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Huang Z, Alkhars H, Gunderman A, Sigounas D, Cleary K, Chen Y. Optimal Concentric Tube Robot Design for Safe Intracerebral Hemorrhage Removal. JOURNAL OF MECHANISMS AND ROBOTICS 2024; 16:081005. [PMID: 38434486 PMCID: PMC10906783 DOI: 10.1115/1.4063979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/05/2024]
Abstract
Purpose The purpose of this paper is to investigate the geometrical design and path planning of Concentric tube robots (CTR) for intracerebral hemorrhage (ICH) evacuation, with a focus on minimizing the risk of damaging white matter tracts and cerebral arteries. Methods To achieve our objective, we propose a parametrization method describing a general class of CTR geometric designs. We present mathematical models that describe the CTR design constraints and provide the calculation of a path risk value. We then use a genetic algorithm to determine the optimal tube geometry for targeting within the brain. Results Our results show that a multi-tube CTR design can significantly reduce the risk of damaging critical brain structures compared to the conventional straight tube design. However, there is no significant relationship between the path risk value and the number and shape of the additional inner curved tubes. Conclusion Considering the challenges of CTR hardware design, fabrication, and control, we conclude that the most practical geometry for a CTR path in ICH treatment is a straight outer tube followed by a planar curved inner tube. These findings have important implications for the development of safe and effective CTRs for ICH evacuation by enabling dexterous manipulation to minimize damage to critical brain structures.
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Affiliation(s)
- Zhefeng Huang
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Hussain Alkhars
- George Washington University School of Medicine, Washington, DC, USA
| | - Anthony Gunderman
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Dimitri Sigounas
- George Washington University School of Medicine, Washington, DC, USA
| | - Kevin Cleary
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Health System, Washington, DC, USA
| | - Yue Chen
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA
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Lu X, Wang X, Chen S, Fan T, Zhao L, Zhong R, Sun G. The rat acute oral toxicity of trifluoromethyl compounds (TFMs): a computational toxicology study combining the 2D-QSTR, read-across and consensus modeling methods. Arch Toxicol 2024; 98:2213-2229. [PMID: 38627326 DOI: 10.1007/s00204-024-03739-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 03/18/2024] [Indexed: 06/13/2024]
Abstract
All areas of the modern society are affected by fluorine chemistry. In particular, fluorine plays an important role in medical, pharmaceutical and agrochemical sciences. Amongst various fluoro-organic compounds, trifluoromethyl (CF3) group is valuable in applications such as pharmaceuticals, agrochemicals and industrial chemicals. In the present study, following the strict OECD modelling principles, a quantitative structure-toxicity relationship (QSTR) modelling for the rat acute oral toxicity of trifluoromethyl compounds (TFMs) was established by genetic algorithm-multiple linear regression (GA-MLR) approach. All developed models were evaluated by various state-of-the-art validation metrics and the OECD principles. The best QSTR model included nine easily interpretable 2D molecular descriptors with clear physical and chemical significance. The mechanistic interpretation showed that the atom-type electro-topological state indices, molecular connectivity, ionization potential, lipophilicity and some autocorrelation coefficients are the main factors contributing to the acute oral toxicity of TFMs against rats. To validate that the selected 2D descriptors can effectively characterize the toxicity, we performed the chemical read-across analysis. We also compared the best QSTR model with public OPERA tool to demonstrate the reliability of the predictions. To further improve the prediction range of the QSTR model, we performed the consensus modelling. Finally, the optimum QSTR model was utilized to predict a true external set containing many untested/unknown TFMs for the first time. Overall, the developed model contributes to a more comprehensive safety assessment approach for novel CF3-containing pharmaceuticals or chemicals, reducing unnecessary chemical synthesis whilst saving the development cost of new drugs.
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Affiliation(s)
- Xinyi Lu
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Chemistry and Life Science, Beijing University of Technology, Beijing, 100124, People's Republic of China
| | - Xin Wang
- Department of Clinical Trials Center, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, 030013, People's Republic of China
| | - Shuo Chen
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Chemistry and Life Science, Beijing University of Technology, Beijing, 100124, People's Republic of China
| | - Tengjiao Fan
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Chemistry and Life Science, Beijing University of Technology, Beijing, 100124, People's Republic of China
- Department of Medical Technology, Beijing Pharmaceutical University of Staff and Workers, Beijing, 100079, China
| | - Lijiao Zhao
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Chemistry and Life Science, Beijing University of Technology, Beijing, 100124, People's Republic of China
| | - Rugang Zhong
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Chemistry and Life Science, Beijing University of Technology, Beijing, 100124, People's Republic of China
| | - Guohui Sun
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Chemistry and Life Science, Beijing University of Technology, Beijing, 100124, People's Republic of China.
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Liu Z, Zhong N, Chen J, Gao B. A modeling method for two-dimensional two-wheeler driving behavior during severe conflict interaction at intersections. ACCIDENT; ANALYSIS AND PREVENTION 2024; 205:107668. [PMID: 38889599 DOI: 10.1016/j.aap.2024.107668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 06/04/2024] [Accepted: 06/04/2024] [Indexed: 06/20/2024]
Abstract
The safety of two-wheelers is a serious public safety issue nowadays. Two-wheelers usually have severe conflict interaction with vehicles at intersections, such as running red lights, which is very likely to cause traffic accidents. Therefore, a model of two-wheeler driving behavior in conflicting interactions can provide guidance for traffic safety management on one hand, and can be used for the development and testing of autonomous vehicles on the other. However, the existing models perform poorly when interacting with vehicles. To address the problems, this paper proposes a modeling method (an improved social force model, ISFM) for two-dimensional two-wheeler driving simulation for conflict interaction at intersections. Based on analysis of naturalistic driving study data, when two-wheelers encounter with vehicles, their driving intentions and trajectories can be categorized into two groups, which are yielding and overtaking. Therefore, the vehicle-related social forces are designed to be a set of two forces rather than a repulsion force in original SFM, which is a yielding force based on the relative distance between the two-wheeler and the vehicle, and an overtaking force based on the velocity of the two-wheeler itself. This opens up the possibilities for modeling the multi-modal driving intention of two-wheelers encountering with cross traffic. Based on ISFM, a bicycle model, a powered two-wheeler (PTW) model and a model of a group of PTWs, are then constructed. Compared to the original SFM, ISFM increases the precision of driving intention prediction by 19.7 % (yielding situation) and 25.0 % (overtaking situation), and reduces the root mean square error between simulated and actual trajectories by 7.8 % and 14.8 % on the bicycle model and the PTW model, respectively. Meanwhile, the model of a group of PTWs also performs well. Finally, the results of ablation experiments also validate the effectiveness of the social force designed based on velocity.
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Affiliation(s)
- Zhenyuan Liu
- School of Automotive Studies, Tongji University, Shanghai, 201804, P.R.China
| | - Naiting Zhong
- School of Automotive Studies, Tongji University, Shanghai, 201804, P.R.China
| | - Junyi Chen
- School of Automotive Studies, Tongji University, Shanghai, 201804, P.R.China.
| | - Bingzhao Gao
- School of Automotive Studies, Tongji University, Shanghai, 201804, P.R.China
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4
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Fazel SAA. Prediction of bubble departing diameter in pool boiling of mixtures by ANN using modified ReLU. Heliyon 2024; 10:e31261. [PMID: 38832267 PMCID: PMC11145198 DOI: 10.1016/j.heliyon.2024.e31261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 04/26/2024] [Accepted: 05/14/2024] [Indexed: 06/05/2024] Open
Abstract
In this research, bubble departure diameter in pool boiling have been measured in aqueous amine and ethylene glycol solutions for various concentrations. The experimental data have been compared with major existing predictive correlations. It is shown that the effect and identity of the independent variables on bubble diameter proposed in the previous studies are inconsistent. The predictions of different correlations have on average a deviation of about 40% from the experimental data. This is mainly due to the complicated interactions between bubbles on the heterogeneous boiling medium, which provides a complex condition. This complexity limits any mathematical modelling of the forces acting on the developing bubbles. Particularly in liquid solutions, where mass transfer by back diffusion through micro-sub-layers adds further complexity. In this work, the classical artificial neural network, ANN, with rectified linear unit, ReLU, activating function, AF, has been modified. This modification is based on adding a numerical matrix to each layer to adjust the slope of AF for each neuron independently. The addition of this parameter, together with the adjustment of the bias matrix, makes the activation function more flexible than the classical ReLU. To find the tuning parameters, a genetic algorithm was implemented instead of the back-propagation technique. It is shown that the predictions of the trained ANN with modified ReLU AF agree within an absolute average error of 10%, which is equal to the total uncertainty of the measurements. Prediction of bubble departing diameter in boiling phenomena is a key parameter for accurate design, operation and optimisation in many industrial systems.
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Affiliation(s)
- Seyed Ali Alavi Fazel
- Department of Chemical Engineering, Mahshahr Branch, Islamic Azad University, Mahshahr, Khuzestan, Iran
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5
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Sun H, Lordi V, Takamura Y, Samanta A. Unraveling the Correlation between the Interface Structures and Tunable Magnetic Properties of La 1-xSr xCoO 3-δ/La 1-xSr xMnO 3-δ Bilayers Using Deep Learning Models. ACS APPLIED MATERIALS & INTERFACES 2024; 16:30166-30175. [PMID: 38780088 DOI: 10.1021/acsami.3c18773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
Perovskite oxides are gaining significant attention for use in next-generation magnetic and ferroelectric devices due to their exceptional charge transport properties and the opportunity to tune the charge, spin, lattice, and orbital degrees of freedom. Interfaces between perovskite oxides, exemplified by La1-xSrxCoO3-δ/La1-xSrxMnO3-δ (LSCO/LSMO) bilayers, exhibit unconventional magnetic exchange switching behavior, offering a pathway for innovative designs in perovskite oxide-based devices. However, the precise atomic-level stoichiometric compositions and chemophysical properties of these interfaces remain elusive, hindering the establishment of surrogate design principles. We leverage first-principles simulations, evolutionary algorithms, and neural network searches with on-the-fly uncertainty quantification to design deep learning model ensembles to investigate over 50,000 LSCO/LSMO bilayer structures as a function of oxygen deficiency (δ) and strontium concentration (x). Structural analysis of the low-energy interface structures reveals that preferential segregation of oxygen vacancies toward the interfacial La0.7Sr0.3CoO3-δ layers causes distortion of the CoOx polyhedra and the emergence of magnetically active Co2+ ions. At the same time, an increase in the Sr concentration and a decrease in oxygen vacancies in the La0.7Sr0.3MnO3-δ layers tend to retain MnO6 octahedra and promote the formation of Mn4+ ions. Electronic structure analysis reveals that the nonuniform distributions of Sr ions and oxygen vacancies on both sides of the interface can alter the local magnetization at the interface, showing a transition from ferromagnetic (FM) to local antiferromagnetic (AFM) or ferrimagnetic regions. Therefore, the exotic properties of La1-xSrxCoO3-δ/La1-xSrxMnO3-δ are strongly coupled to the presence of hard/soft magnetic layers, as well as the FM to AFM transition at the interface, and can be tuned by changing the Sr concentration and oxygen partial pressure during growth. These insights provide valuable guidance for the precise design of perovskite oxide multilayers, enabling tailoring of their functional properties to meet specific requirements for various device applications.
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Affiliation(s)
- Hong Sun
- Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Vincenzo Lordi
- Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Yayoi Takamura
- Department of Materials Science and Engineering, University of California, Davis, Davis, California 95616, United States
| | - Amit Samanta
- Lawrence Livermore National Laboratory, Livermore, California 94550, United States
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Cheng Y, Xu SM, Santucci K, Lindner G, Janitz M. Machine learning and related approaches in transcriptomics. Biochem Biophys Res Commun 2024; 724:150225. [PMID: 38852503 DOI: 10.1016/j.bbrc.2024.150225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Revised: 05/18/2024] [Accepted: 06/03/2024] [Indexed: 06/11/2024]
Abstract
Data acquisition for transcriptomic studies used to be the bottleneck in the transcriptomic analytical pipeline. However, recent developments in transcriptome profiling technologies have increased researchers' ability to obtain data, resulting in a shift in focus to data analysis. Incorporating machine learning to traditional analytical methods allows the possibility of handling larger volumes of complex data more efficiently. Many bioinformaticians, especially those unfamiliar with ML in the study of human transcriptomics and complex biological systems, face a significant barrier stemming from their limited awareness of the current landscape of ML utilisation in this field. To address this gap, this review endeavours to introduce those individuals to the general types of ML, followed by a comprehensive range of more specific techniques, demonstrated through examples of their incorporation into analytical pipelines for human transcriptome investigations. Important computational aspects such as data pre-processing, task formulation, results (performance of ML models), and validation methods are encompassed. In hope of better practical relevance, there is a strong focus on studies published within the last five years, almost exclusively examining human transcriptomes, with outcomes compared with standard non-ML tools.
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Affiliation(s)
- Yuning Cheng
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Si-Mei Xu
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Kristina Santucci
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Grace Lindner
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Michael Janitz
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, 2052, Australia.
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Kang Y, Kim J. ChatMOF: an artificial intelligence system for predicting and generating metal-organic frameworks using large language models. Nat Commun 2024; 15:4705. [PMID: 38830856 PMCID: PMC11148193 DOI: 10.1038/s41467-024-48998-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 05/15/2024] [Indexed: 06/05/2024] Open
Abstract
ChatMOF is an artificial intelligence (AI) system that is built to predict and generate metal-organic frameworks (MOFs). By leveraging a large-scale language model (GPT-4, GPT-3.5-turbo, and GPT-3.5-turbo-16k), ChatMOF extracts key details from textual inputs and delivers appropriate responses, thus eliminating the necessity for rigid and formal structured queries. The system is comprised of three core components (i.e., an agent, a toolkit, and an evaluator) and it forms a robust pipeline that manages a variety of tasks, including data retrieval, property prediction, and structure generations. ChatMOF shows high accuracy rates of 96.9% for searching, 95.7% for predicting, and 87.5% for generating tasks with GPT-4. Additionally, it successfully creates materials with user-desired properties from natural language. The study further explores the merits and constraints of utilizing large language models (LLMs) in combination with database and machine learning in material sciences and showcases its transformative potential for future advancements.
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Affiliation(s)
- Yeonghun Kang
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291, Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea
| | - Jihan Kim
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291, Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea.
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Han F, Yu J, Zhou G, Li S, Sun T. A comparative study on urban waterlogging susceptibility assessment based on multiple data-driven models. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 360:121166. [PMID: 38781876 DOI: 10.1016/j.jenvman.2024.121166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 03/19/2024] [Accepted: 05/10/2024] [Indexed: 05/25/2024]
Abstract
Accurate identification of urban waterlogging areas and assessing waterlogging susceptibility are crucial for preventing and controlling hazards. Data-driven models are utilized to forecast waterlogging areas by establishing intricate relationships between explanatory variables and waterlogging states. This approach tackles the constraints of mechanistic models, which are frequently complex and unable to incorporate socio-economic factors. Previous research predominantly employed single-type data-driven models to predict waterlogging locations and evaluation of their effectiveness. There is a scarcity of comprehensive performance comparisons and uncertainty analyses of different types of models, as well as a lack of interpretability analysis. The chosen study area was the central area of Beijing, which is prone to waterlogging. Given the high manpower, time, and economic costs associated with collecting waterlogging information, the waterlogging point distribution map released by the Beijing Water Affairs Bureau was selected as labeled samples. Twelve factors affecting waterlogging susceptibility were chosen as explanatory variables to construct Random Forest (RF), Support Vector Machine with Radial Basis Function (SVM-RBF), Particle Swarm Optimization-Weakly Labeled Support Vector Machine (PSO-WELLSVM), and Maximum Entropy (MaxEnt). The utilization of diverse single evaluation indicators (such as F-score, Kappa, AUC, etc.) to assess the model performance may yield conflicting results. The Distance between Indices of Simulation and Observation (DISO) was chosen as a comprehensive measure to assess the model's performance in predicting waterlogging points. PSO-WELLSVM exhibited the highest performance with a DISOtest value of 0.63, outperforming MaxEnt (0.78), which excelled in identifying areas highly susceptible to waterlogging, including extremely high susceptibility zones. The SVM-RBF and RF models demonstrated suboptimal performance and exhibited overfitting. The examination of waterlogging susceptibility distribution maps predicted by the four models revealed significant spatial differences due to variations in computational principles and input parameter complexities. The integration of four WSAMs based on logistic regression has been shown to significantly decrease the uncertainty of a single data-driven model and identify the most flood-prone areas. To improve the interpretability of the data model, a geographical detector was incorporated to demonstrate the explanatory capacity of 12 variables and the process of waterlogging. Building Density (BD) exhibits the highest explanatory power in relation to explain waterlogging susceptibility (Q value = 0.202), followed by Distance to Road, Frequency of Heavy Rainstorms (FHR), DEM, etc. The interaction between BD and FHR results in a nonlinear increase in the explanatory power of waterlogging susceptibility. The presence of waterlogging susceptibility risk in the research area can be attributed to the interactions of multiple factors.
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Affiliation(s)
- Feifei Han
- College of Water Sciences, Beijing Normal University, Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, Beijing 100875, China
| | - Jingshan Yu
- College of Water Sciences, Beijing Normal University, Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, Beijing 100875, China; State Environmental Protection Key Laboratory of Land and Sea Ecological Governance and Systematic Regulation, Shandong Academy for Environmental Planning, Jinan 250100, China.
| | - Guihuan Zhou
- College of Water Sciences, Beijing Normal University, Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, Beijing 100875, China
| | - Shuang Li
- College of Water Sciences, Beijing Normal University, Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, Beijing 100875, China
| | - Tong Sun
- College of Water Sciences, Beijing Normal University, Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, Beijing 100875, China
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González-Hernández Y, Perré P. Building blocks needed for mechanistic modeling of bioprocesses: A critical review based on protein production by CHO cells. Metab Eng Commun 2024; 18:e00232. [PMID: 38501051 PMCID: PMC10945193 DOI: 10.1016/j.mec.2024.e00232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 02/12/2024] [Accepted: 02/23/2024] [Indexed: 03/20/2024] Open
Abstract
This paper reviews the key building blocks needed to develop a mechanistic model for use as an operational production tool. The Chinese Hamster Ovary (CHO) cell, one of the most widely used hosts for antibody production in the pharmaceutical industry, is considered as a case study. CHO cell metabolism is characterized by two main phases, exponential growth followed by a stationary phase with strong protein production. This process presents an appropriate degree of complexity to outline the modeling strategy. The paper is organized into four main steps: (1) CHO systems and data collection; (2) metabolic analysis; (3) formulation of the mathematical model; and finally, (4) numerical solution, calibration, and validation. The overall approach can build a predictive model of target variables. According to the literature, one of the main current modeling challenges lies in understanding and predicting the spontaneous metabolic shift. Possible candidates for the trigger of the metabolic shift include the concentration of lactate and carbon dioxide. In our opinion, ammonium, which is also an inhibiting product, should be further investigated. Finally, the expected progress in the emerging field of hybrid modeling, which combines the best of mechanistic modeling and machine learning, is presented as a fascinating breakthrough. Note that the modeling strategy discussed here is a general framework that can be applied to any bioprocess.
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Affiliation(s)
- Yusmel González-Hernández
- Université Paris-Saclay, CentraleSupélec, Laboratoire de Génie des Procédés et Matériaux, Centre Européen de Biotechnologie et de Bioéconomie (CEBB), 3 Rue des Rouges Terres, 51110, Pomacle, France
| | - Patrick Perré
- Université Paris-Saclay, CentraleSupélec, Laboratoire de Génie des Procédés et Matériaux, Centre Européen de Biotechnologie et de Bioéconomie (CEBB), 3 Rue des Rouges Terres, 51110, Pomacle, France
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Li R, Chen S, Xia J, Zhou H, Shen Q, Li Q, Dong Q. Predictive modeling of deep vein thrombosis risk in hospitalized patients: A Q-learning enhanced feature selection model. Comput Biol Med 2024; 175:108447. [PMID: 38691912 DOI: 10.1016/j.compbiomed.2024.108447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 03/23/2024] [Accepted: 04/07/2024] [Indexed: 05/03/2024]
Abstract
Deep vein thrombosis (DVT) represents a critical health concern due to its potential to lead to pulmonary embolism, a life-threatening complication. Early identification and prediction of DVT are crucial to prevent thromboembolic events and implement timely prophylactic measures in high-risk individuals. This study aims to examine the risk determinants associated with acute lower extremity DVT in hospitalized individuals. Additionally, it introduces an innovative approach by integrating Q-learning augmented colony predation search ant colony optimizer (QL-CPSACO) into the analysis. This algorithm, then combined with support vector machines (SVM), forms a bQL-CPSACO-SVM feature selection model dedicated to crafting a clinical risk prognostication model for DVT. The effectiveness of the proposed algorithm's optimization and the model's accuracy are assessed through experiments utilizing the CEC 2017 benchmark functions and predictive analyses on the DVT dataset. The experimental results reveal that the proposed model achieves an outstanding accuracy of 95.90% in predicting DVT. Key parameters such as D-dimer, normal plasma prothrombin time, prothrombin percentage activity, age, previously documented DVT, leukocyte count, and thrombocyte count demonstrate significant value in the prognostication of DVT. The proposed method provides a basis for risk assessment at the time of patient admission and offers substantial guidance to physicians in making therapeutic decisions.
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Affiliation(s)
- Rizeng Li
- Department of General Surgery, The Second Affiliated Hospital of Shanghai University (Wenzhou Central Hospital), Wenzhou, Zhejiang, 325000, China.
| | - Sunmeng Chen
- Department of General Surgery, The Second Affiliated Hospital of Shanghai University (Wenzhou Central Hospital), Wenzhou, Zhejiang, 325000, China.
| | - Jianfu Xia
- Department of General Surgery, The Second Affiliated Hospital of Shanghai University (Wenzhou Central Hospital), Wenzhou, Zhejiang, 325000, China.
| | - Hong Zhou
- Department of General Surgery, The Second Affiliated Hospital of Shanghai University (Wenzhou Central Hospital), Wenzhou, Zhejiang, 325000, China.
| | - Qingzheng Shen
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China.
| | - Qiang Li
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China.
| | - Qiantong Dong
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China.
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11
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Shiddiqi AM, Za'in C, Lathifah A, Ahmad T, Purwitasari D. GA-Sense: Sensor placement strategy for detecting leaks in water distribution networks based on time series flow and genetic algorithm. MethodsX 2024; 12:102612. [PMID: 38385155 PMCID: PMC10879767 DOI: 10.1016/j.mex.2024.102612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Accepted: 02/10/2024] [Indexed: 02/23/2024] Open
Abstract
The detection of leaks in time series flow systems is crucial for efficient and integrated industrial processes. This is especially true when daily demand patterns differ, as this results in fluctuations in the snapshots of water consumption that are commonly used as the basis for placing sensors to detect leaks. This paper introduces a novel method in which the genetic algorithm (GA) is applied to find optimal sensor locations and to enhance the accuracy of leak detection in time series flow data. The method consists of two steps. Firstly, the GA is used to identify the optimal sensor locations using a specific fitness function that accounts for flow patterns, system topology, and leak characteristics. The novelty of the proposed method lies in the weighting scheme of the fitness function, which takes into consideration the frequency of events and the magnitude of leaks at potential locations. Secondly, the selected sensor locations are integrated with an advanced time series data analysis to locate leaks. In this technique, the most consistently performing locations are dynamically selected over time, allowing the model to adapt to varying conditions to maintain optimal sensor placement. Experiments were conducted on a simulated time series flow system with known leak scenarios to evaluate the performance of the proposed method. The results demonstrated the superiority of our GA-based sensor placement strategy in terms of leak detection accuracy and efficiency compared to other methods.•We developed a model called GA-Sense for sensor placement strategy by considering flow patterns to maximize leak detection and localization capabilities.•GA-Sense uses time series data to find strategic sensor locations to identify abnormal flow patterns indicative of leaks.•This approach enhances the accuracy and efficiency of leak detection and localization compared to alternative methods.
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Affiliation(s)
| | - Choiru Za'in
- Department of Computer Science and Information Technology, La Trobe University, Australia
| | - Artya Lathifah
- Department of Industrial and Information Management, National Cheng Kung University, Taiwan
| | - Tohari Ahmad
- Department of Informatics, Institut Teknologi Sepuluh Nopember, Indonesia
| | - Diana Purwitasari
- Department of Informatics, Institut Teknologi Sepuluh Nopember, Indonesia
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12
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Zhao Y, Fu J, Zhang Q, Feng H, Wei W, Chen W, Zhang K, Wu Q. Design of an optically transparent and broadband absorber based on a multi-objective optimization algorithm. OPTICS LETTERS 2024; 49:2942-2945. [PMID: 38824298 DOI: 10.1364/ol.524371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Accepted: 04/29/2024] [Indexed: 06/03/2024]
Abstract
In this Letter, an optically transparent and broadband absorber designed using a multi-objective genetic algorithm (MOGA) is proposed. The absorption of the multilayer lossy frequency selective surface-based absorber is calculated by multilayer absorption equations and equivalent circuit models. To solve the problem of the unbalanced structure absorption bandwidth and thickness, an algorithm is used for optimizing the geometric and sheet resistance parameters of the structure. A multilayer and optically transparent absorber with 90% absorption bandwidth covering a frequency range of 2-18 GHz (S-band to Ku-band) is developed based on the MOGA design method with optical transmittance of 60%. Its total thickness consists of a wavelength of only 0.095, and it has high oblique incidence stability, which makes it useful in the stealth technology and transparent electromagnetic shielding applications.
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Dong Y, Lau HX, Suaini NHA, Kee MZL, Ooi DSQ, Shek LPC, Lee BW, Godfrey KM, Tham EH, Ong MEH, Liu N, Wong L, Tan KH, Chan JKY, Yap FKP, Chong YS, Eriksson JG, Feng M, Loo EXL. A machine-learning exploration of the exposome from preconception in early childhood atopic eczema, rhinitis and wheeze development. ENVIRONMENTAL RESEARCH 2024; 250:118523. [PMID: 38382664 DOI: 10.1016/j.envres.2024.118523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 01/19/2024] [Accepted: 02/18/2024] [Indexed: 02/23/2024]
Abstract
BACKGROUND Most previous research on the environmental epidemiology of childhood atopic eczema, rhinitis and wheeze is limited in the scope of risk factors studied. Our study adopted a machine learning approach to explore the role of the exposome starting already in the preconception phase. METHODS We performed a combined analysis of two multi-ethnic Asian birth cohorts, the Growing Up in Singapore Towards healthy Outcomes (GUSTO) and the Singapore PREconception Study of long Term maternal and child Outcomes (S-PRESTO) cohorts. Interviewer-administered questionnaires were used to collect information on demography, lifestyle and childhood atopic eczema, rhinitis and wheeze development. Data training was performed using XGBoost, genetic algorithm and logistic regression models, and the top variables with the highest importance were identified. Additive explanation values were identified and inputted into a final multiple logistic regression model. Generalised structural equation modelling with maternal and child blood micronutrients, metabolites and cytokines was performed to explain possible mechanisms. RESULTS The final study population included 1151 mother-child pairs. Our findings suggest that these childhood diseases are likely programmed in utero by the preconception and pregnancy exposomes through inflammatory pathways. We identified preconception alcohol consumption and maternal depressive symptoms during pregnancy as key modifiable maternal environmental exposures that increased eczema and rhinitis risk. Our mechanistic model suggested that higher maternal blood neopterin and child blood dimethylglycine protected against early childhood wheeze. After birth, early infection was a key driver of atopic eczema and rhinitis development. CONCLUSION Preconception and antenatal exposomes can programme atopic eczema, rhinitis and wheeze development in utero. Reducing maternal alcohol consumption during preconception and supporting maternal mental health during pregnancy may prevent atopic eczema and rhinitis by promoting an optimal antenatal environment. Our findings suggest a need to include preconception environmental exposures in future research to counter the earliest precursors of disease development in children.
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Affiliation(s)
- Yizhi Dong
- Saw Swee Hock School of Public Health, National University Health System, National University of Singapore, Singapore.
| | - Hui Xing Lau
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), 30 Medical Drive, Singapore, 117609, Singapore.
| | - Noor Hidayatul Aini Suaini
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), 30 Medical Drive, Singapore, 117609, Singapore.
| | - Michelle Zhi Ling Kee
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), 30 Medical Drive, Singapore, 117609, Singapore.
| | - Delicia Shu Qin Ooi
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Khoo Teck Puat-National University Children's Medical Institute, National University Hospital, National University Health System, Singapore.
| | - Lynette Pei-Chi Shek
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
| | - Bee Wah Lee
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
| | - Keith M Godfrey
- School of Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, United Kingdom; NIHR Southampton Biomedical Research Centre, University Hospital Southampton NHS Foundation Trust and University of Southampton, Southampton, United Kingdom; MRC Lifecourse Epidemiology Centre, Faculty of Medicine, University of Southampton, Southampton, United Kingdom.
| | - Elizabeth Huiwen Tham
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), 30 Medical Drive, Singapore, 117609, Singapore; Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Khoo Teck Puat-National University Children's Medical Institute, National University Hospital, National University Health System, Singapore; Human Potential Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
| | - Marcus Eng Hock Ong
- Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore; Health Services and Systems Research, Duke-NUS Graduate Medical School, Singapore, Singapore.
| | - Nan Liu
- Duke-NUS Medical School, National University of Singapore, Singapore; Health Services Research Centre, Singapore Health Services, Singapore, Singapore; Institute of Data Science, National University of Singapore, Singapore.
| | - Limsoon Wong
- School of Computing, National University of Singapore, 13 Computing Drive, Singapore 117417, Singapore.
| | - Kok Hian Tan
- Department of Maternal Fetal Medicine, KK Women's and Children's Hospital (KKH), Singapore.
| | - Jerry Kok Yen Chan
- Duke-NUS Medical School, National University of Singapore, Singapore; Department of Reproductive Medicine, KK Women's and Children's Hospital (KKH), Singapore.
| | - Fabian Kok Peng Yap
- Duke-NUS Medical School, National University of Singapore, Singapore; Department of Paediatrics, KK Women's and Children's Hospital (KKH), Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore.
| | - Yap Seng Chong
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), 30 Medical Drive, Singapore, 117609, Singapore; Department of Obstetrics & Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore.
| | - Johan Gunnar Eriksson
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), 30 Medical Drive, Singapore, 117609, Singapore; Human Potential Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Department of Obstetrics & Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore; Folkhälsan Research Center, Helsinki, Finland; Department of General Practice and Primary Health Care, University of Helsinki, Finland.
| | - Mengling Feng
- Saw Swee Hock School of Public Health, National University Health System, National University of Singapore, Singapore.
| | - Evelyn Xiu Ling Loo
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), 30 Medical Drive, Singapore, 117609, Singapore; Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Human Potential Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Dean's Office, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
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Li C, Zhou Z, Hou L, Hu K, Wu Z, Xie Y, Ouyang J, Cai X. A novel machine learning model for efficacy prediction of immunotherapy-chemotherapy in NSCLC based on CT radiomics. Comput Biol Med 2024; 178:108638. [PMID: 38897152 DOI: 10.1016/j.compbiomed.2024.108638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 04/16/2024] [Accepted: 05/18/2024] [Indexed: 06/21/2024]
Abstract
Lung cancer is categorized into two main types: non-small cell lung cancer (NSCLC) and small cell lung cancer. Of these, NSCLC accounts for approximately 85% of all cases and encompasses varieties such as squamous cell carcinoma and adenocarcinoma. For patients with advanced NSCLC that do not have oncogene addiction, the preferred treatment approach is a combination of immunotherapy and chemotherapy. However, the progression-free survival (PFS) typically ranges only from about 6 to 8 months, accompanied by certain adverse events. In order to carry out individualized treatment more effectively, it is urgent to accurately screen patients with PFS for more than 12 months under this treatment regimen. Therefore, this study undertook a retrospective collection of pulmonary CT images from 60 patients diagnosed with NSCLC treated at the First Affiliated Hospital of Wenzhou Medical University. It developed a machine learning model, designated as bSGSRIME-SVM, which integrates the rime optimization algorithm with self-adaptive Gaussian kernel probability search (SGSRIME) and support vector machine (SVM) classifier. Specifically, the model initiates its process by employing the SGSRIME algorithm to identify pivotal image features. Subsequently, it utilizes an SVM classifier to assess these features, aiming to enhance the model's predictive accuracy. Initially, the superior optimization capability and robustness of SGSRIME in IEEE CEC 2017 benchmark functions were validated. Subsequently, employing color moments and gray-level co-occurrence matrix methods, image features were extracted from images of 60 NSCLC patients undergoing immunotherapy combined with chemotherapy. The developed model was then utilized for analysis. The results indicate a significant advantage of the model in predicting the efficacy of immunotherapy combined with chemotherapy for NSCLC, with an accuracy of 92.381% and a specificity of 96.667%. This lays the foundation for more accurate PFS predictions and personalized treatment plans.
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Affiliation(s)
- Chengye Li
- Department of Pulmonary and Critical Care Medicine, The First Affliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
| | - Zhifeng Zhou
- Wenzhou University Library, Wenzhou, 325035, China.
| | - Lingxian Hou
- Rehabilitation Department, Wenzhou Hospital of Integrated Traditional Chinese and Western Medicine, Wenzhou, 325000, China.
| | - Keli Hu
- Department of Computer Science and Engineering, Shaoxing University, Shaoxing, 312000, China; Information Technology R&D Innovation Center of Peking University, Shaoxing, 312000, China.
| | - Zongda Wu
- Department of Computer Science and Engineering, Shaoxing University, Shaoxing, 312000, China.
| | - Yupeng Xie
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
| | - Jinsheng Ouyang
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
| | - Xueding Cai
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
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15
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Safaie N, Salehi M, Farhadi S, Aligholizadeh A, Mahdizadeh V. Lentinula edodes substrate formulation using multilayer perceptron-genetic algorithm: a critical production checkpoint. Front Microbiol 2024; 15:1366264. [PMID: 38841070 PMCID: PMC11151849 DOI: 10.3389/fmicb.2024.1366264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 04/19/2024] [Indexed: 06/07/2024] Open
Abstract
Shiitake (Lentinula edodes) is one of the most widely grown and consumed mushroom species worldwide. They are a potential source of food and medicine because they are rich in nutrients and contain various minerals, vitamins, essential macro- and micronutrients, and bioactive compounds. The reuse of agricultural and industrial residues is crucial from an ecological and economic perspective. In this study, the running length (RL) of L. edodes cultured on 64 substrate compositions obtained from different ratios of bagasse (B), wheat bran (WB), and beech sawdust (BS) was recorded at intervals of 5 days after cultivation until the 40th day. Multilayer perceptron-genetic algorithm (MLP-GA), multiple linear regression, stepwise regression, principal component regression, ordinary least squares regression, and partial least squares regression were used to predict and optimize the RL and running rate (RR) of L. edodes. The statistical values showed higher prediction accuracies of the MLP-GA models (92% and 97%, respectively) compared with those of the regression models (52% and 71%, respectively) for RL and RR. The high degree of fit between the forecasted and actual values of the RL and RR of L. edodes confirmed the superior performance of the developed MLP-GA models. An optimization analysis on the established MLP-GA models showed that a substrate containing 15.1% B, 45.1% WB, and 10.16% BS and a running time of 28 days and 10 h could result in the maximum L. edodes RL (10.69 cm). Moreover, the highest RR of L. edodes (0.44 cm d-1) could be obtained by a substrate containing 30.7% B, 90.4% WB, and 0.0% BS. MLP-GA was observed to be an effective method for predicting and consequently selecting the best substrate composition for the maximal RL and RR of L. edodes.
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Affiliation(s)
- Naser Safaie
- Department of Plant Pathology, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran
| | - Mina Salehi
- Department of Plant Genetics and Breeding, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran
| | - Siamak Farhadi
- Seed and Plant Improvement Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran
| | - Ali Aligholizadeh
- Department of Plant Pathology, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran
| | - Valiollah Mahdizadeh
- Department of Plant Pathology, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran
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16
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Ge Y, Zhong Y, Murata I, Tamaki S, Yuan N, Sun Y, Ma W, Zou L, Yang Z, Lu L. Efficient optimization of an accelerator neutron source for neutron capture therapy using genetic algorithms. Med Phys 2024. [PMID: 38734991 DOI: 10.1002/mp.17132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 04/19/2024] [Accepted: 05/04/2024] [Indexed: 05/13/2024] Open
Abstract
BACKGROUND In recent years, genetic algorithms have been applied in the field of nuclear technology design, producing superior optimization results compared to traditional methods. They can be employed in the design and optimization of beam shaping assemblies (BSA) BSA to obtain the desired neutron beams. But it should be noted that the direct combination of Monte Carlo methods with genetic algorithms requires a significant amount of computational resources and time. PURPOSE Design and optimize BSA more efficiently to achieve neutron beams that meet specified recommendations. METHODS We propose an approach of NSGA II with crucial variables which are identified by multivariate statistical techniques. This approach significantly reduces the problem sizes, thus reducing the time required for optimization. We illustrate this methodology using the example of BSA design for AB-BNCT. RESULTS The computational efficiency has tripled with crucial variables. By using NSGA II, we obtained optimized models conforming to both the new and old version IAEA BNCT guidelines through a single optimization process and subjected them to phantom analysis. The results demonstrate that models obtained through this method can meet the IAEA recommendations with deep advantage depth (AD) and high absorbed ratio (AR). CONCLUSION The genetic algorithm with crucial variables displays tremendous potential in addressing BSA optimization challenges.
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Affiliation(s)
- Yulin Ge
- Sino-French Institute of Nuclear Engineering and Technology, Sun Yat-sen University, Zhuhai, Guangdong, China
- Department of Sustainable Energy and Environmental Engineering, School of Engineering, Osaka University, Suita, Osaka, Japan
- United Laboratory of Frontier Radiotherapy Technology of Sun Yat-sen University & Chinese Academy of Sciences Ion Medical Technology Co., Ltd, Guangzhou, China
| | - Yao Zhong
- Sino-French Institute of Nuclear Engineering and Technology, Sun Yat-sen University, Zhuhai, Guangdong, China
- Institute of Advanced Energy, Kyoto University, Uji, Kyoto, Japan
| | - Isao Murata
- Department of Sustainable Energy and Environmental Engineering, School of Engineering, Osaka University, Suita, Osaka, Japan
| | - Shingo Tamaki
- Department of Sustainable Energy and Environmental Engineering, School of Engineering, Osaka University, Suita, Osaka, Japan
| | - Nan Yuan
- Sino-French Institute of Nuclear Engineering and Technology, Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Yanbing Sun
- Sino-French Institute of Nuclear Engineering and Technology, Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Wei Ma
- Sino-French Institute of Nuclear Engineering and Technology, Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Liping Zou
- Sino-French Institute of Nuclear Engineering and Technology, Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Zhen Yang
- Sino-French Institute of Nuclear Engineering and Technology, Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Liang Lu
- Sino-French Institute of Nuclear Engineering and Technology, Sun Yat-sen University, Zhuhai, Guangdong, China
- United Laboratory of Frontier Radiotherapy Technology of Sun Yat-sen University & Chinese Academy of Sciences Ion Medical Technology Co., Ltd, Guangzhou, China
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17
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Hu J, Wan J, Xi J, Shi W, Qian H. AI-driven design of customized 3D-printed multi-layer capsules with controlled drug release profiles for personalized medicine. Int J Pharm 2024; 656:124114. [PMID: 38615804 DOI: 10.1016/j.ijpharm.2024.124114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 03/25/2024] [Accepted: 04/10/2024] [Indexed: 04/16/2024]
Abstract
Personalized medicine aims to effectively and efficiently provide customized drugs that cater to diverse populations, which is a significant yet challenging task. Recently, the integration of artificial intelligence (AI) and three-dimensional (3D) printing technology has transformed the medical field, and was expected to facilitate the efficient design and development of customized drugs through the synergy of their respective advantages. In this study, we present an innovative method that combines AI and 3D printing technology to design and fabricate customized capsules. Initially, we discretized and encoded the geometry of the capsule, simulated the dissolution process of the capsule with classical drug dissolution model, and verified it by experiments. Subsequently, we employed a genetic algorithm to explore the capsule geometric structure space and generate a complex multi-layer structure that satisfies the target drug release profiles, including stepwise release and zero-order release. Finally, Two model drugs, isoniazid and acetaminophen, were selected and fused deposition modeling (FDM) 3D printing technology was utilized to precisely print the AI-designed capsule. The reliability of the method was verified by comparing the in vitro release curve of the printed capsules with the target curve, and the f2 value was more than 50. Notably, accurate and autonomous design of the drug release curve was achieved mainly by changing the geometry of the capsule. This approach is expected to be applied to different drug needs and facilitate the development of customized oral dosage forms.
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Affiliation(s)
- Jingzhi Hu
- School of Science, China Pharmaceutical University, Nanjing, PR China
| | - Jiale Wan
- School of Science, China Pharmaceutical University, Nanjing, PR China
| | - Junting Xi
- School of Science, China Pharmaceutical University, Nanjing, PR China
| | - Wei Shi
- Center of Drug Discovery, State Key Laboratory of Natural Medicines and Jiangsu Key Laboratory of Drug Discovery for Metabolic Disease, China Pharmaceutical University, Nanjing, PR China
| | - Hai Qian
- Center of Drug Discovery, State Key Laboratory of Natural Medicines and Jiangsu Key Laboratory of Drug Discovery for Metabolic Disease, China Pharmaceutical University, Nanjing, PR China.
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18
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Feng G. Feature selection algorithm based on optimized genetic algorithm and the application in high-dimensional data processing. PLoS One 2024; 19:e0303088. [PMID: 38723061 PMCID: PMC11081226 DOI: 10.1371/journal.pone.0303088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 04/17/2024] [Indexed: 05/13/2024] Open
Abstract
High-dimensional data is widely used in many fields, but selecting key features from it is challenging. Feature selection can reduce data dimensionality and weaken noise interference, thereby improving model efficiency and enhancing model interpretability. In order to improve the efficiency and accuracy of high-dimensional data processing, a feature selection method based on optimized genetic algorithm is proposed in this study. The algorithm simulates the process of natural selection, searches for possible subsets of feature, and finds the subsets of feature that optimizes the performance of the model. The results show that when the value of K is less than 4 or more than 8, the recognition rate is very low. After adaptive bias filtering, 724 features are filtered to 372, and the accuracy is improved from 0.9352 to 0.9815. From 714 features to 406 Gaussian codes, the accuracy is improved from 0.9625 to 0.9754. Among all tests, the colon has the highest average accuracy, followed by small round blue cell tumor(SRBCT), lymphoma, central nervous system(CNS) and ovaries. The green curve is the best, with stable performance and a time range of 0-300. While maintaining the efficiency, it can reach 4.48 as soon as possible. The feature selection method has practical significance for high-dimensional data processing, improves the efficiency and accuracy of data processing, and provides an effective new method for high-dimensional data processing.
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Affiliation(s)
- Guilian Feng
- School of Physics and Electronic Information Engineering, Qinghai Minzu University, Xining, China
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19
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Liu Y, Zhou X, Wang T, Luo A, Jia Z, Pan X, Cai W, Sun M, Wang X, Wen Z, Zhou G. Genetic algorithm-based semisupervised convolutional neural network for real-time monitoring of Escherichia coli fermentation of recombinant protein production using a Raman sensor. Biotechnol Bioeng 2024; 121:1583-1595. [PMID: 38247359 DOI: 10.1002/bit.28661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 01/02/2024] [Accepted: 01/08/2024] [Indexed: 01/23/2024]
Abstract
As a non-destructive sensing technique, Raman spectroscopy is often combined with regression models for real-time detection of key components in microbial cultivation processes. However, achieving accurate model predictions often requires a large amount of offline measurement data for training, which is both time-consuming and labor-intensive. In order to overcome the limitations of traditional models that rely on large datasets and complex spectral preprocessing, in addition to the difficulty of training models with limited samples, we have explored a genetic algorithm-based semi-supervised convolutional neural network (GA-SCNN). GA-SCNN integrates unsupervised process spectral labeling, feature extraction, regression prediction, and transfer learning. Using only an extremely small number of offline samples of the target protein, this framework can accurately predict protein concentration, which represents a significant challenge for other models. The effectiveness of the framework has been validated in a system of Escherichia coli expressing recombinant ProA5M protein. By utilizing the labeling technique of this framework, the available dataset for glucose, lactate, ammonium ions, and optical density at 600 nm (OD600) has been expanded from 52 samples to 1302 samples. Furthermore, by introducing a small component of offline detection data for recombinant proteins into the OD600 model through transfer learning, a model for target protein detection has been retrained, providing a new direction for the development of associated models. Comparative analysis with traditional algorithms demonstrates that the GA-SCNN framework exhibits good adaptability when there is no complex spectral preprocessing. Cross-validation results confirm the robustness and high accuracy of the framework, with the predicted values of the model highly consistent with the offline measurement results.
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Affiliation(s)
- Yuan Liu
- Department of Pharmaceutical Engineering, Beijing Institute of Petrochemical Technology, Beijing, China
| | - Xiaotian Zhou
- Department of Pharmaceutical Engineering, Beijing Institute of Petrochemical Technology, Beijing, China
| | - Teng Wang
- Department of Pharmaceutical Engineering, Beijing Institute of Petrochemical Technology, Beijing, China
- Beijing Key Laboratory of Enze Biomass and Fine Chemicals, Beijing Institute of Petrochemical Technology, Beijing, China
- Beijing Institute of Petrochemical Technology, College of New Materials and Chemical Engineering, Beijing, China
| | - An Luo
- Department of Pharmaceutical Engineering, Beijing Institute of Petrochemical Technology, Beijing, China
- Beijing Institute of Petrochemical Technology, College of New Materials and Chemical Engineering, Beijing, China
| | - Zhaojun Jia
- Department of Pharmaceutical Engineering, Beijing Institute of Petrochemical Technology, Beijing, China
- Beijing Institute of Petrochemical Technology, College of New Materials and Chemical Engineering, Beijing, China
| | - Xingquan Pan
- Department of Pharmaceutical Engineering, Beijing Institute of Petrochemical Technology, Beijing, China
- Beijing Institute of Petrochemical Technology, College of New Materials and Chemical Engineering, Beijing, China
| | - Weiqi Cai
- Department of Pharmaceutical Engineering, Beijing Institute of Petrochemical Technology, Beijing, China
- Beijing Institute of Petrochemical Technology, College of New Materials and Chemical Engineering, Beijing, China
| | - Mengge Sun
- Department of Pharmaceutical Engineering, Beijing Institute of Petrochemical Technology, Beijing, China
- Beijing Institute of Petrochemical Technology, College of New Materials and Chemical Engineering, Beijing, China
| | - Xuezhong Wang
- Department of Pharmaceutical Engineering, Beijing Institute of Petrochemical Technology, Beijing, China
- Beijing Key Laboratory of Enze Biomass and Fine Chemicals, Beijing Institute of Petrochemical Technology, Beijing, China
- Beijing Institute of Petrochemical Technology, College of New Materials and Chemical Engineering, Beijing, China
| | - Zhenguo Wen
- Department of Pharmaceutical Engineering, Beijing Institute of Petrochemical Technology, Beijing, China
- Beijing Key Laboratory of Enze Biomass and Fine Chemicals, Beijing Institute of Petrochemical Technology, Beijing, China
- Beijing Institute of Petrochemical Technology, College of New Materials and Chemical Engineering, Beijing, China
| | - Guangzheng Zhou
- Beijing Key Laboratory of Enze Biomass and Fine Chemicals, Beijing Institute of Petrochemical Technology, Beijing, China
- Beijing Institute of Petrochemical Technology, College of New Materials and Chemical Engineering, Beijing, China
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Ayunts H, Grigoryan A, Agaian S. Novel Entropy for Enhanced Thermal Imaging and Uncertainty Quantification. ENTROPY (BASEL, SWITZERLAND) 2024; 26:374. [PMID: 38785623 PMCID: PMC11120493 DOI: 10.3390/e26050374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 04/17/2024] [Accepted: 04/22/2024] [Indexed: 05/25/2024]
Abstract
This paper addresses the critical need for precise thermal modeling in electronics, where temperature significantly impacts system reliability. We emphasize the necessity of accurate temperature measurement and uncertainty quantification in thermal imaging, a vital tool across multiple industries. Current mathematical models and uncertainty measures, such as Rényi and Shannon entropies, are inadequate for the detailed informational content required in thermal images. Our work introduces a novel entropy that effectively captures the informational content of thermal images by combining local and global data, surpassing existing metrics. Validated by rigorous experimentation, this method enhances thermal images' reliability and information preservation. We also present two enhancement frameworks that integrate an optimized genetic algorithm and image fusion techniques, improving image quality by reducing artifacts and enhancing contrast. These advancements offer significant contributions to thermal imaging and uncertainty quantification, with broad applications in various sectors.
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Affiliation(s)
- Hrach Ayunts
- Informatics and Applied Mathematics Department, Yerevan State University, Yerevan 0025, Armenia
| | - Artyom Grigoryan
- Department of Electrical and Computer Engineering, The University of Texas at San Antonio, San Antonio, TX 78249, USA;
| | - Sos Agaian
- Computer Science Department, Graduate Center, College of Staten Island (CSI), City University of New York, New York, NY 10314, USA;
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Wei W, Wu H, He Y, Li Q. A multi-objective optimized OLSR routing protocol. PLoS One 2024; 19:e0301842. [PMID: 38669218 PMCID: PMC11051643 DOI: 10.1371/journal.pone.0301842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Accepted: 03/23/2024] [Indexed: 04/28/2024] Open
Abstract
The rapid development of mobile communication devices has brought challenges to wireless networks, where data packets are able to organize and maintain local area networks more freely without the constraints of wired devices. Scholars have developed diverse network protocols on how to ensure data transmission while maintaining its self-organizational nature. However, it is difficult for traditional network protocols to meet the needs of increasingly complex networks. In order to solve the problem that the better node set may not be selected when selecting the node set responsible for forwarding in the traditional OLSR protocol, a multi-objective optimized OLSR algorithm is proposed in this paper, which incorporating a new MPR mechanism and an improved NSGA-II algorithm. In the process of route discovery, the intermediate nodes responsible for forwarding packets are determined by the new MPR mechanism, and then the main parameters in the OLSR protocol are provided by the multi-objective optimization algorithm. Matlab was used to build a self-organizing network in this study. In addition, the conventional OLSR protocol, NSGA-II algorithm and multi-objective simulated annealing algorithm are selected to compare with the proposed algorithm. Simulation results show that the proposed algorithm can effectively reduce packet loss and end-to-end delay while obtaining better results in HV and Spacing, two multi-objective optimization result evaluation metrics.
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Affiliation(s)
- Wenhong Wei
- School of Computer Science and Technology, Dongguan University of Technology, Dongguan, China
| | - Huijia Wu
- School of Computer Science and Technology, Dongguan University of Technology, Dongguan, China
| | - Ying He
- School of Computer, Neusoft Institute Guangdong, Foshan, China
| | - Qingxia Li
- School of Artificial Intelligence, Dongguan City University, Dongguan, China
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22
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Huang G, Tan M, Meng Z, Yan J, Chen J, Qu Q. Optimizing hydropower scheduling through accurate power load prediction: A practical case study. Heliyon 2024; 10:e28312. [PMID: 38571578 PMCID: PMC10987994 DOI: 10.1016/j.heliyon.2024.e28312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Revised: 03/11/2024] [Accepted: 03/15/2024] [Indexed: 04/05/2024] Open
Abstract
Hydropower stations that are part of the grid system frequently encounter challenges related to the uneven distribution of power generation and associated benefits, primarily stemming from delays in obtaining timely load data. This research addresses this issue by developing a scheduling model that combines power load prediction and dual-objective optimization. The practical application of this model is demonstrated in a real-case scenario, focusing on the Shatuo Hydropower Station in China. In contrast to current models, the suggested model can achieve optimal dispatch for grid-connected hydropower stations even when power load data is unavailable. Initially, the model assesses various prediction models for estimating power load and subsequently incorporates the predictions into the GA-NSGA-II algorithm, specifically an enhanced elite non-dominated sorting genetic algorithm. This integration is performed while considering the proposed objective functions to optimize the discharge flow of the hydropower station. The outcomes reveal that the CNN-GRU model, denoting Convolutional Neural Network-Gated Recursive Unit, exhibits the highest prediction accuracy, achieving R-squared and RMSE (i.e., Root Mean Square Error) values of 0.991 and 0.026, respectively. The variance between scheduling based on predicted load values and actual load values is minimal, staying within 5 (m 3 / s ), showcasing practical effectiveness. The optimized scheduling outcomes in the real case study yield dual advantages, meeting both the demands of ship navigation and hydropower generation, thus achieving a harmonious balance between the two requirements. This approach addresses the real-world challenges associated with delayed load data collection and insufficient scheduling, offering an efficient solution for managing hydropower station scheduling to meet both power generation and navigation needs.
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Affiliation(s)
- Guangqin Huang
- Guizhou Wujiang River Navigation Authority, Tongren, 565100, Guizhou, China
| | - Ming Tan
- Guizhou Wujiang River Navigation Authority, Tongren, 565100, Guizhou, China
| | - Zhihang Meng
- College of Mathematics and Information Science, Hebei University, Baoding, 071002, Hebei, China
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, Guangdong, China
| | - Jiaqi Yan
- Guizhou Silin Navigation Authority, Tongren, 565100, Guizhou, China
| | - Jin Chen
- Guizhou Zhongnan Transport Technology co. Ltd., Guiyang, 550000, Guizhou, China
| | - Qiang Qu
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, Guangdong, China
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Yuan T, Mu Y, Wang T, Liu Z, Pirouzi A. Using firefly algorithm to optimally size a hybrid renewable energy system constrained by battery degradation and considering uncertainties of power sources and loads. Heliyon 2024; 10:e26961. [PMID: 38590876 PMCID: PMC10999815 DOI: 10.1016/j.heliyon.2024.e26961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 02/03/2024] [Accepted: 02/22/2024] [Indexed: 04/10/2024] Open
Abstract
In this paper, the planning of a hybrid system of wind turbine units, photovoltaic panels, and battery storage is presented by taking into account the limitation of the storage degradation. The scheme minimizes the construction and maintenance cost of power sources and storage equipment. The constraints of the problem include the operating model of the mentioned elements, the limitation of the number of the mentioned elements, the limitation of the storage degradation, and the power balance in the hybrid system. This scheme is subject to uncertainties of the demand and output power generation of wind turbines and photovoltaics, which are modeled using a scenario-based stochastic optimization. The problem has a mixed-integer non-linear structure, and the paper adopts the firefly algorithm to solve the problem. The contributions of the paper include considering the degradation model of the battery, presenting a stochastic modelling for planning the islanded system, and taking into account the uncertainties of load and renewable power. Finally, based on the numerical results, a low planning cost is obtained for the hybrid system in the case of using renewable resources. Batteries are capable of providing flexibility for the hybrid system so that they can cover oscillations of renewable power with respect to the load. The firefly algorithm can find a reliable optimal solution. Stochastic modeling raises the planning cost of the islanded system in comparison to the deterministic model, but it yields a more reliable solution. The battery degradation model incurs no additional costs in system planning, although it offers a far more precise representation of the battery's behavior.
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Affiliation(s)
- Tianmeng Yuan
- Tangshan Power Supply Company State Grid Jibei Electric Power Co.Ltd, Tangshan, 063000, Hebei, China
| | - Yong Mu
- Tangshan Power Supply Company State Grid Jibei Electric Power Co.Ltd, Tangshan, 063000, Hebei, China
| | - Tao Wang
- Tangshan Power Supply Company State Grid Jibei Electric Power Co.Ltd, Tangshan, 063000, Hebei, China
| | - Ziming Liu
- Tangshan Power Supply Company State Grid Jibei Electric Power Co.Ltd, Tangshan, 063000, Hebei, China
| | - Afshin Pirouzi
- Department of Engineering, Semirom Branch, Islamic Azad University, Semirom, Iran
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24
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Sahimi M. Physics-informed and data-driven discovery of governing equations for complex phenomena in heterogeneous media. Phys Rev E 2024; 109:041001. [PMID: 38755895 DOI: 10.1103/physreve.109.041001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Indexed: 05/18/2024]
Abstract
Rapid evolution of sensor technology, advances in instrumentation, and progress in devising data-acquisition software and hardware are providing vast amounts of data for various complex phenomena that occur in heterogeneous media, ranging from those in atmospheric environment, to large-scale porous formations, and biological systems. The tremendous increase in the speed of scientific computing has also made it possible to emulate diverse multiscale and multiphysics phenomena that contain elements of stochasticity or heterogeneity, and to generate large volumes of numerical data for them. Thus, given a heterogeneous system with annealed or quenched disorder in which a complex phenomenon occurs, how should one analyze and model the system and phenomenon, explain the data, and make predictions for length and time scales much larger than those over which the data were collected? We divide such systems into three distinct classes. (i) Those for which the governing equations for the physical phenomena of interest, as well as data, are known, but solving the equations over large length scales and long times is very difficult. (ii) Those for which data are available, but the governing equations are only partially known, in the sense that they either contain various coefficients that must be evaluated based on the data, or that the number of degrees of freedom of the system is so large that deriving the complete equations is very difficult, if not impossible, as a result of which one must develop the governing equations with reduced dimensionality. (iii) In the third class are systems for which large amounts of data are available, but the governing equations for the phenomena of interest are not known. Several classes of physics-informed and data-driven approaches for analyzing and modeling of the three classes of systems have been emerging, which are based on machine learning, symbolic regression, the Koopman operator, the Mori-Zwanzig projection operator formulation, sparse identification of nonlinear dynamics, data assimilation combined with a neural network, and stochastic optimization and analysis. This perspective describes such methods and the latest developments in this highly important and rapidly expanding area and discusses possible future directions.
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Affiliation(s)
- Muhammad Sahimi
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, California 90089-1211, USA
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25
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Rosa LAS, Brugnago EL, Delben GJ, Rost JM, Beims MW. The influence of hyperchaoticity, synchronization, and Shannon entropy on the performance of a physical reservoir computer. CHAOS (WOODBURY, N.Y.) 2024; 34:043120. [PMID: 38579146 DOI: 10.1063/5.0175001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 03/21/2024] [Indexed: 04/07/2024]
Abstract
In this paper, we analyze the dynamic effect of a reservoir computer (RC) on its performance. Modified Kuramoto's coupled oscillators are used to model the RC, and synchronization, Lyapunov spectrum (and dimension), Shannon entropy, and the upper bound of the Kolmogorov-Sinai entropy are employed to characterize the dynamics of the RC. The performance of the RC is analyzed by reproducing the distribution of random, Gaussian, and quantum jumps series (shelved states) since a replica of the time evolution of a completely random series is not possible to generate. We demonstrate that hyperchaotic motion, moderate Shannon entropy, and a higher degree of synchronization of Kuramoto's oscillators lead to the best performance of the RC. Therefore, an appropriate balance of irregularity and order in the oscillator's dynamics leads to better performances.
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Affiliation(s)
- Lucas A S Rosa
- Departamento de Física, Universidade Federal do Paraná, 81531-980 Curitiba, Paraná, Brazil
| | - Eduardo L Brugnago
- Instituto de Fí sica, Universidade de São Paulo, 05508-090 São Paulo, SP, Brazil
| | - Guilherme J Delben
- Departamento de Ciências Naturais e Sociais, Universidade Federal de Santa Catarina, 89520-000 Curitibanos, SC, Brazil
| | - Jan-Michael Rost
- Max-Planck Institute for the Physics of Complex Systems, Nöthnitzerstr.38, 01187 Dresden, Germany
| | - Marcus W Beims
- Departamento de Física, Universidade Federal do Paraná, 81531-980 Curitiba, Paraná, Brazil
- Max-Planck Institute for the Physics of Complex Systems, Nöthnitzerstr.38, 01187 Dresden, Germany
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26
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Aalloul R, Elaissaoui A, Harkani A, Adhiri R, Benlattar M. A simulation and modeling approach of coupled thermal and electrical behavior of PV panels using the artificial hummingbird algorithm and two-dimensional finite difference-based model. Heliyon 2024; 10:e27244. [PMID: 38515667 PMCID: PMC10955209 DOI: 10.1016/j.heliyon.2024.e27244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 02/26/2024] [Accepted: 02/27/2024] [Indexed: 03/23/2024] Open
Abstract
Accurate estimation of photovoltaic (PV) panels' temperature is crucial for an accurate assessment for both the electrical and thermal aspects and performances. In this study we propose an advanced simulation approach linking a double-diode (DD) electrical model using the Artificial hummingbird algorithm; for parameter extraction; and a two-dimensional finite-difference-based thermal model. The electrical-sub model is firstly validated in comparison to experimental data figuring in literature using three types of PV technologies, with a relative error of about 2%. Then, the coupled model is validated using in-situ experimental setup consisting of the usage of thin-film PV technology, temperature sensors, weather station and an infrared camera. The results from both simulations and experiments exhibit strong alignment with a relative error of not higher than 2%; mainly due to the used material calibration uncertainties and external perturbations. This holistic model can be indeed further optimized, still, it has a potential to advance the development in the research area of PV systems.Future efforts could involve additional experimentation to validate the model for different seasons of the year.
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Affiliation(s)
- Radouane Aalloul
- Laboratory of Engineering and Materials (LIMAT), Faculty of Sciences, Ben M'Sick Hassan II University of Casablanca, Casablanca, 20000, Morocco
- Laboratory of Agricultural Engineering, Energy National Institute of Agricultural Research Settat, Settat, 26000, Morocco
| | - Abdellah Elaissaoui
- Laboratory of Agricultural Engineering, Energy National Institute of Agricultural Research Settat, Settat, 26000, Morocco
| | - Assia Harkani
- Laboratory of Agricultural Engineering, Energy National Institute of Agricultural Research Settat, Settat, 26000, Morocco
| | - Rhma Adhiri
- Laboratory of Engineering and Materials (LIMAT), Faculty of Sciences, Ben M'Sick Hassan II University of Casablanca, Casablanca, 20000, Morocco
| | - Mourad Benlattar
- Matter Physics Laboratory (LPM), Faculty of Sciences, Ben M'Sick Hassan II University of Casablanca, Casablanca, 20000, Morocco
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27
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Han H, Sha R, Dai J, Wang Z, Mao J, Cai M. Garlic Origin Traceability and Identification Based on Fusion of Multi-Source Heterogeneous Spectral Information. Foods 2024; 13:1016. [PMID: 38611322 PMCID: PMC11012206 DOI: 10.3390/foods13071016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 03/20/2024] [Accepted: 03/25/2024] [Indexed: 04/14/2024] Open
Abstract
The chemical composition and nutritional content of garlic are greatly impacted by its production location, leading to distinct flavor profiles and functional properties among garlic varieties from diverse origins. Consequently, these variations determine the preference and acceptance among diverse consumer groups. In this study, purple-skinned garlic samples were collected from five regions in China: Yunnan, Shandong, Henan, Anhui, and Jiangsu Provinces. Mid-infrared spectroscopy and ultraviolet spectroscopy were utilized to analyze the components of garlic cells. Three preprocessing methods, including Multiple Scattering Correction (MSC), Savitzky-Golay Smoothing (SG Smoothing), and Standard Normalized Variate (SNV), were applied to reduce the background noise of spectroscopy data. Following variable feature extraction by Genetic Algorithm (GA), a variety of machine learning algorithms, including XGboost, Support Vector Classification (SVC), Random Forest (RF), and Artificial Neural Network (ANN), were used according to the fusion of spectral data to obtain the best processing results. The results showed that the best-performing model for ultraviolet spectroscopy data was SNV-GA-ANN, with an accuracy of 99.73%. The best-performing model for mid-infrared spectroscopy data was SNV-GA-RF, with an accuracy of 97.34%. After the fusion of ultraviolet and mid-infrared spectroscopy data, the SNV-GA-SVC, SNV-GA-RF, SNV-GA-ANN, and SNV-GA-XGboost models achieved 100% accuracy in both training and test sets. Although there were some differences in the accuracy of the four models under different preprocessing methods, the fusion of ultraviolet and mid-infrared spectroscopy data yielded the best outcomes, with an accuracy of 100%. Overall, the combination of ultraviolet and mid-infrared spectroscopy data fusion and chemometrics established in this study provides a theoretical foundation for identifying the origin of garlic, as well as that of other agricultural products.
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Affiliation(s)
- Hao Han
- School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China; (H.H.); (J.D.); (Z.W.); (J.M.); (M.C.)
- Zhejiang Provincial Key Laboratory for Chemical & Biological Processing Technology of Farm Product, Hangzhou 310023, China
| | - Ruyi Sha
- School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China; (H.H.); (J.D.); (Z.W.); (J.M.); (M.C.)
- Zhejiang Provincial Key Laboratory for Chemical & Biological Processing Technology of Farm Product, Hangzhou 310023, China
| | - Jing Dai
- School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China; (H.H.); (J.D.); (Z.W.); (J.M.); (M.C.)
- Zhejiang Provincial Key Laboratory for Chemical & Biological Processing Technology of Farm Product, Hangzhou 310023, China
| | - Zhenzhen Wang
- School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China; (H.H.); (J.D.); (Z.W.); (J.M.); (M.C.)
- Zhejiang Provincial Key Laboratory for Chemical & Biological Processing Technology of Farm Product, Hangzhou 310023, China
| | - Jianwei Mao
- School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China; (H.H.); (J.D.); (Z.W.); (J.M.); (M.C.)
- Zhejiang Provincial Key Laboratory for Chemical & Biological Processing Technology of Farm Product, Hangzhou 310023, China
| | - Min Cai
- School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China; (H.H.); (J.D.); (Z.W.); (J.M.); (M.C.)
- Zhejiang Provincial Key Laboratory for Chemical & Biological Processing Technology of Farm Product, Hangzhou 310023, China
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28
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Ionescu CM, Copot D, Yumuk E, De Keyser R, Muresan C, Birs IR, Ben Othman G, Farbakhsh H, Ynineb AR, Neckebroek M. Development, Validation, and Comparison of a Novel Nociception/Anti-Nociception Monitor against Two Commercial Monitors in General Anesthesia. SENSORS (BASEL, SWITZERLAND) 2024; 24:2031. [PMID: 38610243 PMCID: PMC11013864 DOI: 10.3390/s24072031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 03/12/2024] [Accepted: 03/20/2024] [Indexed: 04/14/2024]
Abstract
In this paper, we present the development and the validation of a novel index of nociception/anti-nociception (N/AN) based on skin impedance measurement in time and frequency domain with our prototype AnspecPro device. The primary objective of the study was to compare the Anspec-PRO device with two other commercial devices (Medasense, Medstorm). This comparison was designed to be conducted under the same conditions for the three devices. This was carried out during total intravenous anesthesia (TIVA) by investigating its outcomes related to noxious stimulus. In a carefully designed clinical protocol during general anesthesia from induction until emergence, we extract data for estimating individualized causal dynamic models between drug infusion and their monitored effect variables. Specifically, these are Propofol hypnotic drug to Bispectral index of hypnosis level and Remifentanil opioid drug to each of the three aforementioned devices. When compared, statistical analysis of the regions before and during the standardized stimulus shows consistent difference between regions for all devices and for all indices. These results suggest that the proposed methodology for data extraction and processing for AnspecPro delivers the same information as the two commercial devices.
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Affiliation(s)
- Clara M. Ionescu
- Department of Electromechanics, System and Metal Engineering, Ghent University, 9052 Ghent, Belgium; (C.M.I.); (E.Y.); (R.D.K.); (I.R.B.); (G.B.O.); (H.F.); (A.R.Y.)
- Department of Automation, Technical University Cluj-Napoca, Memorandumului Street 20, 400114 Cluj, Romania;
| | - Dana Copot
- Department of Electromechanics, System and Metal Engineering, Ghent University, 9052 Ghent, Belgium; (C.M.I.); (E.Y.); (R.D.K.); (I.R.B.); (G.B.O.); (H.F.); (A.R.Y.)
| | - Erhan Yumuk
- Department of Electromechanics, System and Metal Engineering, Ghent University, 9052 Ghent, Belgium; (C.M.I.); (E.Y.); (R.D.K.); (I.R.B.); (G.B.O.); (H.F.); (A.R.Y.)
- Department of Control and Automation Engineering, Istanbul Technical University, Maslak, Istanbul 34469, Turkey
| | - Robin De Keyser
- Department of Electromechanics, System and Metal Engineering, Ghent University, 9052 Ghent, Belgium; (C.M.I.); (E.Y.); (R.D.K.); (I.R.B.); (G.B.O.); (H.F.); (A.R.Y.)
| | - Cristina Muresan
- Department of Automation, Technical University Cluj-Napoca, Memorandumului Street 20, 400114 Cluj, Romania;
| | - Isabela Roxana Birs
- Department of Electromechanics, System and Metal Engineering, Ghent University, 9052 Ghent, Belgium; (C.M.I.); (E.Y.); (R.D.K.); (I.R.B.); (G.B.O.); (H.F.); (A.R.Y.)
- Department of Automation, Technical University Cluj-Napoca, Memorandumului Street 20, 400114 Cluj, Romania;
| | - Ghada Ben Othman
- Department of Electromechanics, System and Metal Engineering, Ghent University, 9052 Ghent, Belgium; (C.M.I.); (E.Y.); (R.D.K.); (I.R.B.); (G.B.O.); (H.F.); (A.R.Y.)
| | - Hamed Farbakhsh
- Department of Electromechanics, System and Metal Engineering, Ghent University, 9052 Ghent, Belgium; (C.M.I.); (E.Y.); (R.D.K.); (I.R.B.); (G.B.O.); (H.F.); (A.R.Y.)
| | - Amani R. Ynineb
- Department of Electromechanics, System and Metal Engineering, Ghent University, 9052 Ghent, Belgium; (C.M.I.); (E.Y.); (R.D.K.); (I.R.B.); (G.B.O.); (H.F.); (A.R.Y.)
| | - Martine Neckebroek
- Department of Anesthesia, Ghent University Hospital, Corneel Heymanslaan 10, 9000 Ghent, Belgium;
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29
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Yao HT, Marchand B, Berkemer SJ, Ponty Y, Will S. Infrared: a declarative tree decomposition-powered framework for bioinformatics. Algorithms Mol Biol 2024; 19:13. [PMID: 38493130 PMCID: PMC10943887 DOI: 10.1186/s13015-024-00258-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 02/13/2024] [Indexed: 03/18/2024] Open
Abstract
MOTIVATION Many bioinformatics problems can be approached as optimization or controlled sampling tasks, and solved exactly and efficiently using Dynamic Programming (DP). However, such exact methods are typically tailored towards specific settings, complex to develop, and hard to implement and adapt to problem variations. METHODS We introduce the Infrared framework to overcome such hindrances for a large class of problems. Its underlying paradigm is tailored toward problems that can be declaratively formalized as sparse feature networks, a generalization of constraint networks. Classic Boolean constraints specify a search space, consisting of putative solutions whose evaluation is performed through a combination of features. Problems are then solved using generic cluster tree elimination algorithms over a tree decomposition of the feature network. Their overall complexities are linear on the number of variables, and only exponential in the treewidth of the feature network. For sparse feature networks, associated with low to moderate treewidths, these algorithms allow to find optimal solutions, or generate controlled samples, with practical empirical efficiency. RESULTS Implementing these methods, the Infrared software allows Python programmers to rapidly develop exact optimization and sampling applications based on a tree decomposition-based efficient processing. Instead of directly coding specialized algorithms, problems are declaratively modeled as sets of variables over finite domains, whose dependencies are captured by constraints and functions. Such models are then automatically solved by generic DP algorithms. To illustrate the applicability of Infrared in bioinformatics and guide new users, we model and discuss variants of bioinformatics applications. We provide reimplementations and extensions of methods for RNA design, RNA sequence-structure alignment, parsimony-driven inference of ancestral traits in phylogenetic trees/networks, and design of coding sequences. Moreover, we demonstrate multidimensional Boltzmann sampling. These applications of the framework-together with our novel results-underline the practical relevance of Infrared. Remarkably, the achieved complexities are typically equivalent to the ones of specialized algorithms and implementations. AVAILABILITY Infrared is available at https://amibio.gitlabpages.inria.fr/Infrared with extensive documentation, including various usage examples and API reference; it can be installed using Conda or from source.
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Affiliation(s)
- Hua-Ting Yao
- LIX, CNRS UMR 7161, Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau, France.
- Department of Theoretical Chemistry, University of Vienna, Vienna, Austria.
- School of Computer Science, McGill University, Montreal, Canada.
| | - Bertrand Marchand
- LIX, CNRS UMR 7161, Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau, France
| | - Sarah J Berkemer
- LIX, CNRS UMR 7161, Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau, France
- Earth-Life Science Institute, Tokyo Institute of Technology, Tokyo, Japan
| | - Yann Ponty
- LIX, CNRS UMR 7161, Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau, France
| | - Sebastian Will
- LIX, CNRS UMR 7161, Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau, France.
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30
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Su H, Dong Z, Liu Y, Mu Y, Li S, Xia L. Symmetric projection optimizer: concise and efficient solving engineering problems using the fundamental wave of the Fourier series. Sci Rep 2024; 14:6032. [PMID: 38472260 DOI: 10.1038/s41598-024-56521-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 03/07/2024] [Indexed: 03/14/2024] Open
Abstract
The fitness function value is a kind of important information in the search process, which can be more targeted according to the guidance of the fitness function value. Most existing meta-heuristic algorithms only use the fitness function value as an indicator to compare the current variables as good or bad but do not use the fitness function value in the search process. To address this problem, the mathematical idea of the fitting is introduced into the meta-heuristic algorithm, and a symmetric projection optimizer (SPO) is proposed to solve numerical optimization and engineering problems more efficiently. The SPO algorithm mainly utilizes a new search mechanism, the symmetric projection search (SP) method. The SP method quickly completes the fitting of the projection plane, which is located through the symmetry of the two points and finds the minima in the projection plane according to the fitting result. Fitting by using the fitness function values allows the SP to find regions where extreme values may exist more quickly. Based on the SP method, exploration and exploitation strategies are constructed, respectively. The exploration strategy is used to find better regions, and the exploitation strategy is used to optimize the discovered regions continuously. The timing of the use of the two strategies is designed so that the SPO algorithm can converge faster while avoiding falling into local optima. The effectiveness of the SPO algorithm is extensively evaluated using seven test suites, including CEC2017, CEC2019, CEC2020, and CEC2022. It is also compared with two sets of 19 recent competitive algorithms. Statistical analyses are performed using five metrics such as the Wilcoxon test, the Friedman test, and variance. Finally, the practicality of the SPO algorithm is verified by four typical engineering problems and a real spacecraft trajectory optimization problem. The results show that the SPO algorithm can find superior results in 94.6% of the comparison tests and is a promising alternative for solving real-world problems.
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Affiliation(s)
- Haoxiang Su
- Spece Engineering University, Bayi Road, Huairou, Beijing, 101400, China
| | - Zhenghong Dong
- Spece Engineering University, Bayi Road, Huairou, Beijing, 101400, China.
| | - Yi Liu
- Spece Engineering University, Bayi Road, Huairou, Beijing, 101400, China
| | - Yao Mu
- Spece Engineering University, Bayi Road, Huairou, Beijing, 101400, China
| | - Sen Li
- Spece Engineering University, Bayi Road, Huairou, Beijing, 101400, China
| | - Lurui Xia
- Spece Engineering University, Bayi Road, Huairou, Beijing, 101400, China
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31
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Moon SW, Min SK. Gaussian Process Regression-Based Near-Infrared d-Luciferin Analogue Design Using Mutation-Controlled Graph-Based Genetic Algorithm. J Chem Inf Model 2024; 64:1522-1532. [PMID: 38365605 DOI: 10.1021/acs.jcim.3c00870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2024]
Abstract
Molecular discovery is central to the field of chemical informatics. Although optimization approaches have been developed that target-specific molecular properties in combination with machine learning techniques, optimization using databases of limited size is challenging for efficient molecular design. We present a molecular design method with a Gaussian process regression model and a graph-based genetic algorithm (GB-GA) from a data set comprising a small number of compounds by introducing mutation probability control in the genetic algorithm to enhance the optimization capability and speed up the convergence to the optimal solution. In addition, we propose reducing the number of parameters in the conventional GB-GA focusing on efficient molecular design from a small database. We generated a target-specific database by combining active learning and iterative design in the evolutionary methodologies and chose Gaussian process regression as the prediction model for molecular properties. We show that the proposed scheme is more efficient for optimization toward the target properties from goal-directed benchmarks with several drug-like molecules compared to the conventional GB-GA method. Finally, we provide a demonstration whereby we designed D-luciferin analogues with near-infrared fluorescence for bioimaging, which is desirable for effective in vivo light sources, from a small-size data set.
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Affiliation(s)
- Sung Wook Moon
- Departmet of Chemistry, School of Natural Science, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-gil, Ulju-gun, Ulsan 44919, South Korea
| | - Seung Kyu Min
- Departmet of Chemistry, School of Natural Science, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-gil, Ulju-gun, Ulsan 44919, South Korea
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Kim YA, Mousavi K, Yazdi A, Zwierzyna M, Cardinali M, Fox D, Peel T, Coller J, Aggarwal K, Maruggi G. Computational design of mRNA vaccines. Vaccine 2024; 42:1831-1840. [PMID: 37479613 DOI: 10.1016/j.vaccine.2023.07.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 06/23/2023] [Accepted: 07/10/2023] [Indexed: 07/23/2023]
Abstract
mRNA technology has emerged as a successful vaccine platform that offered a swift response to the COVID-19 pandemic. Accumulating evidence shows that vaccine efficacy, thermostability, and other important properties, are largely impacted by intrinsic properties of the mRNA molecule, such as RNA sequence and structure, both of which can be optimized. Designing mRNA sequence for vaccines presents a combinatorial problem due to an extremely large selection space. For instance, due to the degeneracy of the genetic code, there are over 10632 possible mRNA sequences that could encode the spike protein, the COVID-19 vaccines' target. Moreover, designing different elements of the mRNA sequence simultaneously against multiple objectives such as translational efficiency, reduced reactogenicity, and improved stability requires an efficient and sophisticated optimization strategy. Recently, there has been a growing interest in utilizing computational tools to redesign mRNA sequences to improve vaccine characteristics and expedite discovery timelines. In this review, we explore important biophysical features of mRNA to be considered for vaccine design and discuss how computational approaches can be applied to rapidly design mRNA sequences with desirable characteristics.
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Affiliation(s)
| | | | | | | | | | | | | | - Jeff Coller
- Johns Hopkins University, Baltimore, MD, USA
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33
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Ai C, Han S, Yang X, Vegge T, Hansen HA. Graph Neural Network-Accelerated Multitasking Genetic Algorithm for Optimizing Pd xTi 1-xH y Surfaces under Various CO 2 Reduction Reaction Conditions. ACS APPLIED MATERIALS & INTERFACES 2024. [PMID: 38437157 DOI: 10.1021/acsami.3c18734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2024]
Abstract
Palladium (Pd) hydride-based catalysts have been reported to have excellent performance in the CO2 reduction reaction (CO2RR) and hydrogen evolution reaction (HER). Our previous work on doped PdH and Pd alloy hydrides showed that Ti-doped and Ti-alloyed Pd hydrides could improve the performance of the CO2 reduction reaction compared with pure Pd hydride. Compositions and chemical orderings of the surfaces with only one adsorbate under certain reaction conditions are linked to their stability, activity, and selectivity toward the CO2RR and HER, as shown in our previous work. In fact, various coverages, types, and mixtures of the adsorbates, as well as state variables such as temperature, pressure, applied potential, and chemical potential, could impact their stability, activity, and selectivity. However, these factors are usually fixed at common values to reduce the complexity of the structures and the complexity of the reaction conditions in most theoretical work. To address the complexities above and the huge search space, we apply a deep learning-assisted multitasking genetic algorithm to screen for PdxTi1-xHy surfaces containing multiple adsorbates for CO2RR under different reaction conditions. The ensemble deep learning model can greatly speed up the structure relaxations and retain a high accuracy and low uncertainty of the energy and forces. The multitasking genetic algorithm simultaneously finds globally stable surface structures under each reaction condition. Finally, 23 stable structures are screened out under different reaction conditions. Among these, Pd0.56Ti0.44H1.06 + 25%CO, Pd0.31Ti0.69H1.25 + 50%CO, Pd0.31Ti0.69H1.25 + 25%CO, and Pd0.88Ti0.12H1.06 + 25%CO are found to be very active for CO2RR and suitable to generate syngas consisting of CO and H2.
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Affiliation(s)
- Changzhi Ai
- Department of Energy Conversion and Storage, Technical University of Denmark, Anker Engelunds Vej, 2800 Kongens Lyngby, Denmark
| | - Shuang Han
- Department of Energy Conversion and Storage, Technical University of Denmark, Anker Engelunds Vej, 2800 Kongens Lyngby, Denmark
| | - Xin Yang
- Department of Energy Conversion and Storage, Technical University of Denmark, Anker Engelunds Vej, 2800 Kongens Lyngby, Denmark
| | - Tejs Vegge
- Department of Energy Conversion and Storage, Technical University of Denmark, Anker Engelunds Vej, 2800 Kongens Lyngby, Denmark
| | - Heine Anton Hansen
- Department of Energy Conversion and Storage, Technical University of Denmark, Anker Engelunds Vej, 2800 Kongens Lyngby, Denmark
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Halužan Vasle A, Moškon M. Synthetic biological neural networks: From current implementations to future perspectives. Biosystems 2024; 237:105164. [PMID: 38402944 DOI: 10.1016/j.biosystems.2024.105164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 01/03/2024] [Accepted: 02/21/2024] [Indexed: 02/27/2024]
Abstract
Artificial neural networks, inspired by the biological networks of the human brain, have become game-changing computing models in modern computer science. Inspired by their wide scope of applications, synthetic biology strives to create their biological counterparts, which we denote synthetic biological neural networks (SYNBIONNs). Their use in the fields of medicine, biosensors, biotechnology, and many more shows great potential and presents exciting possibilities. So far, many different synthetic biological networks have been successfully constructed, however, SYNBIONN implementations have been sparse. The latter are mostly based on neural networks pretrained in silico and being heavily dependent on extensive human input. In this paper, we review current implementations and models of SYNBIONNs. We briefly present the biological platforms that show potential for designing and constructing perceptrons and/or multilayer SYNBIONNs. We explore their future possibilities along with the challenges that must be overcome to successfully implement a scalable in vivo biological neural network capable of online learning.
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Affiliation(s)
- Ana Halužan Vasle
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Miha Moškon
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
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35
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Kumar A, Kumar V, Ojha PK, Roy K. Chronic aquatic toxicity assessment of diverse chemicals on Daphnia magna using QSAR and chemical read-across. Regul Toxicol Pharmacol 2024; 148:105572. [PMID: 38325631 DOI: 10.1016/j.yrtph.2024.105572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 01/06/2024] [Accepted: 01/26/2024] [Indexed: 02/09/2024]
Abstract
We have modeled here chronic Daphnia toxicity taking pNOEC (negative logarithm of no observed effect concentration in mM) and pEC50 (negative logarithm of half-maximal effective concentration in mM) as endpoints using QSAR and chemical read-across approaches. The QSAR models were developed by strictly obeying the OECD guidelines and were found to be reliable, predictive, accurate, and robust. From the selected features in the developed models, we have found that an increase in lipophilicity and saturation, the presence of electrophilic or electronegative or heavy atoms, the presence of sulphur, amine, and their related functionality, an increase in mean atomic polarizability, and higher number of (thio-) carbamates (aromatic) groups are responsible for chronic toxicity. Therefore, this information might be useful for the development of environmentally friendly and safer chemicals and data-gap filling as well as reducing the use of identified toxic chemicals which have chronic toxic effects on aquatic ecosystems. Approved classes of drugs from DrugBank databases and diverse groups of chemicals from the Chemical and Product Categories (CPDat) database were also assessed through the developed models.
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Affiliation(s)
- Ankur Kumar
- Drug Discovery and Development (DDD) Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Vinay Kumar
- Drug Theoretics and Cheminformatics (DTC) Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Probir Kumar Ojha
- Drug Discovery and Development (DDD) Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Kunal Roy
- Drug Theoretics and Cheminformatics (DTC) Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India.
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36
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Dong Z, Li X, Luan F, Cui C, Ding J, Zhang D. Rolling theory-guided prediction of hot-rolled plate width based on parameter transfer strategy. ISA TRANSACTIONS 2024; 146:352-365. [PMID: 38278755 DOI: 10.1016/j.isatra.2024.01.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Revised: 01/08/2024] [Accepted: 01/08/2024] [Indexed: 01/28/2024]
Abstract
Machine learning performs well in many problems. However, the tendency to generate predictions that violate theoretical knowledge makes it difficult to apply to practical processing. To resolve this situation, this paper combines domain knowledge with a data-driven model, proposes a theory-guided machine learning framework based on a parameter transfer strategy, and applies it to the width prediction of plates after multiple passes of hot rolling. The framework applies a swarm optimization algorithm to the original theoretical model and generates numerous highly-physical consistent samples. The established deep neural network (DNN) model is trained with simulated data, and the parameters are fine-tuned using a parameter transfer strategy combined with actual data to ensure excellent adaptation to the actual environment based on adequate learning of theoretical knowledge. In tests, the proposed model had the best overall prediction performance in this paper. Meanwhile, the developed model is consistent with the existing perception of rolling theory. This allows for the quick and reliable application of machine learning models in production.
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Affiliation(s)
- Zishuo Dong
- The State Key Laboratory of Rolling and Automation, Northeastern University, Shenyang 110819, People's Republic of China
| | - Xu Li
- The State Key Laboratory of Rolling and Automation, Northeastern University, Shenyang 110819, People's Republic of China.
| | - Feng Luan
- School of Computer Science and Engineering, Northeastern University, Shenyang 110819, People's Republic of China
| | - Chunyuan Cui
- The State Key Laboratory of Rolling and Automation, Northeastern University, Shenyang 110819, People's Republic of China
| | - Jingguo Ding
- The State Key Laboratory of Rolling and Automation, Northeastern University, Shenyang 110819, People's Republic of China
| | - Dianhua Zhang
- The State Key Laboratory of Rolling and Automation, Northeastern University, Shenyang 110819, People's Republic of China
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37
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Grazioli G, Tao A, Bhatia I, Regan P. Genetic Algorithm for Automated Parameterization of Network Hamiltonian Models of Amyloid Fibril Formation. J Phys Chem B 2024; 128:1854-1865. [PMID: 38359362 PMCID: PMC10910512 DOI: 10.1021/acs.jpcb.3c07322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Revised: 01/07/2024] [Accepted: 02/05/2024] [Indexed: 02/17/2024]
Abstract
The time scales of long-time atomistic molecular dynamics simulations are typically reported in microseconds, while the time scales for experiments studying the kinetics of amyloid fibril formation are typically reported in minutes or hours. This time scale deficit of roughly 9 orders of magnitude presents a major challenge in the design of computer simulation methods for studying protein aggregation events. Coarse-grained molecular simulations offer a computationally tractable path forward for exploring the molecular mechanism driving the formation of these structures, which are implicated in diseases such as Alzheimer's, Parkinson's, and type-II diabetes. Network Hamiltonian models of aggregation are centered around a Hamiltonian function that returns the total energy of a system of aggregating proteins, given the graph structure of the system as an input. In the graph, or network, representation of the system, each protein molecule is represented as a node, and noncovalent bonds between proteins are represented as edges. The parameter, i.e., a set of coefficients that determine the degree to which each topological degree of freedom is favored or disfavored, must be determined for each network Hamiltonian model, and is a well-known technical challenge. The methodology is first demonstrated by beginning with an initial set of randomly parametrized models of low fibril fraction (<5% fibrillar), and evolving to subsequent generations of models, ultimately leading to high fibril fraction models (>70% fibrillar). The methodology is also demonstrated by applying it to optimizing previously published network Hamiltonian models for the 5 key amyloid fibril topologies that have been reported in the Protein Data Bank (PDB). The models generated by the AI produced fibril fractions that surpass previously published fibril fractions in 3 of 5 cases, including the most naturally abundant amyloid fibril topology, the 1,2 2-ribbon, which features a steric zipper. The authors also aim to encourage more widespread use of the network Hamiltonian methodology for fitting a wide variety of self-assembling systems by releasing a free open-source implementation of the genetic algorithm introduced here.
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Affiliation(s)
- Gianmarc Grazioli
- Department of Chemistry, San
José State University, San Jose, California 95192, United States
| | - Andy Tao
- Department of Chemistry, San
José State University, San Jose, California 95192, United States
| | - Inika Bhatia
- Department of Chemistry, San
José State University, San Jose, California 95192, United States
| | - Patrick Regan
- Department of Chemistry, San
José State University, San Jose, California 95192, United States
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38
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Borówko M. Special Issue "Third Edition: Advances in Molecular Simulation". Int J Mol Sci 2024; 25:2709. [PMID: 38473956 DOI: 10.3390/ijms25052709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 02/19/2024] [Indexed: 03/14/2024] Open
Abstract
Molecular simulation is one of the fastest growing fields in science [...].
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Affiliation(s)
- Małgorzata Borówko
- Department of Theoretical Chemistry, Institute of Chemical Sciences, Faculty of Chemistry, Maria Curie-Skłodowska University, 20-031 Lublin, Poland
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39
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Kiehbadroudinezhad M, Merabet A, Al-Durra A, Hosseinzadeh-Bandbafha H, Wright MM, El-Saadany E. Towards a sustainable environment and carbon neutrality: Optimal sizing of standalone, green, reliable, and affordable water-power cogeneration systems. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 912:168668. [PMID: 38007116 DOI: 10.1016/j.scitotenv.2023.168668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 10/23/2023] [Accepted: 11/15/2023] [Indexed: 11/27/2023]
Abstract
Today, the limited sources of freshwater supply are a significant concern. Exploiting alternative sources, especially seawater, has been the focus, but purifying it is energy-intensive. Integrating desalination with renewable energy is a proposed solution, but it comes with high costs and environmental risks during construction. Hence, this study presents a framework to enhance the modeling, optimization, and evaluation of green water-power cogeneration systems to achieve the sustainability goals of cities and societies. An improved division algorithm (DA) determines the optimal component sizes based on criteria like minimal energy demand, reduced environmental and resource damage, low total life cycle cost (TLCC), and high reliability. Optimization considers varying loss of power supply probability (LPSP) levels (0 %, 2 %, 5 %, and 10 %). The environmental assessment utilizes a life cycle assessment (LCA) approach with IMPACT 2002+ and cumulative energy demand (CED) calculations. The study models the green cogeneration systems based on weather conditions, water demand, and power requirements of Al Lulu Island, Abu Dhabi, UAE. The system comprises photovoltaic panels, wind turbines, tidal generators, and backup systems (fuel cells). Results reveal that TLCC ranges from $186,263 to $486,876 for the highest LPSP. The solar-tidal-based configuration offers the lowest TLCC ($186,263) while substituting solar with wind energy increases TLCC by 160 %. The wind-tidal-based configuration has the lowest specific environmental impact (1020 mPt/yr) and cumulative energy demand (39.06 GJ/yr) for the highest LPSP. In contrast, the solar-tidal-wind-based configuration inflicts the most damage, with 62.63 GJ/yr and 1794 mPt/yr for the highest LPSP. The finding indicates that the DA is faster (100 iterations) than the genetic algorithm (1000 iterations), particle swarm optimization (400 iterations), and artificial bee swarm optimization (300 iterations). The study underscores the solar-tidal-based configuration as the optimal choice across multiple criteria, offering a promising solution for freshwater supply and environmental sustainability on Al Lulu Island.
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Affiliation(s)
| | - Adel Merabet
- Division of Engineering, Saint Mary's University, Halifax, NS B3H 3C3, Canada; Advanced Power and Energy Center, EECS Department, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Ahmed Al-Durra
- Advanced Power and Energy Center, EECS Department, Khalifa University, Abu Dhabi, United Arab Emirates
| | | | - Mark Mba Wright
- Department of Mechanical Engineering, Iowa State University, Ames, IA, USA
| | - Ehab El-Saadany
- Advanced Power and Energy Center, EECS Department, Khalifa University, Abu Dhabi, United Arab Emirates
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40
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Ma SY, Ye XW, Liu ZX, Ding Y, Zhang D, Sun F. Mechanical Behavior Monitoring and Load Inversion Analysis of Large-Diameter Underwater Shield Tunnel during Construction. SENSORS (BASEL, SWITZERLAND) 2024; 24:1310. [PMID: 38400469 PMCID: PMC10892387 DOI: 10.3390/s24041310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 01/28/2024] [Accepted: 02/17/2024] [Indexed: 02/25/2024]
Abstract
The construction of large-diameter shield tunnels underwater involves complex variations in water and earth load outside the tunnel segment, as well as intricate mechanical responses. This study analyzes the variation laws of external loads, axial forces, and bending moments acting on the segment ring during the shield assembly and removal from the shield tail. It accomplishes this through the establishment of an on-site monitoring system based on the Internet of Things (IoT) and proposes a Bayesian-genetic algorithm model to estimate the water and earth pressure. The fluctuation section exhibits a peak load twice as high as that in the stable section. These variations are influenced by Jack thrust, shield shell force, and grouting pressure. The peak load observed in the fluctuation section is twice as high as the load observed in the stable section. During the shield tail removal process, the internal forces undergo significant fluctuations due to changes in both load and boundary conditions, and the peak value of the axial force during the fluctuation section is eight times higher than that during the stable section, while the peak value of the bending moment during the fluctuation section is five times higher than that during the stable section. The earth and water pressure calculated using the inversion analysis method, which relies on the measured internal forces, closely matches the actual measured values. The results demonstrate that the accuracy of the water and earth pressure obtained through inversion analysis is twice as high as that obtained using the full coverage pressure method. These results can serve as a valuable reference for similar projects.
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Affiliation(s)
- Si-Yuan Ma
- Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China; (S.-Y.M.); (X.-W.Y.); (Z.-X.L.)
| | - Xiao-Wei Ye
- Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China; (S.-Y.M.); (X.-W.Y.); (Z.-X.L.)
| | - Zhi-Xiong Liu
- Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China; (S.-Y.M.); (X.-W.Y.); (Z.-X.L.)
| | - Yang Ding
- Department of Civil Engineering, Hangzhou City University, Hangzhou 310015, China
| | - Di Zhang
- China Railway Siyuan Survey and Design Group Co., Ltd., Wuhan 430063, China; (D.Z.); (F.S.)
| | - Feng Sun
- China Railway Siyuan Survey and Design Group Co., Ltd., Wuhan 430063, China; (D.Z.); (F.S.)
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41
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Mehdary A, Chehri A, Jakimi A, Saadane R. Hyperparameter Optimization with Genetic Algorithms and XGBoost: A Step Forward in Smart Grid Fraud Detection. SENSORS (BASEL, SWITZERLAND) 2024; 24:1230. [PMID: 38400385 PMCID: PMC10892895 DOI: 10.3390/s24041230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Revised: 02/07/2024] [Accepted: 02/13/2024] [Indexed: 02/25/2024]
Abstract
This study provides a comprehensive analysis of the combination of Genetic Algorithms (GA) and XGBoost, a well-known machine-learning model. The primary emphasis lies in hyperparameter optimization for fraud detection in smart grid applications. The empirical findings demonstrate a noteworthy enhancement in the model's performance metrics following optimization, particularly emphasizing a substantial increase in accuracy from 0.82 to 0.978. The precision, recall, and AUROC metrics demonstrate a clear improvement, indicating the effectiveness of optimizing the XGBoost model for fraud detection. The findings from our study significantly contribute to the expanding field of smart grid fraud detection. These results emphasize the potential uses of advanced metaheuristic algorithms to optimize complex machine-learning models. This work showcases significant progress in enhancing the accuracy and efficiency of fraud detection systems in smart grids.
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Affiliation(s)
- Adil Mehdary
- LaGes, Hassania School of Public Works, Casablanca 20000, Morocco; (A.M.); (R.S.)
| | - Abdellah Chehri
- Department of Mathematics and Computer Science, Royal Military College of Canada, Kingston, ON K7K 7B4, Canada
| | - Abdeslam Jakimi
- GL-ISI Team, Faculty of Science and Technology Errachidia, Moulay Ismail University, Meknes 50050, Morocco;
| | - Rachid Saadane
- LaGes, Hassania School of Public Works, Casablanca 20000, Morocco; (A.M.); (R.S.)
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42
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Zafar F, Malik SA, Ali T, Daraz A, Afzal AR, Bhatti F, Khan IA. Stabilization and tracking control of underactuated ball and beam system using metaheuristic optimization based TID-F and PIDD2-PI control schemes. PLoS One 2024; 19:e0298624. [PMID: 38354203 PMCID: PMC10866467 DOI: 10.1371/journal.pone.0298624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 01/26/2024] [Indexed: 02/16/2024] Open
Abstract
In this paper, we propose two different control strategies for the position control of the ball of the ball and beam system (BBS). The first control strategy uses the proportional integral derivative-second derivative with a proportional integrator PIDD2-PI. The second control strategy uses the tilt integral derivative with filter (TID-F). The designed controllers employ two distinct metaheuristic computation techniques: grey wolf optimization (GWO) and whale optimization algorithm (WOA) for the parameter tuning. We evaluated the dynamic and steady-state performance of the proposed control strategies using four performance indices. In addition, to analyze the robustness of proposed control strategies, a comprehensive comparison has been performed with a variety of controllers, including tilt integral-derivative (TID), fractional order proportional integral derivative (FOPID), integral-proportional derivative (I-PD), proportional integral-derivative (PI-D), and proportional integral proportional derivative (PI-PD). By comparing different test cases, including the variation in the parameters of the BBS with disturbance, we examine step response, set point tracking, disturbance rejection analysis, and robustness of proposed control strategies. The comprehensive comparison of results shows that WOA-PIDD2-PI-ISE and GWO-TID-F- ISE perform superior. Moreover, the proposed control strategies yield oscillation-free, stable, and quick response, which confirms the robustness of the proposed control strategies to the disturbance, parameter variation of BBS, and tracking performance. The practical implementation of the proposed controllers can be in the field of under actuated mechanical systems (UMS), robotics and industrial automation. The proposed control strategies are successfully tested in MATLAB simulation.
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Affiliation(s)
- Farhan Zafar
- Department of Electrical and Computer Engineering, Faculty of Engineering and Technology, International Islamic University Islamabad (IIUI), Islamabad, Pakistan
| | - Suheel Abdullah Malik
- Department of Electrical and Computer Engineering, Faculty of Engineering and Technology, International Islamic University Islamabad (IIUI), Islamabad, Pakistan
| | - Tayyab Ali
- Department of Electrical and Computer Engineering, Faculty of Engineering and Technology, International Islamic University Islamabad (IIUI), Islamabad, Pakistan
| | - Amil Daraz
- School of Information Science and Engineering, NingboTech University, Ningbo, China
| | - Abdul Rahman Afzal
- Department of Industrial Engineering, University of Business and Technology (UBT) University, Jeddah, Saudi Arabia
| | - Farkhunda Bhatti
- Department of Electronic Engineering, Mehran University of engineering & Technology Jamshoro, Jamshoro, Pakistan
| | - Irfan Ahmed Khan
- Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
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43
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Abdelaal AK, Alhamahmy AIA, Attia HED, El-Fergany AA. Maximizing solar radiations of PV panels using artificial gorilla troops reinforced by experimental investigations. Sci Rep 2024; 14:3562. [PMID: 38347025 PMCID: PMC10861506 DOI: 10.1038/s41598-024-53873-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 02/06/2024] [Indexed: 02/15/2024] Open
Abstract
This article's main objective is to maximize solar radiations (SRs) through the use of the gorilla troop algorithm (GTA) for identifying the optimal tilt angle (OTA) for photovoltaic (PV) panels. This is done in conjunction with an experimental work that consists of three 100 W PV panels tilted at three different tilt angles (TAs). The 28°, 30°, and 50° are the three TAs. The experimental data are collected every day for 181-day and revealed that the TA of 28° is superior to those of 50° and 30°. The GTA calculated the OTA to be 28.445°, which agrees with the experimental results, which show a TA of 28°. The SR of the 28o TA is 59.3% greater than that of the 50° TA and 4.5% higher than that of the 30° TA. Recent methods are used to compare the GTA with the other nine metaheuristics (MHTs)-the genetic algorithm, particle swarm, harmony search, ant colony, cuckoo search, bee colony, fire fly, grey wolf, and coronavirus disease optimizers-in order to figure out the optimal OTA. The OTA is calculated by the majority of the nine MHTs to be 28.445°, which is the same as the GTA and confirms the experimental effort. In only 181-day, the by experimentation it may be documented SR difference between the TAs of 28° and 50° TA is 159.3%. Numerous performance metrics are used to demonstrate the GTA's viability, and it is contrasted with other recent optimizers that are in competition.
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Affiliation(s)
- Ashraf K Abdelaal
- Department of Electric Power and Machine, Faculty of Technology, Suez University, Suez, 43512, Egypt.
| | - Amira I A Alhamahmy
- Department of Electric Power and Machine, Faculty of Technology, Suez University, Suez, 43512, Egypt
| | - Hossam El Deen Attia
- Department of Electric Power and Machine, Faculty of Technology, Suez University, Suez, 43512, Egypt
| | - Attia A El-Fergany
- Department of Electric Power and Machine, Faculty of Engineering, Zagazig University, Zagazig, 44519, Egypt
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44
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Liu X, Zhang Z, Hao Y, Zhao H, Yang Y. Optimized OTSU Segmentation Algorithm-Based Temperature Feature Extraction Method for Infrared Images of Electrical Equipment. SENSORS (BASEL, SWITZERLAND) 2024; 24:1126. [PMID: 38400285 PMCID: PMC10892524 DOI: 10.3390/s24041126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 01/25/2024] [Accepted: 02/06/2024] [Indexed: 02/25/2024]
Abstract
Infrared image processing is an effective method for diagnosing faults in electrical equipment, in which target device segmentation and temperature feature extraction are key steps. Target device segmentation separates the device to be diagnosed from the image, while temperature feature extraction analyzes whether the device is overheating and has potential faults. However, the segmentation of infrared images of electrical equipment is slow due to issues such as high computational complexity, and the temperature information extracted lacks accuracy due to the insufficient consideration of the non-linear relationship between the image grayscale and temperature. Therefore, in this study, we propose an optimized maximum between-class variance thresholding method (OTSU) segmentation algorithm based on the Gray Wolf Optimization (GWO) algorithm, which accelerates the segmentation speed by optimizing the threshold determination process using OTSU. The experimental results show that compared to the non-optimized method, the optimized segmentation method increases the threshold calculation time by more than 83.99% while maintaining similar segmentation results. Based on this, to address the issue of insufficient accuracy in temperature feature extraction, we propose a temperature value extraction method for infrared images based on the K-nearest neighbor (KNN) algorithm. The experimental results demonstrate that compared to traditional linear methods, this method achieves a 73.68% improvement in the maximum residual absolute value of the extracted temperature values and a 78.95% improvement in the average residual absolute value.
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Affiliation(s)
| | - Zhanlong Zhang
- School of Electrical Engineering, Chongqing University, Chongqing 400044, China; (X.L.); (Y.H.); (H.Z.); (Y.Y.)
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45
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Martinez-Mayorga K, Rosas-Jiménez JG, Gonzalez-Ponce K, López-López E, Neme A, Medina-Franco JL. The pursuit of accurate predictive models of the bioactivity of small molecules. Chem Sci 2024; 15:1938-1952. [PMID: 38332817 PMCID: PMC10848664 DOI: 10.1039/d3sc05534e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 01/09/2024] [Indexed: 02/10/2024] Open
Abstract
Property prediction is a key interest in chemistry. For several decades there has been a continued and incremental development of mathematical models to predict properties. As more data is generated and accumulated, there seems to be more areas of opportunity to develop models with increased accuracy. The same is true if one considers the large developments in machine and deep learning models. However, along with the same areas of opportunity and development, issues and challenges remain and, with more data, new challenges emerge such as the quality and quantity and reliability of the data, and model reproducibility. Herein, we discuss the status of the accuracy of predictive models and present the authors' perspective of the direction of the field, emphasizing on good practices. We focus on predictive models of bioactive properties of small molecules relevant for drug discovery, agrochemical, food chemistry, natural product research, and related fields.
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Affiliation(s)
- Karina Martinez-Mayorga
- Institute of Chemistry, Merida Unit, National Autonomous University of Mexico Merida-Tetiz Highway, Km. 4.5 Ucu Yucatan Mexico
- Institute for Applied Mathematics and Systems, Merida Research Unit, National Autonomous University of Mexico Sierra Papacal Merida Yucatan Mexico
| | - José G Rosas-Jiménez
- Department of Theoretical Biophysics, IMPRS on Cellular Biophysics Max-von-Laue Strasse 3 Frankfurt am Main 60438 Germany
| | - Karla Gonzalez-Ponce
- Institute of Chemistry, Merida Unit, National Autonomous University of Mexico Merida-Tetiz Highway, Km. 4.5 Ucu Yucatan Mexico
| | - Edgar López-López
- Department of Chemistry and Graduate Program in Pharmacology, Center for Research and Advanced Studies of the National Polytechnic Institute Mexico City 07000 Mexico
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry National Autonomous University of Mexico Mexico City 04510 Mexico
| | - Antonio Neme
- Institute for Applied Mathematics and Systems, Merida Research Unit, National Autonomous University of Mexico Sierra Papacal Merida Yucatan Mexico
| | - José L Medina-Franco
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry National Autonomous University of Mexico Mexico City 04510 Mexico
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Zhang J, Chen Z, Su Z, Dan Y. High-performance plasmonic mid-infrared bandpass filters by inverse design. NANOTECHNOLOGY 2024; 35:175202. [PMID: 38181440 DOI: 10.1088/1361-6528/ad1b99] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Accepted: 01/05/2024] [Indexed: 01/07/2024]
Abstract
Plasmonic spectral filters composed of periodic nanostructured metal films offer novel opportunities for the development of multispectral imaging technologies in the mid-infrared region. However, traditional plasmonic filters, which typically feature simplistic structures such as nanoholes or nanorings, are constrained by a narrow bandpass and significant crosstalk, leading to limited practical performance. Filters designed using inverse techniques allow a substantial degree of freedom in creating intricate structures that align with desired spectral characteristics, including a quasi-square spectral profile, high transmission, wide full width at half maximum, and reduced crosstalk. In this study, we have utilized an inverse design algorithm to engineer high-performance bandpass filters for the mid-infrared range, achieving an average transmittance exceeding 80% within the bandpass window and below 10% in the stop band, which is comparable to that of commercial multilayer Bragg filters. Nanofabrication processes were employed to transfer the designed pattern into the gold film on ZnS substrate that is transparent in the mid-infrared range. The resulting filters exhibit spectral performance analogous to that of the inversely designed models, making them suitable for direct integration with mid-infrared photodetector arrays in multispectral imaging systems.
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Affiliation(s)
- Jiarui Zhang
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai 2005240, People's Republic of China
- University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
| | - Zeji Chen
- Kunming Institute of Physics, Kunming 650223, People's Republic of China
| | - Zhijuan Su
- Global Institute of Future Technology, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
| | - Yaping Dan
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai 2005240, People's Republic of China
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47
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Mathaba M, Banza J. A comprehensive review on artificial intelligence in water treatment for optimization. Clean water now and the future. JOURNAL OF ENVIRONMENTAL SCIENCE AND HEALTH. PART A, TOXIC/HAZARDOUS SUBSTANCES & ENVIRONMENTAL ENGINEERING 2024; 58:1047-1060. [PMID: 38293764 DOI: 10.1080/10934529.2024.2309102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 01/13/2024] [Indexed: 02/01/2024]
Abstract
Given the severe effects that toxic compounds present in wastewater streams have on humans, it is imperative that water and wastewater streams pollution be addressed globally. This review comprehensively examines various water and wastewater treatment methods and water quality management methods based on artificial intelligence (AI). Machine learning (ML) and AI have become a powerful tool for addressing problems in the real world and has gained a lot of interest since it can be used for a wide range of activities. The foundation of ML techniques involves training of a network with collected data, followed by application of learned network to the process simulation and prediction. The creation of ML models for process simulations requires measured data. In order to forecast and simulate chemical and physical processes such chemical reactions, heat transfer, mass transfer, energy, pharmaceutics and separation, a variety of machine-learning algorithms have recently been developed. These models have shown to be more adept at simulating and modeling processes than traditional models. Although AI offers many advantages, a number of disadvantages have kept these methods from being extensively applied in actual water treatment systems. Lack of evidence of application in actual water treatment scenarios, poor repeatability and data availability and selection are a few of the main problems that need to be resolved.
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Affiliation(s)
- Machodi Mathaba
- Department of Chemical Engineering, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South Africa
| | - JeanClaude Banza
- Department of Chemical Engineering, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South Africa
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Shao X, Kshitij FS, Kim CS. GAILS: an effective multi-object job shop scheduler based on genetic algorithm and iterative local search. Sci Rep 2024; 14:2068. [PMID: 38267469 PMCID: PMC10808125 DOI: 10.1038/s41598-024-51778-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 01/09/2024] [Indexed: 01/26/2024] Open
Abstract
The job shop scheduling problem (JSSP) is critical for building one smart factory regarding resource management, effective production, and intelligent supply. However, it is still very challenging due to the complex production environment. Besides, most current research only focuses on classical JSSP, while flexible JSSP (FJSSP) is more usual. This article proposes an effective method, GAILS, to deal with JSSP and FJSSP based on genetic algorithm (GA) and iterative local search (ILS). GA is used to find the approximate global solution for the JSSP instance. Each instance was encoded into machine and subtask sequences. The corresponding machine and subtasks chromosome could be obtained through serval-time gene selection, crossover, and mutation. Moreover, multi-objects, including makespan, average utilization ratio, and maximum loading, are used to choose the best chromosome to guide ILS to explore the best local path. Therefore, the proposed method has an excellent search capacity and could balance globality and diversity. To verify the proposed method's effectiveness, the authors compared it with some state-of-the-art methods on sixty-six public JSSP and FJSSP instances. The comparative analysis confirmed the proposed method's effectiveness for classical JSSP and FJSSP in makespan, average utilization ratio, and maximum loading. Primarily, it obtains optimal-like solutions for several instances and outperforms others in most instances.
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Affiliation(s)
- Xiaorui Shao
- Industrial Science Technology Research Center, Pukyong National University, Busan, 608737, Korea
| | | | - Chang Soo Kim
- Department of Information Systems, Pukyong National University, Busan, 608737, Korea.
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Cui H, Zhang X, Li Y, Zhao D, Zhang J, Zhao Y. Free light-shape focusing in extreme-ultraviolet radiation with self-evolutionary photon sieves. Sci Rep 2024; 14:1675. [PMID: 38243046 PMCID: PMC10799067 DOI: 10.1038/s41598-024-51902-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 01/10/2024] [Indexed: 01/21/2024] Open
Abstract
Extreme-ultraviolet (EUV) radiation is a promising tool, not only for probing microscopic activities but also for processing nanoscale structures and performing high-resolution imaging. In this study, we demonstrate an innovative method to generate free light-shape focusing with self-evolutionary photon sieves under a single-shot coherent EUV laser; this includes vortex focus shaping, array focusing, and structured-light shaping. The results demonstrate that self-evolutionary photon sieves, consisting of a large number of specific pinholes fabricated on a piece of Si3N4 membrane, are capable of freely regulating an EUV light field, for which high-performance focusing elements are extremely lacking, let alone free light-shape focusing. Our proposed versatile photon sieves are a key breakthrough in focusing technology in the EUV region and pave the way for high-resolution soft X-ray microscopy, spectroscopy in materials science, shorter lithography, and attosecond metrology in next-generation synchrotron radiation and free-electron lasers.
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Affiliation(s)
- Huaiyu Cui
- National Key Laboratory of Science and Technology On Tunable Laser, Harbin Institute of Technology, Harbin, China
| | - Xiuping Zhang
- Key Laboratory of High Power Laser and Physics, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai, 201800, China
| | - You Li
- Key Laboratory of High Power Laser and Physics, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai, 201800, China
| | - Dongdi Zhao
- National Key Laboratory of Science and Technology On Tunable Laser, Harbin Institute of Technology, Harbin, China
| | - Junyong Zhang
- Key Laboratory of High Power Laser and Physics, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai, 201800, China.
| | - Yongpeng Zhao
- National Key Laboratory of Science and Technology On Tunable Laser, Harbin Institute of Technology, Harbin, China.
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Del Priore E, Lampani L. Shape Sensing in Plate Structures through Inverse Finite Element Method Enhanced by Multi-Objective Genetic Optimization of Sensor Placement and Strain Pre-Extrapolation. SENSORS (BASEL, SWITZERLAND) 2024; 24:608. [PMID: 38257700 PMCID: PMC11154501 DOI: 10.3390/s24020608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 01/12/2024] [Accepted: 01/16/2024] [Indexed: 01/24/2024]
Abstract
The real-time reconstruction of the displacement field of a structure from a network of in situ strain sensors is commonly referred to as "shape sensing". The inverse finite element method (iFEM) stands out as a highly effective and promising approach to perform this task. In the current investigation, this technique is employed to monitor different plate structures experiencing flexural and torsional deformation fields. In order to reduce the number of installed sensors and obtain more accurate results, the iFEM is applied in synergy with smoothing element analysis (SEA), which allows the pre-extrapolation of the strain field over the entire structure from a limited number of measurement points. For the SEA extrapolation to be effective for a multitude of load cases, it is necessary to position the strain sensors appropriately. In this study, an innovative sensor placement strategy that relies on a multi-objective genetic algorithm (NSGA-II) is proposed. This approach aims to minimize the root mean square error of the pre-extrapolated strain field across a set of mode shapes for the examined plate structures. The optimized strain reconstruction is subsequently utilized as input for the iFEM technique. Comparisons are drawn between the displacement field reconstructions obtained using the proposed methodology and the conventional iFEM. In order to validate such methodology, two different numerical case studies, one involving a rectangular cantilevered plate and the other encompassing a square plate clamped at the edges, are investigated. For the considered case studies, the results obtained by the proposed approach reveal a significant improvement in the monitoring capabilities over the basic iFEM algorithm with the same number of sensors.
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Affiliation(s)
| | - Luca Lampani
- Dipartimento di Ingegneria Meccanica e Aerospaziale, Sapienza Università di Roma, 00184 Rome, Italy;
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