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Dash S, Ghugar U, Godavarthi D, Mohanty SN. HCSRL: hyperledger composer system for reducing logistics losses in the pharmaceutical product supply chain using a blockchain-based approach. Sci Rep 2024; 14:13528. [PMID: 38866806 PMCID: PMC11169279 DOI: 10.1038/s41598-024-61654-7] [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: 12/07/2023] [Accepted: 05/08/2024] [Indexed: 06/14/2024] Open
Abstract
Blockchain technology uses a secure and decentralised framework for transaction management and data sharing within supply chains. This is particularly crucial in the pharmaceutical industry, where product authenticity and traceability are paramount. Blockchain plays a pivotal role in preventing product loss and counterfeiting, while simultaneously enhancing transparency and efficiency throughout the supply chain. The research introduces a step-by-step approach to implementing a proof-of-concept (PoC) for Supply Chain Risk Management (SCRM) through blockchain technology. This PoC involves simulating a supply chain process to assess feasibility and measure key performance indicators. Engaging stakeholders and gathering feedback is integral to refining the blockchain-based SCRM system. The study rigorously evaluates the performance of the SCRM blockchain across various test scenarios, featuring differing numbers of organizations and clients. Multiple fabric networks are employed to assess the system's scalability and performance under diverse conditions. The results of these comprehensive tests inform practical deployment decisions and highlight areas for potential optimization and further development. So this research provides valuable insights into the application of blockchain in pharmaceutical supply chains, offering a roadmap for implementation and improving supply chain security, efficiency, and transparency.
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Affiliation(s)
- Satyabrata Dash
- Department of Computer Science & Engineering, GITAM School of Technology, GITAM (Deemed to Be University), Vishakhapatnam, India
| | - Umashankar Ghugar
- Department of CSE, School of Engineering, OP Jindal University, Raigarh, CG, India
| | - Deepthi Godavarthi
- School of Computer Science & Engineering (SCOPE), VIT-AP University, Amaravati, Andhra Pradesh, India.
| | - Sachi Nandan Mohanty
- School of Computer Science & Engineering (SCOPE), VIT-AP University, Amaravati, Andhra Pradesh, India
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2
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Gren BA, Antczak M, Zok T, Sulkowska JI, Szachniuk M. Knotted artifacts in predicted 3D RNA structures. PLoS Comput Biol 2024; 20:e1011959. [PMID: 38900780 PMCID: PMC11218946 DOI: 10.1371/journal.pcbi.1011959] [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: 03/08/2024] [Revised: 07/02/2024] [Accepted: 06/01/2024] [Indexed: 06/22/2024] Open
Abstract
Unlike proteins, RNAs deposited in the Protein Data Bank do not contain topological knots. Recently, admittedly, the first trefoil knot and some lasso-type conformations have been found in experimental RNA structures, but these are still exceptional cases. Meanwhile, algorithms predicting 3D RNA models have happened to form knotted structures not so rarely. Interestingly, machine learning-based predictors seem to be more prone to generate knotted RNA folds than traditional methods. A similar situation is observed for the entanglements of structural elements. In this paper, we analyze all models submitted to the CASP15 competition in the 3D RNA structure prediction category. We show what types of topological knots and structure element entanglements appear in the submitted models and highlight what methods are behind the generation of such conformations. We also study the structural aspect of susceptibility to entanglement. We suggest that predictors take care of an evaluation of RNA models to avoid publishing structures with artifacts, such as unusual entanglements, that result from hallucinations of predictive algorithms.
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Affiliation(s)
- Bartosz A. Gren
- Centre of New Technologies, University of Warsaw, Warsaw, Poland
| | - Maciej Antczak
- Institute of Computing Science, Poznan University of Technology, Poznan, Poland
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
| | - Tomasz Zok
- Institute of Computing Science, Poznan University of Technology, Poznan, Poland
| | | | - Marta Szachniuk
- Institute of Computing Science, Poznan University of Technology, Poznan, Poland
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
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3
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Zhang HL, Li B, Shang J, Wang WW, Zhao FY. Source term estimation for continuous plume dispersion in Fusion Field Trial-07: Bayesian inference probability adjoint inverse method. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 915:169802. [PMID: 38215839 DOI: 10.1016/j.scitotenv.2023.169802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Revised: 12/09/2023] [Accepted: 12/29/2023] [Indexed: 01/14/2024]
Abstract
In scenarios involving sudden releases of unidentified gases or concealed pollution emergencies, source control emerges as a critical procedure to safeguard residential air quality. Appropriate inverse source tracking methodology depending on diverse measurement data could be utilized to promptly identify pollutant source parameters. In this study, source term estimation (STE) method, i.e., jointly combining probability adjoint method with the Bayesian inference method, has been proposed. General form of the pollutant inverse transport equation was firstly established. Subsequently, the pollution source information, assumed from single continuous point releases during Fusion Field Trials 2007 under an unsteady wind field, was identified using the Bayesian inference probability adjoint inverse method. Metropolis-Hastings Markov Chain Monte Carlo (MH-MCMC) and Differential Evolution Markov Chain Monte Carlo (DE-MCMC) were then compared as sampling methods for Bayesian inference. Results indicated that the DE-MCMC algorithm has superior convergence and could present higher accuracy of pollutant source information than that of MH-MCMC algorithm, particularly for highly nonlinear and multi-modal distribution systems. Furthermore, the integration of Union standard Adjoint Location Probability (UALP) as prior information into the Bayesian inference probability adjoint inverse method effectively narrowed the sampling range, enhancing both the accuracy and robustness of the proposed approach. Finally, the impact of the covariance matrix on the inverse identification accuracy was explored. Overall, this research has provided insights into the future applicability of this Bayesian inference inversion technique for point source identification.
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Affiliation(s)
- Hong-Liang Zhang
- School of Power and Mechanical Engineering, Wuhan University, Wuhan, Hubei Province, PR China
| | - Bin Li
- School of Power and Mechanical Engineering, Wuhan University, Wuhan, Hubei Province, PR China
| | - Jin Shang
- School of Power and Mechanical Engineering, Wuhan University, Wuhan, Hubei Province, PR China
| | - Wei-Wei Wang
- School of Power and Mechanical Engineering, Wuhan University, Wuhan, Hubei Province, PR China
| | - Fu-Yun Zhao
- School of Power and Mechanical Engineering, Wuhan University, Wuhan, Hubei Province, PR China; School of Civil Engineering, Hunan University of Technology, Zhuzhou, Hunan Province, PR China.
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4
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Jeganathan K, Anzen Koffer V, Lakshmanan K, Loganathan K, Abbas M, Shilpa A. Replacement of failed items in a two commodity retrial queueing-inventory system with multi-component demand and vacation interruption. Heliyon 2024; 10:e24024. [PMID: 38293346 PMCID: PMC10825305 DOI: 10.1016/j.heliyon.2024.e24024] [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: 08/23/2023] [Revised: 12/20/2023] [Accepted: 01/02/2024] [Indexed: 02/01/2024] Open
Abstract
This study investigates a crucial aspect of inventory management, which is the process of replacing failed items. In dynamic commercial environments, it is essential to efficiently and strategically replace failed items to maintain operational efficiency and ensure profitability. We consider a two-commodity retrial queueing-inventory system with vacation interruption. Upon purchasing the first commodity, the second commodity is provided as a complimentary item. In contrast, no item is given as a complimentary for the purchase of the second item. Only the first commodity is stored in a dedicated pooled storage for replacement when it fails. The ( s , Q ) policy governs replenishing the first commodity while the second is replenished through instantaneous ordering. The model considers the multi-component demand rate for customer arrivals. Server vacations are initiated during customer absence in waiting hall or when the first commodity is unavailable. We formulate a level-dependent quasi-birth-and-death process, and its steady-state probability vector is computed using Neuts and Rao's truncation method. The stability condition for the system is derived, and various system performance measures, including expected total cost, number of replaceable items, and customers in the waiting hall and orbit, are established. The comparative analysis between the system with replacement is done with the regular model without replacement, which revealed the efficiency of replacement. The analysis of multi-component demand towards homogeneous arrival highlights the impact of multi-component demand on boosting customer arrival. Also, parametric sensitivity analysis has been conducted numerically over total cost, mean number of failed items for replacement, and mean number of customers in the waiting hall and orbit.
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Affiliation(s)
- K. Jeganathan
- Ramanujan Institute for Advanced Study in Mathematics, University of Madras, Chennai, 600005, India
| | - V. Anzen Koffer
- Ramanujan Institute for Advanced Study in Mathematics, University of Madras, Chennai, 600005, India
| | - K. Lakshmanan
- Department of Mathematics, St. Joseph University, Dimapur, Nagaland, 797115, India
| | - K. Loganathan
- Department of Mathematics and Statistics, Manipal University Jaipur, Jaipur, 303007, Rajasthan, India
| | - Mohamed Abbas
- Electrical Engineering Department, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia
| | - A. Shilpa
- MLR Institute of Technology, Hyderabad, Telangana, India
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5
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Liang HW, Ameri R, Band S, Chen HS, Ho SY, Zaidan B, Chang KC, Chang A. Fall risk classification with posturographic parameters in community-dwelling older adults: a machine learning and explainable artificial intelligence approach. J Neuroeng Rehabil 2024; 21:15. [PMID: 38287415 PMCID: PMC10826018 DOI: 10.1186/s12984-024-01310-3] [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: 10/15/2023] [Accepted: 01/24/2024] [Indexed: 01/31/2024] Open
Abstract
BACKGROUND Computerized posturography obtained in standing conditions has been applied to classify fall risk for older adults or disease groups. Combining machine learning (ML) approaches is superior to traditional regression analysis for its ability to handle complex data regarding its characteristics of being high-dimensional, non-linear, and highly correlated. The study goal was to use ML algorithms to classify fall risks in community-dwelling older adults with the aid of an explainable artificial intelligence (XAI) approach to increase interpretability. METHODS A total of 215 participants were included for analysis. The input information included personal metrics and posturographic parameters obtained from a tracker-based posturography of four standing postures. Two classification criteria were used: with a previous history of falls and the timed-up-and-go (TUG) test. We used three meta-heuristic methods for feature selection to handle the large numbers of parameters and improve efficacy, and the SHapley Additive exPlanations (SHAP) method was used to display the weights of the selected features on the model. RESULTS The results showed that posturographic parameters could classify the participants with TUG scores higher or lower than 10 s but were less effective in classifying fall risk according to previous fall history. Feature selections improved the accuracy with the TUG as the classification label, and the Slime Mould Algorithm had the best performance (accuracy: 0.72 to 0.77, area under the curve: 0.80 to 0.90). In contrast, feature selection did not improve the model performance significantly with the previous fall history as a classification label. The SHAP values also helped to display the importance of different features in the model. CONCLUSION Posturographic parameters in standing can be used to classify fall risks with high accuracy based on the TUG scores in community-dwelling older adults. Using feature selection improves the model's performance. The results highlight the potential utility of ML algorithms and XAI to provide guidance for developing more robust and accurate fall classification models. Trial registration Not applicable.
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Affiliation(s)
- Huey-Wen Liang
- Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan, ROC
| | - Rasoul Ameri
- Department of Information Management, National Yunlin University of Science and Technology, Douliu, Taiwan, ROC
| | - Shahab Band
- International Graduate School of Artificial Intelligence, National Yunlin University of Science and Technology, Douliu, Taiwan, ROC.
- Future Technology Research Center, National Yunlin University of Science and Technology, Douliu, Taiwan, ROC.
| | - Hsin-Shui Chen
- Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital Yulin Branch, Douliu, Taiwan, ROC.
| | - Sung-Yu Ho
- Department of Information Management, National Yunlin University of Science and Technology, Douliu, Taiwan, ROC
| | - Bilal Zaidan
- International Graduate School of Artificial Intelligence, National Yunlin University of Science and Technology, Douliu, Taiwan, ROC
- SP Jain School of Global Management, Sydney, Australia
| | - Kai-Chieh Chang
- Department of Neurology, National Taiwan University Hospital Yulin Branch, Douliu, Taiwan, ROC
| | - Arthur Chang
- Department of Information Management, National Yunlin University of Science and Technology, Douliu, Taiwan, ROC
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6
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Rybarczyk A, Formanowicz D, Formanowicz P. Key Therapeutic Targets to Treat Hyperglycemia-Induced Atherosclerosis Analyzed Using a Petri Net-Based Model. Metabolites 2023; 13:1191. [PMID: 38132873 PMCID: PMC10744714 DOI: 10.3390/metabo13121191] [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: 11/10/2023] [Revised: 11/29/2023] [Accepted: 12/05/2023] [Indexed: 12/23/2023] Open
Abstract
Chronic superphysiological glucose concentration is a hallmark of diabetes mellitus (DM) and a cause of damage to many types of cells. Atherosclerosis coexists with glucose metabolism disturbances, constituting a significant problem and exacerbating its complications. Atherosclerosis in DM is accelerated, so it is vital to slow its progression. However, from the complex network of interdependencies, molecules, and processes involved, choosing which ones should be inhibited without blocking the pathways crucial for the organism's functioning is challenging. To conduct this type of analysis, in silicotesting comes in handy. In our study, to identify sites in the network that need to be blocked to have an inhibitory effect on atherosclerosis in hyperglycemia, which is toxic for the human organism, we created a model using Petri net theory and performed analyses. We have found that blocking isoforms of protein kinase C (PKC)-PKCβ and PKCγ-in diabetic patients can contribute to the inhibition of atherosclerosis progression. In addition, we have discovered that aldose reductase inhibition can slow down atherosclerosis progression, and this has been shown to reduce PKC (β and γ) expression in DM. It has also been observed that diminishing oxidative stress through the inhibitory effect on the AGE-RAGE axis may be a promising therapeutic approach in treating hyperglycemia-induced atherosclerosis. Moreover, the blockade of NADPH oxidase, the key enzyme responsible for the formation of reactive oxygen species (ROS) in blood vessels, only moderately slowed down atherosclerosis development. However, unlike aldose reductase blockade, or direct PKC (β and γ), the increased production of mitochondrial ROS associated with mitochondrial dysfunction effectively stopped after NADPH oxidase blockade. The results obtained may constitute the basis for further in-depth research.
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Affiliation(s)
- Agnieszka Rybarczyk
- Institute of Computing Science, Poznan University of Technology, 60-695 Poznan, Poland;
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-704 Poznan, Poland
- Faculty of Electrical Engineering, Gdynia Maritime University, 81-225 Gdynia, Poland
| | - Dorota Formanowicz
- Department of Medical Chemistry and Laboratory Medicine, Poznan University of Medical Sciences, 60-806 Poznan, Poland;
| | - Piotr Formanowicz
- Institute of Computing Science, Poznan University of Technology, 60-695 Poznan, Poland;
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7
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Zheng Y, Qian X. Go-Game Image Recognition Based on Improved Pix2pix. J Imaging 2023; 9:273. [PMID: 38132691 PMCID: PMC10871096 DOI: 10.3390/jimaging9120273] [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: 10/09/2023] [Revised: 11/30/2023] [Accepted: 12/03/2023] [Indexed: 12/23/2023] Open
Abstract
Go is a game that can be won or lost based on the number of intersections surrounded by black or white pieces. The traditional method is a manual counting method, which is time-consuming and error-prone. In addition, the generalization of the current Go-image-recognition methods is poor, and accuracy needs to be further improved. To solve these problems, a Go-game image recognition based on an improved pix2pix was proposed. Firstly, a channel-coordinate mixed-attention (CCMA) mechanism was designed by combining channel attention and coordinate attention effectively; therefore, the model could learn the target feature information. Secondly, in order to obtain the long-distance contextual information, a deep dilated-convolution (DDC) module was proposed, which densely linked the dilated convolution with different dilated rates. The experimental results showed that compared with other existing Go-image-recognition methods, such as DenseNet, VGG-16, and Yolo v5, the proposed method could effectively improve the generalization ability and accuracy of a Go-image-recognition model, and the average accuracy rate was over 99.99%.
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Affiliation(s)
| | - Xiyuan Qian
- School of Mathematics, East China University of Science and Technology, Shanghai 200237, China;
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8
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Zhang Y, Cao Y, Proctor RW, Liu Y. Emotional experiences of service robots' anthropomorphic appearance: a multimodal measurement method. ERGONOMICS 2023; 66:2039-2057. [PMID: 36803343 DOI: 10.1080/00140139.2023.2182751] [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: 05/03/2022] [Accepted: 02/14/2023] [Indexed: 06/18/2023]
Abstract
Anthropomorphic appearance is a key factor to affect users' attitudes and emotions. This research aimed to measure emotional experience caused by robots' anthropomorphic appearance with three levels - high, moderate, and low - using multimodal measurement. Fifty participants' physiological and eye-tracker data were recorded synchronously while they observed robot images that were displayed in random order. Afterward, the participants reported subjective emotional experiences and attitudes towards those robots. The results showed that the images of the moderately anthropomorphic service robots induced higher pleasure and arousal ratings, and yielded significantly larger pupil diameter and faster saccade velocity, than did the low or high robots. Moreover, participants' facial electromyography, skin conductance, and heart-rate responses were higher when observing moderately anthropomorphic service robots. An implication of the research is that service robots' appearance should be designed to be moderately anthropomorphic; too many human-like features or machine-like features may disturb users' positive emotions and attitudes.Practitioner Summary: This research aimed to measure emotional experience caused by three types of anthropomorphic service robots using a multimodal measurement experiment. The results showed that moderately anthropomorphic service robots evoked more positive emotion than high and low anthropomorphic robots. Too many human-like features or machine-like features may disturb users' positive emotions.
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Affiliation(s)
- Yun Zhang
- School of Economics and Management, Anhui Polytechnic University, Wuhu, P. R. China
| | - Yaqin Cao
- School of Economics and Management, Anhui Polytechnic University, Wuhu, P. R. China
| | - Robert W Proctor
- Department of Psychological Sciences, Purdue University, West Lafayette, USA
| | - Yu Liu
- School of Economics and Management, Anhui Polytechnic University, Wuhu, P. R. China
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9
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Sarzynska J, Popenda M, Antczak M, Szachniuk M. RNA tertiary structure prediction using RNAComposer in CASP15. Proteins 2023; 91:1790-1799. [PMID: 37615316 DOI: 10.1002/prot.26578] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 06/14/2023] [Accepted: 08/08/2023] [Indexed: 08/25/2023]
Abstract
As CASP15 participants, in the new category of 3D RNA structure prediction, we applied expert modeling with the support of our proprietary system RNAComposer. Although RNAComposer is primarily known as an automated web server, its features allow it to be used interactively, for example, for homology-based modeling or assembling models from user-provided structural elements. In the paper, we present various scenarios of applying the system to predict the 3D RNA structures that we employed. Their combination with expert input, comparative analysis of models, and routines to select representative resultant structures form a ready-for-reuse workflow. With selected examples, we demonstrate its application for the in silico modeling of natural and synthetic RNA molecules targeted in CASP15.
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Affiliation(s)
- Joanna Sarzynska
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
| | - Mariusz Popenda
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
| | - Maciej Antczak
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
- Institute of Computing Science, Poznan University of Technology, Poznan, Poland
| | - Marta Szachniuk
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
- Institute of Computing Science, Poznan University of Technology, Poznan, Poland
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10
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Jolfaei AA, Alinaghian M, Bahrami R, Tirkolaee EB. Generalized vehicle routing problem: Contemporary trends and research directions. Heliyon 2023; 9:e22733. [PMID: 38125529 PMCID: PMC10731084 DOI: 10.1016/j.heliyon.2023.e22733] [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: 06/14/2023] [Revised: 11/17/2023] [Accepted: 11/17/2023] [Indexed: 12/23/2023] Open
Abstract
Generalized Vehicle Routing Problem (GVRP) is a challenging operational research problem which has been widely studied for nearly two decades. In this problem, it is assumed that graph nodes are grouped into a number of clusters, and serving any node of a cluster eliminates the need to visit the other nodes of that cluster. The general objective of this problem is to find the set of nodes to visit and determine the service sequence to minimize the total traveling cost. In addition to these general conditions, GVRP can be formulated with different assumptions and constraints to practically create different sub-types and variants. This paper aims to provide a comprehensive survey of the GVRP literature and explore its various dimensions. It first encompasses the definition of GVRP, similar problems, mathematical models, classification of different variants and solution methods developed for GVRPs, and practical implications. Finally, some useful suggestions are discussed to extend the problem. For this review study, Google Scholar, Scopus, Science Direct, Emerald, Springer, and Elsevier databases were searched for keywords, and 160 potential articles were extracted, and eventually, 45 articles were judged to be relevant.
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Affiliation(s)
- Ali Aghadavoudi Jolfaei
- Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran
| | - Mahdi Alinaghian
- Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran
| | - Roghayeh Bahrami
- Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran
| | - Erfan Babaee Tirkolaee
- Department of Industrial Engineering, Istinye University, Istanbul, Turkey
- Department of Industrial Engineering and Management, Yuan Ze University, Taoyuan, Taiwan
- Department of Industrial and Mechanical Engineering, Lebanese American University, Byblos, Lebanon
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11
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Ibarra EJ, Arias-Londoño JD, Zañartu M, Godino-Llorente JI. Towards a Corpus (and Language)-Independent Screening of Parkinson's Disease from Voice and Speech through Domain Adaptation. Bioengineering (Basel) 2023; 10:1316. [PMID: 38002440 PMCID: PMC10669342 DOI: 10.3390/bioengineering10111316] [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: 10/09/2023] [Revised: 11/03/2023] [Accepted: 11/10/2023] [Indexed: 11/26/2023] Open
Abstract
End-to-end deep learning models have shown promising results for the automatic screening of Parkinson's disease by voice and speech. However, these models often suffer degradation in their performance when applied to scenarios involving multiple corpora. In addition, they also show corpus-dependent clusterings. These facts indicate a lack of generalisation or the presence of certain shortcuts in the decision, and also suggest the need for developing new corpus-independent models. In this respect, this work explores the use of domain adversarial training as a viable strategy to develop models that retain their discriminative capacity to detect Parkinson's disease across diverse datasets. The paper presents three deep learning architectures and their domain adversarial counterparts. The models were evaluated with sustained vowels and diadochokinetic recordings extracted from four corpora with different demographics, dialects or languages, and recording conditions. The results showed that the space distribution of the embedding features extracted by the domain adversarial networks exhibits a higher intra-class cohesion. This behaviour is supported by a decrease in the variability and inter-domain divergence computed within each class. The findings suggest that domain adversarial networks are able to learn the common characteristics present in Parkinsonian voice and speech, which are supposed to be corpus, and consequently, language independent. Overall, this effort provides evidence that domain adaptation techniques refine the existing end-to-end deep learning approaches for Parkinson's disease detection from voice and speech, achieving more generalizable models.
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Affiliation(s)
- Emiro J. Ibarra
- Department of Electronic Engineering, Universidad Técnica Federico Santa María, Avenida España 1680, Casilla 110-V, Valparaíso 2390123, Chile; (E.J.I.); (M.Z.)
| | - Julián D. Arias-Londoño
- Escuela Técnica Superior de Ingeneiros de Telecomunicación, Universidad Politécnica de Madrid, Avda, Ciudad Universitaria, 30, 28040 Madrid, Spain;
| | - Matías Zañartu
- Department of Electronic Engineering, Universidad Técnica Federico Santa María, Avenida España 1680, Casilla 110-V, Valparaíso 2390123, Chile; (E.J.I.); (M.Z.)
| | - Juan I. Godino-Llorente
- Escuela Técnica Superior de Ingeneiros de Telecomunicación, Universidad Politécnica de Madrid, Avda, Ciudad Universitaria, 30, 28040 Madrid, Spain;
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12
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Yu L, Vijay M, Sunil J, Vincy VAG, Govindan V, Khan MI, Ali S, Tamam N, Abdullaeva BS. Hybrid deep learning model based smart IOT based monitoring system for Covid-19. Heliyon 2023; 9:e21150. [PMID: 37928011 PMCID: PMC10623272 DOI: 10.1016/j.heliyon.2023.e21150] [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: 03/18/2023] [Revised: 09/04/2023] [Accepted: 10/17/2023] [Indexed: 11/07/2023] Open
Abstract
Recently, COVID-19 becomes a hot topic and explicitly made people follow social distancing and quarantine practices all over the world. Meanwhile, it is arduous to visit medical professionals intermittently by the patients for fear of spreading the disease. This IoT-based healthcare monitoring system is utilized by many professionals, can be accessed remotely, and provides treatment accordingly. In context with this, we designed an IoT-based healthcare monitoring system that sophisticatedly measures and monitors the parameters of patients such as oxygen level, blood pressure, temperature, and heart rate. This system can be widely used in rural areas that are linked to the nearest city hospitals to monitor the patients. The collected data from the monitoring system are stored in the cloud-based data storage and for the classification our approach proposes an innovative Recurrent Convolutional Neural Network (RCNN) based Puzzle optimization algorithm (PO). Based on the outcome further treatments are made with the assistance of physicians. Experimental analyses are made and analyzed the performance with state-of-art works. The availability of more data storage capacity in the cloud can make physicians access the previous data effortlessly.
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Affiliation(s)
- Liping Yu
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, 264005, China
| | - M.M. Vijay
- SCAD College of Engineering and Technology, Tirunelveli, India
| | - J. Sunil
- Department of Computer Science and Engineering, Annai Vailankanni College of Engineering, Kanyakumari, India
| | | | - Vediyappan Govindan
- Department of Mathematics, Hindustan Institute of Technology and Science (Deemed to be University), Padur, Kelambakkam, 603103, India
| | - M. Ijaz Khan
- Department of Mechanical Engineering, Lebanese American University, Kraytem, Beirut, 1102-2801, Lebanon
- Department of Mathematics and Statistics, Riphah International University I-14, Islamabad 44000, Pakistan
| | - Shahid Ali
- School of Electronics Engineering Peking University, Beijing, China
| | - Nissren Tamam
- Department of Physics, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
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Ghani MU, Imran M, Sampathkumar S, Tchier F, Pattabiraman K, Jan AZ. A paradigmatic approach to the molecular descriptor computation for some antiviral drugs. Heliyon 2023; 9:e21401. [PMID: 38027690 PMCID: PMC10658280 DOI: 10.1016/j.heliyon.2023.e21401] [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: 07/20/2023] [Revised: 10/05/2023] [Accepted: 10/20/2023] [Indexed: 12/01/2023] Open
Abstract
In theoretical chemistry, topological indices are commonly employed to model the physico-chemical properties of chemical compounds. Mathematicians frequently use Zagreb indices to calculate a chemical compound's strain energy, melting point, boiling temperature, distortion, and stability. The current global pandemic caused by the new SARS-CoV-2, also known as COVID-19, is a significant public health concern. Various therapy modalities are advised. The issue has become worse since there hasn't been enough counseling. Researchers are looking at compounds that might be used as SARS and MERS therapies based on earlier studies. In several quantitative structure-property-activity relationships (QSPR and QSAR) studies, a variety of physiochemical properties are successfully represented by topological indices, a sort of molecular descriptor that just specifies numerical values connected to a substance's molecular structure. This study investigates several irregularity-based topological indices for various antiviral medicines, depending on the degree of irregularity. In order to evaluate the effectiveness of the generated topological indices, a QSPR was also carried out using the indicated pharmaceuticals, the various topological indices, and the various physiochemical features of these antiviral medicines. The acquired results show a substantial association between the topological indices being studied by the curve-fitting approach and the physiochemical properties of possible antiviral medicines.
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Affiliation(s)
- Muhammad Usman Ghani
- Institute of Mathematics, Khawaja Fareed University of Engineering & Information Technology, Abu Dhabi Road, 64200, Rahim Yar Khan, Pakistan
| | - Muhammad Imran
- Department of Mathematical Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| | - S. Sampathkumar
- Department of Mathematics, SSN College of Engineering, Kalvakkam - 603 110, India
| | - Fairouz Tchier
- Mathematics Department, College of Science, King Saud University, P.O. Box 22452, Riyadh 11495, Saudi Arabia
| | - K. Pattabiraman
- Department of Mathematics Government Arts College, Kumbakonam 612 002, India
| | - Ahmad Zubair Jan
- Wroclaw University of Science and Technology, Faculty of Mechanical Engineering, Poland
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14
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Hossain S, Azam S, Montaha S, Karim A, Chowa SS, Mondol C, Zahid Hasan M, Jonkman M. Automated breast tumor ultrasound image segmentation with hybrid UNet and classification using fine-tuned CNN model. Heliyon 2023; 9:e21369. [PMID: 37885728 PMCID: PMC10598544 DOI: 10.1016/j.heliyon.2023.e21369] [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: 06/14/2023] [Revised: 10/11/2023] [Accepted: 10/20/2023] [Indexed: 10/28/2023] Open
Abstract
Introduction Breast cancer stands as the second most deadly form of cancer among women worldwide. Early diagnosis and treatment can significantly mitigate mortality rates. Purpose The study aims to classify breast ultrasound images into benign and malignant tumors. This approach involves segmenting the breast's region of interest (ROI) employing an optimized UNet architecture and classifying the ROIs through an optimized shallow CNN model utilizing an ablation study. Method Several image processing techniques are utilized to improve image quality by removing text, artifacts, and speckle noise, and statistical analysis is done to check the enhanced image quality is satisfactory. With the processed dataset, the segmentation of breast tumor ROI is carried out, optimizing the UNet model through an ablation study where the architectural configuration and hyperparameters are altered. After obtaining the tumor ROIs from the fine-tuned UNet model (RKO-UNet), an optimized CNN model is employed to classify the tumor into benign and malignant classes. To enhance the CNN model's performance, an ablation study is conducted, coupled with the integration of an attention unit. The model's performance is further assessed by classifying breast cancer with mammogram images. Result The proposed classification model (RKONet-13) results in an accuracy of 98.41 %. The performance of the proposed model is further compared with five transfer learning models for both pre-segmented and post-segmented datasets. K-fold cross-validation is done to assess the proposed RKONet-13 model's performance stability. Furthermore, the performance of the proposed model is compared with previous literature, where the proposed model outperforms existing methods, demonstrating its effectiveness in breast cancer diagnosis. Lastly, the model demonstrates its robustness for breast cancer classification, delivering an exceptional performance of 96.21 % on a mammogram dataset. Conclusion The efficacy of this study relies on image pre-processing, segmentation with hybrid attention UNet, and classification with fine-tuned robust CNN model. This comprehensive approach aims to determine an effective technique for detecting breast cancer within ultrasound images.
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Affiliation(s)
- Shahed Hossain
- Health Informatics Research Laboratory (HIRL), Department of Computer Science and Engineering, Daffodil International University, Dhaka, 1341, Bangladesh
| | - Sami Azam
- Faculty of Science and Technology, Charles Darwin University, Casuarina, 0909, NT, Australia
| | - Sidratul Montaha
- Department of Computer Science, University of Calgary, Calgary, AB, T2N 1N4, Canada
| | - Asif Karim
- Faculty of Science and Technology, Charles Darwin University, Casuarina, 0909, NT, Australia
| | - Sadia Sultana Chowa
- Health Informatics Research Laboratory (HIRL), Department of Computer Science and Engineering, Daffodil International University, Dhaka, 1341, Bangladesh
| | - Chaity Mondol
- Health Informatics Research Laboratory (HIRL), Department of Computer Science and Engineering, Daffodil International University, Dhaka, 1341, Bangladesh
| | - Md Zahid Hasan
- Health Informatics Research Laboratory (HIRL), Department of Computer Science and Engineering, Daffodil International University, Dhaka, 1341, Bangladesh
| | - Mirjam Jonkman
- Faculty of Science and Technology, Charles Darwin University, Casuarina, 0909, NT, Australia
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15
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Sun J, Liu X, Huang Y, Wang F, Sun Y, Chen J, Chu D, Song H. Automatic identification and morphological comparison of bivalve and brachiopod fossils based on deep learning. PeerJ 2023; 11:e16200. [PMID: 37842038 PMCID: PMC10576495 DOI: 10.7717/peerj.16200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 09/07/2023] [Indexed: 10/17/2023] Open
Abstract
Fossil identification is an essential and fundamental task for conducting palaeontological research. Because the manual identification of fossils requires extensive experience and is time-consuming, automatic identification methods are proposed. However, these studies are limited to a few or dozens of species, which is hardly adequate for the needs of research. This study enabled the automatic identification of hundreds of species based on a newly established fossil dataset. An available "bivalve and brachiopod fossil image dataset" (BBFID, containing >16,000 "image-label" data pairs, taxonomic determination completed) was created. The bivalves and brachiopods contained in BBFID are closely related in morphology, ecology and evolution that have long attracted the interest of researchers. We achieved >80% identification accuracy at 22 genera and ∼64% accuracy at 343 species using EfficientNetV2s architecture. The intermediate output of the model was extracted and downscaled to obtain the morphological feature space of fossils using t-distributed stochastic neighbor embedding (t-SNE). We found a distinctive boundary between the morphological feature points of bivalves and brachiopods in fossil morphological feature distribution maps. This study provides a possible method for studying the morphological evolution of fossil clades using computer vision in the future.
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Affiliation(s)
- Jiarui Sun
- State Key Laboratory of Biogeology and Environmental Geology, School of Earth Sciences, China University of Geosciences, Wuhan, Hubei, China
| | - Xiaokang Liu
- State Key Laboratory of Biogeology and Environmental Geology, School of Earth Sciences, China University of Geosciences, Wuhan, Hubei, China
- Department of Biology, University of Fribourg, Fribourg, Switzerland
| | - Yunfei Huang
- School of Geosciences, Yangtze University, Wuhan, Hubei, China
| | - Fengyu Wang
- State Key Laboratory of Biogeology and Environmental Geology, School of Earth Sciences, China University of Geosciences, Wuhan, Hubei, China
| | - Yongfang Sun
- State Key Laboratory of Biogeology and Environmental Geology, School of Earth Sciences, China University of Geosciences, Wuhan, Hubei, China
| | - Jing Chen
- Yifu Museum, China University of Geosciences, Wuhan, Hubei, China
| | - Daoliang Chu
- State Key Laboratory of Biogeology and Environmental Geology, School of Earth Sciences, China University of Geosciences, Wuhan, Hubei, China
| | - Haijun Song
- State Key Laboratory of Biogeology and Environmental Geology, School of Earth Sciences, China University of Geosciences, Wuhan, Hubei, China
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16
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Coşar Soğukkuyu DY, Ata O. Classification of melanonychia, Beau's lines, and nail clubbing based on nail images and transfer learning techniques. PeerJ Comput Sci 2023; 9:e1533. [PMID: 37705653 PMCID: PMC10495933 DOI: 10.7717/peerj-cs.1533] [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: 04/14/2023] [Accepted: 07/21/2023] [Indexed: 09/15/2023]
Abstract
Background Nail diseases are malformations that appear on the nail plate and are classified according to their own signs and symptoms that may be related to other medical conditions. Although most nail diseases have distinct symptoms, making a differential diagnosis of nail problems can be challenging for medical experts. Method One early diagnosis method for any dermatological disease is designing an image analysis system based on artificial intelligence (AI) techniques. This article implemented a novel model using a publicly available nail disease dataset to determine the occurrence of three common types of nail diseases. Two classification models based on transfer learning using visual geometry group (VGGNet) were utilized to detect and classify nail diseases from images. Result and Finding The experimental design results showed good accuracy: VGG16 had a score of 94% accuracy and VGG19 had a 93% accuracy rate. These findings suggest that computer-aided diagnostic systems based on transfer learning can be used to identify multiple-lesion nail diseases.
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Affiliation(s)
| | - Oğuz Ata
- Department of Information Technology, Altinbas University, İstanbul, Turkey
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17
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Dou B, Zhu Z, Merkurjev E, Ke L, Chen L, Jiang J, Zhu Y, Liu J, Zhang B, Wei GW. Machine Learning Methods for Small Data Challenges in Molecular Science. Chem Rev 2023; 123:8736-8780. [PMID: 37384816 PMCID: PMC10999174 DOI: 10.1021/acs.chemrev.3c00189] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023]
Abstract
Small data are often used in scientific and engineering research due to the presence of various constraints, such as time, cost, ethics, privacy, security, and technical limitations in data acquisition. However, big data have been the focus for the past decade, small data and their challenges have received little attention, even though they are technically more severe in machine learning (ML) and deep learning (DL) studies. Overall, the small data challenge is often compounded by issues, such as data diversity, imputation, noise, imbalance, and high-dimensionality. Fortunately, the current big data era is characterized by technological breakthroughs in ML, DL, and artificial intelligence (AI), which enable data-driven scientific discovery, and many advanced ML and DL technologies developed for big data have inadvertently provided solutions for small data problems. As a result, significant progress has been made in ML and DL for small data challenges in the past decade. In this review, we summarize and analyze several emerging potential solutions to small data challenges in molecular science, including chemical and biological sciences. We review both basic machine learning algorithms, such as linear regression, logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), kernel learning (KL), random forest (RF), and gradient boosting trees (GBT), and more advanced techniques, including artificial neural network (ANN), convolutional neural network (CNN), U-Net, graph neural network (GNN), Generative Adversarial Network (GAN), long short-term memory (LSTM), autoencoder, transformer, transfer learning, active learning, graph-based semi-supervised learning, combining deep learning with traditional machine learning, and physical model-based data augmentation. We also briefly discuss the latest advances in these methods. Finally, we conclude the survey with a discussion of promising trends in small data challenges in molecular science.
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Affiliation(s)
- Bozheng Dou
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Zailiang Zhu
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Ekaterina Merkurjev
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Lu Ke
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Long Chen
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Jian Jiang
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Yueying Zhu
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Jie Liu
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Bengong Zhang
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan 48824, United States
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, United States
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18
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Ordu M, Der O. Polymeric Materials Selection for Flexible Pulsating Heat Pipe Manufacturing Using a Comparative Hybrid MCDM Approach. Polymers (Basel) 2023; 15:2933. [PMID: 37447579 DOI: 10.3390/polym15132933] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 06/24/2023] [Accepted: 06/28/2023] [Indexed: 07/15/2023] Open
Abstract
The right choice of polymeric materials plays a vital role in the successful design and manufacture of flexible fluidic systems, as well as heat transfer devices such as pulsating heat pipes. The decision to choose an acceptable polymeric material entails a variety of evaluation criteria because there are numerous competing materials available today, each with its own properties, applications, benefits, and drawbacks. In this study, a comparative hybrid multi-criteria decision-making (MCDM) model is proposed for evaluating suitable polymeric materials for the fabrication of flexible pulsating heat pipes. The decision model consists of fourteen evaluation criteria and twelve alternative materials. For this purpose, three different hybrid MCDM methods were applied to solve the material selection problems (i.e., AHP-GRA, AHP-CoCoSo, and AHP-VIKOR). According to the results obtained, PTFE, PE, and PP showed promising properties. In addition, Spearman's rank correlation analysis was performed, and the hybrid methods used produced consistent rankings with each other. By applying MCDM methods, it was concluded that PTFE is the most suitable material to be preferred for manufacturing flexible pulsating heat pipes. In addition to this result, PE and PP are among the best alternatives that can be recommended after PTFE. The study supports the use of MCDM techniques to rank material choices and enhance the selection procedure. The research will greatly assist industrial managers and academics involved in the selection process of polymeric materials.
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Affiliation(s)
- Muhammed Ordu
- Department of Industrial Engineering, Faculty of Engineering, Osmaniye Korkut Ata University, 80010 Osmaniye, Turkey
| | - Oguzhan Der
- Department of Marine Vehicles Management Engineering, Faculty of Maritime, Bandirma Onyedi Eylul University, 10200 Balikesir, Turkey
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19
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Kochakkashani F, Kayvanfar V, Haji A. Supply chain planning of vaccine and pharmaceutical clusters under uncertainty: The case of COVID-19. SOCIO-ECONOMIC PLANNING SCIENCES 2023; 87:101602. [PMID: 37255585 PMCID: PMC10111859 DOI: 10.1016/j.seps.2023.101602] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 04/17/2023] [Accepted: 04/17/2023] [Indexed: 06/01/2023]
Abstract
As an abrupt epidemic occurs, healthcare systems are shocked by the surge in the number of susceptible patients' demands, and decision-makers mostly rely on their frame of reference for urgent decision-making. Many reports have declared the COVID-19 impediments to trading and global economic growth. This study aims to provide a mathematical model to support pharmaceutical supply chain planning during the COVID-19 epidemic. Additionally, it aims to offer new insights into hospital supply chain problems by unifying cold and non-cold chains and considering a wide range of pharmaceuticals and vaccines. This approach is unprecedented and includes an analysis of various pharmaceutical features such as temperature, shelf life, priority, and clustering. To propose a model for planning the pharmaceutical supply chains, a mixed-integer linear programming (MILP) model is used for a four-echelon supply chain design. This model aims to minimize the costs involved in the pharmaceutical supply chain by maintaining an acceptable service level. Also, this paper considers uncertainty as an intrinsic part of the problem and addresses it through the wait-and-see method. Furthermore, an unexplored unsupervised learning method in the realm of supply chain planning has been used to cluster the pharmaceuticals and the vaccines and its merits and drawbacks are proposed. A case of Tehran hospitals with real data has been used to show the model's capabilities, as well. Based on the obtained results, the proposed approach is able to reach the optimum service level in the COVID conditions while maintaining a reduced cost. The experiment illustrates that the hospitals' adjacency and emergency orders alleviated the service level significantly. The proposed MILP model has proven to be efficient in providing a practical intuition for decision-makers. The clustering technique reduced the size of the problem and the time required to solve the model considerably.
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Affiliation(s)
- Farid Kochakkashani
- Department of Electrical and Computer Engineering, George Washington University, Washington D.C, USA
| | - Vahid Kayvanfar
- Division of Engineering Management and Decision Sciences, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Alireza Haji
- Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran
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20
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Designing the home healthcare supply chain during a health crisis. JOURNAL OF ENGINEERING RESEARCH 2023:100098. [PMCID: PMC10205133 DOI: 10.1016/j.jer.2023.100098] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 05/12/2023] [Accepted: 05/21/2023] [Indexed: 10/28/2023]
Abstract
During the COVID-19 pandemic, sectoral contributors to home healthcare supply chain (HHCSC) corporations highlighted the role of home care services. Pharmacies are located where patients are allocated to them, and nurses are routed and scheduled according to their patients' needs. It is the first study to propose an integrated location-allocation-routing model, which includes all preliminaries necessary to make these decisions. We implement the LP-metric and epsilon-constraint methods to solve this model, and then we discuss the results of these methods. A comparison is also made regarding the objective function values and the time taken to solve the problem. The average, mean ideal distance (MID) (3.74; 3.19), the rate of achievement of two objectives simultaneously (RAS) (1.71; 3.56), and computational time (CPU time) (1.92; 24.92) for two ɛ-constraint and LP-Metric methods is calculated. The superior technique is finally selected by utilizing the TOPSIS. To solve the study’s mathematical model, the LP-metric method is worth implementing. Based on these results, the suggested model for HHCSC companies, and employees’ performance, is efficient during the COVID-19 pandemic.
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21
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Hussain Z, Marcel B, Majeed A, Tsimisaraka RSM. Effects of transport-carbon intensity, transportation, and economic complexity on environmental and health expenditures. ENVIRONMENT, DEVELOPMENT AND SUSTAINABILITY 2023:1-31. [PMID: 37362967 PMCID: PMC10165593 DOI: 10.1007/s10668-023-03297-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 04/25/2023] [Indexed: 06/28/2023]
Abstract
Health and the environment are complex components of the countries, influenced by several factors, especially transport, and economics. Thus, this paper assesses the role of transportation and economic complexity in the environment and public health for the Organization for Economic Co-operation Development (OECD) countries from 2001 to 2020. This study also focuses on the relationship between transport and economic complexity with environmental and healthcare expenditures. Precisely, transport and economic activities stimulate healthcare expenditures through multiple channels. The current study employs the STIRPAT model to investigate the association with transportation, economic complexity, transport-carbon intensity, and healthcare expenditure. Besides, the current research confirms the plausible cross-sectional dependency across countries, and it adopts a second-generation technique. Analytical findings suggest that transportation-carbon intensity is positively and significantly associated with healthcare expenditures. Healthcare and transport-household expenditures increase transport-carbon intensity (TCI) by 75% and 45%, respectively, in the long run. In the contrast, TCI and transport-household expenditures have also a remarkable impact on healthcare expenditures and are estimated approximately 95% in the long run. Moreover, economic growth also upsurges TCI and healthcare expenditures through multiple economic activities. Besides, transport-household expenditures (THE) drastically impact transport-carbon intensity and healthcare expenditures (HEX) through passenger traffic (PTF). Diagnostic upshots unveil that the joint effect of THE and PTF increases TCI and HEX by 4 and 3%, respectively. Finally, findings recommend some policy implications and future research directions for the countries based on empirical outcomes. Countries should regulate economic activities to reduce transport carbon emissions.
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Affiliation(s)
- Zahid Hussain
- School of Finance, Qilu University of Technology (Shandong Academy of Sciences), Jinan, People’s Republic of China
| | | | - Abdul Majeed
- Business School, Huanggang Normal University, Hubei, People’s Republic of China
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22
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Justyna M, Antczak M, Szachniuk M. Machine learning for RNA 2D structure prediction benchmarked on experimental data. Brief Bioinform 2023; 24:7140288. [PMID: 37096592 DOI: 10.1093/bib/bbad153] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 03/15/2023] [Accepted: 03/29/2023] [Indexed: 04/26/2023] Open
Abstract
Since the 1980s, dozens of computational methods have addressed the problem of predicting RNA secondary structure. Among them are those that follow standard optimization approaches and, more recently, machine learning (ML) algorithms. The former were repeatedly benchmarked on various datasets. The latter, on the other hand, have not yet undergone extensive analysis that could suggest to the user which algorithm best fits the problem to be solved. In this review, we compare 15 methods that predict the secondary structure of RNA, of which 6 are based on deep learning (DL), 3 on shallow learning (SL) and 6 control methods on non-ML approaches. We discuss the ML strategies implemented and perform three experiments in which we evaluate the prediction of (I) representatives of the RNA equivalence classes, (II) selected Rfam sequences and (III) RNAs from new Rfam families. We show that DL-based algorithms (such as SPOT-RNA and UFold) can outperform SL and traditional methods if the data distribution is similar in the training and testing set. However, when predicting 2D structures for new RNA families, the advantage of DL is no longer clear, and its performance is inferior or equal to that of SL and non-ML methods.
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Affiliation(s)
- Marek Justyna
- Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965 Poznan, Poland
| | - Maciej Antczak
- Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965 Poznan, Poland
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Noskowskiego 12/14, 61-704 Poznan, Poland
| | - Marta Szachniuk
- Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965 Poznan, Poland
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Noskowskiego 12/14, 61-704 Poznan, Poland
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23
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Bagheri R, Zomorodi P, Rezaeian A. Identifying and ranking key technological capabilities in supply chain sustainability using ISM approach: case of food industry in Iran. ENVIRONMENT, DEVELOPMENT AND SUSTAINABILITY 2023:1-38. [PMID: 37362973 PMCID: PMC10010961 DOI: 10.1007/s10668-023-03091-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 02/25/2023] [Indexed: 06/28/2023]
Abstract
The food industry is one of the strategic industries in developing countries, such as Iran and plays a critical role in the economy, food security, and public health. The growing populations can only have food security when the food industry's supply chain is sustainable. Therefore, due to the sustainable food supply chain's great importance, having technological capabilities compared to others is considered a competitive advantage for the companies involved in the food industry, as it can distinguish them as pioneer actors. Although many technologies have been investigated and used in the sustainable supply chain recently, no study has focused on identifying and ranking key technological capabilities related to the food industry in sustainable supply chain management. Also, we have not found any study using the ISM-MICMAC method to identify, rank, and interdependence between key technology capabilities in supply chain sustainability. Accordingly, the present study sought to identify and rank key technological capabilities in the supply chain sustainability of food industry companies. In this study, after reviewing the relevant literature, eleven technological capabilities in supply chain sustainability were identified. Then using experts' opinions and Interpretive Structural Modelling (ISM), interdependence among the technological capabilities ranked. Finally, dependent and independent drivers were presented using the MICMAC analysis. The ISM analysis results indicated that communication and information technology infrastructure was the most significant driver for other technological capabilities in companies' supply chain sustainability. Moreover, logistic optimization is imperative for improving supply chain sustainability performance. Therefore, if logistic optimization is appropriately implemented, it can improve supply chain sustainability. The present study results can increase supply chain productivity and effectiveness in Iranian food industry companies.
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Affiliation(s)
- Rouholla Bagheri
- Department of Management, Faculty of Economic and Administrative Sciences, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Parisa Zomorodi
- Department of Industrial and Information Management, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran
| | - Ali Rezaeian
- Department of Industrial and Information Management, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran
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24
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An integrated design concept evaluation method based on best-worst entropy and generalized TODIM considering multiple factors of uncertainty. Appl Soft Comput 2023. [DOI: 10.1016/j.asoc.2023.110165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2023]
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25
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Zhao X, Meng L, Tong X, Xu X, Wang W, Miao Z, Mo D. A novel computational fluid dynamic method and validation for assessing distal cerebrovascular microcirculatory resistance. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 230:107338. [PMID: 36640605 DOI: 10.1016/j.cmpb.2023.107338] [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: 07/21/2022] [Revised: 01/02/2023] [Accepted: 01/04/2023] [Indexed: 06/17/2023]
Abstract
BACKGROUND AND OBJECTIVE The non-invasive assessment of microcirculatory resistance could improve the treatment of cerebrovascular stenosis. This study aimed to validate a novel computational strategy for determining the reference value of microcirculatory resistance in patients with cerebrovascular stenosis. METHODS We reconstructed a patient-specific 3-dimensional model of the extracranial-intracranial arteries. A computational strategy incorporating patient-specific pressure-wire measurements was developed to estimate the blood flow rate and microcirculatory resistance. Throughout the computational fluid dynamics (CFD) simulation, the boundary conditions were adjusted according to the developed algorithm. Pearson correlation and Bland-Altman analyses were used to quantify the correlation and agreement between CFD calculations and transcranial Doppler (TCD) assessment. RESULTS A strong correlation was found between the CFD-based and invasive distal pressure measurements (P<0.0001). Meanwhile, the CFD and TCD-based flow measurements were highly correlated (r = 0.853; P = 0.001). Furthermore, there was a correlation between the mean velocity measured by CFD and the mean velocity measured by TCD (r = 0.777; P<0.001). Good agreement was observed between the mass flow by CFD simulation and volumetric flow by TCD (P = 0.0266, mean difference: -0.7814 mmHg, limits of agreement, -4.0905 - 2.5276). However, the mean velocities from CFD simulation were in less agreement with those from the TCD assessment (P = 0.3992, mean difference, -0.0485; limits of agreement, -0.6141 - 0.5170). Results of the CFD simulation indicate that the flow resistance varies greatly between individuals. CONCLUSIONS The computational strategy of incorporating patient-specific pressure-wire measurements may serve as an effective approach to evaluate the actual reference values of microcirculatory resistance. In addition, an individualized assessment of non-invasive flow resistance is necessary for the accurate determination of non-invasive cerebrovascular pressure.
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Affiliation(s)
- Xi Zhao
- Central Research Institute, United Imaging Healthcare, Shanghai, China.
| | | | - Xu Tong
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xiaotong Xu
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | | | - Zhongrong Miao
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Dapeng Mo
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
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Samal S, Nayak R, Jena S, Balabantaray BK. Obscene image detection using transfer learning and feature fusion. MULTIMEDIA TOOLS AND APPLICATIONS 2023; 82:1-29. [PMID: 36846526 PMCID: PMC9942052 DOI: 10.1007/s11042-023-14437-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 04/27/2022] [Accepted: 01/27/2023] [Indexed: 06/18/2023]
Abstract
Deep learning-based methods have been proven excellent performance in detecting pornographic images/videos flooded on social media. However, in a dearth of huge yet well-labeled datasets, these methods may suffer from under/overfitting problems and may exhibit unstable output responses in the classification process. To deal with the issue we have suggested an automatic pornographic image detection method by utilizing transfer learning (TL) and feature fusion. The novelty of our proposed work is TL based feature fusion process (FFP) which enables the removal of hyper-parameter tuning, improves model performance, and lowers the computational burden of the desired model. FFP fuses low-level and mid-level features of the outperforming pre-trained models followed by transferring the learned knowledge to control the classification process. Key contributions of our proposed method are i) generation of a well-labeled obscene image dataset GGOI via Pix-2-Pix GAN architecture for the training of deep learning models ii) modification of model architectures by integrating batch normalization and mixed pooling strategy to obtain training stability (iii) selection of outperforming models to be integrated with the FFP by performing end-to-end detection of obscene images and iv) design of TL based obscene image detection method by retraining the last layer of the fused model. Extensive experimental analyses are performed on benchmark datasets i.e., NPDI, Pornography 2k, and generated GGOI dataset. The proposed TL model with fused MobileNet V2 + DenseNet169 network performs as the state-of-the-art model compared to existing methods and provides average classification accuracy, sensitivity, and F1 score of 98.50%, 98.46% and 98.49% respectively.
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Affiliation(s)
- Sonali Samal
- National Institute of Technology Meghalaya, Shillong, Meghalaya India
| | - Rajashree Nayak
- JIS Institute of Advanced Studies and Research, JISU Kolkata, West Bengal 700091 India
| | - Swastik Jena
- National Institute of Technology Meghalaya, Shillong, Meghalaya India
| | - Bunil Ku. Balabantaray
- Computer Science and Engineering, National Institute of Technology Meghalaya, Shillong, Meghalaya 793003 India
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A Novel Parallel Simulated Annealing Methodology to Solve the No-Wait Flow Shop Scheduling Problem with Earliness and Tardiness Objectives. Processes (Basel) 2023. [DOI: 10.3390/pr11020454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
In this paper, the no-wait flow shop problem with earliness and tardiness objectives is considered. The problem is proven to be NP-hard. Recent no-wait flow shop problem studies focused on familiar objectives, such as makespan, total flow time, and total completion time. However, the problem has limited studies with solution approaches covering the concomitant use of earliness and tardiness objectives. A novel methodology for the parallel simulated annealing algorithm is proposed to solve this problem in order to overcome the runtime drawback of classical simulated annealing and enhance its robustness. The well-known flow shop problem datasets in the literature are utilized for benchmarking the proposed algorithm, along with the classical simulated annealing, variants of tabu search, and particle swarm optimization algorithms. Statistical analyses were performed to compare the runtime and robustness of the algorithms. The results revealed the enhancement of the classical simulated annealing algorithm in terms of time consumption and solution robustness via parallelization. It is also concluded that the proposed algorithm could outperform the benchmark metaheuristics even when run in parallel. The proposed algorithm has a generic structure that can be easily adapted to many combinatorial optimization problems.
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Xu M, Fu Y, Tian B. An ensemble fraud detection approach for online loans based on application usage patterns. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023. [DOI: 10.3233/jifs-222405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
The fraud problem has drastically increased with the rapid growth of online lending. Since loan applications, approvals and disbursements are operated online, deceptive borrowers are prone to conceal or falsify information to maliciously obtain loans, while lenders have difficulty in identifying fraud without direct contacts and lack binding force on customers’ loan performance, which results in the frequent occurrence of fraud events. Therefore, it is significant for financial institutions to apply valuable data and competitive technologies for fraud detection to reduce financial losses from loan scams. This paper combines the advantages of statistical methods and ensemble learning algorithms to design the grouped trees and weighted ensemble algorithm (GTWE), and establishes fraud prediction models for online loans based on mobile application usage behaviors(App behaviors) by logistic regression, extreme gradient boosting (XGBoost), long short-term memory (LSTM) and the GTWE algorithm, respectively. The experimental results show that the fraud prediction model based on the GTWE algorithm achieves outstanding classification effect and stability with satisfactory interpretability. Meanwhile, the fraud probability of customers detected by the fraud prediction model is as high as 84.19%, which indicates that App behaviors have a considerable impact on predicting fraud in online loan application.
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Affiliation(s)
- Meiling Xu
- School of Mathematics, Harbin Institute of Technology, Harbin, China
| | - Yongqiang Fu
- School of Mathematics, Harbin Institute of Technology, Harbin, China
| | - Boping Tian
- School of Mathematics, Harbin Institute of Technology, Harbin, China
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Chen CS, Hu NT. Eye-in-Hand Robotic Arm Gripping System Based on Machine Learning and State Delay Optimization. SENSORS (BASEL, SWITZERLAND) 2023; 23:1076. [PMID: 36772116 PMCID: PMC9919884 DOI: 10.3390/s23031076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 01/12/2023] [Accepted: 01/13/2023] [Indexed: 06/18/2023]
Abstract
This research focused on using RGB-D images and modifying an existing machine learning network architecture to generate predictions of the location of successfully grasped objects and to optimize the control system for state delays. A five-finger gripper designed to mimic the human palm was tested to demonstrate that it can perform more delicate missions than many two- or three-finger grippers. Experiments were conducted using the 6-DOF robot arm with the five-finger and two-finger grippers to perform at least 100 actual machine grasps, and compared to the results of other studies. Additionally, we investigated state time delays and proposed a control method for a robot manipulator. Many studies on time-delay systems have been conducted, but most focus on input and output delays. One reason for this emphasis is that input and output delays are the most commonly occurring delays in physical or electronic systems. An additional reason is that state delays increase the complexity of the overall control system. Finally, it was demonstrated that our network can perform as well as a deep network architecture with little training data and omitting steps, such as posture evaluation, and when combined with the hardware advantages of the five-finger gripper, it can produce an automated system with a gripping success rate of over 90%. This paper is an extended study of the conference paper.
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Di Pilla P, Pareschi R, Salzano F, Zappone F. Listening to what the system tells us: Innovative auditing for distributed systems. FRONTIERS IN COMPUTER SCIENCE 2023. [DOI: 10.3389/fcomp.2022.1020946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
IntroductionIn recent years, software ecosystems have become more complex with the proliferation of distributed systems such as blockchains and distributed ledgers. Effective management of these systems requires constant monitoring to identify any potential malfunctions, anomalies, vulnerabilities, or attacks. Traditional log auditing methods can effectively monitor the health of conventional systems. Yet, they run short of handling the higher levels of complexity of distributed systems. This study aims to propose an innovative architecture for system auditing that can effectively manage the complexity of distributed systems using advanced data analytics, natural language processing, and artificial intelligence.MethodsTo develop this architecture, we considered the unique characteristics of distributed systems and the various signals that may arise within them. We also felt the need for flexibility to capture these signals effectively. The resulting architecture utilizes advanced data analytics, natural language processing, and artificial intelligence to analyze and interpret the various signals emitted by the system.ResultsWe have implemented this architecture in the DELTA (Distributed Elastic Log Text Analyzer) auditing tool and applied it to the Hyperledger Fabric platform, a widely used implementation of private blockchains.DiscussionThe proposed architecture for system auditing can effectively handle the complexity of distributed systems, and the DELTA tool provides a practical implementation of this approach. Further research could explore this approach's potential applications and effectiveness in other distributed systems.
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Investigate the effect of Zn12O12, AlZn11O12, and GaZn11O12 nanoclusters in the carbamazepine drug detection in gas and solvent phases: a comparative DFT study. MONATSHEFTE FUR CHEMIE 2022. [DOI: 10.1007/s00706-022-03025-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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Al-Shourbaji I, Kachare PH, Abualigah L, Abdelhag ME, Elnaim B, Anter AM, Gandomi AH. A Deep Batch Normalized Convolution Approach for Improving COVID-19 Detection from Chest X-ray Images. Pathogens 2022; 12:pathogens12010017. [PMID: 36678365 PMCID: PMC9860560 DOI: 10.3390/pathogens12010017] [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: 11/16/2022] [Revised: 12/12/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022] Open
Abstract
Pre-trained machine learning models have recently been widely used to detect COVID-19 automatically from X-ray images. Although these models can selectively retrain their layers for the desired task, the output remains biased due to the massive number of pre-trained weights and parameters. This paper proposes a novel batch normalized convolutional neural network (BNCNN) model to identify COVID-19 cases from chest X-ray images in binary and multi-class frameworks with a dual aim to extract salient features that improve model performance over pre-trained image analysis networks while reducing computational complexity. The BNCNN model has three phases: Data pre-processing to normalize and resize X-ray images, Feature extraction to generate feature maps, and Classification to predict labels based on the feature maps. Feature extraction uses four repetitions of a block comprising a convolution layer to learn suitable kernel weights for the features map, a batch normalization layer to solve the internal covariance shift of feature maps, and a max-pooling layer to find the highest-level patterns by increasing the convolution span. The classifier section uses two repetitions of a block comprising a dense layer to learn complex feature maps, a batch normalization layer to standardize internal feature maps, and a dropout layer to avoid overfitting while aiding the model generalization. Comparative analysis shows that when applied to an open-access dataset, the proposed BNCNN model performs better than four other comparative pre-trained models for three-way and two-way class datasets. Moreover, the BNCNN requires fewer parameters than the pre-trained models, suggesting better deployment suitability on low-resource devices.
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Affiliation(s)
- Ibrahim Al-Shourbaji
- Department of Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK
| | - Pramod H. Kachare
- Department of Electronics & Telecommunication Engineering, Ramrao Adik Institute of Technology, Nerul, Navi Mumbai 400706, Maharashtra, India
| | - Laith Abualigah
- Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al al-Bayt University, Mafraq 25113, Jordan
- Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan
- Faculty of Information Technology, Middle East University, Amman 11831, Jordan
- Applied Science Research Center, Applied Science Private University, Amman 11931, Jordan
- School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang 11800, Malaysia
- Correspondence: (L.A.); (A.H.G.)
| | - Mohammed E. Abdelhag
- Department of Information Technology and Security, Jazan University, Jazan 45142, Saudi Arabia
| | - Bushra Elnaim
- Department of Computer Science, College of Science and Humanities in Al-Sulail, Prince Sattam bin Abdulaziz University, Riyadh 11671, Saudi Arabia
| | - Ahmed M. Anter
- Egypt-Japan University of Science and Technology (E-JUST), Alexandria 21934, Egypt
- Faculty of Computers and Artificial Intelligence, Beni-Suef University, Benisuef 62511, Egypt
| | - Amir H. Gandomi
- Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW 2007, Australia
- University Research and Innovation Center (EKIK), Óbuda University, 1034 Budapest, Hungary
- Correspondence: (L.A.); (A.H.G.)
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Mahmud S, Hossain MS, Chowdhury MEH, Reaz MBI. MLMRS-Net: Electroencephalography (EEG) motion artifacts removal using a multi-layer multi-resolution spatially pooled 1D signal reconstruction network. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-08111-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
AbstractElectroencephalogram (EEG) signals suffer substantially from motion artifacts when recorded in ambulatory settings utilizing wearable sensors. Because the diagnosis of many neurological diseases is heavily reliant on clean EEG data, it is critical to eliminate motion artifacts from motion-corrupted EEG signals using reliable and robust algorithms. Although a few deep learning-based models have been proposed for the removal of ocular, muscle, and cardiac artifacts from EEG data to the best of our knowledge, there is no attempt has been made in removing motion artifacts from motion-corrupted EEG signals: In this paper, a novel 1D convolutional neural network (CNN) called multi-layer multi-resolution spatially pooled (MLMRS) network for signal reconstruction is proposed for EEG motion artifact removal. The performance of the proposed model was compared with ten other 1D CNN models: FPN, LinkNet, UNet, UNet+, UNetPP, UNet3+, AttentionUNet, MultiResUNet, DenseInceptionUNet, and AttentionUNet++ in removing motion artifacts from motion-contaminated single-channel EEG signal. All the eleven deep CNN models are trained and tested using a single-channel benchmark EEG dataset containing 23 sets of motion-corrupted and reference ground truth EEG signals from PhysioNet. Leave-one-out cross-validation method was used in this work. The performance of the deep learning models is measured using three well-known performance matrices viz. mean absolute error (MAE)-based construction error, the difference in the signal-to-noise ratio (ΔSNR), and percentage reduction in motion artifacts (η). The proposed MLMRS-Net model has shown the best denoising performance, producing an average ΔSNR, η, and MAE values of 26.64 dB, 90.52%, and 0.056, respectively, for all 23 sets of EEG recordings. The results reported using the proposed model outperformed all the existing state-of-the-art techniques in terms of average η improvement.
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Lemenkova P, De Plaen R, Lecocq T, Debeir O. Computer Vision Algorithms of DigitSeis for Building a Vectorised Dataset of Historical Seismograms from the Archive of Royal Observatory of Belgium. SENSORS (BASEL, SWITZERLAND) 2022; 23:56. [PMID: 36616653 PMCID: PMC9824776 DOI: 10.3390/s23010056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 12/04/2022] [Accepted: 12/15/2022] [Indexed: 06/17/2023]
Abstract
Archived seismograms recorded in the 20th century present a valuable source of information for monitoring earthquake activity. However, old data, which are only available as scanned paper-based images should be digitised and converted from raster to vector format prior to reuse for geophysical modelling. Seismograms have special characteristics and specific featuresrecorded by a seismometer and encrypted in the images: signal trace lines, minute time gaps, timing and wave amplitudes. This information should be recognised and interpreted automatically when processing archives of seismograms containing large collections of data. The objective was to automatically digitise historical seismograms obtained from the archives of the Royal Observatory of Belgium (ROB). The images were originallyrecorded by the Galitzine seismometer in 1954 in Uccle seismic station, Belgium. A dataset included 145 TIFF images which required automatic approach of data processing. Software for digitising seismograms are limited and many have disadvantages. We applied the DigitSeis for machine-based vectorisation and reported here a full workflowof data processing. This included pattern recognition, classification, digitising, corrections and converting TIFFs to the digital vector format. The generated contours of signals were presented as time series and converted into digital format (mat files) which indicated information on ground motion signals contained in analog seismograms. We performed the quality control of the digitised traces in Python to evaluate the discriminating functionality of seismic signals by DigitSeis. We shown a robust approach of DigitSeis as a powerful toolset for processing analog seismic signals. The graphical visualisation of signal traces and analysis of the performed vectorisation results shown that the algorithms of data processing performed accurately and can be recommended in similar applications of seismic signal processing in future related works in geophysical research.
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Affiliation(s)
- Polina Lemenkova
- Laboratory of Image Synthesis and Analysis (LISA), École Polytechnique de Bruxelles (Brussels Faculty of Engineering), Université Libre de Bruxelles (ULB), Building L, Campus de Solbosch, Avenue Franklin Roosevelt 50, BE-1050 Brussels, Belgium
| | - Raphaël De Plaen
- Royal Observatory of Belgium, Seismology & Gravimetry (OD2), Avenue Circulaire 3, BE-1180 Uccle, Belgium
| | - Thomas Lecocq
- Royal Observatory of Belgium, Seismology & Gravimetry (OD2), Avenue Circulaire 3, BE-1180 Uccle, Belgium
| | - Olivier Debeir
- Laboratory of Image Synthesis and Analysis (LISA), École Polytechnique de Bruxelles (Brussels Faculty of Engineering), Université Libre de Bruxelles (ULB), Building L, Campus de Solbosch, Avenue Franklin Roosevelt 50, BE-1050 Brussels, Belgium
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Utility of hexagonal boron carbide nanosheets for removing harmful dyes: electronic study via DFT. INORG CHEM COMMUN 2022. [DOI: 10.1016/j.inoche.2022.110322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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A Fuzzy AHP-based approach for prioritization of cost overhead factors in agile software development. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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Flores Gerónimo J, Keramat A, Alastruey J, Duan HF. Computational modelling and application of mechanical waves to detect arterial network anomalies: Diagnosis of common carotid stenosis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 227:107213. [PMID: 36356386 DOI: 10.1016/j.cmpb.2022.107213] [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: 09/04/2022] [Revised: 10/28/2022] [Accepted: 10/31/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVE This paper proposes a novel strategy to localize anomalies in the arterial network based on its response to controlled transient waves. The idea is borrowed from system identification theories in which wave reflections can render significant information about a target system. Cardiovascular system studies often focus on the waves originating from the heart pulsations, which are of low bandwidth and, hence, can hardly carry information about the arteries with the desired resolution. METHODS Our strategy uses a relatively higher bandwidth transient signal to characterize healthy and unhealthy arterial networks through a frequency response function (FRF). We tested our novel approach on data simulated using a one-dimensional cardiovascular model that produced pulse waves in the larger arteries of the arterial network. Specifically, we excited the blood flow from the brachial artery with a relatively high bandwidth flow disturbance and collected the subsequent pressure waveform at peripheral positions. To better differentiate FRFs of healthy and unhealthy networks, we used a FRF that removes the effects of heart pulsations. RESULTS Results demonstrate the ability of the proposed FRF to detect and follow-up on the development of a common carotid artery (CCA) stenosis. We tested distinct geometrical variations of the stenosis (size, length and position) and observed differences between the FRFs of healthy and unhealthy networks in all cases; such differences were mainly due to geometrical variations determined by the stenosis. CONCLUSIONS We have provided a theoretical proof of concept that demonstrates the ability of our novel strategy to detect and track the development of CCA stenosis by using peripheral pressure waves that can be measured non-invasively in clinical practice.
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Affiliation(s)
- Joaquín Flores Gerónimo
- Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
| | - Alireza Keramat
- Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong.
| | - Jordi Alastruey
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Huan-Feng Duan
- Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
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Nishikawa Y, Yoshino T, Sugie T, Nakata Y, Itou K, Ohsawa Y. Explanatory Change Detection in Financial Markets by Graph-Based Entropy and Inter-Domain Linkage. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1726. [PMID: 36554130 PMCID: PMC9778065 DOI: 10.3390/e24121726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 10/25/2022] [Accepted: 11/22/2022] [Indexed: 06/17/2023]
Abstract
In this study, we analyzed structural changes in financial markets under COVID-19 to support investors' investment decisions. Because an explanation of these changes is necessary to respond appropriately to said changes and prepare for similar major changes in the future, we visualized the financial market as a graph. The hypothesis was based on expertise in the financial market, and the graph was analyzed from a detailed perspective by dividing the graph into domains. We also designed an original change-detection indicator based on the structure of the graph. The results showed that the original indicator was more effective than the comparison method in terms of both the speed of response and accuracy. Explanatory change detection of this method using graphs and domains allowed investors to consider specific strategies.
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Affiliation(s)
- Yosuke Nishikawa
- Department of Systems Innovation, School of Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
| | - Takaaki Yoshino
- Nissay Asset Management Corporation, Marunouchi Building, 1-6-6, Marunouchi, Chiyoda-ku, Tokyo 100-8219, Japan
| | - Toshiaki Sugie
- Nissay Asset Management Corporation, Marunouchi Building, 1-6-6, Marunouchi, Chiyoda-ku, Tokyo 100-8219, Japan
| | - Yoshiyuki Nakata
- Nissay Asset Management Corporation, Marunouchi Building, 1-6-6, Marunouchi, Chiyoda-ku, Tokyo 100-8219, Japan
| | - Kakeru Itou
- Nissay Asset Management Corporation, Marunouchi Building, 1-6-6, Marunouchi, Chiyoda-ku, Tokyo 100-8219, Japan
| | - Yukio Ohsawa
- Department of Systems Innovation, School of Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
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Brabec J, Lennartsson F. Editorial for "Investigation of the Inter- and Intra-Scanner Reproducibility and Repeatability of Radiomics Features in Magnetic Resonance Imaging". J Magn Reson Imaging 2022; 56:1569-1570. [PMID: 35403768 DOI: 10.1002/jmri.28190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 02/04/2022] [Indexed: 12/15/2022] Open
Affiliation(s)
- Jan Brabec
- Medical Radiation Physics, Clinical Sciences, Lund University, Lund, Sweden
| | - Finn Lennartsson
- Diagnostic Radiology, Clinical Sciences, Lund University, Lund, Sweden
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Walczak J, Najgebauer P, Wojciechowski A, Scherer R. Ultrasmall fully-convolution GVA-net for point cloud processing. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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41
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An approach to multi-class imbalanced problem in ecology using machine learning. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2022.101822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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A framework for accessing the equilibrium point of a multi-objective decision-making (MODM): a case study. Soft comput 2022. [DOI: 10.1007/s00500-022-07507-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
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López de la Rosa F, Gómez-Sirvent JL, Morales R, Sánchez-Reolid R, Fernández-Caballero A. A deep residual neural network for semiconductor defect classification in imbalanced scanning electron microscope datasets. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Twardawa M, Formanowicz P, Formanowicz D. Chronic Kidney Disease as a Cardiovascular Disorder-Tonometry Data Analyses. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:12339. [PMID: 36231682 PMCID: PMC9566812 DOI: 10.3390/ijerph191912339] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Revised: 09/18/2022] [Accepted: 09/22/2022] [Indexed: 06/16/2023]
Abstract
Tonometry is commonly used to provide efficient and good diagnostics for cardiovascular disease (CVD). There are many advantages of this method, including low cost, non-invasiveness and little time to perform. In this study, the effort was undertaken to check whether tonometry data hides valuable information associated with different stages of chronic kidney disease (CKD) and end-stage renal disease (ESRD) treatment. For this purpose, six groups containing patients at different stages of CKD following different ways of dialysis treatment, as well as patients without CKD but with CVD and healthy volunteers were assessed. It was revealed that each of the studied groups had a unique profile. Only the type of dialysis was indistinguishable a from tonometric perspective (hemodialysis vs. peritoneal dialysis). Several techniques were used to build profiles that independently gave the same outcome: analysis of variance, network correlation structure analysis, multinomial logistic regression, and discrimination analysis. Moreover, to evaluate the classification potential of the discriminatory model, all mentioned techniques were later compared and treated as feature selection methods. Although the results are promising, it could be difficult to express differences as simple mathematical relations. This study shows that artificial intelligence can differentiate between different stages of CKD and patients without CKD. Potential future machine learning models will be able to determine kidney health with high accuracy and thereby classify patients. ClinicalTrials.gov Identifier: NCT05214872.
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Affiliation(s)
- Mateusz Twardawa
- Institute of Computing Science, Poznan University of Technology, 60-965 Poznan, Poland
- ICT Security Department, Poznan Supercomputing and Networking Center Affiliated to the Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-139 Poznan, Poland
| | - Piotr Formanowicz
- Institute of Computing Science, Poznan University of Technology, 60-965 Poznan, Poland
| | - Dorota Formanowicz
- Department of Medical Chemistry and Laboratory Medicine, Poznan University of Medical Sciences, 60-806 Poznan, Poland
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Konings D, Alam F, Faulkner N, de Jong C. Identity and Gender Recognition Using a Capacitive Sensing Floor and Neural Networks. SENSORS (BASEL, SWITZERLAND) 2022; 22:7206. [PMID: 36236306 PMCID: PMC9571660 DOI: 10.3390/s22197206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/01/2022] [Accepted: 09/20/2022] [Indexed: 06/16/2023]
Abstract
In recent publications, capacitive sensing floors have been shown to be able to localize individuals in an unobtrusive manner. This paper demonstrates that it might be possible to utilize the walking characteristics extracted from a capacitive floor to recognize subject and gender. Several neural network-based machine learning techniques are developed for recognizing the gender and identity of a target. These algorithms were trained and validated using a dataset constructed from the information captured from 23 subjects while walking, alone, on the sensing floor. A deep neural network comprising a Bi-directional Long Short-Term Memory (BLSTM) provided the most accurate identity performance, classifying individuals with an accuracy of 98.12% on the test data. On the other hand, a Convolutional Neural Network (CNN) was the most accurate for gender recognition, attaining an accuracy of 93.3%. The neural network-based algorithms are benchmarked against Support Vector Machine (SVM), which is a classifier used in many reported works for floor-based recognition tasks. The majority of the neural networks outperform SVM across all accuracy metrics.
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Fu R, Zheng B, Wen J, Xie L. Research on commodity business value and customer value of e-commerce platforms: Based on consumer psychology and cognition. Front Psychol 2022; 13:985537. [PMID: 36204756 PMCID: PMC9531681 DOI: 10.3389/fpsyg.2022.985537] [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: 07/04/2022] [Accepted: 08/29/2022] [Indexed: 11/13/2022] Open
Abstract
Under the background of economic globalization and COVID-19, online shopping has gradually replaced offline shopping as the main shopping mode. In this paper, consumers' perceptions are introduced into the traditional BCG matrix to form a new BCG matrix, and according to it, the small gifts of a gift e-commerce platform are classified. We then performed a robustness test comparing the BCG matrix with K-means clustering. We found that new BCG matrix can objectively reflect the value of small gifts and provide suggestions for the e-commerce platform to make subsequent product decisions. Then we judge the customer value of the platform based on the improved RFM model and K-means++ clustering, and provide a reasonable customer value classification method for the e-commerce platform. Finally, we comprehensively consider the relationship between the commodity value and customer value, and analyze the preferences of different types of customer groups for different types of small gifts. Our research result shows that small gifts can be divided into 4 categories according to commodity value, namely "stars," "cash cows," "questions marks," and "dogs." These four categories of small gifts can be converted into each other through marketing ploys. Customers can be divided into important retention customers, key loyal customers and general development customers according to their values. Faced with different types of customers, managers can adopt different strategies to extract customer value. However, consumer psychology will affect consumer cognition, and different types of consumers prefer different types of small gifts, so the precise implementation of marketing strategies will effectively improve the profitability of the gift e-commerce platform. Compared with the traditional classification method, the commodity business value classification method proposed in this paper uses management analysis and planning methods, and introduces consumer psychological factors into the commodity and customer classification, so that the classification results are more credible. In addition, we jointly analyze the results of commodity value classification and customer value classification, and analyze in detail the preferences of different valued customer groups for different types of commodities, so as to provide directions for subsequent research on customer preference.
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Affiliation(s)
- Rong Fu
- College of Economics, Hangzhou Dianzi University, Hangzhou, China
| | - Binbin Zheng
- College of Economics, Hangzhou Dianzi University, Hangzhou, China
| | - Juan Wen
- The School of Economics, Xiamen University, Xiamen, China
| | - Luze Xie
- College of Economics, Hangzhou Dianzi University, Hangzhou, China
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Alghazali MW, Al-Hetty HRAK, Ali ZMM, Saleh MM, Suleiman AA, Jalil AT. Non-coding RNAs, another side of immune regulation during triple-negative breast cancer. Pathol Res Pract 2022; 239:154132. [PMID: 36183439 DOI: 10.1016/j.prp.2022.154132] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 08/23/2022] [Accepted: 09/14/2022] [Indexed: 11/16/2022]
Abstract
Triple-negative breast cancer (TNBC) is considered about 12-24 % of all breast cancer cases. Patients experience poor overall survival, high recurrence rate, and distant metastasis compared to other breast cancer subtypes. Numerous studies have highlighted the crucial roles of non-coding RNAs (ncRNAs) in carcinogenesis and proliferation, migration, and metastasis of tumor cells in TNBC. Recent research has demonstrated that long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) play a role in the regulation of the immune system by affecting the tumor microenvironment, the epithelial-mesenchymal transition, the regulation of dendritic cells and myeloid-derived stem cells, and T and B cell activation and differentiation. Immune-related miRNAs and lncRNAs, which have been established as predictive markers for various cancers, are strongly linked to immune cell infiltration and could be a viable therapeutic target for TNBC. In the current review, we discuss the recent updates of ncRNAs, including miRNAs and lncRNAs in TNBC, including their biogenesis, target genes, and biological function of their targets, which are mostly involved in the immune response.
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Affiliation(s)
| | | | - Zahraa Muhsen M Ali
- Department of Medical Laboratory Techniques, Al-Rafidain University College, Iraq
| | - Marwan Mahmood Saleh
- Department of Biophysics, College of Applied Sciences, University of Anbar, Iraq; Medical Laboratory Technology Department, College of Medical Technology, The Islamic University, Najaf, Iraq
| | | | - Abduladheem Turki Jalil
- Medical Laboratories Techniques Department, Al-Mustaqbal University College, Babylon, Hilla 51001, Iraq.
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Salman HE, Jurisch-Yaksi N, Yalcin HC. Computational Modeling of Motile Cilia-Driven Cerebrospinal Flow in the Brain Ventricles of Zebrafish Embryo. Bioengineering (Basel) 2022; 9:bioengineering9090421. [PMID: 36134967 PMCID: PMC9495466 DOI: 10.3390/bioengineering9090421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 07/04/2022] [Accepted: 07/06/2022] [Indexed: 11/16/2022] Open
Abstract
Motile cilia are hair-like microscopic structures which generate directional flow to provide fluid transport in various biological processes. Ciliary beating is one of the sources of cerebrospinal flow (CSF) in brain ventricles. In this study, we investigated how the tilt angle, quantity, and phase relationship of cilia affect CSF flow patterns in the brain ventricles of zebrafish embryos. For this purpose, two-dimensional computational fluid dynamics (CFD) simulations are performed to determine the flow fields generated by the motile cilia. The cilia are modeled as thin membranes with prescribed motions. The cilia motions were obtained from a two-day post-fertilization zebrafish embryo previously imaged via light sheet fluorescence microscopy. We observed that the cilium angle significantly alters the generated flow velocity and mass flow rates. As the cilium angle gets closer to the wall, higher flow velocities are observed. Phase difference between two adjacent beating cilia also affects the flow field as the cilia with no phase difference produce significantly lower mass flow rates. In conclusion, our simulations revealed that the most efficient method for cilia-driven fluid transport relies on the alignment of multiple cilia beating with a phase difference, which is also observed in vivo in the developing zebrafish brain.
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Affiliation(s)
- Huseyin Enes Salman
- Department of Mechanical Engineering, TOBB University of Economics and Technology, Ankara 06510, Turkey
| | - Nathalie Jurisch-Yaksi
- Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, 7491 Trondheim, Norway
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Duan B, Jiang Y. Application Research of IICA Algorithm in a Limited-Buffer Scheduling Problem. INTERNATIONAL JOURNAL OF E-COLLABORATION 2022; 18:1-18. [DOI: 10.4018/ijec.307131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
An improved imperialist competition algorithm (IICA) was proposed to resolve the problem of flexible flow shop scheduling that with limited buffer (LBFFSP). The IICA algorithm adds the elite personal retention strategy, reform operation and the discrete processing on the basis of ICA algorithm. This paper uses an individual selection mechanism based on Hamming distance to improve the ability of initial solution. The prospect is to use the IICA to work out related problems in cloud computing environment. The scheduling problem in the cloud-computing environment needs to consider the optimization of the completion time and cost of using cloud resources. At last, simulation tests and example tests are used to check the usefulness of improved imperialist competition algorithm in solving LBFFSP problem. Compared with standard ICA, the improved ICA has improved in the algorithm evolution process. As such, scheme 4 has a better ability to evolve, and its evolution time is greater than that of scheme 3. Scheme 5 has the best algorithm optimization performance.
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Affiliation(s)
- Bin Duan
- Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang, China & Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China & Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
| | - Yongqing Jiang
- Chongqing Acoustics-Optics-Electronics of China Electronics Technology Group, Chongqing, China
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50
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Xiao Q, Tang W, Zhang C, Zhou L, Feng L, Shen J, Yan T, Gao P, He Y, Wu N. Spectral Preprocessing Combined with Deep Transfer Learning to Evaluate Chlorophyll Content in Cotton Leaves. PLANT PHENOMICS 2022; 2022:9813841. [PMID: 36158530 PMCID: PMC9489230 DOI: 10.34133/2022/9813841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 06/27/2022] [Indexed: 11/16/2022]
Abstract
Rapid determination of chlorophyll content is significant for evaluating cotton's nutritional and physiological status. Hyperspectral technology equipped with multivariate analysis methods has been widely used for chlorophyll content detection. However, the model developed on one batch or variety cannot produce the same effect for another due to variations, such as samples and measurement conditions. Considering that it is costly to establish models for each batch or variety, the feasibility of using spectral preprocessing combined with deep transfer learning for model transfer was explored. Seven different spectral preprocessing methods were discussed, and a self-designed convolutional neural network (CNN) was developed to build models and conduct transfer tasks by fine-tuning. The approach combined first-derivative (FD) and standard normal variate transformation (SNV) was chosen as the best pretreatment. For the dataset of the target domain, fine-tuned CNN based on spectra processed by FD + SNV outperformed conventional partial least squares (PLS) and squares-support vector machine regression (SVR). Although the performance of fine-tuned CNN with a smaller dataset was slightly lower, it was still better than conventional models and achieved satisfactory results. Ensemble preprocessing combined with deep transfer learning could be an effective approach to estimate the chlorophyll content between different cotton varieties, offering a new possibility for evaluating the nutritional status of cotton in the field.
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Affiliation(s)
- Qinlin Xiao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Wentan Tang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Lei Zhou
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
| | - Lei Feng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Jianxun Shen
- Hangzhou Raw Seed Growing Farm, Hangzhou 311115, China
| | - Tianying Yan
- College of Information Science and Technology, Shihezi University, Shihezi 832000, China
| | - Pan Gao
- College of Information Science and Technology, Shihezi University, Shihezi 832000, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Na Wu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
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