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Peinado-Asensi I, Montés N, García E. A framework to reduce energy consumption in a press shop floor based on industrializable IIoT ( I3 oT). Heliyon 2024; 10:e29432. [PMID: 38694072 PMCID: PMC11058704 DOI: 10.1016/j.heliyon.2024.e29432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 04/08/2024] [Accepted: 04/08/2024] [Indexed: 05/03/2024] Open
Abstract
This article presents a framework to reduce energy consumption in a floor shop press based on Industrializable Industrial Internet of Things (I 3 o T ). The I 3 o T proposes the development of IIoT tools using the information available in the system, without adding any additional sensors. Based on this philosophy, we proposed to develop the C360 criterion in our previous works, which allowed to extract all the information available in the stamping presses for the development of I 3 o T applications. In this article, we propose the development of a framework to optimize the parameters accessible from the C360 criterion for energy saving in the stamping process. Regarding the three parameters that can be modified and that affect energy consumption, that is, counterbalance pressure, tonnage and press speed, we will work with the first two in this paper. At the end of the article, the results obtained from the presses installed at Ford factory in Almussafes (Valencia) are shown based on their adjustment.
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Affiliation(s)
- Ivan Peinado-Asensi
- Department of Mathematics, Physics and Technology, Universidad Cardenal Herrera-CEU, CEU Universities, C/ San Bartolomé 55, 46115, Alfara del Patriarca, Valencia, Spain
- Ford Spain, Polígono Industrial Ford S/N, 46440, Almussafes, Valencia, Spain
| | - Nicolás Montés
- Department of Mathematics, Physics and Technology, Universidad Cardenal Herrera-CEU, CEU Universities, C/ San Bartolomé 55, 46115, Alfara del Patriarca, Valencia, Spain
| | - Eduardo García
- Ford Spain, Polígono Industrial Ford S/N, 46440, Almussafes, Valencia, Spain
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2
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Li W, Chen J, Zhu J, Ji X, Fu Z. Research on factor analysis and method for evaluating grouting effects using machine learning. Sci Rep 2024; 14:7782. [PMID: 38565612 PMCID: PMC10987539 DOI: 10.1038/s41598-024-57837-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Accepted: 03/22/2024] [Indexed: 04/04/2024] Open
Abstract
The evaluation of grouting effects constitutes a critical aspect of grouting engineering. With the maturity of the grouting project, the workload and empirical characteristics of grouting effect evaluation are gradually revealed. In the context of the Qiuji coal mine's directional drilling and grouting to limestone aquifer reformation, this study thoroughly analyzes the influencing factors of grouting effects from geological and engineering perspectives, comparing these with various engineering indices associated with drilling and grouting. This led to the establishment of a "dual-process, multi-parameter, and multi-factor" system, employing correlation analysis to validate the selected indices' reasonableness and scientific merit. Utilizing the chosen indices, eight high-performing machine learning models and three parameter optimization algorithms were employed to develop a model for assessing the effectiveness of directional grouting in limestone aquifers. The model's efficacy was evaluated based on accuracy, recall, precision, and F-score metrics, followed by practical engineering validation. Results indicate that the "dual-process, multi-parameter, multi-factor" system elucidates the relationship between influencing factors and engineering parameters, demonstrating the intricacy of evaluating grouting effects. Analysis revealed that the correlation among the eight selected indicators-including the proportion of boreholes in the target rock strata, drilling length, leakage, water level, pressure of grouting, mass of slurry injected, permeability properties of limestone aquifers before being grouted, permeability properties of limestone aquifers after being grouted-is not substantial, underscoring their viability as independent indicators for grouting effect evaluation. Comparative analysis showed that the Adaboost machine learning model, optimized via a genetic algorithm, demonstrated superior performance and more accurate evaluation results. Engineering validation confirmed that this model provides a more precise and realistic assessment of grouting effects compared to traditional methods.
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Affiliation(s)
- Wenxin Li
- Shandong Univ Sci & Technol, Coll Energy & Min Engn, Qingdao, People's Republic of China
| | - Juntao Chen
- Shandong Univ Sci & Technol, Coll Energy & Min Engn, Qingdao, People's Republic of China.
| | - Jun Zhu
- Shandong Univ Sci & Technol, Coll Energy & Min Engn, Qingdao, People's Republic of China
| | - Xinbo Ji
- Shandong Energy Xinwen Mining Group Suncun Coal Mine, Taian, 271219, China
| | - Ziqun Fu
- Shandong Univ Sci & Technol, Coll Energy & Min Engn, Qingdao, People's Republic of China
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3
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Zhang Y, Jiang C, Han Q, Zhang X, Li J, Xiao Y. Coupling simulation of pipeline nodes - Storage tank linkage in urban high-density built-up areas using optimization model. J Environ Manage 2024; 357:120850. [PMID: 38583384 DOI: 10.1016/j.jenvman.2024.120850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 04/01/2024] [Accepted: 04/03/2024] [Indexed: 04/09/2024]
Abstract
Climate change and urbanization contribute to the increased frequency of short-duration intense rainstorms. Traditional solutions often involve multiple scenarios for cost-effectiveness comparison, neglecting the rationality of placement conditions. The effective coupling and coordination of the location, number, size, and cost of storage tanks are crucial to addressing this issue. A three-phase approach is proposed to enhance the dynamic link between drainage pipeline and storage tanks in urban high-density built-up areas, integrating Python language, SWMM, the Elitist Non-Dominated Sorting Genetic Algorithm (NSGA-III), and the Analytic Hierarchy Process (AHP) methods. In the first stage, each node within the pipeline network is considered as a potential storage tank location. In the second stage, factors such as the length and diameter of the upstream connecting pipeline, as well as the suitability of the storage tank location, are assessed. In the third stage, the length and diameter of the downstream connecting pipeline node are evaluated. The results show that the 90 overflow nodes (overflow time >0.5h) have been cleared using the three-phase approach with a 50a (duration = 3h) return period as the rainfall scenario, which meets the flooding limitations. After the completion of the three-phase method configuration, the total overflow and SS loads were reduced by 96.45% and 49.30%, respectively, compared to the status quo conditions. These two indicators have decreased by 48.16 and 9.05%, respectively, compared to the first phase (the traditional method of only replacing all overflow nodes with storage tanks). The proposed framework enables decision-makers to evaluate the acceptability and reliability of the optimal management plan, taking into account their preferences and uncertainties.
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Affiliation(s)
- Yangxuan Zhang
- State Key Laboratory of Eco-hydraulics in Northwest Arid Region of China, Xi'an University of Technology, Xi'an 710048, China
| | - Chunbo Jiang
- State Key Laboratory of Eco-hydraulics in Northwest Arid Region of China, Xi'an University of Technology, Xi'an 710048, China.
| | - Qiaohui Han
- State Key Laboratory of Eco-hydraulics in Northwest Arid Region of China, Xi'an University of Technology, Xi'an 710048, China
| | - Xiang Zhang
- Hubei Key Laboratory of Water System Science for Sponge City Construction, Wuhan University, Wuhan 430072, China
| | - Jiake Li
- State Key Laboratory of Eco-hydraulics in Northwest Arid Region of China, Xi'an University of Technology, Xi'an 710048, China
| | - Yi Xiao
- Hubei Key Laboratory of Water System Science for Sponge City Construction, Wuhan University, Wuhan 430072, China
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4
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Li W, Zhao Y, Zhu Y, Dong Z, Wang F, Huang F. Research progress in water quality prediction based on deep learning technology: a review. Environ Sci Pollut Res Int 2024; 31:26415-26431. [PMID: 38538994 DOI: 10.1007/s11356-024-33058-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 03/20/2024] [Indexed: 05/04/2024]
Abstract
Water, an invaluable and non-renewable resource, plays an indispensable role in human survival and societal development. Accurate forecasting of water quality involves early identification of future pollutant concentrations and water quality indices, enabling evidence-based decision-making and targeted environmental interventions. The emergence of advanced computational technologies, particularly deep learning, has garnered considerable interest among researchers for applications in water quality prediction because of its robust data analytics capabilities. This article comprehensively reviews the deployment of deep learning methodologies in water quality forecasting, encompassing single-model and mixed-model approaches. Additionally, we delineate optimization strategies, data fusion techniques, and other factors influencing the efficacy of deep learning-based water quality prediction models, because understanding and mastering these factors are crucial for accurate water quality prediction. Although challenges such as data scarcity, long-term prediction accuracy, and limited deployments of large-scale models persist, future research aims to address these limitations by refining prediction algorithms, leveraging high-dimensional datasets, evaluating model performance, and broadening large-scale model application. These efforts contribute to precise water resource management and environmental conservation.
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Affiliation(s)
- Wenhao Li
- School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing, China
- Jiangsu Province Engineering Research Center of Environmental Risk Prevention and Emergency Response Technology, School of Environment, Nanjing, 210023, China
| | - Yin Zhao
- School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing, China
| | - Yining Zhu
- Jiangsu Province Engineering Research Center of Environmental Risk Prevention and Emergency Response Technology, School of Environment, Nanjing, 210023, China
- Key Laboratory for Soft Chemistry and Functional Materials of Ministry of Education, Nanjing University of Science and Technology, Nanjing, 210094, Jiangsu, China
| | - Zhongtian Dong
- Key Laboratory for Soft Chemistry and Functional Materials of Ministry of Education, Nanjing University of Science and Technology, Nanjing, 210094, Jiangsu, China
| | - Fenghe Wang
- Jiangsu Province Engineering Research Center of Environmental Risk Prevention and Emergency Response Technology, School of Environment, Nanjing, 210023, China
- Key Laboratory for Soft Chemistry and Functional Materials of Ministry of Education, Nanjing University of Science and Technology, Nanjing, 210094, Jiangsu, China
| | - Fengliang Huang
- School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing, China.
- Jiangsu Province Engineering Research Center of Environmental Risk Prevention and Emergency Response Technology, School of Environment, Nanjing, 210023, China.
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5
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Natarajan SK, Shanmurthy P, Arockiam D, Balusamy B, Selvarajan S. Optimized machine learning model for air quality index prediction in major cities in India. Sci Rep 2024; 14:6795. [PMID: 38514669 PMCID: PMC10958024 DOI: 10.1038/s41598-024-54807-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 02/16/2024] [Indexed: 03/23/2024] Open
Abstract
Industrial advancements and utilization of large amount of fossil fuels, vehicle pollution, and other calamities increases the Air Quality Index (AQI) of major cities in a drastic manner. Major cities AQI analysis is essential so that the government can take proper preventive, proactive measures to reduce air pollution. This research incorporates artificial intelligence in AQI prediction based on air pollution data. An optimized machine learning model which combines Grey Wolf Optimization (GWO) with the Decision Tree (DT) algorithm for accurate prediction of AQI in major cities of India. Air quality data available in the Kaggle repository is used for experimentation, and major cities like Delhi, Hyderabad, Kolkata, Bangalore, Visakhapatnam, and Chennai are considered for analysis. The proposed model performance is experimentally verified through metrics like R-Square, RMSE, MSE, MAE, and accuracy. Existing machine learning models, like k-nearest Neighbor, Random Forest regressor, and Support vector regressor, are compared with the proposed model. The proposed model attains better prediction performance compared to traditional machine learning algorithms with maximum accuracy of 88.98% for New Delhi city, 91.49% for Bangalore city, 94.48% for Kolkata, 97.66% for Hyderabad, 95.22% for Chennai and 97.68% for Visakhapatnam city.
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Affiliation(s)
- Suresh Kumar Natarajan
- School of Computer Science and Engineering, Jain (Deemed-to-be University), Bengaluru, Karnataka, India
| | - Prakash Shanmurthy
- School of Computer Science and Engineering and Information Science, Presidency University, Bengaluru, Karnataka, India
| | | | | | - Shitharth Selvarajan
- School of Built Environment, Engineering and Computing, Leeds Beckett University, Leeds, LS1 3HE, UK.
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Premkumar M, Sinha G, Ramasamy MD, Sahu S, Subramanyam CB, Sowmya R, Abualigah L, Derebew B. Augmented weighted K-means grey wolf optimizer: An enhanced metaheuristic algorithm for data clustering problems. Sci Rep 2024; 14:5434. [PMID: 38443569 PMCID: PMC10914809 DOI: 10.1038/s41598-024-55619-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 02/26/2024] [Indexed: 03/07/2024] Open
Abstract
This study presents the K-means clustering-based grey wolf optimizer, a new algorithm intended to improve the optimization capabilities of the conventional grey wolf optimizer in order to address the problem of data clustering. The process that groups similar items within a dataset into non-overlapping groups. Grey wolf hunting behaviour served as the model for grey wolf optimizer, however, it frequently lacks the exploration and exploitation capabilities that are essential for efficient data clustering. This work mainly focuses on enhancing the grey wolf optimizer using a new weight factor and the K-means algorithm concepts in order to increase variety and avoid premature convergence. Using a partitional clustering-inspired fitness function, the K-means clustering-based grey wolf optimizer was extensively evaluated on ten numerical functions and multiple real-world datasets with varying levels of complexity and dimensionality. The methodology is based on incorporating the K-means algorithm concept for the purpose of refining initial solutions and adding a weight factor to increase the diversity of solutions during the optimization phase. The results show that the K-means clustering-based grey wolf optimizer performs much better than the standard grey wolf optimizer in discovering optimal clustering solutions, indicating a higher capacity for effective exploration and exploitation of the solution space. The study found that the K-means clustering-based grey wolf optimizer was able to produce high-quality cluster centres in fewer iterations, demonstrating its efficacy and efficiency on various datasets. Finally, the study demonstrates the robustness and dependability of the K-means clustering-based grey wolf optimizer in resolving data clustering issues, which represents a significant advancement over conventional techniques. In addition to addressing the shortcomings of the initial algorithm, the incorporation of K-means and the innovative weight factor into the grey wolf optimizer establishes a new standard for further study in metaheuristic clustering algorithms. The performance of the K-means clustering-based grey wolf optimizer is around 34% better than the original grey wolf optimizer algorithm for both numerical test problems and data clustering problems.
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Affiliation(s)
- Manoharan Premkumar
- Department of Electrical & Electronics Engineering, Dayananda Sagar College of Engineering, Kumaraswamy Layout, Bengaluru, Karnataka, 560078, India.
| | - Garima Sinha
- Department of Computer Science and Engineering, Jain University, Ramanagaram, Bengaluru, Karnataka, India
| | - Manjula Devi Ramasamy
- Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India
| | - Santhoshini Sahu
- Department of Computer Science & Engineering, GMR Institute of Technology, Rajam, Srikakulam, Andhra Pradesh, India
| | | | - Ravichandran Sowmya
- Department of Electrical and Electronics Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Laith Abualigah
- Computer Science Department, Al al-Bayt University, Mafraq, 25113, Jordan
- Artificial Intelligence and Sensing Technologies (AIST) Research Center, University of Tabuk, 71491, Tabuk, Saudi Arabia
- Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan
- MEU Research Unit, Middle East University, Amman, 11831, Jordan
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos, 13-5053, Lebanon
- School of Engineering and Technology, Sunway University Malaysia, 27500, Petaling Jaya, Malaysia
- College of Engineering, Yuan Ze University, Taoyuan, Taiwan
- Department of Statistics, College of Natural and Computational Science, Mizan-Tepi University, Tepi Bushira, Ethiopia
| | - Bizuwork Derebew
- Applied science research center, Applied science private university, Amman, 11931, Jordan.
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Yan J, Ren K, Wang T. Improving multidimensional normal cloud model to evaluate groundwater quality with grey wolf optimization algorithm and projection pursuit method. J Environ Manage 2024; 354:120279. [PMID: 38354612 DOI: 10.1016/j.jenvman.2024.120279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 01/25/2024] [Accepted: 02/01/2024] [Indexed: 02/16/2024]
Abstract
Groundwater quality is related to several uncertain factors. Using multidimensional normal cloud model to reduce the randomness and ambiguity of the integrated groundwater quality evaluation is important in environmental research. Previous optimizations of multidimensional normal cloud models have focused on improving the affiliation criteria of the evaluation results, neglecting the weighting scheme of multiple indicators. In this study, a new multidimensional normal cloud model was constructed for the existing one-dimensional normal cloud model (ONCM) by combining the projection-pursuit (PP) method and the Grey Wolf Optimization (GWO) algorithm. The effectiveness and robustness of the model were analyzed. The results showed that compared with ONCM, the new multidimensional normal cloud model (GWOPPC model) integrated multiple evaluation parameters, simplified the modeling process, and reduced the number of calculations for the affiliation degree. Compared with other metaheuristic optimization algorithms, the GWO algorithms converged within 20 iterations during 20 simulations showing faster convergence speed, and the convergence results of all objective functions satisfy the iteration accuracy of 0.001, which indicates that the algorithm is more stable. Compared to the traditional entropy weights (0.27, 0.23, 0.47, 0.44, 0.29, 0.59, 0.12) or principal component weights (0.38, 0.33, 0.42, 0.34, 0.47, 0.29, 0.38), the weight allocation scheme provided by the GWOPP method (0.50, 0.48, 0.05, 0.38, 0.02. 0.51 and 0.32) considers the density of the distribution of all samples in the data set space. Among all 55 groundwater samples, the GWOPPC model has 21 samples with lower evaluation ratings than the fuzzy evaluation method, and 28 samples lower than the Random Forest method or the WQI method, indicating that the GWOPPC model is more conservative under the conditions of considering fuzziness and randomness. This method can be used to evaluate groundwater quality in other areas to provide a basis for the planning and management of groundwater resources.
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Affiliation(s)
- Jiaheng Yan
- Faculty of Architecture and Civil Engineering, Huaiyin Institute of Technology, Huaian, 223003, China.
| | - Ke Ren
- Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, 223003, China.
| | - Tao Wang
- College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, 210098, China
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8
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Jing R, Wang Z, Suo P. Optimization of track and field training methods based on SSA-BP and its effect on athletes' explosive power. Heliyon 2024; 10:e25465. [PMID: 38327462 PMCID: PMC10847653 DOI: 10.1016/j.heliyon.2024.e25465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/11/2023] [Accepted: 01/27/2024] [Indexed: 02/09/2024] Open
Abstract
Digitalization and informationization are important trends in the development of the sports industry. The study first introduced the sparrow search algorithm to improve the generalization ability of traditional neural networks, optimizing the assignment of initial weights and thresholds of neural networks; Secondly, the chicken swarm algorithm is introduced to optimize the training combination, period, and intensity of athletes based on the evaluation results, improving the subjective limitations of traditional training methods. The results of model performance analysis show that the sparrow search algorithm is better than other intelligent optimization algorithms in finding fitted parameters, and the solution error is less than 0.50. The evaluation model performs well in terms of accuracy, recall, average relative error, and R2 evaluation indicators. The model has high repeatability and is suitable for evaluating track and field training methods. The accuracy and computational speed of the chicken swarm algorithm are relatively good; Compared with other optimization models, the chicken swarm algorithm has better optimization ability and accuracy. Friedman test found significant differences in the chicken swarm algorithm, and the optimized training method has a significant positive impact on the explosive power of athletes, and the training period and intensity arrangement are reasonable and more helpful to the improvement of athletic performance. This study improves the scientific rationality of the development of track and field training methods, which is conducive to optimizing the training effect of track and field sports, and facilitates the risk management and personalized training of athletes. At the same time, it greatly promotes the integration and development of sports and computer disciplines.
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Affiliation(s)
- Ruibin Jing
- School of Sports, DongShin University, Dongsindae-gil, Naju-si, Jeollanam-do 58245, South Korea
| | - Zhengwei Wang
- School of Sports, DongShin University, Dongsindae-gil, Naju-si, Jeollanam-do 58245, South Korea
| | - Peng Suo
- School of Physical Education, Shandong Sport University, Jinan 250102, China
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9
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Sharafi M, Samadianfard S, Behmanesh J, Prasad R. Integration of fruit fly and firefly optimization algorithm with support vector regression in estimating daily pan evaporation. Int J Biometeorol 2024; 68:237-251. [PMID: 38060013 DOI: 10.1007/s00484-023-02586-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 10/29/2023] [Accepted: 11/19/2023] [Indexed: 12/08/2023]
Abstract
The purpose of the present study was to predict the pan evaporation values at four stations including Urmia, Makou, Mahabad, and Khoy, located in West Azerbaijan, Iran, using support vector regression (SVR), SVR coupled by fruit fly algorithm (SVR-FOA), and SVR coupled with firefly algorithm (SVR-FFA). Therefore, for the first time, this research has used the combined SVR-FOA to predict pan evaporation. For this purpose, meteorological parameters in the period of 1990-2020 were gathered and then using the Pearson's correlation coefficient, significant inputs for pan evaporation estimation were determined. The correlation evaluation of the parameters showed that the two parameters of wind speed and sunshine hours had the highest correlation with the pan evaporation values, and in addition, these parameters, as input to the models, improved the results and increased the accuracy of the models. The obtained results indicated that at Urmia station, SVR-FFA with the lowest error was the best model. The SVR-FOA also had better performance than the SVR model. Additionally, the result showed that SVR-FOA with the lowest errors had the best capability in pan evaporation estimation at other studied stations. Therefore, it was concluded that FOA with advantages such as simplicity, fewer parameters, easy adjustment, and less calculation can significantly increase the capability of independent SVR models. Hence, based on the overall results, SVR-FOA may be recommended as the most accurate method for pan evaporation estimation.
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Affiliation(s)
- Milad Sharafi
- Department of Water Engineering, Urmia University, Urmia, Iran
| | - Saeed Samadianfard
- Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran
| | - Javad Behmanesh
- Department of Water Engineering, Urmia University, Urmia, Iran.
| | - Ramendra Prasad
- Department of Science, School of Science and Technology, The University of Fiji, Lautoka, Fiji
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10
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Cheng Y, Shen S, Liang X, Liu J, Chen J, Zhang T, Chen E. Communication-efficient federated learning with stagewise training strategy. Neural Netw 2023; 167:460-472. [PMID: 37683460 DOI: 10.1016/j.neunet.2023.08.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 06/06/2023] [Accepted: 08/20/2023] [Indexed: 09/10/2023]
Abstract
The efficiency of communication across workers is a significant factor that affects the performance of federated learning. Though periodic communication strategy is applied to reduce communication rounds in training, the communication cost is still high when the training data distributions are not independently and identically distributed (non-IID) which is common in federated learning. Recently, some works introduce variance reduction to eliminate the effect caused by non-IID data among workers. Nevertheless the provable optimal communication complexity O(log(ST)) and convergence rate O(1/(ST)) cannot be achieved simultaneously, where S denotes the number of sampled workers in each round and T is the number of iterations. To deal with this dilemma, we propose an optimization algorithm SQUARFA that adopts stagewise training framework coupling with variance reduction and uses a quick-start phase in each loop. Theoretical results show that SQUARFA achieves both optimal convergence rate and communication complexity for both strongly convex objectives and non-convex objectives under PL condition, thus fills the gap mentioned above. Then, a variant of SQUARFA yields the optimal theoretical results for general non-convex objectives. We further extend the technique in SQUARFA to the large batch setting and achieve optimal communication complexity. Experimental results demonstrate the superiority of the proposed algorithms.
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Affiliation(s)
- Yifei Cheng
- Anhui Province Key Lab of Big Data Analysis and Application, China; School of Data Science, University of Science and Technology of China, China; State Key Laboratory of Cognitive Intelligence, China.
| | | | - Xianfeng Liang
- Anhui Province Key Lab of Big Data Analysis and Application, China; State Key Laboratory of Cognitive Intelligence, China; School of Computer Science, University of Science and Technology of China, China.
| | - Jingchang Liu
- The Department of Computer Science and Engineering, Hong Kong University of Science and Technology, China.
| | - Joya Chen
- Anhui Province Key Lab of Big Data Analysis and Application, China; State Key Laboratory of Cognitive Intelligence, China; School of Computer Science, University of Science and Technology of China, China.
| | - Tie Zhang
- Anhui Province Key Lab of Big Data Analysis and Application, China; School of Computer Science, University of Science and Technology of China, China.
| | - Enhong Chen
- Anhui Province Key Lab of Big Data Analysis and Application, China; School of Data Science, University of Science and Technology of China, China; State Key Laboratory of Cognitive Intelligence, China; School of Computer Science, University of Science and Technology of China, China.
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11
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Chen L, Sefat SM, Kim KI. An optimal algorithm for mmWave 5G wireless networks based on neural network. Heliyon 2023; 9:e17580. [PMID: 37416690 PMCID: PMC10320281 DOI: 10.1016/j.heliyon.2023.e17580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Revised: 06/14/2023] [Accepted: 06/21/2023] [Indexed: 07/08/2023] Open
Abstract
Fifth generation (5G) wireless networks are based on the use of spectrum blocks above 6 GHz in the millimeter wave (mmWave) range to increase throughput and reduce the overall level of interference in very busy frequency bands below 6 GHz. With the global deployment of the first commercial installations of 5G, the availability of multi-Gbps wireless connections in the mmWave frequency band becomes closer to reality and opens up some unique uses for 5G. Although, mmWave communication is expected to enable high-power radio links and broadband wireless intranet, its main challenges are inherent poor propagation conditions and high transmitter-receiver coordination requirement, which prevent it from realizing its full potential. When smart reflective surfaces are used in mmWave communication, channel state information becomes complex and imprecise. In this study, a hybrid intelligent reflecting surface consisting of a large number of passive components and a small number of RF circuits is proposed as a solution. Then, an improved deep neural network (DNN)-based technique is proposed to estimate the effective channel. The proposed technique provides better channel estimation performance according to the simulation results and improves the quality of service.
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Affiliation(s)
- Liang Chen
- Jilin Provincial Institute of Education, Chang Chun 130022, China
| | - Shebnam M. Sefat
- Department of Computer Science, Independent University, Bangladesh
- Islamic University Centre for Scientific Research, The Islamic University, Najaf, Iraq
| | - Ki-Il Kim
- Department of Computer Science and Engineering, Chungnam National University, Daejeon 34134, South Korea
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12
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Alshammari A. Ensemble recurrent neural network with whale optimization algorithm-based DNA sequence classification for medical applications. Soft comput 2023:1-14. [PMID: 37362270 PMCID: PMC10231859 DOI: 10.1007/s00500-023-08435-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/04/2023] [Indexed: 06/28/2023]
Abstract
The modern data-driven era has facilitated the gathering of large quantities of biomedical and clinical data. The deoxyribonucleic acid gene expression datasets have become a vital focus for the research community because of their capability to detect pathogens via 'biomarkers' or particular modifications in the gene sequence which portray a specific pathogen. Metaheuristic-related feature selection (FS) efficiently filters out only the pertinent genes out of large feature sets to lessen the data storage and computation requirements. This paper embraces the whale optimization algorithm for the FS issue in HD microarray data for the effectual propagation of candidate solutions to reach global optima over sufficient iterations. The chosen data are classified by employing an ensemble recurrent network (ERNN) that retains the amalgamation of long short-term memory, bidirectional long short-term memory, and gated recurrent units. Analysis of this proposed ERNN methodology would be performed by correlating with diverse advanced methodologies, and thus, the ERNN attains 99.59% precision and 99.59% accuracy.
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Affiliation(s)
- Abdulaziz Alshammari
- Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
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13
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Daneshvar NHN, Masoudi-Sobhanzadeh Y, Omidi Y. A voting-based machine learning approach for classifying biological and clinical datasets. BMC Bioinformatics 2023; 24:140. [PMID: 37041456 PMCID: PMC10088226 DOI: 10.1186/s12859-023-05274-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Accepted: 04/05/2023] [Indexed: 04/13/2023] Open
Abstract
BACKGROUND Different machine learning techniques have been proposed to classify a wide range of biological/clinical data. Given the practicability of these approaches accordingly, various software packages have been also designed and developed. However, the existing methods suffer from several limitations such as overfitting on a specific dataset, ignoring the feature selection concept in the preprocessing step, and losing their performance on large-size datasets. To tackle the mentioned restrictions, in this study, we introduced a machine learning framework consisting of two main steps. First, our previously suggested optimization algorithm (Trader) was extended to select a near-optimal subset of features/genes. Second, a voting-based framework was proposed to classify the biological/clinical data with high accuracy. To evaluate the efficiency of the proposed method, it was applied to 13 biological/clinical datasets, and the outcomes were comprehensively compared with the prior methods. RESULTS The results demonstrated that the Trader algorithm could select a near-optimal subset of features with a significant level of p-value < 0.01 relative to the compared algorithms. Additionally, on the large-sie datasets, the proposed machine learning framework improved prior studies by ~ 10% in terms of the mean values associated with fivefold cross-validation of accuracy, precision, recall, specificity, and F-measure. CONCLUSION Based on the obtained results, it can be concluded that a proper configuration of efficient algorithms and methods can increase the prediction power of machine learning approaches and help researchers in designing practical diagnosis health care systems and offering effective treatment plans.
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Affiliation(s)
| | - Yosef Masoudi-Sobhanzadeh
- Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran.
- Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran.
| | - Yadollah Omidi
- Department of Pharmaceutical Sciences, College of Pharmacy, Nova Southeastern University, Florida, 33328, USA.
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14
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Acharya D, Das DK. An optimal internal model proportional integral controller to improve pressure tracking profile of artificial ventilator. Med Biol Eng Comput 2023. [PMID: 36920642 DOI: 10.1007/s11517-023-02795-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Accepted: 01/29/2023] [Indexed: 03/16/2023]
Abstract
Mechanical ventilator (MV) is a breathing support medical equipment used during medical surgery or in an intensive care unit (ICU) for critical patients to reduce the breathing work of the patient under ventilation. In this work, a piston-driven mechanical ventilator is studied. Such types of ventilators are generally used as anaesthesia ventilators with volume-controlled modes. A modern ventilator can operate in different modes, such as volume control, pressure control, or both. Pressure-controlled mode may be better suited for some cases, such as patients undergoing laparoscopic surgery, as compared to volume-controlled mode because it may increase compliance during pneumoperitoneum, enhance oxygenation, and lower the stress response postoperatively. The pressure profile of a PCV must exactly track the pressure profile of a patient under ventilation to avoid ventilator-induced diaphragmatic dysfunction. Therefore, an optimal internal model control base proportional integral (IMC-PI) controller is proposed. The optimum parameter of the filter component of the IMC-PI controller is found by an enhanced class topper optimization (ECTO) algorithm. The performance of the proposed controller is analyzed in different scenarios and compared with existing results. Graphical Abstract Optimal internal model proportional integral controller for human respiratory ventilator.
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15
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Yuan Y, Shen Q, Wang S, Ren J, Yang D, Yang Q, Fan J, Mu X. Coronavirus Mask Protection Algorithm: A New Bio-inspired Optimization Algorithm and Its Applications. J Bionic Eng 2023; 20:1-19. [PMID: 37361682 PMCID: PMC9976690 DOI: 10.1007/s42235-023-00359-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 02/08/2023] [Accepted: 02/11/2023] [Indexed: 06/28/2023]
Abstract
Nowadays, meta-heuristic algorithms are attracting widespread interest in solving high-dimensional nonlinear optimization problems. In this paper, a COVID-19 prevention-inspired bionic optimization algorithm, named Coronavirus Mask Protection Algorithm (CMPA), is proposed based on the virus transmission of COVID-19. The main inspiration for the CMPA originated from human self-protection behavior against COVID-19. In CMPA, the process of infection and immunity consists of three phases, including the infection stage, diffusion stage, and immune stage. Notably, wearing masks correctly and safe social distancing are two essential factors for humans to protect themselves, which are similar to the exploration and exploitation in optimization algorithms. This study simulates the self-protection behavior mathematically and offers an optimization algorithm. The performance of the proposed CMPA is evaluated and compared to other state-of-the-art metaheuristic optimizers using benchmark functions, CEC2020 suite problems, and three truss design problems. The statistical results demonstrate that the CMPA is more competitive among these state-of-the-art algorithms. Further, the CMPA is performed to identify the parameters of the main girder of a gantry crane. Results show that the mass and deflection of the main girder can be improved by 16.44% and 7.49%, respectively.
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Affiliation(s)
- Yongliang Yuan
- School of Mechanical and Power Engineering, Henan Polytechnic University, Jiaozuo, 454003 China
| | - Qianlong Shen
- School of Mechanical and Power Engineering, Henan Polytechnic University, Jiaozuo, 454003 China
| | - Shuo Wang
- School of Mechanical Engineering, Dalian University of Technology, Dalian, 116024 China
| | - Jianji Ren
- School of Software, Henan Polytechnic University, Jiaozuo, 454003 China
| | - Donghao Yang
- School of Software, Henan Polytechnic University, Jiaozuo, 454003 China
| | - Qingkang Yang
- School of Mechanical and Power Engineering, Henan Polytechnic University, Jiaozuo, 454003 China
| | - Junkai Fan
- School of Mechanical and Power Engineering, Henan Polytechnic University, Jiaozuo, 454003 China
| | - Xiaokai Mu
- School of Mechanical Engineering, Dalian University of Technology, Dalian, 116024 China
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16
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Kong R, Ma N, Liu P, Zhou X. Dual trace gas detection using a compact two-channel multipass cell with dense and line spot patterns. Heliyon 2023; 9:e13677. [PMID: 36879754 PMCID: PMC9984412 DOI: 10.1016/j.heliyon.2023.e13677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 02/07/2023] [Accepted: 02/08/2023] [Indexed: 02/13/2023] Open
Abstract
A highly sensitive dual-gas sensor based on a two-channel multipass cell (MPC) was designed and developed for simultaneous detection of atmospheric methane (CH4) and carbon dioxide (CO2) by using two distributed feedback lasers emitting at 1653 nm and 2004 nm. The nondominated sorting genetic algorithm was applied to intelligently optimize the MPC configuration and accelerate the dual-gas sensor design process. A compact and novel two-channel MPC was used to achieve two optical path lengths of 27.6 m and 2.1 m in a small volume of 233 cm3. Simultaneous measurements of CH4 and CO2 in the atmosphere were performed to demonstrate the stability and robustness of the gas sensor. According to the Allan deviation analysis, the optimal detection precision for CH4 and CO2 was 4.4 ppb at an integration time of 76 s and 437.8 ppb at an integration time of 271 s, respectively. The newly developed dual-gas sensor exhibits superior characteristics of high sensitivity and stability, cost-effectiveness and simple structure, which make it well-suited for multiple trace gas sensing in various applications, including environmental monitoring, safety inspections and clinical diagnosis.
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Affiliation(s)
- Rong Kong
- Center for Advanced Quantum Studies, Applied Optics Beijing Area Major Laboratory, Department of Physics, Beijing Normal University, Beijing, 100875, China
| | - Ningyi Ma
- Center for Advanced Quantum Studies, Applied Optics Beijing Area Major Laboratory, Department of Physics, Beijing Normal University, Beijing, 100875, China
| | - Peng Liu
- Center for Advanced Quantum Studies, Applied Optics Beijing Area Major Laboratory, Department of Physics, Beijing Normal University, Beijing, 100875, China
| | - Xin Zhou
- Center for Advanced Quantum Studies, Applied Optics Beijing Area Major Laboratory, Department of Physics, Beijing Normal University, Beijing, 100875, China
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17
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Qu Z, Li Y, Jiang X, Niu C. An innovative ensemble model based on multiple neural networks and a novel heuristic optimization algorithm for COVID-19 forecasting. Expert Syst Appl 2023; 212:118746. [PMID: 36089985 PMCID: PMC9444161 DOI: 10.1016/j.eswa.2022.118746] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 07/08/2022] [Accepted: 08/30/2022] [Indexed: 06/15/2023]
Abstract
During the global fight against the novel coronavirus pneumonia (COVID-19) epidemic, accurate outbreak trend forecasting has become vital for outbreak prevention and control. Effective COVID-19 outbreak trend prediction remains a complex and challenging issue owing to the significant fluctuations in the COVID-19 data series. Most previous studies have limitations only using individual forecasting methods for outbreak modeling, ignoring the combination of the advantages of different prediction methods, which may lead to insufficient results. Therefore, this paper develops a novel ensemble paradigm based on multiple neural networks and a novel heuristic optimization algorithm. First, a new hybrid sine cosine algorithm-whale optimization algorithm (SCWOA) is exercised on 15 benchmark tests. Second, four neural networks are used as predictors for the COVID-19 outbreak forecasting. Each predictor is given a weight, and the proposed SCWOA is used to optimize the best matching weights of the ensemble model. The daily COVID-19 series collected from three of the most-affected countries were taken as the test cases. The experimental results demonstrate that different neural network models have different performances in various complex epidemic prediction scenarios. The SCWOA-based ensemble model can outperform all comparable models with its high accuracy and robustness.
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Affiliation(s)
- Zongxi Qu
- School of Management, Lanzhou University, Lanzhou 730000, China
- Research Center for Emergency Management, Lanzhou University, Lanzhou 730000, China
| | - Yutong Li
- School of Management, Lanzhou University, Lanzhou 730000, China
- Research Center for Emergency Management, Lanzhou University, Lanzhou 730000, China
| | - Xia Jiang
- Affiliated Hospital of Northwest Minzu University/Second Provincial People's Hospital of Gansu, Lanzhou 730099, China
| | - Chunhua Niu
- School of Management, Lanzhou University, Lanzhou 730000, China
- Research Center for Emergency Management, Lanzhou University, Lanzhou 730000, China
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18
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Zhao L, Zhao X, Li Y, Shi Y, Zhou H, Li X, Wang X, Xing X. Applicability of hybrid bionic optimization models with kernel-based extreme learning machine algorithm for predicting daily reference evapotranspiration: a case study in arid and semiarid regions, China. Environ Sci Pollut Res Int 2023; 30:22396-22412. [PMID: 36289123 DOI: 10.1007/s11356-022-23786-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 10/19/2022] [Indexed: 06/16/2023]
Abstract
The accurate prediction of daily reference crop evapotranspiration (ETO) enables effective management decision-making for agricultural water resources; this is crucial for developing water-efficient agriculture. To improve the accuracy of ETO forecasts in data-deficient areas, this study uses a decision tree algorithm (classification and regression tree [CART]) to obtain the effects of various factors on ETO at typical stations in arid and semiarid regions of China. A combination of factors with considerable influence on the model was selected as the input for constructing a kernel-extreme-learning-machine (KELM) daily reference evapotranspiration prediction model, and three bionic optimization algorithms (i.e., sparrow search optimization algorithm, Harris Hawks optimization algorithm, and lion swarm optimization algorithm) were integrated to optimize KELM prediction model parameters and improve the accuracy of daily reference evapotranspiration prediction. The results indicate that temperature (maximum or minimum temperature) is the primary factor influencing ETO, and the range of importance is 0.399-0.554. RH and Ra are also key factors influencing ETO; the hybrid model optimized using the bionic optimization algorithm provides advantages over the independent KELM model, and the SSA-KELM model has the highest accuracy among hybrid models, with a root-mean-square error of 0.408-1.964, R2 of 0.545-0.982, mean absolute error of 0.273-1.086, and Nash-Sutcliffe efficiency coefficient of 0.658-0.967. The top five factors extracted using the CART algorithm are recommended as inputs for constructing the SSA-KELM model for ETO estimation in arid and semiarid regions of China, and this model can also serve as a reference for ETO forecasting in similar regions.
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Affiliation(s)
- Long Zhao
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, 471000, Henan Province, China
| | - Xinbo Zhao
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, 471000, Henan Province, China
| | - Yuanze Li
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, 471000, Henan Province, China
| | - Yi Shi
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, 471000, Henan Province, China
| | - Hanmi Zhou
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, 471000, Henan Province, China
| | - Xiuzhen Li
- College of Horticulture and Plant Protection, Henan University of Science and Technology, Luoyang, 471000, Henan Province, China
| | - Xiaodong Wang
- College of Agriculture, Henan University of Science and Technology, Henan Province, Luoyang, 471000, China
| | - Xuguang Xing
- Key Laboratory for Agricultural Soil and Water Engineering in Arid Area of Ministry of Education, Northwest A&F University, Yangling, 712100, Shaanxi Province, China.
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19
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Benghanem M, Lekouaghet B, Haddad S, Soukkou A. Optimization of pv cells/modules parameters using a modified quasi-oppositional logistic chaotic rao-1 (QOLCR) algorithm. Environ Sci Pollut Res Int 2023; 30:44536-44552. [PMID: 36692712 DOI: 10.1007/s11356-022-24941-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 12/19/2022] [Indexed: 01/25/2023]
Abstract
Since the behavior of photovoltaic (PV) modules under different operational conditions is highly nonlinear, predicting the performance of PV systems in industrial applications is becoming a major challenge issue. Moreover, the most important information required to configure an optimal PV system is unavailable in all manufacturer's datasheets. In this context, a novel method is recommended to optimize PV cells/module parameters with the ability to correctly characterize the I-V and P-V curves of different PV models. In the present article, a chaotic map is incorporated in the so-called quasi-oppositional Rao-1 algorithm to improve its efficiency, and the resulting algorithm is named quasi-oppositional logistic chaotic Rao-1 (QOLCR) algorithm. Numerical results indicate that the QOLCR algorithm has presented very good performance in terms of accuracy and robustness. The idea is to minimize the root mean square error (RMSE) between the estimated and the actual data. Simulation results in the single diode model give an RMSE of value [Formula: see text], and in the double diode model, an RMSE of value [Formula: see text] has been reached as the minimum value among the other compared optimization methods. Hence, the QOLCR approach also converges faster than the basic Rao-1 algorithm and its other variants. Moreover, the modified QO Rao-1 algorithm shows its perfectness and could be involved as tools for optimal designing of PV systems.
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Affiliation(s)
- Mohamed Benghanem
- Physics Department, Faculty of Science, Islamic University of Madinah, Madinah, Kingdom of Saudi Arabia.
| | - Badis Lekouaghet
- RE Laboratory, Electronics Department, MSB Jijel University, Jijel, Algeria
| | - Sofiane Haddad
- RE Laboratory, Electronics Department, MSB Jijel University, Jijel, Algeria
| | - Ammar Soukkou
- RE Laboratory, Electronics Department, MSB Jijel University, Jijel, Algeria
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20
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Cheng C, Gu X, Fei Z, Xiao P. An Algorithm-optimized Scheme for In-situ Synthesis of DNA Microarrays. Comb Chem High Throughput Screen 2023; 26:1609-1617. [PMID: 36654466 DOI: 10.2174/1386207326666230118114032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Revised: 12/02/2022] [Accepted: 12/06/2022] [Indexed: 01/19/2023]
Abstract
BACKGROUND The cost of synthetic DNA has limited applications in frontier science and technology fields such as synthetic biology, DNA storage, and DNA chips. OBJECTIVE The objective of this study is to find an algorithm-optimized scheme for the in-situ synthesis of DNA microarrays, which can reduce the cost of DNA synthesis. METHODS Here, based on the characteristics of in-situ chemical synthesis of DNA microarrays, an optimization algorithm was proposed. Through data grading, the sequences with the same base at as many different features as possible were synthesized in parallel to reduce synthetic cycles. RESULTS AND DISCUSSION The simulation results of 10 and 100 randomly selected sequences showed that when level=2, the reduction ratio in the number of synthetic cycles was the largest, 40% and 32.5%, respectively. Subsequently, the algorithm-optimized scheme was applied to the electrochemical synthesis of 12,000 sequences required for DNA storage. The results showed that compared to the 508 cycles required by the conventional synthesis scheme, the algorithm-optimized scheme only required 342 cycles, which reduced by 32.7%. In addition, the reduced 166 cycles reduced the total synthesis time by approximately 11 hours. CONCLUSIONS The algorithm-optimized synthesis scheme can not only reduce the synthesis time of DNA microarrays and improve synthesis efficiency, but more importantly, it can also reduce the cost of DNA synthesis by nearly 1/3. In addition, it is compatible with various in-situ synthesis methods of DNA microarrays, including soft-lithography, photolithography, a photoresist layer, electrochemistry and photoelectrochemistry. Therefore, it has very important application value.
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Affiliation(s)
- Chu Cheng
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Xingyue Gu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Zhongjie Fei
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Pengfeng Xiao
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
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21
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Jena B, Naik MK, Panda R, Abraham A. A novel minimum generalized cross entropy-based multilevel segmentation technique for the brain MRI/dermoscopic images. Comput Biol Med 2022; 151:106214. [PMID: 36308899 DOI: 10.1016/j.compbiomed.2022.106214] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 09/20/2022] [Accepted: 10/15/2022] [Indexed: 12/27/2022]
Abstract
BACKGROUND One of the challenging and the primary stages of medical image examination is the identification of the source of any disease, which may be the aberrant damage or change in tissue or organ caused by infections, injury, and a variety of other factors. Any such condition related to skin or brain sometimes advances in cancer and becomes a life-threatening disease. So, an efficient automatic image segmentation approach is required at the initial stage of medical image analysis. PURPOSE To make a segmentation process efficient and reliable, it is essential to use an appropriate objective function and an efficient optimization algorithm to produce optimal results. METHOD The above problem is resolved in this paper by introducing a new minimum generalized cross entropy (MGCE) as an objective function, with the inclusion of the degree of divergence. Another key contribution is the development of a new optimizer called opposition African vulture optimization algorithm (OAVOA). The proposed optimizer boosted the exploration, skill by inheriting the opposition-based learning. THE RESULTS The experimental work in this study starts with a performance evaluation of the optimizer over a set of standards (23 numbers) and IEEE CEC14 (8 numbers) Benchmark functions. The comparative analysis of test results shows that the OAVOA outperforms different state-of-the-art optimizers. The suggested OAVOA-MGCE based multilevel thresholding approach is carried out on two different types of medical images - Brain MRI Images (AANLIB dataset), and dermoscopic images (ISIC 2016 dataset) and found superior than other entropy-based thresholding methods.
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Affiliation(s)
- Bibekananda Jena
- Dept. of Electronics and Communication Engineering, Anil Neerukonda Institute of Technology & Science, Sangivalasa, Visakhapatnam, Andhra Pradesh, 531162, India.
| | - Manoj Kumar Naik
- Faculty of Engineering and Technology, Siksha O Anusandhan, Bhubaneswar, Odisha, 751030, India.
| | - Rutuparna Panda
- Dept of Electronics and Telecommunication Engineering, Veer Surendra Sai University of Technology, Burla, Odisha, 768018, India.
| | - Ajith Abraham
- Machine Intelligence Research Labs, Scientific Network for Innovation and Research Excellence, WA, 98071-2259, USA.
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22
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Hassan E, Shams MY, Hikal NA, Elmougy S. The effect of choosing optimizer algorithms to improve computer vision tasks: a comparative study. Multimed Tools Appl 2022; 82:16591-16633. [PMID: 36185324 PMCID: PMC9514986 DOI: 10.1007/s11042-022-13820-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 06/30/2022] [Accepted: 09/06/2022] [Indexed: 06/16/2023]
Abstract
Optimization algorithms are used to improve model accuracy. The optimization process undergoes multiple cycles until convergence. A variety of optimization strategies have been developed to overcome the obstacles involved in the learning process. Some of these strategies have been considered in this study to learn more about their complexities. It is crucial to analyse and summarise optimization techniques methodically from a machine learning standpoint since this can provide direction for future work in both machine learning and optimization. The approaches under consideration include the Stochastic Gradient Descent (SGD), Stochastic Optimization Descent with Momentum, Rung Kutta, Adaptive Learning Rate, Root Mean Square Propagation, Adaptive Moment Estimation, Deep Ensembles, Feedback Alignment, Direct Feedback Alignment, Adfactor, AMSGrad, and Gravity. prove the ability of each optimizer applied to machine learning models. Firstly, tests on a skin cancer using the ISIC standard dataset for skin cancer detection were applied using three common optimizers (Adaptive Moment, SGD, and Root Mean Square Propagation) to explore the effect of the algorithms on the skin images. The optimal training results from the analysis indicate that the performance values are enhanced using the Adam optimizer, which achieved 97.30% accuracy. The second dataset is COVIDx CT images, and the results achieved are 99.07% accuracy based on the Adam optimizer. The result indicated that the utilisation of optimizers such as SGD and Adam improved the accuracy in training, testing, and validation stages.
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Affiliation(s)
- Esraa Hassan
- Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh, 33516 Egypt
| | - Mahmoud Y. Shams
- Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh, 33516 Egypt
| | - Noha A. Hikal
- Department of Information Technology, Faculty of Computers and Information, Mansoura University, Mansoura, 35516 Egypt
| | - Samir Elmougy
- Department of Computer Science, Faculty of Computers and Information, Mansoura University, Mansoura, 35516 Egypt
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23
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Xie W, Wu WZ, Liu C, Goh M. Generalized fractional grey system models: The memory effects perspective. ISA Trans 2022; 126:36-46. [PMID: 34366121 DOI: 10.1016/j.isatra.2021.07.037] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 07/26/2021] [Accepted: 07/26/2021] [Indexed: 06/13/2023]
Abstract
In recent years, grey models based on fractional-order accumulation and/or derivatives have attracted considerable research interest because they offer better performance in handling limited samples with uncertainty than integer-order grey models; however, there remains room for improvement. This paper considers a more flexible and general structure for the fractional grey model by incorporating a generalized fractional-order derivative (GFOD) that complies by memory effects, resulting in the development of a generalized fractional grey model (denoted as GFGM(1,1)). Specifically, we comprehensively analyse the modelling mechanism of the proposed GFGM(1,1) model, involving model parameter estimation and time response function derivation, and discuss the link between the proposed approach and existing special cases. Then, to further improve the efficacy of the proposed approach, four mainstream metaheuristic algorithms are employed to ascertain the orders of fractional accumulation and derivatives. Finally, we carry out a series of simulation studies and a real-world application case to demonstrate the applicability and advantage of the our approach. The numerical results show that GFGM(1,1) outperforms other benchmarks, and some significant insights are obtained from the numerical experiments.
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Affiliation(s)
- Wanli Xie
- Institute of EduInfo Science and Engineering, Nanjing Normal University, Nanjing 210097, China.
| | - Wen-Ze Wu
- School of Economics and Business Administration, Central China Normal University, Wuhan 430079, China.
| | - Chong Liu
- School of Science, Northeastern University, Shenyang 110819, China.
| | - Mark Goh
- NUS Business School, National University of Singapore, The Logistics Institute Asia Pacific, National University of Singapore, Singapore.
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24
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Song C, Yao L. Application of artificial intelligence based on synchrosqueezed wavelet transform and improved deep extreme learning machine in water quality prediction. Environ Sci Pollut Res Int 2022; 29:38066-38082. [PMID: 35067886 DOI: 10.1007/s11356-022-18757-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 01/15/2022] [Indexed: 06/14/2023]
Abstract
Water quality prediction is the basis for the prevention and control of water pollution. In this paper, to address the problem of low prediction accuracy of existing empirical models due to the non-smoothness and nonlinearity of water quality series, a novel water quality forecasting model integrating synchrosqueezed wavelet transform and deep extreme learning machine optimized with the sparrow search algorithm (SWT-SSA-DELM) was proposed. First, the water quality series was denoised by SWT to reduce the non-stationarity and randomness of water quality series. Then, construct DELM by combining ELM and an autoencoder, and an innovative metaheuristic algorithm, SSA, was used to optimize the hyperparameters of the DELM. Finally, the constructed feature vector was used as the input of the DELM, and the proposed water quality prediction model SWT-SSA-DELM was trained and tested with the data sets of Xinchengqiao and Xiaolangdi in the Yellow River Basin, China. Models such as ELM and DELM alone, as well as their improved form based on ensemble learning, long short-term memory network (LSTM), autoregressive integrated moving average (ARIMA) were adopted as comparison models. The results make it evident that the model presented, linking the ability to ensure convergence to the global optima of the SSA with the nonlinear mapping of the DELM, outperforms similar models in terms of predictive performance, with average MAE, MAPE, and RMSE of 0.15, 2.02%, and 0.21 in the test stage, which is 72.82%, 72.88%, and 74.32% lower than the baseline ELM model, respectively.
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Affiliation(s)
- Chenguang Song
- School of Engineering and Technology, China University of Geosciences (Beijing), Beijing, 100083, China
| | - Leihua Yao
- School of Engineering and Technology, China University of Geosciences (Beijing), Beijing, 100083, China.
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Dehghani M, Hubálovský Š, Trojovský P. A new optimization algorithm based on average and subtraction of the best and worst members of the population for solving various optimization problems. PeerJ Comput Sci 2022; 8:e910. [PMID: 35494852 PMCID: PMC9044275 DOI: 10.7717/peerj-cs.910] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 02/10/2022] [Indexed: 06/14/2023]
Abstract
In this paper, a novel evolutionary-based method, called Average and Subtraction-Based Optimizer (ASBO), is presented to attain suitable quasi-optimal solutions for various optimization problems. The core idea in the design of the ASBO is to use the average information and the subtraction of the best and worst population members for guiding the algorithm population in the problem search space. The proposed ASBO is mathematically modeled with the ability to solve optimization problems. Twenty-three test functions, including unimodal and multimodal functions, have been employed to evaluate ASBO's performance in effectively solving optimization problems. The optimization results of the unimodal functions, which have only one main peak, show the high ASBO's exploitation power in converging towards global optima. In addition, the optimization results of the high-dimensional multimodal functions and fixed-dimensional multimodal functions, which have several peaks and local optima, indicate the high exploration power of ASBO in accurately searching the problem-solving space and not getting stuck in nonoptimal peaks. The simulation results show the proper balance between exploration and exploitation in ASBO in order to discover and present the optimal solution. In addition, the results obtained from the implementation of ASBO in optimizing these objective functions are analyzed compared with the results of nine well-known metaheuristic algorithms. Analysis of the optimization results obtained from ASBO against the performance of the nine compared algorithms indicates the superiority and competitiveness of the proposed algorithm in providing more appropriate solutions.
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Affiliation(s)
- Mohammad Dehghani
- Department of Mathematics/Faculty of Science, University of Hradec Králové, Hradec Kralove, Czech Republic
| | - Štěpán Hubálovský
- Department of Applied Cybernetics/Faculty of Science, University of Hradec Králové, Hradec Kralove, Czech Republic
| | - Pavel Trojovský
- Department of Mathematics/Faculty of Science, University of Hradec Králové, Hradec Kralove, Czech Republic
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Bac BH, Nguyen H, Thao NTT, Duyen LT, Hanh VT, Dung NT, Khang LQ, An DM. Performance evaluation of nanotubular halloysites from weathered pegmatites in removing heavy metals from water through novel artificial intelligence-based models and human-based optimization algorithm. Chemosphere 2021; 282:131012. [PMID: 34118630 DOI: 10.1016/j.chemosphere.2021.131012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Revised: 05/21/2021] [Accepted: 05/24/2021] [Indexed: 06/12/2023]
Abstract
The efforts of this study aimed to evaluate the feasibility of the nanotubular halloysites in weathered pegmatites (NaHWP) for removing heavy metals (i.e., Cd2+, Pb2+) from water. Furthermore, two novel intelligent models, such as teaching-learning-based optimization (TLBO)-artificial neural network (ANN), and TLBO-support vector regression (SVR), named as TLBO-ANN and TLBO-SVR models, respectively, were proposed to predict the Cd2+ and Pb2+ absorption efficiencies from water using the NaHWP absorbent. Databases used, including 53 experiments for Pb2+ absorption and 56 experiments for Cd2+ absorption from water, under the catalysis of different conditions, such as initial concentration of Pb2+ and Cd2+, solution pH, adsorbent weight, and contact time. Subsequently, the TLBO-ANN and TLBO-SVR models were developed and applied to predict the efficiencies of Cd2+ and Pb2+ absorption from water, aiming to evaluate the role as well as the effects of different conditions on the absorption efficiencies using the NaHWP absorbent. The standalone ANN and SVM models were also taken into consideration and compared with the proposed hybrid models (i.e., TLBO-ANN and TLBO-SVR). The results showed that the NaHWP detected in a Kaolin mine (Vietnam) with 70% nanotubular halloysites is a potential adsorbent for water treatment to eliminate heavy metals from water. The two novel hybrid models proposed, i.e., TLBO-ANN and TLBO-SVR, also yielded the dominant performances and accuracies in predicting the Cd2+ and Pb2+ absorption efficiencies from water, i.e., RMSE = 1.190 and 1.102, R2 = 0.951 and 0.957, VAF = 94.436 and 95.028 for the TLBO-ANN and TLBO-SVR models, respectively, in predicting the Pb2+ absorption efficiency from water; RMSE = 3.084 and 3.442, R2 = 0.971 and 0.965, VAF = 96.499 and 96.415 for the TLBO-ANN and TLBO-SVR models, respectively, in predicting the Cd2+ absorption efficiency from water. Furthermore, the validation results also demonstrated these findings in practice through 23 experiments with the accuracies of 98.3% and 98.37% for the TLBO-ANN and TLBO-SVR models, respectively, in predicting the Pb2+ absorption efficiency from water; the accuracies of 98.3% and 97.46% for the TLBO-ANN and TLBO-SVR models, respectively, in predicting the Cd2+ absorption efficiency from water. Besides, solution pH was evaluated as the most critical parameter that can be adjusted to enhance the performance of the absorption of the heavy metals in this study. By using the NaHWP absorbent and the novel proposed intelligent models developed, heavy metals can be eliminated entirely from water, providing pure water/clean freshwater without any risk of adverse health effects for the short term or long term.
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Affiliation(s)
- Bui Hoang Bac
- Department of Exploration Geology, Faculty of Geosciences and Geoengineering, Hanoi University of Mining and Geology, 18 Vien st., Duc Thang ward, Bac Tu Liem dist., Hanoi, Viet Nam; Centre for Excellence in Analysis and Experiment, Hanoi University of Mining and Geology, 18 Vien st., Duc Thang ward, Bac Tu Liem dist., Hanoi, Viet Nam.
| | - Hoang Nguyen
- Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, 18 Vien st., Duc Thang ward, Bac Tu Liem dist., Hanoi, Viet Nam; Centre for Mining, Electro-Mechanical Research, Hanoi University of Mining and Geology, 18 Vien st., Duc Thang ward, Bac Tu Liem dist., Hanoi, Viet Nam.
| | - Nguyen Thi Thanh Thao
- Department of Exploration Geology, Faculty of Geosciences and Geoengineering, Hanoi University of Mining and Geology, 18 Vien st., Duc Thang ward, Bac Tu Liem dist., Hanoi, Viet Nam
| | - Le Thi Duyen
- Centre for Excellence in Analysis and Experiment, Hanoi University of Mining and Geology, 18 Vien st., Duc Thang ward, Bac Tu Liem dist., Hanoi, Viet Nam; Department of Chemistry, Faculty of Basic Science, Hanoi University of Mining and Geology, 18 Vien st., Duc Thang ward, Bac Tu Liem dist., Hanoi, Viet Nam
| | - Vo Thi Hanh
- Centre for Excellence in Analysis and Experiment, Hanoi University of Mining and Geology, 18 Vien st., Duc Thang ward, Bac Tu Liem dist., Hanoi, Viet Nam; Department of Chemistry, Faculty of Basic Science, Hanoi University of Mining and Geology, 18 Vien st., Duc Thang ward, Bac Tu Liem dist., Hanoi, Viet Nam
| | - Nguyen Tien Dung
- Department of Exploration Geology, Faculty of Geosciences and Geoengineering, Hanoi University of Mining and Geology, 18 Vien st., Duc Thang ward, Bac Tu Liem dist., Hanoi, Viet Nam
| | - Luong Quang Khang
- Department of Exploration Geology, Faculty of Geosciences and Geoengineering, Hanoi University of Mining and Geology, 18 Vien st., Duc Thang ward, Bac Tu Liem dist., Hanoi, Viet Nam
| | - Do Manh An
- Department of Exploration Geology, Faculty of Geosciences and Geoengineering, Hanoi University of Mining and Geology, 18 Vien st., Duc Thang ward, Bac Tu Liem dist., Hanoi, Viet Nam
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Hao Y, Niu X, Wang J. Impacts of haze pollution on China's tourism industry: A system of economic loss analysis. J Environ Manage 2021; 295:113051. [PMID: 34182342 DOI: 10.1016/j.jenvman.2021.113051] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Revised: 06/05/2021] [Accepted: 06/07/2021] [Indexed: 06/13/2023]
Abstract
Haze pollution not only negatively influences public health but also causes great economic losses. However, most previous studies have mainly focused on health-related economic losses, while the negative effects of haze pollution on the tourism industry are often ignored, leading to the unsustainable development of tourism. In this context, contrasting with previous research perspectives, this article selected several representative tourist cities from East China, South China, Central China, North China, Northwest China, Southwest China, and Northeast China as research objects in an empirical study, developing an economic loss analysis system to quantitatively evaluate the losses in the tourism industry caused by haze pollution. This system uses the satin bower bird optimization-based distribution estimation method to identify the optimal distribution of haze pollution, demonstrating superior performance to the traditional estimation method. Meanwhile, the optimal distribution of haze pollution is employed to measure the probability of different concentration limits in each area. Furthermore, the economic loss formula of the tourism industry is proposed in the devised system, calculating the economic loss caused by haze pollution at different degrees. The results show that haze pollution in different degrees has caused varying degrees of losses to China's tourism industry. In terms of seasonality and regionality, different seasons and different regions produce different results. Compared with summer, autumn and winter haze pollution is more severe, creating obvious seasonal differences. There is also a regional agglomeration effect, whereby the regional distribution of haze pollution is consistent with each region's economic development.
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Affiliation(s)
- Yan Hao
- Business School, Shandong Normal University, Jinan, 250014, China
| | - Xinsong Niu
- School of Statistics, Dongbei University of Finance and Economics, Dalian, 116025, China.
| | - Jianzhou Wang
- Macau Institute of Systems Engineering, Macau University of Science and Technology, Macau, 999078, China
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Szczepanski R, Tarczewski T, Grzesiak LM. Application of optimization algorithms to adaptive motion control for repetitive process. ISA Trans 2021; 115:192-205. [PMID: 33451802 DOI: 10.1016/j.isatra.2021.01.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 12/22/2020] [Accepted: 01/04/2021] [Indexed: 06/12/2023]
Abstract
The application of optimization algorithms to adaptive motion control is proposed in this paper. In order to provide optimal system response, optimization algorithm is used as adjustment mechanism of controller coefficients. Most of optimization algorithms are not able to work in continuous optimization mode and with non-constant search space (i.e. dataset). For this reason, the introduction of a novel approach, Adaptive Procedure for Optimization Algorithms (APOA), that allows to apply most of optimization algorithms to adaptation process seems to be necessary. Such an algorithm is innovative, due to its universality in terms of applied optimization algorithm. Its application allows to obtain optimal motion control in modern electric drives. The proposed APOA is presented together with the novel desired-response adaptive system (DRAS) approach for repetitive processes. This solution provides unchanged system response regardless of plant parameters variation or external disturbances. The developed adaptive approach based on optimization algorithm is implemented in permanent magnet synchronous motor (PMSM) drive to adapt state feedback speed controller during moment of inertia variations. Since the proposed DRAS is model-free approach, the adaptation procedure is immune to issues related to plant parameters mismatch. The obtained results prove that proposed adaptive speed controller for PMSM assures desired system response regardless of the moment of inertia variation.
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Affiliation(s)
- Rafal Szczepanski
- Department of Automatics and Measurement Systems, Nicolaus Copernicus University, Grudziadzka 5, 87-100 Torun, Poland.
| | - Tomasz Tarczewski
- Department of Automatics and Measurement Systems, Nicolaus Copernicus University, Grudziadzka 5, 87-100 Torun, Poland.
| | - Lech M Grzesiak
- Institute of Control and Industrial Electronics, Warsaw University of Technology, Koszykowa 75, 00-662 Warsaw, Poland.
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Jalaee SA, Shakibaei A, Horry HR, Akbarifard H, GhasemiNejad A, Robati FN, Zarin NA. A new hybrid metaheuristic method based on biogeography-based optimization and particle swarm optimization algorithm to estimate money demand in Iran. MethodsX 2021; 8:101226. [PMID: 34434749 PMCID: PMC8374190 DOI: 10.1016/j.mex.2021.101226] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Accepted: 01/07/2021] [Indexed: 11/24/2022] Open
Abstract
Money demand is one of the most important economic variables which are a critical component in appointing and choosing appropriate monetary policy, because it determines the transmission of policy-driven change in monetary aggregates to the real sector. In this paper, the data of economic indicators in Iran are presented for estimating the money demand using biogeography-based optimization (BBO) algorithm, particle swarm optimization (PSO) algorithm, and a new hybrid metaheuristic method based on biogeography-based optimization and particle swarm optimization algorithm (BBPSO). The data are used in two forms (i.e. linear and exponential) to estimate money demand values based on true liquidity, Consumer price index, GDP, lending interest rate, Inflation, and official exchange rate. The available data are partly used for finding optimal or near-optimal values of weighting parameters (1974–2013) and partly for testing the models (2014–2018). The performance of methods is evaluated using mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). According to the simulation results, the proposed method (i.e. BBPSO) outperformed the other models. The findings proved that the recommended method was an appropriate tool for effective money demand prediction in Iran. These data were the result of a comprehensive look at the most influential factors for money market demand. With this method, the demand side of this market was clearly defined. Along with other markets, the consequences of economic policy could be analyzed and predicted. • The article provides a method for observing the effect of economic scenarios on the money market and the analysis obtained by this proposed method allows experts, public sector economics, and monetary economist to see a clearer explanation of the country's liquidity plan. • The method presented in this article can be beneficial for the policy makers and monetary authorities during their decision-making process.
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Affiliation(s)
- Sayyed Abdolmajid Jalaee
- Department of Economics, Faculty of Management and Economics, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Alireza Shakibaei
- Department of Economics, Faculty of Management and Economics, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Hamid Reza Horry
- Department of Economics, Faculty of Management and Economics, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Hossein Akbarifard
- Department of Economics, Faculty of Management and Economics, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Amin GhasemiNejad
- Department of Economics, Faculty of Management and Economics, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Fateme Nazari Robati
- Department of Economics, Faculty of Management and Economics, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Naeeme Amani Zarin
- Department of Economics, Faculty of Management and Economics, Shahid Bahonar University of Kerman, Kerman, Iran
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Jalaee SA, Shakibaei A, Akbarifard H, Horry HR, GhasemiNejad A, Nazari Robati F, Amani Zarin N, Derakhshani R. A novel hybrid method based on Cuckoo optimization algorithm and artificial neural network to forecast world's carbon dioxide emission. MethodsX 2021; 8:101310. [PMID: 34434830 PMCID: PMC8374256 DOI: 10.1016/j.mex.2021.101310] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 03/11/2021] [Indexed: 12/04/2022] Open
Abstract
This paper deals with the global energy consumption to forecast future projections based on primary energy, global oil, coal and natural gas consumption using a hybrid Cuckoo optimization algorithm and information of British Petroleum Company plc and BP Amoco plc. The Artificial Neural Network (ANN) has some significant disadvantages, such as training slowly, easiness to fall into local optimal point, and sensitivity of the initial weights and bias. To overcome the shortcomings, an improved ANN structure, that is optimized by the Cuckoo Optimization Algorithm (COA), is proposed in this paper (COANN). The performance of the COANN is evaluated with Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (CC) between the output of the model and the actual dataset. Finally, CO2 emission in the world by 2050 is forecasted using COANN. The findings showed that COANN is a helpful and reliable tool for monitoring global warming. This proposed method will assist experts, policy planners and researchers who study greenhouse gases.The method can be used as a potential tool for policymakers and governments to make policy on global warming monitoring and control. The proposed method can play a key role in the global climate changes policies and can have a significant impact on the efficiency or inefficiency of government's intervention policies.
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Affiliation(s)
- Sayyed Abdolmajid Jalaee
- Department of Economics, Faculty of Management and Economics, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Alireza Shakibaei
- Department of Economics, Faculty of Management and Economics, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Hossein Akbarifard
- Department of Economics, Faculty of Management and Economics, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Hamid Reza Horry
- Department of Economics, Faculty of Management and Economics, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Amin GhasemiNejad
- Department of Economics, Faculty of Management and Economics, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Fateme Nazari Robati
- Department of Economics, Faculty of Management and Economics, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Naeeme Amani Zarin
- Department of Economics, Faculty of Management and Economics, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Reza Derakhshani
- Department of Geology, Shahid Bahonar University of Kerman, Kerman, Iran.,Department of Earth Sciences, Utrecht University, Utrecht, the Netherlands
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Liu C, Zhang S, Gao Y, Wang Y, Sheng L, Gao H, Fung JCH. Optimal estimation of initial concentrations and emission sources with 4D-Var for air pollution prediction in a 2D transport model. Sci Total Environ 2021; 773:145580. [PMID: 33582338 DOI: 10.1016/j.scitotenv.2021.145580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 01/13/2021] [Accepted: 01/28/2021] [Indexed: 06/12/2023]
Abstract
Attributing sources of air pollution events by deploying an efficient observational network is an important and interesting problem in air quality control and forecast studies, but it is very challenging. In order to estimate the sensitivities of pollution events to emission sources, a comprehensive framework is built based on a horizontal 2-dimensional transport model and its adjoint in solving this problem. In an analysis of an idealized air pollution event of PM2.5 over the region of North China, an objective function is defined to optimally estimate the initial concentrations and emission sources through a series of minimization procedures. Results by means of the 4-dimensional variational approach show that, with the optimal initial conditions and emission sources, the model can successfully forecast the pollution event in a few days. The optimal observing network based on sensitivity analysis takes only one third of the cost but greatly retains predictability skill compared to the full-grid observing system, while nearly no predictability skill is detectable if the same number of observational sites is randomly deployed. We evaluate air pollution predictability in the point of focusing on to what degree the root mean square errors between the modeled concentration and the targeted air pollution are limited by the optimal observational network. Results show that air pollution predictability in association with the optimal observational network is limited in the time scales about 6 days. With the high efficiency and in an economic fashion, such a sensitivity-based optimal observing system holds promise for accurately predicting an air pollution event in the targeted area once the adjoint and variational procedure of a realistic atmosphere model including transport and chemical processes is performed.
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Affiliation(s)
- Caili Liu
- The College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, China
| | - Shaoqing Zhang
- The College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, China; Key Laboratory of Physical Oceanography, MOE, Institute for Advanced Ocean Study, Frontiers Science Center for Deep Ocean Multispheres and Earth System (FDOMES), Ocean University of China, China; Ocean Dynamics and Climate Function Lab, Pilot National Laboratory for Marine Science and Technology (QNLM), Qingdao, China; International Laboratory for High-Resolution Earth System Model and Prediction (iHESP), Qingdao, China.
| | - Yang Gao
- Key Laboratory of Marine Environment and Ecology, and Frontiers Science Center for Deep Ocean Multispheres and Earth System (FDOMES), Ministry of Education, Ocean University of China, Qingdao 266100, China; Ocean Dynamics and Climate Function Lab, Pilot National Laboratory for Marine Science and Technology (QNLM), Qingdao, China.
| | - Yuhang Wang
- School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA 30332, United States of America
| | - Lifang Sheng
- The College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, China
| | - Huiwang Gao
- Key Laboratory of Marine Environment and Ecology, and Frontiers Science Center for Deep Ocean Multispheres and Earth System (FDOMES), Ministry of Education, Ocean University of China, Qingdao 266100, China
| | - J C H Fung
- Division of Environment and Sustainability, Hong Kong University of Science and Technology, China
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Song C, Yao L, Hua C, Ni Q. A water quality prediction model based on variational mode decomposition and the least squares support vector machine optimized by the sparrow search algorithm (VMD-SSA-LSSVM) of the Yangtze River, China. Environ Monit Assess 2021; 193:363. [PMID: 34041601 DOI: 10.1007/s10661-021-09127-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 05/16/2021] [Indexed: 05/12/2023]
Abstract
Accurate and reliable water quality forecasting is of great significance for water resource optimization and management. This study focuses on the prediction of water quality parameters such as the dissolved oxygen (DO) in a river system. The accuracy of traditional water quality prediction methods is generally low, and the prediction results have serious autocorrelation. To overcome nonstationarity, randomness, and nonlinearity of the water quality parameter data, an improved least squares support vector machine (LSSVM) model was proposed to improve the model's performance at two gaging stations, namely Panzhihua and Jiujiang, in the Yangtze River, China. In addition, a hybrid model that recruits variational mode decomposition (VMD) to denoise the input data was adopted. A novel metaheuristic optimization algorithm, the sparrow search algorithm (SSA) was also implemented to compute the optimal parameter values for the LSSVM model. To validate the proposed hybrid model, standalone LSSVM, SSA-LSSVM, VMD-LSSVM, support vector regression (SVR), as well as back propagation neural network (BPNN) were considered as the benchmark models. The results indicated that the VMD-SSA-LSSVM model exhibited the best forecasting performance among all the peer models at Panzhihua station. Furthermore, the model forecasting results applied at Jiujiang were consistent with those at Panzhihua station. This result further verified the accuracy and stability of the VMD-SSA-LSSVM model. Thus, the proposed hybrid model was effective method for forecasting nonstationary and nonlinear water quality parameter series and can be recommended as a promising model for water quality parameter forecasting.
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Affiliation(s)
- Chenguang Song
- School of Engineering and Technology, China University of Geosciences (Beijing), Beijing, 100083, China.
| | - Leihua Yao
- School of Engineering and Technology, China University of Geosciences (Beijing), Beijing, 100083, China.
| | - Chengya Hua
- School of Engineering and Technology, China University of Geosciences (Beijing), Beijing, 100083, China
| | - Qihang Ni
- School of Engineering and Technology, China University of Geosciences (Beijing), Beijing, 100083, China
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Yang Q, Chen Q, Niu T, Feng E, Yuan J. Robustness analysis and identification for an enzyme-catalytic complex metabolic network in batch culture. Bioprocess Biosyst Eng 2021; 44:1511-1524. [PMID: 33687551 DOI: 10.1007/s00449-021-02535-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Accepted: 02/09/2021] [Indexed: 11/25/2022]
Abstract
Bioconversion of glycerol to 1,3-propanediol is a promising way to mitigate the shortage of energy. To maximize the production of 1,3-propanediol, it needs to control precisely microbial fermentation process. However, it might consume lots of human and material resources when conducting experimental tests many times. In this study, a nonlinear enzyme-catalytic dynamical system is developed to describe the bioconversion process of glycerol to 1,3-propanediol, especially continuous piecewise linear functions are used as identification parameters. The existence, uniqueness and continuity of solutions are also discussed. Then, considering the fact that the concentration of intracellular substances is difficult to measure in experiments, a new quantitative definition of biological robustness is introduced as a performance index to determine the identification parameters related to intracellular substances. Meanwhile, a two-phase optimization algorithm is constructed to solve the identification model. By comparison with the experimental data, it can be found that the present nonlinear dynamical system can describe the fermentation process very well. Finally, the present nonlinear dynamical system and the corresponding optimal identification parameters might be useful in future studies on the batch culture of glycerol to 1,3-propanediol.
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Affiliation(s)
- Qi Yang
- School of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin, 541004, Guangxi, People's Republic of China
| | - Qunbin Chen
- School of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin, 541004, Guangxi, People's Republic of China.
| | - Teng Niu
- School of Mathematical Sciences, Dalian University of Technology, Dalian, 116024, Liaoning, People's Republic of China
| | - Enmin Feng
- School of Mathematical Sciences, Dalian University of Technology, Dalian, 116024, Liaoning, People's Republic of China
| | - Jinlong Yuan
- Department of Mathematics, School of Science, Dalian Maritime University, Dalian, 116026, Liaoning, People's Republic of China
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Hesami M, Naderi R, Tohidfar M. Introducing a hybrid artificial intelligence method for high-throughput modeling and optimizing plant tissue culture processes: the establishment of a new embryogenesis medium for chrysanthemum, as a case study. Appl Microbiol Biotechnol 2020; 104:10249-10263. [PMID: 33119796 DOI: 10.1007/s00253-020-10978-1] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 10/13/2020] [Accepted: 10/25/2020] [Indexed: 11/30/2022]
Abstract
Data-driven models in a combination of optimization algorithms could be beneficial methods for predicting and optimizing in vitro culture processes. This study was aimed at modeling and optimizing a new embryogenesis medium for chrysanthemum. Three individual data-driven models, including multi-layer perceptron (MLP), adaptive neuro-fuzzy inference system (ANFIS), and support vector regression (SVR), were developed for callogenesis rate (CR), embryogenesis rate (ER), and somatic embryo number (SEN). Consequently, the best obtained results were used in the fusion process by a bagging method. For medium reformulation, effects of eight ionic macronutrients on CR, ER, and SEN and effects of four vitamins on SEN were evaluated using data fusion (DF)-non-dominated sorting genetic algorithm-II (NSGA-II) and DF-genetic algorithm (GA), respectively. Results showed that DF models with the highest R2 had superb performance in comparison with all other individual models. According to DF-NSGAII, the highest ER and SEN can be obtained from the medium containing 14.27 mM NH4+, 38.92 mM NO3-, 22.79 mM K+, 5.08 mM Cl-, 3.34 mM Ca2+, 1.67 mM Mg2+, 2.17 mM SO42-, and 1.44 mM H2PO4-. Based on the DF-GA model, the maximum SEN can be obtained from a medium containing 0.61 μM thiamine, 5.93 μM nicotinic acid, 0.25 μM biotin, and 0.26 μM riboflavin. The efficiency of the established-optimized medium was experimentally compared to Murashige and Skoog medium (MS) for embryogenesis of five chrysanthemum cultivars, and results indicated the efficiency of optimized medium over MS medium.Key points• MLP, SVR, and ANFIS were fused by a bagging method to develop a data fusion model.• NSGA-II and GA were linked to the data fusion model for establishing and optimizing a new embryogenesis medium.• The new culture medium (HNT) had better efficiency than MS medium.
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Affiliation(s)
- Mohsen Hesami
- Gosling Research Institute for Plant Preservation, Department of Plant Agriculture, University of Guelph, Guelph, ON, Canada.,Department of Horticultural Science, Faculty of Agriculture, University of Tehran, Karaj, Iran
| | - Roohangiz Naderi
- Department of Horticultural Science, Faculty of Agriculture, University of Tehran, Karaj, Iran.
| | - Masoud Tohidfar
- Department of Plant Biotechnology, Faculty of Sciences & Biotechnology, Shahid Beheshti University, G.C., Tehran, Iran
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Li B, Wang H, Li G, Liu J, Zhang Z, Gu K, Yang H, Qiao A, Du J, Liu Y. A patient-specific modelling method of blood circulatory system for the numerical simulation of enhanced external counterpulsation. J Biomech 2020; 111:110002. [PMID: 32898825 DOI: 10.1016/j.jbiomech.2020.110002] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 07/09/2020] [Accepted: 08/14/2020] [Indexed: 11/17/2022]
Abstract
Lumped parameter model (LPM) is a common numerical model for hemodynamic simulation of human's blood circulatory system. The numerical simulation of enhanced external counterpulsation (EECP) is a typical biomechanical simulation process based on the LPM of blood circulatory system. In order to simulate patient-specific hemodynamic effects of EECP and develop best treatment strategy for each individual, this study developed an optimization algorithm to individualize LPM elements. Physiological data from 30 volunteers including approximate aortic pressure, cardiac output, ankle pressure and carotid artery flow were clinically collected as optimization objectives. A closed-loop LPM was established for the simulation of blood circulatory system. Aiming at clinical data, a sensitivity analysis for each element was conducted to identify the significant ones. We improved the traditional simulated annealing algorithm to iteratively optimize the sensitive elements. To verify the accuracy of the patient-specific model, 30 samples of simulated data were compared with clinical measurements. In addition, an EECP experiment was conducted on a volunteer to verify the applicability of the optimized model for the simulation of EECP. For these 30 samples, the optimization results show a slight difference between clinical data and simulated data. The average relative root mean square error is lower than 5%. For the subject of EECP experiment, the relative error of hemodynamic responses during EECP is lower than 10%. This slight error demonstrated a good state of optimization. The optimized modeling algorithm can effectively individualize the LPM for blood circulatory system, which is significant to the numerical simulation of patient-specific hemodynamics.
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Affiliation(s)
- Bao Li
- Department of Biomedical Engineering, College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China.
| | - Hui Wang
- The Eighth Affiliated Hospital, Sun Yat-sen University, ShenZhen, GuangDong, China
| | - Gaoyang Li
- Institute of Fluid Science, Tohoku University, Sendai, Miyagi, Japan
| | - Jian Liu
- Peking University People's Hospital, Beijing, China
| | - Zhe Zhang
- Peking University Third Hospital, Beijing, China
| | - Kaiyun Gu
- Peking University Third Hospital, Beijing, China
| | - Haisheng Yang
- Department of Biomedical Engineering, College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China
| | - Aike Qiao
- Department of Biomedical Engineering, College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China
| | - Jianhang Du
- The Eighth Affiliated Hospital, Sun Yat-sen University, ShenZhen, GuangDong, China
| | - Youjun Liu
- Department of Biomedical Engineering, College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China.
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Dadvar AA, Vahidi J, Hajizadeh Z, Maleki A, Reza Bayati M. Experimental study on classical and metaheuristics algorithms for optimal nano-chitosan concentration selection in surface coating and food packaging. Food Chem 2020; 335:127681. [PMID: 32739803 DOI: 10.1016/j.foodchem.2020.127681] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 07/03/2020] [Accepted: 07/24/2020] [Indexed: 11/26/2022]
Abstract
In this study the Lagrange interpolation optimization algorithm based on two variables with respect to all experimental replicates (POA), was compared with two other heuristics methods (WOA and GOA). Modification of the apple surface by an edible nano coating solution in food packaging was used as case study. The experiment was performed as a factorial test based on completely randomized design by 100 permutations data sets. Results showed a significant difference between the three optimization methods (POA, WOA and GOA) which indicates the necessity of optimization and also efficiency of the present POA. The optimum result by POA, similar to a rose petal property, could rise 72% in surface contact angle (CA). The scanning electron microscopy (SEM) images of the derived surfaces showed almost a uniform spherical nanoparticles morphology. Remarkable advantages of this new approach are no additional material requirement, healthful, easy, inexpensive, fast and affordable technique for surface improvement.
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Affiliation(s)
- Ali Akbar Dadvar
- Department of Mathematics, Iran University of Science and Technology, Tehran, Iran; Catalysts and Organic Synthesis Research Laboratory, Department of Chemistry, Iran University of Science and Technology, Tehran 16846-13114, Iran
| | - Javad Vahidi
- Department of Mathematics, Iran University of Science and Technology, Tehran, Iran
| | - Zoleikha Hajizadeh
- Catalysts and Organic Synthesis Research Laboratory, Department of Chemistry, Iran University of Science and Technology, Tehran 16846-13114, Iran
| | - Ali Maleki
- Catalysts and Organic Synthesis Research Laboratory, Department of Chemistry, Iran University of Science and Technology, Tehran 16846-13114, Iran.
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Masoudi-Sobhanzadeh Y, Masoudi-Nejad A. Synthetic repurposing of drugs against hypertension: a datamining method based on association rules and a novel discrete algorithm. BMC Bioinformatics 2020; 21:313. [PMID: 32677879 PMCID: PMC7469914 DOI: 10.1186/s12859-020-03644-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 07/06/2020] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Drug repurposing aims to detect the new therapeutic benefits of the existing drugs and reduce the spent time and cost of the drug development projects. The synthetic repurposing of drugs may prove to be more useful than the single repurposing in terms of reducing toxicity and enhancing efficacy. However, the researchers have not given it serious consideration. To address the issue, a novel datamining method is introduced and applied to repositioning of drugs for hypertension (HT) which is a serious medical condition and needs some improved treatment plans to help treat it. RESULTS A novel two-step data mining method, which is based on the If-Then association rules as well as a novel discrete optimization algorithm, was introduced and applied to the synthetic repurposing of drugs for HT. The required data were also extracted from DrugBank, KEGG, and DrugR+ databases. The findings indicated that based on the different statistical criteria, the proposed method outperformed the other state-of-the-art approaches. In contrast to the previously proposed methods which had failed to discover a list on some datasets, our method could find a combination list for all of them. CONCLUSION Since the proposed synthetic method uses medications in small dosages, it might revive some failed drug development projects and put forward a suitable plan for treating different diseases such as COVID-19 and HT. It is also worth noting that applying efficient computational methods helps to produce better results.
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Affiliation(s)
- Yosef Masoudi-Sobhanzadeh
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
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Li X, Yang S, Xiao D, Wang S. Numerical reconstruction of turbid slab optical properties using global optimization algorithms. Lasers Med Sci 2021; 36:43-54. [PMID: 32277407 DOI: 10.1007/s10103-020-03001-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2019] [Accepted: 03/25/2020] [Indexed: 10/24/2022]
Abstract
The detection and reconstruction of the optical properties within turbid slabs/plate parallel mediums have been widely investigated for its applications in medical diagnosis, atmosphere detection, etc., where the scattering of light would be expected. Although the scattering signal can be utilized for diagnostics purposes, the multiple scattering in the intermediate scattering regime (with an optical depth ~ 2-9) has posed a remarkable challenge. Existing optical tomography methods usually only reconstruct the reduced scattering coefficient to investigate the properties of the scattering target, while reconstruction efforts in analyzing the exact scattering phase function are rare. Solving such issues can provide much more information for proper interpretation of the characteristics of the turbid slab. This work demonstrates an inversion method based on optimization algorithms and the angular distribution of the transmitted light at the entrance plane and the exit plane of the sought medium. Candidate phase functions were pre-calculated and the optimization algorithm is able to reconstruct the phase function spatial distribution of the turbid slab with a satisfactory computational cost. Parametric studies were also performed to analyze the performance of each optimization algorithm used and the sensitivity of this Markov reconstruction scheme to noise.
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Abstract
Deep learning is one of the subsets of machine learning that is widely used in artificial intelligence (AI) field such as natural language processing and machine vision. The deep convolution neural network (DCNN) extracts high-level concepts from low-level features and it is appropriate for large volumes of data. In fact, in deep learning, the high-level concepts are defined by low-level features. Previously, in optimization algorithms, the accuracy achieved for network training was less and high-cost function. In this regard, in this study, AdaptAhead optimization algorithm was developed for learning DCNN with robust architecture in relation to the high volume data. The proposed optimization algorithm was validated in multi-modality MR images of BRATS 2015 and BRATS 2016 data sets. Comparison of the proposed optimization algorithm with other commonly used methods represents the improvement of the performance of the proposed optimization algorithm on the relatively large dataset. Using the Dice similarity metric, we report accuracy results on the BRATS 2015 and BRATS 2016 brain tumor segmentation challenge dataset. Results showed that our proposed algorithm is significantly more accurate than other methods as a result of its deep and hierarchical extraction.
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Affiliation(s)
- Farnaz Hoseini
- Department of Computer Engineering, Rasht Branch, Islamic Azad University, Rasht, Iran
| | - Asadollah Shahbahrami
- Department of Computer Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran
| | - Peyman Bayat
- Department of Computer Engineering, Rasht Branch, Islamic Azad University, Rasht, Iran
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Bhowmick SS, Bhattacharjee D, Rato L. Identification of tissue-specific tumor biomarker using different optimization algorithms. Genes Genomics 2018; 41:431-443. [PMID: 30535858 DOI: 10.1007/s13258-018-0773-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2018] [Accepted: 12/03/2018] [Indexed: 11/25/2022]
Abstract
BACKGROUND Identification of differentially expressed genes, i.e., genes whose transcript abundance level differs across different biological or physiological conditions, was indeed a challenging task. However, the inception of transcriptome sequencing (RNA-seq) technology revolutionized the simultaneous measurement of the transcript abundance levels for thousands of genes. OBJECTIVE In this paper, such next-generation sequencing (NGS) data is used to identify biomarker signatures for several of the most common cancer types (bladder, colon, kidney, brain, liver, lung, prostate, skin, and thyroid) METHODS: Here, the problem is mapped into the comparison of optimization algorithms for selecting a set of genes that lead to the highest classification accuracy of a two-class classification task between healthy and tumor samples. As the optimization algorithms Artificial Bee Colony (ABC), Ant Colony Optimization, Differential Evolution, and Particle Swarm Optimization are chosen for this experiment. A standard statistical method called DESeq2 is used to select differentially expressed genes before being feed to the optimization algorithms. Classification of healthy and tumor samples is done by support vector machine RESULTS: Cancer-specific validation yields remarkably good results in terms of accuracy. Highest classification accuracy is achieved by the ABC algorithm for Brain lower grade glioma data is 99.10%. This validation is well supported by a statistical test, gene ontology enrichment analysis, and KEGG pathway enrichment analysis for each cancer biomarker signature CONCLUSION: The current study identified robust genes as biomarker signatures and these identified biomarkers might be helpful to accurately identify tumors of unknown origin.
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Affiliation(s)
- Shib Sankar Bhowmick
- Department of Electronics and Communication Engineering, Heritage Institute of Technology, Kolkata, 700107, India.
| | - Debotosh Bhattacharjee
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032, India
| | - Luis Rato
- Department of Informatics, University of Evora, 7004-516, Evora, Portugal
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Yilmaz B, Aras E, Nacar S, Kankal M. Estimating suspended sediment load with multivariate adaptive regression spline, teaching-learning based optimization, and artificial bee colony models. Sci Total Environ 2018; 639:826-840. [PMID: 29803053 DOI: 10.1016/j.scitotenv.2018.05.153] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2018] [Revised: 04/20/2018] [Accepted: 05/13/2018] [Indexed: 06/08/2023]
Abstract
The functional life of a dam is often determined by the rate of sediment delivery to its reservoir. Therefore, an accurate estimate of the sediment load in rivers with dams is essential for designing and predicting a dam's useful lifespan. The most credible method is direct measurements of sediment input, but this can be very costly and it cannot always be implemented at all gauging stations. In this study, we tested various regression models to estimate suspended sediment load (SSL) at two gauging stations on the Çoruh River in Turkey, including artificial bee colony (ABC), teaching-learning-based optimization algorithm (TLBO), and multivariate adaptive regression splines (MARS). These models were also compared with one another and with classical regression analyses (CRA). Streamflow values and previously collected data of SSL were used as model inputs with predicted SSL data as output. Two different training and testing dataset configurations were used to reinforce the model accuracy. For the MARS method, the root mean square error value was found to range between 35% and 39% for the test two gauging stations, which was lower than errors for other models. Error values were even lower (7% to 15%) using another dataset. Our results indicate that simultaneous measurements of streamflow with SSL provide the most effective parameter for obtaining accurate predictive models and that MARS is the most accurate model for predicting SSL.
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Affiliation(s)
- Banu Yilmaz
- Karadeniz Technical University, Faculty of Technology, Department of Civil Engineering, Trabzon, Turkey
| | - Egemen Aras
- Karadeniz Technical University, Faculty of Technology, Department of Civil Engineering, Trabzon, Turkey.
| | - Sinan Nacar
- Karadeniz Technical University, Faculty of Engineering, Department of Civil Engineering, Trabzon, Turkey
| | - Murat Kankal
- Uludağ University, Faculty of Engineering, Department of Civil Engineering, Bursa, Turkey
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Abstract
Modern laboratory techniques allow studying NMDA receptors (NMDAR) either anatomically with specific antibodies coupled to sophisticated confocal microscopy, or physiologically by live imaging or electrophysiological techniques. However, NMDARs are not fixed in time and space and changes in their composition and/or distribution on the post-synaptic membrane may significantly impact the synaptic strength and overall function. The computational modeling approach therefore constitutes a complementary tool for investigating the properties of biological systems based on the knowledge provided by the lab experiments.Here, we describe a general computational method aiming at developing kinetic Markov-chain based models of NMDARs subtypes capable of reproducing various experimental results. These models are then used to make predictions on additional (non-obvious) properties and on their role in synaptic function under various physiological and pharmacological conditions. For the purpose of this book chapter, we will focus on the method used to develop a NMDAR model that includes pharmacological site of action of different compounds. Notably, this elementary model can subsequently be included in a neuron model (not described in detail here) to explore the impact of their differential distribution on synaptic functions.
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Hassanien AE, Tharwat A, Own HS. Computational model for vitamin D deficiency using hair mineral analysis. Comput Biol Chem 2017; 70:198-210. [PMID: 28923545 DOI: 10.1016/j.compbiolchem.2017.08.015] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2017] [Revised: 08/09/2017] [Accepted: 08/22/2017] [Indexed: 01/03/2023]
Abstract
Vitamin D deficiency is prevalent in the Arabian Gulf region, especially among women. Recent studies show that the vitamin D deficiency is associated with a mineral status of a patient. Therefore, it is important to assess the mineral status of the patient to reveal the hidden mineral imbalance associated with vitamin D deficiency. A well-known test such as the red blood cells is fairly expensive, invasive, and less informative. On the other hand, a hair mineral analysis can be considered an accurate, excellent, highly informative tool to measure mineral imbalance associated with vitamin D deficiency. In this study, 118 apparently healthy Kuwaiti women were assessed for their mineral levels and vitamin D status by a hair mineral analysis (HMA). This information was used to build a computerized model that would predict vitamin D deficiency based on its association with the levels and ratios of minerals. The first phase of the proposed model introduces a novel hybrid optimization algorithm, which can be considered as an improvement of Bat Algorithm (BA) to select the most discriminative features. The improvement includes using the mutation process of Genetic Algorithm (GA) to update the positions of bats with the aim of speeding up convergence; thus, making the algorithm more feasible for wider ranges of real-world applications. Due to the imbalanced class distribution in our dataset, in the second phase, different sampling methods such as Random Under-Sampling, Random Over-Sampling, and Synthetic Minority Oversampling Technique are used to solve the problem of imbalanced datasets. In the third phase, an AdaBoost ensemble classifier is used to predicting the vitamin D deficiency. The results showed that the proposed model achieved good results to detect the deficiency in vitamin D.
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Affiliation(s)
- Aboul Ella Hassanien
- Faculty of Computers and Information, Cairo University, Egypt; Scientific Research Group in Egypt (SRGE), Egypt1.
| | - Alaa Tharwat
- Faculty of Engineering, Suez Canal University, Egypt; Faculty of Computer Science and Engineering, Frankfurt University of Applied Sciences, 60318 Frankfurt am Main, Germany; Scientific Research Group in Egypt (SRGE), Egypt1.
| | - Hala S Own
- Department of Solar and Space Research, National Research Institute of Astronomy and Geophysics, El-Marsad Street, P.O. Box 11421 Helwan, Egypt.
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Jereesh AS, Govindan VK. Immuno-hybrid algorithm: a novel hybrid approach for GRN reconstruction. 3 Biotech 2016; 6:222. [PMID: 28330294 PMCID: PMC5065543 DOI: 10.1007/s13205-016-0536-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2016] [Accepted: 10/03/2016] [Indexed: 11/28/2022] Open
Abstract
Bio-inspired algorithms are widely used to optimize the model parameters of GRN. In this paper, focus is given to develop improvised versions of bio-inspired algorithm for the specific problem of reconstruction of gene regulatory network. The approach is applied to the data set that was developed by the DNA microarray technology through biological experiments in the lab. This paper introduced a novel hybrid method, which combines the clonal selection algorithm and BFGS Quasi-Newton algorithm. The proposed approach implemented for real world E. coli data set and identified most of the relations. The results are also compared with the existing methods and proven to be efficient.
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Affiliation(s)
- A. S. Jereesh
- Department of Computer Science, Cochin University of Science and Technology, Cochin, Kerala India
| | - V. K. Govindan
- Department of Computer Science and Engineering, Indian Institute of Information Technology Pala, Kottayam, Kerala India
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Song Y, Ge Y, Wang J, Ren Z, Liao Y, Peng J. Spatial distribution estimation of malaria in northern China and its scenarios in 2020, 2030, 2040 and 2050. Malar J 2016; 15:345. [PMID: 27387921 PMCID: PMC4936159 DOI: 10.1186/s12936-016-1395-2] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2015] [Accepted: 06/15/2016] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Malaria is one of the most severe parasitic diseases in the world. Spatial distribution estimation of malaria and its future scenarios are important issues for malaria control and elimination. Furthermore, sophisticated nonlinear relationships for prediction between malaria incidence and potential variables have not been well constructed in previous research. This study aims to estimate these nonlinear relationships and predict future malaria scenarios in northern China. METHODS Nonlinear relationships between malaria incidence and predictor variables were constructed using a genetic programming (GP) method, to predict the spatial distributions of malaria under climate change scenarios. For this, the examples of monthly average malaria incidence were used in each county of northern China from 2004 to 2010. Among the five variables at county level, precipitation rate and temperature are used for projections, while elevation, water density index, and gross domestic product are held at their present-day values. RESULTS Average malaria incidence was 0.107 ‰ per annum in northern China, with incidence characteristics in significant spatial clustering. A GP-based model fit the relationships with average relative error (ARE) = 8.127 % for training data (R(2) = 0.825) and 17.102 % for test data (R(2) = 0.532). The fitness of GP results are significantly improved compared with those by generalized additive models (GAM) and linear regressions. With the future precipitation rate and temperature conditions in Special Report on Emission Scenarios (SRES) family B1, A1B and A2 scenarios, spatial distributions and changes in malaria incidences in 2020, 2030, 2040 and 2050 were predicted and mapped. CONCLUSIONS The GP method increases the precision of predicting the spatial distribution of malaria incidence. With the assumption of varied precipitation rate and temperature, and other variables controlled, the relationships between incidence and the varied variables appear sophisticated nonlinearity and spatially differentiation. Using the future fluctuated precipitation and the increased temperature, median malaria incidence in 2020, 2030, 2040 and 2050 would significantly increase that it might increase 19 to 29 % in 2020, but currently China is in the malaria elimination phase, indicating that the effective strategies and actions had been taken. While the mean incidences will not increase even reduce due to the incidence reduction in high-risk regions but the simultaneous expansion of the high-risk areas.
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Affiliation(s)
- Yongze Song
- />School of Land Science and Technology, China University of Geosciences, Beijing, China
- />State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
| | - Yong Ge
- />State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
| | - Jinfeng Wang
- />State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- />Key Laboratory of Surveillance and Early Warning on Infectious Diseases, Chinese Center for Diseases Control and Prevention, Beijing, China
| | - Zhoupeng Ren
- />State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- />Key Laboratory of Surveillance and Early Warning on Infectious Diseases, Chinese Center for Diseases Control and Prevention, Beijing, China
- />University of Chinese Academy of Sciences, Beijing, China
| | - Yilan Liao
- />State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
| | - Junhuan Peng
- />School of Land Science and Technology, China University of Geosciences, Beijing, China
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Solvang HK, Frigessi A, Kaveh F, Riis MLH, Lüders T, Bukholm IRK, Kristensen VN, Andreassen BK. Gene expression analysis supports tumor threshold over 2.0 cm for T-category breast cancer. EURASIP J Bioinform Syst Biol 2016; 2016:6. [PMID: 26900390 PMCID: PMC4746218 DOI: 10.1186/s13637-015-0034-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/03/2014] [Accepted: 12/23/2015] [Indexed: 11/17/2022]
Abstract
Tumor size, as indicated by the T-category, is known as a strong prognostic indicator for breast cancer. It is common practice to distinguish the T1 and T2 groups at a tumor size of 2.0 cm. We investigated the 2.0-cm rule from a new point of view. Here, we try to find the optimal threshold based on the differences between the gene expression profiles of the T1 and T2 groups (as defined by the threshold). We developed a numerical algorithm to measure the overall differential gene expression between patients with smaller tumors and those with larger tumors among multiple expression datasets from different studies. We confirmed the performance of the proposed algorithm by a simulation study and then applied it to three different studies conducted at two Norwegian hospitals. We found that the maximum difference in gene expression is obtained at a threshold of 2.2–2.4 cm, and we confirmed that the optimum threshold was over 2.0 cm, as indicated by a validation study using five publicly available expression datasets. Furthermore, we observed a significant differentiation between the two threshold groups in terms of time to local recurrence for the Norwegian datasets. In addition, we performed an associated network and canonical pathway analyses for the genes differentially expressed between tumors below and above the given thresholds, 2.0 and 2.4 cm, using the Norwegian datasets. The associated network function illustrated a cellular assembly of the genes for the 2.0-cm threshold: an energy production for the 2.4-cm threshold and an enrichment in lipid metabolism based on the genes in the intersection for the 2.0- and 2.4-cm thresholds.
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Affiliation(s)
- Hiroko K Solvang
- Department of Marine Mammals, Institute of Marine Research, C. Sundts Gate 64, Bergen, 5004 Norway
| | - Arnoldo Frigessi
- Department of Biostatistics, Institute of Basic Medical Science, University of Oslo, Norway and Statistics for Innovation-(sfi)2, Oslo, Norway
| | - Fateme Kaveh
- Medical Genetics Department, Oslo University Hospital (Ullevål), Oslo, Norway
| | - Margit L H Riis
- Department of Surgery, Akershus University Hospital, Lørenskog, Norway ; Department of Molecular Biology and Laboratory Sciences (EpiGen), Institute of Clinical Medicine, Akershus University Hospital, Lørenskog, Norway
| | - Torben Lüders
- Department of Surgery, Akershus University Hospital, Lørenskog, Norway ; Department of Molecular Biology and Laboratory Sciences (EpiGen), Institute of Clinical Medicine, Akershus University Hospital, Lørenskog, Norway
| | - Ida R K Bukholm
- Department of Surgery, Akershus University Hospital, Lørenskog, Norway ; Institute of Clinical Medicine, University of Oslo, Norwegian Center of HPH Network, Oslo, Norway
| | - Vessela N Kristensen
- Department of Molecular Biology and Laboratory Sciences (EpiGen), Institute of Clinical Medicine, Akershus University Hospital, Lørenskog, Norway ; Department of Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, Oslo, Norway
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Roland M, Tjardes T, Otchwemah R, Bouillon B, Diebels S. An optimization algorithm for individualized biomechanical analysis and simulation of tibia fractures. J Biomech 2015; 48:1119-24. [PMID: 25698239 DOI: 10.1016/j.jbiomech.2015.01.015] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2014] [Revised: 01/13/2015] [Accepted: 01/14/2015] [Indexed: 12/01/2022]
Abstract
An algorithmic strategy to determine the minimal fusion area of a tibia pseudarthrosis to achieve mechanical stability is presented. For this purpose, a workflow capable for implementation into clinical routine workup of tibia pseudarthrosis was developed using visual computing algorithms for image segmentation, that is a coarsening protocol to reduce computational effort resulting in an individualized volume-mesh based on computed tomography data. An algorithm detecting the minimal amount of fracture union necessary to allow physiological loading without subjecting the implant to stresses and strains that might result in implant failure is developed. The feasibility of the algorithm in terms of computational effort is demonstrated. Numerical finite element simulations show that the minimal fusion area of a tibia pseudarthrosis can be less than 90% of the full circumferential area given a defined maximal von Mises stress in the implant of 80% of the total stress arising in a complete pseudarthrosis of the tibia.
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Affiliation(s)
- M Roland
- Saarland University, Chair of Applied Mechanics, D-66123 Saarbrücken, Germany.
| | - T Tjardes
- Department of Trauma Surgery, Orthopedic Surgery and Sportstraumatology Cologne Merheim Medical Center, Chair of University of Witten/Herdecke, Ostmerheimerstr. 200, D-51109 Cologne, Germany.
| | - R Otchwemah
- Department of Trauma Surgery, Orthopedic Surgery and Sportstraumatology Cologne Merheim Medical Center, Chair of University of Witten/Herdecke, Ostmerheimerstr. 200, D-51109 Cologne, Germany
| | - B Bouillon
- Department of Trauma Surgery, Orthopedic Surgery and Sportstraumatology Cologne Merheim Medical Center, Chair of University of Witten/Herdecke, Ostmerheimerstr. 200, D-51109 Cologne, Germany
| | - S Diebels
- Saarland University, Chair of Applied Mechanics, D-66123 Saarbrücken, Germany
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