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arXiv 2021年

作者： Marcelino, Carolina Gil Leite, G.M.C. Delgado, C.A.D.M. De Oliveira, L.B. Wanner, E.F. Jiménez-Fernández, S. Salcedo-Sanz, S. Department of Signal Processing and Communications Universidad de Alcalá Spain Institute of Computing Federal University of Rio de Janeiro Brazil Post-Graduate Program in Systems Engineering and Computer Science Federal University of Rio de Janeiro Brazil Computing Department Federal Center of Technology Education of Minas Gerais Brazil

This paper tackles the short-term hydro-power unit commitment problem in a multi-reservoir system - a cascade-based operation scenario. For this, we propose a new mathematical modelling in which the goal is to maximize the total energy production of the hydro-power plant in a sub-daily operation, and, simultaneously, to maximize the total water content (volume) of reservoirs. For solving the problem, we discuss the Multi-objective Evolutionary Swarm Hybridization (MESH) algorithm, a recently proposed multiobjective swarm intelligence-based optimization method which has obtained very competitive results when compared to existing evolutionary algorithms in specific applications. The MESH approach has been applied to find the optimal water discharge and the power produced at the maximum reservoir volume for all possible combinations of turbines in a hydro-power plant. The performance of MESH has been compared with that of well-known evolutionary approaches such as NSGA-II, NSGA-III, SPEA2, and MOEA/D in a realistic problem considering data from a hydro-power energy system with two cascaded hydro-power plants in Brazil. Results indicate that MESH showed a superior performance than alternative multi-objective approaches in terms of efficiency and accuracy, providing a profit of $412,500 per month in a projection analysis carried out. © 2021, CC BY-NC-ND.

关键词： Multiobjective optimization

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The Importance of Exploration and Exploitation Innovation in Emerging Economies

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Journal of Open Innovation: Technology, Market, and Complexity 2022年第3期8卷

作者： Moreira, António Carrizo Navaia, Eurico Colarinho Ribau, Cláudia Department of Economics Management Industrial Engineering and Tourism University of Aveiro Aveiro 3810-193 Portugal GOVCOPP—Research Unit on Governance Competitiveness and Public Policies University of Aveiro Aveiro 3810-193 Portugal INESCTEC—Institute for Systems and Computer Engineering Technology and Science Porto 4200-465 Portugal Faculdade de Ciências Sociais e Humanidades Universidade Zambeze Beira 369 Sofala Mozambique Instituto Superior de Contabilidade e Administração University of Aveiro Aveiro 3810-193 Portugal

Innovation capabilities are among the main driving sources of export performance;however, the literature on how exploration and exploitation innovation influence export performance in the context of emerging economies is scarce. As such, the purpose of this paper is to assess the relationship between innovation capabilities and export performance, mediated by exploration and exploitation innovation. For that, an online questionnaire was implemented surveying 250 Mozambican Small and Medium Enterprises (SMEs) in the agro-industry, agro-processing, and fishing sectors. Based on a Partial Least Squares Structural Equation Model (PLS-SEM) relating innovation capabilities, exploitation, and exploration innovation to export performance, it is possible to state that innovation capabilities influence the export performance of SMEs in this emerging country. Moreover, exploration and exploitation innovation exert important mediation effects, the latter being more prevalent than the former. The results constitute a major contribution as it is possible to claim that, in the context of emerging economies, Mozambican SMEs have managed to enhance their export performance, based on innovation capabilities, but, also, to master their ambidexterity with exploiting capabilities, normally linked to their current technological trajectories, being more prevalent that exploitation capabilities, which are focused on the pursuit of radical innovation. © 2022 by the authors.

关键词： exploitation innovation exploration innovation export performance innovation capabilities internationalization Mozambique PLS-SEM SMEs

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Estimation of obesity levels based on computational intelligence

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Informatics in Medicine Unlocked 2020年 21卷

作者： Cervantes, Rodolfo Cañas Palacio, Ubaldo Martinez Department of Computer Science and Electronics Universidad de la Costa CUC. Faculty Teacher Systems Engineering Program Colombia

Obesity is a worldwide disease that affects people of all ages and gender;in consequence, researchers have made great efforts to identify factors that cause it early. In this study, an intelligent method is created, based on supervised and unsupervised techniques of data mining such as Simple K-Means, Decision Trees (DT), and Support Vector Machines (SVM) to detect obesity levels and help people and health professionals to have a healthier lifestyle against this global epidemic. In this research the primary source of collection was from students 18 and 25 years old at institutions in the countries of Colombia, Mexico, and Peru. The study takes a dataset relating to the main causes of obesity, based on the aim to reference high caloric intake, a decrease of energy expenditure due to the lack of physical activity, alimentary disorders, genetics, socioeconomic factors, and/or anxiety and depression. In the selected dataset, 178 students participated in the study, 81 male and 97 female. Using algorithms including Decision Tree, Support Vector Machine (SVM), and Simple K-Means, the results show a relevant tool to perform a comparative analysis among the mentioned algorithms. © 2020 The Authors

关键词： Decision trees Obesity Simple k-means Support vector machines

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A machine learning approach for classifying healthy and infarcted patients using heart rate variabilities derived vector magnitude

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Healthcare Analytics 2022年 2卷 100121-100121页

作者： Agrawal, Rupesh Kumar Sewani, Rahim R. Delen, Dursun Benjamin, Bruce Department of Informatics Northern Kentucky University Highland Heights 41099 KY United States Department of Biomedical Sciences Oklahoma State University Center for Health Sciences Tulsa 74107 OK United States Center for Health Systems Innovation Department of Management Science and Information Sciences Oklahoma State University Tulsa 74106 OK United States Faculty of Engineering and Natural Sciences Istinye University Istanbul Turkey

According to the World Health Organization, Heart disease is the number one killer of humans, with coronary heart disease (CHD) being the most common type of heart disease. CHD leads to myocardial ischemia (MI) or infarction. Several clinical tests are available to assist physicians in diagnosing MI or infarcted (unhealthy) patients. However, diagnostic tests can be costly, invasive, and unreliable in identifying patients with declining coronary health conditions. This study investigated the application of Machine Learning (ML) techniques on the Vector Magnitude (VM) data of heart signals generated via Vectorcardiography (VCG) to classify unhealthy patients from healthy patients. Patients with MI, a CHD, are identified as ill patients. Three machine-learning classification techniques: Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Decision Trees (DT), were applied to classify healthy and unhealthy (MI) patients. The heart signal dataset was acquired from the Physikalisch-Technische Bundesanstalt (PTB) Diagnostic electrocardiogram (ECG) Database. A 10-fold cross-validation sampling method was used to improve the predictability of the sample. Results from ML techniques produced high classification sensitivity, specificity, and accuracy. ML analysis findings indicated that DT is the best predictor for classification accuracy, followed by SVM and ANN. The future study goal is to expand the study with forward-looking data and the right sample size for clinical validity and support the high accuracy results. © 2022 The Author(s)

关键词： Classification Coronary disease Heartrate variability k-fold cross-validation

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Information Relaxation and A Duality-Driven Algorithm for Stochastic Dynamic programs

arXiv

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arXiv 2020年

作者： Chen, Nan Ma, Xiang Liu, Yanchu Yu, Wei Department of Systems Engineering and Engineering Management Chinese University of Hong Kong Shatin Hong Kong College Sun Yat-sen University Guangzhou China 1 Wallich Street #20-01 Guoco Tower Singapore

We use the technique of information relaxation to develop a duality-driven iterative approach to obtaining and improving confidence interval estimates for the true value of finite-horizon stochastic dynamic programming problems. We show that the sequence of dual value estimates yielded from the proposed approach in principle monotonically converges to the true value function in a finite number of dual iterations. Aiming to overcome the curse of dimensionality in various applications, we also introduce a regression-based Monte Carlo algorithm for implementation. The new approach can be used not only to assess the quality of heuristic policies, but also to improve them if we find that their duality gap is large. We obtain the convergence rate of our Monte Carlo method in terms of the amounts of both basis functions and the sampled states. Finally, we demonstrate the effectiveness of our method in an optimal order execution problem with market friction and in an inventory management problem in the presence of lost sale and lead time. Both examples are well known in the literature to be difficult to solve for optimality. The experiments show that our method can significantly improve the heuristics suggested in the literature and obtain new policies with a satisfactory performance guarantee. Copyright © 2020, The Authors. All rights reserved.

关键词： Inventory control

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Optimising phase change materials for ventilated building components in sustainable building design: a comprehensive review

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Energy and Buildings 2025年

作者： Farhan Lafta Rashid Mudhar A. Al-Obaidi Wadhah Amer Hatem Najah M.L. Al Maimuri Arman Ameen Shabbir Ahmad Ephraim Bonah Agyekum Saif Ali Kadhim Karrar A. Hammoodi Petroleum Engineering Department College of Engineering University of Kerbala Karbala 56001 Iraq Technical Instructor Training Institute Middle Technical University Baghdad 10074 Iraq Middle Technical University Technical Institute of Baquba Baquba Diyala 32001 Iraq Building and Construction Techniques Engineering Department College of Engineering and Engineering Techniques Al-Mustaqbal University Hillah Babylon 51001 Iraq Department of Building Engineering Energy Systems and Sustainability Science University of Gävle 801 76 Gävle Sweden Graduate Program of Ocean Engineering School of Engineering Universidade Federal do Rio Grande Rio Grande Italia Avenue km 8 96201-900 Brazil Institute of Geophysics and Geomatics China University of Geosciences Wuhan 430074 China Department of Nuclear and Renewable Energy Ural Federal University Named after the First President of Russia Boris Yeltsin 19 Mira Street Ekaterinburg 620002 Russia Western Caspian University 31 Istiglaliyyat Street AZ1001 Baku Azerbaijan Istanbul Okan University Tuzla Campus 34959 Tuzla Istanbul Turkey Mechanical Engineering Department University of Technology – Iraq Baghdad Iraq College of Engineering University of Al Maarif Al Anbar Iraq

The rise in energy-efficient, sustainable building operations necessitates the development of advanced innovations that reduce heating and cooling demands while maintaining comfortable indoor thermal conditions. Phase change materials (PCMs) demonstrate significant potential as a stabilization mechanism through latent heat storage for managing residential indoor temperatures; however, their integration into systems requires further optimisation. This review examines PCM-assisted ventilation technologies—specifically ventilated façades, roofs, and windows—highlighting their energy performance, which ranges from 7.7 % to 32.8 %, and their capacity to reduce peak temperatures by 2.5 °C to 7.02 °C. These improvements are influenced by PCM thickness of 15–35  mm and melting temperatures of 15–37 °C. The combination of PCM-enhanced hybrid systems with natural or mechanical ventilation has shown greater operational effectiveness, particularly in hot climates and when integrated with adaptive control systems to achieve optimal performance. Despite their benefits, widespread adoption of PCM technologies is hindered by high material costs, hysteresis effects, and limited discharge cycle efficiency. Nevertheless, PCM-enhanced ventilation systems have proven capable of supporting sustainable, low-energy buildings. Continued research is needed to develop cost-effective design strategies, automated control mechanisms, and climate-responsive optimisations to fully realize their potential.

关键词： Energy-efficient ventilation systems Phase change materials (PCMs) Residential buildings Sustainable thermal management

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On complementary duals both fixed points

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Bulletin of the Kyushu Institute of Technology - Pure and Applied Mathematics 2020年第67期2020卷 1-28页

作者： Iwamoto, S. Kimura, Y. Fujita, T. Kyushu University Fukuoka813-0012 Japan Department of Management Science and Engineering Faculty of Systems Science and Technology Akita Prefectural University Yurihonjo Akita015-0055 Japan Department of Basic Sciences Faculty of Engineering Kyushu Institute of Technology Tobata Kitakyushu804-8550 Japan

We consider a quadratic minimization (primal) problem with both ﬁxed endpoints and its associated maximization (dual) problem from a view point of complementarity. We focus on a pair of linear terms, which generates the respective quadratic functions (sums of squares) through an elementary inequality. We show that a complementary identity plays a fundamental part in establishing a dual relation between primal and dual. The identity produces the pair with an equality condition. The condition turns out to be a linear system of 2n-equation in 2n-variable. The system yields a couple of solutions, one is a minimum solution and the other is a maximum one. In the n-variable pair, both the solutions turn out to be complementary. The optimal solution is characterized by the backward Fibonacci sequence. The duality is enhanced through conjugate function. The solution is also given by dynamic programming. Thus Fibonacci complementary duality is established through the complementary identity approach. © 2020, Kyushu Institute of Technology. All rights reserved.

关键词： Linear systems

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ENIGMA-Meditation: Worldwide Consortium for Neuroscientific Investigations of Meditation Practices

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Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 2025年第4期10卷 425-436页

作者： Ganesan, Saampras Barrios, Fernando A. Batta, Ishaan Bauer, Clemens C.C. Braver, Todd S. Brewer, Judson A. Brown, Kirk Warren Cahn, Rael Cain, Joshua A. Calhoun, Vince D. Cao, Lei Chetelat, Gaël Ching, Christopher R.K. Creswell, J. David Dagnino, Paulina Clara Davanger, Svend Davidson, Richard J. Deco, Gustavo Dutcher, Janine M. Escrichs, Anira Eyler, Lisa T. Fani, Negar Farb, Norman A.S. Fialoke, Suruchi Fresco, David M. Garg, Rahul Garland, Eric L. Goldin, Philippe Hafeman, Danella M. Jahanshad, Neda Kang, Yoona Khalsa, Sahib S. Kirlic, Namik Lazar, Sara W. Lutz, Antoine McDermott, Timothy J. Pagnoni, Giuseppe Piguet, Camille Prakash, Ruchika S. Rahrig, Hadley Reggente, Nicco Saccaro, Luigi F. Sacchet, Matthew D. Siegle, Greg J. Tang, Yi-Yuan Thomopoulos, Sophia I. Thompson, Paul M. Torske, Alyssa Treves, Isaac N. Tripathi, Vaibhav Tsuchiyagaito, Aki Turner, Matthew D. Vago, David R. Valk, Sofie Zeidan, Fadel Zalesky, Andrew Turner, Jessica A. King, Anthony P. Department of Biomedical Engineering The University of Melbourne Carlton VIC Australia Contemplative Studies Centre Melbourne School of Psychological Sciences The University of Melbourne Melbourne VIC Australia Systems Lab of Neuroscience Neuropsychiatry and Neuroengineering The University of Melbourne Parkville VIC Australia Universidad Nacional Autónoma de México Instituto de Neurobiolgía México Querétaro Mexico Center for Translational Research in Neuroimaging and Data Science: Georgia State University Georgia Institute of Technology and Emory University Atlanta GA United States Department of Psychology Northeastern University Boston Massachusetts Brain and Cognitive Science McGovern Institute for Brain Research Massachusetts Institute of Technology Cambridge Massachusetts Department of Psychological and Brain Sciences Washington University St. Louis Missouri Department of Behavioral and Social Sciences Brown University School of Public Health Providence Rhode Island Department of Psychology Carnegie Mellon University Pittsburgh PA United States University of Southern California Department of Psychiatry & Behavioral Sciences Los Angeles California University of Southern California Center for Mindfulness Science Los Angeles California Institute for Advanced Consciousness Studies Santa Monica California Department of Psychiatry and Behavioral Health The Ohio State University College of Medicine Columbus OH United States Normandie University Université de Caen Normandie INSERM U1237 Neuropresage Team Cyceron Caen France Imaging Genetics Center Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine of the University of Southern California Los Angeles California Desert-Pacific Mental Illness Research Education and Clinical Center VA San Diego Healthcare System San Diego California Computational Neuroscience Group Center for Brain and Cognition Department of Information and Communication Technologies Universitat Pompeu Fa

Meditation is a family of ancient and contemporary contemplative mind-body practices that can modulate psychological processes, awareness, and mental states. Over the last 40 years, clinical science has manualized meditation practices and designed various meditation interventions that have shown therapeutic efficacy for disorders including depression, pain, addiction, and anxiety. Over the past decade, neuroimaging has been used to examine the neuroscientific basis of meditation practices, effects, states, and outcomes for clinical and nonclinical populations. However, the generalizability and replicability of current neuroscientific models of meditation have not yet been established, because they are largely based on small datasets entrenched with heterogeneity along several domains of meditation (e.g., practice types, meditation experience, clinical disorder targeted), experimental design, and neuroimaging methods (e.g., preprocessing, analysis, task-based, resting-state, structural magnetic resonance imaging). These limitations have precluded a nuanced and rigorous neuroscientific phenotyping of meditation practices and their potential benefits. Here, we present ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis)–Meditation, the first worldwide collaborative consortium for neuroscientific investigations of meditation practices. ENIGMA-Meditation will enable systematic meta- and mega-analyses of globally distributed neuroimaging datasets of meditation using shared, standardized neuroimaging methods and tools to improve statistical power and generalizability. Through this powerful collaborative framework, existing neuroscientific accounts of meditation practices can be extended to generate novel and rigorous neuroscientific insights that account for multidomain heterogeneity. ENIGMA-Meditation will inform neuroscientific mechanisms that underlie therapeutic action of meditation practices on psychological and cognitive attributes, thereby advancing the f

关键词： Consortium ENIGMA Magnetic resonance imaging (MRI) Meditation Mindfulness Neuroimaging

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A Preliminary Study of Fintech Industry: A Two-Stage Clustering Analysis for Customer Segmentation in the B2B Setting

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Journal of Business-to-Business Marketing 2019年第2期26卷 197-207页

作者： Alireza Sheikh Tohid Ghanbarpour Davoud Gholamiangonabadi Department of Management Science and Technology Amirkabir University of Technology Tehran Iran Department of Industrial Engineering and Management Systems Amirkabir University of Technology Tehran Iran Department of Industrial Engineering and Management Systems Amirkabir University of Technology Tehran Iran

ABSTRACTABSTRACTThis practitioner note proposes a new approach considering two-stage clustering and LRFMP model (Length, Recency, Frequency, Monetary and Periodicity) simultaneously for customer segmentation and behavior analysis and applies it among the Iranian Fintech companies. In this practitioner note, the K-means clustering algorithm and LRFMP model are combined in the customer segmentation process. After initial clustering, for a better understanding of valuable customers, additional clustering is implemented in segments that needed further investigation. This approach contributes to a better interpretation of different customer segments. Customer segments, consisting of 23524 business customers are analysed based on their characteristics and appropriate strategies are recommended accordingly. The first stage clustering result shows that customers are best segmented into four groups. The first and fourth segments are clustered again and the final 11 groups of customers are determined. This note provides a systematic and practical approach for researchers and practitioners for segmentation, interpretation, and targeting of customers especially in the B2B setting and the Fintech industry and helps managers to make effective marketing strategies and enhance customer relationship and marketing intelligence.

关键词： Customer Segmentation RFM Model B2B Fintech Industry Cluster Analysis

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On Network Flow Maximization via Multihop Backhauling and UAVs: An Integer programming Approach

On Network Flow Maximization via Multihop Backhauling and UA...

引用

IEEE Vehicular Technology Conference

作者： Abdullateef Almohamad Mazen O. Hasna Tamer Khattab Mohamed Haouari Electrical Engineering Department Qatar University Engineering Management and Systems Engineering Old Dominion University

Although small cells (SC) densification approach plays a prominent role in achieving the data rate and coverage requirements in 5G networks, it poses serious challenges concerning the flexible and cost efficient back-hauling solutions. The traditional terrestrial backhauling hubs are subject to limited line of sight probabilities in such dense networks. Having this challenge in hand, and recognizing the increasing interest in the unmanned aerial vehicle (UAV) enabled communication systems, we address the problem of wireless multihop backhauling of SCs using UAV hubs. We present two linear optimization programs that optimize the SC-UAV association and the SC-SC formation in order to maximize the total backhaul flow. Some practical constraints are considered, such as backhaul reliability, association criteria, SC relaying capacity and the number of available links at each SC. Numerical simulations show that the approach allowing partial demand fulfillment of the SCs outperforms the binary one in terms of accumulated rate and the percentage of associated SCs, even at low number of maximum hops and links allowed.

关键词： Unmanned aerial vehicles Spread spectrum communication Wireless communication Base stations Logic gates Signal to noise ratio Reliability

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