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Analyzing key users’ behavior trends in volunteer-based networks

Journal of Big Data 2025年第1期12卷 1-26页

作者： Piterman, Nofar Makov, Tamar Fire, Michael Department of Software and Information Systems Engineering Ben-Gurion University Beersheba Israel Department of Management Guilford Glazer Faculty of Business and Management Ben-Gurion University Beersheba Israel

The use of online social platforms and networks has surged over the past decade and continues to grow in popularity. In many social networks, volunteers play a central role, and their behavior in volunteer-based networks has been studied extensively. Here, we explore the development of volunteer-based social networks, focusing on the activities and behaviors of the most influential users. We introduce two innovative algorithms: the first outlines the evolution of volunteers’ behavior patterns over time, while the second employs machine learning techniques to forecast their future behavior, including whether they will remain active donors or become mainly recipients, and vice-versa. These algorithms allowed us to analyze the factors that significantly influence behavior predictions. We utilized data from over 2.4 million users on a peer-to-peer food-sharing online platform. Using our algorithm, we identified four key user behavior patterns over time to evaluate our algorithms. Moreover, we succeeded in forecasting future active donor key users and predicting the key users that would change their behavior toward donors, with an accuracy of up to 89.6%. The insights gained from our analysis not only shed light on the behavioral patterns of key users in volunteer-driven networks but also highlight the potential of machine learning in enhancing community engagement and building strategies for the future. © The Author(s) 2025.

关键词： Tweets

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Informative Path Planning for Nano-Surgical Robot Adaptive Drug Delivery 59

Informative Path Planning for Nano-Surgical Robot Adaptive D...

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59th Annual Conference on Information Sciences and systems, CISS 2025

作者： Alla, Sujatha Bheesetty, Nagesh Park, Hyoshin John Old Dominion University Engineering Management and Systems Engineering Norfolk United States Old Dominion University Department of Data Science Norfolk United States

ISBN: (纸本)9798331513269

Nano-surgical robots are transforming precision medicine by enabling minimally invasive procedures at a microscopic scale. Informative path planning for these robots is critical for ensuring safety, efficiency, and high precision in navigating sensitive anatomical and uncertain environments. This study adapts the RRT*-IT (Rapidly Exploring Random Tree-Information Theoretic) algorithm, traditionally utilized in robotics, to address the unique challenges faced by nano-surgical robots. The proposed methodology offers significant advancements in sub-millimeter precision navigation, reducing collision risks and enhancing procedural efficiency. The adaptive path planning system optimizes a trade-off between collision avoidance and information gain while minimizing surgical trajectory. The algorithm dynamically adjusts paths by incorporating probabilistic models for obstacle detection and uncertainty reduction, accommodating changes in tissue density, obstacle dimensions, and operational constraints. The performance of proposed approach are validated through various scenarios involving dynamic uncertain environments. The findings hold promise for broader applications in automated drug delivery setting a foundation for intelligent nano-robotic systems in healthcare. © 2025 IEEE.

关键词： Robot programming

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Improving Power Response of Single-Controllable VSG-Based Active Distribution Network

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CSEE Journal of Power and Energy systems 2025年第1期11卷 102-114页

作者： Shah Fahad Arman Goudarzi Pierluigi Siano Ji Xiang Department of Electrical and Computer Engineering Missouri University of Science and TechnologyUSA University of Victoria Canada Department of Management&Innovation Systems University of SalernoItaly Department of Electrical and Electronic Engineering Science University of JohannesburgJohannesburg2006South Africa National Laboratory of Industrial Control Technology College of Electrical EngineeringZhejiang UniversityHangzhou310058China

Controlling an active distribution network(ADN)from a single PCC has been advantageous for improving the performance of coordinated Intermittent RESs(IRESs).Recent studies have proposed a constant PQ regulation approach at the PCC of ADNs using coordination of non-MPPT based ***,due to the intermittent nature of DGs coupled with PCC through uni-directional broadcast communication,the PCC becomes vulnerable to transient *** address this challenge,this study first presents a detailed mathematical model of an ADN from the perspective of PCC regulation to realize rigidness of PCC against ***,an H_(∞)controller is formulated and employed to achieve optimal performance against disturbances,consequently,ensuring the least oscillations during transients at ***,an eigenvalue analysis is presented to analyze convergence speed limitations of the newly derived system ***,simulation results show the proposed method offers superior performance as compared to the state-of-the-art methods.

关键词： Cooperative control of DGs hierarchical control system high penetration of intermittent RESs H_(∞)control optimal control in power systems single-controllable active distribution networks

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Ontology-Based Functional Analysis of an Aerospace System

Ontology-Based Functional Analysis of an Aerospace System

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Johnson, Hamilton E. Sureshkumar, Mayuranath Kannan, Hanumanthrao Thomas, L. Dale Mechanical and Aerospace Engineering Department University of Alabama in Huntsville HuntsvilleAL35899 United States Industrial and Systems Engineering and Engineering Management Department University of Alabama in Huntsville HuntsvilleAL35899 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

systems engineering often employs "-ilities", such as affordability, to represent various aspects of a system. While these "-ilities" are widely used, exact definitions for the terms can vary between individuals. While most definitions of affordability include some cost notion, only a subset balance cost against a benefit. Additionally, some definitions treat affordability as a binary constraint, a system either is affordable or it is not. Others define it in more continuous terms which could be treated as an objective to be maximized. While multiple definitions can be readily observed from different sources, the degree to which definitions for a concept such as affordability differs within a single organization has not been thoroughly studied. This paper presents results from a survey completed by 172 employees of NASA Marshall Space Flight Center. Definitions of affordability from each participant are categorized ac-cording to how cost and benefit notions are used in the definition. Eight different categories of definitions were observed;however, 86 of the 172 definitions were categorized as involving a cost constraint with no clear benefit component. Due to the variety of definitions of affordability observed, practitioners should be cautious about assuming what stakeholders and fellow decision makers mean when using the term affordability. The use of affordability definitions categorized as involving only a cost constraint may result in the elimination of alternatives in a design space, rather than driving decision makers to continuously improve affordability. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： NASA

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Genetic Algorithms with Neural Cost Predictor for Solving Hierarchical Vehicle Routing Problems

Genetic Algorithms with Neural Cost Predictor for Solving Hi...

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作者： Sobhanan, Abhay Park, Junyoung Park, Jinkyoo Kwon, Changhyun Department of Industrial and Management Systems Engineering University of South Florida TampaFL33620 United States Department of Industrial and Systems Engineering KAIST Daejeon34141 Korea Republic of OMELET Daejeon34141 Korea Republic of

When vehicle routing decisions are intertwined with higher-level decisions, the resulting optimization problems pose significant challenges for computation. Examples are the multi-depot vehicle routing problem (MDVRP), where customers are assigned to depots before delivery, and the capacitated location routing problem (CLRP), where the locations of depots should be determined first. A simple and straightforward approach for such hierarchical problems would be to separate the higher-level decisions from the complicated vehicle routing decisions. For each higher-level decision candidate, we may evaluate the underlying vehicle routing problems to assess the candidate. As this approach requires solving vehicle routing problems multiple times, it has been regarded as impractical in most cases. We propose a novel deep learning-based approach called the genetic algorithm with neural cost predictor to tackle the challenge and simplify algorithm developments. For each higher-level decision candidate, we predict the objective function values of the underlying vehicle routing problems using a pretrained graph neural network without actually solving the routing problems. In particular, our proposed neural network learns the objective values of the HGS-CVRP open-source package that solves capacitated vehicle routing problems. Our numerical experiments show that this simplified approach is effective and efficient in generating high-quality solutions for both MDVRP and CLRP and that it has the potential to expedite algorithm developments for complicated hierarchical problems. We provide computational results evaluated in the standard benchmark instances used in the literature. © 2024 INFORMS.

关键词： Genetic algorithms

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A Comparative Study of Optimized-LSTM Models Using Tree-Structured Parzen Estimator for Traffic Flow Forecasting in Intelligent Transportation

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Computers, Materials & Continua 2025年第5期83卷 3369-3388页

作者： Hamza Murad Khan Anwar Khan Santos Gracia Villar Luis Alonso DzulLopez Abdulaziz Almaleh Abdullah M.Al-Qahtani Department of Electronics University of PeshawarPeshawar25120KPKPakistan Department of Project Management(L.A.D.L) Higher Polytechnic School(S.G.V)Universidad Europea del AtlanticoSantander39011Spain Faculty of Engineering Universidad Internacional IberoamericanaCampeche24560Mexico Faculty of Engineering Universidade Internacional do CuanzaCuitoEN250BieAngola Department of Project Management Universidad Internacional IberoamericanaCampeche24560Mexico Department of Project Management Department of Management SciencesUniversid de La RomanaLa Romana22000Dominican Reublic Information Systems Department College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia Department of Electrical and Electronic Engineering College of Engineering&Computer ScienceJazan UniversityJazan45142Saudi Arabia

Traffic forecasting with high precision aids Intelligent Transport systems(ITS)in formulating and optimizing traffic management *** algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational *** address this problem,this paper uses the Tree-structured Parzen Estimator(TPE)to tune the hyperparameters of the Long Short-term Memory(LSTM)deep learning *** Tree-structured Parzen Estimator(TPE)uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad *** ensures fast convergence in tuning the hyperparameter values in the deep learning model for performing prediction while still maintaining a certain degree of *** also overcomes the problem of converging to local optima and avoids timeconsuming random search and,therefore,avoids high computational complexity in prediction *** proposed scheme first performs data smoothing and normalization on the input data,which is then fed to the input of the TPE for tuning the *** traffic data is then input to the LSTM model with tuned parameters to perform the traffic *** three optimizers:Adaptive Moment Estimation(Adam),Root Mean Square Propagation(RMSProp),and Stochastic Gradient Descend with Momentum(SGDM)are also evaluated for accuracy prediction and the best optimizer is then chosen for final traffic prediction in TPE-LSTM *** results verify the effectiveness of the proposed model in terms of accuracy of prediction over the benchmark schemes.

关键词： Short-term traffic prediction sequential time series prediction TPE tree-structured parzen estimator LSTM hyperparameter tuning hybrid prediction model

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Location-Allocation of a Biorefinery Based on Linguistic Mixed Integer Nonlinear Mathematical Modeling

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IEEE Access 2025年 13卷 83373-83393页

作者： Fan, Lei Karimi, Negar Reza Feylizadeh, Mohammad Khorshidnia, Mohsen Li, Yongzheng Jiangsu University of Science and Technology School of Economics and Management Jiangsu Zhenjiang212100 China Isfahan University of Technology Department of Industrial and Systems Engineering Isfahan*** Iran Islamic Azad University Shiraz Branch Department of Industrial Engineering Shiraz*** Iran Islamic Azad University Najafabad Branch Department of Industrial Engineering Najafabad*** Iran Jiangsu University of Science of Technology School of Naval Architecture and Ocean Engineering Jiangsu Zhenjiang212100 China

Significant research is currently being conducted to explore renewable energy sources, such as biomass fuel, in response to recent fluctuations in fossil fuel prices and environmental concerns. However, utilizing biomass energy poses challenges due to the widespread distribution of feedstock and the need to transport multiple low-energy-density materials to conversion facilities. In this investigation, a Linguistics Mixed Integer Nonlinear Programming (LMINLP) approach with Z-number computations was employed to analyze the optimal allocation of locations for a bio-refinery supply chain network. This proposed method significantly impacted the product process, time value for plutocrats, installation investment costs, and transportation network. This research aims to construct an LMINLP model to determine the optimal position of a single installation within a bio-refinery. The primary goal was to make well-informed investment decisions regarding technologies, facilities, capacities, and site selections. A firefly algorithm incorporating a piecewise linearization approach was utilized to solve the model and achieve global optimality. The model’s effectiveness was demonstrated through numerical examples, including those from the ethanol industry. © 2013 IEEE.

关键词： Mixed-integer linear programming

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Optimal coordination of Production, Logistics, and Maintenance 4.0: A framework for Multi-Site Stamping Plants 6

Optimal coordination of Production, Logistics, and Maintenan...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Zerrad, Raoua Diaz, Kamar Mechkouri, Meriem Hayani Sefiani, Naoufal Department of Mechanical Engineering Research Laboratory in Engineering Innovation and Management of Industrial Systems Faculty of Sciences and Techniques Abdelmalek Essaadi University Tangier Morocco Department of Industrial Engineering and Maintenance Computer Engineering Production and Maintenance Laboratory University of Lorraine Metz France

This paper presents a comprehensive framework for optimal coordination of production, logistics, and maintenance in multi-site stamping plants within the context of Industry 4.0. By integrating key technologies such as the Internet of Things, cloud computing and big data analytics, we developed a system that would enable real-time data sharing, predictive analytics, and intelligent decision-making. This framework aims to enhance operational efficiency, reduce downtime, and improve service rate. Additionally, our approach aims to optimize spare parts inventory management across different sites, minimizing inventory costs and lead times by ensuring timely availability and efficient sourcing of spare parts. © 2024 The Authors. Published by Elsevier B.V.

关键词： Inventory control

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Multi-period hub network design from a dual perspective: An integrated approach considering congestion, demand uncertainty, and service quality optimization

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European Journal of Operational Research 2025年第1期326卷 78-95页

作者： Bayram, Vedat Aydoğan, Çiya Kargar, Kamyar University of Kent Kent Business School Department of Analytics Operations and Systems Centre for Logistics and Sustainability Analytics Canterbury Kent United Kingdom Department of Industrial Engineering TED University Ankara Turkey Department of Industrial Engineering Middle East Technical University Ankara Turkey Lancaster University Management School Department of Management Science Lancaster LA1 4YX United Kingdom

This study introduces a hub network design problem that considers three key factors: congestion, demand uncertainty, and multi-periodicity. Unlike classical models, which tend to address these factors separately, our model considers them simultaneously, providing a more realistic representation of hub network design challenges. Our model also incorporates service level considerations of network users, extending beyond the focus on transportation costs. Service quality is evaluated using two measures: travel time and the number of hubs visited during travel. Moreover, our model allows for adjustments in capacity levels and network structure throughout the planning horizon, adding a dynamic and realistic aspect to the problem setting. The inherent nonlinear nonconvex integer programming problem is reformulated into a mixed-integer second-order cone programming (SOCP) problem. To manage the model's complexity, we propose an exact solution algorithm based on Benders decomposition, where the sub-problems are solved using a column generation technique. The efficacy of the solution approach is demonstrated through extensive computational experiments. Additionally, we discuss the benefits of each considered feature in terms of transportation costs and their impact on network structure, providing insights for the field. © 2025 The Authors

关键词： Travel time

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Tomography of Quantum States From Structured Measurements via Quantum-Aware Transformer

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IEEE Transactions on Cybernetics 2025年第6期55卷 2571-2582页

作者： Ma, Hailan Sun, Zhenhong Dong, Daoyi Chen, Chunlin Rabitz, Herschel University of New South Wales School of Engineering and Technology CanberraACT2600 Australia The Australian National University School of Engineering CanberraACT2601 Australia University of Technology Sydney Australian Artificial Intelligence Institute Faculty of Engineering and Information Technology SydneyNSW2007 Australia Nanjing University School of Management and Engineering Department of Control and Systems Engineering Nanjing210093 China Princeton University Department of Chemistry PrincetonNJ08544 United States

Quantum state tomography (QST) is the process of reconstructing the state of a quantum system (mathematically described as a density matrix) through a series of different measurements, which can be solved by learning a parameterized function to translate experimentally measured statistics into physical density matrices. However, the specific structure of quantum measurements for characterizing a quantum state has been neglected in previous work. In this article, we explore the similarity between highly structured sentences in natural language and intrinsically structured measurements in QST. To fully leverage the intrinsic quantum characteristics involved in QST, we design a quantum-aware transformer (QAT) model to capture the complex relationship between measured frequencies and density matrices. In particular, we query quantum operators in the architecture to facilitate informative representations of quantum data and integrate the Bures distance into the loss function to evaluate quantum state fidelity, thereby enabling the reconstruction of quantum states from measured data with high fidelity. Extensive simulations and experiments (on IBM quantum computers) demonstrate the superiority of the QAT in reconstructing quantum states with favorable robustness against experimental noise. © 2013 IEEE.

关键词： Quantum computers

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