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Analysis of Supply Chain Sustainability Drivers in the Oil and Gas Industry under Covid-19 Pandemic

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Journal of systems Science and systems engineering 2024年第2期33卷 131-161页

作者： Sujan Piya department of Industrial engineering and engineering management University of SharjahSharjahUnited Arab Emirates

The supply chain of many industries,including Oil and Gas,was significantly affected by the disruption caused by the Covid ***,in turn,had a knock-on effect on other industries around the *** the impact of the disruption posed a major challenge for the *** study contributes to the existing literature by identifying and analyzing the most significant drivers that affected the sustainability of the Oil and Gas supply chain during the Covid *** drivers were identified based on an extensive literature review and a survey conducted with experts working in the Oil and Gas ***-criteria decision-making methodologies were used to analyze these *** analysis from the fuzzy analytical hierarchy process found that the most important drivers for the sustainability of the Oil and gas supply chain during the pandemic were"Risk management capacity","Government regulation"and"Health and safety of employees".On the other hand,the driver"Community Pressure"was found to be of the least ***,the study integrated the results of the fuzzy analytical hierarchy process with the fuzzy technique for order of preference by similarity to ideal solution to calculate the supply chain sustainability index.A case example was demonstrated to rank the industries based on such *** study can support the governmental institutions in benchmarking the Oil and Gas industry based on its sustainability ***,the outcomes of the study will help industrial decision makers prioritize the drivers the company should focus and devise strategies based on the priority to improve the sustainability of their supply chain during severe *** will be crucial as the World health organization has cautioned that the world may encounter another pandemic in the near future.

关键词： Supply chain sustainability drivers Oil and gas supply chain Fuzzy analytical hierarchy process Fuzzy technique for order of preference by similarity to ideal solution Covid-19

Exploiting sound for emerging applications of extracellular vesicles

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Nano Research 2024年第2期17卷 462-475页

作者： Zhuhao Wu Hongwei Cai Chunhui Tian Zheng Ao Lei Jiang Feng Guo department of Intelligent systems engineering Indiana UniversityBloomingtonIN 47405USA

Extracellular vesicles are nano-to micro-scale,membrane-bound particles released by cells into extracellular space,and act as carriers of biomarkers and therapeutics,holding promising potential in translational ***,the challenges remain in handling and detecting extracellular vesicles for disease diagnosis as well as exploring their therapeutic capability for disease ***,we review the recent engineering and technology advances by leveraging the power of sound waves to address the challenges in diagnostic and therapeutic applications of extracellular vesicles and biomimetic *** first introduce the fundamental principles of sound waves for understanding different acoustic-assisted extracellular vesicle *** discuss the acoustic-assisted diagnostic methods including the purification,manipulation,biosensing,and bioimaging of extracellular ***,we summarize the recent advances in acoustically enhanced therapeutics using extracellular vesicles and biomimetic ***,we provide perspectives into current challenges and future clinical applications of the promising extracellular vesicles and biomimetic nanovesicles powered by sound.

关键词： extracellular vesicles exosomes biomimetic nanovesicles acoustics disease diagnostics therapeutics

Distributed Energy and Reserve Scheduling in Local Energy Communities Using L-BFGS Optimization

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CSEE Journal of Power and Energy systems 2024年第3期10卷 942-952页

作者： Mohammad Dolatabadi Alireza Zakariazadeh Alberto Borghetti Pierluigi Siano department of management&Innovation systems University of SalernoSalernoItaly department of Electrical Electronicand Information EngineeringUniversity of BolognaBolognaItaly University of Science and Technology of Mazandaran BehshahrIran department of Power systems National University of Science and Technology POLITEHNICA BucharestBucharestRomania IEEE

Encouraging citizens to invest in small-scale renewable resources is crucial for transitioning towards a sustainable and clean energy *** energy communities(LECs)are expected to play a vital role in this ***,energy scheduling in LECs presents various challenges,including the preservation of customer privacy,adherence to distribution network constraints,and the management of computational *** paper introduces a novel approach for energy scheduling in renewable-based LECs using a decentralized optimization *** proposed approach uses the Limitedmemory Broyden–Fletcher–Goldfarb–Shanno(L-BFGS)method,significantly reducing the computational effort required for solving the mixed integer programming(MIP)*** incorporates network constraints,evaluates energy losses,and enables community participants to provide ancillary services like a regulation reserve to the grid *** assess its robustness and efficiency,the proposed approach is tested on an 84-bus radial distribution *** indicate that the proposed distributed approach not only matches the accuracy of the corresponding centralized model but also exhibits scalability and preserves participant privacy.

关键词： Distributed optimization flexibility services L-BFGS method local energy community renewables reserve

Resilience indices from a family of recovery functions

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Fundamental Research 2024年第1期4卷 13-20页

作者： Loon Ching Tang Lijuan Shen department of Industrial systems engineering and management National University of Singapore117576 Singapore ETH Zurich Future Resilient SystemsSingapore-ETH Centre138602 Singapore

Defining and measuring resilience using a unified framework has been a topic of intense *** article presents a perspective on how resilience could be quantitatively assessed through a set of *** starts with a brief explanation of resilience in the context of supply chain and a quick summary of existing quantitative measures of *** then discusses how resilience could be quantified in a constructive manner so that the resulting metrics are representative of the performance throughout the system's life *** particular,it is proposed that resilience should be evaluated according to different time periods,***,during and after a disruption has *** dimensions of resilience,namely reliability,robustness,recovery and reconfigurability,can then be used to make up a set of indices for *** numerical illustration,these indices are computed based on recovery data arising from Hurricane Sandy in October ***,it is postulated that resilience will be the performance metric that complements productivity and sustainability as the third pillar for measuring success of organizations,and in turn,that of sovereign countries in their quests for developing smart cities.

关键词： systems resilience Recovery function Recovery index Robustness index Reconfigurability

Toward Human-centered XAIin Practice:A survey

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Machine Intelligence Research 2024年第4期21卷 740-770页

作者： Xiangwei Kong Shujie Liu Luhao Zhu department of Data Science and management engineering School of ManagementZhejiang UniversityHangzhou 310058China

Human adoption of artificial intelligence(AI)technique is largely hampered because of the increasing complexity and opacity of AI *** AI(XAI)techniques with various methods and tools have been developed to bridge this gap between high-performance black-box AI models and human ***,the current adoption of XAI technique stil lacks"human-centered"guidance for designing proper solutions to meet different stakeholders'needs in XAI *** first summarize a human-centered demand framework to categorize different stakeholders into five key roles with specific demands by reviewing existing research and then extract six commonly used human-centered XAI evaluation measures which are helpful for validating the effect of *** addition,a taxonomy of XAI methods is developed for visual computing with analysis of method *** clearer human demands and XAI methods in mind,we take a medical image diagnosis scenario as an example to present an overview of how extant XAI approaches for visual computing fulfil stakeholders'human-centered demands in *** we check the availability of open-source XAI tools for stakeholders'*** survey provides further guidance for matching diverse human demands with appropriate XAI methods or tools in specific applications with a summary of main challenges and future work toward human-centered XAI in practice.

关键词： Artificial intelligence(AI)application explainable AI(XAI) human-centered design visual computing medical diagnosis.

Recent Developments in Authentication Schemes Used in Machine-Type Communication Devices in Machine-to-Machine Communication: Issues and Challenges

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Computers, Materials & Continua 2024年第4期79卷 93-115页

作者： Shafi Ullah Sibghat Ullah Bazai Mohammad Imran Qazi Mudassar Ilyas Abid Mehmood Muhammad Asim Saleem Muhmmad Aasim Rafique Arsalan Haider Ilyas Khan Sajid Iqbal Yonis Gulzar Kauser Hameed department of Computer engineering Balochistan University of Information TechnologyEngineering and Management SciencesQuetta87300Pakistan department of Information Technology Balochistan University of Information TechnologyEngineeringand Management SciencesQuetta87300Pakistan department of Information systems College of Computer Sciences and Information TechnologyKing Faisal UniversityHofuf in Al-Ahsa31982Saudi Arabia department of management Information systems College of Business AdministrationKing Faisal UniversityHofuf in Al-Ahsa31982Saudi Arabia department of Software engineering College of ComputingRiphah International UniversityFaisalabad44000Pakistan department of Electrical engineering Balochistan University of Information Technology Engineeringand Management SciencesQuetta87300Pakistan department of Mathematics College of Science Al-ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia

Machine-to-machine (M2M) communication plays a fundamental role in autonomous IoT (Internet of Things)-based infrastructure, a vital part of the fourth industrial revolution. Machine-type communication devices(MTCDs) regularly share extensive data without human intervention while making all types of decisions. Thesedecisions may involve controlling sensitive ventilation systems maintaining uniform temperature, live heartbeatmonitoring, and several different alert systems. Many of these devices simultaneously share data to form anautomated system. The data shared between machine-type communication devices (MTCDs) is prone to risk dueto limited computational power, internal memory, and energy capacity. Therefore, securing the data and devicesbecomes challenging due to factors such as dynamic operational environments, remoteness, harsh conditions,and areas where human physical access is difficult. One of the crucial parts of securing MTCDs and data isauthentication, where each devicemust be verified before data transmission. SeveralM2Mauthentication schemeshave been proposed in the literature, however, the literature lacks a comprehensive overview of current M2Mauthentication techniques and the challenges associated with them. To utilize a suitable authentication schemefor specific scenarios, it is important to understand the challenges associated with it. Therefore, this article fillsthis gap by reviewing the state-of-the-art research on authentication schemes in MTCDs specifically concerningapplication categories, security provisions, and performance efficiency.

关键词： Authentication cyber security internet of things machine-type communication devices machine-to-machine communication

Autonomous Vehicle Platoons In Urban Road Networks:A Joint Distributed Reinforcement Learning and Model Predictive Control Approach

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 141-156页

作者： Luigi D’Alfonso Francesco Giannini Giuseppe Franzè Giuseppe Fedele Francesco Pupo Giancarlo Fortino IEEE the department of Computer engineering ModelingElectronics and SystemsUniversitàdella CalabriaVia Pietro BucciCubo 42-CRende(CS)87036Italy the department of Mechanical engineering Energy Engineering and ManagementUniversitàdella CalabriaVia Pietro BucciCubo 42-CRende(CS)87036Italy

In this paper, platoons of autonomous vehicles operating in urban road networks are considered. From a methodological point of view, the problem of interest consists of formally characterizing vehicle state trajectory tubes by means of routing decisions complying with traffic congestion criteria. To this end, a novel distributed control architecture is conceived by taking advantage of two methodologies: deep reinforcement learning and model predictive control. On one hand, the routing decisions are obtained by using a distributed reinforcement learning algorithm that exploits available traffic data at each road junction. On the other hand, a bank of model predictive controllers is in charge of computing the more adequate control action for each involved vehicle. Such tasks are here combined into a single framework:the deep reinforcement learning output(action) is translated into a set-point to be tracked by the model predictive controller;conversely, the current vehicle position, resulting from the application of the control move, is exploited by the deep reinforcement learning unit for improving its reliability. The main novelty of the proposed solution lies in its hybrid nature: on one hand it fully exploits deep reinforcement learning capabilities for decisionmaking purposes;on the other hand, time-varying hard constraints are always satisfied during the dynamical platoon evolution imposed by the computed routing decisions. To efficiently evaluate the performance of the proposed control architecture, a co-design procedure, involving the SUMO and MATLAB platforms, is implemented so that complex operating environments can be used, and the information coming from road maps(links,junctions, obstacles, semaphores, etc.) and vehicle state trajectories can be shared and exchanged. Finally by considering as operating scenario a real entire city block and a platoon of eleven vehicles described by double-integrator models, several simulations have been performed with the a

关键词： Distributed model predictive control distributed reinforcement learning routing decisions urban road networks

Auto-tuning PVT data using multi-objective optimization:Application of NSGA-II algorithm

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Petroleum 2024年第1期10卷 135-149页

作者： Abdolhadi Zarifi Mohammad Madani Mohammad Jafarzadegan Reservoir engineering systems Petroleum Engineering DepartmentMain Office Building of National Iranian South Oil Company(NISOC)AhvazIran Reservoir engineering Division Petroleum Engineering DepartmentPetroleum Engineering and Development Company(PEDEC)TehranIran Combined Planning management National Iranian Oil Company(NIOC)TehranIran

Reservoir simulation is known as perhaps the most widely used,accurate,and reliable method for field development in the petroleum *** integral part of a reliable reservoir simulation process is to consider robust and rigorous tuned EOS ***,EOS models are tuned iteratively through arduous workflows against experimental PVT ***,this comes with a number of drawbacks such as forcingly using weight factors,which upon alteration adversely affects the optimization *** objective of the current work is thus to introduce an auto-tune PVT matching tool using NSGA-II multi-objective *** order to illustrate the robustness of the presented technique,three different PVT samples are used,including two black-oil and one gas condensate *** utilize PengRobinson EOS during all the manual and auto-tuning *** of auto-tuned EOS-generated results with those of experimental and computed statistical error values for these samples clearly show that the proposed method is *** addition,the proposed method,contrary to the manual matching process,provides the engineer with several matched solutions,which allows them to select a match based on the engineering background to be best amenable to the problem at *** addition,the proposed technique is fast,and can output several solutions within less time compared to the traditional manual matching method.

关键词： Auto-tuning PVT Equation of state NSGA-II Multi-objective optimization

Energy-Saving Distributed Flexible Job Shop Scheduling Optimization with Dual Resource Constraints Based on Integrated Q-Learning Multi-Objective Grey Wolf Optimizer

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Computer Modeling in engineering & Sciences 2024年第8期140卷 1459-1483页

作者： Hongliang Zhang Yi Chen Yuteng Zhang Gongjie Xu School of management Science and engineering Anhui University of TechnologyMa’anshan243032China Laboratory of Multidisciplinary management and Control of Complex systems of Anhui Higher Education Institutes Anhui University of TechnologyMa’anshan243032China department of Industrial engineering School of Mechanical EngineeringNorthwestern Polytechnical UniversityXi’an710072China

The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing *** DFJSP research only considers machine constraints and ignores worker *** one critical factor of production,effective utilization of worker resources can increase ***,energy consumption is a growing concern due to the increasingly serious environmental ***,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this *** solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling *** further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are *** strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto *** effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 *** results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality.

关键词： Distributed flexible job shop scheduling problem dual resource constraints energy-saving scheduling multi-objective grey wolf optimizer Q-learning