Article
Green & Sustainable Science & Technology
Lang Liu, Yutao Pu, Zhenwei Liu, Junjie Liu
Summary: This paper builds a green closed-loop supply chain model using generalized stochastic Petri nets (GSPN) and converts it into a Markov model to calculate the steady-state probability. The model is analyzed from the perspectives of time performance and operation efficiency of each link. Compared to previous studies, this paper finds that when the whole green closed-loop supply chain system reaches a dynamic equilibrium state, the product has a steady-state probability at all stages, and decision makers need to focus on the supervision and management of key links such as marketing, packaging processing, market feedback, and market demand formulation. Managers of green closed-loop supply chain systems need to adjust their decision-making strategies in a timely manner to ensure efficient operation of the system. This paper provides theoretical support for improving the operational efficiency of green closed-loop supply chain system and offers new ideas for research on green closed-loop supply chain operation mode.
Article
Engineering, Industrial
Shigui Ma, Yong He, Ran Gu
Summary: This paper examines the impact of supply disruption on the optimal decision-making and profits of supply chain members. The key is to revise the optimal advertising strategies and advertising subsidy scheme in response to changes in the original product supply. Several propositions are analytically derived to obtain the optimal advertising strategies and the advertising subsidy scheme before and after the supply disruption, providing management insights for managers to redesign advertising strategies and subsidy schemes under supply disruption.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2021)
Article
Green & Sustainable Science & Technology
Qing Zhang, Weiguo Fan, Jianchang Lu, Siqian Wu, Xuechao Wang
Summary: Due to the globalization of supply and production, supply chain management has strengthened the connection between upstream and downstream enterprises. However, the complex relationship between nodes makes the supply chain system more vulnerable, and interruption at any location can cause irreparable damage. Quantitative analysis of interruption events in different locations is essential for formulating effective mitigation strategies to achieve the recovery of node enterprises.
Article
Biochemical Research Methods
Alexandru Oarga, Bridget P. Bannerman, Jorge Julvez
Summary: Despite the slow pace of new drug production due to high cost and uncertain success, high-throughput technologies and computational methods can be used to identify vulnerabilities in biological models and facilitate novel drug development. However, the current approach only considers topological data, neglecting dynamic information and potentially leading to misidentified drug targets.
Article
Engineering, Industrial
A. Vasilyev, J. Andrews, S. J. Dunnett, L. M. Jackson
Summary: This paper develops a novel model for dynamic reliability analysis of a polymer electrolyte membrane fuel cell system to consider multi-state dynamics and ageing. By combining physical and stochastic sub-models, the study investigates the effects of operating conditions on system reliability. Monte Carlo simulations demonstrate the significant influence of purging and load cycles on the longevity of the fuel cell system.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2021)
Article
Environmental Sciences
Leyi Shi, Shanshan Du, Yifan Miao, Songbai Lan
Summary: This research focuses on the performance evaluation of satellite network moving target defense system. By constructing models, theoretical reasoning, and simulation experiments, key factors affecting the performance of satellite networks were identified, providing a theoretical basis for system design and performance optimization.
Article
Engineering, Multidisciplinary
Vipul Garg, Gopika Vinod, Mahendra Prasad, T. V. Santhosh, N. B. Shrestha, J. Chattopadhyay
Summary: Smart transmitters are increasingly being used in safety critical systems. However, the reliability modeling of these digital systems is more complex due to their processing and diagnostic features. The conventional Fault Tree Analysis (FTA) method has limitations in modeling digital systems, which require detailed consideration of dynamic system interactions and failure modes. The Dynamic Flowgraph Methodology (DFM) is an optimistic approach that combines multi-valued logic modeling and analysis capabilities to handle these interactions. This paper demonstrates the potential of using the hybrid DFM - Petri Net approach for dynamic reliability analysis of smart transmitters.
QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL
(2023)
Article
Computer Science, Interdisciplinary Applications
Jie Ding, Xiao Chen, Hui Sun, Wei Yan, Huixing Fang
Summary: The green supply chain system aims to reduce production costs, stimulate economic growth, and address ecological issues. Current design methods for the green supply chain system, including costly conventional discrete event simulation, and limited compositional modeling using Petri nets. Researchers proposed a hybrid modeling technique to combine Petri nets with process algebra for improved compositionality.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Engineering, Chemical
Kai Huang, Jian Wang, Jinxin Zhang
Summary: The automobile industry is crucial for the national economy, and a well-functioning automobile supply chain is essential for sustainable economic and social development. However, there is a lack of comprehensive bibliometric review in the field of automotive supply chain disruption risk management. This paper fills this gap by conducting a comprehensive bibliometric analysis of 866 journal articles from 2000 to 2022, providing insights into research topics, trends, key features, and future directions in automotive supply chain disruption risk management.
Article
Chemistry, Multidisciplinary
Tomasz Rak, Dariusz Rzonca
Summary: Simulation models are scientific tools that use software to solve complex mathematical problems, benefiting areas such as performance engineering and communications systems. To achieve more accurate results, researchers should utilize detailed models and analyze system operations in the early modeling stages. The study introduces the use of the QPME tool based on queueing Petri nets to model the system stream generator, proposing an alternative design model that better meets customer needs. Adjusting queueing Petri net models appropriately can help produce better data streams, as shown by the study results.
APPLIED SCIENCES-BASEL
(2021)
Article
Engineering, Multidisciplinary
Jacek Skorupski, Agnieszka A. Tubis, Sylwia Werbinska-Wojciechowska, Adam Wroblewski
Summary: Recently, there has been increasing attention on supply chain risk management and several methods and tools have been developed in this area. However, most of these focus on the direct consequences of disruptions in supply chains. This article introduces an operational risk analysis method for supply chains that evaluates the consequences of adverse events based on direct supply process disruptions to customers and recovery time for the supply system. The implementation of this method in an automotive industry company confirms the importance of differentiating between direct and indirect consequences in risk assessment and suggests the need for a two-fold interpretation of the results for proper risk management tool selection.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART O-JOURNAL OF RISK AND RELIABILITY
(2023)
Article
Nuclear Science & Technology
Mohan Rao Mamdikar, Vinay Kumar, Sharda Bharti, Pooja Singh
Summary: Unified Modeling Language (UML) is powerful in designing and modeling safety-critical systems, but lacks the ability to illustrate dynamic behavior. To address this limitation, we propose a framework combining UML and Petri Net to capture system requirements and perform in-depth reliability analysis.
PROGRESS IN NUCLEAR ENERGY
(2023)
Article
Computer Science, Hardware & Architecture
Khizar Hameed, Saurabh Garg, Muhammad Bilal Amin, Byeong Kang
Summary: This paper proposes a formal modeling, analysis, and verification solution for clone node attacks in IoT. The proposed scheme is modeled using High-Level Petri Nets (HLPNs) and Z-specification language, and the functional and logical correctness are verified. Coloured Petri Nets (CPNs) are employed to extend the modeling work and validate functional and logical correctness as well.
Article
Business
Christina Oberg
Summary: This study explores the ongoing disruption caused by metal additive manufacturing to supply chains, revealing disruptions occurring at multiple positions and the efforts of challenged parties to speed transformation. The research contributes to theorizing about episodic positions during disruption and provides unique empirical data capturing supply chain changes and disruptive, transformational impacts.
SUPPLY CHAIN MANAGEMENT-AN INTERNATIONAL JOURNAL
(2022)
Article
Automation & Control Systems
Yanhua Du, Yongchuan Zhou, Hesuan Hu
Summary: Obtaining assignment plans of roles and users for business processes or workflow processes under security requirements is crucial for enterprises. However, existing methods fail to account for the dynamics of users and roles, and cannot fulfill security requirements. In this article, we propose a new approach that utilizes a role and user assignment graph (RUAG) based on Petri nets to record assignment plans and extract optimal compositions that meet security requirements for multiple concurrent business processes. Our approach improves accuracy, enhances efficiency, and reduces costs compared to existing methods.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)