Safety Decision-Making for Autonomous Vehicles Integrating Passenger Physiological States by fNIRS by Xiaofei Zhang, Haoyi Zheng, Jun Li, Zongsheng Xie, Huamu Sun, Hong Wang

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Research on the Design and Simulation of Missile Intelligent Agent Autopilot Integrated With Deep Reinforcement Learning by Jianqi Wang, Shengtao Long, Su Wang, Kaiyu Zhan

“…This paper proposes an innovative method for missile autopilot design based on the deep deterministic policy gradient (DDPG) algorithm. Under the framework of deep reinforcement learning, by integrating the missile’s dynamic characteristics to optimize the reward function and network structure, an adaptive control model capable of adapting to complex flight conditions was constructed. …”
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Accelerated Benders’ Decomposition for Integrated Forward/Reverse Logistics Network Design under Uncertainty by Vahab Vahdat, Mohammad Ali Vahdatzad

“…In this paper, a two-stage stochastic programming modelling is proposed, to design a multi-period, multistage, and single-commodity integrated forward/reverse logistics network design problem under uncertainty. …”
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Modeling and Solving the Knapsack Problem with a Multi-Objective Equilibrium Optimizer Algorithm Based on Weighted Congestion Distance by Ziqian Wang, Xin Huang, Yan Zhang, Danju Lv, Wei Li, Zhicheng Zhu, Jian’e Dong

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Artificial intelligence, generative artificial intelligence and research integrity: a hybrid systemic review by Khalid H. Arar, Hamit Özen, Gülşah Polat, Selahattin Turan

“…The study also identifies risks such as biased algorithms, plagiarism risk, false information production, and potential damage to academic integrity. …”
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Enhanced Spatial Clustering for Crime Analysis: Novel Advances in Ward-Like and SKATER Algorithms for Brazilian Public Security by Raydonal Ospina, Alexsandra Gomes De Lima, Cristiano Ferraz, Cecilia Castro, Carlos Martin-Barreiro, Victor Leiva

“…The resulting clusters partly, but not fully, coincide with Recife’s official Integrated Security Areas (Áreas Integradas de Segurança in Portuguese), suggesting that a data-driven redrawing of boundaries could improve police resource allocation and the monitoring of local safety policies. …”
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Optimized memory allocation in edge-PLCs using Deep Q-Networks and bidirectional LSTM with Quantum Genetic Algorithm by N. Naveen Kumar, S. Saravana, S. Balamurugan, P. Seshu Kumar, S. Suresh

“…Our findings underscore the significance of integrating advanced machine learning techniques with quantum-inspired optimization algorithms to address complex challenges in industrial IoT environments, offering a promising avenue for enhancing memory allocation efficiency in edge computing systems.…”
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Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm by Xuehui Fan, Ruixue Ye, Yan Gao, Kaiwen Xue, Zeyu Zhang, Jing Xu, Jingpu Zhao, Jun Feng, Yulong Wang

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Resource Scheduling Method for Integration of TT＆C and Observation Based on Multi-Agent Deep Reinforcement Learning by Siyue CHENG, Haoran LI, Weigang BAI, Di ZHOU, Yan ZHU

“…With the development of satellite communication technology and the continuous expansion of the constellation scale, the integration of TT＆amp;C and observation technology has become the mainstream trend.The large constellation scale, many scheduling objects and complex operation joint control bring great challenges to the integrated resource scheduling of satellite network TT＆amp;C and observation.Subject to the low solution effi ciency and complex constraints of scheduling algorithms, the traditional TT＆amp;C resource scheduling technology adopts the advance injection TT＆amp;C instructions to perform tasks according to the fi xed deployment, which is diffi cult to meet the scheduling needs of emergencies and emergency tasks.Therefore, a kind of resource scheduling method based on multi-agent actor-Agent Actor-Critic Deterministic Policy Gradient Algorithms (MADDPG) was presented.With centralized training and distributed execution, the multi-agent model of integrated task of TT＆amp;C and observation was established.By analyzed the scheduling strategy of neighbor agent, the response speed of local information was improved.According to the model and constraints in the integrated resource scheduling problem of TT＆amp;C and observation, selected signifi cant and interpretable constraints, then established the multi-agent resource scheduling reinforcement learning model, and carried on the simulation test.The simulation results showed that the task benefi t of this method was 22% higher than the traditional method.…”
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Global Cross-Market Trading Optimization Using Iterative Combined Algorithm: A Multi-Asset Approach with Stocks and Cryptocurrencies by Kansuda Pankwaen, Sukrit Thongkairat, Worrawat Saijai

“…This study presents an advanced adaptive trading framework that integrates Deep Reinforcement Learning (DRL) with the Iterative Model Combining Algorithm (IMCA) to overcome the critical limitations of static ensemble methods in global portfolio optimization. …”
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Integrated Guidance and Control for Strap-Down Flight Vehicle: A Deep Reinforcement Learning Approach by Qinglong Zhang, Bin Zhao, Yifu Jiang, Jingyan Zhang, Jiale Zhang

“…By leveraging the data-driven nature of the deep reinforcement learning (DRL) algorithm, this method mitigates the challenges associated with integrated guidance and control (IGC) method design arising from model dependencies, thereby addressing the inherent complexity of the IGC model. …”
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RETRACTED ARTICLE: Image Processing Algorithm for Mechanical Properties Testing of High-Temperature Materials Based on Time-Frequency Analysis by Dong Sun, Yong Wei

“…We, the Editor and Publisher of the Journal Of Experimental Nanoscience, have retracted the following article, which is part of the Special Issue titled Adaptive Nanotechnology for Environmental Sustainability:Dong Sun & Yong Wei (2023) Image processing algorithm for mechanical properties testing of hightemperature materials based on time‐frequency analysis, Journal of Experimental Nanoscience, 18:1, DOI: 10.1080/17458080.2023.2170357Following publication, concerns were raised about the peer review and decision‐making processes for this special issue.After an investigation by the Taylor & Francis Publishing Ethics & Integrity team, in full cooperation with the Editor‐in‐Chief, it was confirmed that the article was not reviewed appropriately, in line with the Journal’s peer review standards and policy.As the stringency of the peer review process is core to the integrity of the publication process, the Editor and Publisher have decided to retract the article. …”
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Research and Experimental Verification of an Efficient Subframe Lightweighting Method Integrating SIMP Topology and Size Optimization by Jihui Zhuang, Fan Zeng

“…A topology optimization model was established using the Solid Isotropic Material with Penalization (SIMP) method and solved using the Method of Moving Asymptotes (MMA) algorithm. Integration of the SIMP method was achieved on the Abaqus-Matlab (2022x) platform via Python (3.11.0) and Matlab (R2022a) coding, forming an effective optimization framework. …”
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Investigating Catching Hotspots of Fishing Boats: A Framework Using BeiDou Big Data and Deep Learning Algorithms by Fen Wang, Xingyu Liu, Tanxue Chen, Hongxiang Feng, Qin Lin

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Impact of agricultural industry transformation based on deep learning model evaluation and metaheuristic algorithms under dual carbon strategy by Xuan Zhao, Weiyun Tang, Qiuyan Liu, Hongtao Cao, Fei Chen

“…Compared with traditional methods—such as deep neural networks, support vector machines, and linear regression—the proposed model effectively integrates static and dynamic agricultural data. Static features, including farmland distribution and soil types, are extracted using Convolutional Neural Networks, while temporal trends in variables such as weather patterns and policy changes are captured by the Long Short-Term Memory network. …”
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A Hybrid Model Integrating Variational Mode Decomposition and Intelligent Optimization for Vegetable Price Prediction by Gao Wang, Shuang Xu, Zixu Chen, Youzhu Li

“…This study proposes a hybrid forecasting model integrating variational mode decomposition (VMD), the Fruit Fly Optimization Algorithm (FOA), and a gated recurrent unit (GRU). …”
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Prospects for the Integration of Google Trends Data and Official statistics to Assess social Comfort and Predict the Financial situation of the Population by M. V. Shakleinaa, M. I. Volkova, K. I. Shaklein, S. R. Yakiro

“…The proposed algorithm for working with big data allowed to determine the components of social comfort (“Education and Training”, “Safety”, “Leisure and free time”), for which it is necessary to directly integrate big data in the system of primary statistical accounting with further data processing and obtaining composite indicators. …”
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An Integrated Supplier-Buyer Inventory Model with Conditionally Free Shipment under Permissible Delay in Payments by Chia-Hsien Su

“…Algorithm for computing the optimal policies is derived. …”
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Integrated Systemic Modeling of Production Scheduling, Maintenance, and Quality Control in Closed-Loop Supply Chains by Javad Rahim, Ali Morovati Sharifabadi, Davood Andalib Ardakani

“…The proposed model integrates preventive and corrective maintenance policies, scheduling, and quality management into a unified system that minimizes costs while enhancing reliability and sustainability. …”
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Evaluating Recycling Initiatives for Landfill Diversion in Developing Economies Using Integrated Machine Learning Techniques by Muyiwa Lawrence Adedara, Ridwan Taiwo, Olusola Olaitan Ayeleru, Hans-Rudolf Bork

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