Using Artificial Intelligence in Employment: Problems and Prospects of Legal Regulation by D. A. Novikov

“…Objective: to identify the legal problems of using artificial intelligence in hiring employees and the main directions of solving them.Methods: formal-legal analysis, comparative-legal analysis, legal forecasting, legal modeling, synthesis, induction, deduction.Results: a number of legal problems arising from the use of artificial intelligence in hiring were identified, among which are: protection of the applicant’s personal data, obtained with the use of artificial intelligence; discrimination and unjustified refusal to hire due to the bias of artificial intelligence algorithms; legal responsibility for the decision made by a generative algorithm during hiring. …”
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Study on energy saving strategy and Nash equilibrium of base station in cognitive radio network by Xiao-tong MA, Shun-fu JIN, Jian-ping LIU, Zhan-qiang HUO

“…From the perspective of economics, a profit function was constructed and a nonlinear optimization algorithm was designed to investigate the Nash equilibrium and the socially optimal behavior of the secondary user packets, then a pricing policy of licensed spectrum for secondary users was formulated. …”
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Study on energy saving strategy and Nash equilibrium of base station in cognitive radio network by Xiao-tong MA, Shun-fu JIN, Jian-ping LIU, Zhan-qiang HUO

“…From the perspective of economics, a profit function was constructed and a nonlinear optimization algorithm was designed to investigate the Nash equilibrium and the socially optimal behavior of the secondary user packets, then a pricing policy of licensed spectrum for secondary users was formulated. …”
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Estimation and validation of solubility of recombinant protein in E. coli strains via various advanced machine learning models by Wael A. Mahdi, Adel Alhowyan, Ahmad J. Obaidullah

“…The integration of these models within an AdaBoost framework, coupled with advanced hyperparameter tuning via the Firefly Algorithm (FA), demonstrates a novel strategy for improving predictive accuracy and model robustness. …”
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Preference learning based deep reinforcement learning for flexible job shop scheduling problem by Xinning Liu, Li Han, Ling Kang, Jiannan Liu, Huadong Miao

“…To address this, this paper proposes a Preference-Based Mask-PPO (PBMP) algorithm, which leverages the strengths of preference learning and invalid action masking to optimize FJSP solutions. …”
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Robust Multi-Stage Planning of Park-Level Integrated Energy System Considering Source-Load Uncertainties by JIANG Xunpu, BAO Zhejing, YU Miao, GUO Chuangxin, GUO Yuanyue, WANG Jian

“…[Objective] A robust multi-stage planning model is established for a park-level integrated energy system （PIES） to minimize the total life-cycle present-value cost while considering source-load uncertainties， optimal construction sequencing， and tiered carbon trading to enhance economic efficiency and low-carbon benefits. …”
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Interpretability Study on the Fault Diagnosis Model of the Heat pipe/ Vapor Compression Composite Air Conditioning System by ZHANG Yiqi, HUANG Shuoquan, LI Xiuming, DI Yanqiang, SONG Mengjie, HAN Zongwei

“…Applying data-driven fault diagnosis models to data center air conditioning systems can significantly improve operational reliability. …”
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Establishing strength prediction models for low-carbon rubberized cementitious mortar using advanced AI tools by Fu Limei, Xu Feng

“…This research addresses this gap by evaluating the predictability of machine learning approaches for evaluating the CS of rubberized mortar (RM) incorporating supplementary cementitious materials. Among the tested algorithms, including bagging, gradient boosting, and AdaBoost, the bagging model achieved the highest accuracy (R 2 = 0.975). …”
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Joint Three-Task Optical Performance Monitoring with High Performance and Superior Generalizability Using a Meta-Learning-Based Convolutional Neural Network-Attention Algorithm and... by Di Zhang, Junyao Shi, Yameng Cao, Yan Ling Xue

“…The meta-learning algorithms can learn optimal initial model parameters across multiple related tasks, enabling them to quickly adapt to new tasks through fine-tuning with a small amount of data. …”
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Distributed Coordinated Dispatch Model for Multi-area Interconnected Integrated Energy Systems Based on Sequential Cone Programming by Yujie REN, Yuhan HUANG, Zhenbo WEI

“…The solution of the distributed algorithm based on ATC is close to the global optimal solution of the distributed algorithm. …”
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Seasonally Adaptive VMD-SSA-LSTM: A Hybrid Deep Learning Framework for High-Accuracy District Heating Load Forecasting by Yu Zhang, Keyong Hu, Lei Lu, Qingqing Yang, Min Fang

“…To improve the accuracy of heating load forecasting and effectively address the energy waste caused by supply–demand imbalances and uneven thermal distribution, this study innovatively proposes a hybrid prediction model incorporating seasonal adjustment strategies. …”
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Soil Organic Carbon Prediction and Mapping in Morocco Using PRISMA Hyperspectral Imagery and Meta-Learner Model by Yassine Bouslihim, Abdelkrim Bouasria, Budiman Minasny, Fabio Castaldi, Andree Mentho Nenkam, Ali El Battay, Abdelghani Chehbouni

“…This study presents a novel meta-learner framework that combines multiple machine learning algorithms and spectra processing algorithms to optimize SOC prediction using the PRISMA hyperspectral satellite imagery in the Doukkala plain of Morocco. …”
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HouseGanDi: A Hybrid Approach to Strike a Balance of Sampling Time and Diversity in Floorplan Generation by Azmeraw Bekele Yenew, Beakal Gizachew Assefa, Elefelious Getachew Belay

“…Evaluation of diversity using FID demonstrates an average 15.5% improvement over the state-of-the-art houseDiffusion model, with a 41% reduction in generation time. …”
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Risk-Aware Lane Change and Trajectory Planning for Connected Autonomous Vehicles Based on a Potential Field Model by Tao Wang, Dayi Qu, Kedong Wang, Chuanbao Wei, Aodi Li

“…These candidate trajectories are further assessed using a minimum safety distance model derived from potential field theory, with optimization focusing on safety, comfort, and efficiency. …”
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A CART-Based Model for Analyzing the Shear Behaviors of Frozen–Thawed Silty Clay and Structure Interface by Fengpan Zhu, Bo Wang, Zhiqiang Liu

“…The physical and mechanical properties of the soil–structure interface under the freeze–thaw condition are complex, making empirical shear strength models poorly applicable. This study employs integrated machine learning algorithms to model the shear behavior of frozen–thawed silty clay and the structure interface. …”
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Predictive Modeling of Acute Respiratory Distress Syndrome Using Machine Learning: Systematic Review and Meta-Analysis by Jinxi Yang, Siyao Zeng, Shanpeng Cui, Junbo Zheng, Hongliang Wang

“…Future research should standardize model development, optimize model performance, and explore how to better integrate predictive models into clinical practice to improve ARDS diagnosis and risk stratification. …”
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Multi-Objective Cluster Classification and Voltage Control Approach for Active Distribution Network Considering Resource Reserve Degree by Jing WANG, Yi YUAN, Yinchi SHAO, Jinqi ZHANG, Ran DING, Yanjiang GONG

“…Then, the K-means clustering algorithm is used to improve the discrete particle swarm optimization (DPSO) algorithm to convert the cluster classification into an optimization solution problem. …”
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Research on primary frequency regulation and VSC-HVDC frequency synchronization control coordination method for large-scale hydropower DC export regional grid by Hongchun Shu, Huaibin Gao, Zongxue Shao, Yongzan Cui, Wei Zhao

“…An evolutionary algorithm is used to solve the optimal parameters hierarchically progressively. …”
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Diagnostic Models for Differentiating COVID-19-Related Acute Ischemic Stroke Using Machine Learning Methods by Eylem Gul Ates, Gokcen Coban, Jale Karakaya

“…Various feature selection algorithms were applied to identify the most relevant features, which were then used to train and evaluate machine learning classification models. …”
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Porosity prediction of tight reservoir rock using well logging data and machine learning by Yawen He, Hongjun Zhang, Zhiyu Wu, Hongbo Zhang, Xin Zhang, Xiaojing Zhuo, Xiaoli Song, Sha Dai, Wei Dang

“…These models are further optimized with the particle swarm optimization (PSO) algorithm to enhance their predictive accuracy. …”
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