On the problem of the korenization and ukrainization of state authorities in the 1920s–1930s (based on the materials of the North Caucasus) by E. V. Tufanov

“…In this paper, based on the decisions of party congresses, the analysis of the formation of the legislative framework for the implementation of one of the facets of Soviet national policy – the policy of Korenization and Ukrainization as an integral part of this area of activity is carried out. …”
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Link-State-Aware Proactive Data Delivery in Integrated Satellite–Terrestrial Networks for Multi-Modal Remote Sensing by Ranshu Peng, Chunjiang Bian, Shi Chen, Min Wu

“…This paper proposes multi-modal-MAPPO, a novel multi-modal deep reinforcement learning (MDRL) framework designed for a proactive data push in large-scale integrated satellite–terrestrial networks (ISTNs). By integrating satellite cache states, user cache states, and multi-modal data attributes (including imagery, metadata, and temporal request patterns) into a unified Markov decision process (MDP), our approach pioneers the application of the multi-actor-attention-critic with parameter sharing (MAPPO) algorithm to ISTNs push tasks. …”
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Integrating Machine Learning for Enhanced Agricultural Productivity: A Focus on Bananas and Arecanut in the Context of India’s Economic Growth by B. S. Saruk, G. Mokesh Rayalu

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Cyber-Secure IoT and Machine Learning Framework for Optimal Emergency Ambulance Allocation by Jonghyuk Kim, Sewoong Hwang

“…Supervised regression algorithms—Random Forest, XGBoost, and LightGBM—were trained on 2061 center-month observations. …”
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Estimating canopy height in tropical forests: Integrating airborne LiDAR and multi-spectral optical data with machine learning by Brianna J. Pickstone, Hugh A. Graham, Andrew M. Cunliffe

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Deep neural network approach integrated with reinforcement learning for forecasting exchange rates using time series data and influential factors by T. Soni Madhulatha, Dr. Md. Atheeq Sultan Ghori

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Understanding acceptance and resistance toward generative AI technologies: a multi-theoretical framework integrating functional, risk, and sociolegal factors by Priyanka Shrivastava

“…Ethical concerns, including algorithmic bias, data ownership, and the labor market impact of AI, are addressed to offer a more holistic understanding of resistance behavior. …”
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Integrating Artificial Intelligence into Service Innovation, Business Development, and Legal Compliance: Insights from the Hainan Free Trade Port Era by Yincheng Li, Shumin Wang, Muhammad Bilawal Khaskheli

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Simulation of Spatial and Temporal Dynamics in Habitat Quality amid Rapid Urbanization based on Random Forest Algorithm: A Lingui District, Guilin City Case Study by Zhang Xiaoyan, Liu Hanxin, Yang Xin, Wu Gang, Tan Junjie

“…This study, centering on the rapidly urbanizing Lingui District, assesses and forecasts land use conversion and habitat quality evolution from 2000 to 2030, using the random forest algorithm, employing land use data from 2000, 2010, and 2020 through an integrated application of the PLUS- InVEST model. …”
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Privacy-Preserving Hierarchical Reinforcement Learning Framework for Task Offloading in Low-Altitude Vehicular Fog Computing by Zhiwei Wei, Jingxin Mao, Bing Li, Rongqing Zhang

“…At the local DRL level, we design an Attention-enhanced Federated Proximal Policy Optimization (AFedPPO) algorithm to enable decentralized training and execution (DTDE) for task offloading, which is privacy-preserving, effective, and scalable for the low-altitude VFC systems. …”
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IMSBA: A Novel Integrated Sensing and Communication Beam Allocation Based on Multi-Agent Reinforcement Learning for mmWave Internet of Vehicles by Jinxiang Lai, Deqing Wang, Yifeng Zhao

“…To address these challenges, this paper proposes an integrated sensing and communication (ISAC) beam allocation algorithm, termed IMSBA, which jointly optimizes beam direction, transmission power, and spectrum resource allocation to effectively mitigate the interference between I2V and V2V while maximizing the overall network performance. …”
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The Future of Employment: Evaluating the Impact of STI Foresight Exercises by Ricardo Seidl da Fonseca

“…The impact evaluation exercise relating STI Foresight and employment proposed here converges and integrates different scientific sectors such as the interdependence between employment and welfare framework; the role and weight of technology change to employment dimension; the prospects of emerging and future technologies impacting employment in future industry; the contribution of science, technology and innovation (STI) policies to promoting the generation and real application of new technologies. …”
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Adaptive Control of VSG Inertia Damping Based on MADDPG by Demu Zhang, Jing Zhang, Yu He, Tao Shen, Xingyan Liu

“…In the centralized training phase, each agent’s critic network shares global observation and action information to guide the actor network in policy optimization. In the decentralized execution phase, agents observe frequency deviations and the rate at which angular frequency changes, using reinforcement learning algorithms to adjust the virtual inertia <i>J</i> and damping coefficient <i>D</i> in real time. …”
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Strategic Traffic Management in Mixed Traffic Road Networks: A Methodological Approach Integrating Game Theory, Bilevel Optimization, and C-ITS by Areti Kotsi, Ioannis Politis, Evangelos Mitsakis

“…The integration of Connected Vehicles into conventional traffic systems presents significant challenges due to the diverse behaviors and objectives of different drivers. …”
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Application of machine learning algorithms to model predictors of informed contraceptive choice among reproductive age women in six high fertility rate sub Sahara Africa countries by Mequannent Sharew Melaku, Lamrot Yohannes, Berhanu Sharew, Mintesnot Hawaz Derseh, Eliyas Addisu Taye

“…The LGBM classifier outperformed among machine learning algorithms and achieved 73% accuracy and an AUC of 0.80. …”
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Integral Reinforcement Learning-Based Online Adaptive Dynamic Event-Triggered Control Design in Mixed Zero-Sum Games for Unknown Nonlinear Systems by Yuling Liang, Zhi Shao, Hanguang Su, Lei Liu, Xiao Mao

“…In this paper, multiplayer mixed zero-sum games (MZSGs) are studied by the means of an integral reinforcement learning (IRL) algorithm under the dynamic event-triggered control (DETC) mechanism for completely unknown nonlinear systems. …”
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Neural-Driven heuristic for strip packing trained with Black-Box optimization by Mariusz Kaleta, Tomasz Śliwiński

“…Our method learns decision policies by optimizing fill factor improvements over a large dataset of problem instances. …”
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Joint optimization of edge computing and caching in NDN by Yu ZHANG, Min CHENG

“…Named data networking (NDN) is architecturally easier to integrate with edge computing as its routing is based on content names and its nodes have caching capabilities.Firstly, an integrated framework was proposed for implementing dynamic coordination of networking, computing and caching in NDN.Then, considering the variability of content popularity in different regions, a matrix factorization-based algorithm was proposed to predict local content popularity, and deep reinforcement learning was used to solve the the problem of joint optimization for computing and caching resource allocation and cache placement policy with the goal of maximizing system operating profit.Finally, the simulation environment was built in ndnSIM.The simulation results show that the proposed scheme has significant advantages in improving cache hit rate, reducing the average delay and the load on the remote servers.…”
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Machine learning based classification of catastrophic health expenditures: a cross-sectional study of Korean low-income households by Seok Min Ji, Jeewuan Kim, Kyu Min Kim

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Coastal Urban Ecological Security Pattern Identification Integrating Land Subsidence Factors: A Deep Learning-Based Case Study of Zhuhai City by Yuan Shaoxiong, Gong Qinghua, Ye Yuyao, Wang Jun, Hao Yinlei, Zhang Yaze, Liu Bowen

“…Future research should focus on utilizing high-resolution spatiotemporal data, refining algorithms, and developing mechanisms to translate research findings into practical urban planning and ecological management policies.…”
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