Negotiating digital traces by Helene O. I. Gundhus, Pernille Erichsen Skjevrak, Christin Thea Wathne

“… Drawing on two empirical cases in different Norwegian police units, we explore how the increasing data gathering, recording, sorting, standardizing, and integration required by the Norwegian police's Intelligence Doctrine is experienced by users. …”
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Digital Empowerment in Social Work: Leveraging AI to Enhance Educational Access in Developing Nations by Zvinodashe Revesai, Benjamin Tungwa, Telson Anesu Chisosa, Vanessa Runyararo Meki

“…However, this research points out the need for addressing the digital divide and ethical issues associated with artificial intelligence, including problems of privacy and algorithmic bias. The study concludes by making a call for further research into models of safe and equitable AI integration in social work education.…”
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Impact of EU Laws on AI Adoption in Smart Grids: A Review of Regulatory Barriers, Technological Challenges, and Stakeholder Benefits by Bo Nørregaard Jørgensen, Saraswathy Shamini Gunasekaran, Zheng Grace Ma

“…The analysis delves into regulatory barriers such as data protection requirements, algorithmic transparency mandates, and liability concerns that can limit the scope and scale of AI deployment. …”
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Teaching postsecondary students about the ethics of artificial intelligence: A scoping review protocol. by Calvin Hillis, Maushumi Bhattacharjee, Batool AlMousawi, Tarik Eltanahy, Sara Ono, Marcus Hui, Ba' Pham, Michelle Swab, Gordon V Cormack, Maura R Grossman, Ebrahim Bagheri, Zack Marshall

“…This review will inform future research, policy development, and teaching practices, offering valuable insights for educators, policymakers, and researchers working towards responsible AI integration. …”
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Land cover changes in grassland landscapes: combining enhanced Landsat data composition, LandTrendr, and machine learning classification in google earth engine with MLP-ANN scenari... by Cecilia Parracciani, Daniela Gigante, Onisimo Mutanga, Stefania Bonafoni, Marco Vizzari

“…The LandTrendr (LT) algorithm was used in GEE to collect ground control points in no-change areas automatically. …”
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Application of Reinforcement Learning in Controlling Quadrotor UAV Flight Actions by Shang-En Shen, Yi-Cheng Huang

“…The research investigates three RL algorithms suitable for discrete action training. The Deep Q Network (DQN), Advantage Actor–Critic (A2C), and Proximal Policy Optimization (PPO) were combined with three different reward and punishment design mechanisms for training and testing. …”
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Preliminary study on the construction of a data privacy protection course based on a teaching-in-practice range by Zhe SUN, Hong NING, Lihua YIN, Binxing FANG

“…Since China’s Data Security Law, Personal Information Protection Law and related laws were formalized, demand for privacy protection technology talents has increased sharply, and data privacy protection courses have been gradually offered in the cyberspace security majors of various universities.Building on longstanding practices in data security research and teaching, the teaching team of “Academician Fang Binxing’s Experimental Class” (referred to as “Fang Class”) at Guangzhou University has proposed a teaching method for data privacy protection based on a teaching-in-practice range.In the selection of teaching course content, the teaching team selected eight typical key privacy protection techniques including anonymity model, differential privacy, searchable encryption, ciphertext computation, adversarial training, multimedia privacy protection, privacy policy conflict resolution, and privacy violation traceability.Besides, the corresponding teaching modules were designed, which were deployed in the teaching practice range for students to learn and train.Three teaching methods were designed, including the knowledge and application oriented teaching method which integrates theory and programming, the engineering practice oriented teaching method based on algorithm extension and adaptation, and the comprehensive practice oriented teaching method for practical application scenarios.Then the closed loop of “learning-doing-using” knowledge learning and application was realized.Through three years of privacy protection teaching practice, the “Fang class” has achieved remarkable results in cultivating students’ knowledge application ability, engineering practice ability and comprehensive innovation ability, which provided useful discussion for the construction of the initial course of data privacy protection.…”
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Mitigating spatial hallucination in large language models for path planning via prompt engineering by Hongjie Zhang, Hourui Deng, Jie Ou, Chaosheng Feng

“…To address this, we propose S2ERS, an LLM-based technique that integrates entity and relation extraction with the on-policy reinforcement learning algorithm Sarsa for optimal path planning. …”
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Deep Reinforcement Learning-Based Active Disturbance Rejection Control for Trajectory Tracking of Autonomous Ground Electric Vehicles by Xianjian Jin, Huaizhen Lv, Yinchen Tao, Jianning Lu, Jianbo Lv, Nonsly Valerienne Opinat Ikiela

“…The framework integrates active disturbance rejection control (ADRC) for real-time disturbance estimation and compensation with a deep deterministic policy gradient (DDPG)-based deep reinforcement learning (DRL) algorithm for dynamic optimization of controller parameters to improve tracking accuracy and robustness. …”
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Long-Term (2015–2024) Daily PM_2.5 Estimation in China by Using XGBoost Combining Empirical Orthogonal Function Decomposition by Jiacheng Jiang, Jiaxin Dong, Yu Ding, Wenjia Ni, Jie Yang, Siwei Li

“…A two-step PM_2.5 estimation model is established based on a machine learning algorithm and a spatio-temporal decomposition method. …”
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Enhancing BVR Air Combat Agent Development With Attention-Driven Reinforcement Learning by Andre R. Kuroswiski, Annie S. Wu, Angelo Passaro

“…We propose a novel approach that introduces a task-based layer, leveraging domain expertise to optimize decision-making and training efficiency. By integrating multi-head attention mechanisms into the policy model and employing an improved DQN algorithm, agents dynamically select context-aware tasks, enabling the learning of efficient emergent behaviors for variable engagement conditions. …”
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Finding a New Balance Point: Intelligent Optimization of Multi-Target Cognitive Electronic Reconnaissance Strategy for Unmanned Aerial Vehicles by Yun Zhang, Shixun You, Yunbin Yan, Qiaofeng Ou, Xiang Zhu

“…By grouping radar positions, solving for multiple pseudo-targets, and integrating these pseudo-targets, we ultimately obtain an invisible pseudo-target that spans the entire radar detection range. …”
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Towards Intelligent Unmanned Adversarial Games: A Reinforcement Learning Framework with the PHP-ROW Method by Guoqing Shi, Yi Cao, Dinghan Wang, Qiming Yang, Jiandong Zhang, Zhuoyong Shi

“…The superiority of the PHP-ROW method is showcased by contrasting it against the conventional proximal policy optimization (PPO) algorithm. Conclusively, the utility and efficacy of the presented framework are corroborated through human–machine adversarial game simulations in a hyper-realistic environment. …”
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Aortic Pressure Control Based on Deep Reinforcement Learning for <i>Ex Vivo</i> Heart Perfusion by Shangting Wang, Ming Yang, Yuan Liu, Junwen Yu

“…Subsequently, an aortic pressure control method based on the Deep Deterministic Policy Gradient (DDPG) algorithm is proposed. This method enables the regulation of the blood pump and the realization of closed-loop control. …”
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A novel hybrid model based on MPA-VMD, QRMGM and KDE for carbon price prediction by Dabin Zhang, Yufeng Ye, Yongmei Fang, Jing Zhou

“…Thirdly, the MGM was integrated with quantile regression (QR) to predict the conditional quantiles of each subsequence. …”
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Elevator Control Simulation Using Fuzzy Logic Management by Mehr Ali Qasimi, Asmatullah Nashir

“…This work addresses the problem by developing an elevator group controller using a fuzzy algorithm. This project is designed to handle the necessary passenger traffic density while maintaining acceptable passenger waiting times by integrating a fuzzy controller into an elevator system. …”
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BucketAugment: Reinforced Domain Generalisation in Abdominal CT Segmentation by David Jozef Hresko, Peter Drotar

“…<italic>Methods:</italic> BucketAugment leverages principles from the Q-learning algorithm and employs validation loss to search for an optimal policy within a search space comprised of distributed stacks of 3D volumetric augmentations, termed &#x2018;buckets.…”
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Optimizing the dynamic treatment regime of outpatient rehabilitation in patients with knee osteoarthritis using reinforcement learning by Sijia Liu, Jiawei Luo, Chengqi He

“…Then, based on the key features screened out, a dynamic treatment recommendation system was constructed by using deep reinforcement learning algorithms, including Deep Deterministic Policy Gradien(DDPG), Deep Q-Network(DQN) and Batch-Constrained Q-learning(BCQ). …”
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Sentiment Classification of Public Perception on LHKPN Using SVM and Naive Bayes by Ahmad Rijal Hermawan Hermawan, Isa Faqihuddin Hanif

“…These findings indicate that public sentiment toward the LHKPN initiative is largely favorable, despite persistent concerns surrounding integrity and trustworthiness in asset reporting. This study highlights the effectiveness of sentiment analysis in gauging public opinion and informing future policy improvements.…”
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Energy Management in Microgrids Using Model-Free Deep Reinforcement Learning Approach by Odia A. Talab, Isa Avci

“…To optimize decision-making, an actor-critic-based Deep Deterministic Policy Gradient (DDPG) algorithm is developed, leveraging reinforcement learning (RL) to adapt dynamically to changing system conditions. …”
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