Closed‐loop economics in waste management: financing procedures and results by R. I. Yabbarova

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Research on artificial intelligence-driven container relocation problem for green ports by Sisi Zheng, Jin Sha, Yinying Kong, Yougan Wang

“…IntroductionContainer relocation in port yards represents a canonical NP-hard problem, characterized by high-dimensional nonlinear constraints and stringent real-time decision-making requirements.MethodsThis study proposes a unified framework integrating an Intelligent Decision-Driven Model (IDDM), an Adaptive Data Generator (ADG), and an Optimization–Learning Closed-Loop Framework (OLCF).ResultsThe IDDM leverages heuristic search and machine learning within a multi-stage decision mechanism to mitigate the curse of dimensionality; in two-dimensional scenarios involving 50–100 containers, the model achieves an average response time of 9.83 ± 0.12 µs and reduces relocation operations by 61.68%. …”
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Enhancing Continuum Robotics Accuracy Using a Particle Swarm Optimization Algorithm and Closed-Loop Wire Transmission Model for Minimally Invasive Thyroid Surgery by Na Guo, Haoyun Zhang, Xingshuai Li, Xinnan Cui, Yang Liu, Jiachen Pan, Yajuan Song, Qinjian Zhang

“…By integrating rigid mechanisms and continuum joints within a closed-loop cable-driven framework, the system achieves a balance between flexibility in narrow spaces and operational stiffness. …”
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A Hybrid Digital-4E Strategy for comorbid migraine and depression: a medical hypothesis on an AI-driven, neuroadaptive, and exposome-aware approach by Parisa Gazerani

“…This paper proposes a Hybrid Digital-4E Strategy, deployed on an AI-driven neuroadaptive digital health platform, integrating closed-loop therapy, digital phenotyping, and exposome tracking to enable real-time, personalized care.MethodsGrounded in the 4E cognition framework (Embodied, Embedded, Enactive, and Extended cognition), this strategy reconceptualizes migraine-depression as an interactive system rather than two separate conditions. …”
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An Interactive Human-in-the-Loop Framework for Skeleton-Based Posture Recognition in Model Education by Jing Shen, Ling Chen, Xiaotong He, Chuanlin Zuo, Xiangjun Li, Lin Dong

“…Among all evaluated methods, the Transformer model achieved the best accuracy of 92.7% on the dataset, demonstrating the effectiveness of our closed-loop framework in supporting pose classification and model training. …”
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Creation of Parcelas 5.0 using the 5S framework (social, sustainable, smart, sensing and safe) to improve traditional farming in Mexico by Arturo Molina, Alondra Michel Sánchez López, Emigdio Martínez Jiménez, Ana Lidia Barcenas Cortés, Pedro Ponce

“…Socially, this solution empowers local communities by providing access to training, creating jobs, and enhancing food security through localised food production. …”
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A Framework for Constructing Large-Scale Dynamic Datasets for Water Conservancy Image Recognition Using Multi-Role Collaboration and Intelligent Annotation by Xueying Song, Xiaofeng Wang, Ganggang Zuo, Jiancang Xie

“…This paper proposes a method that integrates multi-role collaboration with automated annotation to address these issues. The framework introduces two new roles, data augmentation specialists and automatic annotation operators, to establish a closed-loop process that includes dynamic classification adjustment, data augmentation, and intelligent annotation. …”
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Active location and recovery of unbalance problems in smart distribution networks by LI Yihong, PAN Yicheng, MA Meng, WANG Ping

“…The time-series causal inference was introduced into distribution network anomaly analysis, establishing a comprehensive "detection-localization-regulation" solution framework for the first time. By integrating Granger causality tests with adaptive interval detection algorithms, the method achieves unbalanced root cause localization without requiring pre-training or physical topology dependencies. …”
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Research on rehabilitation robot control based on port-Hamiltonian systems and fatigue dissipation port compensation by Jingjing Li, Zhen Chen, Zhen Chen, Jian Li, Hongyu Yan, Zhen Li, Minshan Feng, Minshan Feng, Jiawen Zhan, Liwei Shao

“…Experimental validation further showed that, compared to fixed damping parameters, the proposed fatigue compensation approach reduced muscle fatigue accumulation by 45% and increased training duration by 40%.DiscussionThe proposed fatigue-adaptive control framework was shown to enhance the safety, effectiveness, and physiological adaptability of rehabilitation training. …”
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Enhancing wound healing through deep reinforcement learning for optimal therapeutics by Fan Lu, Ksenia Zlobina, Nicholas A. Rondoni, Sam Teymoori, Marcella Gomez

“…We propose an adaptive closed-loop control framework that incorporates deep learning, optimal control and reinforcement learning to accelerate wound healing. …”
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A Review of Hierarchical Control Strategies for Lower-Limb Exoskeletons in Children with Cerebral Palsy by Ziwei Kang, Hui Li, Yang Wang, Hongliu Yu

“…This study adopts a structured narrative review approach, referencing the PRISMA framework to enhance transparency in the literature selection. …”
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Interdisciplinary medical education practices: building a case-driven interdisciplinary simulation system based on public datasets by Kangli Qiu, Tianshu Zeng, Wenfang Xia, Miaomiao Peng, Wen Kong

“…Abstract Background Recent advancements in medical education underscore the importance of training professionals who are proficient in multiple disciplines. …”
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Deep learning for sunflower (Helianthus annuus L.) mapping and stand counting: Trade-offs between closed vs. open-access methods by Maria Villamil-Mahecha, Harsh Pathak, Nitin Rai, Paul Overby, Xin Sun

“…These results suggests that although closed-loop commercial software such as ArcGIS Pro provides DL features for model training, it still remains limited in adapting to custom, high-resolution, agriculture-centered applications. …”
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Control of Linear-Threshold Brain Networks via Reservoir Computing by Michael McCreesh, Jorge Cortes

“…Given the impracticality of evaluating closed-form control signals, particularly with growing network complexity, we provide a framework where a reservoir of a larger size than the network is trained to drive the activity to the desired pattern. …”
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Dynamic Gradient Descent and Reinforcement Learning for AI-Enhanced Indoor Building Environmental Simulation by Xiaolong Chen, Haohao Yang, Hongfeng Zhang, Cora Un In Wong

“…We propose a novel dynamic gradient descent (DGD) framework integrated with reinforcement learning (RL) for AI-enhanced indoor environmental simulation, addressing the limitations of static optimization in dynamic settings. …”
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Optimization Design of Lazy-Wave Dynamic Cable Configuration Based on Machine Learning by Xudong Zhao, Qingfen Ma, Jingru Li, Zhongye Wu, Hui Lu, Yang Xiong

“…To address this challenge, this study proposes a closed-loop optimization framework that couples machine learning with intelligent optimization algorithms for a dynamic cable configuration design. …”
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Deep Reinforcement Learning-Based Impact Angle-Constrained Adaptive Guidance Law by Zhe Hu, Wenjun Yi, Liang Xiao

“…We introduce a parameter to the super-twisting algorithm and subsequently improve an intelligent parameter-adaptive algorithm grounded in the Twin-Delayed Deep Deterministic Policy Gradient (TD3) framework. During the guidance phase, a pre-trained reinforcement learning model is employed to directly map the missile’s state variables to the optimal adaptive parameters, thereby significantly enhancing the guidance performance. …”
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