Machine-Learning-Based Rollover Risk Prediction for Autonomous Trucks: A Dynamic Stability Analysis by Heung-Shik Lee

“…This study addresses this gap by conducting dynamic simulations of standardized rollover tests to evaluate the static stability factor (SSF) and by developing a machine-learning-based model for predicting rollover risk. …”
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InsNet-CRAFTY v1.0: integrating institutional network dynamics powered by large language models with land use change simulation by Y. Zeng, C. Brown, C. Brown, M. Byari, J. Raymond, T. Schmitt, M. Rounsevell, M. Rounsevell, M. Rounsevell

“…Despite errors in information and behaviours by the LLM agents, the network maintains overall behavioural believability. …”
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Enhanced Wind Power Forecasting Using Graph Convolutional Networks with Ramp Characterization and Error Correction by Xin He, Yichen Ma, Jiancang Xie, Gang Zhang, Tuo Xie

“…This study proposes a wind power prediction approach based on graph convolutional networks, incorporating ramp feature recognition and error correction mechanisms. …”
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DualDyConvNet: Dual-Stream Dynamic Convolution Network via Parameter-Efficient Fine-Tuning for Predicting Motor Prognosis in Subacute Stroke by Yunjeong Jang, Joohye Jeong, Yun Kwan Kim, Da-Hye Kim, Wanjoo Park, Laehyun Kim, Yun-Hee Kim, Minji Lee

“…In this study, we propose a novel framework, called dual-stream dynamic convolution network (DualDyConvNet), to predict motor recovery for two months using resting-state electroencephalogram data in the subacute phase. …”
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Use of Ecological Networks to Reveal Interspecific Fungal Interactions from 150 Years of Foray Records by Zhiru Guoli, Nathan Edward Charles Smith

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Effective Urban Region Representation Learning Using Heterogeneous Urban Graph Attention Network (HUGAT) by Namwoo Kim, Yoonjin Yoon

“…Objective: This study aims to develop a model that effectively represents urban regions by incorporating both spatial features and human activity patterns, in order to better understand and predict urban dynamics. Methods: We propose a novel urban region representation model called the Heterogeneous Urban Graph Attention Network (HUGAT). …”
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Research on Time Constant Test of Thermocouples Based on QNN-PID Controller by Chenyang Xu, Xiaojian Hao, Pan Pei, Tong Wei, Shenxiang Feng

“…This paper simulates and analyzes the effects of adjusting PID parameters using quantum neural networks. By comparing this with the method of optimizing PID parameters with BP neural networks, the superiority of the designed QNN-PID controller is proven. …”
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Analysis of Anti-Jamming Performance of HF Access Network Based on Asymmetric Frequency Hopping by Ruijie Duan, Liang Jin, Xiaofei Lan

“…The primary focus of this paper lies in addressing the inadequate anti-dynamic jamming capability of the link layer within high-frequency (HF) access networks. …”
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<italic>DynaTrack</italic>: Low-Power Channel-Aware Dynamic Smartphone Tracking Using UWB DL-TDOA by Junyoung Choi, Sagnik Bhattacharya, Joohyun Lee

“…It comprises a NLOS probability predictor based on a convolutional neural network (CNN), a dynamic ranging frequency control module, and an IMU sensor-based ranging filter. …”
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Deep Fusion of Skeleton Spatial–Temporal and Dynamic Information for Action Recognition by Song Gao, Dingzhuo Zhang, Zhaoming Tang, Hongyan Wang

“…Focusing on the issue of the low recognition rates achieved by traditional deep-information-based action recognition algorithms, an action recognition approach was developed based on skeleton spatial–temporal and dynamic features combined with a two-stream convolutional neural network (TS-CNN). …”
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Soft actor-critic algorithm and improved GNN model in secure access control of disaggregated optical networks by Zhenqian Zhao, Yuhe Wang

“…Abstract To address the challenges of coordinated defense amid dynamic topology evolution and multidimensional security threats in decomposed optical networks, this study introduces the Graph-Entangled Security Actor-Critic (GESAC) model. …”
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Monitoring and investigation to control the brain network disease under immunotherapy by using fractional operator by Muhammad Farman, Ammara Talib, Khadija Jamil, Kottakkaran Sooppy Nisar, Aceng Sambas, Mustafa Bayram, Mohamed Hafez

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Deep learning-driven insights into the transmission dynamics of hepatitis B virus with treatment by Muhammad Farhan, Saif Ullah, Waseem, Muath Suliman, Abdul Baseer Saqib, Mohammed Qeshta

“…Furthermore, to enhance the accuracy of epidemiological modeling, we develop a novel computational technique using deep neural networks (DNNs) to solve the Caputo HBV model. To support our theoretical findings, we conduct a comprehensive evaluation of the DNN-generated solutions by benchmarking them against standard numerical results and assessing them through multiple phases of training, validation, testing, error distribution analysis, and regression evaluation. …”
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Optimizing early hip maturation strategies in neonatal hip immaturity and dysplasia: a cohort study and network meta-analysisResearch in context by Yu-Cheng Su, Ying-Yu Wang, Pei-Chun Lai, Yu-Kang Tu, Chih-Kai Hong, Ming-Tung Huang, Chii-Jeng Lin, Su-Ying Lee, Che-Wei Lin, Ching-Ju Fang, Po-Ting Wu, Fa-Chuan Kuan, Kai-Lan Hsu, Jou-Hua Wang, Chien-An Shih

“…Second, we performed a network meta-analysis (NMA) registered in PROSPERO (CRD42024591239) to synthesize broader evidence on treatment effectiveness. …”
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Research on Grouting Dynamic Monitoring Based on Borehole–Tunnel Joint Resistivity Method by Cheng Wang, Lei Zhou, Liangjun Yan, Bofan Li

“…By integrating three-dimensional (3D) electrode arrays in both tunnels and boreholes with 3D resistivity inversion technology, this approach enables fully automated underground data acquisition and real-time processing, facilitating comprehensive dynamic monitoring of grout propagation. A case study was conducted on a coal mine fault grouting project, where tunnel and borehole survey lines were deployed to construct a 3D cross-monitoring network, overcoming the limitations of traditional 2D data acquisition. …”
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Predicting ionic conductivity in solids from the machine-learned potential energy landscape by Artem Maevskiy, Alexandra Carvalho, Emil Sataev, Volha Turchyna, Keian Noori, Aleksandr Rodin, A. H. Castro Neto, Andrey Ustyuzhanin

“…One can achieve significant computational advantages by leveraging them as the foundation for traditional methods of assessing the ionic conductivity, such as molecular dynamics or nudged elastic band techniques. …”
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An Efficient Semantic Segmentation Framework with Attention-Driven Context Enhancement and Dynamic Fusion for Autonomous Driving by Jia Tian, Peizeng Xin, Xinlu Bai, Zhiguo Xiao, Nianfeng Li

“…To validate the effectiveness of the proposed network, experiments were conducted on a single RTX 3090 GPU. …”
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Dynamic Modeling and Simulation of Wind Turbine Unit Primary Frequency Regulation Considering Multi-domain Coupling Characteristics by Zhanyang JI, Yang HU, Lingxing KONG, Ziqiu SONG, Dan DENG, Jizhen LIU

“…Thirdly, based on balanced sampling of simulation operating data under discrete operating conditions, and guided by physical prior information, subspace identification and deep neural network algorithms are employed to conduct multi-input-multi-output modeling and simulation verification of the unit's primary frequency modulation dynamics across the full range of operating conditions. …”
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A multi-stage weakly supervised design for spheroid segmentation to explore mesenchymal stem cell differentiation dynamics by Arash Shahbazpoor Shahbazi, Farzin Irandoost, Reza Mahdavian, Seyedehsamaneh Shojaeilangari, Abdollah Allahvardi, Hossein Naderi-Manesh

“…Accurate image segmentation is crucial for analyzing the morphological features of the spheroids during the experimental period and for understanding MSC differentiation dynamics for therapeutic applications. Therefore, we developed an innovative, weakly supervised model, aided by convolutional neural networks, to perform label-free spheroid segmentation. …”
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Water Leakage Pre-Localization in Drinking Water Networks via the Cosmic-Ray Neutron Sensing Technique by Luca Morselli, Federica Lorenzi, Andrea Basso, Luca Stevanato

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