Quantitative evaluation and obstacle factor diagnosis of drug regulatory capacity in China. by Mingming Zhai, Liwen Huang, Shijie Sun, Liying Cao, Xueqiong Yue, Yuanxia Hu

“…<h4>Methods</h4>Using the methods of literature research, expert interviews, investigation and analysis, the quantitative evaluation indicator system of supervision ability was established in all directions; the indicator data were collected and quantified; the indicator weight setting algorithm of the evaluation system was improved and the indicator weight was set by combining AHP and entropy method; the differences among eastern, central, and western provincial-level regions were analyzed by variance analysis; panel data were constructed for spatio-temporal evolution analysis; obstacle factor diagnosis model was used to analyze the obstacle factors.…”
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Prediction of Bus Arrival Time Based on Gated Recurrent Unit Neural Networks by LU Juntian;SUN Ling;SHI Quan

“…Furthermore, combining more than 50 million pieces of raw data, the model uses Spark elastic distributed data set in distributed Hadoop cluster to clean data and site matching algorithm to match source data, Lasso algorithm to optimize feature options and remove interference. …”
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Target Detection in Low Grazing Angle with Adaptive OFDM Radar by Yang Xia, Zhiyong Song, Zaiqi Lu, Hao Wu, Qiang Fu

“…Then, we propose an algorithm to optimally design the transmitted subcarrier weights to improve the detection performance. …”
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Design and Experiment of a Single-Disk Silage Corn Harvester by Wenxuan Wang, Wei Sun, Hui Li, Xiaokang Li, Yongwei Yuan

“…In addition, the main program was optimized by writing the program of the SMPSO algorithm. …”
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Research and application of flexible starting method for metro trains with permanent magnet motors based on PI control by LIU Zhicheng, CHEN Xijun, HE Ye, HU Hui

“…To address train rollback protection and control, an incremental proportional-integral (PI) algorithm was introduced, by establishing an active rollback prevention and control system model specifically for the train starting process, this approach enhanced the robustness of train starting on inclined track sections. …”
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Joint Resource Scheduling of the Time Slot, Power, and Main Lobe Direction in Directional UAV Ad Hoc Networks: A Multi-Agent Deep Reinforcement Learning Approach by Shijie Liang, Haitao Zhao, Li Zhou, Zhe Wang, Kuo Cao, Junfang Wang

“…The algorithm with the main lobe direction scheduling improves performance by 67.06% compared to the algorithm without the main lobe direction scheduling.…”
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Federated learning for digital twin applications: a privacy-preserving and low-latency approach by Jie Li, Dong Wang

“…Our approach introduces an improved Paillier encryption method with a new hyperparameter and pre-calculates multiple random intermediate values during the key generation stage, significantly reducing encryption time and thereby expediting model training. …”
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A novel double machine learning approach for detecting early breast cancer using advanced feature selection and dimensionality reduction techniques by Suganya Athisayamani, Tamilazhagan S, A. Robert Singh, Jae-Yong Hwang, Gyanendra Prasad Joshi

“…This approach effectively captures both structured features and non-linear patterns, making it suitable for datasets with complex dependencies. The second model pairs eXtreme Gradient Boosting (XGBoost), a highly efficient boosting algorithm for tabular data, with an Artificial Neural Network (ANN). …”
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Association of MTHFR Polymorphisms with H-Type Hypertension: A Systemic Review and Network Meta-Analysis of Diagnostic Test Accuracy by Yixuan Kong, Jinghui Zheng, Lijuan Li, Liying Lu, Jie Wang

“…The results indicated that the dominant model was an optimal diagnosis model for excluding diseases, which could reduce a missed diagnosis rate and further improve the accuracy of disease diagnosis. …”
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GDnet-IP: Grouped Dropout-Based Convolutional Neural Network for Insect Pest Recognition by Dongcheng Li, Yongqi Xu, Zheming Yuan, Zhijun Dai

“…Specifically, we optimized the base model by selecting appropriate optimizers, fine-tuning the dropout probability, and adjusting the learning rate decay strategy. …”
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Human-based metaheuristics and non-parametric learning for groundwater-prone area mapping by Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi-Niaraki, Seyedeh Zeinab Shogrkhodaei, Biswajeet Pradhan, Soo-Mi Choi

“…This research introduces a novel approach combining human-based metaheuristics—Teaching Learning Based Optimization (TLBO) and Cultural Algorithms (CA)—with non-parametric Decision Tree (DT) models. …”
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Research on intelligent energy management strategies for connected range-extended electric vehicles based on multi-source information by Xuewen Zhai, Hanwu Liu, Wencai Sun, Zihang Su

“…The Euclidean distance between consecutive traffic scenario matrices is used as a basis for similarity to optimize speed and predict future vehicle speeds. Moreover, a multi-objective intelligent EMS based on deep reinforcement learning (DRL) is employed, utilizing the Deep Deterministic Policy Gradient (DDPG) algorithm to comprehensively consider vehicle dynamics, energy consumption economy, and the degradation of batteries. …”
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Reconstruction for Scanning LiDAR with Array GM-APD on Mobile Platform by Di Liu, Jianfeng Sun, Wei Lu, Sining Li, Xin Zhou

“…This method avoids the need for field-of-view registration, improves data utilization, and reduces the complexity of the algorithm while eliminating the effect of LiDAR motion. …”
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A Method of Locating and Measuring Train Wheel Tread Defects Based on YOLOv3-tiny by JIN Kairong, WANG Junping, CHEN Shenglan

“…In order to solve the key problems that the detection frame is too large and too small, traditional image algorithms such as Fourier transform, band-stop filter, and threshold segmentation are used to construct a defect size measurement model, contours of roughly located defects are extracted and detection frame size is optimized, and finally location and size of defect are accurately calculated. …”
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Federated Synergy: Hierarchical Multi-Agent Learning for Sustainable Edge Computing in IIoT by S. Benila, K. Devi

“…It employs multi-agent reinforcement learning to optimize resource allocation and task offloading by leveraging local Actor-Critic models on IoT edge devices. …”
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Simulation and test study of transmission characteristics of V-shaped rubber belt CVT by TIAN Zhewen, TIAN Hao, CHEN Shuoyan

“…ObjectiveThe dynamic matching problem of continuously variable transmission （CVT） with the engine and Baja racing car was subjected to research analysis.MethodsThe parameters of key components of CVT were first optimized by genetic algorithm in Matlab software, then the dynamic modeling and simulation of CVT and the whole vehicle were carried out in Matlab/Simulink and RecurDyn, and finally the vehicle test was carried out on the chassis dynamometer.ResultsThe results of modeling simulation and vehicle test show that the dynamic index of Baja racing car is significantly improved after the optimization of the parameters of key components of CVT. …”
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A geometric approach for accelerating neural networks designed for classification problems by Mohsen Saffar, Ahmad Kalhor, Ali Habibnia

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An interpretable integrated machine learning framework for genomic selection by Jinbu Wang, Jia Zhang, Wenjie Hao, Wencheng Zong, Mang Liang, Fuping Zhao, Longchao Zhang, Lixian Wang, Huijiang Gao, Ligang Wang

“…Moreover, we introduced the NuSVR model, which achieved the highest accuracy among nine compared algorithms. …”
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MRI-based intratumoral and peritumoral radiomics for assessing deep myometrial invasion in patients with early-stage endometrioid adenocarcinoma by Jing Yang, Yang Liu, Xiaolong Liu, Yaoxin Wang, Xianhong Wang, Conghui Ai, Qiu Bi, Ying Zhao

“…Integrated models were constructed by ensemble and stacking algorithms based on the above radiomics models. …”
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Vehicle Reidentification via Multifeature Hypergraph Fusion by Wang Li, Zhang Yong, Yuan Wei, Shi Hongxing

“…To merge multiple feature descriptors, an efficient and appropriate representation is particularly necessary, and a hypergraph is naturally suitable for modeling high-order relationships. In addition, the spatiotemporal correlation of traffic status between cameras is the constraint beyond the image, which can greatly improve the re-ID accuracy of different vehicles with similar appearances. …”
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