Interactive Operation Strategy for Multi-scenario County-Level Multi-microgrid Based on ADMM by Chongbiao ZHANG, Chenwen QIAN, Hongyan YU, Yanling PENG, Jinwei CHEN

“…Firstly, a scheduling model for independent microgrid operation is established to realize the optimal scheduling strategy in the day-ahead pre-scheduling plan. …”
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The future of critical care: AI-powered mortality prediction for acute variceal gastrointestinal bleeding and acute non-variceal gastrointestinal bleeding patients by Zhou Liu, Guijun Jiang, Liang Zhang, Palpasa Shrestha, Yugang Hu, Yi Zhu, Guang Li, Yuanguo Xiong, Liying Zhan

“…In case of ANGIB patients, gradient boosting model proven to be the optimal machine learning models, with the AUC of 0.985 ± 0.002, accuracy of 0.948 ± 0.009, precision of 0.949 ± 0.009, recall of 0.968 ± 0.009, and F1 score of 0.959 ± 0.007. …”
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MED-AGNeT: An attention-guided network of customized augmentation of samples based on conditional diffusion for textile defect detection by Jun Liu, Haolin Li, Hao Liu, Jiuzhen Liang

“…Ultimately, AGNet’s true positive rate (TPR), positive predictive value (PPV), and f-measure exceed those of the state-of-the-art (SOTA) algorithms by 1.88%, 0.05%, and 0.77%, respectively, and with a consistent model architecture, its parameter quantity is reduced by 56%.…”
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Acute Appendicitis Remains a Great Mimicker – The Pitfalls in the Differential Diagnosis and Tactics - A Case Report by Georgi Popivanov, Marina Konaktchieva, Vladimir Vasilev, Kirien Kjossev, Marin Penkov, Dimitar Penchev

“…Acute appendicitis (AA) is the most common non-traumatic abdominal emergency. Despite the improved knowledge, experience, and technological advance, its diagnosis remains a challenge. …”
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Comprehensive Review of Robotics Operating System-Based Reinforcement Learning in Robotics by Mohammed Aljamal, Sarosh Patel, Ausif Mahmood

“…To improve decision making, robots need a dependable framework to facilitate communication between different modules and the optimal action for real-world applications. …”
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Lymph Node Assessment in Endometrial Cancer: Towards Personalized Medicine by Fabien Vidal, Arash Rafii

“…Finally, the use of peroperative algorithm for risk determination could improve patient's staging with a reduction of lymphadenectomy-related morbidity.…”
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An abnormal traffic detection method for chain information management system network based on convolutional neural network by Chao Liu, Chunxiang Liu, Changrong Liu

“…Finally, the conditional random field (CRF) determines the optimal label sequence based on the conditional probability distribution and applies the Viterbi algorithm to complete the sequence labeling of network traffic in chain information management system. …”
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Short-Term Power Prediction for Wind Farm and Solar Plant Clusters Based on Machine Learning Method by Yang CUI, Zhenghong CHEN, Peihua XU

“…Firstly, the machine learning-based Bisecting K-Means(BKM) clustering algorithm is used to reasonably divide the wind farms and PV stations in the region into clusters; Secondly, based on the correlation between of the historical power data of each power station and the total historical power data in the region, a representative power station is selected for each region; Thirdly, after optimizing and correcting the NWP(numerical weather prediction) model of each representative power station, a short-term power prediction framework model is established using BP neural network based on the cluster division of wind farms and PV power plants. …”
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Urban Freight Management with Stochastic Time-Dependent Travel Times and Application to Large-Scale Transportation Networks by Shichao Sun, Zhengyu Duan, Dongyuan Yang

“…Based on that, the proposed STD-VRP model can be converted into solving a normal time-dependent VRP (TD-VRP), and algorithms for such TD-VRPs can also be introduced to obtain the solution. …”
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To accurately predict lymph node metastasis in patients with mass-forming intrahepatic cholangiocarcinoma by using CT radiomics features of tumor habitat subregions by Pengyu Chen, Zhenwei Yang, Peigang Ning, Hao Yuan, Zuochao Qi, Qingshan Li, Bo Meng, Xianzhou Zhang, Haibo Yu

“…This model is expected to provide personalized decision support to clinicians and help to optimize treatment plans and improve patient outcomes.…”
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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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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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Verification of the method for classifying the technical state of a turboshaft engine fuel regulator in the space of operational process parameters under factory test conditions by Ihor Ohanian, Sergiy Yepifanov

“…Recommendations for further improvement of the method include using expert systems and developing effective model identification algorithms. …”
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Real-Time Human Group Detection and Clustering in Crowded Environments Using Enhanced Multi-Object Tracking by Hyunmin Lee, Donggoo Kang, Hasil Park, Sangwoo Park, Dasol Jeong, Joonki Paik

“…To address these limitations, we propose a novel algorithm that integrates an optimized YOLOv8 model with DeepSORT tracking, enhancing both detection accuracy and real time performance. …”
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Simulation and Test of a Fuel Cell Hybrid Golf Cart by Jingming Liang, Qifei Jian

“…This paper establishes the simulation model of fuel cell hybrid golf cart (FCHGC), which applies the non-GUI mode of the Advanced Vehicle Simulator (ADVISOR) and the genetic algorithm (GA) to optimize it. …”
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Comparative Assessment of Several Effective Machine Learning Classification Methods for Maternal Health Risk by Md Nurul Raihen, Sultana Akter

“…Maternal risk analysis can improve prenatal care, improve mother and baby health, and optimize healthcare resources by identifying misclassified observations using machine learning algorithms such as LDA, QDA, KNN, Decision Tree, Random Forest, Bagging, and Support Vector Machine, all of which have a significant impact on maternity health risk assessment. …”
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Fingerprint Classification Based on Multilayer Extreme Learning Machines by Axel Quinteros, David Zabala-Blanco

“…In this study, we introduce, for the first time, the use of a multilayer extreme learning machine (M-ELM) for fingerprint classification, aiming to improve training efficiency. A comparative analysis is conducted with CNNs and unbalanced extreme learning machines (W-ELMs), as these represent the most influential methodologies in the literature. …”
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Precision Weed Management for Straw-Mulched Maize Field: Advanced Weed Detection and Targeted Spraying Based on Enhanced YOLO v5s by Xiuhong Wang, Qingjie Wang, Yichen Qiao, Xinyue Zhang, Caiyun Lu, Chao Wang

“…Secondly, we proposed an improved YOLO v5s network, incorporating a Convolutional Block Attention Module (CBAM), FasterNet feature extraction network, and a loss function to optimize the network structure and training strategy. …”
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Electromagnetic Transient Parallel Simulation of Large-Scale New Energy Grid Connection Based on PSCAD/EMTDC by Bin CAO, Liqiang WANG, Yongfei ZHAO, Xiuqi ZHANG

“…The simulation results show that the cluster parallel technology can effectively improve the simulation efficiency of the new energy grid-connected electromagnetic transient model. …”
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An efficient cost calculation method for disparity estimation of stereo matching considering shadow occlusion by Hongjun Sha, Wei Yuan, Xunping Wang, Xiuliu Yuan, Shunichi Koshimura

“…The qualitative and quantitative evaluation results show that MCSP is superior to two classical cost calculation methods and shows good transferability and applicability in the popular disparity estimation algorithms, which can significantly enhance the definition of the edges of the ground objects and improve the quality of the disparity estimation in the shadow occlusion areas.…”
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