DS-YOLOv7: Dense Small Object Detection Algorithm for UAV by Tao Sun, Haonan Chen, Haiying Liu, Lixia Deng, Lida Liu, Shuang Li

“…The experimental verification on the public data set VisDrone2019 shows that mAP50 and mAP50-95 have increased by 4.3% and 3.3% respectively, F1 scores have increased by 3.81%, model volume has decreased by 23.3MB, parameters have decreased by 13 million, and the improved algorithm is more conducive to UAV deployment and dense small object detection.…”
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Athlete movement control training based on dynamic time warping algorithm by Luo Dai, Yueqiang Zhao

“…Subsequently, a quality assessment model for action completion is built based on an improved dynamic time-warping algorithm. …”
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Automated diabetes detection prediction system based on patients’ medical data by S.V. Pidopryhora, Yu.V. Bogoyavlenska

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Optimum problem of piezoelectric laminated composite plate using genetic algorithm by Le Kim Ngoc, Tran Ich Thinh

“…A nine-node isoparametric rectangular element with 5 degrees of freedom for the generalized displacements and 2 electrical degrees of freedom at each node is used. Optimization techniques based on genetic algorithm (GAs) are applied in order to maximize the piezoelectric actuator efficiency, improve the structural performance. …”
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Model‐Free Deep Reinforcement Learning with Multiple Line‐of‐Sight Guidance Laws for Autonomous Underwater Vehicles Full‐Attitude and Velocity Control by Chengren Yuan, Changgeng Shuai, Zhanshuo Zhang, Jianguo Ma, Yuan Fang, YuChen Sun

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Enhancing phase change thermal energy storage material properties prediction with digital technologies by Minghao Yu, Jing Liu, Cheng Chen, Mingyue Li

“…To address these limitations, the integration of digital technologies, such as computational modeling and machine learning (ML), has become increasingly important.MethodsThis paper proposes a hybrid multiscale modeling framework that integrates molecular dynamics (MD) simulations, finite element methods (FEM) from continuum mechanics, and supervised ML algorithms—including deep neural networks and gradient boosting regressors—to enable accurate and efficient prediction of material properties across scales. …”
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Research on Dynamic Performance of Autonomous-rail Rapid Tram by ZHONG Hanwen, LI Xiaoguang, XIAO Lei, YANG Yong, ZHANG Chenlin, HUANG Ruipeng, YUAN Xiwen

“…Through detailed Simpack dynamic model, the simulation research was carried out to provide guidance for optimization and improvement of vehicle dynamic performance. …”
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Development and validation of novel machine learning-based prognostic models and propensity score matching for comparison of surgical approaches in mucinous breast cancer by Chunmei Chen, Jundong Wu, Yutong Fang, Yong Li, Qunchen Zhang

“…We have successfully developed 6 optimal prognostic models utilizing the XGBoost algorithm to accurately predict the survival of MBC patients. …”
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CRASHWORTHINESS DESIGN AND OPTIMIZATION FOR COLLISION POST OF TRAIN by SHI YueQing, QIN RuiXian, CHEN BingZhi

“…In order to improve the crashworthiness of the collision post structure, the optimal cross-section configuration of the collision post was obtained based on the topology optimization method, and the impacting finite element model of the collision post was established in the explicit dynamics software Ls-Dyna. …”
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Object Detection Algorithm Based on Feature Enhancement and Anchor-object Matching by LI Cheng-yan, ZHAO Shuai, CHE Zi-xuan

“…In order to improve the detection accuracy of SSD (Single Shot MultiBox Detector), an A-SSD (Anchor-object SSD) object detection algorithm based on Anchor-object matching is proposed. …”
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Modeling Techniques and Boundary Conditions in Abdominal Aortic Aneurysm Analysis: Latest Developments in Simulation and Integration of Machine Learning and Data-Driven Approaches by Burcu Ramazanli, Oyku Yagmur, Efe Cesur Sarioglu, Huseyin Enes Salman

“…Research on abdominal aortic aneurysms (AAAs) primarily focuses on developing a clear understanding of the initiation, progression, and treatment of AAA through improved model accuracy. High-fidelity hemodynamic and biomechanical predictions are essential for clinicians to optimize preoperative planning and minimize therapeutic risks. …”
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A deep learning-based algorithm for the detection of personal protective equipment. by Bo Tong, Guan Li, Xiangli Bu, Yang Wang, Xingchen Yu

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Recommendation Algorithm of Web-Based Learning Resources considering Timeliness and Popularity by Xiaolu Han

“…Accordingly, an improved collaborative filtering algorithm (TRCP) is proposed to improve the accuracy of learning resource recommendation. …”
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3D animation design image detail enhancement based on intelligent fuzzy algorithm by Pu Haitao, Pu Yuang

“…The image was divided into superpixel regions using SLIC (Simple Linear Iterative Clustering) algorithm, and local features such as texture, contrast, and edge intensity were extracted; in the SRGAN model, the generator improved image resolution through deep residual blocks and Convolutional Neural Network (CNN), while the discriminator optimized the generated image quality through adversarial training; at the same time, a Fuzzy Logic System (FLS) was constructed to dynamically adjust the image fuzzy degree; channel and spatial attention modules in the generator were integrated to enhance key area details. …”
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Distributionally Robust Operation for Flexible Distribution Networks Considering Multi-correlation of Renewable Power Generation by Zhiwei LIU, Yue MA, Zhicheng SHA, Yunshu SHAO, Yuanfang NIU, Xiaoming DONG, Chengfu WANG

“…Finally, the column and constraint generation algorithm is adopted to solve the model, and a simulation analysis is conducted using an improved IEEE 33-bus test system as an example, verifying the effectiveness and practicality of the proposed method.…”
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Study on debris flow vulnerability of ensemble learning model based on spy technology A case study of upper Minjiang river basin by Yutao Chen, Ning Li, Fucheng Xing, Han Xiang, Zilong Chen

“…This suggests that the SPY technique is able to improve the prediction accuracy and reliability of the model, especially effective in reducing the misclassification of non-prone areas. …”
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Do We Need to Add the Type of Treatment Planning System, Dose Calculation Grid Size, and CT Density Curve to Predictive Models? by Reza Reiazi, Surendra Prajapati, Leonardo Che Fru, Dongyeon Lee, Mohammad Salehpour

“…Solutions such as multi-institutional data harmonization and domain adaptation techniques are essential to improve model generalizability and robustness. These strategies support the better integration of predictive modeling into clinical workflows, ultimately optimizing patient outcomes and personalized treatment strategies.…”
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Development and validation of a machine learning model for online predicting the risk of in heart failure: based on the routine blood test and their derived parameters by Jianchen Pu, Yimin Yao, Xiaochun Wang

“…This online forecasting tool not only processes a large amount of data but also continuously optimizes and adjusts the accuracy of the model according to the latest medical research and clinical data. …”
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EPRSA: interference resource scheduling algorithms for air-ground communication networks by Wenpeng Wu, Zhenhua Wei, Haiyang You, Xiaoqing Li, Jianwei Zhan, Zhaoguang Zhang

“…Based on this, this paper proposes the interference link margin in the mathematical model and normalizes and quantifies the optimization objectives. …”
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Algorithm of underdetermined convolutive blind source separation for high reverberation environment by Yuan XIE, Tao1 ZOU, Weijun SUN, Shengli XIE

“…To separate the underdetermined convolutive mixture signals in the high reverberation environment, a novel algorithm of underdetermined convolutive blind source separation was proposed.Aiming at the influence of high reverberation environment, a global impulse response network was designed to weaken reverberation echo, improving signal quality.A new mathematical model of time-frequency mixing signals was established based on the global impulse response network.The global impulse response matrix which shortened the length of the traditional impulse response, reduced the approximation error of model transformation caused by high reverberation.The real-time update learning rules of model parameters were designed based on the theory of nonnegative matrix factorization, and the source signal separation problem was converted into the model parameter optimization problem, achieving blind source separation of mixing signals.Experimental results show that the proposed algorithm can effectively realize the blind source separation of Chinese and English speech and music signals, and the comparision with existing popular algorithms verified the superiority of the proposed algorithm.…”
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