Personalized Federated Learning for Heterogeneous Residential Load Forecasting by Xiaodong Qu, Chengcheng Guan, Gang Xie, Zhiyi Tian, Keshav Sood, Chaoli Sun, Lei Cui

“…Based on the principle of generative adversarial network (GAN), the algorithm achieves the balance between privacy and prediction accuracy throughout the game. …”
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Research of application of DFT-modulated filter bank in systems with significant spectral component amplification by N. S. Sanko, M. I. Vashkevich

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ScRNA-seq reveals novel immune-suppressive T cells and investigates CMV-TCR-T cells cytotoxicity against GBM by Wei Gao, Qing Liu, Xiang Lin, Yiming Zhao, Minghua Wu, Shuai Chen, Peng Du, Meng Huang, Cheng Huang, Xiangyu Wang, Xinmiao Long, Zuping Zhang, Yuzhe Li, Kun Deng, Xiaoling She, Minfu Zhang, Shiyi Wang, Yinfei Du

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Research on High Arch Dam Deformation Monitoring Model with Deep Capturing Related Features in Factor-time Dimensions by XUE Jianghan, ZHANG Pengtao, TIAN Jichen, LU Xiang, CHEN Jiankang, Guo Yinju

“…However, at the present stage, the dam prediction model based on machine learning mostly adopts the means of data preprocessing, using optimization algorithm, and using the model's characteristics to stack multiple models, lacking in in-depth consideration of the physical mechanism of dam deformation. …”
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Tensor-Based Efficient Federated Reinforcement Learning for Cyber-Physical-Social Intelligence by Xin Nie, Laurence T. Yang, Fulan Fan, Zecan Yang

“…By leveraging tensor to model state-action values and employing tensor decomposition techniques for dimensionality reduction, this method effectively reduces model parameters and communication overhead while maintaining strong interpretability, accelerates algorithm convergence speed. …”
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Informative Path Planning Using Physics-Informed Gaussian Processes for Aerial Mapping of 5G Networks by Jonas F. Gruner, Jan Graßhoff, Carlos Castelar Wembers, Kilian Schweppe, Georg Schildbach, Philipp Rostalski

“…Physics-informed mean functions, including a detailed ray-tracing simulation, are integrated into the GP models to enhance the extrapolation performance of the GP prediction. As a central element of the GP prediction, a quantitative evaluation of different mean functions is conducted. …”
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Investigation on the Temperature Distribution of Asphalt Overlay on the Existing Cement Concrete Pavement in Hot-Humid Climate in Southern China by Zuzhong Li, Yayun Zhang, Chunguang Fa, Xiaoming Zou, Haiwei Xie, Huaxin Chen, Rui He

“…This research is to analyze the impact of meteorological factors on temperature at 2 cm depth in asphalt overlay by ReliefF algorithm and also obtain the temperature prediction model using MATLAB. …”
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FocusDet: an efficient object detector for small object by Yanli Shi, Yi Jia, Xianhe Zhang

“…SIOU multi-dimension accurately locates the prediction box, and SoftNMS uses the Gaussian algorithm to remove redundant prediction boxes. …”
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Digital mapping of peat thickness and extent in Finland using remote sensing and machine learning by Jonne Pohjankukka, Timo A. Räsänen, Timo P. Pitkänen, Arttu Kivimäki, Ville Mäkinen, Tapio Väänänen, Jouni Lerssi, Aura Salmivaara, Maarit Middleton

“…Additionally, we provided an approach for evaluating spatial prediction uncertainty based on the models’ internal prediction agreement. …”
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An integrated approach of feature selection and machine learning for early detection of breast cancer by Jing Zhu, Zhenhang Zhao, Bangzheng Yin, Canpeng Wu, Chan Yin, Rong Chen, Youde Ding

“…With the increasing application of machine learning technology in the medical field, algorithm-based diagnostic tools provide new possibilities for early prediction of breast cancer. …”
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Automated Detection of Reduced Ejection Fraction Using an ECG-Enabled Digital Stethoscope by Ling Guo, PhD, Gregg S. Pressman, MD, Spencer N. Kieu, BS, Scott B. Marrus, MD, PhD, George Mathew, PhD, John Prince, PhD, Emileigh Lastowski, MS, Rosalie V. McDonough, MD, MSc, Caroline Currie, BA, John N. Maidens, PhD, Hussein Al-Sudani, MD, Evan Friend, BA, Deepak Padmanabhan, MD, Preetham Kumar, MD, Edward Kersh, MD, Subramaniam Venkatraman, PhD, Salima Qamruddin, MD

“…Results: The CNN model demonstrated an area under the receiver operating characteristic curve of 0.85, with a sensitivity of 77.5%, specificity of 78.3%, positive predictive value of 20.3%, and negative predictive value of 98.0%. …”
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Machine Learning-Assisted Hartree–Fock Approach for Energy Level Calculations in the Neutral Ytterbium Atom by Kaichen Ma, Chen Yang, Junyao Zhang, Yunfei Li, Gang Jiang, Junjie Chai

“…The workflow incorporates enhanced ElasticNet and XGBoost algorithms, refined using entropy weight methodology to optimize performance. …”
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A mineral-strength conversion model based on LIBS technology and rapid batch testing and application of uniaxial compressive strength by Qinghe ZHANG, Xiaorui WANG, Chuanbing WANG, Weiguo LI, Fengqiang GONG, Xinsheng ZHANG, Jingguo LI

“…The mass fraction of mineral components is analyzed via the support vector regression (SVR) algorithm. In the end, a mineral-strength conversion model is established to calculate the UCS from the predicted values of mineral component concentrations, and its rationality and scientific validity are validated by the standard mechanical tests. …”
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Human-Annotated Label Noise and Their Impact on ConvNets for Remote Sensing Image Scene Classification by Longkang Peng, Tao Wei, Xuehong Chen, Xiaobei Chen, Rui Sun, Luoma Wan, Jin Chen, Xiaolin Zhu

“…We found that 1) human-annotated label noise exhibits significant class and instance dependence; 2) an additional 1% of human-annotated label noise in training data leads to a 0.5% reduction in the overall accuracy of ConvNets classification; and 3) the error pattern of ConvNet predictions was strongly correlated with that of participant's labels. …”
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Design, Development and Implementation of iALERTS (Informatics Analytics for Long-term Evaluation and Repercussions Tracking of SARS-CoV-2 Infection): A Research Protocol by Krishna Mohan Surapaneni, Manmohan Singhal, Ashish Joshi

“…This platform employs machine learning algorithms to predict the likelihood of PASC development among Coronavirus Disease-19 (COVID-19) survivors. …”
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A Review of Personalised Cardiac Computational Modelling Using Electroanatomical Mapping Data by Ovais A Jaffery, Lea Melki, Gregory Slabaugh, Wilson W Good, Caroline H Roney

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Planificación y optimización asistida por computadora de secuencias de ensamble mecánico // Computer aided Planning and optimization for mechanical assembly. by L. L. Tomás-García

“…The<br />generated assembly sequences are preprocessed and optimized for the assembly Process Planning<br />using Genetic Algorithms. This approach integrates the geometric and technological information of<br />the assembly process, which allows reducing the number of elements and sequences to be<br />processed with the consequent processing time and cost reduction.…”
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Modern fixation techniques versus traditional tension band wiring for olecranon fractures: a systematic review and meta-analysis of functional outcomes, healing time, and complicat... by Chengjing Wang, Changqing Li

“…Larger standardized trials are needed to confirm these preliminary conclusions and refine evidence-based treatment algorithms.…”
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Surface Clay and Mineral Exploration Using Hyperspectral Imaging: Advanced Techniques for Geospatial Analysis by S. Raut, N. Andure, A. Kadam, A. Dongare, A. Fulari, G. M. Sayyad, S. B. Sayyad, S. K. Shaikh, S. Sawant

“…The study employs advanced HSI processing techniques, including Minimum Noise Fraction (MNF) transformation for noise reduction and feature space optimization, and Pixel Purity Index (PPI) for endmember extraction, followed by mineral classification using Spectral Angle Mapper (SAM). …”
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Unveiling the power of high-dimensional cytometry data with cyCONDOR by Charlotte Kröger, Sophie Müller, Jacqueline Leidner, Theresa Kröber, Stefanie Warnat-Herresthal, Jannis Bastian Spintge, Timo Zajac, Anna Neubauer, Aleksej Frolov, Caterina Carraro, DELCODE Study Group, Frank Jessen, Simone Puccio, Anna C. Aschenbrenner, Joachim L. Schultze, Tal Pecht, Marc D. Beyer, Lorenzo Bonaguro

“…The comprehensive suite of features of cyCONDOR, including guided pre-processing, clustering, dimensionality reduction, and machine learning algorithms, facilitates the seamless integration of cyCONDOR into clinically relevant settings, where scalability and disease classification are paramount for the widespread adoption of HDC in clinical practice. …”
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