Modeling and optimization of power consumption for multi-core processors: A thermodynamic perspectiveModeling-and-Optimization-of-Power-Consumption-for-Multi-core-Processors-A-Ther... by Xiaoxuan Peng, Litao Yan, Qiang Li, Xiaohu Ge, Yonghui Li

“…Based on the relationship between processor power consumption and temperature, a spatio-temporal optimization algorithm is designed to reduce the power consumption of multi-core processors, which achieved maximum 22.2% power consumption reduction compared with unoptimized processors. …”
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Supporting self-management with an internet intervention for low back pain in primary care: a RCT (SupportBack 2) by Adam W A Geraghty, Taeko Becque, Lisa C Roberts, Jonathan Hill, Nadine E Foster, Lucy Yardley, Beth Stuart, David A Turner, Gareth Griffiths, Frances Webley, Lorraine Durcan, Alannah Morgan, Stephanie Hughes, Sarah Bathers, Stephanie Butler-Walley, Simon Wathall, Gemma Mansell, Malcolm White, Firoza Davies, Paul Little

“…At 12 months, there were small but significant reductions in kinesiophobia in both intervention arms, compared to usual care. …”
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TransUNet Image 3D Reconstruction with Hyperparameter Optimization by Xiaofang Wang, Zhihao Luo, Mingrui Gou, Kerui Mao, Liang Zhou

“…The method employs Restricted Boltzmann Machine (RBM) for depth prediction and an optimized ant colony algorithm for network parameter optimization. …”
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Consumer Happiness in the Purchase of Electric Vehicles: a Fuzzy Logic Model by Fernando Lámbarry-Vilchis, Aboud Barsekh Onji, Leticia Refugio Chavarría López, Paola Judith Maldonado Colín

“…This research was conducted using a fuzzy Delphi method survey targeting a specific consumer group and two fuzzy inference systems: a multi-input single-output FIS model and an FIS Tree employing a hierarchical fuzzy inference structure, which leverages the survey's training data to optimize the models using different machine learning algorithms. The FIS tree model demonstrated superior efficacy in predicting the consumer satisfaction index, achieving an average forecast error of 0.65%. …”
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Vaginal dysbiosis as a factor contributing to the development of recurrent lower urinary tract infections by I. V. Kosova, V. A. Barsegian, L. A. Sinyakova, I. V. Lukianov, D. N. Kolbasov

“…The examination was performed according to the algorithm developed in the Department of Urology and Surgical Andrology, Russian Medical Academy of Continuous Professional Education. …”
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Association between the development of sepsis and the triglyceride-glucose index in acute pancreatitis patients: a retrospective investigation utilizing the MIMIC-IV database by Lin Xu, Xuejing Li, Na Zhang, Chunmei Guo, Pan Wang, Min Gao, Yanhui Zhang, Lixin Zhao

“…Utilizing the formula ln[(triglycerides mg/dl) × (glucose mg/dl)/2], the TyG index was calculated. The Boruta algorithm and Xgboost model were used for feature selection in order to pinpoint the important variables affecting results. …”
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Validation of ICD‐10 Consensus Code Set for Cirrhosis Detection Using Electronic Health Records in an Asian Population by Jason Pik‐Eu Chang, Hong‐Yi Lin, Pooi‐Ling Loi, Jeanette Pei‐Xuan Ng, Marianne De Roza, Rahul Kumar, Hiang‐Keat Tan, Chanda Kendra Ho, Wei‐Quan Teo, Amber Hwa Hwa Chung, Prema Raj

“…We evaluated the consensus code set's sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) in identifying cirrhosis cases. …”
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A prognostic glycolysis-related gene signature in osteosarcoma: implications for metabolic programming, immune microenvironment, and drug response by Naiqiang Zhu, Jingyi Hou, Yu Zhang, Ning Yang, KaiKai Ding, Chengbing Chang, Yanqi Liu, Haipeng Gu, Bin Chen, Xu Wei, Liguo Zhu

“…The prognostic efficacy of this risk signature was assessed via Kaplan–Meier curve analysis and Cox regression, evaluating its independence as a prognostic indicator. Additionally, the predictive potential of the risk model for drug response was evaluated using the “OncoPredict” package. …”
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Epigenetic age and fertility timeline: testing an epigenetic clock to forecast in vitro fertilization success rate by Davide Marinello, Marco Reschini, Giorgia Di Stefano, Giorgia Carullo, Maíra Casalechi, Letizia Tarantini, Benedetta Albetti, Valentina Bollati, Paola Viganò, Edgardo Somigliana, Letizia Li Piani

“…Conclusions Epigenetic clocks may enhance IVF success prediction, particularly in women between 31 and 35. …”
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Generating Deeply-Engineered Technical Features for Basketball Video Understanding by Shaohua Fang, Guifeng Wang, Yongbin Li, Yue Yu, Jun Li

“…Our main contributions include: 1) an LSTM-based deep learning architecture for player action recognition and prediction; 2) a clustering-based algorithm for basketball court and line detection; and 3) a keyframe selection technique for basketball videos based on spatial-temporal scoring. …”
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Simultaneous and Proportional Control Based on an Enhanced Musculoskeletal Model by Lizhi Pan, Diyi Liu, Ruyi Wang, Jinhua Li

“…For offline experiment, another two MMs were established and taken as the control groups for validation of the proposed DC-NMF-MM, including the MM totally taking the NMF algorithm (T-NMF-MM) and that partly taking the NMF algorithm (P-NMF-MM) for predicting the wrist pronation/supination only. …”
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TCN–Transformer Spatio-Temporal Feature Decoupling and Dynamic Kernel Density Estimation for Gas Concentration Fluctuation Warning by Yanping Wang, Longcheng Zhang, Zhenguo Yan, Jun Deng, Yuxin Huang, Zhixin Qin, Yuqi Cao, Yiyang Wang

“…This study addresses the problems of multi-source data redundancy, insufficient feature capture timing, and delayed risk warning in the prediction of gas concentration in fully mechanized coal-mining operations by constructing a three-pronged technical approach that integrates feature dimensionality reduction, hybrid modeling, and intelligent early warning. …”
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Diagnostic Value of F-FDG PET/CT Radiomics in Lymphoma: A Systematic Review and Meta-Analysis by Chaoying Liu MD, Jun Zhao PhD, Heng Zhang PhD, Xinye Ni PhD

“…Six meta-regressions were conducted on study performance, considering sample size, image modality, region of interest (ROI) selection, ROI segmentation, radiomics mode, and algorithms. Results In total, 20 studies classified as type 2a or above according to the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) statement were included for this systematic review and meta-analysis. …”
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Modelling of River-Groundwater Interactions under Rainfall Events Based on a Modified Tank Model by Wen Nie, Yong-chang Liang, Lin Chen, Wei Shao

“…Parameter training of our tank model includes two algorithms: (i) the nonincremental learning algorithm-based model can predict the pore water pressure (PWP) in a slope and river under a 65 mm/hr rainfall event (coarse material) with Nash–Sutcliffe efficiency (NSE) = 0.427 and −0.909 and (ii) the incremental learning algorithm-based model can predict the PWP in a slope and river with NSE = 0.994 and 0.995. …”
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Evaluasi Algoritma Machine Learning untuk Klasifikasi dan Prediksi Penggunaan Lahan by Fajar Nugraha, Dwi Putro Tejo Baskoro, Suria Darma Tarigan

“…This study aimed to evaluate machine learning algorithms in land use classification and prediction and analyzed land use change from 2002 to 2032. …”
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Impact Assessment of Various Methods for Control of Synchronous Generator Excitation on Quality of Transient Processes by Y. D. Filipchik

“…The algorithm makes it possible to improve quality of the transient processes due to reduction of oscillation range concerning as an active power so a δ-angle as well.…”
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Regulating neural data processing in the age of BCIs: Ethical concerns and legal approaches by Hong Yang, Li Jiang

“…Brain–computer interfaces (BCIs) have seen increasingly fast growth under the help from AI, algorithms, and cloud computing. While providing great benefits for both medical and educational purposes, BCIs involve processing of neural data which are uniquely sensitive due to their most intimate nature, posing unique risks and ethical concerns especially related to privacy and safe control of our neural data. …”
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Distinguishing novel coronavirus influenza A virus pneumonia with CT radiomics and clinical features by Lianyu Sui, Huan Meng, Jianing Wang, Wei Yang, Lulu Yang, Xudan Chen, Liyong Zhuo, Lihong Xing, Yu Zhang, Jingjing Cui, Xiaoping Yin

“…Firstly, voxel-based gray-level discretization (binWidth = 25) and Z-Score normalization were applied to preprocess the patient's ROI and normalize the extracted features. Then, the most predictive radiomic features were selected and their corresponding coefficients were evaluated using the correlation coefficient algorithm and the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. …”
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Cell Outage Detection and Compensation in Two-Tier Heterogeneous Networks by Wenqian Xue, Hengzhi Zhang, Yong Li, Dong Liang, Mugen Peng

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Analysis of IoT-Collected Radish Growth Data Using Deep Neural Networks by Ivan P. Malashin, Vadim S. Tynchenko, Andrei P. Gantimurov, Vladimir A. Nelyub, Aleksei S. Borodulin

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