An FPGA-accelerated multi-level AI-integrated simulation framework for multi-time domain power systems with high penetration of power converters by Chen Liu, Peng Su, Hao Bai, Xizheng Guo, Alber Filbà Martínez, Jose Luis Dominguez Garcia

“…The framework structures systems hierarchically using energy transmission functions and unified energy information flow-based surrogate models with defined ports, ensuring compatibility with artificial intelligence algorithms. By integrating AI techniques, such as back propagation neural networks, the framework predicts variables with high computational complexity, improving accuracy and simulation efficiency. …”
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Identification of genes related to fatty acid metabolism in type 2 diabetes mellitus by Ji Yang, Yikun Zhou, Jiarui Zhang, Yongqin Zheng, Jundong He

“…Differential expression analysis, WGCNA, machine learning algorithms, diagnostic analysis, and validation were employed to identify key feature genes. …”
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Electrical discharge machining: Recent advances and future trends in modeling, optimization, and sustainability by Muhamad Taufik Ulhakim, Sukarman, Khoirudin, Dodi Mulyadi, Hendri Susilo, Rohman, Muji Setiyo

“…Advanced modeling techniques, such as finite element analysis (FEA) and artificial intelligence (AI)-driven simulations, have improved the accuracy of process predictions, enabling real-time adjustments and precise control of machining parameters. …”
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Does the hepatologist still need to rely on aminotransferases in clinical practice? A reappraisal of the role of a classic biomarker in the diagnosis and clinical management of chr... by Patrizia Burra, Calogero Cammà, Pietro Invernizzi, Fabio Marra, Maurizio Pompili

“…ALT, a sensitive and cost-effective marker of liver injury, remains pivotal in predicting clinical outcomes and guiding interventions in several chronic liver diseases including metabolic dysfunction-associated steatotic liver disease, and chronic viral hepatitis. …”
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Deep learning in next-generation vaccine development for infectious diseases by Manojit Bhattacharya, Yi-Hao Lo, Srijan Chatterjee, Arpita Das, Zhi-Hong Wen, Chiranjib Chakraborty

“…However, integrated frameworks connecting the bioinformatics and DL approaches are rapidly progressing, which are necessary for DL-assisted epitope prediction and the subsequent steps for vaccine development. …”
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Application of Fourier transform infrared spectroscopy to exhaled breath analysis for detecting helicobacter pylori infection by Meiqi Qiu, Fei Liao, Yulin Tan, Junlong Zhang, Changjun Zheng, Hanyu Wang, Huangming Zhuang, Wanli Xiong, Qingfang Xie, Weiguo Dong

“…Individual exhalation spectral data after deducting baseline spectral data were used as the basis for the training and test sets through K-center clustering algorithm. Results: A total of 278 samples were collected (63 H. pylori infection cases, 215 healthy controls). …”
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Echocardiographic Left Ventricular Function in the Third Year After COVID-19 Hospitalization: A Follow-Up Pilot Study in South-East of Romania by Constantin-Marinel Vlase, Cristian Gutu, Roxana Elena Bogdan Goroftei, Andreea Boghean, Traian Florin Daniel Iordachi, Anca-Adriana Arbune, Manuela Arbune

“…<i>Conclusions</i>: COVID-19 is an independent predictive factor for GLS impairment, which can indicate myocardial contractile dysfunction, even in patients with asymptomatic heart disease. …”
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Deep Learning-Based Adaptive Downsampling of Hyperspectral Bands for Soil Organic Carbon Estimation by Mohammad Rahman, Shyh Wei Teng, Manzur Murshed, Manoranjan Paul, David Brennan

“…The recent supervised band selection algorithm BSDR improves accuracy but does not retain spectral continuity. …”
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Online variational Gaussian process for time series data by Weidong Wang, Mian Muhammad Yasir Khalil, Leta Yobsan Bayisa

“…In this paper, we propose the OnLine Variational Gaussian Process (OLVGP) algorithm, which introduces a novel approach for dynamically managing the number of inducing points based on the concept of eigenfunction inducing features. …”
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Integrated bioinformatics and clinical data identify three novel biomarkers for osteoarthritis diagnosis and synovial immune by Zheng Zhu, Bizhi Tu, Cheng Peng, Xun Xu, Peizhi Lu, Rende Ning

“…The Venn diagram, random forest, SVM-RFE algorithm, and multivariate analysis were employed to determine SID-related hub genes in OA synovium. …”
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Biomarkers associated with cell-in-cell structure in kidney renal clear cell carcinoma based on transcriptome sequencing by Zehua Wang, Zhongxiao Zhang

“…The accuracy of these biomarkers was verified by using the receiver operating characteristic (ROC) curve as well as in vitro experiments (CCK-8 assay, wound healing assay, Transwell assay, and quantitative real-time PCR). The CIBERSORT algorithm was applied to explore the association between immune infiltration and the biomarkers. …”
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Scenario Modelling for Reproducing Investment Potential of Institutional Sectors in the Regions of the Siberian Federal District by I. V. Naumov, A. V. Trynov, A. O. Safonov

“…The authors developed an algorithm of scenario modelling for reproducing investment potential of institutional sectors in regional systems. …”
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Identification of exosome-related genes associated with prognosis and immune infiltration features in pancreatic cancer by Jie Wang, Ming Qing, Jie Gui, Pingyong Zhong, Hao Hua

“…Additionally, CIBERSORT was used to estimate immune cell infiltration levels. Lastly, the TIDE algorithm was leveraged to evaluate the connection between gene expression and drug sensitivity. …”
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Development of a prognostic model for chemotherapy response and identification of TNFAIP2 as a target in colorectal cancer by Cheng Zhou, Chuan-Hai Xu, Min Xu, Xin-Kun Huang, Bin Zhu

“…The tumor microenvironment (TME) was assessed using the MCP-counter algorithm, and CRC cell experiments were performed to evaluate changes in drug sensitivity following oxaliplatin exposure. …”
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A Novel Hybrid Deep Learning Framework for Evaluating Field Evapotranspiration Considering the Impact of Soil Salinity by Yao Rong, Weishu Wang, Peijin Wu, Pu Wang, Chenglong Zhang, Chaozi Wang, Zailin Huo

“…Data‐driven models exhibit strong predictive ET capabilities, yet significant limitations like naive extrapolation hamper wider generalization. …”
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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

“…A network meta-analysis of diagnostic test and Thakkinstian’s algorithm were used to select the most appropriate genetic model, along with false-positive report probability (FPRP) for noteworthy associations. …”
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Integrating taxonomic and phenotypic information through FISH-enhanced flow cytometry for microbial community dynamics analysis by Valérie Mattelin, Josefien Van Landuyt, Frederiek-Maarten Kerkhof, Yorick Minnebo, Nico Boon

“…As a reference to correlate increased distinction with the incorporation of taxonomic information, 16S rRNA gene sequencing was used. Finally, a predictive algorithm was trained to correctly classify samples in the differently treated groups. …”
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A Low-Cost Antipodal Vivaldi Antenna-Based Peanut Defect Rate Detection System by Yuanyuan Yin, Fangyan Ma, Xiaohong Liu, Shuhao Wang, Junjie Xia, Liqing Zhao

“…By implementing the XGBoost algorithm, we established predictive models for defect rate quantification (regression) and defect grade classification. …”
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Advancing Marine Surveillance: A Hybrid Approach of Physics Infused Neural Network for Enhanced Vessel Tracking Using Automatic Identification System Data by Tasmiah Haque, Md Asif Bin Syed, Srinjoy Das, Imtiaz Ahmed

“…Recognizing the strengths and limitations of the LSTM model, we propose a hybrid machine-learning algorithm that integrates LSTM with a physics-based model. …”
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PhyIndBC: Development of a machine learning tool for screening of potential breast cancer inhibitors from phytochemicalsGitHub by Agneesh Pratim Das, Subhash M. Agarwal

“…., k-nearest neighbor (KNN), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB) were combined with various molecular fingerprints (MACCS and Morgan2) to develop multiple predictive models. Among these models, the RF algorithm coupled with the MACCS fingerprint emerged as the best performing model. …”
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