Sodium Propionate Alleviates Atopic Dermatitis by Inhibiting Ferroptosis via Activation of LTBP2/FABP4 Signaling Pathway by Xie A, Li W, Ye D, Yin Y, Wang R, Wang M, Yu R

“…Analysis using three algorithms identified potential therapeutic targets of SP. …”
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Tensor RT optimized driver drowsiness detection system using edge device by Chandramohan Dhasarathan, Sambasivam Gnanasekaran, Arnab Pattanayak, Gourav Kumar, Kartik Vig, Vaibhav Narain, K.M. Deva Narayan, Sunidhi Garg

“…In this paper, alongwith the proposed project, we have analysed and reviewed the latest research, studies, and experiments in the field of driver drowsiness detection and prediction analysing systems. The proposed approach utilizes multiple models to improve and accurately detecting driver drowsiness, with the models being InceptionV3, ResNet50, VGG-16, and MobileNetV1. …”
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Identifying Therapeutic Targets and Potential Drugs for Diabetic Retinopathy: Focus on Oxidative Stress and Immune Infiltration by Peng H, Hu Q, Zhang X, Huang J, Luo S, Zhang Y, Jiang B, Sun D

“…Immune infiltration analysis and regulatory networks were constructed. Drug prediction was validated through molecular docking, and hub gene expression was confirmed in dataset and animal models.Results: Compared to the control group, 91 DEOSGs were found. …”
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Constructing a Glioblastoma Prognostic Model Related to Fatty Acid Metabolism Using Machine Learning and Identifying F13A1 as a Potential Target by Yushu Liu, Hui Deng, Ping Song, Mengxian Zhang

“…On the basis of 10 kinds of machine learning methods, we used 101 combinations of algorithms to construct prognostic models and obtain the best model. …”
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Transfer Learning Estimation and Transferability of LNC and LMA Across Different Datasets by Yingbo Wang, Mengzhu He, Lin Sun, Yong He, Zengwei Zheng

“…The LNC and LMA estimation performance in transfer models established by partial least squares regression (PLS), support vector regression (SVR), extreme gradient boosting (XGB), and random forest regression (RFR) algorithms across different datasets were employed, in which the RFR transfer models performed good prediction results. …”
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Use of artificial intelligence in the diagnosis, treatment and surveillance of patients with kidney cancer by E. Yu. Timofeeva, С. R. Azilgareeva, A. O. Morozov, M. S. Taratkin, D. V. Enikeev

“…AI shows great prospects in the diagnosis urinary system lesions, in the ability to differentiate benign and malignant neoplasm (due to machine learning systems), as well as in predicting the histological subtype of the tumor. AI can be used at the intraoperative stage (thanks to the integration of virtual 3D models during surgical interventions), which reduces the frequency of thermal ischemia and damage to the kidney cavity system. …”
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Pioneering CPMI framework for accurate state-of-health assessment in Lithium ion battery power management using FBG sensors by Taher M. Ghazal, Ali Q. Saeed, Mosleh M. Abualhaj, Taj-Aldeen Naser Abdali, Munir Ahmad

“…Integrating FBG sensors with the CPMI framework provides a robust Li-ion battery SOH monitoring solution, enabling predictive maintenance strategies and enhanced power management capabilities. …”
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In-situ real-time monitoring of flavonoids extraction from propolis extracts based on near infrared spectroscopy by WANG Wuli, MA Haile, LI Han, ZHANG Yong, XIA Hong

“…The Rp (prediction correlation coefficient) and RMSEP (the root mean square prediction error) were 0.929 1 and 0.242 7 respectively, which showed that the in-situ monitoring of the model had good regression and prediction ability.ConclusionNear-infrared spectroscopy can be used to quickly and accurately monitor the flavonoid content in the extraction process of propolis residue, and realize real-time monitoring in situ.…”
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Short-term forecasting method for net load considering the impact of high proportion of distributed renewable energy by Yuhang Song, Haibo Zhang, Hanru Wu, Joseph Ndonji

“…Example validation shows that the proposed combination algorithm not only enhances the prediction accuracy, but also exhibits excellent generalization capabilities.…”
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Comprehensive analysis of regulatory B Cell related genes in prognosis and therapeutic response in lung adenocarcinoma by Liangyu Zhang, Liangyu Zhang, Jianshen Zeng, Jianshen Zeng, Xun Zhang, Xun Zhang, Menglong Zhang, Menglong Zhang, Yilin Lin, Yilin Lin, Fancai Lai, Fancai Lai

“…The BREGI demonstrated robust predictive power for LUAD patient prognosis across various cohorts. …”
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TRAINING SYSTEM OF FUTURE SPECIALISTS: QUALITY CONTROL by Vladimir A. Romanov, Valentina N. Kormakova, Elena N. Musaelian

“…The strategy of the development of the process of quality control training in educational professional organizations and a predictive model of the system of quality control training for future engineers and workers have been created on the analysis and synthesis of a quantitative specification of the quality, the obtained experience and success in control training of future specialists in educational professional organizations in recent economic and educational conditions.Scientific novelty. …”
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Plasma FGF2 and YAP1 as novel biomarkers for MCI in the elderly: analysis via bioinformatics and clinical study by Yejing Zhao, Yejing Zhao, Xiang Wang, Jie Zhang, Yanyan Zhao, Yi Li, Ji Shen, Ying Yuan, Jing Li

“…Therefore, the high expression of plasma FGF2 and YAP1 proteins may be independent predictive risk factors for MCI in the elderly. Our findings may provide targets for the development of early minimally invasive, efficient, and convenient screening tools, and even for the treatment of AD in the future.…”
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Investigating Spatial Effects through Machine Learning and Leveraging Explainable AI for Child Malnutrition in Pakistan by Xiaoyi Zhang, Muhammad Usman, Ateeq ur Rehman Irshad, Mudassar Rashid, Amira Khattak

“…The use of machine learning algorithms, specifically XGBoost and Random Forest, highlighted the significant role played by the average value in the neighbourhood in predicting childhood stunting in nearby districts, confirming that the spatial spillover effect is not bounded by geographical boundaries. …”
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Identification of subtypes and biomarkers associated with disulfidptosis-related ferroptosis in ulcerative colitis by Yinghao Jiang, Hongyan Meng, Xin Zhang, Jinguang Yang, Chengxin Sun, Xiaoyan Wang

“…Next, the hub genes were identified by differential analysis and WGCNA algorithms, and three machine learning algorithms were used to screen biomarkers for UC from hub genes. …”
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AFM Image Segmentation Based on Wave Growth of Local Maximum Regions with their Selection in Order of Decreasing Values by V. V. Rabtsevich, V. Y. Tsviatkou

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Quantitative Analysis of Structural Parameters Importance of Helical Temperature Microfiber Sensor by Artificial Neural Network by Juan Liu, Minghui Chen, Hang Yu, Jinjin Han, Hongyi Jia, Zhili Lin, Zhijun Wu, Jixiong Pu, Xining Zhang, Hao Dai

“…The relative output intensities of HMF sensor at different temperatures are predicted by the BPNN with the HMF’s structural parameters as the input variables. …”
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Classification of SERS spectra for agrochemical detection using a neural network with engineered features by Mateo Frausto-Avila, Monserrat Ochoa-Elias, Jose Pablo Manriquez-Amavizca, María del Carmen González-López, Gonzalo Ramírez-García, Mario Alan Quiroz-Juárez

“…The model demonstrates strong predictive performance, achieving high precision and recall values across all classes, with an overall classification accuracy of 98.5% for organophosphate pesticides and their mixtures. …”
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Multi-stage Optimization Forecast of Short-term Power Load Based on VMD and PSO-SVR by Wenwu LI, Qiang SHI, Dan LI, Qunyong HU, Yun TANG, Jinchao MEI

“…In the second stage, phase space reconstruction is used to optimize and reorganize each sequence component, and establish support vector regression（SVR）prediction model for each component. In the third stage, the particle swarm optimization（PSO）algorithm is applied to optimize the internal parameters of the SVR model to facilitate better training and forecasting. …”
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Application of Big Data Technology in the EMU Fault Early Warning by WU Zhenyi

“…It uses a method of combining equipment fault mechanism and artificial intelligence algorithm to construct a fault early warning and prediction model of key components of EMU, which reflects the working status of a component with its key physical properties and identify potential faults in advance. …”
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CYLD as a key regulator of myocardial infarction-to-heart failure transition revealed by multi-omics integration by Jingya Xu, Jingya Xu, Zhonghua Dong, Zhaodong Li, Xuan Wang, Xuan Wang

“…Single-cell transcriptomic analysis demonstrated that CYLD exhibited the strongest correlation with the transition from MI to HF; using random forest modelling, we validated its predictive value in this context.DiscussionIn conclusion, our study identified CYLD as a critical regulator of the transition from MI to HF. …”
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