Exploring shared pathogenic mechanisms and biomarkers in hepatic fibrosis and inflammatory bowel disease through bioinformatics and machine learning by Shangkun Li, Haoyu Li, Mingran Qi

“…The key diagnostic biomarkers were determined via a protein-protein interaction (PPI) network combined with two machine learning algorithms. The logistic regression model was subsequently developed based on these key genes. …”
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Short-term Wind Power Forecasting Based on BWO‒VMD and TCN‒BiGRU by LU Jing, ZHANG Yanru, WANG Rui

“…Given the instability and high volatility of wind power generation, this study proposes a short-term wind power prediction method based on BWO‒VMD and TCN‒BiGRU to improve the accuracy of wind power prediction and better support the energy transition under the “dual carbon” strategy.MethodsA short-term wind power generation prediction model based on the beluga whale optimization (BWO) algorithm, variational mode de-composition (VMD), temporal convolutional network (TCN), and bidirectional gated recurrent unit (BiGRU) was carefully proposed to improve the prediction accuracy of wind power generation, particularly considering its inherent instability and high volatility. …”
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Molecular biomarkers in salivary diagnostic materials: Point-of-Care solutions — PoC-Diagnostics and -Testing by Ziyad S. Haidar

“…Recent advancements in nanomaterials and fabrication techniques, coupled with emerging computational approaches such as artificial intelligence (AI), machine learning, and deep learning, have revolutionized high-throughput screening and laboratory automation. AI-driven algorithms now process and analyze salivary proteomic data with remarkable accuracy, identifying patterns and biomarkers associated with diseases such as oral cancer at an early stage. …”
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A Small-Sample Scenario Optimization Scheduling Method Based on Multidimensional Data Expansion by Yaoxian Liu, Kaixin Zhang, Yue Sun, Jingwen Chen, Junshuo Chen

“…Firstly, based on spatial correlation, the daily power curves of PV power plants with measured power are screened, and the meteorological similarity is calculated using multicore maximum mean difference (MK-MMD) to generate new energy output historical data of the target distributed PV system through the capacity conversion method; secondly, based on the existing daily load data of different types, the load historical data are generated using the stochastic and simultaneous sampling methods to construct the full historical dataset; subsequently, for the sample imbalance problem in the small-sample scenario, an oversampling method is used to enhance the data for the scarce samples, and the XGBoost PV output prediction model is established; finally, the optimal scheduling model is transformed into a Markovian decision-making process, which is solved by using the Deep Deterministic Policy Gradient (DDPG) algorithm. …”
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Civil Aircraft Landing Attitude Ultra-Limit Warning System Based on mRMR-LSTM by Fei Lu, Tong Jing, Chunsheng Xie, Haonan Chen

“…Then, the Max-Relevance and Min-Redundancy algorithm was applied to screen the QAR (Quick Access Recorder) parameters with the highest correlation with the predictor variables, and the LSTM network model was established to predict the pitch and roll angles of the aircraft landing, respectively. …”
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Oxidative stress-related genes in uveal melanoma: the role of CALM1 in modulating oxidative stress and apoptosis and its prognostic significance by Yue Wu, Xiaoyan Cai, Menghan Hu, Runyan Cao, Yong Wang

“…Protein–protein interaction (PPI) networks were constructed to identify hub genes, and machine learning algorithms were utilized to screen for diagnostic genes, employing methods such as least absolute shrinkage and selection operator (LASSO) regression, random forest, support vector machine (SVM), gradient boosting machine (GBM), neural network algorithm (NNET), and eXtreme gradient boosting (XGBoost). …”
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Machine learning-aided discovery of T790M-mutant EGFR inhibitor CDDO-Me effectively suppresses non-small cell lung cancer growth by Rui Zhou, Ziqian Liu, Tongtong Wu, Xianwei Pan, Tongtong Li, Kaiting Miao, Yuru Li, Xiaohui Hu, Haigang Wu, Andrew M. Hemmings, Beier Jiang, Zhenzhen Zhang, Ning Liu

“…Identification of new selective EGFR-T790M inhibitors has proven challenging through traditional screening platforms. With great advances in computer algorithms, machine learning improved the screening rates of molecules at full chemical spaces, and these molecules will present higher biological activity and targeting efficiency. …”
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Remote Sensing for Urban Biodiversity: A Review and Meta-Analysis by Michele Finizio, Federica Pontieri, Chiara Bottaro, Mirko Di Febbraro, Michele Innangi, Giovanna Sona, Maria Laura Carranza

“…Our analysis incorporated technical (e.g., sensor, platform, algorithm), geographic (e.g., country, city extent, population) and ecological (biodiversity target, organization level, biome) meta-data, examining their frequencies, temporal trends (Generalized Linear Model—GLM), and covariations (Cramer’s V). …”
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A negative combined effect of exposure to maternal Mn-Cu-Rb-Fe metal mixtures on gestational anemia, and the mediating role of creatinine in the Guangxi Birth Cohort Study (GBCS):... by Yuen Zhong, Yu Bao, Hong Cheng, Chaoqun Liu, Shengzhu Huang, Hualong Qiu, Honglin Huang, Jiajun Ren, Hailiu Jin, Caitong He, Long Tian, Yu Zhang, Bangzhu Luo, Tao Liang, Mujun Li, Zengnan Mo, Longman Li, Xiaobo Yang

“…We utilized twelve machine learning (ML) algorithms to independently screen for effective metal mixtures, assess their combined impacts and dose-response relationships on gestational anemia, and estimate the mediating role of kidney function. …”
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Artificial Intelligence in the Non-Invasive Detection of Melanoma by Banu İsmail Mendi, Kivanc Kose, Lauren Fleshner, Richard Adam, Bijan Safai, Banu Farabi, Mehmet Fatih Atak

“…The use of artificial intelligence (AI)-based technologies in dermatology has emerged in recent years to assist in the diagnosis of melanoma that may be more accessible to all patients and more accurate than current methods of screening. …”
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DIPOLE ANTENNAS WITH A SECTOR-SHAPED RADIATION PATTERN by M. M. Gorobets, N. P. Yeliseyeva, S. L. Berdnyk, O. M. Horobets

“…Results. The algorithms and calculation programs developed allow studying the electrodynamic characteristics of the antenna over a wide range of screen electrical dimensions and distances between the dipole and the screen. …”
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Two-Stage Dispatch of CCHP Microgrid Based on NNC and DMC by Suhao CHEN, Yue WU, Wei ZENG, Xiaohui YANG, Xiaopeng WANG, Yunfei WU

“…In the online optimization stage, a finite-time domain optimization model based on dynamic matrix control algorithm is established to track and optimize the offline optimization results with feedback correction to reduce the influence of uncertainty factors. …”
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Smart Insole-Based Plantar Pressure Analysis for Healthy and Diabetic Feet Classification: Statistical vs. Machine Learning Approaches by Dipak Kumar Agrawal, Watcharin Jongpinit, Soodkhet Pojprapai, Wipawee Usaha, Pattra Wattanapan, Pornthep Tangkanjanavelukul, Timporn Vitoonpong

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Combined use of near infrared spectroscopy and chemometrics for the simultaneous detection of multiple illicit additions in wheat flour by Xinyi Dong, Ying Dong, Jinming Liu, Siting Wu

“…Compared to regression models built with competitive adaptive reweighted sampling and genetic algorithm for feature wavelength selection, the performance improved significantly, enhancing generalization capability. …”
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"Non-destructive and rapid determination of bound styrene content of styrene-butadiene rubber latex using near-infrared spectroscopy" by LI Yan, ZHONG Ming-li, ZHU Shi-yong, CUI Jia-min, ZHANG Jian-ping, CHEN Shi-long

“…A non-destructive and rapid determination of bound styrene content in styrene-butadiene rubber latex was studied using near-infrared spectroscopy diffuse transmission method combined with chemometrics, bound styrene content in styrene-butadiene rubber latex was determined by refractive index method, near-infrared spectral data of styrene-butadiene rubber latex were collected using Fourier transform near-infrared spectrometer, Kennard-Stone algorithm was used to divide the calibration set and validation set, partial least squares regression quantitative analysis model was established by combining the spectral preprocessing methods, such as multiple scattering correction method, second-order derivatives and Norris smoothing, etc, and the influence of screening spectral feature variables by interval partial least squares algorithm on the quantitative ana-lysis model was finally investigated. …”
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An efficient hybrid Hopfield convolutional neural network for detecting spam bots in Twitter platform by A.V. Santhosh Kumar, N. Suresh Kumar, R. Kanniga Devi

“…The extracted features are then subjected to feature selection, where a meta-heuristic-based optimization algorithm called the Binary Golden Search Optimization algorithm (BGSO) is used. …”
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State-of-the-Art Review on the Application of Unmanned Aerial Vehicles (UAVs) in Power Line Inspections: Current Innovations, Trends, and Future Prospects by Bongumsa Mendu, Nhlanhla Mbuli

“…Unmanned aerial vehicles (UAVs) make power line inspections more safe, efficient, and cost-effective, replacing risky manual checks and expensive helicopter surveys while overcoming challenges like stability and regulations. …”
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The Use of NIR Spectroscopy and Chemometrics to Identify the Thermal Treatment of Milk in Fiore Sardo PDO Cheese to Detect Fraud by Marco Caredda, Alessio Silvio Dedola, Massimo Pes, Margherita Addis

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A Dual-Variable Selection Framework for Enhancing Forest Aboveground Biomass Estimation via Multi-Source Remote Sensing by Dapeng Chen, Hongbin Luo, Zhi Liu, Jie Pan, Yong Wu, Er Wang, Chi Lu, Lei Wang, Weibin Wang, Guanglong Ou

“…The dual-variable selection strategy integrates SHAP with the Pearson correlation coefficient (PC), RF, EN, and Lasso to enhance feature screening robustness and interpretability. The results of the study showed that Lasso-SHAP dual-variate screening was more stable than SHAP univariate screening. …”
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Identification and Validation of Circadian Rhythm‐Related Genes Involved in Intervertebral Disc Degeneration and Analysis of Immune Cell Infiltration via Machine Learning by Yongbo Zhang, Liuyang Chen, Sheng Yang, Rui Dai, Hua Sun, Liang Zhang

“…Results Six hub genes related to CRs (CCND1, FOXO1, FRMD8, NTRK2, PRRT1, and TFPI) were screened out. Immune infiltration analysis revealed that the IVDD group had significantly more M0 macrophages and significantly fewer follicular helper T cells than those of the control group. …”
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