A traceability model for upper corner gas in fully mechanized mining faces based on XGBoost-SHAP by SHENG Wu, WANG Lingzi

“…Case analysis results showed that: ① the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) of the XGBoost model were 0.93, 0.007, and 0.008, respectively, indicating the highest goodness of fit and the lowest errors compared with random forest (RF), support vector regression (SVR), and gradient boosting decision tree (GBDT). ② The mean relative error of the XGBoost model was 4.478%, demonstrating higher accuracy and better generalization performance compared with the other models. ③ Based on the mean absolute SHAP values of input features, the gas concentration at T1 on the working face had the greatest influence on the gas concentration in the upper corner, followed by the gas concentration in the upper corner extraction pipeline, with the gas content and roof pressure of the mining coal seam following closely. …”
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Soft sensor modeling method for Pichia pastoris fermentation process based on substructure domain transfer learning by Bo Wang, Jun Wei, Le Zhang, Hui Jiang, Cheng Jin, Shaowen Huang

“…Secondly, the optimal subspace domain adaptation method integrating multiple metrics is used to obtain the optimal projection matrices $${{W}_{s}}$$ W s and $${{W}_{t}}$$ W t that are coupled with each other, and the data of source and target domains are projected to the corresponding subspace to perform spatial alignment, so as to reduce the discrepancy between the sample data of different working conditions. Finally, based on the source and target domain data after substructure domain adaptation, the least squares support vector machine algorithm is used to establish the prediction model. …”
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Integrating geodetic infrastructures for GNSS displacements analysis in Chile: A case study with REDGEOMIN (2019–2022) by José Antonio Tarrío Mosquera, Valeria Vásquez Tejo, Catalina Cáceres Venegas, Miguel Marten-Allendes, Juan Giglio Gutierrez, Fernando Isla Rodríguez, Marcelo Caverlotti Silva, Gabriel Jeldres Vergara, Rodrigo Urrutia Vidal, Cristian Mardones Castro

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Integrated Bioinformatics and Experimental Validation Reveal Macrophage Polarization-Related Biomarkers for Osteoarthritis Diagnosis by He Q, Liu L, Hu X, Lin L, Song Z, Xia Y, Lin Q, Wei J, Li S

“…Least absolute shrinkage and selection operator (LASSO), random forest (RF), and support vector machine recursive feature elimination (SVM-RFE) algorithms were used to identify hub genes and construct a diagnostic model validated through internal datasets and multiple external bulk RNA-seq and single-cell RNA-seq data. …”
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A Computational Model of Attention-Guided Visual Learning in a High-Performance Computing Software System by Alice Ahmed, Md. Tanim Hossain

“…The model includes an all-attention layer with embedded input vectors, non-contextual vectors containing generic task-relevant information, and self-attentional and feedforward layers. …”
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Antiviral Activity of Double-Stranded Ribonucleic Acid and Interferon Alpha Composition in the Model of Experimental Influenza Infection of Mice by S. G. Gamaley, М. O. Skarnovich, E. V. Makarevich, О. Yu. Mazurkov, L. N. Shishkina, О. S. Ivanova, G. M. Levagina, Е. D. Danilenko

“…The specific antiviral activity of the preparations and synergistic effect of the components within the compositions were shown in L-68 and L-929 cell cultures. The aim of this work was to study antiviral activity of intranasal forms of the pharmaceutical compositions containing yeast dsRNA and recombinant human interferon-alpha-2b in a model of lethal influenza infection in mice. …”
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Smart grid stability prediction model using two-way attention based hybrid deep learning and MPSO by Umesh Kumar Lilhore, Surjeet Dalal, Magdalena Radulescu, Marinela Barbulescu

“…As far as we know, it is the first work to suggest a dynamic short-term load prediction model that considers different significant features and enables precise predicting outcomes. …”
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American Sign Language Recognition Model Using Complex Zernike Moments and Complex-Valued Deep Neural Networks by Selda Bayrak, Vasif Nabiyev, Celal Atalar

“…In the developed model, complex Zernike moments are used to obtain the feature vector of character images. …”
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Machine learning-based study of hardness in polypropylene/carbon nanotube and low-density polyethylene/carbon nanotube composites by Harshit Sharma, Gaurav Arora, Raj Kumar, Suman Debnath, Suchart Siengchin

“…Abstract In the present work, the hardness prediction of polypropylene/carbon nanotubes (PP/CNT) and low-density polyethylene/carbon nanotubes (LDPE/CNT) composite materials, processed by microwave technique, has been explored using machine learning models i.e. …”
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Measurement-Based Neural Network Technique for Modeling the Low-Frequency Electric Field Radiated Behavior of Satellite Units by Anna N. Lampou, Anargyros T. Baklezos, Konstantinos K. Spyridakis, Dimitrios A. Rigas-Papakonstantinou, Ioannis O. Vardiambasis, Christos D. Nikolopoulos

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Contribution au développement d’un modèle heuristique des temporalités en contexte de réchauffement climatique by Matthieu Le Duff, Gaëlle Lefer-Sauvage, Miki Mori

“…The focus on climate change serves as a vector to more broadly address how researchers might understand, define and model the construction of time in Mayotte. …”
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Assessment of Landslide Susceptibility Based on the Two-Layer Stacking Model—A Case Study of Jiacha County, China by Zhihan Wang, Tao Wen, Ningsheng Chen, Ruixuan Tang

“…These landslide conditioning factors were integrated into a total of 4660 Stacking ensemble learning models, randomly combined by 10 base-algorithms, including AdaBoost, Decision Tree (DT), Gradient Boosting Decision Tree (GBDT), k-Nearest Neighbors (kNNs), LightGBM, Multilayer Perceptron (MLP), Random Forest (RF), Ridge Regression, Support Vector Machine (SVM), and XGBoost. …”
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Hybrid pre trained model based feature extraction for enhanced indoor scene classification in federated learning environments by Monica Dutta, Deepali Gupta, Vikas Khullar, Sapna Juneja, Roobaea Alroobaea, Pooja Sapra

“…Deep Learning (DL) models, especially Convolutional Neural Networks (CNNs), have improved classification accuracy significantly by extracting the image features. …”
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Research on Interval Probability Prediction and Optimization of Vegetation Productivity in Hetao Irrigation District Based on Improved TCLA Model by Jie Ren, Delong Tian, Hexiang Zheng, Guoshuai Wang, Zekun Li

“…This work introduces a scientific approach for precisely forecasting alterations in regional vegetation productivity using the proposed multimodal TCLA model, significantly enhancing global vegetation resource management and ecological conservation techniques.…”
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Transferable approaches to CRISPR-Cas9 induced genome editing in non-model insects: a brief guide by Hassan M. M. Ahmed, Lisha Zheng, Vera S. Hunnekuhl

“…We focus our review on experimental work in insects, but due to the ubiquitous functionality of the CRISPR-Cas system many considerations are transferable to other non-model organisms.…”
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Modeling of railway track integrating a shock-absorbing mat at the sleepers-ballast interface using eigenfunctions of displacement by C. A. Moubeke, Adoukatl Chanceu, R. P. Lemanlé Sanga, G. E. Ntamack, S. Charif D’Ouazzane

“…Therefore, many studies have been focused on solutions to attenuate, even cancel vibrations effects on the track and nearby environment. This work has presented a modeling of a ballasted railway track with a shock-absorbing mat or Under Sleepers Rubber Mat (USRm) at the sleepers/ ballast interface. …”
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LLM-Based Unknown Function Automated Modeling in Sensor-Driven Systems for Multi-Language Software Security Verification by Liangjun Deng, Qi Zhong, Jingcheng Song, Hang Lei, Wenjuan Li

“…This provides a foundational validation of our method’s feasibility, particularly in reducing modeling time while maintaining quality. This work is the first to integrate LLMs into formal verification, offering a scalable and automated verification solution for sensor-driven software, blockchain smart contracts, and WebAssembly systems, expanding the scope of secure IoT development.…”
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Category-Based Sentiment Analysis of Sindhi News Headlines Using Machine Learning, Deep Learning, and Transformer Models by Safdar Ali Soomro, Siti Sophiayati Yuhaniz, Mazhar Ali Dootio, Ghulam Mujtaba, Jawaid Ahmed Siddiqui

“…Experimental results show that traditional ML models outperform DL and transformer-based models on the SNHD dataset. …”
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A comparative analysis of variants of machine learning and time series models in predicting women’s participation in the labor force by Rasha Elstohy, Nevein Aneis, Eman Mounir Ali

“…Various machine learning (ML) algorithms, such as support vector machine (SVM), neural network, K-nearest neighbor (KNN), linear regression, random forest, and AdaBoost, in addition to popular time series algorithms, including autoregressive integrated moving average (ARIMA) and vector autoregressive (VAR) models, have been applied to an actual dataset from the public sector. …”
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Predicting soil chemical characteristics in the arid region of central Iran using remote sensing and machine learning models by Azita Molaeinasab, Hossein Bashari, Mostafa Tarkesh Esfahani, Saeid Pourmanafi, Norair Toomanian, Bahareh Aghasi, Ahmad Jalalian

“…We employed 34 environmental covariates derived from Landsat 8 imagery and a digital elevation model, combined with 96 surface soil samples (0 to 20 cm depth), to assess the performance of six machine-learning models: Random Forest (RF), Classification and Regression Tree (CART), Support Vector Regression (SVR), Generalized Additive Model (GAM), Generalized Linear Model (GLM), and an ensemble approach. …”
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