Optimizaition of a structure of machines’ fleet for winter maintainence of urban road network taking into account probabilistic assessment of climate factors by T. V. Bobrova, I. V. Sleptsov

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Investigating translation for Indic languages with BLOOMZ-3b through prompting and LoRA fine-tuning by Aarathi Rajagopalan Nair, Deepa Gupta, B. Premjith

“…Abstract In the domain of natural language processing, the rise of Large Language Models and Generative AI represents a noteworthy transition, enabling machines to understand and generate text resembling that produced by humans. …”
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Selection of Landsat 8 OLI Levels, Monthly Phases, and Spectral Variables on Identifying Soil Salinity: A Study in the Yellow River Delta by Guosheng Ni, Yang Guan, Xiaoguang Zhang, Yi Yang, Yu Li, Xinwei Liu, Ziguo Rong, Min Ju

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Optimization of grinding process parameters for slender tubes through orthogonal experiments and grey relational analysis by Lianzhi Zhang

“…The influence of these parameters and their interactions on MRR and surface roughness (Sq) was studied, subsequently, the regression model constructed accordingly can predict machining performance. …”
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Hybrid Deep Learning Techniques for Improved Anomaly Detection in IoT Environments by Hanan Abbas Mohammad

“…Hence, a number of researchers are attempting to build an intrusion detection system utilizing machine learning and deep learning algorithms. In this work, a novel attack detection model is proposed by superimposing Whale Optimization Algorithm and Bidirectional Long Short-Term Memory (WB-LSTM) together. …”
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Cogging Torque Mitigation using the Unequal Rotor Slot Arcs Method based on Magnetic Permeance Distribution for Flux-Switching Machines by Emrah Cetin

“…Furthermore, to demonstrate the effects of MPD, a mathematical model is developed. Parametric optimization is implemented to obtain the optimal cogging torque value that also maintains a convenient average torque output. …”
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New Maps of Lunar Surface Oxide Abundances and Mg# Using an Optimized Ensemble Learning Algorithm by Chaofa Bian, Kefei Zhang, Yunzhao Wu, Suqin Wu, Yu Lu, Yabo Duan, Huajing Wu, Zhenxing Zhao, Wei Wu

“…Among the models tested, the SXL algorithm (stacking of support vector machine regression, extreme gradient boosting, and linear regression), which was selected from a stack of 2 or 3 out of six typical algorithms, achieved the highest inversion accuracy. …”
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Exploring the relationship between audio–visual perception in Fuzhou universities and college students' attention restoration quality using machine learning by Shaofeng Chen, Zhengyan Chen, Jiawen Hong, Xiaowen Zhuang, Chenxi Su, Zheng Ding

“…XGBoost models and SHAP interpretability analysis were employed to reveal the effects and interaction mechanisms of variables.Results(1) Attention recovery quality is significantly higher in liberal arts and agricultural/forestry universities than in science and engineering universities, with boundary effects and the synergistic design of humanistic soundscapes being key factors; (2) SHAP analysis identifies humanistic soundscapes, natural soundscapes, and color complexity as core influencing factors, with their effects exhibiting significant threshold characteristics; (3) Linear interaction mechanisms among audiovisual elements are discovered, such as the interaction between vegetation density and building enclosure degree enhancing recovery efficacy, and the synergistic design of musical soundscapes and paving materials can optimize perceptual experiences.ConclusionBy innovatively integrating multi-source data and machine learning techniques, this study systematically analyzes the relationship between campus audiovisual environments and attention recovery, breaking through the limitations of traditional linear analysis. …”
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Scalable Cold-Start Optimization in Serverless Computing: Leveraging Function Fusion With PanOpticon Simulator by Ranjan Kumar Behera, Anisha Kumari, Sung-Bae Cho

“…Serverless computing has transformed cloud computing with its inherent scalability, pay-as-you-go pricing model, and low-latency execution. However, cold-start delays arising from the on-demand initialization of execution environments such as containers or virtual machines remain a persistent bottleneck, particularly for latency-sensitive applications. …”
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Machine learning application to predict binding affinity between peptide containing non-canonical amino acids and HLA-A0201. by Shan Jiang, Zhaoqian Su, Nathaniel Bloodworth, Yunchao Liu, Cristina E Martina, David G Harrison, Jens Meiler

“…Our model demonstrates robust performance, with 5-fold cross-validation yielding an R2 value of 0.477 and a root-mean-square error (RMSE) of 0.735, indicating strong predictive capability for peptides with NCAAs. …”
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A New Design Approach for Milkrun-Based In-Plant Supply in Manufacturing Systems by Tamás Bányai, Ádám Francuz

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Determining optimal strategies for primary prevention of cardiovascular disease: a synopsis of an evidence synthesis study by Olalekan A Uthman, Lena Al-Khudairy, Chidozie Nduka, Rachel Court, Jodie Enderby, Seun Anjorin, Hema Mistry, G J Melendez-Torres, Sian Taylor-Phillips, Aileen Clarke

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Composition Design and Property Prediction for AlCoCrCuFeNi High-Entropy Alloy Based on Machine Learning by Cuixia Liu, Meng Meng, Xian Luo

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Experimental and machine learning-based identification of a solar thermal system for domestic hot water and direct solar floor heating by Yassine Bouguergour, Sayeh Menhoudj, Abderrahmane Mejedoub Mokhtari, Karim Dehina, Abdelatif Zairi, Romain Mege, Mohammed-Hichem Benzaama

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Mapping 30-m cotton areas based on an automatic sample selection and machine learning method using Landsat and MODIS images by Zhuting Tan, Zhengyu Tan, Juhua Luo, Hongtao Duan

“…Next, cotton was identified based on the croplands from 2000 to 2020 by using the machine learning model. Finally, the performance was evaluated, and the spatiotemporal distribution characteristics of cotton planting areas were analyzed. …”
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Mechanical properties of 3D-Printed aluminum, nickel, and titanium using a hybrid machine learning and computational mechanics approach by Sara Samine, Mohamed Karouchi, Maria Zemzami, Nabil Hmina, Soufiane Belhouideg

“…Initial simulations using CASTEP provide benchmark mechanical values, which are subsequently used to train and validate the Ridge regression model. The results reveal outstanding predictive accuracy, with R2 values surpassing 0.999 across all properties and minimal mean squared errors. …”
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A novel hybrid SCS-LSTM model for intelligent flood forecasting in data-scarce small mountainous catchments by Yuan Ximin, Wang Tianzhi, Tian Fuchang, Wang Xiaojiao

“…By generating 117 synthetic time-series hydrographs, this method provides process-informed training datasets for machine learning models. Three hybrid architectures (SCS-BPNN, SCS-GRU, SCS-LSTM) are rigorously compared to identify the optimal approach for mountainous PUBs. …”
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Monolithic 3D Oscillatory Ising Machine Using Reconfigurable FeFET Routing for Large‐Scalability and Low‐Power Consumption by Joon Pyo Kim, Song‐Hyeon Kuk, Hyun Wook Kim, Jaeyong Jeong, Juhyuk Park, Bong Ho Kim, Jongmin Kim, Aida Todri‐Sanial, Sanghyeon Kim

“…Abstract Ising machines are attractive for efficiently solving NP‐hard combinatorial optimization problems (COPs). …”
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Early Warning System for Debt Group Migration: The Case of One Commercial Bank in Vietnam by Nguyen Quoc Hung, Trinh Hoang Viet, Phuong Truong Viet, Ly Truong Thi Minh

“…This study utilizes machine learning models, including Logistic Regression, Support Vector Machine, Decision Tree, and Random Forest, in the early warning system for debt group migration in a Vietnamese commercial bank. …”
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Understanding and detection of process instabilities in wire arc directed energy deposition additive manufacturing using meltpool imaging and machine learning by André Ramalho, Anis Assad, Benjamin Bevans, Fernando Deschamps, Telmo G. Santos, J.P. Oliveira, Prahalada Rao

“…These signatures were used in a machine learning model trained to autonomously detect process instabilities. …”
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