Progressive technologies of electrophysical treatment for regulation of stress-strain states of elements of welded structures by Leonid Lobanov, Pavlo Ustymenko, Yuriy Sydorenko, Mykola Pashchyn

Get full text

Scheduling of home energy management systems for price-based demand response and end-users discomfort reduction by Kamyab Gholam-Reza

“…The home energy management system (HEMS) can effectively participate in price-based demand response programs, significantly reducing electricity costs by optimizing the usage times of shift-able household appliances such as washing machines, dishwashers, and others. …”
Get full text

Impact of ITH on PRAD patients and feasibility analysis of the positive correlation gene MYLK2 applied to PRAD treatment by Chuanyu Ma, Chuanyu Ma, Guandu Li, Xiaohan Song, Xiaochen Qi, Tao Jiang

“…The results of the CMap data suggested that NU.1025 was the most likely drug to treat PRAD. The results of our machine learning model constructed based on ITH-score suggest that the random survival forest (RSF) model performs well in both the training and validation sets and has the potential to be used as a clinical prediction model. …”
Get full text

Unraveling the neural dynamics of mathematical interference in english reading: A novel approach with deep learning and fNIRS data by Zhijie Liang, Ling Wang, Jianyu Su, Bo Sun, Daifa Wang, Juan Yang

“…The AC-LSTM model achieves an exceptional accuracy rate of 99.8 %, surpassing other machine learning and deep learning models. …”
Get full text

Predicting Survival Rates in Brain Metastases Patients from Non‐Small Cell Lung Cancer Using Radiomic Signatures Associated with Tumor Immune Heterogeneity by Fuxing Deng, Gang Xiao, Guilong Tanzhu, Xianjing Chu, Jiaoyang Ning, Ruoyu Lu, Liu Chen, Zijian Zhang, Rongrong Zhou

“…Bidirectional stepwise logistic regression is employed to identify significant variables, facilitating the construction of a prognostic model, which is benchmarked against four machine learning algorithms. …”
Get full text

Intra- and peritumoral radiomics nomogram based on DCE-MRI for the early prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer by Yun Zhu, Shuni Zhang, Wei Wei, Li Yang, Lingling Wang, Ying Wang, Ye Fan, Haitao Sun, Zongyu Xie

“…Radiomics features were extracted from intratumoral region and 2-mm, 4-mm, 6-mm, 8-mm peritumoral regions on DCE-MRI images, and selected the optimal peritumoral region. The intratumoral radiomics model (IRM), 2-mm, 4-mm, 6-mm, 8-mm peritumoral radiomics model (PRM), the combined intra- and the optimal peritumoral radiomics model (CIPRM) were constructed based on five machine learning algorithms, and then the radiomics scores (Rad-score) were obtained. …”
Get full text

Dynamic taxonomy generation for future skills identification using a named entity recognition and relation extraction pipeline by Luis Jose Gonzalez-Gomez, Sofia Margarita Hernandez-Munoz, Abiel Borja, Fernando A. Arana-Salas, Jose Daniel Azofeifa, Jose Daniel Azofeifa, Julieta Noguez, Patricia Caratozzolo, Patricia Caratozzolo

“…A custom pipeline was used for PDF text extraction, tokenization, and lemmatization to standardize the data. The models were trained and evaluated using over 1,700 annotated documents, with the training process optimized for both entity recognition and relationship prediction accuracy.ResultsThe NER and RE models demonstrated promising performance. …”
Get full text

Analysis of Temperature Characteristics of Double-Row Spherical Roller Bearings Based on CFD by Chengguo Fu, Ting Chen, Hui Yang, Haibo Li, Yuhao Li, Yaqi Zhang, Weiwei He, Hongbin Cong

“…The findings of this research are as follows: (1) The numerical model demonstrates high accuracy, with a relative error of less than 5% when comparing the experimental temperature values of the jaw crusher bearing to the simulated values. (2) Under diverse operating conditions, the inner ring of the bearing has the highest temperature of all parts of the bearing, while the bearing cavity’s flow field has the lowest temperature. (3) The average temperature amplitude across different areas of the bearing system will rise as a result of increases in radial load or the bearing rotational speed. (4) When the grease filling volume increases from minimal to maximal, the average temperature in each bearing area initially decreases before subsequently rising, with the optimal grease filling amount identified as 60%. …”
Get full text

Enhancing the Efficiency of Routing Strategies in WSNs Using Live Streaming Algorithms by Hayder Jasim Alhamdane, Mohsen Nickray

“…However, sometimes problems arise, such as poor flexibility, focusing on a single operative, and relying on precise algebraic models. Machine learning techniques can adapt to environmental changes and employ multiple agents to make informed decisions, providing new ideas for energy-saving and intelligent routing algorithms in wireless networks. …”
Get full text

Investigation of Spot Size Variations in Multiroom Proton Therapy Systems and Their Clinical Significance: An In silico Study by Umesh Bharat Gayake, Bhushankumar J. Patil, Kantaram Darekar, Sanjay D. Dhole, Lalit Chaudhary, Siddhartha Laskar

“…Gamma analysis showed superior passing rates for the model ± 0.3 mm, particularly at 2%/2 mm and 3%/2 mm criteria, confirming its suitability for optimal treatment accuracy. …”
Get full text

A hybrid hierarchical health monitoring solution for autonomous detection, localization and quantification of damage in composite wind turbine blades for tinyML applications by Nikhil Holsamudrkar, Shirsendu Sikdar, Akshay Prakash Kalgutkar, Sauvik Banerjee, Rakesh Mishra

“…TinyML hardware can run ML models efficiently with low power consumption. This paper presents a Hybrid Hierarchical Machine-Learning Model (HHMLM) that leverages acoustic emission (AE) data to identify, classify, and locate different types of damage using the single unified model. …”
Get full text

Multi-Modal CLIP-Informed Protein Editing by Mingze Yin, Hanjing Zhou, Yiheng Zhu, Miao Lin, Yixuan Wu, Jialu Wu, Hongxia Xu, Chang-Yu Hsieh, Tingjun Hou, Jintai Chen, Jian Wu

“…Background: Proteins govern most biological functions essential for life, and achieving controllable protein editing has made great advances in probing natural systems, creating therapeutic conjugates, and generating novel protein constructs. Recently, machine learning-assisted protein editing (MLPE) has shown promise in accelerating optimization cycles and reducing experimental workloads. …”
Get full text

Hybrid Algorithm via Reciprocal-Argument Transformation for Efficient Gauss Hypergeometric Evaluation in Wireless Networks by Jianping Cai, Zuobin Ying

“…Consequently, our method significantly enhances the feasibility of tractable optimization in ultra-dense non-uniform cellular networks, bridging the computational gap in large-scale wireless performance modeling.…”
Get full text

Scenario-Based Economic Analysis of Underwater Biofouling Using Artificial Intelligence by Min-Ho Park, Jae-Jung Hur, Gwi-Ho Yun, Won-Ju Lee

“…We confirmed that using the novel framework we presented, the optimal hull cleaning timing could be determined for oceangoing vessels worldwide, considering economic impact based on data and machine learning models.…”
Get full text

An Enhanced Tree Ensemble for Classification in the Presence of Extreme Class Imbalance by Samir K. Safi, Sheema Gul

“…The efficacy of the proposed method is assessed using twenty benchmark problems for binary classification with moderate to extreme class imbalance, comparing it against other well-known methods such as optimal tree ensemble (OTE), SMOTE random forest (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>R</mi><mi>F</mi></mrow><mrow><mi>S</mi><mi>M</mi><mi>O</mi><mi>T</mi><mi>E</mi></mrow></msub></mrow></semantics></math></inline-formula>), oversampling random forest (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi mathvariant="normal">R</mi><mi mathvariant="normal">F</mi></mrow><mrow><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow></msub></mrow></semantics></math></inline-formula>), under-sampling random forest (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi mathvariant="normal">R</mi><mi mathvariant="normal">F</mi></mrow><mrow><mi mathvariant="normal">U</mi><mi mathvariant="normal">S</mi></mrow></msub></mrow></semantics></math></inline-formula>), k-nearest neighbor (k-NN), support vector machine (SVM), tree, and artificial neural network (ANN). …”
Get full text

MoNetViT: an efficient fusion of CNN and transformer technologies for visual navigation assistance with multi query attention by Liliek Triyono, Liliek Triyono, Rahmat Gernowo, Prayitno

“…Traditional CNNs handle image segmentation well, but transformers excel at capturing long-range dependencies, essential for machine vision tasks. Our study introduces MoNetViT (Mini-MobileNet MobileViT), a lightweight model combining CNNs and MobileViT in a dual-path encoder to optimize global and spatial image details. …”
Get full text

Unraveling the TEC-associated landscape in hepatocellular carcinoma: a comprehensive study based on multi-omics analyses by Jianwei Lan, Longhui Xie, Dekun Song, Pengpeng Liu, Quanyan Liu

“…These subtypes exhibit unique immune landscapes and biological characteristics, including pathway activation and differential responses to immunotherapy and targeted treatments. Using machine learning, we developed a robust prognostic scoring model to predict patient outcomes and therapy responsiveness, which was validated across independent cohorts. …”
Get full text

Inner pace: A dynamic exploration and analysis of basketball game pace. by Fei Zhang, Qing Yi, Rui Dong, Jin Yan, Xiao Xu

“…Compared to traditional methods, our approach provides a finer-grained analysis of game pace dynamics and offers actionable insights for optimizing coaching strategies. This study not only advances the understanding of basketball game rhythm but also establishes a robust framework for integrating machine learning and statistical models in sports analysis.…”
Get full text

Automatic feature selection and weighting in molecular systems using Differentiable Information Imbalance by Romina Wild, Felix Wodaczek, Vittorio Del Tatto, Bingqing Cheng, Alessandro Laio

“…Abstract Feature selection is essential in the analysis of molecular systems and many other fields, but several uncertainties remain: What is the optimal number of features for a simplified, interpretable model that retains essential information? …”
Get full text

The Kratos Framework for Heterogeneous Astrophysical Simulations: Fundamental Infrastructures and Hydrodynamics by Lile Wang

“…Focusing on the hydrodynamics module as an example and foundation for more complex simulations, optimizations and adaptations have been implemented for heterogeneous devices that allow for accurate and fast computations, especially the mixed-precision method that maximizes its efficiency on consumer-level GPUs while holding the conservation laws to machine accuracy. …”
Get full text