Outdoor Environment Design Optimization of an Office Building Based on Indoor Thermal Conditions and Building Energy Performance by Yaolin Lin, Tao Huang, Wei Yang, Melissa Chan, Chun-Qing Li, Mingqi Dai, Pengju Chen

“…The outdoor environment greatly affects indoor thermal conditions, yet few investigations have been carried out to optimize the outdoor environment physical parameters that lead to improvements in the indoor thermal environment. …”
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Effects of varying loading rates on the Brazilian splitting characteristics of coal-rock composites by CHEN Yan, WANG Jiahao, DENG Liangtao, HONG Zijie, RONG Tenglong, HOU Zhiqiang

“…The stress-crack strain curve exhibited three stages before the peak： crack closure， elasticity， and crack propagation. The crack parameters demonstrated a pronounced dependency on loading rates.ConclusionsThese findings provide valuable theoretical insights into the tensile failure mechanism of coal-rock composite structures.…”
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RESEARCH OF ORIENTATION OF THE MODEL AND SCANNING SPEED TO THE RESULTING SURFACE ROUGHNESS IN THE SELECTIVE LASER MELTING TECHNOLOGY by Jan Milde, Marcle Kuruc, Jakub Hrbál, Patrik Dobrovszky, Tomáš Macháč

“…The metallic powder employed in the study was austenitic stainless steel SS 316L. Surface parameters (Ra, Rz, Rq) were meticulously measured (five times) for each print. …”
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Steel Surface Defect Detection Technology Based on YOLOv8-MGVS by Kai Zeng, Zibo Xia, Junlei Qian, Xueqiang Du, Pengcheng Xiao, Liguang Zhu

“…To address the problems of low efficiency and poor accuracy in the manual inspection process, intelligent detection technology based on machine learning has been gradually applied to the detection of steel surface defects. …”
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Enhancing intrusion detection in wireless sensor networks using a Tabu search based optimized random forest by Vivek Kumar Pandey, Shiv Prakash, Tarun Kumar Gupta, Priyanshu Sinha, Tiansheng Yang, Rajkumar Singh Rathore, Lu Wang, Sabeen Tahir, Sheikh Tahir Bakhsh

“…Tuning these parameters manually is tedious, as the combinations will be exponential. …”
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State of Health Estimation of Li-Ion Battery via Incremental Capacity Analysis and Internal Resistance Identification Based on Kolmogorov–Arnold Networks by Jun Peng, Xuan Zhao, Jian Ma, Dean Meng, Shuhai Jia, Kai Zhang, Chenyan Gu, Wenhao Ding

“…Three commonly used machine learning methods (BP, LSTM, TCN) and two hybrid algorithms (LSTM-KAN and TCN-KAN) were used to establish the SOH estimation model. …”
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Design and performance analysis of wear-resistant micro-groove cutting tool: Numerical simulation and experimental study by Feilong Du, Fukuan Chen, Tao Zhou, Hongfei Yao, Xuefeng Zhao, Dongwei Zhu, Lin He

“…This investigation establishes a novel computational framework integrating finite element modeling (FEM), machine learning (ML)-based predictive analysis, and multi-objective genetic algorithm (GA) optimization for micro-groove tool design. …”
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Personalized Federated Learning for Heterogeneous Residential Load Forecasting by Xiaodong Qu, Chengcheng Guan, Gang Xie, Zhiyi Tian, Keshav Sood, Chaoli Sun, Lei Cui

“…As a novel distributed machine learning (ML) technique, it only exchanges model parameters without sharing raw data. …”
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Predicting the Unconfined Compressive Strength of Rice Husk Ash – Treated Fine-grained Soils by Rizgar A. Blayi, Jamal I. Kakrasul, Samir M. Hamad

“…The findings present that these predictive models provide a hybrid empirical–machine learning approach, and an accurate alternative to traditional UCS testing, significantly reducing the need for laboratory experiments. …”
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Life extension of wind turbine drivetrains by means of SCADA data: Case study of generator bearings in an onshore wind farm by Kelly Tartt, Abbas Mehrad Kazemi-Amiri, Amir R. Nejad, James Carroll, Alasdair McDonald

“…These metrics are defined based on the differences between the actual temperatures of the components, that might have undergone some damage and the model predicted temperatures of those components if they would have remained healthy, throughout years of operation. A machine learning model is used, along with selected SCADA input parameters, to predict the healthy state temperature of components. …”
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Modular deep learning approach for wind farm power forecasting and wake loss prediction by S. Ally, S. Ally, T. Verstraeten, T. Verstraeten, P.-J. Daems, A. Nowé, J. Helsen, J. Helsen

“…<p>Power production of offshore wind farms depends on many parameters and is significantly affected by wake losses. …”
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Instagram fake profile detection using an ensemble learning method by Bharti Goyal, Nasib Singh Gill, Preeti Gulia, Noha Alduaiji, Piyush Kumar Shukla, Shreyas J

“…To find these profiles, we use a number of analytical parameters. Using machine learning is one of the main reasons for developing a model to effectively combat these false accounts. …”
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Fractional Optimizers for LSTM Networks in Financial Time Series Forecasting by Mustapha Ez-zaiym, Yassine Senhaji, Meriem Rachid, Karim El Moutaouakil, Vasile Palade

“…This study investigates the theoretical foundations and practical advantages of fractional-order optimization in computational machine learning, with a particular focus on stock price forecasting using long short-term memory (LSTM) networks. …”
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Toward improving precision and complexity of transformer-based cost-sensitive learning models for plant disease detection by Manh-Tuan Do, Manh-Hung Ha, Duc-Chinh Nguyen, Oscal Tzyh-Chiang Chen, Oscal Tzyh-Chiang Chen

“…This study introduces an automated system for early disease detection in plants that enhanced a lightweight model based on the robust machine learning algorithm. In particular, we introduced a transformer module, a fusion of the SPP and C3TR modules, to synthesize features in various sizes and handle uneven input image sizes. …”
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Efficient Design of a Terahertz Metamaterial Dual-Band Absorber Using Multi-Objective Firefly Algorithm Based on a Multi-Cooperative Strategy by Guilin Li, Yan Huang, Yurong Wang, Weiwei Qu, Hu Deng, Liping Shang

“…Traditional design processes are complex and time-consuming. Machine learning-based methods, such as neural networks and deep learning, require a large number of simulations to gather training samples. …”
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Butterfly magnetoreception based neighbour awareness strategy protocol for autonomous aerial vehicles by Janjhyam Venkata Naga Ramesh, C. Dastagiraiah, Suraya Mubeen, W. Deva Priya, M. Kameswara Rao, B. H. K. Bhagat Kumar

“…NAS protocol integrates Butterfly Magnetoreception Mechanism (BMM) and Machine Learning (ML) algorithms. BMM enhances navigational safety by reducing congestion and minimising decision-making delays during real-time events. …”
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Mechanical and physical properties of dental floss: a comparative cost analysis by Thiago Silva Peres, Izabela Batista Cordeiro, Ianca Daniele Oliveira de Jesus, Roberta de Oliveira Alves, Carlos José Soares, Priscilla Barbosa Ferreira Soares

“…The maximum load (N) and elongation (mm) were measured using a universal testing machine (Instron EL3000). The dental floss width (µm) and filament diameter (µm) were measured using a scanning electron microscope. …”
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Small Sample Fiber Full State Diagnosis Based on Fuzzy Clustering and Improved ResNet Network by Xiangqun Li, Jiawen Liang, Jinyu Zhu, Shengping Shi, Fangyu Ding, Jianpeng Sun, Bo Liu

“…The optical time domain reflectometer (OTDR) curve features of communication fibers exhibit subtle differences among their normal, subhealthy, and faulty operating states, making it challenging for existing machine learning-based fault diagnosis algorithms to extract these minute features. …”
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Advanced beamforming and reflection control in intelligent reflecting surfaces with integrated channel estimation by Sakhshra Monga, Anmol Rattan Singh, Nitin Saluja, Chander Prabha, Shivani Malhotra, Asif Karim, Md. Mehedi Hassan

“…This study presents a machine learning framework that directly maximises the beamformers at the BS and the reflective coefficients at the IRS, bypassing conventional methods that estimate channels before optimising system parameters. …”
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Fusion of Visible and Infrared Images Using a Reinforcement Learning System Based on Fuzzy Logic and Convolution Optimized with Wild Horse Algorithm by Mahvash Zarimeidani, Amir Amirabadi, Nasrin Amiri, Iman Ahanian, Siavash Es’haghi

“…Existing fusion approaches based on machine learning still struggle with how to better preserve the detail information in the source images. …”
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