Analysis of the Strength of Assembly Joints - Welded Joints of Various Construction Materials by Anna Rudawska, Piotr Penkała, Paweł Chochowski, Andrzej Tkaczyk, Tetiana Vitenko

“…Welded joints samples were made with appropriate parameters, according to the welding methods. Strength tests of welded joints on MTS BIONIX 370.02 testing machine, in accordance with the PN-EN 1465 standardwere provided.Based on the obtained test results, it can be seen that the value of stresses is affected by both the welding method and the type of joint structure, while the type of material to be welded should also be taken into account.…”
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Res-SE-ConvNet: A Deep Neural Network for Hypoxemia Severity Prediction for Hospital In-Patients Using Photoplethysmograph Signal by Talha Ibn Mahmud, Sheikh Asif Imran, Celia Shahnaz

“…There has been research conducted for the detection of severity level using various parameters and bio-signals and feeding them in a machine learning algorithm. …”
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Robust Short-Term Wind Speed Forecasting Using Adaptive Shallow Neural Networks by Matrenin P.V., Manusov V.Z., Igumnova E.A.

“…This work aims to develop a machine learning model for short-term wind speed forecasting with acceptable accuracy but high robustness and the pos-sibility of automatic online retraining. …”
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Thermal and carbonation resistance of tunnel concrete: Experimental evaluation and hybrid ANN–GPR modeling under fire–CO₂ exposure by Amirhossein Fatemi, Ahmad Ganjali, Reza Babaei Semiromi, Pejman Aminian

“…Ultrasonic pulse velocity (UPV) measurements showed strong correlation with both strength loss and carbonation depth, supporting UPV as a reliable non-destructive evaluation method. A hybrid machine learning model combining artificial neural networks (ANN) and Gaussian process regression (GPR) was developed to predict residual compressive strength and carbonation depth based on UPV and exposure parameters. …”
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Active ramp-down control and trajectory design for tokamaks with neural differential equations and reinforcement learning by Allen M. Wang, Cristina Rea, Oswin So, Charles Dawson, Darren T. Garnier, Chuchu Fan

“…The policy training environment is a hybrid physics and machine learning model trained on simulations of the SPARC primary reference discharge (PRD) ramp-down, an upcoming burning plasma scenario which we use as a testbed. …”
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IQGO: Iterative Quantum Gate Optimiser for Quantum Data Embedding by Tautvydas Lisas, Ruairi de Frein

“…Quantum kernel methods and Variational Quantum Classifiers (VQCs) have recently gained significant interest in the field of Machine Learning (ML). They have the potential to achieve superior generalisation whilst using smaller datasets and fewer parameters compared to their classical counterparts. …”
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Symbolic Framework for Evaluation of NOMA Modulation Impairments Based on Irregular Constellation Diagrams by Nenad Stefanovic, Vladimir Mladenovic, Borisa Jovanovic, Ron Dabora, Asutosh Kar

“…We explicitly address several important design and measurement parameters and their relationship to different tasks, including variable constellation processing, carrier and symbol synchronization, and pulse shaping, focusing on quadrature amplitude modulation (QAM). …”
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Height of Hydraulic Fracture Zone Based on PSO_LSSVM Model by Hebin Zhang, Tingting Wang, Bin Wu, Haijun Feng

“…To achieve its effective prediction, this study selects four main control parameters for prediction, including the proportion coefficient of hard rock in the overlying strata, the inclination distance of the working face, and the thickness and depth of mining. …”
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Research on the Design of an On-Line Lubrication System for Wire Ropes by Fan Zhou, Yuemin Wang, Ruqing Gong

“…Kinematic modeling and grease consumption analysis guided greasing parameters optimization, validated through simulations and practical tests. …”
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A fully automated hybrid approach for processing high-frequency surface settlement data by Changyu Wang, Zude Ding, Annan Zhou, Zekun Zhu

“…The proposed method minimises manual intervention and reduces reliance on empirical design through the integration of the Mel Frequency Cepstral Coefficient (MFCC) based Convolutional Neural Networks (CNN), Extreme Learning Machine (ELM), and Variational Modal Decomposition (VMD) algorithms. …”
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Optimising the Selection of Input Variables to Increase the Predicting Accuracy of Shear Strength for Deep Beams by Mohammed Majeed Hameed, Faidhalrahman Khaleel, Mohamed Khalid AlOmar, Siti Fatin Mohd Razali, Mohammed Abdulhakim AlSaadi

“…Therefore, developing an efficient algorithm to select the optimal input parameters that have the highest information content to represent the target and minimise redundant data is very important. …”
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Threshold-free multi-attributes physical layer authentication based on expectation–conditional maximization channel estimation in Internet of Things by Tao Jing, Hongyan Huang, Yue Wu, Qinghe Gao, Yan Huo, Jiayu Sun

“…To overcome the uncertainty, machine learning–based authentication approaches have been employed to implement threshold-free authentication. …”
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Development and validation of a cardiac surgery-associated acute kidney injury prediction model using the MIMIC-IV database. by Yang Xu, Chunxiao Song, Wenping Wei, Runfeng Miao

“…By systematically comparing multiple machine learning approaches, our study highlights the utility of combining temporal physiological metrics to enhance AKI risk stratification. …”
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Coupling an Autonomous UAV With a ML Framework for Sustainable Environmental Monitoring and Remote Sensing by Faris A. Almalki, Shafaa M. Salem, Waad M. Fawzi, Norah S. Alfeteis, Shams A. Esaifan, Anoud S. Alharthi, Reef Z. Alnefaiey, Qamar H. Naith

“…This paper describes a pioneering approach to develop smart agriculture using multimission drones equipped with dual cognitive modules (brains) that are powered by a machine learning (ML) framework. The first brain uses deep reinforcement learning (DRL) principles to enable autonomous flight, allowing drones to navigate complex agricultural terrain with agility and flexibility. …”
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Investigating the hydrogen renaissance in the global energy transition with AI integration by Abderrahim Lakhouit

“…AI plays a pivotal role in optimizing hydrogen production methods, such as electrolysis, by enhancing process efficiencies through machine learning models that predict and optimize operational parameters. …”
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Structural Evolution and Mechanical Properties of FeCoNiAlTi HEA Coatings Fabricated by Plasma Surfacing by WANG Yonghong, ZHANG Chunlin, ZHANG Shihan, YU Jianping, LIU Yingfu, JIE Zhiwen

“…In order to deeply study the influence of the surfacing current on the microstructure and mechanical properties of FeCoNiAlTi highentropy alloy coatings in the plasma surfacing process parameters， five different surfacing currents (140， 160， 180， 200， 220 A) were used for coating preparation， and the microstructure， hardness， wear resistance and tensile properties of the coatings were evaluated by XRD， OM，SEM， micro-Vickers hardness tester， profilometer， friction wear tester and universal testing machine， respectively.Results showed that the porosity and elemental segregation of the coating were significantly improved with the increase of the surfacing current，but there was no significant effect on the coating phase composition.The coating mainly consisted of the typical FCC solid solution phase and Co3 Ti precipitation phase.When the surfacing current was 180 A， the surface microhardness of the obtained T3 coating was up to 322.77 HV0.2， which was about twice that of the substrate.At the same time， the T3 coating showed the best wear resistance， with a maximum wear depth of only 2.144 μm， an average friction coefficient of 0.362 and the lowest wear rate of 3.83×10－6 mm3/(N·m).In addition， the tensile strength and elongation of the T3 coating were 948 MPa and 26.61%， respectively， showing excellent strength and toughness.…”
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Frequency-Based Density Estimation and Identification of Partial Discharges Signal in High-Voltage Generators via Gaussian Mixture Models by Krissana Romphuchaiyapruek, Sarawut Wattanawongpitak

“…However, recent advancements in signal processing and machine learning have facilitated the development of automated tools to identify and categorize PD patterns, aiding those without extensive experience. …”
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The applications of CT with artificial intelligence in the prognostic model of idiopathic pulmonary fibrosis by Zeyu Chen, Zheng Lin, Zihan Lin, Qi Zhang, Haoyun Zhang, Haiwen Li, Qing Chang, Jianqi Sun, Feng Li

“…Recently, several studies attempted to build prognostic models by extracting predictive variates from pulmonary function data, basic information, or chest computed tomography (CT) and CT-derived parameters with clinical characteristics. Artificial intelligence (AI) algorithms, including principal component analysis, support vector machine, random survival forest, and convolutional neural network, could be applied to the procedure of IPF prognostic model, that is, region of interest extraction, image feature selection, clinical feature selection, and model construction. …”
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Failure Strain and Related Triaxiality of Aluminum 6061-T6, A36 Carbon Steel, 304 Stainless Steel, and Nitronic 60 Metals, Part I: Experimental Investigation by Ron Harwell, Robert Spears, Arya Ebrahimpour

“…These tests are performed at quasi-static speeds using Universal Testing Machines (UTMs) in accordance with ASTM E8 and ASTM E9 standards. …”
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A proposed deep learning model for multichannel ECG noise reduction by Jay Prakash Maurya, Manish Manoria, Sunil Joshi

“…Comparison of ECG signal patterns is very difficult manually, and machine-based interpretation is a demand of society. …”
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