Experimental study of load transfer mechanisms of onshore wind turbine foundations by Janet Modu, Laurent Briançon, Jean-François Georgin, Eric Antoinet

“…To reduce environmental impacts and limit greenhouse gas emissions, this practice appears to be far from optimal. This paper therefore focuses on assessing the suitability of a 1g small-scale model as a tool to support an evolutionary design enabling reuse of existing foundations during repowering. …”

Re-Evaluating Deep Learning Attacks and Defenses in Cybersecurity Systems by Meaad Ahmed, Qutaiba Alasad, Jiann-Shiun Yuan, Mohammed Alawad

“…The experiment was conducted by leveraging a deep learning model as a classifier with the three aforementioned datasets. …”
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Data‐Driven Design of Mechanically Hard Soft Magnetic High‐Entropy Alloys by Mian Dai, Yixuan Zhang, Xiaoqing Li, Stephan Schönecker, Liuliu Han, Ruiwen Xie, Chen Shen, Hongbin Zhang

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An Efficient Anomalous Sound Detection System for Microcontrollers by Yi-Cheng Lo, Tsung-Lin Tsai, Chieh-Wen Yang, An-Yeu Wu

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Study of grain spreading and cooling process based on non equilibrium thermal simulation by ZHAO Chao, CHEN Tao, ZHOU Zhonglin, YANG Jian

“…The relationship equations between the discharge temperature and the grain residue processing capacity, and exhaust air volume were determined, which were convenient for predicting the discharge temperature in case of process changes.ConclusionThrough process verification, the non-equilibrium thermal simulation method is accurate and effective in simulating the spreading and cooling process. The optimal process conditions for this spreading and cooling machine are about 20 cm in thickness of grain, 0.15 m/s in plate chain movement and 5 000 m3/h exhaust air volume.…”
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3D-Printed soft pneumatic actuators: enhancing flexible gripper capabilities by Shivashankar Hiremath, Kevin Amith Mathias, Tae-Won Kim

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Entropy-extreme concept of data gaps filling in a small-sized collection by Viacheslav Kovtun, Krzysztof Grochla, Mohammed Al-Maitah, Saad Aldosary, Oleksii Kozachko

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Garbage prediction using regression analysis for municipal corporations of Indian cities by Raj Kumar Sharma, Manisha Jailia

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MLR-predictor: a versatile and efficient computational framework for multi-label requirements classification by Summra Saleem, Summra Saleem, Muhammad Nabeel Asim, Ludger Van Elst, Markus Junker, Andreas Dengel, Andreas Dengel

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SineKAN: Kolmogorov-Arnold Networks using sinusoidal activation functions by Eric Reinhardt, Dinesh Ramakrishnan, Sergei Gleyzer

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GNNSeq: A Sequence-Based Graph Neural Network for Predicting Protein–Ligand Binding Affinity by Somanath Dandibhotla, Madhav Samudrala, Arjun Kaneriya, Sivanesan Dakshanamurthy

“…To overcome these limitations, we developed GNNSeq, a novel hybrid machine learning model that integrates a Graph Neural Network (GNN) with Random Forest (RF) and XGBoost. …”
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Evaluating and Forecasting the Probability of Lightning Occurrence in Rasht City by Afsaneh Ghasemi, Jamil Amanollahi

“…False negative rate = 0.198 was identified as the optimal model in predicting lightning in future and with respect to reliable outputs with maximum accuracy, precision and least prediction error, the Support Vector Machine model has a good performance which can be used to forecast the probability of lightning occurrencein Rasht City. …”
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Predicting Filter Medium Performances in Chamber Filter Presses with Digital Twins Using Neural Network Technologies by Dennis Teutscher, Tyll Weber-Carstanjen, Stephan Simonis, Mathias J. Krause

“…Additionally, the model predicts the filter medium’s lifespan, aiding in maintenance planning and resource sustainability. …”
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Integrating inflow control valve control with LSTM networks for oil production forecasting in horizontal intelligent well application by Liang Zhang, Zhong Cheng, Zhouzheng Hao, Kai Zuo, Kefei Liu

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Method for Analyzing the Importance of Quality and Safety Influencing Factors in Automotive Body Manufacturing Process—A Comprehensive Weight Evaluation Method to Reduce Subjective... by Ying Xiang, Long Guo, Shaoqian Ji, Shengchao Zhu, Zhiming Guo, Hu Qiao

“…To address the issue of subjectivity in traditional technique for order of preference by similarity to an ideal solution (TOPSIS) evaluation methods, this paper employs the coefficient of variation method for objective analysis of criterion-level indicators, the trapezoidal fuzzy number method for subjective analysis of criterion-level indicators, and establishes a model for optimizing target weight that balances subjective and objective approaches. …”
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Mapping rapeseed (Brassica napus L.) aboveground biomass in different periods using optical and phenotypic metrics derived from UAV hyperspectral and RGB imagery by Chuanliang Sun, Chuanliang Sun, Weixin Zhang, Genping Zhao, Qian Wu, Wanjie Liang, Ni Ren, Hongxin Cao, Lidong Zou, Lidong Zou

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Spatial and Temporal Patterns of Grassland Species Diversity and Their Driving Factors in the Three Rivers Headwater Region of China from 2000 to 2021 by Mingxin Yang, Ang Chen, Wenqiang Cao, Shouxin Wang, Mingyuan Xu, Qiang Gu, Yanhe Wang, Xiuchun Yang

“…Among models based on diverse variable selection and machine learning methods, the random forest (RF) combined stepwise regression (STEP) model was found to be the optimal model for estimating grassland species diversity in this study, which had an R² of 0.44 and an RMSE of 2.56 n/m² on the test set. …”
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Mechanical properties of self compacting concrete reinforced with hybrid fibers and industrial wastes under elevated heat treatment by Kennedy C. Onyelowe, Shadi Hanandeh, Viroon Kamchoom, Ahmed M. Ebid, Susana Monserrat Zurita Polo, Vilma Fernanda Noboa Silva, Rodney Orlando Santillán Murillo, Vicente Javier Parra León, Krishna Prakash Arunachalam

“…Finally, various performance metrics are used to evaluate the reliability of the models. The results show that the machine learning models show varying degrees of predictive accuracy, with the Kstar and XNV models consistently outperforming others across all mechanical properties. …”
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Enhancing cotton irrigation with distributional actor–critic reinforcement learning by Yi Chen, Meiwei Lin, Zhuo Yu, Weihong Sun, Weiguo Fu, Liang He

“…We used soil and plant state indicators from 5 experimental groups with varying irrigation treatments to calibrate and validate the DSSAT model. Subsequently, we innovatively integrated a distributional reinforcement learning method—an effective machine learning technique for continuous control problems. …”
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Computed tomography-based radiomics predicts prognostic and treatment-related levels of immune infiltration in the immune microenvironment of clear cell renal cell carcinoma by Shiyan Song, Wenfei Ge, Xiaochen Qi, Xiangyu Che, Qifei Wang, Guangzhen Wu

“…Therefore, the CT-based ML achieved good predictive results in predicting immune infiltration in ccRCC, with the ExtraTrees machine learning algorithm being optimal. Conclusion The use of radiomics model based on renal CT images can be noninvasively used to predict the immune infiltration level of ccRCC as well as combined with clinical information to create columnar plots predicting total survival in people with ccRCC and to predict responsiveness to ICI therapy, findings that may be useful in stratifying the prognosis of patients with ccRCC and guiding clinical practitioners to develop individualized regimens in the treatment of their patients.…”
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