Linker-GPT: design of Antibody-drug conjugates linkers with molecular generators and reinforcement learning by An Su, Yanlin Luo, Chengwei Zhang, Hongliang Duan

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Cutting-edge approaches to specific energy prediction in TBM disc cutters: Integrating COSSA-RF model with three interpretative techniques by Jian Zhou, Zijian Liu, Chuanqi Li, Kun Du, Haiqing Yang

“…Accurate prediction of the SE of tunnel boring machine disc cutters is important for optimizing the crushing process, reducing energy consumption, and minimizing machine wear. …”
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The suppressive role of GLS in radiosensitivity and irradiation-induced immune response in LUAD: integrating bioinformatics and experimental insights by Peicheng Jiang, Zhifeng Jiang, Su Li, Su Li, Ye-Xiong Li, Yuqiong Chen, Xinyan Li

“…Furthermore, the established GLS-DSBr model serves as a robust predictive tool for prognosis and effects of radiotherapy and immunotherapy, which assists personalized treatment optimization in LUAD.…”
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A Generalized Convolutional Neural Network Model Trained on Simulated Data for Fault Diagnosis in a Wide Range of Bearing Designs by Amirmasoud Kiakojouri, Ling Wang

“…A novel hybrid signal processing method is employed to enhance feature extraction and reduce domain shifts between simulated and real-world data. The optimized CNN model, trained on simulated data, is tested using experimental and real-world vibration signals from laboratory bearings and jet engine components. …”
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Metaheuristic Prediction Models for Kerf Deviation in Nd-YAG Laser Cutting of AlZnMgCu1.5 Alloy by Arulvalavan Tamilarasan, Devaraj Rajamani

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Data-driven framework for prediction of mechanical properties of waste glass aggregates concrete by Kennedy C. Onyelowe, Shadi Hanandeh, Viroon Kamchoom, Ahmed M. Ebid, Hamza Imran, Miguel Angel Duque Vaca, Greys Carolina Herrera Morales, Nestor Ulloa, Krishna Prakash Arunachalam

“…The practical implications of this research extend to sustainable machine learning-based concrete design, where AI-driven optimization can help reduce the reliance on conventional trial-and-error methods. …”
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Modelling, implementation and analysis of double-side slotted axial flux PMGs suitable to small-scale wind energy conversion systems by Ravindran S., Prabhakaran Koothu Kesavan, David Banjerdpongchai, Prem P.

“…This paper introduces a design methodology for a Double-Sided Slotted Axial Flux PMG (DSAFPMGs), aiming to address the shortcomings of RFPMGs. The machine model is developed in the Ansys Maxwell and finite element analysis of machine is perfomed using Altair Flux software packages. …”
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An improved finite set model predictive control of SRM drive based on a voltage vectors strategy for low torque ripple by Deepak Mohanraj, Devakirubakaran S, Praveen Kumar Balachandran

“…This paper proposes a Finite Set-Model Predictive Control (FS-MPC) for an analytical model of a non-linearity SRM machine to analyze the torque ripple performance. …”
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Advancing Spike Sorting Through Gradient‐Based Preprocessing and Nonlinear Reduction With Agglomerative Clustering by Mohammad Amin Lotfi, Fatemeh Zareayan Jahromy, Mohammad Reza Daliri

“…Method Unsupervised mathematical methods in spike sorting possess an advantage over supervised machine learning and deep learning models as they require no training and involve lower computational costs. …”
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A multi-level classification model for corrosion defects in oil and gas pipelines using meta-learner ensemble (MLE) techniques by Adamu Abubakar Sani, Mohamed Mubarak Abdul Wahab, Nasir Shafiq, Kamaludden Usman Danyaro, Nasir Khan, Adamu Tafida, Arsalaan Khan Yousafzai

“…This study demonstrates the application of stacking ensemble techniques in predicting corrosion risks and optimizing pipeline maintenance strategies. It provides vital information for improving pipeline safety and optimizing predictive maintenance practices by providing an in-depth assessment of various machine learning models, especially when real-time monitoring systems are integrated.…”
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Self-trainable and adaptive sensor intelligence for selective data generation by Arghavan Rezvani, Wenjun Huang, Hanning Chen, Yang Ni, Mohsen Imani

“…One promising solution involves deploying compact machine learning models near sensors, enabling intelligent identification and transmission of only relevant data frames. …”
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Research on Bearing Fault Diagnosis Method Based on IPSO-RVM by ZHANG Han, ZOU Fang-hao, MENG Liang, SU Yuan-hao, XU Tong-le

“…Aiming at the problems of poor classification effect of support vector machine in traditional particle swarm optimization support vector machine and inaccuracy of traditional particle swarm optimization in bearing fault diagnosis, an improved particle swarm optimization method for correlation vector machine is proposed in this paper.By using the adaptive inertia weight and acceleration factor, the search speed is faster in the early stage and the convergence speed is faster in the late stage.The classification models of improved particle swarm optimization correlation vector machine (IPSO-RVM), improved particle swarm optimization support vector machine (IPSO-SVM) and particle swarm optimization support vector machine (PSO-SVM) were constructed respectively for comparative experiments.The simulation results show that the classification accuracy of IPSO-RVM is 5.8% higher than IPSO-SVM and 8.7% higher than PSO-SVM.The simulation results show that the classification accuracy of IPSO-RVM is 5.8% higher than ipSO-SVM and 8.7% higher than PSO-SVM.The running time of IPSO-RVM is 0.58 s and 4.28 s slower than that of IPSO-SVM and PSO-SVM, respectively. …”
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Design and damping characterization of sandwich composite made of particle-filled hollow spheres and steel sheets by Xin Zhou, Lars Penter, Ulrike Jehring, Hartmut Göhler, Thomas Weißgärber, Steffen Ihlenfeldt

“…To address manufacturing tolerances, a finite element (FE) model-based optimization was developed to accurately determine PHSS material parameters. …”
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Data-Driven Proactive Early Warning of Grid Congestion Probability Based on Multiple Time Scales by Haobo Fu, Ruizhuo Wang, Bingxu Zhai, Yuanzhuo Li, Pengyuan Li, Rui Zhang, Haoyuan He, Siyang Liao

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Research on residual stress and deformation control of domestic 7050 aluminum alloy thin-wall bearing frame turning by HU Yue, ZHANG Shengwei, JIANG Ruisong, HUANG Chongxiang

“…The results show that the optimized machining surface residual stress is reduced to 15.6 MPa，and the maximum radial deformation of the bearing frame is 1.59 mm，which is about 19% less than the original turning parameters，achieving the goal of controlling and optimizing the processing deformation of the domestic 7050 aluminum alloy aviation thin-walled bearing frame.…”
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Generative autoencoder to prevent overregularization of variational autoencoder by YoungMin Ko, SunWoo Ko, YoungSoo Kim

“…In machine learning, data scarcity is a common problem, and generative models have the potential to solve it. …”
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TAE Predict: An Ensemble Methodology for Multivariate Time Series Forecasting of Climate Variables in the Context of Climate Change by Juan Frausto Solís, Erick Estrada-Patiño, Mirna Ponce Flores, Juan Paulo Sánchez-Hernández, Guadalupe Castilla-Valdez, Javier González-Barbosa

“…The ensemble combines Long Short-Term Memory neural networks, Random Forest regression, and Support Vector Machines, optimizing their contributions using heuristic algorithms such as Particle Swarm Optimization. …”
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Analysis of the dynamic behavior of onboard rotors in non-linear regimes under the influence of parametric uncertainties by Trevilato Thales R. B., Cavallini Jr Aldemir Ap, Steffen Jr Valder

“…In recent decades, a significant advance in the understanding of the dynamic behavior of rotors has been observed, including the capacity to model and forecast the physical behavior of these systems. …”
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Self-Sensing of Piezoelectric Micropumps: Gas Bubble Detection by Artificial Intelligence Methods on Limited Embedded Systems by Kristjan Axelsson, Mohammadhossien Sheikhsarraf, Christoph Kutter, Martin Richter

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A double-layer model for improving the estimation of wheat canopy nitrogen content from unmanned aerial vehicle multispectral imagery by Zhen-qi LIAO, Yu-long DAI, Han WANG, Quirine M. KETTERINGS, Jun-sheng LU, Fu-cang ZHANG, Zhi-jun LI, Jun-liang FAN

“…The results showed that the inversion of winter wheat LAI, CPP and CNC by the combination of SFs+TFs greatly improved the estimation accuracy compared with that by using only the SFs. The RBFNN and BPNN models outperformed the other machine learning models in estimating winter wheat LAI, CPP and CNC. …”
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