Algae-Mamba: A Spatially Variable Mamba for Algae Extraction From Remote Sensing Images by Yaoteng Zhang, Shuaipeng Wang, Yanlong Chen, Shiqing Wei, Mingming Xu, Shanwei Liu

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Semi-supervised gearbox fault diagnosis under variable working conditions based on masked contrastive learning by ZHANG Huiyun, ZUO Fangjun, LI Hang, YU Xi

“…To address the problem that it is difficult to label variable working condition gearbox fault samples and the significant data distribution discrepancies in practical engineering, which result in reduced accuracy of fault diagnosis models, a semi-supervised gearbox fault diagnosis method based on masked contrastive learning is proposed. …”
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Multimodal bearing fault classification under variable conditions: A 1D CNN with transfer learning by Tasfiq E. Alam, Md Manjurul Ahsan, Shivakumar Raman

“…Overall, this multimodal 1D CNN framework with late fusion and TL strategies lays a foundation for more accurate, adaptable, and efficient bearing fault classification in industrial environments with variable operating conditions.…”
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Discretization-independent surrogate modeling of physical fields around variable geometries using coordinate-based networks by James Duvall, Karthik Duraisamy

“…Two methods toward generalization are proposed and compared: design-variable multilayer perceptron (DV-MLP) and design-variable hypernetworks (DVH). …”
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Fault Diagnosis Based On Improved Information Entropy And 1dcnn For Marine Turbocharger Rotor With Variable Speed by Hu Lei, Hu Haoran, Hu Nao, Liu Luyuan, Dong Fei, Yang Jianguo, Zhong Jiahong

“…In addition, the uncertainty in the feature parameters used for diagnosis under variable rotational speeds leads to low accuracy in fault identification. …”
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Digital three-stage recursive-separable image processing filter with variable sizes of scanning multielement aperture by A. V. Kamenskiy, T. M. Akaeva, D. A. Grebenshchikova

“…The aim of the work is to develop a type of recursively separable digital filter with variable sizes of a scanning multielement aperture which allows the number of computational operations to be reduced while maintaining the efficiency of filtering input data (images).Methods. …”
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FAULT DIAGNOSIS OF GEARBOX UNDER VARIABLE WORKING CONDITION BASED ON WEIGHTED SUBDOMAIN ADAPTIVE ADVERSARIAL NETWORK by ZHANG Huiyun, ZUO Fangjun, YU Xi, YANG Ting

“…In practical engineering, gearboxes are subject to complex and variable operating environments, which hinder the ability of a single vibration signal to accurately and effectively represent fault information under different working conditions. …”
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Multi-Factor Deep Learning Model for Sea Surface Temperature Forecasting by Yuting Yang, Kin-Man Lam, Junyu Dong, Yakun Ju

“…To address these challenges, we propose a multi-sensor SST prediction model that integrates Long Short-Term Memory (LSTM) networks, convolutional neural networks (CNNs), and an attention mechanism to directly incorporate physical variables such as temperature, salinity, density, and current velocity. …”
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Robust Long-Term Hand Grasp Recognition With Raw Electromyographic Signals Using Multidimensional Uncertainty-Aware Models by Yuzhou Lin, Ramaswamy Palaniappan, Philippe De Wilde, Ling Li

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Maize Seed Variety Classification Based on Hyperspectral Imaging and a CNN-LSTM Learning Framework by Shuxiang Fan, Quancheng Liu, Didi Ma, Yanqiu Zhu, Liyuan Zhang, Aichen Wang, Qingzhen Zhu

“…This method successfully minimized data dimensionality, reduced variable collinearity, and boosted the model’s stability and computational efficiency. …”
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Spatio-Temporal Collaborative Perception-Enabled Fault Feature Graph Construction and Topology Mining for Variable Operating Conditions Diagnosis by Jiaxin Zhao, Xing Wu, Chang Liu, Feifei He

“…Finally, we develop a graph residual convolutional network to mine topological information from multi-source spatio-temporal features under complex operating conditions. …”
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Development of Bimodal Emotion Recognition System Based on Skin Temperature and Heart Rate Variability Using Hybrid Neural Networks by Sayat Orynbassar, Duygun Erol Barkana, Evan Yershov, Madiyar Nurgaliyev, Ahmet Saymbetov, Batyrbek Zholamanov, Gulbakhar Dosymbetova, Ainur Kapparova, Nursultan Koshkarbay, Nurzhigit Kuttybay, Askhat Bolatbek, Kymbat Kopbay, Dinara Almen

“…This study aims to develop a new bimodal emotion recognition system based on skin temperature (SKT) and heart rate variability (HRV) using hybrid neural networks. Notably, these physiological signals can be measured remotely, addressing the limitations of direct measurement methods. …”
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Sharp L2 Norm Convergence of Variable-Step BDF2 Implicit Scheme for the Extended Fisher–Kolmogorov Equation by Yang Li, Qihang Sun, Naidan Feng, Jianjun Liu

“…A variable-step BDF2 time-stepping method is investigated for simulating the extended Fisher-Kolmogorov equation. …”
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Research on fault diagnosis method for variable condition planetary gearbox based on SKN attention mechanism and deep transfer learning by Nai-Qiu Huang, Meng-Meng Song, Yao-Hong Tang, Li-Xia Huang, Zhi-Wen Chen

“…However, the multi-coupling fault characteristics, accompanied by data fuzziness and distribution differences, present certain challenges to diagnostic research. Under variable operating conditions, the fault data to be diagnosed becomes more prominently inconsistent in distribution, leading to suboptimal fault recognition rates in diagnostic models. …”
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An interpretable wheat yield estimation model using an attention mechanism-based deep learning framework with multiple remotely sensed variables by Mingqi Li, Pengxin Wang, Kevin Tansey, Yue Zhang, Fengwei Guo, Junming Liu, Hongmei Li

“…The attention weights indicated that the most significant variable influencing wheat yield was FPAR, followed by LAI and VTCI. …”
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Spacecraft Intelligent Fault Diagnosis under Variable Working Conditions via Wasserstein Distance-Based Deep Adversarial Transfer Learning by Gang Xiang, Kun Tian

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A Predictive Method for Greenhouse Soil Pore Water Electrical Conductivity Based on Multi-Model Fusion and Variable Weight Combination by Jiawei Zhao, Peng Tian, Jihong Sun, Xinrui Wang, Changjun Deng, Yunlei Yang, Haokai Zhang, Ye Qian

“…The model utilizes highly correlated environmental variables to forecast soil pore water EC. The experimental results demonstrate that the PCLBX model achieves a mean square error (MSE) of 0.0016, a mean absolute error (MAE) of 0.0288, and a coefficient of determination (R²) of 0.9778. …”
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Vibration under variable magnitude moving distributed masses of non-uniform Bernoulli-Euler beam resting on Pasternak elastic foundation by T. O. Awodola, S. A. Jimoh, B. B. Awe

“…In order to obtain the solution, a technique based on the method of Galerkin with the series representation of Heaviside function is first used to reduce the equation to second order ordinary differential equations with variable coefficients. Thereafter the transformed equations are simplified using (i) The Laplace transformation technique in conjunction with convolution theory to obtain the solution for moving force problem and (ii) finite element analysis in conjunction with Newmark method to solve the analytically unsolvable moving mass problem because of the harmonic nature of the moving load. …”
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A Rolling-Bearing-Fault Diagnosis Method Based on a Dual Multi-Scale Mechanism Applicable to Noisy-Variable Operating Conditions by Jing Kang, Taiyong Wang, Ye Wei, Usman Haladu Garba, Ying Tian

“…To address the performance degradation encountered by current convolutional neural network-based rolling-bearing-fault diagnosis methods due to significant noise interference and variable working conditions in industrial settings, we propose a rolling-bearing-fault diagnosis method based on dual multi-scale mechanism applicable to noisy-variable operating conditions. …”
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Notes on the Free Additive Convolution by Shokrya S. Alshqaq, Raouf Fakhfakh, Fatimah Alshahrani

“…The investigation of free additive convolution is a key concept in free probability theory, offering a framework for studying the sum of freely independent random variables. …”
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