Analysis of Time-Fractional Delay Partial Differential Equations Using a Local Radial Basis Function Method by Kamran, Kalsoom Athar, Zareen A. Khan, Salma Haque, Nabil Mlaiki

“…Delay partial differential equations have significant applications in numerous fields, such as population dynamics, control systems, neuroscience, and epidemiology, where they are required to efficiently model the effects of past states on current system behavior. …”
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Tomographic task solution using a dichotomous discretization scheme in polar coordinates and partial system matrices invariant to rotations by A. А. Manushkin, N. N. Potrachov, A. V. Stepanov, E. Yu. Usachev

“…A reconstruction algorithm was developed on the basis of the use of partial system matrices corresponding to the dichotomous division of the image field into partial annular reconstruction regions. …”
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Detection of failures in HV surge arrester using chaos pattern with deep learning neural network by Chun‐Chun Hung, Meng‐Hui Wang, Shiue‐Der Lu, Cheng‐Chien Kuo

“…This study integrates chaotic systems with Convolutional Neural Networks (CNN) to diagnose faults in HV surge arresters. …”
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Spectrophotometric Simultaneous Kinetic Determination of Iodide and Iodate Using Partial Least-Squares Calibration Method in a Single Kinetic Run

“…The Savitzky-Golay convolution method is used for calculating and smoothing the rate data. …”
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Temporal–Spatial Partial Differential Equation Modeling of Land Cover Dynamics via Satellite Image Time Series and Sparse Regression by Ming Kang, Zheng Zhang, Zhitao Zhao, Keli Shi, Junfang Zhao, Ping Tang

“…Our approach leverages <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1</mn><mo>×</mo><mn>1</mn></mrow></semantics></math></inline-formula> convolutional kernels within a convolutional neural network (CNN) solver to approximate derivatives, enabling the discovery of interpretable equations that generalize across temporal–spatial domains. …”
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Enhancing Autonomous Driving With Spatial Memory and Attention in Reinforcement Learning by Matvey Gerasyov, Andrey V. Savchenko, Ilya Makarov

“…Through experiments in the Atari benchmark and multiple autonomous driving environments, our approach outperforms agents using classical convolutional and recurrent neural networks. Further analysis reveals repeated interpretive patterns in attention distribution among trained agents. …”
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Context-Aware Deep Learning Model for Yield Prediction in Potato Using Time-Series UAS Multispectral Data by Suraj A. Yadav, Xin Zhang, Nuwan K. Wijewardane, Max Feldman, Ruijun Qin, Yanbo Huang, Sathishkumar Samiappan, Wyatt Young, Francisco G. Tapia

“…The proposed feature engineering and prediction model followed a two-fold approach: first, adoption of partial least squares regression (PLSR) algorithm to extract features relevant to yield, and second, a novel context-aware attention and residual connection convolution-bidirectional gated recurrent unit bidirectional long short-term memory-network (CAR Conv1D-BiGRU-BiLSTM-Net) to exploit time-series multifeatures information to predict final yield. …”
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An interpretable deep learning model for the accurate prediction of mean fragmentation size in blasting operations by Baoqian Huan, Xianglong Li, Jianguo Wang, Tao Hu, Zihao Tao

“…To address the limitations of conventional blasting fragmentation size prediction methods in terms of prediction accuracy and applicability, this study proposes an NRBO-CNN-LSSVM model for predicting mean fragmentation size, which integrates Convolutional Neural Networks (CNN), Least Squares Support Vector Machines (LSSVM), and the Newton-Raphson Optimizer (NRBO). …”
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Research on Atlantic surface pCO2 reconstruction based on machine learning by Jiaming Liu, Jie Wang, Xun Wang, Yixuan Zhou, Runbin Hu, Haiyang Zhang

“…Ocean acidification is transforming marine ecosystems at an unprecedented rate, which in turn requires the estimation of sea surface carbon dioxide partial pressure (pCO2) as a crucial metric to gauge acidification. …”
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Spatiotemporal Forecasting of Solar and Wind Energy Production: A Robust Deep Learning Model with Attention Framework by Md. Shadman Abid, Razzaqul Ahshan, Mohammed Al-Abri, Rashid Al Abri

“…In this context, a novel robust deep learning model, termed the Convolutional Neural Network-Bidirectional Long Short-Term Memory model with spatiotemporal attention mechanism (CNN-BiLSTM-STA), is developed in this study. …”
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Study on Color Detection of Korla Fragrant Pears by Near-Infrared Spectroscopy Combined with PLSR by Yifan Xia, Yang Liu, Hong Zhang, Jikai Che, Qing Liang

“…The full-spectrum data were pre-processed using six methods: Savitzky–Golay convolution smoothing (SGCS), Savitzky–Golay convolution derivative (SGCD), multiplicative scatter correction (MSC), vector normalization (VN), min–max normalization (MMN), and standard normal variate transformation (SNV). …”
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Double Local Fractional Yang–Laplace Transform for Local Fractional PDEs on Fractal Domains by Djelloul Ziane, Mountassir Hamdi Cherif, Carlo Cattani, Abdelhamid Mohammed Djaouti

“…This study introduces a novel analytical technique known as the double local fractional Yang–Laplace transform method (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>L</mi><mi>F</mi><msubsup><mi>L</mi><mrow><mi>ζ</mi></mrow><mn>2</mn></msubsup></mrow></semantics></math></inline-formula>) and rigorously investigates its foundational properties, including linearity, differentiation, and convolution. The proposed method is formulated via double local fractional integrals, enabling a robust mechanism for addressing local fractional partial differential equations defined on fractal domains, particularly Cantor sets. …”
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Performance and Computational Efficiency of LMS Adaptive Volterra Equalizers for Nonlinear TWTA Distortion in Satellite Communications by Jerome J. Malone, Byeong Kil Lee

“…Difference in performance between fully-coupled and partially-decoupled adaptive equalizers was found to be small, rarely exceeding ~1.5 dB mean-square error. …”
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Nature-inspired MPPT algorithms for solar PV and fault classification using deep learning techniques by S. Senthilkumar, V. Mohan, S. P. Mangaiyarkarasi, R. Gandhi Raj, K. Kalaivani, N. Kopperundevi, M. Chinnadurai, M. Nuthal Srinivasan, L. Ramachandran

“…To further classify the faults in solar PV systems, random forest (RF), artificial neural network (ANN), support vector machine (SVM), and convolutional neural network (CNN) models are employed. …”
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Robust Face Detection and Identification under Occlusion using MTCNN and RESNET50 by Eiman Wahab, Wajeeha Shafique, Habiba Amir, Sameena Javed, Muhammad Marouf

“…We used the face detector algorithm called Multi-Task Cascaded Convolutional Neural Network (MTCNN) for face detection with 99.8% accuracy. …”
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DGA domain detection and botnet prevention using Q-learning for POMDP by Y. V. Bubnov, N. N. Ivanov

“…The described method implies the detection of generated domain names in DNS queries using a neural network with parallel organization of convolutional and bidirectional recurrent layers. The effectiveness of the method is based on the assumption that generated domain names are used to create a botnet for merging. …”
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Fractional Calculus Approach for Variational Problems: Characterization of Sufficient Optimality Conditions and Duality by Ved Prakash Dubey, Devendra Kumar, Jagdev Singh, Dumitru Baleanu

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LCDDN-YOLO: Lightweight Cotton Disease Detection in Natural Environment, Based on Improved YOLOv8 by Haoran Feng, Xiqu Chen, Zhaoyan Duan

“…Furthermore, it integrates Partial Convolution (PConv) and Distributed Shift Convolution (DSConv) into the C2f module, called PDS-C2f. …”
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DCFE-YOLO: A novel fabric defect detection method. by Lei Zhou, Bingya Ma, Yanyan Dong, Zhewen Yin, Fan Lu

“…Finally, the feature fusion network integrates Partial Convolution and Efficient Multi-scale Attention, optimizing the fusion of information across different feature levels and spatial scales, which enhances the richness and accuracy of feature representations while reducing computational complexity. …”
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A lightweight UAV target detection algorithm based on improved YOLOv8s model by Fubao Ma, Ran Zhang, Bowen Zhu, Xirui Yang

“…To tackle these challenges, this paper proposes LW-YOLOv8, a lightweight object detection algorithm based on the YOLOv8s model for UAV deployment. First, Cross Stage Partial Convolutional Neural Network (CNN) Transformer Fusion Net (CSP-CTFN) is proposed. …”
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