On the effectiveness of neural operators at zero-shot weather downscaling by Saumya Sinha, Brandon Benton, Patrick Emami

“…While neural operator-based downscaling models perform better than interpolation and a simple convolutional baseline, we show the surprising performance of an approach that combines a powerful transformer-based model with parameter-free interpolation at zero-shot weather downscaling. …”
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Research on channel estimation based on joint perception and deep enhancement learning in complex communication scenarios by Xin Liu, Shanghong Zhao, Yanxia Liang, Shahid Karim

“…The introduction of reinforcement learning enables the model to continuously optimize decisions in dynamic channel environments, improve the robustness of channel estimation, and quickly adjust the IRS reflection parameters when the channel state changes to adapt to complex communication conditions. …”
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Fractional-Order Adaptive P-Laplace Equation-Based Art Image Edge Detection by Ming Chen

“…The adaption is mainly reflected in the model algorithm by constructing an exponential parameter relation that is closely related to the image, which can dynamically adjust the parameter values, thus making the model algorithm more practical. …”
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Ground Segmentation for LiDAR Point Clouds in Structured and Unstructured Environments Using a Hybrid Neural–Geometric Approach by Antonio Santo, Enrique Heredia, Carlos Viegas, David Valiente, Arturo Gil

“…This paper introduces a hybrid framework that synergizes multi-resolution polar discretization with sparse convolutional neural networks (SCNNs) to address these challenges. …”
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A physical state prediction method based on reduce order model and deep learning applied in virtual reality by Pengbo Yu, Qiyu Liu, Qiyu Liu, Qiyu Liu, Qiyu Liu, Siyun Yi, Ming Zhu, Yangheng Hu, Gexiang Zhang, Gexiang Zhang

“…This method firstly integrates data dimensionality reduction and temporal convolutional network (TCN) to pre-capture time-series data from numerical simulation results, and then employs Kolmogorov–Arnold Networks (KAN) to approximate nonlinear characteristics to improved Long Short-Term Memory (LSTM) network, thereby predict time-series simulation data accurately to achieves realistic and responsive dynamic displays. …”
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“Locality – Adaptation” Research of Hydropower Resettlement Communities in the Jinsha River Basin: A Case Study of Ludila Hydropower Station by Fang WANG, Zhuoqi LI, Haoyi XU, Jiaqi YAN

“…At the settlement scale, the Mask Region-based Convolutional Neural Network (Mask R-CNN) deep learning model is utilized to identify architectural spatial features, categorizing three typical building types: traditional pitched-roof buildings, uniformly planned flat-roof buildings, and color steel plate-modified structures. …”
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Intelligent Forecasting for Solar Flares Using Magnetograms from SDO/SHARP, SDO/HMI, and ASO-S/FMG by Xuebao Li, Hongwei Ye, Yanfang Zheng, Ting Li, Jiaben Lin, Shunhuang Zhang, Pengchao Yan, Yongshang Lv, Noraisyah Mohamed Shah, Xuefeng Li, Xiaotian Wang, Yingbo Liu, Rui Wang, Jinfang Wei, Changtian Xiang, Honglei Jin

“…We compare the performance of four models for flare forecasting, including a convolutional neural network (CNN), CNN-BiLSTM, vision transformer (ViT), and MViT. …”
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A multi-scale cross-dimension interaction approach with adaptive dilated TCN for RUL prediction by Zhe Lu, Bing Li, Changyu Fu, Liang Xu, Bai Jiang, Zelong Li, Junbao Wu, Siye Jia

“…First, a dynamic adaptive dilation factor is incorporated into the TCN, thereby enabling the model to adjust its receptive field dynamically, which facilitates the capture of long- and short-term dependencies across different scales, allowing a more comprehensive representation of equipment degradation patterns. …”
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A Hybrid CNN-LSTM Model With Attention Mechanism for Improved Intrusion Detection in Wireless IoT Sensor Networks by Pendukeni Phalaagae, Adamu Murtala Zungeru, Abid Yahya, Boyce Sigweni, Selvaraj Rajalakshmi

“…Additionally, the improved dynamic PSO algorithm optimizes feature selection in real time, enhancing classification accuracy and adaptability to evolving IoT network threats. …”
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STID-Net: Optimizing Intrusion Detection in IoT with Gradient Descent by James Deva Koresh Hezekiah, Usha Nandini Duraisamy, Kalaichelvi Nallusamy, Avudaiammal Ramalingam, Saranya Chandran, Murugesan Rajeswari Thiyagupriyadharsan, Periasamy Selvaraju, Rajagopal Maheswar

“…Existing methods often struggle in capturing complex and irregular patterns from dynamic intrusion data, making them not suitable for different IoT applications. …”
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An Integrated Preprocessing and Drift Detection Approach With Adaptive Windowing for Fraud Detection in Payment Systems by Hadi M. R. Al Lawati, Anazida Zainal, Bander Ali Saleh Al-Rimy, Mohammad Al-Azawi, Mohamad Nizam Kassim, Sultan Ahmed Almalki, Tami Abdulrahman Alghamdi

“…These insights demonstrate the significance of dynamic pre-processing and drift-aware approaches in the context of real-time fraud detection. …”
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Short-Term Traffic Flow Prediction: A Method of Combined Deep Learnings by Chuanxiang Ren, Chunxu Chai, Changchang Yin, Haowei Ji, Xuezhen Cheng, Ge Gao, Heng Zhang

“…Moreover, a dynamic optimal weighted coefficient algorithm (DOWCA) is proposed to calculate the dynamic weights of CNN-LSTM-attention and CNN-GRU-attention with the goal of minimizing the sum of squared errors of the CDLP model. …”
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BiDGCNLLM: A Graph–Language Model for Drone State Forecasting and Separation in Urban Air Mobility Using Digital Twin-Augmented Remote ID Data by Zhang Wen, Junjie Zhao, An Zhang, Wenhao Bi, Boyu Kuang, Yu Su, Ruixin Wang

“…Using Remote ID data, we propose BiDGCNLLM, a hybrid prediction framework that integrates a Bidirectional Graph Convolutional Network (BiGCN) with Dynamic Edge Weighting and a reprogrammed Large Language Model (LLM, Qwen2.5–0.5B) to capture spatial dependencies and temporal patterns in drone speed trajectories. …”
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Early detection of ICU-acquired infections using high-frequency electronic health record data by Meri R. J. Varkila, Giacomo Lancia, Maarten van Smeden, Marc J. M. Bonten, Cristian Spitoni, Olaf L. Cremer

“…Earlier identification of these complications may facilitate better clinical management and improve outcomes. We developed a dynamic prediction model that leveraged high-frequency longitudinal data to estimate infection risk 48 h ahead of clinically overt deterioration. …”
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Hybrid deep learning-enabled framework for enhancing security, data integrity, and operational performance in Healthcare Internet of Things (H-IoT) environments by Nithesh Naik, Neha Surendranath, Sai Annamaiah Basava Raju, Chennaiah Madduri, Nagaraju Dasari, Vinod Kumar Shukla, Vathsala Patil

“…A dynamic Trust-Aware Controller (TAC) is introduced to compute real-time trust scores using anomaly likelihood, context entropy, and historical behavior. …”
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MambaPose: A Human Pose Estimation Based on Gated Feedforward Network and Mamba by Jianqiang Zhang, Jing Hou, Qiusheng He, Zhengwei Yuan, Hao Xue

“…The direct use of convolutional downsampling reduces selectivity for different stages of information flow. …”
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CoastVisionNet: transformer with integrated spatial-channel attention for coastal land cover classification by Li Yang, Liu Yijun, Wenhao Deng

“…Coastal zones, being highly dynamic and spatially heterogeneous, require sophisticated semantic modeling strategies that account for both spectral variability and spatial morphology. …”
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Contextual Deep Semantic Feature Driven Multi-Types Network Intrusion Detection System for IoT-Edge Networks by Shaho Hassen, Ahmed Abdlrazaq

“…However, the at-hand edge-IoT systems being decentralized, infrastructure-less, and dynamic remain vulnerable to man-in-the-middle attacks, intrusion, denial of service attacks, etc. …”
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Research on Bearing Fault Diagnosis Method for Varying Operating Conditions Based on Spatiotemporal Feature Fusion by Jin Wang, Yan Wang, Junhui Yu, Qingping Li, Hailin Wang, Xinzhi Zhou

“…Then, a parallel processing mechanism of the Convolutional Neural Network (SECNN) based on the Squeeze-and-Excitation module and the Bidirectional Long Short-Term Memory network (BiLSTM) is employed to dynamically adjust weights, capturing high-dimensional spatiotemporal features. …”
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Deep Learning-Based Sound Source Localization: A Review by Kunbo Xu, Zekai Zong, Dongjun Liu, Ran Wang, Liang Yu

“…In UAV surveillance, lightweight models integrated with spatiotemporal Transformers address dynamic modeling of non-stationary noise spectra and edge computing efficiency constraints. …”
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