SVHAE: Spectral Variability-Aware Hybrid Autoencoder for Hyperspectral Underwater Target Detection by Suresh Aala, Sravan Kumar Sikhakolli, Sunil Chinnadurai, Anuj Deshpande, Karthikeyan Elumalai, Md. Abdul Latif Sarker, Hala Mostafa

“…Hyperspectral imaging (HSI) has evolved as an important tool for many applications, including remote sensing, crime investigation, target detection, disease diagnosis, and anomaly detection. …”
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Development of Recurrent Neural Networks for Thermal/Electrical Analysis of Non-Residential Buildings Based on Energy Consumptions Data by Elisa Belloni, Flavia Forconi, Gabriele Maria Lozito, Martina Palermo, Michele Quercio, Francesco Riganti Fulginei

“…Uncertainty analyses are carried out to evaluate the behavior effectiveness of the artificial neural networks (ANNs) in different weather conditions, and the root mean square error (RMSE) is calculated in terms of mean air temperatures. …”
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An attention mechanism and fully connected layer-based dual-branch network for bioluminescent tomographic reconstruction by Lin Wang, Chenrui Pan, Minghua Zhao, Xin Cao, Xueli Chen

“…However, the simplified optical transmission models and the ill-posed inverse reconstruction limit its wide applications. The development of deep learning has provided new potential for extending the applications of optical BLT. …”
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AI-Integrated AR as an Intelligent Companion for Industrial Workers: A Systematic Review by Steven Yoo, Sakib Reza, Hamid Tarashiyoun, Akhil Ajikumar, Mohsen Moghaddam

“…Augmented reality (AR) has gained significant attention in recent years for its applications in training and assistance in various industrial settings. …”
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Context-Aware Multimodal Fusion with Sensor-Augmented Cross-Modal Learning: The BLAF Architecture for Robust Chinese Homophone Disambiguation in Dynamic Environments by Yu Sun, Yihang Qin, Wenhao Chen, Xuan Li, Chunlian Li

“…Furthermore, the sensor-augmented module significantly improves robustness under noise (speech SNR to 18.6 dB at 75 dB noise, 12.7% reduction in word error rates). By bridging gaps among tonal phonetics, contextual semantics, and computational efficiency, BLAF establishes a scalable paradigm for robust Chinese homophone disambiguation in industrial NLP applications. …”
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The Prediction of the Valve Opening Required for Slugging Control in Offshore Pipeline Risers Based on Empirical Closures and Valve Characteristics by Jiqiang Fu, Quanhong Wu, Jie Sun, Hanxuan Wang, Suifeng Zou

“…A field case with a flexible S-shape riser proves the good applicability of the model (with a deviation smaller than ±2%). …”
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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

“…The results showed that the NRBO-CNN-LSSVM model achieved remarkable prediction accuracy on the training dataset, with a coefficient of determination (R 2) as high as 0.9717 and a root mean square error (RMSE) as low as 0.0285. On the test set, the model maintained high prediction accuracy, with an R 2 value of 0.9105 and an RMSE of 0.0403. …”
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Effects of Konjac Glucomannan and Curdlan on the 3D Printability and Physicochemical Properties of Germinated Brown Rice Gel by Chun Bai, Ran Liu, Liuyang Shen, Yu Zhuang, Jiaying Hu

“…Specifically, under the KGM to CD ratio of 3:1 (KC3) formulation obtained by combining 2.25% KGM and 0.75% CD, the printing accuracy was highest with a minimized error of 4.97 ± 0.45%, and optimal structural stability was maintained within 5 h post-printing. …”
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Simulation of the Dynamic Response to Sinusoidal Excitation of the Elastic Porous Metal Mesh Damper by Yichuan Shao, Shibin Song, Taia Xin, Jinyu Wang, Xiaobo Xie

“…However, the design of a metal mesh damper relies heavily on the trial-and-error methodology for tuning fabrication parameters, which prevents it from widespread use in rotor-bearing applications. …”
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An Image-Free Single-Pixel Detection System for Adaptive Multi-Target Tracking by Yicheng Peng, Jianing Yang, Yuhao Feng, Shijie Yu, Fei Xing, Ting Sun

“…Quantitative evaluations under 1920 × 1080 pixel resolution reveal a normalized root mean square error (NRMSE) of 0.00785, confirming the method’s capability for robust multi-target tracking in practical applications.…”
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A flexible and lightweight signcryption scheme for underwater wireless sensor networks by Sabir Shah, Nadeem Sarwar, Abdu Salam, Farhan Amin, Faizan Ullah, Asfandyar Khan, Isabel de la Torre, Mónica Gracia Villar, Helena Garay

“…However, It has several challenges including security, node mobility, limited bandwidth, and high error rates. Thus, to solve these issues, herein we propose a new lightweight Signcryption scheme for UWSNs. …”
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Remote Sensing-Based Multiscale Analysis of Total and Groundwater Storage Dynamics over Semi-Arid North African Basins by Abdelhakim Amazirh, Youness Ouassanouan, Houssne Bouimouass, Mohamed Wassim Baba, El Houssaine Bouras, Abdellatif Rafik, Myriam Benkirane, Youssef Hajhouji, Youness Ablila, Abdelghani Chehbouni

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Development of intelligent controller for high performance electric drives with hybrid CSA and RERNN technique by J. Prabhakaran, P. Thirumoorthi, M. Mathankumar, S. P. Mangaiyarkarasi

“…Abstract Electric drives play a crucial role in various industrial applications, requiring precise control and adaptability to changing parameters. …”
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GazeCapsNet: A Lightweight Gaze Estimation Framework by Shakhnoza Muksimova, Yakhyokhuja Valikhujaev, Sabina Umirzakova, Jushkin Baltayev, Young Im Cho

“…Our results show that GazeCapsNet achieves state-of-the-art (SOTA) performance on several benchmark datasets, including ETH-XGaze and Gaze360, achieving a mean angular error (MAE) reduction of up to 15% compared to existing models. …”
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Effortless alkalinity analysis using AI and smartphone technology, no equipment needed, from freshwater to saltwater by Zachary Y. Han, Zihan Zheng, Alan Y. Han, Huichun Zhang

“…After examining the effects of container type, lighting condition, ML algorithms, and sample size, we obtained the best models with R2 values of 0.868 ± 0.024 and 0.978 ± 0.008, and root-mean-square-error values of 29.5 ± 2.6 and 14.1 ± 2.0 for freshwater and saltwater, respectively. …”
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A Novel Six-Dimensional Chimp Optimization Algorithm—Deep Reinforcement Learning-Based Optimization Scheme for Reconfigurable Intelligent Surface-Assisted Energy Harvesting in Batt... by Mehrdad Shoeibi, Anita Ershadi Oskouei, Masoud Kaveh

“…Compared to benchmark algorithms, our approach achieves higher gains in harvested power, an improvement in the data rate at a transmit power of 20 dBm, and a significantly lower root mean square error (RMSE) of 0.13 compared to 3.34 for standard RL and 6.91 for the DNN, indicating more precise optimization of RIS phase shifts.…”
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A Structured Dataset for Automated Grading: From Raw Data to Processed Dataset by Ibidapo Dare Dada, Adio T. Akinwale, Ti-Jesu Tunde-Adeleke

“…The experimental results demonstrate that transformer-based models outperform traditional methods, with Longformer achieving the highest Spearman’s Correlation of 0.77 and the lowest Mean Squared Error (MSE) of 0.04, indicating a strong alignment between model predictions and human grading. …”
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Validity and reliability of the Chinese version of the Jenkins Sleep Scale among university students in China by Mengyuan Zhao, Garry Kuan, Ke Zhou, Yee Cheng Kueh

“…Background The Chinese version of the Jenkins Sleep Scale (JSS-C) is widely used; however, it lacks formal validation, and its applicability to Chinese university students has not been examined. …”
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A Novel Fuzzy PIDF Enhancing PIDF Controller Tuned in Two Stages by TLBO and PSO Algorithms for Reliable AVR Performance by Mokhtar Shouran, Mohammed Alenezi

“…The tuning process for both stages employs the well-known Integral Time Absolute Error (ITAE) cost function in conjunction with the TLBO and PSO algorithms. …”
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Control of Nonlinear Systems Using Fuzzy Techniques Based on Incremental State Models of the Variable Type Employing the “Extremum Seeking” Optimizer by Basil Mohammed Al-Hadithi, Gilberth André Loja Acuña

“…The main contribution of this work is that the proposed scheme achieves zero steady-state error for reference inputs and disturbances up to order N−1 by employing the incremental-N formulation. …”
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