Prototype of Swiftlet Nest Moisture Content Measurement Using Resistance Sensor and Machine Learning by Ratu Anggriani Tangke Parung, Hanna Arini Parhusip, Suryasatriya Trihandaru

“…The results demonstrated a strong correlation between predicted and actual moisture content (R² = 0.9759), with the neural network model attaining a mean squared error (MSE) of 0.01. This method holds significant potential to improve the efficiency and cost-effectiveness of moisture measurement for swiftlet nests and similar applications.…”
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Effectiveness of Explainable Artificial Intelligence (XAI) Techniques for Improving Human Trust in Machine Learning Models: A Systematic Literature Review by In-On Wiratsin, Chaiyong Ragkhitwetsagul

“…Future research should focus on these areas to further improve the applicability and effectiveness of XAI techniques in diverse domains.…”
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Sparse Representation of Deformable 3D Organs with Spherical Harmonics and Structured Dictionary by Dan Wang, Ahmed H. Tewfik, Yingchun Zhang, Yunhe Shen

“…This algorithm is also generalized into applications of organs with both interior and exterior surfaces. …”
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Enhancing security and usability with context aware multi-biometric fusion for continuous user authentication by Ayeswarya S., John Singh K.

“…This research contributes to the development of adaptive, context-aware biometric systems, advancing both security and user experience in real-world applications.…”
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CrystalShift: A Versatile Command-Line Tool for Crystallographic Structural Data Analysis, Modification, and Format Conversion Prior to Solid-State DFT Calculations of Organic Crys... by Ilona A. Isupova, Denis A. Rychkov

“…These options are especially valuable for studying the mechanical properties of molecular crystals with potential applications in organic materials science. Written in the C programming language, <i>CrystalShift</i> offers computational efficiency and compatibility with widely used crystallographic formats such as CIF, POSCAR, and XYZ. …”
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A novel two-dimensional reversible data hiding method with high embedding capacity in H.264/advanced video coding by Yi Chen, Hongxia Wang, Xiaoxu Tang, Yong Liu, Hanzhou Wu, Yanli Chen

“…Because it can be applied in some applications, such as error concealment and privacy protection. …”
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Buried PE Pipeline Location Method Based on Double-Tree Complex Wavelet Cross-Correlation Delay by Yang Li, Hanyu Zhang, Zhuo Xu, Ao Zhang, Xianfa Liu, Pengyao Sun, Xianchao Sun

“…The results indicate that the maximum error is 4.6% for asymmetric arrangements and less than 1% for symmetric arrangements. …”
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Self-Calibrating TSEP for Junction Temperature and RUL Prediction in GaN HEMTs by Yifan Cui, Yutian Gan, Kangyao Wen, Yang Jiang, Chunzhang Chen, Qing Wang, Hongyu Yu

“…Gallium nitride high-electron-mobility transistors (GaN HEMTs) are critical for high-power applications like AI power supplies and robotics but face reliability challenges due to increased dynamic ON-resistance (R_{DS_ON}) from electrical and thermomechanical stresses. …”
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Accelerated commercial battery electrode-level degradation diagnosis via only 11-point charging segments by Yu Tian, Cheng Lin, Xiangfeng Meng, Xiao Yu, Hailong Li, Rui Xiong

“…Through transfer learning, such a method can also be adapted to different battery chemistries, indicating the enticing potential for wide applications.…”
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High-precision speed control of induction motors using a multi-pulse voltage source converter and advanced observer-based strategies by Carlos E. Castañeda, Antonio Valderrabano-Gonzalez, Hossam A. Gabbar, Vijay K. Sood

“…The effectiveness of the proposed system is validated through a set of simulations conducted in Matlab/Simulink, demonstrating its capability to achieve high precision in motor drive applications.…”
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A Hybrid Deep Learning Model for Enhanced Structural Damage Detection: Integrating ResNet50, GoogLeNet, and Attention Mechanisms by Vikash Singh, Anuj Baral, Roshan Kumar, Sudhakar Tummala, Mohammad Noori, Swati Varun Yadav, Shuai Kang, Wei Zhao

“…Our findings indicate that the hybrid deep neural network introduced in this study significantly outperformed standalone architectures such as ResNet50 and GoogLeNet, making it a highly effective solution for applications in disaster response and infrastructure maintenance.…”
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Improved Isolation of Ultra-High-Molecular-Weight Genomic DNA Suitable for Third-Generation Sequencing by Ayşe Öykü Ova, Enrique Joffre, Reza Zandi Shafagh, Mariana F. G. Assunção, Roman Y. Sidorov, Lilia M. A. Santos, Volker M. Lauschke, Ute Römling

“…Although a variety of protocols to isolate high-molecular-weight genomic DNA exist, the isolation and preservation of ultra-high-molecular-weight genomic DNA of sufficient quality and length for error-free third-generation sequencing remains challenging. …”
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Sagnac Interference-Based Contact-Type Fiber-Optic Vibration Sensor by Hongmei Li, Longhuang Tang, Lijie Zhang, Wenjuan Huang, Rong Cao, Cheng Huang, Xiaobo Hu, Yifei Sun, Jia Shi

“…The experimental results show that the relative measurement error of the fiber-optic vibration sensor for healthy and faulty bearings is less than 1.8%, which verifies the effectiveness and accuracy of the sensor. …”
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Empirical Study of Magnet Distance on Magneto-Mechanical Resonance Frequency by Knopp Tobias, Mohn Fabian, Foerger Fynn, Thieben Florian, Hackelberg Niklas, Faltinath Jonas, Tsanda Artyom, Boberg Marija, Möddel Martin

“…Experimental analysis of the resonance frequency and comparison with the model values show both qualitative and quantitative agreement with an average relative error of only 0.8 %. This validates the implemented model and shows the suitability of our magneticmechanical resonator made from cylindrical permanent magnets for sensing applications.…”
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Nonlinearities Estimation in Optical Fiber Communication: Current Progress, Challenges and Perspectives by Zulfiqar Ahmad, Muhammad Ali Qureshi, Asjad Amin

“…These nonlinearities have a significant impact on optical performance (i.e., bit error rate, optical signal-to-noise-ratio, Q-factor) and capacity by limiting the data rates. …”
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LevelWAN: a cost-effective, open-source IoT system for water level monitoring in highly dynamic aquatic environments by Ilane Cherif, Frédéric Cherqui, Franck Perret, Bastien Bourjaillat, Lionel Lord, Jean-Luc Bertrand-Krajewski, Nicolas Walcker, Maria Gisi, Laëtitia Bacot, Oldrich Navratil

“…Its accuracy was assessed against a high-precision professional device, demonstrating an error margin of less than 1 cm. Additionally, LevelWAN was developed with a user-friendly design to facilitate accessibility for non-experts, aligning with the needs of citizen science initiatives and educational applications.…”
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Numerical and experimental analysis of temperature-dependent wear in rocket sled shoe materials by Boyi Qi, Ling Qi, Peiyao Tian, Mingshi Wang, Fan Zhang, Wenbo Zhuang, Weixing Zhao, Lei Wang, Kun Sun

“…Results revealed strong agreement between simulations and experiments, with an average wear depth error below 20 % and pronounced wear localization at the slider front and center under increased speeds. …”
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Enhanced Single-Diode Solar Cell Model: Analytical Solutions Using Lambert W Function and Circuit Innovations by Martin Calasan, Snezana Vujosevic, Kristina Bakic

“…Finally, the proposed approach enhances the mathematical precision in modeling solar cell behavior and provides a reliable framework for optimizing solar energy systems, contributing to improved performance and efficiency in practical applications.…”
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GIS isolation switch contact temperature wireless monitoring by Liu Haoli, Li Jiaqi, Zhang Jiuye, Zhao Yubo, Zhang Jinhua, Wang Liang

“…It was found that the average absolute error of monitoring is 0.47%. This method can save economic costs and achieve the required accuracy for engineering applications.…”
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A neural network-based synthetic diagnostic of laser-accelerated proton energy spectra by Christopher J. G. McQueen, Robbie Wilson, Timothy P. Frazer, Martin King, Matthew Alderton, Ewan F. J. Bacon, Ewan J. Dolier, Thomas Dzelzainis, Jesel K. Patel, Maia P. Peat, Ben C. Torrance, Ross J. Gray, Paul McKenna

“…Trained on data from fewer than 700 laser-plasma interactions, the model achieves an error level of 13.5%, and improves with more data. …”
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