Physics-integrated neural networks for improved mineral volumes and porosity estimation from geophysical well logs by Prasad Pothana, Kegang Ling

“…This method reduces uncertainties in reservoir characterization by improving the reliability of mineralogy and porosity predictions, providing a more robust tool for decision-making in various subsurface geoscience applications.…”
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Integrated Platform for Bio-Image Processing and Identification of Cervical Cancer by Larisa-Dalina LAZĂR, Diogen BABUC, Todor IVAŞCU

“…A future direction is to explore the applications of Vision Transformers, which have demonstrated excellent performance in capturing complex patterns in image data, to further improve data preprocessing and classification accuracy. …”
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Automated Lane-Level Road Geometry Estimation Using Microscopic Trajectory Data by Junhua Wang, Chengmin Li, Ting Fu, Lanfang Zhang, Anae Sobhani, Jiangtian Xue

“…This has posed a challenge for trajectory tracking, particularly for real-time applications. To address this challenge, this paper proposes a method for estimating lane-level road geometrics using microscopic trajectory data. …”
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Pulsatile Physiological Control of Blood Pump-Cardiovascular System Based on Feedforward Compensation by Yanjun Bao, Teng Jing, Weimin Ru, Ling Zhou

“…Simulation shows that the designed feed-forward compensation link reduces the tracking error of the pulsatile physiological control by 80%. …”
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Compound Mask for Divergent Wave Imaging in Medical Ultrasound by Zahraa Alzein, Marco Crocco, Daniele D. Caviglia

“…Unlike the previous work on plane waves, the proposed approach is not limited to linear probe geometries but works seamlessly also with convex ones, thus expanding the scope of applicability of divergent wave imaging. Moreover, it handles arbitrary spatial arrangements of virtual sources generating divergent waves. …”
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Improving LUR testing accuracy and practicability for prestressed concrete sleepers through bending stiffness and visco-plastic deflection-based analysis techniques by Ferhat Çeçen, Bekir Aktaş, Hakan Öztürk, İrfan Ş. Öztürk, M. Burhan Navdar

“…Furthermore, this approach significantly reduces the potential for human error and holds promise for future automation. These advancements make the proposed method highly applicable for both quality control and design of prestressed concrete railway sleepers, warranting further validation across different sleeper types and prestressed concrete structures.…”
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GAN-based solar radiation forecast optimization for satellite communication networks by Chao Chen, Xin Liu, Shizhong Zhao, Muhammad Bilal

“…It reduces the Root Mean Square Error by approximately 3.2% and increases the Structural Similarity Index from 0.84 to 0.87 when compared to the baseline ED-AttUNet model. …”
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Deep Learning-Based FSS Spectral Characterization and Cross-Band Migration by Lei Gong, Xuan Liu, Pan Zhou, Liguo Wang, Zhiqiang Yang

“…However, these methods are typically confined to designing FSSs within the spectral ranges defined by their datasets, significantly limiting their applicability. This paper systematically analyzes the impact of material and geometric parameters of FSSs on their spectral characteristics, thereby establishing a theoretical foundation for the cross-band transfer learning capability of neural networks. …”
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EMVAS: end-to-end multimodal emotion visualization analysis system by Xianxun Zhu, Heyang Feng, Erik Cambria, Yao Huang, Ming Ju, Haochen Yuan, Rui Wang

“…Abstract Accurately interpreting human emotions is crucial for enhancing human–machine interactions in applications such as driver monitoring, adaptive learning, and smart environments. …”
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Spatial clustering between socioeconomic inequalities and COVID-19 mortality rate in Africa by Ropo Ebenezer Ogunsakin, Johnson Adedeji Olusola, Kemi Funlayo Akeju, Adigun Kehinde Abimbola

“…These results highlight the need to draw preventive and response policies applicable to infectious diseases with more significant consideration of the different geographical points in the region.…”
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Machine Learning-Based Environment-Aware GNSS Integrity Monitoring for Urban Air Mobility by Oguz Kagan Isik, Ivan Petrunin, Antonios Tsourdos

“…This study introduces a novel machine learning-based GNSS integrity monitoring framework that incorporates environment recognition to create environment-specific error models. Using a comprehensive Hardware-in-the-Loop (HIL) simulation setup, extensive data were generated for suburban, urban, and urban canyon environments to train and validate the models. …”
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Multi-Task Learning for Joint Indoor Localization and Blind Channel Estimation in OFDM Systems by Maria Camila Molina, Iness Ahriz, Lounis Zerioul, Michel Terré

“…Results based on experimental data using the proposed solution show a 50th percentile localization error of 1.62 m for 3-tap channels and 0.89 m for 10-tap channels.…”
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Reliability and validity of OpenPose for measuring HKA angle in dynamic walking videos in patients with knee osteoarthritis by Fanghong Ge, Changjiang Wu, Fangjun Ge, Shenghao Xu, Jianlin Xiao

“…The pose estimation method provides a safe, cost-effective, and user-friendly solution for monitoring lower limb alignment, with promising applications in remote healthcare and rehabilitation management. …”
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Transfer Learning-Based Detection of Pile Defects in Low-Strain Pile Integrity Testing by Övünç Öztürk, Tuğba Özacar, Bora Canbula

“…Low-strain pile integrity testing (LSPIT) is widely used for defect detection; however, conventional manual interpretation of reflectograms is both time-consuming and susceptible to human error. This study presents a deep learning-driven approach utilizing transfer learning with convolutional neural networks (CNNs) to automate pile defect detection. …”
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Broadband Polarization-Insensitive Metamaterial Perfect Absorbers Using Topology Optimization by Sze Ming Fu, Yan Kai Zhong, Nyan Ping Ju, Ming-Hsiang Tu, Bo-Ruei Chen, Albert Lin

“…Here, nontapered shape-optimized HMM absorbers are proposed, which facilitates the fabrication and promotes the large-area applications such as thermophotovoltaics (TPV). In the synthesis of the optimal patterns, we use 5-harmonic rigorously coupled wave analysis (RCWA) and experimental trials to shorten the trial-and-error time. …”
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Unveiling the hidden depths: advancements in underwater image enhancement using deep learning and auto-encoders by Jaisuraj Bantupalli, Amal John Kachapilly, Sanjukta Roy, Pavithra L. K.

“…The performance of the model is evaluated and compared with various traditional and deep learning based image enhancement techniques using the quality measures structural similarity index (SSIM), peak signal-to-noise ratio (PSNR) and mean squared error (MSE). This research aims to address the critical limitations of current techniques by offering a superior method for underwater image enhancement by improving color fidelity and better information extraction capabilities for various applications. …”
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Robust higher-order numerical scheme for solving time-fractional singularly perturbed parabolic partial differential equations with large delay in time by Habtamu Getachew Kumie, Awoke Andargie Tiruneh, Getachew Adamu Derese

“…The uniform stability analysis and the bounds of the truncation error are performed. The convergence of the numerical scheme is proved in the maximum norm. …”
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Calculating the Optimal Point Cloud Density for Airborne LiDAR Landslide Investigation: An Adaptive Approach by Zeyuan Liao, Xiujun Dong, Qiulin He

“…This method solves the problem of determining point cloud density in landslide surveys using airborne LiDAR and provides direct guidance for practical applications.…”
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A novel framework to identify delamination location/size in BFRP pipe based on convolutional neural network (CNN) algorithm hybrid with capacitive sensors by Wael A. Altabey

“…The proposed method results converge with available traditional methods in the literature for assessing the delamination location/size such as the response surface methodology (RSM), and the error band from the diagonal line is less than 4.86 and 1.14 degrees for location and size respectively, thus validating the proposed technique's reliability, accuracy, and applicability for the relevant structures.…”
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Correctness Coverage Evaluation for Medical Multiple-Choice Question Answering Based on the Enhanced Conformal Prediction Framework by Yusong Ke, Hongru Lin, Yuting Ruan, Junya Tang, Li Li

“…Conformal Prediction (CP) is now recognized as a robust framework within the broader domain of machine learning, offering statistically rigorous guarantees of marginal (average) coverage for prediction sets. However, the applicability of CP in medical QA remains to be explored. …”
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