bluuebunny/crossref_metadata_2025 huggingface dataset:
Ocular Biometry Distribution and One-Year Growth in Eight-Year-Old Southern European Schoolchildren Under the CISViT Project
Authors: Gharbi Mariam El , Guisasola Laura , Galdón Alba , Vinuela-Navarro Valldeflors , Pérez-Corral Joan , Tomás Núria , Vila-Vidal Núria
Published: February 2025

Objective: To analyse variations in axial length (AL), corneal radius (CR) and the AL/CR ratio over one year in eight-year-old schoolchildren, considering sex, ethnicity and refractive error. Methods: Vision screenings were conducted in 16 schools in Terrassa (Barcelona, Spain) with eight-year-old children as part of the CISViT project. Measurements included ocular biometrics (AL and CR) and non-cycloplegic autorefraction for refractive error. Parental questionnaires provided demographic data (birth date, ethnicity). The same procedures were repeated after one year. Results: Ocular biometric parameters differed by sex and ethnicity. Boys and children of Maghreb descent had longer AL and flatter CR than girls and Caucasian children (p < 0.001 for both visits). The AL/CR ratio was higher in boys than girls (p = 0.002 in the initial visit and p = 0.011 in the follow-up visit) but consistent across ethnicities (p = 0.291 and p = 0.390). AL and AL/CR ratio differed significantly by refractive error status (p < 0.001 in both visits), increasing in more myopic children, while CR showed no significant difference. In myopic children, the AL/CR ratio exceeded 3.0, and typical sex-based biometric differences diminished. Growth rates for AL and AL/CR ratio were similar across sex and ethnicity, indicating minimal demographic influence. Conclusions: AL and CR differ significantly by sex and ethnicity, with demographic differences evident in baseline measurements but not in growth rates over one year. The consistency of the AL/CR ratio across ethnicities, despite sex-based differences, supports its utility as a reliable metric for assessing refractive development in diverse populations.
An Analytical Prior Selection Procedure for Empirical Bayesian Analysis Using Resampling Techniques: A Simulation-Based Approach Using the Pancreatic Adenocarcinoma Data from the SEER Database
Authors: Chakraborty Aditya , Pant Mohan D.
Published: February 2025

Introduction: In the field of medical research, empirical Bayesian analysis has emerged as an increasingly applicable approach. This statistical framework offers greater flexibility, enabling researchers to incorporate prior information and rigorously estimate parameters of interest. However, the selection of suitable prior distributions can be a challenging endeavor, with profound implications for the resulting inferences. To address this challenge, this study proposes a new analytical procedure that leverages resampling techniques to guide the choice of priors in Bayesian analysis. Subject and Methods: The study group consisted of patients who had been diagnosed and had died of pancreatic adenocarcinoma (cause-specific death) who had undergone both chemotherapy and radiation at stage IV of cancer. The data were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Initially, the most suitable probabilistic behavior of the survival times of patients was identified parametrically via goodness-of-fit (GOF) tests, and afterward, empirical Bayesian analysis (EBA) was performed using resampling techniques (bootstrapping and the jackknife method). The Hamiltonian Monte Carlo (HMC) method was used to obtain the posterior distribution. Results: The most appropriate data distribution was found to be a two-parameter log-normal via GOF tests. A sensitivity analysis, followed by a simulation study, was performed to validate the analytical method. The performance of bootstrapped and jackknifed empirical Bayesian estimates was compared with maximum likelihood (ML) methods at each simulation stage. The empirical Bayesian estimates were found to be consistent with the ML estimates. Finally, a comparison was made among the parametric, Kaplan–Meier and empirical Bayesian survival estimates at different time points to illustrate the validity of the method. Conclusions: Determining the appropriate prior distribution is one of the crucial components in Bayesian analysis, as it can significantly influence the resulting inferences. The cautious selection of the prior information is essential, as it encapsulates the researcher’s beliefs or external prior knowledge about the parameters of interest. In the Bayesian framework, empirical resampling methods, such as bootstrapping and jackknifing, can offer valuable insights into the significance of prior selection, thus improving the consistency of statistical inferences. However, the analytical procedure is based on the time-to-event data, and the prior selection procedure can be extended to any real data, where Bayesian analysis is needed for decision-making and uncertainty quantification.
Real-Time Gaze Estimation Using Webcam-Based CNN Models for Human–Computer Interactions
Authors: Vidhya Visal , Resende Faria Diego
Published: February 2025

Gaze tracking and estimation are essential for understanding human behavior and enhancing human–computer interactions. This study introduces an innovative, cost-effective solution for real-time gaze tracking using a standard webcam, providing a practical alternative to conventional methods that rely on expensive infrared (IR) cameras. Traditional approaches, such as Pupil Center Corneal Reflection (PCCR), require IR cameras to capture corneal reflections and iris glints, demanding high-resolution images and controlled environments. In contrast, the proposed method utilizes a convolutional neural network (CNN) trained on webcam-captured images to achieve precise gaze estimation. The developed deep learning model achieves a mean squared error (MSE) of 0.0112 and an accuracy of 90.98% through a novel trajectory-based accuracy evaluation system. This system involves an animation of a ball moving across the screen, with the user’s gaze following the ball’s motion. Accuracy is determined by calculating the proportion of gaze points falling within a predefined threshold based on the ball’s radius, ensuring a comprehensive evaluation of the system’s performance across all screen regions. Data collection is both simplified and effective, capturing images of the user’s right eye while they focus on the screen. Additionally, the system includes advanced gaze analysis tools, such as heat maps, gaze fixation tracking, and blink rate monitoring, which are all integrated into an intuitive user interface. The robustness of this approach is further enhanced by incorporating Google’s Mediapipe model for facial landmark detection, improving accuracy and reliability. The evaluation results demonstrate that the proposed method delivers high-accuracy gaze prediction without the need for expensive equipment, making it a practical and accessible solution for diverse applications in human–computer interactions and behavioral research.
Hybrid CNN–BiLSTM–DNN Approach for Detecting Cybersecurity Threats in IoT Networks
Authors: Agbor Bright Agbor , Stephen Bliss Utibe-Abasi , Asuquo Philip , Luke Uduak Onofiok , Anaga Victor
Published: February 2025

The Internet of Things (IoT) ecosystem is rapidly expanding. It is driven by continuous innovation but accompanied by increasingly sophisticated cybersecurity threats. Protecting IoT devices from these emerging vulnerabilities has become a critical priority. This study addresses the limitations of existing IoT threat detection methods, which often struggle with the dynamic nature of IoT environments and the growing complexity of cyberattacks. To overcome these challenges, a novel hybrid architecture combining Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Deep Neural Networks (DNN) is proposed for accurate and efficient IoT threat detection. The model’s performance is evaluated using the IoT-23 and Edge-IIoTset datasets, which encompass over ten distinct attack types. The proposed framework achieves a remarkable 99% accuracy on both datasets, outperforming existing state-of-the-art IoT cybersecurity solutions. Advanced optimization techniques, including model pruning and quantization, are applied to enhance deployment efficiency in resource-constrained IoT environments. The results highlight the model’s robustness and its adaptability to diverse IoT scenarios, which address key limitations of prior approaches. This research provides a robust and efficient solution for IoT threat detection, establishing a foundation for advancing IoT security and addressing the evolving landscape of cyber threats while driving future innovations in the field.
Contrast-Enhanced CT-Based Radiomics Nomogram for Prediction of Pathologic T3a Upstaging in Clinical T1 RCC
Authors: Yin Di , Wang Keruo , Xu Hongyi , Guo Yunfei , Qian Baoxin , Duan Dengyi , Li Yiming , Zhang Wenyi , Li Zhengyang , Zhao Yang
Published: February 2025

Background/Objectives: To develop a nomogram for the preoperative prediction of pathologic T3a (pT3a) upstaging in patients with clinical T1(cT1) renal cell carcinoma (RCC). Methods: A total of 169 cT1 patients with RCC with preoperative contrast-enhanced CT (CECT) and clinical data were enrolled in this study. Afterwards, the sample was split randomly into training and testing sets in a 7:3 ratio. Radiomics features were extracted and selected from the whole primary tumor on CECT images to develop radiomics signatures. The nomogram was constructed using the obtained radiomics signature and clinical risk factors. The predictive performance of different models was evaluated and visualized using receiver operator characteristic (ROC) curves. Results: In total, 26 radiomics features were selected for the radiomics signature construction. The radiomics signature yielded area under the curve (AUC) values of 0.945 and 0.873 in the training and testing sets, respectively. The nomogram integrating radiomics signature and predictive clinical factors, including tumor size and neutrophil–lymphocyte ratio (NLR), achieved higher predictive performance in the training [AUC, 0.958; 95% confidence interval (CI): 0.921, 0.995] and testing (AUC, 0.913; 95% CI: 0.814, 1.000) sets. Good calibration was achieved for the nomogram in both the training and testing sets (Brier score = 0.082 and 0.098). Decision curve analysis (DCA) demonstrated that the nomogram was clinically useful in predicting pT3a upstaging, with a corresponding net benefit of 0.378. Conclusions: The proposed nomogram can preoperatively predict pT3a upstaging in cT1 RCC and serve as a non-invasive imaging marker to guide individualized treatment.
Optimized Lightweight Architecture for Coronary Artery Disease Classification in Medical Imaging
Authors: Abdusalomov Akmalbek , Mirzakhalilov Sanjar , Umirzakova Sabina , Kalandarov Ilyos , Mirzaaxmedov Dilmurod , Meliboev Azizjon , Cho Young Im
Published: February 2025

Background/Objectives: The early and accurate detection of Coronary Artery Disease (CAD) is crucial for preventing life-threatening complications, particularly among athletes engaged in high-intensity endurance sports. This demographic faces unique cardiovascular risks, as prolonged and intense physical exertion can exacerbate underlying CAD conditions. Studies indicate that while athletes typically exhibit enhanced cardiovascular health, this demographic is not immune to Coronary Artery Disease (CAD) risks. Research has shown that approximately 1–2% of competitive athletes suffer from CAD-related complications, with sudden cardiac arrest being the leading cause of mortality in athletes over 35 years old. High-intensity endurance sports can exacerbate underlying CAD conditions due to the prolonged physical stress placed on the cardiovascular system, making early detection crucial. This study aimed to develop and evaluate a lightweight deep learning model for CAD detection tailored to the unique challenges of diagnosing athletes. Methods: This study introduces a lightweight deep learning model specifically designed for CAD detection in athletes. By integrating ResNet-inspired residual connections into the VGG16 architecture, the model achieves a balance of high diagnostic accuracy and computational efficiency. By incorporating ResNet-inspired residual connections into the VGG16 architecture, the model enhances gradient flow, mitigates vanishing gradient issues, and improves feature extraction of subtle morphological variations in coronary lesions. Its lightweight design, with only 1.2 million parameters and 3.5 GFLOPs, ensures suitability for real-time deployment in resource-constrained clinical environments, such as sports clinics and mobile diagnostic systems, where rapid and efficient diagnostics are essential for high-risk populations. Results: The proposed model achieved superior performance compared to state-of-the-art architectures, with an accuracy of 90.3%, recall of 89%, precision of 90%, and an AUC-ROC of 0.912. These metrics highlight its robustness in detecting and classifying CAD in athletes. The model lightweight architecture, with only 1.2 million parameters and 3.5 GFLOPs, ensures computational efficiency and suitability for real-time clinical applications, particularly in resource-constrained settings. Conclusions: This study demonstrates the potential of a lightweight, deep learning-based diagnostic tool for CAD detection in athletes, achieving a balance of high diagnostic accuracy and computational efficiency. Future work should focus on integrating broader dataset validations and enhancing model explainability to improve adoption in real-world clinical scenarios.
Application of the Fault Injection Method for the Verification of the Behavior of Multiple Unmanned Aircraft Systems Flying in Formation
Authors: Rodríguez Iván Felipe , Ambrosio Ana María , Traslaviña Danny Stevens , Orduy Jaime Enrique , Melo Pedro Fernando
Published: February 2025

This research aims to present an analysis of the behavior of multiple Remotely Piloted Aircraft Systems (multi-RPAS) flying in formation, a key aspect of advanced aerial mobility in the aerospace industry. This involves the positioning and relative distance in three dimensions (3D) of two RPAS, taking into account their operational requirements and limitations, recognizing the operating states, and addressing potential situations encountered during formation flight. For this study, the “Conformance and Fault Injection—CoFI” methodology is employed. This methodology guides the user towards a comprehensive understanding of the system and enables the creation of a set of finite state machines representing the system’s behavior under study. Consequently, models and requirements for the behavior of multi-RPAS flying in formation are presented. By applying the CoFI methodology to inject faults into the operation and predict behavior in anomalous situations, both normal and abnormal behavior models, as well as the flight behavior requirements of the multi-RPAS formation, are outlined. This analysis is expected to facilitate the identification of formation flight behavior in multi-RPAS, thereby reducing associated operational risks.
UAV-Based Pseudolite Navigation System Architecture Design and the Flight Path Optimization
Authors: Guo Ruocheng , Yuan Hong , Zhang Yang , Chen Xiao , Zhang Guanbing
Published: February 2025

In a scenario where GNSS signal is blocked due to interference or occlusion, it is of considerable value to establish a regional navigation system providing emergency services for ground users by using long-endurance and long-range fixed-wing Unmanned Aerial Vehicles (UAVs). The main work of this paper consists of two parts. First, we designed a set of UAV-based pseudolite navigation system (UAV-PNS) architecture based on fixed-wing UAVs. Then, considering the flight cost of the UAV swarm, the optimization of the UAV swarm’s flight path aimed at improving regional navigation performance was studied. In this paper, the fitness functions for UAVs’ flight path optimization are proposed, taking into account the navigation and positioning performance, the aircraft utilization rate of UAVs under flight constraints, and the response speed of the system to the emergency mission. Based on this, an acceptance–rejection mutated non-dominated sorting genetic algorithm III (ARMNSGA-III) is proposed for the UAVs’ flight path optimization. The research results show that the flight path strongly guarantees navigation service performance with constraints on the operating cost. The ARMNSGA-III proposed in this paper can provide a 44.01% algorithm timeliness improvement compared to the NSGA-III in the flight path optimization, supporting rapid establishment and continuous service of the UAV-PNS in emergency scenarios.
Quantifying Unknown Multiqubit Entanglement Using Machine Learning
Authors: Wang Yukun , Wang Shaoxuan , Xing Jincheng , Du Yuxuan , Wu Xingyao
Published: February 2025

Entanglement plays a pivotal role in numerous quantum applications, and as technology progresses, entanglement systems continue to expand. However, quantifying entanglement is a complex problem, particularly for multipartite quantum states. The currently available entanglement measures suffer from high computational complexity, and for unknown multipartite entangled states, complete information about the quantum state is often necessary, further complicating calculations. In this paper, we train neural networks to quantify unknown multipartite entanglement using input features based on squared entanglement (SE) and outcome statistics data produced by locally measuring target quantum states. By leveraging machine learning techniques to handle non-linear relations between outcome statistics and entanglement measurement SE, we achieve high-precision quantification of unknown multipartite entanglement states with a linear number of measurements, avoiding the need for global measurements and quantum state tomography. The proposed method exhibits robustness against noise and extends its applicability to pure and mixed states, effectively scaling to large-scale multipartite entanglement systems. The results of the experiment show that the predicted entanglement measures are very close to the actual values, which confirms the effectiveness of the proposed method.
A Novel Evaluation of Income Class Boundaries Using Inflection Points of Probability Density Functions: A Case Study of Brazil
Authors: Bittencourt Rafael , Pereira Hernane Borges de Barros , Moret Marcelo A. , Da Cunha Lima Ivan C. , Galam Serge
Published: February 2025

Categorizing a population into different income classes is important for creating effective policies and analyzing markets. Our study develops a statistical method based on a nationwide survey of income distribution. We use these data to create a cumulative distribution function with a metalogistic distribution and its probability density function. We propose a new way to divide the population into income classes by using the inflection points of the probability density function as the class boundaries. As a case study, we apply this method to income data from Brazil between 2012 and 2022. We identify five income classes, with both their boundaries and the distribution of the population changing over time. To check our approach, we calculate the Gini coefficient and find that our results closely match official figures, with a root mean square deviation of less than 1%. By using individual income instead of family income, we avoid distortions caused by the fact that poorer families tend to be larger than wealthier ones. In the end, we identify five main income classes, with their boundaries shifting each year, reflecting the changing nature of income distribution in society.
A Review of Model Predictive Control for Grid-Connected PV Applications
Authors: Alharbi Yousef , Darwish Ahmed , Ma Xiandong
Published: February 2025

This paper presents the latest advancements in model predictive control (MPC) for grid-connected power inverters in renewable energy applications. It focuses on grid-connected PV systems employing MPC techniques. Two main categories of MPC are introduced: continuous control MPC (CC MPC) and predetermined control MPC (PC MPC). In CC MPC, a modulator is required to generate the control signal, whereas in PC MPC, the MPC controller directly performs the control process and generates the control pulses. Consequently, PC MPC is preferred for most power converter applications due to its lower computational complexity and simpler implementation. However, ensuring a fixed switching frequency remains a significant challenge when using this control strategy for power electronic converters. Moreover, the computation requirements of MPC strategies are challenging, due to the large number of online calculations. Even with the significant improvements in DSPs, computation complexity is still a continuing issue, especially for applications requiring a high frequency. This paper also examines the design considerations for both types of MPC in PV applications. Lastly, it reviews recent developments in grid-connected inverters utilizing MPC strategies, with a focus on system stability, converter topology, and control objectives.
A Seamless Authentication Scheme for Edge-Assisted Internet of Vehicles Environments Using Chaotic Maps
Authors: Son Seunghwan , Kwon DeokKyu , Park Youngho
Published: February 2025

Internet of Vehicles (IoV) is a concept that combines IoT and vehicular ad hoc networks. In IoV environments, vehicles constantly move and communicate with other roadside units (edge servers). Due to the vehicles’ insufficient computing power, repetitive authentication procedures can be burdensome for automobiles. In recent years, numerous authentication protocols for IoV environments have been proposed. However, there is no study that considers both re-authentication and handover authentication situations, which are essential for seamless communication in vehicular networks. In this study, we propose a chaotic map-based seamless authentication scheme for edge-assisted IoV environments. We propose authentication protocols for initial, handover, and re-authentication situations and analyze the security of our scheme using informal methods, the real-or-random (RoR) model, and the Scyther tool. We also compare the proposed scheme with existing schemes and show that our scheme has superior performance and provides more security features. To our knowledge, This paper is the first attempt to design an authentication scheme considering both handover and re-authentication in the IoV environment.
CSP-DCPE: Category-Specific Prompt with Deep Contextual Prompt Enhancement for Vision–Language Models
Authors: Wu Chunlei , Wu Yixiang , Xu Qinfu , Zi Xuebin
Published: February 2025

Recently, prompt learning has emerged as a viable technique for fine-tuning pre-trained vision–language models (VLMs). The use of prompts allows pre-trained VLMs to be quickly adapted to specific downstream tasks, bypassing the necessity to update the original pre-trained weights. Nevertheless, much of the existing work on prompt learning has focused primarily on the utilization of non-specific prompts, with little attention paid to the category-specific data. In this paper, we present a novel method, the Category-Specific Prompt (CSP), which integrates task-oriented information into our model, thereby augmenting its capacity to comprehend and execute complex tasks. In order to enhance the exploitation of features, thereby optimizing the utilization of the combination of category-specific and non-specific prompts, we introduce a novel deep prompt-learning method, Deep Contextual Prompt Enhancement (DCPE). DCPE outputs features with rich text embedding knowledge that changes in response to input through attention-based interactions, thereby ensuring that our model contains instance-oriented information. Combining the above two methods, our architecture CSP-DCPE contains both task-oriented and instance-oriented information, and achieves state-of-the-art average scores on 11 benchmark image-classification datasets.
Mechanoreceptor-Inspired Tactile Sensor Topological Configurations for Hardness Classification in Robotic Grippers
Authors: Sharma Yash , Guo Claire , Beatty Matthew , Justham Laura , Ferreira Pedro
Published: February 2025

Human hands have the unique ability to classify material properties, such as hardness, using mechanoreceptors and tactile information. Previous studies have demonstrated hardness classification using Commercial Off-The-Shelf (COTS) sensors but lacked robotic integration considerations. This study explores the integration of multiple COTS sensors, inspired by mechanoreceptors, for classifying material hardness. The sensors were used to classify objects into three categories—hard, soft, and flexible—based on the qualitative Shore hardness scale. The aim was to identify the optimal sensor topology configuration that delivers high accuracy, using machine learning algorithms provided in the literature. The results suggest that the Random Forest Classifier is the most suitable algorithm, showcasing accuracies ranging from 90% to 98.7%, across various sensor topologies. The ‘PFV’ topology, comprising a potentiometer (P), force sensor (F), and vibration sensor (V), achieved the highest accuracy of 98.7%, while the ‘FPV’ and ‘FVP’ recorded accuracies between 96% and 97.5%. The topology of FPV and FVP have the most closely related configuration to that of mechanoreceptors; however, the results show that PFV outperforms this configuration. While the PFV topology marginally outperforms the mechanoreceptor-inspired configurations, the results demonstrate that bio-inspired sensor arrangements provide a robust solution for hardness classification in robotics. The PFV topology performs better than FPV in terms of prediction speed, with an average prediction time of 8.31 ms (millisecond) for PFV versus 13.93 ms for FPV. PFV and FPV achieved 12 and 13 correct predictions, respectively, out of 18 objects. The faster prediction times of PFV make it particularly advantageous for applications requiring quick and accurate decision-making for robotic applications.
Rewriting a Generative Model with Out-of-Domain Patterns
Authors: Gao Panpan , Sun Hanxu , Chen Gang , Li Minggang
Published: February 2025

Generative Adversarial Networks (GANs) can synthesize images with the same distribution as the training dataset through random vectors. However, there is still no proper way to explain the generation rules in the generator and modify those rules. Although there have been some works on rewriting the rules in generative models in recent years, these works mainly focus on rewriting the rules between the patterns it has already learned. In this paper, we cast the problem of rewriting as rewriting the rule in the generator with out-of-domain patterns. To the best of our knowledge, this is the first time this problem has been investigated. To address this problem, we propose the Re-Generator framework. Specifically, our method projects out-of-domain images into the feature space of a specific layer in the generator and designs a mechanism to enable the generator to decode that feature back to the original image as possible. Besides that, we also suggest viewing multiple layers of the model as nonlinearity associative memory and design a rewriting strategy with better performance based on this. Finally, the results of the experiments on various generative models in multiple datasets show that our method can effectively use the patterns that the model has never seen before as rules for rewriting.
PPF-Net: Efficient Multimodal 3D Object Detection with Pillar-Point Fusion
Authors: Zhang Lingxiao , Li Changyong
Published: February 2025

Detecting objects in 3D space using LiDAR is crucial for robotics and autonomous vehicles, but the sparsity of LiDAR-generated point clouds limits performance. Camera images, rich in semantic information, can effectively compensate for this limitation. We propose a simpler yet effective multimodal fusion framework to enhance 3D object detection without complex network designs. We introduce a cross-modal GT-Paste data augmentation method to address challenges like 2D object acquisition and occlusions from added objects. To better integrate image features with sparse point clouds, we propose Pillar-Point Fusion (PPF), which projects non-empty pillars onto image feature maps and uses an attention mechanism to map semantic features from pillars to their constituent points, fusing them with the points’ geometric features. Additionally, we design the BD-IoU loss function, which measures 3D bounding box similarity, and a joint regression loss combining BD-IoU and Smooth L1, effectively guiding model training. Our framework achieves consistent improvements across KITTI benchmarks. On the validation set, PFF (PV-RCNN) achieves at least 1.84% AP improvement in Cyclist detection performance across all difficulty levels compared to other multimodal SOTA methods. On the test set, PPF-Net excels in pedestrian detection for moderate and hard difficulty levels and achieves the best results in low-beam LiDAR scenarios.
Millimeter-Wave SAR Imaging for Sub-Millimeter Defect Detection with Non-Destructive Testing
Authors: Yalcinkaya Bengisu , Aydin Elif , Kara Ali
Published: February 2025

This paper introduces a high-resolution 77–81 GHz mmWave Synthetic Aperture Radar (SAR) imaging methodology integrating low-cost hardware with modified radar signal characteristics specifically for NDT applications. The system is optimized to detect minimal defects in materials, including low-reflectivity ones. In contrast to the existing studies, by optimizing key system parameters, including frequency slope, sampling interval, and scanning aperture, high-resolution SAR images are achieved with reduced computational complexity and storage requirements. The experiments demonstrate the effectiveness of the system in detecting optically undetectable minimal surface defects down to 0.4 mm, such as bonded adhesive lines on low-reflectivity materials with 2500 measurement points and sub-millimeter features on metallic targets at a distance of 30 cm. The results show that the proposed system achieves comparable or superior image quality to existing high-cost setups while requiring fewer data points and simpler signal processing. Low-cost, low-complexity, and easy-to-build mmWave SAR imaging is constructed for high-resolution SAR imagery of targets with a focus on detecting defects in low-reflectivity materials. This approach has significant potential for practical NDT applications with a unique emphasis on scalability, cost-effectiveness, and enhanced performance on low-reflectivity materials for industries such as manufacturing, civil engineering, and 3D printing.
Synergy of Internet of Things and Software Engineering Approach for Enhanced Copy–Move Image Forgery Detection Model
Authors: Assiri Mohammed
Published: February 2025

The fast development of digital images and the improvement required for security measures have recently increased the demand for innovative image analysis methods. Image analysis identifies, classifies, and monitors people, events, or objects in images or videos. Image analysis significantly improves security by identifying and preventing attacks on security applications through digital images. It is crucial in diverse security fields, comprising video analysis, anomaly detection, biometrics, object recognition, surveillance, and forensic investigations. By integrating advanced software engineering models with IoT capabilities, this technique revolutionizes copy–move image forgery detection. IoT devices collect and transmit real-world data, improving software solutions to detect and analyze image tampering with exceptional accuracy and efficiency. This combination enhances detection abilities and provides scalable and adaptive solutions to reduce cutting-edge forgery models. Copy–move forgery detection (CMFD) has become possibly a major active research domain in the blind image forensics area. Between existing approaches, most of them are dependent upon block and key-point methods or integration of them. A few deep convolutional neural networks (DCNN) techniques have been implemented in image hashing, image forensics, image retrieval, image classification, etc., that have performed better than the conventional methods. To accomplish robust CMFD, this study develops a fusion of soft computing with a deep learning-based CMFD approach (FSCDL-CMFDA) to secure digital images. The FSCDL-CMFDA approach aims to integrate the benefits of metaheuristics with the DL model for an enhanced CMFD process. In the FSCDL-CMFDA method, histogram equalization is initially performed to improve the image quality. Furthermore, the Siamese convolutional neural network (SCNN) model is used to learn complex features from pre-processed images. Its hyperparameters are chosen by the golden jackal optimization (GJO) model. For the CMFD process, the FSCDL-CMFDA technique employs the regularized extreme learning machine (RELM) classifier. Finally, the detection performance of the RELM method is improved by the beluga whale optimization (BWO) technique. To demonstrate the enhanced performance of the FSCDL-CMFDA method, a comprehensive outcome analysis is conducted using the MNIST and CIFAR datasets. The experimental validation of the FSCDL-CMFDA method portrayed a superior accuracy value of 98.12% over existing models.
Pole-to-Pole Fault Current Impact Factors Analysis Based on Equivalent Impedance for Modular Multilevel Converter High-Voltage Direct Current System
Authors: Zhao Qi , Li Kuan , Tan Jinlong , Zhang Lu , Zhang Shuobo
Published: February 2025

The fault current in MMC HVDC systems suffers from a quick rising rate and no zero-cross problems, which makes it hard to cut off and has become the main problem when promoting MMC dc systems. Among the different types of faults, the pole-to-pole fault is the most serious one. Hence, to clarify the discharging mechanism during pole-to-pole faults and determine the influential factors, this paper proposes a simplified modeling method for MMC HVDC fault current impact factors analysis. The proposed method uses the simplified MMC circuit when faults happen, and the frequency domain model is then established for fault current analysis. Considering that the fault period concerned always is less than 10 ms, the high-frequency part impedance characteristics of the established model are carefully studied when device parameters are varied. The smoothing reactor has a significant influence on the high-frequency impedance, while the arm inductance and arm resistance have much less impact on high-frequency impedance. This demonstrates, theoretically, that the pole-to-pole fault current is basically influenced by the smoothing reactor rather than other impact factors. The calculation results based on the model also prove that fault current will change a lot if the smoothing reactor’s parameters are different. The analysis verifies the accuracy of the equivalent model and supplies a theoretical analysis tool for MMC HVDC fault current analysis.
An Optimized Additive Manufacturing Strategy for Low-Impedance Electronics
Authors: Hehn Tobias , Zimmer Felix , Klein Markus , Holtmannspötter Jens
Published: February 2025

The additive manufacturing of electronics allows the printing of complex structures, such as antenna matrixes, motors, and system-in-packages, directly into a three-dimensional shape. This freedom enables new design approaches for various applications in different fields. The additive manufacturing system under investigation uses inkjet technology, which is capable of applying a conductive and a non-conductive ink layer-by-layer at the desired places. This layer-by-layer approach creates a rough outline of the conductive material that negatively impacts high-frequency signal transmission. This article proposes a blockwise printing strategy to smooth the contours of the conductive material, enhancing its high-frequency properties. The evaluation of representative geometries, resembling printed samples that can be obtained using a custom slicing software, showed that the blockwise strategy reduced electrical losses by a factor of two compared to the conventional layer-by-layer approach. Numerical simulations demonstrated that this method achieves improved impedance performance in materials with low-loss tangents. These findings highlight the potential of alternative additive manufacturing strategies to optimize high-frequency performance in electronic devices.
Statistical Upscaling Prediction Method of Photovoltaic Cluster Power Considering the Influence of Sand and Dust Weather
Authors: Yang Hu , Liu Chang , Wang Zhao , Wang Hongqing
Published: February 2025

Photovoltaic (PV) clusters in deserts such as the Gobi and other regions are frequently affected by sand and dust, which causes great deviation in power prediction and seriously threatens the safe operation of new power systems. For this reason, this paper proposes a short-term cluster PV power prediction method based on statistical upscaling, considering the effect of sand and dust. Firstly, the sand and dust events are identified, and then time series generative adversarial networks (TimeGANs) are used to solve the problem of small sample scarcity in sand and dust and construct a power correction model for sand and dust scenes. Secondly, for different weather scenes, a combination of conventional prediction and correction prediction is used to solve the problem of large differences in the predictability of a single model. Finally, a statistical upscaling method is utilized to calculate the cluster prediction power to solve the prediction difficulties of large-scale newly installed PV field stations. Through a case study and comparison with other models and methods, the cluster prediction method established in this paper effectively improves the prediction accuracy of the power of large-scale PV clusters affected by sand and dust, with the RMSE reduced by 8.28%.
Planning for Medium- and Heavy-Duty Electric Vehicle Charging Infrastructure in Distribution Networks to Support Long-Range Electric Trucks
Authors: Then Joshua , Agalgaonkar Ashish P. , Muttaqi Kashem M.
Published: February 2025

Electrification of the transport sector introduces operational issues in the electricity distribution network, such as excessive voltage deviation, substation overloading, and adverse power quality impacts on other network loads. These concerns are expected to grow as electrification expands to incorporate heavy vehicles such as trucks and buses due to their greater energy requirements and higher charging loads. Two strategies are proposed to support medium- and heavy-duty chargers which address their high power demand and mitigate power quality disturbances and the overloading of substations. The first is a dedicated feeder connected at the secondary of the substation directly to the charging station which aims to reduce the impact of high load on other customers. The second is the addition of a dedicated substation that solely provides power for charging stations in major corridors, alleviating stress on existing zone substations. Hosting capacity is measured using a voltage deviation index, describing the deviation in line voltage, which should experience a sag of no more than 6% of the nominal voltage, and a substation charging capacity index, describing the available capacity of each zone substation as a ratio of its total power capacity. Verification of the proposed strategies was performed on an MV-LV distribution network representative of an industrial Australian town with heavy-vehicle charging. Results showed that the network could handle ten 250 kW chargers, which was tripled to 35 with a dedicated feeder. The dedicated feeder alternatively allowed up to 10 megawatt-scale chargers, which was again tripled when a dedicated substation was introduced.
The Experimental and Numerical Modeling Challenges Related to Multiphase Flows in Subsurface Resource Exploitation
Authors: Creux Patrice
Published: February 2025

Scientists are continuously seeking new methods to identify and predict multiphase flows in porous media using experimental characterization, modeling, and numerical simulations to improve the efficiency of subsurface industry processes for extraction and storage processes [...]
Preparation and Characterization of Ru-Based Catalyst for Power to Gas Applications
Authors: Colelli Leonardo , Grancini Alberto , Mattei Enrico , Bassano Claudia , Vilardi Giorgio
Published: February 2025

Heterogeneous catalysis plays a crucial role in various industrial processes, representing a key aspect also in the energy transition for the development of new technologies. Among them, Power to Gas (PtG), belonging to the e-fuels category, requires a deep study of catalysis to convert CO2 and green hydrogen coming from the water electrolysis with renewable power into synthetic methane, contributing to carbon-neutral goals and net-zero emission targets. In this context, the preparation and characterization of Ru-based catalyst on alumina support are carried out through a patented experimental procedure to evaluate performance parameters for PtG applications. Two main preparations are performed to assess the differences of the final product, which is a 0.5 wt% Ru on 1/8” alumina sphere support in the dry form. In the first case, a laboratory-scale production is carried out to produce 300 g of catalyst (Batch 1), while in the second one, the preparation is brought to 3 kg of catalyst (Batch 2) by using a pilot plant. In both cases, wet impregnation technique is used to prepare the Ru-based catalyst. Beyond the production, analytical tests are performed to evaluate the main features of the product and ascertain the differences between the two productions.
Prof. Em. Dr. med. Alex Vermeulen (1927–2023)—A Giant in Endocrinology
Authors: De Crée Carl
Published: February 2025

Alex Vermeulen (1927–2023) was a leading Belgian endocrinologist whose name will forever remain linked to testosterone and androgen metabolism. As a dedicated scientist and clinician, he made seminal contributions to endocrinology throughout his career. These included the development of chromatography and radioimmunoassays of steroid hormones. His work also focused on the biological significance and metabolism of corticosteroids and androgens, and he defined key concepts in the role of steroid hormones in the human menstrual cycle, pregnancy, and menopause. His love for math, endocrinology, and problem-solving led to a formula for the estimation of free testosterone in serum, which has not been improved upon to date and is still in use worldwide. He contributed to enhancing our understanding of the role that male sex hormones may play in a variety of clinical problems in endocrinology, including bone health, type 2 diabetes, and, especially, endocrine function in aging males. Alex Vermeulen literally was “a giant in endocrinology”. Beyond his scientific contributions, Vermeulen was a wise and engaging mentor, a Renaissance man, and an aficionado of the finer things in life. He owned an eclectic choice of modern artworks, all of which he bequeathed to the Ghent Museum of Fine Arts, thus significantly enhancing the museum’s art patrimony.
Trade-Off Between Leaf Mass Area and Phosphorus Concentration Alters Resource-Use Strategy of Understory Plants Under Long-Term Nitrogen and Phosphorus Addition in a Subtropical Forest
Authors: Yong Yutong , Huang Pei , Xu Siyi , Yang Xiongfei , Bao Jiehuan , Zheng Zemei
Published: February 2025

Leaf economic spectrum (LES) traits (e.g., leaf mass area and leaf nutrient concentrations) are effective indicators of an acquisitive or conservative resource use strategy for plants. The increased atmospheric deposition of nitrogen (N) and phosphorus (P) alters soil nutrient availability, thereby affecting plant LES traits. However, how the LES traits and their trade-offs affect the resource-use strategies of understory plants under long-term N and P additions is still unclear. Based on a fertilization plot including four treatments (control (CK), N addition (+N, 100 kg N hm−1 yr−1), P addition (+P, 50 kg P hm−1 yr−1), and combined N and P additions (+NP, 100 kg N hm−1 yr−1 + 50 kg P hm−1 yr−1)) conducted over 12 years in a subtropical broad-leaved evergreen forest, this study addresses the differential response of four LES traits (leaf mass per area (LMA), leaf N concentration (Nmass), leaf P concentration (Pmass) and leaf net photosynthesis per unit mass (Amass)) to fertilization in five dominant understory plants (Camellia fraternal, Eurya muricata, Eurya rubiginosa, Rhododendron ovatum, and Symplocos sumuntia) to test whether trade-offs between plant traits closely related to light resources play an important role in influencing plant resource-use strategies. The results show that, compared to the CK treatment, the LMA was significantly increased by 12.5% to 12.8% under +N treatment, and the Nmass was significantly increased by 25.9% and 23.6% under +N and +NP treatments, while Pmass and Amass were significantly increased by about 23% and 15~50%, respectively, after P addition. There was a highly significant negative correlation between the response of LMA and Pmass, irrespective of the addition of N and P alone or together. The increase in LMA under +N treatment made the resource-use strategy of the understory plants more conservative. Meanwhile, the understory plants tended to rapidly acquire resources by decreasing LMA while increasing Pmass under +P and +NP treatments. Our results suggest that, under long-term N and P additions, understory plants with limited light availability change their resource-use strategies mainly through the trade-off between leaf LMA and Pmass, which should be considered to capture the long-term adaptive strategies of understory plants against a background of intense atmospheric N and P deposition.
Heat Shock Factors in the European Eel: Gene Characterization and Expression Response to Different Environmental Conditions and to Induced Sexual Maturation
Authors: Ferrão Leonor , Pérez Luz , Asturiano Juan F. , Morini Marina
Published: February 2025

Heat shock factors (HSFs) are transcription factors that regulate responses to environmental changes and play roles in physiological mechanisms like spermatogenesis. This study analyzed the HSF gene family and their expression profiles in the European eel under different environmental conditions and during testis maturation. Six HSF genes were identified in the studied vertebrates, in which the eel presents two HSF1 paralogs (HSF1a and HSF1b), likely resulting from the teleost whole-genome duplication event, while only one paralog is present for the other HSF (HSF2, HSF4, and HSF5). All five HSF genes were highly expressed in the testis, but some were also detected in the brain, intestine, and gills. Our findings showed that HSF1 changed their expression in response to different temperature and salinity conditions, suggesting that these may support males in perceiving the temperature and salinity changes possibly found during reproductive migration. During hCGrec-induced spermatogenesis, HSF genes presented a decreasing expression profile throughout testis maturation (with significant differences in HSF1a and HSF4), except HSF5, which showed the highest levels after 4 weeks of hormonal treatment. Our study indicates that HSF genes are potentially implicated in the response to environmental changes perception and during gonadal maturation.