bluuebunny/crossref_metadata_2025 huggingface dataset:
MtAD-Net: Multi-Threshold Adaptive Decision Net for Unsupervised Synthetic Aperture Radar Ship Instance Segmentation
Authors: Xue Junfan , Yin Junjun , Yang Jian
Published: February 2025

In synthetic aperture radar (SAR) images, pixel-level Ground Truth (GT) is a scarce resource compared to Bounding Box (BBox) annotations. Therefore, exploring the use of unsupervised instance segmentation methods to convert BBox-level annotations into pixel-level GT holds great significance in the SAR field. However, previous unsupervised segmentation methods fail to perform well on SAR images due to the presence of speckle noise, low imaging accuracy, and gradual pixel transitions at the boundaries between targets and background, resulting in unclear edges. In this paper, we propose a Multi-threshold Adaptive Decision Network (MtAD-Net), which is capable of segmenting SAR ship images under unsupervised conditions and demonstrates good performance. Specifically, we design a Multiple CFAR Threshold-extraction Module (MCTM) to obtain a threshold vector by a false alarm rate vector. A Local U-shape Feature Extractor (LUFE) is designed to project each pixel of SAR images into a high-dimensional feature space, and a Global Vision Transformer Encoder (GVTE) is designed to obtain global features, and then, we use the global features to obtain a probability vector, which is the probability of each CFAR threshold. We further propose a PLC-Loss to adaptively reduce the feature distance of pixels of the same category and increase the feature distance of pixels of different categories. Moreover, we designed a label smoothing module to denoise the result of MtAD-Net. Experimental results on the dataset show that our MtAD-Net outperforms traditional and existing deep learning-based unsupervised segmentation methods in terms of pixel accuracy, kappa coefficient, mean intersection over union, frequency weighted intersection over union, and F1-Score.
Hybrid Deterministic Sensing Matrix for Compressed Drone SAR Imaging and Efficient Reconstruction of Subsurface Targets
Authors: Jo Hwi-Jeong , Lee Heewoo , Choi Jihoon , Lee Wookyung
Published: February 2025

Drone-based synthetic aperture radar (SAR) systems have increasingly gained attention due to their potential for rapid surveillance in localized areas. This paper presents a novel approach to SAR processing for subsurface target detection from a lightweight drone platform. The limited processing capacity and memory resources of small SAR platforms demand efficient recovery performance for high-resolution imaging. Compressed sensing (CS) algorithms are widely used to mitigate data storage requirements, yet they often suffer from challenges related to computational burden and detection errors. CS theory exploits signal sparsity and the incoherence of sensing matrices to reconstruct target information from reduced data measurements. Although random sensing matrices are commonly employed to ensure the independence of measured data, they incur high computational cost and memory resources. While deterministic sensing matrices provide fast data recovery, they suffer from increased internal interference, leading to degraded performance in noisy environments. This paper proposes a novel hybrid sensing matrix and recovery algorithm for efficient target detection in small drone-based SAR platforms. After establishing the principles of signal sampling and recovery, SAR imaging simulations are conducted to evaluate the performance of the proposed method with respect to data compression, processing speed, and recovery accuracy. For verification, a custom-built drone SAR platform is utilized to recover subsurface targets obscured by high-clutter backgrounds. Experimental results demonstrate the effective recovery of buried target images, highlighting the potential of the proposed method for practical applications in high-clutter environments.
Exploiting Weighted Multidirectional Sparsity for Prior Enhanced Anomaly Detection in Hyperspectral Images
Authors: Liu Jingjing , Jin Jiashun , Xiu Xianchao , Liu Wanquan , Zhang Jianhua
Published: February 2025

Anomaly detection (AD) is an important topic in remote sensing, aiming to identify unusual or abnormal features within the data. However, most existing low-rank representation methods usually use the nuclear norm for background estimation, and do not consider the different contributions of different singular values. Besides, they overlook the spatial relationships of abnormal regions, particularly failing to fully leverage the 3D structured information of the data. Moreover, noise in practical scenarios can disrupt the low-rank structure of the background, making it challenging to separate anomaly from the background and ultimately reducing detection accuracy. To address these challenges, this paper proposes a weighted multidirectional sparsity regularized low-rank tensor representation method (WMS-LRTR) for AD. WMS-LRTR uses the weighted tensor nuclear norm for background estimation to characterize the low-rank property of the background. Considering the correlation between abnormal pixels across different dimensions, the proposed method introduces a novel weighted multidirectional sparsity (WMS) by unfolding anomaly into multimodal to better exploit the sparsity of the anomaly. In order to improve the robustness of AD, we further embed a user-friendly plug-and-play (PnP) denoising prior to optimize the background modeling under low-rank structure and facilitate the separation of sparse anomalous regions. Furthermore, an effective iterative algorithm using alternate direction method of multipliers (ADMM) is introduced, whose subproblems can be solved quickly by fast solvers or have closed-form solutions. Numerical experiments on various datasets show that WMS-LRTR outperforms state-of-the-art AD methods, demonstrating its better detection ability.
Detection of Parabolic Antennas in Satellite Inverse Synthetic Aperture Radar Images Using Component Prior and Improved-YOLOv8 Network in Terahertz Regime
Authors: Yang Liuxiao , Wang Hongqiang , Zeng Yang , Liu Wei , Wang Ruijun , Deng Bin
Published: February 2025

Inverse Synthetic Aperture Radar (ISAR) images of space targets and their key components are very important. However, this method suffers from numerous drawbacks, including a low Signal-to-Noise Ratio (SNR), blurred edges, significant variations in scattering intensity, and limited data availability, all of which constrain its recognition capabilities. The terahertz (THz) regime has reflected excellent capacity for space detection in terms of showing the details of target structures. However, in ISAR images, as the observation aperture moves, the imaging features of the extended structures (ESs) undergo significant changes, posing challenges to the subsequent recognition performance. In this paper, a parabolic antenna is taken as the research object. An innovative approach for identifying this component is proposed by using the advantages of the Component Prior and Imaging Characteristics (CPICs) effectively. In order to tackle the challenges associated with component identification in satellite ISAR imagery, this study employs the Improved-YOLOv8 model, which was developed by incorporating the YOLOv8 algorithm, an adaptive detection head known as the Dynamic head (Dyhead) that utilizes an attention mechanism, and a regression box loss function called Wise Intersection over Union (WIoU), which addresses the issue of varying sample difficulty. After being trained on the simulated dataset, the model demonstrated a considerable enhancement in detection accuracy over the five base models, reaching an mAP50 of 0.935 and an mAP50-95 of 0.520. Compared with YOLOv8n, it improved by 0.192 and 0.076 in mAP50 and mAP50-95, respectively. Ultimately, the effectiveness of the suggested method is confirmed through the execution of comprehensive simulations and anechoic chamber tests.
Practical Guidelines for Performing UAV Mapping Flights with Snapshot Sensors
Authors: Maes Wouter H.
Published: February 2025

Uncrewed aerial vehicles (UAVs) have transformed remote sensing, offering unparalleled flexibility and spatial resolution across diverse applications. Many of these applications rely on mapping flights using snapshot imaging sensors for creating 3D models of the area or for generating orthomosaics from RGB, multispectral, hyperspectral, or thermal cameras. Based on a literature review, this paper provides comprehensive guidelines and best practices for executing such mapping flights. It addresses critical aspects of flight preparation and flight execution. Key considerations in flight preparation covered include sensor selection, flight height and GSD, flight speed, overlap settings, flight pattern, direction, and viewing angle; considerations in flight execution include on-site preparations (GCPs, camera settings, sensor calibration, and reference targets) as well as on-site conditions (weather conditions, time of the flights) to take into account. In all these steps, high-resolution and high-quality data acquisition needs to be balanced with feasibility constraints such as flight time, data volume, and post-flight processing time. For reflectance and thermal measurements, BRDF issues also influence the correct setting. The formulated guidelines are based on literature consensus. However, the paper also identifies knowledge gaps for mapping flight settings, particularly in viewing angle pattern, flight direction, and thermal imaging in general. The guidelines aim to advance the harmonization of UAV mapping practices, promoting reproducibility and enhanced data quality across diverse applications.
Mapping Harmful Algae Blooms: The Potential of Hyperspectral Imaging Technologies
Authors: Arias Fernando , Zambrano Maytee , Galagarza Edson , Broce Kathia
Published: February 2025

Harmful algae blooms (HABs) pose critical threats to aquatic ecosystems and human economies, driven by their rapid proliferation, oxygen depletion capacity, toxin release, and biodiversity impacts. These blooms, increasingly exacerbated by climate change, compromise water quality in both marine and freshwater ecosystems, significantly affecting marine life and coastal economies based on fishing and tourism while also posing serious risks to inland water bodies. This article examines the role of hyperspectral imaging (HSI) in monitoring HABs. HSI, with its superior spectral resolution, enables the precise classification and mapping of diverse algae species, emerging as a pivotal tool in environmental surveillance. An array of HSI techniques, algorithms, and deployment platforms are evaluated, analyzing their efficacy across varied geographical contexts. Notably, hyperspectral sensor-based studies achieved up to 90% classification accuracy, with regression-based chlorophyll-a (Chl-a) estimations frequently reaching coefficients of determination (R2) above 0.80. These quantitative findings underscore the potential of HSI for robust HAB diagnostics and early warning systems. Furthermore, we explore the current limitations and future potential of HSI in HAB management, highlighting its strategic importance in addressing the growing environmental and economic challenges posed by HABs. This paper seeks to provide a comprehensive insight into HSI’s capabilities, fostering its integration in global strategies against HAB proliferation.
A Multi-Scale Fusion Deep Learning Approach for Wind Field Retrieval Based on Geostationary Satellite Imagery
Authors: Zhang Wei , Wu Yapeng , Fan Kunkun , Song Xiaojiang , Pang Renbo , Guoan Boyu
Published: February 2025

Wind field retrieval, a crucial component of weather forecasting, has been significantly enhanced by recent advances in deep learning. However, existing approaches that are primarily focused on wind speed retrieval are limited by their inability to achieve real-time, full-coverage retrievals at large scales. To address this problem, we propose a novel multi-scale fusion retrieval (MFR) method, leveraging geostationary observation satellites. At the mesoscale, MFR incorporates a cloud-to-wind transformer model, which employs local self-attention mechanisms to extract detailed wind field features. At large scales, MFR incorporates a multi-encoder coordinate U-net model, which incorporates multiple encoders and utilises coordinate information to fuse meso- to large-scale features, enabling accurate and regionally complete wind field retrievals, while reducing the computational resources required. The MFR method was validated using Level 1 data from the Himawari-8 satellite, covering a geographic range of 0–60°N and 100–160°E, at a resolution of 0.25°. Wind field retrieval was accomplished within seconds using a single graphics processing unit. The mean absolute error of wind speed obtained by the MFR was 0.97 m/s, surpassing the accuracy of the CFOSAT and HY-2B Level 2B wind field products. The mean absolute error for wind direction achieved by the MFR was 23.31°, outperforming CFOSAT Level 2B products and aligning closely with HY-2B Level 2B products. The MFR represents a pioneering approach for generating initial fields for large-scale grid forecasting models.
Reconstruction for Scanning LiDAR with Array GM-APD on Mobile Platform
Authors: Liu Di , Sun Jianfeng , Lu Wei , Li Sining , Zhou Xin
Published: February 2025

Array Geiger-mode avalanche photodiode (GM-APD) Light Detection and Ranging (LiDAR) has the advantages of high sensitivity and long imaging range. However, due to its operating principle, GM-APD LiDAR requires processing based on multiple-laser-pulse data to complete the target reconstruction. Therefore, the influence of the device’s movement or scanning motion during GM-APD LiDAR imaging cannot be ignored. To solve this problem, we designed a reconstruction method based on coordinate system transformation and the Position and Orientation System (POS). The position, attitude, and scanning angles provided by POS and angular encoders are used to reduce or eliminate the dynamic effects in multiple-laser-pulse detection. Then, an optimization equation is constructed based on the negative-binomial distribution detection model of GM-APD. The spatial distribution of photons in the scene is ultimately computed. This method avoids the need for field-of-view registration, improves data utilization, and reduces the complexity of the algorithm while eliminating the effect of LiDAR motion. Moreover, with sufficient data acquisition, this method can achieve super-resolution reconstruction. Finally, numerical simulations and imaging experiments verify the effectiveness of the proposed method. For a 1.95 km building scene with SBR ~0.137, the 2 × 2-fold super-resolution reconstruction results obtained by this method reduce the distance error by an order of magnitude compared to traditional methods.
Research on the eLoran/GNSS Combined Positioning Algorithm and Altitude Optimization
Authors: Yang Man , Yan Baorong , Yang Chaozhong , Jiang Xiang , Li Shifeng
Published: February 2025

With the widespread use of the Global Navigation Satellite System (GNSS), its signal vulnerabilities and security issues have become increasingly exposed. Enhanced Long-Range Navigation (eLoran), as a backup system for the GNSS, has gradually attracted widespread attention. This paper investigates and optimizes the eLoran/GNSS combined positioning algorithm. The main research contributions are as follows: (a) Correcting the incorrect application of spatial coordinate transformation relations in the existing literature and re-deriving the eLoran/GNSS combined positioning algorithm based on the Andoyer–Lambert formula. (b) Correcting the eLoran pseudorange positioning equation for altitude in the combined positioning algorithm, compensating for the lack of altitude parameters in eLoran to improve positioning accuracy. (c) Verifying the correctness of the algorithm through simulation analysis, exploring the impact of errors on the algorithm, and evaluating whether the correction of altitude contributes to improving positioning accuracy. (d) Verifying the simulation results through actual measurement analysis.
Recent Advances in Deep Learning-Based Spatiotemporal Fusion Methods for Remote Sensing Images
Authors: Lian Zilong , Zhan Yulin , Zhang Wenhao , Wang Zhangjie , Liu Wenbo , Huang Xuhan
Published: February 2025

Remote sensing images captured by satellites play a critical role in Earth observation (EO). With the advancement of satellite technology, the number and variety of remote sensing satellites have increased, which provide abundant data for precise environmental monitoring and effective resource management. However, existing satellite imagery often faces a trade-off between spatial and temporal resolutions. It is challenging for a single satellite to simultaneously capture images with high spatial and temporal resolutions. Consequently, spatiotemporal fusion techniques, which integrate images from different sensors, have garnered significant attention. Over the past decade, research on spatiotemporal fusion has achieved remarkable progress. Nevertheless, traditional fusion methods often encounter difficulties when dealing with complicated fusion scenarios. With the development of computer science, deep learning models, such as convolutional neural networks (CNNs), generative adversarial networks (GANs), Transformers, and diffusion models, have recently been introduced into the field of spatiotemporal fusion, resulting in efficient and accurate algorithms. These algorithms exhibit various strengths and limitations, which require further analysis and comparison. Therefore, this paper reviews the literature on deep learning-based spatiotemporal fusion methods, analyzes and compares existing deep learning-based fusion algorithms, summarizes current challenges in this field, and proposes possible directions for future studies.
Enhanced U-Net with Multi-Module Integration for High-Exposure-Difference Image Restoration
Authors: Jian Bo-Lin , Chang Hong-Li , Chen Chieh-Li
Published: February 2025

Machine vision systems have become key unmanned vehicle (UAV) sensing systems. However, under different weather conditions, the lighting direction and the selection of exposure parameters often lead to insufficient or missing object features in images, which could fail to perform various tasks. As a result, images need to be restored to secure information that is accessible when facing a light exposure difference environment. Many applications require real-time and high-quality images; therefore, efficiently restoring images is also important for subsequent tasks. This study adopts supervised learning to solve the problem of images under lighting discrepancies using a U-Net as our main architecture of the network and adding suitable modules to its encoder and decoder, such as inception-like blocks, dual attention units, selective kernel feature fusion, and denoising blocks. In addition to the ablation study, we also compared the quality of image light restoration with other network models using BAID and considered the overall trainable parameters of the model to construct a lightweight, high-exposure-difference image restoration model. The performance of the proposed network was demonstrated by enhancing image detection and recognition.
ZPTM: Zigzag Path Tracking Method for Agricultural Vehicles Using Point Cloud Representation
Authors: Yang Shuang , Zhang Engen , Liu Yufei , Du Juan , Yin Xiang
Published: February 2025

Automatic navigation, as one of the modern technologies in farming automation, enables unmanned driving and operation of agricultural vehicles. In this research, the ZPTM (Zigzag Path Tracking Method) was proposed to reduce the complexity of path planning by using a point cloud consisting of a series of anchor points with spatial information, which are obtained from orthophotos taken by UAVs (Unmanned Aerial Vehicles) to represent the curved path in the zigzag. A local straight path was created by linking two adjacent anchor points, forming the local target path to be tracked, which simplified the navigation algorithm for zigzag path tracking. A nonlinear feedback function was established, using both lateral and heading errors as inputs for determining the desired heading angle of agricultural vehicles, which were guided along the local target path with minimal errors. A GUI (Graphic User Interface) was designed on the navigation terminal to visualize and monitor the working process of agricultural vehicles in automatic navigation, displaying interactive controls and components, including representations of the zigzag path and the agricultural vehicle using affine transformation. A high-clearance sprayer equipped with an automatic navigation system was utilized as the test platform to evaluate the proposed ZPTM. Zigzag navigation tests were conducted to explore the impact of path tracking parameters, including path curvature, moving speed, and spacing between anchor points, on zigzag navigation performance. Based on these tests, a regression model was established to optimize these parameters for achieving accurate and smooth movement. Field test results showed that the maximum error, average error, and RMS (Root Mean Square) error in the zigzag navigation were 3.30 cm, 2.04 cm, and 2.27 cm, respectively. These results indicate that the point cloud path-based ZPTM in this research demonstrates adequate stability, accuracy, and applicability in zigzag navigation.
Synthetic Meshes in Pelvic Organ Prolapse: A Narrative Review
Authors: Lasri Sami , Alshamsi Hend , Campeau Lysanne
Published: February 2025

Introduction: Pelvic organ prolapse is a common condition that can affect 50% of parous women. The surgical management can be divided into two approaches: A trans-vaginal and a trans-abdominal approach. In view of current controversies and discrepancies between guidelines, this review aims to scope the historically available data on synthetic meshes in the management of POP mainly on outcomes and complications of the trans-vaginal approach and the trans-abdominal approach. Methods: This study is a narrative review of the use of synthetic meshes in POP surgery. The different indications, the results, and comparisons to other surgical management were collected using MEDLINE and Google Scholar. Results: Regarding the trans-vaginal approach, 31 articles were included. The anatomical success rate is high, around 90%. However, this technique was recently considered cost-ineffective mostly because of high rates of erosions, ranging from 4 to 40% depending on the series. Obesity seems to be the most important risk factor of mesh erosion, followed by age and smoking. Regarding the trans-abdominal approach, 36 articles were included. In the literature, anatomical success is between 70 and 95%, with an erosion rate between 0 and 11%. Minimally invasive sacrocolpopexy and open sacrocolpopexy seem to be equally effective on anatomical outcomes and recurrence rates. Concomitant total hysterectomy might be effective but may be associated with more mesh erosions. Concomitant laparoscopic supracervical hysterectomy may be the preferred option for patients with cervical and uterine lesions but should not be performed for the sole purpose of reducing the occurrence of endometrial carcinoma, especially when uterine preservation seems effective and is associated with less blood loss and shorter operating time. Conclusion: Our review reports limited application for trans-vaginal repair because of high complications rates, leading countries to suspend their utilization. Our review reports a gold standard application for trans-abdominal sacrocolpopexy. The use of synthetic meshes in trans-abdominal sacrocolpopexy for POP repair provide durable cure rates with a lower rate of mesh-related complications and therefore may be considered the gold standard approach.
The Impact of Video Consent on Patient Satisfaction When Undergoing Percutaneous Nephrolithotomy: A Randomized Control Trial
Authors: Sharma Kartik , Choudhary Gautam Ram , Navriya Shiv Charan , Kudunthail Jeena Raju , Bhirud Deepak Prakash , Singh Mahendra , Sandhu Arjun Singh
Published: February 2025

Introduction: Consent-taking for surgery evolved from a historical paternalistic approach to informed consent in the mid-20th century. Modern healthcare models prioritize patient-centric care, and the use of multimedia tools may overcome challenges such as language barriers and complex medical surgical steps. This study evaluates the impact of an educational video on patient satisfaction for those undergoing percutaneous nephrolithotomy (PCNL), a procedure where explaining complexities verbally can be challenging. Materials and Methods: A randomized control trial was conducted at a tertiary care center in India from July 2022 to April 2024. A total of 232 adult patients scheduled for PCNL were randomly assigned to a study group (Group A) or a control group (Group B). Group A viewed an educational video about PCNL, while Group B provided standard written consent. The video, presented in patients’ native languages, covered procedural details, potential outcomes, and post-operative care. Patient satisfaction was assessed using a 10-question questionnaire at discharge, with scores ranging from one (poor) to five (best). Statistical analysis was performed using SPSS software to compare outcomes between the two groups. Results: The study found that Group A exhibited significantly higher satisfaction compared to Group B across all domains. Group A demonstrated a better understanding of the procedure, improved knowledge of post-operative care, reduced anxiety, and a greater awareness of potential complications. Specifically, the mean satisfaction scores for Group A were higher in understanding the procedure (13.15 vs. 10.00), post-operative care (8.46 vs. 6.84), and overall anxiety (8.65 vs. 6.96). The video also improved patients’ comprehension regarding potential complications and the need for further procedures. Complication rates and hospital stay durations were similar between both groups. Discussion: The educational video significantly enhanced patient satisfaction and the understanding of PCNL. This multimedia approach provided a consistent, clear explanation of the procedure, which improved patient comprehension and reduced anxiety, irrespective of literacy levels. These findings support the integration of video-assisted consent in pre-operative education to enhance patient engagement and satisfaction. Conclusions: The use of an educational video for consent in PCNL improves patient understanding and satisfaction. This method effectively complements traditional consent processes, providing a valuable tool for patient education in complex procedures.
The Silent Burden of De Novo Metastatic Prostate Cancer in the Middle East: A Call for Region-Specific Screening Guidelines
Authors: Abbas Noura , Chehade Laudy , Abdul Sater Zahi , Shamseddine Ali
Published: February 2025

Background: Prostate cancer is a significant global health concern, with rising incidence and disease burden in the Middle East (ME). This review aims to explore the current state of prostate cancer epidemiology in the ME, particularly in low- to middle-income settings, investigating trends in incidence and mortality, assessing challenges related to de novo metastatic prostate cancer, and evaluating the need for region-specific screening guidelines. Methods: We conducted a comprehensive narrative review of epidemiological data on prostate cancer in the ME, examining trends in incidence and mortality, de novo metastatic cases, and current screening practices. Additionally, we assessed the applicability of international guidelines for prostate cancer screening to the ME context. Results: The ME exhibits a rising trend in prostate cancer incidence, with a mortality-to-incidence ratio of 0.3–0.4, compared to 0.1 in the United States, reflecting significant differences in healthcare access and quality that contribute to poorer outcomes. The incidence rates are particularly high in Lebanon, reaching 37.2 per 100,000 in 2012. De novo metastatic prostate cancer is also more prevalent in the ME, often exceeding 20–30%, with a value of 23% reported in Lebanon and reaching 54% in a study including six Middle Eastern countries, compared to 4–14% in the United States. Our review identified a critical need for enhanced screening and early detection efforts tailored to the ME’s unique epidemiological and socio-cultural factors. Conclusions: The substantial burden of de novo metastatic prostate cancer in the ME underscores the need for region-specific screening guidelines. Tailored approaches, including increased awareness, early detection, and resource-stratified strategies, are essential to address the unique epidemiological and socio-cultural factors of the ME and improve patient outcomes.
A Review of the Intraoperative Use of Artificial Intelligence in Urologic Surgery
Authors: Guduguntla Arjun , Al-Khanaty Abdullah , Davey Catherine E. , Patel Oneel , Ta Anthony , Ischia Joseph
Published: February 2025

Introduction: Future evolutions of artificial intelligence (AI) will support autonomous surgery, conducted without the need for human decision making and implementation, but we have not yet achieved this level of technology. Presently, the predominant applications of AI in urological surgery are achieved using the tool of computer vision. This review aims to summarise potential intra-operative AI tools for urologists. Method: A systematic search was conducted through Scopus, PubMed, Embase, and Medline by two independent reviewers, with a third to resolve any conflicts. As a rule, only original articles describing the use or potential use of artificial intelligence intra-operatively in urologic surgery were included. A total of 60 articles were reviewed. Key content and findings: There is significant research investigating the ability to diagnose bladder tumours using AI assistance at the time of cystoscopy, with studies showing the ability to also grade tumour based on appearance and differentiate between carcinoma in situ and indeterminate lesions. With the aid of AI, kidney stones can accurately be identified and diagnosed morphologically intra-operatively. Various studies show the ability to overlay 2D and 3D anatomical models on a surgeon’s screen, as well as correctly identify important anatomical landmarks and surgical instruments, with AI support. All types of intra-operative data can be analysed with AI to assess surgeon performance, predict post-operative outcomes such as continence post prostatectomy, and recognise complications such as bleeding and ischemia. Conclusions: AI holds great potential for urologists during surgery to improve safety, diagnostic accuracy, identification of anatomical structures and surgical instruments, assessment of the surgeon for self-evaluation, and prediction of post-operative outcomes. Before the use of AI as an aid during surgery becomes standard practice, more prospective studies are needed to evaluate its real-world application, feasibility, and costs.
Digital Transformation in African Heritage Preservation: A Digital Twin Framework for a Sustainable Bab Al-Mansour in Meknes City, Morocco
Authors: Serbouti Imane , Chenal Jérôme , Tazi Saâd Abdesslam , Baik Ahmad , Hakdaoui Mustapha
Published: February 2025

The advent of digital transformation has redefined the preservation of cultural heritage and historic sites through the integration of Digital Twin technology. Initially developed for industrial applications, Digital Twins are now increasingly employed in heritage conservation as dynamic, digital replicas of physical assets and environments. These systems enable detailed, interactive approaches to documentation, management, and preservation. This paper presents a detailed framework for implementing Digital Twin technology in the management of heritage buildings. By utilizing advanced methods for data collection, processing, and analysis, the framework creates a robust data hub for Digital Twin Heritage Buildings (DTHB). This architecture enhances real-time monitoring, improves accuracy, reduces operational costs, and enables predictive maintenance while minimizing invasive inspections. Focusing on Bab Al-Mansour Gate in Meknes, Morocco, a significant cultural landmark, this research outlines the workflow for developing a Bab Al-Mansour DTHB platform. The platform monitors structural health and detects damage over time, offering a dynamic tool for conservation planning. By integrating innovative technologies with data-driven solutions, this study provides a replicable model for preserving heritage sites, addressing critical gaps in real-time monitoring, resource optimization, and environmental risk mitigation.
From Passion to Burnout: The Role of Work–Family Conflict and Job Satisfaction in the Workplace
Authors: Sousa Cátia , Ferro Ana Sofia
Published: February 2025

In the modern workplace, understanding the dynamics of work passion and its consequences on employee well-being is crucial. This quantitative study, conducted with Portuguese workers, seeks to examine and validate the Dualistic Model of Passion, which distinguishes between harmonious and obsessive passion, while also exploring the intricate relationships among work passion, job satisfaction, work–family conflict, and burnout. Using mediation analyses and structural equation modeling (SEM), a convenience sample of 326 participants (169 men and 157 women), aged between 19 and 69 years (M = 41.31, SD = 10.52), was analyzed to uncover how different types of work passion—obsessive and harmonious—impact professional outcomes and well-being. The findings reveal that obsessive passion for work is a significant predictor of work–family conflict, which, in turn, escalates the risk of burnout. In contrast, harmonious passion is positively associated with increased job satisfaction and acts as a buffer against the negative effects of work–family conflict. Notably, contrary to initial assumptions, obsessive passion does not directly precipitate burnout. Mediation analyses confirmed that work–family conflict plays a key role in this process, while SEM results demonstrated strong model fit indices, reinforcing its relevance and applicability in organizational settings. These results offer crucial insights for both theoretical advancement and practical applications, highlighting the importance of fostering a work environment that nurtures harmonious passion. By doing so, organizations can mitigate conflict and burnout while promoting higher levels of job satisfaction among employees.
Measuring Restrictive Masculinity: Development and Implementation Within University Students
Authors: Noel Jonathan K. , Morais Madison A. , Nosal Abigail G. , Gately Kelsey A. , Ramsland Short Korina , Rosenthal Samantha R.
Published: February 2025

Restrictive masculinity is a rigid construct of how to define manliness that is associated with several health harms. The current study sought to develop a brief questionnaire to measure restrictive masculinity. The questionnaire items were based on the restrictive masculinity norms defined by the Organisation for Economic Co-operation and Development (OECD) Development Centre and refined through expert input and focus group sessions. The restrictive masculinity questionnaire was then administered to n = 506 university students via an online survey. Exploratory factor analysis was used to identify the questionnaire’s latent structure, and sociodemographic, behavioral, and mental health predictors of holding restrictive masculinity norms were explored. A twelve-item questionnaire containing two latent constructs (i.e., political/economic sphere and private sphere) and three measured variables had a good model fit (RMSEA [90%CI] = 0.05 [0.04,0.07], CFI = 0.98, TLI = 0.96, and SRMR = 0.03). Participants identifying as heterosexual cis-males and reporting illicit substance use were positively associated with restrictive masculinity norms. Although further development is needed, this 12-item questionnaire may be a useful tool to measure restrictive masculinity norms. Early interventions should be considered to reduce the impact of restrictive masculinity on individual and population health.
Analysis of Magnitude and Direction of Body Composition Asymmetries in Male Brazilian Jiu-Jitsu Athletes: Pilot Study
Authors: Ojeda-Aravena Alex , Kons Rafael L. , Azócar-Gallardo Jairo , Dopico-Calvo Xurxo , Fernández Aida , Tuesta-Roa Marcelo , Cresp-Barria Mauricio , Olivares-Arancibia Jorge , Báez-San Martín Eduardo
Published: February 2025

In order to optimize body composition and its components, it is of interest to analyze inter-limb asymmetries in athletes of popular combat sports such as Brazilian Jiu-Jitsu (BJJ). This study aimed to assess the magnitude and direction of body composition asymmetry in competitive male BJJ athletes. Seventeen experienced and competitive male BJJ athletes (age 34.59 ± 8.00 years, 172.94 ± 5.46 cm, BJJ experience 7.88 ± 5.57 years, and 3.71 ± 1.05 days of weekly volume training), including Guard Fighters (n = 7) and Pass Fighters (n = 10), participated in this study. In a single session, whole-body and segmental upper limb and lower limb body composition (BC) was assessed utilizing dual-energy X-ray absorptiometry (DXA). The assessment included total mass (TM), fat mass (FM), fat mass percentage (%FM), fat-free mass (FFM), and bone mineral content (BMC). Absolute differences between limbs were analyzed using a paired t-test. A comparison of BC asymmetries according to combat styles was conducted using an independent t-test. The effect size (ES) was interpreted as Cohen’s d. The main results revealed significant asymmetries in the upper limbs (ULs) of the total group; greater values were found on the left side for TM (p = 0.009, ES = 0.725), FM (p = 0.016, ES = 0.650), FFM (p = 0.026, ES = 0.594), and BMC (p < 0.001, ES = 0.993). In Pass Fighters, differences favored the right side in TM (p = 0.003, ES = 1.277), FM (p = 0.009, ES = 1.039), FFM (p = 0.011, ES = 1.000), and BMC (p < 0.001, ES = 1.916). In contrast, Guard Fighters showed no discrepancies in these parameters. No notable disparities were observed in the lower limbs. This pilot study reveals that BJJ athletes present significant asymmetries in BC, particularly in the ULs, with a marked predominance on the right side, especially in Pass Fighters.
Open Deep Venous Arterialization for No-Option Chronic Limb-Threatening Ischemia: A Variable and Adaptable Technique
Authors: Alsabbagh Yaman , Erben Young , Farres Houssam
Published: February 2025

Background: Patients with no-option chronic limb-threatening ischemia (NoCLTI), lacking suitable distal arteries for conventional revascularization, face major limb amputation. The 1-year mortality rate after major amputation is 48.3%, increasing to 70.9% in 3 years. Open deep venous arterialization (DVA) offers a promising alternative for limb salvage, achievable through open, endovascular, or hybrid approaches. We aim to provide a comprehensive, step-by-step guide to performing open DVA in NoCLTI patients, addressing preoperative and postoperative considerations as well as the technical details of the procedure. Methods: Patient selection for open DVA focuses on individuals with NoCLTI at high risk for amputation. Preoperative assessments include evaluating risk factors, determining limb threat severity using the Wound, Ischemia, and foot Infection (WIfI) score, and mapping anatomical patterns via the Global Limb Anatomic Staging System (GLASS). The procedure involves identifying the target artery using Doppler ultrasound, performing microdissection to expose the artery and vein, ligating proximal vein branches, and creating a side-to-side anastomosis. Venous valves are disrupted with a valvulotome to allow antegrade flow. A proximal bypass graft may be applied if necessary. Results: Postoperatively, patients are monitored for 2–4 days with frequent Doppler assessments. Anticoagulation therapy begins with a heparin drip, transitioning to oral agents and/or dual antiplatelet therapy. Wound care includes deferred debridement for 2–4 weeks and may involve negative-pressure therapy. Follow-up involves weekly visits for the first month, and then at 3 months, and every 6 months thereafter, with surveillance using transcutaneous oxygen measurement, the toe–brachial index, and arterial duplex ultrasound. Conclusions: Open DVA represents a viable limb salvage option for patients with NoCLTI, potentially avoiding major amputations and improving quality of life. Success depends on careful patient selection, a meticulous surgical technique, and comprehensive postoperative care.
Assessment of Debris Flow Triggering Rainfall Using Parameter-Elevation Relationships on an Independent Slope Model
Authors: Jo Bum-Hee , Chung Taek-Kyu , Kim Inhyun
Published: February 2025

The increasing frequency of extreme weather events such as typhoons and heavy rains, driven by climate change, has intensified debris flow risks during Korea’s monsoon season, causing considerable human and economic losses. In South Korea, where mountainous terrain covers 64% of the country, localized downpours exacerbate the risk of debris flows, endangering communities and critical infrastructure. To enhance resilience and ensure sustainable risk management, the Korea Expressway Corporation developed a quantitative debris flow risk assessment system based on sensitivity and vulnerability indicators. An early warning system utilizing rainfall thresholds was subsequently introduced. However, discrepancies between rainfall data from local AWS stations and actual site conditions compromised its predictive accuracy. This study addresses those limitations by integrating the Parameter-elevation Regressions on Independent Slopes Model (PRISM) into the early warning system to enhance prediction accuracy at debris flow occurrence and non-occurrence points. Comparative analysis revealed that the PRISM-enhanced system significantly improved predictive performance. Furthermore, cumulative rainfall data from five highway sites validated the system’s reliability in short-term prediction while offering a sustainable, data-driven framework for long-term debris flow risk management. This approach strengthens adaptive infrastructure strategies, promoting more resilient transportation networks and improving public safety while minimizing environmental impacts.
Assessment of Safe and Sustainable Operation for Freight Transportation Company Based on Tire Set Configurations Used in Its Trucks’ Fleet
Authors: Žuraulis Vidas , Pečeliūnas Robertas , Misevičius Tomas
Published: February 2025

This article investigates the safety potential of a freight transportation company, considering tire set selection as one of the most important aspects to ensure safe driving and a reliable transportation service. The revision of tire sets selection in large vehicle fleets is attributed to a new regulation from the United Nations to maintain non-deteriorating tire wet braking performance up to a minimum allowable wear limit, encouraging both safety and sector sustainability, as a significant part of tires are currently replaced before reaching a tread depth of 3 mm. In this research, an experimental test was conducted that involved four maneuvers with a truck using ten different sets of tires (including new and retreaded) to determine which set performs better in critical driving conditions. The results are then analyzed using the TOPSIS method where the most efficient set of tires and the best alternatives are selected. Finally, the safety of trucks on the road using the appropriate set of tires is evaluated by the estimated accident reduction potential. It should be mentioned that the optimal selection of the truck tire set is also important for sustainable transportation, as the pollution of worn tires remains a relevant environmental issue.
Sustainability of Global Trade: The Impact of Executive Green Awareness on the Global Green Value Chain of Enterprises
Authors: Huang Xiaobing , Xie Jiawei
Published: February 2025

In the context of economic globalization, international trade facilitates cross-border production and consumption but raises concerns such as carbon transfer from corporate trade activities. This study investigates the influence of executives’ green awareness on the global green value chain (GGVC) using matched data from Chinese customs and enterprises, along with newly constructed GVC net flow indicators and econometric models. From 2007 to 2016, executives’ green awareness in Chinese A-share listed companies significantly promoted GVC development, with proactive, green-oriented executives driving greater green upgrades. Variations in executives’ backgrounds, firm types, regional education levels, and highly digitalized production environments further shaped the effectiveness of green leadership. The findings provide empirical evidence and insights into green export management practices.
Enhanced Fault Detection and Classification in AC Microgrids Through a Combination of Data Processing Techniques and Deep Neural Networks
Authors: Taheri Behrooz , Hosseini Seyed Amir , Hashemi-Dezaki Hamed
Published: February 2025

This paper introduces an innovative method for the intelligent protection of AC microgrids that incorporate renewable energy sources and electric vehicle charging stations. To extract relevant features, current signals from both sides of the distribution line are sampled. Subsequently, the differential current is calculated, and the resultant signals are processed using Compressed Sensing Theory and Variational Mode Decomposition to extract key features. These extracted features serve as input data for training the proposed wide and deep learning model. The proposed method was evaluated on a microgrid that incorporated electric vehicle chargers and wind turbines. The results indicate that this approach can effectively identify and categorize different types of faults in AC microgrids. Moreover, it demonstrates stable and dependable performance in the face of typical transients, and its accuracy is not influenced by uncertainties in the microgrid topology.
Carbon Footprint Comparison of Rapeseed and Palm Oil: Impact of Land Use and Fertilizers
Authors: Tarigan Suria , Pradiko Iput , Darlan Nuzul H. , Kristanto Yudha
Published: February 2025

Palm oil is being criticized as an unsustainable product by the EU due to its association with deforestation and high carbon emissions. However, the producers in Indonesia do not acknowledge the criticisms. Therefore, this study aimed to compare the carbon footprint of a representative EU-produced vegetable oil, rapeseed oil, with Indonesian palm oil. The analysis is divided into two stages, namely (a) land use conversion (LUC) as well as (b) plantation and oil processing. LUC entailed the conversion of native vegetation, such as forest areas and grassland, to vegetable oil crops. The carbon opportunity cost was used to account for the LUC contribution to the carbon footprint. For plantation and oil processing stages, the LCA SIMAPRO was adopted. The results showed that when vegetable oil replaced high-carbon-storage vegetation such as forests, the LUC carbon footprint of rapeseed oil and palm oil production were 2.09 and 1.49 t CO2eq t−1 oil, respectively. Replacing low-carbon-storage vegetation, namely shrub/grassland, led to 0.05 and −0.43 t CO2eq t−1 of repressed and palm oils, respectively. Based on the LCA SIMAPRO, the carbon footprints of plantation and oil processing stages were 1.05 and 0.88 t CO2eq t−1 oil for rapeseed and palm oils, respectively. The cultivation of oil palm in peatland generated a higher total carbon footprint (i.e., combined LUC and plantation and oil processing stages) than rapeseed oil (13.8 to 3.14 t CO2eq t−1 oil). However, in non-peatland areas, the total carbon footprint of palm oil was lower than rapeseed oil (2.37 to 3.14 t CO2eq t−1 oil) when replacing tropical forest and temperate forest vegetation, respectively. The total footprint was 1.2 to 0.45 t CO2eq t−1 oil when both replaced shrub/grassland. The higher productivity of oil palm and lower fertilizer requirement contributed primarily to the lower carbon footprint in non-peatland areas.