bluuebunny/crossref_metadata_2025 huggingface dataset:
SVIADF: Small Vessel Identification and Anomaly Detection Based on Wide-Area Remote Sensing Imagery and AIS Data Fusion
Authors: Chen Lihang , Hu Zhuhua , Chen Junfei , Sun Yifeng
Published: February 2025

Small target ship detection and anomaly analysis play a pivotal role in ocean remote sensing technologies, offering critical capabilities for maritime surveillance, enhancing maritime safety, and improving traffic management. However, existing methodologies in the field of detection are predominantly based on deep learning models with complex network architectures, which may fail to accurately detect smaller targets. In the classification domain, most studies focus on synthetic aperture radar (SAR) images combined with Automatic Identification System (AIS) data, but these approaches have significant limitations: first, they often overlook further analysis of anomalies arising from mismatched data; second, there is a lack of research on small target ship classification using wide-area optical remote sensing imagery. In this paper, we develop SVIADF, a multi-source information fusion framework for small vessel identification and anomaly detection. The framework consists of two main steps: detection and classification. To address challenges in the detection domain, we introduce the YOLOv8x-CA-CFAR framework. In this approach, YOLOv8x is first utilized to detect suspicious objects and generate image patches, which are then subjected to secondary analysis using CA-CFAR. Experimental results demonstrate that this method achieves improvements in Recall and F1-score by 2.9% and 1.13%, respectively, compared to using YOLOv8x alone. By integrating structural and pixel-based approaches, this method effectively mitigates the limitations of traditional deep learning techniques in small target detection, providing more practical and reliable support for real-time maritime monitoring and situational assessment. In the classification domain, this study addresses two critical challenges. First, it investigates and resolves anomalies arising from mismatched data. Second, it introduces an unsupervised domain adaptation model, Multi-CDT, for heterogeneous multi-source data. This model effectively transfers knowledge from SAR–AIS data to optical remote sensing imagery, thereby enabling the development of a small target ship classification model tailored for optical imagery. Experimental results reveal that, compared to the CDTrans method, Multi-CDT not only retains a broader range of classification categories but also improves target domain accuracy by 0.32%. The model extracts more discriminative and robust features, making it well suited for complex and dynamic real-world scenarios. This study offers a novel perspective for future research on domain adaptation and its application in maritime scenarios.
A Comparison of Capacitive Soil Moisture Sensors in Different Substrates for Use in Irrigation Systems
Authors: Gümüser Mehmet Akif , Pichlhöfer Alexander , Korjenic Azra
Published: February 2025

Smart irrigation systems play a crucial role in water management, particularly in urban greening applications aimed at mitigating urban heat islands and enhancing environmental sustainability. These systems rely on soil moisture sensors to optimize water usage, ensuring that irrigation is precisely tailored to plant needs. This study evaluates the performance of four commercially available capacitive soil moisture sensors—TEROS 10, SMT50, Scanntronik, and DFROBOT—across three different substrates under controlled laboratory conditions. A total of 380 measurements were conducted to assess sensor accuracy, reliability, and the influence of insertion technique on measurement variability. Results indicate that while all sensors adequately cover the moisture ranges critical for plant health, their accuracy varies significantly, highlighting the necessity of substrate-specific calibration. TEROS 10 exhibited the lowest relative deviation and highest measurement consistency, making it the most reliable among the tested sensors. DFROBOT, despite being the least expensive, performed comparably to SMT50 and Scanntronik in certain conditions. The findings provide valuable insights for selecting and calibrating soil moisture sensors in smart irrigation applications, ultimately contributing to improved water efficiency, plant vitality, and sustainable building-integrated greenery.
Noise Directivity Reconstruction Radiated from Unmanned Underwater Vehicle’s Propeller Using the Equivalent Source Method
Authors: Jiang Shuai , Tan Liwen , Gu Ruichong , Peng Zilong
Published: February 2025

Noise directivity reconstruction and prediction of noise levels at long ranges from such sources as unmanned underwater vehicles (UUVs) or aircraft are important practical problems. The equivalent source method can be used to reconstruct and predict the sound propagation of such directional complex volume sources in the far field. However, the selection of the elementary source configurations for the equivalent source method has a certain degree of blindness. In this paper, a method for selecting elementary source configurations was proposed, considering the correlation coefficients that exhibit a strong correlation with the directivity function. It is then applied to reconstruct the noise directivity pattern radiated from a real UUV. The results demonstrate that this method can achieve higher accuracy in reconstructing complex radiated sound sources using fewer elementary source configurations.
Advanced Predictive Analytics for Fetal Heart Rate Variability Using Digital Twin Integration
Authors: Lwin Tunn Cho , Zin Thi Thi , Tin Pyke , Kino Emi , Ikenoue Tsuyomu
Published: February 2025

Fetal heart rate variability (FHRV) is a critical indicator of fetal well-being and autonomic nervous system development during labor. Traditional monitoring methods often provide limited insights, potentially leading to delayed interventions and suboptimal outcomes. This study proposes an advanced predictive analytics approach by integrating approximate entropy analysis with a hidden Markov model (HMM) within a digital twin framework to enhance real-time fetal monitoring. We utilized a dataset of 469 fetal electrocardiogram (ECG) recordings, each exceeding one hour in duration, to ensure sufficient temporal information for reliable modeling. The FHRV data were preprocessed and partitioned into parasympathetic and sympathetic components based on downward and non-downward beat detection. Approximate entropy was calculated to quantify the complexity of FHRV patterns, revealing significant correlations with umbilical cord blood gas parameters, particularly pH levels. The HMM was developed with four hidden states representing discrete pH levels and eight observed states derived from FHRV data. By employing the Baum–Welch and Viterbi algorithms for training and decoding, respectively, the model effectively captured temporal dependencies and provided early predictions of the fetal acid–base status. Experimental results demonstrated that the model achieved 85% training and 79% testing accuracy on the balanced dataset distribution, improving from 78% and 71% on the imbalanced dataset. The integration of this predictive model into a digital twin framework offers significant benefits for timely clinical interventions, potentially improving prenatal outcomes.
Comparative Analysis of YOLO Series Algorithms for UAV-Based Highway Distress Inspection: Performance and Application Insights
Authors: Yang Ziyi , Lan Xin , Wang Hui
Published: February 2025

Established unmanned aerial vehicle (UAV) highway distress detection (HDD) faces the dual challenges of accuracy and efficiency, this paper conducted a comparative study on the application of the YOLO (You Only Look Once) series of algorithms in UAV-based HDD to provide a reference for the selection of models. YOLOv5-l and v9-c achieved the highest detection accuracy, with YOLOv5-l performing well in mean and classification detection precision and recall, while YOLOv9-c showed poor performance in these aspects. In terms of detection efficiency, YOLOv10-n, v7-t, and v11-n achieved the highest levels, while YOLOv5-n, v8-n, and v10-n had the smallest model sizes. Notably, YOLOv11-n was the best-performing model in terms of combined detection efficiency, model size, and computational complexity, making it a promising candidate for embedded real-time HDD. YOLOv5-s and v11-s were found to balance detection accuracy and model lightweightness, although their efficiency was only average. When comparing t/n and l/c versions, the changes in the backbone network of YOLOv9 had the greatest impact on detection accuracy, followed by the network depth_multiple and width_multiple of YOLOv5. The relative compression degrees of YOLOv5-n and YOLOv8-n were the highest, and v9-t achieved the greatest efficiency improvement in UAV HDD, followed by YOLOv10-n and v11-n.
Enhancing E-Nose Performance via Metal-Oxide Based MEMS Sensor Arrays Optimization and Feature Alignment for Drug Classification
Authors: Kong Ruiwen , Shen Wenfeng , Gao Yang , Lv Dawu , Ai Ling , Song Weijie , Tan Ruiqin
Published: February 2025

This article introduces a novel approach to improve electronic nose classification accuracy by optimizing sensor arrays and aligning features. This involves selecting the best sensor combinations and reducing redundant information for better odor recognition. We employ a feature alignment algorithm to address the discrepancies that impede model sharing between electronic nose devices. Our research focuses on overcoming challenges associated with material selection and the constraints of transferring classification models across different electronic nose devices for drug classification. We fabricated six SnO2-based MEMS gas sensors using physical vapor deposition. The ReliefF algorithm was employed to rank and score each sensor’s contribution to drug classification, identifying the optimal sensor array. We then applied feature alignment from transfer learning to enhance model sharing among three inconsistent devices. This study resolves the issue of electronic noses being hard to use on the same database due to hardware inconsistencies in batch production, laying the groundwork for future mass production.
Quantitative Angle Measurement of the Inclined Surface Crack Based on Laser Ultrasonics
Authors: Li Haiyang , Zhang Rui , Pan Qianghua , Wang Pengfei
Published: February 2025

To measure the angles of oblique surface cracks on a specimen, the method of using the time difference between the reflected and scattered waves generated by the interaction of the surface waves and the crack is proposed. Based on the reflection and scattering waves’ paths at the surface crack, an analytical equation that involves the crack depth, width, and inclined angle is developed. On the basis of establishing the ratio of width to depth, ∆t-φ curves and estimated error images without the width term can be analyzed in detail. The finite element simulation and experimental results for aluminum alloy samples show that a crack’s angle can be detected using the time difference method. The effects of the width term in the analytical equation on the estimated angles of the surface cracks are also verified. Measurement of the quantitative angle of inclined surface cracks is first regarded as a multiparameter inversion problem. The simulation and the experimental discussion of the interrelation between the depth, width, and angle of the inclined surface crack in this work are very meaningful for quantitative determination using laser ultrasound.
Systematic Security Analysis of Sensors and Controls in PV Inverters: Threat Validation and Countermeasures
Authors: Yang Fengchen , Pan Kaikai , Yan Chen , Ji Xiaoyu , Xu Wenyuan
Published: February 2025

As renewable energy sources (RES) continue to expand and the use of power inverters has surged, inverters have become crucial for converting direct current (DC) from RES into alternating current (AC) for the grid, and their security is vital for maintaining stable grid operations. This paper investigates the security vulnerabilities of photovoltaic (PV) inverters, specifically focusing on their internal sensors, which are critical for reliable power conversion. It is found that both current and voltage sensors are susceptible to intentional electromagnetic interference (IEMI) at frequencies of 1 GHz or higher, even with electromagnetic compatibility (EMC) protections in place. These vulnerabilities can lead to incorrect sensor readings, disrupting control algorithms. We propose an IEMI attack that results in three potential outcomes: Denial of Service (DoS), physical damage to the inverter, and power output reduction. These effects were demonstrated on six commercial single-phase and three-phase PV inverters, as well as in a real-world microgrid, by emitting IEMI signals from 100 to 150 cm away with up to 20 W of power. This study highlights the growing security risks of power electronics in RES, which represent an emerging target for cyber-physical attacks in future RES-dominated grids. Finally, to cope with such threats, three detection methods that are adaptable to diverse threat scenarios are proposed and their advantages and disadvantages are discussed.
Antenna Design and Optimization for 5G, 6G, and IoT
Authors: Jahanbakhsh Basherlou Haleh , Ojaroudi Parchin Naser , See Chan Hwang
Published: February 2025

In the ever-expanding domain of wireless communication, the continuous evolution of network technologies has underscored the critical role of antenna design and optimization [...]
Ray-Based Physical Modeling and Simulation of Multibeam Sonar for Underwater Robotics in ROS-Gazebo Framework
Authors: Choi Woen-Sug
Published: February 2025

While sonar sensors are crucial for underwater robotics perception, the key challenge lies in traditional multibeam sonar simulation’s lack of comprehensive physics-based interaction models. Such missing physical aspects lead to sonar imagery discrepancies, such as the absence of coherent imaging systems and speckle noise effects exposing risks of over-fitted control designs of the systems using the sonar perceptions. Previous research addressed this gap by introducing a physics-based simulation approach by direct calculation of the point-scattering model equations from perception data obtained from rasterization. However, the raster-based method could not control the resolution of data to pipeline into image generation, and its limitation was explicitly presented in local search scenarios where the distance between data is large. To eliminate those limitations and extend capabilities without losing the quality of the image, this paper introduces a ray-based approach to replace the raster-based method when obtaining the perception data from the simulated world to pipeline into physical equation calculations. The results of the ray-based and raster-based models are compared for the front floating object and the ground grazing local search scenario to confirm that the ray-based method maintains equal quality of sonar image generation, including physical characteristics, but it has more flexibility and capability in control of data resolution for correct sonar image generation.
On the Identification of Mobile and Stationary Zone Mass Transfer Resistances in Chromatography
Authors: Adrover Alessandra , Desmet Gert
Published: February 2025

A robust and elegant approach, based on the Two-Zone Moment Analysis (TZMA) method, is proposed to assess the contributions of the mobile and stationary zones, HCm and HCs, to the C term HC in the van Deemter equation for plate height. The TZMA method yields two formulations for HCm and HCs, both fully equivalent in terms of HC, yet offering different decompositions of the contributions from the mobile and stationary zones. The first formulation proposes an expression for the term HCs that has strong similarities, but also significant differences, from the well-known and widely used one proposed by Giddings. While it addresses the inherent limitation of Giddings’ approach—namely, the complete decoupling of transport phenomena in the moving and stationary zones—it introduces the drawback of a non-unique decomposition of HC. Despite this, it proves highly valuable in highlighting the limitations and flaws of Giddings’ method. In contrast, the second formulation not only properly accounts for the interaction between the moving and stationary zones, but provides a unique and consistent decomposition of HC into its components. Three different geometries are investigated in detail: the 2D triangular array of cylinders (pillar array columns), the 2D array of rectangular pillars (radially elongated pillar array columns) and the 3D face-centered cubic array of spheres. It is shown that Giddings’ approach significantly underestimates the HCs term, especially for porous-shell particles. Its accuracy is limited, being reliable only when intra-particle diffusivity (Ds) and the zone retention factor (k″) are very low, or when axially invariant systems are considered.
Environmental Quality and Poverty in Montevideo: A Spatial Approach to Exploring Environmental Injustices
Authors: Camacho Lorenzo Soledad , Albert Yolanda Pérez , Alberich González Joan
Published: February 2025

The distributive environmental justice approach focuses on analysing the spatial patterns of environmental effects and identifying situations of inequality between different social groups. These inequalities may be related to specific impacts or be the result of accumulated benefits or harms to certain populations, the latter aspect being less investigated globally and, in particular, in Latin America. This work aims to analyse the existence of environmental injustices in the city of Montevideo (Uruguay). For this purpose, an environmental quality index (EQI) composed of five subindices is proposed: pollution, exposure to risks, health impacts, habitat quality and availability of green spaces, and their relationship with the level of poverty is evaluated through descriptive analyses and spatial regression models. The results reveal an inverse relationship between the cumulative environmental quality and poverty level, this being especially marked in the subindices of environmental pollution and habitat quality. In contrast, the availability of green spaces presents a more favourable situation for the poorest groups of the population. This study highlights the importance of analysing environmental injustices through multiple indicator-based approaches and highlights the need to incorporate these perspectives into the study of cities with high levels of segregation.
Intensity vs. Volume in Professional Soccer: Comparing Congested and Non-Congested Periods in Competitive and Training Contexts Using Worst-Case Scenarios
Authors: Douchet Tom , Michel Antoine , Verdier Julien , Babault Nicolas , Gosset Marius , Delaval Benoit
Published: February 2025

Background: Understanding the balance between intensity and volume during training and competition is crucial for optimizing players’ performance and recovery in professional soccer. While worst-case scenarios (WCSs) are commonly used to assess peak match demands, little is known about how the time spent within WCS thresholds varies across congested and non-congested periods, especially when considering differences in playing time. This study examines the time spent at different percentages of WCSs during congested and non-congested periods for players with lower and higher playing times throughout training sessions and matches. Methods: Data were collected from a professional soccer team across a congested and non-congested match period. Twenty players were divided into two groups based on playing time: the top 10 playing times (PT 1–10) and the bottom 10 playing times (PT 11–20). WCS thresholds for total distance (TD) and the distance covered above 20 km·h−1 (D20) were quantified in 10% increments, starting from 50% and increasing up to >100%. The time spent at each threshold was compared between periods and groups for the integrated soccer exercises performed during all training sessions. Repeated measures of ANOVA were used to analyze differences between playing time groups and periods. Results: During training, players spent significantly more time within the 50–90% WCS TD and WCS D20 thresholds during non-congested periods compared to congested periods (p < 0.05). However, no significant differences were observed in the time spent for >90% of the WCSs between periods (p > 0.05). Both PT 1–10 and PT 11–20 groups exhibited similar patterns of WCS achievement, with small effect sizes observed for a few indicators. Conclusion: Coaches should design training sessions that replicate or exceed match demands, particularly during non-congested periods. Future strategies should integrate larger-sided games with longer durations and dissociated contents to better individualize and optimize training loads, especially for non-starters.
A Study on the Impact of New Urbanisation on Green Total Factor Productivity in Agriculture in Jilin Province
Authors: Wang Liu , Zhao Guiyu
Published: February 2025

New urbanisation is crucial for agricultural green transformation and ensuring food and ecological security. Taking Jilin Province as its focus, this study constructs a new urbanisation index system covering four dimensions—population, economy, society, and ecology—and adopts the entropy method to assess its development level and measures agricultural green total factor productivity (GTFP) with the SBM-GML model, which accounts for non-desired outputs. The analysis of panel data and a fixed effects model from 2008 to 2022 finds that both new urbanisation and agricultural GTFP in Jilin Province show an upward trend. Additionally, new urbanisation has a significant positive impact on agricultural GTFP and indirectly enhances efficiency through the proportion of employees in the primary industry. Regional analyses show that the central region has a leading urbanisation level, but regional development is imbalanced; the growth of agricultural GTFP mainly relies on green technology progress, and the decline in technical efficiency requires careful attention. Based on this, it is recommended to promote urban–rural integration and high-quality agricultural development by optimising the spatial layout of new urbanisation, promoting agricultural technological innovation, and promoting industrial restructuring and synergistic development policies.
Impact of the Type of Energetic Material on the Fume Emission in Open-Pit Mining
Authors: Biessikirski Andrzej , Dworzak Michał , Pytlik Mateusz , Nachlik Sonia
Published: February 2025

This study examines the fume emissions from various energetic materials utilized in open-pit mining, emphasizing the influence of chemical composition on their environmental impact. The analysis of fume emissions based on data from an open-pit mine reveals that the annual consumption of approximately 89.7 tons of ANFO, 121.4 tons of emulsion, or 137.8 tons of dynamite can result in total COx and NOx emissions ranging between 16,432.88 and 21,834.07 m3. The use of TNT boosters in ANFO and emulsion energetic material further amplified emissions; however, substituting TNT with dynamite for priming achieved a notable reduction in overall fumes by approximately 9–9.5%, depending on the energetic material used. The scale effect of energetic material mass highlighted the importance of optimized formulations for large-scale blasting. A three-year predictive model indicated fluctuations in energetic material demand, with reductions anticipated as deposits deplete. The result of this study offers pathways for reducing emissions and process optimization, particularly in large-scale mining operations, where the blasting technique is the major extraction method.
Social Sustainability and Subjective Well-Being: A Study on Italian Inner Areas
Authors: Capecchi Stefania , Corduas Marcella , Piccolo Domenico
Published: February 2025

Social sustainability relies on the promotion of social processes and structures that ensure the basic needs of individuals and communities are met while also encouraging constructive interactions among them. This paper offers an overview of the characteristics of Italy’s peripheral rural areas and presents findings from a targeted survey conducted across three southern Italian regions. Evaluations collected from a sample of residents were analyzed using the class of CUB models, which are suitable for preference and opinion data. Subjective perceptions of well-being and quality of life, community participation, and the quality of social relationships were examined. Residents’ opinions on the available services in the areas, including economic facilities, digitalization, and transportation, were also considered. Our research indicates that perceptions of well-being in the peripheral areas of Italy are remarkably influenced by individuals’ assessments of their income adequacy. Our findings establish a strong connection between a positive self-assessment of well-being and the belief that income can sufficiently meet needs. Exploring the perceptual dimensions of well-being can offer valuable insights for stakeholders and decision-makers in developing policy efforts and community-led social innovations, which are critical for fighting sentiments of marginalization in rural areas caused by economic perceptions.
A Combined Forecasting Model Based on a Modified Pelican Optimization Algorithm for Ultra-Short-Term Wind Speed
Authors: Guo Lei , Xu Chang , Ai Xin , Han Xingxing , Xue Feifei
Published: February 2025

Ultra-short-term wind speed forecasting is crucial for ensuring the safe grid integration of wind energy and promoting the efficient utilization and sustainable development of renewable energy sources. However, due to the arbitrary, intermittent, and volatile nature of wind speed, achieving satisfactory forecasts is challenging. This paper proposes a combined forecasting model using a modified pelican optimization algorithm, variational mode decomposition, and long short-term memory. To address issues in the current combination model, such as poor optimization and convergence performance, the pelican optimization algorithm is improved by incorporating tent map-based population initialization, Lévy flight strategy, and classification optimization concepts. Additionally, to obtain the optimal parameter combination, the modified pelican optimization algorithm is used to optimize the parameters of variational mode decomposition and long short-term memory, further enhancing the model’s predictive accuracy and stability. Wind speed data from a wind farm in China are used for prediction, and the proposed combined model is evaluated using six indicators. Compared to the best model among all compared models, the proposed model shows a 10.05% decrease in MAE, 4.62% decrease in RMSE, 17.43% decrease in MAPE, and a 0.22% increase in R2. The results demonstrate that the proposed model has better accuracy and stability, making it effective for wind speed prediction in wind farms.
Optimizing UAV Spraying for Sustainability: Different System Spray Drift Control and Adjuvant Performance
Authors: Semenišin Michail , Steponavičius Dainius , Kemzūraitė Aurelija , Savickas Dainius
Published: February 2025

Agricultural spraying, despite modern technological advances, still poses the problem of downwind spray drift, which contributes to environmental contamination and ecological imbalance, which are critical sustainability concerns. This study investigated the effect of lateral wind on different unmanned aerial vehicle (UAV) spraying systems under semi-controlled conditions, additionally evaluating the impact of four tank-mix adjuvants (drift reduction agents (DRAs)) at varying concentrations on spray effectiveness, droplet size, and deposition compared to water as a control. By examining UAV-specific spray dynamics, this research provides insights into sustainable drift reduction strategies that minimize environmental impacts. For the UAV spraying performance trials, three UAVs with different spraying configurations were tested, TTA M6E, XAG XP2020, and DJI T30, to identify the most effective system for minimizing downwind spray drift. For the DRA effectiveness trials, four commercially available adjuvants were evaluated at different concentrations utilizing the T30 UAV, which was chosen because it produces the highest proportion of fine droplets. The DRA products included an ionic/non-ionic surfactant (DRA No. 1), silicone-based wetting agents (DRA Nos. 2 and 3), and a silicone-based spreader-adhesive (DRA No. 4). This study showed that, among the tested UAV spray systems, M6E and XP2020 performed better in low-wind conditions, while T30 was more suitable for stable target area deposition in windy conditions but produced higher quantities of fine droplets prone to drifting further. Lateral wind contributes significantly to spray drift, as shown by the results, with increased wind speed causing an additional drift of up to 2 m downwind for all systems. The study also showed that all the tested DRAs exhibit the potential to mitigate drift and improve crop coverage, contributing to more efficient resource use and reduced environmental impacts. All the DRA products either reduce the drift distance by up to 3 m or decrease the deposition by up to 67% compared to water. However, DRA No. 1 showed the best results out of all the tested products in terms of drift control, while DRA No. 4 showed the best target area coverage and adequate drift control capabilities. More field research is required to validate the effectiveness in real-life application scenarios. In summary, the following management measures can be used to control droplet drift using UAV spraying systems, in order of importance: selecting a UAV and nozzles that are optimal for the specific requirements of the spraying task, planning applications in correlation with lateral wind speed, and the use of DRAs.
Coalescing the Chaos for Catchment Connections: A Framework Inspired by Wānaka for Catalyzing Community Action for One Health
Authors: Bell Amanda , Gregorini Pablo , Kane Prue , Youngman Ben , Gordon Iain J.
Published: February 2025

Across the globe, ecosystems are degraded and humanity is impacting the biosphere amongst multiple domains, exceeding sustainability boundaries, in, for example, biodiversity loss and air pollution. To address this issue, people are calling for a socio-ecological systems approach. The objective of this paper is to demonstrate how we used our experience to develop a catchment approach to planning and on-ground intervention, using the Upper Clutha catchment in the South Island of Aotearoa/New Zealand to demonstrate and report the importance of collaboration in achieving coordinated catchment management. This experience can be represented by the culmination of multiple workstreams that make up the Wānaka Way Framework—a prototype in action of how we work as a community to pursue One Health. Here, we demonstrate that this is achieved through trusted relationships, the co-design of tools, and knowledge creation and sharing. We highlight how the learnings from this catchment management approach can be applied more broadly.
Risk Factors for Recurrence in Patients Surgically Treated for Chronic Subdural Hematomas: A Single Institutional Experience
Authors: Lampros Marios , Katsiou Ilektra-Theodora , Kafritsas Georgios , Metaxas Dimitrios , Zagorianakou Panagiota , Zigouris Andreas , Skiada Dionysoula , Michos Euaggelos , Pachatouridis Dimitrios , Alexiou George A. , Voulgaris Spyridon
Published: February 2025

Background: Chronic subdural hematoma (CSDH) is a common neurosurgical condition that usually occurs in the elderly. Surgical evacuation of the hematoma with burr holes is considered the standard of care for the treatment of patients with CSDH. However, a high risk of recurrence, up to 34%, after hematoma evacuation has been reported, while the risk factors linked with recurrence have not been studied in depth. In the present study, we set out to identify risk factors of recurrence in patients surgically treated for CSDH. Methods: We retrospectively studied patients with symptomatic CSDH who were treated surgically in our institute during a five-year period. All the patients were treated by single or double burr hole drainage. The data for this study were extracted by the pre-operative and post-operative CT scan of each patient. These data include the sex, the midline shift, the width, and the presence of acute clots and subdural air collection post-operatively. A univariate analysis was performed. A receiver operating characteristic (ROC) analysis was utilized to detect the hematoma’s width more efficiently, differentiating patients that developed recurrence. Results: A total of 222 patients were included in this study. Recurrence occurred in 20 (9.0%) patients. The univariate analysis showed that the presence of acute clots post-operatively are significant factors related with recurrence (OR = 4.01, CI 95% 1.55–10.33, and p = 0.002). There was no significant relationship between the recurrence rate and the hematoma’s width, sex, the midline shifts pre-operatively or post-operatively, the subdural space, and air collection post-operatively. Conclusions: The presence of acute clots in the post-operative CT scan is a potential, previously unexplored, risk factor associated with the recurrence of CSDH.
Dynamic Optimization of Tunnel Construction Scheduling in a Reverse Construction Scenario
Authors: Wei Jianying , Liu Yuming , Lu Xiaochun , Zhao Rong , Wang Gan
Published: February 2025

Tunnel construction, a critical aspect of railway engineering, is a repetitive process with distinct linear characteristics. While the Linear Scheduling Method (LSM) is widely used for scheduling optimization in linear projects, it struggles to accommodate dynamic construction sequences, reverse construction, and flexible team allocation. Minimizing the project duration is a primary objective in tunnel construction scheduling optimization. To optimize tunnel construction, we propose a duration-shortening method using additional working surfaces, adaptable to multi-segment and multi-team scenarios. A dynamic optimization model is developed for tunnel construction scheduling, integrating LSM, soft logic, Work Breakdown Structure (WBS), and Resource Breakdown Structure (RBS) within a dynamic scheduling framework. This model analyzes logical relationships, work continuity, temporal and spatial constraints, and resource variation, focusing on reverse construction. The Mixed-Integer Programming (MIP) approach is used to build the mathematical model, solved with both exact algorithms and Genetic Algorithms (GA), and implemented in Python 3.12.7. Both algorithms perform well, with the GA excelling at handling complex constraints. Case studies confirm the method’s effectiveness in optimizing durations, devising flexible schedules, and improving efficiency and practicality. This research provides both theoretical insights and practical guidance for tunnel construction scheduling optimization in railway engineering.
A Novel Phantom for Standardized Microcalcification Detection Developed Using a Crystalline Growth System
Authors: Wu Dee H. , Preskitt Caroline , Stratemeier Natalie , Lau Hunter , Ponnam Sreeja , Koya Supriya
Published: February 2025

Background/Objectives: The accurate detection of microcalcifications in mammograms is critical for the early detection of breast cancer. However, the variability between different manufacturers is significant, particularly with digital breast tomosynthesis (DBT). Manufacturers have many design differences, including sweep angles, detector types, reconstruction techniques, filters, and focal spot construction. This study outlined the development of an innovative phantom model using crystallizations to improve the accuracy of imaging microcalcifications in DBT. The goal of these models was to achieve consistent evaluations, thereby reducing the variability between different scanners. Methods: We created a novel phantom model that simulates different types of breast tissue densities with calcifications. Furthermore, these crystalline-grown phantoms can more accurately represent the physiological shapes and compositions of microcalcifications than do other available phantoms for calcifications and can be evaluated on different systems. Microcalcification patterns were generated using the evaporation of sodium chloride, transplantation of calcium carbonate crystals, and/or injection of hydroxyapatite. These patterns were embedded in multiple layers within the wax to simulate various depths and distributions of calcifications with the ability to generate a large variety of patterns. Results: The tomosynthesis imaging revealed phantoms that utilized calcium carbonate crystals showed demonstrable visualization differences between the 3D DBT reconstructions and the magnification/2D view, illustrating the model’s value. The phantom was able to highlight changes in the contrast and resolution, which is crucial for accurate microcalcification evaluation. Conclusions: Based on the crystalline growth, this phantom model offers an important new standardized target for evaluating DBT systems. By promoting standardization, especially through the development of advanced breast calcification phantoms, this work and design aimed to contribute to improving earlier and more accurate breast cancer detection.
Deep Learning for Ultrasonographic Assessment of Temporomandibular Joint Morphology
Authors: Lasek Julia , Nurzynska Karolina , Piórkowski Adam , Strzelecki Michał , Obuchowicz Rafał
Published: February 2025

Background: Temporomandibular joint (TMJ) disorders are a significant cause of orofacial pain. Artificial intelligence (AI) has been successfully applied to other imaging modalities but remains underexplored in ultrasonographic evaluations of TMJ. Objective: This study aimed to develop and validate an AI-driven method for the automatic and reproducible measurement of TMJ space width from ultrasonographic images. Methods: A total of 142 TMJ ultrasonographic images were segmented into three anatomical components: the mandibular condyle, joint space, and glenoid fossa. State-of-the-art architectures were tested, and the best-performing 2D Residual U-Net was trained and validated against expert annotations. The algorithm for joint space width measurement based on TMJ segmentation was proposed, calculating the vertical distance between the superior-most point of the mandibular condyle and its corresponding point on the glenoid fossa. Results: The segmentation model achieved high performance for the mandibular condyle (Dice: 0.91 ± 0.08) and joint space (Dice: 0.86 ± 0.09), with notably lower performance for the glenoid fossa (Dice: 0.60 ± 0.24), highlighting variability due to its complex geometry. The TMJ space width measurement algorithm demonstrated minimal bias, with a mean difference of 0.08 mm and a mean absolute error of 0.18 mm compared to reference measurements. Conclusions: The model exhibited potential as a reliable tool for clinical use, demonstrating accuracy in TMJ ultrasonographic analysis. This study underscores the ability of AI-driven segmentation and measurement algorithms to bridge existing gaps in ultrasonographic imaging and lays the foundation for broader clinical applications.
Exposure to Nanoplastics During Pregnancy Induces Brown Adipose Tissue Whitening in Male Offspring
Authors: Shen Zhaoping , Tian Kai , Tang Jiayi , Wang Lin , Zhang Fangsicheng , Yang Lingjuan , Ge Yufei , Jiang Mengna , Zhao Xinyuan , Yang Jinxian , Chen Guangdi , Wang Xiaoke
Published: February 2025

Background: Polystyrene nanoplastics (PSNPs) have been recognized as emerging environmental pollutants with potential health impacts, particularly on metabolic disorders. However, the mechanism by which gestational exposure to PSNPs induces obesity in offspring remains unclear. This study, focused on the whitening of brown adipose tissue (BAT), aims to elucidate the fundamental mechanisms by which prenatal exposure to PSNPs promotes obesity development in mouse offspring. Methods and Results: Pregnant dams were subjected to various doses of PSNPs (0 µg/µL, 0.5 µg/µL, and 1 µg/µL), and their offspring were analyzed for alterations in body weight, adipose tissue morphology, thermogenesis, adipogenesis, and lipophagy. The findings revealed a notable reduction in birth weight and an increase in white adipocyte size in adult offspring mice. Notably, adult male mice exhibited BAT whitening, correlated with a negative dose-dependent downregulation of UCP1 expression, indicating thermogenesis dysfunction. Further investigation revealed augmented lipogenesis evidenced by the upregulation of FASN, SREBP-1c, CD36, and DGAT2 expression, coupled with the inhibition of lipophagy, indicated by elevated levels of mTOR, AKT, and p62 proteins and reduced levels of LC3II/LCI and Lamp2 proteins in male offspring. Conclusions: These findings indicate that gestational PSNP exposure plays a role in the development of obesity in offspring through the whitening of brown adipose tissue, which is triggered by lipogenesis and lipophagy inhibition, providing a novel insight into the metabolic risks associated with gestational PSNPs exposure.
Challenges in Elucidating HIV-1 Genetic Diversity in the Middle East and North Africa: A Review Based on a Systematic Search
Authors: Sallam Malik , Al-Khatib Arwa Omar , Sabra Tarneem , Al-Baidhani Saja , Al-Mahzoum Kholoud , Aleigailly Maryam A. , Sallam Mohammed
Published: February 2025

The extensive genetic diversity of HIV-1 represents a major challenge to public health interventions, treatment, and successful vaccine design. This challenge is particularly pronounced in the Middle East and North Africa (MENA) region, where limited data among other barriers preclude the accurate characterization of HIV-1 genetic diversity. The objective of this review was to analyze studies conducted in the MENA region to delineate possible barriers that would hinder the accurate depiction of HIV-1 genetic diversity in this region. A systematic search of PubMed/MEDLINE and Google Scholar was conducted for published records on HIV-1 genetic diversity in the English language up until 1 October 2024 across 18 MENA countries. The pre-defined themes of challenges/barriers included limited sampling, data gaps, resource and infrastructure constraints, HIV-1-specific factors, and socio-cultural barriers. A total of 38 records were included in the final review, comprising original articles (55.3%), reviews (21.1%), and sequence notes (10.5%). Libya (15.8%), Morocco (13.2%), Saudi Arabia, and MENA as a whole (10.5% for each) were the primary sources of the included records. Of the 23 records with original MENA HIV-1 sequences, the median number of sequences was 46 (range: 6–193). The identified barriers included the following: (1) low sampling density; (2) limited clinical data (21.7% with no data, 60.9% partial data, and 17.4% with full data); (3) reliance solely on population sequencing and insufficient use of advanced sequencing technologies; (4) lack of comprehensive recombination analysis; and (5) socio-cultural barriers, including stigma with subsequent under-reporting among at-risk groups. The barriers identified in this review can hinder the ability to map the genetic diversity of HIV-1 in the MENA. Poor characterization of HIV-1’s genetic diversity in the MENA would hinder efforts to optimize prevention strategies, monitor drug resistance, and develop MENA-specific treatment protocols. To overcome these challenges, investment in public health/research infrastructure, policy reforms to reduce stigma, and strengthened regional collaboration are recommended.
Multivalent Exosome Based Protein Vaccine: A “Mix and Match” Approach to Epidemic Viruses’ Challenges
Authors: Cacciottolo Mafalda , Hsieh Li-En , Li Yujia , LeClaire Michael J. , Mora Ciana L. , Lau Christy , Dwyer Charles , Elliott Kristi , Sun Minghao
Published: February 2025

Background: Endemic viruses are becoming increasingly the norm, and the development of a rapid and effective vaccine is necessary. Methods: Here, we used our StealthXTM exosome platform to express either Influenza H3 (StealthXTM-Hemagglutinin, STX-H3), SARS-CoV-2 Delta spike (StealthXTM-Spike, STX-S) or respiratory syncytial virus proteins (StealthXTM-RSV fusion protein, STX-RSV) protein on the membrane surface and facilitate their trafficking to the exosomes. Results: The administration of exosomes carrying one of the antigens by themselves resulted in a strong immune response with the production of a potent humoral and cellular immune response in mice. Interestingly, these effects were obtained with the administration of nanograms of protein and without adjuvant. We tested the possibility of manufacturing a multivalent vaccine by combining exosomes expressing either STX-H3, STX-RSV or STX-S exosomes in the same formulation, in a “mix and match” approach. Mice immunized with the cocktail vaccine showed an increased immune response against all three antigens received. Conclusions: The results further demonstrated that our STX trivalent cocktail vaccine elicited a strong immune response, and the magnitude of the responses was comparable to the single antigen administered individually. Our data show that our exosome platform has enormous potential to revolutionize vaccinology by rapidly facilitating antigen presentation, to tackle the fast-evolving viral infections.
Recent Advances in the Etiology, Diagnosis, and Treatment of Marmoset Wasting Syndrome
Authors: Bakker Jaco , Nederlof Remco A. , Stumpel Job , de la Garza Melissa A.
Published: February 2025

One of the primary challenges encountered in managing captive callitrichids is a group of gastrointestinal disorders that are collectively known as Marmoset Wasting Syndrome (MWS). Unfortunately, MWS is still not well understood. It is suggested that MWS has a multifactorial etiology; stress, infection, malnutrition, malabsorption, and enteritis or colitis may be associated with MWS. Usually, animals demonstrate more than one of the following clinical signs: progressive weight loss, alopecia, chronic diarrhea, muscle atrophy, hypoproteinemia, and anemia. Recent advances in diagnostics, mostly noninvasive fecal and urine tests, yielded promising results. Good treatment results have been achieved with the administration of glucocorticoids and tranexamic acid, although no cure has been found yet. Probiotics and gel diets also demonstrated to be potentially useful. However, once animals are affected by MWS, treatments eventually become ineffective, and animals will inevitably require euthanasia to prevent further suffering. Preventative measures should be aimed at reducing stress and optimizing housing, husbandry, and diets. More research is warranted to elucidate the pathogenesis of MWS in captive callitrichids and to refine diagnostic and treatment methods.
Beyond Safety: Barriers to Shared Autonomous Vehicle Utilization in the Post-Adoption Phase—Evidence from Norway
Authors: Wu Sinuo , Falk Kristin , Myklebust Thor
Published: February 2025

The usage rates of shared autonomous vehicles (SAVs) have become a pressing concern following their increased deployment. While prior research has focused on initial user acceptance, post-adoption behavior remains underexplored. As SAV deployment matures, public concerns have expanded beyond safety to encompass service requirements, challenging the relevance of earlier findings to current commercialization efforts. This study investigates the factors shaping SAV utilization through an empirical study in Norway, where autonomous buses have operated for several years. Through mixed methods, we first analyzed responses from 106 participants to 43 SAV users and 63 witnesses of SAV operations. The results revealed that concerns had shifted from technological anxiety to service-related factors. Through purposive interviews with individuals who showed acceptance of SAVs but did not adopt them as their primary mode of transportation, we explored the gap between high acceptance and low usage. Our findings provide insights into long-term SAV deployment and guidelines for improving usage rates, highlighting the importance of addressing service characteristics such as information transparency, vehicle appearance, speed, and convenience, rather than focusing solely on safety in commercial settings.