Search Results (29,355)

Vascular diseases remain a major global health burden despite remarkable technological advances in vascular surgery and endovascular therapies. Conditions such as peripheral arterial disease, abdominal aortic aneurysm, carotid stenosis, chronic venous disease, diabetic vasculopathies, and vascular chronic ulcers are not only biological entities but are deeply shaped by social structures, cultural norms, and economic inequalities. This article introduces Vascular Sociology as an interdisciplinary field that integrates vascular surgery with medical sociology to provide a more comprehensive understanding of vascular health and disease. Drawing on classical and contemporary sociological theory, including concepts such as social determinants of health, embodiment, illness narratives, and the disease–illness–sickness triad, the article argues that vascular pathology reflects cumulative social exposures across the life course. Socially patterned behaviors, work conditions, food environments, healthcare access, gender norms, and geographic inequalities profoundly influence disease onset, progression, treatment decisions, and outcomes. The paper highlights how surgical success is contingent not only on technical excellence but also on patients’ social contexts, including health literacy, trust in institutions, caregiving resources, and the capacity to adhere to long-term follow-up and rehabilitation. By outlining conceptual foundations, epidemiological evidence, and mixed-methods research strategies, the article positions Vascular Sociology as a framework capable of bridging biomedical knowledge with lived experience. This approach expands the definition of vascular outcomes to include social reintegration, identity transformation, and equity of care, ultimately aiming to improve patient-centered practice, reduce disparities, and inform more socially responsive vascular health policies. Full article

Fenbendazole is an important anti-parasitic medicine widely used in the veterinary field and has recently been considered as a possible anti-cancer agent in humans by some researchers. Fenbendazole encounters challenges in its usage due to its limited aqueous solubility, which consequently impacts its therapeutic efficacy. In this work, an in vitro mechanistic investigation was conducted to evaluate the compatibility, amorphization behaviour and dissolution profile of fenbendazole dispersed in Soluplus^® using the solid dispersion approach via hot-melt extrusion. Three different fenbendazole/Soluplus^® ratios were formulated and characterised through systematic experimentation. Powder X-Ray Diffraction (PXRD), Differential Scanning Calorimetry (DSC), Scanning Electron Microscopy (SEM), Energy Dispersive X-Ray (EDX) and Fourier Transform Infrared Spectroscopy (FTIR) were employed for thermal, physical, chemical and morphological analyses. The solubility of the drug formulation during a dissolution test was investigated using Ultraviolet–Visible (UV–Vis) spectrophotometric measurements. In vitro dissolution testing in acidic and neutral media was employed as a controlled environment to compare dissolution behaviour among different loadings. The extrudates demonstrated markedly enhanced apparent solubility compared to neat fenbendazole, with the 5% formulation showing the highest dissolution rate (approximately 85% after 48 h). This improvement can be attributed to better wetting properties and drug dispersion within the Soluplus^® matrix. This innovative strategy holds promise in surmounting fenbendazole’s solubility limitations, presenting a comprehensive solution to enhance its therapeutic effectiveness. Full article

Validation of ICON model configurations optimized over a limited domain is essential before accepting new semi-empirical parameters that influence the behavior of subgrid-scale schemes. Because such parameters can modify the dynamics of a numerical weather prediction (NWP) model in highly nonlinear ways, we analyze one season of forecasts (December 2022, January and February 2023) generated with the NWP ICON-LAM through the lens of machine learning–based diagnostics as a complement to traditional evaluation metrics. The goal is to extract physically interpretable information on the model behavior induced by the optimized parameters. This work represents the first part of a wider study exploring machine learning tools for model validation, focusing on two specific approaches: Empirical Orthogonal Functions (EOFs), which are widely used in meteorology and climate science, and autoencoders, which are increasingly adopted for their nonlinear feature extraction capability. In this first part, EOF analysis is used as the primary tool to decompose weather fields from observed reanalysis and forecast datasets. Hourly 2-m temperature forecasts for winter 2022–2023 from multiple regional ICON configurations are compared against downscaled ERA5 data and in situ observations from ground station. EOF analyses revealed that the optimized configurations demonstrate a high skill in predicting surface temperature. From the signal error decomposition, the fourth EOF mode is effective particularly during night-time hours, and contributes to enhancing the performance of ICON. Analyses based on autoencoders will be presented in a companion paper (Part II). Full article

Examining pedestrian crossing violations at high-risk road midblock crossings has become essential, particularly in high-speed corridors, as a result of accidents at crossings resulting in fatalities. Hence, this article investigates such behavior in Alexandria, Egypt, as a credible case study in a developing country. According to our research methodology, a comprehensive dataset of over 2400 field-observed video recordings was used for real-life data collection. Machine learning (ML) models, such as CatBoost and gradient boosting (GB), were employed to predict crossing decisions. The models showed that risky behavior is strongly influenced by waiting time, crossing time, and the number of crossing attempts. The highest predictive performance was achieved by CatBoost and gradient boosting, indicating strong interpersonal influence within small groups engaging in unsafe road-crossing behavior. In the same context, the Shapley additive explanation (SHAP) values for these variables were 3, 2, and 0.60, respectively. Subsequently, based on SHAP sensitivity analysis, the results show that pedestrian crossing time (s) had the highest tendency to push the model towards class 1 (e.g., crossing illegally), while total time (s) and age group (40–60 Y) had a significant negative influence on model prediction converging to class 0 (e.g., crossing illegally). The results also showed that shorter exposure times increase the likelihood of crossing illegally. This research work is among the few studies that employ a behavior-based approach to understanding pedestrian behavior at midblock crossings. This study offers actionable insights and valuable information for urban designers and transportation planners when considering the design of midblock crossings. Full article

The accidental contamination of olives by mineral hydrocarbons, such as diesel, motor lubricants, and hydraulic fluids from agricultural machinery, has become a growing concern in the olive oil industry. In response, European regulatory bodies are working on establishing new standards to address this issue. This study explores the feasibility of using Metal Oxide Semiconductor (MOS) gas sensors as a non-invasive method for detecting such contaminants on freshly harvested olives across different maturity stages. By assessing the sensitivity and selectivity of MOS sensors, this research aims to identify hydrocarbons that may adhere to the olive surface during harvesting and processing. The study involves controlled laboratory contamination scenarios, with samples exposed to various hydrocarbons to evaluate the relative response of individual MOS sensors under reproducible conditions. Findings from this research may provide valuable insights into rapid and cost-effective detection systems, supporting quality control and regulatory compliance in olive oil production, and contributing to the safety and traceability of olive-derived products. As a feasibility study, the results provide a basis for future developments involving multivariate analysis, field-contaminated samples, and industrial implementation. Full article

The principal objective of this work is to develop an optimized procedure that guarantees the reproducibility of results across different applications and laboratories, facilitating potential field applications of methodologies for Structural Health Monitoring in aerospace structures. The focus is to accurately detect and localize impact areas on planar structures using in situ transducers and Machine Learning (ML) techniques. The research concentrates on an aluminum plate where impacts are generated by metal spheres of different masses dropped from a fixed height. The resulting Lamb waves are detected by PZT sensors glued on the surface. Various data processing and feature extraction algorithms are implemented and compared to extract the differences in Time of Flight (ΔToF). The obtained features are used for training ML classification models. Then, the influence of various parameters in signal acquisition and data processing are assessed along with the reproducibility of the results. For this reason, an interlaboratory comparison is conducted in which the trained models are applied to data collected under varying conditions. The experimental results show that the most influencing factors for impact area classification are the algorithm for ΔToF estimation, the number of training points used in ML models, the type of classification model, the distribution of the impact points on the component, and their balance in the classification area. This evidence suggests approaches for reducing both issues, therefore improving the reproducibility of results. Full article

Conventional object detectors represent each object by a deterministic bounding box, regressing its center and size from RGB images. However, such discrete parameterization ignores the inherent uncertainty in object appearance and geometric projection, which can be more naturally modeled as a probabilistic density field. Recent works have introduced Gaussian-based formulations that treat objects as distributions rather than boxes, yet they remain limited to 2D images or require late fusion between image and depth modalities. In this paper, we propose a unified Gaussian-based framework for direct 3D object detection from RGB-D inputs. Our method is built upon a vision transformer backbone to effectively capture global context. Instead of separately embedding RGB and depth features or refining depth within region proposals, our method takes a full four-channel RGB-D tensor and predicts the mean and covariance of a 3D Gaussian distribution for each object in a single forward pass. We extend a pretrained vision transformer to accept four-channel inputs by augmenting the patch embedding layer while preserving ImageNet-learned representations. This formulation allows the detector to represent both object location and geometric uncertainty in 3D space. By optimizing divergence metrics such as the Kullback–Leibler or Bhattacharyya distances between predicted and target distributions, the network learns a physically consistent probabilistic representation of objects. Experimental results on the SUN RGB-D benchmark demonstrate that our approach achieves competitive performance compared to state-of-the-art point-cloud-based methods while offering uncertainty-aware and geometrically interpretable 3D detections. Full article

The reliability of high-strength bolted connections is critical to the safety of large-scale engineering structures. This study proposes a non-contact quantitative method for detecting bolt loosening based on the self-magnetic flux leakage (SMFL) effect. Systematic experiments were carried out on M14-12.9 bolts, using nine independent specimens tested under six torque levels, to reveal the intrinsic relationship between bolt preload and the “magnetic valley” feature of the surface leakage field. For quantitative evaluation, the absolute value of the differential peak magnetic field, |ΔPMF|, is defined as the core feature parameter. The results show that, in the reference specimen group, |ΔPMF| exhibits a pronounced linear relationship with the applied torque (R² > 0.96), and the corresponding linear regression parameters display good consistency across the nine specimens (RSD ≈ 4%). Comparative tests on two additional bolt specifications clarify how bolt strength grade and geometric size influence the detection sensitivity and linearity. To address lift-off effects, measurements on a representative specimen at four lift-off heights were used to construct a simplified bivariate linear compensation model, which significantly reduces lift-off-induced bias within the working range h = 10–16 mm. Finally, a hierarchical diagnostic scheme for bolt loosening that incorporates lift-off compensation is established on the basis of |ΔPMF|, providing a feasible approach for rapid assessment of bolt loosening under complex service conditions. Full article

Recent reports have revealed that the number of lower limb amputees worldwide has increased as a result of war, accidents, and vascular diseases and that transfemoral amputation accounts for 39% of cases, highlighting the need to develop an improved functional prosthetic knee joint that improves the amputee’s ability to resume activities of daily living. To enable transfemoral prosthesis users to walk on level ground, accurate prediction of the intended knee joint angle is critical for transfemoral prosthesis control. Therefore, the purpose of this research was to develop a technique for estimating knee joint angle utilizing a long short-term memory (LSTM) network and kinematic data collected from inertial measurement units (IMUs). The proposed LSTM network was trained and tested to estimate the contralateral knee angle using data collected from twenty able-bodied subjects using a lab-developed sensory gadget, which included four IMUs. Accordingly, the present work represents a feasibility investigation conducted on able-bodied individuals rather than a clinical validation for amputee gait. This study contributes to the field of bionics by mimicking the natural biomechanical behavior of the human knee joint during gait cycle to improve the control of artificial prosthetic knees. The proposed LSTM model learns the contralateral knee’s motion patterns in able-bodied gait and demonstrates the potential for future application in prosthesis control, although direct generalization to amputee users is outside the scope of this preliminary study. The contralateral LSTM models exhibited a real-time RMSE range of 2.48–2.78° and a correlation coefficient range of 0.9937–0.9991. This study proves the effectiveness of LSTM networks in estimating contralateral knee joint angles and shows their real-time performance and robustness, supporting its feasibility while acknowledging that further testing with amputee participants is required. Full article

The advent of electric propulsion for new aircraft designs necessitates the optimization of propeller aerodynamic performance and the reduction of acoustic signatures. Variable-pitch propellers present a promising solution, offering the flexibility to adjust blade angles in response to different flight conditions. The study investigates the performance of blade pitch configurations tailored to specific flight conditions. Rather than a dynamic pitch change, the research evaluates discrete pitch settings coupled with corresponding advance ratios to identify optimal operating points. Findings show that increasing collective pitch in response to a higher advance ratio (forward flight) successfully maintains aerodynamic efficiency and thrust, with an expected increase in torque. While this adjustment leads to an anticipated rise in noise due to higher aerodynamic loading, results reveal that a collective pitch increment of $+5°$ actively suppresses broadband noise at frequencies above 2 kHz. Analysis of the flow field and surface pressure fluctuations indicates this suppression is directly attributed to the mitigation of outboard propeller stall. Ultimately, this work demonstrates the feasibility of using collective pitch adjustments not only to enhance flight performance but also to actively control and suppress components of the propeller noise signature, such as the broadband noise. Full article

Hair dyes are widely used across all socioeconomic groups and regions worldwide. However, some studies indicate that these products contain substances known to be toxic to a wide variety of organisms. Moreover, dyeing practices generate effluents that may carry the toxicity of hair dyes into the environment. Due to these facts, there is great concern about the impacts these products may have on the environment, as well as on the health of their users and professionals in the field of cosmetology. This scoping review analyzed 184 publications from major databases (PubMed, SciELO, Scopus, Google Scholar, and MEDLINE). Ultimately, 126 scientific studies published between 1981 and 2024 were included based on methodological rigor and their relevance to the One Health framework. According to the literature, the components of hair dyes can induce adverse responses in biological systems, ranging from reversible topical irritations to severe systemic effects. Among the studies evaluated, more than half reported significant toxicological or genotoxic associations related to oxidative dye components such as p-phenylenediamine and its derivatives. These compounds are frequently associated with various types of human cancers, including breast, prostate, bladder, skin, ocular cancers, and brain tumors. In addition to their effects on humans, hair dyes exhibit ecotoxicity, which may threaten the maintenance of ecosystems exposed to their residues. The reported environmental impacts result from effluent emissions after successive hair washes that release unreacted dye residues. Due to the low biodegradability of these compounds, conventional wastewater treatment methods are often ineffective, leading to environmental accumulation and changes in aquatic ecosystems, soil fertility, and trophic balance. Data on the toxicity of hair dye effluents remain scarce and sometimes contradictory, particularly regarding the effects of their transformation products and metabolites. Overall, the evidence underscores the need for continuous monitoring, updated risk assessments, and the adoption of advanced treatment technologies specific to beauty salon effluents. The information presented in this work may support further studies and guide public management agencies in developing policies for mitigating the impacts of hair dye pollutants within the One Health perspective. Full article

The grid-forming static var generator (SVG) is a key device that supports the stable operation of power grids with a high penetration of renewable energy. The cooling efficiency of its forced water-cooling system directly determines the reliability of the entire unit. However, existing wired monitoring methods suffer from complex cabling and limited capacity to provide a full perception of the water-cooling condition. To address these limitations, this study develops a wireless monitoring system based on multi-source information fusion for real-time evaluation of cooling efficiency and early fault warning. A heterogeneous wireless sensor network was designed and implemented by deploying liquid-level, vibration, sound, and infrared sensors at critical locations of the SVG water-cooling system. These nodes work collaboratively to collect multi-physical field data—thermal, acoustic, vibrational, and visual information—in an integrated manner. The system adopts a hybrid Wireless Fidelity/Bluetooth (Wi-Fi/Bluetooth) networking scheme with electromagnetic interference-resistant design to ensure reliable data transmission in the complex environment of converter valve halls. To achieve precise and robust diagnosis, a three-layer hierarchical weighted fusion framework was established, consisting of individual sensor feature extraction and preliminary analysis, feature-level weighted fusion, and final fault classification. Experimental validation indicates that the proposed system achieves highly reliable data transmission with a packet loss rate below 1.5%. Compared with single-sensor monitoring, the multi-source fusion approach improves the diagnostic accuracy for pump bearing wear, pipeline micro-leakage, and radiator blockage to 98.2% and effectively distinguishes fault causes and degradation tendencies of cooling efficiency. Overall, the developed wireless monitoring system overcomes the limitations of traditional wired approaches and, by leveraging multi-source fusion technology, enables a comprehensive assessment of cooling efficiency and intelligent fault diagnosis. This advancement significantly enhances the precision and reliability of SVG operation and maintenance, providing an effective solution to ensure the safe and stable operation of both grid-forming SVG units and the broader power grid. Full article

Unmanned aerial vehicles open new possibilities for developing technologies that support more sustainable and efficient agriculture. This paper presents a non-invasive method for repelling rodents from crop fields using ultrasound. The proposed system is implemented as a spherical-cap ultrasound loudspeaker array consisting of eight transducers, mounted on a drone that overflies the field while emitting sound in the 20–70 kHz range. The hardware design includes both the loudspeaker array and a custom printed circuit board hosting power amplifiers and a signal generator tailored to drive multiple ultrasonic transducers. In parallel, a genetic algorithm is used to compute flight paths that maximize coverage and increase the probability of driving rodents away from the protected area. As part of the validation phase, artificial intelligence models for rodent detection using a thermal camera are developed to provide quantitative feedback on system performance. The complete prototype is evaluated through a series of experiments conducted both in controlled laboratory conditions and in the field. Field trials highlight which parts of the concept are already effective and identify open challenges that need to be addressed in future work to move from a research prototype toward a deployable product. Full article

Solar-aided power generation offers a pathway to reduce the carbon dioxide emissions from existing coal-fired plants. This study addresses the gap in comparing different solar integration modes by conducting a thermo-economic analysis of a 600 MW coal-fired system retrofitted with a solar tower. Four integration modes were designed and rigorously compared, encompassing series and parallel configurations at either the high-exergy reheater or the lower-exergy economizer. A detailed thermodynamic model was developed to simulate its off-design and annual performance. The results showed that integration at the primary reheater outperformed the economizer integration. Specifically, the parallel configuration at the primary reheater (Mode II) achieved the highest annual solar-to-electricity efficiency of 18.43% at a thermodynamically optimal heliostat field area of 125,025.6 m². Economic analysis revealed a trade-off, with the minimum levelized cost of energy (LCOE) of −0.00929 USD/kWh for Mode II occurring at the economically optimal area of 321,494 m² due to greater coal and emission savings. Sensitivity analysis across two other locations confirmed that the annual solar-to-electricity efficiency and LCOE are directly influenced by solar resource quality, but the thermodynamically optimal and economically optimal heliostat field area remain consistent. This work demonstrates that parallel integration with the primary reheater presents a favorable and practical configuration, balancing high solar-to-electricity conversion efficiency with favorable economics for hybrid solar–coal power plants. Full article

FLASH radiotherapy requires real-time, non-invasive beam monitoring systems capable of operating under ultra-high dose rate (UHDR) conditions without perturbing the therapeutic beam. In this work, we characterized the extraction system of Supersonic Gas Curtain-based Ionization Profile Monitor (SGC-IPM) for its capabilities as a transverse beam profile and position monitor for FLASH protons. The monitor utilizes a tilted gas curtain intersected by the incident beam, leading to the generation of ions that are extracted through a tailored electrostatic field, and detected using a two stage microchannel plate (MCP) coupled to a phosphor screen and CMOS camera. CST Studio Suite was employed to conduct electrostatic and particle tracking simulations evaluating the ability of the extraction system to measure both beam profile and position. The ion interface, at the interaction region of proton beam and gas curtain, was modeled with realistic proton beam parameters and uniform gas curtain density distributions. The ion trajectory was tracked to evaluate the performance across multiple beam sizes. The simulations suggest that the extraction system can reconstruct transverse beam profiles for different proton beam sizes. Simulations also supported the system’s capability as a beam position monitor within the boundary defined by the beam size, the dimensions of the extraction system, and the height of the gas curtain. Some simulation results were benchmarked against experimental data of 28 MeV proton beam with 70 nA average beam current. This study will further help to optimize the design of the extraction system to facilitate the integration of SGC-IPM in medical accelerators. Full article