Search Results (621)

This work presents a high-fidelity simulation of the Peach Bottom turbine trip (PBTT) benchmark using the Virtual Environment for Reactor Applications (VERA), a multiphysics reactor modeling tool developed by the U.S. Department of Energy’s Consortium for Advanced Simulation of Light Water Reactors energy innovation hub. The PBTT benchmark, based on a 1977 transient event at the end of cycle 2 in a General Electric Type-4 boiling water reactor (BWR), is a critical test case for validating core physics models with thermal feedback during rapid reactivity events. VERA was employed to perform end-to-end, pin-resolved simulations from conditions at the beginning of cycle 1 through the turbine-trip transient, incorporating detailed neutron transport, fuel depletion, and subchannel thermal hydraulics. The simulation reproduced key benchmark observables with high accuracy: the peak power excursion occurred at 0.75 s, matching the scram time and closely aligning with the benchmark average of 0.742 s; the simulated maximum power spike was approximately 7600 MW, which is within 3% of the benchmark average of 7400 MW; and void-collapse dynamics were consistent with benchmark expectations. Reactivity predictions during cycles 1 and 2 remained within 1500 pcm and 400 pcm of criticality, respectively. These results confirm VERA’s ability to model complex coupled neutronic and thermal hydraulic behavior in a BWR turbine-trip transient, which will support its use in future studies of modeling dryout, fuel performance, and uncertainty quantification for transients of this type. Full article

Civil aviation safety remains a critical concern globally, with continuous efforts aimed at reducing accidents and fatalities. This paper focuses on the comprehensive evaluation of civil aviation safety in the Slovak Republic over the past several years, with the main objective of identifying prevailing trends and key risk factors. A comprehensive analysis of 155 accidents and incidents was conducted based on selected operational parameters. Logistic regression was applied to identify potential causal factors influencing various levels of injury severity in aviation accidents. Moreover, the prediction model can also be used to predict the probability of specific injury severity for accidents with given parameter values. The results indicate a clear declining trend in the annual number of aviation safety events; however, the fatality rate has stagnated or slightly increased in recent years. Human error, particularly mistakes and intentional violations of procedures, was identified as the dominant causal factor across all sectors of civil aviation, including flight operations, airport management, maintenance, and air navigation services. Despite technological advancements and regulatory improvements, human-related failures persist as a major safety challenge. The findings highlight the critical need for targeted strategies to mitigate human error and enhance overall aviation safety in the Slovak Republic. Full article

Glucose-6-phosphate dehydrogenase (G6PD) deficiency, the most common enzymatic disorder, affects over 500 million people worldwide and is often linked to exercise intolerance due to oxidative stress, but its true impact on physical performance remains unclear. This study aimed to evaluate the physiological and metabolic effects of G6PD deficiency on endurance capacity. Using humanized mice carrying the African G6PD variant [V68M; N126D] (hG6PD_A−), we show that despite reduced pentose phosphate pathway activity, these mice exhibit a 10.8% increase in treadmill critical speed (CS)—suggesting enhanced endurance capacity. Multi-omics profiling across red blood cells, plasma, skeletal muscle, spleen, kidney, and liver reveals metabolic adaptations, including elevated glycolysis, fatty acid oxidation, and increased mitochondrial activity, alongside heightened oxidative phosphorylation in muscle and accelerated red blood cell turnover in the spleen and liver. These findings indicate that systemic metabolic reprogramming may offset antioxidant deficiencies, potentially conferring a performance advantage. Given that G6PD deficiency affects up to 13% of African Americans and is associated with cardiovascular health disparities, our results challenge conventional exercise restrictions and highlight the need for personalized exercise guidelines for affected individuals. Full article

Over the past 50 years, coastal erosion has become an increasingly critical issue worldwide, and Colombia’s Caribbean coast is no exception. In urban areas, this challenge is further complicated by hard protection structures, which, while often implemented as immediate solutions, can disrupt sediment transport and trigger unintended long-term consequences. This study examines shoreline changes in Riohacha, the capital of La Guajira Department, over a 35-year period (1987–2022), focusing on the impacts of coastal protection structures—specifically, the construction of seven groins and a seawall between 2006 and 2009—on coastal dynamics. Using twelve images (photographs and satellite) and the Digital Shoreline Analysis System (DSAS), the evolution of both beaches and cliffs has been analyzed. The results reveal a dramatic shift in shoreline behavior: erosion rates of approximately 0.5 m/year prior to the interventions transitioned to accretion rates of up to 11 m/year within the groin field, where rapid infill occurred. However, this sediment retention has exacerbated erosion in downstream cliff areas, with retreat rates reaching 1.8 ± 0.2 m/year. To anticipate future coastal evolution, predictive models were applied through 2045, providing insights into potential risks for infrastructure and urban development. These findings highlight the need for a strategic, long-term approach to coastal management that considers both the benefits and unintended consequences of engineering interventions. Full article

Traffic in large cities is increasing due to continuous urbanization, the construction of new housing complexes and the accompanying new street network. The growth of cities creates prerequisites for increasing the intensity of transport, pedestrian, and bicycle flows, especially during peak periods. To improve the conditions in which traffic flows, it is necessary to introduce an effective method for reducing delays that arise at intersections, especially those regulated by traffic light systems. One of the possible approaches to this is to coordinate the operation of traffic light systems. The main thing in this is to determine relatively accurate times for the movement of individual flows, for which adequate traffic models are needed. This article presents a model of the movement of transport flows when starting from the first intersection in a coordinated mode of operation of traffic light systems. This is of particular importance when determining the times of individual signals and, above all, has an impact on the moment for switching on the permitting signal at the next intersection. The presented model aims to provide an opportunity to determine accurate times of passage of vehicles through consecutive intersections that operate in a coordinated mode of traffic light systems. Full article

Tourist satisfaction models typically assume that service performance dimensions carry the same weight for all travelers. Drawing on Bourdieu, we reconceptualize age, gender, and region of origin as demographic capital, durable resources that mediate how visitors decode service cues. Using a SERVPERF-based survey of 407 international travelers departing Quito (Ecuador), we test measurement invariance across six sociodemographic strata with multi-group confirmatory factor analysis. The four-factor SERVPERF core (Access, Lodging, Extra-hotel Services, Attractions) holds, yet partial metric invariance emerges: specific loadings flex with demographic capital. Gen-Z travelers penalize transport reliability and safety; female visitors reward cleanliness and empathy; and Latin American guests are the most critical of basic organization. These patterns expose a boundary condition for universalistic satisfaction models and elevate demographic capital from a descriptive tag to a structuring construct. Managerially, we translate the findings into segment-sensitive levers, visible security for youth and regional markets, gender-responsive facility upgrades, and dual eco-luxury versus digital-detox bundles for long-haul segments. By demonstrating when and how SERVPERF fractures across sociodemographic lines, this study intervenes in three theoretical conversations: (1) capital-based readings of consumption, (2) the search for boundary conditions in service-quality measurement, and (3) the shift from segmentation to capital-sensitive interpretation in emerging markets. The results position Ecuador as a critical case and provide a template for destinations facing similar performance–perception mismatches in the Global South. Full article

Knowledge extraction and sharing is one of the biggest challenges organizations face to ensure successful and long-lasting knowledge repositories. The North Carolina Department of Transportation (NCDOT) commissioned a web-based knowledge management program called Communicate Lessons, Exchange Advice, Record (CLEAR) for end-users to promote employee-generated innovation and to institutionalize organizational knowledge. Reusing knowledge from an improperly managed database is problematic and potentially causes substantial financial loss and reduced productivity for an organization. Poorly managed databases can hinder effective knowledge dissemination across the organization. Data-driven dashboards offer a promising solution by facilitating evidence-driven decision-making through increased information access to disseminate, understand and interpret datasets. This paper describes an effort to create data visualizations in Tableau for CLEAR’s gatekeeper to monitor content within the knowledge repository. Through the three web-based strategic dashboards relating to lessons learned and best practices, innovation culture index, and website analytics, the information displays will aid in disseminating useful information to facilitate decision-making and execute appropriate time-critical interventions. Particular emphasis is placed on utility-related issues, as data from the NCDOT indicate that approximately 90% of projects involving utility claims experienced one or two such incidents. These claims contributed to an average increase in project costs of approximately 2.4% and schedule delays averaging 70 days. The data dashboards provide key insights into all 14 NCDOT divisions, supporting the gatekeeper in effectively managing the CLEAR program, especially relating to project performance, cost savings, and schedule improvements. The chronological analysis of the CLEAR program trends demonstrates sustained progress, validating the effectiveness of the dashboard framework. Ultimately, these data dashboards will promote organizational innovation in the long run by encouraging end-user participation in the CLEAR program. Full article

The exploration of the statistical characteristics and distribution patterns of workplace accidents can help to reveal the intrinsic features and general laws of safety issues, which is essential for forecasting and decision making in safe production. Here, we conduct the detailed analysis of the distribution characteristics between the fatality number and the frequency of workplace accidents based on the in-depth data mining of various industries. The results show that the distribution between the fatality number and the frequency of workplace accidents follows a power-law distribution. Moreover, the exponents of such power-law distributions in different industries exhibit significant industry dependence, with the characteristic values of the power-law exponents in the coal mining industry, the hazardous chemicals industry, the transportation industry, and the construction industry being 1.55, 2.16, 2.15, and 2.92, respectively. Meanwhile, the temporal variation in the power-law distribution exponent in each industry can be used for the short-term prediction and evaluation of safe production, which will inform the decision making of the safety management department. Last, but not the least, the results of this study provide the theoretical basis for Heinrich’s Law and confirm that a substantial reduction in the number of small-scale accidents can effectively help control the frequency of large-scale fatal accidents. Full article

As transportation infrastructure systems continue to expand, the demand for unmanned aerial vehicle (UAV) technologies in the assessment of urban infrastructure is expected to grow substantially, due to their strong potential for efficient data collection and post-processing. UAVs offer numerous advantages in infrastructure assessment, including enhanced time and cost efficiency, improved safety, and the ability to capture high-quality data. Furthermore, integrating various data-collecting sensors enhances the versatility of UAVs, enabling the acquisition of diverse data types to support comprehensive infrastructure evaluations. Numerous post-processing software applications utilizing various structure-from-motion (SfM) techniques have been developed, significantly facilitating the assessment process. However, researchers’ efforts to find the potentialities of this technology will be in vain if its applications are not utilized effectively in the practical field. Therefore, this study aims to determine the adaptation condition of UAV technologies in different Department of Transportation (DOT) and Federal Highway Administration (FHWA) agencies to assess transportation infrastructure systems. This study also explores the quantitative analysis of benefits and challenges/barriers, expectations for every UAV and post-processing software, and the cutting-edge features that should be integrated with UAVs to effectively evaluate transportation infrastructure systems. A comprehensive survey form was distributed to all 50 DOTs and the FHWA, and 35 complete responses were recorded from 27 DOTs and the FHWA. The survey results show that 25 agencies currently use UAVs for roads or highways, and 23 DOTs for bridges, confirming these as the most commonly assessed infrastructure systems. The top benefits found in this study include safety, cost effectiveness, and time efficiency (mean ratings: 3.95–4.28), while weather, FAA regulations, and airspace restrictions are the main challenges. Respondents emphasize the need for longer flight times, better automation, and advanced data tools, underscoring growing adoption and highlighting the need to overcome technical, regulatory, and data privacy challenges for optimal UAV integration within transportation infrastructure systems management. Full article

Extreme weather events pose increasing risks to air transport operations, affecting flight safety, scheduling, and infrastructure resilience. This paper provides a comprehensive bibliometric analysis of scientific literature addressing the impacts of extreme weather on aviation, based on 1000 documents retrieved from the Web of Science Core Collection (2010–2024). Using VOSviewer software, keyword co-occurrence, overlay visualization, co-authorship networks, and citation analyses were conducted. Results revealed a clear thematic shift from environmental impact assessments toward research emphasizing operational resilience, technological adaptation, and mitigation strategies. Collaboration networks highlighted strong international cooperation, particularly among institutions in the United States, Germany, and the United Kingdom, with growing contributions from emerging research regions. Highly cited studies predominantly focused on emissions modeling and operational mitigation measures. Despite notable advances, the field remains fragmented and geographically uneven, underscoring the need for broader interdisciplinary integration and empirical validation of adaptation strategies. This paper offers a systematic overview of the evolving research landscape and identifies critical directions for future efforts to enhance the resilience and sustainability of global air transport systems under increasing climatic volatility. Full article

Computer vision is one of the key technologies of advanced driver assistance systems (ADAS), but the incorporation of a vision-based driver assistance system (still) poses a great challenge due to the special characteristics of the algorithms, the neural network architecture, the constraints, and the strict hardware/software requirements that need to be met. The aim of this study is to show the influence of image resolution in traffic lane detection using a virtual dataset from virtual simulation environment (CARLA) combined with a real dataset (TuSimple), considering four performance parameters: Mean Intersection over Union (mIoU), F1 precision score, Inference time, and processed frames per second (FPS). By using a convolutional neural network (U-Net) specifically designed for image segmentation tasks, the impact of different input image resolutions (512 × 256, 640 × 320, and 1024 × 512) on the efficiency of traffic line detection and on computational efficiency was analyzed and presented. Results indicate that a resolution of 512 × 256 yields the best trade-off, offering high mIoU and F1 scores while maintaining real-time processing speeds on a standard CPU. A key contribution of this work is the demonstration that combining synthetic and real datasets enhances model performance, especially when real data is limited. The novelty of this study lies in its dual analysis of simulation-based data and image resolution as key factors in training effective lane detection systems. These findings support the use of synthetic environments in training neural networks for autonomous driving applications. Full article

Retreading is a cornerstone in the remanufacturing process of tires, facilitating the extraction of maximum kilometers (Km) from a tire carcass. Tire remanufacturing plays a crucial role in conserving raw materials, reducing environmental impacts, and lowering the overall operating costs. This study employs predictive modeling techniques to forecast tire performance and optimize resource allocation, departing from traditional approaches, for a bus transport system in India. Machine learning models, including linear regression, ensemble boosted trees, and neural network models, were used. Two scenarios were devised: Scenario I addressed premature failures and optimizing performance to reduce tire procurement and Scenario II used targeted interventions, such as eliminating new tire condemnations and optimizing retread (RT) strategies, and could potentially salvage 169 tires from premature retirement. The results achieved R² values of 0.44, 0.51, and 0.45 and improved values for the test datasets of 0.46, 0.52 and 0.44. By leveraging these models, decision-makers can substantially improve tire mileage, reduce premature condemnations, increase tire production, and drive cost savings in fleet operations. Notably, this approach contributes to enhanced operational efficiency and promotes sustainability by cutting costs by 15–25%, improving tire mileage by 20–30%, and reducing environmental impacts by up to 25%. These results demonstrate the broader implications of predictive modelling as a decision-support tool, highlighting its capacity to drive economic and environmental benefits across industrial logistics and sustainable development. Full article

Dysbiosis, or an altered microbiota composition, has been implicated in chronic endometrial inflammation and recurrent implantation failure. Despite growing research on the relationship between the genital microbiome and reproductive health, few studies have examined its role in ectopic pregnancy. Therefore, our study focuses on the microbiota of the cervical canal in women diagnosed with an ectopic pregnancy. Material and methods: The study group consisted of nine women of a reproductive age who were hospitalized at the Department of Maternal and Child Health, Gynecology and Obstetrics, Clinical Hospital of the University of Poznań, between February and September 2023. In nine patients, an ectopic pregnancy was diagnosed based on a transvaginal ultrasound examination. The swabs were collected for quantitative microbiological culture (using Amies transport medium). The microbiological analyses involved quantitative culture on selected selective and differential media, following the Standard Operating Procedure developed by the Institute of Microecology. Results: A reduced Lactobacillus spp. count (≤5 × 10⁷ CFU/mL) was observed in 78% of the patients participating in the study, including those that produce H₂O₂, i.e., with strong protective properties for the environment of the female reproductive tract. The molecular analyses revealed Ureaplasma spp. (U. parvum and U. urealyticum) in 33% of the samples (three patients). However, Chlamydia trachomatis and Mycoplasma genitalium were not detected in any of the analyzed samples. Conclusions: The ease of obtaining material and the minimally invasive nature of lower reproductive tract examinations may allow for the evaluation of microbiota imbalances, helping to identify individuals at an increased risk of reproductive complications. Full article

Healthcare providers performing aerosol-generating procedures (AGPs) face significant infection risks, emphasizing the critical need for effective aerosol containment systems. In this study, we developed and validated a negative pressure chamber enhanced with an innovative aerodynamic cap structure designed to optimize aerosol containment. Initially, computational fluid dynamics (CFD) simulations were performed to evaluate multiple structural improvement ideas, including air curtains, bidirectional suction, and aerodynamic cap structures. Among these, the aerodynamic cap was selected due to its superior predicted containment performance, practical feasibility, and cost-effectiveness. The CFD analyses employed realistic transient boundary conditions, precise turbulence modeling using the shear stress transport (SST) k–ω model, and detailed droplet evaporation dynamics under realistic humidity conditions. A full-scale prototype incorporating the selected aerodynamic cap was fabricated and evaluated using physical polyalphaolefin (PAO) particle leakage tests and biological aerosol validation with aerosolized Bacillus subtilis. For the physical leakage tests, the chamber opening was divided into nine sections, and the aerosol dispersion was tested in three distinct directions: ceiling-directed, toward the suction hole, and opposite the suction hole. These tests demonstrated significantly stabilized airflow and substantial reductions in aerosol leakage, consistently maintaining containment levels below the critical threshold of 0.3%, especially under transient coughing conditions. The biological aerosol experiments, conducted in a simulated emergency department environment, involved aerosolizing bacteria continuously for one hour. The results confirmed the effectiveness of the aerodynamic cap structure in achieving at least a one millionth (10⁻⁶) reduction in the aerosolized bacterial leakage compared to the control conditions. These findings highlight the importance and effectiveness of advanced CFD modeling methodologies in accurately predicting aerosol dispersion and improving containment strategies. Although further studies assessing the structural durability, long-term operational ease, and effectiveness against pathogenic microorganisms are required, the aerodynamic cap structure presents a promising, clinically practical infection control solution for widespread implementation during aerosol-generating medical procedures. Full article

Progressive advancements in the global economy and technology have propelled human civilization forward; however, they have also inflicted significant harm on the global ecological environment. In the present era, electric vehicle (EV) technology is playing a vital role due to its environmentally friendly technological advances. However, widespread adoption of EVs has been hindered by their limited travel range, inadequate charging infrastructure, and high costs. This can be closely observed when we assess the adoption of electric vehicles (EVs) among motorcycle taxi drivers, commonly called ‘pilots,’ in Goa, India. Motorcycle taxis are crucial in Goa’s transportation network, providing affordable, efficient, and door-to-door services, especially in regions with limited public transport options. However, the rising costs of petrol and vehicle maintenance have adversely affected the income of these pilots, prompting concerns about their willingness to adopt EVs. This study aims to analyze the factors prompting the behavioral intention to adopt EVs by motorcycle taxi pilots in Goa, India, focusing on six key determinants: charging infrastructure, effort expectancy, performance expectancy, price value, social influence, and satisfaction with incentive policies. A quantitative approach was employed, utilizing stratified proportionate random sampling techniques to collect data from 242 motorcycle taxi pilots registered with the Goa State Government Transport Department. It was analyzed using partial least squares-structural equation modeling (PLS-SEM) through Smart-PLS 4.0 software. The research highlights that performance expectancy and price value are the potential motivators for the adoption of electric vehicles. These findings suggest that pilots are more likely to embrace EVs when they perceive tangible benefits in performance and find the cost reasonable in relation to the value offered. The results offer actionable insights for policymakers, manufacturers, and other stakeholders. These insights can guide strategic decisions and policy frameworks aimed at fostering a sustainable and user-centric transportation ecosystem. Full article