Search Results (735)

In this pilot study, the crossing behavior of elementary school students commuting on bicycles was investigated with the objective of enhancing safety around pedestrian crossings within school zones. With a noticeable increase in crashes involving young cyclists near schools, this research assessed the effectiveness of visual nudges in the form of red strips displaying “CYCLISTS DISMOUNT” instructions. Initial observations indicated a lack of compliance with dismounting regulations. After the initial observations, a specific elementary school was selected for the implementation of the nudging intervention and additional pre- (N = 91) and post-intervention (N = 71) observations. The pre-intervention observations again revealed poor adherence to the regulations requiring cyclists to dismount at specific points. Following our targeted intervention, the post-intervention observations marked an improvement in compliance. Indeed, the visual nudge effectively communicated the necessity of dismounting at a critical location, leading to a higher rate of adherence among cyclists (52.74% pre-intervention, 97.18% post-intervention). Although it also indirectly affected the behavior of the accompanying adult, who more often held hands with their children while crossing, this effect was weaker than the direct effect on dismounting behavior (20.88% pre-intervention, 39.44% post-intervention). The findings of the current pilot study underscore the possible impact of nudging on behavior and advocate for a combined approach utilizing physical nudges to bolster safety within school zones. Follow-up research, including, for instance, multiple sites, long-term effects, or children traveling alone, is called for. Full article

Railway systems occupy a predominant role in urban transport, providing efficient, high-capacity mobility. Progress in rail transport allows fast traveling, whilst environmental concerns and CO₂ emissions are on the rise. The integration of railway systems with renewable energy source (RES)-based stations presents a promising avenue to improve the sustainability, reliability, and efficiency of urban transport networks. A storage system is needed to both ensure a continuous power supply and meet train demand at the station. Batteries (BTs) offer high energy density, while supercapacitors (SCs) offer both a large number of charge and discharge cycles, and high-power density. This paper proposes a hybrid RES (photovoltaic and wind), combined with batteries and supercapacitors constituting the hybrid energy storage system (HESS). One major drawback of trains is the long charging time required in stations, so they have been fitted with SCs to allow them to charge up quickly. A new fuzzy energy management strategy (F-EMS) is proposed. This supervision strategy optimizes the power flow between renewable energy sources, HESS, and trains. DC bus voltage regulation is involved, maintaining BT and SC charging levels within acceptable ranges. The simulation results, carried out using MATLAB/Simulink, demonstrate the effectiveness of the suggested fuzzy energy management strategy for various production conditions and train demand. Full article

At the Politecnica Salesiana University (UPS) in Guayaquil, Ecuador, urban mobility challenges were addressed with the aim of improving students’ quality of life and promoting sustainability. This study evaluated the technical, economic, and social feasibility of implementing a shared transportation (carpooling) system using a quantitative-descriptive approach. Surveys were applied to a stratified sample of 256 students to analyze transportation habits. Route planning was performed using ArcGIS software, and costs were calculated with Microsoft Excel. Social impact assessment involved focus groups and analysis of variables such as changes in mobility patterns, system acceptance, and perceived safety, comfort, and accessibility. Key indicators included the percentage of students willing to participate in the pilot (82.7%), satisfaction with travel time savings (85.7% fully satisfied), and positive perceptions of safety and comfort. The results suggest that the proposed system is not only economically viable but also widely accepted by students, contributing to reduced stress, travel time, and single-occupancy vehicle use. This study demonstrates the feasibility of shared transport in urban universities and provides a replicable model to guide sustainable mobility policies that improve safety, comfort, and efficiency in student commuting. Full article

Optimizing passenger train line planning is a complex task that involves balancing operational costs and passenger service quality. This study investigates the adjustment and optimization of train line plans to better align with passenger demand and operational constraints, while minimizing systematic costs. These costs include train operation expenses (e.g., line usage fees and station service fees), passenger travel costs, and hidden costs such as imbalances in station stops. Line usage fees refer to charges for using railway tracks, whereas station service fees cover services provided at train stations. The optimization process employs a Simulated Annealing Algorithm to adjust train compositions, capacity configurations, and stop patterns to better match passenger demand. The results indicate a 13.89% reduction in the objective function value, reflecting improved overall efficiency. Notably, most costs are reduced, including train operating costs and passenger travel costs. However, ticketing service fees—which are calculated as a percentage of passenger fare revenue—increased slightly due to additional backtracking in passenger travel paths, which raised the total fare collected. Overall, the optimization improves the operational performance of the train network, enhancing both efficiency and service quality. Full article

Designing optimal heritage tourism routes in secondary cities involves complex trade-offs between cultural richness, travel time, carbon emissions, spatial coherence, and group satisfaction. This study addresses the Personalized Group Trip Design Problem (PGTDP) under real-world constraints by proposing DRL–IMVO–GAN—a hybrid multi-objective optimization framework that integrates Deep Reinforcement Learning (DRL) for policy-guided initialization, an Improved Multiverse Optimizer (IMVO) for global search, and a Generative Adversarial Network (GAN) for local refinement and solution diversity. The model operates within a digital twin of Warin Chamrap’s old town, leveraging 92 POIs, congestion heatmaps, and behaviorally clustered tourist profiles. The proposed method was benchmarked against seven state-of-the-art techniques, including PSO + DRL, Genetic Algorithm with Multi-Neighborhood Search (Genetic + MNS), Dual-ACO, ALNS-ASP, and others. Results demonstrate that DRL–IMVO–GAN consistently dominates across key metrics. Under equal-objective weighting, it attained the highest heritage score (74.2), shortest travel time (21.3 min), and top satisfaction score (17.5 out of 18), along with the highest hypervolume (0.85) and Pareto Coverage Ratio (0.95). Beyond performance, the framework exhibits strong generalization in zero- and few-shot scenarios, adapting to unseen POIs, modified constraints, and new user profiles without retraining. These findings underscore the method’s robustness, behavioral coherence, and interpretability—positioning it as a scalable, intelligent decision-support tool for sustainable and user-centered cultural tourism planning in secondary cities. Full article

Plasmodium falciparum malaria remains a major cause of morbidity and mortality, particularly in endemic regions. We report the case of a 21-year-old male with recent travel to an endemic area (Guapi, Colombia), who presented with febrile symptoms, severe respiratory distress, and oxygen saturation below 75%, necessitating orotracheal intubation. During the procedure, he developed pulseless electrical activity cardiac arrest, achieving return of spontaneous circulation after advanced resuscitation. Diagnosis was confirmed by thick blood smear, demonstrating P. falciparum infection. The patient progressed to multiorgan failure, including acute respiratory distress syndrome with capillary leak pulmonary edema, refractory distributive shock, acute kidney injury with severe hyperkalemia, and consumptive thrombocytopenia. Management included invasive mechanical ventilation, vasopressor support, sedation-analgesia, neuromuscular blockade, methylene blue, unsuccessful hemodialysis due to hemorrhagic complications, and platelet transfusions. Despite these interventions, the patient experienced a second cardiac arrest and died. This case highlights the severity and rapid progression of severe malaria with multisystem involvement, underscoring the critical importance of early diagnosis and intensive multidisciplinary management. It also emphasizes the need for preventive strategies for travelers to endemic areas and the development of clinical protocols to improve outcomes in complicated malaria. Full article

Terahertz (THz) frequencies, spanning from 0.1 to 1 THz, are poised to play a pivotal role in the development of future 6G wireless communication systems. These systems aim to utilize photonic technologies to enable ultra-high data rates—on the order of terabits per second—while maintaining low latency and high efficiency. In this work, we present a novel photonic method for generating sub-THz vector signals within the THz band, employing a semiconductor optical amplifier (SOA) and phase modulator (PM) to create an optical frequency comb, combined with in-phase and quadrature (IQ) modulation techniques. We demonstrate, both through simulation and experimental setup, the generation and successful transmission of a 0.1 THz vector. The process involves driving the PM with a 12.5 GHz radio frequency signal to produce the optical comb; then, heterodyne beating in a uni-traveling carrier photodiode (UTC-PD) generates the 0.1 THz radio frequency signal. This signal is transmitted over distances of up to 30 km using single-mode fiber. The resulting 0.1 THz electrical vector signal, modulated with quadrature phase shift keying (QPSK), achieves a bit error ratio (BER) below the hard-decision forward error correction (HD-FEC) threshold of ${3.8 \times 10}^{-3}$. To the best of our knowledge, this is the first experimental demonstration of a 0.1 THz photonic vector THz wave based on an SOA and a simple PM-driven optical frequency comb. Full article

Background: While efforts have been made to control meningococcal disease or carriage during mass gatherings (MGs), it is still a significant problem. This meta-analysis aims to assess the prevalence and predictors of meningitis carriage during MGs and travel. Methodology: PubMed, Scopus, Embase, and Cochrane were searched from their conception to January 2025. Cohort and cross-sectional studies assessing the prevalence of meningitis carriage and its serotype related to MGs and/or travel, and risk factors associated with its spread, were considered. The Newcastle–Ottawa scale was used for the quality assessment of studies. Results: Out of 1301 studies, 25 were considered for this meta-analysis. The largest geographic area involved was Saudi Arabia. A meta-analysis of 24 studies identified a pooled prevalence rate of meningococcal disease or carriage of 15.9% (95%CI: 4.45–27.4%) and the most frequent infecting organisms to be Serotype C (13.9%; 95%CI: −14.7 to 42.5; 4 studies) and A (11.5%; 95%CI: −2.13 to 25.2; 9 studies) among those at MGs or traveling. Age, gender, smoking history, and the vaccination status did not affect the infection risk. Conclusions: There is an increased prevalence of meningococcal disease and carriage, especially Serogroups A and C, associated with MGs and travel. New interventions and methodologies should be undertaken to control and prevent meningococcal disease or carriage transmission during such events. Full article

Intelligent transport systems are revolutionising all aspects of modern life, increasing the efficiency of commerce, modern living, and international travel. Intelligent transport systems are systems of systems comprised of cyber, physical, and social nodes. They represent unique opportunities but also have potential threats to system operation and correctness. The emergent behaviour in Complex Cyber–Physical–Social Systems (C-CPSSs), caused by events such as cyber-attacks and network outages, have the potential to have devastating effects to critical services across society. It is therefore imperative that the risk of cascading failure is minimised through the fortifying of these systems of systems to achieve resilient mission assurance. This work designs and implements a programmatic model to validate the value of cascading failure simulation and analysis, which is then tested against a C-CPSS intelligent transport system scenario. Results from the model and its implementations highlight the value in identifying both critical nodes and percolation of consequences during a cyber failure, in addition to the importance of including social nodes in models for accurate simulation results. Understanding the relationships between cyber, physical, and social nodes is key to understanding systems’ failures that occur because of or that involve cyber systems, in order to achieve cyber and system resilience. Full article

Participation in horse-related activities frequently involves relocating horses from the home stable to an activity venue, which might require local, regional, or international travel. In these circumstances, horses are exposed to unfamiliar surroundings and experience changes to their daily routines, which could have negative welfare impacts. An online survey was conducted in 2021 to ask experienced horse sector participants about the horse management practices that they perceived worked well and provided for positive horse welfare when undertaken at venues. Qualitative analysis identified four themes: ‘managing venues’, ‘monitoring fitness to participate’, ‘maintaining a healthy equine digestive system’, and ‘using horse behaviors to inform decision-making’. The findings indicate that activity-related individuals selected practices that assisted horses to adapt to venue surroundings, remain calm, and stay healthy. The co-authors propose that experienced participants recognize that practices include both provisions (inputs) and outcomes (the horse’s subjective experiences), resonating with the Five Freedoms and Five Domains models. For horse activity organizations proposing to implement the Five Domains model, the findings indicate that reviewing practices and implementing updates is timely and achievable. The authors propose that continuously updating practices will contribute to safeguarding horses and maintaining the sector’s social license to operate. Full article

This paper proposes an incentive scheme based on a public–private partnership (PPP) to encourage low-carbon travel behavior by inducing the mode choice shift from private cars to public transit. The scheme involves three key entities: travelers, the government, and the private sector. Travelers can choose between private cars and public transit, producing different emissions. As the leader, the government aims to reduce total emission to a certain level with limited budgets. The private sector, as an intermediary, invests subsidies in low-carbon rewards to attract green travelers and benefits from a larger user pool. A two-layer multi-objective optimization model is proposed, which includes travel time, monetary cost, and emission. The objective of the upper level is to maximize the utilities of the private sector and minimize social costs to the government. The lower layer is the user equilibrium of the travelers. The numerical results obtained through heuristic algorithms demonstrate that the proposed scheme can achieve a triple-win situation, where all stakeholders benefit. Moreover, sensitivity analysis finds that prioritizing pollution control strategies will be beneficial to the government only if the unit pollution control cost coefficient is below a low threshold. Contrary to intuition, larger government subsidies do not necessarily lead to better promotion of low-carbon travel. Full article

Choosing a rural destination today often comes down to one thing: how authentic it feels. In countries like Romania, where tradition is still woven into daily life, travelers are looking for something real and sustainable—but what exactly does that mean? And how can we measure it? This study takes a different approach. We created an Authenticity Index using fuzzy logic, a method that makes space for in-between answers and soft boundaries. It helped us capture how people actually perceive things like local food, architecture, and natural scenery—without forcing their opinions into rigid categories. We tested the index with real guest feedback from rural accommodation. The results showed that guests consistently valued sensory experiences—like nature and food—more than activities that required deeper cultural involvement, such as workshops or folk demonstrations. Instead of just producing a number, the index turned out to be a guide. It gives hosts a better idea of what really matters to their guests—even when those preferences are not always easy to define. More than that, it brings together what theory says with what visitors actually feel, supporting more sustainable tourism practices. And in rural tourism, that connection can make all the difference. Full article

This paper presents a reinforcement learning (RL) approach for solving the team orienteering problem under both deterministic and dynamic travel time conditions. The proposed method builds on the transformer architecture and is trained to construct routes that adapt to real-time variations, such as traffic and environmental changes. A key contribution of this work is the model’s ability to generalize across problem instances with varying numbers of nodes and vehicles, eliminating the need for retraining when problem size changes. To assess performance, a comprehensive set of experiments involving 27,000 synthetic instances is conducted, comparing the RL model with a variable neighborhood search metaheuristic. The results indicate that the RL model achieves competitive solution quality while requiring significantly less computational time. Moreover, the RL approach consistently produces feasible solutions across all dynamic instances, demonstrating strong robustness in meeting time constraints. These findings suggest that learning-based methods can offer efficient, scalable, and adaptable solutions for routing problems in dynamic and uncertain environments. Full article

Trichophyton indotineae is emerging globally from its origin in India, presenting with a terbinafine resistance and causing significant clinical burden. We report herein the first four confirmed cases of T. indotineae dermatophytoses in Malaysia, which were diagnosed based on the microscopic examination of skin scrapings using potassium hydroxide (KOH) wet mount, followed by confirmation via culture and Internal Transcribed Spacer (ITS1) sequencing. In contrast to conventional Trichophyton infections, T. indotineae dermatophytoses demonstrate extensive cutaneous involvement and marked inflammation with erythematous lesions. All cases exhibited a chronic course lasting more than three months, with evidence of person-to-person transmission. Although one patient reported a travel to Singapore, three had no recent travel history, suggesting possible local transmission. The isolates produced characteristic white, cottony colonies with radial mycelial growth on Mycosel agar after incubation at 30 °C for four days. Three patients responded well to oral itraconazole (200 mg daily), with reduced inflammation and erythematous lesions observed two weeks after treatment initiation. The occurrence of T. indotineae particularly among patients without a travel history, suggests a potential endemic establishment. This fungal pathogen warrants consideration in cases of extensive or recalcitrant dermatophytoses. Further investigations into the diagnostic methods, antifungal susceptibility profiles, and epidemiological risk factors of Malaysian strains are warranted to enhance clinical management and inform public health interventions. Full article

While airlines can directly quantify carbon emissions based on flight-specific fuel burn data, such data, along with data on other gaseous emissions that do not scale linearly with fuel consumption, are often unavailable to external stakeholders, necessitating the reliance on estimation models. Emissions are thus approximated from known quantities, with most usually from the fuel burned and distance travelled. Emission approximators developed for the aviation industry thus involve some degree of approximation and assumptions, as well as different exogenous and endogenous factors. As a result, such solutions differ primarily due to the significant methodological variations they incorporate. This paper assesses carbon emission approximators developed to valorize emissions generated by flight operations. It reveals the significance and sources of the misestimation of emissions by focusing on the ICAO Carbon Emission Calculator (ICEC), ICAO CORSIA CO₂ Estimation and Reporting Tool (CERT) and EUROCONTROL’ Advanced Emission Model (AEM) and Small Emitters Tool (SET). Thereby, the main research findings indicate considerable estimation uncertainty among the reviewed solutions, ranging from 1.77% to 27.95% on average compared to the baseline, which translates to statistical confidence levels ranging from 15% to 77.50% on average with respect to a 95% confidence threshold. Full article