Heavy-duty trucks (HDTs) are a dominant source of urban nitrogen oxide (NOX) emissions, yet their in-use emissions often diverge from regulatory limits. We present a multi-source data fusion framework to map truck NOX emissions at high resolution and evaluate targeted mitigation strategies for sustainable urban management. Using Shanghai as a case study, we integrate over one billion trajectory records, 2,513 plume-chasing tests, and remote on-board diagnostics (OBD) data from 40,726 HDTs, fused to derive emission factor distributions and activity levels. These data feed into a dynamic, road-level, hourly inventory disaggregated by emission standard and usage pattern. Results reveal strong spatial heterogeneity, with freight corridors and port-related links as hotspots, and highly skewed distributions within the fleet: the top 20% of trucks contribute 44% of total NOX, including not only China IV vehicles but also China V and poorly performing China VI models. We compared two control strategies: phasing out all China IV trucks versus targeted removal of the highest-emitting 20%. Under an idealized high-emitter prioritization scenario, the targeted strategy achieves ∼40% greater overall reduction and delivers larger benefits on major freight corridors. These findings highlight the potential of multi-source big data for targeted emission management, offering an effective pathway toward cleaner, more sustainable cities.
This review aims to fill a critical gap-phytosanitation methods for inert surfaces, such as farming equipment, containers, tools, and shoes, to mitigate plant pathogen establishment and expansion. Although a core component to food system security and ecosystem stability, especially for mitigating emerging pathogen impacts, this is one of the least studied disease management tools and often lacks robust science-based practices. We herein synthesize what is known about inert surface phytosanitation practices across diverse microbiological and plant pest systems and highlight opportunities for improving both phytosanitation practices and approaches used for phytosanitation science. Basic frameworks are first established for the types of plant pathogen propagules that are spread on surfaces and dispersal risks posed by key inert surface types. This is followed by a discussion of primary surface phytosanitation methods, including physical, chemical, and heat-based approaches. Case studies of nursery/greenhouse and farm equipment phytosanitation are used to demonstrate both systems-strategies for application of phytosanitation best management practices and methods for developing science-based practices using the hazard analysis for critical control points (HACCP) approach. Opportunities for growth discussed throughout include the use of pathogen-specific analyses and epidemiological modeling to improve phytosanitation science and engineering advancements to make phytosanitation practices more efficient. Taken together, it is hoped that this synthesis can both function as a resource for research/extension practitioners looking to develop and improve phytosanitation practices and provide impetus for innovation.
[This corrects the article DOI: 10.3389/fvets.2026.1789484.].
Field data analysis has shown that SUVs and pickup trucks cause more torso injuries than sedans, and the rapid increasing proportion of SUVs among the U.S. vehicle fleet will likely increase the importance of pedestrian torso protection. The objective of this study is to use finite element (FE) vehicle and human body models to investigate effects of vehicle front-end geometry and stiffness characteristics on pedestrian injuries, specifically focusing on SUVs and pickup trucks and pedestrian torso injuries. Front-end geometries of 74 U.S. vehicles, including 41 with hood leading edge (HLE) > 1000 mm and 33 with 750 mm < HLE < 1000 mm, were collected and analyzed using principal component analysis (PCA). The resulting parametric vehicle front-end geometry model was then linked to an FE generic vehicle (GV) model, so that the GV model can be morphed into a wide range of vehicle front-end geometries representing the fleet. Impact simulations using GHBMC F05, M50, and M95 pedestrian models and three detailed vehicle FE models were conducted with the pedestrian perpendicular to the vehicle front-end located at the center of the vehicle. These simulation results were used to calibrate the stiffness values and contact definitions of the hood and hood leading edge components of the morphed GV models. After GV model calibration, several parametric studies were conducted, resulting in a total of 306 vehicle-to-pedestrian crash simulations using 34 morphed GV models with varied front-end geometric and stiffness characteristics and three pedestrian models under three impact velocities (30, 40, and 50 kph). Pedestrian torso injuries were measured by lateral torso deflections at 17 locations across the chest and abdomen regions. Multiple regression was used to test the significance of the variables. PCA results showed that the top three principal components (PCs) captured over 90% of the variation in vehicle front-end geometries, primarily reflecting HLE height/length, HLE roundness, and overall front-end shape. Simulation results suggested that HLE height and impact velocity were the two dominant factors influencing pedestrian torso injury predictions. Torso injury metrics were the highest when the HLE height was equal to or slightly lower than (<150 mm) the pedestrian's mid-sternum height. In addition, increased HLE roundness and a more compliant HLE were associated with reduced pedestrian torso injuries. This study generated a comprehensive set of vehicle-to-pedestrian impact simulation data, enabling a systematic evaluation of how vehicle front-end geometric and stiffness characteristics influence pedestrian torso injuries.
Road traffic crashes remain a leading cause of fatalities worldwide, underscoring the need for accurate data to guide prevention strategies and evidence-based policymaking. However, crash databases often suffer from misclassification, underreporting, and inconsistencies, particularly in alcohol-involved cases, which limits the reliability of safety analyses. This study addresses this issue by identifying and quantifying Misclassified Alcohol-Involved Crashes (MAICs) using a Natural Language Processing (NLP) framework based on the BERT model. The framework analyzed 371,062 crash records from Iowa (2016-2022) and identified 3,895 misclassified alcohol-involved crashes (MAICs) out of 19,177 alcohol-involved cases predicted by the model, corresponding to an overall misclassification rate of 20.35% and a confidence interval of 18.86%-21.85%. To examine the factors contributing to these errors, a mixed-effects Probit Logit regression model was applied, incorporating behavioral, environmental, and roadway attributes. Results indicated that fatal and nighttime crashes were less likely to be misclassified, whereas crashes involving older or younger drivers, heavy trucks, and vulnerable road users showed higher odds of misclassification. A Local Indicators of Spatial Association (LISA) analysis revealed significant county-level clusters of misclassifications, suggesting regional differences in enforcement and reporting practices.
Accurately estimating the causal effect of human factors on crash severity in mountainous freeways is pivotal for developing effective safety strategies. Although previous studies have investigated human factors, they typically focus on estimating average effects under specific conditions, often conflating statistical correlations with causal relationships. Consequently, the underlying causal mechanisms in freeway crashes remain unclear. To address this limitation, this study proposes a dynamic weighted double machine learning framework that integrates LightGBM and XGBoost models to estimate confounder-outcome and treatment-outcome relationships. By optimizing model weights and using non-human-caused crashes as a control group, the effects of five human factors are isolated through counterfactual reasoning. The heterogeneous treatment effects of human factors on crash severity are quantified, and causal relationships are analyzed across variables such as weather, slope, truck traffic volume, and vehicle type. The results reveal significant heterogeneity in crash severity attributable to human factors compared to non-human factors. Inadequate safety distance often co-occurs with other high-risk conditions, amplifying crash severity. Reversing behavior is particularly sensitive to weather conditions. The causal pathways of distracted or fatigued driving and driving in the wrong lane are influenced by daily truck traffic volume and road slope. Additionally, interactions between slope and vehicle type significantly affect the severity of overloaded crashes. These findings underscore the need for targeted interventions addressing specific human factors in high-risk scenarios. Consequently, enforcement against reversing and overloading on steep slopes with high truck volume should be intensified, and heavy trucks should be restricted to right lanes on high-risk segments.
Slopes often emerge as traffic bottlenecks, yet not all slopes lead to congestion. The relationship between slope capacity and factors like grade and length is complex and non-linear. Accurately estimating road slope capacity and mitigating traffic congestion remain challenges in traffic management. Drivers instinctively adjust vehicle acceleration or braking to counteract gravity, influencing vehicle speed and road capacity. However, traditional models often overlook these compensatory adjustments, leading to inaccurate predictions. This study introduces a novel approach using vehicle trajectory data and the Expectation-Maximization (EM) algorithm to estimate driver compensations on slopes. The algorithm separates observed acceleration into baseline (flat road) and compensatory components. Data from field experiments with cars, trucks, and buses reveal that compensatory acceleration decreases with speed and remains predictable across different slopes. These findings enhance our understanding of slope impacts on traffic flow and provide valuable insights for traffic management and infrastructure design.
Harmful Algal Blooms (HABs) are pervasive in freshwater and marine waters requiring advances in monitoring, prevention and control of active blooms. Prevention through interception of nutrient runoff that feed HABs is critical to reducing future impacts. This research provides a logical convergence of complementary goals of mitigating the severity of HABs and increasing beneficial use of dredged sediment conveniently sourced from Confined Disposal Facilities (CDFs). This work innovates use of additive manufacturing (AM), or 3D printing, to generate porous, geometrically complex sediment structures to adsorb and intercept nutrients in runoff water prior to discharge into surface water. While this technology application is widely applicable, proof-of-principle of nutrient removal capability is demonstrated using a 3D printed dredged sediment mound that was strategically placed on a 3 by 3-foot physical landscape model that was also completely 3D printed. The sediment mound structure was specifically printed into parallel roadways forming channels to increase preferential infiltration of run-off water into the otherwise clayey sediment. Nutrients were measured before and after pumped runoff water passed through either amended (biochar, commercial resins) or unamended dredged sediment structures and demonstrated that the unamended marine-sourced dredged sediment had sufficient capacity to almost immediately reduce concentrations of phosphate by 79 to 98% and nitrate by 93 to 99% from runoff discharge. This concept may be scaled using hoppers, excavators and trucks to move sediment from nearby CDFs to a runoff site for field demonstration and application.
Food trucks (FTs) are becoming increasingly popular in Saudi Arabia. However, inadequate food safety practices and limited consumer awareness of foodborne pathogens may increase the risk of foodborne illnesses associated with consumption from FTs. Therefore, we assessed consumer knowledge, attitudes, associations with sociodemographic characteristics, and dietary patterns among FT customers. A cross-sectional study was conducted in the Makkah region, Saudi Arabia, between March to October 2025. An online questionnaire was completed by 500 adults covering sociodemographic characteristics, food safety knowledge, attitudes, pathogen awareness, and dietary intake. Data were analyzed using the independent t-test, one-way analysis of variance, and linear regression. The mean scores for awareness, knowledge, and attitudes were 16.7 ± 3.8, 14.7 ± 3.3, and 30.8 ± 5.3, respectively. Women had considerably higher knowledge and awareness scores than men (p < 0.0001). Significant correlations were found between food safety knowledge and attitude scores (r  =  0.531, p  <  0.001), food safety knowledge and awareness scores (r  =  0.633, p  <  0.001), and attitude and awareness scores (r  =  0.429, p  <  0.001). A trend toward high fruit consumption was observed among participants with high knowledge and awareness scores. These findings highlight the need for targeted consumer education to improve pathogen awareness among FT customers.
To evaluate preventive achievements and identify emerging trends and challenges regarding road traffic injuries (RTIs) in China from 1997 to 2023. Ecological trend study. Data on road traffic crashes, injuries, and deaths were extracted from China's National Bureau of Statistics. Joinpoint regression analysis was employed to identify significant temporal trends and compute the annual percentage change (APC) and average annual percentage change (AAPC). All outcome variables were log-transformed prior to analysis due to skewed distributions, as determined by normality assessment. Over the 27-year period, the number of road traffic crashes and associated casualties showed three distinct phases: a rapid increase until 2001, a significant decline until around 2011, and a subsequent stabilization or slight rebound. While motor vehicle-related incidents initially drove these trends, injuries (APC 2010-2023 = 12.88, P < 0.01; AAPC = 6.54, P < 0.01) and deaths (APC 2011-2019 = 13.57, P < 0.01; APC 2019-2023 = 5.06, P < 0.01) involving non-motor vehicles have risen sharply since around 2010. This surge has partially offset earlier gains in overall RTI prevention and now poses a substantial threat to national road safety. Notably, deaths exhibited a strong resurgence post-2011, exceeding 5000 in 2023. Despite commendable gains in reducing motor vehicle-related RTIs, China now faces the acute challenge of a rising injury burden involving non-motor vehicles. Consequently, future road safety initiatives must be formulated on a holistic approach that integrates enhanced vehicle safety, road infrastructure optimized for vulnerable users, and precise behavioral interventions targeting high-risk populations. The findings provide an evidence base for prioritizing non-motor vehicle safety in China's next phase of road safety strategy and offer methodological insights for similar trend analyses in other low- and middle-income countries experiencing rapid e-bike adoption.
Sepsis is defined as a dysregulated host response to infection leading to organ dysfunction. It represents a major global health concern, particularly in childhood. The underlying pathophysiological and genetic mechanisms remain insufficiently understood. Using samples and clinical data from 650 children enrolled in the Swiss Pediatric Sepsis Study, a national multicentre cohort for culture-proven bacterial sepsis, we conducted within-cohort analyses and a separate case-control analysis in 510 cases and 994 controls, testing genome-wide polymorphisms for association with sepsis susceptibility and, in cases only, with disease characteristics. In the within-cohort analysis, no significant genome-wide associations were found when assessing host, microbiological, and outcome features. In the case-control analysis, we identified one locus significantly associated with sepsis susceptibility, encompassing the CTNNAL1 and ELP1 genes. Our results suggest contribution of genetic modulators to susceptibility for sepsis in children. The Swiss Pediatric Sepsis Study received funding from the Swiss National Science Foundation (342730_153158/1 and 320030_201060/1), the Swiss Society of Intensive Care, the Bangerter Foundation, the Vinetum and Borer Foundation, the Foundation for the Health of Children and Adolescents, and the Sanofi-Aventis Suisse. LJS was supported by the NOMIS and the Thomas and Doris Ammann Foundation.
Metabolic dysfunction-associated steatohepatitis (MASH) is a chronic liver disease characterized by persistent inflammation, oxidative stress, and progressive fibrosis. There is currently no effective pharmacological therapy for MASH. We investigated whether two defined probiotic bacteria strains, Lactobacillus delbrueckii subsp. lactis (L. lactis) CKDB001 and Lactiplantibacillus plantarum (L. plantarum) Q180, attenuate MASH pathology in association with modulation of gut-liver redox signaling. Using diet-induced preventive and therapeutic mouse models of MASH, we showed that oral administration of these strains significantly improved hepatic steatosis, robustly attenuates fibrosis, and reduced inflammatory and oxidative stress markers. Probiotic treatment was associated with increased intestinal glutathione availability, and was accompanied by activation of hepatic nuclear factor erythroid 2-related factor 2 (Nrf2) signaling and upregulation of canonical antioxidant enzymes, consistent with improved hepatic redox homeostasis and reduced hepatocellular injury. Microbiome profiling revealed successful intestinal persistence of the administered strains and enrichment of other bacterial taxa associated with gut barrier integrity and metabolic resilience, including Akkermansia muciniphila, Parabacteroides goldsteinii, and Mediterraneibacter butyricigenes. Functional prediction analysis further suggested enhancement of microbial glutathione metabolism pathways, supporting a potential role for microbiota-driven redox modulation in host protection. Therapeutic efficacy was maintained after disease establishment and under conditions recapitulating features of a lean MASH-like phenotype, highlighting obesity-independent mechanisms of action. Collectively, our findings support a probiotic-driven association between intestinal glutathione dynamics and hepatic Nrf2 activation within the gut-liver axis, providing a mechanistically informed and translationally relevant framework for MASH intervention.
Waste collection is a critical sector, and maintaining a healthy working environment is essential. This study aimed to (i) characterize waste collectors' exposure to bioaerosols, (ii) examine associations between workday exposure to bioaerosols and serum inflammatory markers, and between week-exposure and self-reported symptoms, and (iii) identify hygiene practices, work tasks, and waste types associated with reported symptoms. In a cross-sectional study, exposure was measured among 56 waste collectors using personal air samplers. Blood samples collected at the end of the workday were analyzed for three inflammatory markers. Participants completed a questionnaire on health symptoms, hygiene practices, and work tasks. Individual week-exposure estimates were derived from exposure measurements among 105 collectors, accounting for season, waste type, and work task. Inflammatory markers indicated low-grade systemic inflammation and were positively associated with workday exposure to anaerobic bacteria. Week-exposure to anaerobic bacteria was associated with increased odds of runny nose and sore throat. Eating in the truck cab was associated with eye symptoms and diarrhea. Use of hand sanitizer was associated with lower odds of nasal symptoms and colds. Cleaning of the truck waste compartment was associated with higher exposure and increased odds of eye and airway symptoms. These findings suggest that anaerobic bacterial exposure, work tasks, and hygiene behaviors may contribute to inflammatory responses and symptoms among waste collectors. The results should be interpreted as hypothesis-generating and support the need for confirmatory intervention studies to evaluate causal relationships and preventive measures related to residual and biowaste collection, truck cleaning, and hygiene practices.
Aggressive riding behavior is a key contributing factor to road accidents, particularly in motorcycling, where rider dynamics directly influence vehicle stability and control. Despite growing interest in objective behavioral assessment, validated classification frameworks specific to motorcycles remain scarce in the literature. This pilot study investigated the feasibility of a standard deviation-based method for classifying aggressive riding behavior in a single experienced motorcyclist navigating two distinct environments: an urban route (UR) and a suburban national route (SNR). The participant completed two 20 min rides under real-world conditions. The UR was characterized by frequent accelerations, braking, speed bumps, and traffic lights, whereas the SNR features low traffic density and minimal interruptions. Longitudinal acceleration data were continuously recorded using a Vicon Blue Trident measurement unit mounted on the motorcycle seat. Drawing on the threshold principles established in automotive research, an environment-specific classification framework was developed to categorize riding events into normal, aggressive, and dangerous levels for both acceleration and deceleration maneuvers. The derived thresholds revealed pronounced environmental differences: UR thresholds (acceleration: 2.122 m/s2; deceleration: -2.134 m/s2) were approximately three times lower than those observed in the SNR (acceleration: 6.16 m/s2; deceleration: -7.09 m/s2). From more than four million recorded data points, approximately 88% of the riding behavior was classified as normal in both routes. In the UR, 9.27% of events were identified as aggressive and 4.37% as dangerous, compared with 7.27% aggressive and 5.35% dangerous events in the SNR. These preliminary findings suggest that environment-specific thresholds may be essential for accurately characterizing motorcycle riding behavior, and caution against the direct application of fixed automotive criteria to motorcycle safety analyses. All findings are specific to one rider on two routes and must not be extrapolated to other motorcyclists, vehicle types, or road contexts without replication.
Melasma is an acquired disorder of hypermelanosis that most commonly affects sun-exposed facial skin in women. Extra-facial involvement and occurrence in male patients are uncommon. We present a case of a 66-year-old Hispanic male with asymptomatic, symmetric hyperpigmented patches on the bilateral forearms in the setting of chronic occupational ultraviolet (UV) exposure as a truck driver. Notably, the patient had a history of severe facial burns two years prior to presentation, with complete sparing of facial skin hyperpigmentation. A punch biopsy demonstrated increased basal layer melanin pigmentation with few dermal melanophages and no significant inflammatory infiltrate, consistent with epidermal melasma. The patient was treated with topical azelaic acid and photoprotection counseling but reported minimal improvement, likely related to ongoing UV exposure and occupational constraints. This case highlights an uncommon presentation of extra-facial melasma in a male patient and suggests that prior cutaneous injury may influence melanocytic responsiveness. It also emphasizes the role of chronic UV exposure in disease distribution and persistence.
Comprehensive studies on hepatitis B, C and D virus (HBV, HCV and HDV) infections in high-risk groups are lacking in Africa. This study investigated viral markers among nine high-risk populations in three locations of Burkina Faso. Socio-demographic and clinical data, as well as blood samples, were collected from sex workers, men who have sex with men, prisoners, health care workers, lorry drivers, illegal gold miners, hairdressers, haemodialysis patients and human immunodeficiency virus patients. Serum samples were screened using serological and molecular tests; detected viruses were sequenced. Overall, 1373 participants were enrolled. HBsAg seroprevalence was 13.5% and anti-HBc seroprevalence was 75.5%. Among HBsAg-positive samples, HBeAg, anti-HBe and HBV DNA were detected in 18.3%, 79.4% and 30.1% respectively. Total anti-HDV antibodies were detected in 4 (2.1%) samples and HDV RNA in 3 (75.0%). Anti-HCV prevalence was 8.5% and HCV RNA was detected in 13.7%. HBV/HCV coinfection was found in 4 (0.3%) cases. Virus characterization identified genotype 1 for HDV, genotypes A and E for HBV, genotypes 1 and 2 for HCV. The results showed higher prevalences of HBV and HCV infections than in the general population. They underline the need of targeted programs for these high-risk groups to support the 2030 hepatitis elimination goals.
Motorization has undeniably enhanced mobility and convenience, but it comes at a significant cost, as the increasing number of vehicles has led to a surge in road traffic injuries (RTIs) now a major global cause of disability and death. Over the past decade, the significant growth in two-wheelers has coincided with a rise in RTIs, creating an increasing concern for young individuals. This mixed-method study aims to explore epidemiology, assess their impact on undergraduate students, and gather key recommendations from stakeholders to improve road safety and reduce injury rates. The quantitative data collection was conducted among undergraduate students in the four colleges in Kathmandu district. The total number of participants in this study was 1217 students of 17-31 years of age. Furthermore, for the qualitative data collection, 5 Key informant interviews were conducted among various stakeholders. The prevalence of RTI in 2021-2022 was 23%. The most common injuries were bruises in the upper and lower limbs. It was seen that only 78% of the respondents had a driving license. The injuries led to academic loss and caused financial strain due to healthcare and vehicle repair expenses. 5 key recommendations to decrease the ever-increasing burden of RTI have emerged from the qualitative data analysis. The study highlighted a high prevalence and impact of RTIs among undergraduate students along with 5 key recommendations that came forward from this study could be instrumental for road safety in Nepal.
Working in the ambulance service is highly demanding, requiring new professionals to manage diverse, unpredictable patient situations from day one. New professionals often report limited self-confidence, emotional strain, and isolation, which can contribute to burnout, and turnover. Despite these challenges, in Sweden, education required for working in the ambulance service covers only a fraction of the essential competencies. Structured induction programmes are therefore critical for supporting new professionals. Although there is extensive knowledge about induction in hospital settings, little is known about induction in the ambulance service. Therefore, the aim of this study was to map the induction process of novice professionals in Swedish ambulance services. A descriptive study was conducted based on qualitative data from induction programme materials from 23 ambulance organisations, representing all 21 healthcare regions in Sweden. Data was analysed using document analysis. Analysis yielded six categories describing the content of the introductory training; Transportation and navigation; Cooperation and communication; Systematic work approaches and structures, Prerequisites for care and nursing; Safe healthcare environment; Organizational knowledge, and a seventh category describing Pedagogical approaches. The findings of this study highlight a need for more evidence-based and pedagogically coherent induction programmes within ambulance services. Induction should support not only technical and clinical competence, but also interpersonal, ethical, and person-centred aspects of care, while fostering professional socialisation, belonging, and psychological safety. As this study was based on documentary analysis, further research is needed to examine how different induction designs influence learning, clinical competence, and patient safety in ambulance care.
Efficient allocation of physician-staffed emergency medical services (EMS) is crucial for optimal resource use in urban prehospital systems. The rendezvous model differs fundamentally from the traditional ambulance model: It deploys a lighter, nontransport-capable, passenger vehicle that may offer operational advantages, although comparative evidence with regard to traditional models remains limited. In this study we aimed to evaluate the impact of a physician-staffed rendezvous model on response times and physician-staffed crew availability within the EMS system in Košice, Slovak Republic. We conducted a retrospective, cross-sectional study, analyzing all primary responses by physician-staffed EMS units in the Košice region from January 2023-March 2025. In August 2024, one of three traditional, physician-staffed transport units was replaced by a physician-staffed rendezvous unit, yielding a post-intervention system with two transport units and one rendezvouz unit, a faster, physician-staffed non-transport vehicle that provides specialized medical care on scene. We extracted time intervals in minutes-response time, time on scene, time to transport initiation, and total time until crew availability-from the national EMS database and compared them to the pre- and post-introduction of the rendezvous model. We analyzed data using non-parametric statistical tests (Mann-Whitney U test, Kruskal-Wallis test), and we conducted a multivariable ordinary least squares regression to adjust for potential confounders. Of 11,347 eligible cases, 11,094 met the inclusion criteria (8,389 patients treated during the pre-intervention period and 2,705 during the post-intervention period). Of these, 488 patients (4.4% of the overall cohort and 18.0% of the post-intervention cases) were managed by the rendezvous unit. Following rendezvous unit implementation, the mean response time was reduced compared to that of the standard physician-staffed transport units (-0.77 minutes per response; P < .001). The rendezvous unit demonstrated reductions in time to crew availability across districts, with absolute decreases ranging from 11.12 to 18.01 minutes; these differences were statistically significant in all districts except the undefined/border region (P < .001 for each comparison). The time spent on scene was slightly longer for the rendezvous unit in most districts, although these differences did not reach statistical significance. The time to transport initiation showed mixed trends. Ordinary least squares regression confirmed the independent association between rendezvous unit implementation and shorter response times. Replacing a standard physician-staffed ambulance with a lighter, faster, non-transport rendezvous vehicle improved operational efficiency by reducing response times and expediting physician-staffed crew availability. These findings suggest that the rendezvous model can enhance system-level performance in urban EMS settings by supporting more flexible physician deployment and informing decisions on resource allocation within tiered prehospital systems.
In-wheel motor bearings in electric vehicles operate in harsh environments where strong background noise often masks early fault features, limiting the accuracy of traditional diagnostic methods. This study proposes an intelligent fault diagnosis framework integrating improved Successive Variational Mode Decomposition (SVMD) with a ResNet-Kolmogorov-Arnold Network (ResNet-KAN). To enhance feature extraction, a multi-strategy Crested Porcupine Optimizer (CPO) is employed to adaptively optimise SVMD parameters. Subsequently, a Gramian angular difference field (GADF) reconstruction strategy transforms one-dimensional vibration signals into two-dimensional images to improve spatial distinguishability. Finally, a ResNet-KAN model, featuring a ReLU-based non-linear classification head, is developed to capture complex fault boundaries more effectively than traditional linear layers. Experimental results demonstrate that the CPO-SVMD method increases the kurtosis of extracted components by at least 25.6% compared to traditional optimisation methods. Furthermore, the ResNet-KAN model achieves an identification accuracy exceeding 98% on the in-wheel motor bearing dataset, outperforming 2DCNN, ResNet, and ViT models by at least 2%. This integrated approach provides a robust, high-precision solution for the intelligent condition monitoring and early warning of in-wheel motor drive systems under complex, high-noise operating conditions.