Search Results (43)

Traffic variability along heavily congested signalised urban corridors undermines roadway safety, reduces energy efficiency, weakens operational reliability, and can hinder emergency response. Although many simulation-based studies have examined the impacts of Autonomous Vehicles (AVs), relatively few have combined high-resolution congestion observations with link-level microscopic calibration in a real urban network, particularly when evaluating implications for emergency mobility. This study develops and calibrates a microscopic Aimsun traffic simulation model for the Atakum district of Samsun, Türkiye, using a 10 min Google Traffic Index (GTI) observation stream converted into a four-level ordinal congestion scale. The calibration process began with an origin–destination (OD) matrix derived from 2020 traffic counts and was refined through link-level GTI synchronization, iterative OD scaling on mismatched corridors, and signal retiming at key intersections. GTI was validated as an ordinal congestion proxy through both categorical agreement and volumetric consistency, achieving 83% class agreement and GEH values below 5 for more than 90% of links. Five AV penetration scenarios (0%, 25%, 50%, 75%, and 100%) were simulated under peak-hour conditions. Network performance was evaluated using delay, stop time, mean speed, throughput, missed turns, and total journey time, while emergency mobility was assessed along a representative ambulance corridor on Atatürk Boulevard using seconds per kilometre. The results indicate that increasing AV penetration improves flow stability more clearly than nominal capacity. Mean speed increased from 36.2 to 39.2 km/h, delay and stop time declined steadily, and throughput remained nearly constant at 22.2–22.5 thousand vehicles/h. Along the ambulance corridor, travel time improved by 11.5%, from 112.4 to 99.4 s/km, between the baseline and full automation scenarios. These findings provide scenario-based evidence that, within a calibrated signalised urban network, increasing AV penetration can enhance operational stability and emergency response efficiency. More broadly, the study demonstrates the practical value of integrating GTI-based congestion observations with microscopic simulation for AV impact assessment in real urban networks. Full article

Processes for generating clean hydrogen from waste plastics through thermochemical methods such as pyrolysis and gasification are a promising solution for both waste management and clean energy initiatives. Then, this derived hydrogen powers the fuel cell, which produces electricity that can be directly fed to charge electric vehicles (EVs). Although this complex process has many challenges related to energy efficiency during the conversion processes—starting from the generation of hydrogen from thermochemical processes and hydrogen storage and followed by fueling the fuel cells and charging EV infrastructure—the simplistic conceptual modeling developed for this research demonstrates how an ecosystem of such processes can be made feasible commercially. Clean hydrogen generated using known techniques reported in the literature is promising for commercialization, but harnessing hydrogen from plastics offers additional benefits, such as reducing greenhouse gas (GHG) emissions. Overall, the feasibility of clean hydrogen using this methodology is not limited by potential cost inefficiencies, especially when savings from GHG emissions reduction are taken into account. EVs have become commercially viable thanks to high-energy-density Li-ion batteries. And therefore, research continues to optimize charging performance through the integration of renewable energy and battery storage systems. This study examines another potential of clean hydrogen: its use as a power source in grids, especially V-2-G (vehicle-to-grid) systems. Additionally, direct current (DC) power from a fuel cell powers an EV charger at DC input voltages for e-ambulances. In particular, this designed system operates on DC voltages throughout the power system, combining high-voltage direct current (HVDC) lines, renewable energy sources, DC-DC converters, DC EV chargers, and other supporting components. The literature review identified gaps in plastics production, waste management, and processes for converting them into useful energy. The presented model is a stepping stone towards a novel, innovative process for clean hydrogen production to power electric vehicle charging infrastructure for emergency response systems in healthcare, thereby improving public safety. The limitations of the study would be governed by the effective establishment of locations where waste management services are performed (for example, landfills) and adoption by local government authorities with deregulated power systems. Full article

Ambulance offload delay (AOD) occurs when incoming ambulance patients cannot be transferred promptly from paramedics to emergency department (ED) staff, usually due to ED and hospital congestion. This study empirically examines how AOD affects ambulance system efficiency in Nova Scotia, Canada. Using 12 months of call data from an integrated provincial EMS system and the electronic patient care reporting system, the analysis quantifies AOD impacts on the number of ambulances at EDs, turnaround time, total call time, response time, and ambulance availability across all regions. Findings show that AOD in the Central Region negatively affects all performance measures locally and in adjacent regions, prolonging turnaround and total call times, lengthening response times, and reducing ambulance availability where resources are shared. These results highlight the scale of AOD’s system-wide impact and provide a generalizable methodological framework that other EMS operators can adapt to assess and manage AOD in their specific operational contexts, recognizing that region-specific factors significantly influence outcomes. Full article

Background: Rapid helicopter air ambulance (HAA) response is a cornerstone of emergency medical logistics, yet the “time-to-care” metric remains highly sensitive to uncertainties in base posture, readiness, and operational disruptions. This study evaluates how these factors jointly influence response-time reliability and identifies strategies for improving service performance. Methods: A Monte Carlo simulation was developed to model the end-to-end HAA mission chain, including dispatch, wheels-up delay, en-route flight, and patient handoff, while accounting for uncertainty from weather, airspace congestion, and flight dynamics. Scenario experiments incorporated training improvements and alternative response protocols (Ground vs. Airborne Standby). Results: Simulation results indicate that operational factors reduced mean and tail response times, with Airborne Standby reducing the probability of exceeding a 45 min threshold by over 90% in urban night scenarios. Performance gains were most prominent in rural service areas and night operations, where disruption risks were highest. Conclusions: The findings offer evidence-based guidance for EMS logistics planners by clarifying how standby policies and readiness enhancements mitigate logistical risks. Full article

Background: Spasticity is a complex and multifactorial condition resulting from upper motor neuron injury. It manifests through muscle contractions, pain, limited range of motion, and clonus, which significantly impair daily activities and quality of life. High-frequency spinal cord stimulation (HF SCS) has shown optimal results in treating chronic neuropathic pain, but its potential role in spasticity remains underexplored. This study aimed to evaluate the efficacy of HF SCS in patients with spasticity. Methods: From April 2021 to July 2024, six patients with spasticity from various etiologies underwent SCS implantation at our institution. Clinical evaluations including the use of the Visual Analog Scale (VAS), Douleur Neuropathique 4 (DN4), and the Ashworth score, as well as ambulation ability and clonus episodes, were performed preoperatively and at a minimum of six months post-surgery. Subjective assessments of motor function, including coordination, movement efficiency, and postural transitions, were also recorded. Results: The mean age of patients was 50.12 ± 9.41 years, with follow-up averaging 24.32 ± 10.83 months. Statistically significant improvements were observed in VAS (p = 0.0412) and DN4 (p = 0.0422) scores, alongside a reduction in clonus episodes. All patients reported subjective improvements in coordination, movement efficiency, and postural transitions. Ambulation remained stable or improved in all cases. No perioperative complications or sensory/motor side effects were noted. Conclusions: HF SCS offers a promising approach to managing spasticity, with improvements in motor function, ambulation, and postural transitions. These findings support further investigation into HF SCS for spasticity, with multicenter trials needed to optimize treatment protocols and identify the most responsive patient populations. Full article

Background/objectives: The effectiveness of emergency medical procedures administered to a patient in a life-threatening condition depends, to a large degree, on the knowledge and skills of medical response team personnel. Their competencies can be developed through participation in training and then verified during emergency medicine championships. Methods: The research was conducted on the basis of one of the tasks carried out during the ‘16th International Winter Championships in Emergency Medicine’. The task was completed by 28 Polish emergency response teams from ambulance stations across the country. The teams carried out a simulated scenario related to procedures with a patient with hypoglycaemia. The teams’ interventions were assessed in accordance with European Resuscitation Council (ERC) guidelines by judges selected from among academic lecturers and ERC instructors. Results: The research showed that 86% of the teams obtained the maximum number of points for adhering to safety procedures. Further, 61% of the teams obtained the maximum of 6 points for the initial assessment, with the average number of points obtained by the teams being 5.54. The average number of points for the physical examination was 21.04, with only one team obtaining the maximum result of 26 points. Additionally, 57% of the teams obtained the maximum number of 6 points for the medical consultation, with the average obtained by the teams being 5.43. The teams obtained, on average, 8.18 points for the correct treatment of hypoglycaemia, with 68% of the teams obtaining the maximum of 9 points. The research demonstrated a positive correlation between the quality of patient examination and the collection of medical data, and the effectiveness of hypoglycaemia treatment. It was also shown that if the team leader had completed an ALS course, they obtained higher scores for the treatment of hypoglycaemia, although this finding is specific to this scenario. Conclusions: The teams demonstrated generally high performance in a simulated hypoglycaemia scenario. More complete assessment and history-taking were associated with higher treatment scores. Correct treatment was achieved in 79% of ALS-led teams versus 44% of non-ALS teams, although this observation is specific to this simulation and should not be generalised. Full article

Sepsis remains a critical medical emergency caused by a dysregulated immune response to infection, with timely detection and intervention being essential for improving survival rates. Traditional methods often rely on clinician intuition and structured scoring systems, which may be time-intensive and prone to variability. To address these limitations, Machine Learning (ML) offers a powerful alternative, bringing precision and efficiency to sepsis detection. This study investigates both white-box and complex black-box ML models applied to patient data collected across the continuum of care, including monitoring at the urgent care, en route in ambulances, and diagnostics conducted within hospital emergency department settings themselves. White-box models, such as logistic regression and decision trees, are valued for their interpretability, allowing healthcare providers to understand and trust the reasoning behind predictions. Meanwhile, black-box models like deep neural networks and support vector machines deliver superior accuracy but pose challenges in clinical transparency. This trade-off between explainability and performance is explored in detail, supported by experimental results aimed at identifying the most effective computational strategies for early sepsis recognition across diverse healthcare environments. Full article

Optimizing ambulance deployment is a critical task in emergency medical services (EMS), as it directly affects patient outcomes and system efficiency. This study proposes a cyber-secure, machine learning-based framework for predicting region-specific ambulance allocation and response times across South Korea. The model integrates heterogeneous datasets—including demographic profiles, transportation indices, medical infrastructure, and dispatch records from 229 EMS centers—and incorporates real-time IoT streams such as traffic flow and geolocation data to enhance temporal responsiveness. Supervised regression algorithms—Random Forest, XGBoost, and LightGBM—were trained on 2061 center-month observations. Among these, Random Forest achieved the best balance of accuracy and interpretability (MSE = 0.05, RMSE = 0.224). Feature importance analysis revealed that monthly patient transfers, dispatch variability, and high-acuity case frequencies were the most influential predictors, underscoring the temporal and contextual complexity of EMS demand. To support policy decisions, a Lasso-based simulation tool was developed, enabling dynamic scenario testing for optimal ambulance counts and dispatch time estimates. The model also incorporates the coefficient of variation (CV) of workload intensity as a performance metric to guide long-term capacity planning and equity assessment. All components operate within a cyber-secure architecture that ensures end-to-end encryption of sensitive EMS and IoT data, maintaining compliance with privacy regulations such as GDPR and HIPAA. By integrating predictive analytics, real-time data, and operational simulation within a secure framework, this study offers a scalable and resilient solution for data-driven EMS resource planning. Full article

Firefighters experience high noise levels from various sources, such as sirens, alarms, pumps, and emergency vehicles. Unlike industrial workers who experience continuous noise exposure, firefighters are subject to intermittent high-intensity noise, increasing their risk of noise-induced hearing loss (NIHL). Despite global concerns regarding firefighters’ auditory health, research on Korean firefighters remains limited. This study aimed to assess personal noise exposure among Korean firefighters across three primary job roles—fire suppression, rescue, and emergency medical services (EMS)—and to predict worst-case noise exposure scenarios. This study included 115 firefighters from three fire stations (one urban, two suburban). We measured personal noise exposure using dosimeters attached near the ear following the Korean Ministry of Employment and Labor (MOEL) and International Organization for Standardization (ISO) criteria. Measurements included threshold levels of 80 dBA, exchange rates of 5 dB (MOEL) and 3 dB (ISO), and a peak noise criterion of 140 dBC. We categorized firefighters’ activities into routine tasks (shift handovers, equipment checks, training) and emergency responses (fire suppression, rescues, EMS calls). We performed statistical analyses to compare noise levels across job roles, vehicle types, and specific tasks. The worst-case exposure scenarios were estimated using 10th percentile recorded noise levels. The average 8 h time-weighted noise exposure levels varied significantly by job role. Rescue personnel exhibited the highest mean noise exposure (MOEL: 71.4 dBA, ISO: 81.2 dBA; p < 0.05), whereas fire suppression (MOEL: 66.5 dBA, ISO: 74.2 dBA) and EMS personnel (MOEL: 68.6 dBA, ISO: 73.0 dBA) showed no significant difference. Peak noise levels exceeding 140 dBC were most frequently observed in rescue operations (33.3%), followed by fire suppression (30.2%) and EMS (27.2%). Among vehicles, noise exposure was the highest for rescue truck occupants. Additionally, EMS personnel inside ambulances had significantly higher noise levels than drivers (p < 0.05). Certain tasks, including shift handovers, equipment checks, and firefighter training, recorded noise levels exceeding 100 dBA. Worst-case scenario predictions indicated that some work conditions could lead to 8 h average exposures surpassing MOEL (91.4 dBA) and ISO (98.7 dBA) limits. In this study, Korean firefighters exhibited relatively low average noise levels. However, when analyzing specific tasks, exposure was sufficiently high enough to cause hearing loss. Despite NIHL risks, firefighters rarely used hearing protection, particularly during routine tasks. This emphasizes the urgent need for hearing conservation programs, including mandatory hearing protection during high-noise activities, noise exposure education, and the adoption of communication-friendly protective devices. Future research should explore long-term auditory health outcomes and assess the effectiveness of noise control measures. Full article

This retrospective multicentre study investigated haemorrhagic myelopathy as a rare complication of steroid-responsive meningitis-arteritis (SRMA) in nine dogs. The affected dogs exhibited varied neurological deficits, including cervical hyperesthesia, generalised stiffness, ambulatory tetraparesis, and, in the most severe cases, paraplegia without nociception. MRI findings primarily localised haemorrhagic lesions to the thoracolumbar (T3-L3) region, with intradural–extramedullary haemorrhages being the most common type. Most cases responded favourably to immunosuppressive therapy with prednisolone, either alone or in combination with cytarabine. Surgical intervention, performed in a case of compressive extradural haemorrhage, led to a successful recovery of ambulation. Two cases presented or developed paraplegia without nociception, despite immunosuppression. These findings emphasise the importance of advanced imaging and timely therapeutic interventions in addressing atypical and severe manifestations of SRMA. Full article

Introduction: Hereditary transthyretin amyloidosis (ATTRv) is a severe, multisystemic, autosomal dominant disease with variable penetrance caused by mutations in the TTR gene generating protein misfolding and accumulation of amyloid fibrils. The diagnosis is usually challenging because ATTRv may initially manifest with nonspecific multisystemic symptoms. Conversely, an early diagnosis is needed to start timely appropriate therapy. Hence, screening models have been proposed to improve ATTRv diagnosis. In this study, we propose a genetic screening model based on predefined “red flags” followed by “cascading screening” on first-degree relatives of patients who tested positive. Materials and methods: After obtaining written informed consent, genetic testing on salivary swabs was performed in individuals who met at least two major red flags for ATTRv (age > 65 years old, progressive sensory or sensorimotor neuropathy not responsive to steroids or immunomodulant therapies, recent and unexplained weight loss associated with gastrointestinal signs and symptoms, diagnosis of cardiac amyloidosis, bilateral or relapsing carpal tunnel syndrome, unexplained autonomic dysfunction) or one major flag and two minor flags (family history of neuropathy, ambulation disorders or cardiopathy, sudden cardiac death, a bedridden, wheelchaired patient without specific diagnosis excluding upper motor neuron diseases, infections, juvenile cardiac disease, ocular disorders, lumbar spine stenosis, biceps tendon rupture). Results: In the first screening phase, 29 suspected cases (individuals meeting at least two major red flags or one major red flag and two minor red flags) underwent genetic testing. One patient (3.5%) was diagnosed with hereditary transthyretin amyloidosis with polyneuropathy (ATTRv-PN), carrying the Phe64Leu mutation. Then, cascade screening allowed for early recognition of two additional individuals (two pre-symptomatic carriers) among two first-degree relatives (100%). The identified patient was a 72-year-old man who had a family history of both cardiopathy, neuropathy, and a diagnosis of juvenile cardiac disease and progressive sensorimotor neuropathy unresponsive to steroids or immunomodulant therapies. Conclusions: ATTRv is a progressive and often fatal disease that should be promptly diagnosed and treated to stop progression and reduce mortality. Systematic screening for ATTRv yielded increased recognition of the disease in our neurological clinic. A focused approach for the screening of ATTRv-PN could lead to an earlier diagnosis and identification of asymptomatic carriers, enabling timely intervention through close clinical monitoring and early treatment initiation at symptom onset. Full article

This study focuses on Vehicle-Integrated Photovoltaic (VIPV) strategy adopted as an energy supply vector in disaster scenarios. As a matter of fact, energy supply may be a very critical issue in a disaster context, when grid networks may be damaged. Emergency vehicles, including ambulances and trucks, as well as mobile units such as containers and operating rooms, can be equipped with photovoltaic modules and can serve as mobile emergency energy sources, supporting both vehicle operations and disaster relief efforts. A methodology was developed to estimate energy production under unpredictable disaster conditions, by adapting existing VIPV simulation approaches. Obtained results show that VIPV strategy, even under minimal daily energy generation, can be a useful aid for disaster resilience and emergency prompt response. Ambulance performance, analyzed for worst-case scenarios (e.g., December), shows that they can power medical devices for 1 to 15 h daily. Additionally, the ambulance can generate up to 2 $\mathrm{M}\mathrm{W}\mathrm{h}$ annually, reducing CO₂ emissions by up to 0.5 tons. In optimal configurations, mobile operating rooms can generate up to 120 times the daily energy demand for medical devices. Full article

Emergency medical services (EMSs) are critical to reducing fatalities and improving patient outcomes in emergencies such as traffic accidents, where response time is a decisive factor. This study proposes a comprehensive and systematic approach to designing and optimizing EMS systems tailored for urban traffic accidents. By integrating Geographic Information Systems (GISs), hypercube queuing models, Economic Value Added (EVA) calculations, and multi-criteria decision-making (MCDM) techniques, we developed a model that balances service efficiency, financial sustainability, and equitable access to emergency care. The hypercube queuing model was applied to estimate key performance metrics, such as response time, coverage, and the GINI index for equity, under varying numbers of ambulances and demand scenarios. In addition, EVA was calculated for different configurations of leased and owned ambulances, offering a financial perspective to assess the viability of public–private partnerships (PPPs) in EMSs. Using the fuzzy Analytic Hierarchy Process (AHP) and CoCoSo (Combined Compromise Solution) methods, this study identified the optimal number of ambulances required to minimize response time, maximize coverage, and ensure financial sustainability. The proposed approach has been applied to a real case in Colombia. Furthermore, integrating leased ambulances offers a financially viable solution with positive EVA values that guarantee the long-term sustainability of the public–private partnership. This paper advances the literature by providing a practical framework for optimizing EMS systems, particularly in developing countries where financial constraints and resource limitations represent significant challenges. The proposed methodology improves service efficiency and economic sustainability and ensures equity in access to life-saving care. Full article

Out-of-hospital cardiac arrests (OHCAs) have high mortality rates, worsened by limited access to automated external defibrillators (AEDs). This study analyzed OHCA response times, identified areas with prolonged ambulance travel times, and proposed optimal AED locations in a medium-sized city in southern Brazil. Data from 278 non-traumatic OHCA cases (2019–2022) in patients over 18 years old, with ambulance response times under 20 min, were included. Spatial survival analysis assessed the probability of exceeding the recommended 5-min (300 s) ambulance response time. The maximal covering location problem identified 100 strategic AED sites within a 150-s reach for bystanders. AED and ambulance travel times were compared using the Wilcoxon test (p < 0.01). Defibrillation occurred in 89 cases (31.01%), and bystander CPR was performed in 149 cases (51.92%). Despite these efforts, 77% of patients died. The median ambulance response time was 11.63 min, exceeding 5 min in most cases, particularly at peak times like 11 a.m. AED placement in selected locations could cover 76% of OHCA occurrences, with a mean AED travel time of 320 s compared to 709 s for ambulances. Strategic AED placement could enhance early defibrillation and improve survival outcomes. Full article

Background/Objectives: In acute stroke, often-prolonged hospital transport times present an opportunity for early interventions to salvage brain tissue. Remote ischemic conditioning (RIC), where brief cycles of ischemia–reperfusion in a limb are induced to protect the brain, is a promising treatment for this setting. We assessed the usability of a novel RIC system in a simulated emergency response scenario. Methods: Paramedics were asked to use the RIC device in an emergency stroke care and ambulance transport simulation, overseen by a confederate. Feedback on device use was collected through questionnaires, including the System Usability Scale (SUS) and the NASA Task Load Index (NASA-TLX), and a semi-structured interview. Questionnaire responses were summarized using descriptive statistics; interview transcripts were analyzed thematically. Results: Nine paramedics (including the confederate) participated, with a mean of 10.0 ± 10.3 years of professional experience. Questionnaire responses indicated high device usability (mean SUS score: 85.3 ± 12.9 out of 100) and low task-related demands, effort, and frustration (mean NASA-TLX domain scores: ≤3.9 out of 20). Seven paramedics stated they would use the device in daily practice. They expressed concerns related to display screen clarity, interference with standard procedures, cable management, device fragility, and patient discomfort. Suggested improvements included adding indicators of device performance and refining the cuff design. Conclusions: While the device was considered easy to use, paramedics also identified important areas of improvement. With a small, localized study sample, our findings are primarily applicable to the refinement of the RICovery system for use in future clinical trials in the same healthcare setting. However, feedback on the importance of mitigating potential interference of newly introduced procedures with those already established, robustness of equipment, and effective paramedic–patient communication may also help inform the design of other pre-hospital interventions. Full article