Search Results (120)

Rapid urban population growth has intensified traffic congestion in smart cities. This has resulted in longer travel times, higher fuel consumption, increased environmental pollution, greater operational costs, and slower emergency response services. Existing traffic congestion prediction models primarily rely on traffic-flow and temporal features; the effects of road accidents and peak-hour conditions are not adequately addressed. This limitation is particularly significant in smart cities where both recurrent congestion (peak-hour demand) and non-recurrent congestion (road accidents) influence traffic conditions they have a significant impact on the performance of the road network. This study introduces a novel Historical Accident-Aware Peak-Hour GAN-GRU (APG-GRU) framework. The proposed framework employs a data processing pipeline integrating traffic data with historical accident-related features to predict traffic congestion using these features. Extensive experiments are conducted on a novel integrated dataset consist on Automatic Number Plate Recognition (ANPR) traffic data and ANPR traffic observations with historical accident features. The results demonstrate that the APG-GRU framework achieved superior performance on the integrated features dataset, attaining an accuracy of 97.50%, a congested precision of 91.86%, a congested recall of 97.31%, and a congested F1-score of 94.51%, outperforming both the ANPR traffic-only dataset and all baseline models. The APG-GRU framework significantly outperforms a suite of benchmark models, including XGBoost, Long Short-Term Memory (LSTM), and Random Forest as baselines, which achieved accuracies between 84% and 95.5% with correspondingly lower precision, recall, and F1-scores. External validation using a traffic dataset collected from Lahore, Pakistan, further demonstrated the robustness and generalizability of the proposed APG-GRU framework. A web-based interface developed for the APG-GRU framework to visualize accident hotspots and route-level traffic conditions. Routes with smooth traffic flow are highlighted in green, whereas congested routes are highlighted in red, demonstrating the practical applicability of the proposed framework for smart city traffic management systems. Full article

Background and Clinical Significance: Cerebral amyloid angiopathy (CAA) is a neurovascular disorder characterized by the deposition of amyloid beta (Aβ) peptides within the walls of small-to-medium-sized cerebral vessels, leading to vascular fragility and an increased risk of lobar intracerebral hemorrhage (ICH), cognitive decline, and recurrent stroke. CAA is an important cause of spontaneous ICH in elderly patients and may be underrecognized, particularly when presenting with acute neurologic symptoms that mimic ischemic stroke. Early identification has significant implications for management, prognosis, and secondary prevention. Case Presentation: An 80-year-old male presented to the emergency department with incoherent speech, rambling, and severe headache concerning for acute stroke. His medical history was notable for a prior cerebrovascular accident, hypertension, diabetes mellitus, benign prostatic hyperplasia, and recent evaluation for dementia-like symptoms. Initial neuroimaging revealed a 3.2 cm intraparenchymal hemorrhage in the left occipital lobe with surrounding edema. Subsequent MRI demonstrated a lobar hemorrhage pattern suggestive of CAA based on imaging findings and clinical context. The patient was admitted to the intensive care unit (ICU) for close neurologic monitoring. He remained hemodynamically stable with no new motor or sensory deficits. Over a three-day hospital course, his speech and visual deficits improved. Blood pressure was carefully controlled, and repeat imaging demonstrated stable hemorrhage without progression. He was diagnosed with probable CAA and discharged home with supportive services. Conclusions: This case highlights the importance of considering cerebral amyloid angiopathy in elderly patients presenting with spontaneous lobar intracerebral hemorrhage and cognitive symptoms. Prompt recognition and appropriate neuroimaging are critical for diagnosis, risk stratification, and guiding management. Full article

Accurate simulation modeling of the danger zone and real-time visualization of the toxic cloud spread during a fire and explosion at an industrial facility in a nearby urban area are in demand by rescue services conducting evacuation. Using a cellular automaton method allows us to create an optimal predictive model of the danger zone spread, combine modeling accuracy with computational speed, and consider multiple input variables and the cascading nature of an accident during visualization. The objective of this study was to develop a mobile application for calculating the parameters of the danger zone during an accident at an industrial facility caused by a toxic cloud spreading into an urban area, based on the selection of a cellular automaton algorithm. The primary objective of the study was a highly detailed visualization of the danger zone with several predicted values of toxic substance concentrations in the air. The authors developed a cellular automaton-based model, which forms the basis of the mobile application. It takes into account several variables characterizing chemicals in the explosion and fire zone, climate factors, occupancy, building parameters, and the availability of respiratory protection. The FireSoft Mobile app was developed using the Visual Studio 2022 development environment, C# 10.0, and .NET MAUI, adapted for Android 8.0 and higher. The mobile app was tested to visualize a cloud of toxic pollutants forming a hazardous zone in an urban agglomeration for cases involving an ammonia tank explosion and a large fire involving a large amount of polyvinyl chloride. The results demonstrate the app’s feasibility and effectiveness in predicting, planning, and managing evacuation measures during accidents at an industrial facility. Full article

Background/Objectives: Cervical spine injuries (CSIs) are life-threatening conditions commonly resulting from road traffic accidents and falls; improper first aid management can significantly worsen neurological outcomes. Public awareness and correct first aid response are critical for preventing secondary injury; despite this, available data from the southern provinces of Saudi Arabia remain insufficient. This study aimed to assess public awareness and first aid preparedness for CSIs across four southern regions of Saudi Arabia. Methods: A cross-sectional design was employed across multiple regions, encompassing 1179 adults from Jazan, Aseer, Al-Baha, and Najran between 2025 and 2026. A validated online questionnaire was employed for data collection to assess CSI awareness, recognition of injury signs, and appropriate first aid responses. Awareness scores of ≥60% were classified as good. Multiple linear regression analysis was performed to identify independent predictors of awareness. Results: The mean awareness score was 16.0 ± 4.8 out of a possible total of 20 points, corresponding to 80% of the total score, with 87% of participants demonstrating good awareness. The majority of respondents recognized the importance of spinal immobilization (89%), maintaining head–neck alignment (95%), and contacting emergency services before intervention (93%). Correct responses to emergency scenarios were recorded in 83% of participants. Notably, only 39% had attended formal medical or trauma training, and merely 3% reported real-life first aid experience. Training attendance (β = 1.39, p < 0.001) and marital status (married; β = 1.37, p = 0.004) were identified as independent predictors of higher awareness scores. Conclusions: Although general public awareness of CSI first aid is high, formal training participation remains critically low, revealing a substantial gap between knowledge and practice. Integrating structured first aid training into community, workplace, and primary healthcare settings is essential to improve trauma outcomes and reduce preventable disability. Full article

Inadequate vehicle maintenance management is one of the main causes of road accidents and elevated operating costs in light vehicles. This paper addresses this problem through the development and implementation of a low-cost integrated system for preventive maintenance management and alerts. The device, based on an open-hardware architecture (Arduino Mega 2560), integrates Global Positioning System (GPS) and mobile communication (GSM/LTE) modules to monitor distance traveled in real time and notify the user via SMS about the proximity of critical services such as oil changes, brake inspections, and timing-belt replacements. Its technical contribution lies in the integration of non-intrusive virtual ignition, filtered GPS-based odometry, configurable MicroSD-based persistence, and progressive SMS alert logic into a low-cost aftermarket system for conventional vehicles without OBD-II dependence. Experimental validation was conducted in the city of Guayaquil using a 2012 Hyundai Accent. Field tests were carried out in three scenarios: a dense urban route, a peripheral road, and interurban routes. Results showed satisfactory accuracy with a global average percentage error of 3.98% compared to the vehicle’s odometer and 100% effectiveness in sending alerts under the tested conditions (20/20 events; exact 95% binomial confidence interval: 83.2–100.0%). These results provide strong evidence of technical feasibility for the proposed architecture under the tested conditions in a representative single-vehicle proof-of-concept, while broader cross-vehicle validation remains necessary before generalizing the system to the wider diversity of aging fleets. Full article

Hallux fractures, particularly those involving the proximal phalanx, can lead to significant functional impairments if not promptly managed. This report introduces a novel, minimally invasive technique for hallux fracture fixation using hypodermic needles. This technique is specifically designed for resource-limited settings such as disasters or emergencies. A 30-year-old female sustained an open proximal phalanx fracture of the left hallux with interphalangeal joint dislocation following a motor vehicle accident. Under local anesthesia in the emergency service settings, fracture reduction and stabilization were achieved using two hypodermic needles. A 21-gauge needle was inserted intramedullary through the medial aspect of the hallux to stabilize the fracture, while an 18-gauge needle was placed distally to secure alignment. Postoperative radiographs confirmed proper alignment, and the needles were removed after four weeks. The patient achieved pain-free ambulation with a full range of motion within six months, with no complications noted. This technique offers a cost-effective, rapid, and practical alternative to traditional methods, eliminating the need for fluoroscopy or specialized surgical tools. It is particularly valuable in emergency and resource-constrained environments, providing favorable clinical and radiological outcomes. This approach demonstrates significant potential for managing hallux fractures in challenging settings and warrants further validation in broader orthopedic practice. Full article

Fires caused by electric vehicle (EV) batteries can pose hazardous situations during accidents and during the servicing of critically damaged vehicles. Managing and preventing such fires requires a thorough understanding of the underlying causes and processes. This article analyses lithium-ion (Li-ion) batteries in electric vehicles, demonstrating the effects of cell overheating, the production of runaway gases, and the resulting thermal catastrophe. We examine the composition of cell eruption gases (CEGs) and their implications for fire protection. Based on these findings, we assess the conditions for safe battery storage, safety guidelines for servicing electric and hybrid vehicles, fire suppression methods, and measures following fire suppression. Additionally, we analyze the unique characteristics of EV fire incidents and, based on these, outline the implementation requirements and safety technologies for repair bays designed to service critically damaged electric and hybrid vehicles. Finally, we propose implementing repair stations suitable for safe servicing operations and accrediting such repair bays, including a flowchart detailing the implementation process. Although this concept is still in its early stages, its implementation would significantly enhance the safety of servicing operations and the effective management of hazardous situations. Full article

Battery-powered applications are rapidly spreading in handheld, domestic, business and power storage appliances and in propelling a range of electric vehicles. Fast developments of new battery technology sparked an equally fast development of a new and wide range of applications, showing new safety problems at the same time. The acceptability of these new safety risks across the range has so far not been thoroughly assessed due to lack of statistical incident data. This study explores the wide range of new technology-based battery applications where people are exposed to these hazards, gathers credible incident scenarios and assesses currently available means for incident prevention and mitigation. Battery fire, explosion and toxic fume incidents are emerging as key safety issues in aerospace, shipping, transport and storage, waste handling, the high-risk chemical industry, domestic appliances, industrial power storage, road traffic and carparks. Incidents are causing severe injuries, death and considerable environmental impacts and financial losses. Implementation of both preventive and repressive safety measures is ongoing, yet complicated due to re-ignition and chemicals involved in battery fires. New firefighting strategies and techniques are needed. The authors present an indicative risk assessment based on the presence of risk factors, as derived from a triangulation of experiences reported from practice, scientific literature findings and expert interviews, thereby initiating a risk-based perspective. Several ways to move forward are recommended. Full article

Emergency response to construction safety accidents is the focus of this study. Despite the abundance of data and materials available for emergency response in construction safety, the unstructured nature of the knowledge and the disordered state of storage have limited the timely application of this knowledge in decision-making for emergency response. In this study, scenario-response theory, natural language processing, and deep learning technologies were employed to construct a domain knowledge graph for emergency response in the field of safety accidents. First, based on scenario-response theory and domain-specific materials, four categories of scenario domains and 14 types of scenario elements were identified. Second, according to the mapping relationships between scenario elements and emergency response knowledge, 14 entity types and 10 relationship types were determined, thereby forming the knowledge structure pattern of this field. Subsequently, 4877 entities and 5783 relationships were extracted by means of the BERT-BiLSTM-CRF model and the BERT-CNN model, with F1 values reaching approximately 0.8. Finally, the Neo4j graph database was adopted for data storage, and a domain knowledge graph was constructed. Based on this graph, services such as knowledge association, knowledge retrieval, and intelligent question-answering were implemented. These services effectively addressed key challenges in information acquisition and decision support for on-site safety management, thereby significantly enhancing response efficiency and quality while strengthening overall safety management practices within the construction industry. Full article

Establishing secure vehicular communication is essential for the development of autonomous driving capabilities and to make new functionalities possible, such as smart traffic management, or systems aimed at avoiding or mitigating traffic accidents. In this scenario, where the deployment of a Public Key Infrastructure (PKI) may be difficult, the use of identity-based cryptography is proposed as a good alternative, because this approach simplifies encryption by enabling dynamic key generation and secure communication, without requiring prior key exchanges, making it highly scalable and efficient. In this way, this paper proposes a communication protocol applicable to V2V (vehicle-to-vehicle) and V2X (vehicle-to-all) communications, replacing schemes based on PKI with identity-based cryptographic schemes using the VIN (Vehicle Identification Number) as an unequivocal vehicle identifier. This paper also describes a prototype implementation in a conventional vehicle and the performance metrics of the system. Through a defined proof of concept, we obtained various quantitative results, demonstrating the importance of the processors used for the encryption and decryption operations required. The proposed system provides secure and flexible vehicle identification, with multiple practical applications. It can enables digital authentication, support toll payments without extra hardware, and facilitate V2X communication via the VIN for improved traffic management and safety. Additionally, it can streamline processes in repair workshops and optimize fuel payment and tracking at service stations. Full article

Objectives: The primary objective was to compare the usage of Hazardous Materials (HazMat) Protective Personal Equipment (PPE) and ordinary PPE when performing basic and advanced health care support maneuvers in a prehospital setting, evaluating the effectiveness of several procedures, defined as the mean success rate of each. The secondary objective was to evaluate the presence of a learning effect, with improvements in the success rate and/or procedure timing. Methods: This was a prospective within-subjects (repeated-measures) study conducted on Emergency Medical Services (EMS) responders within their Chemical-Biological-Radiological-Nuclear-Explosive (CBRNe) training institutional programme. Volunteers performed a trial sequence of eight lifesaving procedures four times. During the first trial sequence, they wore standard clothing; during the three successive trials, they wore full HazMat PPE equipment. The primary outcomes were changes in success rate and time interval across the four trials. Results: A total of 146 EMS responders volunteered for the experiment. Procedure success rates remained high overall, with the most notable initial drop observed for video-assisted intubation (≈−10%). The only statistically significant delay in the first HazMat trial compared with baseline was for intravenous access (median +30 s; p < 0.001). In the two successive HazMat trials, success rates and timings improved, with median values coming close to baseline. However, only 61% of participants completed the entire drill due to tolerance limits of the equipment. Conclusions: HazMat PPE, while physically and ergonomically demanding, has minimal impact on most lifesaving procedures, though it may reduce intubation success and delay intravenous access. Tolerance to prolonged use is a key limitation, but dexterity improves rapidly with brief practice. EMS responders can benefit from continuous training practice, while manufacturers could explore ergonomic and tolerance improvements in their PPE equipment. Full article

This study aims to guide the management and service of highways towards a more efficient and intelligent direction, and also provides intelligent and green data support for achieving sustainable development goals. The forecasting of traffic flow at highway stations serves as the cornerstone for spatiotemporal analysis and is vital for effective highway management and control. Despite considerable advancements in data-driven traffic flow prediction, the majority of existing models fail to differentiate between directions. Specifically, entrance flow prediction has applications in dynamic route guidance, disseminating real-time traffic conditions, and offering optimal entrance selection suggestions. Meanwhile, exit flow prediction is instrumental for congestion and accident alerts, as well as for road network optimization decisions. In light of these needs, this study introduces an enhanced heterogeneous spatiotemporal graph network model tailored for predicting highway station traffic flow. To accurately capture the dynamic impact of upstream toll stations on the target station’s flow, we devise an influence probability matrix. This matrix, in conjunction with the covariance matrix across toll stations, updated graph structure data, and integrated external weather conditions, allows the attention mechanism to assign varied combination weights to the target toll station from temporal, spatial, and external standpoints, thereby augmenting prediction accuracy. We undertook a case study utilizing traffic flow data from the Chengdu-Chengyu station on the Sichuan Highway to gauge the efficacy of our proposed model. The experimental outcomes indicate that our model surpasses other baseline models in performance metrics. This study provides valuable insights for highway management and control, as well as for reducing traffic congestion. Furthermore, this research highlights the importance of using data-driven approaches to reduce carbon emissions associated with transportation, enhance resource allocation at toll plazas, and promote sustainable highway transportation systems. Full article

Mechanised agricultural operations are often performed individually, under minimal supervision and across a wide range of unfavourable working conditions, resulting in a complex mixture of hazards and external stressors that severely affect safety conditions. Socio-technical and environmental constraints significantly affect safety culture and require continuous performance adjustments to overcome timing pressures, resource limitations, and unstable weather conditions. This study introduces a FRAM-based safety culture model that embeds the thoroughness-efficiency trade-off (ETTO) in four distinct operational modes that adhere to specific safety cultures, namely, thoroughness, risk awareness, compliance, and efficiency. This model has been instantiated for mechanised ploughing: foreground task functions were coupled with background functions that represent socio-technical constraints and environmental variability, while severity classes for potential incidents were derived from the US OSHA accident database. The framework was also supported by a semi-quantitative Resonance Index based on severity and coupling strength, the Total Resonance Index (TRI), to assess how variability propagates in foreground functions and to identify hot-spot functions where small adjustments can escalate into high resonance and hazardous conditions. Results showed that the negative effects on functional resonance generated by safety detriment on TRI observed between compliance and effective working modes were three times larger than the drift between risk awareness and compliance, demonstrating that efficiency comes with a much higher cost than keeping safety at compliance levels. Extending the proposed approach with quantitative assessments could further support the management of socio-technical and environmental drivers in mechanised farming, strengthening the role of safety as a competitive asset for enhancing resilience and service quality. Full article

Background: Examining medico-legal cases within hospitals aids in identifying care-related problems, facilitating necessary corrections and emphasizing successful preventive measures. The case of Meyer Children’s Hospital is particularly noteworthy as it offers insights into the evolution of litigation in regard to a tertiary pediatric hospital. Methods: The study sample comprised 158 malpractice claims received by Meyer Children’s Hospital from 1 January 2010 to 31 December 2023, which were managed by the Claims Management Committee (CMC) responsible for civil liability within the hospital. In this observational retrospective study, the following variables were analyzed: (1) The characteristics of the patients (age–sex) and the manner in which they interacted with the hospital, ultimately resulting in the compensation claim (method of access, area of specialty, outcomes based on the International Classification of Patient Safety (ICPS)). (2) Medico-legal factors: the details of the compensation claim, the significant issues noted in various cases, and the findings of the medico-legal inquiry conducted by the CMC. In cases of ADR (Alternative Dispute Resolution), we evaluated the nature of the procedure, the results, and the amount of compensation awarded. Results: We conducted a descriptive statistical analysis to delineate the trend of claims and identify specific deficiencies within structures or departments over time. Invasive procedures and surgical operations were identified as the leading causes of accidents, resulting in heightened mortality rates and serious injuries. The most common errors observed were diagnostic and therapeutic. Conclusions: The data that emerged highlighted a low rate of claims (11.28/year) and a low claim/service ratio (0.0002%), suggesting a high level of safety of patient care at the hospital. The acceptance rate (32%), the percentage of rejected cases (48/158~30% of total, or 48/99~49% of resolved claims), the average compensation (EUR 68,312), and the percentage of cases (92%) with judicial opinions consistent with those of the CMC indicate a tendency to pursue exploratory compensation requests and the effectiveness of CMC’s activity. Meanwhile, the predominant error types (surgical and diagnostic) are in accordance with national and international data. Finally, the scarcity of disputes concerning informed consent reflects the impressive effectiveness of the communication strategies utilized by the pediatricians at this center. 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