Accident Cases Research Articles

Analysis of Safety Distances in Fire Explosions at Indoor Tank Storage Facilities Containing Flammable Liquids in Neighborhood Living Facilities

This study analyzed the minimum safe distance required for rescue activities in a flammable liquid fire explosion in an indoor tank storage facility within a neighborhood living facility. The safe distance was derived using guidelines, accident data, and simulations of accident cases. The considered chemicals were acetone, which was used as the refined oil, and N-dodecane, which was used as the diesel. The Busan bathhouse fire explosion in 2023 was selected as the accident case. The results indicated that the safe distance in the guidelines was 50 m, and the safe distance for accident cases considering only thermal radiation was 30 m. The simulation results showed that the radiation heat and overpressure of refined oil were in the ranges of 17~52 m and 26~129 m, respectively, whereas the radiation heat of diesel oil was in the range of 19~47 m. Hence, it is reasonable to apply a minimum safety distance of 50 m, as set by the fire brigade, upon the initial arrival of a flammable liquid leak accident. To provide relief activities for a chemical rescue team arriving in the middle of an accident, it is necessary to create and provide data that can be used to set appropriate safety distances based on the characteristics of the material and environment.

Boosting Algorithms for the Accident Severity Classification

<div><i>Background:</i> Road accident severity estimation is a critical aspect of road safety analysis and traffic management. Accurate severity estimation contributes to the formulation of effective road safety policies. Knowledge of the potential consequences of certain behaviors or conditions can contribute to safer driving practices. Identifying patterns of high-severity accidents allows for targeted improvements in terms of overall road safety. <i>Objective:</i> This study focuses on analyzing road accidents by utilizing real data, i.e., US road accidents open database called “CRSS.” It employs advanced machine learning models such as boosting algorithms such as LGBM, XGBoost, and CatBoost to predict accident severity classification based on various parameters. The study also aims to contribute to road safety by providing predictive insights for stakeholders, functional safety engineering community, and policymakers using KABCO classification systems. The article includes sections covering theoretical methodology, data analysis, model development, evaluation, performance metrics, and implications for improving road safety measures by comparing the performance of different boosting algorithms on the CRSS dataset. This study aims to identify the most effective machine learning algorithm to integrate into our product line in the near future, enabling accurate prediction of both accident severity and occurrence. <i>Results and Conclusions:</i> This study addresses challenges in evaluating performance metrics for different severity classes within unbalanced datasets, emphasizing the impact of dominant classes like Class O (O = no apparent injury) on overall accuracy. The investigation reveals the limitations and conservatism associated with imbalanced data in boosting models, hinting at a potential ceiling in their performance around 80%. Comparative analysis of algorithms, including CatBoost, XGBoost, and LGBM, demonstrates comparable performance even in the case of applying KNN algorithm for pre-processing, based on various metrics, especially accuracy, <i>F</i><sub>1</sub>-score, ROC-AUC, and PR-AUC for all severity classes. XGBoost with KNN algorithm did not show any significant performance improvement compared to the XGBoost without KNN algorithm. The study includes performance metrics, such as <i>F</i><sub>1</sub>-score, CM upper triangle, ROC-AUC, and PR-AUC applied to an accident analysis case study. Future work directions involve extending the application of CatBoost, XGBoost, and other algorithms to diverse datasets, exploring the capabilities of deep neural networks, refining dataset preparation for accuracy improvement, and creating unified tools for hazard analysis and risk assessment.</div>

Occurrence Type Classification for Establishing Prevention Plans Based on Industrial Accident Cases Using the KoBERT Model

Occurrence Type Classification for Establishing Prevention Plans Based on Industrial Accident Cases Using the KoBERT Model

Research on safety risk factors of metro shield tunnel construction in China based on social network analysis

PurposeDuring metro construction using the shield method, the construction process's complexity, the construction environment's variability, and other factors can easily lead to tunnel construction accidents. This paper aims to explore the interconnections between risk factors and related accident types, as well as the risk chain formed between risk factors, and to analyze the key risk factors and vulnerabilities in shield tunnel construction through empirical data.Design/methodology/approachBased on the social network analysis theory, the connection of various risk factors in subway shield tunnel construction is explored, and the mechanism of multiple risk factors is studied. Through literature analysis, articles on safety risk factors in metro shield tunnel construction are organized and studied, and the identified safety risk factors can comprehensively reflect the significant risks that need to be concerned in metro shield tunnel construction.FindingsThe results show that a small world characterizes the SNA network of safety risk factors for metro shield tunnel construction: The frequency of association between the five risk factors “unsafe behavior,” “site management,” “safety supervision and inspection,” “safety education system” and “safety protection” is higher than that of other factors. Only a few risks, such as “site management,” “safety supervision and inspection,” and “rapid response capability,” directly lead to accidents. In addition, risk factors such as the “safety education system” and “safety protection” will indirectly cause unsafe behaviors of construction personnel.Research limitations/implicationsDuring construction, the probability of occurrence of risk factors may vary with the construction phase and area and is not considered in this paper. In addition, although this paper identifies, determines and analyzes the risk factors affecting the safety of metro shield tunnel construction, including the importance of each risk factor and the connection between them, more detailed information before and after the accident could not be obtained based on the accident investigation report alone. Therefore, future research can collect the same accident case from more sources to obtain more information.Practical implicationsThe theory of accident causation has been improved at the theoretical level. The identified safety risk factors can comprehensively reflect the significant risks that need to be paid attention to in metro shield tunnel construction. From a practical point of view, the results of the study provide a basis for the rational control of the risk factors in the construction of subway shield tunnels, which can help guide practitioners to do a good job of risk prevention before the construction of metro shield tunnels and reduce the probability of related accidents.Originality/valueThis study expands the application of social network analysis in the field of subway tunnel construction risk, quantitatively analyzes the key risk factors and vulnerabilities in shield method tunnel construction and proposes policy recommendations for future metro tunnel construction safety management.

Design System of Solar-Powered Safety Lamp Integrated Proximity Sensor as a Solution for Accident Prevention at Blind Spot Areas

The curved contour of the road creates blind spots which increase the risk of traffic accidents. Throughout 2019, there were 107,500 accident cases recorded in Indonesia. Therefore, efforts are needed to provide road infrastructure that supports driving safety, such as traffic signs to reduce the risk of accidents in blind spot areas. This research aims to design a smart safety light warning system that is integrated with solar-based street lighting. The design uses a design-based research approach referring to several established standards including Indonesia National Standard (SNI) 7931: 2008, SNI IEC 60529:2014, Regulation of the Minister of Transportation of the Republic of Indonesia Number PM 13 of 2014, Manual on Uniform Traffic Control Devices (MUTCD) issued by the Federal Highway Administration (FHWA) of the United States Department of Transportation (USDOT), and Article 115 of Minister of Transportation Republic of Indonesia Regulation PM 27 of 2018. This study used Design-Based Research (DBR) as a research methodology. The result of this study is a prototype design and development of smart safety light warning system which can be optimized. Validation of the design and circuit is conducted using prototyping modeling 1:20 from the original dimensions to measure the function and performance of the circuit. The prototype is capable of turning on warning lights and siren notifications when detecting objects with a maximum distance of 350 cm as measured by the HC – SR04 sensor. Charging the stored solar panels via the accumulator allows the technology to continue working for 45 hours without charging. For power efficiency purposes, the technology is equipped with an LDR (Light Dependent Resistor) sensor with a minimum limit of 341-lux which regulates the lighting. The development of these safety lights will increase driver awareness when passing through blind spot areas and have positive implications for reducing the number of accidents in Indonesia.

Research on scenario extrapolation and emergency decision-making for fire and explosion accidents at university laboratories based on BN-CBR

To solve the problems of suddenness, uncertainty and untimely emergency decision-making related to fire and explosion accidents in university laboratories, a combined method of BN and CBR is introduced to analyze laboratory accidents. By summarizing the characteristics of 72 accident cases worldwide, four scenario elements with key roles are extracted by combining the public safety triangle theoretical model; a BN is established from the macro perspective, which is based on the construction of dynamic scenarios; the evolution path is analyzed via BN theory; and the probability of occurrence of accidents is quantified from the microscopic perspective, with a focus on the analysis of the accidental evolution process. A case similarity calculation is carried out via CBR, and the construction of a BN-CBR-assisted decision-making model is completed, verified and corrected in an case study. The results show that the BN-CBR model can quickly determine the accident evolution path and the most similar historical cases, and its quantitative probability calculation enables one to comprehensively grasp the real-time state of the whole accident and the emergency response in a timely manner, which provides a new way to approach emergency decision-making of accidents.

Pattern Of Fatal Head Injury Due To Vehicular Accidents In Tertiary Care Centre

Head injury is an important cause of mortality worldwide. The present study was undertaken on 100 cases of vehicular accidents, which were autopsied at Thirunelveli Medical college over a period of one year from 2019-2020.Most of accidents has taken place during 8PM to 12 AM with a marked male preponderance of 83%.Vulnerable age group was found to be 21 to 40 years. Two wheeler occupants were commonly involved. Skull fractures were present in 65% of cases. Base of skull fractures found to be 23%,fracture of vault were found in 12%,linear fracture 50% .Among intra cranial hemorrhages, Subdural and sub arachnoid hemorrhage found to be 80.9%.

Study of Pattern of Craniofacial Injuries in Road Traffic Accident Cases Autopsied in Midnapore Medical College

Background: The objective of this study was to investigate the pattern of craniofacial injuries in victims of fatal road traffic accidents, observed through autopsies conducted at Midnapore Medical College, West Midnapore from January 2021 to December 2023. Methods: It is a retrospective, cross- sectional observational study. Data was collected by thorough analysis of the injuries documented in post-mortem reports of all the alleged road traffic accident cases where victim sustained Cranio- facial injuries within a time span of 3 years, from 1st January 2021 to 31st December, 2023. Data was analysed using Microsoft Excel. Conclusion: The study highlights the necessity of context-specific interventions to address the unique injury patterns observed in West Midnapore's road traffic accidents. Improving road infrastructure, raising traffic safety awareness, and promoting protective measures like seat belts and helmets can reduce severe craniofacial injuries. The prevalence of maxillofacial fractures underscores the face's vulnerability, with long-term physical and psychological impacts. Effective prevention strategies, such as strict traffic regulations and awareness campaigns on the dangers of distracted or impaired driving, are essential. Despite limitations, the study provides valuable insights into craniofacial injury patterns in fatal accidents, stressing the need for enhanced road safety, stronger traffic laws, and better post-accident care. Interdisciplinary collaboration among healthcare professionals, law enforcement, and policymakers is crucial for developing comprehensive strategies to manage and prevent these injuries. By examining regional injury characteristics and comparing them with other studies, this research contributes to the broader understanding of craniofacial trauma, offering significant implications for improving road safety, trauma care, and forensic practices.

Coupled Analysis of Risk Factor for Tailing Pond Dam Failure Accident Based on N–K Model and SNA

The failure of tailings pond dams represents a complex coupled system involving various risk factors, including human, governance, facilities, and environmental aspects. It is crucial to identify key risk factors at the system level to enhance the safety management of tailings ponds. We analyzed 74 cases of tailings pond dam failure accidents, both domestically and internationally, from the perspectives of human, governance, facility, and environment. We employed the 2–4 Model to identify and extract the causes of dam failures, summarizing these into four primary risk factors and 40 secondary risk factors, while constructing a risk coupling mechanism model. The natural killing (N–K) model was implemented to analyze the risk coupling values of primary risk factors and quantify these couplings. The N–K model facilitated an analysis of the risk coupling values of first-level risk factors, while social network analysis (SNA) was employed to visualize the relationships among second-level risk factors and assess the centrality and accessibility of nodes within the risk factor network. The out-degree of the risk nodes was corrected by integrating the N–K model with the SNA, leading to the identification of key risk factors associated with tailings pond dam failures and the formulation of corresponding safety prevention and control strategies. The findings indicate that managing multi-risk factor coupling is an effective approach to mitigating the occurrence of tailings pond dam failure accidents. Notably, unfavorable environmental risk factors significantly contribute to the coupling of human–governance–facility–environmental risks, necessitating targeted management strategies. Furthermore, inadequate safety supervision, weak safety awareness, inadequate receipt and inspection, and irregular operation represent additional key risk factors requiring focused prevention and control efforts.

Revealing the coupled evolution process of construction risks in mega hydropower engineering through textual semantics

Mega hydropower engineering (MHE) involves complex construction processes, harsh environments, and highly mobile personnel, equipment, and other resources, resulting in a high incidence of construction risks and significant difficulties in safety control. Previous studies extensively explored the identification and governance of risks in MHE. However, these studies did not adequately utilize accident report case texts, and the coupled evolutionary law of construction risks remains unclear. To address this gap, this study developed a Bert-based Global Pointer Network for Named Entity-Relation Joint Extraction (Bert-GPLinker) to extract risk factors and their relationships. Second, the extracted risk factors and their relationships were employed to construct a knowledge graph, integrating text matching and path inference algorithms to propose a coupled evolutionary path reasoning method for construction risks. Finally, the coupled interactions among the risk factors were considered, and the interaction matrix principle was utilized to quantitatively assess the importance of each risk factor in the knowledge graph. The results showed that (1) the F1 value of Bert-GPLinker on the self-built dataset was 91.90%, indicating better model performance; (2) the coupled evolutionary reasoning system can quickly deduce the evolutionary path with the highest probability of any risk node and the types of accidents that may result; and (3) among the top 25 risk factors in terms of importance, those in the management system category accounted for 52%, with higher importance mainly concentrated in inspection, supervision, and training.

A systematic literature review on occupational accident factors in the rail construction industry: lessons learned from a quarter-century of studies globally.

The rail construction industry is notable for its large scale, substantial investment, extensive stakeholders involvement, long construction period, and intricate operation and technology. This industry is among the most dangerous due to the highest number of occupational accident cases worldwide. Therefore, it is crucial to analyse and identify the existing literature on occupational accident factors in rail construction. To address the research aim, the study identified the factors that contribute to occupational accidents using systematic review methodology. This systematic literature review adheres to the rigorous Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. Relevant publications from the past 25 years were retrieved from Scopus, Web of Science (WoS), and Science Direct electronic databases. Through a meticulous review of 43 selected publications, five accident factor themes were discovered: worker, workplace, materials and equipment, organizational, and environmental influences. The detailed analysis of these themes has led to the identification of 19 specific sub-factors within these categories, providing a granular understanding of the intricate elements contributing to accidents. This study offers a foundational understanding of accident factors in the rail construction industry, paving the way for targeted OSH interventions aimed at preventing occupational accidents in the future.

Ship recycling process in Bangladesh and a survey-based risk assessment with mitigation proposal

Ship recycling industries play a significant role in the economic development of South Asian countries, including Bangladesh. In the wake of entering the Hong Kong Convention (HKC) in June 2025, the industry aims to improve its overall safety standards cost-effectively. However, due to the restrictive nature of the industry, solutions based on field surveys are limited. This study successfully articulates the indigenous industry knowledge of the recycling process in Bangladesh after carefully studying the practices followed by the existing ship recycling yards in the country. Based on field surveys conducted across 18 ship recycling yards in Bangladesh, the risk patterns inherent in the ship recycling industry are studied; for example, highly hazardous tasks in the ship recycling process are found to be (i) explosion hazards during primary cutting operations at the intertidal zone, (ii) falling hazards during inspections at the outer anchorage, (iii) falling and crushing hazards during block cutting and handling heavy objects. Industry experts and scholars consider the perception of the risk pattern regarding the current practices of ship recycling activities to cover a broader perspective of this issue. The correlation of survey outcomes with actual accident cases is comparable. Finally, practical recommendations for minimising risks have been recommended, especially in high-risk workplace areas. The study adds knowledge to the existing literature by giving a clear picture of evidence-based findings for this sector and helps improve the industry's overall safety standard.

The Importance of the Role of Emergency Paramedics in Quickly Transporting Traffic Accident Cases to Health Facilities

This study aims to know the effect of ambulance transportation in reducing the rate of injuries for people injured in traffic accidents, the importance of the role of emergency paramedics in reducing the rate of injury for people transported to health facilities, a questionnaire was prepared via Google Drive and distributed to the population between the ages of 25-55 years from Men and women in the holy city of Mecca. The questionnaire was distributed via the social networking program (WhatsApp) for the purpose of distancing. 400 questionnaires were distributed, and 380 responses were obtained via email to the principal researcher. it concluded that everyone mentioned the importance of paramedics in transporting all dangerous cases (whether traffic accidents or other) in record time and with a very high degree of professionalism.

A Simulation-Based Study on Securing Data Sharing for Situational Awareness in a Port Accident Case

The cyber–physical systems (CPSs) of various stakeholders from the mobility, logistics, and security sectors are needed to enable smart and secure situational awareness operations in a port environment. The motivation for this research arises from the challenges caused by some unexpected events, such as accidents, in such a multi-stakeholder critical environment. Due to the scale, complexity, and cost and safety challenges, a simulation-based approach was selected as the basis for the study. Prototype-level experimental solutions for dataspaces for secure data sharing and visualization of situational awareness were developed. The secure data-sharing solution relies on the application of verifiable credentials (VCs) to ensure that data consumers have the required access rights to the data/information shared by the data prosumer. A 3D virtual digital twin model is applied for visualizing situational awareness for people in the port. The solutions were evaluated in a simulation-based execution of an accident scenario where a forklift catches fire while loading a docked ship in a port environment. The simulation-based approach and the provided solutions proved to be practical and enabled the smooth study of disaster-type situations. The realized concept of dataspaces is successfully applied here for both daily routine operations and information sharing during accidents in the simulation-based environment. During the evaluation, needs for future research related to perception, comprehension, projection, trust, and security as well as performance and quality of experience were detected. Especially, distributed and secure viewpoints of objects and stakeholders toward real-time situational awareness seem to require further studies.

Advanced Risk Assessment for Deep Excavation in Karst Regions Using Improved Dempster–Shafer and Dynamic Bayesian Networks

This study presents a novel risk-assessment methodology for deep foundation pit projects in karst regions, aimed at enhancing project safety and decision-making processes. This approach amalgamates fuzzy dynamic Bayesian networks with a refined Dempster–Shafer (DS) evidence theory to tackle the intricate uncertainties present in such contexts. A comprehensive risk index system, derived from historical accident cases, relevant standards, and the literature, encompasses environmental, design, construction, and management factors. Initial probabilities for each risk factor are determined through the integration of expert knowledge and fuzzy theory. The enhanced Dempster–Shafer theory is utilized to fuse diverse information sources, culminating in a robust and dynamic risk evaluation model. This model leverages real-time monitoring data to dynamically assess and adjust risk levels throughout the construction process. The validation of the proposed method is demonstrated through a detailed case study of the Guangzhou Tangxi Section 1 deep foundation pit project, which effectively identified critical risk factors and facilitated proactive construction strategy adjustments. To further evaluate the reliability of the methodology, comparisons were made with three alternative methods, and applications were conducted on three additional deep foundation pit projects. These comparative analyses confirm the superior reliability and applicability of the proposed methodology across varied scenarios.

Internet of things based smart framework for the safe driving experience of two wheelers

Several parameters affect our brain's neuronal system and can be identified by analyzing electroencephalogram (EEG) signals. One of the parameters is alcoholism, which affects the pattern of our EEG signals. By analyzing these EEG signals, one can derive information regarding the alcoholic or normal stage of an individual. Many road accident cases around the world, including drinking and driving scenarios, which result in loss of life, have been reported. Another reason for such incidents is that riders avoid wearing helmets while driving two-wheelers. Many road accident cases involving two-wheelers, including drinking, driving, overspeeding, and nonwearing helmets, have been reported. Therefore, to solve such issues, the present work highlights the features of an intelligent model that can predict the alcoholism level of the subject, wearing of a helmet, vehicle speed, location, etc. The system is designed with the latest technologies and is smart enough to make decisions. The system is based on multilayer perceptron, histogram of oriented gradients (HoG) feature extraction, and random forest to make decisions in real time. The accuracy of the proposed method is approximately 95%, which will reduce the fatality rate due to road accidents. The system is tested under different working environments, i.e., indoor and outdoor, and satisfactory outcomes are observed.

MAKG: A maritime accident knowledge graph for intelligent accident analysis and management

With the increasing frequency of human activities at sea, maritime accidents are occurring more often. Analyzing and mining maritime accident cases can help uncover the causal mechanisms behind these incidents, thereby enhancing maritime safety. As an emerging technology for knowledge management and mining, knowledge graphs offer significant support for the storage, reasoning, and decision-making processes related to maritime accidents.In this study, we established a knowledge graph construction and application framework for maritime accidents to facilitates the extraction and management of maritime knowledge from unstructured texts. First, 581 accident reports released by the China Maritime Safety Administration over the past decade (2014–2023) were used as the data basis for analysis and construction of the maritime accident ontology structure using the seven-step method, which comprises 8 entity types, 8 relationship types, and 18 attribute entity types. Second, We proposed MBERT-BiLSTM-CRF-SF, a named entity recognition model based on domain pretraining and self-training, to reduce graph construction costs. This model achieved state-of-the-art performance in the maritime domain, with an F1 score of 0.910 ± 0.006, which is about 5% higher than the mainstream model. In addition, we proposed an entity alignment method based on font and semantics to refine knowledge further. On the basis of the proposed method, we constructed a large, high-quality maritime accident knowledge graph (MAKG) system that contains 16,099 entities and 20,809 relationship instances. Finally, we reduced the complexity of applying knowledge graphs by integrating the CRISPE prompt learning framework of the large language model, and experiments on graph traversal, pattern recognition, and aggregation analysis were conducted to assess the quality of MAKG. Results demonstrate that MAKG can effectively enhance the efficiency of querying and reasoning about maritime accident information, thus providing significant support for the prevention and management of maritime accidents.

Law Enforcement of Police Discretion to Resolve Traffic Accidents at the Investigation Level

Traffic accidents are one of the serious problems that are often faced in the context of law enforcement, especially in countries with high traffic density such as Indonesia. This study aims to evaluate how discretionary decisions taken by police investigators can affect the case settlement process, taking into account aspects of justice and public interest. The study utilized policy documents as the primary data source, focusing on analyzing these documents through a systematic document analysis and content analysis approach. The document analysis technique will help in organizing and summarizing these documents to identify relevant information and protocols regarding the application of discretion. The content analysis will then be employed to examine the specifics of how discretion is framed and regulated within these policies, revealing the underlying principles and standards guiding police decision-making in traffic accident cases. This research would contribute to refining policies and practices related to police discretion, ensuring they effectively balance flexibility with adherence to legal and ethical standards.

Application of tuberosity suture combined with autogenous bone grafting in reverse total shoulder arthroplasty for elderly patients with proximal humeral fractures

To analyze the effectiveness of tuberosity suture combined with autogenous bone grafting in reverse total shoulder arthroplasty for elderly patients with proximal humerus fracture. A clinical data of 28 patients with fresh proximal humerus fractures, who met the selection criteria and admitted between June 2014 and April 2022, was retrospectively analyzed. There were 7 males and 21 females. Age ranged from 65 to 81 years, with an average of 73.8 years. The causes of injury were 21 cases of fall, 6 cases of traffic accident, and 1 case of falling from height. The time from injury to operation ranged from 5 to 20 days with an average of 9.2 days. There were 8 cases of Neer three-part fracture and 20 cases of four-part fracture. The reverse total shoulder arthroplasty was performed, and the greater and lesser tuberosities were sutered and reconstructed with autogenous bone grafting. After operation, the Constant score, American Society for Shoulder Surgery (ASES) score, and visual analogue scale (VAS) score were used to evaluate shoulder function and pain; and the active range of motion of the shoulder joint was recorded, including flexion, external rotation, and internal rotation. X-ray films were taken to observe the position of prosthesis. According to the evaluation criteria proposed by Boileau, the healing of greater tuberosity was evaluated, and the effectiveness was compared between the patients with healed and non-healed (displacement and absorption) greater tuberosity. All incisions healed by first intention after operation. All patients were followed up 24-106 months, with an average of 60.9 months. At last follow-up, the VAS score of shoulder joint ranged from 0 to 6 (mean, 1.1). The Constant score ranged from 45 to 100 (mean, 80.1). The ASES score ranged from 57 to 100 (mean, 84.7). The active range of motions of shoulder joint were 60°-160° (mean, 118°) in flexion, 0°-50° (mean, 30°) in external rotation, and corresponding to reaching the S 5-T 8 vertebral body level in internal rotation. During follow-up, no shoulder joint re-dislocation or severe shoulder instability occurred, and no revision surgery was performed. X-ray film reexamination showed that there was no loosening of the prosthesis. According to the evaluation criteria proposed by Boileau, the greater tuberosity fused in 22 cases (78.6%), displaced in 3 cases (10.7%), and absorbed in 3 cases (10.7%). The shoulder joint function and pain related evaluation indicators in the healed group were significantly better than those in the non-healed group ( P<0.05). Tuberosity suture combined with autogenous bone grafting is a relatively simple procedure that provides a reliable fixation for the anatomic recovery of greater and lesser tuberosities and is beneficial for the recovery of shoulder function in elderly patients with proximal humeral fractures.

Possibilities to evaluate the vehicle pre-collision travel velocity in the case of a frontal impact with a fixed object, through numerical modelling

Abstract The present paper evaluates, from a physical-mathematical perspective, the pre-collision travel speed of a vehicle, in the case of a frontal impact with a rigid, fixed pole-type obstacle. The evaluation considers both energy methods and deformation coefficients, taking into account the parameters resulting from the primary investigation of the accident site. The reconstruction of such road accidents aims to determine the kinematic parameters that influence the behavior of the involved vehicle, and to allow the analysis of avoidance possibilities of such events. The developed working algorithm can be applied to solve a large number of road accident cases by changing the input data and to obtain the necessary kinematic and dynamic parameters for the accident reconstruction process. Thus, it is possible to establish the dynamics of such road traffic events, which facilitates the assessment and comparison of different conditions under study. The validation of the fore-mentioned reconstruction algorithm is performed by comparing the results obtained through numerical modelling with those determined experimentally. The relative error between the two is, as well, assessed.