Fires constitute a significant threat due to the pollutants emitted and the destruction they cause. People who take part in fire-fighting operations must be equipped with appropriate tools, including special clothing that will allow them to work and guarantee safety. One of the threats are compounds from the PAH group, which are characterized by high toxicity and carcinogenicity. Therefore, it is important that the materials used constitute a barrier to contamination. Various materials from which individual elements of special firefighter's clothing are made were tested. Additionally, the importance of height on the possibility of sorption of PAH compounds on a given type of material was analyzed. Based on the results obtained, it was found that the type of material and the zone in which the clothing items are used are important in the sorption processes of pollutants. Data in this area may be important for taking further actions regarding the modification of materials used in special fire brigade clothing and in their cleaning processes.

Continuous technological progress places significant demands on the materials used in increasingly modern devices. An important parameter is often the long-term thermal resistance of the material. The use of heat-resistant polymer materials worked well in technologically advanced products. An economically justified direction in searching for new materials is the area of polymer nanocomposite materials. It is necessary to appropriately select both the polymer matrix and the nanofillers best able to demonstrate the synergistic effect. A promising area of exploration for such nanocomposites is the use of organosilicon polymers, which results from the unique properties of these polymers related to their structure. This review presents the results of the analysis of the most important literature reports regarding organosilicon polymer nanocomposites with increased thermal resistance. Particular attention was paid to modification methods of silicone nanocomposites focused on the increase of their thermal resistance related to the modification of siloxane molecular structure and by making nanocomposites using inorganic additives, carbon nanomaterials. Attention was also paid to such important issues as the influence of the dispersion of additives in the polymer matrix on the thermal resistance of silicone nanocomposites and the possibility of modifying the polymer matrix and permanently introducing nanofillers thanks to the presence of various reactive groups. The thermal stability mechanism of these nanocomposites was also analysed.

This concept paper explores possible collective identities for a future wildland fire workforce. Taking inspiration from the work of futurists who foresee an end to the dominant fire exclusion/suppression paradigm, and assuming an emerging fire restoration/resilience paradigm shift replaces it, the paper engages in speculative explorations of the process and product of this paradigm shift on future collective identities for a workforce doing ecological fire management. Social constructionist assumptions from symbolic interactionist sociological theory, Gramscian political theory's concept of hegemony, and New-Social-Movement theory's concept of collective identity all provide the intellectual foundations for the discussion. This concept paper argues that in order to actualize a paradigm shift, more than advances in scientific research or reforms of government policies will be required--the wildland fire community will need to become (or join) a social movement engaged in collective actions. An imaginary social movement, the "Greenfire revolution," is invented to help illustrate how the selected theories and concepts might apply in the social construction of ecological fire management and the collective identities of its future workforce.

Background: By 2025, road traffic injuries are projected to rank third in the global burden of disease, posing a significant challenge that affects health, social well-being, and economic aspects. According to data from the Romanian Police National Statistics Center, there has been an average of 342 traffic accidents per year involving pediatric patients over the past 10 years. Materials and Methods: A retrospective research study was conducted, encompassing 358 cases of road traffic accidents identified for the study. Data collection was conducted at "St. Mary’s" Emergency Hospital for Children in Iasi, Romania, from January 2015 to December 2022. To facilitate statistical analyses, participants were categorized into three specific age groups, correlating between the anatomical region affected and different types of accidents was examined, and the classification of injury types was determined based on the characteristics of the harm inflicted. Results: An oscillating trend is observed from 2015 to 2020, with its lowest value recorded in 2017 at around 6.8% and its peak in 2019 at 20.1%. Notably, post-pandemic (Covid-19), the cases underwent a substantial decline of approximately 50%. At least 78.7% of those who did not undergo orthopedic reduction required surgery, whereas among those who underwent orthopedic reduction, only 23.4% needed surgery. Regarding the frequency of complications 7,9% of the total cases experienced complications. Conclusion: The increased frequency of pediatric traffic accidents is attributed to the extensive services provided by "St. Mary’s" Emergency Hospital for Children, Iasi, to a significant number of patients from both rural and urban areas across the seven counties of the Moldova, Romania.

Occupational accidents, despite continuous safety updates, are still a scourge in the occupational and forensic spheres, constituting, among other things, the subject of a large share of litigation. Demographic data can help to understand the areas where the application of health surveillance is lacking. This meta-analysis sets out to analyze data from studies on accidents at work, focusing on the correlation between the areas in which accidents occur and whether or not personal safety equipment is used, about the different regulations in force. For the selection of the data, a systematic review was carried out according to the PRISMA guidelines, with the primary objective of identifying the trend of occupational accidents in specific geographical areas, which differ in terms of the attention paid to preventive aspects. The data we highlighted showed, regarding the type of accident, substantial differences between low-income countries and industrialized countries (stratified according to the Human Development Index) and, an overall indifference as to whether or not individual safety devices were used, revealing that, despite the continuous normative evolution in the field of safety at work, even today, the investigative data on the actual application of the regulations, during accidents at work, is underestimated and little researched.

In this study, unlike conventional methods for producing vanadium (3.5+) electrolyte by VOSO4 and V2O5, a batch-type hydrothermal reactor was used to produce a vanadium (3.5+) electrolyte for vanadium redox flow batteries through a reduction reaction. The starting material, V2O5, was mixed with different concentrations of citric acid (0.8M, 1.2M, 1.6M, 2.0M) as reducing agent and stirred using a hot plate at 90°C for 60 minutes to achieve complete dispersion in the solution. The resulting solution was then subjected to a hydrothermal reduction reaction in a furnace at 150°C for 24 hours to produce vanadium (3.5+). The mixed state of the produced vanadium (3+) and vanadium (4+) was confirmed using UV-vis spectroscopy. Electrochemical properties were investigated through CV analysis, confirming that the optimal concentration was 1.6M. Charge and discharge experiments were conducted to compare the current efficiency, energy efficiency, and voltage efficiency of the electrolyte prepared by the hydrothermal reduction process and the electrolyte prepared by VOSO4. As a result, the electrolyte produced through the hydrothermal reduction process showed improved performance in all efficiencies. The results shows that vanadium (3.5+) electrolyte could be easily produced through a reaction process using citric acid.

This research investigates the fatigue behaviour and fracture mechanics of high-performance concrete (HPC), including various compositions such as HPC with basalt aggregates (HPC-B), HPC with gravel (HPC-G), and high-strength coarse mortar (CM) static and cyclic tensile loading within the special priority program SPP 2020. The study aims to integrate fracture mechanics into structural analysis to enhance design guidelines for slender cross-sections and safety-related high-performance structural components. The experimental investigations reveal HPC-B's re-markable superiority, displaying higher compressive strength, modulus of elasticity, and tensile strength compared to HPC-G and CM. A modified disk-shaped compact tension (MDCT) based on ASTM standards, aided by digital image correlation (DIC) unveils fracture behaviour, empha-sizing fracture energy as a crucial parameter. HPC-B exhibits improved crack resistance and notch sensitivity reduction attributed to crushed basalt aggregates and enhanced interfacial transition zone (ITZ). The research scrutinizes factors like material characterization, aggregate morphology, stress levels, and displacement rate on crack formation. High-cycle fatigue tests show HPC-B's superior performance, and post-fatigue analysis reveals enhanced residual fracture toughness attributed to nano-level structural changes, stress redistribution and aggregate-matrix interac-tion. 3D image analysis via Computed Tomography (CT) scans captures mesostructural crack propagation and provide quantitative insights. This study advances the understanding of HPC fatigue behaviour, emphasizing the interplay of aggregate types, morphologies, and their dy-namic response to cyclic loading, offering valuable insights for optimizing design guidelines and fostering innovation in structural engineering.

Prescribed fire is increasingly utilized for conservation and restoration goals, yet there is limited empirical evidence supporting its effectiveness in reducing wildfire-induced damages to valued resources of significant worth—whether natural, cultural, or economic. This study evaluates the efficacy of prescribed fire in reducing wildfire severity to LANDFIRE-defined vegetation classes and valued resources impacted by the 2017 West Mims event. Wildfire severity, measured using the differenced normalized burn ratio (dNBR) index, was highly heterogeneous both within and between vegetation classes; however, profound differences between treated and untreated areas were evident. The beneficial effects of prescribed fire were most pronounced within ca. two-years post-treatment but remained evident beyond three-years relative to untreated areas. For example, actively managed areas that were treated with prescribed fire just one month prior to the West Mims event exhibited an 88% reduction in mean wildfire severity relative to unmanaged areas. When post-treatment duration reached 38 months, mean wildfire severity remained 17% lower than untreated areas. Findings from this study reveal significant reductions in both average and peak wildfire severities in areas treated with frequent fire, supporting burn rotations of one to two years to balance fuel reduction and ecological conservation.

Road traffic accidents are associated with different factors, such as non-signalized road networks, inexperienced drivers, lack of communication skills, distraction, and visual or cognitive impairment of road users, which have led to this increase in traffic accidents. Understanding these factors in alignment with road user communication skills can provide solutions that can reduce traffic fatalities. Road users’ communication is predominantly formal in nature, but informal means of communication also exist but are not given proper attention during the driving training and licence testing processes. Therefore, this study investigated communication skills between drivers as a factor contributing to traffic accidents in South African cities. Consequently, 16 traffic scenarios of communication between drivers were simulated using PTV Vissim and Blender software, and a semistructured interview questionnaire was used to collect data from South African road users. The questionnaire was further used to evaluate the confidence level of road users concerning informal communication. The data were analysed via regression analysis to establish the relationship between traffic accidents and miscommunication between drivers. The information gathered and analysed from this study helps to understand the informal communication types between drivers and establish where communication loss exists, which ultimately leads to a traffic accident. The results also show that misinterpretation of communication signals can present the possibility of traffic safety risk.

Occupational incidents in construction workplaces present a persistent challenge, with severe consequences for both workers and project outcomes. This study aims to examine the specific causal factors contributing to these incidents, focusing on the interaction between worker-related, environmental, and procedural factors. The objective is to identify key contributors to workplace accidents and develop a unified predictive model to mitigate future risks. To achieve this, we employed a machine learning approach on a comprehensive accidentology dataset collected from contractors across multiple construction sites. The dataset includes variables such as worker ex-perience, environmental conditions, adherence to safety protocols, and more. By analyzing direct, indirect, and root causes, the methodology uncovers hidden patterns and interdependencies that traditional analysis might overlook. The study’s findings indicate that worker experience and environmental factors are the most significant contributors to incident occurrence, with a clear interaction effect between these variables. The results not only confirm previous research but also offer enhanced predictive capabilities for future safety measures. This research demonstrates the value of machine learning in generating data-driven insights, ultimately aiding in the develop-ment of targeted interventions to improve safety standards in the construction industry.

The increasing frequency and intensity of wildfires necessitate innovative solutions to enhance firefighting capabilities. This review provides a comprehensive evaluation of vehicles specifically designed for direct forest fire suppression, focusing on their speed of fire response, and encompasses both ground and aerial units. The study explores the functionality and speed capacity of various fire suppression vehicles, including fire trucks, firefighting aircrafts and helicopters, robotic firefighting vehicles and uncrewed aerial vehicles (UAVs). Key features such as mobility, water capacity, and advanced technologies including Artificial Intelligence (AI)-driven navigation and remote sensing capabilities are evaluated. The review also addresses the operational challenges these vehicles face in wildland environments. Additionally, it identifies emerging trends and innovations in fire suppression vehicle technology, offering insights into future developments that aim to enhance the effectiveness and safety of firefighting operations in forest fires. This overview aims to serve as a valuable resource for researchers, fire departments, and emergency response planners focused on advancing fire suppression strategies in forest.

Fires in houses are inevitable and they cause loss of life and major repair costs for the damage structure and contents. There is a clear need to prevent its ignition and spread to adjacent areas. This article presents the steps taken, namely: contacts, data collection, tests, analysis and conclusions regarding the accident resulting from the outbreak and spread of a fire, on December 25, 2020, in a traditional Portuguese house in Arganil, Portugal. The owners and the court requested to carry out an expert opinion by a person with specialized technical-scientific knowledge, which led to this work as an expert. the work consists of carrying out observations and data analysis. Then, the observed and analysed data were laboratory tested in order to consistently identify characteristics that lead to the ignition and propagation of the fire. This work identified construction problems, materials used and heat sources that are responsible for the accident. The conclusions may be used to avoid future incidents or accidents with houses of same construction type.

Transportation accidents in Brazil rank as the second leading cause of hospitalizations due to external causes, significantly impacting the organization of the healthcare system.. While all road users are at risk, pedestrians, cyclists, and motorcyclists are particularly vulnerable. Although governments and societies advocate for increased bicycling as a sustainable urban transportation option, the uneven accessibility of cycling infrastructure poses accident risks. Addressing the gap in research on this issue, this study investigates the trends and contributing factors of bicycle traffic accidents in the metropolitan area of Maringá, Paraná, Brazil. This ecological time series study analyzed 848 pre-hospital care records from SAMU and SIATE of Maringá-PR, spanning January 2010 to December 2019. The results reveal a rise in accident frequency over the study period, with the majority of victims being males aged 11 to 20 years. Accidents predominantly occurred between noon and 8:00 p.m., with peak occurrences on Fridays and Saturdays. Serious accidents were most frequent on Tuesdays and Fridays. These findings align with existing literature and underscore the importance of incorporating data from SIATE and SAMU into urban accident prevention strategies. Additionally, the study highlights the need for further research on this topic.

The number of fishers working on commercial fishing vessels worldwide is approximately 159,800 according to the Food and Agriculture Organization of the United Nations. The International Labour Organization estimates that the mean annual number of deaths during fishing is approximately 24,000. Most guidelines for the prevention of marine accidents have been developed with a focus on vessels involved in international navigation. As these guidelines may have parts that are unsuitable for application in small-scale boats operating in coastal areas, appropriate modifications are required. This study analyzed quantitative data on 1,790 marine accidents that occurred in trap fishery, a globally common type of fishery, and data obtained through a survey of 101 fishers in South Korea. A guideline to reduce the rate of fatal accidents was developed using the Bayesian network, a method used for the analysis of accidents, as suggested in the formal safety assessment protocol. The results of data analysis and the developed guidelines are expected to be beneficial to all fishers involved in trap fishery across many countries worldwide.

As Li-ion batteries use is spreading, incidents in large energy storage systems (stationary storage containers, …) or in large-scale cell and battery storages (warehouse, recyclers, …), often leading to fire, are occurring on regular basis. Water remains one of the most efficient fire extinguishing agents for tackling those battery incidents and large quantities are usually necessary. Since bat-teries contains various potentially harmful components (metals and their oxides or salts, sol-vents…) and that thermal runway induced battery incidents are accompanied with complex and potentially multistage fume emissions (containing both gas and particles) the potential impact of fire run-off water on the environment should be considered and assessed carefully. The tests presented in this paper focus on the analysis of the composition of run-off waters used to spray NMC Li-ion modules under thermal runaway. It highlights that waters used for fire-fighting are susceptible to contain many metals, including Ni, Mn, Co, Li and Al mixed with other carbonaceous species (soot, tarballs) and sometimes undecomposed solvents used in the elec-trolyte. Extra-polation of pollutant concentrations compared with PNEC values showed that, for large scale incidents, run-off water could be potentially hazardous to the environment.

The number of migrant workers in Saudi Arabia (SA) has gradually increased, particularly in the construction industry, where migrant workers make up 89% of the workforce. They continue to experience exposure to dangerous working conditions and increased risk for occupational injury and hazards due to the work they tra-ditionally perform. To address this challenge, this study explores the differences between migrant and domestic workers’ injury and occupational accident rates in SA’s construction industry. Data were analysed from 121,999 (98.7% migrant and 1.28% domestic workers) reported accidents and injuries obtained from the General Or-ganisation for Social Insurance (GOSI) between 2014 and 2019. Chi-square test was used to examine the asso-ciations of occupational accidents and injuries among migrant and domestic workers. Migrant workers suffered more falls, strikes, collisions, rubbing and abrasions (damage caused by scraping, producing wounds), bodily reactions (e.g., reaction to chemicals), and car accidents than domestic workers. The results also show that migrant workers aged 30-39 and domestic workers 20-29 experienced more severe injuries and increased seasonal mortality throughout the six-year period examined. In addition, domestic workers achieved a higher proportion of full recovery across all types of accidents except transport and car accidents. The findings emphasise the need for ongoing safety education, training, and improved safety measures to protect the health and safety of construction workers, especially migrant workers.

Nanocellulose (CNC) seems to be a promising surfactant, which, together with expandable graphite (EG), forms the essence of an effective natural-based fire retardant wood coating. In our research, the most suitable composition of the mixture was tested concerning good solubility, dispersion, and consistency. Favorable results were achieved with the formulation composed of a 4% CNC alkaline solution with 80 wt.% of selected EG. Subsequently, six different EG were used to prepare these wood fire retardant coatings. The effectiveness of treatments was verified using a test with a radiant heat source, where the test samples' relative weight loss, relative burning rate, and surface temperature during 600 sec were evaluated. All prepared formulations can be characterized as more or less equally effective. However, the best results were obtained with EG of GG 200 – 100 N, where the mass loss of the sample was 8.10 ± 1.24%. Very good results were also achieved by graphite 25 E + 180 HPH (8.70 ± 0.89%), which is similar to the previous one, even according to the microscopic assessment of the coating as well as the expanded layer. Graphite type 25 K + 180 (8.86 ± 0.65 %) shows the expanded layer's best cohesion, coating uniformity, and ease of application. The results of this work confirmed that the CNC coating itself has significant retardation effects.

Forest fires can occur suddenly and have significant environmental, economic, and social consequences. Timely and accurate evaluation and prediction of their progression, particularly the spread speed in difficult-to-access areas, are essential for emergency management departments to proactively implement prevention strategies and scientifically extinguish fires. This paper provides a comprehensive analysis of advanced technologies for predicting forest fire spread in China and globally. Incorporating remote sensing (RS) technology and forest fire science as the theoretical foundation, and utilizing the Wang Zhengfei forest fire spread model as the technical framework, this study constructs a forest fire spread model based on remote sensing interpretation. The model optimizes the method for acquiring parameters and enhances algorithm accuracy. By considering regional landforms (ridge lines, valley lines, slopes) and vegetation coverage, this paper establishes visual interpretation markers for identifying hotspots within the spread model. Utilizing statistical data from pixels within the fire line zone transforms the methodology for predicting forest fire spread speed, resulting in greater accuracy compared to conventional fixed-point predictions. Finally, the model was applied to the case of a forest fire in Mianning County, Sichuan Province, China and verified using high-time resolution mid-infrared image data and previous research findings. The results demonstrate that the predicted fire spread area from this RS-enabled model is consistent with both high-time resolution geostationary satellite data and previous research outcomes, indicating high reliability. This model can provide crucial fire spread information to relevant emergency departments, enabling effective preemptive measures and scientific firefighting strategies.

Tunnel fires are generally detected using various sensors, including measuring temperature, CO concentration, and smoke concentration. To address the ambiguity and inconsistency in multi-sensor data, this paper proposes a tunnel fire detection method based on an improved Dempster-Shafer (DS) evidence theory for multi-sensor data fusion. To solve the problem of evidence conflict in DS theory, a two-level multi-sensor data fusion framework is adopted. The first level of fusion involves feature fusion of the same type of sensor data, removing ambiguous data to obtain characteristic data, and calculating the basic probability assignment (BPA) function through the feature interval. The second level fusion derives basic probability numbers from the BPA, calculates the degree of evidence conflict, normalizes to obtain the relative conflict degree, and optimizes the BPA using the trust coefficient. The classical DS evidence theory is then used to integrate and obtain the probability of tunnel fire occurrence. Different heat release rates, tunnel wind speeds, and fire locations are set, forming four fire scenarios. Sensor monitoring data under each simulation condition are extracted and fused using the improved DS evidence theory. The results show that the proposed improved DS evidence theory method detects the probability of fire occurrence in the four scenarios as 67.5%, 71.0%, 82.8%, and 83.5%, respectively, and identifies fire occurrence in approximately 2.4 seconds, an improvement of 64.7% to 70% over traditional methods. This demonstrates the feasibility and superiority of the proposed method, highlighting its significant importance in ensuring personnel safety.

Fire detection is crucial for ensuring human safety and minimizing property damage. By utilizing advanced technologies, we can identify fires early, before they escalate. Autonomous fire detection systems are particularly vital in high-risk areas with minimal human presence. To address this need, we propose an automated fire-detection model based on YOLOv8. We modified the architecture of YOLOv8 and achieved impressive performances. Through comprehensive analysis of different YOLO models and their predecessors, we identified and addressed substantial gaps in this field. Also, by augmenting the framework with additional capabilities, we surpassed previous models, enabling real-time fire detection. Our proposed model achieves impressive performances, with 98% accuracy in fire detection and 97.8% in smoke detection. Moreover, recall rates for fire and smoke are 97.1% and 97.4%, respectively, with a mean average precision (mAP) accuracy of 99.1% for both cases. Finally, we applied an explainable artificial intelligence technique to interpret our proposed model and its results. This study lays the foundation for future research aimed at enhancing fire detection efficiency. The reliability of our modified model presents promising opportunities for future advancements, ensuring increased effectiveness and precision in efficient fire detection.

Wildfires have always been a part of the history of Mediterranean forests. However, forest regeneration after a wildfire is not certain. It depends on many factors, some of which may be influenced by land management activities. Failure of regeneration will cause a regime shift in the ecosystem, reducing the provision of ecosystem services and ultimately leading to desertification. How can we increase Mediterranean forests’ resilience to fire? To answer this question, we did a literature review, investigating chains of processes that allow forests to regenerate (which we label “regeneration mechanisms”), and assessed the impact of selected management practices documented in the WOCAT database on the regeneration mechanisms. We identified three distinct regeneration mechanisms that enable Mediterranean forests to recover, as well as the time frame before and after a fire in which they are at work, and factors that can hinder or support resilience. The three regeneration mechanisms enabling a forest to regenerate after a fire consist of regeneration (1) from a seed bank; (2) from resprouting individuals; and (3) from unburned plants that escaped the fire. Management practices were grouped into four categories: (1) fuel breaks, (2) fuel management, (3) afforestation, and (4) mulching. We assessed how and under what conditions land management modifies the ecosystem’s resilience. The results show that land management influences resilience by interacting with resilience mechanisms before and after the fire, and not just by modifying the fire regime. Our analysis demonstrates a need for adaptive – i.e. context- and time-specific – management strategies.

Fire is a serious security threat that can lead to casualties, property damage, and environmental damage. However, despite the availability of object detection algorithms, challenges persist in detecting fires and smoke. These challenges include slow convergence speed, poor performance in detecting small targets, and high computational cost limiting deployments. In this paper, Fire smoke and human detection based on ConvNeXt and Mixed encoder (FCM-DETR), an end-to-end object detection algorithm based on Deformable DETR, is proposed. Firstly, we introduce ConvNeXt to take the place of Resnet, which greatly reduces the amount of computation and improves the ability to extract irregular flame features. Secondly, to effectively process multi-scale features, the original encoder is decoupled into two modules. Then Mixed encoder, an innovative encoder structure, is proposed, resulting in excellent performance of multi-scale fire and smoke features fusion. What can’t be overlooked is that the encoder block is compatible with any DETR-based models. Finally, the convergence speed is accelerated and the mean of average precision is improved by applying a novel loss function called Powerful IoU v2. The experimental results indicate that our model achieves the best detection accuracy compared to other models, with mAP reaching 66.7%, mAPs and Accuracyfire achieving impressive 50.2% and 98.05%, respectively.

The growing requirements regarding the safety of using polymers and their composites are related to the emergence of more effective, sustainable, and hazardous-limited fire retardants (FR). Significant amounts of FR are usually required to effectively affect the polymer burning behavior, while the knowledge of their recycling potential is still insufficient. At the same time, concerns are related not only to the reduced effectiveness of flame retardancy but, above all, to the potential deterioration of mechanical properties caused by the degradation of temperature-affected additives under processing conditions. This study describes the impact of four-time reprocessing of bio-based polyamide 11 (PA11) modified with an intumescent flame retardant (IFR) system composed of ammonium polyphosphate (APP), melamine cyanurate (MC) and pentaerythritol (PER) and its composites containing additionally short basalt fibers (BF). Composites manufactured by twin-screw extrusion were subjected to four reprocessing cycles by injection molding, comprehensively analyzing structural, mechanical, and fire behavior changes in each cycle. The obtained results confirmed the safety of using the proposed flame-retardant system for polyamide, processed under the recommended process parameters, without the risk of significant changes in the structure despite the partial increase in flammability caused mainly by the degradation of the polymer.

Increased wildfire activity is the most significant natural disturbance affecting forest ecosystems, with a strong impact on their natural regeneration. This study presents a comprehensive analysis of post-fire forest recovery using Landsat time series data from 2000 to 2023 in the Middle Volga region of the Russian Federation. The analyses utilised the LandTrendr algorithm in Google Earth Engine (GEE) cloud computing platform to examine Normalized Burn Ratio (NBR) spectral metrics and quantify the forest recovery at low, moderate, and high burn severity (BS) levels. To assess the spatio-temporal trends of the recovery, the Mann–Kendall statistical test and Theil–Sen’s slope estimator was applied. The results suggested that the post-fire spectral recovery is significantly influenced by the degree of the BS on affected areas. The higher the class of BS, the faster and more extensive the reforestation of the area occurs. About 91% (40,446 ha) of the first 5-year forest recovery after the wildfire belong to the BS classes of moderate and high severity. A regression model showed that land surface temperature (LST) was more significant to post-fire recovery than the variability in precipitation (Pr), explaining about 65% of the variance in post-fire recovery. This study provides new insights into the post-fire forest recovery dynamics and scientific bases for the cost-effective management strategies under changing climate conditions.

The research was aimed at comparing the fire characteristics of different types of pepper in the context of designing explosion prevention. The following characteristics were studied: explosion pressure pmax and Kst at selected concentrations, ignition temperature of the deposited dust layer from the hot surface and minimum initiation energy. The comparison of the chemical properties of the used types of pepper was performed using TG/DSC. Results of the measurements suggest that different types of peppers exhibit different explosion characteristics. Each of the samples reached the maximum value of the explosion pressure, and their rate of pressure rise increased at different concentrations. The volume of the explosion chamber used also influenced the explosion characteristics. It is a consequence of the fact that the explosion characteristics strongly depend on the mechanism of action of a particular igniter. The minimum effect on the safety characteristics was observed when measuring the minimum initiation energy and the minimum ignition temperature of dust layer from the hot surface. The results of the measurements suggest that different types of peppers exhibit different explosion characteristics. This information should then be considered in explosion prevention.

Fire detection from satellite sensors relies on an accurate estimation of the unperturbed state of a target pixel, from which an anomaly can be isolated. Methods for estimating the radiation budget of a pixel without fire depend upon training data derived from the location's recent history of brightness temperature variation over the diurnal cycle, which can be vulnerable to cloud contamination and the effects of weather. This study proposes a new method that utilises the common solar budget found at a given latitude in conjunction with an area's local solar time to aggregate a broad-area training dataset, which can be used to model the expected diurnal temperature cycle of a location. This training data is then used in a temperature fitting process with the measured brightness temperatures in a pixel, and compared to pixel-derived training data and contextual methods of background temperature determination. Results of this study show similar accuracy between clear-sky medium wave infrared upwelling radiation and the diurnal temperature cycle estimation compared to previous methods, with demonstrable improvements in processing time and training data availability. This method can be used in conjunction with brightness temperature thresholds to provide a baseline for upwelling radiation, from which positive thermal anomalies such as fire can be isolated.

This research work deals with traffic accidents that are very concurrent in several countries, which in turn contribute to the mortality rate from 19 to 25 years of age and up. In the research work, immediate notifications for traffic accidents were carried out through a mobile application (app) where it will have an emergency button which will help the injured a lot, since the nearest help centers such as the police, fire brigades and the nearest hospitals, so that they can provide support to the people who suffered the accident. In the project, the implementation of Scrum Methodology was used since it served us an amount for the development of the prototypes of the App, and in turn with their roles it did not serve much help for the project, however, passers-by are of vital importance already that they will be the ones to make said report when observing a traffic accident and thus notify the nearest aid centers. As data collection for the creation of the software functions, a survey was carried out with more than 30 people, the functions applied in the application were programmed with the Dart language and generated under the flutter tool in order to be able to distribute it on different platforms such as Play Store and AppStore. The results obtained were the most anticipated since the application fulfills its function, thus reducing deaths and improving the speed of attention to traffic accidents and thus saving a life that is in danger.

This paper examines cognitive biases which affect the ability of decision makers to make rational decisions in an organizational context. The motivation for this analysis begins with the observation of catastrophic accidents caused by human error but in an organizational context. This paper expands on the concept of cognitive bias to define organizational biases which are the factors that affect decisions in an organizational context. The paper distinguishes between organizational biases, which are the focus of this paper, and individual biases, which are biases experienced by individuals but may have organizational consequences. The purpose of this paper is to identify methods to mitigate the risks of organizational accidents, accidents which involve many people operating at different levels of an organization. The methodology is to identify those decisions that would address the specific organizational biases. The focus of this paper is the decisions for mitigating the risks associated with decisions in an organizational context. Results are shown for seven organizational biases, six specific case studies, and four decision options. This paper concludes that organizational biases are intrinsically different from individual biases and that these differences lead to different decision options from those that mitigate individual biases; however, they may exist concurrently.

Grasping the spatial and temporal patterns of forest fires, along with the key factors that drive them and the ability to forecast such events accurately, is essential for the successful management of forests. In China's southern region, forest fires significantly endanger the ecological system, public safety, and economic well-being. Through the application of Geographic Information Systems (GIS) and LightGBM (Light Gradient Boosting Machine) model, this study investigates the determinants of fire incidents and formulates a predictive model for forest fire occurrences, alongside a zoning strategy, within the Central-South area. The results indicate: (i) Spatially, fire points exhibit significant clustering and autocorrelation characteristics; (ii) The Central-South Forest Fire Prediction Model shows exceptional accuracy, reliability, and predictive power, with high performance metrics across training and validation sets, including over 85% accuracy, precision, recall, and F1 scores, along with AUC values above 89%, underscoring its efficacy in forecasting forest fires and distinguishing between fire events; (iii) Throughout the year, forest fire risks in the Central-South region of China vary by region and season, with risk spikes from March to May in Guangdong, Guangxi, Hunan, Hubei, and Hainan. From June to August, localized risks are observed in Heyuan and Huangshi, while from September to November, an increase in risk is noted in Guangdong (Meizhou, Heyuan, Shaoguan), Guangxi (Nanning, Hezhou, Yulin), and Hunan (Binzhou, Yongzhou, Hengyang) due to cooler, drier conditions and leaf litter accumulation. From December to February, the risk continues in specific areas across Guangdong, Guangxi, Hunan, and Hubei.

The increasing growth in road users who are unfamiliar with traffic rules and regulations provides a serious safety problem for Pakistani traffic conditions. This study has two main objectives. First, it seeks to validate the reliability and applicability of the Dula Dangerous Driving Index (DDDI) in Pakistan. Second, it intends to investigate the effects of DDDI, sociodemographic characteristics, seat belt use, and driving training on the occurrence of traffic accidents. A total of 623 Pakistani individuals completed a questionnaire as part of this study. The questionnaire consists of items related to dangerous driving behaviors, sociodemographic characteristics, seat belt use, driving instruction, and accident involvement. The DDDI's accuracy was evaluated by comparing it to self-reported traffic incidents and seat belt compliance. To discover predictors within the DDDI dimensions, generalized linear models were used. A binary logistic regression analysis was also performed to evaluate the factors that influence traffic accidents. DDDI dimensions, demographic characteristics, seat belt usage, and driving training were all considered in this study. The use of seat belts while driving reduced the likelihood of an accident by 25%. On the other hand, aggressive driving and risky driving increased the chances of getting involved in traffic accidents by 36%% and 20%. Furthermore, the chances of being involved in a traffic accident decreased by 21% as the drivers age increased. The results also illustrate that Pakistani drivers are more aggressive and risk taker compared to Chinese drivers who are more disciplined. This study suggests that lack of comprehensive and standardized driving training and education programs, in consistent enforcement of traffic laws, the quality of road infrastructure and lack of public awareness campaigns can be attributed to adverse road safety situations in country.

This manuscript presents a study on the spatial relationships between bike accidents, the built environment, land use, and transportation network characteristics in Budapest, Hungary using Geographic Weighted Regression (GWR). The sample period included bike crash data between 2017 and 2022. The findings provide insights into the spatial distribution of bike crashes and their severity, which can be useful for designing targeted interventions to improve bike safety in Budapest and be useful for policymakers and city planners in developing effective strategies to reduce the severity of bike crashes in urban areas. The study reveals that the built environment features, such as traffic signals, road crossings, and bus stops, are positively correlated with the bike crashes index, particularly in the inner areas of the city. However, traffic signals have a negative correlation with the bike crash index in the suburbs, where they may contribute to making roads safer for cyclists. The study also shows that commercial activity and PT stops have a higher impact on bike crashes in the northern and western districts. The GWR analysis further suggests that one-way roads and higher speed limits are associated with more severe bike crashes, while green and recreational areas are generally safer for cyclists. Future research should be focused on the traffic volume and bikes trips’ effects on the severity index.

Abstract: Wildfires in forest ecosystems exert substantial ecological, economic, and social impacts. The effectiveness of fire management hinges on precise pre‐fire risk assessments to inform mitigation efforts. This study aims to investigate the relationship between predictions from pre‐fire risk assessments and outcomes observed through post‐fire burn severity analyses. In this study, forest fire risk was assessed with the Fuzzy Analytical Hierarchy Process, in which fire behavior factors were used as input. The degree of burn was determined using the Random Forest method using 11519 training points and 400 test points on Sentinel‐2 satellite images in three different classes. According to the results obtained from 266 selected test points located within the forest boundaries, all primary factors showed an increased effect in areas with high burn severity. Climate, in particular, emerged as the most influential factor, accounting for 52% of the overall impact. However, in cases of high fire severity, climate proved to be the most effective risk factor, ac‐counting for 67%. It followed by topography with 50% accuracy at high fire intensity. In the risk assessment is based on the FAHP method, climate was assigned the highest weight value among other factors with 32.2%. Topography emerged as the second most effective risk factor with 27 %. To evaluate the results more comprehensively both visually and statistically, two regions with different stand canopy characteristics were selected within the study area. While high burn severity had the highest accuracy in Case 1 area, moderate burn severity had the highest in Case 2 area. During the days when the fire continued, the direction of spread was obtained from MODIS images. In this way, the fire severity depending on the direction of fire progression was also interpreted. Through an analysis of various case studies and existing literature, the research underscores both the strengths and limitations inherent in predicting forest fire behavior‐based pre‐fire risk assessments. Furthermore, it emphasizes the necessity of continuous improvement to increase the success of forest fire management.

Mountain fire can become more complex than fires at lower elevation due to the complex interaction of fire, topography, and weather. The Gell River Fire in Tasmania, Australia occurred in rugged terrain where there are abundant fire sensitive vegetation communities, as well as the presence of infrastructure including high-voltage transmission lines. The fire began at the end of December 2018 and lasted a few months, with a final burnt area of approximately 350km² despite significant fire suppression effort. The fire was investigated by employing wind vector maps, numerical weather model vertical sounding charts (NWMVS) and Prototype 2, which is an integrated fire simulator and can detect lateral fire channeling (LFC). Our analysis of the fire found its spread was likely to be introduced into a valley by forced channeling (FC), which is modified synoptic wind, and showed rapid spread in the valley. The simulated fire also showed wider spread than the observed data in the valley, with the simulated fire impacting highly sensitive vegetation communities on the fringes of the valley. This alludes to some potential conclusions: (1) The loss of fire sensitive vegetation would have increased if fire suppression activity had not been conducted. (2) Spotting fires could be produced by LFC because these spotting fire would allow spreading fire in a shorter period. (3) Heterogeneity of vegetation, such as combination of buttongrass and forest, could help carry fire rapidly in the valley with LFC. Fire can propagate faster in buttongrass than in forests while the forests allow the spotting fire.

This paper deals with the analysis of trends in road accidents on major highways in Brazil. Using updated time series techniques, our results indicate that a low degree of long memory was detected in the series with shocks having transitory effects over time. We further find that the number of accidents taking place in Brazil has been reducing over time, though in the presence of negative shocks, the recovery is not going to be immediate due to the long memory nature of the data. Despite the absence of relevant investment relating to infrastructure expansion, it is worth mentioning the consolidation of a nationwide tolled road system in Brazil involving concessions to private administrators, alongside more severe traffic laws that can impose limitations on driving licences.

The paper aims in assessing risks of mandible fractures consequent to impacts or sport accidents. The role of the structural stiffness of mandible, related to disocclusion state, is evaluated through numerical simulations using the finite element method (FEM). It has been assumed that the quasi-static stress field, due to distributed forces developed during accidents, could explain the common types of mandibular fractures. Geometric model of adolescent mandible was built, upon the basis of medical imaging, in CAD software with distinction between cortical layer and inner spongy bone. The finite element model of disoccluded mandible was next created. Mandibular condyles were supposed jammed in the maxillary fossae. The total force of 700 N, simulating an impact on mandible, has been sequentially applied in three distinct areas: centrally, at canine zone and at the mandibular angle. Clinically most frequent fractures of mandible were recognized through the analysis of maximal principal stress and maximal principal strain fields. Mandibular fracture during accidents can be analyzed at satisfactory level using linear quasi-static FE models for designing protections in sport and transport. The proposed approach can be improved by introducing more realistic interactions between condylar processes and fossae.

Using lightweight fire-rated board (LFRB) presents cost-effective opportunities for various passive fire protection measures. The aim of the project is to develop a LFRB with enhanced fire resistance, acoustic properties, and mechanical properties. These properties were determined using the Bunsen burner, furnace, energy dispersive X-Ray, impedance tube instrument, and Instron universal testing machine. To fabricate the LFRBs, vermiculite and perlite were blended with flame-retardant bind-ers, and four types of LFRBs were produced. A fire test was conducted to compare the fire-resistance performance of the LFRBs with a commercially available flame retardant board. The B2 prototype showed exceptional fire-resistant properties, with a temperature reduction of up to 73.0 °C as compared to the commercially available fire-rated magnesium board. Incorporating nano chicken eggshell into the specially formulated flame-retardant binder preserved the LFRBs’ structural integrity, enabling them to withstand fire for up to 120 minutes with an equilibrium temperature of 92.6 °C. This approach also provided an absorption coefficient of α = 2.0, a high flexural strength of 3.54 MPa, and effective flame retardancy properties with a low oxygen/carbon ratio of 2.60. These results make the LFRBs valuable for passive fire protection applications in the construction and building materials industry.

Weather surveillance radars routinely detect smoke of various origin. Of particular significance to the meteorological community are wildfires in forests and/or prairies. For example, one responsibility of the National Weather Service in the USA is to forecast fire outlooks as well as to monitor wild fire evolution. Polarimetric variables have enabled relatively easy recognitions of smoke plumes in data fields of weather radars. Presented here are the fields of these variables from smoke plumes caused by grass fire, brush fire, and forest fire. Histograms of polarimetric data from plumes contrast these three cases. Most of the data are from the polarimetric Weather Surveillance Radar 1988 Doppler (WSR-88D aka Nexrad, 10 cm wavelength) hence the wavelength does not influence these comparisons. Nevertheless, in one case simultaneous observations of a plume by the operational Terminal Doppler Weather Radar (TDWR, 5 cm wavelength) and a WSR-88D is used to infer backscattering characteristic and hence sizes of dominant contributors to the returns. In addition, comparisons with observations by other investigators of plumes from urban area but at a 5 cm wavelength are made. To interpret some measurements Computational Electromagnetics (CEM) tools are applied.

The aim of this study was to describe the trend of traffic accident mortality and social inequalities in Ecuador for the period 2011-2020, distributed by year, gender, age group, geographic location, and type of accident, alongside social disparities. An ecological approach was used to evaluate traffic accident mortality data. The average traffic accident mortality rate in the country during this period was 19.1 per 100,000 inhabitants, with rates being significantly higher in males (31.3) than in females (7.2). The highest mortality rate was recorded in 2011, predominantly impacting the 60 and older age group, the Santo Domingo de los Tsáchilas province, and the Amazon region. The study also noted a declining trend in these rates over the years, with an Annual Percentage Variation of -1.2%. A notable finding was the decrease in traffic accident mortality inequality based on per capita income in 2019, compared to 2011, which rose by 247.7%, whereas the inequality based on illiteracy rates decreased by 18.9% from 2014 to 2019. These findings highlight the significant decrease in traffic accident mortality rates across Ecuador, though marked disparities remain evident across different genders, age groups, and provinces.

Globally, there are 1.35 million road fatalities every year, which are estimated to cost governments approximately US$ 518 billion, making road fatalities the 8th leading cause of death across all age groups and the leading cause of death of children and young adults. In South Africa, despite tremendous governmental efforts to curb the soaring trajectory of road accidents, the annual number of road fatalities has increased by 26% in recent years. By fitting a structural equation model (SEM) and a GARCH Model (Generalized Auto-Regressive Conditional Heteroskedastic-ity) to analyze and predict future trend of road accidents (number of road accidents, number of casualties, number of fatal crashes and number of persons killed) in South Africa, we propose and test a complex metamodel that integrates multiple causality relationships. We show an increasing trend of road accidents over time, a trend that is predictable by number of vehicles in the country, the population of the country and the total distance travelled by vehicles. We further show that death rate linked to road accidents is on average 23.14 deaths per 100,000 persons and is pre-dictable following the equation: y = -0.0114x2+1.2378x-2.2627 (R2=0.76) with y = death rate and x = year. Finally, in the next decade, the number of road accidents is predicted to be roughly constant at 617,253 accidents but can reach 1 896 667 accidents in the worst-case scenario. The number of casualties was also predicted to be roughly constant at 93 531 over time, although this number may reach 661 531 in the worst-case scenario. However, although the number of fatal crashes may decrease in the next decade, it is forecasted to reach 11 241 within the next 10 years with the worse scenario estimated at 19 034 within the same period. At the same time, the number of persons killed in fatal crashes is also predicted to be roughly constant at 14 739 but may also reach 172 784 in the worse scenario. Overall, the present study reveals perhaps the positive effects of government initiatives to curb road accidents and their consequences; we call for more stronger actions for a drastic reduction in road accident events in South Africa.

An analysis of workplace accidents in the mining sector has been done using the database from the Spanish administration between the period 2005-2015 and applying data mining techniques. Data has been processed by means of the software Weka. Two scenarios were chosen regarding the accidents database, surface and underground mining. The most important variables involved in occupation accidents and their association rules have been determined. These rules are formed by several predictor variables that cause an accident, defining its characteristics and context. This study exposes the 20 most important association rules of the sector, either surface or underground mining, based on statistical confidence levels of each rule obtained by Weka. The outcomes display the most typical immediate causes with the percentage of accident basis of each association rule. The most typical immediate cause is body movement with physical effort or overexertion and type of accident is physical effort or overexertion. On the other hand, the second most important immediate cause and type of accident change in both scenarios. Data mining techniques have been proved as a very powerful tool to find out the root of the accidents, apply corrective measures and verify their effectiveness, either for public or private companies.

This paper deals with identifying the accident black spots and the influencing factors causing accidents using factored analysis in the medium-sized city (Tirunelveli) in India. From the literature review, the geospatial technique to identify the black spots and the factors causing accidents was used for analysis. The most influencing factors driving the accident were identified and ranked based on the repetitive occurrence of accidents in the black spot area. The spearman ranking system obtained the correlation among the factors causing accidents. The factor analysis technique was utilized to identify the key factors driving the repetitive accidents and group them. This study will help transportation planners to understand the factors causing accidents and take appropriate measures to reduce the casualties in the road construction planning stage and existing conditions.

In order to achieve the dual carbon goal and simplify the decarbonization process, a CO2 recovery process of the fire-flooding exhaust was designed. The process adopted the two-stage membrane separation method to recover CO2 and obtain high purity CO2 rich flow, and then through the mixed refrigerant liquefaction and rectification column, food grade liquid CO2 was obtained. HYSYS process simulation shows that for the fire-flooding exhaust with a flow rate of 1000 kg/h and a given composition, the process can achieve a CO2 recovery of 69.02%, total product sales S=2086.8 yuan/h, and specific power consumption of 2.818 kW∙h/kg. On this basis, a hybrid simulation platform of HYSYS & Matlab was established. Taking the specific power consumption of the product as the objective function, sequential quadratic programming (SQP) algorithm was adopted to optimize the process, and the optimized specific power consumption was reduced by 18.8%. Finally, by adjusting N2/CO2 components, the adaptability of the optimization condition was analyzed.

Annually, the oak forests of the Zagros Mountain chains, in western Iran and northeastern Iraq, face recurring challenges posed by forest fires, particularly in the Kurdo-Zagrosian forests situated in western Iran and northeastern Iraq. Assessing fire susceptibility relies significantly on vegetation condition. Integrating in situ data, Remote Sensing (RS) data, alongside the integration of Geographical Information Systems (GIS), presents a cost-effective and precise approach to capturing environmental conditions before, during, and after fire events, minimizing the need for extensive fieldwork. This study refines and applies the Zagros Grass Index (ZGI), a local vegetation index tailored to discern between grass-covered surfaces from tree canopies in Zagros forests, identifying the grass masses as the most flammable fuel type. Utilizing Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) product as input, from 2013 to 2022, the ZGI aims to mitigate the influence of tree canopies, isolating NDVI values solely attributable to grass cover. By incorporating phenological characteristics of forest trees and grass species, the ZGI outperforms NDVI in mapping grass-covered areas that are crucial for fire susceptibility assessment in the study region. Results demonstrate a strong overlap between ZGI-based maps and recorded fire occurrences, validating the efficacy of the index in fire susceptibility estimation.

Polyurethane is used extensively in the furniture industry, primarily in beds and sofas. There is a significant volume of research pertaining to the flammability of polyurethane materials, but very little regarding smoke toxicity. Given toxic fire effluents are a primary cause of injury and fatalities, more work needs to be done to test the toxicity of polyurethane materials and the fire retardants they are coated/bound with. The aim of the review is to provide a comprehensive analysis of the decomposition of polyurethane and discuss the development and application of modern fire retardants and their effect on smoke toxicity. This work then provides discussion on the challenges faced when attempting to quantify the toxicity of the effluent produced during combustion. Future work should aim to reduce the toxicity and flammability of polyurethane simultaneously by; reducing the pyrolysis of the polymer, preventing the escape of volatile products, reducing radicals within the system and lowering the temperature of the system. Before this can be achieved, a better understanding of smoke toxicity and the ability to test materials is needed.

Fires pose a significant threat to human life and property. An autonomous fire detection and fighting robot has the potential to mitigate the challenges associated with firefighting. This paper presents the implementation of an autonomous fire detection and fighting robot equipped with sensors, algorithms, and actuators to detect, locate, and suppress fires autonomously. The robot's mechanical and electrical components were designed, prototyped, and tested in a controlled environment to evaluate its ability to navigate through obstacles, detect fires, and suppress them autonomously. The results show that the robot effectively detects and suppresses fires, making it a promising solution for firefighting applications. The study contributes to the development of autonomous robots for firefighting, enhancing the safety of firefighters and the effectiveness of firefighting operations.

Roadway accidents significantly contribute to intermittent congestion and increased CO2 emissions on freeways. This research introduces a statistical approach designed to predict the rise in CO2 emissions resulting from traffic disturbances or jams triggered by such incidents. It also assesses the influence of varying levels of accident management effectiveness in different situations. To construct these scenarios, the study employs VISSIM, a traffic modeling software, incorporating diverse factors such as traffic volume, vehicle types, incident durations, and freeway lane counts. It then produces traffic flow characteristics in the form of vehicle paths. The emission estimates are derived by correlating these simulated vehicle paths with emission rates from the MOVES model. The study then applies a regression analysis to examine the connection between the increase in emissions and various influencing factors. The findings indicate that this approach efficiently reflects the impact of variables like accident duration, vehicle mix, and traffic volume on CO2 emissions across different lane configurations. The accuracy of these predictions is also confirmed. The outcomes suggest the model's potential usage in guiding efforts to lower emissions and determining the optimal duration of incident management, particularly in terms of lane closure, to mitigate emission impacts. This paper also discusses the limitations of the model and the future improvement direction.

Daily thousands of people and goods move along Brazilian Federal highways. Traffic accidents are numerous on these highways and have a significant impact, whether on the economy or the health system. Identifying predictor variables, the probability of an event occurring and how to mitigate them are of paramount importance for the actions of the transit authorities that manage these roads. The main contribution of this study is the development of a predictive machine learning model which uses open data to shows graphically the critical points in the highways. This model is fully reproducible and can be applied to any region worldwide helping to minimize the number of accidents and to prevent deaths by automotive collisions. For this study, 43 variables were analyzed supporting the identification of the causes of accidents with fatal victims on the main highways in the south of Brazil. RoadLytics is proposed as a supervised machine learning model, using the Random Forest algorithm to analyze about 33 thousand occurrences between 2017 and 2020. An exploratory analysis of the data was carried out to support the modeling and to facilitate data visualization. In this sense, heat maps were developed to support the analysis and identification of potential risk areas. The results show that BR386 highway registers the highest number of fatal occurrences, regardless of the season. Additionally, concerning the weather conditions, the analysis shows that 52% of accidents occurred in favorable conditions, such as clear skies, victimizing 501 people. The driver’s lack of attention is the main reason for the accidents’ occurrences. Applying the developed model, an accuracy of 77% was achieved for the classification of fatal accidents.

This research presents a comprehensive framework for efficiently generating forest fire datasets from Google Earth Engine data sources. The primary contribution of this work lies in providing a methodology to swiftly extract forest fire factors without the need for permissions or access to private datasets, rendering the dataset openly accessible and shared without barriers. Furthermore, given that the remote sensing data used is a global dataset, it can be applied in any region without restrictions. In this study, Peninsular Malaysia is chosen as a case study to demonstrate the framework's effectiveness. The generated dataset includes essential variables including the climate and environment, landcover, topography, and anthropogenic factors facilitating the analysis of fire occurrences. The methodology empowers data scientists, enabling them to leverage their analytical skills on the extracted dataset without requiring specialized remote sensing knowledge. Additionally, this study also showcases the adoption of large language models, specifically GPT-4 with the Noteable plugin, as a tool for conducting preliminary analyses on the generated dataset. Sample analyses reveal that several key features, including the KBDI, LST, PDSI, climate water deficit, and precipitation, significantly impact forest fire occurrences in Peninsular Malaysia. Despite the successful application of the GPT-4 with Noteable plugin, certain limitations and challenges are identified, highlighting the necessity for further validation of the tool's applicability and limitations. This study encourages future research to (1) adopt the proposed framework in other regions, (2) explore more detailed analyses encompassing all variables, and (3) leverage machine learning for advanced forecasting.

Water mist fire suppression technology has attracted an increasing interest from the field of fire protection services such as fire safety for buildings, ships, spacecraft, libraries and museums due to its non-toxic and high efficiency in the suppression of a wide variety of fires. To support the technological development of water mist fire suppression system and its application areas, this review introduces the concept of water mist system and discusses its suppression mechanisms in comparison with other fire protection systems. The recent application areas of water mist system are surveyed for class A fires involving combustible solid materials such as wood, paper and textiles; class B fires involving flammable liquids such as petrol, oils, lubricants, paints and waxes; class C fires involving flammable gasses such as natural gas and liquefied petroleum gas; fires involving electrical (class E) equipment such as computers and information technology facilities; and the class F fires involving flammable cooking oils and fats. Finally, the paper concludes the review by identifying the current research trends, and providing the future direction for water mist technology and applications.

Based on Chinese CDCP report on COVID-19, 14% of patients presented severe disease and 5% critical conditions. The average case-fatality rate was 2.3%, but mortality was as high as 49% in patients with critical illness. Serious life threatening thromboembolic complications have been found in 71·4% of non-survivors and micro/macro angiopathic coagulopathy has been found, also at autopsy, with highly increased neutrophil number, fibrinogen, concentrations of D-dimer and FDPs and NETs, ATIII decrease and normal number of platelets. A cytokine storm and interaction between inflammation and coagulation has been advocated as explanation of hypercoagulability. In this paper, it’s hypothesised that SARS-CoV-2 infection of alveolar cells induces recruitment of innate responder neutrophils, which release proteases and NETs inducing endothelial damage/endotheliopathy and imbalance of the four major proteolytic cascades (coagulation, complement, fibrinolysis and kallikrein) with prevalence of activators over inhibitors and consequent thrombotic complications. Platelets adhesion to damaged endothelium and the presence of ULVWF multimers, due to decreased ADAMTS13, contributes to the state of hypercoagulability. Neutrophil innate “unfriendly fire” response can be identified as the trigger of a “proteolytic storm”, responsible for subsequent well known prothrombotic condition and “cytokine storm”. The hypothesis explains also the pathology of recently described systemic “Kawasaki Disease like” vasculitis cases in Covid-19 young ill patients.

The Victorian Government Inquiry into wildfires that killed 173 people in 2009 has driven an Australian policy shift from self-evacuation or staying and defending a well-prepared property (‘go or stay’) to self-evacuation under catastrophic fire weather (‘leave early’). The Inquiry also led to the establishment of national ‘performance standards’ for Private Fire Shelters (PFS, that also known as bunkers). Nonetheless, incorporation of PFS into national bushfire policy remains embryonic with only Victoria having streamlined accreditation and planning approval processes. Arguments against PFS include potentially engendering complacency about preparing dwellings to survive fire and encourage risky behavior in response to a fire threat. Counteracting these arguments is research that shows that residents without PFS have low engagement with bushfire preparation and typically delay evacuation. In any case, because wildfire is unpredictable it is accepted that self-evacuation plans must have fallback positions that include sheltering ‘in place’ from the bushfire, making properly used and well maintained PFS an important element of bushfire safety. A less discussed barrier to PFS uptake outside Victoria appears to hinge on a lack of clarity about obligations for their design, certification, and consistency with planning approvals. The escalating Australian fire crisis demands much greater research and development in legal frameworks, policy and planning processes for PFS, as well as design and construction standards.

Accurate and prompt determination of fire types is essential for effective firefighting and reducing damages. However, traditional methods such as smoke detection, visual analysis, and wireless signals pose unavailability in identifying fire types. This paper introduces FireSonic, an acoustic sensing system that leverages commercial speakers and microphones to actively probe the fire using acoustic signals, effectively identifying indoor fire types. By incorporating beamforming technology, FireSonic first enhances signal clarity and reliability, thus mitigating signal attenuation and distortion. To establish a reliable correlation between fire types and sound propagation, FireSonic quantifies the Heat Release Rate (HRR) of flames by analyzing the relationship between fire heated areas and sound wave propagation delays. Furthermore, the system extracts spatiotemporal features related to fire from channel measurements. Experimental results demonstrate that FireSonic attains an average fire type classification accuracy of 95.5% and a detection latency of less than 400 ms, satisfying the requirements for real-time monitoring. This system significantly enhances the formulation of targeted firefighting strategies, boosting fire response effectiveness and public safety.

PFAS are a variety of ecologically persistent compounds of anthropogenic origin loosely included in many industrial products, in which the carbon chain can be fully (perfluoroalkyl substances) or partially (polyfluoroalkyl substances) fluorinated. Their ubiquitous presence in many environmental compartments over the years, and highly long-lasting nature, arose concern because of the possible adverse effects of PFAS on ecosystem and human health. Between the remediation technologies, adsorption has been demonstrated to be a maneageble and cost-effective method for the removal of PFAS in aqueous media. This study tested two novel and eco-friendly adsorbents (pinewood and date seeds biochar) on 6 different PFAS (PFOS, GenX, PFHxA, PFOA, PFDA, PFTeDA). Batch sorption tests (24 hours) were carried out to evaluate the removal efficiency of each PFAS substance in relation to the two biochars. All samples of liquid phase were analysed by a developed and then well-established method: i) pre-treatment (centrifugation and filtration); ii) determination by high-performance liquid chromatography coupled with mass spectrometry (HPLC-MS/MS). Results evidenced a comparable adsorption capacity of both materials, but greater for the long-chain PFAS. Such findings may lead to a promising path for the use of waste origin materials in the PFAS remediation field.

The rate of fires, and particularly residential-fires, is a serious concern in industrialized countries. However, there is considerable uncertainty regarding the reported numbers of residential-fire incidents as official figures are based on fires reported to fire response agencies only. This population-based study aims to quantify the total number of residential-fire incidents regardless of reporting status. The cohort comprised linked person-level data from Fire and Rescue New South Wales (FRNSW) and health system and death records. It includes all persons residing at a residential address in New South Wales, Australia, that experienced a fire between 1 January 2005 – 31 December 2014. Capture-Recapture method was used to estimate the underreporting number of residential fire-related incidents. Over the study period, 43,707 residential-fire incidents were reported to FRNSW and there were 2,795 residential fire-related health service utilization of which 2,380 were not reported. Using Capture-Recapture method, the total number of residential-fire incidents was estimated at 267,815 to 319,719 which is more than six times the official records. This study found that 15% of residential-fire incidents that were identified in health administrative dataset were reported. The residential-fire incidents that were not reported occurred mainly in socio-economically disadvantaged areas among the males and adults.

Fires are the leading cause of death, serious injury and property damage. In the past, schools, temples and government offices had more frequent fires than they should. Statistics showed that the number of fires between 2017 to 2022 amounted 13,593 cases which mostly occurred in the school, temple and government offices (40.0% of all buildings). Moreover, it causes more damage among disabled especially the blinds who has a limited vision. Therefore, this cross-sectional purpose of this study was to assess fire risk including management model in school for the blind. The fire checklists, brainstorming and Analytic Hierarchy Process (AHP) were applied to estimate the fire risk in school for the blind building. The findings revealed an inherent fire hazard factors with a risk score equal to 3.2830 and evacuation factors with a risk score equal to 3.3178 were acceptable risk except the fire control factors with a risk score equal to 1.4320 was unacceptable risk may cause an impact on life, health, property and public communities. Eventually, efforts should be made to supervise those risk factors by designing suitable activities to reduce undesirable conditions in school for the blind.

Background: In long-term care facilities, there are many residents who do not have the ability to seek shelter by themselves in the case of an emergency. Thus, it is extremely important that the staff of nursing homes are equipped with correct disaster prevention concepts, emergency survival responses, and hazard mitigation measures. Purpose: Discuss the intervention effectiveness of different fire prevention and emergency response trainings at nursing homes and the relationship and predictivity of awareness to self-efficacy. Method: Recruit staff from two nursing homes through purposive sampling, using a two-team pre-and post-test design to collect results from 41 individuals in the experiment group and 40 individuals in the control group. The research tool is the “Nursing Home Fire Prevention and Emergency Response Awareness and Self-Efficacy Scale,” to compare the effectiveness of advanced and general fire safety trainings. Result: After receiving improved advanced fire safety training, the total score and the result of the experiment group on fire prevention and emergency response awareness and self-efficacy had both performed better than the control group who received general fire safety training (p < .001); fire prevention and emergency response awareness has significant and positive correlation with self-efficacy (r=.601, p < .001), and awareness is a significant predictor variable to self-efficacy (p < .001). Conclusion/Practical Application: This study finds that the key to improving learning effectiveness includes adding fire science concept chapter when creating fire safety training material in order to strengthen basic awareness; fire safety training should comprehensively introduce all related duty responsibilities of staff fire defense formation, in turn enabling mutual responsive support for the needs of the site; also, to become familiarized with the knowledge requires appropriate frequency of training and enhancing the staff’s awareness to fire prevention and emergency response, which is the most important key of learning effectiveness.

This work presents the dynamics of forest clearing in the Brazilian Amazon during the period 2006–2019 in which includes the approval of the new Brazilian Forest Code in 2012. The study was carried out in the Brazilian Amazon, Pará State and in the municipality of Novo Progresso (Pará State). The analysis was based on deforestation and fire hotspot datasets issued by the Brazilian Institute for Space Research (INPE), produced based on optical and thermal sensors onboard different satellites. Deforestation data was also used to assess greenhouse gas (GHG) emissions from the slash-and-burn practices. The work showed a good correlation between the occurrence of fires in the newly deforested area in the municipality of Novo Progresso and the slash-and-burn practices. The same trend was also observed in the Pará State, suggesting a common practice along the deforestation arch. The study indicated positive coefficients of determination of 0.72 and 0.66 between deforestation and fire occurrences for the municipality of Novo Progresso and Pará State, respectively. The increased number of fire occurrences in the primary forest suggests possible ecosystem degradation. Deforestation reported for 2019 surpassed 10,000 km2, a significant intensification comparatively higher than the previous ten years which was on an average of 6,760 km2. The steady increase of deforestation in the Amazon after 2012 has been a worldwide concern because of the forest loss itself as well as the massive GHG emitted in the Brazilian Amazon (295 million tons of net CO2 equivalent in the year 2019). Better correlation of deforestation and fires occurrences reported from satellite images confirmed the slash-and-burn practice and the secondary effect of deforestation, which degrades primary forest surrounding the deforested areas.

The peri-urban and urban forests in Greece occupy a total area of 105.353 ha. In these vulnerable ecosystems, fire constitutes a natural disaster presenting particular challenges and specific difficulties. These include the high number of visitors as well as forests characteristics - such as the presence of particularly flammable tree species and high accumulation of combustible biomass – that make the on-start of fires more likely. The main purpose of the current research is to identify the optimum combination of silvicultural treatments to efficiently reduce potential forest fire severity and to facilitate their successful suppression by firefighting crews. In order to simulate the basic fire environment of urban forests, two main experimental plots were established and several tree and topographical characteristics were measured. Additionally, the NEXUS wildfire system was used to simulate forest fire potential behavior before and after the adoption of the silvicultural treatments that altered critical characteristics of the forest fire environment. The results clearly show that specific silvicultural prescriptions altered the type of forest fire spreading potential, revealing the overall efficiency of preventing actions during forest management.

Ensuring fire safety is an important condition for the successful operation of industrial enterprises. It is necessary to detect the threat of fire before ignition. An indicator of danger is the appearance of gas, which occurs as a result of unauthorized heating of equipment. Gas sensors make it possible to detect the appearance of gases characteristic of the decomposition of materials at the stage of smouldering and pyrolysis, which cause a change in the composition of the atmosphere. This paper presents a mathematical method for the rational placement of gas analyzers for premises of potentially hazardous industries. It is the rational placement of gas sensors, which ensures safety, taking into account economic feasibility that determines the effectiveness of their use. For the first time, the analysis of gas accumulation during the movement of the flow from the source to the ceil-ing, then propagation to the location of the sensors, is used for the first time to select the placement of gas sensors. Depending on the permissible values of the gas volume, the placement and number of gas sensors are selected. The calculation of the conditions for the rational placement of gas sen-sors for the most common indicators, such as carbon monoxide and hydrogen at different sizes and heights of rooms, is carried out. The method of calculating the location of gas analyzers makes it possible to diagnose and predict fire hazard at technological facilities on the basis of gas control, and compare the obtained values with the maximum permissible values. This method provides cal-culation of the coordinates of the gas source from the coordinates of the triggered sensors. Knowing the coordinates of all sensors, the coordinates of the sensors that gave the alarm, and the formula for determining the volume of the gas cloud under these sensors, it is possible to assess the localization of the source of dangerous gas in the protected room. This increases the efficiency of the fire safety system. The method makes it possible to control the layout of sensors depending on the volume of protected premises, types of production, the creation of new sensors, changes in the composition of hazardous gas in different technological conditions.

Despite various interventions in road safety work, fatal and severe road traffic accidents (RTAs) remain a significant challenge leading to human suffering and economic costs. Understanding the multicausal nature of RTAs, where multiple conditions and factors interact, is crucial for developing effective prevention measures in road safety work. This study investigates the multivariate statistical analysis of co-occurring conditions in RTAs, focusing on single-vehicle accidents with single occupancy and personal injury on Austrian roads outside built-up areas from 2012 to 2019. The aim is to detect recurring combinations of accident-related variables, referred to as blackpatterns (BPs), using the Austrian RTA database. The study proposes Fisher’s exact test to estimate the relationship between an accident-related variable and fatal and severe RTAs (severe casualties). In terms of pattern recognition, the study develops the maximum combination value (MCV) of accident-related variables, a procedure to search through all possible combinations of variables to find the one that has the highest frequency. The accident investigation proceeds with the application of pattern recognition methods, including binomial logistic regression and a newly developed method, the PATTERMAX-method, created to accurately detect and analyse variable-specific BPs in RTA data. Findings indicate significant BPs contributing to severe accidents. The combination of binomial logistic regression and the PATTERMAX-method appears to be a promising approach to investigate severe accidents, providing both insights into detailed variable combinations and their impact on accident severity.

The objective of the study was to report the health-related quality of life (HRQOL) following road traffic accidents (RTAs) among children. A community-based survey using EuroQol five-dimension questionnaire (EQ-5D-5L) in Hindi was used to collected data from community. The survey included 2620 households from urban and rural areas of Ujjain, India. From these households 229 children aged 5–18 years with a history of RTA in the last 1 year were identified, with 27%, 63%, and 10% children reporting mild, moderate, and severe injury based on length of hospitalization. Motorcycles, bicycles, and pedestrians constituted most RTAs. Helmet use was low (12%). EQ-5D-5L revealed that the most severe and extreme problem was pain and discomfort, whereas the least severe problem was usual activity and self-care. The most common (65%) injuries were either abrasion or fracture and dislocation. EQ-5D-5L severity index was maximum (mean 72) for lower extremity injuries. The results of the present study highlight the ability and requirement for quality of life measurement using EQ-5D-5L among children post-RTA.

In recent decades the Mediterranean area observed an increase in large fires, despite the decrease in the number of fire ignitions, following a worldwide tendency. Climate change has been the main culprit for large burnt areas, and alterations of forest ecosystems, resulting in the increased recurrence of forest fires. In Portugal, the analogous trend resulted in the destruction of large rural, urban and industrial areas, where after the 2017 months of June and October catastrophic events, the government implemented the 2019 specific policies for preventing fire outbreaks and defending the forest against fires. This compulsory policy demanded that landowners and local governments alike needed to contact specific authorities requesting authorization for their intentions to clean properties in and around urban areas, roads, and forest limits. Despite policy and careful monitoring, ignitions still occur, and in many cases resulting from the implementation of such policies. The study analyses the before and after policy implementation, considering 6904 ignitions records from the period between 2014 and 2023 from the central area of Portugal, covering the transition area between the agricultural fields of the Tagus valley and the Monte-junto-Estrela Mountain range, the district of Santarém. The authorization to use fire in clearance practices is granted according to a set of weather conditions, temperature, wind intensity, and relative humidity, when analyzing fire ignitions with the average daily wind intensity at the site of the occurrence, we observed that more than half of wildfires occur on days with winds between 10 and 20 Km/h, whereas intensities greater than 30 Km/h only 1% of the total occurrences, and of these 22% occurred due to the use of fire in the burning of agricultural leftovers and cut and piled bushes. The circulation of the thermal depression in the Iberian Peninsula gives rise to increasing intensity throughout the day, reaching a maximum in the late afternoon, this factor combined with the misjudgment of safe conditions leads us to suggest stricter intervals and the need for awareness of daily variation in conditions.

Fire outbreaks continue to cause damage despite the improvements in fire-detection tools and algorithms. It is still challenging to implement a well performing and optimized approach, which is sufficiently accurate, and has tractable complexity and low false alarming rate. Small amount of fire and identification of fire from a long distance is also a challenge in previously proposed techniques. In this study, we propose a novel hybrid model based on Convolutional Neural Networks (CNN) to detect and analyze fire intensity. 21 convolutional layers, 24 Rectified Linear Unit (ReLU) layers, 6 pooling layers, 3 fully connected layers, 2 dropout layers, and a softmax layer are included in the proposed 57-layer CNN model. Our proposed model performs instance segmentation in order to distinguish between fire and non-fire events. To reduce the intricacy of the proposed model, we also propose a key-frame extraction algorithm. The proposed model uses Internet of Things (IoT) devices to alert the relevant person by calculating the severity of fire. Our proposed model is tested on a publicly available dataset having fire and normal videos. The achievement of 95.25 % classification accuracy, 0.09% False Positive Rate (FPR), 0.65 percent False Negative Rate (FNR), and a prediction time of 0.08 seconds validates the proposed system.

Variety of harmful gases are produced in asphalt mixture after mixing, paving and rolling process. Effective measures must be tak-en to suppress the asphalt pavement in the tunnel due to fire accidents and other toxic gases and fumes, reducing the human health during the construction process. In this study, a flame retardant and smoke suppressant (compound) with Mg(OH)2 as the main component was developed, the flame retardant asphalt mixture and asphalt mastics were prepared to evaluate the flame retard-ant-smoke suppressant properties and performance effects. Firstly, its low and high temperature performances were investigated with the bending beam rheometer (BBR) and dynamic shear rheological (DSR), respectively. Then, the indoor combustion test and the cone calorimeter test were used to evaluate the fire retardant smoke suppression effect of the asphalt mastic. Thirdly, the flame retardant effect of asphalt mastic mixed with the compound was further analyzed by thermogravimetric (TG) test and scanning electron microscopy (SEM). The pyrolysis temperature, mass loss and microscopic state of asphalt surface were used to verify and explain the flame retardant reaction effect and process of the compound. Finally, the asphalt mixture performance was evaluated, as well as the flame retardant smoke suppression effect was verified by asphalt mixture combustion tests. The results showed that the flame retardant smoke suppression time of the flame retardant asphalt mixture was reduced by 66% and the smoke emission area was reduced by 20%. The flame retardant smoke suppression effect of the asphalt mixture was improved by 44%. The flame-retardant and smoke-suppressing compound and the asphalt mixture with the compound prepared in this study meet the asphalt mixture performance and flame retardant smoke suppression function, providing an option for application of fire retardant and smoke-suppressing asphalt pavement materials in tunnels.

Tropical forest and savanna biomes are pivotal in the functioning of the Earth system. Tropical forests are one of the largest terrestrial biosphere carbon pools, whereas savannas exchange carbon between the biosphere and atmosphere via frequent and extensive landscape fires. Both are biodiverse and under increasing threat due to land clearing and anthropogenic climate change. Reliable mapping of tropical forest and savanna is essential to provide understanding of how anthropogenic impacts are affecting the extent of these biomes. Using Google Maps satellite imagery, we manually classified 24,239 random points as forest, savanna, or anthropogenic landscapes within the tropics. Because fire and climate are correlated, we developed separate geospatial models to rank the importance of climate, topography, and human influence on vegetation present. This modelling confirmed that those areas with more fires had lower probabilities of tropical forest, that forest was most likely in areas with high mean annual rainfall with little seasonal variation in precipitation, and that anthropogenic factors disrupt this environmental predictability. We found there were environments where tropical forest and savanna were equally probable are geographically restricted. These relationships suggest that future drier climates projected under anthropogenic climate change, combined with clearing and burning that have reduced tropical forest extent to a subset of its theoretical distribution, will lead to irreversible loss of tropical forests. Our modelling provides global mapping that can be used track further changes to distribution of rainforests.

Fires are complex multi-physics problems that span wide spatial scale ranges. Capturing this complexity in computationally affordable numerical simulations for process studies and “outer-loop” techniques (e.g., optimization and uncertainty quantification) is a fundamental challenge in reacting flow research. Further complications arise for propagating fires where a priori knowledge of the fire spread rate and direction is typically not available. In such cases, static mesh refinement at all possible fire locations is a computationally inefficient approach to bridging the wide range of spatial scales relevant to fire behavior. In the present study, we address this challenge by incorporating adaptive mesh refinement (AMR) in fireFoam, an OpenFOAM solver for simulations of complex fire phenomena involving pyrolyzing solid surfaces. The AMR functionality in the extended solver, called fireDyMFoam, is load balanced, models gas, solid, and liquid phases, and allows us to dynamically track regions of interest, thus avoiding inefficient over-resolution of areas far from a propagating flame. We demonstrate the AMR capability and computational efficiency for fire spread on vertical panels, showing that the AMR solver reproduces results obtained using much larger statically refined meshes, but at a substantially reduced computational cost. We then leverage the computational efficiency of the AMR solver to demonstrate an optimization framework for fire suppression based on the open-source Dakota toolkit.

In the last decades droughts, deforestation and wildfires have become recurring phenomena that have affected both human activities and natural ecosystems in Amazonia. The time an ecosystem requires to recover from carbon losses is a crucial metric to evaluate disturbance impacts on forests. However, the factors influencing and controlling the recovery time and its spatiotemporal patterns at the regional scale are still poorly understood. In this study, we combined forest growth model, remote sensing and field plots, to map Amazonia-wide (300-ha resolution) impact and recovery time of aboveground biomass (AGB) after drought, fire and a combination of logging and fire. Our simulated results indicate that AGB decreases by 4%, 19% and 46% in forests disturbed by drought, fire and logging + fire, respectively, with an average AGB recovery time of 27 years for drought, 44 years for burned and 63 years for logged + burned areas and with maximum values reaching 184 years in areas of high fire intensity. Our findings provide two major insights in the spatial and temporal patterns of drought and wildfire in the Amazon: 1) the recovery time of the forests takes longer in the southeastern part of the basin, and, 2) as droughts and wildfires become more frequent – since the intervals between the disturbances is getting shorter than forest regeneration – potentially causing a long-lasting damage in these fragile ecosystems and a permanent degradation.

Forest fire is a major ecological disaster, which has economic, social and environmental impacts on humans and also causes the loss of biodiversity. Forest officials issue the warnings to the public on the basis of fire danger index classes. There is no fire danger index for the country India due to the sparsely distributed meteorological stations. Previous studies suggest that Static Fire Danger Index (SFDI) as well as Dynamic Fire Danger Index (DFDI) have been derived from the satellite datasets. In this study, we have made an attempt to integrate both the Static and Dynamic fire danger indices and also used the Near Real Time (NRT) data sets that can be available for download through NASA FTP server after one hour of the satellite overpass. In this study, DFDI has been calculated from the MODIS Terra NRT Land Surface Temperature (MOD11_L2) and MODIS TERRA NRT surface reflectance MOD09. Finally, The Forest Fire Danger Index (FFDI) has been developed from the static and dynamic fire danger indices by the additive model and the overall accuracy was around 81.27%. Thus, the FFDI has been useful to predict the fire danger accurately and can be useful anywhere, where the meteorological stations are un-available.

Fire safety is a critical concern in various industries necessitating the development of sustainable and effective fire-resistant materials. Sustainable fire-resistant polysaccharide-based composite aerogels are regarded as innovative solutions in fire safety applications, and as such, research in this field has increased consistently over the past few years. Despite the plethora of literature on this important subject, only a few studies have attempted to map the global research of sustainable fire-resistant polysaccharide-based composite aerogels to identify the geospatial collaborative network and trend of research. This study utilizes a scientometric review of global trends in sustainable fire-resistant polysaccharide-based composite aerogels research between 2003–2023 by VOSviewer and biblioshiny to analyze co-author, co-word, co-citation, clusters, and geospatial map. A total of 234 bibliographic records from the Scopus database were analyzed to generate the study's research power networks and geospatial map. The most significant contributions in sustainable fire-resistant polysaccharide-based composite aerogels comes from China, the United States, Australia, Canada, and India with records of 194, 20, 11, 9, and 8 respectively. The top five sources for articles in this area of research include ACS Applied Materials and Interfaces, Chemical Engineering Journal, Composite Engineering, ACS Sustainable Chemistry and Engineering, and Carbohydrate Polymers. The application of sustainable fire-resistant polysaccharide-based composite aerogels spans the engineering and construction fields. The versatility in the fabrication and customization allows for seamless integration into diverse applications. The article concludes by emphasizing the significance of sustainable fire-resistant polysaccharide-based composite aerogels as a promising advancement in fire safety technology, combining sustainability, fire resistance, versatility, and mechanical strength to address critical challenges in the field. This study provides readers with an extensive understanding of the salient research themes, trends, and patterns of sustainable fire-resistant polysaccharide-based composite aerogel research worldwide.

Background: We studied Riveaux Road Fire, which was ignited by multiple lightning strikes in January 2019 and burnt more than 637.19 km² in southern Tasmania, Australia. Aims: We focused on fire weather, such as identification of dynamic wind and vegetation type, in a valley of the study area. Methods: We employed two methods: numerical weather model vertical sounding (NWMVS) and the use of a fire simulator, to quantify and examine the contribution of dynamic winds to fire behaviour. The NWMVSs allow rapid diagnosis of changes in wind, temperature, dew point temperature and cloud coverage. Prototype 2 is a fire simulator based on the specification of Australian Fire Danger Rating System (AFDRS). Key results: We found fires to be guided by terrain-forced channelling primarily and by downslope wind conditionally in the valleys. In addition, the fire intensity periodically changed with the magnitude of surface wind, in buttongrass moorland, in which the fire often smoulders, during the fire period according to the satellite image. Conclusions and Implications: Therefore, there should be caution for not only terrain and dynamic wind but also vegetation type during fire spread in rugged terrain.

Forests, integral to human civilization, hold immense value and play a vital role in maintaining ecological harmony. Despite India's goal of extensive forest coverage, significant progress is still needed. Uncontrolled forest fires pose a severe threat, particularly in Uttarakhand's Pauri Garhwal district. To address this challenge, a comprehensive study examined surface and subsurface hydrological factors influencing the forest fire occurrences, such as elevation, aspect, slope, vegetation, proximity to human settlements, proximity to waterbodies, Active faults and lineament density. A total of 15 such factors were integrated with advanced techniques of remote sensing and GIS and coupled with historical fire data to create a precise forest fire risk map using the support vector machine algorithm. Forest fire risk map was classified into 5 distinct risk zones, Very High Risk (47.38 Km2), High Risk (275.98 km2, Moderate Risk (985.49 km2), Low Risk (1741.17 km2) and Very Low Risk (2374.11 km2) aiding in proactive fire management. By embracing this innovative tool, decision-makers can protect forests, preserve biodiversity, and ensure a sustainable future for generations to come.

Forest fires are one of the world's deadliest natural disasters. Early detection of forest fires can help minimize the damage to ecosystems and forest life. In this paper, we propose an improved fire detection method YOLOv5-IFFDM for YOLOv5. Firstly, the fire and smoke detection accuracy and the network perception accuracy of small targets are improved by adding attention mechanism in the backbone network. Secondly, the loss function is improved and the SoftPool pyramid pooling structure is used to improve the regression accuracy and detection effect of the model and the robustness of the model. In addition, random Mosaic augmentation technique is used to enhance the data to increase the generalization ability of the model, and re-clustering of flame and smoke detection a priori frames are used to improve the accuracy and speed. Finally, the parameters of the convolutional and normalization layers of the trained model are homogeneously merged to further reduce the model responsibility to improve the detection speed. Experimental results on homemade forest fire and smoke datasets show that this algorithm has high detection accuracy and fast detection speed, with average accuracy of fire up to 90.5% and smoke up to 84.3%, and detection speed up to 75 FPS (frames per second transmission), which can meet the requirements of real-time and efficient fire detection.

Fire-related deaths are issues for forensic pathologists particularly in ascertaining if death occurred before or during fire. The authors highlight a unique bronchiolar epithelial cytological clue (nuclear heat-induced elongation) determined by active inhalation of hot gases and fumes, not yet described in the literature at distal small airways level.

Very few multi-species or ecosystem comparisons of post-fire vertebrate herbivore activity and food preference exist to inform fire-management and conservation strategies. We inferred post-fire (1-3 years) native and introduced vertebrate herbivore activity and attraction to six diverse temperate vegetation communities (grassland to rainforest) from scat counts. We hypothesised that where fire reduced herbaceous and grassy vegetation (‘fodder’), vertebrate herbivores would decline, and that post-fire preferences of native versus exotic herbivores would differ significantly. Instead, we found evidence for a ‘fire and fodder reversal phenomenon’ whereby native macropod and exotic rabbit scats were more abundant after fire in consistently ‘fodder-poor’ vegetation types (e.g wet forests) but more less abundant after fire in previously fodder-rich vegetation communities (e.g. grassland). Fodder cover predicted native macropod, wombat, and introduced deer activity and bareground cover was strongly associated with introduced herbivore activity only, with the latter indicating post-fire competition for food sources due to their abundance in high altitude open ecosystems. We therefore found environmental and vegetation predictors for each individual species/group and suggest broadscale multi-environment, multispecies observations to be informative for conservation management in potentially overlapping post-fire niches.

Fire accidents are causing a huge human death as well as economic losses throughout the world every year. But a novel fire detection and extinguishing system could reduce these losses to a great extent. We are proposing a cost effective and yet very efficient system which has been developed in our laboratory and evaluated while in operation. Our system consists of two units: detection unit and extinguishing unit. For detection purposes we used smoke, IR and temperature sensors. While for extinguishing purposes we used both foam and water. We used a PC to process and control our system through LabVIEW software of National Instrument, USA. The detection unit will send signal through a wireless module to PC if fire is detected. Then PC (LabVIEW) will process the signal and using relay module first of all the electric power and gas lines will be turned off. The extinguishing unit will then release foam or water to extinguish the fire. The system will send SMS to the predefined numbers using GSM module. Our system is location independent as it uses GSM and can be operated from remote location using wireless module. After performance evaluation it was found to be fast and reliable.

Repeated fires cause peatlands to degrade. Fire management has been carried out, but fires continue to occur, especially during the dry season. Through social capital that applies in the community based on the local wisdom of the Banjar people in environmental management, it is hoped that peatland fires can be overcome in this area. This research aims to analyze the social capital of the Banjar people in mitigating land fires based on local wisdom and the environment. The sample in this study was 250 people who live in an area prone to fires on peatlands in Banjarbaru, South Kalimantan, Indonesia. There are three variables in this study, namely Trust (T), Network (N) and Norm (N). There are 33 Likert scale questions (1-4, strongly agree-strongly disagree). We also conducted interviews with 20 members of the Fire Care Community (MPA) to obtain information about the social capital of the Banjar community in mitigating land fires. The analysis used is Structural Equation Modeling (SEM). The study results show norms have the greatest influence on disaster mitigation of peatland fires compared to trust and social networks. Norms in the community in the form of values from peatlands, rules, norms and sanctions regulate the community more to deal with the problem of peatland fires than trust and social networks

In this study, we focus on the effects of fuel bed representation and fire heat and smoke distribution in a coupled fire-atmosphere simulation platform for two landscape-scale fires: the 2018 Camp Fire and the 2021 Caldor Fire. The fuel bed representation in the coupled fire-atmosphere simulation platform WRF-Fire currently includes only surface fuels. Thus, we enhance the model by adding canopy fuel characteristics and heat release, for which a method to calculate the heat generated from canopy fuel consumption is developed and implemented in WRF-Fire. Furthermore, the current WRF-Fire heat and smoke distribution in the atmosphere is replaced with a heat-conserving Truncated Gaussian (TG) function and its effects are evaluated. The simulated fire perimeters of case studies are validated against semi-continuous, high-resolution fire perimeters derived from NEXRAD radar observations. Furthermore, simulated plumes of the two fire cases are compared to NEXRAD radar reflectivity observations followed by buoyancy analysis using simulated temperature and vertical velocity fields. The results show that while the improved fuel bed and the TG heat release scheme have small effects on the simulated fire perimeters of the wind-driven Camp Fire, they affect the propagation direction of the plume-driven Caldor Fire, leading to better-matching fire perimeters with the observations. However, the improved fuel bed representation together with the TG heat smoke release scheme leads to more realistic plume structure in comparison to the observations in both fires. The buoyancy analysis also depicts more realistic fire-induced temperature anomalies and atmospheric circulation when the fuel bed is improved.

Through the use of machine learning algorithms like the Support Vector Machine, it has been show that burn extent can be accurately mapped from hyperspatial drone imagery in both grasslands and forests. Despite these successes, hyperspatial imagery must be acquired via drones, requiring large amounts of time and resources to capture areas much smaller than the large catastrophic fires which result in the majority of the lands burned each year by wildland fires. To overcome this difficulty, high spatial resolution satellite imagery from Worldview2 can be substituted for hyperspatial drone imagery, allowing for larger regions of images to be acquired more easily and efficiently. Additionally, Worldview2 trades spatial resolution for spectral resolution and extent, capturing images in 8 multispectral bands as opposed to 3 band imagery in the visible spectra. This research examines the utility of each of the 8 bands observed in Worldview2 imagery using an Iterative Dichotomiser 3 decision tree, then uses these bands to map burn extent and biomass consumption. Several classifications of burn extent and biomass consumption are produced and compared based on the bands used as inputs. The results show that using Worldview2 imagery to map burn extent and biomass consumption results in highly accurate maps, with slight improvements when additional bands are added.

Fire behavior of natural rubber latex foam under different thickness conditions(d=1,2and 5cm)were explored though a little of experiments by using the self-built small scale experimental platform. It can be shown that the flame spread law of thermally thin and thermally thickness are different. Natural rubber latex foam with thickness of 2cm show higher fire risk, which value of flame spread rate, maximum flame height, maximum mass loss rate and maximum temperature is 0.00293m/s, 851.875mm, 1.83g/s, 948K,repectively.That may because the thickness of residue formed of thermally thick materials is larger than the thin one, obstructing the contact of the natural rubber latex foam with fresh air .In addition, a special phenomenon is noticed that during the second stage, the bottom unburned zone located in the four edges(thermally thin material) and middle player(thermally thick material).

Accurate fire severity mapping is essential for understanding the impacts of wildfires on vegetation dynamics in arid savannas. The frequent wildfires in these biomes often cause topkill, where vegetation experiences above-ground combustion, but the below-ground root structures survive, allowing for subsequent regrowth post-burn. Investigating post-fire regrowth is crucial for maintaining ecological balance, elucidating fire regimes, and enhancing the knowledge base of land managers regarding vegetation response. This study examined the relationship between bush fire severity and post-burn coppicing/regeneration events of woody vegetation in the Kalahari Desert of Botswana. Utilizing UAV-derived RGB imagery combined with a Random Forest (RF) classification algorithm, we aimed to enhance the precision of burn severity mapping at a fine spatial resolution. Our research focused on a 1 km2 plot within the Modisa Wildlife Reserve, extensively burnt by the Kgalagadi Transfrontier Fire of 2021. The UAV imagery, captured at various intervals post-burn, provided detailed orthomosaics and canopy height models, facilitating precise land cover classification and burn severity assessment. The RF model achieved an overall accuracy of 79.5% and effectively identified key burn severity indicators, including green vegetation, charred grass, and ash deposits. Our analysis revealed a &gt;50% probability of woody vegetation regrowth in high severity burn areas six months post-burn, highlighting the resilience of these ecosystems. This study demonstrates the efficacy of low-cost UAV photogrammetry for fine scale burn severity assessment and provides valuable insights into post-fire vegetation recovery, thereby aiding land management and conservation efforts in savannas.

This study develops an intelligent fire suppression device that links microcapsule-based IoT to extinguish early fires, such as blind spots that cannot be seen by humans or places that are difficult to recognize fires. The microcapsule is a micro-unit collection that collects Novak 1230 gas generated in the semiconductor production process. The microcapsule is molded into a form with a fire suppression function, and when a fire occurs, the molded body explodes and absorbs ambient oxygen to suppress the fire. The complex sensor IoT executes smoke and heat detection generated when a fire is suppressed within 10 seconds, which derives the reliability of the detector by notifying the fire and detecting the ignition point through communication linkages such as Ieee 485 and WiFi or LoRa.

This project involves the development of Fire Suppression System (FSS) leveraging of Home automation system using Graphical System design (LabVIEW) software and Arduino microcontroller for real-time monitoring and control. The system integrates an Infrared (IR) flame sensor, an MQ2 gas sensor to detect and suppress fire hazards efficiently. The LabVIEW interface facilitates real-time monitoring of sensor data, allowing users to visualize and analyze the environmental conditions. Through this system, fires are automatically subdued, enhancing safety without requiring human intervention. This advances fire safety by swiftly containing threats and minimizing potential damage.

This paper proposes an Integrated Fire Management (IFM) framework that can be used to support communities and resource managers in finding effective and efficient approaches to prevent damaging fires, as well as maintain desirable fire regimes in Kenya. Designing and implementing an IFM approach in Kenya calls for a systematic understanding of the various uses of fire and the underlying perceptions and traditional ecological knowledge of the local people. The here proposed IFM framework allows an evaluation of the risks posed by fires, while balancing them with their beneficial ecological and economic effects, and thus developing effective fire management approaches. A case study of the proposed IFM framework was conducted in Gathiuru Forest that is part of the larger Mt. Kenya Forest Ecosystem. Focus group discussions were held with key resource persons, primary and secondary data on socio-economic activities were studied, fire and weather records were analyzed and the current fire management plans were consulted. Questionnaires were used to assess how the IFM is implemented in the Gathiuru Forest Station. The results show that the proposed IFM framework is scalable and can be applied in places with fire-dependent ecosystems as well as in places with fire-sensitive ecosystems in Kenya. The effectiveness is dependent on the active participation, formulation and implementation of the IFM activities by the main stakeholder groups (Kenya Forest Service (KFS), Kenya Wildlife Service (KWS), and the Community Forest Associations (CFA)). The proposed IFM framework helps in implementing cost-effective approaches to prevent damaging fires and maintain desirable fire regimes in Kenya.

Older adults in low-and middle-income countries experience a disproportionate burden of non-communicable diseases (NCDs). Unintentional injuries are among the major NCDs, and falls are the second leading cause of these injuries and deaths worldwide, including in Thailand. We aimed to culturally adapt the CDC’s Stopping Elderly Accidents, Deaths, and Injuries (STEADI) for Thai older adults and explore the feasibility, appropriateness, and acceptability of using STEADI in primary care via trained community health workers (CHWs) and care managers (CMs). STEADI takes a coordinated care approach that consists of three steps: screening, assessing, and intervening. In Step a, CHWs screened fall risk in 20 community-dwelling older adults using three key questions and found that all of them had fall risk, then CHWs screened with a Stay Independent questionnaire (range 0-14) and found that 100% have high fall risk (total scores 9.7± 2.4). In Step b, CMs assessed balance, vision, footwear, postural hypotension, medications, and CHWs assessed home hazards. They found that 50% had poor balance, 70% took 4+ medications,75% fell on the walkway, and 70% had no bathroom modifications. In Step c, individual participants received fall prevention interventions to mitigate their specific fall risk factors. CHWs and CMs indicated high acceptability (19.20±.1.31 of 20 total), appropriateness (18.80± 1.79 of 20 total), and feasibility (18.60±1.67 of 20 total) of the Thai-STEADI intervention. Our study showed that the community-based multifactorial Thai-STEADI delivered by CHWs and CMs is feasible and acceptable to prevent falls in older adults with limited access to health care.

Polymers and their processing by engineering production technologies (injection, moulding or additive manufacturing) are being increasingly used. An explosive atmosphere can be created by the powder form of these polymer materials, and introduction of preventive safeguards to control safety is required for their use. Determination of the fire parameters of powder samples of Polyamide PA12, Polypropylene, and ultra-high molecular weight polyethylene (UHMW Polyethylene) is the subject of the current article. The results showed? that one of the samples used was not flammable and thus safe for use in terms of explosiveness. Two samples were flammable and explosive. The lower explosive limit was 30 g.m-3 (Polyamide PA12) and 60 g.m-3 (UHMW Polyethylene). The maximum explosion pressure of the samples was 6.47 (UHMW Polyethylene) and 6.76 bar (Polyamide PA 12). The explosion constant Kst of the samples was 116.6 bar.m.s-1 (Polyamide PA 12) and 97.1 bar.m.s-1 (UHMW Polyethylene). Therefore, when using polymers in production technologies, it is necessary to know their fire parameters, and to design effective explosion prevention measures for flammable and explosive polymers.

Fire is a common disturbance in the Brazilian savanna (Cerrado), wherein high-frequency fires drive the vegetation structure, composition, function, and dynamics of savanna ecosystems. Under climate change pressure, further understanding of fire-vegetation relationships and interactions can provide new approaches for establishing strategies for integrated fire management and promote savanna vegetation recovery post-fire. To understand how 15 years of yearly manipulated burning affects the vertical and horizontal structure of the vegetation, the species composition and diversity metrics (species richness, Shannon's diversity, and Pielou's evenness), and the aboveground carbon stocks, we surveyed all woody plant species with a diameter greater than three centimeters in 15 plots of a typical Brazilian savanna (cerrado stricto sensu) at an experimental research station in central Brazil (Cerrado biome). Fifteen plots (five per treatment) were differently affected by fire events over a decade, comprising three treatments: (i) annual fire, (ii) legacy fire (> 11 years since the last fire event), and control (not burned in the past 30 years). Non-metric multidimensional scaling (NMDS) analysis indicated a significant effect of fire on the species composition among treatments and that some species benefited from fire, such as Erythroxylum suberosum, whereas others propagated better without fire, such as Camisala montana and Dalbergia miscolobium. Over a decade of annual fire events led to decreases in Shannon's diversity, species richness, and stem density, which were significantly lower in the annual fire treatment than in the control and legacy fire treatments. Stem density by diameter and height size class (except for the 1-2 m class and above 8 m) was higher in the control than in the annual fire treatment, but the number of dead trees did not differ between the control and annual fire treatments. Our results also showed that fire was a factor in changes in the evaluated parameters, such as annual fire treatment, which reduced the amount of biomass and, therefore, the carbon stock. This study suggests that, if burned yearly, typical Brazilian savannas can become less biodiverse in terms of woody plant species and negatively affect their fire resilience. Therefore, fire management practices should focus on determining the frequency with which these ecosystems may benefit the most from fire disturbances.

The paper aims at presenting the results obtained in the development of a system allowing the detection and monitoring of forest fires and the continuous comparison of their intensity when several events occur simultaneously, as usually happens in the European Mediterranean countries during the summer season. The system, called SFIDE (Satellite FIre DEtection), exploits a geostationary satellite sensor (SEVIRI on board of MSG satellite series). The algorithm was developed several years ago in the framework of a project (SIGRI) funded by the Italian Space Agency (ASI). This algorithm has been completely reviewed in order to enhance its efficiency by reducing false alarms rate preserving a high sensitivity. Due to the very low spatial resolution of SEVIRI images (4x4 km² at Mediterranean latitude) the sensitivity of the algorithm should be very high to detect even small fires. The improvement of the algorithm has been obtained by: introducing the sun elevation angle in the computation of the preliminary thresholds to identify potential thermal anomalies (hot spots), introducing a contextual analysis in the detection of clouds and in the detection of night-time fires. The results of the algorithm have been validated in the Sardinia region by using ground true data provided by the regional Corpo Forestale e di Vigilanza Ambientale (CFVA). A significant reduction of the commission error (less than 10%) has been obtained with respect to the previous version of the algorithm and also with respect to fire-detection algorithms based on low earth orbit satellites.

Sudden water pollution accident is a tough environmental challenge to solve due to its uncertainty, unpredictability and destructiveness. Therein, the dangerous goods transportation accidents on the cross-tributary bridges are increasing worldwide with the acceleration of industrialization and urbanization, posing threats to the water ecological environment and human health. To prevent and control the risk, this study evaluated the risk of sudden water pollution accidents associated with dangerous goods transportation on the cross-tributary bridges by using the expert judgment fuzzy language method and Bayesian network model. Baiyangdian lake as the largest freshwater shallow lake in North China was chosen as a study case. Different accident scenarios were simulated to put forward risk prevention and control measures. The results showed that the primary risk factors were divided into five categories: personnel factors, vehicle factors, tank factors, weather factors, and road environment factors. The probability of dangerous goods transportation accidents was a possible event (P=0.115), with a high risk level. Furthermore, the Baiyangdian lake would suffer from serious environmental pollution and water quality degradation without prevention and control measures by simulating the oil leakage accident. Therefore, emergency prevention and control measures are expected to be formulated and implemented before and after the occurrence of the sudden water pollution accidents. This study provides useful insights into risk prevention and control strategies for sudden water pollution accidents, and the framework can be made applicable to other rivers facing similar risk challenges worldwide.

Introduction: Road traffic accidents are a real pandemic and incur expenses amounting to 1-2% of every country’s GDP. AESLEME (Association for the Study of Spinal Cord Injuries) – devoted to teaching road safety and health to prevent road accidents – has celebrated its 30th anniversary. AESLEME’s instructors are health workers and people with spinal cord injuries caused by road accidents: their presentations – teaching road safety and sharing information on irreversible injuries – are enhanced by personal stories that help schoolchildren to acquire knowledge on this matter. However, until now, we had not assessed how far this acquisition of knowledge had reached. Methods used: Two multiple-choice tests were given to each of the 8,106 students (12-14 years) who took part. Of the four possible answers, only one of them was correct. The first multiple-choice test was taken before the presentation and the second was taken one month later. Results: After assessing the answers, there was a change in the tendency of the number of correct answers before/after answers for the multiple-choice test, and the number of correct one´s rose one month after the presentation. This increase is statistically significant (p&lt;0.01) and represents a national increase of 61% in the number of correct answers, although this varies from 8% to 278% depending on the region. Conclusions: The assessment, involving over 8,000 people, showed that there has been an improvement in road safety knowledge thanks to education provided by AESLEME’s instructors, and a statistically significant increase was obtained throughout Spain, with an average of 61% (confidence level 95: 53% and 64%).

Understanding that organizational communication is crucial for the consistent functioning of any establishment, this research assesses its influence on work-life balance, organizational climate, and job satisfaction among Portuguese firefighters, a rarely studied group. Effective organizational communication is identified as a potential pathway for nurturing a favorable work setting and bolstering employee contentment, essential elements of work-life balance. Data compiled from a group of 102 firefighters, using a sociodemographic questionnaire, the Work Social Climate Scale, and Portuguese versions of the ICA Communication Audit Survey Instrument and the Labor Satisfaction Questionnaire S20/23, unveil multiple significant correlations among various dimensions of the ICA, the Work Social Climate Scale, and communication metrics. Explicitly, correlations were discerned between received and sent communications and aspects of implication, support, and cohesion. Links were also evident between desired communications and the control dimension. Positive and statistically significant relationships were further observed between received and sent communications, all five satisfaction dimensions, necessary outgoing communications and the physical environment, and required outgoing communications and benefits, highlighting the pivotal role of adept communication in augmenting work-life balance, organizational climate, and job satisfaction among firefighters.

The article presents results of research consisting in an attempt to assess the influence of temperature, heating time and cooling method on microstructure and residual strength properties of steel previously tempered during the production process. Simulated environmental conditions to which high-strength bolts, commonly used in steel construction, were subjected, were intended to reflect conditions of a natural fire that may occur in public facilities where the obligation to ensure safety of users and rescue teams is of key importance, also required by law. Furthermore, the tests carried out also comprised a simulated effect of a rescue and firefighting operation using shock, rapid cooling of some of the heated bolts. Samples cut out from the tested bolts, after they were properly prepared, were subjected to microstructural tests using light microscopy, scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), phase analysis with the use of an X-ray diffractometer (XRD) and quantitative analysis of the microstructure involving, inter alia, measuring the surface area of grains, their equivalent diameter and mean diameter. As a result of the tests, considerable microstructure changes were identified occurring in the bolt material as a result of exposing it to fire conditions, leading to a change in key, from the point of view of structural safety, mechanical properties. The results of the microstructure tests were compared with the results of previous strength tests, including hardness of the material after the heat treatment and the residual tensile strength of the material Rm. A conducted comparative analysis showed a significant effect of all such factors as the temperature level of the simulated fire, its duration and the fire-fighting method on the mechanical properties of bolts. Results obtained were provided with required comments and the concept of using the microstructure for the post-fire assessment of steel structures was referred to.

In the present work we have studied the short-term edaphic recovery of a burned pine forest of Pinus pinaster Aiton in Cerro del Castillo (Sierra de Guadarrama, Central Spain) in which a hydrological-forestry restoration was being carried out after the fire occurred in July 2022. For this purpose, variations in physicochemical properties (conductivity, relative humidity, organic matter and pH) and collembolan communities were analyzed by comparing the soils of a natural pine forest and a burned pine forest during the 24 weeks after the fire in 3 different samplings. With the data obtained, a series of diversity indices were calculated and statistical analyses such as ANOVA, hierarchical classification and principal component analysis were developed, with the aim of understanding in greater depth the recovery of collembolan populations, the changes in these physicochemical variables and the relationships between both parameters. This study shows how the fire reduced the amount of organic matter and water, and increased soil pH and conductivity, affecting edaphic fauna populations. In the short term, there was an increase in relative humidity and a decrease in electrical conductivity. The edaphic fauna communities of the burned pine forest increased their diversity and equity without reaching the values of the natural pine forest. At the specific level, however, the springtail populations matched the richness and even reached a very slightly higher Shanon-Wiener diversity index than that the natural pine forest populations in just 24 weeks. Finally, it was observed that the samples from the burned pine forest with recovered collembolan populations had the closest physicochemical parameters to those of the natural pine forest.

Fire-related cues could alter vegetation community composition by promoting or excluding different types of plants. Smoke-derived compounds have been a hot topic in plant and crop physiology. There are some fire-prone areas in Australia, both Americas, some Mediterranean countries and regions with extensive prescribed or illegal burning like central European, which are subjected to a direct impact of fire (heat, minerals accumulation from the burning matter) and/or the indirect one (smoke) and undergo compositional and structural changes. This review first updates information about the effect of the compounds on plants' kingdoms and focuses on the research advances in the field of smoke compounds and attempts to gather and summarize the recent state of research and opinions on the role of smoke-derived compounds in plants' lives. We finish our review by discussing major research gaps, which include: Why plants respond to smoke chemicals? Is the response of seeds to smoke and smoke compounds an evolutionary-driven trait allowing plants to adapt to local environment?

The purpose of this paper is to develop an index system for measuring the compliance of high-rise residential buildings with fire requirements and also to develop an index system for measuring the fire response efficiency, which is linked to the index of compliance with fire requirements. The higher the compliance rate, the greater the chance of a successful response. The two systems de-pend on the automation of the firefighting system management processes using the techniques of the fourth industrial revolution and developed based on the consultation of subject matter experts in the field. The main elements of the indexes were identified, which were based mainly on several variables, including the fire administrative system, the firefighting system, the residents, the loca-tion of the high-rise residential buildings from the fire extinguishing station, and the height of the high-rise residential building. The two systems can contribute to improving emergency prepared-ness in high-rise residential buildings in the Emirate of Sharjah and are also considered as a measurement index for compliance with fire requirements in the Emirate of Sharjah.

Forest fire identification is important for forest resource protection. Effective monitoring of forest fires requires the deployment of multiple monitors with different viewpoints, while most traditional recognition models can only recognize images from a single source. By ignoring the information from images with different viewpoints, these models produce high rates of missed and false alarms. In this paper, we propose a graph neural network model based on the similarity of dynamic features of multi-view images to improve the accuracy of forest fire recognition. The input features of the nodes on the graph are converted into relational features of different gallery pairs by establishing pairs (nodes) representing different viewpoint images and gallery images. The new feature library relationship is used to update the image gallery with dynamic features in order to achieve the estimation of similarity between images and improve the image recognition rate of the model. In addition, to reduce the complexity of image pre-processing process and extract key features in images effectively, this paper also proposes a dynamic feature extraction method for fire regions based on image segment ability. By setting the threshold value of HSV color space, the fire region is segmented from the image, and the dynamic features of successive frames of the fire region are extracted. The experimental results show that, compared with the baseline method Resnet, this paper's method is more effective in identifying forest fires, and its recognition accuracy is improved by 2%. And the scheme of this paper can adapt to different forest fire scenes, with better generalization ability and anti-interference ability.

In the steel industries, workers are exposed to heat and ambient thermal stresses on a daily basis, leading to discomfort and limited performance. In this study, the main purpose is to investigate the effect of climate heat stress on the rate of accidents in the workplace for workers for 5 consecutive years. The data of this study were received without any sampling through the HSE Center for Steel Industry and meteorological data from 2015 to 2019 from Isfahan Meteorological station. The daily number of casualties among workers in the steel industry during 2015-2019 by adjusting seasonal patterns, months, effects of the day of the week and other meteorological factors on the average daily temperature using the studied model has a decreasing effect. Eviews software (version 8) was used to model and investigate the relationship between events and meteorological variables. The mean temperature was at least 40.2-2 and at most 70.34 ° C, respectively. In the time-series study for the main model, the number of accidents shows a direct relationship with the average temperature and wind speed. Climatic indices of humidity and rainfall have the least impact on accidents compared to temperature and wind speed. A strong correlation was shown between the increase in average ambient temperature and the rate of accidents over the past 5 years. Given the fundamental differences in studies of environmental exposure and wind speed over heat stress, further analysis in workers should be considered.

Abstract: Petrol is considered the most common fire accelerant. However, the identification and classification of petrol sources through the years has been proven to be a challenging field in the investigation of fire debris analysis. This research explored the possibility of identifying petrol sources by high field NMR methods accompanied by ML (Machine Learning). The automated identification and classification of petrol brands were achieved for first time based on the ML clas-sification model developed in this research. A hierarchical classification model was constructed using local classifiers to categorize neat or weathered petrol into its sources.

Expandable graphite (EG) was modified with a charring agent and organic-inorganic hybridised intumescent flame retardants (MEG) were synthesized. This study uses a cone calorimeter (CCT) and a DaqPRO 5300 radiation heat flow meter to evaluate the fire-resistant properties influenced by MEG on intumescent fire-retardant coatings. The impact of MEG on the thermal degradation of these coatings was investigated through the use of thermogravimetric analysis (TGA). The results obtained by CCT demonstrated that the incorporation of MEG markedly diminished the heat release rate and total heat release rate of the coating, in addition to enhancing the char residue compared to coatings with only expandable graphite (EG). Furthermore, TGA results demonstrate that adding MEG increases the weight of the char residue at elevated temperatures, suggesting improved thermal stability. Based on these findings, MEG exhibits a synergistic flame retardant effect when combined with intumescent fire retardant (IFR) systems. This synergy not only improves the flame-retardant properties of the coatings but also enhances their overall thermal stability, making MEG a promising additive for developing more efficient fire-retardant materials. Thus, MEG-modified coatings offer superior protection against fire hazards, highlighting their potential for practical applications in fire safety.

Project managers should balance a variety of resource elements in building projects while taking into account many major concerns, including time, cost, quality, risk, and the environment. This study presents a framework for resource trade-offs in project scheduling based on the Building Information Modeling (BIM) methodology and metaheuristic algorithms. First, a new metaheuristic algorithm called Fire Hawk Optimizer (FHO) is used. Using project management software and the BIM process, a 3D model of the construction is created. In order to maximize quality while minimizing time, cost, risk, and CO2 in the project under consideration, an optimization problem is created, and the FHO's capability for solving it is assessed. A predefined stopping condition is taken into account while doing 30 independent optimization runs to obtain the statistical metrics, such as the mean, standard deviation, and the required number of objective function evaluations. The results show that the FHO algorithm is capable of producing competitive and exceptional outcomes when it comes to trade-off various resource options in projects.

In order to assess the human exposure risks from the release of contaminants from water pipes made of polyvinyl chloride (PVC), experiments were carried out by subjecting the PVC pipe material to burning and leaching conditions followed by analysis of the emission and leachate samples. The emissions of burning pipes were analyzed by both infrared spectrometry and gas chromatography-mass spectrometry (GC-MS). The emission results indicate the presence of chlorinated components including chlorine dioxide, methyl chloride, methylene chloride, allyl chloride, vinyl chloride, ethyl chloride, 1-chlorobutane, tetrachloroethylene, chlorobenzene, and hydrogen chloride were detected in the emissions of burning PVC pipes. Furthermore, the concentrations of benzene, 1,3-butadiene, methyl methacrylate, carbon monoxide, acrolein, and formaldehyde were found at levels capable of affecting human health adversely. The analysis of PVC pipe leachates using GC-MS shows that there are 40-60 tentatively identified compounds, mostly long-chain hydrocarbons such as tetradecane, hexadecane, octadecane, and docosane, were released when the burned PVC materials were soaked in deionized water for one week. Quantitative analysis shows that 2-butoxyethanol, 2-ethyl-1-hexanol, and diethyl phthalate were found in the burned PVC polymer at the average levels of 2.7, 14.0, and 3.1 micrograms per gram (g/g) of pipe material. This study has significant implications for understanding the benzene contamination of drinking water in the aftermath of wildfires that burned polymer pipes in California.