Fire Response Research Articles

Behaviour of concrete building units incorporating waste plastic eco-aggregate (RESIN8) subjected to fire conditions

Plastic waste has started to be incorporated within construction systems with potential benefits including improved sustainability, decreased costs, lighter construction units and enhanced thermal performance. However, the addition of plastics has an unknown impact on the fire response and associated behaviour of recycled construction products. Hence, this work seeks to answer the question: does the incorporation of waste plastic in masonry systems influence fire risk? This study investigates the effect of using the RESIN8 eco-aggregate in bricks, an aggregate based on processed waste plastic. This is done by exposing concrete bricks containing 0%, 5%, 10%, 20%, 30%, 40% and 50% RESIN8 by volume to a heat flux of 30 kW/m2 for an hour. The replacement content of the RESIN8 samples influenced both smoke production and mass loss rates, with high volumes of smoke observed for the highest replacement content of RESIN8. The capacity of the RESIN8 brick units was also found to drop by up to 34.7% of the original compressive strength. It is shown that low replacement rates (e.g. 5 and 10%) are unlikely to affect fire risk significantly, but higher replacement rates may be a concern due to smoke production and heat release rates.

Thermally-induced self-cutting surfaced reduced graphene oxide fibers for smart fire alarm

Fire is the most frequent catastrophic incident and the design and development of fire sensors with self-cutting and ultra-sensitive fire alarm response are extremely urgent. Here, conductive reduced graphene oxide fibers (RGOF) with a layered structure were prepared by roll-folding and twisting the chemically reduced graphene oxide (RGO) films. The roll-folding and twisting can promote the expansion of the RGOF when exposed to high temperature or flame attack, which makes the RGOF easy to fracture. Based on this unique self-cutting property of RGOF, a new fire detection and alarm device has been developed. The ultra-fast alarm response (∼1 s) of the RGOF is triggered once encountering a flame. The RGOF also has a high-temperature alarm function with a minimum alarm temperature of 150 °C. Interestingly, this RGOF can also act like a fuse and cut off conductive circuits in time when it encounters abnormally high circuit voltages. This discovery expands the range of applications for RGO and provides new ideas for the design of advanced fire alarm sensors.

Facile construction of a flexible smart core-sheath flax yarns with temperature-responsive resistance for ultra-fast fire-alarm response

In order to improve the fire safety of combustible materials in daily life, the intelligent sensor with high sensitivity and wireless remote warning ability is urgently needed for early real-time fire detection. Herein, the intelligent core-sheath flax-GO/CS/PDA yarn prepared using the electrostatic interaction between graphene oxide (GO) sheets, chitosan (CS) molecules and polydopamine (PDA). According to the rapid reduction of resistance of GO stimulated at high temperature, it is reduced to reduced GO (rGO), forming a conductive network circuit. Due to the effective protection of the GO/CS/PDA layer on the flax yarn, it prevents the exchange of heat and oxygen, and improves the thermal stability and flame retardant of flax yarn. What's more, in the case of assembling 1 trilayer, the prepared intelligent core-sheath yarns achieve sensitive fire warning response time (∼3 s). The results show that the prepared core-sheath flax-GO/CS/PDA intelligent yarn have excellent flame retardancy and super-fast fire response speed at low temperature (<300 ℃). More importantly, the signal processing information received by the fire alarm device is transmitted to the wireless communication device via Bluetooth. Therefore, when the abnormal temperature is detected, the communication equipment will immediately emit an alarm sound. This study provides a new strategy and foresight for realizing the ideal fire performance and real-time temperature warning response time based on GO, and expands the application of GO network in early fire detection and wireless sensor warning.

Orexin recruits non-selective cationic conductance and endocannabinoid to dynamically modulate firing of caudal pontine reticular nuclear neurones.

The neuropeptide orexin is involved in motor circuit function. However, its modulation on neuronal activities of motor structures, integrating orexin's diverse downstream molecular cascades, remains elusive. By combining whole-cell patch-clamp recordings and neuropharmacological methods, we revealed that both non-selective cationic conductance (NSCC) and endocannabinoids (eCBs) are recruited by orexin signalling on reticulospinal neurones in the caudal pontine reticular nucleus (PnC). The orexin-NSCC cascade provides a depolarizing force that proportionally enhances the firing-responsive gain of these neurones. Meanwhile, the orexin-eCB cascade selectively attenuates excitatory synaptic strength in these neurones by activating presynaptic cannabinoid receptor type 1. This cascade restrains the firing response of the PnC reticulospinal neurones to excitatory inputs. Intriguingly, non-linear or linear interactions between orexin postsynaptic excitation and presynaptic inhibition can influence the firing responses of PnC reticulospinal neurones in different directions. When presynaptic inhibition is in the lead, non-linear interactions can prominently downregulate or even gate the firing response. Conversely, linear interactions occur to promote the firing response, and these linear interactions can be considered a proportional reduction in the contribution of depolarization to firing by presynaptic inhibition. Through the dynamic employment of these interactions, adaptive modulation may be achieved by orexin to restrain or even gate the firing output of the PnC to weak/irrelevant input signals and facilitate those to salient signals. KEY POINTS: This study investigated the effects of orexin on the firing activity of PnC reticulospinal neurones, a key element of central motor control. We found that orexin recruited both the non-selective cationic conductances (NSCCs) and endocannabinoid (eCB)-cannabinoid receptor type 1 (CB1R) system to pontine reticular nucleus (PnC) reticulospinal neurones. The orexin-NSCC cascade exerts a postsynaptic excitation that enhances the firing response, whereas the orexin-eCB-CB1R cascade selectively attenuates excitatory synaptic strength that restrains the firing response. The postsynaptic and presynaptic actions of orexins occur in an overlapping time window and interact to dynamically modulate firings in PnC reticulospinal neurones. Non-linear interactions occur when presynaptic inhibition of orexin is in the lead, and these interactions can prominently downregulate or even gate firing responses in PnC reticulospinal neurones. Linear interactions occur when postsynaptic excitation of orexin is in the lead, and these interactions can promote the firing response. These linear interactions can be considered a proportional reduction of the contribution of depolarization to firing by presynaptic inhibition.

HUBUNGAN PENGETAHUAN PERAWAT RUANG RAWAT INAP TENTANG PENGGUNAAN APAR (ALAT PEMADAM API RINGAN) DENGAN TERJADINYA RESIKO BENCANA KEBAKARAN DI RUMAH SAKIT UMUM SEMBIRING DELITUA

The hospital is one of the high-rise buildings that have the potential for fires originating from electricity, stoves, and storing flammable materials for example fuel and medical gas and installation of power plants such as generator sets which are the source of fires. Fire is an unwanted and uncontrollable occurrence of fire. The purpose of this study was to examine the relationship between knowledge of inpatient nurses regarding the use of fire extinguishers with the occurrence of fire disaster risk at Sembiring Delitua General Hospital.The design of this study used an observational analytic method through a cross sectional approach. The study population were 158 people, with sample of 61 nurses used the proportional stratified random sampling method. The research instrument was questionnaire. Data Relationship between Inpatient Nurse Knowledge about the Use of Fire Extinguishers and the Occurrence of Fire Disaster Risk in the Post-Covid-19 Pandemic at Sembiring Delitua Hospital.analysis methods were univariate and bivariate with chi square test. The results showed that from 61 nurses, 47 people (77%) had poor knowledge about the use of fire extinguishers and resulted in a high risk of fire disaster and 14 people (23%) have good knowledge about the use of fire extinguishers and with a low risk of fire disasters. The results of the univariate analysis showed that there was a significant relationship between nurses' knowledge of the use of fire extinguishers and the occurrence of fire disaster at Sembiring General Hospital with p = 0.003 &lt; 0.05. Conclusion: nurses' knowledge about the use of fire extinguishers has a relationship with the occurrence of fire disaster risk with 4,170 times greater chance. Suggestion: the management provides training and simulations to health workers such as nurses on how to use fire extinguishers and fire emergency response.

Using Landsat time series and bi-temporal GEDI to compare spectral and structural vegetation responses after fire

Passive and active spaceborne remote sensing technologies play a key role in monitoring forests across large areas, particularly when combining the advantages of both sensor technologies. This study investigates the link between spectral and structural change metrics following forest fire disturbance, collected from Landsat satellites and the Global Ecosystem Dynamics Investigation (GEDI) mission, respectively. The relationships were analysed across 1849 GEDI sampling locations (footprints), spread across south-east Australian forests. To assess structural change on the footprint level, simulated pre-fire GEDI observations were compared with real GEDI observations from one year after the fires. Results show relatively strong fire responses across Landsat spectral indices, with a median decline to between 46.1 % (Normalised Burn Ratio 2) and 77 % (Normalised Difference Vegetation Index) of pre-fire levels. GEDI’s structural change metrics demonstrated a markedly different response, with most showing an even more pronounced decline. In contrast, canopy height demonstrated a less substantial decline, dropping to 82.7 %. Results also suggest that fire severity and forest type impact the fire response of some of the examined spectral and structural metrics. In particular, taller forests and increased fire severity were associated with a more pronounced post-fire decline. The findings of this study highlight the large variation of forest structural responses to fire and their divergence from spectral change metrics, and emphasise the potential of integrating GEDI observations into wall-to-wall spectral forest change monitoring. The concept of bi-temporal GEDI observations as demonstrated in this study is promising, as it captures both pre- and post-disturbance structure on the footprint level and might enhance modelling of forest structural change in future approaches.

Cost–benefit analysis of partnership working between fire and rescue and health services across England and Wales during the COVID-19 pandemic

ObjectivesFire and rescue services undertook a range of additional activities to support the National Health Service (NHS) in managing extreme service demand during the COVID-19 pandemic. This study aims to...

Satellite observed response of fire dynamics to vegetation water content and weather conditions in Southeast Asia

Fires greatly affect terrestrial ecosystems, climate, human health, and economy. Under the threat of global warming, assessing fire occurrence frequency and fire danger is critical to mitigate fire’s negative impacts. The state-of-the-art satellite microwave observation provides the unique opportunity to monitor vegetation water content (VWC) for multiple vegetation strata, yet still lacks associated investigation in fire dynamics. Here, we applied the multiple microwave Emissivity Difference Vegetation Indexes (EDVIs) as VWC proxies of different vegetation strata to explore their influence on fire occurrence frequency (given by the number of fires, NOF) and the cumulative fire radiant energy (FRE) in mainland Southeast Asia during 2015–2019; and compared to that with the commonly used fire weather indices from the Canadian Forest Fire Weather Index system. Then, we identified the optimal regression models to depict the relationship of fires with EDVIs and fire weather indices, respectively; and incorporated both kinds of indices in models to retrospectively predict NOF and FRE. Results show that: (1) the monthly mean NOF and FRE nonlinearly decrease with EDVI in mainland Southeast Asia, and 40–60% of the variances are explained by EDVI, which further improve to 60–70% by incorporating EDVIs of two different vegetation layers. (2) In comparison, NOF and FRE nonlinearly increase with fire weather indices, and connections are stronger than that with EDVI; nevertheless, combing fire weather indices with EDVI, especially with EDVIs of two vegetation layers provides almost the highest explanation for fires. (3) The log-linear and/or log-quadratic models are identified to outperform the other models in depicting the fire-EDVI and fire-fire weather indices relationships; and incorporating EDVI and fire weather indices in models generally improves the prediction accuracy. These findings reveal the differentiated VWC effects on NOF and FRE among vegetation strata and quantify the strong nonlinear response of fires to VWC and weather conditions in mainland Southeast Asia, which could provide new insight for fire prediction and fire danger assessments with the utility of microwave observations.

Impact of partially damaged passive protection on the fire response of bolted steel connections using finite element analysis

This article aims to quantify the impact of a potential failure of passive fire protection on the ultimate response of a top and seat steel connection with double web angles. A numerical, finite element analysis scheme is proposed considering the real, semi-rigid behaviour of the connection, using unilateral contact-friction laws between the interfaces of the beam, the column, and the steel angles. The model has been validated by previous experimental research at ambient temperatures. Scenarios of unprotected connections, undamaged and partially damaged fire protections are numerically tested. A change in the failure mode and a reduction of the strength equal to 28% for standard fire and 35% for hydrocarbon fire arise for the model with the damaged protection. In this case, maximum temperatures locally at the beam reach the ones of the unprotected connection (900 °C), which is more than 800 °C higher than the connection with undamaged protection. Significant temperature increases of more than 288 °C and 406 °C for standard and hydrocarbon fires also arise on the top angle, compared to the model with undamaged fire protection.

Spinal neuromodulation mitigates myocardial ischemia-induced sympathoexcitation by suppressing the intermediolateral nucleus hyperactivity and spinal neural synchrony.

Myocardial ischemia disrupts the cardio-spinal neural network that controls the cardiac sympathetic preganglionic neurons, leading to sympathoexcitation and ventricular tachyarrhythmias (VTs). Spinal cord stimulation (SCS) is capable of suppressing the sympathoexcitation caused by myocardial ischemia. However, how SCS modulates the spinal neural network is not fully known. In this pre-clinical study, we investigated the impact of SCS on the spinal neural network in mitigating myocardial ischemia-induced sympathoexcitation and arrhythmogenicity. Ten Yorkshire pigs with left circumflex coronary artery (LCX) occlusion-induced chronic myocardial infarction (MI) were anesthetized and underwent laminectomy and a sternotomy at 4-5 weeks post-MI. The activation recovery interval (ARI) and dispersion of repolarization (DOR) were analyzed to evaluate the extent of sympathoexcitation and arrhythmogenicity during the left anterior descending coronary artery (LAD) ischemia. Extracellular in vivo and in situ spinal dorsal horn (DH) and intermediolateral column (IML) neural recordings were performed using a multichannel microelectrode array inserted at the T2-T3 segment of the spinal cord. SCS was performed for 30 min at 1 kHz, 0.03 ms, 90% motor threshold. LAD ischemia was induced pre- and 1 min post-SCS to investigate how SCS modulates spinal neural network processing of myocardial ischemia. DH and IML neural interactions, including neuronal synchrony as well as cardiac sympathoexcitation and arrhythmogenicity markers were evaluated during myocardial ischemia pre- vs. post-SCS. ARI shortening in the ischemic region and global DOR augmentation due to LAD ischemia was mitigated by SCS. Neural firing response of ischemia-sensitive neurons during LAD ischemia and reperfusion was blunted by SCS. Further, SCS showed a similar effect in suppressing the firing response of IML and DH neurons during LAD ischemia. SCS exhibited a similar suppressive impact on the mechanical, nociceptive and multimodal ischemia sensitive neurons. The LAD ischemia and reperfusion-induced augmentation in neuronal synchrony between DH-DH and DH-IML pairs of neurons were mitigated by the SCS. These results suggest that SCS is decreasing the sympathoexcitation and arrhythmogenicity by suppressing the interactions between the spinal DH and IML neurons and activity of IML preganglionic sympathetic neurons.

An Intelligent Path Planning Mechanism for Firefighting in Wireless Sensor and Actor Networks

Forests have an important role in environmental preservation and maintenance. The primary threat is forest fires, which have disastrous repercussions. As a result, it is critical to identify and extinguish a fire before it spreads and destroys resources. To that end, we propose a forest fire detection and fighting mechanism using Wireless Sensor and Actor Networks (WSANs). Temperature sensors are utilized to detect fires, and actors (robots) are employed to extinguish them. Sensors and robots are distributed at random throughout the forest, forming clusters. Clustering, sleep/active scheduling for the sensors, and energy harvesting (EH)/moving modes for the robots, are used to extend and maximize the sensors/robots lifetime in the WSAN. In such a network, robots should move to the fire site as quickly as possible. To do this, we further propose a robot routing mechanism that focuses on determining the shortest path for each firefighting robot. In particular, each firefighting robot equipped with on-board processing uses a fuzzy Q-learning (FQL)-based trajectory mechanism to learn the shortest path to the fire zone in the least amount of time. Simulations are conducted to demonstrate the benefits of employing the proposed framework for a rapid and effective fire response. When compared to the traditional QL, the total approaching rate (a measure of how quickly the firefighting robots can reach the fire) to the fire spot is greater when utilizing the proposed FQL-based strategy.

Relationship between threshold and bifurcations for paradoxical firing responses along with seizure induced by inhibitory stimulation

Inhibition-induced enhancement or paradoxical response of firing was related to Hopf bifurcation instead of saddle-node bifurcation on an invariant cycle (SNIC), due to the “negative” threshold and rotated vector fields. In the present letter, by changing multiple parameters to adjust the vector fields, the condition for the paradoxical response and “negative” threshold is extended to SNIC near a codimension-2 bifurcation appearing prior to the Hopf bifurcation, which presents a comprehensive relationship between bifurcations and threshold. Especially, the result for a special current can well explain the enhanced firing along with seizure induced by inhibitory interneuron, implying that SNIC far from the codimension-2 bifurcation of pyramidal neuron is a potential candidate to avoid seizure.

Intelligent cyclic fire warning sensor based on hybrid PBO nanofiber and montmorillonite nanocomposite papers decorated with phenyltriethoxysilane

An abundance of early warning graphene-based nano-materials and sensors have been developed to avoid and prevent the critical fire risk of combustible materials. However, there are still some limitations that should be addressed, such as the black color, high-cost and single fire warning response of graphene-based fire warning materials. Herein, we report an unexpected montmorillonite (MMT)-based intelligent fire warning materials that have excellent fire cyclic warning performance and reliable flame retardancy. Combining phenyltriethoxysilane (PTES) molecules, poly(p-phenylene benzobisoxazole) nanofiber (PBONF), and layers of MMT to form a silane crosslinked 3D nanonetwork system, the homologous PTES decorated MMT-PBONF nanocomposites are designed and fabricated via a sol–gel process and low temperature self-assembly method. The optimized nanocomposite paper shows good mechanical flexibility (good recovery after kneading or bending process), high tensile strength of ∼81 MPa and good water resistance. Furthermore, the nanocomposite paper exhibits high-temperature flame resistance (almost unchanged structure and size after 120 s combustion), sensitive flame alarm response (∼0.3 s response once exposure onto a flame), cyclic fire warning performance (>40 cycles), and adaptability to complex fire situations (several fire attack and evacuation scenarios), showing promising applications for monitoring the critical fire risk of combustible materials. Therefore, this work paves a rational way for design and fabrication of MMT-based smart fire warning materials that combine excellent flame shielding and sensitive fire alarm functions.

Precession and obliquity forcing of the South African monsoon revealed by sub-tropical fires

While the influence of precession on monsoon at low latitudes through insolation forcing is well-known, the role of obliquity is still debated since its influence on the distribution of incoming solar radiation is small in these regions. In southern Africa, long marine and terrestrial sedimentary records attest of a precessional influence on the South African monsoon at orbital time scale. The obliquity signal is occasionally observed in the geological records although modeling results suggest an influence of precession and obliquity on summer monsoon. Here, we present a record of microscopic charcoal from core MD96-2098 located off Namibia covering the past 184,000 years. Our record of fire activity reveals cyclic changes at frequencies of 23, 58 and 12 kyr−1 and lacks the obliquity signal at 41 kyr−1. Changes in fire over southern Africa are interpreted as shifts in large and intense fires spreading in open-grassland savanna as a result of orbitally-driven changes in rainfall intensity associated with the South African monsoon. We show that, despite the absence of a 41 kyr obliquity imprint, the presence of 23, 58 and 12 kyr−1 frequencies likely stems from a nonlinear response of fire to precipitation controlled by a combination of precession and obliquity frequencies, supporting the influence of obliquity on the South African monsoon.

Animal population decline and recovery after severe fire: Relating ecological and life history traits with expert estimates of population impacts from the Australian 2019-20 megafires

Catastrophic megafires can increase extinction risks; identifying species priorities for management and policy support is critical for preparing and responding to future fires. However, empirical data on population loss and recovery post-fire, especially megafire, are limited and taxonomically biased. These gaps could be bridged if species' morphological, behavioural, ecological and life history traits indicated their fire responses. Using expert elicitation that estimated population changes following the 2019–20 Australian megafires for 142 terrestrial and aquatic animal species (from every vertebrate class, one invertebrate group), we examined whether expert estimates of fire-related mortality, mortality in the year post-fire, and recovery trajectories over 10 years/three generations post-fire, were related to species traits. Expert estimates for fire-related mortality were lower for species that could potentially flee or shelter from fire, and that associated with fire-prone habitats. Post-fire mortality estimates were linked to diet, diet specialisation, home range size, and susceptibility to introduced herbivores that damage or compete for resources. Longer-term population recovery estimates were linked to diet/habitat specialisation, susceptibility to introduced species; species with slower life histories and shorter subadult dispersal distances also had lower recovery estimates. Across animal groups, experts estimated that recovery was poorest for species with pre-fire population decline and more threatened conservation status. Sustained management is likely needed to recover species with habitat and diet specialisations, slower life histories, pre-existing declines and threatened conservation statuses. This study shows that traits could help inform management priorities before and after future megafires, but further empirical data on animal fire response is essential.

System-level analysis of a self-centring moment-resisting frame under post-earthquake fire

Post-earthquake fire is a multi-hazard combination with cascading effects, of which catastrophic consequences are not only caused by the earthquake but exacerbated by the triggered fire. Only a few studies focused on the system-level structural analysis in earthquake-fire sequence, mostly on the conventional moment-resisting frame. Despite the self-centring system being a novel structure with excellent seismic performance, its post-earthquake fire response is still unclear due to limited research. Accordingly, this study aims to investigate the system-level behaviour of a self-centring system under post-earthquake fire. A numerical model of the six-storey prototype frame is established, and a two-stage bilinear material model is proposed to reflect the combined effects of pre-induced damage and temperature on material properties. A total of 11 ground motions (DBE and MCE levels) followed by 3 fires on different storeys compose the post-earthquake fire scenarios. A welded moment-resisting frame with reduced beam sections (WR-MRF) is selected for comparison, with the DBE response designed the same as the self-centring frame (SC-MRF). Results show that the SC-MRF exhibits smaller responses to the preceding earthquake and subsequent fire than the WR-MRF. Post-earthquake fire mainly affects two inter-storey drift ratios in each scenario, while it has a negligible effect on others which remain unchanged from the residual status after the earthquake. An obvious increase in structural responses can be found from fire-only (FO) to post-MCE fire (P-MCE-F) scenarios, and deformation is slightly larger in multi-floor fires than in single-floor ones. The findings unveil the post-earthquake fire responses of a seismic-resilient system with self-centring mechanism and provide a comparative assessment against the conventional structure. The methodology, including the proposed material model, can be further extended for analysis on other systems to understand and enhance the comprehensive performance in earthquake-fire sequence.

Mechanical–thermal–chemical coupled modeling techniques and numerical simulation methods for the firearm igniting process

AbstractThe firing and ignition of firearms is a complex process involving the coupled effects of mechanical, thermal, and chemical mechanisms. The theoretical modeling and numerical simulation methods for primer mixture impact and ignition process are investigated to reveal the response patterns of characteristic variables, such as primer output pressure and the crater on the primer cup that forms when the firing pin strikes the primer to ignite the primer mixture. Theoretical models such as the elastoplastic mechanical response of the primer mixture, the pore collapse hot spot model, and the Lee‐Tarver ignition and growth model are established by studying the impact response, the initiation mechanism, the hot spot formation mechanism, and the energy release mechanism of the primer mixture. Constitutive models of the primer mixture and the primer cup material are established based on a dynamic compression test of the primer mixture under passive confining pressure and a quasi‐static tensile test of the primer cup material. A numerical model of the primer mixture impact and ignition performance is constructed by using ANSYS LS‐DYNA software. The simulation parameters are serially simulated and calibrated through the 3D total Lagrange algorithm. The firing and ignition response process of a small‐caliber firearm is simulated with the 3D Activation Likelihood Estimation (ALE) algorithm. The relative error between the simulated results and the test results is below 15 %, which validates the feasibility of the numerical simulation methods for the firing and ignition performances of firearms.

Fire Safety Management Strategies in Selected Markets of Lusaka City

This study investigated the effectiveness of fire safety management strategies in selected markets of Lusaka city. The study employed a descriptive research design, which involved qualitative data collection and analysis. The study used purposive sampling to select markets and participants for the study. A total of 2 markets. From each market, 10 marketeers were sampled conveniently. 2 market managers were also sampled purposefully. The study collected data using two main methods: Focus Group Discussions (FGD) and in-depth interviews. The marketeers were interviewed individually and FGD guide was further administered to get more insights. This was meant to collect opinion and insights that could not be collected during interviews. The in-depth interviews were conducted with a subset of the participants to obtain more detailed and nuanced information. The data collected from the in-depth interviews and FGD were analyzed using thematic analysis to identify patterns and themes in the data. The findings of the study indicated that effective fire safety management in markets required a combination of proactive measures and emergency preparedness. Proactive measures included regular risk assessments, proper storage of flammable materials, regular maintenance of electrical systems, and the installation of fire safety equipment such as alarms and sprinklers. Emergency preparedness strategies included the creation of emergency evacuation plans, the establishment of communication protocols, and the training of market staff on fire safety procedures. The study also found that collaboration between market management, vendors, and local fire departments is crucial to effective fire safety management in markets. The study highlights the need for ongoing education and training for vendors and market staff to maintain awareness of fire safety hazards and emergency response procedures. It also underscores the importance of regular reviews and updates of fire safety management plans to ensure they remain effective and relevant. Overall, the study provides valuable insights into effective fire safety management strategies in markets, and emphasizes the need for continued attention and investment in fire safety management to protect people, property, and businesses from the devastating effects of fires.

Herbaceous vegetation responses to experimental fire in savannas and forests depend on biome and climate.

Fire-vegetation feedbacks potentially maintain global savanna and forest distributions. Accordingly, vegetation in savanna and forest ecosystems should have differential responses to fire, but fire response data for herbaceous vegetation have yet to be synthesized across biomes. Here, we examined herbaceous vegetation responses to experimental fire at 30 sites spanning four continents. Across a variety of metrics, herbaceous vegetation increased in abundance where fire was applied, with larger responses to fire in wetter and in cooler and/or less seasonal systems. Compared to forests, savannas were associated with a 4.8 (±0.4) times larger difference in herbaceous vegetation abundance for burned versus unburned plots. In particular, grass cover decreased with fire exclusion in savannas, largely via decreases in C4 grass cover, whereas changes in fire frequency had a relatively weak effect on grass cover in forests. These differential responses underscore the importance of fire for maintaining the vegetation structure of savannas and forests.

Detection of forest fire using deep convolutional neural networks with transfer learning approach

Forest fires caused by natural causes such as climate change, temperature increase, lightning strikes, volcanic activity or human effects are among the world’s most dangerous, deadly, and destructive disasters. Detection, prevention, and extinguishing forest fires is challenging. In addition, forest fires can cause habitat destruction that cannot be controlled in time and cause great material and moral losses. Therefore, fast and accurate Detection of forest fires is vital in emergency response. Here, in solving the problem, the transfer learning method from deep learning sub-topics can be used, which allows the application of pre-trained networks to a new problem. The Fire Luminosity Airborne-based Machine learning Evaluation dataset (consisting of forest fire images) obtained by Unmanned Aerial Vehicle was used in this study. In the Detection of forest fire images in the dataset, InceptionV3, DenseNet121, ResNet50V2, NASNetMobile, VGG-19 deep learning algorithms, transfer learning techniques that can produce more successful results than networks trained from scratch, and hybrid proposed with Support Vector Machine, Random Forest, Bidirectional Long Short-Term Memory, Gated Recurrent Unit algorithms methods have been applied. In the classification study with the Fire Luminosity Airborne-based Machine learning Evaluation dataset in performance measurement, 97.95% accuracy was obtained from the DenseNet121 model, which was started with random weights. In the transfer learning study using ImageNet weights, satisfactory results were obtained with 99.32% accuracy in the DenseNet121 model. We anticipate that working in forest fire detection and response can be entirely satisfactory.