Fire Location Research Articles

A computer-vision-based deep learning model of smoke diffusion

Smoke is a lethal hazard in fire accidents and the biggest obstacle to emergency rescue. Predicting its diffusion range is important in fire command and emergency rescue. This article utilizes computer-vision-based deep learning methods to learn the characteristics of smoke diffusion and achieve real-time prediction of smoke-temperature fields. Firstly, a tunnel fire was simulated to verify the reliability of the deep learning model in predicting smoke-temperature fields. Thirty-six sets of fire simulation conditions were established with various values of the fire source location and heat release rate (HRR) and longitudinal ventilation airflow velocity. This created a database of 17,280 smoke-temperature fields. Based on windless scenario with the fire source located in the middle of the tunnel, it was determined that the SiLU activation function provides the best nonlinear modelling ability and stability in training neural networks. Based on the smoke-temperature-field database, a U-Net network was constructed to predict the smoke diffusion process of the tunnel fire. Under different scenarios, the U-Net model achieved 90 % accuracy in smoke-temperature-field prediction. The proposed U-Net model was then used to predict smoke dispersion in a petroleum storage depot scenario. The results show that this approach can help to delineate safe areas during fire emergencies.

Multi-source heterogeneous data fusion prediction technique for the utility tunnel fire detection

Diverse and complex fire environment in modern utility tunnels with multiple uncertainties make fire detection difficult to be achieved accurately. This study aims to develop an intelligent fire detection technique to address the difficulty. In the technique, initially, a lightweight image segmentation method is proposed for initial estimation of the fire source location. Then, the multi-source heterogeneous data fusion fire detection is implemented for fire source localization and ceiling temperature distribution prediction based on Gauss model and the improved multi-particle swarm optimization (MPSO) algorithm. Additionally, the results of the case study support the ability of the intelligent fire detection technique through compared with the experiment results and the previous methods, which can be used to achieve precise and stable fire source localization and ceiling temperature prediction in the utility tunnel fire.

Forest fire susceptibility assessment under small sample scenario: A semi-supervised learning approach using transductive support vector machine

Forest fires threaten global ecosystems, socio-economic structures, and public safety. Accurately assessing forest fire susceptibility is critical for effective environmental management. Supervised learning methods dominate this assessment, relying on a substantial dataset of forest fire occurrences for model training. However, obtaining precise forest fire location data remains challenging. To address this issue, semi-supervised learning emerges as a viable solution, leveraging both a limited set of collected samples and unlabeled data containing environmental factors for training. Our study employed the transductive support vector machine (TSVM), a key semi-supervised learning method, to assess forest fire susceptibility in scenarios with limited samples. We conducted a comparative analysis, evaluating its performance against widely used supervised learning methods. The assessment area for forest fire susceptibility lies in Dayu County, Jiangxi Province, China, renowned for its vast forest cover and frequent fire incidents. We analyzed and generated maps depicting forest fire susceptibility, evaluating prediction accuracies for both supervised and semi-supervised learning methods across various small sample scenarios (e.g., 4, 8, 12, 16, 20, 24, 28, and 32 samples). Our findings indicate that TSVM exhibits superior prediction accuracy compared to supervised learning with limited samples, yielding more plausible forest fire susceptibility maps. For instance, at sample sizes of 4, 16, and 28, TSVM achieves prediction accuracies of approximately 0.8037, 0.9257, and 0.9583, respectively. In contrast, random forests, the top performers in supervised learning, demonstrate accuracies of approximately 0.7424, 0.8916, and 0.9431, respectively, for the same small sample sizes. Additionally, we discussed three key aspects: TSVM parameter configuration, the impact of unlabeled sample size, and performance within typical sample sizes. Our findings support semi-supervised learning as a promising approach compared to supervised learning for forest fire susceptibility assessment and mapping, particularly in scenarios with small sample sizes.

Propagation of NO2 originated in intense fires in the Paraná River Delta analyzed from satellite observations

Wildfires in the Paraná River Delta started to be a common situation in the year 2008 and during the first years of the 2020́s decade: 2020, 2021, 2022. These fires affected natural areas, primarily the islands within the delta, situated in front of Rosario city (32.95 °S, 60.67 °W, 25 meters above sea level (m.a.s.l.)), Argentina. They mostly occur during the period between June and September each year and are more intense when a strong La Niña event is present. The aim of this study is to examine the association between the propagation of high intensity nitrogen dioxide (NO2) plumes and the significant fire events that occurred in the Paraná River Delta from 6 to 17 September 2022. Satellite data of NO2 obtained from the TROPOspheric Monitoring Instrument (TROPOMI) on board the European Space Agency (ESA) Sentinel-5 Precursor satellite, air mass forward trajectories performed with The Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT), Fire Radiative Power (FRP) data provided by Visible Infrared Imaging Radiometer Suite (VIIRS) on board The National Oceanic and Atmospheric Administration - 20 (NOAA-20) and Suomi National Polar-orbiting Partnership (S-NPP) satellites, Aerosol Optical Depth (AOD) from VIIRS S-NPP and Smoke Injection Height from Multi-Angle Implementation of Atmospheric Correction (MAIAC) Thermal Technique were employed. The trajectories followed by the air masses coming from the major fire sources locations nearby Rosario city and its surrounding area, were analyzed at altitudes of 20, 500, 1000, 1500 and 2000 m above ground level (AGL) in order to evaluate the impact of the NO2 clouds present in the atmosphere and carried by the winds, at different sites of Rosario and its adjacent region. The NO2 plumes has the potential to pose a significant health risk for this region, inhabited by an approximate population of one and a half million individuals. This risk is contingent upon the altitude where the highest levels of NO2 in the tropospheric column are found. Tropospheric vertical column densities (VCDs) of NO2 in the range of 4.68×1014 to 1.66×1016 molecules/cm2 were determined. The results indicate a robust correlation between elevated tropospheric NO2 VCD values and intense fires, as characterized by their FRP. Additionally, high tropospheric NO2 VCDs were observed to impact cities and towns located in close proximity to the fires. In particular, the unusually high tropospheric NO2 VCDs determined for the towns of Fighiera and Pueblo Esther, situated along the coast and near the Paraná River Delta, are noteworthy. This is uncommon considering they are small towns in rural areas that typically have good air quality. The results regarding FRP, provided by VIIRS on board NOAA-20 and S-NPP satellites, indicate that the fire characterization of hotspots during the considered period had a mean value in the range of 20.17–21.47 MW. On specific days (12, 13 and 16 September 2022), the cumulative (total) daily values were very high, ranging from 2000 to 10000 MW. Furthermore, on 12 and 13 September 2022, the smoke aerosols did not reach significant altitudes in the atmosphere, remaining within the range of 0 to 1500 m AGL. During the fire period, AOD values close to 1 were observed within the fire plume. Notably, Puerto San Martín (0.27), San Lorenzo (0.22), and Ibarlucea (0.20) recorded higher average AOD values compared to other sites. The remaining sites generally exhibited lower average AOD values, with a slight distinction between rural locations (Pueblo Esther: 0.14 and Fighiera: 0.14) and the city center of Rosario (0.09).

Development of an IoT-based firefighting drone for enhanced safety and efficiency in fire suppression

An uncontrolled fire poses severe threats to both humans and the environment, making firefighting a perilous and complex task. Traditional fire suppression methods are inefficient, costly, and without thorough testing, leading to delays in gaining control over fire outbreaks. This paper presents a novel firefighting drone aimed at mitigating risks to firefighters by extinguishing fires and providing real-time imaging, gas concentration and fire location data monitoring. The proposed intelligent quadcopter utilizes the Pixhawk PX4 microcontroller for precise control and the Pixhawk Telemetry system for data processing. The proposed device is constructed from an ultra-strength S500 Quadcopter frame, NodeMCU, Arduino Nano, various gas sensors, a servo motor to extinguish the fire and a camera to detect fire events in real time. Equipped with an FPV camera and a video transmitter, it transmits live video feed to the ground, enabling efficient navigation using the Flysky I6X controller. The intended position and height of the drone are controlled using an adaptive optimization technique known as fuzzy-based backstepping control. This article demonstrates the effectiveness of the device by collecting and analyzing gas emissions data from controlled burns of various materials. The drone successfully measured concentrations of CO, CO2, O3, SO2, and NO2 in affected areas, providing valuable insights for firefighting operations. Different levels of gases have been measured depending on the concentration from burning alcohol, clothes, plastic materials, paper, leaves, and so on. The novelty of this work lies in the development and comprehensive analysis of an IoT-based firefighting drone conducting extensive real-time experiments.

Biometric door lock system with automated fire detection alert

The reconciliation of biometric advancements and fire recognition components in shrewd homes is critical for access control and crisis readiness. The Blynk mobile app is used to access this project's biometric door lock and automated fire detection system. For access authorization, the biometric system uses cutting-edge fingerprint or facial recognition technology. The mechanized fire location framework screens the climate for potential fire dangers and triggers an alert when distinguished. The Blynk application gives controller and checking abilities, permitting inhabitants to lock or open the entryway, get ongoing warnings, and remotely screen the fire discovery framework's status. The microcontroller coordinates communications between the frameworks, outfitted with Wi-Fi availability. The Blynk application point of interaction is planned with easy-to-understand gadgets, permitting inhabitants to control and screen the coordinated frameworks. Safety efforts, for example, encoded correspondence conventions and secure stockpiling of biometric information, shield client protection. This all-encompassing approach to residential security ensures that their homes can effectively handle access control and emergencies, providing residents with security and peace of mind.

Experimental and numerical study of the interaction between tempered glass fallout and enclosure fire dynamics

Tempered glass, as an important type of safety glazing, is extensively used in high-rise buildings. However, little is known about the fallout behavior of tempered glass in the compartment with different fire locations. Therefore, in this work, the exposed framed glass panes with the dimension of 815 × 815 × 6 mm3 were heated by a pool fire in the compartment. The fuel pans were placed at six different locations in the compartment. It was found that the fallout time of tempered glass increased with a decrease in the distance between the fuel pan and glass panes; when the fire was moved to the wall side, the fallout time of tempered glass was longer. Besides, the temperature gradient of the glass surface at the exposed area was insignificant when the fire source was at the center line of the doorway opening, while there was an evident temperature gradient of the glass surface on the height direction due to the apparent smoke layer in the upper of the compartment for near wall fire locations. In addition, CFD numerical simulations were conducted using FDS software to observe the temperature and velocity fields inside the compartment with different fire locations. It was found that when the fire was moved to the wall side, the circulation flow was formed at both the high-temperature region near the fire source and the low-temperature region on the left of the compartment.

Assessment of post-earthquake fire capacity of RC frames without seismic design

To study the importance of post-earthquake fire events, 500 reinforced concrete (RC) frames were designed without seismic considerations. These consist in three-bays frames with a variation in the number of floors from two to six (100 frames each). Numerical analyses were performed in SAFIR to investigate the influence of different types of damage, damage location, fire location and fire curves on the post-earthquake fire resistance of the structures. It was observed that the seismic damage can significantly decrease the fire resistance of the frames. In an earthquake scenario it can be expected an increase in the response times of the firefighters as well as malfunctioning and damage of active and passive firefighting measures, which means that the use of parametric fire curves without active firefighting measures can be a better approach instead of the use of the fire curve ISO 834. It was observed, for the analysed cases, that parametric fire curves without active firefighting measures led to lower collapse times when compared to the fire curve ISO 834. On the other hand, when the active firefighting measures are considered, there is a very significant number of structures that do not collapse, which indicates the importance that these firefighting measures can have in a fire after an earthquake. The development of these analysis emphasizes the impacts of applying different fire curves on the damaged structure which consequently brings attention to an adequate definition of the fire curves.

Comparison of Different Models to Simulate Forest Fire Spread: A Case Study

With the development of computer technology, forest fire spread simulation using computers has gradually developed. According to the existing research on forest fire spread, the models established in various countries have typical regional characteristics. A fire spread model established in a specific region is only suitable for the local area, and there is still a great deal of uncertainty as to whether or not the established model is suitable for fire spread simulation for the same fuel in other regions. Although many fire spread models have been established, the fuel characteristics applicable to each model, such as the fuel loading, fuel moisture content, combustibility, etc., are not similar. It is necessary to evaluate the applicability of different fuel characteristics to different fire spread models. We combined ground investigation, historical data collection, model improvements, and statistical analysis to establish a multi-model forest fire spread simulation method (FIRER) that shows the burning time, perimeter, burning area, overlap area, and spread rate of fire sites. This method is a large-scale, high-resolution fire growth model based on fire spread in eight directions on a regular 30 m grid. This method could use any one of four different physical models (McArthur, Rothermel, FBP, and Wang Zhengfei (China)) for fire behavior. This method has an option to represent fire breaks from roads, rivers, and fire suppression. We can evaluate which model is more suitable in a specific area. This method was tested on a single historical lightning fire in the Daxing’an Mountains. Different scenarios were tested and compared: using each of the four fire behavior models, with fire breaks on or off, and with a single or suspected double fire ignition location of the historical fire. The results show that the Rothermel model is the best model in the simulation of the Hanma lightning fire; the overlap area is 5694.4 hm2. Meanwhile, the real fire area in FIRER is 5800.9 hm2; both the Kappa and Sørensen values exceed 0.8, providing high accuracy in fire spread simulations. FIRER performs well in the automatic identification of fire break zones and multiple ignited points. Compared with FARSITE, FIRER performs well in predicting accuracy. Compared with BehavePlus, FIRER also has advantages in simulating large-scale fire spread. However, the complex data preparation stage of FIRER means that FIRER still has great room for improvement. This research provides a practical basis for the comparison of the practicability and applicability of various fire spread models and provides more effective practical tools and a scientific basis for decision-making and the management of fighting forest fires.

Abnormal gamma phase-amplitude coupling in the parahippocampal cortex is associated with network hyperexcitability in Alzheimer's disease.

While animal models of Alzheimer's disease (AD) have shown altered gamma oscillations (∼40 Hz) in local neural circuits, the low signal-to-noise ratio of gamma in the resting human brain precludes its quantification via conventional spectral estimates. Phase-amplitude coupling (PAC) indicating the dynamic integration between the gamma amplitude and the phase of low-frequency (4-12 Hz) oscillations is a useful alternative to capture local gamma activity. In addition, PAC is also an index of neuronal excitability as the phase of low-frequency oscillations that modulate gamma amplitude, effectively regulates the excitability of local neuronal firing. In this study, we sought to examine the local neuronal activity and excitability using gamma PAC, within brain regions vulnerable to early AD pathophysiology-entorhinal cortex and parahippocampus, in a clinical population of patients with AD and age-matched controls. Our clinical cohorts consisted of a well-characterized cohort of AD patients (n = 50; age, 60 ± 8 years) with positive AD biomarkers, and age-matched, cognitively unimpaired controls (n = 35; age, 63 ± 5.8 years). We identified the presence or the absence of epileptiform activity in AD patients (AD patients with epileptiform activity, AD-EPI+, n = 20; AD patients without epileptiform activity, AD-EPI-, n = 30) using long-term electroencephalography (LTM-EEG) and 1-hour long magnetoencephalography (MEG) with simultaneous EEG. Using the source reconstructed MEG data, we computed gamma PAC as the coupling between amplitude of the gamma frequency (30-40 Hz) with phase of the theta (4-8 Hz) and alpha (8-12 Hz) frequency oscillations, within entorhinal and parahippocampal cortices. We found that patients with AD have reduced gamma PAC in the left parahippocampal cortex, compared to age-matched controls. Furthermore, AD-EPI+ patients showed greater reductions in gamma PAC than AD-EPI- in bilateral parahippocampal cortices. In contrast, entorhinal cortices did not show gamma PAC abnormalities in patients with AD. Our findings demonstrate the spatial patterns of altered gamma oscillations indicating possible region-specific manifestations of network hyperexcitability within medial temporal lobe regions vulnerable to AD pathophysiology. Greater deficits in AD-EPI+ suggests that reduced gamma PAC is a sensitive index of network hyperexcitability in AD patients. Collectively, the current results emphasize the importance of investigating the role of neural circuit hyperexcitability in early AD pathophysiology and explore its potential as a modifiable contributor to AD pathobiology.

Two-dimensional inverse problem of fire location in the closed goaf of coal mine based on optical fiber sensors.

Monitoring the temperature to determine the fire source locations is essential for controlling the spontaneous combustion in the goaf. Optical fiber sensors are employed to measure the temperature distribution in the goaf. However, due to changes in the geological conditions and the influence of the falling rocks in the goaf, only sensors on the upper side of the uncompacted goaf, due to inclination and coal pillar, may remain. Unilateral sensors are located on the upper side of the goaf, while fire occurs in the center. To investigate the issue with linear unilateral sensors, a two-dimensional inverse method has been developed to determine the location of fire sources by considering heat transfer after a fire inside the goaf. The equations were theoretically solved using Green's function method to obtain the internal temperature distribution of the physical model of the goaf. Sensitivity analysis identified the most crucial parameters in the process of spontaneous heating at different temperature. The fire source location can be determined using a loop method based on the model calculations. We considered a case to validate the model. Accurately identifying the fire source location in the goaf using the unilateral sensors has an essential theoretical and practical significance for fire prevention and fighting.

AIoT-enabled digital twin system for smart tunnel fire safety management

High traffic flow in a confined tunnel makes fire safety a critical issue. This paper proposed a digital twin framework for tunnel fire safety management in real-time, driven by dynamic sensor data and AIoT technologies. A deep learning model trained by the Transformer network and simulation dataset is used to predict real-time fire location and size. Then, the AI model is integrated into a 3D digital twin platform developed by the game engine Unity 3D. The performance of the proposed digital twin framework is demonstrated using numerical experiments and large-scale tunnel fire tests. Results show that the established AI model achieved promising accuracy in predicting fire location and power for both numerical and experimental data. The digital twin platform can also visualize the 3D fire scene that supports evacuation, firefighting, and emergency rescue. This research demonstrates the feasibility of using a 3D environment and digital twin in real-time fire safety management.

Component-based models of beam-column joints exposed to fire in steel framed structures against progressive collapse

This paper is supplementary research following the previous experimental and numerical studies on the progressive collapse behaviors of beam-column substructures with different joint configurations under middle-column removal scenarios induced by fire. The component-based models (CBMs) of beam-column joints exposed to fire, which include the welded web-welded flange (WW) joint, as well as the bolted web-welded flange (BW) joint, were proposed based on refined FE models established in previous studies. Subsequently, the proposed CBMs with detailed spring arrangements and constitutive parameters were incorporated into the ABAQUS software to validate the collapse responses of beam-column joints obtained from both test results and refined solid FE models. A comparison investigation with experimental data indicates that the proposed CBMs can accurately reflect the nonlinear collapse behaviors of different fire-exposed beam-column joints with relatively high computational efficiency. Moreover, the validated CBMs are further incorporated into the collapse analysis of a multi-story planar steel framed structure exposed to fire, by compiling the VUSDFLD subroutine, to realize the component deletion process. The parameter effects of fire location, joint configurations, and loading ratios on collapse behaviors and force redistributions of framed structures exposed to fire were investigated. Based on analysis results, it is shown that the component failure temperature is quite sensitive to whether the specific joint configurations are considered in the analysis of fire-resistant progressive collapse in planar framed structures, which should be paid sufficient attention to in the performance evaluation of collapse resistance. For those planar framed structures with larger loading ratios, it is recommended to increase the section of the frame column or carry out fire protection to prevent the progressive collapse of the structure.

Optimal ventilation mode for double fires in a typical interchange subway station

This paper investigates the optimal ventilation mode for double fires in a typical interchange subway station. Numerical simulations were carried out in a full-scale subway station with different heat release rates, ventilation modes, and fire source locations. Results demonstrate that when double fires occur, activating ventilation systems in one line may affect the smoke spread in other lines due to the existence of transfer channels. For the platform-tunnel mixed exhaust mode, when the fire size is small, the optimal TVF exhaust volume range exists. There is no optimal smoke exhaust volume for double fires with the large fire size or near the ends of the platform. Under such circumstances, the subway staff should take some measures to hinder the unwanted smoke flow path.

A BIM-FDS Based Evacuation Assessment of Complex Rail Transit Stations under Post-Earthquake Fires for Sustainable Buildings

Post-earthquake fire is considered as a catastrophic secondary disaster to personal and property safety, especially in complex rail transit station. This is primarily attributed to intricate infrastructure, densely populated floors, and the unrestricted layout of these areas. The aim of this study is to evaluate the evacuation capacity of complex railway stations under post-earthquake fires, and provide sustainable recommendations for building design. In this article, an evacuation assessment of a complex rail transit station under the post-earthquake fire for sustainable buildings was conducted from the internal environment and external rescue based on Building Information Modeling (BIM) and Fire Dynamic Simulation (FDS). The internal environment evacuation assessment simulation experiments were conducted in six hypothetical high-risk scenarios. In addition, the external rescue assessment was based on investigation of the route and the required rescue time during different periods of holidays and workdays. The results show that (1) The influence caused by different sizes of fire area in the power distribution room is smaller than those in the train at the platform floor. (2) In fire scenarios with the same fire area but different fire locations, the temperature is more affected than the CO concentration in the power distribution room. (3) It shows slight differences between single-floor fire and double-floor fire on evacuation of small area fire in power distribution room. Meanwhile, optimized design recommendations are proposed to reduce the risk of emergency evacuation in both internal and external environments of rail transit stations for sustainable future buildings, which include strategically locating the power distribution room away from public areas, installing fire-resistant doors around the room, increasing the quantity of smoke detectors and alarms with regular maintenance, minimizing the size of the power distribution room, developing specific emergency plans for train fires, and incorporating small fire stations in urban planning near complex public buildings to mitigate post-earthquake road obstruction challenges.

Single Room Fire Traceability and Prediction Models Based on Deep Learning Methods

ABSTRACT When a fire occurs, fast recognition on key information of the fire based on limited data available and predict fire smoke motion trends from limited fire information is desirable to develop effective emergency strategies. The proposed models include fire intensity traceability model and fire position traceability model based on Back Propagation Neural Network (BPNN). Smoke prediction model is proposed based on Transpose Convolutional Neural Network (TCNN). The numerical model is first validated by the single-room fire test, and then a numerical database of 165 transient room-fire scenarios is established under various fire locations, fire sizes, and vent sizes. The model test is conducted in new fire intensity and vent size scenarios, with R2 coefficients of 0.965 and 0.95, respectively. The experimental data validation shows that relative errors were less than 10%. The position traceability model has Kappa values of 0.758 and 0.75. The average visibility error values of the smoke prediction model’s output images in the validation of the test set generally fall within the range of ± 1.5 m. The results indicate that the models have good accuracy and strong adaptability to different compartment fire scenarios which can provides effective support for fire rescue and emergency strategy development in fire scenes.

Study of spatial temperature features of road tunnel fires under multiple factors

AbstractAfter a highway tunnel fire, the rapid change of the internal ambient temperature directly affects the personal safety of pedestrians. Based mainly on the lateral possibility of fire location and considering the coupling effect of multiple factors in a two‐lane highway tunnel, this study systematically carried out 63 full‐size tunnel fire simulation studies relying on Fluent software. The smoke temperature distribution patterns in the transverse and longitudinal spaces of the tunnel after the fire were revealed. This study shows that the smoke temperature below the tunnel vault is susceptible to the effect of sidewall restraint, making the maximum temperature of the tunnel vault of the offset fire source large; considering the lateral location of the fire source, the maximum temperature prediction formula of the tunnel vault is modified to expose the change law of the longitudinal temperature decay of the vault. This paper reveals for the first time the law of temperature distribution changes at the characteristic height of the human eye on the evacuation platform in the tunnel; the systematic study of the fire source cross‐sectional temperature distribution changes at the key lining structure points, and it is found that a low‐temperature space conducive to personnel escape exists in the lower part of the tunnel away from the fire source side. The results of the study are beneficial to the design of safe evacuation paths in the tunnel and the active emergency evacuation of personnel in the near‐fire source area.

Forest Fire Detection System for Preventing Flora & Fauna from Fire Using Computer Vision

Forest fires present a significant threat to ecosystems, endangering both human and animal life while causing irreversible environmental damage. To address this pressing issue in this research paper introduces an innovative & Affordable forest fire detection system that utilises the HSV algorithm and various computer vision libraries with the use of AI. Our advance system aims to detect & respond to forest fires in real-time, mitigating potential harm to flora and fauna. Our proposed System employs deploy camera sensors distributed at the Sensitive Parts of the forest, continually monitoring the environment. The HSV algorithm enhances the system's capability to accurately identify fire-related hues, ensuring reliable fire detection. Upon detecting a fire, an automatic alarm is triggered, initiating a series of measures to protect the ecosystem proactively. In response to a detected fire, the system automatically reroutes animal pathways to minimize their exposure to danger. Concurrently, the system sends immediate notifications to the local fire department and forest authorities via direct email, providing vital information on the fire's location and severity. This rapid communication facilitates timely intervention and improves the chances of successful firefighting efforts. We can put our system at Jim Corbett National Park & at different Sensitive forest Regions. Furthermore, the system features live monitoring capabilities, enabling authorities to observe the fire's real-time progression. This live feed supports informed decision making and facilitates the swift deployment of resources to contain and extinguish the fire. Through the integration of advanced computer vision technology, our system aims to redefine forest fire detection & response mechanisms, ultimately safeguarding the delicate balance of flora and fauna in ecosystems.

Safety analysis of fire evacuation from Drilling and Production Platforms (DPP)

Offshore platforms are vulnerable to fire accidents and it is essential to conduct safety analysis in case of an evacuation and take measures to improve worker evacuation efficiency and reduce casualties. Based on the proposed Credible and Representative Fire Scenarios (CRFS) methodology, nine fire scenarios were selected and simulated. The safety analysis of fire evacuation was performed based on the coupled injury model and the modified social force model, while the effects of wind direction, wind speed, fire location and pre-evacuation time on the probability of death (POD) were investigated. The results demonstrated that the wind from the stern led to a higher coverage rate of smoke (CRS) on the decks and higher POD values. The wind from the bow, larboard and starboard sides was shown to be more conducive to the dissipation of smoke. As a result, the overall POD values were relatively low. The magnitude of wind speed affects the accumulation behavior of smoke between different decks, and the lower the wind speed, the higher the CRS on the decks and POD of the workers.The pool fire located on the lower deck was more likely to cause injuries than the fire on the working deck. In addition, it was found that the trend between POD and pre-evacuation time shows a non-linear relationship, thus the scenario with the most severe fire threat was used as an example for regression analysis. An approach was proposed to determine the fire-evacuation safety time windows in the event of a fire accident and the time windows in different areas of the nine selected scenarios were predicted. Finally, the time windows obtained from the regression curves and the proposed method were compared.

Non-Faradaic optoelectrodes for safe electrical neuromodulation

Nanoscale optoelectrodes hold the potential to stimulate optically individual neurons and intracellular organelles, a challenge that demands both a high-density of photoelectron storage and significant charge injection. Here, we report that zinc porphyrin, commonly used in dye-sensitized solar cells, can be self-assembled into nanorods and then coated by TiO2. The J-aggregated zinc porphyrin array enables long-range exciton diffusion and allows for fast electron transfer into TiO2. The formation of TiO2(e−) attracts positive charges around the neuron membrane, contributing to the induction of action potentials. Far-field cranial irradiation of the motor cortex using a 670 nm laser or an 850 nm femtosecond laser can modulate local neuronal firing and trigger motor responses in the hind limb of mice. The pulsed photoelectrical stimulation of neurons in the subthalamic nucleus alleviates parkinsonian symptoms in mice, improving abnormal stepping and enhancing the activity of dopaminergic neurons. Our results suggest injectable nanoscopic optoelectrodes for optical neuromodulation with high efficiency and negligible side effects.