Early Fire Research Articles

Dbf4 Zn-Finger Motif Is Specifically Required for Stimulation of Ctf19-Activated Origins in Saccharomyces cerevisiae

Eukaryotic genomes are replicated in spatiotemporal patterns that are stereotypical for individual genomes and developmental profiles. In the model system Saccharomyces cerevisiae, two primary mechanisms determine the preferential activation of replication origins during early S phase, thereby largely defining the consequent replication profiles of these cells. Both mechanisms are thought to act through specific recruitment of a rate-limiting initiation factor, Dbf4-dependent kinase (DDK), to a subset of licensed replication origins. Fkh1/2 is responsible for stimulation of most early-firing origins, except for centromere (CEN)-proximal origins that recruit DDK via the kinetochore protein Ctf19, which is required for their early firing. The C-terminus of Dbf4 has been implicated in its recruitment to origins via both the Fkh1/2 and Ctf19 mechanisms. Here, we show that the Zn-finger motif within the C-terminus is specifically required for Dbf4 recruitment to CENs to stimulate CEN-proximal/Ctf19-dependent origins, whereas stimulation of origins via the Fkh1/2 pathway remains largely intact. These findings re-open the question of exactly how Fkh1/2 and DDK act together to stimulate replication origin initiation.

The Demographic Response of Grass Species to Fire Treatments in a Guinean Savanna

Fighting tree encroachment using fire promotes C4 perennial grasses but likely affects their demography according to the fire date during the dry season. We analyzed the impact of four fire treatments (early, middle, late and no fire) on the demography of the four dominant perennial grasses (Andropogon canaliculatus, Andropogon schirensis, Hyparrhenia diplandra and Loudetia simplex) in a Guinean savanna of West Africa (Lamto, Côte d’Ivoire). We carried out a yearly demographic monitoring of each grass individual during five years (2015–2019) on three plots by treatment and parametrized a size-classified matrix model with five circumference classes. The results showed that A. schirensis, H. diplandra and L. simplex declined (λ < 1.0) under late fire and will disappear after 10 years, as did L. simplex under the middle fire. Stasis influenced the most λ values and stable class distribution was nearly achieved in all species under all treatments. The size of L. simplex should increase under early and late fires. Our results suggest that late fire is the most detrimental fire regime for grasses in this Guinean area, contrary to early and middle fires, which could be recommended to savanna managers.

Pyramid Attention Based Early Forest Fire Detection Using UAV Imagery

Early forest fire detection using unmanned aerial vehicle (UAV) imagery is widely implemented in recent years. However, as the size of moving object in UAV imagery changes dramatically, the issues of focusing more informative areas of an image, large-scale variation as well as temporal consistency preservation have yet to be resolved due to the wide field of vision of UAVs in flight. To address these issues, in this work, we concerned the visual characteristics of early stage forest fire in UAV imagery and employ pyramid attention mechanism. We engineered a novel convolutional neural network (CNN) based on the state-of-the-art backbone by stacking our proposed PyrAtten blocks. We built a large dataset of early stage forest fire, including real world aerial photographs shot using our UAV prototype. Extensive evaluation of PyrAtten demonstrates its efficiency and effectiveness in detecting multi-scale objects of fire and smoke in UAV imagery, even if the object takes up a very small proportion of the whole image. Compared with existing similarly-sized networks, average detecting accuracy of PyrAtten-ResNet-50 reaches 97.5%, with negligible increase in computational overhead.

Camera-based wildfire smoke detection for foggy environments

Smoke is the first visible sign of forest fires and the most commonly used feature for early forest fire detection using data from cameras. However, one of the natural challenges is the dense fog that appears in forests, which decreases the detection accuracy or triggers false alarms. In this study, we propose a system with a deep neural network-based image preprocessing approach that significantly improves the smoke segmentation and classification performance by dehazing the camera view. Our experimental results provide that the classification models reach 99% F1 score for the correct classification of smoke when the image dehazing method is used before the training process. The smoke localization system achieves 60% average precision when the mask region-based convolutional neural network is used with the ResNet101-FPN backbone. The proposed approach can be utilized for all smoke segmentation frameworks to increase fire detection performance.

Detection of 2-ethyl hexanol and dioctyl phthalate with potentiometric sensor based on ordered ZnO nanorods sensing electrode

In this work, a planar mixed-potential sensor was prepared by in situ growth of ZnO nanorods sensing electrode on yttria-stabilized zirconia (YSZ) electrolyte. The potentiometric sensing properties of the sensor toward two signature gases of early PVC cable fires, 2-ethyl hexanol (2-EH) and dioctyl phthalate (DOP), were studied. High response values of −205.1 mV and −54.1 mV were achieved at 400 °C for ∼171–193 ppm 2-EH and ∼89–109 ppt DOP for ZnO nanorods-based sensor, which were 1.7 and 4.1 times higher than those of ZnO nanoparticles-based sensor, respectively. Furthermore, ZnO nanorods exhibited much better 2-EH and DOP selectivity. The improvement of the gas-sensing performance of ZnO nanorods may be ascribed to the unique three-dimensional electrode morphology.

FFireNet: Deep Learning Based Forest Fire Classification and Detection in Smart Cities

Forests are a vital natural resource that directly influences the ecosystem. Recently, forest fire has been a serious issue due to natural and man-made climate effects. For early forest fire detection, an artificial intelligence-based forest fire detection method in smart city application is presented to avoid major disasters. This research presents a review of the vision-based forest fire localization and classification methods. Furthermore, this work makes use of the forest fire detection dataset, which solves the classification problem of discriminating fire and no-fire images. This work proposes a deep learning method named FFireNet, by leveraging the pre-trained convolutional base of the MobileNetV2 model and adding fully connected layers to solve the new task, that is, the forest fire recognition problem, which helps in classifying images as forest fires based on extracted features which are symmetrical. The performance of the proposed solution for classifying fire and no-fire was evaluated using different performance metrics and compared with other CNN models. The results show that the proposed approach achieves 98.42% accuracy, 1.58% error rate, 99.47% recall, and 97.42% precision in classifying the fire and no-fire images. The outcomes of the proposed approach are promising for the forest fire classification problem considering the unique forest fire detection dataset.

Development of an Early Fire Detection Technique Using a Passive Infrared Sensor and Deep Neural Networks

Early detection of fire is key to mitigate fire related damages. This paper presents a differential pyro-electric infrared (PIR) sensor and deep neural networks (DNNs) based method to detect fire in real-time. Since the PIR sensor is sensitive to sudden body motions and emits a continuous time-varying signal, experiments are carried out to collect human and fire motions using a PIR sensor. These signals are processed using one-dimensional continuous wavelet transform to perform feature extraction. The corresponding wavelet coefficients are converted into RGB spectrum images that are then used as inputs for a deep convolutional neural network. Various pre-trained DNN architectures are adopted to train and identify the collected data for background (no motion), human motion, and fire categories: small quasi-static and spreading fires. Experimental results show that the ShuffleNet architecture yields the highest prediction accuracy of 87.8%. Experimental results for the real-time strategy which works at a speed of 12 frames-per-second show 95.34% and 92.39% fire and human motion detection accuracy levels respectively.

Research on the Risk Classification of Cruise Ship Fires Based on an Attention-Bp Neural Network

Abstract Due to the relatively closed environment, complex internal structure, and difficult evacuation of personnel, it is more difficult to prevent ship fires than land fires. In this paper, taking the large cruise ship as the research object, the physical model of a cruise cabin fire is established through PyroSim software, and the safety indexes such as smoke temperature, CO concentration, and visibility are numerically simulated. An Attention-BP neural network model is designed for realizing the intelligent identification of a cabin fire and dividing the risk level, which integrates the diagnosis results of multiple neural network models through the self-Attention mechanism and adaptively distributes the weight of each BP neural network model. The proposed model can provide decision-making reference for subsequent fire-fighting measures and personnel evacuation. Experimental results show that the proposed Attention-BP neural network model can effectively realize the early warning of the fire risk level. Compared with other machine learning algorithms, it has the highest stability and accuracy and reduces the uncertainty of early cabin fire warning.

Tailored P/Si-decorated graphene oxide-based fire sensor for sensitive detection at low-temperature via local and remote wireless transmission

Fire sensors are highly promising in maintaining public fire safety. However, it has certain deficiencies in sensitivity and intelligence even after its development for several years. Developing an advanced fire sensor remains a considerable challenge. This work proposes a novel early thermos-sensitive fire sensor based on phosphorous (P) / silicon (Si) - decorated graphene oxide (GO) by layer-by-layer assembly (LBL) technology. This early-warning film can achieve an ultrafast response within 1 s at a low temperature of 250 °C or an actual fire during the simulation scenario. Intriguingly, an intelligent custom-made wireless communication system is designed to show warning messages on multiple local and long-range computer screens at distances up to 20 km from the target fire area. Furthermore, the pre-designed functional solution can be a fire-warning nano-coating layer wrapped into flexible polyurethane (FPU) foam. This illustrates the feasibility of tuning novel fire-warning nano-coating onto flammable FPU for fire sensors. These results may provide new insights into advanced smart low-temperature fire-warning coating strategies and a new generation of wireless combined fire sensors.

FOREST FIRE DETECTION USING DEEP LEARNING

Early forest fire detection is of great importance to avoid the huge damage of forests caused by fires. Early fire detection focuses on smoke detection. The forest area is gradually decreased because of increasing forest fire and human activities. The satellite sensor is used to collect the forest thermal image in different places and analyze the data in these images to detect the fire region if they occur. Image processing technique can effectively predict the fire in the forest. The input image is pre- processed to enhance the image quality, because the input image has the noise, so the pre- processing technique is used to eliminate the noise in this system and enhance the image quality. The pre-processed image is taking to the segmentation process; it processes the image to adjacent the forest sub-area. In this system, the affected area is separately detected, and it gives the accurate forest fire in this system because the output image intensity is better to stabilize the average value of the image. In our proposed system we propose a deep learning method that uses a Convolutional Neural Network (CNN) to predict the forest fire detection. The convolutional layer is the main building block of the convolutional neural network. Usually, the layers of the network are fully connected in which a neuron in the next layer is connected to all the neurons in the previous layer. We are going to detect the fire in the forest result based on the accuracy which we get in train and test of the dataset based CNN algorithm using that we show the graph result. KEYWORDS CNN– Convolutional neural network.

‘Fall girl’: Vertical evacuation and the aesthetics of emergency

When the Conservative Member of Parliament and Leader of the House of Commons Jacob Rees-Mogg complained on London’s LBC radio that the 72 victims of the 2017 Grenfell fire did not use common sense and simply leave the building, and that he could not understand how it had ‘anything to do with race or class’, he fell into a trap which is now at least 150 years old. This has seen the art and act of evacuating – especially tall buildings – blamed on the evacuees themselves. It is also revealing of an aesthetics of erasure which silences a classed, raced and gendered politics which has served to render certain subjects and bodies as not only victims, but culpable. The vertical evacuee has been considered too slow, too big, too indecisive, too passionate, too weak, too much, too many – too inadequate. In this paper, and in building on a wider politics of verticality and mobility, I pull on several threads of the gendered and raced geographies of high-rise vertical evacuation focusing particularly on the solidaristic movements and expressions of the workers. The paper explores the history of tall building evacuations, focusing on early high-rise factory fires, their investigation, and subsequent changes to fire regulation, building technologies and working practices in North America which affected predominantly young, female and migrant working labour.

A Multi-Sensor Data-Fusion Method Based on Cloud Model and Improved Evidence Theory.

The essential factors of information-aware systems are heterogeneous multi-sensory devices. Because of the ambiguity and contradicting nature of multi-sensor data, a data-fusion method based on the cloud model and improved evidence theory is proposed. To complete the conversion from quantitative to qualitative data, the cloud model is employed to construct the basic probability assignment (BPA) function of the evidence corresponding to each data source. To address the issue that traditional evidence theory produces results that do not correspond to the facts when fusing conflicting evidence, the three measures of the Jousselme distance, cosine similarity, and the Jaccard coefficient are combined to measure the similarity of the evidence. The Hellinger distance of the interval is used to calculate the credibility of the evidence. The similarity and credibility are combined to improve the evidence, and the fusion is performed according to Dempster’s rule to finally obtain the results. The numerical example results show that the proposed improved evidence theory method has better convergence and focus, and the confidence in the correct proposition is up to 100%. Applying the proposed multi-sensor data-fusion method to early indoor fire detection, the method improves the accuracy by 0.9–6.4% and reduces the false alarm rate by 0.7–10.2% compared with traditional and other improved evidence theories, proving its validity and feasibility, which provides a certain reference value for multi-sensor information fusion.

Alat Pendeteksi Kebakaran Dini Berbasis Internet Of Things (IoT) Menggunakan NodeMCU Dan Telegram

One of the unpredictable disasters is fire. Fires often occur in densely populated areas and are triggered by a variety of factors, for example an electric short circuit. Usually a fire disaster is only realized when the fire has grown and has spread to all places. Based on these problems, a solution is needed to prevent the occurrence of fire disasters, namely by designing an Early Fire Detection Tool Based On Internet of Things (IoT) Using NodeMCU And Telegram. The microcontroller used in this study is NodeMCU which is equipped with WiFi ESP8266. This tool uses two fire sensors, namely the LM393 type fire sensor and the KY-026 type fire sensor, besides that this tool also uses buzzer which is used to provide warnings in the form of sound and Light Emitting Diode which are used as a sign that the microcontroller is connected to Telegram. This study produces a device that can send notifications in the form of messages to the user's telegram if the two fire sensors detect the presence of fire in which the two fire sensors can detect the source of the fire up to a distance of 50 cm. This tool can make it easier for users to monitor the condition of the room in real - time by sending messages through the telegram application and this tool will reply to the message automatically according to the conditions of the two fire sensors.

Identification of the Ignition Point of High Voltage Cable Trenches Based on Ceiling Temperature Distribution

With the improvement of the development level of power systems, high voltage cable trenches are being widely used in power systems. The high voltage cable trench is characteristically strongly sealed, which makes it difficult to find a fire in the trench. In order to judge the development of an early cable fire, the relationship between the ceiling temperature distribution and the location of cable ignition points was studied. According to the idea of symmetry, this paper adopts the experimental method of symmetrical double ignition points, carries out several groups of longitudinally symmetrical cable fire combustion experiments with high voltage cable trenches as the carrier, and establishes an equation for calculating the fitness of the relationship between the ceiling temperature and the distance from the ignition point. The results show that the temperature measurement results of the 1:1 high voltage cable trench experimental platform are accurate; in the longitudinal direction, the ceiling temperature decreases from the ignition point to both sides, and the temperature change curve has obvious stage characteristics; within a range of 5 m from the expected ignition point, the average identification accuracy reaches 97.0%. In the early stage of a substation cable trench fire, the two-point temperature measurement and ignition point identification method of high voltage cable trenches based on ceiling temperature distribution proposed in this paper can effectively identify the location of the ignition point along the longitudinal direction of cable trench.

Determination of human DNA replication origin position and efficiency reveals principles of initiation zone organisation.

Replication of the human genome initiates within broad zones of ∼150 kb. The extent to which firing of individual DNA replication origins within initiation zones is spatially stochastic or localised at defined sites remains a matter of debate. A thorough characterisation of the dynamic activation of origins within initiation zones is hampered by the lack of a high-resolution map of both their position and efficiency. To address this shortcoming, we describe a modification of initiation site sequencing (ini-seq), based on density substitution. Newly replicated DNA is rendered ‘heavy-light’ (HL) by incorporation of BrdUTP while unreplicated DNA remains ‘light-light’ (LL). Replicated HL-DNA is separated from unreplicated LL-DNA by equilibrium density gradient centrifugation, then both fractions are subjected to massive parallel sequencing. This allows precise mapping of 23,905 replication origins simultaneously with an assignment of a replication initiation efficiency score to each. We show that origin firing within early initiation zones is not randomly distributed. Rather, origins are arranged hierarchically with a set of very highly efficient origins marking zone boundaries. We propose that these origins explain much of the early firing activity arising within initiation zones, helping to unify the concept of replication initiation zones with the identification of discrete replication origin sites.

The Design of Microcontroller Based Early Warning Fire Detection System for Home Monitoring

Fire is a type of disaster that can occur anytime and anywhere as a result of any accidental or intentional causes. Without exception, houses are also very vulnerable to fire. To anticipate the catastrophic effects of fire that can destroy houses, advanced technology, such as the Internet of Things (IoT) can be utilized to detect the smoke and fire. This study aims to design an early warning fire detection system for home monitoring using smoke detection sensors based on Arduino microcontroller together with NodeMCU ESP8266. This early warning fire detection system is expected to function by notifying homeowners when detecting the presence of smoke in their homes. With the aid of this detection system, the issue of potential damage, death, or material loss caused by fire can be significantly reduced. The results and testing of the designed system will be discussed in the paper.

Investigating the Impact of Using IR Bands on Early Fire Smoke Detection from Landsat Imagery with a Lightweight CNN Model

Smoke plumes are the first things seen from space when wildfires occur. Thus, fire smoke detection is important for early fire detection. Deep Learning (DL) models have been used to detect fire smoke in satellite imagery for fire detection. However, previous DL-based research only considered lower spatial resolution sensors (e.g., Moderate-Resolution Imaging Spectroradiometer (MODIS)) and only used the visible (i.e., red, green, blue (RGB)) bands. To contribute towards solutions for early fire smoke detection, we constructed a six-band imagery dataset from Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) with a 30-metre spatial resolution. The dataset consists of 1836 images in three classes, namely “Smoke”, “Clear”, and “Other_aerosol”. To prepare for potential on-board-of-small-satellite detection, we designed a lightweight Convolutional Neural Network (CNN) model named “Variant Input Bands for Smoke Detection (VIB_SD)”, which achieved competitive accuracy with the state-of-the-art model SAFA, with less than 2% of its number of parameters. We further investigated the impact of using additional Infra-Red (IR) bands on the accuracy of fire smoke detection with VIB_SD by training it with five different band combinations. The results demonstrated that adding the Near-Infra-Red (NIR) band improved prediction accuracy compared with only using the visible bands. Adding both Short-Wave Infra-Red (SWIR) bands can further improve the model performance compared with adding only one SWIR band. The case study showed that the model trained with multispectral bands could effectively detect fire smoke mixed with cloud over small geographic extents.

Hidden signatures of early fire at Evron Quarry (1.0 to 0.8 Mya)

Pyrotechnology is a key element of hominin evolution. The identification of fire in early hominin sites relies primarily on an initial visual assessment of artifacts' physical alterations, resulting in potential underestimation of the prevalence of fire in the archaeological record. Here, we used a suite of spectroscopic techniques to counter the absence of visual signatures for fire and demonstrate the presence of burnt fauna and lithics at the Lower Paleolithic (LP) open-air site of Evron Quarry (Israel), dated between 1.0 and 0.8 Mya and roughly contemporaneous to Gesher Benot Ya'aqov where early pyrotechnology has been documented. We propose reexamining finds from other LP sites lacking visual clues of pyrotechnology to yield a renewed perspective on the origin, evolution, and spatiotemporal dispersal of the relationship between early hominin behavior and fire use.

DeepFire: A Novel Dataset and Deep Transfer Learning Benchmark for Forest Fire Detection

Forest fires pose a potential threat to the ecological and environmental systems and natural resources, impacting human lives. However, automated surveillance system for early forest fire detection can mitigate such calamities and protect the environment. Therefore, we propose a UAV-based forest fire fighting system with integrated artificial intelligence (AI) capabilities for continuous forest surveillance and fire detection. The major contributions of the proposed research are fourfold. Firstly, we explain the detailed working mechanism along with the key steps involved in executing the UAV-based forest fire fighting system. Besides, a robust forest fire detection system requires precise and efficient classification of forest fire imagery against no-fire. Moreover, we have curated a novel dataset (DeepFire) containing diversified real-world forest imagery with and without fire to assist future research in this domain. The DeepFire dataset consists of 1900 colored images with 950 each for fire and no-fire classes. Next, we investigate the performance of various supervised machine learning classifiers for the binary classification problem of detecting forest fire. Furthermore, we propose a VGG19-based transfer learning solution to achieve improved prediction accuracy. We assess and compare the performance of several machine learning approaches such as k -nearest neighbors, random forest, naive Bayes, support vector machine, logistic regression, and the proposed approach for accurately identifying fire and no-fire images in the DeepFire dataset. The simulation results demonstrate the efficacy of the proposed approach in terms of accurate classification, where it achieves the mean accuracy of 95% with 95.7% precision and 94.2% recall.

Multi-Scale Video Flame Detection for Early Fire Warning Based on Deep Learning

The widespread use of renewable energy resources requires more immediate and effective fire alarms as a preventive measure. The fire is usually weak in the initial stages, which is not conducive to detection and identification. This paper validates a solution to resolve that problem by a flame detection algorithm that is more sensitive to small flames. Based on Yolov3, the parallel convolution structure of Inception is used to obtain multi-size image information. In addition, the receptive field of the convolution kernel is increased with the dilated convolution so that each convolution output contains a range of information to avoid information omission of tiny flames. The model accuracy has improved by introducing a Feature Pyramid Network in the feature extraction stage that has enhanced the feature fusion capability of the model. At the same time, a flame detection database for early fire has been established, which contains more than 30 fire scenarios and is suitable for flame detection under various challenging scenes. Experiments validate the proposed method not only improves the performance of the original algorithm but are also advantageous in comparison with other state-of-the-art object detection networks, and its false positives rate reaches 1.2% in the test set.