How to effectively evaluate the firing precision of weapon equipment at low cost is one of the core contents of improving the test level of weapon system.A new method to evaluate the firing precision of the MLRS consi...How to effectively evaluate the firing precision of weapon equipment at low cost is one of the core contents of improving the test level of weapon system.A new method to evaluate the firing precision of the MLRS considering the credibility of simulation system based on Bayesian theory is proposed in this paper.First of all,a comprehensive index system for the credibility of the simulation system of the firing precision of the MLRS is constructed combined with the group analytic hierarchy process.A modified method for determining the comprehensive weight of the index is established to improve the rationality of the index weight coefficients.The Bayesian posterior estimation formula of firing precision considering prior information is derived in the form of mixed prior distribution,and the rationality of prior information used in estimation model is discussed quantitatively.With the simulation tests,the different evaluation methods are compared to validate the effectiveness of the proposed method.Finally,the experimental results show that the effectiveness of estimation method for firing precision is improved by more than 25%.展开更多
This article delves into the analysis of performance and utilization of Support Vector Machines (SVMs) for the critical task of forest fire detection using image datasets. With the increasing threat of forest fires to...This article delves into the analysis of performance and utilization of Support Vector Machines (SVMs) for the critical task of forest fire detection using image datasets. With the increasing threat of forest fires to ecosystems and human settlements, the need for rapid and accurate detection systems is of utmost importance. SVMs, renowned for their strong classification capabilities, exhibit proficiency in recognizing patterns associated with fire within images. By training on labeled data, SVMs acquire the ability to identify distinctive attributes associated with fire, such as flames, smoke, or alterations in the visual characteristics of the forest area. The document thoroughly examines the use of SVMs, covering crucial elements like data preprocessing, feature extraction, and model training. It rigorously evaluates parameters such as accuracy, efficiency, and practical applicability. The knowledge gained from this study aids in the development of efficient forest fire detection systems, enabling prompt responses and improving disaster management. Moreover, the correlation between SVM accuracy and the difficulties presented by high-dimensional datasets is carefully investigated, demonstrated through a revealing case study. The relationship between accuracy scores and the different resolutions used for resizing the training datasets has also been discussed in this article. These comprehensive studies result in a definitive overview of the difficulties faced and the potential sectors requiring further improvement and focus.展开更多
The mechanism of lightning that ignites a forest fire and the lightning that occurs above a forest fire are explained at the molecular level. It is based on two phenomena, namely, internal charge separation inside the...The mechanism of lightning that ignites a forest fire and the lightning that occurs above a forest fire are explained at the molecular level. It is based on two phenomena, namely, internal charge separation inside the atmospheric cloud particles and the existence of a layer of positively charged hydrogen atoms sticking out of the surface of the liquid layer of water on the surface of rimers. Strong turbulence-driven collisions of the ice particles and water droplets with the rimers give rise to breakups of the ice particles and water droplets into positively and negatively charged fragments leading to charge separation. Hot weather in a forest contributes to the updraft of hot and humid air, which follows the same physical/chemical processes of normal lightning proposed and explained recently[1]. Lightning would have a high probability of lighting up and burning the dry biological materials in the ground of the forest, leading to a forest (wild) fire. The burning of trees and other plants would release a lot of heat and moisture together with a lot of smoke particles (aerosols) becoming a strong updraft. The condition for creating lightning is again satisfied which would result in further lightning high above the forest wild fire.展开更多
This work studied the effects of firing temperatures on the refractory properties of insulating firebricks produced from a blend of hydrometallurgically purified clay, high alumina cement and sawdust. Twenty grams out...This work studied the effects of firing temperatures on the refractory properties of insulating firebricks produced from a blend of hydrometallurgically purified clay, high alumina cement and sawdust. Twenty grams out of a bulk (1000 Kg) of clay obtained from Ipetumodu in Nigeria was analyzed for size range, consequent upon which the remaining bulk was sieved to 100 μm, being the average size. The bulk was there after leached under a predetermined condition (1.6 mol/dm3 of oxalic acid at 70oC for 150 min and 200 rev/min agitation speed) and cylindrical samples (5 cm diameter by 5 cm high) containing different quantities of high alumina cement (5% - 20%) and sawdust (1% - 5%) were prepared, dried at 110oC and subsequently fired at 900oC, 1100oC, 1300oC and 1500oC, at the rate of 4oC/min and soaked for 2 hrs. These samples were subjected to different refractory tests (permanent linear change, modulus of rupture, bulk density, cold crushing strength and apparent porosity). Even though samples containing more than 20% alumina crumbled at elevated temperatures, it was still observed that the bricks performed to expectations at lower alumina contents, even at 1500oC. The sample containing 3% sawdust and 10% alumina cement however, gave the desired requirement for preparing good insulating firebricks with reliable phase integrity, as revealed by scanning electron microscopy (SEM).展开更多
Evaluation system of small arms firing has an important effect in the context of military domain. A partially automated evaluation system has been conducted and performed at the ground level. Automation of such system...Evaluation system of small arms firing has an important effect in the context of military domain. A partially automated evaluation system has been conducted and performed at the ground level. Automation of such system with the inclusion of artificial intelligence is a much required process. This papers puts focus on designing and developing an AI-based small arms firing evaluation systems in the context of military environment. Initially image processing techniques are used to calculate the target firing score. Additionally, firing errors during the shooting have also been detected using a machine learning algorithm. However, consistency in firing requires an abundance of practice and updated analysis of the previous results. Accuracy and precision are the basic requirements of a good shooter. To test the shooting skill of combatants, firing practices are held by the military personnel at frequent intervals that include 'grouping' and 'shoot to hit' scores. Shortage of skilled personnel and lack of personal interest leads to an inefficient evaluation of the firing standard of a firer. This paper introduces a system that will automatically be able to fetch the target data and evaluate the standard based on the fuzzy systems.Moreover it will be able to predict the shooter performance based on linear regression techniques.Thereby, it compares with recognized patterns to analyze the individual expertise and suggest improvements based on previous values. The paper is developed on a Small Arms Firing Skill Evaluation System, which makes the whole process of firing and target evaluation faster with better accuracy. The experiment has been conducted on real-time scenarios considering the military field and shows a promising result to evaluate the system automatically.展开更多
Vegetation fires become the concern worldwide due to their substantial impacts on climate and environment,and in particular in the circum-Arctic.Assessing vegetation fires and associated emissions and causes can impro...Vegetation fires become the concern worldwide due to their substantial impacts on climate and environment,and in particular in the circum-Arctic.Assessing vegetation fires and associated emissions and causes can improve understanding of fire regime and provide helpful information for vegetation fires solution.In this study,satellitebased vegetation fires and emissions during 2001–2020 were investigated and contributions of different types of fires were analyzed.Furthermore,climate anomalies related to extreme vegetation fires were explored.The main results showed that the region south of the Arctic circle(50°N-67°N)experienced a greater number of vegetation fires compared to the Arctic(north of 67°N).During 2001–2020,interannual variability of vegetation fires between 50°N and 67°N appeared to be decreasing while emissions(including carbon,dry matter,PM_(2.5),and BC)appeared to be increasing overall,which were contributed by the increasing summer boreal forest fires in this region largely.In the Arctic,vegetation fires and emissions increased in recent years distinctly,and those were dominated by the summer forest fires.Spatially,large increases of vegetation fires were located in the eastern Siberia and northern North America while large decreases were located in the northwestern Eurasia mainly.Additionally,in the Arctic,the unprecedented vegetation fires were observed in the eastern Siberia and Alaska in 2019 and in the eastern Siberia in 2020,which could be attributed to high pressure,high near-surface temperature,and low air moisture anomalies.Meanwhile,obvious anticyclonic anomalies in Alaska in 2019 and in the eastern Siberia in 2020 and cyclonic anomalies in the western Siberia in 2019,also played an important role on fire occurrences making drier conditions.展开更多
Understanding the mechanisms and risks of forest fires by building a spatial prediction model is an important means of controlling forest fires.Non-fire point data are important training data for constructing a model,...Understanding the mechanisms and risks of forest fires by building a spatial prediction model is an important means of controlling forest fires.Non-fire point data are important training data for constructing a model,and their quality significantly impacts the prediction performance of the model.However,non-fire point data obtained using existing sampling methods generally suffer from low representativeness.Therefore,this study proposes a non-fire point data sampling method based on geographical similarity to improve the quality of non-fire point samples.The method is based on the idea that the less similar the geographical environment between a sample point and an already occurred fire point,the greater the confidence in being a non-fire point sample.Yunnan Province,China,with a high frequency of forest fires,was used as the study area.We compared the prediction performance of traditional sampling methods and the proposed method using three commonly used forest fire risk prediction models:logistic regression(LR),support vector machine(SVM),and random forest(RF).The results show that the modeling and prediction accuracies of the forest fire prediction models established based on the proposed sampling method are significantly improved compared with those of the traditional sampling method.Specifically,in 2010,the modeling and prediction accuracies improved by 19.1%and 32.8%,respectively,and in 2020,they improved by 13.1%and 24.3%,respectively.Therefore,we believe that collecting non-fire point samples based on the principle of geographical similarity is an effective way to improve the quality of forest fire samples,and thus enhance the prediction of forest fire risk.展开更多
Climate change has an impact on forest fire patterns.In the context of global warming,it is important to study the possible effects of climate change on forest fires,carbon emission reductions,carbon sink effects,fore...Climate change has an impact on forest fire patterns.In the context of global warming,it is important to study the possible effects of climate change on forest fires,carbon emission reductions,carbon sink effects,forest fire management,and sustainable development of forest ecosystems.This study is based on MODIS active fire data from 2001-2020 and the influence of climate,topography,vegetation,and social factors were integrated.Temperature and precipitation information from different scenarios of the BCC-CSM2-MR climate model were used as future climate data.Under climate change scenarios of a sustainable low development path and a high conventional development path,the extreme gradient boosting model predicted the spatial distribution of forest fire occurrence in China in the 2030s(2021-2040),2050s(2041-2060),2070s(2061-2080),and2090s(2081-2100).Probability maps were generated and tested using ROC curves.The results show that:(1)the area under the ROC curve of training data(70%)and validation data(30%)were 0.8465 and 0.8171,respectively,indicating that the model can reasonably predict the occurrence of forest fire in the study area;(2)temperature,elevation,and precipitation were strongly correlated with fire occurrence,while land type,slope,distance from settlements and roads,and slope direction were less strongly correlated;and,(3)based on future climate change scenarios,the probability of forest fire occurrence will tend to shift from the south to the center of the country.Compared with the current climate(2001-2020),the occurrence of forest fires in 2021-2040,2041-2060,2061-2080,and 2081-2100 will increase significantly in Henan Province(Luoyang,Nanyang,S anmenxia),Shaanxi Province(Shangluo,Ankang),Sichuan Province(Mianyang,Guangyuan,Ganzi),Tibet Autonomous Region(Shannan,Linzhi,Changdu),Liaoning Province(Liaoyang,Fushun,Dandong).展开更多
Considering the fact that memristors have the characteristics similar to biological synapses, a fractional-order multistable memristor is proposed in this paper. It is verified that the fractional-order memristor has ...Considering the fact that memristors have the characteristics similar to biological synapses, a fractional-order multistable memristor is proposed in this paper. It is verified that the fractional-order memristor has multiple local active regions and multiple stable hysteresis loops, and the influence of fractional-order on its nonvolatility is also revealed. Then by considering the fractional-order memristor as an autapse of Hindmarsh–Rose(HR) neuron model, a fractional-order memristive neuron model is developed. The effects of the initial value, external excitation current, coupling strength and fractional-order on the firing behavior are discussed by time series, phase diagram, Lyapunov exponent and inter spike interval(ISI) bifurcation diagram. Three coexisting firing patterns, including irregular asymptotically periodic(A-periodic)bursting, A-periodic bursting and chaotic bursting, dependent on the memristor initial values, are observed. It is also revealed that the fractional-order can not only induce the transition of firing patterns, but also change the firing frequency of the neuron. Finally, a neuron circuit with variable fractional-order is designed to verify the numerical simulations.展开更多
Forest fires are a significant threat to the environment, causing ecological damage, economic losses, and posing a threat to human life. Hence, timely detection and prevention of forest fires are critical to minimizin...Forest fires are a significant threat to the environment, causing ecological damage, economic losses, and posing a threat to human life. Hence, timely detection and prevention of forest fires are critical to minimizing their impact. In this paper, we review the current state-of-the-art methods in forest fire detection and prevention using predictions based on weather conditions and predictions based on forest fire history. In particular, we discuss different Machine Learning (ML) models that have been used for forest fire detection. Further, we present the challenges faced when implementing the ML-based forest fire detection and prevention systems, such as data availability, model prediction errors and processing speed. Finally, we discuss how recent advances in Deep Learning (DL) can be utilized to improve the performance of current fire detection systems.展开更多
To reduce production costs and make full and reasonable use of raw materials,high alumina bricks were prepared using tabular corundum and mullite as aggregates,sillimanite as intermediate particles,and white fused cor...To reduce production costs and make full and reasonable use of raw materials,high alumina bricks were prepared using tabular corundum and mullite as aggregates,sillimanite as intermediate particles,and white fused corundum powder,α-alumina micropowder,and Suzhou soil as the matrix,firing at different temperatures(1420,1440,1460,1480,1500 and 1520℃)for 4 h.The apparent porosity(AP),the bulk density(BD),the cold crushing strength(CCS),the thermal shock resistance(TSR),the refractoriness under load(RUL)and the creep rate of the samples were tested.The effects of the firing temperature on the creep rate(1450℃×50 h,under a load of 0.2 MPa)of the samples were studied.The results show that with the sillimanite addition of 22.5 mass%,the sample fired at 1460℃for 4 h performs the best comprehensive properties:the AP of 17.5%,the BD of 2.75 g·cm^(-3),the CCS of 100.5 MPa,the TSR number of 35 cycles,the RUL of 1682℃,and the creep rate of-0.428%,which can prolong the service life of furnaces.展开更多
In the last two decades, unprecedented changes have taken place in the frequency and severity of wildfires;in different regions of the world, some fires were even classified as megafires. Although there are studies ab...In the last two decades, unprecedented changes have taken place in the frequency and severity of wildfires;in different regions of the world, some fires were even classified as megafires. Although there are studies about the diverse effects of fire, which have made significant theoretical contributions, a comprehensive review of the changes in fire research is required to understand worldwide patterns, particularly in those countries where fire activity is on the rise, such is the case of Mexico. The objective of this study was to analyze the trends in the research on wildfires published in Mexico and worldwide over a 40-year timescale. For this purpose, the Web of Science database, bibliometric tools, and the keywords TI = Forest fire* OR TI = Wildfire* were used to extract as many articles as possible related to fires from 1980 to 2020, without being restricted to those studies whose title included any of the variants of the keywords. There were 8458 publications about fires in the vegetation cover, with a notable increase in the frequency of studies in the previous decade;52% of the studies were concentrated in five countries and 20% of the articles focused on the study of different aspects of the soil. Mexico ranks thirteenth in volume of scientific production and studies in the country have focused mainly on the description of the quantitative relationship between the size of the affected area and the number of occurrences in the landscape, meanwhile, studies on fires and the consequences on the biotic interactions have been little explored.展开更多
To reduce greenhouse gases emission and increase the renewable energy uti</span><span style="font-family:Verdana;">lization portion in the world, the biomass gasification coupled with a coal-fire...To reduce greenhouse gases emission and increase the renewable energy uti</span><span style="font-family:Verdana;">lization portion in the world, the biomass gasification coupled with a coal-fired </span><span style="font-family:Verdana;">boiler power generation system is studied. It is a challenge to achieve optimum performance for the coupled system. The models of biomass gasification coupled with co-firing of coal in a boiler have been established. A comparative study of three kinds of biomass (Food Rubbish, Straw and Wood Pellets) has </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">been </span></span></span><span><span><span><span style="font-family:Verdana;">done. The syngas produced in a 10 t/h gasifier is fed to a 330 MWe coal-fired boiler for co-combustion, and the co-firing performances have been compared with pure coal combustion case under the conditions of constant boiler load. Results show that co-firing decreases the furnace combustion temperature and raises the flue gas temperature for Food Rubbish and Straw, while, flue gases temperature decrease in case of Wood Pellets. At the same time NO<sub>x</sub> and SO<sub>x</sub> emissions have reduced. The system efficiencies at constant load for Food Rubbish, Straw and Wood Pellets are 83.25%, </span><span style="font-family:Verdana;">83.88% and 82.56% when the optimum conditions of gasification and co-firing </span><span style="font-family:Verdana;">process are guaranteed.展开更多
Fire whirls cause an increase in fire damage. This study clarified the unsteady behavior of fire whirls, considering that instantaneous changes in the temperature and flame shape of fire whirls can affect the damage t...Fire whirls cause an increase in fire damage. This study clarified the unsteady behavior of fire whirls, considering that instantaneous changes in the temperature and flame shape of fire whirls can affect the damage to the surrounding area. Numerical simulations of a lab-scale flame that simulates a fire whirl were performed to investigate the changes in gas temperature and velocity fields under various fuel inflow velocities. The flow field was obtained by solving a continuity equation and a three-dimensional Navier-Stokes equation, and the turbulence was resolved using a large eddy simulation. A chemical equilibrium partially premixed combustion model was used, and radiation effects were considered. The time-averaged gas temperature distribution along the burner central axis revealed that the gas temperature decreased monotonically from upstream to downstream. The time-averaged velocity distribution along the burner central axis showed that the velocity decreased as one moved downstream, but the decrease was uneven. The time variation of the gas temperature demonstrated that the higher the fuel inflow velocity, especially near the burner, the greater the gas temperature flutter. Furthermore, the larger the fuel inflow velocity, the larger the flame swell and wobble. The results showed that the fuel inflow velocity affected temperature fluctuation and flame undulating movement.展开更多
基金National Natural Science Foundation of China(Grant Nos.11972193 and 92266201)。
文摘How to effectively evaluate the firing precision of weapon equipment at low cost is one of the core contents of improving the test level of weapon system.A new method to evaluate the firing precision of the MLRS considering the credibility of simulation system based on Bayesian theory is proposed in this paper.First of all,a comprehensive index system for the credibility of the simulation system of the firing precision of the MLRS is constructed combined with the group analytic hierarchy process.A modified method for determining the comprehensive weight of the index is established to improve the rationality of the index weight coefficients.The Bayesian posterior estimation formula of firing precision considering prior information is derived in the form of mixed prior distribution,and the rationality of prior information used in estimation model is discussed quantitatively.With the simulation tests,the different evaluation methods are compared to validate the effectiveness of the proposed method.Finally,the experimental results show that the effectiveness of estimation method for firing precision is improved by more than 25%.
文摘This article delves into the analysis of performance and utilization of Support Vector Machines (SVMs) for the critical task of forest fire detection using image datasets. With the increasing threat of forest fires to ecosystems and human settlements, the need for rapid and accurate detection systems is of utmost importance. SVMs, renowned for their strong classification capabilities, exhibit proficiency in recognizing patterns associated with fire within images. By training on labeled data, SVMs acquire the ability to identify distinctive attributes associated with fire, such as flames, smoke, or alterations in the visual characteristics of the forest area. The document thoroughly examines the use of SVMs, covering crucial elements like data preprocessing, feature extraction, and model training. It rigorously evaluates parameters such as accuracy, efficiency, and practical applicability. The knowledge gained from this study aids in the development of efficient forest fire detection systems, enabling prompt responses and improving disaster management. Moreover, the correlation between SVM accuracy and the difficulties presented by high-dimensional datasets is carefully investigated, demonstrated through a revealing case study. The relationship between accuracy scores and the different resolutions used for resizing the training datasets has also been discussed in this article. These comprehensive studies result in a definitive overview of the difficulties faced and the potential sectors requiring further improvement and focus.
文摘The mechanism of lightning that ignites a forest fire and the lightning that occurs above a forest fire are explained at the molecular level. It is based on two phenomena, namely, internal charge separation inside the atmospheric cloud particles and the existence of a layer of positively charged hydrogen atoms sticking out of the surface of the liquid layer of water on the surface of rimers. Strong turbulence-driven collisions of the ice particles and water droplets with the rimers give rise to breakups of the ice particles and water droplets into positively and negatively charged fragments leading to charge separation. Hot weather in a forest contributes to the updraft of hot and humid air, which follows the same physical/chemical processes of normal lightning proposed and explained recently[1]. Lightning would have a high probability of lighting up and burning the dry biological materials in the ground of the forest, leading to a forest (wild) fire. The burning of trees and other plants would release a lot of heat and moisture together with a lot of smoke particles (aerosols) becoming a strong updraft. The condition for creating lightning is again satisfied which would result in further lightning high above the forest wild fire.
文摘This work studied the effects of firing temperatures on the refractory properties of insulating firebricks produced from a blend of hydrometallurgically purified clay, high alumina cement and sawdust. Twenty grams out of a bulk (1000 Kg) of clay obtained from Ipetumodu in Nigeria was analyzed for size range, consequent upon which the remaining bulk was sieved to 100 μm, being the average size. The bulk was there after leached under a predetermined condition (1.6 mol/dm3 of oxalic acid at 70oC for 150 min and 200 rev/min agitation speed) and cylindrical samples (5 cm diameter by 5 cm high) containing different quantities of high alumina cement (5% - 20%) and sawdust (1% - 5%) were prepared, dried at 110oC and subsequently fired at 900oC, 1100oC, 1300oC and 1500oC, at the rate of 4oC/min and soaked for 2 hrs. These samples were subjected to different refractory tests (permanent linear change, modulus of rupture, bulk density, cold crushing strength and apparent porosity). Even though samples containing more than 20% alumina crumbled at elevated temperatures, it was still observed that the bricks performed to expectations at lower alumina contents, even at 1500oC. The sample containing 3% sawdust and 10% alumina cement however, gave the desired requirement for preparing good insulating firebricks with reliable phase integrity, as revealed by scanning electron microscopy (SEM).
文摘Evaluation system of small arms firing has an important effect in the context of military domain. A partially automated evaluation system has been conducted and performed at the ground level. Automation of such system with the inclusion of artificial intelligence is a much required process. This papers puts focus on designing and developing an AI-based small arms firing evaluation systems in the context of military environment. Initially image processing techniques are used to calculate the target firing score. Additionally, firing errors during the shooting have also been detected using a machine learning algorithm. However, consistency in firing requires an abundance of practice and updated analysis of the previous results. Accuracy and precision are the basic requirements of a good shooter. To test the shooting skill of combatants, firing practices are held by the military personnel at frequent intervals that include 'grouping' and 'shoot to hit' scores. Shortage of skilled personnel and lack of personal interest leads to an inefficient evaluation of the firing standard of a firer. This paper introduces a system that will automatically be able to fetch the target data and evaluate the standard based on the fuzzy systems.Moreover it will be able to predict the shooter performance based on linear regression techniques.Thereby, it compares with recognized patterns to analyze the individual expertise and suggest improvements based on previous values. The paper is developed on a Small Arms Firing Skill Evaluation System, which makes the whole process of firing and target evaluation faster with better accuracy. The experiment has been conducted on real-time scenarios considering the military field and shows a promising result to evaluate the system automatically.
基金supported by the Chinese Academy of Sciences (QYZDJ-SSW-DQC039,QYZDY-SSW-DQC021,131B62KYSB20180003)the National Natural Science Foundation of China (41721091,42201157)the State Key Laboratory of Cryospheric Science (SKLCS-ZZ-2022).
文摘Vegetation fires become the concern worldwide due to their substantial impacts on climate and environment,and in particular in the circum-Arctic.Assessing vegetation fires and associated emissions and causes can improve understanding of fire regime and provide helpful information for vegetation fires solution.In this study,satellitebased vegetation fires and emissions during 2001–2020 were investigated and contributions of different types of fires were analyzed.Furthermore,climate anomalies related to extreme vegetation fires were explored.The main results showed that the region south of the Arctic circle(50°N-67°N)experienced a greater number of vegetation fires compared to the Arctic(north of 67°N).During 2001–2020,interannual variability of vegetation fires between 50°N and 67°N appeared to be decreasing while emissions(including carbon,dry matter,PM_(2.5),and BC)appeared to be increasing overall,which were contributed by the increasing summer boreal forest fires in this region largely.In the Arctic,vegetation fires and emissions increased in recent years distinctly,and those were dominated by the summer forest fires.Spatially,large increases of vegetation fires were located in the eastern Siberia and northern North America while large decreases were located in the northwestern Eurasia mainly.Additionally,in the Arctic,the unprecedented vegetation fires were observed in the eastern Siberia and Alaska in 2019 and in the eastern Siberia in 2020,which could be attributed to high pressure,high near-surface temperature,and low air moisture anomalies.Meanwhile,obvious anticyclonic anomalies in Alaska in 2019 and in the eastern Siberia in 2020 and cyclonic anomalies in the western Siberia in 2019,also played an important role on fire occurrences making drier conditions.
基金financially supported by the National Natural Science Fundation of China(Grant Nos.42161065 and 41461038)。
文摘Understanding the mechanisms and risks of forest fires by building a spatial prediction model is an important means of controlling forest fires.Non-fire point data are important training data for constructing a model,and their quality significantly impacts the prediction performance of the model.However,non-fire point data obtained using existing sampling methods generally suffer from low representativeness.Therefore,this study proposes a non-fire point data sampling method based on geographical similarity to improve the quality of non-fire point samples.The method is based on the idea that the less similar the geographical environment between a sample point and an already occurred fire point,the greater the confidence in being a non-fire point sample.Yunnan Province,China,with a high frequency of forest fires,was used as the study area.We compared the prediction performance of traditional sampling methods and the proposed method using three commonly used forest fire risk prediction models:logistic regression(LR),support vector machine(SVM),and random forest(RF).The results show that the modeling and prediction accuracies of the forest fire prediction models established based on the proposed sampling method are significantly improved compared with those of the traditional sampling method.Specifically,in 2010,the modeling and prediction accuracies improved by 19.1%and 32.8%,respectively,and in 2020,they improved by 13.1%and 24.3%,respectively.Therefore,we believe that collecting non-fire point samples based on the principle of geographical similarity is an effective way to improve the quality of forest fire samples,and thus enhance the prediction of forest fire risk.
基金funded by the National Postdoctoral Innovative Talents Support Plan China Postdoctoral Science Foundation (BX20220038)Key R&D Projects in Hainan Province (ZDYF2021SHFZ256)。
文摘Climate change has an impact on forest fire patterns.In the context of global warming,it is important to study the possible effects of climate change on forest fires,carbon emission reductions,carbon sink effects,forest fire management,and sustainable development of forest ecosystems.This study is based on MODIS active fire data from 2001-2020 and the influence of climate,topography,vegetation,and social factors were integrated.Temperature and precipitation information from different scenarios of the BCC-CSM2-MR climate model were used as future climate data.Under climate change scenarios of a sustainable low development path and a high conventional development path,the extreme gradient boosting model predicted the spatial distribution of forest fire occurrence in China in the 2030s(2021-2040),2050s(2041-2060),2070s(2061-2080),and2090s(2081-2100).Probability maps were generated and tested using ROC curves.The results show that:(1)the area under the ROC curve of training data(70%)and validation data(30%)were 0.8465 and 0.8171,respectively,indicating that the model can reasonably predict the occurrence of forest fire in the study area;(2)temperature,elevation,and precipitation were strongly correlated with fire occurrence,while land type,slope,distance from settlements and roads,and slope direction were less strongly correlated;and,(3)based on future climate change scenarios,the probability of forest fire occurrence will tend to shift from the south to the center of the country.Compared with the current climate(2001-2020),the occurrence of forest fires in 2021-2040,2041-2060,2061-2080,and 2081-2100 will increase significantly in Henan Province(Luoyang,Nanyang,S anmenxia),Shaanxi Province(Shangluo,Ankang),Sichuan Province(Mianyang,Guangyuan,Ganzi),Tibet Autonomous Region(Shannan,Linzhi,Changdu),Liaoning Province(Liaoyang,Fushun,Dandong).
基金Project supported by the National Key Research and Development Program of China (Grant No.2018AAA0103300)the National Natural Science Foundation of China (Grant Nos.62171401 and 62071411)。
文摘Considering the fact that memristors have the characteristics similar to biological synapses, a fractional-order multistable memristor is proposed in this paper. It is verified that the fractional-order memristor has multiple local active regions and multiple stable hysteresis loops, and the influence of fractional-order on its nonvolatility is also revealed. Then by considering the fractional-order memristor as an autapse of Hindmarsh–Rose(HR) neuron model, a fractional-order memristive neuron model is developed. The effects of the initial value, external excitation current, coupling strength and fractional-order on the firing behavior are discussed by time series, phase diagram, Lyapunov exponent and inter spike interval(ISI) bifurcation diagram. Three coexisting firing patterns, including irregular asymptotically periodic(A-periodic)bursting, A-periodic bursting and chaotic bursting, dependent on the memristor initial values, are observed. It is also revealed that the fractional-order can not only induce the transition of firing patterns, but also change the firing frequency of the neuron. Finally, a neuron circuit with variable fractional-order is designed to verify the numerical simulations.
文摘Forest fires are a significant threat to the environment, causing ecological damage, economic losses, and posing a threat to human life. Hence, timely detection and prevention of forest fires are critical to minimizing their impact. In this paper, we review the current state-of-the-art methods in forest fire detection and prevention using predictions based on weather conditions and predictions based on forest fire history. In particular, we discuss different Machine Learning (ML) models that have been used for forest fire detection. Further, we present the challenges faced when implementing the ML-based forest fire detection and prevention systems, such as data availability, model prediction errors and processing speed. Finally, we discuss how recent advances in Deep Learning (DL) can be utilized to improve the performance of current fire detection systems.
文摘To reduce production costs and make full and reasonable use of raw materials,high alumina bricks were prepared using tabular corundum and mullite as aggregates,sillimanite as intermediate particles,and white fused corundum powder,α-alumina micropowder,and Suzhou soil as the matrix,firing at different temperatures(1420,1440,1460,1480,1500 and 1520℃)for 4 h.The apparent porosity(AP),the bulk density(BD),the cold crushing strength(CCS),the thermal shock resistance(TSR),the refractoriness under load(RUL)and the creep rate of the samples were tested.The effects of the firing temperature on the creep rate(1450℃×50 h,under a load of 0.2 MPa)of the samples were studied.The results show that with the sillimanite addition of 22.5 mass%,the sample fired at 1460℃for 4 h performs the best comprehensive properties:the AP of 17.5%,the BD of 2.75 g·cm^(-3),the CCS of 100.5 MPa,the TSR number of 35 cycles,the RUL of 1682℃,and the creep rate of-0.428%,which can prolong the service life of furnaces.
文摘In the last two decades, unprecedented changes have taken place in the frequency and severity of wildfires;in different regions of the world, some fires were even classified as megafires. Although there are studies about the diverse effects of fire, which have made significant theoretical contributions, a comprehensive review of the changes in fire research is required to understand worldwide patterns, particularly in those countries where fire activity is on the rise, such is the case of Mexico. The objective of this study was to analyze the trends in the research on wildfires published in Mexico and worldwide over a 40-year timescale. For this purpose, the Web of Science database, bibliometric tools, and the keywords TI = Forest fire* OR TI = Wildfire* were used to extract as many articles as possible related to fires from 1980 to 2020, without being restricted to those studies whose title included any of the variants of the keywords. There were 8458 publications about fires in the vegetation cover, with a notable increase in the frequency of studies in the previous decade;52% of the studies were concentrated in five countries and 20% of the articles focused on the study of different aspects of the soil. Mexico ranks thirteenth in volume of scientific production and studies in the country have focused mainly on the description of the quantitative relationship between the size of the affected area and the number of occurrences in the landscape, meanwhile, studies on fires and the consequences on the biotic interactions have been little explored.
文摘To reduce greenhouse gases emission and increase the renewable energy uti</span><span style="font-family:Verdana;">lization portion in the world, the biomass gasification coupled with a coal-fired </span><span style="font-family:Verdana;">boiler power generation system is studied. It is a challenge to achieve optimum performance for the coupled system. The models of biomass gasification coupled with co-firing of coal in a boiler have been established. A comparative study of three kinds of biomass (Food Rubbish, Straw and Wood Pellets) has </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">been </span></span></span><span><span><span><span style="font-family:Verdana;">done. The syngas produced in a 10 t/h gasifier is fed to a 330 MWe coal-fired boiler for co-combustion, and the co-firing performances have been compared with pure coal combustion case under the conditions of constant boiler load. Results show that co-firing decreases the furnace combustion temperature and raises the flue gas temperature for Food Rubbish and Straw, while, flue gases temperature decrease in case of Wood Pellets. At the same time NO<sub>x</sub> and SO<sub>x</sub> emissions have reduced. The system efficiencies at constant load for Food Rubbish, Straw and Wood Pellets are 83.25%, </span><span style="font-family:Verdana;">83.88% and 82.56% when the optimum conditions of gasification and co-firing </span><span style="font-family:Verdana;">process are guaranteed.
文摘Fire whirls cause an increase in fire damage. This study clarified the unsteady behavior of fire whirls, considering that instantaneous changes in the temperature and flame shape of fire whirls can affect the damage to the surrounding area. Numerical simulations of a lab-scale flame that simulates a fire whirl were performed to investigate the changes in gas temperature and velocity fields under various fuel inflow velocities. The flow field was obtained by solving a continuity equation and a three-dimensional Navier-Stokes equation, and the turbulence was resolved using a large eddy simulation. A chemical equilibrium partially premixed combustion model was used, and radiation effects were considered. The time-averaged gas temperature distribution along the burner central axis revealed that the gas temperature decreased monotonically from upstream to downstream. The time-averaged velocity distribution along the burner central axis showed that the velocity decreased as one moved downstream, but the decrease was uneven. The time variation of the gas temperature demonstrated that the higher the fuel inflow velocity, especially near the burner, the greater the gas temperature flutter. Furthermore, the larger the fuel inflow velocity, the larger the flame swell and wobble. The results showed that the fuel inflow velocity affected temperature fluctuation and flame undulating movement.