Single-file Movement of Ants Stressed by a High Temperature

Single-file movement is a universal pattern in both nature and human society. In this paper, we investigate single-file movement of ants (Camponotus japonicus) driven by a high temperature in a narrow channel. Here, ants were placed in a chamber. The chamber was connected to a narrow channel which was 10 cm long and 0.6 cm wide so that the ants can escape through it one by one. Both chamber and narrow channel were in high temperature environment. In the channel, the random pause was observed due to the characteristic of ants. Moreover, ants were inclined to following the preceding one and trying to overtake it, which is different from the movement in natural investigation. On the other hand, the speed increased with distance headway when the distance headway is less than 0.26 cm, that is less than the body size of an ant. Furthermore, touching phenomenon was observed. When the following ants touched the preceding one, they could reduce speed, stop or move backward. On the contrary, the preceding ants increased their speed. Thus, the touching effect in multiple ants experiment can enhance the evacuation efficiency

Numerical simulation of decomposition of Polymer Nano-composites: Investigation of the Influence of the Char Structure

In recent years, nano-particles such as nano-clays, carbon nanotubes and graphenes have been extensively used in flame-retardant polymeric materials. The surface char layer formed in combustion acts as protective barriers that limit the heat transfer into the unpyrolysed polymer and volatilization of combustible degradation products and diffusion of oxygen into the material. A numerical simulation tool Thermakin is used to simulate the thermal decomposition of the neat polymers (polypropylene (PP), Acrylonitrile Butadiene Styrene (ABS)) and corresponding nano-composites (PP/multi-walled carbon nanotube (PP/MWCNT) and ABS/ graphene nano-sheets /NiFe-layered double hydroxide hybrid (ABS/GNS-LDH) in cone calorimetry experiments. PP/MWCNT forms a porous network while ABS/GNS-LDH forms a compact, dense char layer during combustion. With appropriate input parameters, the heat release rates (or mass loss rates) are predicted very well. Finally, the effect of input parameters on model outputs are discussed

Numerical Simulation of Decomposition of Polymer Nano-composites: Investigation of the Influence of the Char Structure

AbstractIn recent years, nano-particles such as nano-clays, carbon nanotubes and graphenes have been extensively used in flame-retardant polymeric materials. The surface char layer formed in combustion acts as protective barriers that limit the heat transfer into the unpyrolysed polymer and volatilization of combustible degradation products and diffusion of oxygen into the material. A numerical simulation tool Thermakin is used to simulate the thermal decomposition of the neat polymers (polypropylene (PP), Acrylonitrile Butadiene Styrene (ABS)) and corresponding nano-composites (PP/multi-walled carbon nanotube (PP/MWCNT) and ABS/ graphene nano-sheets /NiFe-layered double hydroxide hybrid (ABS/GNS-LDH) in cone calorimetry experiments. PP/MWCNT forms a porous network while ABS/GNS-LDH forms a compact, dense char layer during combustion. With appropriate input parameters, the heat release rates (or mass loss rates) are predicted very well. Finally, the effect of input parameters on model outputs are discussed

Building Fire Evacuation: An IoT-Aided Perspective in the 5G Era

Complex and tall buildings have been constructed in many cities recently. Fire safety should be a major concern of building designers, engineers, and governments. Previous fire experience has made us understand the importance of acquiring fire-ground information to facilitate firefighting operations, evacuation processes, rescues, etc. Recently, the rapid advancement in Information Technology, Data Analytics, and other detection and monitoring systems has provided the basis for fire safety researchers to re-think fire safety strategies in the built environment. Amongst all fire safety studies, evacuation in tall buildings, including elevator evacuations, has attracted much attention. IoT-aided building fire evacuation is a new concept of the building evacuation mode, which improves the building evacuation process by making decisions of escape based on the real-time fire-ground information, such as the fire environment and occupant situations. Focusing on IoT applications in building fire evacuation, this paper explores the advantages and insufficiencies of current smart building fire evacuation systems. A conceptual design of an IoT-aided building fire evacuation control system is described. The system is introduced in the sequence of information needs, information sources and data transmission, and potential services and applications. Finally, new insights into promising 5G technologies for future building fire evacuations are discussed

Integration of Multiple Spectral Indices and a Neural Network for Burned Area Mapping Based on MODIS Data

Since wildfires have occurred frequently in recent years, accurate burned area mapping is required for wildfire severity assessment and burned land reconstruction. Satellite remote sensing is an effective technology that can provide valuable information for wildfire assessment. However, the common approaches based on using a single satellite image to promptly detect the burned areas have low accuracy and limited applicability. This paper develops a new burned area mapping method that surpasses the detection accuracy of previous methods, while still using a single Moderate Resolution Imaging Spectroradiometer (MODIS) sensor image. The key innovation is integrating optimal spectral indices and a neural network algorithm. We used the traditional empirical formula method, multi-threshold method and visual interpretation method to extract the sample sets of five typical types (burned area, vegetation, cloud, bare soil, and cloud shadow) from the MODIS data of several wildfires in the American states of Nevada, Washington and California in 2016. Afterward, the separability index M was adopted to assess the capacity of seven spectral bands and 13 spectral indices to distinguish the burned area from four unburned land cover types. Based on the separability analysis between the burned area and unburned areas, the spectral indices with an M value higher than 1.0 were employed to generate the training sample sets that were assessed to have an overall accuracy of 98.68% and Kappa coefficient of 97.46%. Finally, we utilized a back-propagation neural network (BPNN) to learn the spectral differences of different types from the training sample sets and obtain the output burned area map. The proposed method was applied to three wildfire cases in the American states of Idaho, Nevada and Oregon in 2017. A comparison of detection results between the new MODIS-based burned area map and the reference burned area map compiled from Landsat-8 Operational Land Imager (OLI) data indicates that the proposed method can effectively exploit the spectral characteristics of various land cover types. Also, this new method can achieve higher accuracy with the reduction of commission error (CE, >10%) and omission error (OE, >6%) compared to the traditional empirical formula method. The new burned area mapping method could help managers and the public perform more effective wildfire assessments and emergency management

Spatiotemporal Heterogeneity of Forest Fire Occurrence Based on Remote Sensing Data: An Analysis in Anhui, China

A forest fire is a destructive disaster that is difficult to handle and rescue and can pose a significant threat to ecosystems, society, and humans. Since driving factors and their effects on forest fires change over time and space, exploring the spatiotemporal patterns of forest fire occurrence should be addressed. To better understand the patterns of forest fire occurrence and provide valuable insights for policy making, we employed the Geographically and Temporally Weighted Regression (GTWR) model to investigate the varying spatiotemporal correlations between driving factors (vegetation, topography, meteorology, social economy) and forest fires in Anhui province from 2012 to 2020. Then we identified the dominant factors and conducted the spatiotemporal distribution analysis. Moreover, we innovatively introduced nighttime light as a socioeconomic driving factor of forest fires since it can directly reflect more comprehensive information about the social economy than other socioeconomic factors commonly used in previous studies. This study applied remote sensing data since the historical statistic data were not detailed. Here, we obtained the following results. (1) There was a spatial autocorrelation of forest fires in Anhui from 2012 to 2020, with high-high aggregation of forest fires in eastern cities. (2) The GTWR model outperformed the Ordinary Least Squares (OLS) regression model and the Geographically Weighted Regression model (GWR), implying the necessity of considering temporal heterogeneity in addition to spatial heterogeneity. (3) The relationships between driving factors and forest fires were spatially and temporally heterogeneous. (4) The forest fire occurrence was mainly dominated by socioeconomic factors, while the dominant role of vegetation, topography, and meteorology was relatively limited. It’s worth noting that nighttime light played the most extensive dominant role in forest fires of Anhui among all the driving factors in the years except 2015

Analysis of Multifractal and Organization/Order Structure in Suomi-NPP VIIRS Normalized Difference Vegetation Index Series of Wildfire Affected and Unaffected Sites by Using the Multifractal Detrended Fluctuation Analysis and the Fisher–Shannon Analysis

The analysis of vegetation dynamics affected by wildfires contributes to the understanding of ecological changes under disturbances. The use of the Normalized Difference Vegetation Index (NDVI) of satellite time series can effectively contribute to this investigation. In this paper, we employed the methods of multifractal detrended fluctuation analysis (MFDFA) and Fisher–Shannon (FS) analysis to investigate the NDVI series acquired from the Visible Infrared Imaging Radiometer Suite (VIIRS) of the Suomi National Polar-Orbiting Partnership (Suomi-NPP). Four study sites that were covered by two different types of vegetation were analyzed, among them two sites were affected by a wildfire (the Camp Fire, 2018). Our findings reveal that the wildfire increases the heterogeneity of the NDVI time series along with their organization structure. Furthermore, the fire-affected and fire-unaffected pixels are quite well separated through the range of the generalized Hurst exponents and the FS information plane. The analysis could provide deeper insights on the temporal dynamics of vegetation that are induced by wildfire

A Monte Carlo analysis of the effect of heat release rate uncertainty on available safe egress time

Available safe egress time is an important criterion to determine occupant safety in performance-based fire protection design of buildings. There are many factors affecting the calculation of available safe egress time, such as heat release rate, smoke toxicity and the geometry of the building. Heat release rate is the most critical factor. Due to the variation of fuel layout, initial ignition location and many other factors, significant uncertainties are associated with heat release rate. Traditionally, fire safety engineers prefer to ignore these uncertainties, and a fixed value of heat release rate is assigned based on experience. This makes the available safe egress time results subjective. To quantify the effect of uncertainties in heat release rate on available safe egress time, a Monte Carlo simulation approach is implemented for a case study of a single hypothetical fire compartment in a commercial building. First, the effect of deterministic peak heat release rate and fire growth rate on the predicted available safe egress time is studied. Then, the effect of uncertainties in peak heat release rate and fire growth rate are analyzed separately. Normal and log-normal distributions are employed to characterize peak heat release rate and fire growth rate, respectively. Finally, the effect of uncertainties in both peak heat release rate and fire growth rate on available safe egress time are analyzed. Illustrations are also provided on how to utilize probabilistic functions, such as the cumulative density function and complementary cumulative distribution function, to help fire safety engineers develop proper design fires