Extraordinary Passive Safety in Cars Using a Sensor Network Model

Context: The automobile industry has included active and passive safety. Active safety incorporates elements to avoid crashes and collisions. Some elements are ABS brakes and stabilization bars, among others. On the other hand, passive safety avoids or minimizes damage to the occupants in the event of an accident. Some passive safety features include seat belts and front and curtain airbags for the driver and other occupants. Method: In this research work, we propose a new category called Extraordinary Passive Safety (XPS). A model of a sensor network was designed to inspect the conditions inside the car to detect fire, smoke, gases, and extreme temperatures. The sensors send data to a device (DXPS) capable of receiving and storing the data. Results: Each sensor collects data and sends it to the DXPS every period. The sensor sends 0s while there is no risk, and 1s when it detects a risk. When the DXPS receives a 1, the pattern is evaluated, and the risk is identified. Since there are several sensors, the reading pattern is a set of 0s (000000). When a pattern with one or more 1s (000100, 010101) is received, the DXPS can send an alert or activate a device. Conclusions: The proposed solution could save the lives of children left in the car or people trapped when the car catches fire. As future work, it is intended to define the devices to avoid or minimize damage to the occupants such as oxygen supply, gas extraction, regulating the temperature, among others.

Experimental and Numerical Study on the Plume Rising in Stairwell of Buildings

In recent years, it has been observed that when a fire occurs in a multi-use facility, a toxic fire smoke rapidly rises through the vertical shaft and spreads due to the chimney effect and hot buoyancy. Generally, the fire smoke spreads rapidly through a number of evacuation passages installed for safe evacuation, which adversely affects an emergency situation. Due to the lack of this knowledge among the occupants, the majority of the occupants are evacuated using the stairwells, getting suffocated by poisonous smoke and suffering serious injuries. The present study considered the fire smoke spreading vertically through the stairwell. For this purpose, the power of the heat source and the area of the ventilation windows connected to the stairwell were modified, and the movement and diffusion of the hot plume rising vertically in the stairwell were observed. For the experiment, a 1/20 scaled-down stairwell model was employed, and the temperature ‘T’ and the vertical velocity ‘w’ of the hot plume rising inside the stairwell were measured using a 60 W-180 W heat source power. Numerical analysis was performed using FDS under similar conditions, and the results were compared with the experimental results.

Semantic Segmentation and Analysis on Sensitive Parameters of Forest Fire Smoke Using Smoke-Unet and Landsat-8 Imagery

Forest fire is a ubiquitous disaster which has a long-term impact on the local climate as well as the ecological balance and fire products based on remote sensing satellite data have developed rapidly. However, the early forest fire smoke in remote sensing images is small in area and easily confused by clouds and fog, which makes it difficult to be identified. Too many redundant frequency bands and remote sensing index for remote sensing satellite data will have an interference on wildfire smoke detection, resulting in a decline in detection accuracy and detection efficiency for wildfire smoke. To solve these problems, this study analyzed the sensitivity of remote sensing satellite data and remote sensing index used for wildfire detection. First, a high-resolution remote sensing multispectral image dataset of forest fire smoke, containing different years, seasons, regions and land cover, was established. Then Smoke-Unet, a smoke segmentation network model based on an improved Unet combined with the attention mechanism and residual block, was proposed. Furthermore, in order to reduce data redundancy and improve the recognition accuracy of the algorithm, the conclusion was made by experiments that the RGB, SWIR2 and AOD bands are sensitive to smoke recognition in Landsat-8 images. The experimental results show that the smoke pixel accuracy rate using the proposed Smoke-Unet is 3.1% higher than that of Unet, which could effectively segment the smoke pixels in remote sensing images. This proposed method under the RGB, SWIR2 and AOD bands can help to segment smoke by using high-sensitivity band and remote sensing index and makes an early alarm of forest fire smoke.

High-resolution smoke forecasting for the 2018 Camp Fire in California

Smoke from the 2018 Camp Fire in Northern California blanketed a large part of the region for two weeks, creating poor air quality in the “unhealthy” range for millions of people. The NOAA Global System Laboratory’s HRRR-Smoke model was operating experimentally in real time during the Camp Fire. Here, output from the HRRR-Smoke model is compared to surface observations of PM2.5 from AQS and PurpleAir sensors as well as satellite observation data. The HRRR-Smoke model grid at 3-km resolution successfully simulated the evolution of the plume during the initial phase of the fire (8-10 November 2018). Stereoscopic satellite plume height retrievals were used to compare with model output (for the first time, to the authors’ knowledge), showing that HRRR-Smoke is able to represent the complex 3D distribution of the smoke plume over complex terrain. On 15-16 November, HRRR-Smoke was able to capture the intensification of PM2.5 pollution due to a high pressure system and subsidence that trapped smoke close to the surface; however, HRRR-Smoke later underpredicted PM2.5 levels due to likely underestimates of the fire radiative power (FRP) derived from satellite observations. The intensity of the Camp Fire smoke event and the resulting pollution during the stagnation episodes make it an excellent test case for HRRR-Smoke in predicting PM2.5 levels, which were so high from this single fire event that the usual anthropogenic pollution sources became insignificant. The HRRR-Smoke model was implemented operationally at NOAA/NCEP in December 2020, now providing essential support for smoke forecasting as the impact of US wildfires continues to increase in scope and magnitude.

Archaeometric evidence for the earliest exploitation of lignite from the bronze age Eastern Mediterranean

This paper presents the earliest evidence for the exploitation of lignite (brown coal) in Europe and sheds new light on the use of combustion fuel sources in the 2nd millennium BCE Eastern Mediterranean. We applied Thermal Desorption/Pyrolysis–Gas Chromatography-Mass Spectrometry and Polarizing Microscopy to the dental calculus of 67 individuals and we identified clear evidence for combustion markers embedded within this calculus. In contrast to the scant evidence for combustion markers within the calculus samples from Egypt, all other individuals show the inhalation of smoke from fires burning wood identified as Pinaceae, in addition to hardwood, such as oak and olive, and/or dung. Importantly, individuals from the Palatial Period at the Mycenaean citadel of Tiryns and the Cretan harbour site of Chania also show the inhalation of fire-smoke from lignite, consistent with the chemical signature of sources in the northwestern Peloponnese and Western Crete respectively. This first evidence for lignite exploitation was likely connected to and at the same time enabled Late Bronze Age Aegean metal and pottery production, significantly by both male and female individuals.

Impact of forest fire smoke on the state of the central nervous system of rats

Introduction. The adverse negative effect of forest fire smoke on human health represents a unique interdisciplinary challenge to the scientific community. The influence of forest fire smoke on locomotor activity, cognitive indices, and brain bioelectrical activity parameters in exposed rats is presented. Materials and methods. Experimental studies were carried out on outbred white male rats. The animals of the experimental group were exposed to smoke inhalation forest fire for one day. Immediately after the end of the exposure, the animals were examined, including testing in an open field and Morris water maze, as well as an electroencephalographic examination. Results. At twenty-four-hour exposure to wildfire smoke in the model, conditions showed increasing motor and research activity of male rats against the backdrop of growing anxiety. Disorders of indicators of spatial memory and navigation learning were not revealed. On the encephalogram of the exposed animals, in comparison with the control group, the δ-rhythm range predominated, more pronounced in the leads of the right hemisphere. A decrease in the power spectrum and the average amplitude β1-rhythm, as well as a tendency to decrease the average amplitude of θ-rhythm, were revealed. The indices of the primary EEG rhythms did not have statistically significant differences when compared with the control group. Conclusion. The results showed that forest fire smoke leads to changes in the bioelectric activity of brain structures and dysregulation of individual behaviour in animals, all of which may indicate the formation of increased levels of stressing beyond physiological adaptation.

PDAM–STPNNet: A Small Target Detection Approach for Wildland Fire Smoke through Remote Sensing Images

The target detection of smoke through remote sensing images obtained by means of unmanned aerial vehicles (UAVs) can be effective for monitoring early forest fires. However, smoke targets in UAV images are often small and difficult to detect accurately. In this paper, we use YOLOX-L as a baseline and propose a forest smoke detection network based on the parallel spatial domain attention mechanism and a small-scale transformer feature pyramid network (PDAM–STPNNet). First, to enhance the proportion of small forest fire smoke targets in the dataset, we use component stitching data enhancement to generate small forest fire smoke target images in a scaled collage. Then, to fully extract the texture features of smoke, we propose a parallel spatial domain attention mechanism (PDAM) to consider the local and global textures of smoke with symmetry. Finally, we propose a small-scale transformer feature pyramid network (STPN), which uses the transformer encoder to replace all CSP_2 blocks in turn on top of YOLOX-L’s FPN, effectively improving the model’s ability to extract small-target smoke. We validated the effectiveness of our model with recourse to a home-made dataset, the Wildfire Observers and Smoke Recognition Homepage, and the Bowfire dataset. The experiments show that our method has a better detection capability than previous methods.