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The obscuration thresholds for various smoke detectors and combustibles, required as an input parameter in fire simulation, were measured to predict the accurate activation time of detectors. One ionization detector and nine photoelectric detectors were selected. A fire detector evaluator, which can uniformly control the velocity and smoke concentration, was utilized. Filter paper, liquid fuels, and polymer pellets were employed as smoke-generation combustibles. The nominal obscuration thresholds of the considered detectors were 15 %/m, but the ionization detectors activated at approximately 40 %/m and 16 %/m, respectively, on applying filter paper and kerosene. In contrast, the reverse obscuration thresholds were found quantitatively according to the combustibles in the photoelectric detector. This phenomenon was caused by differences in the color of the smoke particles according to the combustibles, which is explained by single-scattering albedo (ratio of light scattering to light extinction). The obscuration thresholds for liquid fuels (kerosene, heptane and toluene) as well as fire types of polymer plastic pellets were also measured for several photoelectric detectors. A database of obscuration thresholds was thereby established according to the detector and combustible types, and it is expected to provide useful information for predicting more accurate detector activation time and required safe egress time (REST).

The forest fire has been threatening the forest ecosystem and has brought huge economic losses to humans. Traditional fire detection systems use ion-optical smoking type and other physical or chemical means to discover a fire, which is not suitable for outdoor forest fires with long-distance and large-area characteristics. This paper presents the early fire detection algorithm based on smoke dynamic characteristics and its main part includes smoke color model, dynamic feature extraction and fire area connected component analysis. Through standard video database performance testing and compared to the algorithm and the traditional fire detection method, we can achieve early warning of a fire and can effectively reduce false and negative rate of fire monitoring system.

Our purpose was to quantify 36 inorganic chemical elements in aerosols from disposable electronic cigarettes (ECs) and electronic hookahs (EHs), examine the effect of puffing topography on elements in aerosols, and identify the source of the elements. Thirty-six inorganic chemical elements and their concentrations in EC/EH aerosols were determined using inductively coupled plasma optical emission spectroscopy, and their source was identified by analyzing disassembled atomizers using scanning electron microscopy and energy dispersive X-ray spectroscopy. Of 36 elements screened, 35 were detected in EC/EH aerosols, while only 15 were detected in conventional tobacco smoke. Some elements/metals were present in significantly higher concentrations in EC/EH aerosol than in cigarette smoke. Concentrations of particular elements/metals within EC/EH brands were sometimes variable. Aerosols generated at low and high air-flow rates produced the same pattern of elements, although the total element concentration decreased at the higher air flow rate. The relative amount of elements in the first and last 60 puffs was generally different. Silicon was the dominant element in aerosols from all EC/EH brands and in cigarette smoke. The elements appeared to come from the filament (nickel, chromium), thick wire (copper coated with silver), brass clamp (copper, zinc), solder joints (tin, lead), and wick and sheath (silicon, oxygen, calcium, magnesium, aluminum). Lead was identified in the solder and aerosol of two brands of EHs (up to 0.165 μg/10 puffs). These data show that EC/EH aerosols contain a mixture of elements, including heavy metals, with concentrations often significantly higher than in conventional cigarette smoke. While the health effects of inhaling mixtures of heated metals is currently not known, these data will be valuable in future risk assessments involving EC/EH elements/metals.

In order for detection of a fire in fields, it is effective to detect smoke since it often behaves as a precursor of the fire. One preferable way for early detection is to use visual information from outdoor cameras that widely monitor the filed. There have been many attempts to detect smokes via optical sensors on digital cameras using optical flow methods, but not fully successful from practical-use aspects. It is because the area of smokes occupying on the footage by outdoor cameras is not necessarily large enough. Moreover, in case of urban cities, discrimination of the smokes from other moving objects such as cars, trees and turbines is not easy. Herein we propose a novel method to detect daytime smokes based on variance of optical flow and characteristics of HSV (hue-saturation-value) color. We apply the method to a set of footage of three days obtained in an industrial zone in Japan. Successful results are obtained as over 90% of smokes are detected. Notable is that this method is independent of solar radiation conditions on sunny and cloudy days.

Evidence-based interventions (EBIs) often require adaptation to be effective for new communities and/or cultural contexts. This paper describes the process for adapting an evidence-based smoke-free homes (SFHs) intervention to be culturally appropriate for households in Armenia and Georgia. The intervention, including three mailed packages (“mailings”) and a coaching call, was adapted using a systematic multi-step adaptation process involving: (i) focus groups (n = 8) among adults in Armenia and Georgia, who smoked cigarettes or lived in a household with someone who smoked; (ii) consulting with in-country research team experts and local community leaders; and (iii) collaboratively deciding on critical adaptations, which differed slightly by country. Adaptations spanned across intervention components. While adaptations were largely surface-level (e.g. Armenia- and Georgia-relevant facts, color themes, imagery of individuals, homes, and settings), the process identified needed deep structure changes. For example, the nature of the challenges and solutions addressed, the narratives used for role modeling, and the imagery were adapted to better reflect the smoking-related social norms and dynamics (e.g. greater smoking prevalence among men vs. women, difficulty asking guests/elders to smoke outside), household composition (i.e. multigenerational), types of homes (e.g. ease of access to outdoor spaces), and types of tobacco used (i.e. heated tobacco products). The adapted interventions maintained the core elements and underlying theoretical approach but included adaptations to ensure cultural appropriateness and relevance. This should yield an effective intervention, which will be assessed next. The description of this multi-step adaptation process could inform future efforts to disseminate and implement EBIs across settings globally.

Smoke detection is an advance caution to the unforeseen great damage events. Therefore, it is required to identify the smoke in the course of initial stages for preventing fire events. A new technique is proposed to lessen the rate of incorrect alarm by identify the smoke and examine its distinctive texture attributes. Initially, the smoke-colored regions are segmented based on color at the YUV color locality. Then the tentative frame differencing is used to segment the candidate smoke region from the smoke-colored region. In the next phase, the candidate distinctive texture attributes in the smoke region are extracted using Co-occurrence of Hamming Distance based Local Binary pattern (CoHDLBP) and Co-occurrence of Local Binary pattern (CoLBP); these features include homogeneity, energy, correlation and contrast. Finally, the ELM classifier is proficient for the take-out features from the candidate smoke region, and then the decision has been taken with the assistance of a smoke alarm. Investigational outcomes proved that the suggested smoke recognition process executes better compared with all the usual smoke recognition methods by achieving better detection accuracy and processing time.

The environmental challenges the world faces have never been greater or more complex. Global areas that are covered by forests and urban woodlands are threatened by large-scale forest fires that have increased dramatically during the last decades in Europe and worldwide, in terms of both frequency and magnitude. To this end, rapid advances in remote sensing systems including ground-based, unmanned aerial vehicle-based and satellite-based systems have been adopted for effective forest fire surveillance. In this paper, the recently introduced 360-degree sensor cameras are proposed for early fire detection, making it possible to obtain unlimited field of view captures which reduce the number of required sensors and the computational cost and make the systems more efficient. More specifically, once optical 360-degree raw data are obtained using an RGB 360-degree camera mounted on an unmanned aerial vehicle, we convert the equirectangular projection format images to stereographic images. Then, two DeepLab V3+ networks are applied to perform flame and smoke segmentation, respectively. Subsequently, a novel post-validation adaptive method is proposed exploiting the environmental appearance of each test image and reducing the false-positive rates. For evaluating the performance of the proposed system, a dataset, namely the “Fire detection 360-degree dataset”, consisting of 150 unlimited field of view images that contain both synthetic and real fire, was created. Experimental results demonstrate the great potential of the proposed system, which has achieved an F-score fire detection rate equal to 94.6%, hence reducing the number of required sensors. This indicates that the proposed method could significantly contribute to early fire detection.

The spatial characteristics, movement characteristics and color characteristics of smoke are important features that distinguish them to other objects. In order to make full use of these three features, we proposed a deep convolutional network called Full High Resolution Network(FHRNet).This network consists of two parts: Spatio-Temporal-aware Sub-network (STS) and Color-aware Sub-network (CS). We build high -resolution residual symmetrical units and embed the two sub-networks to ensure the integrity of two dimensional features.In the STS, the residual symmetrical unit extracts the spatial semantic characteristics of smoke from every frame, and combine them into a feature sequence, then the spatio-temporal perceptron is used to extract the spatio-temporal characteristics of smoke to further improve the characteristic expression. In the CS, the color feature of picture is converted into color feature matrix, which is easier to make the residual symmetrical unit to extract the color feature of smoke. We constructed a smoke vedio datasets which have a diverse background to avoid producing over-fitting situation.The experimental results show that mthod we proposed can effectively extract the color features and the spatio-temporal features of smoke and our method can effectively detect smoke.

Detecting fire and smoke is essential for maintaining safety in urban, industrial, and outdoor settings. This study suggests a unique concatenated convolutional neural network (CNN) model that combines deep learning with hybrid preprocessing methods, such as contour-based algorithms and color characteristics analysis, to provide reliable and accurate fire and smoke detection. A benchmark dataset with a variety of situations, including dynamic surroundings and changing illumination, the D-Fire dataset was used to assess the technique. Experiments show that the suggested model outperforms both conventional techniques and the most advanced YOLO-based methods, achieving accuracy (0.989) and recall (0.983). In order to reduce false positives and false negatives, the hybrid architecture uses preprocessing to enhance Regions of Interest (ROIs). Additionally, pooling and fully linked layers provide computational efficiency and generalization. In contrast to current approaches, which frequently concentrate only on fire detection, the model’s dual smoke and fire detection capabilities increase its adaptability. Although preprocessing adds a little computing expense, the methodology’s excellent accuracy and resilience make it a dependable option for safety-critical real-world applications. This study sets a new standard for smoke and fire detection and provides a route forward for future developments in this crucial area.

In this study, liquid smoke production waste gas (LSPW) was used as a substitute for carbon monoxide (CO) and filter smoke in handling frozen tuna steaks. Tuna steaks were packed in plastic bags, then inside the bag, tuna steak were sprayed with CO and LSPW, and stored at 4°C for two days. After that, each package was vacuumed and stored at -25°C for four weeks. Observation of quality deterioration of tuna steak started from the beginning of storage until the fourth week. The results showed a significant difference in the average TPC value of tuna steak sprayed with CO and LSPW during frozen storage. While the values of pH, TVB, myoglobin, and color were not significantly different. Until the end of the observation, the quality of tuna steak was still relatively good with myoglobin content values of CO and LSPW treatments, respectively: 36.65±4.78 and 42.04±4.47 mg%, color 34.67±9.07 and 35.67±8.50 °Hue, pH 6.47±0.15 and 6.60±0.10, TVB-N 18.04±0.93 and 17.86±0.98 mg N/100g, histamine 1.92±0.05 and 1.85±0.13 ppm, and TPC 7.41x102 and 8.70x102cfu/g.