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Early fire detection and notification techniques provide fire prevention and safety information to blind and visually impaired (BVI) people within a short period of time in emergency situations when fires occur in indoor environments. Given its direct impact on human safety and the environment, fire detection is a difficult but crucial problem. To prevent injuries and property damage, advanced technology requires appropriate methods for detecting fires as quickly as possible. In this study, to reduce the loss of human lives and property damage, we introduce the development of the vision-based early flame recognition and notification approach using artificial intelligence for assisting BVI people. The proposed fire alarm control system for indoor buildings can provide accurate information on fire scenes. In our proposed method, all the processes performed manually were automated, and the performance efficiency and quality of fire classification were improved. To perform real-time monitoring and enhance the detection accuracy of indoor fire disasters, the proposed system uses the YOLOv5m model, which is an updated version of the traditional YOLOv5. The experimental results show that the proposed system successfully detected and notified the occurrence of catastrophic fires with high speed and accuracy at any time of day or night, regardless of the shape or size of the fire. Finally, we compared the competitiveness level of our method with that of other conventional fire-detection methods to confirm the seamless classification results achieved using performance evaluation matrices.

Coal remains one of the key raw materials used in the energy industry to generate electricity and heat. As a result, diagnostics of the combustion process is still an important topic of scientific research. Correct implementation of the process allows the emission of pollutants into the atmosphere to be kept at a compliant level. Therefore, it is important to conduct the process in a manner that will not exceed these standards. A preliminary analysis of the measurement signals was carried out, and signal predictions of flame intensity changes were determined using the autoregressive moving average (ARMA) model. Different fuzzy neural network architectures have been investigated. Binary and multi-class classifications of flame states were conducted. The best results were obtained from the ANFIS_grid partition model, producing an accuracy of 95.46% for binary classification and 79.08% for multi-class classification. The accuracy of the recognition of flame states and the high convergence of the determined predictions with measurement signals validate the application of the proposed approach in diagnosing or controlling the combustion process of pulverized coal and its mixtures with biomass. Expert decisions determine the range of acceptable states.

Brominated flame retardants (BFRs) have been using to reduce the flammability of plastics contained in many products, such as household articles, furniture, mattresses, textiles or insulation. Considering the fact that these compounds may be released into the environment leading to the exposure of living organisms, it is necessary to study their possible effects and mechanisms of action. Proteins play a crucial role in all biological processes. For this reason, a simple model of human serum albumin (HSA) was chosen to study the mechanism of BFRs’ effect on proteins. The study determined interactions between selected BFRs, i.e., tetrabromobisphenol A (TBBPA), tetrabromobisphenol S (TBBPS), 2,4-dibromophenol (2,4-DBP), 2,4,6-tribromophenol (2,4,6-TBP) and pentabromophenol (PBP), and HSA by measurement of fluorescence of intrinsic tryptophan and absorbance of circular dichroism (CD). In addition, in order to understand the possible effect of these compounds in their native environment, the effect of BFRs on membrane proteins of human erythrocytes (red blood cells, RBCs) was also assessed. Among bromophenols, PBP had the strongest oxidative effect on RBC membrane, and 2,4-DBP demonstrated the weakest fluorescence-quenching effect of both membrane tryptophan and HSA. By contrast to PBP, 2,4-DBP and 2,4,6-TBP caused spatial changes of HSA. We have observed that among all analyzed BFRs, TBBPA caused the strongest oxidation of RBC membrane proteins and the model HSA protein, causing reduction of fluorescence of tryptophan contained in them. TBBPA also changed albumin conformation properties, leading to impairment of the α-helix structure. However, TBBPS had the weakest oxidative effect on proteins among studied BFRs and did not affect the secondary structure of HSA.

The classification and recognition of flame images play an important role in avoiding forest fires. Deep learning technology has shown good performance in flame image recognition tasks. In order to further improve the accuracy of classification, this paper combines deep learning technology with the idea of three-way decision-making. First, a ResNet34 network is used for initial classification. The probability value calculated by the SoftMax function is used as the decision evaluation criterion for initial classification. Using the idea of three-way decision-making, the flame image is divided into positive domain, negative domain, and boundary domain based on decision evaluation indicators. Furthermore, we perform secondary classification on images divided into boundary domains. In the secondary classification, a DualArchClassNet structure was constructed to extract new features and combine them with the features of the initial classification. The integrated features are optimized and used to reclassify images in uncertain domains to improve overall classification accuracy. The experimental results show that the proposed method improves the accuracy of flame image recognition compared to using a single ResNet34 network.

Residential dust has been used as a medium for assessing human exposures to a constellation of indoor contaminants including radionuclides, persistent organic pollutants, metals, allergens, and tobacco smoke. Here, we review and comment on investigations of household dust levels of particular analytes of health significance, namely polybrominated diphenyl ethers, polychlorinated biphenyls, and polycyclic aromatic hydrocarbons. In doing so, we not only describe methods for collecting and analyzing residential dust, but also describe global patterns in dust levels. Aside from geographic location, we discuss several potential determinants for dust levels of these contaminants. We also review previous estimates of the contribution of dust to overall intake of these three chemical classes and show how residential-dust measurements could be useful in either augmenting or replacing questionnaire-based assessment of human exposures in epidemiological studies. We conclude our review with a discussion of the current gaps in knowledge of worldwide dust levels and suggestions for how residential-dust measurements could be used to describe human exposures to chemicals in developing countries.

Polypropylene (PP) is a commodity plastic known for high rigidity and crystallinity, which is suitable for a wide range of applications. However, high flammability of PP has always been noticed by users as a constraint; therefore, a variety of additives has been examined to make PP flame-retardant. In this work, research papers on the flame retardancy of PP have been comprehensively reviewed, classified in terms of flame retardancy, and evaluated based on the universal dimensionless criterion of Flame Retardancy Index (FRI). The classification of additives of well-known families, i.e., phosphorus-based, nitrogen-based, mineral, carbon-based, bio-based, and hybrid flame retardants composed of two or more additives, was reflected in FRI mirror calculated from cone calorimetry data, whatever heat flux and sample thickness in a given series of samples. PP composites were categorized in terms of flame retardancy performance as Poor, Good, or Excellent cases. It also attempted to correlate other criteria like UL-94 and limiting oxygen index (LOI) with FRI values, giving a broad view of flame retardancy performance of PP composites. The collected data and the conclusions presented in this survey should help researchers working in the field to select the best additives among possibilities for making the PP sufficiently flame-retardant for advanced applications.

Tetrabromobisphenol A (TBBPA) is a known endocrine disruptor employed in a range of consumer products and has been predominantly found in different environments through industrial processes and in human samples. In this review, we aimed to summarize published scientific evidence on human biomonitoring, toxic effects and mode of action of TBBPA in humans. Interestingly, an overview of various pretreatment methods, emerging detection methods, and treatment methods was elucidated. Studies on exposure routes in humans, a combination of detection methods, adsorbent-based treatments and degradation of TBBPA are in the preliminary phase and have several limitations. Therefore, in-depth studies on these subjects should be considered to enhance the accurate body load of non-invasive matrix, external exposure levels, optimal design of combined detection techniques, and degrading technology of TBBPA. Overall, this review will improve the scientific comprehension of TBBPA in humans as well as the environment, and the breakthrough for treating waste products containing TBBPA.

In 2019, we introduced Flame Retardancy Index (FRI) as a universal dimensionless index for the classification of flame-retardant polymer materials (Polymers, 2019, 11(3), 407). FRI simply takes the peak of Heat Release Rate (pHRR), Total Heat Release (THR), and Time-To-Ignition (ti) from cone calorimetry data and quantifies the flame retardancy performance of polymer composites with respect to the blank polymer (the reference sample) on a logarithmic scale, as of Poor (FRI ˂ 100), Good (100 ≤ FRI ˂ 101), or Excellent (FRI ≥ 101). Although initially applied to categorize thermoplastic composites, the versatility of FRI was later verified upon analyzing several sets of data collected from investigations/reports on thermoset composites. Over four years from the time FRI was introduced, we have adequate proof of FRI reliability for polymer materials ranking in terms of flame retardancy performance. Since the mission of FRI was to roughly classify flame-retardant polymer materials, its simplicity of usage and fast performance quantification were highly valued. Herein, we answered the question “does inclusion of additional cone calorimetry parameters, e.g., the time to pHRR (tp), affect the predictability of FRI?”. In this regard, we defined new variants to evaluate classification capability and variation interval of FRI. We also defined the Flammability Index (FI) based on Pyrolysis Combustion Flow Calorimetry (PCFC) data to invite specialists for analysis of the relationship between the FRI and FI, which may deepen our understanding of the flame retardancy mechanisms of the condensed and gas phases.

Background Early disruption of the microbial community may influence life-long health. Environmental toxicants can contaminate breast milk and the developing infant gut microbiome is directly exposed. We investigated whether environmental toxicants in breastmilk affect the composition and function of the infant gut microbiome at 1 month. We measured environmental toxicants in breastmilk, fecal short-chain fatty acids (SCFAs), and gut microbial composition from 16S rRNA gene amplicon sequencing using samples from 267 mother-child pairs in the Norwegian Microbiota Cohort (NoMIC). We tested 28 chemical exposures: polychlorinated biphenyls (PCBs), polybrominated flame retardants (PBDEs), per- and polyfluoroalkyl substances (PFASs), and organochlorine pesticides. We assessed chemical exposure and alpha diversity/SCFAs using elastic net regression modeling and generalized linear models, adjusting for confounders, and variation in beta diversity (UniFrac), taxa abundance (ANCOM), and predicted metagenomes (PiCRUSt) in low, medium, and high exposed groups. Results PBDE-28 and the surfactant perfluorooctanesulfonic acid (PFOS) were associated with less microbiome diversity. Some sub-OTUs of Lactobacillus , an important genus in early life, were lower in abundance in samples from infants with relative “high” (> 80th percentile) vs. “low” (< 20th percentile) toxicant exposure in this cohort. Moreover, breast milk toxicants were associated with microbiome functionality, explaining up to 34% of variance in acetic and propionic SCFAs, essential signaling molecules. Per one standard deviation of exposure, PBDE-28 was associated with less propionic acid (− 24% [95% CI − 35% to − 14%] relative to the mean), and PCB-209 with less acetic acid (− 15% [95% CI − 29% to − 0.4%]). Conversely, PFOA and dioxin-like PCB-167 were associated with 61% (95% CI 35% to 87%) and 22% (95% CI 8% to 35%) more propionic and acetic acid, respectively. Conclusions Environmental toxicant exposure may influence infant gut microbial function during a critical developmental window. Future studies are needed to replicate these novel findings and investigate whether this has any impact on child health.

Manual diagnosis of skin cancer is time-consuming and expensive; therefore, it is essential to develop automated diagnostics methods with the ability to classify multiclass skin lesions with greater accuracy. We propose a fully automated approach for multiclass skin lesion segmentation and classification by using the most discriminant deep features. First, the input images are initially enhanced using local color-controlled histogram intensity values (LCcHIV). Next, saliency is estimated using a novel Deep Saliency Segmentation method, which uses a custom convolutional neural network (CNN) of ten layers. The generated heat map is converted into a binary image using a thresholding function. Next, the segmented color lesion images are used for feature extraction by a deep pre-trained CNN model. To avoid the curse of dimensionality, we implement an improved moth flame optimization (IMFO) algorithm to select the most discriminant features. The resultant features are fused using a multiset maximum correlation analysis (MMCA) and classified using the Kernel Extreme Learning Machine (KELM) classifier. The segmentation performance of the proposed methodology is analyzed on ISBI 2016, ISBI 2017, ISIC 2018, and PH2 datasets, achieving an accuracy of 95.38%, 95.79%, 92.69%, and 98.70%, respectively. The classification performance is evaluated on the HAM10000 dataset and achieved an accuracy of 90.67%. To prove the effectiveness of the proposed methods, we present a comparison with the state-of-the-art techniques.