Current Smoke Research Articles

Ignition detection method based on real-time measurements of a hazardous parameter in the environment

The object of this study is a selective average of the dangerous parameters of the gas environment during the ignition of materials. Theoretical substantiation of the fire detection method based on testing the null hypothesis regarding the current difference of the specified selective averages of an arbitrary dangerous parameter of the gas environment has been performed. In this case, the significance of the current difference with respect to selective averages allows detection of ignition in real-time observation of an arbitrary dangerous parameter of the gas environment. The method makes it possible to set the level of significance for the current difference and at the same time provide for the maximum power of fire detection. Laboratory experiments were conducted to verify the proposed method for detecting ignition based on the current difference of the selective averages of the measured dangerous parameters of the gas environment corresponding to the training and control general population. The results of verification showed that at a given level of significance, the method allows detecting current fires of materials based on significant differences in sample means. It was established that the current difference in the concentration of carbon monoxide during ignition and after ignition of alcohol, paper, wood shavings, and textiles is –0.459 and 8.296, –0.152 and 4.299, –0.027 and 6.9, –0.262 and 2.3, respectively. Current smoke density differences are 0.043 and 0.391, 0.012, and 0.923, –0.139, and –0.235, 0.034, and 0.129, and temperatures are –0.01 and 10.635, 0.53 135 and 2.726, respectively. This means that the current difference is significant and is due not to a random nature but to the appearance of a persistent effect from the ignition of the material. In practice, research results can be used to detect fires in real time in order to prevent them from growing into an uncontrolled fire

Use of current automatic smoke evacuation system in flexible gastrointestinal endoscopy: Its feasibility and potential usefulness.

Flexible endoscopy does not have a system that can automatically evacuate surgical smoke generated in the gastrointestinal lumen. We aimed to investigate the feasibility and potential usefulness of automatic smoke evacuation systems in flexible endoscopy. [Bench] After surgical smoke generated in the stomach was evacuated by the evacuator, the degree of residual smoke and gastric luminal collapse were evaluated to optimize the evacuator settings. [Animal] Insufflation, suction, and total operation time to complete the protocol of 10 cauterizations of the gastric mucosa were measured in three groups: "manual suction only," "manual suction with automatic evacuation (50% force)," and "manual suction with automatic evacuation (70% force)." The stability of endoscopic visualization and operability was evaluated by 10 endoscopists blinded to those suction settings, and the number of manual suctions, insufflations, and total operation time were measured. [Bench] The degree of residual smoke and gastric luminal collapse were inversely correlated. [Animal] When the automatic evacuator was partially used, there was no difference in the insufflation time, but the suction time (vs 50%; p = 0.011, vs. 70%; p = 0.011) and total operation time (vs. 50%; p = 0.012, vs. 70%; p = 0.036) were significantly reduced compared to manual operation only. Furthermore, manual suction with automatic evacuation (50% force) significantly improved the stability of endoscopic visualization and operability compared to manual operation only (p = 0.041, p = 0.0085). The automatic smoke evacuation in flexible gastrointestinal endoscopy was potentially feasible and useful by improving the device setting.

Assessment of Cardiovascular Diseases Knowledge among Adults Population in Sulaimani City: A Descriptive Study

Background: The increasing prevalence of cardiovascular disease has emerged as a significant global and public health issue within the category of non-communicable diseases. In this study, we have to assess cardiovascular diseases knowledge and risk factors among adult population in Sulaimani City. Materials and Methods: A descriptive study was done with 131 people in Sulaimani city was collected the data includes socio demographic, clinical characteristics, and information about some risk factors of cardiovascular disease through direct interview. Results: The results of the present study revealed that 60.3%, 39.7% of the participants were males and female respectively. Most of them married and from urban area, 35.9% was graduated from primary school. 51.9% of them were unemployed. 76.3% of them had insufficient economic state. 81% of them were current and second-hand smoke. More than half of them had no information about heart disease, it is risk factors. Most 80.2% of them did not do regular exercise. Conclusion: From this present study, we have concluded that insufficient knowledge of the participants regarding risk factors of heart disease, especially food drug interaction and drug- drug interaction. More than half and nearly a quarter of them did not sure about existing dyslipidemia and hypertension respectively. The majority of them did not perform regular daily exercise. Keywords: cardiovascular diseases, knowledge, adults, population, Sulaimani city, descriptive study

The effect of biomass smoke exposure on quality-of-life among Ugandan patients treated for tuberculosis: A cross-sectional analysis

More than half the global population burns biomass fuels for cooking and home heating, especially in low-middle income countries. This practice is a prominent source of indoor air pollution and has been linked to the development of a variety of cardiopulmonary diseases, including Tuberculosis (TB). The purpose of this cross-sectional study was to investigate the association between current biomass smoke exposure and self-reported quality of life scores in a cohort of previous TB patients in Uganda. We reviewed medical records from six TB clinics from 9/2019-9/2020 and conducted phone interviews to obtain information about biomass smoke exposure. A random sample of these patients were asked to complete three validated quality-of-life surveys including the St. Georges Respiratory Questionnaire (SGRQ), the EuroQol 5 Dimension 3 Level system (EQ-5D-3L) which includes the EuroQol Visual Analog Scale (EQ-VAS), and the Patient Health Questionnaire 9 (PHQ-9). The cohort was divided up into 3 levels based on years of smoke exposure–no-reported smoke exposure (0 years), light exposure (1–19 years), and heavy exposure (20+ years), and independent-samples-Kruskal-Wallis testing was performed with post-hoc pairwise comparison and the Bonferroni correction. The results of this testing indicated significant increases in survey scores for patients with current biomass exposure and a heavy smoke exposure history (20+ years) compared to no reported smoke exposure in the SGRQ activity scores (adj. p = 0.018) and EQ-5D-3L usual activity scores (adj. p = 0.002), indicating worse activity related symptoms. There was a decrease in EQ-VAS scores for heavy (adj. p = 0.007) and light (adj. p = 0.017) exposure groups compared to no reported exposure, indicating lower perceptions of overall health. These results may suggest worse outcomes or baseline health for TB patients exposed to biomass smoke at the time of treatment and recovery, however further research is needed to characterize the effect of indoor air pollution on TB treatment outcomes.

Prevalence, associated factors, and gene polymorphisms of obesity in Tibetan adults in Qinghai, China

ObjectivesTo explore the prevalence and associated factors of obesity in Tibetan adults in Qinghai, China, and to determine the association between the FTO (rs1121980 and rs17817449) and MC4R gene (rs17782313 and rs12970134) polymorphisms with obesity.MethodsA cross-sectional survey was conducted in 2015 in Qinghai to selected Tibetan adults aged 20 to 80 years. Prevalence of obesity (BMI ≥ 28 kg/m2) and overweight (BMI 24 ~ 27.9 kg/m2) were evaluated. Multivariable logistic models were used to determine the associated factors. Pair-matched subjects of obesity cases and normal-weight controls were selected for the gene polymorphism analyses. Conditional logistic models were used to assess the association between gene polymorphisms with obesity. Additive and multiplicative gene-environment interactions were tested.ResultsA total of 1741 Tibetan adults were enrolled. The age- and sex- standardized prevalence of obesity and overweight was 18.09% and 31.71%, respectively. Male sex, older age, heavy level of leisure-time exercise, current smoke, and heavy level of occupational physical activity were associated with both obesity and overweight. MC4R gene polymorphisms were associated with obesity in Tibetan adults. No significant gene-environment interaction was detected.ConclusionThe prevalence of obesity and overweight in Tibetan adults was high. Both environmental and genetic factors contributed to the obesity prevalent.

Associations of childhood and current secondhand smoke exposure at home with current secondhand smoke exposure at work: A cross-sectional analysis of the Singapore multi-ethnic cohort.

Relationships between secondhand smoke exposure (SHSE) in various temporal and physical settings are not fully studied despite its adverse impacts on human health, especially in multi-ethnic Asian populations. We investigated associations of childhood and current SHSE at home (SHSEhome) with current SHSE at work (SHSEwork) in Singapore and its relation to sources of daily smokers at home. This cross-sectional study identified 925 healthy, never smoker working adults from the Singapore Multi-Ethnic Cohort (2004-2010). Firstly, the multiple logistic regression model estimated the adjusted odds ratios (AORs) of SHSEhome. Subsequently, sources of daily smokers entered through an additional model building process using the former as a base. Current adults (AOR=2.05; 95% CI: 1.28-3.29) and childhood SHSEhome (AOR=1.43; 95% CI: 0.93-2.19) had a positive and no association with current SHSEwork, respectively. These findings persisted when smoker identity-related variables entered the model: child (AOR=3.56; 95% CI: 1.19-10.64) for current daily smokers; father (AOR=2.30; 95% CI: 0.94-5.64) and sibling (AOR=2.97; 95% CI: 1.55-5.68) for childhood. Compared to no childhood SHSEhome, only those living with their fathers and siblings who smoked daily at home had significantly higher odds of reporting current SHSEwork (AOR=3.70; 95% CI: 1.88-7.30). Current SHSEhome was a risk factor for current SHSEwork, with risks elevated among those who smoke daily at home and living with their children. Childhood SHSEhome becomes a risk factor when daily household smokers include fathers and siblings. Deformalizing smoking could consider interpersonal dynamics of daily smokers at home with family members in different temporal settings, to reduce SHSEwork.

A study of engine room smoke detection based on proactive machine vision model for intelligent ship

Fire disaster causes damage to ships, pollute the environment, and threatens people's lives, so making early detection and reasonable decision is essential for avoiding catastrophic accidents. Smoke, as the main feature of fire, plays a significant role in early fire identification. However, challenged by the complex engine room (E/R) environment and shooting issues, the recognition performances of current smoke detection methods based on machine vision are unsatisfactory at the early fire stage. To address the issues, this paper proposes a proactive machine vision model based on the fusion of the transfer learning method and proactive perception technology for smoke detection. Firstly, a smoke images database is established based on similar environments (indoor and ship fire scenes) for the transfer learning module training, where more smoke features are extracted and learned from complex scenes. Afterward, real-time images are input into the trained model for local significance analysis which is applied as a triggering criterion for active smoke perception. When the significance indicator reaches the trigger condition, the proactive perception technology adopts reinforcement learning and proactive vision for further identifying detailed information about scenarios. By this processing, the internal and external parameters of the camera are adjusted to narrow and focus the targets of interest in the current scene. Finally, the ship E/R workshop and distribution box fire cases are selected to validate the proposed method. The experiment results indicate that the proposed model outperforms the existing techniques in accuracy as it has the potential to detect earlier smoke features.

Seeing Through the Blind: Belief About Treatment Randomization and Smoking Cessation Outcome Among People With Current or Past Major Depressive Disorder Who Smoke in a Placebo-Controlled Trial of Varenicline.

Blinding participants to randomization is a cornerstone of science. However, participant beliefs about their allocation can influence outcomes. We examined blind integrity, the association between trial arm belief and cessation, and potential mechanisms linking treatment arm and treatment arm belief among people with major depressive disorder (MDD) who smoke receiving varenicline in a placebo-controlled trial. 175 participants were asked at the end of treatment (EOT) if they thought they received placebo, varenicline, or were not sure. We assessed the relationship between treatment arm belief and actual treatment allocation, examined the association between treatment arm belief and EOT cessation, and evaluated changes in craving, withdrawal, side effects, depression symptoms, and smoking reward as mediators through which treatment arm was believed. Treatment arm belief was significantly associated with actual arm assignment (χ2(2) = 13.0, p = .002). Participants in the varenicline arm were >3 times as likely to believe they were taking varenicline, versus "not sure" (RR = 3.05 [1.41-6.60], p = .005). Participants in the placebo arm were just as likely to believe they were taking placebo versus "not sure" (χ2[2] = 0.75, p = .69). Controlling for treatment arm, belief that one received varenicline was significantly associated with an increase in cessation rate (OR = 5.91 [2.06-16.92], p = .001). Change in the rewarding experience of smoking may mediate participant ability to discern getting varenicline (B = 0.077 [0.002-0.192], p < .05). Participants receiving varenicline can discern that they received varenicline and this belief is associated with higher cessation rates. Research is needed to continue to examine how participants correctly identify their allocation to varenicline. Data come from the trial NCT02378714. The present study adds to the sparse literature on blind integrity, particularly in the field of tobacco cessation. Randomized clinical trial participants can discern their assignment to varenicline, and believing that one received varenicline was associated with significantly higher cessation rates. Identifying treatment arm allocation may be associated with changes in the rewarding aspects of smoking that have been well documented with varenicline use. Masking allocation to varenicline is challenging. The effects of this medication in clinical trials may represent both pharmacological effects and participants' abilities to recognize that they are receiving the medication.

Endoscopic image classification algorithm based on Poolformer.

Image desmoking is a significant aspect of endoscopic image processing, effectively mitigating visual field obstructions without the need for additional surgical interventions. However, current smoke removal techniques tend to apply comprehensive video enhancement to all frames, encompassing both smoke-free and smoke-affected images, which not only escalates computational costs but also introduces potential noise during the enhancement of smoke-free images. In response to this challenge, this paper introduces an approach for classifying images that contain surgical smoke within endoscopic scenes. This classification method provides crucial target frame information for enhancing surgical smoke removal, improving the scientific robustness, and enhancing the real-time processing capabilities of image-based smoke removal method. The proposed endoscopic smoke image classification algorithm based on the improved Poolformer model, augments the model's capacity for endoscopic image feature extraction. This enhancement is achieved by transforming the Token Mixer within the encoder into a multi-branch structure akin to ConvNeXt, a pure convolutional neural network. Moreover, the conversion to a single-path topology during the prediction phase elevates processing speed. Experiments use the endoscopic dataset sourced from the Hamlyn Centre Laparoscopic/Endoscopic Video Dataset, augmented by Blender software rendering. The dataset comprises 3,800 training images and 1,200 test images, distributed in a 4:1 ratio of smoke-free to smoke-containing images. The outcomes affirm the superior performance of this paper's approach across multiple parameters. Comparative assessments against existing models, such as mobilenet_v3, efficientnet_b7, and ViT-B/16, substantiate that the proposed method excels in accuracy, sensitivity, and inference speed. Notably, when contrasted with the Poolformer_s12 network, the proposed method achieves a 2.3% enhancement in accuracy, an 8.2% boost in sensitivity, while incurring a mere 6.4 frames per second reduction in processing speed, maintaining 87 frames per second. The results authenticate the improved performance of the refined Poolformer model in endoscopic smoke image classification tasks. This advancement presents a lightweight yet effective solution for the automatic detection of smoke-containing images in endoscopy. This approach strikes a balance between the accuracy and real-time processing requirements of endoscopic image analysis, offering valuable insights for targeted desmoking process.

The Role of Fuel Characteristics and Heat Release Formulations in Coupled Fire-Atmosphere Simulation

In this study, we focus on the effects of fuel bed representation and fire heat and smoke distribution in a coupled fire-atmosphere simulation platform for two landscape-scale fires: the 2018 Camp Fire and the 2021 Caldor Fire. The fuel bed representation in the coupled fire-atmosphere simulation platform WRF-Fire currently includes only surface fuels. Thus, we enhance the model by adding canopy fuel characteristics and heat release, for which a method to calculate the heat generated from canopy fuel consumption is developed and implemented in WRF-Fire. Furthermore, the current WRF-Fire heat and smoke distribution in the atmosphere is replaced with a heat-conserving Truncated Gaussian (TG) function and its effects are evaluated. The simulated fire perimeters of case studies are validated against semi-continuous, high-resolution fire perimeters derived from NEXRAD radar observations. Furthermore, simulated plumes of the two fire cases are compared to NEXRAD radar reflectivity observations, followed by buoyancy analysis using simulated temperature and vertical velocity fields. The results show that while the improved fuel bed and the TG heat release scheme have small effects on the simulated fire perimeters of the wind-driven Camp Fire, they affect the propagation direction of the plume-driven Caldor Fire, leading to better-matching fire perimeters with the observations. However, the improved fuel bed representation, together with the TG heat smoke release scheme, leads to a more realistic plume structure in comparison to the observations in both fires. The buoyancy analysis also depicts more realistic fire-induced temperature anomalies and atmospheric circulation when the fuel bed is improved.

Prediction of 6-year risk of bladder cancer using the HUNT Lung Cancer Risk Model in ever-smokers: A HUNT study.

e16581 Background: Bladder cancer is the 6th most common cancer in men and 17th most common cancer in women and the mortality is high when detected in advanced stage. Previous or current cigarette smoke is the main risk factor. There is no screening program for bladder cancer. Given the pathophysiological similarity with lung cancer, we postulated that a lung cancer risk model could work for bladder cancer. The validated HUNT Lung Cancer Risk Model (HUNT LCM) predicts lung cancer in 6 years with an area under the ROC curve (AUC) of 0.87. Here we aimed to test the performance of the HUNT LCM in the prospective HUNT2 cohort for a 6-year prediction of bladder cancer. Methods: The HUNT2 cohort from Norway, a prospective population study of 65 200 individuals 19-100 years of age, was followed up for a median of 15 years. This population was linked with the Norwegian Cancer Registry and all cases diagnosed with bladder cancer were registered. The HUNT Lung Cancer Model was used to predict risk of bladder cancer within 6 years. Results: In the HUNT2 cohort 38 846 ever-smokers were identified. 82.6% of those developing bladder cancer were ever-smokers. 31 269 had all the variables needed for the model and of those, 191 developed bladder cancer in six years. The HUNT Lung Cancer Model prediction of bladder cancer in 6 years AUC was 0.751. Conclusions: To our knowledge, this is the first time a lung cancer risk prediction model has been applied to predict bladder cancer. The performance of the model is lower than for lung cancer but still has a good performance. This may open a path for screening and early detection of bladder cancer.

COVID-19 and smoking: Considerations after two years

COVID-19 and smoking: Considerations after two years

A prospective ultrasound study of cigarette smoking and uterine leiomyomata incidence and growth

Uterine leiomyomata (fibroids) are common, benign neoplasms that contribute substantially to gynecologic morbidity. Some existing epidemiologic studies indicate that cigarette smoking is associated with lower uterine leiomyomata risk. However, no prospective studies have systematically screened an entire study population for uterine leiomyomata using transvaginal ultrasound or evaluated the association between cigarette smoking and uterine leiomyomata growth. This study aimed to examine the association between cigarette smoking and uterine leiomyomata incidence and growth in a prospective ultrasound study. We enrolled 1693 residents from the Detroit metropolitan area into the Study of Environment, Lifestyle, and Fibroids during 2010 to 2012. Eligible participants were aged 23 to 34 years, had an intact uterus but no previous diagnosis of uterine leiomyomata, and self-identified as Black or African American. We invited participants to complete a baseline visit and 4 follow-up visits over approximately 10 years. At each visit, we used transvaginal ultrasound to assess uterine leiomyomata incidence and growth. Participants provided extensive self-reported data throughout follow-up including exposures to active and passive cigarette smoking in adulthood. We excluded participants who did not return for any follow-up visits (n=76; 4%). We fit Cox proportional hazards regression models to estimate hazard ratios and 95% confidence intervals for the association between time-varying smoking history and incidence rates of uterine leiomyomata. We fit linear mixed models to estimate the percentage difference and 95% confidence intervals for the association between smoking history and uterine leiomyomata growth. We adjusted for sociodemographic, lifestyle, and reproductive factors. We interpreted our results based on magnitude and precision rather than binary significance testing. Among 1252 participants without ultrasound evidence of uterine leiomyomata at baseline, uterine leiomyomata were detected in 394 participants (31%) during follow-up. Current cigarette smoking was associated with a lower uterine leiomyomata incidence rate (hazard ratio, 0.67; 95% confidence interval, 0.49-0.92). Associations were stronger among participants who had smoked for longer durations (≥15 years vs never: hazard ratio, 0.49; 95% confidence interval, 0.25-0.95). The hazard ratio for former smokers was 0.78 (95% confidence interval, 0.50-1.20). Among never smokers, the hazard ratio for current passive smoke exposure was 0.84 (95% confidence interval, 0.65-1.07). Uterine leiomyomata growth was not appreciably associated with current (percent difference,-3%; 95% confidence interval,-13% to 8%) or former (percent difference,-9%; 95% confidence interval,-22% to 6%) smoking. We provide evidence from a prospective ultrasound study that cigarette smoking is associated with lower uterine leiomyomata incidence.

Experimental Study on Particle Size Distribution Characteristics of Aerosol for Fire Detection

Current optical fire smoke detectors use scattering light intensity as an indicator of smoke concentration and trigger fire alarms when the intensity exceeds a threshold value. However, such detectors are prone to false alarms caused by non-fire aerosols since both fire smokes and non-fire aerosols scatter light. Thus, in order to reduce false alarms caused by non-fire aerosols such as dust and water vapor, fire detectors must be capable of distinguishing fire smoke from non-fire aerosols. Since the light scattering signals depend on the particle size information of aerosols, it is essential to study and characterize the particle size distribution of fire smoke and non-fire aerosols for differentiating them. In this paper, a comprehensive aerosol experimental platform is built to measure the particle size distribution of various typical fire smokes and non-fire aerosols. Through the conducted experiments, we note that there are significant differences in the particle size distributions of typical fire smokes and non-fire aerosols, with a boundary of about 1μm. The experimental results provide fundamental data support of the particle size distribution for developing a better fire detector that accurately identifies smoke.

Dietary inflammatory index and risk of non-alcoholic fatty liver disease and advanced hepatic fibrosis in US adults.

This study aims to investigate whether the Dietary Inflammatory Index (DII) is associated with non-alcoholic fatty liver disease (NAFLD) and advanced hepatic fibrosis (AHF) among non-institutionalized adults in the United States. Utilizing data from the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2016, a total of 10,052 adults aged ≥18 years were included in the analysis. We used multivariable analysis, controlling for demographic variables, to evaluate the association between DII and NAFLD and AHF, a restricted cubic spline (RCS) was used to model the non-linear relationship between DII and NAFLD. For 10,052 participants, DII ranges from -4.63 to 5.47. Compared with quartile 1, higher DII group were associated with higher levels of female, separated/divorced, lower education level, heavy alcohol use, current smoke status, BMI, poverty income ratio, and waist circumference. DII also showed a significantly positive correlation with ALT, AST. In the fully adjusted multivariable model, DII was positively associated with the presence of NAFLD (OR 1.09, 1.06-1.13 CI, p trend <0.0001), and AHF (OR 1.15, 1.07-1.23 CI, p trend <0.001). The association remained statistically significant after stratified by gender in terms of NAFLD, but in case of AHF only in males (Q4 vs. Q1: OR 2.68, 1.63-4.41 CI, p trend <0.0001) was statistically significant. In the RCS models, the relation of DII and NAFLD started increase rapidly until around 1.80 and then started relatively flat afterward. Higher pro-inflammatory level was associated with higher risk of NAFLD in males and females, and with higher risk of AHF in males but not in females. Therefore, strategies to promote an Zhang anti-inflammatory diet should be considered to prevent and ameliorate NAFLD and AHF in adults.

Urinary cotinine and exposure to passive smoke in children and adolescents in Germany – Human biomonitoring results of the German Environmental Survey 2014–2017 (GerES V)

Passive smoking is a preventable and significant cause of many serious health problems, with children being particularly at risk. In the fifth German Environmental Survey (GerES V), conducted from 2014 to 2017, information reflecting the extent of passive smoke exposure in children and adolescents was collected by interview-based questionnaires and human biomonitoring (HBM) analyses of cotinine in urine from 2260 participants, aged 3–17 years. Based on these population-representative data, we describe current passive smoke exposure stratified by different subgroups and identify specific exposure determinants using multivariate logistic regression. The questionnaire data revealed that 42% of children and adolescents lived with at least one smoker in the household. Quantifiable concentrations of cotinine could be detected in 56% of the participants. The overall median concentration of cotinine was 0.2 μg/L, with children and adolescents of low socioeconomic status found to be a group particularly affected by passive smoke with higher cotinine concentrations (median = 1.2 μg/L). In the multiple analysis, the most significant predictor of cotinine levels derived from the questionnaire was passive smoking at home (odds ratio (OR) 13.07 [95CI: 4.65, 36.70]). However, parental smoking and passive smoking among friends and relatives could also be identified as independent factors influencing elevated cotinine levels. The comparison between the previous cycle GerES IV (2003–2006) on 3–14-year-olds and GerES V shows that tobacco smoke exposure of children decreased significantly. This decrease is likely an effect of extensive non-smoker protection laws being enforced 2007–2008 on federal and state level. This is reflected by a halving of urinary cotinine concentrations. Nevertheless, our results indicate that passive smoke is still a relevant source of harmful pollutants for many children and adolescents in Germany, and thus support the need for further efforts to reduce passive smoke exposure, especially in the private environment.

Asthma Comorbidities: Frequency, Risk Factors, and Associated Burden in Children and Adolescents.

Identifying asthma comorbidities in children is fundamental for improving disease management. We aimed to investigate the frequency of allergy-related comorbidities in children and adolescents with asthma, and to identify associated risk factors and disease burden. Between September 2015 and December 2018, 508 asthmatic patients (5–17 years) were consecutively enrolled. Parents answered a standardized questionnaire on the history of disease and risk factors. Comorbidities were classified based on the involvement of respiratory and/or extra-respiratory districts: asthma only (A, 13%), asthma with respiratory comorbidities (AR, 37%), asthma with extra-respiratory comorbidities (AER, 10%), and asthma with both respiratory and extra-respiratory comorbidities (ARER, 40%). Multinomial logistic regression showed that membership in the AR group was significantly associated with a maternal history of asthma (OR = 3.08, 95% CI: 1.23–7.72), breastfeeding ≥ three months (OR = 1.92, 1.06–3.46), early mold exposure (OR = 2.39, 1.12–5.11), and current environmental tobacco smoke exposure (OR = 2.06, 1.11–3.83). Membership in the AER group was significantly associated with the female gender (OR = 3.43, 1.54–7.68), breastfeeding ≥ three months (OR = 2.77, 1.23–6.22). ARER was significantly associated with all the aforementioned exposures. Patients with AR reported exacerbations in the last 12 months more frequently (p = 0.009). Several personal and environmental risk factors are associated with comorbidities in asthmatic children and adolescents, possibly worsening the disease burden.

A high-precision forest fire smoke detection approach based on ARGNet

The occurrence of forest fires can lead to ecological damage, property loss, and human casualties. Current forest fire smoke detection methods do not sufficiently consider the characteristics of smoke with high transparency and no clear edges and have low detection accuracy, which cannot meet the needs of complex aerial forest fire smoke detection tasks. In this paper, we propose Adjacent layer composite network based on a recursive feature pyramid with deconvolution and dilated convolution and global optimal nonmaximum suppression (ARGNet) for high-accuracy detection of forest fire smoke. First, the Adjacent layer composite network is proposed to enhance the extraction of smoke features with high transparency and no clear edges, and SoftPool in it is used to retain more feature information of smoke. Then, a recursive feature pyramid with deconvolution and dilated convolution (RDDFPN) is proposed to fuse shallow visual features and deep semantic information in the channel dimension to improve the accuracy of long-range aerial smoke detection. Finally, global optimal nonmaximum suppression (GO-NMS) sets the objective function to globally optimize the selection of anchor frames to adapt to the aerial photography of multiple smoke locations in forest fire scenes. The experimental results show that the ARGNet parametric number on the UAV-IoT platform is as low as 53.48 M, mAP reaches 79.03%, mAP50 reaches 90.26%, mAP75 reaches 82.35%, FPS reaches 122.5, and GFLOPs reaches 55.78. Compared with other mainstream methods, it has the advantages of real-time detection and high accuracy.

Adaptive linear feature-reuse network for rapid forest fire smoke detection model

Current mainstream smoke detection methods have some problems, such as missing report, large deviation of detection box and slow detection speed. Therefore, we propose an Adaptive Linear Feature-Reuse Network for Rapid Forest Fire Smoke Detection (ALFRNet). Firstly, we designed Double Linear Feature-Reuse Module(DLFR Module) to reduce information loss in the process of the acquisition of smoke images；and Hybrid Attention-Guided Module (HAG Module) was proposed to reduce the interference caused by blurred image and to emphasize the expression of smoke characteristics. At the convolutional layer of the proposed network, a novel Adaptive Depthwise Convolution Module (ADC Module) which can effectively solve the problem of difficulty in recognition caused by too small smoke targets in images was used. Besides, we adopt Cluster NMS (CNMS) in purpose of avoiding large deviation of the detection box. It can adapt to smoke target with fuzzy edge and improve the performance of detection. Finally, we build a smoke detection system based on the Internet of things. The experimental results show that our method achieves 87.26% mAP50 at 43 FPS on NVIDIA TITAN Xp. Compared with other mainstream methods, it has the better performance of quicker speed, higher accuracy and more accurate position of detection box.

Fire and smoke detection with deep learning: A review

This paper outlines a state-of-the-art method for smoke and fire detection utilizing Convolutional Neural Networks (CNNs). The current smoke detectors installed in buildings pose a challenge for effective fire detection. The inefficiency of traditional methods in terms of speed and cost led to the exploration of using Artificial Intelligence (AI) to identify and alert from Closed Circuit Television (CCTV) footage. In this paper, an analytical overview of AI is conducted by using a selfcreated dataset of video frames containing flames and smoke. The data undergoes pre-processing before being used to train a CNN-based machine learning model. The goal of this review study is to understand the available literature in the field and propose a highly accurate, cost-effective, and simple system for fire detection in various scenarios.