The diversion effect caused by the linked structure in a metro tunnel with cross-passage complicates the impact of longitudinal fire source location on the smoke backflow layering behavior that has not been clarified,...The diversion effect caused by the linked structure in a metro tunnel with cross-passage complicates the impact of longitudinal fire source location on the smoke backflow layering behavior that has not been clarified,despite the fact that the scenario exists in practice.A series of laboratory-scale experiments were conducted in this study to investigate the smoke back-layering length in a model tunnel with cross-passage.The heat release rate,the velocity of longitudinal air flow,and the location of the fire source were all varied.It was found that the behavior of smoke backflow for the fire source located at the upstream of bifurcation point resembles a single-hole tunnel fire.As the fire source’s position shifts downstream from the bifurcation point,the length of smoke back-layering progressively increases.A competitive interaction exists between airflow diversion and smoke diversion during smoke backflow,significantly affecting the smoke back-layering length in the main tunnel.The dimensionless smoke back-layering length model was formulated in a tunnel featuring a cross-passage,taking into account the positions of longitudinal fire sources.The dimensionless smoke back-layering length exhibits a positive correlation with the 17/18 power of total heat release rate Q and a negative correlation with the 5/2 power of longitudinal ventilation velocity V.展开更多
A simultaneous boost in toughness and fire safety of epoxy(EP)is achieved through solvent-free one-step neutralization of phytic acid with 1,8-diaminooctane to yield a multifunctional bio-based curing agent,PA-DAO.Whe...A simultaneous boost in toughness and fire safety of epoxy(EP)is achieved through solvent-free one-step neutralization of phytic acid with 1,8-diaminooctane to yield a multifunctional bio-based curing agent,PA-DAO.When used as the sole hardener,5 wt%PA-DAO increased the tensile,flexural,and impact strengths by 165%,81%,and 455%,respectively,over the parent amine system,whereas the tensile and flexural toughness increased by 1387% and 775%,respectively.At 25 wt% loading,the resin attained a UL-94 V-0 rating and a limiting oxygen index of 28.1%,accompanied by a 71% reduction in the peak heat-release rate and a 53%suppression of total smoke production.This facile,green protocol provides scalable access to ultra-tough,intrinsically flame-retardant epoxy networks without external plasticizers or additives.展开更多
In fire rescue scenarios,traditional manual operations are highly dangerous,as dense smoke,low visibility,extreme heat,and toxic gases not only hinder rescue efficiency but also endanger firefighters’safety.Although ...In fire rescue scenarios,traditional manual operations are highly dangerous,as dense smoke,low visibility,extreme heat,and toxic gases not only hinder rescue efficiency but also endanger firefighters’safety.Although intelligent rescue robots can enter hazardous environments in place of humans,smoke poses major challenges for human detection algorithms.These challenges include the attenuation of visible and infrared signals,complex thermal fields,and interference frombackground objects,all ofwhichmake it difficult to accurately identify trapped individuals.To address this problem,we propose VIF-YOLO,a visible–infrared fusion model for real-time human detection in dense smoke environments.The framework introduces a lightweight multimodal fusion(LMF)module based on learnable low-rank representation blocks to end-to-end integrate visible and infrared images,preserving fine details while enhancing salient features.In addition,an efficient multiscale attention(EMA)mechanism is incorporated into the YOLOv10n backbone to improve feature representation under low-light conditions.Extensive experiments on our newly constructedmultimodal smoke human detection(MSHD)dataset demonstrate thatVIF-YOLOachievesmAP50 of 99.5%,precision of 99.2%,and recall of 99.3%,outperforming YOLOv10n by a clear margin.Furthermore,when deployed on the NVIDIA Jetson Xavier NX,VIF-YOLO attains 40.6 FPS with an average inference latency of 24.6 ms,validating its real-time capability on edge-computing platforms.These results confirm that VIF-YOLO provides accurate,robust,and fast detection across complex backgrounds and diverse smoke conditions,ensuring reliable and rapid localization of individuals in need of rescue.展开更多
Accurate detection of smoke and fire sources is critical for early fire warning and environmental monitoring.However,conventional detection approaches are highly susceptible to noise,illumination variations,and comple...Accurate detection of smoke and fire sources is critical for early fire warning and environmental monitoring.However,conventional detection approaches are highly susceptible to noise,illumination variations,and complex environmental conditions,which often reduce detection accuracy and real-time performance.To address these limitations,we propose Lightweight and Precise YOLO(LP-YOLO),a high-precision detection framework that integrates a self-attention mechanism with a feature pyramid,built upon YOLOv8.First,to overcome the restricted receptive field and parameter redundancy of conventional Convolutional Neural Networks(CNNs),we design an enhanced backbone based on Wavelet Convolutions(WTConv),which expands the receptive field through multifrequency convolutional processing.Second,a Bidirectional Feature Pyramid Network(BiFPN)is employed to achieve bidirectional feature fusion,enhancing the representation of smoke features across scales.Third,to mitigate the challenge of ambiguous object boundaries,we introduce the Frequency-aware Feature Fusion(FreqFusion)module,in which the Adaptive Low-Pass Filter(ALPF)reduces intra-class inconsistencies,the offset generator refines boundary localization,and the Adaptive High-Pass Filter(AHPF)recovers high-frequency details lost during down-sampling.Experimental evaluations demonstrate that LP-YOLO significantly outperforms the baseline YOLOv8,achieving an improvement of 9.3%in mAP@50 and 9.2%in F1-score.Moreover,the model is 56.6%and 32.4%smaller than YOLOv7-tiny and EfficientDet,respectively,while maintaining real-time inference speed at 238 frames per second(FPS).Validation on multiple benchmark datasets,including D-Fire,FIRESENSE,and BoWFire,further confirms its robustness and generalization ability,with detection accuracy consistently exceeding 82%.These results highlight the potential of LP-YOLO as a practical solution with high accuracy,robustness,and real-time performance for smoke and fire source detection.展开更多
Smoke generator constitute an important class of pesticide formulations widely used in protected agriculture,forestry,mushroom cultivation,and storage environments.Unlike conventional sprays,smoke generator rely on he...Smoke generator constitute an important class of pesticide formulations widely used in protected agriculture,forestry,mushroom cultivation,and storage environments.Unlike conventional sprays,smoke generator rely on heat-induced phase transitions of active ingredients to produce fine aerosolized particles that disperse through Brownian motion,thereby markedly improving application efficiency.Despite their long history and broad utility,the development of smoke generator has largely stagnated over the past two decades.Here,we provide a comprehensive assessment of their historical evolution,registration landscape,physicochemical mechanisms,and current deployment in agricultural systems.Based on this analysis,we outline key directions for nextgeneration smoke generator technology.First,transitioning from chemical heating to electric heating is essential to enable automation and unmanned pesticide delivery.Second,expanding the air-purification functionality of smoke formulations offers a promising strategy to suppress airborne pest and pathogen populations.Finally,integrating principles of crystal engineering to modulate particle morphology and interfacial affinity may overcome current limitations in deposition efficiency and biological performance.Together,these advances will underpin the development of high-efficiency,intelligent smoke generator and support precision plant protection and sustainable intensification in protected agriculture.展开更多
To understand the smoke level and NO_(x)emission characteristics of in-use construction machinery in Beijing,we selected 905 construction machines in Beijing from August 2022 to April 2023 to monitor the emission leve...To understand the smoke level and NO_(x)emission characteristics of in-use construction machinery in Beijing,we selected 905 construction machines in Beijing from August 2022 to April 2023 to monitor the emission level of smoke and NO_(x).The exhaust smoke level and excessive emission situation of different machinery types were identified,and their NO_(x)emission levels were monitored according to the free acceleration method.We investigated the correlation of NO_(x)and smoke emission,and proposed suggestions for controlling pollution discharge from construction machinery in the future.The results show that the exhaust smoke level was 0–2.62 m^(−1),followed a log-normal distribution(μ=-1.73,δ=1.09,R^(2)=0.99),with a 5.64%exceedance rate.Differenceswere observed amongmachinery types,with low-power engine forklifts showing higher smoke levels.The NO_(x)emission range was 71–1516 ppm,followed a normal distribution(μ=565.54,δ=309.51,R^(2)=0.83).Differences among machinery types were relatively small.Engine rated net power had the most significant impact on NO_(x)emissions.Thus,NO_(x)emissions from construction machinery need further attention.Furthermore,we found a weak negative correlation(p<0.05)between the emission level of smoke and NO_(x),that is the synergic emission reduction effect is poor,emphasizing the need for NO_(x)emission limits.In the future,the oversight in Beijing should prioritize phasing out ChinaⅠand ChinaⅡmachinery,and monitor emissions from highpower engine ChinaⅢmachinery.展开更多
The present study monitored bacterial succession,physicochemical properties,and volatile organic compounds(VOCs)changes in smoked chicken legs with modified atmosphere packaging(MAP,60% CO_(2) and 40%N_(2))during a 25...The present study monitored bacterial succession,physicochemical properties,and volatile organic compounds(VOCs)changes in smoked chicken legs with modified atmosphere packaging(MAP,60% CO_(2) and 40%N_(2))during a 25-day storage period at 4℃.After 15 days of storage,S erratia proteamaculans and Pseudomonas fragi became the predominant bacteria.Furthermore,physicochemical properties changed significantly,as evidenced by an increase in thiobarbituric acid reactive substances and b*(yellowness)value,and a decrease in hardness.A total of 65 VOCs were identified during storage.Correlation between bacterial succession and quality indicators(including VOCs and physicochemical properties)allowed the identification of 26 core dominant bacteria,including S.proteamaculans,Psychrobacter alimentarius,Pseudomonas putida,and Pseudomonas poae,which were positively related to spoilage VOCs(e.g.,1-octen-3-ol,1-pentanol,and 3-methyl-1-butanol)and could be defined as specific spoilage organisms(SSOs).The results of this study provide a systematic approach to predict SSOs in smoked chicken legs during storage,which can also provide a basis for product safety.展开更多
To improve the catalytic performance of La_(0.6)Sr_(0.4)Co_(0.2)Fe_(0.8)O_(3)(LSCF)towards carbon soot,we utilized the impregnation method to incorporate Ag into the prepared LSCF catalyst.We conducted a series of cha...To improve the catalytic performance of La_(0.6)Sr_(0.4)Co_(0.2)Fe_(0.8)O_(3)(LSCF)towards carbon soot,we utilized the impregnation method to incorporate Ag into the prepared LSCF catalyst.We conducted a series of characterization tests and evaluated the soot catalytic activity of the composite catalyst by comparing it with the LaCoO_(3) group,LaFeO_(3) group,and catalyst-free group.The results indicate that the Ag-LSCF composite catalyst exhibits the highest soot catalytic activity,with the characteristic temperature values of 376.3,431.1,and 473.9℃at 10%,50%,and 90%carbon soot conversion,respectively.These values are 24.8,20.2,and 23.1℃lower than those of the LSCF group.This also shows that LSCF can improve the catalytic activity of soot after compounding with Ag,and reflects the necessity of using catalysts in soot combustion reaction.XPS characterization and BET test show that Ag-LSCF has more abundant surface-adsorbed oxygen species,larger specific surface area and pore volume than LSCF,which also proves that Ag-LSCF has higher soot catalytic activity.展开更多
BACKGROUND Smoking is a leading cause of carcinogenesis in the head and neck region,representing a critical public health issue.Identifying genotoxic damage in smokers can provide valuable insights for developing prev...BACKGROUND Smoking is a leading cause of carcinogenesis in the head and neck region,representing a critical public health issue.Identifying genotoxic damage in smokers can provide valuable insights for developing preventive interventions.AIM To assess genotoxic damage through the micronucleus assay in exfoliated buccal mucosa cells from users of conventional tobacco,reverse smoking,cannabis,electronic cigarettes,and non-smokers.METHODS A cross-sectional study was conducted with 100 participants divided into five groups:20 conventional tobacco smokers,20 reverse smokers,20 electronic cigarette users,20 cannabis users,and 20 non-smokers.Exfoliated buccal mucosa cells were analyzed using Giemsa and Papanicolaou staining to identify micronuclei(MN)as markers of genotoxic damage.RESULTS MN were present in 86%of the samples.Statistically significant differences were observed in the median micronucleus count between conventional,reverse,and electronic cigarette smokers compared to non-smokers(P<0.001),while no significant difference was found for cannabis smokers(P=0.89).A significant correlation was identified between the presence of oral lesions and micronucleus count(P=0.03).Regression analysis ruled out alcohol as a confounding factor.CONCLUSION This study identified genotoxic damage associated with various smoking habits,except for cannabis use,highlighting the need for public health interventions to reduce smoking and mitigate its genotoxic effects.These findings provide a foundation for future research and the implementation of preventive policies.展开更多
In rapid urban development,outdoor parking lots have become essential components of urban transportation systems.However,the increasing number of parking lots is accompanied by a rising risk of vehicle fires,posing a ...In rapid urban development,outdoor parking lots have become essential components of urban transportation systems.However,the increasing number of parking lots is accompanied by a rising risk of vehicle fires,posing a serious challenge to public safety.As a result,there is a critical need for fire warning systems tailored to outdoor parking lots.Traditional smoke detection methods,however,struggle with the complex outdoor environment,where smoke characteristics often blend into the background,resulting in low detection efficiency and accuracy.To address these issues,this paper introduces a novel model named Dynamic Contextual Transformer YOLO(DCT-YOLO),an advanced smoke detection method specifically designed for outdoor parking lots.We introduce an innovative Dynamic Channel-Spatial Attention(DCSA)mechanism to improve the model’s focus on smoke features,thus improving detection accuracy.Additionally,we incorporate Contextual Transformer Networks(CoTNet)to better adapt to the irregularity of smoke patterns,further enhancing the accuracy of smoke region detection in complex environments.Moreover,we developed a new dataset that includes a wide range of smoke and fire scenarios,improving the model’s generalization capability.All baseline models were trained and evaluated on the same dataset to ensure a fair and consistent comparison.The experimental results on this dataset demonstrate that the proposed algorithm yields a mAP@0.5 of 85.1%and a mAP@0.5:0.95 of 55.7%,representing improvements of 15.0%and 14.9%,respectively,over the baseline model.These results highlight the effectiveness of the proposed method in accurately detecting smoke in challenging outdoor environments.展开更多
The corrosion behavior and life of Sn−3.0Ag−0.5Cu solder joints were investigated through fire smoke exposure experiments within the temperature range of 45−80℃.The nonlinear Wiener process and Arrhenius equation wer...The corrosion behavior and life of Sn−3.0Ag−0.5Cu solder joints were investigated through fire smoke exposure experiments within the temperature range of 45−80℃.The nonlinear Wiener process and Arrhenius equation were used to establish the probability distribution function and prediction model of the solder joint’s average life and individual remaining useful life.The results indicate that solder joint resistance shows a nonlinear growth trend with time increasing.After 24 h,the solder joint transforms from spherical to rose-like shapes.Higher temperatures accelerate solder joint failure,and the relationship between failure time and temperature conforms to the Arrhenius equation.The predicted life of the model is in good agreement with experimental results,demonstrating the effectiveness and accuracy of the model.展开更多
With rapid urbanization,fires pose significant challenges in urban governance.Traditional fire detection methods often struggle to detect smoke in complex urban scenes due to environmental interferences and variations...With rapid urbanization,fires pose significant challenges in urban governance.Traditional fire detection methods often struggle to detect smoke in complex urban scenes due to environmental interferences and variations in viewing angles.This study proposes a novel multimodal smoke detection method that fuses infrared and visible imagery using a transformer-based deep learning model.By capturing both thermal and visual cues,our approach significantly enhances the accuracy and robustness of smoke detection in business parks scenes.We first established a dual-view dataset comprising infrared and visible light videos,implemented an innovative image feature fusion strategy,and designed a deep learning model based on the transformer architecture and attention mechanism for smoke classification.Experimental results demonstrate that our method outperforms existing methods,under the condition of multi-view input,it achieves an accuracy rate of 90.88%,precision rate of 98.38%,recall rate of 92.41%and false positive and false negative rates both below 5%,underlining the effectiveness of the proposed multimodal and multi-view fusion approach.The attention mechanism plays a crucial role in improving detection performance,particularly in identifying subtle smoke features.展开更多
Poly(vinyl chloride)(PVC)materials are produced with high smoke and toxic gases during combustion,when commercial flame-retardant additives are incorporated.Here,rare-earth yttrium stannate(Y_(2)Sn_(2)O_(7)),which is ...Poly(vinyl chloride)(PVC)materials are produced with high smoke and toxic gases during combustion,when commercial flame-retardant additives are incorporated.Here,rare-earth yttrium stannate(Y_(2)Sn_(2)O_(7)),which is superior to commercial flame retardants,was designed to enhance the smoke suppression and toxicity reduction performance of PVC materials without damaging their mechanical properties.After the addition of 15 wt%Y_(2)Sn_(2)O_(7)(PVC/Y_(2)Sn_(2)O_(7)),the PVC composites achieved a V-0 rating,whereas the pure PVC material achieved a V-2 rating.The peak heat release rate of PVC/Y_(2)Sn_(2)O_(7) composite was reduced from 282.7 kW/m^(2)(pure PVC)to 243.6 kW/m^(2).In addition,the maximum smoke density(Ds-max)of PVC/Y_(2)Sn_(2)O_(7) was 263 m^(2)/m^(2),a decrease of 48.5%compared to pure PVC materials(511 m^(2)/m^(2)),indicating its outstanding ability for smoke suppression.Compared to Sb_(2)O_(3),Y_(2)Sn_(2)O_(7) can effectively reduce the release of the toxic gas CO(decreasing by 37.5%).Furthermore,the tensile strength of PVC can reach as high as 16.1 MPa.Compared with five widely used commercial flame retardants,Y_(2)Sn_(2)O_(7) demonstrates superior performance,positioning it as a promising alternative to prospective candidates.Therefore,this study developed a rare-earth flame retardant and offers a promising design to improve the fire safety of PVC composites.展开更多
This study proposes a multi-scene smoke detection algorithm based on a multi-feature extraction method to address the problems of varying smoke shapes in different scenes,difficulty in locating and detecting transluce...This study proposes a multi-scene smoke detection algorithm based on a multi-feature extraction method to address the problems of varying smoke shapes in different scenes,difficulty in locating and detecting translucent smoke,and variable smoke scales.First,the convolution module of feature extraction in YOLOv5s backbone network is replaced with asymmetric convolution block re-parameterization convolution to improve the detection of different shapes of smoke.Then,coordinate attention mechanism is introduced in the deeper layer of the backbone network to further improve the localization of translucent smoke.Finally,the detection of smoke at different scales is further improved by using the feature pyramid convolution module instead of the standard convolution module of the feature pyramid in the model.The experimental results demonstrate the feasibility and superiority of the proposed model for multi-scene smoke detection.展开更多
Atrium spaces,common in modern construction,provide significant fire safety challenges due to their large vertical openings,which facilitate rapid smoke spread and reduce sprinkler effectiveness.Traditional smoke mana...Atrium spaces,common in modern construction,provide significant fire safety challenges due to their large vertical openings,which facilitate rapid smoke spread and reduce sprinkler effectiveness.Traditional smoke management systems primarily rely on make-up air to replace the air expelled through vents.Inadequate calibration,particularly with air velocity,can worsen fire conditions by enhancing oxygen supply,increasing soot production,and reducing visibility,so endangering safe evacuation.This study investigates the impact of make-up air velocity on smoke behaviour in atrium environments through 24 simulations performed using the FireDynamics Simulator(FDS).Scenarios include various fire intensities(1,3,5 MW)and make-up air velocities(1–3.5 m/s),with fire sources located at the centre,northeast,and southwest corners.The simulation model was validated using updated full-scale fire test data with polystyrene fuel,leading to heightened soot density and reduced smoke clear height.This Research design diverges from other studies that predominantly utilized propane pool fires and concentrated on axisymmetric(Fire at the center of the atrum),Northeast and Southeast corners of the atrium scenarios by using polystyrene-a widely accessible construction material and examining several asymetric fire sites,so providing a more authentic depiction of atrium fire settings.Research reveals that increased air velocities,especially when directed at the fire,result in greater soot density and reduced smoke clearance due to intensified combustion.The northeastern region consistently displayed high temperature readings,highlighting the importance of fire source positioning in smoke behaviour.The study recommends limiting make-up air velocity to 1 m/s to avert turbulence and guarantee safety.This research provides critical insights for fire safety design and aligns with the United Nations Sustainable Development Goals,namely SDG 9 and SDG 11,by promoting safer and more resilient construction practices in urban environments.展开更多
Objective Cigarette smoking exacerbates the progression of pulmonary tuberculosis(TB).The role of tertiary lymphoid structures(TLS)in chronic lung diseases has gained attention;however,it remains unclear whether smoki...Objective Cigarette smoking exacerbates the progression of pulmonary tuberculosis(TB).The role of tertiary lymphoid structures(TLS)in chronic lung diseases has gained attention;however,it remains unclear whether smoking-exacerbated lung damage in TB is associated with TLS.This study aimed to analyze the characteristics of pulmonary TLS in smokers with TB and to explore the possible role of TLS in smoking-related lung injury in TB.Methods Lung tissues from 36 male patients(18 smokers and 18 non-smokers)who underwent surgical resection for pulmonary TB were included in this study.Pathological and immunohistological analyses were conducted to evaluate the quantity of TLS,and chest computed tomography(CT)was used to assess the severity of lung lesions.The correlation between the TLS quantity and TB lesion severity scores was analyzed.The immune cells and chemokines involved in TLS formation were also evaluated and compared between smokers and non-smokers.Results Smoker patients with TB had significantly higher TLS than non-smokers(P<0.001).The TLS quantity in both the lung parenchyma and peribronchial regions correlated with TB lesion severity on chest CT(parenchyma:r=0.5767;peribronchial:r=0.7373;both P<0.001).Immunohistochemical analysis showed increased B cells,T cells,and C-X-C motif chemokine ligand 13(CXCL13)expression in smoker patients with TB(P<0.001).Conclusion Smoker TB patients exhibited increased pulmonary TLS,which was associated with exacerbated lung lesions on chest CT,suggesting that cigarette smoking may exacerbate lung damage by promoting TLS formation.展开更多
Fire detection has held stringent importance in computer vision for over half a century.The development of early fire detection strategies is pivotal to the realization of safe and smart cities,inhabitable in the futu...Fire detection has held stringent importance in computer vision for over half a century.The development of early fire detection strategies is pivotal to the realization of safe and smart cities,inhabitable in the future.However,the development of optimal fire and smoke detection models is hindered by limitations like publicly available datasets,lack of diversity,and class imbalance.In this work,we explore the possible ways forward to overcome these challenges posed by available datasets.We study the impact of a class-balanced dataset to improve the fire detection capability of state-of-the-art(SOTA)vision-based models and propose the use of generative models for data augmentation,as a future work direction.First,a comparative analysis of two prominent object detection architectures,You Only Look Once version 7(YOLOv7)and YOLOv8 has been carried out using a balanced dataset,where both models have been evaluated across various evaluation metrics including precision,recall,and mean Average Precision(mAP).The results are compared to other recent fire detection models,highlighting the superior performance and efficiency of the proposed YOLOv8 architecture as trained on our balanced dataset.Next,a fractal dimension analysis gives a deeper insight into the repetition of patterns in fire,and the effectiveness of the results has been demonstrated by a windowing-based inference approach.The proposed Slicing-Aided Hyper Inference(SAHI)improves the fire and smoke detection capability of YOLOv8 for real-life applications with a significantly improved mAP performance over a strict confidence threshold.YOLOv8 with SAHI inference gives a mAP:50-95 improvement of more than 25%compared to the base YOLOv8 model.The study also provides insights into future work direction by exploring the potential of generative models like deep convolutional generative adversarial network(DCGAN)and diffusion models like stable diffusion,for data augmentation.展开更多
基金funded by the National Natural Science Foundation of China(NSFC)under Grant No.52278415the National Key Research and Development Program of China under Grant No.2022YFC3801104+2 种基金Hebei Provincial Department of Education Project under Grant No.QN2025304the Innovation Fund Project of Hebei University of Engineering under Grant No.SJ2401002066the Sichuan Science and Technology Program under Grant No.2023YFS0407。
文摘The diversion effect caused by the linked structure in a metro tunnel with cross-passage complicates the impact of longitudinal fire source location on the smoke backflow layering behavior that has not been clarified,despite the fact that the scenario exists in practice.A series of laboratory-scale experiments were conducted in this study to investigate the smoke back-layering length in a model tunnel with cross-passage.The heat release rate,the velocity of longitudinal air flow,and the location of the fire source were all varied.It was found that the behavior of smoke backflow for the fire source located at the upstream of bifurcation point resembles a single-hole tunnel fire.As the fire source’s position shifts downstream from the bifurcation point,the length of smoke back-layering progressively increases.A competitive interaction exists between airflow diversion and smoke diversion during smoke backflow,significantly affecting the smoke back-layering length in the main tunnel.The dimensionless smoke back-layering length model was formulated in a tunnel featuring a cross-passage,taking into account the positions of longitudinal fire sources.The dimensionless smoke back-layering length exhibits a positive correlation with the 17/18 power of total heat release rate Q and a negative correlation with the 5/2 power of longitudinal ventilation velocity V.
基金financially supported by Zhejiang Provincial"Leading Force"and"Leading Bird"R&D Key Project(No.2022C03128)Basic Public Welfare Research Plan of Zhejiang Province(No.LY21C160003)+1 种基金National Natural Science Foundation(No.51991355)Zhejiang A&F University Scientific Research Start-up Fund(No.2023LFR134)。
文摘A simultaneous boost in toughness and fire safety of epoxy(EP)is achieved through solvent-free one-step neutralization of phytic acid with 1,8-diaminooctane to yield a multifunctional bio-based curing agent,PA-DAO.When used as the sole hardener,5 wt%PA-DAO increased the tensile,flexural,and impact strengths by 165%,81%,and 455%,respectively,over the parent amine system,whereas the tensile and flexural toughness increased by 1387% and 775%,respectively.At 25 wt% loading,the resin attained a UL-94 V-0 rating and a limiting oxygen index of 28.1%,accompanied by a 71% reduction in the peak heat-release rate and a 53%suppression of total smoke production.This facile,green protocol provides scalable access to ultra-tough,intrinsically flame-retardant epoxy networks without external plasticizers or additives.
基金funded by the National Natural Science Foundation of China under Grant 62306128the Leading Innovation Project of Changzhou Science and Technology Bureau underGrant CQ20230072+2 种基金the Basic Science Research Project of Jiangsu Provincial Department of Education under Grant 23KJD520003the Science and Technology Development Plan Project of Jilin Provinceunder Grant 20240101382JCthe National KeyR esearch and Development Program of China under Grant 2023YFF1105102.
文摘In fire rescue scenarios,traditional manual operations are highly dangerous,as dense smoke,low visibility,extreme heat,and toxic gases not only hinder rescue efficiency but also endanger firefighters’safety.Although intelligent rescue robots can enter hazardous environments in place of humans,smoke poses major challenges for human detection algorithms.These challenges include the attenuation of visible and infrared signals,complex thermal fields,and interference frombackground objects,all ofwhichmake it difficult to accurately identify trapped individuals.To address this problem,we propose VIF-YOLO,a visible–infrared fusion model for real-time human detection in dense smoke environments.The framework introduces a lightweight multimodal fusion(LMF)module based on learnable low-rank representation blocks to end-to-end integrate visible and infrared images,preserving fine details while enhancing salient features.In addition,an efficient multiscale attention(EMA)mechanism is incorporated into the YOLOv10n backbone to improve feature representation under low-light conditions.Extensive experiments on our newly constructedmultimodal smoke human detection(MSHD)dataset demonstrate thatVIF-YOLOachievesmAP50 of 99.5%,precision of 99.2%,and recall of 99.3%,outperforming YOLOv10n by a clear margin.Furthermore,when deployed on the NVIDIA Jetson Xavier NX,VIF-YOLO attains 40.6 FPS with an average inference latency of 24.6 ms,validating its real-time capability on edge-computing platforms.These results confirm that VIF-YOLO provides accurate,robust,and fast detection across complex backgrounds and diverse smoke conditions,ensuring reliable and rapid localization of individuals in need of rescue.
基金supported by the National Natural Science Foundation of China(No.62203163)the Scientific Research Project of Hunan Provincial Education Department(No.24A0519)+1 种基金the Hunan Provincial Natural Science Foundation(No.2025JJ60407)the Postgraduate Scientific Research Innovation Project of Hunan Province(No.CX2024100).
文摘Accurate detection of smoke and fire sources is critical for early fire warning and environmental monitoring.However,conventional detection approaches are highly susceptible to noise,illumination variations,and complex environmental conditions,which often reduce detection accuracy and real-time performance.To address these limitations,we propose Lightweight and Precise YOLO(LP-YOLO),a high-precision detection framework that integrates a self-attention mechanism with a feature pyramid,built upon YOLOv8.First,to overcome the restricted receptive field and parameter redundancy of conventional Convolutional Neural Networks(CNNs),we design an enhanced backbone based on Wavelet Convolutions(WTConv),which expands the receptive field through multifrequency convolutional processing.Second,a Bidirectional Feature Pyramid Network(BiFPN)is employed to achieve bidirectional feature fusion,enhancing the representation of smoke features across scales.Third,to mitigate the challenge of ambiguous object boundaries,we introduce the Frequency-aware Feature Fusion(FreqFusion)module,in which the Adaptive Low-Pass Filter(ALPF)reduces intra-class inconsistencies,the offset generator refines boundary localization,and the Adaptive High-Pass Filter(AHPF)recovers high-frequency details lost during down-sampling.Experimental evaluations demonstrate that LP-YOLO significantly outperforms the baseline YOLOv8,achieving an improvement of 9.3%in mAP@50 and 9.2%in F1-score.Moreover,the model is 56.6%and 32.4%smaller than YOLOv7-tiny and EfficientDet,respectively,while maintaining real-time inference speed at 238 frames per second(FPS).Validation on multiple benchmark datasets,including D-Fire,FIRESENSE,and BoWFire,further confirms its robustness and generalization ability,with detection accuracy consistently exceeding 82%.These results highlight the potential of LP-YOLO as a practical solution with high accuracy,robustness,and real-time performance for smoke and fire source detection.
基金supported by National Key Research and Development Program of China(2022YFD1700500)Earmarked Fund for Shandong Agriculture Research System(SDARS-05).
文摘Smoke generator constitute an important class of pesticide formulations widely used in protected agriculture,forestry,mushroom cultivation,and storage environments.Unlike conventional sprays,smoke generator rely on heat-induced phase transitions of active ingredients to produce fine aerosolized particles that disperse through Brownian motion,thereby markedly improving application efficiency.Despite their long history and broad utility,the development of smoke generator has largely stagnated over the past two decades.Here,we provide a comprehensive assessment of their historical evolution,registration landscape,physicochemical mechanisms,and current deployment in agricultural systems.Based on this analysis,we outline key directions for nextgeneration smoke generator technology.First,transitioning from chemical heating to electric heating is essential to enable automation and unmanned pesticide delivery.Second,expanding the air-purification functionality of smoke formulations offers a promising strategy to suppress airborne pest and pathogen populations.Finally,integrating principles of crystal engineering to modulate particle morphology and interfacial affinity may overcome current limitations in deposition efficiency and biological performance.Together,these advances will underpin the development of high-efficiency,intelligent smoke generator and support precision plant protection and sustainable intensification in protected agriculture.
基金supported by the Energy Foundation(No.G-2203-33693).
文摘To understand the smoke level and NO_(x)emission characteristics of in-use construction machinery in Beijing,we selected 905 construction machines in Beijing from August 2022 to April 2023 to monitor the emission level of smoke and NO_(x).The exhaust smoke level and excessive emission situation of different machinery types were identified,and their NO_(x)emission levels were monitored according to the free acceleration method.We investigated the correlation of NO_(x)and smoke emission,and proposed suggestions for controlling pollution discharge from construction machinery in the future.The results show that the exhaust smoke level was 0–2.62 m^(−1),followed a log-normal distribution(μ=-1.73,δ=1.09,R^(2)=0.99),with a 5.64%exceedance rate.Differenceswere observed amongmachinery types,with low-power engine forklifts showing higher smoke levels.The NO_(x)emission range was 71–1516 ppm,followed a normal distribution(μ=565.54,δ=309.51,R^(2)=0.83).Differences among machinery types were relatively small.Engine rated net power had the most significant impact on NO_(x)emissions.Thus,NO_(x)emissions from construction machinery need further attention.Furthermore,we found a weak negative correlation(p<0.05)between the emission level of smoke and NO_(x),that is the synergic emission reduction effect is poor,emphasizing the need for NO_(x)emission limits.In the future,the oversight in Beijing should prioritize phasing out ChinaⅠand ChinaⅡmachinery,and monitor emissions from highpower engine ChinaⅢmachinery.
基金funded by the National Natural Science Foundation of China(U22A20547)the Major Science and Technology Projects of Heilongjiang Province(2021ZX12B05 and 2020ZX07B02)。
文摘The present study monitored bacterial succession,physicochemical properties,and volatile organic compounds(VOCs)changes in smoked chicken legs with modified atmosphere packaging(MAP,60% CO_(2) and 40%N_(2))during a 25-day storage period at 4℃.After 15 days of storage,S erratia proteamaculans and Pseudomonas fragi became the predominant bacteria.Furthermore,physicochemical properties changed significantly,as evidenced by an increase in thiobarbituric acid reactive substances and b*(yellowness)value,and a decrease in hardness.A total of 65 VOCs were identified during storage.Correlation between bacterial succession and quality indicators(including VOCs and physicochemical properties)allowed the identification of 26 core dominant bacteria,including S.proteamaculans,Psychrobacter alimentarius,Pseudomonas putida,and Pseudomonas poae,which were positively related to spoilage VOCs(e.g.,1-octen-3-ol,1-pentanol,and 3-methyl-1-butanol)and could be defined as specific spoilage organisms(SSOs).The results of this study provide a systematic approach to predict SSOs in smoked chicken legs during storage,which can also provide a basis for product safety.
文摘To improve the catalytic performance of La_(0.6)Sr_(0.4)Co_(0.2)Fe_(0.8)O_(3)(LSCF)towards carbon soot,we utilized the impregnation method to incorporate Ag into the prepared LSCF catalyst.We conducted a series of characterization tests and evaluated the soot catalytic activity of the composite catalyst by comparing it with the LaCoO_(3) group,LaFeO_(3) group,and catalyst-free group.The results indicate that the Ag-LSCF composite catalyst exhibits the highest soot catalytic activity,with the characteristic temperature values of 376.3,431.1,and 473.9℃at 10%,50%,and 90%carbon soot conversion,respectively.These values are 24.8,20.2,and 23.1℃lower than those of the LSCF group.This also shows that LSCF can improve the catalytic activity of soot after compounding with Ag,and reflects the necessity of using catalysts in soot combustion reaction.XPS characterization and BET test show that Ag-LSCF has more abundant surface-adsorbed oxygen species,larger specific surface area and pore volume than LSCF,which also proves that Ag-LSCF has higher soot catalytic activity.
文摘BACKGROUND Smoking is a leading cause of carcinogenesis in the head and neck region,representing a critical public health issue.Identifying genotoxic damage in smokers can provide valuable insights for developing preventive interventions.AIM To assess genotoxic damage through the micronucleus assay in exfoliated buccal mucosa cells from users of conventional tobacco,reverse smoking,cannabis,electronic cigarettes,and non-smokers.METHODS A cross-sectional study was conducted with 100 participants divided into five groups:20 conventional tobacco smokers,20 reverse smokers,20 electronic cigarette users,20 cannabis users,and 20 non-smokers.Exfoliated buccal mucosa cells were analyzed using Giemsa and Papanicolaou staining to identify micronuclei(MN)as markers of genotoxic damage.RESULTS MN were present in 86%of the samples.Statistically significant differences were observed in the median micronucleus count between conventional,reverse,and electronic cigarette smokers compared to non-smokers(P<0.001),while no significant difference was found for cannabis smokers(P=0.89).A significant correlation was identified between the presence of oral lesions and micronucleus count(P=0.03).Regression analysis ruled out alcohol as a confounding factor.CONCLUSION This study identified genotoxic damage associated with various smoking habits,except for cannabis use,highlighting the need for public health interventions to reduce smoking and mitigate its genotoxic effects.These findings provide a foundation for future research and the implementation of preventive policies.
基金supported by the Major Scientific and Technological Special Project of Guizhou Province([2024]014).
文摘In rapid urban development,outdoor parking lots have become essential components of urban transportation systems.However,the increasing number of parking lots is accompanied by a rising risk of vehicle fires,posing a serious challenge to public safety.As a result,there is a critical need for fire warning systems tailored to outdoor parking lots.Traditional smoke detection methods,however,struggle with the complex outdoor environment,where smoke characteristics often blend into the background,resulting in low detection efficiency and accuracy.To address these issues,this paper introduces a novel model named Dynamic Contextual Transformer YOLO(DCT-YOLO),an advanced smoke detection method specifically designed for outdoor parking lots.We introduce an innovative Dynamic Channel-Spatial Attention(DCSA)mechanism to improve the model’s focus on smoke features,thus improving detection accuracy.Additionally,we incorporate Contextual Transformer Networks(CoTNet)to better adapt to the irregularity of smoke patterns,further enhancing the accuracy of smoke region detection in complex environments.Moreover,we developed a new dataset that includes a wide range of smoke and fire scenarios,improving the model’s generalization capability.All baseline models were trained and evaluated on the same dataset to ensure a fair and consistent comparison.The experimental results on this dataset demonstrate that the proposed algorithm yields a mAP@0.5 of 85.1%and a mAP@0.5:0.95 of 55.7%,representing improvements of 15.0%and 14.9%,respectively,over the baseline model.These results highlight the effectiveness of the proposed method in accurately detecting smoke in challenging outdoor environments.
基金National Natural Science Foundation of China (No. 52206180)Fundamental Research Funds for the Central Universities,China (No. WK2320000050)。
文摘The corrosion behavior and life of Sn−3.0Ag−0.5Cu solder joints were investigated through fire smoke exposure experiments within the temperature range of 45−80℃.The nonlinear Wiener process and Arrhenius equation were used to establish the probability distribution function and prediction model of the solder joint’s average life and individual remaining useful life.The results indicate that solder joint resistance shows a nonlinear growth trend with time increasing.After 24 h,the solder joint transforms from spherical to rose-like shapes.Higher temperatures accelerate solder joint failure,and the relationship between failure time and temperature conforms to the Arrhenius equation.The predicted life of the model is in good agreement with experimental results,demonstrating the effectiveness and accuracy of the model.
基金supported by the National Natural Science Foundation of China(32171797)Chunhui Project Foundation of the Education Department of China(HZKY20220026).
文摘With rapid urbanization,fires pose significant challenges in urban governance.Traditional fire detection methods often struggle to detect smoke in complex urban scenes due to environmental interferences and variations in viewing angles.This study proposes a novel multimodal smoke detection method that fuses infrared and visible imagery using a transformer-based deep learning model.By capturing both thermal and visual cues,our approach significantly enhances the accuracy and robustness of smoke detection in business parks scenes.We first established a dual-view dataset comprising infrared and visible light videos,implemented an innovative image feature fusion strategy,and designed a deep learning model based on the transformer architecture and attention mechanism for smoke classification.Experimental results demonstrate that our method outperforms existing methods,under the condition of multi-view input,it achieves an accuracy rate of 90.88%,precision rate of 98.38%,recall rate of 92.41%and false positive and false negative rates both below 5%,underlining the effectiveness of the proposed multimodal and multi-view fusion approach.The attention mechanism plays a crucial role in improving detection performance,particularly in identifying subtle smoke features.
基金financially supported by the Science and Technology Breakthrough Project of Inner Mongolia(Nos.2024KJTW0006 and 2023B2551)Central Guiding Science and Technology Development Fund of Inner Mongolia(No.2022ZY0100)+3 种基金Natural Science Foundation of Anhui Province(No.2408085ME140)National Key Laboratory of Baiyunobo Rare Earth Resource Research and Comprehensive UtilizationExperimental Center of Engineering and Material Science from the University of Science and Technology of ChinaInstruments Center for Physical Science,University of Science and Technology of China。
文摘Poly(vinyl chloride)(PVC)materials are produced with high smoke and toxic gases during combustion,when commercial flame-retardant additives are incorporated.Here,rare-earth yttrium stannate(Y_(2)Sn_(2)O_(7)),which is superior to commercial flame retardants,was designed to enhance the smoke suppression and toxicity reduction performance of PVC materials without damaging their mechanical properties.After the addition of 15 wt%Y_(2)Sn_(2)O_(7)(PVC/Y_(2)Sn_(2)O_(7)),the PVC composites achieved a V-0 rating,whereas the pure PVC material achieved a V-2 rating.The peak heat release rate of PVC/Y_(2)Sn_(2)O_(7) composite was reduced from 282.7 kW/m^(2)(pure PVC)to 243.6 kW/m^(2).In addition,the maximum smoke density(Ds-max)of PVC/Y_(2)Sn_(2)O_(7) was 263 m^(2)/m^(2),a decrease of 48.5%compared to pure PVC materials(511 m^(2)/m^(2)),indicating its outstanding ability for smoke suppression.Compared to Sb_(2)O_(3),Y_(2)Sn_(2)O_(7) can effectively reduce the release of the toxic gas CO(decreasing by 37.5%).Furthermore,the tensile strength of PVC can reach as high as 16.1 MPa.Compared with five widely used commercial flame retardants,Y_(2)Sn_(2)O_(7) demonstrates superior performance,positioning it as a promising alternative to prospective candidates.Therefore,this study developed a rare-earth flame retardant and offers a promising design to improve the fire safety of PVC composites.
基金the Natural Science Foundation of Zhejiang Province(Nos.LY20F020015 and LY21F020015)the National Natural Science Foundation of China(Nos.61972121 and 61902099)。
文摘This study proposes a multi-scene smoke detection algorithm based on a multi-feature extraction method to address the problems of varying smoke shapes in different scenes,difficulty in locating and detecting translucent smoke,and variable smoke scales.First,the convolution module of feature extraction in YOLOv5s backbone network is replaced with asymmetric convolution block re-parameterization convolution to improve the detection of different shapes of smoke.Then,coordinate attention mechanism is introduced in the deeper layer of the backbone network to further improve the localization of translucent smoke.Finally,the detection of smoke at different scales is further improved by using the feature pyramid convolution module instead of the standard convolution module of the feature pyramid in the model.The experimental results demonstrate the feasibility and superiority of the proposed model for multi-scene smoke detection.
文摘Atrium spaces,common in modern construction,provide significant fire safety challenges due to their large vertical openings,which facilitate rapid smoke spread and reduce sprinkler effectiveness.Traditional smoke management systems primarily rely on make-up air to replace the air expelled through vents.Inadequate calibration,particularly with air velocity,can worsen fire conditions by enhancing oxygen supply,increasing soot production,and reducing visibility,so endangering safe evacuation.This study investigates the impact of make-up air velocity on smoke behaviour in atrium environments through 24 simulations performed using the FireDynamics Simulator(FDS).Scenarios include various fire intensities(1,3,5 MW)and make-up air velocities(1–3.5 m/s),with fire sources located at the centre,northeast,and southwest corners.The simulation model was validated using updated full-scale fire test data with polystyrene fuel,leading to heightened soot density and reduced smoke clear height.This Research design diverges from other studies that predominantly utilized propane pool fires and concentrated on axisymmetric(Fire at the center of the atrum),Northeast and Southeast corners of the atrium scenarios by using polystyrene-a widely accessible construction material and examining several asymetric fire sites,so providing a more authentic depiction of atrium fire settings.Research reveals that increased air velocities,especially when directed at the fire,result in greater soot density and reduced smoke clearance due to intensified combustion.The northeastern region consistently displayed high temperature readings,highlighting the importance of fire source positioning in smoke behaviour.The study recommends limiting make-up air velocity to 1 m/s to avert turbulence and guarantee safety.This research provides critical insights for fire safety design and aligns with the United Nations Sustainable Development Goals,namely SDG 9 and SDG 11,by promoting safer and more resilient construction practices in urban environments.
基金supported by the Peking University Medicine Fund of Fostering Young Scholars'Scientific&Technological Innovation[grant number BMU2024YFJHPY014]the Fundamental Research Funds for the Central Universities+1 种基金the Key Clinical Projects of Peking University Third Hospital[grant number BYSYZD2022014]the Capital’s Funds for Health Improvement and Research[grant number 2022-2G-40910]。
文摘Objective Cigarette smoking exacerbates the progression of pulmonary tuberculosis(TB).The role of tertiary lymphoid structures(TLS)in chronic lung diseases has gained attention;however,it remains unclear whether smoking-exacerbated lung damage in TB is associated with TLS.This study aimed to analyze the characteristics of pulmonary TLS in smokers with TB and to explore the possible role of TLS in smoking-related lung injury in TB.Methods Lung tissues from 36 male patients(18 smokers and 18 non-smokers)who underwent surgical resection for pulmonary TB were included in this study.Pathological and immunohistological analyses were conducted to evaluate the quantity of TLS,and chest computed tomography(CT)was used to assess the severity of lung lesions.The correlation between the TLS quantity and TB lesion severity scores was analyzed.The immune cells and chemokines involved in TLS formation were also evaluated and compared between smokers and non-smokers.Results Smoker patients with TB had significantly higher TLS than non-smokers(P<0.001).The TLS quantity in both the lung parenchyma and peribronchial regions correlated with TB lesion severity on chest CT(parenchyma:r=0.5767;peribronchial:r=0.7373;both P<0.001).Immunohistochemical analysis showed increased B cells,T cells,and C-X-C motif chemokine ligand 13(CXCL13)expression in smoker patients with TB(P<0.001).Conclusion Smoker TB patients exhibited increased pulmonary TLS,which was associated with exacerbated lung lesions on chest CT,suggesting that cigarette smoking may exacerbate lung damage by promoting TLS formation.
基金supported by a grant from R&D Program Development of Rail-Specific Digital Resource Technology Based on an AI-Enabled Rail Support Platform,grant number PK2401C1,of the Korea Railroad Research Institute.
文摘Fire detection has held stringent importance in computer vision for over half a century.The development of early fire detection strategies is pivotal to the realization of safe and smart cities,inhabitable in the future.However,the development of optimal fire and smoke detection models is hindered by limitations like publicly available datasets,lack of diversity,and class imbalance.In this work,we explore the possible ways forward to overcome these challenges posed by available datasets.We study the impact of a class-balanced dataset to improve the fire detection capability of state-of-the-art(SOTA)vision-based models and propose the use of generative models for data augmentation,as a future work direction.First,a comparative analysis of two prominent object detection architectures,You Only Look Once version 7(YOLOv7)and YOLOv8 has been carried out using a balanced dataset,where both models have been evaluated across various evaluation metrics including precision,recall,and mean Average Precision(mAP).The results are compared to other recent fire detection models,highlighting the superior performance and efficiency of the proposed YOLOv8 architecture as trained on our balanced dataset.Next,a fractal dimension analysis gives a deeper insight into the repetition of patterns in fire,and the effectiveness of the results has been demonstrated by a windowing-based inference approach.The proposed Slicing-Aided Hyper Inference(SAHI)improves the fire and smoke detection capability of YOLOv8 for real-life applications with a significantly improved mAP performance over a strict confidence threshold.YOLOv8 with SAHI inference gives a mAP:50-95 improvement of more than 25%compared to the base YOLOv8 model.The study also provides insights into future work direction by exploring the potential of generative models like deep convolutional generative adversarial network(DCGAN)and diffusion models like stable diffusion,for data augmentation.
