The development of infrared engineering technologies for extreme environments remains a formidable challenge due to the inherent trade-offs among optical performance,thermal stability,and mechanical integrity in therm...The development of infrared engineering technologies for extreme environments remains a formidable challenge due to the inherent trade-offs among optical performance,thermal stability,and mechanical integrity in thermal photonic metamaterials(TPMs).This work introduces a novel multi-obj ective design framework and demonstrates the design,fabrication,and validation of a TPM operating under extreme temperatures up to 1873 K.We have established a holistic design framework integrating temperaturedependent neural network and Pareto multi-obj ective optimization to co-optimize spectral response,component light-weighting,and structural efficiency.The framework achieves 100 times faster computation than genetic algorithms.The performance of the designed TPM was evaluated under various atmospheric models and detection distances.The TPM achieved a peak radiance suppression efficiency of 82%and a maximum attenuation of-7.4 dB at 1200-1500 K.Experimentally,we fabricated an all-dielectric TPM using a refractory TiO_(2)/BeO multilayer stack with only 5 layers and 2um total thickness.The optimized structure shows high reflectivity(0.62 at 3-5 um;0.48 at 8-14μm)for radiative suppression and high emissivity(0.87 at 5-8μm)for radiative cooling.The TPM withstands 1873 K for 12 h in air with less than 3%spectral drift,retaining excellent mechanical properties.On high-temperature components,it achieves 40-50%radiative suppression and 40-60 K(~10.1 kW m^(-2))radiative cooling at 1100 K,endures over 20 times thermal shock cycles(>150 K s^(-1),700-1500 K),and maintains stable performance over 5 cycles,with 78%visible and 98%microwave transmittance.This work establishes a new paradigm in the design and application of photonic materials for extreme environments.展开更多
Driven by rapid advances in deep learning,object detection has been widely adopted across diverse application scenarios.However,in low-light conditions,critical visual cues of target objects are severely degraded,posi...Driven by rapid advances in deep learning,object detection has been widely adopted across diverse application scenarios.However,in low-light conditions,critical visual cues of target objects are severely degraded,posing a significant challenge for accurate low-light object detection.Existing methods struggle to preserve discriminative features while maintaining semantic consistency between low-light and normal-light images.For this purpose,this study proposes a DL-YOLO model specially tailored for low-light detection.To mitigate target feature attenuation introduced by repeated downsampling,we design aMulti-Scale FeatureConvolution(MSF-Conv)module that captures rich,multi-level details via multi-scale feature learning,thereby reducing model complexity and computational cost.For feature fusion,we integrated the C3k2-DWRmodule by embedding the Dilation-wise Residual(DWR)mechanism into the 2-core optimized Cross Stage Partial(C3)framework,achieving efficient feature integration.In addition,we replace conventional localization losses with WIoU(Weighted Intersection over Union),which dynamically adjusts gradient gain according to sample quality,thereby improving localization robustness and precision.Experiments on the ExDark dataset demonstrate that DL-YOLO delivers strong low-light detection performance.The relevant code is published at https://github.com/cym0997/DL-YOLO.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
In modern industrial production,foreign object detection in complex environments is crucial to ensure product quality and production safety.Detection systems based on deep-learning image processing algorithms often fa...In modern industrial production,foreign object detection in complex environments is crucial to ensure product quality and production safety.Detection systems based on deep-learning image processing algorithms often face challenges with handling high-resolution images and achieving accurate detection against complex backgrounds.To address these issues,this study employs the PatchCore unsupervised anomaly detection algorithm combined with data augmentation techniques to enhance the system’s generalization capability across varying lighting conditions,viewing angles,and object scales.The proposed method is evaluated in a complex industrial detection scenario involving the bogie of an electric multiple unit(EMU).A dataset consisting of complex backgrounds,diverse lighting conditions,and multiple viewing angles is constructed to validate the performance of the detection system in real industrial environments.Experimental results show that the proposed model achieves an average area under the receiver operating characteristic curve(AUROC)of 0.92 and an average F1 score of 0.85.Combined with data augmentation,the proposed model exhibits improvements in AUROC by 0.06 and F1 score by 0.03,demonstrating enhanced accuracy and robustness for foreign object detection in complex industrial settings.In addition,the effects of key factors on detection performance are systematically analyzed,providing practical guidance for parameter selection in real industrial applications.展开更多
With the increasing emphasis on personal information protection,encryption through security protocols has emerged as a critical requirement in data transmission and reception processes.Nevertheless,IoT ecosystems comp...With the increasing emphasis on personal information protection,encryption through security protocols has emerged as a critical requirement in data transmission and reception processes.Nevertheless,IoT ecosystems comprise heterogeneous networks where outdated systems coexist with the latest devices,spanning a range of devices from non-encrypted ones to fully encrypted ones.Given the limited visibility into payloads in this context,this study investigates AI-based attack detection methods that leverage encrypted traffic metadata,eliminating the need for decryption and minimizing system performance degradation—especially in light of these heterogeneous devices.Using the UNSW-NB15 and CICIoT-2023 dataset,encrypted and unencrypted traffic were categorized according to security protocol,and AI-based intrusion detection experiments were conducted for each traffic type based on metadata.To mitigate the problem of class imbalance,eight different data sampling techniques were applied.The effectiveness of these sampling techniques was then comparatively analyzed using two ensemble models and three Deep Learning(DL)models from various perspectives.The experimental results confirmed that metadata-based attack detection is feasible using only encrypted traffic.In the UNSW-NB15 dataset,the f1-score of encrypted traffic was approximately 0.98,which is 4.3%higher than that of unencrypted traffic(approximately 0.94).In addition,analysis of the encrypted traffic in the CICIoT-2023 dataset using the same method showed a significantly lower f1-score of roughly 0.43,indicating that the quality of the dataset and the preprocessing approach have a substantial impact on detection performance.Furthermore,when data sampling techniques were applied to encrypted traffic,the recall in the UNSWNB15(Encrypted)dataset improved by up to 23.0%,and in the CICIoT-2023(Encrypted)dataset by 20.26%,showing a similar level of improvement.Notably,in CICIoT-2023,f1-score and Receiver Operation Characteristic-Area Under the Curve(ROC-AUC)increased by 59.0%and 55.94%,respectively.These results suggest that data sampling can have a positive effect even in encrypted environments.However,the extent of the improvement may vary depending on data quality,model architecture,and sampling strategy.展开更多
Antibiotic resistance genes(ARGs) are recognized as a primary threat to the sustainability of environment and human health in the 21^(st) century.Nanomaterials(NMs) have attracted substantial attention due to their un...Antibiotic resistance genes(ARGs) are recognized as a primary threat to the sustainability of environment and human health in the 21^(st) century.Nanomaterials(NMs) have attracted substantial attention due to their unique dimensions and structures.Unfortunately,emerging evidence suggests that NMs may facilitate the transmission of ARGs.It is crucial to elucidate how NMs affect the evolution and dissemination of ARGs.The current review comprehensively examines the role of NMs in the widespread transmission of ARGs in aquatic environments and the underlying mechanisms involved in the process.It aims to clarify the effects and mechanisms of NMs on the horizontal gene transfer processes that are associated with ARGs,including the enhancement of cell membrane permeability,the formation of nanopores on membranes,promotion of mutagenesis,and the generation of reactive oxygen species(ROSs).Furthermore,the trade-off between the removal of ARGs and horizontal transfer has been elucidated.The review aspires to guide future research directions,advance knowledge on the implications of NMs in the field of ARGs' transmission,and provide a theoretical foundation for the development of safer and more effective applications of NMs.展开更多
With the rapid development of artificial intelligence,intelligent air combat maneuver decision-making(ACMD)has garnered global attention.Although deep reinforcement learning provides a promising approach to ACMD,exist...With the rapid development of artificial intelligence,intelligent air combat maneuver decision-making(ACMD)has garnered global attention.Although deep reinforcement learning provides a promising approach to ACMD,existing methods often suffer from rigid reward functions and limited adaptability to evolving adversarial strategies.Moreover,most research assumes open airspace,overlooking the influence of potential obstacles.In this paper,we address one-on-one within-visual-range ACMD in obstructed environments,and propose an improved Soft Actor-Critic(SAC)algorithm trained under a curriculum self-play framework.A maneuver strategy mirroring inference module is integrated to estimate each other's likely positions when visual obstruction occurs.By leveraging curriculum learning to guide progressive experience accumulation and self-play for adversarial evolution,our method enhances both training efficiency and tactical diversity.We further integrate an attention mechanism that dynamically adjusts the weights of sub-rewards,enabling the learned policy to adapt to rapidly changing air combat situations.Numerical simulations demonstrate that our enhanced SAC converges more quickly and achieves higher win rates than other baseline methods.An animation is available at bilibili.com/video/BV1BHVszHE98 for better illustration.展开更多
The atmospheric corrosion behavior of 510L low alloy steel subjected to acid-cleaned surface(ACS)and eco-pickled surface(EPS)treatments is systematically examined.After 1 year of atmospheric exposure,both ACS-and EPS-...The atmospheric corrosion behavior of 510L low alloy steel subjected to acid-cleaned surface(ACS)and eco-pickled surface(EPS)treatments is systematically examined.After 1 year of atmospheric exposure,both ACS-and EPS-treated samples demonstrate protective ability index values exceeding 2,indicating robust protective properties of the developed rust layers.The corrosion rates of ACS-and EPS-treated samples are similar.During the initial corrosion stage,γ-FeOOH emerges as the dominant corrosion product.With the prolonged atmospheric exposure,γ-FeOOH content progressively decreases through phase transformation into thermodynamically stableα-FeOOH and densely structured Fe_(3)O_(4),which markedly suppresses the corrosion of the steel.Notably,the corrosion rate of the coated EPS sample is obviously lower than that of the coated ACS counterpart,which is ascribed to the distinctive micro-roughness of EPS-treated surfaces that promote mechanical interlocking with protective coatings.展开更多
As large,room-scale environments become increasingly common,their spatial complexity increases due to variable,unstructured elements.Consequently,demand for room-scale service robots is surging,yet most technologies r...As large,room-scale environments become increasingly common,their spatial complexity increases due to variable,unstructured elements.Consequently,demand for room-scale service robots is surging,yet most technologies remain corridor-centric,and autonomous navigation in expansive rooms becomes unstable even around static obstacles.Existing approaches face several structural limitations.These include the labor-intensive requirement for large-scale object annotation and continual retraining,as well as the vulnerability of vanishing point or linebased methods when geometric cues are insufficient.In addition,the high cost of LiDAR and 3D perception errors caused by limited wall cues and dense interior clutter further limit their effectiveness.To address these challenges,we propose a zero-shot vision-based algorithm for robust 3D map reconstruction in geometry-deficient room-scale environments.The algorithm operates in three layers:Layer 1 performs dimension-wise boundary detection;Layer 2 estimates vanishing points,refines the precise perspective space,and extracts a floor mask;and Layer 3 conducts 3D spatial mapping and obstacle recognition.The proposed method was experimentally validated across various geometric-deficient room-scale environments,including lobbies,seminar rooms,conference rooms,cafeterias,and museums—demonstrating its ability to reliably reconstruct 3D maps and accurately recognize obstacles.Experimental results show that the proposed algorithm achieved an F1 score of 0.959 in precision perspective space detection and 0.965 in floor mask extraction.For obstacle recognition and classification,it obtained F1 scores of 0.980 in obstacle absent areas,0.913 in solid obstacle environments,and 0.939 in skeleton-type sparse obstacle environments,confirming its high precision and reliability in geometric-deficient room-scale environments.展开更多
Federated Learning(FL)provides an effective framework for efficient processing in vehicular edge computing.However,the dynamic and uncertain communication environment,along with the performance variations of vehicular...Federated Learning(FL)provides an effective framework for efficient processing in vehicular edge computing.However,the dynamic and uncertain communication environment,along with the performance variations of vehicular devices,affect the distribution and uploading processes of model parameters.In FL-assisted Internet of Vehicles(IoV)scenarios,challenges such as data heterogeneity,limited device resources,and unstable communication environments become increasingly prominent.These issues necessitate intelligent vehicle selection schemes to enhance training efficiency.Given this context,we propose a new scenario involving FL-assisted IoV systems under dynamic and uncertain communication conditions,and develop a dynamic interval multi-objective optimization algorithm to jointly optimize various factors including training experiments,system energy consumption,and bandwidth utilization to meet multi-criteria resource optimization requirements.For the problem at hand,we design a dynamic interval multi-objective optimization algorithm based on interval overlap detection.Simulation results demonstrate that our method outperforms other solutions in terms of accuracy,training cost,and server utilization.It effectively enhances training efficiency under wireless channel environments while rationally utilizing bandwidth resources,thus possessing significant scientific value and application potential in the field of IoV.展开更多
To assess the effectiveness of vaccination in contaminated environments,this study introduces a modeling framework that encompasses two transmission routes,namely direct human-to-human contact and indirect human-to-en...To assess the effectiveness of vaccination in contaminated environments,this study introduces a modeling framework that encompasses two transmission routes,namely direct human-to-human contact and indirect human-to-environment contact,as well as the implementation of new M72/AS01_(E)vaccine.Motivated by this,a coupled age-structured tuberculosis(TB)model is proposed.Its well-posedness requirement is verified using the integrated semigroup theory.Furthermore,this study presents a comprehensive analysis of threshold dynamics associated with the proposed model.Specifically,the global stability of the disease-free and positive steady states is demonstrated by employing Lyapunov functionals.Lastly,the effects of the vaccination with M72/AS01_(E)and contaminated environments on TB control are numerically simulated.Experimental results indicate that high concentrations of Mycobacterium tuberculosis in contaminated environments may somewhat impede TB control efforts,but that large-scale deployment of new vaccine could significantly reduce the prevalence of TB.展开更多
There is a lack of systematic understanding of coal-forming environment classification and its influences on coal petrological characteristics,a coal-forming mire classification scheme applicable to the petroleum indu...There is a lack of systematic understanding of coal-forming environment classification and its influences on coal petrological characteristics,a coal-forming mire classification scheme applicable to the petroleum industry is proposed based on modern ecological peatland frameworks.The formation,evolutionary processes,and diagnostic criteria of coal-forming environments are systematically clarified.The results show that:(1)modern peatlands can be classified according to hydrological conditions,vegetation types,and geomorphic settings,and coal-forming mires can be divided into low moor,transitional,and high moor peat mires based on geomorphology;(2)the development of coal-forming environments includes three modes:subaqueous peat infilling,autochthonous peat accumulation in wetlands,and mire development in arid regions;(3)peat accumulation is jointly controlled by plant production and decomposition,hydrological disturbances,and sediment input,and the peat-to-coal thickness ratio varies with coalification;(4)diagnostic criteria for low moor,transitional,and high moor peat mires are established based on ash yield,gamma-ray log responses,and vitrinite-to-inertinite ratios;and(5)transgression-regression processes exert a key control on peat mire evolution,directly influencing peat thickness and continuity,while the evolution of low moor,transitional,and high moor mires governs coal maceral assemblages and thereby affects hydrocarbon generation potential and reservoir properties of coals.The coal-forming environment classification and identification system developed in this study effectively reveals the vertical heterogeneity of coals in the Ordos Basin,providing theoretical and practical guidance for efficient exploration and development of coal-rock gas.展开更多
To meet the requirements of electromagnetic(EM)theory and applied physics,this study presents an overview of the state-of-the-art research on obtaining the EM properties of media and points out potential solutions tha...To meet the requirements of electromagnetic(EM)theory and applied physics,this study presents an overview of the state-of-the-art research on obtaining the EM properties of media and points out potential solutions that can break through the bottlenecks of current methods.Firstly,based on the survey of three mainstream approaches for acquiring EM properties of media,we identify the difficulties when implementing them in realistic environments.With a focus on addressing these problems and challenges,we propose a novel paradigm for obtaining the EM properties of multi-type media in realistic environments.Particularly,within this paradigm,we describe the implementation approach of the key technology,namely“multipath extraction using heterogeneous wave propagation data in multi-spectrum cases”.Finally,the latest measurement and simulation results show that the EM properties of multi-type media in realistic environments can be precisely and efficiently acquired by the methodology proposed in this study.展开更多
With the rapid development of the marine economy,marine microbiologically influenced corrosion(MIC)has garnered increasing attention.However,most studies have not analyzed the MIC process over continuous and extended ...With the rapid development of the marine economy,marine microbiologically influenced corrosion(MIC)has garnered increasing attention.However,most studies have not analyzed the MIC process over continuous and extended periods,failing to provide a comprehensive understanding of MIC mechanisms at different stages.In this study,the corrosion behavior of EH36 steel caused by Halomonas titanicae in an aerobic enriched seawater over a 30-d incubation period was investigated driven by big data.The results revealed that the corrosion by H.titanicae against EH36 steel evolved dynamically over time.During the initial stages,the aerobic respiration of H.titanicae consumed significant amounts of oxygen,which suppressed the cathodic oxygen reduction process,thereby inhibiting corrosion compared to the abiotic conditions.As time progressed,the accumulation of corrosion products slowed the abiotic corrosion,while the biotic corrosion accelerated due to a shift from aerobic to anaerobic respiration by H.titanicae,utilizing Fe0 and nitrate as electron donors and acceptors,respectively.The big data results are consistent with the weight loss and electrochemical data,demonstrating the reliability of using big data monitoring techniques to characterize microbial corrosion processes.展开更多
Aiming to address the Unmanned Aerial Vehicle(UAV) formation collision avoidance problem in Three-Dimensional(3-D) low-altitude environments where dense various obstacles exist, a fluid-based path planning framework n...Aiming to address the Unmanned Aerial Vehicle(UAV) formation collision avoidance problem in Three-Dimensional(3-D) low-altitude environments where dense various obstacles exist, a fluid-based path planning framework named the Formation Interfered Fluid Dynamical System(FIFDS) with Moderate Evasive Maneuver Strategy(MEMS) is proposed in this study.First, the UAV formation collision avoidance problem including quantifiable performance indexes is formulated. Second, inspired by the phenomenon of fluids continuously flowing while bypassing objects, the FIFDS for multiple UAVs is presented, which contains a Parallel Streamline Tracking(PST) method for formation keeping and the traditional IFDS for collision avoidance. Third, to rationally balance flight safety and collision avoidance cost, MEMS is proposed to generate moderate evasive maneuvers that match up with collision risks. Comprehensively containing the time and distance safety information, the 3-D dynamic collision regions are modeled for collision prediction. Then, the moderate evasive maneuver principle is refined, which provides criterions of the maneuver amplitude and direction. On this basis, an analytical parameter mapping mechanism is designed to online optimize IFDS parameters. Finally, the performance of the proposed method is validated by comparative simulation results and real flight experiments using fixed-wing UAVs.展开更多
Human interaction with natural environments is gaining increasing attention in environmental sciences as research consistently shows that access to green spaces,clean air,and biodiversity plays a crucial role in enhan...Human interaction with natural environments is gaining increasing attention in environmental sciences as research consistently shows that access to green spaces,clean air,and biodiversity plays a crucial role in enhancing physical health,reducing stress,and improving overall well-being.This study conducts a bibliometric and visualization-based analysis of forest therapy research,emphasizing its physiological and psychological benefits.Using the Web of Science database,we identified and analyzed 414 studies from 1998 to 2023.Through CiteSpace and VOSviewer,we mapped these documents to examine research trends,publication networks,leading scholars and institutions,key journals,and thematic evolution.Findings indicate that forest therapy research is predominantly concentrated in East Asia,North America,Australia,and Europe,with strong collaborative networks among authors and institutions.The concentration of publications,research evolution,and keyword trends reflect the development of forest therapy research.The analysis further identifies sixteen research clusters and discusses two research themes:physiological and psychological effects.By analyzing how the natural environment contributes to human well-being,we provide a comprehensive and visually structured understanding of forest therapy as an intersection of environmental science,public health and well-being,and ecosystem conservation.Our findings offer a new perspective for future interdisciplinary research,emphasizing the need for well-designed clinical trials to substantiate forest therapy’s diverse health effects and its role in promoting sustainable interactions between human societies and natural environments.展开更多
With the rapid development of drone technology,drones are increasingly used in urban environments,but they also bring many security risks,such as illegal reconnaissance,smuggling,and terrorist attacks.Therefore,it is ...With the rapid development of drone technology,drones are increasingly used in urban environments,but they also bring many security risks,such as illegal reconnaissance,smuggling,and terrorist attacks.Therefore,it is of great significance to study the anti-UAV technology in the urban environment.This paper analyzes the advantages and disadvantages of existing technologies and their applicability in the urban environment from the aspects of UAV detection,identification,and countermeasures,and discusses the future development trend of anti-UAV technology,aiming to provide a reference for urban safety protection.展开更多
To the editor:Adverse home environments(AHE),characterised by family conflict,parental separation or dysfunctional parenting,are linked to negative mental health outcomes in children and adults.12 AHE disproportionate...To the editor:Adverse home environments(AHE),characterised by family conflict,parental separation or dysfunctional parenting,are linked to negative mental health outcomes in children and adults.12 AHE disproportionately affect children with neurodevelopmental disorders such as attention-deficit/hyperactivity disorder(ADHD),which is characterised by inattention,hyperactivity/impulsivity and functional impairments.3 Apart from core symptoms,including inattention and hyperactivity,disruptive behaviour disorders(DBD),such as oppositional defiant disorder(ODD)and conduct disorder(CD),may be associated with AHE.Conduct problems are risk factors for ODD.And CD has become a main concern for childhood mental health.展开更多
Background:Cold temperatures cause blood vessels to constrict,shallow breathing,and slight thickening of the blood.Working in extremely cold environments can have negative effects on health,yet there are currently no ...Background:Cold temperatures cause blood vessels to constrict,shallow breathing,and slight thickening of the blood.Working in extremely cold environments can have negative effects on health,yet there are currently no effective biomarkers to monitor these health conditions.Proteins are important intermediate phenotypes that can provide a theoretical basis for understanding disease pathophysiology.Proteins in the circulatory system reflect the physiological status of individuals,and plasma proteins have significant potential as biomarkers for various health conditions.Methods:In this study,we employed the Mendelian randomization(MR)method to analyze the effects of freezing temperatures on over 2900 plasma proteins.Subsequently,the selected plasma proteins were subjected to causal analysis in relation to 55 diseases,including respiratory disorders,cardiovascular diseases,various cancers,and oral diseases.The aim was to identify proteins that could serve as biomarkers for health status.Results:Our results indicate that cold environments may affect the concentrations of 78 plasma proteins.Further MR analysis revealed that nine of these plasma proteins are associated with the risk of respiratory disorders,cardiovascular diseases,various cancers,and oral diseases.Conclusion:These proteins show promise as biomarkers for monitoring the hazards and risks faced by individuals working in cold environments.These findings provide valuable insights into the biological mechanisms underlying occupational hazards.展开更多
When a fire breaks out in a high-rise building,the occlusion of smoke and obstacles results in dearth of crucial information concerning people in distress,thereby creating a challenge in their detection.Given the rest...When a fire breaks out in a high-rise building,the occlusion of smoke and obstacles results in dearth of crucial information concerning people in distress,thereby creating a challenge in their detection.Given the restricted sensing range of a single unmanned aerial vehicle(UAV)cam-era,enhancing the target recognition rate becomes challenging without target information.To tackle this issue,this paper proposes a multi-agent autonomous collaborative detection method for multi-targets in complex fire environments.The objective is to achieve the fusion of multi-angle visual information,effectively increasing the target’s information dimension,and ultimately address-ing the problem of low target recognition rate caused by the lack of target information.The method steps are as follows:first,the you only look once version5(YOLOv5)is used to detect the target in the image;second,the detected targets are tracked to monitor their movements and trajectories;third,the person re-identification(ReID)model is employed to extract the appearance features of targets;finally,by fusing the visual information from multi-angle cameras,the method achieves multi-agent autonomous collaborative detection.The experimental results show that the method effectively combines the visual information from multi-angle cameras,resulting in improved detec-tion efficiency for people in distress.展开更多
基金supported by National Key Research and Development Program of China(2024YFA1210500,2023YFB4606105)Fundamental Research Center Projects(52488301)of National Natural Science Foundation of China(NSFC)+1 种基金Key Research Program of Frontier Sciences(ZDBS-LYJSC030)of Chinese Academy of SciencesWestern Light Program(xbzg-zdsys-202402)of Chinese Academy of Sciences。
文摘The development of infrared engineering technologies for extreme environments remains a formidable challenge due to the inherent trade-offs among optical performance,thermal stability,and mechanical integrity in thermal photonic metamaterials(TPMs).This work introduces a novel multi-obj ective design framework and demonstrates the design,fabrication,and validation of a TPM operating under extreme temperatures up to 1873 K.We have established a holistic design framework integrating temperaturedependent neural network and Pareto multi-obj ective optimization to co-optimize spectral response,component light-weighting,and structural efficiency.The framework achieves 100 times faster computation than genetic algorithms.The performance of the designed TPM was evaluated under various atmospheric models and detection distances.The TPM achieved a peak radiance suppression efficiency of 82%and a maximum attenuation of-7.4 dB at 1200-1500 K.Experimentally,we fabricated an all-dielectric TPM using a refractory TiO_(2)/BeO multilayer stack with only 5 layers and 2um total thickness.The optimized structure shows high reflectivity(0.62 at 3-5 um;0.48 at 8-14μm)for radiative suppression and high emissivity(0.87 at 5-8μm)for radiative cooling.The TPM withstands 1873 K for 12 h in air with less than 3%spectral drift,retaining excellent mechanical properties.On high-temperature components,it achieves 40-50%radiative suppression and 40-60 K(~10.1 kW m^(-2))radiative cooling at 1100 K,endures over 20 times thermal shock cycles(>150 K s^(-1),700-1500 K),and maintains stable performance over 5 cycles,with 78%visible and 98%microwave transmittance.This work establishes a new paradigm in the design and application of photonic materials for extreme environments.
文摘Driven by rapid advances in deep learning,object detection has been widely adopted across diverse application scenarios.However,in low-light conditions,critical visual cues of target objects are severely degraded,posing a significant challenge for accurate low-light object detection.Existing methods struggle to preserve discriminative features while maintaining semantic consistency between low-light and normal-light images.For this purpose,this study proposes a DL-YOLO model specially tailored for low-light detection.To mitigate target feature attenuation introduced by repeated downsampling,we design aMulti-Scale FeatureConvolution(MSF-Conv)module that captures rich,multi-level details via multi-scale feature learning,thereby reducing model complexity and computational cost.For feature fusion,we integrated the C3k2-DWRmodule by embedding the Dilation-wise Residual(DWR)mechanism into the 2-core optimized Cross Stage Partial(C3)framework,achieving efficient feature integration.In addition,we replace conventional localization losses with WIoU(Weighted Intersection over Union),which dynamically adjusts gradient gain according to sample quality,thereby improving localization robustness and precision.Experiments on the ExDark dataset demonstrate that DL-YOLO delivers strong low-light detection performance.The relevant code is published at https://github.com/cym0997/DL-YOLO.
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.
文摘In modern industrial production,foreign object detection in complex environments is crucial to ensure product quality and production safety.Detection systems based on deep-learning image processing algorithms often face challenges with handling high-resolution images and achieving accurate detection against complex backgrounds.To address these issues,this study employs the PatchCore unsupervised anomaly detection algorithm combined with data augmentation techniques to enhance the system’s generalization capability across varying lighting conditions,viewing angles,and object scales.The proposed method is evaluated in a complex industrial detection scenario involving the bogie of an electric multiple unit(EMU).A dataset consisting of complex backgrounds,diverse lighting conditions,and multiple viewing angles is constructed to validate the performance of the detection system in real industrial environments.Experimental results show that the proposed model achieves an average area under the receiver operating characteristic curve(AUROC)of 0.92 and an average F1 score of 0.85.Combined with data augmentation,the proposed model exhibits improvements in AUROC by 0.06 and F1 score by 0.03,demonstrating enhanced accuracy and robustness for foreign object detection in complex industrial settings.In addition,the effects of key factors on detection performance are systematically analyzed,providing practical guidance for parameter selection in real industrial applications.
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.RS-2023-00235509Development of security monitoring technology based network behavior against encrypted cyber threats in ICT convergence environment).
文摘With the increasing emphasis on personal information protection,encryption through security protocols has emerged as a critical requirement in data transmission and reception processes.Nevertheless,IoT ecosystems comprise heterogeneous networks where outdated systems coexist with the latest devices,spanning a range of devices from non-encrypted ones to fully encrypted ones.Given the limited visibility into payloads in this context,this study investigates AI-based attack detection methods that leverage encrypted traffic metadata,eliminating the need for decryption and minimizing system performance degradation—especially in light of these heterogeneous devices.Using the UNSW-NB15 and CICIoT-2023 dataset,encrypted and unencrypted traffic were categorized according to security protocol,and AI-based intrusion detection experiments were conducted for each traffic type based on metadata.To mitigate the problem of class imbalance,eight different data sampling techniques were applied.The effectiveness of these sampling techniques was then comparatively analyzed using two ensemble models and three Deep Learning(DL)models from various perspectives.The experimental results confirmed that metadata-based attack detection is feasible using only encrypted traffic.In the UNSW-NB15 dataset,the f1-score of encrypted traffic was approximately 0.98,which is 4.3%higher than that of unencrypted traffic(approximately 0.94).In addition,analysis of the encrypted traffic in the CICIoT-2023 dataset using the same method showed a significantly lower f1-score of roughly 0.43,indicating that the quality of the dataset and the preprocessing approach have a substantial impact on detection performance.Furthermore,when data sampling techniques were applied to encrypted traffic,the recall in the UNSWNB15(Encrypted)dataset improved by up to 23.0%,and in the CICIoT-2023(Encrypted)dataset by 20.26%,showing a similar level of improvement.Notably,in CICIoT-2023,f1-score and Receiver Operation Characteristic-Area Under the Curve(ROC-AUC)increased by 59.0%and 55.94%,respectively.These results suggest that data sampling can have a positive effect even in encrypted environments.However,the extent of the improvement may vary depending on data quality,model architecture,and sampling strategy.
基金supported by the State Key Laboratory of Urban Water Resource and Environment (Harbin Institute of Technology) (No.2022TS13)the key projects of National Natural Science Foundation of China (No.2019YFC0408503)the Key Research Program of Wuhan (No.2022022202015015)。
文摘Antibiotic resistance genes(ARGs) are recognized as a primary threat to the sustainability of environment and human health in the 21^(st) century.Nanomaterials(NMs) have attracted substantial attention due to their unique dimensions and structures.Unfortunately,emerging evidence suggests that NMs may facilitate the transmission of ARGs.It is crucial to elucidate how NMs affect the evolution and dissemination of ARGs.The current review comprehensively examines the role of NMs in the widespread transmission of ARGs in aquatic environments and the underlying mechanisms involved in the process.It aims to clarify the effects and mechanisms of NMs on the horizontal gene transfer processes that are associated with ARGs,including the enhancement of cell membrane permeability,the formation of nanopores on membranes,promotion of mutagenesis,and the generation of reactive oxygen species(ROSs).Furthermore,the trade-off between the removal of ARGs and horizontal transfer has been elucidated.The review aspires to guide future research directions,advance knowledge on the implications of NMs in the field of ARGs' transmission,and provide a theoretical foundation for the development of safer and more effective applications of NMs.
基金support of the National Key Research and Development Plan(No.2021YFB3302501)the financial support of the National Science Foundation of China(No.12161076)the financial support of the Fundamental Research Funds for the Central Universities(No.DUT25GF207).
文摘With the rapid development of artificial intelligence,intelligent air combat maneuver decision-making(ACMD)has garnered global attention.Although deep reinforcement learning provides a promising approach to ACMD,existing methods often suffer from rigid reward functions and limited adaptability to evolving adversarial strategies.Moreover,most research assumes open airspace,overlooking the influence of potential obstacles.In this paper,we address one-on-one within-visual-range ACMD in obstructed environments,and propose an improved Soft Actor-Critic(SAC)algorithm trained under a curriculum self-play framework.A maneuver strategy mirroring inference module is integrated to estimate each other's likely positions when visual obstruction occurs.By leveraging curriculum learning to guide progressive experience accumulation and self-play for adversarial evolution,our method enhances both training efficiency and tactical diversity.We further integrate an attention mechanism that dynamically adjusts the weights of sub-rewards,enabling the learned policy to adapt to rapidly changing air combat situations.Numerical simulations demonstrate that our enhanced SAC converges more quickly and achieves higher win rates than other baseline methods.An animation is available at bilibili.com/video/BV1BHVszHE98 for better illustration.
基金support by National Natural Science Foundation of China(Grant No.52401103)Key Scientific Research Project in Shanxi Province(Grant No.202302050201015)+3 种基金Central Guiding Science and Technology Development of Local Fund(Grant No.YDZJSK20231A046)the Special Fund for Science and Technology Innovation Teams of Shanxi Province(202204051001004)Science and Technology Cooperation and Exchange Special Project of Shanxi Province(202404041101038)Postgraduate Education Innovation Project of Shanxi Province(Grant No.2024SJ304).
文摘The atmospheric corrosion behavior of 510L low alloy steel subjected to acid-cleaned surface(ACS)and eco-pickled surface(EPS)treatments is systematically examined.After 1 year of atmospheric exposure,both ACS-and EPS-treated samples demonstrate protective ability index values exceeding 2,indicating robust protective properties of the developed rust layers.The corrosion rates of ACS-and EPS-treated samples are similar.During the initial corrosion stage,γ-FeOOH emerges as the dominant corrosion product.With the prolonged atmospheric exposure,γ-FeOOH content progressively decreases through phase transformation into thermodynamically stableα-FeOOH and densely structured Fe_(3)O_(4),which markedly suppresses the corrosion of the steel.Notably,the corrosion rate of the coated EPS sample is obviously lower than that of the coated ACS counterpart,which is ascribed to the distinctive micro-roughness of EPS-treated surfaces that promote mechanical interlocking with protective coatings.
基金supported by Kyonggi University Research Grant 2025.
文摘As large,room-scale environments become increasingly common,their spatial complexity increases due to variable,unstructured elements.Consequently,demand for room-scale service robots is surging,yet most technologies remain corridor-centric,and autonomous navigation in expansive rooms becomes unstable even around static obstacles.Existing approaches face several structural limitations.These include the labor-intensive requirement for large-scale object annotation and continual retraining,as well as the vulnerability of vanishing point or linebased methods when geometric cues are insufficient.In addition,the high cost of LiDAR and 3D perception errors caused by limited wall cues and dense interior clutter further limit their effectiveness.To address these challenges,we propose a zero-shot vision-based algorithm for robust 3D map reconstruction in geometry-deficient room-scale environments.The algorithm operates in three layers:Layer 1 performs dimension-wise boundary detection;Layer 2 estimates vanishing points,refines the precise perspective space,and extracts a floor mask;and Layer 3 conducts 3D spatial mapping and obstacle recognition.The proposed method was experimentally validated across various geometric-deficient room-scale environments,including lobbies,seminar rooms,conference rooms,cafeterias,and museums—demonstrating its ability to reliably reconstruct 3D maps and accurately recognize obstacles.Experimental results show that the proposed algorithm achieved an F1 score of 0.959 in precision perspective space detection and 0.965 in floor mask extraction.For obstacle recognition and classification,it obtained F1 scores of 0.980 in obstacle absent areas,0.913 in solid obstacle environments,and 0.939 in skeleton-type sparse obstacle environments,confirming its high precision and reliability in geometric-deficient room-scale environments.
基金supported in part by the Central Guidance for Local Science and Technology Development Funds under Grant No.YDZJSX2025D049Shanxi Provincial Graduate Innovation Research Program under Grant No.2024KY652.
文摘Federated Learning(FL)provides an effective framework for efficient processing in vehicular edge computing.However,the dynamic and uncertain communication environment,along with the performance variations of vehicular devices,affect the distribution and uploading processes of model parameters.In FL-assisted Internet of Vehicles(IoV)scenarios,challenges such as data heterogeneity,limited device resources,and unstable communication environments become increasingly prominent.These issues necessitate intelligent vehicle selection schemes to enhance training efficiency.Given this context,we propose a new scenario involving FL-assisted IoV systems under dynamic and uncertain communication conditions,and develop a dynamic interval multi-objective optimization algorithm to jointly optimize various factors including training experiments,system energy consumption,and bandwidth utilization to meet multi-criteria resource optimization requirements.For the problem at hand,we design a dynamic interval multi-objective optimization algorithm based on interval overlap detection.Simulation results demonstrate that our method outperforms other solutions in terms of accuracy,training cost,and server utilization.It effectively enhances training efficiency under wireless channel environments while rationally utilizing bandwidth resources,thus possessing significant scientific value and application potential in the field of IoV.
文摘To assess the effectiveness of vaccination in contaminated environments,this study introduces a modeling framework that encompasses two transmission routes,namely direct human-to-human contact and indirect human-to-environment contact,as well as the implementation of new M72/AS01_(E)vaccine.Motivated by this,a coupled age-structured tuberculosis(TB)model is proposed.Its well-posedness requirement is verified using the integrated semigroup theory.Furthermore,this study presents a comprehensive analysis of threshold dynamics associated with the proposed model.Specifically,the global stability of the disease-free and positive steady states is demonstrated by employing Lyapunov functionals.Lastly,the effects of the vaccination with M72/AS01_(E)and contaminated environments on TB control are numerically simulated.Experimental results indicate that high concentrations of Mycobacterium tuberculosis in contaminated environments may somewhat impede TB control efforts,but that large-scale deployment of new vaccine could significantly reduce the prevalence of TB.
基金Supported by the China National Science and Technology Major Project(2025ZD1405700)。
文摘There is a lack of systematic understanding of coal-forming environment classification and its influences on coal petrological characteristics,a coal-forming mire classification scheme applicable to the petroleum industry is proposed based on modern ecological peatland frameworks.The formation,evolutionary processes,and diagnostic criteria of coal-forming environments are systematically clarified.The results show that:(1)modern peatlands can be classified according to hydrological conditions,vegetation types,and geomorphic settings,and coal-forming mires can be divided into low moor,transitional,and high moor peat mires based on geomorphology;(2)the development of coal-forming environments includes three modes:subaqueous peat infilling,autochthonous peat accumulation in wetlands,and mire development in arid regions;(3)peat accumulation is jointly controlled by plant production and decomposition,hydrological disturbances,and sediment input,and the peat-to-coal thickness ratio varies with coalification;(4)diagnostic criteria for low moor,transitional,and high moor peat mires are established based on ash yield,gamma-ray log responses,and vitrinite-to-inertinite ratios;and(5)transgression-regression processes exert a key control on peat mire evolution,directly influencing peat thickness and continuity,while the evolution of low moor,transitional,and high moor mires governs coal maceral assemblages and thereby affects hydrocarbon generation potential and reservoir properties of coals.The coal-forming environment classification and identification system developed in this study effectively reveals the vertical heterogeneity of coals in the Ordos Basin,providing theoretical and practical guidance for efficient exploration and development of coal-rock gas.
基金supported by the Beijing Natural Science Foundation(No.L212029)the National Natural Science Foundation of China(No.62271043).
文摘To meet the requirements of electromagnetic(EM)theory and applied physics,this study presents an overview of the state-of-the-art research on obtaining the EM properties of media and points out potential solutions that can break through the bottlenecks of current methods.Firstly,based on the survey of three mainstream approaches for acquiring EM properties of media,we identify the difficulties when implementing them in realistic environments.With a focus on addressing these problems and challenges,we propose a novel paradigm for obtaining the EM properties of multi-type media in realistic environments.Particularly,within this paradigm,we describe the implementation approach of the key technology,namely“multipath extraction using heterogeneous wave propagation data in multi-spectrum cases”.Finally,the latest measurement and simulation results show that the EM properties of multi-type media in realistic environments can be precisely and efficiently acquired by the methodology proposed in this study.
基金financially supported by the National Natural Science Foundation of China(Nos.U2106206,52471079,42276212,and 42176043)the Natural Science Foundation of Shandong Province(ZR2024ME047)+1 种基金the National Materials Corrosion and Protection Data Center(No.2023DATAFU20-01)The authors wish to acknowledge Sen Wang,Haiyan Yu,Xiaomin Zhao from State Key Laboratory of Microbial Technology,Shandong University for the assistance in the SEM analysis。
文摘With the rapid development of the marine economy,marine microbiologically influenced corrosion(MIC)has garnered increasing attention.However,most studies have not analyzed the MIC process over continuous and extended periods,failing to provide a comprehensive understanding of MIC mechanisms at different stages.In this study,the corrosion behavior of EH36 steel caused by Halomonas titanicae in an aerobic enriched seawater over a 30-d incubation period was investigated driven by big data.The results revealed that the corrosion by H.titanicae against EH36 steel evolved dynamically over time.During the initial stages,the aerobic respiration of H.titanicae consumed significant amounts of oxygen,which suppressed the cathodic oxygen reduction process,thereby inhibiting corrosion compared to the abiotic conditions.As time progressed,the accumulation of corrosion products slowed the abiotic corrosion,while the biotic corrosion accelerated due to a shift from aerobic to anaerobic respiration by H.titanicae,utilizing Fe0 and nitrate as electron donors and acceptors,respectively.The big data results are consistent with the weight loss and electrochemical data,demonstrating the reliability of using big data monitoring techniques to characterize microbial corrosion processes.
基金supported in part by the National Natural Science Foundations of China(Nos.61175084,61673042 and 62203046)the China Postdoctoral Science Foundation(No.2022M713006).
文摘Aiming to address the Unmanned Aerial Vehicle(UAV) formation collision avoidance problem in Three-Dimensional(3-D) low-altitude environments where dense various obstacles exist, a fluid-based path planning framework named the Formation Interfered Fluid Dynamical System(FIFDS) with Moderate Evasive Maneuver Strategy(MEMS) is proposed in this study.First, the UAV formation collision avoidance problem including quantifiable performance indexes is formulated. Second, inspired by the phenomenon of fluids continuously flowing while bypassing objects, the FIFDS for multiple UAVs is presented, which contains a Parallel Streamline Tracking(PST) method for formation keeping and the traditional IFDS for collision avoidance. Third, to rationally balance flight safety and collision avoidance cost, MEMS is proposed to generate moderate evasive maneuvers that match up with collision risks. Comprehensively containing the time and distance safety information, the 3-D dynamic collision regions are modeled for collision prediction. Then, the moderate evasive maneuver principle is refined, which provides criterions of the maneuver amplitude and direction. On this basis, an analytical parameter mapping mechanism is designed to online optimize IFDS parameters. Finally, the performance of the proposed method is validated by comparative simulation results and real flight experiments using fixed-wing UAVs.
文摘Human interaction with natural environments is gaining increasing attention in environmental sciences as research consistently shows that access to green spaces,clean air,and biodiversity plays a crucial role in enhancing physical health,reducing stress,and improving overall well-being.This study conducts a bibliometric and visualization-based analysis of forest therapy research,emphasizing its physiological and psychological benefits.Using the Web of Science database,we identified and analyzed 414 studies from 1998 to 2023.Through CiteSpace and VOSviewer,we mapped these documents to examine research trends,publication networks,leading scholars and institutions,key journals,and thematic evolution.Findings indicate that forest therapy research is predominantly concentrated in East Asia,North America,Australia,and Europe,with strong collaborative networks among authors and institutions.The concentration of publications,research evolution,and keyword trends reflect the development of forest therapy research.The analysis further identifies sixteen research clusters and discusses two research themes:physiological and psychological effects.By analyzing how the natural environment contributes to human well-being,we provide a comprehensive and visually structured understanding of forest therapy as an intersection of environmental science,public health and well-being,and ecosystem conservation.Our findings offer a new perspective for future interdisciplinary research,emphasizing the need for well-designed clinical trials to substantiate forest therapy’s diverse health effects and its role in promoting sustainable interactions between human societies and natural environments.
文摘With the rapid development of drone technology,drones are increasingly used in urban environments,but they also bring many security risks,such as illegal reconnaissance,smuggling,and terrorist attacks.Therefore,it is of great significance to study the anti-UAV technology in the urban environment.This paper analyzes the advantages and disadvantages of existing technologies and their applicability in the urban environment from the aspects of UAV detection,identification,and countermeasures,and discusses the future development trend of anti-UAV technology,aiming to provide a reference for urban safety protection.
基金supported by National Natural Science Foundation Youth Project (81901386)the Fundamental Research Funds for the Central Universities (YG2025ZD07)+5 种基金the National Science and Technology Innovation 2030 Major Project of China (2021ZD0203900)National Natural Science Foundation of China (NSFC) grant (82422029)the Science and Technology Commission of Shanghai Municipality (24Y22800200, 22QA1407900)NSFC grant (82271530)Innovation teams of high-level universities in Shanghaithe Scientific Research and Innovation Team of Liaoning Normal University (24TD004).
文摘To the editor:Adverse home environments(AHE),characterised by family conflict,parental separation or dysfunctional parenting,are linked to negative mental health outcomes in children and adults.12 AHE disproportionately affect children with neurodevelopmental disorders such as attention-deficit/hyperactivity disorder(ADHD),which is characterised by inattention,hyperactivity/impulsivity and functional impairments.3 Apart from core symptoms,including inattention and hyperactivity,disruptive behaviour disorders(DBD),such as oppositional defiant disorder(ODD)and conduct disorder(CD),may be associated with AHE.Conduct problems are risk factors for ODD.And CD has become a main concern for childhood mental health.
基金funded by the Health Commission of Heilongjiang Province(Project Number:20230808010517).
文摘Background:Cold temperatures cause blood vessels to constrict,shallow breathing,and slight thickening of the blood.Working in extremely cold environments can have negative effects on health,yet there are currently no effective biomarkers to monitor these health conditions.Proteins are important intermediate phenotypes that can provide a theoretical basis for understanding disease pathophysiology.Proteins in the circulatory system reflect the physiological status of individuals,and plasma proteins have significant potential as biomarkers for various health conditions.Methods:In this study,we employed the Mendelian randomization(MR)method to analyze the effects of freezing temperatures on over 2900 plasma proteins.Subsequently,the selected plasma proteins were subjected to causal analysis in relation to 55 diseases,including respiratory disorders,cardiovascular diseases,various cancers,and oral diseases.The aim was to identify proteins that could serve as biomarkers for health status.Results:Our results indicate that cold environments may affect the concentrations of 78 plasma proteins.Further MR analysis revealed that nine of these plasma proteins are associated with the risk of respiratory disorders,cardiovascular diseases,various cancers,and oral diseases.Conclusion:These proteins show promise as biomarkers for monitoring the hazards and risks faced by individuals working in cold environments.These findings provide valuable insights into the biological mechanisms underlying occupational hazards.
文摘When a fire breaks out in a high-rise building,the occlusion of smoke and obstacles results in dearth of crucial information concerning people in distress,thereby creating a challenge in their detection.Given the restricted sensing range of a single unmanned aerial vehicle(UAV)cam-era,enhancing the target recognition rate becomes challenging without target information.To tackle this issue,this paper proposes a multi-agent autonomous collaborative detection method for multi-targets in complex fire environments.The objective is to achieve the fusion of multi-angle visual information,effectively increasing the target’s information dimension,and ultimately address-ing the problem of low target recognition rate caused by the lack of target information.The method steps are as follows:first,the you only look once version5(YOLOv5)is used to detect the target in the image;second,the detected targets are tracked to monitor their movements and trajectories;third,the person re-identification(ReID)model is employed to extract the appearance features of targets;finally,by fusing the visual information from multi-angle cameras,the method achieves multi-agent autonomous collaborative detection.The experimental results show that the method effectively combines the visual information from multi-angle cameras,resulting in improved detec-tion efficiency for people in distress.
