Health risk assessment of trace metal(loid)s in agricultural soils based on Monte Carlo simulation coupled with positive matrix factorization model in Chongqing, southwest China

This study aimed to investigate the pollution characteristics, source apportionment, and health risks associated with trace metal(loid)s(TMs) in the major agricultural producing areas in Chongqing, China. We analyzed the source apportionment and assessed the health risk of TMs in agricultural soils by using positive matrix factorization(PMF) model and health risk assessment(HRA) model based on Monte Carlo simulation. Meanwhile, we combined PMF and HRA models to explore the health risks of TMs in agricultural soils by different pollution sources to determine the priority control factors. Results showed that the average contents of cadmium(Cd), arsenic (As), lead(Pb), chromium(Cr), copper(Cu), nickel(Ni), and zinc(Zn) in the soil were found to be 0.26, 5.93, 27.14, 61.32, 23.81, 32.45, and 78.65 mg/kg, respectively. Spatial analysis and source apportionment analysis revealed that urban and industrial sources, agricultural sources, and natural sources accounted for 33.0%, 27.7%, and 39.3% of TM accumulation in the soil, respectively. In the HRA model based on Monte Carlo simulation, noncarcinogenic risks were deemed negligible(hazard index <1), the carcinogenic risks were at acceptable level(10^(-6)<total carcinogenic risk ≤ 10^(-4)), with higher risks observed for children compared to adults. The relationship between TMs, their sources, and health risks indicated that urban and industrial sources were primarily associated with As, contributing to 75.1% of carcinogenic risks and 55.7% of non-carcinogenic risks, making them the primary control factors. Meanwhile, agricultural sources were primarily linked to Cd and Pb, contributing to 13.1% of carcinogenic risks and 21.8% of non-carcinogenic risks, designating them as secondary control factors.

Analysis of debris flow control effect and hazard assessment in Xinqiao Gully,Wenchuan M_(s)8.0 earthquake area based on numerical simulation

Xinqiao Gully is located in the area of the 2008 Wenchuan M_(s)8.0 earthquake in Sichuan province,China.Based on the investigation of the 2023"6-26"Xinqiao Gully debris flow event,this study assessed the effectiveness of the debris flow control project and evaluated the debris flow hazards.Through field investigation and numerical simulation methods,the indicators of flow intensity reduction rate and storage capacity fullness were proposed to quantify the effectiveness of the engineering measures in the debris flow event.The simulation results show that the debris flow control project reduced the flow intensity by41.05%to 64.61%.The storage capacity of the dam decreases gradually from upstream to the mouth of the gully,thus effectively intercepting and controlling the debris flow.By evaluating the debris flow of different recurrence intervals,further measures are recommended for managing debris flow events.

E2E-MFERC:AMulti-Face Expression Recognition Model for Group Emotion Assessment

In smart classrooms, conducting multi-face expression recognition based on existing hardware devices to assessstudents’ group emotions can provide educators with more comprehensive and intuitive classroom effect analysis,thereby continuouslypromotingthe improvementof teaching quality.However,most existingmulti-face expressionrecognition methods adopt a multi-stage approach, with an overall complex process, poor real-time performance,and insufficient generalization ability. In addition, the existing facial expression datasets are mostly single faceimages, which are of low quality and lack specificity, also restricting the development of this research. This paperaims to propose an end-to-end high-performance multi-face expression recognition algorithm model suitable forsmart classrooms, construct a high-quality multi-face expression dataset to support algorithm research, and applythe model to group emotion assessment to expand its application value. To this end, we propose an end-to-endmulti-face expression recognition algorithm model for smart classrooms (E2E-MFERC). In order to provide highqualityand highly targeted data support for model research, we constructed a multi-face expression dataset inreal classrooms (MFED), containing 2,385 images and a total of 18,712 expression labels, collected from smartclassrooms. In constructing E2E-MFERC, by introducing Re-parameterization visual geometry group (RepVGG)block and symmetric positive definite convolution (SPD-Conv) modules to enhance representational capability;combined with the cross stage partial network fusion module optimized by attention mechanism (C2f_Attention),it strengthens the ability to extract key information;adopts asymptotic feature pyramid network (AFPN) featurefusion tailored to classroomscenes and optimizes the head prediction output size;achieves high-performance endto-end multi-face expression detection. Finally, we apply the model to smart classroom group emotion assessmentand provide design references for classroom effect analysis evaluation metrics. Experiments based on MFED showthat the mAP and F1-score of E2E-MFERC on classroom evaluation data reach 83.6% and 0.77, respectively,improving the mAP of same-scale You Only Look Once version 5 (YOLOv5) and You Only Look Once version8 (YOLOv8) by 6.8% and 2.5%, respectively, and the F1-score by 0.06 and 0.04, respectively. E2E-MFERC modelhas obvious advantages in both detection speed and accuracy, which can meet the practical needs of real-timemulti-face expression analysis in classrooms, and serve the application of teaching effect assessment very well.

IoT Smart Devices Risk Assessment Model Using Fuzzy Logic and PSO

Increasing Internet of Things(IoT)device connectivity makes botnet attacks more dangerous,carrying catastrophic hazards.As IoT botnets evolve,their dynamic and multifaceted nature hampers conventional detection methods.This paper proposes a risk assessment framework based on fuzzy logic and Particle Swarm Optimization(PSO)to address the risks associated with IoT botnets.Fuzzy logic addresses IoT threat uncertainties and ambiguities methodically.Fuzzy component settings are optimized using PSO to improve accuracy.The methodology allows for more complex thinking by transitioning from binary to continuous assessment.Instead of expert inputs,PSO data-driven tunes rules and membership functions.This study presents a complete IoT botnet risk assessment system.The methodology helps security teams allocate resources by categorizing threats as high,medium,or low severity.This study shows how CICIoT2023 can assess cyber risks.Our research has implications beyond detection,as it provides a proactive approach to risk management and promotes the development of more secure IoT environments.

Assessment of Ecosystems Damages Caused by Russian War against Ukraine

The question of the impact of war on ecosystems still remains secondary in the internal and external policy of states, society and the agenda of international organizations. From the point of view of losses in monetary terms, the values of ecosystem damages obtained in the work, which are a consequence of the impact of hostilities on the environment, correspond to the annual budgets of the largest countries in the world or exceed them. The presented calculations significantly exceed the known normative methods, the use of which in the conditions of war is limited in space and time. Objective difficulties associated with the uncertainty of many processes of the development of ecological systems and their reaction to the multifactorial impact of war are also significant limitations. Therefore, as part of the study, a method of assessing the impact of war on the environment is proposed, which is based on the patterns of energy flows in ecosystems from the moment it is binding by producers. This made it possible to take into account in the calculations the principle of functional integrity of the ecological system, according to which the destruction or damage of the components of a functionally whole environment will necessarily cause negative phenomena in the development of ecological systems. The results are presented in the form of real values of ecological losses in energy and monetary equivalents, as consequences of the loss of ecosystem services. As the results of the research show, the minimum amount of damage to ecosystems from Russian tanks is 43,500 USD per day. Environmental damage from Russian fighter jets has been estimated at $1.5 billion per week since the start of the war. Noise from military operations causes losses of at least 2.3 billion US dollars per year. The obtained results create prerequisites for improving the system of ensuring environmental safety at the local, state, and international levels and transferring the obtained solutions into safety-shaping practice.

Distribution, Mobility, and Health Risks Assessment of Trace Metals in River Sediments from Intense Agricultural Activity Areas in West Africa

The economy of West African countries is mainly based on agriculture. However, the trace metal(loid)s contamination status in rivers is relatively unknown in the region. In this work, 45 surface sediments collected from the Bandama, Comoé, and Bia Rivers in south and south eastern Côte d’Ivoire (West Africa), were analyzed for total metal concentrations and chemical speciation. The results showed that the river sediments were considerably contaminated by Cd and moderately contaminated by As, Cu, Pb, and Zn. Significant spatial variations were observed among the stations but not between the rivers. Metals Cd and Cu were likely to cause more ecological risks. The speciation analysis unravelled that the metal(loid)s partitioned mainly in the residual fraction, with the potential mobile fraction varying from 14% to 28%. The study calls for establishment of strict policies relative to the application of fertilizers and agrochemicals and mining activities to protect the environment and human health risks.

High resolution radar target adaptive detector and performance assessment

The high resolution radar target detection is addressed in the non-Gaussian clutter. An adaptive detector is derived for range-spread target based on a novel covariance matrix estimator. It is proved that the new detector is constant false alarm rate （CFAR） to both of the clutter covariance matrix structure and power level theoretically for match cases. The simulation results show that the new detector is almost CFAR for mismatch cases, and it outperforms the existing adaptive detector based on the sample covariance matrix. It also shows that the detection performance improves, as the number of pulses, the number of secondary data or the clutter spike increases. In addition, the derived detector is robust to different subsets, estimated clutter group sizes and correlations of clutter. Importantly, the number of iterations for practical application is just one.

Sensitivity Detection Technique and Resistance Risk Assessment of Magnaporthe grisea to Tricyclazole

In vitro detection method for the sensitivity of Magnaporthe grisea to tricyclazole was studied, and the potential resistance risk of blast disease to tricyclazole was assessed. Both EC50 of hyphal melanization (EC50-H) and minimum inhibitive concentration of melanization in appressorial (MIC-A) by inhibitor tricyclazole showed positive correlation to the EC50 of tricyclazole against blast disease tested in vivo, with relative co-efficiency (R5) of 0.8995 and 0.8244, respectively. However, stability and reproducibility of EC50-H were better than those of MIC-A, suggesting that it could be used to detect the sensitivity of M. grisea to tricyclazole in vitro. Tricyclazole sensitivity of the progenies derived from single spores of the most sensitive isolate DY2 and the least sensitive isolate GY6 detected in sensitivity monitoring in 2000 was not stable, with mean EC50 values of 4.4968 μg/mL and 5.4010 ug/mL, respectively, indicating that the difference in EC50 between DY2 and GY6 was not caused probably by resistance variation. EC50 of GY6 did not increase significantly when continuously selected for twenty generations under the selection pressure of tricyclazole in vivo. However, the sensitivity of DY2 was decreased by 10-fold after selected for twenty generations. The results suggested that tricyclazole was still low resistance risk for M. grisea in China.

Quantitative risk assessment & leak detection criteria for a subsea oil export pipeline

A quantitative risk assessment （QRA） based on leak detection criteria （LDC） for the design of a proposed subsea oil export pipeline is presented in this paper. The objective of this QRA/LDC study was to determine if current leak detection methodologies were sufficient, based on QRA results, while excluding the use of statistical leak detection; if not, an appropriate LDC for the leak detection system would need to be established. The famous UK PARLOC database was used for the calculation of pipeline failure rates, and the software POSVCM from MMS was used for oil spill simulations. QRA results revealed that the installation of a statistically based leak detection system （LDS） can significantly reduce time to leak detection, thereby mitigating the consequences of leakage. A sound LDC has been defined based on QRA study results and comments from various LDS vendors to assist the emergency response team （ERT） to quickly identify and locate leakage and employ the most effective measures to contain damage.

Detection and Analysis of Pesticide Residues in Chinese Chives and Risk Assessment of Dietary Intake

[Objectives]This study was conducted to understand the status of pesticide residues and dietary intake risk of Chinese chives in Tangshan area. [Methods] Sixty eight kinds of pesticide residues in 415 Chinese chive samples collected from Tangshan area were qualitatively and quantitatively determined by high-performance liquid chromatography-tandem mass spectrometry(HPLC-MS/MS) and gas chromatography(GC) in 2020. [Results] The results showed that 41 kinds of pesticide residues were detected in the 415 Chinese chive samples, and the detection rate was 69.4%(288/415), and there was a combination of pesticides in many samples. According to the National Food Safety Standard―Maximum Residue Limits of Pesticides in Food(GB 2763-2019), the residues of 12 pesticides exceeded the maximum residue limits(MRLs), and the unqualified rate was 38.07%(158/415). The highest detection rate of clothianidin was 41.20%(171/415), but there was no MRL in GB 2763-2019. The next was procymidone, the detection rate of which was 35.42%(147/415), and the over-standard rate was 30.12%(125/415). Forbidden and restricted pesticides were detected in some samples. According to the dietary exposure risk assessment, the NEDI/ADI values were all less than 1 and the intake risk was within acceptable range. In Tangshan area, the types of pesticides used in Chinese chive production are complex, and there are risks of multi-residue pollution and use of banned and restricted pesticides and unregistered pesticides. It is suggested that routine monitoring of pesticide residues and management of pesticide use should be strengthened to ensure the quality and safety of agricultural products. [Conclusions] This study provides a theoretical basis for the safe production of Chinese chive and the standardized and rational use of pesticides.

Using of Synthetic Images on Accuracy Assessment of Change Detection

Land cover change detection is the major goal in multitemporal remote sensing studies. It is well known that remotely-sensed images of the same area acquired on different dates tend to be affected by radiometric differences and registration problems. These influences are considered as noise in the process and may induce the user to both： signalling false changes and masking real surface changes. The difference image produced by subtracting two co-registered images is a standard initial step in change detection algorithms. This image naturally appears to be noisier than the original ones and has at least two populations： （1） the noise-like and （2） the real changes. The problem that arises is how to discriminate them. There are several approaches to perform change detection reported in the literature and some studies have employed synthetic images. By using synthetic images, the accuracy assessment of specific algorithm can be done more accurately. The question at this point is： what is the acceptable noise level to be added on the synthetic images to simulate a real problem？ This paper attempts to answer this question by suggesting values of SNR （signal-to-noise ratio） obtained from experiments performed on TM-Landsat-5 and CCD-CBERS-2B images.

Network Intrusion Traffic Detection Based on Feature Extraction

With the increasing dimensionality of network traffic,extracting effective traffic features and improving the identification accuracy of different intrusion traffic have become critical in intrusion detection systems(IDS).However,both unsupervised and semisupervised anomalous traffic detection methods suffer from the drawback of ignoring potential correlations between features,resulting in an analysis that is not an optimal set.Therefore,in order to extract more representative traffic features as well as to improve the accuracy of traffic identification,this paper proposes a feature dimensionality reduction method combining principal component analysis and Hotelling’s T^(2) and a multilayer convolutional bidirectional long short-term memory(MSC_BiLSTM)classifier model for network traffic intrusion detection.This method reduces the parameters and redundancy of the model by feature extraction and extracts the dependent features between the data by a bidirectional long short-term memory(BiLSTM)network,which fully considers the influence between the before and after features.The network traffic is first characteristically downscaled by principal component analysis(PCA),and then the downscaled principal components are used as input to Hotelling’s T^(2) to compare the differences between groups.For datasets with outliers,Hotelling’s T^(2) can help identify the groups where the outliers are located and quantitatively measure the extent of the outliers.Finally,a multilayer convolutional neural network and a BiLSTM network are used to extract the spatial and temporal features of network traffic data.The empirical consequences exhibit that the suggested approach in this manuscript attains superior outcomes in precision,recall and F1-score juxtaposed with the prevailing techniques.The results show that the intrusion detection accuracy,precision,and F1-score of the proposed MSC_BiLSTM model for the CIC-IDS 2017 dataset are 98.71%,95.97%,and 90.22%.

Clinicopathological features and prognosis assessment of extranodal follicular dendritic cell sarcoma

AIM: To establish a model for prognosis assessment of extranodal follicular dendritic cell (FDC) sarcoma.METHODS: Nine lesions were examined by routine and molecular approaches.Clinicopathological factors from the new cases and 97 reported cases were analyzed for their prognostic values.RESULTS: The current lesions were found in f ive male and four female patients,located mainly in the head and neck area and averaging 7.2 cm in size.Six patients had recurrence or metastasis and three remained free of disease.The 106 patients (male/female ratio,1.1:1) were aged from 9 to 82 years (median,44 years).The tumor sizes ranged from 1.5 to 21 cm (mean,7.4 cm).Abdominal/pelvic region was affected most frequently (43%).Surgical resection was performed in 100 patients,followed by radiation and/or chemotherapy in 35 of them.Follow-up data were available in 91 cases,covering a period of 3-324 mo (mean,27 mo;median,19 mo).Of the informative cases,38 (42%) had recurrence or metastasis,and 12 (13%) died of the disease.These tumors were classif ied histologically into lowand high-grade lesions.A size ≥ 5 cm (P = 0.003),highgrade histology (P = 0.046) and a mitotic count ≥ 5/10 HPF (P = 0.013) were associated with tumor recurrence.The lesions were def ined as low-,intermediateand high-risk tumors,and their recurrence rates were 16%,46% and 73%,and their mortality rates 0%,4% and 45%,respectively.CONCLUSION: Extranodal FDC tumors behave like soft tissue sarcomas.Their clinical outcomes are variable and can be evaluated according to their sizes and grades.

超声S-Detect技术联合乳腺简化MRI对早期乳腺癌的诊断效能

目的 探讨超声S-Detect技术联合乳腺简化MRI对早期乳腺癌诊断效能的分析。方法 选取2020年1月至2021年12月进行乳腺超声和MRI检查的85个病灶。患者年龄>14岁,且未进行手术及新辅助化疗治疗,肿瘤病灶直径≤2 cm,并进行穿刺或手术获得病理结果。以乳腺穿刺肿瘤病理结果为金标准,评估超声S-Detect技术、乳腺简化MRI及二者联合检查的诊断效能。结果 S-Detect、简化MRI及两者联合诊断的灵敏度分别为:69.5%,81.4%,98.3%,特异度分别为:88.5%,80.8%,76.9%,准确率分别为:75.3%,81.2%,91.8%,与病理一致性检验Kappa值分别为:0.50,0.58,0.80,AUC分别为:0.79,0.81,0.88。差异均有统计学意义(Z=1.979,P<0.05;Z=2.096,P<0.05)。结论 超声S-Detect与乳腺简化MRI技术联合对早期乳腺癌检查的灵敏度、准确率以及与病理一致性比单独使用S-Detect技术或简化MRI高,具有较高的诊断效能,可以对早期乳腺癌的早发现早治疗起到积极作用并为临床科室制定治疗方案提供有效帮助。

Condition assessment of structures under unknown support excitation

A new method is proposed to assess the condition of structures under unknown support excitation by simultaneously detecting local damage and identifying the support excitation from several structural dynamic responses. The support excitation acting on a structure is modeled by orthogonal polynomial approximations, and the sensitivities of structural dynamic response with respect to its physical parameters and orthogonal coefficients are derived. The identification equation is based on Taylor＇s first order approximation, and is solved with the damped least-squares method in an iterative procedure. A fifteen-story shear building model and a five-story three-dimensional steel frame structure are studied to validate the proposed method. Numerical simulations with noisy measured accelerations show that the proposed method can accurately detect local damage and identify unknown support excitation from only several responses of the structure. This method provides a new approach for detecting structural damage and updating models with unknown input and incomplete measured output information.

A wave energy resource assessment in the China's seas based on multi-satellite merged radar altimeter data

Wave energy resources are abundant in both offshore and nearshore areas of the China＇s seas. A reliable assessment of the wave energy resources must be performed before they can be exploited. First, for a water depth in offshore waters of China, a parameterized wave power density model that considers the effects of the water depth is introduced to improve the calculating accuracy of the wave power density. Second, wave heights and wind speeds on the surface of the China＇s seas are retrieved from an AVISO multi-satellite altim-eter data set for the period from 2009 to 2013. Three mean wave period inversion models are developed and used to calculate the wave energy period. Third, a practical application value for developing the wave energy is analyzed based on buoy data. Finally, the wave power density is then calculated using the wave field data. Using the distribution of wave power density, the energy level frequency, the time variability indexes, the to-tal wave energy and the distribution of total wave energy density according to a wave state, the offshore wave energy in the China＇s seas is assessed. The results show that the areas of abundant and stable wave energy are primarily located in the north-central part of the South China Sea, the Luzon Strait, southeast of Taiwan in the China＇s seas; the wave power density values in these areas are approximately 14.0–18.5 kW/m. The wave energy in the China’s seas presents obvious seasonal variations and optimal seasons for a wave energy utilization are in winter and autumn. Except for very coastal waters, in other sea areas in the China＇s seas, the energy is primarily from the wave state with 0.5 m≤Hs≤4 m, 4 s≤Te≤10 s whereHs is a significant wave height andTe is an energy period; within this wave state, the wave energy accounts for 80% above of the total wave energy. This characteristic is advantageous to designing wave energy convertors （WECs）. The practical application value of the wave energy is higher which can be as an effective supplement for an energy con-sumption in some areas. The above results are consistent with the wave model which indicates fully that this new microwave remote sensing method altimeter is effective and feasible for the wave energy assessment.

Reachability-Based Confidence-Aware Probabilistic Collision Detection in Highway Driving

Risk assessment is a crucial component of collision warning and avoidance systems for intelligent vehicles.Reachability-based formal approaches have been developed to ensure driving safety to accurately detect potential vehicle collisions.However,they suffer from over-conservatism,potentially resulting in false–positive risk events in complicated real-world applications.In this paper,we combine two reachability analysis techniques,a backward reachable set(BRS)and a stochastic forward reachable set(FRS),and propose an integrated probabilistic collision–detection framework for highway driving.Within this framework,we can first use a BRS to formally check whether a two-vehicle interaction is safe;otherwise,a prediction-based stochastic FRS is employed to estimate the collision probability at each future time step.Thus,the framework can not only identify non-risky events with guaranteed safety but also provide accurate collision risk estimation in safety-critical events.To construct the stochastic FRS,we develop a neural network-based acceleration model for surrounding vehicles and further incorporate a confidence-aware dynamic belief to improve the prediction accuracy.Extensive experiments were conducted to validate the performance of the acceleration prediction model based on naturalistic highway driving data.The efficiency and effectiveness of the framework with infused confidence beliefs were tested in both naturalistic and simulated highway scenarios.The proposed risk assessment framework is promising for real-world applications.

Cross-Modal Consistency with Aesthetic Similarity for Multimodal False Information Detection

With the explosive growth of false information on social media platforms, the automatic detection of multimodalfalse information has received increasing attention. Recent research has significantly contributed to multimodalinformation exchange and fusion, with many methods attempting to integrate unimodal features to generatemultimodal news representations. However, they still need to fully explore the hierarchical and complex semanticcorrelations between different modal contents, severely limiting their performance detecting multimodal falseinformation. This work proposes a two-stage detection framework for multimodal false information detection,called ASMFD, which is based on image aesthetic similarity to segment and explores the consistency andinconsistency features of images and texts. Specifically, we first use the Contrastive Language-Image Pre-training(CLIP) model to learn the relationship between text and images through label awareness and train an imageaesthetic attribute scorer using an aesthetic attribute dataset. Then, we calculate the aesthetic similarity betweenthe image and related images and use this similarity as a threshold to divide the multimodal correlation matrixinto consistency and inconsistencymatrices. Finally, the fusionmodule is designed to identify essential features fordetectingmultimodal false information. In extensive experiments on four datasets, the performance of the ASMFDis superior to state-of-the-art baseline methods.

Probabilistic seismic landslide hazard assessment: a case study in Tianshui, Northwest China

Probabilistic analysis in the field of seismic landslide hazard assessment is often based on an estimate of uncertainties of geological, geotechnical,geomorphological and seismological parameters.However, real situations are very complex and thus uncertainties of some parameters such as water content conditions and critical displacement are difficult to describe with accurate mathematical models. In this study, we present a probabilistic methodology based on the probabilistic seismic hazard analysis method and the Newmark’s displacement model. The Tianshui seismic zone(105°00′-106°00′ E, 34°20′-34°40′ N) in the northeastern Tibetan Plateau were used as an example. Arias intensity with three standard probabilities of exceedance(63%, 10%, and 2% in 50 years) in accordance with building design provisions were used to compute Newmark displacements by incorporating the effects of topographic amplification.Probable scenarios of water content condition were considered and three water content conditions(dry,wet and saturated) were adopted to simulate the effect of pore-water on slope. The influence of 5 cm and 10 cm critical displacements were investigated in order to analyze the sensitivity of critical displacement to the probabilities of earthquake-induced landslide occurrence. The results show that water content in particular, have a great influence on the distribution of high seismic landslide hazard areas. Generally, the dry coverage analysis represents a lower bound for susceptibility and hazard assessment, and the saturated coverage analysis represents an upper bound to some extent. Moreover, high seismic landslide hazard areas are also influenced by the critical displacements. The slope failure probabilities during future earthquakes with critical displacements of 5 cm can increase by a factor of 1.2 to 2.3 as compared to that of 10 cm. It suggests that more efforts are required in order to obtain reasonable threshold values for slope failure. Considering the probable scenarios of water content condition which is varied with seasons, seismic landslide hazard assessments are carried out for frequent, occasional and rare earthquake occurrences in the Tianshui region, which can provide a valuable reference for landslide hazard management and infrastructure design in mountainous seismic zones.

An Improved YOLOv5s-Based Smoke Detection System for Outdoor Parking Lots

In the rapidly evolving urban landscape,outdoor parking lots have become an indispensable part of the city’s transportation system.The growth of parking lots has raised the likelihood of spontaneous vehicle combus-tion,a significant safety hazard,making smoke detection an essential preventative step.However,the complex environment of outdoor parking lots presents additional challenges for smoke detection,which necessitates the development of more advanced and reliable smoke detection technologies.This paper addresses this concern and presents a novel smoke detection technique designed for the demanding environment of outdoor parking lots.First,we develop a novel dataset to fill the gap,as there is a lack of publicly available data.This dataset encompasses a wide range of smoke and fire scenarios,enhanced with data augmentation to ensure robustness against diverse outdoor conditions.Second,we utilize an optimized YOLOv5s model,integrated with the Squeeze-and-Excitation Network(SENet)attention mechanism,to significantly improve detection accuracy while maintaining real-time processing capabilities.Third,this paper implements an outdoor smoke detection system that is capable of accurately localizing and alerting in real time,enhancing the effectiveness and reliability of emergency response.Experiments show that the system has a high accuracy in terms of detecting smoke incidents in outdoor scenarios.