Scene Characteristics Research Articles

What drives viewers’ engagement in travel live streaming: a mixed-methods study from perceived value perspective

Purpose The purpose of this study is to explore the factors influencing viewers’ engagement intention in travel live streaming (TLS) from a perceived value perspective. Design/methodology/approach This study used a mixed-methods approach. In Study 1, 48 semistructured interviews were analyzed based on grounded theory and perceived value theory, and a research framework was established to investigate the impact of viewers’ engagement intentions in TLS. In Study 2, partial least squares structural equation modeling (PLS-SEM) was used to empirically validate survey data from 255 TLS viewers. Findings Through an analysis of the interview content, it was found that the expertise and interaction of the live streamer in TLS as well as the immersion, aesthetics and novelty of the live streaming scene are key influencing factors that affect the engagement of TLS viewers. This finding was confirmed through empirical research. Practical implications This research provides practical suggestions for live streamers, TLS platforms and local government to increase viewer engagement. Specifically, it provides methods and directions for the individual improvement of live streamers, further promotes the development and construction of the platform and underscores the importance of government initiatives in policy support and regulatory framework development. Originality/value This study focuses on the less-researched field of TLS. Using a mixed-methods approach combining interviews and PLS-SEM, this study explores the key factors that affect the engagement of TLS viewers based on the characteristics of live streamers and live streaming scenes.

LRSDet: lightweight remote sensing target detection network with local-global information awareness and rapid sample assignment

Abstract With the rapid development of aerospace and unmanned aerial vehicles, using neural networks for object detection in optical remote sensing images (O-RSI) has encountered heightened challenges. The optical remote sensing images have the characteristics of complex geometric scenes, dense groups of objects, and significant multi-scale variations of objects; researchers need to use more complex models to achieve higher accuracy. However, this complexity also brings challenges to the application of lightweight scenes. Therefore, to cope with the trade-off challenge between model complexity and detection accuracy, we propose a lightweight network model LRSDet in this study. The model integrates local and global information processing mechanisms and introduces a fast positive sample assignment strategy to adapt to resource-constrained embedded and mobile platforms. By constructing a lightweight feature extraction network and a lightweight path aggregation network and incorporating the ESM-Attention module, the feature extraction capability of the model in complex remote sensing scenarios is significantly improved. In addition, the application of a dynamic soft threshold strategy further optimizes the positive sample selection process and improves the detection efficiency of the model. Experimental on the O-RSI datasets DIOR, NWPU VHR-10, and RSOD, while analyzing model real-time performance on aerial video and embedded devices, outperforming other state-of-the-art methods.

Dynamic indoor mapping for AVP: Crowdsourcing mapping without prior maps

AbstractHigh‐definition maps are essential for autonomous vehicle navigation, but indoor parking lots remain poorly mapped due to high costs. To address this, a crowdsourcing model gathers data from consumer‐grade sensors in mass‐produced vehicles to create semantic maps. Indoor parking lots lack GNSS signals, and most of them do not have high‐definition maps or navigation maps as references, making it difficult to ensure the accuracy of the final mapping results. Additionally, the semantic information of indoor parking lots is relatively limited, and the geometric features are overly similar, which significantly impacts the accuracy of point cloud registration. Therefore, this article proposes a crowdsourcing‐based approach, where vehicles generate local semantic maps at the client end and upload them to the cloud. Leveraging the scene characteristics of indoor parking lots, the cloud optimizes and fits a large amount of crowdsourced data to obtain a high‐precision base map without prior information. Enhanced ICP point cloud registration merges subsequent maps with the base. Additionally, parking space occupancy information is provided. This map can furnish the necessary information for Autonomous Valet Parking (AVP) tasks. Evaluation on the BEVIS dataset shows a root mean square error of 0.482446 m for vehicle localization on the cloud‐based map.

Ensemble width estimation in HRTF-convolved binaural music recordings using an auditory model and a gradient-boosted decision trees regressor

Binaural audio recordings become increasingly popular in multimedia repositories, posing new challenges in indexing, searching, and retrieval of such excerpts in terms of their spatial audio scene characteristics. This paper presents a new method for the automatic estimation of one of the most important spatial attributes of binaural recordings of music, namely “ensemble width.” The method has been developed using a repository of 23,040 binaural excerpts synthesized by convolving 192 multi-track music recordings with 30 sets of head-related transfer functions (HRTF). The synthesized excerpts represented various spatial distributions of music sound sources along a frontal semicircle in the horizontal plane. A binaural auditory model was exploited to derive the standard binaural cues from the synthesized excerpts, yielding a dataset representing interaural level and time differences, complemented by interaural cross-correlation coefficients. Subsequently, a regression method, based on gradient-boosted decision trees, was applied to the formerly calculated dataset to estimate ensemble width values. According to the obtained results, the mean absolute error of the ensemble width estimation averaged across experimental conditions amounts to 6.63° (SD 0.12°). The accuracy of the method is the highest for the recordings with ensembles narrower than 30°, yielding the mean absolute error ranging between 0.8° and 10.2°. The performance of the proposed algorithm is relatively uniform regardless of the horizontal position of an ensemble. However, its accuracy deteriorates for wider ensembles, with the error reaching 25.2° for the music ensembles spanning 90°. The developed method exhibits satisfactory generalization properties when evaluated both under music-independent and HRTF-independent conditions. The proposed method outperforms the technique based on “spatiograms” recently introduced in the literature.

Research on Vehicle-Road Intelligent Capacity Redistribution and Cost Sharing in the Context of Collaborative Intelligence

The vehicle-road collaborative intelligence approach has become an industry consensus. It can efficiently tackle the technical hurdles and reduce the performance requirements and costs of on-board perception and computing devices. There is a need for in-depth quantitative studies to optimize the allocation of vehicle-road intelligent capabilities for collaborative intelligence. However, current research tends to focus more on qualitative analysis, and there is little research on the redistribution of vehicle and roadside intelligent capabilities. In this paper, we present a model for distributing perception and computing capabilities between vehicle-side and roadside, ensuring to meet the needs of various autonomous driving levels. Meanwhile, the collaborative intelligence approach will also introduce the costs of intelligent infrastructure deployment, energy, and maintenance. Different roads have varying scene characteristics and usage intensities. It is necessary to conduct a cost-effectiveness analysis of the intelligent deployment of different road types. A vehicle-road cost allocation model is developed based on the lifecycle traveled distance of vehicles and the lifecycle traffic flow of various roads to evaluate the function-cost effectiveness. Our study presents several vehicle-road intelligent schemes that meet the needs of various autonomous driving levels and selects Beijing for case analysis. The results indicate that primary intelligent infrastructure can reduce the lifecycle cost of the vehicle-side intelligent scheme for intermediate autonomous driving from ¥65,301 to ¥37,703, and advanced intelligent infrastructure can reduce the lifecycle cost for advanced autonomous driving from ¥126,938 to ¥42,180. Considering the distributed cost of vehicle-side and roadside, urban roads in Beijing have higher function-cost effectiveness compared to highways, especially urban expressways, which are expected to generate 43.3 times the vehicle-function-cost benefits after the advanced intelligent upgrades. The corresponding research findings can serve as a reference for city managers to make decisions on intelligent road deployment.

Association between EMS response times, low socioeconomic status, and poorer outcomes in out-of-hospital cardiac arrest: the MEDIC multicenter retrospective cohort study for disparities in access to prehospital critical care in the Paris metropolitan area

Background and importance Prolonged emergency medical services’ response times (EMS-RT) are associated with poorer outcomes in out-of-hospital cardiac arrest (OHCA). The patient access time interval (PATI), from vehicle stop until contact with patient, may be increased in areas with low socioeconomic status (SES). Objectives The objective of this study is to identify predictors of prolonged EMS-RT intervals, and to evaluate associations with clinical outcomes in OHCAs occurring in the largest metropolitan area in France. Design Using the Utstein-style, prospectively implemented, population-based SDEC registry for OHCAs, we conducted a multicenter, region-wide, retrospective cohort study of EMS dispatches for OHCA cases occurring in the 152 cities of the Greater Paris area, France, between January 1, 2017 and December 31, 2018. Settings and participants Adult, nontraumatic, EMS-assessed, non-EMS witnessed OHCAs. Exposure Geographic location and scene-level SES. Outcome measures and analysis The primary outcome was the EMS-RT interval, from activation until arrival at patient’s side. As secondary outcomes, we evaluated patient access outcomes of: (1) dispatch-to-patient contact interval (‘EMS-RT’); and (2) vehicle scene arrival-to-patient contact interval (PATI); and patient clinical outcomes of: (1) death; and (2) unfavorable neurological status, both at 30 days. Area-level SES was assessed at census tract level using the European Deprivation Index (EDI; continuous, and divided into quintiles, Q5 = most deprived). We fitted multilevel mixed-effects regression models to identify predictors of patient access outcomes, and their association with clinical outcomes. Main results We included 4082 cases; the median EMS-RT was 10.85 min (interquartile range [8.87–13.15]), and 138 (3.4%) survived to hospital discharge. Independent predictors of increased EMS-RT and PATI were age >65, female sex, residential location, occurrence at elevated floors, arrest unwitnessed by a bystander, and low EDI (all P < 0.018). After multivariable analysis, an overall EMS-RT interval >8 min was associated with higher mortality and poorer neurological status at hospital discharge (both P < 0.001). Conclusion In OHCA cases occurring in the Greater Paris metropolitan area, after adjustment for scene characteristics, EMS delays until patient contact were longer in neighborhoods of low SES, and were associated with poorer clinical outcomes.

Point cloud semantic segmentation of grotto scenes using the knowledge-guided deep learning method

ABSTRACT In recent years, the deep learning-based semantic segmentation for point clouds has demonstrated remarkable capabilities in processing 3D urban scenes for applications such as three-dimensional reconstruction, semantic modeling, and augmented reality. However, research on grottoes scenes is very limited. It is currently unclear how existing neural architectures for point cloud semantic segmentation perform in grotto scenes, and how to effectively incorporate the unique characteristics of grotto scenes to enhance the performance of deep neural networks. This study proposed a method for point cloud semantic segmentation of grotto scenes, combining knowledge with deep learning approaches. The method adopted knowledge to guide the creation of benchmark datasets, the design of a neural network called GSS-Net, and the correction of segmentation errors in the results of deep learning. The results show that the proposed method outperforms four existing mainstream models without the correction of segmentation results. Moreover, a set of ablation studies verified the effectiveness of each proposed module. This method not only improves the accuracy of point cloud semantic segmentation in grotto scenes but also enhances the interpretability of network designs. It provides new insights into the application of knowledge-guided deep learning models in grotto scenes.

Enhancing Place Emotion Analysis with Multi-View Emotion Recognition from Geo-Tagged Photos: A Global Tourist Attraction Perspective

User−generated geo−tagged photos (UGPs) have emerged as a valuable tool for analyzing large−scale tourist place emotions with unprecedented detail. This process involves extracting and analyzing human emotions associated with specific locations. However, previous studies have been limited to analyzing individual faces in the UGPs. This approach falls short of representing the contextual scene characteristics, such as environmental elements and overall scene context, which may contain implicit emotional knowledge. To address this issue, we propose an innovative computational framework for global tourist place emotion analysis leveraging UGPs. Specifically, we first introduce a Multi−view Graph Fusion Network (M−GFN) to effectively recognize multi−view emotions from UGPs, considering crowd emotions and scene implicit sentiment. After that, we designed an attraction−specific emotion index (AEI) to quantitatively measure place emotions based on the identified multi−view emotions at various tourist attractions with place types. Complementing the AEI, we employ the emotion intensity index (EII) and Pearson correlation coefficient (PCC) to deepen the exploration of the association between attraction types and place emotions. The synergy of AEI, EII, and PCC allows comprehensive attraction−specific place emotion extraction, enhancing the overall quality of tourist place emotion analysis. Extensive experiments demonstrate that our framework enhances existing place emotion analysis methods, and the M−GFN outperforms state−of−the−art emotion recognition methods. Our framework can be adapted for various geo−emotion analysis tasks, like recognizing and regulating workplace emotions, underscoring the intrinsic link between emotions and geographic contexts.

One-Shot Any-Scene Crowd Counting With Local-to-Global Guidance.

Due to different installation angles, heights, and positions of the camera installation in real-world scenes, it is difficult for crowd counting models to work in unseen surveillance scenes. In this paper, we are interested in accurate crowd counting based on the data collected by any surveillance camera, that is to count the crowd from any scene given only one annotated image from that scene. To this end, we firstly pose crowd counting as a one-shot learning task. Through the metric-learning, we propose a simple yet effective method that firstly estimates crowd characteristics and then transfers them to guide the model to count the crowd. Specifically, to fully capture these crowd characteristics of the target scene, we devise the Multi-Prototype Learner to learn the prototypes of foreground and density from the limited support image using the Expectation-Maximization algorithm. To learn the adaptation capability for any unseen scene, estimated multi prototypes are proposed to guide the crowd counting of query images in a local-to-global way. CNN is utilized to activate the local features. And transformer is introduced to correlate global features. Extensive experiments on three surveillance datasets suggest that our method outperforms the SOTA methods in the few-shot crowd counting.

Prognostic Scores for Mortality in Invasive Mechanically Ventilated Burn Patients.

Managing burn patients is a challenge requiring a multidisciplinary team with the ability to predict complications and act early to avoid them. There are few studies characterizing the population of critically ill burn patients in need of ventilatory support. This study aimed to describe the population of burn patients in need of invasive mechanical ventilation supportand assess in-hospital mortality and the factors associated with it. A longitudinal retrospective study was conducted, including burn patients admitted to a tertiary hospital burn unit over five consecutive years, who required invasive mechanical ventilation support during their hospitalization. Demographic data, comorbidities, characteristics of the injury scene, etiology, and characteristics of the burn were collected.Length of mechanical ventilation and hospitalization as well as mortality rate were evaluated.The determination of mortality predictors and the prognostic performance of mortality prediction scores were analyzed. A one-year follow-up was performed to evaluate the survival of discharged patients. A total of 141 patients were included in this study; 68.1% patients were malewith a median age of 58 years. The mean percentage of total body surface area (TBSA) burned was 24.5%. Home incidents were the most frequent, and fire was the most common cause of burns (80.9% of patients). The mean Abbreviated Burn Severity Index Score (ABSI) was 7.83, with an area under the curve in receiver operating characteristic curve (ROC) analysis (AUC_ROC) of 0.725; the mean Belgium Outcome of Burn Injury (BOBI) score was 3.45, with AUC_ROC of 0.740 and mean R-Baux of 89.1 and AUC_ROC of 0.834. The mean duration of invasive mechanical ventilation was 16.9±19.3 days. Age (p<0.001), length of mechanical ventilation (p<0.001), length of hospitalization (p<0.001), higher degree of burn (p=0.001), TBSA (p=0.040), and the presence of buttock burn (0.006) were associated with mortality in this sample. In-hospital mortality was 29.8%. The survival group had a 12% death rate at one-year follow-up, mostly in the first three months after discharge. Age, duration of mechanical ventilation, length of hospitalization, higher degree of burn, TBSA, and the presence of buttock burn were associated with mortality in this sample. R-Baux score was the most accurate test score to predict mortality in this challenging group of patients.

Arbitrary stylized light-field generation for three-dimensional light-field displays based on radiance fields

Three-dimensional (3D) light-field display technology can reconstruct the spatial characteristics of 3D scenes and provide users with an immersive visual experience without the need for any additional external devices. Here, an arbitrary stylized light-field generation method for 3D light-field displays is presented, and the tripartite constraints are addressed by conducting style transformation in a refined feature space. A multi-dimensional feature refinement module is designed to learn which aspects and regions should be prioritized within the high-level feature grid of the scene, which allows content and style patterns to be better matched and blended. To preserve more structural details and reduce artifacts, a novel global quality and local structure joint loss function is used for optimization. A mask-guided light-field coding method based on ray-casting is employed to accelerate the synthesis of stylized light-field images. Experimental results demonstrate that the proposed method can generate higher-quality stylized 3D light-field content with any given style in a zero-shot manner. Additionally, the method provides two user control extensions to further enrich the ways for the personalized editing of 3D content displayed on 3D light-field displays.

The Potential of Neural Radiance Fields and 3D Gaussian Splatting for 3D Reconstruction from Aerial Imagery

Abstract. In this paper, we focus on investigating the potential of advanced Neural Radiance Fields (NeRFs) and 3D Gaussian Splatting for 3D scene reconstruction from aerial imagery obtained via sensor platforms with an almost nadir-looking camera. Such a setting for image acquisition is convenient for capturing large-scale urban scenes, yet it poses particular challenges arising from imagery with large overlap, very short baselines, similar viewing direction and almost the same but large distance to the scene, and it therefore differs from the usual object-centric scene capture. We apply a traditional approach for image-based 3D reconstruction (COLMAP), a modern NeRF-based approach (Nerfacto) and a representative for the recently introduced 3D Gaussian Splatting approaches (Splatfacto), where the latter two are provided in the Nerfstudio framework. We analyze results achieved on the recently released UseGeo dataset both quantitatively and qualitatively. The achieved results reveal that the traditional COLMAP approach still outperforms Nerfacto and Splatfacto approaches for various scene characteristics, such as less-textured areas, areas with high vegetation, shadowed areas and areas observed from only very few views.

Depth-Guided Dehazing Network for Long-Range Aerial Scenes

Over the past few years, the applications of unmanned aerial vehicles (UAVs) have greatly increased. However, the decrease in clarity in hazy environments is an important constraint on their further development. Current research on image dehazing mainly focuses on normal scenes at close range or mid-range, while ignoring long-range scenes such as aerial perspective. Furthermore, based on the atmospheric scattering model, the inclusion of depth information is essential for the procedure of image dehazing, especially when dealing with images that exhibit substantial variations in depth. However, most existing models neglect this important information. Consequently, these state-of-the-art (SOTA) methods perform inadequately in dehazing when applied to long-range images. For the purpose of dealing with the above challenges, we propose the construction of a depth-guided dehazing network designed specifically for long-range aerial scenes. Initially, we introduce the depth prediction subnetwork to accurately extract depth information from long-range aerial images, taking into account the substantial variance in haze density. Subsequently, we propose the depth-guided attention module, which integrates a depth map with dehazing features through the attention mechanism, guiding the dehazing process and enabling the effective removal of haze in long-range areas. Furthermore, considering the unique characteristics of long-range aerial scenes, we introduce the UAV-HAZE dataset, specifically designed for training and evaluating dehazing methods in such scenarios. Finally, we conduct extensive experiments to test our method against several SOTA dehazing methods and demonstrate its superiority over others.

Tell Me What You Do, I’ll Tell You Who You Are: Predicting Offender-Victim Relationships in Sexual Homicide

ABSTRACT This study fills a gap in the existing literature by differentiating between sexual homicides committed by strangers and those by acquaintances. Utilizing data from the Sexual Homicide International Database, which encompasses 772 cases from France and Canada, the research focuses on using victimological and crime scene characteristics to predict the victim-offender relationship in sexual homicides. Employing a comprehensive methodological approach, the study uses bivariate analysis, sequential binary logistic regression, and an artificial neural network (ANN) model. These methods help in examining the correlations and predictive values of various factors in determining the nature of the victim-offender relationship. The findings highlight significant differences in the modus operandi of stranger and acquaintance offenders. Stranger offenders are more likely to exhibit violent, premeditated actions involving weapons, while acquaintance offenders tend to use verbal aggression, exploiting their existing relationship with the victim. Theoretically, results provide empirical insights into the dynamics of sexual homicides, expanding the understanding of offender behavior and crime scene analysis. Practically, it offers valuable guidance for law enforcement in criminal investigations and resource allocation.

A Novel Network Framework on Simultaneous Road Segmentation and Vehicle Detection for UAV Aerial Traffic Images.

Unmanned Aerial Vehicle (UAV) aerial sensors are an important means of collecting ground image data. Through the road segmentation and vehicle detection of drivable areas in UAV aerial images, they can be applied to monitoring roads, traffic flow detection, traffic management, etc. As well, they can be integrated with intelligent transportation systems to support the related work of transportation departments. Existing algorithms only realize a single task, while intelligent transportation requires the simultaneous processing of multiple tasks, which cannot meet complex practical needs. However, UAV aerial images have the characteristics of variable road scenes, a large number of small targets, and dense vehicles, which make it difficult to complete the tasks. In response to these issues, we propose to implement road segmentation and on-road vehicle detection tasks in the same framework for UAV aerial images, and we conduct experiments on a self-constructed dataset based on the DroneVehicle dataset. For road segmentation, we propose a new algorithm C-DeepLabV3+. The new algorithm introduces the coordinate attention (CA) module, which can obtain more accurate segmentation target location information and make the segmentation target edges more continuous. Also, the improved algorithm introduces the cascade feature fusion module to prevent the loss of detail information in road segmentation and to obtain better segmentation performance. For vehicle detection, we propose an improved algorithm S-YOLOv5 by adding a parameter-free lightweight attention module SimAM. Finally, the proposed road segmentation-vehicle detection framework is utilized to unite the C-DeepLabV3+ and S-YOLOv5 algorithms for the implementation of the serial tasks. The experimental results show that on the constructed ViDroneVehicle dataset, the C-DeepLabV3+ algorithm has an mPA value of 98.75% and an mIoU value of 97.53%, which can better segment the road area and solve the problem of occlusion. The mAP value of the S-YOLOv5 algorithm has an mAP value of 97.40%, which is more than YOLOv5's 96.95%, which effectively reduces the vehicle omission and false detection rates. By comparison, the results of both algorithms are superior to multiple state-of-the-art methods. The overall framework proposed in this paper has superior performance and is capable of realizing high-quality and high-precision road segmentation and vehicle detection from UAV aerial images.

Path guiding for wavefront path tracing: A memory efficient approach for GPU path tracers

We propose a path-guiding algorithm to be incorporated into the wavefront style of path tracers (WFPTs). As WFPTs are primarily implemented on graphics processing units (GPUs), the proposed method aims to leverage the capabilities of the GPUs and reduce the hierarchical data structure and memory usage typically required for such techniques. To achieve this, our algorithm only stores the radiant exitance on a single global sparse voxel octree (SVO) data structure. Probability density functions required to guide the rays are generated on-the-fly using this data structure. The proposed approach reduces the scene-related persistent memory requirements compared to other path-guiding techniques while producing similar or better results depending on scene characteristics. To our knowledge, our algorithm is the first one that incorporates path guiding into a WFPT.

A Fault Restoration Method for DC-interconnected Distribution Systems with Renewable Resources Considering Multiple Fault Scenes

Background:: The distribution systems are gradually transformed into active systems with the large-scale integration of renewable resources, which brings greater potential to the fault recovery of the distribution networks and higher requirements for the power scheduling capability under fault conditions. Power electronic devices such as soft open points (SOPs) can break the radial constraints of the traditional distribution network, provide cross-station power support in fault scenarios, and enhance the resilience of the distribution networks. Methods:: This paper focuses on the distribution systems with multi-transformer flexible interconnection, and a multi-device coordinated fault restoration method is proposed. Firstly, the characteristics of different transformer fault scenes and the fault restoration process coordinated by the bus-bar switches and SOPs in each scene are sorted out. Secondly, an optimization model of the fault restoration for DC-interconnected distribution system is established considering the network operation and the equipment constraints in the fault restoration process. Result:: The proposed model aims at the maximization of the weighted load restoration amount, and is transformed into a second-order cone programming model to be solved quickly. Lastly, the effectiveness of the proposed method is verified in a typical flexible interconnection distribution system. Conclusion:: The simulation results prove that it can effectively improve the capability of preserving power supply for important loads after transformer failure.

Identification of Shipborne VHF Radio Based on Deep Learning with Feature Extraction

In the feature identification of maritime VHF radio communication signals, shipborne VHF communication technology follows the same international technical standards formulated by IMO, uses analog communication technology and uses the same communication channel in the same area, and cannot effectively achieve signal feature identification by adding feature elements in the process of signal modulation. How to effectively identify the ship using VHF radio has always been a technical difficulty in the field of ship perception. In this paper, based on the convolutional neural network, combined with the feasibility of CAM feature extraction and BiLSTM feature extraction in non-cooperative signal recognition, a deep learning recognition model of shipborne VHF radio communication signals is established, and the deep learning approach is employed to discern the features of VHF signals, thereby accomplishing the identification and classification of transmitting VHF radio stations. Several experiments are designed according to the characteristics of ship communication scenes at sea. The experimental data show that the method proposed in this paper can provide a new feasible path for ship target perception in terms of radio signal characteristics and identification.

Characterization of fire investigators’ polyaromatic hydrocarbon exposures using silicone wristbands

BackgroundExposures to polyaromatic hydrocarbons (PAHs) contribute to cancer in the fire service. Fire investigators are involved in evaluations of post-fire scenes. In the US, it is estimated that there are up to 9000 fire investigators, compared to approximately 1.1 million total firefighting personnel. This exploratory study contributes initial evidence of PAH exposures sustained by this understudied group using worn silicone passive samplers. ObjectivesEvaluate PAH exposures sustained by fire investigators at post-fire scenes using worn silicone passive samplers. Assess explanatory factors and health risks of PAH exposure at post-fire scenes. MethodsAs part of a cross-sectional study design, silicone wristbands were distributed to 16 North Carolina fire investigators, including eight public, seven private, and one public and private. Wristbands were worn during 46 post-fire scene investigations. Fire investigators completed pre- and post-surveys providing sociodemographic, occupational, and post-fire scene characteristics. Solvent extracts from wristbands were analyzed via gas chromatography-mass spectrometry (GC-MS). Results were used to estimate vapor-phase PAH concentration in the air at post-fire scenes. ResultsFire investigations lasted an average of 148 minutes, standard deviation ± 93 minutes. A significant positive correlation (r=0.455, p<.001) was found between investigation duration and PAH concentrations on wristbands. Significantly greater time-normalized PAH exposures (p=0.039) were observed for investigations of newer post-fire scenes compared to older post-fire scenes. Regulatory airborne PAH exposure limits were exceeded in six investigations, based on exposure to estimated vapor-phase PAH concentrations in the air at post-fire scenes. DiscussionHigher levels of off-gassing and suspended particulates at younger post-fire scenes may explain greater PAH exposure. Weaker correlations are found between wristband PAH concentration and investigation duration at older post-fire scenes, suggesting reduction of off-gassing PAHs over time. Exceedances of regulatory PAH limits indicate a need for protection against vapor-phase contaminants, especially at more recent post-fire scenes.

AM-UNet: Field Ridge Segmentation of Paddy Field Images Based on an Improved MultiResUNet Network

In order to solve the problem of image boundary segmentation caused by the irregularity of paddy fields in southern China, a high-precision segmentation method based on the improved MultiResUNet model for paddy field mapping is proposed, combining the characteristics of paddy field scenes. We introduce the attention gate (AG) mechanism at the end of the encoder–decoder skip connections in the MultiResUNet model to generate the weights and highlight the response of the field ridge area, add an atrous spatial pyramid pooling (ASPP) module after the end of the encoder down-sampling, use an appropriate combination of expansion rates to improve the identification of small-scale edge details, use 1 × 1 convolution to improve the range of the sensory field after bilinear interpolation to increase the segmentation accuracy, and, thus, construct the AM-UNet paddy field ridge segmentation model. The experimental results show that the IoU, precision, and F1 value of the AM-UNet model are 88.74%, 93.45%, and 93.95%, respectively, and that inference time for a single image is 168ms, enabling accurate and real-time segmentation of field ridges in a complex paddy field environment. Thus, the AM-UNet model can provide technical support for the development of vision-based automatic navigation systems for agricultural machines.