Hazard Detection Research Articles

Intelligent detection method with 3D ranging for external force damage monitoring of power transmission lines

The rapid development of smart grid requires more and more reliable power supply. The external force damage to transmission lines can significantly impact the safety and stability of the power grid, potentially causing electric shock accidents. Existing methods for monitoring external force damage in transmission corridors lack the ability to provide effective warnings based on the real-time distance between potential hazards and power lines. Additionally, limited computational power and storage capacity at edge terminals restrict the efficient deployment of high-precision (high complexity) visual algorithms. This study presents, for the first time, a lightweight intelligent detection method integrating detection and three-dimensional (3D) ranging. A regression loss optimized for small objects is introduced to compensate for the shortcomings of lightweight networks in detection accuracy. Simultaneously, based on the influence of convolutions in various modules of the baseline model on performance, lightweight improvements are made to the detector architecture using Omni-Dimensional Dynamic Convolution and Distribution Shifting Convolution. Finally, a 3D ranging module is integrated into the detector, involving operations such as 2D3D information matching and back-projection transformation. This method innovatively achieves automated ranging and hierarchical warning. Its effectiveness is validated in transmission corridor scenarios under various weather conditions and surveillance video. The results demonstrate that our method outperforms other algorithms in hazard detection accuracy and lightweight performance. Moreover, the distance prediction error rate is below 1.6%. The hierarchical warning solutions can be applied to more scenarios.

Concealed hazardous object detection for terahertz images with cross-feature fusion transformer

Terahertz (THz) waves can penetrate many non-metallic materials (such as paper, plastic, and clothing), making them highly valuable in security inspection areas. However, the low contrast between target objects and the background in THz images poses problems for object detection and recognition tasks. Though many schemes have been proposed to tackle the problems, the low detection accuracy and high computational complexity remain challenges. To address these challenges, we propose a concealed hazardous object detection method called Cross-Feature Fusion Transformer YOLO (CFT-YOLO) for THz images. We design a Multi-Channel Spatial Feature Module (MCSFM) to achieve cross-channel and cross-spatial information transfer between feature maps. MCSFM also realizes the feature parameter transfer between the backbone and neck of the network. Additionally, we develop a CrossFuse-Transformer (CF-Former) module that leverages self-attention to fully exploit the correlations and structural information between different feature maps. We conduct a series of experiments on public datasets. The results show that CFT-YOLO outperforms the other advanced methods in THz hazardous object detection tasks. Moreover, the CFT-YOLO achieves a better balance between model complexity and detection accuracy compared to YOLOv8s, making it more practical for real-world applications.

Identifying regional hotspots of heatwaves, droughts, floods, and their co-occurrences

In this paper we present a framework to aid in the selection of optimal environmental indicators for detecting and mapping extreme events and analyzing trends in heatwaves, meteorological and hydrological droughts, floods, and their compound occurrence. The framework uses temperature, precipitation, river discharge, and derived climate indices to characterize the spatial distribution of hazard intensity, frequency, duration, co-occurrence, and dependence. The relevant climate indices applied are Standardized Precipitation Index, Standardized Precipitation and Evapotranspiration Index (SPEI), Standardized Streamflow Index, heatwave indices based on fixed (HWIS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_\ extrm{S}$$\\end{document}) and anomalous temperatures (HWIE\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_\ extrm{E}$$\\end{document}), and Daily Flood Index (DFI). We selected suitable environmental indicators and corresponding thresholds for each hazard based on estimated extreme event detection performance using receiver operating characteristics (ROC), area under curve (AUC), and accuracy, which is defined as the proportion of correct detections. We assessed compound hazard dependence using a Likelihood Multiplication Factor (LMF). We tested the framework for the case of Sweden, using daily data for the period 1922–2021. The ROC results showed that HWIS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_\ extrm{S}$$\\end{document}, SPEI12 and DFI are suitable indices for representing heatwaves, droughts, and floods, respectively (AUC > 0.83). Application of these indices revealed increasing heatwave and flood occurrence in large areas of Sweden, but no significant change trend for droughts. Hotspots with LMF > 1, mostly concentrated in Northern Sweden from June to August, indicated that compound drought-heatwave and drought-flood events are positively correlated in those areas, which can exacerbate their impacts. The novel framework presented here adds to existing hydroclimatic hazard research by (1) using local data and historical records of extremes to validate indicator-based hazard hotspots, (2) evaluating compound hazards at regional scale, (3) being transferable and streamlined, (4) attaining satisfactory performance for indicator-based hazard detection as demonstrated by the ROC method, and (5) being generalizable to various hazard types.

Vapochromic wool fibers for hazardous ammonia detection using xanthohumol biomolecule from natural extract of common hop (Humulus lupulus L.)

Ammonia is a colorless gas, yet it can be fatal if inhaled or ingested in high enough concentrations. Herein, a solid-state colorimetric smart wool (WL) sensor for ammonia was developed. Common hop (Humulus lupulus L.) is a natural resource of spectroscopical dyestuff known as xanthohumol (XN). Wool fabrics were dyed with different concentrations of xanthohumol extract using the high-temperature high-pressure method in the presence of a mordant. The coloration parameters and absorption spectra were employed to explore the yellow-to-white colorimetric shift of the wool fabric after it was exposed to aqueous ammonia. The wool fabric showed an excellent detection limit of 5 to 125 ppm. When the ammonia concentration was increased, the absorbance spectra demonstrated a hypsochromic shift from 498 nm to 367 nm. This could be attributed to changes in the molecular structure of xanthohumol that happen owing to intramolecular charge delocalization. Using transmission electron microscopy (TEM), the mordant/xanthohumol nanoparticles were measured to have diameters of 15–40 nm. The xanthohumol-finished wool fabrics showed good colorfastness properties. The incorporation of mordant/xanthohumol nanoparticles into wool fabrics showed no negative effects on their stiffness or air-permeability.

The Biopolymer Active Surface for Optical Fibre Sensors.

Optical fibre sensors have the potential to be overly sensitive and responsive, making them useful in various applications to detect the presence of pollutants in the environment, toxic gasses, or pesticides in soil. Deoxyribonucleic acid (DNA) as biopolymer active surfaces for fibre sensors can be designed to detect specific molecules or compounds accurately. In the article, we propose to use an optical fibre taper and DNA complex with surfactant-based sensors to offer a promising approach for gas detection, including ammonia solution, 1,4 thioxane, and trimethyl phosphate imitating hazardous agents. The presented results describe the influence of the adsorption of evaporation of measured agents to the DNA complex layer on a light leakage outside the structure of an optical fibre taper. The DNA layer with additional gas molecules becomes a new cladding of the taper structure, with the possibility to change its properties. The process of adsorption causes a change in the layer's optical properties surrounding a taper-like refractive index and increasing layer diameter, which changes the boundary condition of the structure and interacts with light in a wide spectral range of 600-1200 nm. The research's novelty is implementing a DNA complex active surface as the biodegradable biopolymer alignment for optical devices like in-line fibre sensors and those enabled for hazardous agent detection for substances appearing in the environment as industrial or even warfare toxic agents.

Verification and Validation for a Digital Twin for Augmenting Current SORA Practices with Air-to-Air Collision Hazards Prediction from Small Uncooperative Flying Objects

Future autonomous Unmanned Aerial Vehicles (UAV) missions will take place in highly cluttered urban environments. As a result, the UAV must be able to autonomously evaluate risks and react to unforeseen hazards. The current regulatory framework for missions implements SORA guidelines for hazard detection, but its application to air-to-air collision is limited. This research defined a rigorous verification and validation framework (V&V) for digital twins for use in future autonomous UAV missions. The researchers designed a sentry mission for a UAV to evaluate its capacity to detect small uncooperative flying objects. A digital twin of the DJI M300 vision system was built using a game engine and a V&V framework was developed to assure the quality of results in both virtual and real-world scenarios. The results showed the capability of the digital twin to identify vulnerabilities and worst-case scenarios in UAV mission operations, and how it can assist remote pilots in identifying air-to-air collision hazards. Furthermore, the probability of air-to-air collision was calculated for three sentry patterns, and the results were validated in the field. This research demonstrated the capability to identify vulnerabilities and worst-case scenarios in UAV mission operations. We present how the digital twin of an operational theatre can be exploited to assist remote pilots with the identification of air-to-air collision hazards of small uncooperative objects. Furthermore, we discuss how these results can be used to enhance current SORA-based risk assessment practices.

Hazardous Chemicals Detection and Classification Through Millimeter Wave and Machine Learning

Hazardous Chemicals Detection and Classification Through Millimeter Wave and Machine Learning

A Deep-Learning-Based Algorithm for Landslide Detection over Wide Areas Using InSAR Images Considering Topographic Features.

The joint action of human activities and environmental changes contributes to the frequent occurrence of landslide, causing major hazards. Using Interferometric Synthetic Aperture Radar (InSAR) technique enables the detailed detection of surface deformation, facilitating early landslide detection. The growing availability of SAR data and the development of artificial intelligence have spurred the integration of deep learning methods with InSAR for intelligent geological identification. However, existing studies using deep learning methods to detect landslides in InSAR deformation often rely on single InSAR data, which leads to the presence of other types of geological hazards in the identification results and limits the accuracy of landslide identification. Landslides are affected by many factors, especially topographic features. To enhance the accuracy of landslide identification, this study improves the existing geological hazard detection model and proposes a multi-source data fusion network termed MSFD-Net. MSFD-Net employs a pseudo-Siamese network without weight sharing, enabling the extraction of texture features from the wrapped deformation data and topographic features from topographic data, which are then fused in higher-level feature layers. We conducted comparative experiments on different networks and ablation experiments, and the results show that the proposed method achieved the best performance. We applied our method to the middle and upper reaches of the Yellow River in eastern Qinghai Province, China, and obtained deformation rates using Sentinel-1 SAR data from 2018 to 2020 in the region, ultimately identifying 254 landslides. Quantitative evaluations reveal that most detected landslides in the study area occurred at an elevation of 2500-3700 m with slope angles of 10-30°. The proposed landslide detection algorithm holds significant promise for quickly and accurately detecting wide-area landslides, facilitating timely preventive and control measures.

Mitigation of urban road collapses based on machine learning via integrating susceptibility assessment and geophysical detection validation

Road collapse is a frequent and damaging disaster in cities. The complexity and uncertainty inherent in urban environments pose significant challenges to mitigating road collapses. This paper presents a novel framework integrating machine learning-based susceptibility assessment and geophysical detection validation for urban road collapse risk reduction. Three oversampling techniques, random oversampling, synthetic minority oversampling technique for nominal and continuous features (SMOTENC), and adaptive synthetic sampling (ADASYN), are first utilized to implement data augmentation on urban road collapse accident samples. Subsequently, three machine learning models, support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), are developed to evaluate road collapse susceptibility by extracting collapse-inducing patterns from historical accident data. Particularly, on-site geophysical hazard detection is conducted to validate the assessment results. The results demonstrate that XGBoost with SMOTENC achieves satisfactory performance in identifying road collapses with accuracy (0.9608) and AUC (0.9796). The spatial distribution of road collapse susceptibility in Shanghai central area follows a high-moderate-low pattern from northwest to southeast. The geophysical detection reveals a correlation between higher road collapse susceptibility and increased severity of underground diseases, validating the generalization capacity of XGBoost in actual operational environments. Additionally, the structural problems of underground pipelines are identified as the most influential factors for urban road collapse. This research offers valuable insights for urban road collapse mitigation and resilience improvement of transportation infrastructure.

Mechanisms for Data Acquisition to Train Artificial Intelligence Models for Detecting Increased Susceptibility to Fire Situations by Using Internet of Things Devices and Satellite Systems

Aim: Exploration and developing mechanisms of advanced data acquisition necessary for training an artificial intelligence model capable of effectively detecting areas with increased susceptibility to fire situations. The study focuses on utilizing data from satellite missions and ground-based sensors, which provide both high-resolution imagery and precise data on temperature, humidity, and other environmental factors. By analysing these diverse data sources, the research aims to create a comprehensive and efficient model capable of early detection of potential fire hazards, which is crucial for prevention for fire-prone situations. Project and methods: It centres on a project that aims to enhance fire detection and management through the integration of artificial intelligence with data acquired from satellite systems and internet of things devices. The methodologies employed in this project involve a combination of advanced data acquisition, machine learning techniques, and the synthesis of diverse environmental data to train artificial intelligence models that can predict and detect fire incidents more effectively. Results: Significant advancements in fire detection and management have been demonstrated through the integration of artificial intelligence (AI) with satellite data and IoT: 1. Enhanced monitoring capabilities the use of satellite data systems enabled real-time monitoring of thermal anomalies and vegetation health, crucial for early detection and effective monitoring of wildfires. This real-time capability allowed for quicker responses and more informed decision-making in firefighting efforts. 2. Effective integration of data sources: the integration of satellite and surface data proved to be effective in enhancing the predictive capabilities of the fire management systems. This comprehensive approach allowed for a better understanding of fire dynamics and contributed to more accurate and timely predictions. Conclusions: It could be emphasize the significant benefits and future potential of integrating artificial intelligence with satellite and internet of things data for improving fire detection and management. The integration of satellite imagery and internet of things sensor data is essential for enhancing the predictive accuracy of artificial intelligence systems. This integration allows for a comprehensive assessment of fire risks, providing actionable intelligence that is critical for prevention for fire-prone situations. These conclusions underscore the transformative potential of artificial intelligence in enhancing fire management systems. Keywords: data acquisition, artificial intelligence, IoT, satellite data systems, fire management systems

Defining public warning failure

AbstractPublic warning failure is an undertheorized concept. A shared definition of public warning failure is needed to help stakeholders assess responsibility in the aftermath of government nonuse, misuse, or misunderstanding of public warning systems, especially mobile device‐based systems that have become a critical element of the public warning ecosystem. Although some uncertainty in the disaster information environment is inevitable, a shared definition of public warning failure could help clarify the roles and responsibilities of government officials and agencies across the arc of disaster communication: public preparation, hazard detection, warning message creation, message dissemination, and public response monitoring. In the context of earthquake early warning system performance, warning failure has been defined as false alert, missed alert, inaccurate alert, no alert, and late alert. Building upon this typology, in this essay, we propose an all‐hazards definition of public warning failure, illustrate it using a conceptual model, and assess the conceptual model using examples drawn from media reporting.

A portability self-powered sensor facilitates sensitive Cd2+ detection: Dual mechanism and three quantitative mode

As a common heavy metal contaminant, Cd2+ has adverse effects on food safety and consumer health. It is very important for human health to realize highly sensitive Cd2+ detection methods. The self-powered sensing system based on enzyme biofuel cells (EBFCs) does not need an external power supply, which can simplify the experimental equipment and has great application value in portable detection. Thus, the biosensor is innovatively integrated into the screen-printed electrode to construct a new type of portable sensor suitable for on-site and real-time Cd2+ detection. Hybridization chain reaction (HCR) combined with the Cd2+-dependent deoxyribose (DNAzyme) signal amplification strategy is used to enhance the detection sensitivity while specifically recognizing the Cd2+. Moreover, the self-powered sensor combines with smartphones to realize quantitative Cd2+ detection without other instruments and has the characteristic of Effectively improving the hazard detection technology is essential to ensure food safety. Portability, simplicity, and speed are suitable for real-time Cd2+ detection in the field. The dual mechanism and three quantitative modes combining colorimetric and two electrical signals output modes are adopted to realize the visualization and accurate detection. A series of research results confirm that this strategy is of great significance to strengthen the development of intelligent Cd2+ technology, expand the application of self-powered sensing technology, and improve the safety detection system.

Understanding the awareness of health and safety signs amongst health care workers in Pakistan - A cross-sectional study.

Safety signs are very important communication tools for accident prevention, fire safety, health hazard information, and emergency evacuation. However, they are helpful only when properly designed and understood by employees. The purpose of the present study was to assess the awareness of health and safety signs amongst health care workers including doctors, dentists and paramedics in different health care sectors across Pakistan. Data was collected via Google forms circulated through WhatsApp social media to predetermined groups of health care professionals to assess their understanding of safety signs across different health sectors. The survey included questions pertaining to awareness of 19 different health and safety signs complied with International Organization for Standardization 1710 and the Safety Signs and Signal Regulations 1996 chosen randomly. A total of 987 people participated in our study and were asked to comprehend the meaning of nineteen health and safety signs. The mean comprehension score for 19 signs was 42.2%. The mean score for warning signs was the lowest and fire safety signs was highest. The lowest comprehension scores were for oxygen cylinder sign (W029) 7.5% and highest for first aid sign (E003) 75.9%. Only two signs, that are first aid (E003) and mandatory gloves (M009) had acceptable comprehensive score of 75.9% and 73.7% respectively as per ISO 7010 i.e. >67%. Statistically significant differences were found only for trip hazard sign (W007) with respect to education and for risks of bomb explosion (W002), ionising radiation (W009), evacuation assembly point (E007), location of automated external heart defibrillator (E010) and mandatory gloves (M009) with work experience. Based on our results, we conclude that there is dire need of special and frequent training to better recognize the safety signs amongst health care employees since these kinds of interventions promote early detection of hazards and their associated risks. Thus, we propose that health care safety sign training must be included in every health care profession curriculum.

Metabolomics of Benzene Exposure and Development of Biomarkers for Exposure Hazard Assessment.

Benzene, a common industrial solvent, poses significant health risks including poisoning and hematopoietic diseases. However, its precise toxicity mechanisms remain unclear. To assess the health impact of prolonged benzene exposure through metabolomic analyses of exposed workers and benzene-poisoned mice, aiming to identify biomarkers and minimize occupational hazards. This study compared 18 benzene-exposed workers with 18 non-exposed workers, matching for age, lifestyle, and BMI. The metabolites in the workers' samples were analyzed using ultra-high-performance liquid chromatography and mass spectrometry. A larger study included 118 exposed and 158 non-exposed workers, incorporating surveys and routine blood and urine tests with differential metabolites targeted via an enzyme-linked immunosorbent assay. The animal studies consisted of two 15- and 60-day benzene staining and control experiments on 28 C57BL/6J mice, followed by sample collection and organ analysis. The data analysis employed eXtensible Computational Mass Spectrometry (XCMS), Python, MetaboAnalyst 6.0, and SPSS24.0. The exposed workers exhibited altered metabolites indicating external benzene exposure, lower glucose levels, and changes in white blood cell counts and urinary ketone bodies. The plasma metabolomics revealed disturbances in energy and lipid metabolism. The benzene-exposed mice displayed reduced weight gain, behavioral changes, and organ damage. Oxidative stress and abnormal purine and lipid metabolism were observed in both the long-term benzene-exposed workers and benzene-exposed mice. Metabolic markers for the early detection of benzene exposure hazards were identified, underscoring the need to mitigate occupational risks.

Evaluating Precipitation Events Using GPM IMERG 30-Minute Near-Real-Time Precipitation Estimates

Abstract Accurately detecting and estimating precipitation at near–real time (NRT) is of utmost importance for the early detection and monitoring of hydrometeorological hazards. The precipitation product, Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), provides NRT 0.1° and 30-min precipitation estimates across the globe with only a 4-h latency. This study was an evaluation of the GPM IMERG version 6 level-3 early run 30-min precipitation product for precipitation events from 2014 through 2020. The purpose of this research was to identify when, where, and why GPM IMERG misidentified and failed to detect precipitation events in California, Nevada, Arizona, and Utah in the United States. Precipitation events were identified based on 15-min precipitation from gauges and 30-min precipitation from the IMERG multisatellite constellation. False-positive and false-negative precipitation events were identified and analyzed to determine their characteristics. Precipitation events identified by gauges had longer duration and had higher cumulative precipitation than those identified by GPM IMERG. GPM IMERG had many false event detections during the summer months, suggesting possible virga event detection, which is when precipitation falls from a cloud but evaporates before it reaches the ground. The frequency and timing of the merged passive microwave (PMW) product and forward propagation were responsible for IMERG overestimating cumulative precipitation during some precipitation events and underestimating others. This work can inform experts that are using the GPM IMERG NRT product to be mindful of situations where GPM IMERG–estimated precipitation events may not fully resolve the hydrometeorological conditions driving these hazards. Significance Statement Accurately estimating rainfall to detect and monitor a precipitation event at near–real time is of utmost importance for hydrometeorological hazards. We used a state-of-the-art rainfall estimation product called GPM IMERG that uses infrared and passive microwave measurements collected from a constellation of satellites to produce near-real-time rainfall estimates every 30 min worldwide. The purpose of our research was to identify when, where, and why GPM IMERG falsely detected and missed precipitation events. Our results suggest that the frequency and timing of passive microwave precipitation with forward propagation were responsible for IMERG missing events, overestimating total rainfall during some precipitation events, and underestimating total rainfall in other precipitation events. Our future work will further investigate precipitation events using the GPM IMERG version 7 near-real-time product.

A Sensitive Lead-Free Perovskite Photodetector for Self-Powered Blue Light Hazard Detection Application

A Sensitive Lead-Free Perovskite Photodetector for Self-Powered Blue Light Hazard Detection Application

Enhancing smoke management in underground parking areas using jet fan systems with a focus on dead-end areas

This study delves into the utilization of jet fans in underground parking structures for smoke management during fires, with a specific focus on enhancing smoke control near critical areas adjacent to dead-end sections through innovative jet fan designs. Through the analysis of airflow patterns and computational fluid dynamic simulations, the research aims to effectively contain smoke and prevent its dispersion to unaffected zones. Findings underscore the significance of parking layout designs in ventilation strategies and soot dispersion rates. Altering the downward orientation of the jet fan outlet results in a notable portion of smoke being confined within the parking facility. Additionally, widening the jet fan outlet, as proposed in the design, can enhance smoke evacuation in all directions. Two hazard detection scenarios were executed within the parking structure following the British standard (BS), with one scenario simulated as a fire ignition source. The chosen scenario considers all escape routes and evacuation procedures within the underground parking area based on architectural specifications. By employing OpenFOAM software with the buoyantBoussinesqPimpleFOAM solver, the study assesses the efficacy of this approach, achieving a reduction in airflow irregularities of over 30%. It also provides valuable insights for designers to refine their comprehension of jet fan design, recommending tailored designs for specific zones. This research offers practical guidance for designers to optimize jet fan configurations for effective smoke control in underground parking facilities.

SAM‐Y: Attention‐enhanced hazardous vehicle object detection algorithm

AbstractVehicle transportation of hazardous chemicals is one of the important mobile hazards in modern logistics, and its unsafe factors bring serious threats to people's lives, property and environmental safety. Although the current object detection algorithm has certain applications in the detection of hazardous chemical vehicles, due to the complexity of the transportation environment, the small size and low resolution of the vehicle target etc., object detection becomes more difficult in the face of a complex background. In order to solve these problems, the authors propose an improved algorithm based on YOLOv5 to enhance the detection accuracy and efficiency of hazardous chemical vehicles. Firstly, in order to better capture the details and semantic information of hazardous chemical vehicles, the algorithm solves the problem of mismatch between the receptive field of the detector and the target object by introducing the receptive field expansion block into the backbone network, so as to improve the ability of the model to capture the detailed information of hazardous chemical vehicles. Secondly, in order to improve the ability of the model to express the characteristics of hazardous chemical vehicles, the authors introduce a separable attention mechanism in the multi‐scale target detection stage, and enhances the prediction ability of the model by combining the object detection head and attention mechanism coherently in the feature layer of scale perception, the spatial location of spatial perception and the output channel of task perception. Experimental results show that the improved model significantly surpasses the baseline model in terms of accuracy and achieves more accurate object detection. At the same time, the model also has a certain improvement in inference speed and achieves faster inference ability.

Hazard Perception in Visually Impaired Drivers Who Use Bioptic Telescopes.

Bioptic telescopic spectacles can allow individuals with central vision impairment to obtain or maintain driving privileges. The purpose of this study was to (1) compare hazard perception ability among bioptic drivers and traditionally licensed controls, (2) assess the impact of bioptic telescopic spectacles on hazard perception in drivers with vision impairment, and (3) analyze the relationships among vision and hazard detection in bioptic drivers. Visual acuity, contrast sensitivity, and visual field were measured for each participant. All drivers completed the Driving Habits Questionnaire. Hazard perception testing was conducted using commercially available first-person video driving clips. Subjects signaled when they could first identify a traffic hazard requiring a change of speed or direction. Bioptic drivers were tested with and without their bioptic telescopes in alternating blocks. Hazard detection times for each clip were converted to z-scores, converted back to seconds using the average response time across all videos, and then compared among conditions. Twenty-one bioptic drivers and 21 normally sighted controls participated in the study. The hazard response time of bioptic drivers was improved when able to use the telescope (5.4 ± 1.4 seconds vs 6.3 ± 1.8 seconds without telescope); however, it remained significantly longer than for controls (4.0 ± 1.4 seconds). Poorer visual acuity, contrast sensitivity, and superior visual field sensitivity loss were related to longer hazard response times. Drivers with central vision loss had improved hazard response times with the use of bioptic telescopic spectacles, although their responses were still slower than normally sighted control drivers. The use of a bioptic telescope by licensed, visually impaired drivers improves their hazard detection speed on a video-based task, lending support to their use on the road.

3D Spiky Needle‐Clustered Ag@Au Plasmonic Nanoarchitecture for Highly Sensitive and Machine Learning‐Assisted Detection of Multiple Hazardous Molecules

AbstractTo develop a field applicable hazardous molecular detection system, highly sensitive and multiplex detection capability is required for practical utilization. Here, a paper‐based 3D spiky needle‐clustered gold grown on silver (Ag@Au) plasmonic nanoarchitecture (3D‐SNCP) is fabricated through whole solution process. The developed substrate is investigated by scanning electron microscopy (SEM), transmission electron microscopy (TEM) and X‐ray diffraction (XRD) to find out morphological development mechanism. Also, finite‐domain time difference (FDTD) simulation is conducted for the observation of electromagnetic field (E‐field) distribution. After surface‐enhanced Raman scattering (SERS) characterization, the 3D‐SNCP is utilized for ultra‐sensitive and multiplex hazardous molecular detection, such as bipyridine pesticides including paraquat (PQ), diquat (DQ), and difenzoquat (DIF). Then, each of pesticide molecular Raman signals are trained by a machine learning technique of multinomial logistic regression (MLR), followed by multiplex classificationf of blank, PQ, DQ, DIF, and four mixture types of each pesticide, spiked in real agricultural matrix. The developed 3D‐SNCP substrate combined with the machine learning method successfully verifies the multiple pesticides and it is expected to be applied for various hazardous molecular detection in much complicated matrix environments.