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The Hindu Kush Himalaya (HKH) is one of the most flood-prone regions in the world, yet heavy cloud cover and limited in situ observations have hampered efforts to monitor the impact of heavy rainfall, flooding, and inundation during severe weather events. This paper introduces HydroSAR, a Sentinel-1 SAR-based hazard monitoring service which was co-developed with in-region partners to provide year-round, low-latency weather hazard information across the HKH. This paper describes the end user-focused concept and overall design of the HydroSAR service. It introduces the main processing algorithms behind HydroSAR's broad product portfolio, which includes qualitative visual layers as well as quantitative products measuring the surface water extent and water depth. We summarize the cloud-based implementation of the developed service, which provides the capability to scale automatically with the event size. A performance assessment of our quantitative algorithms is described, demonstrating the capabilities to map the flood extent and water depth with an accuracy of >90% and <1 m, respectively. An application of the HydroSAR service to the 2023 South Asia monsoon seasons showed that monsoon floods peaked near 6 August 2023 and covered 11.6% of Bangladesh in water. At the peak of the flood season, nearly 13.5% of Bangladesh's agriculture areas were affected. [ABSTRACT FROM AUTHOR]

Heatwaves are often driven by subtropical persistent highs and quasi-stationary Rossby waves along with regional factors such as soil moisture. Both temperature and humidity variability play an important role in the regional heatwave patterns. Studies have differentiated the dry and moist heatwave-related extremes both in terms of their driving mechanism and in their impact on human health. With the observed increase in the intensity, frequency and duration of heatwaves in recent decades, the impact-based forecasting and monitoring (IBF) of heatwaves has assumed significance. Current operational IBF indices, especially over the Indian region are empirical and do not necessarily consider the modal dynamics or their role in the impact of dry and moist heat-related discomfort. In this study, based on two large-scale intraseasonal modes of summer temperature variability, the driving mechanisms of dry and moist heat extremes over the Indian region are identified. The regional scale impact identified based on the Heat Index is driven by these two large-scale modes. The first mode represents the conditions conducive for the formation of the dry heat extremes. The second mode represents the regional circulation pattern that supports the moisture inflow towards the coastal states. This inflow combined with the modal temperature pattern drives the moist heat extremes over India. Based on the location of the principal components of these modes in the phase space, the heat-related health risk associated with the dominant modes is assessed. Warnings for different phases of dry and moist heat extremes occurring over the Indian region are also proposed for operational use. [ABSTRACT FROM AUTHOR]

Since their discovery in 1981, quantum dots (QDs) have been the center of attention in optoelectronic fields owing to their excellent properties such as clearly discrete band structure, high luminous efficiency, and tunable energy bandgap via dot size control. Among them, the bandgap increase characteristic based on the quantum confinement effect opens a new horizon in the field of deep ultraviolet photodetectors (DUV PDs). Emerging applications such as risk monitoring, wireless optical communication, and chemical sensing with extremely low environmental interference raise the significance of DUV PDs. Nonetheless, the limited material selection for DUV absorption has been the main obstacle for further applications. Along this line, this review systematically revisits the recent advances in QD‐based DUV PDs, using bandgap‐widened QDs, with a focus on the synthetic method, device architecture, and interactions among constituent components. Various QDs categorized into carbon, oxide, sulfide, and nitride are reviewed with the specific characteristics of each substance. The future prospects of QD‐based DUV PDs in terms of practical applications and the current challenges are discussed. [ABSTRACT FROM AUTHOR]

This study introduces a new automated system that blends multi-satellite information and citizen science data for reliable and timely observations of lake and river ice in under-observed northern regions. The system leverages the Google Earth Engine resources to facilitate the analysis and visualization of ice conditions. The adopted approach utilizes a combination of moderate and high-resolution optical data, along with radar observations. The results demonstrate the system's capability to accurately detect and monitor river ice, particularly during key periods, such as the freeze-up and the breakup. The integration citizen science data showed added values in the validation of remote sensing products, as well as filling gaps whenever satellite observations cannot be collected due to cloud obstruction. Moreover, it was shown that citizen science data can be converted to valuable quantitative information, such as the case of ice thickness, which is very useful when combined with ice extent derived from remote sensing. In this study, citizen science data were employed for the quantitative assessment of the remote sensing product. Obtained results showed a good agreement between the product and observed river status, with a Critical Success Index of 0.82. Notably, the system has shown effectiveness in capturing the spatial and temporal evolution of snow and ice conditions, as evidenced by its application in analyzing specific ice jam events in 2023. The study concludes that the developed system marks a significant advancement in river ice monitoring, combining technological innovation with community engagement. [ABSTRACT FROM AUTHOR]

Objectives: Global navigation satellite system (GNSS) is an essential tool for landslide monitoring. On the one hand, influenced by unmodeled errors such as multipath, some abnormal fluctuations will occur in the monitoring sequence of GNSS, which will have a negative impact on the accurate discrimination of hazard warning, and even cause serious consequences such as missed warning and false warning. On the other hand, the state of landslide hazard bodies is predictable throughout their life cycle, such as the commonly used three phase law: Initial acceleration stage, constant velocity stage and instability acceleration stage. However, this feature information is not fully utilized in the GNSS solution process. Methods: For the above problems, the state space model of GNSS hazard monitoring is analyzed, and a new real-time filtering algorithm is proposed considering the state characteristics of the hazard body. The algorithm models the historical state information of monitoring points through the adaptive adjustment of the size of the sliding window, and then reasonably adjusts the current state parameters, so as to obtain a more reliable real-time solution sequence. The monitoring data of Heifangtai loess landslide in Gansu Province are selected for experimental verification. Results: The experimental results show that compared with the conventional results, the monitoring accuracy of the new real-time filtering algorithm for floating point solution and fixed solution can be improved by 97.6%, 87.5% and 89.6% in the east, north and up directions, respectively. The monitoring accuracy of fixed solution can be improved by 50.0%, 14.3% and 18.8%. The fixed ambiguity rate can be increased from 97.1% to 99.9%. Conclusions: The new real-time filtering algorithm not only improves the accuracy of monitoring results, but also improves the fixed rate of the ambiguity, which effectively reduces the influence of abnormal sequence fluctuation on hazard monitoring and warning. [ABSTRACT FROM AUTHOR]

A multi-parameter comprehensive early warning method for coal pillar-type rockburst risk based on the deep neural network (DNN) is proposed in this study. By utilizing preprocessed data from the surveillance of coal pillar impact hazards in Yangcheng Coal Mine, this study incorporates training samples derived from three distinct coal pillar-type impact hazard monitoring methodologies: microseismic monitoring, borehole cutting analysis, and real-time stress monitoring. The data characteristics of the monitoring data were extracted, evaluated, classified, and verified by monitoring the data of different working faces. This method was applied to develop the depth of multi-parameter neural network comprehensive early warning software in engineering practice. The results showed that the accuracy of the depth for burst monitoring data processing is improved by 6.89%–16.87% compared to the traditional monitoring methods. This method has a better early warning effect to avoid the occurrence of coal pillar rockburst hazard.

Fire metrics such as fire front location and rate of spread (ROS) are critical to understanding the behavior of prescribed fires and wildfires. This paper proposes a new method for prescribed grass fire evolution mapping and ROS measurement using multitemporal thermal orthomosaics collected by a small fixed-wing Unmanned Aircraft System (UAS) at low altitudes. The proposed method provides a low-cost, safe, and effective solution for active grass fire monitoring and fire metric measurement in areas that may be challenging for a typical rotor-wing UAS to cover due to endurance and size constraints. The proposed method is demonstrated using a prescribed grass fire data set collected by the KHawk fixed-wing UAS over a 13 ha. Kansas tallgrass prairie field on 8 October 2019. Repeat-pass thermal images collected by the KHawk UAS during about 10 min of the burning were grouped and processed to produce multitemporal orthomosaics with a spatial resolution of about 0 ˜.23 m and a horizontal position error of about 1.5 m. The resulting orthomosaics were further processed for fire front extraction and the measurement of fire front location and ROS. The head fire ROS of this grass burn was observed to be between 0.2 and 0.4 m s − 1 with a mean value of 0.27 m s − 1 . [ABSTRACT FROM AUTHOR]

The aim of this study was to model bus drivers' crash involvement risk as a function of their demographic factors and driving behaviour. 177 bus drivers completed the bus driver risk index questionnaire. 19.2% of respondents had been involved in at least one at-fault crash during the past 3 years. In addition, age group and having a second job were found to affect the crash involvement rate. Smoker drivers reported significantly lower scores in patient driving and anxious driving factors. Results also indicated that vehicle ownership could improve hazard monitoring and thrill-seeking behaviours of bus drivers. Logistic regression suggested that drivers ≥55 years old are 5.4 times more likely to be involved in crashes, compared with drivers <35 years old. Model results also implied that the odds of crash involvement would decrease by 39% for each one-unit improvement in the scores of hazard monitoring behaviour. Implications are further discussed. [ABSTRACT FROM AUTHOR]

During explosive volcanic eruptions, large quantities of tephra can be dispersed and deposited over wide areas. Following deposition, subsequent aeolian remobilisation of ash can potentially exacerbate primary impacts on timescales of months to millennia. Recent ash remobilisation events (e.g., following eruptions of Cordón Caulle 2011; Chile, and Eyjafjallajökull 2010, Iceland) have highlighted this to be a recurring phenomenon with consequences for human health, economic sectors, and critical infrastructure. Consequently, scientists from observatories and Volcanic Ash Advisory Centers (VAACs), as well as researchers from fields including volcanology, aeolian processes and soil sciences, convened at the San Carlos de Bariloche headquarters of the Argentinian National Institute of Agricultural Technology to discuss the “state of the art” for field studies of remobilised deposits as well as monitoring, modeling and understanding ash remobilisation. In this article, we identify practices for field characterisation of deposits and active processes, including mapping, particle characterisation and sediment traps. Furthermore, since forecast models currently rely on poorly-constrained dust emission schemes, we call for laboratory and field measurements to better parameterise the flux of volcanic ash as a function of friction velocity. While source area location and extent are currently the primary inputs for dispersion models, once emission schemes become more sophisticated and better constrained, other parameters will also become important (e.g., source material volume and properties, effective precipitation, type and distribution of vegetation cover, friction velocity). Thus, aeolian ash remobilisation hazard and associated impact assessment require systematic monitoring, including the development of a regularly-updated spatial database of resuspension source areas.

Introduction: Examining the effect of bus drivers’ demographic characteristics as well as workplace conditions on their traffic behavior and performance is important not only for drivers’ health and safety, but also for the safety of bus passengers. The aim of this study was to investigate the relationship between demographic variables and work shift schedule on the behaviors pertaining to “hazard monitoring”, “relax driving”, “fatigue proneness”, and “thrill seeking” among intercity bus drivers. Material and Method: In this cross-sectional study a sample of 321 bus drivers were randomly selected from Tehran intercity bus terminals. The BDRI questionnaire was used to collect data. The Pearson’s correlation coefficient and the ANOVA and Tukey’s HSD tests were used to analyze the data. Result: In general, the findings showed that age, driving experience and rest time period positively correlate to lower fatigue, lower thrill seeking and more relaxed driving. Drivers with higher vehicle age were found to perform worse than others in hazard monitoring and to be more prone to the stress and fatigue. Also, fatigue and relaxed driving were significantly related to the marital status and cigarette usage. In addition, drivers who were college graduate or above were found to perform worse than other drivers considering all of the four factors. Furthermore, those who drive in mountainous routes were found to perform worse than other drivers in hazard monitoring and fatigue proneness. Conclusion: Since the age and driving experience were found to influence fatigue proneness, hazard monitoring and relaxed driving, thus, setting up specific training courses for different driver age groups aiming to enhance their awareness concerning on managing these factors might help drivers improve their performance in these factors. Furthermore, regarding the effect of rest period and route type on the drivers’ fatigue and hazard monitoring ability, appropriate regulations are needed to mitigate the effect of these factors on the performance of drivers.

River corridors play a crucial environmental, economic, and societal role yet also represent one of the world's most dangerous natural hazards, making monitoring imperative to improve our understanding and to protect people. Remote sensing offers a rapidly growing suite of methods by which river corridor monitoring can be performed efficiently, at a range of scales and in difficult environmental conditions. This paper aims to evaluate the current state and assess the potential future of river corridor monitoring, whilst highlighting areas that require further investigation. We initially review established methods that are used to undertake river corridor monitoring, framed by the context and scales upon which they are applied. Subsequently, we review cutting edge technologies that are being developed and focussed around unmanned aerial vehicle and multisensor system advances. We also "horizon scan" for future methods that may become increasingly prominent in research and management, citing examples from within and outside of the fluvial domain. Through review of the literature, it has become apparent that the main gap in fluvial remote sensing lies in the trade‐off between resolution and scales. However, prioritising process measurements and simultaneous multisensor data collection is likely to offer a bigger advance in understanding than purely from better surveying methods alone. Challenges regarding the legal deployment of more complex systems, as well as effectively disseminating data into the science community, are amongst those that we propose need addressing. However, the plethora of methods currently available means that researchers and monitoring agencies will be able to identify suitable techniques for their needs. [ABSTRACT FROM AUTHOR]

The aim of this study was to examine the influence of demographic, environmental, and occupational factors as well as behavioral characteristics of intercity bus drivers on their crash involvement risk. A total of 107 intercity bus drivers from Tehran, Iran, participated in the study. Logistic regression model suggested that smokers, those who drive during night to morning, less experienced drivers, and those who operate older buses are more likely to be involved in crashes. In addition, a 1-unit increase in the weekly driving hours might significantly increase the drivers' crash involvement risk. The model results also indicated that hazard monitoring, fatigue proneness, and thrill seeking might be considered other significant predictors of crash involvement risk. Implications of results are discussed. [ABSTRACT FROM AUTHOR]

Rural hamlets in the Southeast Sahel, because of their pastoral setting and dependence on local agricultural production, are particularly vulnerable to flash flooding, loss of soil and the inundation of arable land caused by sudden storm events. High resolution satellite precipitation estimates (SPEs) could prove essential for detecting, monitoring, predicting and mitigating the effects of such hydrometeorological extremes. The focus of this report is on assessing – in the context of the needs of the local community − the effectiveness of what has become the gold standard for such applications in Africa and elsewhere since 1998: Tropical Rainfall Monitoring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) program’s 0.25° × 0.25°, daily 3B42 Version 7 satellite-gauge merged product. The scientific community now has almost 20 years of TMPA data that will likely play a significant role well into the future for retrospective studies in conjunction with the next generation (post-2014) Global Precipitation Mission (GPM) products. To assess the 3B42 product for the purposes of this report, we employ a pixel-to-point comparison of 3B42 daily time series with synchronous gauge data at four well-separated gauge sites. While others have found notable agreement between 3B42 and gauge observations aggregated over large areas and seasonal to annual time periods, it is a different matter downscaling to daily values at local sites, where we find a disturbing number of ‘false alarms’ and ‘missed events’. More than 90% of the total annual rainfall estimated by the 3B42 is contributed by daily events that are misaligned in time or intensity with gauge events. This largely concurs with the findings of our prior reports based on CPC-RFE2 0.1° × 0.1° daily values. Thus, users might be cautious in using the TMPA 3B42, CPC-RFE2 or, by inference, any SPE, as deterministic metrics for detecting and monitoring short term (i.e. ≤ 1 day), extreme storm events in this area of the Sahel. [ABSTRACT FROM AUTHOR]

Falls at construction sites account for approximately 50% of all accidents reported in the US annually, making them the leading cause of injuries and fatalities. Although there have been several studies characterizing fall-related hazards and emerging technologies, a systematic review of literature examining the intersection of fall from height (FFH) risk factors and emerging technological solutions is missing. This paper addresses this gap by conducting a systematic literature review to investigate FFH risk factors and the use of technologies to prevent falls from heights on construction sites. The review analyzed 94 research articles published between 2002 and 2022 and identified the most common FFH hazards, including missing guardrails, lack of personal protective equipment, lack of knowledge and training, loss of balance and unsafe behavior, violation of safety rules, and poor site management. The review also revealed that individual technologies are insufficient to address most fall hazards, and there is a need for an integrated system of various technologies to improve fall prevention on construction sites. • Fall from height (FFH) risks factors and technologies to mitigate these risks in the construction industry are reviewed. • A total of 94 relevant articles published between 2002 and 2022 are systemically reviewed. • Seven most frequently occurring FFH hazards were identified. • Individual technology-based models and systems did not sufficiently all FFH hazards. • Need to mitigate FFH hazards using an integrated system consisting of multiple smart technologies. [ABSTRACT FROM AUTHOR]

In this work, we extend a previously introduced framework for safety supervisory control with the ingredient of Temporal Logic (TL) to improve both accident prevention and dynamic risk assessment. We examine the synergies obtained from integrating model-based hazard modeling/monitoring with the verification of safety properties expressed in TL. This expanded framework leverages tools and ideas from Control Theory and Computer Science, and is meant to guide safety intervention both on-line and off-line, either during the design stages or during operation to support operator's situational awareness and decision-making in the face of emerging hazardous situations. We illustrate these capabilities and the insight that results from the integration of the proposed ingredients through a detailed case study. The study involves a runway overrun by a business jet, and it shows how hardware, software, and operators’ control actions and responses can be integrated within the proposed framework. The aircraft suffered from a faulty logic in the Full Authority Digital Engine Computer (FADEC), which prevented the pilot from activating the thrust reversers in a particular operational scenario. We examine the accident sequence against three system safety principles expressed in TL: the fail-safe principle, the defense-in-depth principle, and the observability-in-depth principle. The framework is implemented in Simulink and Stateflow, and is shown to provide important feedback for dynamic risk assessment and accident prevention. When applied on-line, it provides warning signs to support the sensemaking of emerging hazardous situations, and identifying adverse conditions that are closer to being released. When applied off-line, it provides diagnostic information regarding missing or inadequate safety features embedded in the system. For the specific case study, we propose a new TL safety constraint (based on speed measurements and the history of pressure sensors from the landing gears) to be incorporated in this and other aircraft FADEC, and that could have prevented the hazardous situation, in this case a rejected takeoff following tire explosion, from turning into a deadly accident. We conclude with some recommendations to prevent similar accident recurrences and to improve accident prevention. [ABSTRACT FROM AUTHOR]

The industrial area of Oreokastro, NW of the city of Thessaloniki, is monitored using radar interferometry to determine the spatial evolution of the underlying ground deformation trends. Previous studies, using SAR data acquired between 1992 and 1999, have revealed subsidence; however, the driving mechanism has not been, so far, solidly explained. Here, SAR satellite data from ERS 1, 2 and ENVISAT missions, acquired between 1992 and 2010, are analysed to enhance our understanding of the ground displacement trends and provide a thorough interpretation of the phenomena. The analysis confirms a subsiding displacement pattern from 1992 to 1999, whereas the recent data indicate that after 2003 the motion direction has changed to uplift. This whole monitoring of subsidence and the subsequent uplift is a rarely documented phenomenon, and in the case of Oreokastro is not reflecting a natural process; on the contrary, the driver is anthropogenic, related to the regional aquifer activity. Our study also highlights the fact that the local faults act as groundwater barriers and captures the existence of a possible previously unknown tectonic structure. [ABSTRACT FROM AUTHOR]

In the context of hazard monitoring, using sensor web technology to monitor anddetect hazardous conditions in near-real-time can result in large amounts of spatial data thatcan be used to drive analysis at an instrumented site. These data can be used for decisionmaking and problem solving, however as with any analysis problem the success ofanalyzing hazard potential is governed by many factors such as: the quality of the sensordata used as input; the meaning that can be derived from those data; the reliability of themodel used to describe the problem; the strength of the analysis methods; and the ability toeffectively communicate the end results of the analysis. For decision makers to make use ofsensor web data these issues must be dealt with to some degree. The work described in thispaper addresses all of these areas by showing how raw sensor data can be automaticallytransformed into a representation which matches a predefined model of the problem context.This model can be understood by analysis software that leverages rule-based logic andinference techniques to reason with, and draw conclusions about, spatial data. These toolsare integrated with a well known Geographic Information System (GIS) and existinggeospatial and sensor web infrastructure standards, providing expert users with the toolsneeded to thoroughly explore a problem site and investigate hazards in any domain.

Pāhoehoe lava flows are a major component of Hawaiian eruptive activity, and an important part of basaltic volcanism worldwide. In recent years, pāhoehoe lava has destroyed homes and threatened parts of Hawai'i with inundation and disruption. In this study, we use oblique helicopter-borne thermal images to create high spatial resolution (~ 1 m) georeferenced thermal maps of the active pāhoehoe flow on Kīlauea Volcano's East Rift Zone. Thermal maps were created on 27 days during 2014–2016 in the course of operational monitoring, encompassing a phase of activity that threatened the town of Pāhoa. Our results illustrate and reinforce how pāhoehoe flows are multicomponent systems consisting of the vent, master tube, distributary tubes, and surface breakouts. The thermal maps accurately depict the distribution and character of pāhoehoe breakouts through time, and also delineate the subsurface lava tube. Surface breakouts were distributed widely across the pāhoehoe flow, with significant portions concurrently active well upslope of the flow front, often concentrated in clusters of activity that evolved through time. Gradual changes to surface breakout distribution and migration relate to intrinsic processes in the flow, including the slow evolution of the distributary tube system. Abrupt disruptions to this system, and the creation of new breakouts (and associated hazards), were triggered by extrinsic forcing—namely fluctuations in lava supply rate at the vent which disrupted the master lava tube. Although the total area of a pāhoehoe flow has been suggested to relate to effusion rate, our results show that changes in the proportion of expansion vs. overplating can complicate this relationship. By modifying existing techniques, we estimate time-averaged discharge rates for the flow during 2014–2016 generally in the range of 1–2 m 3 s − 1 (mean: 1.3 ± 0.4 m 3 s − 1 )—less than half of Kīlauea's typical eruption rate on the East Rift Zone and suggestive of a weak eruptive regime during 2014–2016. We caution, however, that this discharge rate approach requires further independent corroboration. The thermal maps provide the first synoptic characterization of pāhoehoe flow activity at high spatial resolution, essential both for operational hazard assessment and fundamental understanding of pāhoehoe behavior. [ABSTRACT FROM AUTHOR]

Probabilistic Risk Assessment (PRA) is a staple in the engineering risk community, and it has become to some extent synonymous with the entire quantitative risk assessment undertaking. Limitations of PRA continue to occupy researchers, and workarounds are often proposed. After a brief review of this literature, we propose to address some of PRA׳s limitations by developing a novel framework and analytical tools for model-based system safety, or safety supervisory control, to guide safety interventions and support a dynamic approach to risk assessment and accident prevention. Our work shifts the emphasis from the pervading probabilistic mindset in risk assessment toward the notions of danger indices and hazard temporal contingency. The framework and tools here developed are grounded in Control Theory and make use of the state-space formalism in modeling dynamical systems. We show that the use of state variables enables the definition of metrics for accident escalation, termed hazard levels or danger indices, which measure the “proximity” of the system state to adverse events, and we illustrate the development of such indices. Monitoring of the hazard levels provides diagnostic information to support both on-line and off-line safety interventions. For example, we show how the application of the proposed tools to a rejected takeoff scenario provides new insight to support pilots’ go/no-go decisions. Furthermore, we augment the traditional state-space equations with a hazard equation and use the latter to estimate the times at which critical thresholds for the hazard level are (b)reached. This estimation process provides important prognostic information and produces a proxy for a time-to-accident metric or advance notice for an impending adverse event. The ability to estimate these two hazard coordinates, danger index and time-to-accident, offers many possibilities for informing system control strategies and improving accident prevention and risk mitigation. Finally we develop a visualization tool, termed hazard temporal contingency map, which dynamically displays the “coordinates” of a portfolio of hazards. This tool is meant to support operators’ situational awareness by providing prognostic information regarding the time windows available to intervene before hazardous situations become unrecoverable, and it helps decision-makers prioritize attention and defensive resources for accident prevention. In this view, emerging risks and hazards are dynamically prioritized based on the temporal vicinity of their associated accident(s) to being released, not on probabilities or combination of probabilities and consequences, as is traditionally done (off-line) in PRA. This approach offers novel capabilities, complementary to PRA, for improving risk assessment and accident prevention. It is hoped that this work helps to expand the basis of risk assessment beyond its reliance on probabilistic tools, and that it serves to enrich the intellectual toolkit of risk researchers and safety professionals. [ABSTRACT FROM AUTHOR]

The present study examined the impact of Primacy/Recency Effects and Hazard Monitoring on driver attributions. Participants viewed a simulated near collision from the perspective of a trailing motorist. The amount of error free driving prior to the near collision varied between two groups, where the near incident occurred either early or later in their viewing experience. They were then given the opportunity to provide judgments of the offending driver based on how safe, dangerous, risky, and skilled the driver was in general, and to evaluate their overall performance. Results showed a Primacy Effect dominance in that judgments of the driver were most negative in the early group, but this was moderated by high Hazard Monitoring for ratings of “dangerous” and “safe”. This suggests that judgments of other drivers are likely to be quick and based on early information, but are impacted by personal factors such as a tendency to monitor for hazards. [ABSTRACT FROM AUTHOR]

In environmental monitoring applications, having multiple cameras focus on common scenery increases robustness of the system. To save energy based on user demand, successive refinement image coding is important, as it allows us to progressively request better image quality. By exploiting the broadcast nature and correlation between multiview images, we investigate a two-camera setup and propose a novel two-encoder successive refinement scheme which imitates a ping-pong game. For the bivariate Gaussian case, we prove that this scheme is successively refinable on the theoretical rate-distortion limit of distributed coding (Wagner surface) under arbitrary settings. For stereo-view images, we develop a practical successive refinement coding algorithm using the same idea. The simulation results show that this scheme operates close to the distributed coding bound. [ABSTRACT FROM AUTHOR]

This study aims to predict traffic behavior using personality factors that induce drivers' vulnerability to stress (aggression, dislike of driving, proneness to fatigue, risk monitoring, sensation seeking) in accordance with the transactional model of stress and driver fatigue developed by Matthews (2001, 2002). 338 participants were asked to fill in the Driver Stress Inventory and the Driver Behavior Questionnaire. We aimed to achieve the best prediction models for behavior in traffic. The study results support the utility of the transactional model in predicting and explaining aberrant behaviors. It shows that by measuring personality traits in the specific context of behavior occurrence, one can capture other effects compared to those captured by a general predisposition toward a behavior. [ABSTRACT FROM AUTHOR]

Civil infrastructures are always subjected to various types of hazard and deterioration. These conditions require systematic efforts to assess the exposure and vulnerability of infrastructure, as well as producing strategic countermeasures to reduce the risks. This paper describes the needs for and concept of advanced sensor technologies for risk assessment of civil infrastructure in Japan. Backgrounds of the infrastructure problems such as natural disasters, difficult environment, limited resource for maintenance, and increasing requirement for safety are discussed. The paper presents a concept of risk assessment, which is defined as a combination of hazard and structural vulnerability assessment. An overview of current practices and research activities toward implementing the concept is presented. This includes implementation of structural health monitoring (SHM) systems for environment and natural disaster prevention, improvement of stock management, and prevention of structural failure. [ABSTRACT FROM AUTHOR]

Abstract: The ability to monitor and rapidly react to remote detection of volcanic activity has been greatly improved through use of the Autonomous Sciencecraft Experiment (ASE), an advanced software application installed on a spacecraft in Earth orbit. ASE is a NASA New Millennium Program experiment demonstrating science-driven autonomous command and control of a spacecraft. Flying on the Earth Observing-1 (EO-1) spacecraft, ASE successfully detected thermal emission from the Mt. Erebus lava lake on 7 May 2004, having analyzed a Hyperion hyperspectral data product on board the spacecraft. EO-1 was re-tasked by ASE to obtain a follow-up observation 7 h later and sent a notification of detection of volcanic activity to the ground. The entire process was carried out autonomously. Initial acquisition to receipt on the ground of the positive detection took less than 3 h, a process that without ASE would have taken weeks. The ASE Thermal Classifier has detected several styles of effusive volcanic activity: active lava lakes, pahoehoe flow fields, open channel flows and lava domes. ASE successfully demonstrated that science-driven spacecraft operation greatly enhances science return per returned byte through the identification of the most valuable data, allowing prioritization of downlink products and the discarding of null data sets. This technology has applications on missions elsewhere in the solar system. Modified thermal classifiers can be used for detecting and monitoring active volcanism on the jovian satellite Io, the neptunian moon Triton, and searching for active volcanism on Mars and icy satellites. The success of ASE is an incentive for future instrument and mission designers to consider on-board data-processing requirements (especially data storage capacity, number of processors and processor speed, and RAM) in order to take advantage of this flight-proven technology. [Copyright &y& Elsevier]