Quantifying Vision Zero: Crash avoidance in rural and motorway accident scenarios by combination of ACC, AEB, and LKS projected to German accident occurrence.

Objective: The Vision Zero initiative pursues the goal of eliminating all traffic fatalities and severe injuries. Today's advanced driver assistance systems (ADAS) are an important part of the strategy toward Vision Zero. In Germany in 2018 more than 26,000 people were killed or severely injured by traffic accidents on motorways and rural roads due to road accidents. Focusing on collision avoidance, a simulative evaluation can be the key to estimating the performance of state-of-the-art ADAS and identifying resulting potentials for system improvements and future systems. This project deals with the effectiveness assessment of a combination of ADAS for longitudinal and lateral intervention based on German accident data. Considered systems are adaptive cruise control (ACC), autonomous emergency braking (AEB), and lane keeping support (LKS). Methods: As an approach for benefit estimation of ADAS, the method of prospective effectiveness assessment is applied. Using the software rateEFFECT, a closed-loop simulation is performed on accident scenario data from the German In-Depth Accident Study (GIDAS) precrash matrix (PCM). To enable projection of results, the simulative assessment is amended with detailed single case studies of all treated cases without PCM data. Results: Three categories among today's accidents on German rural roads and motorways are reported in this study: Green, grey, and white spots. Green spots identify accidents that can be avoided by state-of-the-art ADAS ACC, AEB, and LKS. Grey spots contain scenarios that require minor system modifications, such as reducing the activation speed or increasing the steering torque. Scenarios in the white category cannot be addressed by state-of-the-art ADAS. Thus, which situations demand future systems are shown. The proportions of green, grey, and white spots are determined related to the considered data set and projected to the entire GIDAS. Conclusions: This article describes a systematic approach for assessing the effectiveness of ADAS using GIDAS PCM data to be able to project results to Germany. The closed-loop simulation run in rateEFFECT covers ACC, AEB, and LKS as well as relevant sensors for environment recognition and actuators for longitudinal and lateral vehicle control. Identification of green spots evaluates safety benefits of state-of-the-art level 0-2 functions as a baseline for further system improvements to address grey spots. Knowing which accidents could be avoided by standard ADAS helps focus the evolution of future driving functions on white spots and thus aim for Vision Zero.

The effects of socioecological factors on variation of communicable diseases: A multiple-disease study at the national scale of Vietnam.

OBJECTIVE: To examine the effects of socioecological factors on multiple communicable diseases across Vietnam. METHODS: We used the Moran's I tests to evaluate spatial clusters of diseases and applied multilevel negative binomial regression models using the Bayesian framework to analyse the association between socioecological factors and the diseases queried by oral, airborne, vector-borne, and animal transmission diseases. RESULTS AND SIGNIFICANCE: The study found that oral-transmission diseases were spatially distributed across the country; whereas, the airborne-transmission diseases were more clustered in the Northwest and vector-borne transmission diseases were more clustered in the South. Most of diseases were sensitive with climatic factors. For instance, a 1°C increase in average temperature is significantly associated with 0.4% (95CI, 0.3-0.5), 2.5% (95%CI, 1.4-3.6), 0.9% (95%CI, 0.6-1.4), 1.1% (95%CI), 5% (95%CI, 3-.7.4), 0.4% (95%CI, 0.2-0.7), and 2% (95%CI, 1.5-2.8) increase in risk of diarrhoea, shigellosis, mumps, influenza, dengue, malaria, and rabies respectively. The influences of socio-economic factors on risk of communicable diseases are varied by factors with the biggest influence of population density. The research findings reflect an important implication for the climate change adaptation strategies of health sectors. A development of weather-based early warning systems should be considered to strengthen communicable disease prevention in Vietnam.

Spatiotemporal variation of hand-foot-mouth disease in relation to socioecological factors: A multiple-province analysis in Vietnam.

BACKGROUND: Hand-foot-and-mouth disease (HFMD) is a significant public health issue in Asia-pacific countries. Numerous studies have examined the relationship between socio-ecological factors and HFMD however the research findings were inconsistent. This study examined the association between socio-ecologic factors and HFMD in multiple provinces across Vietnam. METHODS: We applied a spatial autoregressive model using a Bayesian framework to examine the relationship between HFMD and socio-demographic factors. We used a Generalized Linear Model (GLD) with Poisson family to examine the province-specific association between monthly HFMD and climatic factors while controlling for spatial lag, seasonality and long-term trend of HFMD. Then, we used a random-effect meta-analysis to generate pooled effect size of climate-HFMD association for regional and country scale. RESULTS: One percent increase in newborn breastfed within 1h of birth, households with permanent houses, and households accessed to safe water resulted in 1.57% (95% CI: -2.25, -0.93), 0.96% (-1.66, -0.23), and 1.13% (-2.16, -0.18) reduction in HFMD incidence, respectively. At the country-level, HFMD increased 7% (RR: 1.07; 95%CI: 1.052-1.088) and 3.1% (RR: 1.031, 95%CI: 1.024-1.039) for 1°C increase in monthly temperature above 26°C and 1% increase in monthly humidity above 76%. Whereas, HFMD decreased 3.1% associated with 1mm increase in monthly cumulative rainfalls. The climate-HFMD relationship was varied by regions and provinces across the country. CONCLUSIONS: The findings reflect an important implication for the climate change adaptation strategies and public-health decision, of which development of weather-based early warning systems should be considered to strengthen communicable disease prevention system.

The impact of prevention and control of infectious disease law on diarrhoea control: a 5-year evaluation in multiple provinces in Vietnam.

To address to burden of infectious diseases such as diarrhoea, the Vietnamese government has enacted the Law on Prevention and Control of Infectious Diseases (LPCIDs) since July 2008. However, no evaluation of the impact of the LPCID has been conducted. This study aims to evaluate the impact of the LPCID on diarrhoeal control for the 5 years following the implementation of LPCID in Vietnam. We used an interrupted time series design using a segmented regression analysis to estimate the 'province-level' impact of LPCID and then used random-effect meta-analysis to estimate the pooled effect sizes of the 'country-level' impact of LPCID on diarrhoeal control throughout Vietnam. The results show that the impacts varied by provinces. They were classified in four groups: 'positive impact, positive impact without sustainability, possibly positive impact, no or negative impact' of the LPCID. The meta-analysis indicated that the country-level impact of the LPCID became significant at 11 months after the LPCID took effect, with a decrease in level of diarrhoea of 9.7% (coefficient, -0.097; 95% CI: -19.1 to - 0.002) and a permanent downward trend of diarrhoea at a rate of 1.1% per month (coefficient, -0.011; 95% CI: -0.02 to - 0.003); whereas the trend in diarrhoea before the LPCID took effect was unchanging (coefficient, 0.002; 95% CI, 0-0.004). At 12, 24, 36, 48 and 60 months following the LPCID implementation date the levels of diarrhoea decreased by 10.9% (coefficient, -0.109; 95% CI: -0.203 to - 0.015), P < 0.01), 21.8% (coefficient, -0.218; 95% CI: -0.338 to - 0.098), P < 0.01), 31% (coefficient, -0.31; 95% CI: -0.474 to - 0.145), P < 0.01), 46.8% (coefficient, -0.468; 95% CI: -0.667 to - 0.27), P < 0.01), 48.2% (coefficient, -0.482; 95% CI: -0.708 to - 0.256), P < 0.01) respectively. The findings of this study reveal the effectiveness of the LPCID in reducing diarrhoea incidence in Vietnam. However, further studies should be conducted to better understanding the cost-effectiveness, acceptability, and sustainability of each component of the LPCID.

Temporal and spatial analysis of hand, foot, and mouth disease in relation to climate factors: A study in the Mekong Delta region, Vietnam.

This study examined the temporal and spatial patterns of hand, foot, and mouth disease (HFMD) in the Mekong Delta region in Vietnam. A time-series analysis was used to examine the temporal patterns of HFMD in relation to climate factors while a retrospective space-time scan was used to detect the high-risk space-time clusters of this disease. A 1°C increase in average temperature was associated with 5.6% increase in HFMD rate at lag 5days (95% CI 0.3-10.9). A 1% increase in humidity had equal influence of 1.7% increases on HFMD rate at both lag 3days and 6days (95% CI 0.7-2.7 and 95% CI 0.8-2.6, respectively). An increase in 1 unit of rainfall was associated with a 0.5% increase of HFMD rate on the lag 1 and 6days (95% CI 0.2-0.9 and 95% CI 0.1-0.8, respectively). The predictive model indicated that the peak of HFMD was from October to December - the rainy season in the Mekong Delta region. Most high-risk clusters were located in areas with high population density and close to transport routes. The findings suggest that HFMD is influenced by climate factors and is likely to increase in the future due to climate change related weather events.

Heatwave and risk of hospitalization: A multi-province study in Vietnam.

The effects of heatwaves on morbidity in developing and tropical countries have not been well explored. The purpose of this study was to examine the relationship between heatwaves and hospitalization and the potential influence of socio-economic factors on this relationship in Vietnam. Generalized Linear Models (GLM) with Poisson family and Distributed Lag Models (DLM) were applied to evaluate the effect of heatwaves for each province (province-level effect). A random-effects meta-analysis was applied to calculate the pooled estimates (country-level effects) for 'all causes', infectious, cardiovascular, and respiratory admissions queried by lag days, regions, sex, and ages. We used random-effects meta-regression to explore the potential influence of socio-economic factors on the relationship between heatwaves and hospitalization. The size of province-level effects varied across provinces. The pooled estimates show that heatwaves were significantly associated with a 2.5% (95%CI: 0.8-4.3) and 3.8% (95%CI, 1.5-6.2) increase in all causes and infectious admissions at lag 0. Cardiovascular and respiratory admissions (0.8%, 95%CI: -1.6-3.3; 2.2%, 95%CI: -0.7-5.2) were not significantly increased after a heatwave event. The risk of hospitalization due to heatwaves was higher in the North than in the South for all causes (5.4%, 95%CI: -0.1-11.5 versus 1.3%, 95%CI: 0.1-2.6), infectious (11.2%, 95%CI: 3.1-19.9 versus 3.2%, 95%CI: 0.7-5.7), cardiovascular (7.5%, 95%CI: 1.1-14.4 versus -1.2%, 95%CI: -2.6-2.3), and respiratory diseases (2.7%, 95%CI: -5.4-11.5 versus 2.1%, 95%CI: -0.8-1.2). A non-significant influence of socio-economic factors on the relationship between heatwave and hospitalization was observed. This study provides important evidence and suggests implications for the projected impacts of climate change related extreme weather. Climate change adaptation programs of the health sector should be developed to protect residents from the effects of extreme weather events such as heatwaves in Vietnam.

Heavy rainfall and risk of infectious intestinal diseases in the most populous city in Vietnam.

The association between heavy rainfall and infectious intestinal diseases (IID) has not been well described and little research has been conducted in developing countries. This study examines the association between heavy rainfall and hospital admissions for IID in Ho Chi Minh City, the most populous city in Vietnam. An interrupted time-series method was used to examine the effect of each individual heavy rainfall event (HRE) on IID. The percentage changes in post-HRE level and trends of IID were estimated for 30days following each HRE. Then a random-effect meta-analysis was used to quantify the pooled estimate of effect sizes of all HREs on IID. The pooled estimates were calculated over a 21day lag period. The effects of a HRE on IID varied across individual HREs. The pooled estimates indicate that the levels of IID following a HRE increased from 7.3% to 13.5% for lags from 0 to 21days, however statistically significant increases were only observed for lags from 4 to 6days (13.5%, 95%CI: 1.4-25.4; 13.3%, 95%CI: 1.5-25.0; and 12.9%, 95%CI: 1.6-24.1 respectively). An average decrease of 0.11% (95%CI: -0.55-0.33) per day was observed for the post-HRE trend. This finding has important implications for the projected impacts on residents living in this city which is highly vulnerable to increased heavy rainfall associated with climate change. Adaptation and intervention programs should be developed to prevent this additional burden of disease and to protect residents from the adverse impacts of extreme weather events.

The spatial distribution of vulnerability to the health impacts of flooding in the Mekong Delta, Vietnam.

Flooding causes significant public health issues. The Mekong Delta has been considered the region to be the most vulnerable to flooding in Vietnam. This study assessed the spatial vulnerability of the health impacts of flooding in the Mekong Delta region, Vietnam. This study applied a vulnerability assessment framework which was computed as the function of three dimensions: exposure, sensitivity, and adaptive capacity. The indicators for each dimension were derived from the relevant literature, consultations with experts, and data availability. An analytic hierarchy process (AHP) and a principal component analysis (PCA) were used to determine the weight of indicators. Vulnerability indexes (VIs) were then computed for each province. A total of 29 indicators (sensitivity index, 14; adaptive capacity index, 13; and exposure index, 2) were employed to evaluate the vulnerability to the health impacts of flooding at a provincial level. The results of AHP revealed that the highest VIs were found in the Dong Thap and An Giang provinces (VI, 1.948 and 1.574, respectively). VIs were distributed with higher indexes in upstream provinces close to a river than in coastal provinces. PCA generated three components from the 29 indicators, and the VIs computed from the PCA method are in substantial agreement with the AHP method (ICC = 0.71, p < 0.05). The vulnerability to the health impacts of flooding varies from province to province in the Mekong Delta region in Vietnam. Individual plans for health preparedness and adaption to flooding should be developed for each province in the Mekong Delta region.

The effects of high temperature on cardiovascular admissions in the most populous tropical city in Vietnam.

This study examined the short-term effects of temperature on cardiovascular hospital admissions (CHA) in the largest tropical city in Southern Vietnam. We applied Poisson time-series regression models with Distributed Lag Non-Linear Model (DLNM) to examine the temperature-CHA association while adjusting for seasonal and long-term trends, day of the week, holidays, and humidity. The threshold temperature and added effects of heat waves were also evaluated. The exposure-response curve of temperature-CHA reveals a J-shape relationship with a threshold temperature of 29.6 °C. The delayed effects temperature-CHA lasted for a week (0-5 days). The overall risk of CHA increased 12.9% (RR, 1.129; 95%CI, 0.972-1.311) during heatwave events, which were defined as temperature ≥ the 99th percentile for ≥2 consecutive days. The modification roles of gender and age were inconsistent and non-significant in this study. An additional prevention program that reduces the risk of cardiovascular disease in relation to high temperatures should be developed.

Temporal and spatial assessment of river surface water quality using multivariate statistical techniques: a study in Can Tho City, a Mekong Delta area, Vietnam.

The present study is an evaluation of temporal/spatial variations of surface water quality using multivariate statistical techniques, comprising cluster analysis (CA), principal component analysis (PCA), factor analysis (FA) and discriminant analysis (DA). Eleven water quality parameters were monitored at 38 different sites in Can Tho City, a Mekong Delta area of Vietnam from 2008 to 2012. Hierarchical cluster analysis grouped the 38 sampling sites into three clusters, representing mixed urban-rural areas, agricultural areas and industrial zone. FA/PCA resulted in three latent factors for the entire research location, three for cluster 1, four for cluster 2, and four for cluster 3 explaining 60, 60.2, 80.9, and 70% of the total variance in the respective water quality. The varifactors from FA indicated that the parameters responsible for water quality variations are related to erosion from disturbed land or inflow of effluent from sewage plants and industry, discharges from wastewater treatment plants and domestic wastewater, agricultural activities and industrial effluents, and contamination by sewage waste with faecal coliform bacteria through sewer and septic systems. Discriminant analysis (DA) revealed that nephelometric turbidity units (NTU), chemical oxygen demand (COD) and NH3 are the discriminating parameters in space, affording 67% correct assignation in spatial analysis; pH and NO2 are the discriminating parameters according to season, assigning approximately 60% of cases correctly. The findings suggest a possible revised sampling strategy that can reduce the number of sampling sites and the indicator parameters responsible for large variations in water quality. This study demonstrates the usefulness of multivariate statistical techniques for evaluation of temporal/spatial variations in water quality assessment and management.

Distortion-tolerant 3D recognition of underwater objects using neural networks.

A method for distortion-tolerant recognition of objects in water using three-dimensional (3D) integral imaging with a neural network classification architecture is presented. Recognition algorithms are developed and experimental results are presented with rotation-variable 3D objects. To test the robustness of the system, objects are placed under a variety of water conditions, including variable Maalox-induced scattering levels and occlusion using pine needles. Neural networks have long been used for two-dimensional recognition and have recently been used for 3D digital holographic recognition. To the best of our knowledge, this is the first use of neural networks for passive 3D integral imaging and recognition of underwater objects.

Three-dimensional object-distortion-tolerant recognition for integral imaging using independent component analysis.

Independent component analysis (ICA) aims at extracting unknown components from multivariate data assuming that the underlying components are mutually independent. This technique has been successfully applied to the recognition and classification of objects. We present a method that combines the benefits of ICA and the ability of the integral imaging technique to obtain 3D information for the recognition of 3D objects with different orientations. Our recognition is also possible when the 3D objects are partially occluded by intermediate objects.