Assessment of Social Vulnerability to Floods in the Floodplain of Northern Italy

Practices for reducing the impacts of floods are becoming more and more advanced, centered on communities and reaching out to vulnerable populations. Vulnerable individuals are characterized by social and economic attributes and by societal dynamics rooted in each community. These indicators can magnify the negative impacts of disasters together with the capacity of each individual to cope with these events. The Social Vulnerability Index (SoVI) provides an empirical basis to compare social differences in various spatial scenarios and for specific environmental hazards. This research shows the application of the SoVI to the floodplain of northern Italy, based on the use of 15 census variables. The chosen study area is of particular interest for the high occurrence of flood events coupled with a high level of human activity, landscape transformations, and an elevated concentration of assets and people. The analysis identified a positive spatial autocorrelation across the floodplain that translates into the spatial detection of vulnerable groups, those that are likely to suffer the most from floods. In a second stage, the output of the index was superimposed on the flood hazard map of the study area to analyze the resulting risk. The Piemonte and Veneto regions contain the main areas prone to flood \u201csocial\u201d risk, highlighting the need for a cohesive management approach at all levels to recognize local capacities and increase communication, awareness, and preparedness to mitigate the undesirable effects of such events

Improving the Performance of Online Neural Transducer Models

Having a sequence-to-sequence model which can operate in an online fashion is important for streaming applications such as Voice Search. Neural transducer is a streaming sequence-to-sequence model, but has shown a significant degradation in performance compared to non-streaming models such as Listen, Attend and Spell (LAS). In this paper, we present various improvements to NT. Specifically, we look at increasing the window over which NT computes attention, mainly by looking backwards in time so the model still remains online. In addition, we explore initializing a NT model from a LAS-trained model so that it is guided with a better alignment. Finally, we explore including stronger language models such as using wordpiece models, and applying an external LM during the beam search. On a Voice Search task, we find with these improvements we can get NT to match the performance of LAS

Spatial Variation of NO2 and Its Impact Factors in China: An Application of Sentinel-5P Products

As an important tropospheric trace gas and precursor of photochemical smog, the accumulation of NO2 will cause serious air pollution. China, as the largest developing country in the world, has experienced a large amount of NO2 emissions in recent decades due to the rapid economic growth. Compared with the traditional air pollution monitoring technology, the rapid development of the remote sensing monitoring method of atmospheric satellite has gradually become the critical technical means of global atmospheric environmental monitoring. To reveal the NO2 pollution situation in China, based on the latest NO2 products from Sentinel-5P TROPOMI, the spatial\u2013temporal characteristics and impact factors of troposphere NO2 column concentration of mainland China in the past year (February 2018 to January 2019) were analyzed on two administrative levels for the first time. Results show that the monthly fluctuation of tropospheric NO2 column concentration has obvious characteristics of \u201chigh in winter and low in summer\u201d, while the spatial distribution forms a \u201chigh in East and low in west\u201d pattern, bounded by Hu Line. The comparison of Coefficient of Variation (CV) and spatial autocorrelation models at two kinds of administrative scales indicates that although the spatial heterogeneity of NO2 column concentration is less affected by the observed scale, there is a \u201cdelayed effect\u201d of about one month in the process of NO2 column concentration fluctuation. Besides, the impact factors analysis based on Spatial Lag Model (SLM) and Geographic Weighted Regression (GWR) reveals that there is a positive correlation between nighttime light intensity, the secondary and tertiary industries proportion and NO2 column concentration. Furthermore, for regions with serious NO2 pollution in North China Plain, the whole society electricity consumption and vehicle ownership also play a positive role in increasing the NO2 column concentration. This study will enlighten the government and policy makers to formulate policies tailored to local conditions, to more effectively implement NO2 emission reduction and air pollution prevention

Research on the Preparation and Performance of Binary Modified Maltodextrin Water-Reducing Agent

Using maltodextrin (MD) of different dextrose equivalent (DE) values, 1,3-propanesultone, and maleic anhydride as raw materials, a novel binary modified maltodextrin (BMMD) was synthesized and further applied as a water-reducing agent. Its structure was characterized by Fourier transform infrared (FT-IR) and UV. The rheological behavior of the sample solution and strengths for concrete were also determined and the adsorption was tested by TOC. The influence of the process parameters to degree of substitution (DS) and the dosage on the fluidity of cement paste were investigated. The results show that the optimal conditions of sulfonation were the MD of DE 15, m (sulfonated agent)/m (MD) of 1.4, the catalyst amount of 1% by mass MD, and the reaction time of 12 h; the optimal conditions of esterification were m (esterified agent)/m (SMD) of 0.6, the reaction temperature of 90°C, and the reaction time of 4 h. The optimal dosages of sulfonated maltodextrin (SMD) and BMMD were 0.475% and 0.45%, respectively. In this dosage, the main dispersion capacity of BMMD attributes to two kinds of anchoring groups (SO3-  &  COO-) and the appropriate molecular weight of MD