Early warning system for shallow landslides using rainfall threshold and slope stability analysis

A combined cluster and regression analysis were performed for the first time to identify rainfall threshold that triggers landslide events in Amboori, Kerala, India. Amboori is a tropical area that is highly vulnerable to landslides. The 2, 3, and 5-day antecedent rainfall data versus daily rainfall was clustered to identify a cluster of critical events that could potentially trigger landslides. Further, the cluster of critical events was utilized for regression analysis to develop the threshold equations. The 5-day antecedent (x-variable) vs. daily rainfall (y-variable) provided the best fit to the data with a threshold equation of y = 80.7–0.1981x. The intercept of the equation indicates that if the 5-day antecedent rainfall is zero, the minimum daily rainfall needed to trigger the landslide in the Amboori region would be 80.7 mm. The negative coefficient of the antecedent rainfall indicates that when the cumulative antecedent rainfall increases, the amount of daily rainfall required to trigger monsoon landslide decreases. The coefficient value indicates that the contribution of the 5-day antecedent rainfall is ∼20% to the landslide trigger threshold. The slope stability analysis carried out for the area, using Probabilistic Infinite Slope Analysis Model (PISA-m), was utilized to identify the areas vulnerable to landslide in the region. The locations in the area where past landslides have occurred demonstrate lower Factors of Safety (FS) in the slope stability analysis. Thus, rainfall threshold analysis together with the FS values from slope stability can be suitable for developing a simple, cost-effective, and comprehensive early-warning system for shallow landslides in Amboori and similar regions

A novel approach for optimal weight factor of DT-CWT coefficients for land cover classification using MODIS data.

International audiencePresently, there is a need to explore the possibility to maximize the use of MODIS (Moderate Resolution Imaging Spectroradiometer) data as it has very good spectral (36 bands) and temporal resolution whereas its spatial resolution is moderate i.e. 250m, 500m, and 1km. Because of its moderate spatial resolution, its application for land cover classification is limited. Therefore, in this paper, an attempt has been made to enhance its spatial resolution and utilize the information contained in the different bands together to achieve good land cover classification accuracy, so that, in future, MODIS data can be used more effectively. For resolution enhancement, modified dual tree complex wavelet transform (DT-CWT) has been employed, where DT-CWT has been modified by critically analyzing the effect of weight factor of the DT-CWT coefficients on land cover classification. For this purpose, image statistics parameter like Mean of the image has also been considered. The proposed technique has been applied on the six bands of MODIS data which have spatial resolution of 500m. It is observed that weight factor of the high-frequency sub-bands is quite sensitive for computation of classification accuracy. Index Terms— DT-CWT, Resolution enhancement, wavelets, weights, MODIS 1.INTRODUCTION Satellite images are being used in various applications such as geoscience studies, astronomy and geographical information systems where their resolution plays a critical role but on the other hand, directly obtaining a high resolution data is an another herculean task because of high cost of sensor. Land cover classification from satellite data is a central topic in satellite imaging applications. Therefore, it becomes a necessity to develop and utilize a reliable resolution enhancement technique to obtain accurate information as much as possible as per application from the freely available moderate resolution satellite data. In this regard, many image resolution enhancement techniques have been developed which are interpolations (nearest neighbor, bilinear and bicubic) and wavelets (DWT, SWT, WZP etc.) based. Interpolation techniques [1] have been widely used for resolution enhancement but it results in loss of edges (i.e., high frequency components) of an image. Nowadays, resolution enhancement is being carried out in the wavelet domain. There are many wavelet transforms which have acquired the place. Discrete wavelet transform (DWT) [2] has also been widely used in order to preserve the high-frequency components of the image but its disadvantage is that it ends up with some ringing artifacts into the image since it is not found to be shift-invariant because of decimations and suppression of wavelet coefficients exploited by DWT. It basically suffers from four shortcomings i.e., oscillations, shift variance, aliasing and lack of directionality which can lead to some artifacts in the image and difficulties in signal modeling. Hence, the DWT has somewhat disappointed the researchers for satellite images. Therefore, in order to alleviate all these drawbacks of DWT [2,3], a new kind of wavelet was introduced by Kingsbury which is known as DT-CWT (Dual tree complex wavelet transform) [1,3]. It possesses shift-invariant property and has the capability of improving directional resolution (because of good directional sensitivity) as compared to that of the decimated DWT. That's why, DT-CWT has been employed in this paper for resolution enhancement of moderate resolution satellite images. It is foremost to discover the possibility of maximizing the use of freely available satellite data like MODIS. It consists of several bands in which different information is present, but has certain limitations as well like low spatial resolution i.e. 500m which is a major obstacle in obtaining that information accurately. Many researchers have worked on resolution enhancement techniques for visualization enhancement whereas in this paper, main motive is to enhance the land cover classification accuracy which is not reported much for satellite images like MODIS yet. Variance minimization [4] has also been explored by several researchers for weights optimization but it is somewhat 4528 978-1-5090-3332-4/16/$31.0

COVIDiSTRESS Global Survey dataset on psychological and behavioural consequences of the COVID-19 outbreak

This N = 173,426 social science dataset was collected through the collaborative COVIDiSTRESS Global Survey - an open science effort to improve understanding of the human experiences of the 2020 COVID-19 pandemic between 30th March and 30th May, 2020. The dataset allows a cross-cultural study of psychological and behavioural responses to the Coronavirus pandemic and associated government measures like cancellation of public functions and stay at home orders implemented in many countries. The dataset contains demographic background variables as well as measures of Asian Disease Problem, perceived stress (PSS-10), availability of social provisions (SPS-10), trust in various authorities, trust in governmental measures to contain the virus (OECD trust), personality traits (BFF-15), information behaviours, agreement with the level of government intervention, and compliance with preventive measures, along with a rich pool of exploratory variables and written experiences. A global consortium from 39 countries and regions worked together to build and translate a survey with variables of shared interests, and recruited participants in 47 languages and dialects. Raw plus cleaned data and dynamic visualizations are available.Measurement(s) psychological measurement center dot anxiety-related behavior trait center dot Stress center dot response to center dot Isolation center dot loneliness measurement center dot Emotional Distress Technology Type(s) Survey Factor Type(s) geographic location center dot language center dot age of participant center dot responses to the Coronavirus pandemic Sample Characteristic - Organism Homo sapiens Sample Characteristic - Location global Machine-accessible metadata file describing the reported data:Peer reviewe

Stress and worry in the 2020 coronavirus pandemic: Relationships to trust and compliance with preventive measures across 48 countries in the COVIDiSTRESS global survey

The COVIDiSTRESS global survey collects data on early human responses to the 2020 COVID-19 pandemic from 173 429 respondents in 48 countries. The open science study was co-designed by an international consortium of researchers to investigate how psychological responses differ across countries and cultures, and how this has impacted behaviour, coping and trust in government efforts to slow the spread of the virus. Starting in March 2020, COVIDiSTRESS leveraged the convenience of unpaid online recruitment to generate public data. The objective of the present analysis is to understand relationships between psychological responses in the early months of global coronavirus restrictions and help understand how different government measures succeed or fail in changing public behaviour. There were variations between and within countries. Although Western Europeans registered as more concerned over COVID-19, more stressed, and having slightly more trust in the governments' efforts, there was no clear geographical pattern in compliance with behavioural measures. Detailed plots illustrating between-countries differences are provided. Using both traditional and Bayesian analyses, we found that individuals who worried about getting sick worked harder to protect themselves and others. However, concern about the coronavirus itself did not account for all of the variances in experienced stress during the early months of COVID-19 restrictions. More alarmingly, such stress was associated with less compliance. Further, those most concerned over the coronavirus trusted in government measures primarily where policies were strict. While concern over a disease is a source of mental distress, other factors including strictness of protective measures, social support and personal lockdown conditions must also be taken into consideration to fully appreciate the psychological impact of COVID-19 and to understand why some people fail to follow behavioural guidelines intended to protect themselves and others from infection. The Stage 1 manuscript associated with this submission received in-principle acceptance (IPA) on 18 May 2020. Following IPA, the accepted Stage 1 version of the manuscript was preregistered on the Open Science Framework at https://osf.io/g2t3b. This preregistration was performed prior to data analysis

Stress and worry in the 2020 coronavirus pandemic : relationships to trust and compliance with preventive measures across 48 countries in the COVIDiSTRESS global survey

The COVIDiSTRESS global survey collects data on early human responses to the 2020 COVID-19 pandemic from 173 429 respondents in 48 countries. The open science study was co-designed by an international consortium of researchers to investigate how psychological responses differ across countries and cultures, and how this has impacted behaviour, coping and trust in government efforts to slow the spread of the virus. Starting in March 2020, COVIDiSTRESS leveraged the convenience of unpaid online recruitment to generate public data. The objective of the present analysis is to understand relationships between psychological responses in the early months of global coronavirus restrictions and help understand how different government measures succeed or fail in changing public behaviour. There were variations between and within countries. Although Western Europeans registered as more concerned over COVID-19, more stressed, and having slightly more trust in the governments' efforts, there was no clear geographical pattern in compliance with behavioural measures. Detailed plots illustrating between-countries differences are provided. Using both traditional and Bayesian analyses, we found that individuals who worried about getting sick worked harder to protect themselves and others. However, concern about the coronavirus itself did not account for all of the variances in experienced stress during the early months of COVID-19 restrictions. More alarmingly, such stress was associated with less compliance. Further, those most concerned over the coronavirus trusted in government measures primarily where policies were strict. While concern over a disease is a source of mental distress, other factors including strictness of protective measures, social support and personal lockdown conditions must also be taken into consideration to fully appreciate the psychological impact of COVID-19 and to understand why some people fail to follow behavioural guidelines intended to protect themselves and others from infection. The Stage 1 manuscript associated with this submission received in-principle acceptance (IPA) on 18 May 2020. Following IPA, the accepted Stage 1 version of the manuscript was preregistered on the Open Science Framework at https://osf.io/g2t3b. This preregistration was performed prior to data analysis.Peer reviewe

Stress and worry in the 2020 coronavirus pandemic: relationships to trust and compliance with preventive measures across 48 countries in the COVIDiSTRESS global survey

The COVIDiSTRESS global survey collects data on early human responses to the 2020 COVID-19 pandemic from 173 429 respondents in 48 countries. The open science study was co-designed by an international consortium of researchers to investigate how psychological responses differ across countries and cultures, and how this has impacted behaviour, coping and trust in government efforts to slow the spread of the virus. Starting in March 2020, COVIDiSTRESS leveraged the convenience of unpaid online recruitment to generate public data. The objective of the present analysis is to understand relationships between psychological responses in the early months of global coronavirus restrictions and help understand how different government measures succeed or fail in changing public behaviour. There were variations between and within countries. Although Western Europeans registered as more concerned over COVID-19, more stressed, and having slightly more trust in the governments' efforts, there was no clear geographical pattern in compliance with behavioural measures. Detailed plots illustrating between-countries differences are provided. Using both traditional and Bayesian analyses, we found that individuals who worried about getting sick worked harder to protect themselves and others. However, concern about the coronavirus itself did not account for all of the variances in experienced stress during the early months of COVID-19 restrictions. More alarmingly, such stress was associated with less compliance. Further, those most concerned over the coronavirus trusted in government measures primarily where policies were strict. While concern over a disease is a source of mental distress, other factors including strictness of protective measures, social support and personal lockdown conditions must also be taken into consideration to fully appreciate the psychological impact of COVID-19 and to understand why some people fail to follow behavioural guidelines intended to protect themselves and others from infection. The Stage 1 manuscript associated with this submission received in-principle acceptance (IPA) on 18 May 2020. Following IPA, the accepted Stage 1 version of the manuscript was preregistered on the Open Science Framework at https://osf.io/g2t3b. This preregistration was performed prior to data analysis

Early warning system for shallow landslides using rainfall threshold and slope stability analysis

A combined cluster and regression analysis were performed for the first time to identify rainfall threshold that triggers landslide events in Amboori, Kerala, India. Amboori is a tropical area that is highly vulnerable to landslides. The 2, 3, and 5-day antecedent rainfall data versus daily rainfall was clustered to identify a cluster of critical events that could potentially trigger landslides. Further, the cluster of critical events was utilized for regression analysis to develop the threshold equations. The 5-day antecedent (x-variable) vs. daily rainfall (y-variable) provided the best fit to the data with a threshold equation of y = 80.7–0.1981x. The intercept of the equation indicates that if the 5-day antecedent rainfall is zero, the minimum daily rainfall needed to trigger the landslide in the Amboori region would be 80.7 mm. The negative coefficient of the antecedent rainfall indicates that when the cumulative antecedent rainfall increases, the amount of daily rainfall required to trigger monsoon landslide decreases. The coefficient value indicates that the contribution of the 5-day antecedent rainfall is ∼20% to the landslide trigger threshold. The slope stability analysis carried out for the area, using Probabilistic Infinite Slope Analysis Model (PISA-m), was utilized to identify the areas vulnerable to landslide in the region. The locations in the area where past landslides have occurred demonstrate lower Factors of Safety (FS) in the slope stability analysis. Thus, rainfall threshold analysis together with the FS values from slope stability can be suitable for developing a simple, cost-effective, and comprehensive early-warning system for shallow landslides in Amboori and similar regions. Keywords: Landslide, Cluster analysis, Rainfall threshold analysis, Factor of safety, Slope stability analysis, PISA-

Not Available

Not AvailableThe levels of trace elements (As, Hg, Cr, Cd, Pb, Co, Ni, Cu, Mn and Zn) in commercially important fish species sampled from fish markets of Adelaide, Australia; canned fish from South Australian supermarkets; and fish markets of West Bengal, India were determined by inductively coupled plasma mass spectrometry (ICP-MS) after microwave digestion. Mercury was determined by using triple quadrupole ICP-MS. The accuracy of the methods was assessed with a certified standard reference material (NRCC-DORM-3 dogfish protein), and the results were compared with values reported in the literature. The results indicated considerable variations in the accumulation of trace elements among the fish species. The relationship between species with respect to trace element concentrations was examined using cluster analysis, which showed Indian fish species forming distinct groups from the others. Other than As in sardines, whiting and snapper and Hg in swordfish and snapper, the trace element concentrations were within permissible limits recommended by various standards. Based on the estimated daily intake (EDI), fish samples analysed in this study can be considered safe for human consumption as per the recommended daily dietary allowance limit fixed by various agencies. Continuous monitoring and assessments of fish metal(loid) content are needed to generate more data and safeguard human health.Not Availabl

Hydrogen Intensity and Real-Time Analysis Experiment: 256-element array status and overview

International audienceThe Hydrogen Intensity and Real-time Analysis Experiment (HIRAX) is a radio interferometer array currently in development, with an initial 256-element array to be deployed at the South African Radio Astronomy Observatory Square Kilometer Array site in South Africa. Each of the 6 m, f  /  0.23 dishes will be instrumented with dual-polarization feeds operating over a frequency range of 400 to 800 MHz. Through intensity mapping of the 21 cm emission line of neutral hydrogen, HIRAX will provide a cosmological survey of the distribution of large-scale structure over the redshift range of 0.775  <  z  <  2.55 over ∼15,000 square degrees of the southern sky. The statistical power of such a survey is sufficient to produce ∼7  %   constraints on the dark energy equation of state parameter when combined with measurements from the Planck satellite. Additionally, HIRAX will provide a highly competitive platform for radio transient and HI absorber science while enabling a multitude of cross-correlation studies. We describe the science goals of the experiment, overview of the design and status of the subcomponents of the telescope system, and describe the expected performance of the initial 256-element array as well as the planned future expansion to the final, 1024-element array

COVIDiSTRESS Global Survey dataset on psychological and behavioural consequences of the COVID-19 outbreak

Funder: Consejo Nacional de Ciencia y Tecnología (CONCYT); doi: https://doi.org/10.13039/501100007350Funder: Research Foundation Flanders (FWO) postdoctoral fellowshipFunder: The HSE University Basic Research ProgramFunder: JSPS KAKENHI Grant JP20K14222Abstract: This N = 173,426 social science dataset was collected through the collaborative COVIDiSTRESS Global Survey – an open science effort to improve understanding of the human experiences of the 2020 COVID-19 pandemic between 30th March and 30th May, 2020. The dataset allows a cross-cultural study of psychological and behavioural responses to the Coronavirus pandemic and associated government measures like cancellation of public functions and stay at home orders implemented in many countries. The dataset contains demographic background variables as well as measures of Asian Disease Problem, perceived stress (PSS-10), availability of social provisions (SPS-10), trust in various authorities, trust in governmental measures to contain the virus (OECD trust), personality traits (BFF-15), information behaviours, agreement with the level of government intervention, and compliance with preventive measures, along with a rich pool of exploratory variables and written experiences. A global consortium from 39 countries and regions worked together to build and translate a survey with variables of shared interests, and recruited participants in 47 languages and dialects. Raw plus cleaned data and dynamic visualizations are available