Do Natural Disasters Affect Human Capital? An Assessment Based on Existing Empirical Evidence

The last few years have seen a notable increase in the number of studies investigating the causes and effects of natural disasters in many dimensions. This paper seeks to review and assess available empirical evidence on the ex-post microeconomic effects of natural disasters on the accumulation of human capital, focusing on consumption, nutrition, education and health, including mental health. Three major findings come forward from this work. First, disasters appear to bring substantial damages to human capital, including death and destruction, and produce deleterious consequences on nutrition, education, health and many income-generating processes. Furthermore, some of these detrimental effects are both large and long-lasting. Second, there is a large degree of heterogeneity in the size – but not much in the direction – of the impacts on different socioeconomic groups. Yet, an empirical regularity across natural hazards is that the poorest carry the heaviest burden of the effects of disasters across different determinants and outcomes of human capital. Finally, although the occurrence of natural hazards is mostly out of control of authorities, there still is a significant room for policy action to minimize their impacts on the accumulation of human capital. We highlight the importance of flexible safety nets as well as the double critical role of accurate and reliable information to monitor risks and vulnerabilities, and identify the impacts and responses of households once they are hit by a disaster. The paper also lays out existing knowledge gaps, particularly in regard to the need of improving our understanding of the impacts of disasters on health outcomes, the mechanisms of transmission and the persistence of the effects in the long-run.natural disasters, human capital accumulation

Insight into the Sealing Capacity of Mudrocks determined using a Digital Rock Physics Workflow

Primary objective: To better understand seal capacity in mudrocks and to determine the conditions under which a mudrock seal fails by allowing a non-wetting fluid to percolate. Hypothesis: Mudrock seals can fail below the fracture pressure if there exists a percolating pathway formed due to a continuous and sufficiently large pore-throat system. Procedure: We used SEM images of uncemented muds obtained at various depths (< 1.1 km burial) in the Kumano Basin offshore Japan for the study. Image mosaics were filtered and segmented using conventional and machine-learning techniques to identify the pore space, silt, and clay grains. We applied a 3D stochastic technique for pore space reconstruction from the SEM images and simulated capillary drainage in the resulting 3D volumes by the lattice Boltzmann method (LBM) using Stampede 2. Conclusion: Results showed that porosity and permeability decreased with depth, and capillary threshold pressure values increased. However, increasing silt content at a particular depth counteracted this behavior, due to better preservation of larger pores and throats.Texas Advanced Computing Center (TACC

PySimFrac: A Python Library for Synthetic Fracture Generation, Analysis, and Simulation

In this paper, we introduce Pysimfrac, a open-source python library for generating 3-D synthetic fracture realizations, integrating with fluid simulators, and performing analysis. Pysimfrac allows the user to specify one of three fracture generation techniques (Box, Gaussian, or Spectral) and perform statistical analysis including the autocorrelation, moments, and probability density functions of the fracture surfaces and aperture. This analysis and accessibility of a python library allows the user to create realistic fracture realizations and vary properties of interest. In addition, Pysimfrac includes integration examples to two different pore-scale simulators and the discrete fracture network simulator, dfnWorks. The capabilities developed in this work provides opportunity for quick and smooth adoption and implementation by the wider scientific community for accurate characterization of fluid transport in geologic media. We present Pysimfrac along with integration examples and discuss the ability to extend Pysimfrac from a single complex fracture to complex fracture networks

Mitigation of Spatial Nonstationarity with Vision Transformers

Spatial nonstationarity, the location variance of features' statistical distributions, is ubiquitous in many natural settings. For example, in geological reservoirs rock matrix porosity varies vertically due to geomechanical compaction trends, in mineral deposits grades vary due to sedimentation and concentration processes, in hydrology rainfall varies due to the atmosphere and topography interactions, and in metallurgy crystalline structures vary due to differential cooling. Conventional geostatistical modeling workflows rely on the assumption of stationarity to be able to model spatial features for the geostatistical inference. Nevertheless, this is often not a realistic assumption when dealing with nonstationary spatial data and this has motivated a variety of nonstationary spatial modeling workflows such as trend and residual decomposition, cosimulation with secondary features, and spatial segmentation and independent modeling over stationary subdomains. The advent of deep learning technologies has enabled new workflows for modeling spatial relationships. However, there is a paucity of demonstrated best practice and general guidance on mitigation of spatial nonstationarity with deep learning in the geospatial context. We demonstrate the impact of two common types of geostatistical spatial nonstationarity on deep learning model prediction performance and propose the mitigation of such impacts using self-attention (vision transformer) models. We demonstrate the utility of vision transformers for the mitigation of nonstationarity with relative errors as low as 10%, exceeding the performance of alternative deep learning methods such as convolutional neural networks. We establish best practice by demonstrating the ability of self-attention networks for modeling large-scale spatial relationships in the presence of commonly observed geospatial nonstationarity

Gas chromatography–tandem mass spectrometry with atmospheric pressure chemical ionization for fluorotelomer alcohols and perfluorinated sulfonamides determination

Ionization and in source-fragmentation behavior of four fluorotelomer alcohols (FTOH) (4:2 FTOH, 6:2 FTOH, 8:2 FTOH and 10:2 FTOH) and four N-alkyl fluorooctane sulfonamides/-ethanols (N-MeFOSA, N-EtFOSA, N-MeFOSE and N-EtFOSE) by APCI has been studied and compared with the traditionally used EI and CI. Protonated molecule was the base peak of the APCI spectrum in all cases giving the possibility of selecting it as a precursor ion for MS/MS experiments. Following, CID fragmentation showed common product ions for all FOSAs/FOSEs (C4F7 and C3F5). Nevertheless, the different functionality gave characteristic pattern fragmentations. For instance, FTOHs mainly loss H2O + HF, FOSAs showed the losses of SO2 and HF while FOSEs showed the losses of H2O and SO2. Linearity, repeatability and LODs have been studied obtaining instrumental LODs between 1 and 5 fg. Finally, application to river water and influent and effluent waste water samples has been carried out in order to investigate the improvements in detection capabilities of this new source in comparison with the traditionally used EI/CI sources. Matrix effects in APCI have been evaluated in terms of signal enhancement/suppression when comparing standards in solvent and matrix. No matrix effects were observed and concentrations found in samples were in the range of 1–100 pg L−1 far below the LODs achieved with methods previously reported. Unknown related perfluoroalkyl substances, as methyl-sulfone and methyl-sulfoxide analogues for FTOHs, were also discovered and tentatively identified.The authors wish to acknowledge the financial support received from Spanish Ministry of Economy and Competitiveness under the project CTQ2012-30836 and from the Agency for Administration of University and Research Grants (Generalitat de Catalunya, Spain) under the project 2014 SGR-539. They are very grateful to the Serveis Centrals d’Instrumentació Científica (SCIC) of University Jaume I for the use of the GC XevoTQ-S

Music Self-Efficacy for Performance: An explanatory model based on Social Support

Personal perceptions of self-efficacy are particularly relevant in the field of music performance, which is oriented toward the outward expressions of one’s own ability through public performances. Within this context, a number of personal variables, including social support and performance anxiety, have been shown to be associated with musical success and are therefore relevant for research that seeks to understand the four sources of self-efficacy (mastery experiences, vicarious observation, verbal persuasion, physiological states) that are integral components of Bandura’s (2002) Social Learning Theory. Previous research, as well as observed differences among musicians associated with educational level (preuniversity) and gender (male/female), underpins the context of this study, which presents evidence regarding the factors that are capable of mediating perceptions of self-efficacy for musical performance. Specifically, the main objectives of this study were to more clearly understand relations between social support, public performance, musical performance anxiety, and self-efficacy using structural equation modeling and to compare these results according to gender. A battery of questionnaires was submitted to 359 preuniversity Spanish music students. Results highlight the relevance of family support for self-efficacy in public performance: directly and mediated through musical performance anxiety. The role of teachers and peers appeared to be relevant only for boys and was mediated through performance anxiety. Public performances lead to a greater degree of musical self-efficacy, but only in girls. Further research shall be required in order to improve pedagogical methods and help teachers increasingly individualize their teaching

A study of the Suess effect using a raised peat bog as historical archive

The radiocarbon content in a peat core from Gävle, Sweden, 61.0 oN, 17.0 oE, has been studied. This is a raised peat bog which only receives material from atmospheric deposition. There has been an increased use of fossil fuels by industries and also locally by transports and heating of domestic buildings. There has been fallout of 14C from nuclear tests during the 1950ies and 1960ies and also from the Chernobyl accident in 1986. There is also emission of 14C from nuclear facilities. The 14C/12C ratio from the Chernobyl accident is unclear since it was a graphite moderated reactor and the graphite was burning. The core was sampled in 2008 and was previously dated using the 210Pb method, giving a growth rate of 0.15 mm/yr. The top 21 cm have been analyzed to obtain radiocarbon content by Accelerator Mass Spectrometry (AMS) at the Centro Nacional de Aceleradores (CNA), Seville Spain. Using 0.5 cm samples, information about the last 140 years could be obtained with resolution better than 4 years. Results show a clear depletion of F14C levels in the area, the so called SUESS effect with maximum levels of only F14C=1.2333±0.0043, and the absence of a clear nuclear tests peak

Learning a General Model of Single Phase Flow in Complex 3D Porous Media

Modeling effective transport properties of 3D porous media, such as permeability, at multiple scales is challenging as a result of the combined complexity of the pore structures and fluid physics - in particular, confinement effects which vary across the nanoscale to the microscale. While numerical simulation is possible, the computational cost is prohibitive for realistic domains, which are large and complex. Although machine learning models have been proposed to circumvent simulation, none so far has simultaneously accounted for heterogeneous 3D structures, fluid confinement effects, and multiple simulation resolutions. By utilizing numerous computer science techniques to improve the scalability of training, we have for the first time developed a general flow model that accounts for the pore-structure and corresponding physical phenomena at scales from Angstrom to the micrometer. Using synthetic computational domains for training, our machine learning model exhibits strong performance (R$^2$=0.9) when tested on extremely diverse real domains at multiple scales

Intra-breed genetic diversity characterization of the Iberian pig

Ponencia publicada en ITEA, vol.104El desenvolvimiento en el tiempo de subpoblaciones aisladas adscritas a un mismo tipo racial es el origen de la diversidad natural que surge en toda raza animal enriqueciéndola. El Cerdo Ibérico no ha sido ajeno a este proceso, acumulando a lo largo de los siglos una gran heterogeneidad intrarracial, reflejada en un valor alto (0,19) para el FST de Wright entre las subpoblaciones analizadas. En el presente trabajo abordaremos el estudio de esta diversidad genética interna del Cerdo Ibérico con especial atención a las cuatro estirpes principales (Negro Lampiño, Entrepelado, Retinto y Torbiscal), sin descuidar, no obstante, otras estirpes y líneas que la integran. Para ello partiremos de diferentes estudios de caracterización de las estirpes y líneas del Cerdo Ibérico. Resaltaremos no sólo sus diferencias genéticas sino también las habidas entre sus productos para consumo en fresco (solomillos), en los que la estirpe Negro Lampiño muestra los porcentajes de proteína, capacidad de retención de agua (CRA) e infiltración grasa intramuscular más elevados (23.74, 17.06 y 5.28, respectivamente), definiendo una calidad diferenciada. Finalmente aportaremos una clasificación que explique la estructura interna del Cerdo Ibérico.The evolution in time of isolated subpopulations assigned to a same breed is the origin of the natural diversity that arises in any breed animal enriching it. The Iberian Pig breed has not been unaware of this process, accumulating throughout the centuries a great intra-breed heterogeneity that is reflected by a high FST value (0.19) among the subpopulations analyzed. In the present study we will undertake the assessment of the internal genetic diversity of the Iberian Pig breed with special attention to the four main strains (Negro Lampiño, Entrepelado, Retinto and Torbiscal), without forgetting others strains and lines that integrate Iberian Pig Breed. To that purpose, we based on different characterization studies of the strains and lines of the Iberian Pig breed. We emphasize not only their genetic differences but also the differences among their meat products for fresh consumption (tenderloin) by strain, in which Negro Lampiño shows the higher percentages of protein, water-holding capacity (ARC) and intramuscular fat infiltration (23.74, 17.06 and 5.28, respectively), defining a differentiated quality. Finally, we expose a classification to explain the population structure of the Iberian Pig Breed