Miracle-dz/UniFAR-FTUs huggingface dataset:
Measuring /s/ variation among younger generations in a migrant settlement in Lima, Peru

Abstract This study analyzes the pronunciation of /s/ in the Spanish of young adults in a newer migrant settlement in metropolitan Lima, Peru. The participants have parents and/or grandparents who migrated to Lima primarily from the Andean provinces, where the sibilant pronunciation of /s/ prevails. The study examines a variety of social factors, including migrant generation, family origin, gender, education, occupation, and social networks to determine the factors that correlate with /s/ weakening, which is more prevalent in Classic Limeño Spanish than in Andean Spanish (Caravedo 1990; Hundley 1983; Klee and Caravedo 2006). A proportional-odds mixed effects model was used to treat the repeated measurement categorical data on a continuum of acoustical variation ([s]>[h]>Ø) and the advantages of this model are explained. Results indicate that an important social predictor of /s/ variation is migrant generation: there is a progressive weakening in /s/ with each subsequent migrant generation. In addition to the generational effect, higher levels of education correlate with less /s/ weakening. Two variables were more weakly correlated with /s/ pronunciation: gender and social networks. Overall, the results indicate that young adults in this community, especially those of the third-generation, seem to be assimilating to some degree to coastal norms of /s/ weakening, although there is also a possibility that they may be forming their own norm.
A prediction model for frequency spectrum of blast‐induced seismic wave in viscoelastic medium

A prediction model for frequency spectrum of blast‐induced seismic waves is established. The effect of explosive sources is considered in this model. Our model implies that the frequency spectrum of blast‐induced seismic wave is mainly influenced by the initial pressure and the adiabatic exponent of explosives. The dominant frequency increases with the decreasing of initial pressure or the increasing of adiabatic exponent. In addition, this prediction model is verified by the experiment. The error of the dominant frequency is 4%–6%. It is indicated that the proposed model in this paper can reasonably predict the frequency spectrum of blast‐induced seismic waves, and then, we can provide a better frequency spectrum by optimizing the explosion source.
Genetics of complex disease

To find true genetic associations in complex diseases, such as allergic rhinitis and chronic rhinosinusitis, is a difficult task. Both are polygenetic diseases where it is believed that genetic variation in several genes make up their phenotypes. In addition, the environment of the affected individuals also play an important role, further obscuring eventual associations. Today most genetic association studies of polygenetic disease perform tests on the whole-genome level, but this has not always been the case. By 2012 a total of 56 genetic association studies had been published in allergic rhinitis and by 2013 a total of 27 studies in chronic rhinosinusitis. In but a few exceptions, all studies of the two diseases targeted candidate genes in small case and control groups. Replication attempts of significant associations from previous studies has shown some promise by pointing out more likely candidates. However, these associations do not explain the heritability of the studied diseases to any great extent. To find this missing heritability researchers have to look beyond common variants and instead focus on other potential targets. Re-sequencing studies of candidate genes could find rare variants with much higher effects sizes and studies of RNA and proteins should also prove to be helpful. New technologies and advances in molecular biology are looking promising but it is still hard to interpret all the new data. Polygenetic disorders are hard to study, low effect sizes makes novel candidate genes hard to discover. However, it is a numbers game, as more and more studies are made, more information will be available and eventually the genetic component, however big or small it is, of complex diseases will be solved.
Trends and Interannual Variability in Terrestrial Water Storage Over the Eastern United States, 2003–2016

We examine the relative contributions of root zone soil moisture (SM) and groundwater (GW) storage to trends and interannual variability in total water storage (TWS) from essentially the entire Gravity Recovery and Climate Experiment (GRACE) record (2003–2016). Our study region is the Eastern United States where GW generally is shallow, and we use observations and simulations from a suite of land surface models (LSMs) where observations are not available. We first consider Illinois, which has a long‐term network of SM sensors and observation wells. We assessed the uncertainties in GRACE TWS in comparison with the observation‐based TWS using observed SM, well‐derived GW, and model‐reconstructed snow water equivalent. We evaluated LSM SM compared with observations over Illinois; generally good agreement encouraged us to use the LSM‐ensemble average SM, well‐derived GW, and Snow Data Assimilation System snow water equivalent over the entire Eastern United States to examine the relative contribution of storage components to GRACE TWS over this larger domain. Taken together, the three components explained 2%–85% of interannual variability in GRACE TWS across the 2‐digit Hydrologic Unit Code regions. Root zone SM was the dominant contributor to the interannual variability (but not trends) of GRACE TWS over much of the Eastern United States. We also compared three independent approaches to estimating GW: well data, baseflow observations, and GRACE. Despite much smaller magnitudes of variation, baseflow‐derived GW correlated more highly with well‐derived GW over the eastern‐most and Upper Mississippi regions, while GRACE‐derived GW matched better with well‐derived GW over the remaining regions.
Playing the climate game: climate change impacts, resilience and adaptation in the climate-dependent sport sector

ABSTRACT The aims of this study were to understand issues that climate change poses for major Australian sport stadia and the organizations that manage them, and any organizational responses to such issues. Like climate-dependent agriculture and tourism, the sport sector is potentially vulnerable to climate change impacts, yet has largely been overlooked in empirical research. The results reveal four primary climate change issues: organizational uncertainty; greater management complexity and cost risks associated with water and energy resources, and waste outputs. No revenue opportunities were evident. The results demonstrate that while most physical impacts are manageable, the primacy of commercial and operational imperatives determine organizational responses ahead of government climate policy, and any direct climate “signal” to adapt. Ten factors shape three organizational responses that we have typed using Berkhout’s [2012, Adaptation to climate change by organizations. Wiley Interdisciplinary Reviews: Climate Change, 3(1), 91–106] adaptation framework. The results challenge the assumption that climate change impacts and responses are limited to non-sport and leisure industries.
Automatic detection of forest fire disturbance based on dynamic modelling from MODIS time-series observations

ABSTRACT Forest disturbances provide an important reference and a basis for studying the carbon cycle, biodiversity, and eco-social development. Remote sensing is a promising data source for monitoring forest ecosystem dynamics and detecting disturbance areas. This research used a seasonal trend method to model Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time series from 2007 to 2011 recursively with a fixed-size temporal sliding window and a step length of 1 (i.e. 16 days). Model parameter variations were monitored to detect changes in the structure of the time-series data. Significant changes in the time-series structure were captured as disturbance signals. The method was applied to the 2009 Minto Flats fire in Alaska, USA, and the forest-disturbance detection results obtained using the proposed method essentially agreed with the Monitoring Trends of Burned Severity data set. This result indicates that the proposed method can reliably reveal the occurrence of forest fire disturbances. Moreover, because the model parameter variations reflect the disturbance signal, and the modelling and detection process requires only MODIS NDVI time-series data without any other ancillary ground information, the disturbance area can be detected effectively and automatically.
Micromechanical properties of macerals in coals from the Ukrainian Donets Basin: maturity trends and implications for unconventional hydrocarbon production

The mechanical properties of different types of organic matter (macerals) are considered as an important influencing factor for the fracture behavior of organic matter-rich shales and coals, and hence, fracture permeability that directly controls the producibility of unconventional resources such as shale gas or coal bed methane. In an attempt to obtain maceral-specific material properties, nanoindentation tests were carried out on three coal samples from the Ukrainian Donets Basin, covering a maturity range from 0.6 to 1.1 %Rr (vitrinite reflectance). Vitrinite, liptinite and inertinite macerals within these coals were identified based on optical microscopy, and hardness and elastic modulus (Er) were determined after the standard method established by Oliver & Pharr (1992) for all three maceral groups, using a Cube Corner-Tip indenter in load-controlled mode with a trapezoidal loading profile at a maximum load of 1000 μN (maximum load was held for 10 s; loading/unloading rate: 500 μN/s). The results indicate that the maceral groups are affected differently by maturity changes; The obtained hardness (0.44±0.05 GPa at 0.62 %Rr; 0.45±0.02 GPa at 1.1 %Rr) and Er values (4.90±0.33 GPa at 0.62 %Rr; 5.08±0.23 GPa at 1.1 %Rr) of investigated vitrinites do not show a significant response to maturity changes, while liptinites show increasing hardness (0.37±0.02 GPa at 0.62 %Rr; 0.42±0.02 GPa at 0.93 %Rr) and decreasing Er (4.78±0.68 GPa at 0.62 %Rr; 4.02±0.15 GPa at 0.93 %Rr) with increasing maturity. Time-dependent deformation might be responsible for the decrease in Er of liptinites with increasing maturity, which needs to be further evaluated. The influence of maturity changes on the mechanical properties of inertinites depends strongly on the maceral subgroup. Increasing hardness (2.86±0.20 GPa at 0.62 %Rr; 3.20±0.19 GPa at 1.1 %Rr) and Er (9.31±0.64 GPa at 0.62 %Rr; 11.98±0.45 GPa at 1.1 %Rr) with increasing degree of coalification was observed for fusinite macerals, while the values remained constant in case of semifusinite. The different deformation behavior of the three maceral groups is reflected in the variable shapes of the obtained load-displacement curves. The main indentation response of vitrinite and liptinite is elasto-plastic, while the deformation behavior of fusinite is mainly elastic. Nanoindentation tests on isolated vitrinite particles in shales yielded significantly higher (up to an order of magnitude) hardness and Er compared to vitrinites in coals of comparable maturity. Further investigations will help to evaluate the influence of compaction and mineral matrix effects on the discrepancy of micromechanical properties of organic matter in shales and coals.
Effects of sleep quality and physical activity measured by a Fitbit and self-rated questionnaire on the health-related quality of life in community-dwelling older adults: A cross-sectional study

Purpose: This study aimed to identify influencing factors on health-related quality of life by examining relationships among sleep quality, physical activity, and health-related quality of life in older adults living in the community using a Fitbit and self-rated questionnaire. Methods: The participants were adults aged 65 years and above who were recruited from one senior university and three senior citizen centers. A total of 106 participants were included in the analysis. The data were analyzed with t-test, ANOVA, Pearson’s correlations, and multiple regression analysis. Results: According to multiple regression analysis regarding objectively measured sleep quality and physical activity using a Fitbit, the factors affecting the health-related quality of life were age (β=-.30, p=.006), subjective health status as ‘moderate’ (β=.33, p=.001), and subjective health status as ‘good’ (β=.41, p=.001). In terms of using a self-rated questionnaire, the factors affecting health-related quality of life were age (β=-.27, p=.010), subjective health status as ‘moderate’ (β=.32, p=.001), subjective health status as ‘good’ (β=.37, p=.001), and physical activity (β=.18, p=.030). Conclusion: Although the findings may not be generalizable to community-dwelling older adults due to our convenience samples, this study suggests that customized physical activity programs are required to improve health-related quality of life in community-dwelling older adults of different age groups.
Quantification of soil CO 2 emissions under the influence of climate change on the Qinghai-Tibet Plateau based on freely accessible data

Ecosystems in alpine permafrost regions like the Qinghai-Tibet Plateau are highly sensitive to global warming. Climate change potentially triggers large carbon loss from this ecosystem and thus exerts a relevant positive feedback to the climate warming. However, quantifying soil carbon dynamics in these remote areas is a challenge, because data on belowground biomass (BGB) as important carbon supplier for soils are scarce. Our current study aims at the approximation of BGB for the Qinghai-Tibet Plateau. We compared six regression models based on January mean temperature, July mean temperature, mean annual temperature (MAT), mean annual precipitation (MAP) and elevation on their ability to predict BGB for the entire plateau. We used the
Evaluation of snow water equivalent datasets over the Saint‐Maurice river basin region of southern Québec

A 10‐km gridded snow water equivalent (SWE) dataset is developed over the Saint‐Maurice River basin region in southern Québec from kriging of observed snow survey data for evaluation of SWE products. The gridded SWE dataset covers 1980–2014 and is based on manual gravimetric snow surveys carried out on February 1, March 1, March 15, April 1, and April 15 of each snow season, which captures the annual maximum SWE (SWEM) with a mean interpolation error of ±19%. The dataset is used to evaluate SWEM from a range of sources including satellite retrievals, reanalyses, Canadian regional climate models, and the Canadian Meteorological Centre operational snow depth analysis. We also evaluate a number of solid precipitation datasets to determine their contribution to systematic errors in estimated SWEM. None of the evaluated datasets is able to provide estimates of SWEM that are within operational requirements of ±15% error, and insufficient solid precipitation is determined to be one of the main reasons. The Climate System Forecast Reanalysis is the only dataset where snowfall is sufficiently large to generate SWEM values comparable to observations. Inconsistencies in precipitation are also found to have a strong impact on year‐to‐year variability in SWEM dataset performance and spread. Version 3.6. 1 of the Canadian Land Surface Scheme land surface scheme driven with ERA‐Interim output downscaled by Version 5.0. 1 of the Canadian Regional Climate Model was the best physically based model at explaining the observed spatial and temporal variability in SWEM (root‐mean‐square error [RMSE] = 33%) and has potential for lower error with adjusted precipitation. Operational snow products relying on the real‐time snow depth observing network performed poorly due to a lack of real‐time data and the strong local scale variability of point snow depth observations. The results underscore the need for more effort to be invested in improving solid precipitation estimates for use in snow hydrology applications.
Heart Disease Prediction using Machine Learning

Over the past few decades, heart-related diseases, or cardiovascular diseases (CVDs) have emerged as the leading cause of death not only in India but worldwide. They are the main cause of many diseases in the world. Machine learning algorithms and techniques have been applied to various clinical trials to automate the analysis of large and complex data. Recently, many scientists have been using AI techniques to support life and experts to detect heart-related diseases in the care industry. Compared to the brain, which is the largest organ in the human body, the heart is the next largest organ. Blood is pumped and sent to all the organs of the body. Cardiovascular disease prediction plays an important role in clinical practice. Informative studies will be useful in anticipating more data that can help predict various diseases and focus on treatment. There is a lot of data about the patients that are seen each month. The stored data can be a source for predicting future disease outbreaks. Some methods of data mining and artificial intelligence are used to predict heart diseases, such as artificial neural networks (ANN), animation, etc. To reduce the number of people who die from heart disease, we need to do it quickly and have a way to detect it. Data mining techniques and artificial intelligence calculations help experts make better cancer predictions and diagnoses. The main goal of this research project is to use AI statistics to predict coronary heart disease in patients.
Estimation de mouvement des obstacles mobiles: une approche statistique

The main objective of this work is to search for a motion estimation technique for vehicles and pedestrians having the following properties: It should produce estimations with a long Time Horizon. It should be as general as possible and work with many diJerent kinds of objects. It should be fast enough to give estimations in real time. We propose a motion estimation technique based on pairwise clustering which verifies the required properties. We have implemented and tested our approach, comparing it with a technique that we consider to represent the state of the art in clustering techniques. In order to perform the comparison, we propose a benchmark that can be used to test other motion estimation techniques.
Structure downdip of deep low‐frequency earthquakes in western Shikoku, Japan, revealed by P and S waves propagating at slow apparent velocities from intraslab earthquakes

For P waves traveling downdip of deep low‐frequency earthquakes (LFEs) from intraslab earthquakes in western Shikoku, Japan, we observed distinct later arrivals with slow apparent velocities comparable to velocities of the lower crust. The apparent velocities of predominant S waves were also in the range of lower crust speeds. Such observations suggest that the low‐velocity oceanic crust connects with the island arc crust beneath the western Seto Inland Sea, which may be due to direct contact or the presence of low‐velocity wedge mantle between the crusts. As high‐velocity mantle is not likely near deep LFEs in the region, deep LFEs occur either at the crust‐crust boundary or at the boundary between the oceanic crust and the overriding low‐velocity mantle. Observations of later arrivals differed between earthquakes in eastern and western Shikoku, implying that the location of the high‐velocity mantle can vary and affect temperatures along the zone where deep LFEs occur.
Planar Lensing Lithography: Enhancing the Optical Near Field.

In 2000, a controversial paper by John Pendry surmised that a slab of negative index material could act as a perfect lens, projecting images with resolution detail beyond the limits of conventional lensing systems. A thin silver slab was his realistic suggestion for a practical near-field superlens – a ‘poor-mans perfect lens’. The superlens relied on plasmonic resonances rather than negative refraction to provide imaging. This silver superlens concept was experimentally verified by the author using a novel near-field lithographic technique called Planar Lensing Lithography (PLL), an extension of a previously developed Evanescent Near-Field Optical Lithography (ENFOL) technique. This thesis covers the computational and experimental efforts to test the performance of a silver superlens using PLL, and to compare it with the results produced by ENFOL. The PLL process was developed by creating metal patterned conformable photomasks on glass coverslips and adapting them for use with an available optical exposure system. After sub-diffraction-limited ENFOL results were achieved with this system additional spacer and silver layers were deposited onto the masks to produce a near-field test platform for the silver superlens. Imaging through a silver superlens was achieved in a near-field lithography environment for sub-micron, sub-wavelength, and sub-diffraction-limited features. The performance of PLL masks with 120-, 85-, 60-, and 50-nm thick silver layers was investigated. Features on periods down to 145-nm have been imaged through a 50-nm thick silver layer into a thin photoresist using a broadband mercury arc lamp. The quality of the imaging has been improved by using 365 nm narrowband exposures, however, resolution enhancement was not achieved. Multiple layer silver superlensing has also been experimentally investigated for the first time; it was proposed that a multi-layered superlens could achieve better resolution than a single layer lens for the same total silver thickness. Using a PLL mask with two 30-nm thick silver layers gave 170-nm pitch sub-diffraction-limited resolution, while for a single layer mask with the same total thickness (60 nm) resolution was limited to a 350-nm pitch. The proposed resolution enhancement was verified, however pattern fidelity was reduced, the result of additional surface roughness. Simulation and analytical techniques have been used to investigate and understand
Attention-Based Learning for Predicting Drug-Drug Interactions in Knowledge Graph Embedding Based on Multisource Fusion Information

Drug combinations can reduce drug resistance and side effects and enable the improvement of disease treatment efficacy. Therefore, how to effectively identify drug-drug interactions (DDIs) is a challenging problem. Currently, there exist several approaches that leverage advanced representation learning and graph-based techniques for DDIs prediction. While these methods have demonstrated promising results, a limited number of approaches effectively utilize the potential of knowledge graphs (KGs), which provide information on drug attributes and multirelation among entities. In this work, we introduce a novel attention-based KGs representation learning framework. To encode drug SMILES sequence, a pretrained model is used, while molecular structure information is mapped as the initialization of nodes within the KG using a message-passing neural network. Additionally, the knowledge-aware graph attention network is employed to capture the drug and its topological neighbor representation in the KG representation module. To prevent the oversmoothing problem, the residual layer is used in the DDI prediction module. Comprehensive experiments on several datasets have demonstrated that the proposed method outperforms the state-of-the-art algorithms on the DDI prediction task across a range of evaluation metrics. It achieves an accuracy of 0.924 and an AUC of 0.9705 on the KEGG dataset and attains an ACC of 0.9777 and an AUC of 0.9959 on the OGB-biokg dataset. These experimental findings affirm that our approach is a dependable model for predicting the association of drugs.