Miracle-dz/UniFAR-FTUs huggingface dataset:
Regional consequences of demographic change : regional development and disparities in a context of ageing and shrinking population in Germany

Demographic change represents the defining trend in population development of the 21st century on a global, national, and regional level. Although some countries are yet to see its impacts, others, such as Germany, have already begun experiencing the effects of demographic change. Sub-replacement level fertility since the 1970s coupled with increases in life expectancy have slowed German population growth and cause pronounced shifts in the age composition. These changes are even more noticeable on a sub-national level, where, in the context of national-level demographic change, shrinking and ageing regions are no longer transitory and exceptional but a wide-spread and permanent phenomenon. Despite the relative predictability of these trends, there is little empirical research on the consequences of demographic change on regional socio-economic conditions. This thesis analyses demographic change using the case of Germany on a regional level and considers how changes in population size and age composition affect economic outcomes and their geographical patterns. It focuses on three aspects that are central to processes of regional economic development. First, it investigates the role of demographic change in the provision of public services, using the example of primary school closures in response to falling student numbers. Second, it studies the effect of population ageing on availability and composition of human capital in regional labour markets. Third, it examines the relationship between regional age structures and patterns of internal migration, testing for evidence whether the two trends may be mutually reinforcing. Using administrative and micro-data for 332 German district regions between 1996 and 2010, this thesis documents the current relevance of demographic change for socioeconomic conditions and emphasises its future role in shaping regional economic development in ageing and shrinking countries.
Processing Choices Affect Ocean Mass Estimates From GRACE

Accurate estimates of ocean mass change are necessary to infer steric sea level change from sea level changes measured with satellite altimeters. Published studies using the GRACE satellite mission indicated a large range in trends (∼1–2 mm/y) with reported standard errors of 0.1–0.3 mm/y. Here, we show that a large part of this discrepancy (up to 0.6 mm/y) can be explained by which model is used to account for the effect of glacial isostatic adjustment. The second-largest contribution (0.3–0.4 mm/y) is related to the way how different studies have restored atmospheric and oceanic signals which have been removed during the GRACE gravity estimation process. Here, two processing strategies, which previously resulted in differing ocean mass trends are considered. The ’direct’ method uses the standard GRACE Stokes coefficients, while the ’inverse’ method applies a joint inversion of data from GRACE and altimetry. After accounting for differences in processing corrections, global mean ocean mass estimates from the direct, the mascon and inverse approach agree with each other on global scales within less than 0.1 mm/y. Using the A et al. [2013] GIA model, we provide a reconciled monthly time series of global mean ocean mass, which suggests that ocean mass has increased by 1.43 mm/y over 2002.6– 2014.5, with an amplified rate of 1.75 mm/y over 2002.6-2016.5 which covers almost the complete GRACE time span. However, we note that estimates as low as 1.05 mm/y cannot be ruled out when other published GIA corrections with lower mass-equivalent signals over Antarctica are used.
Wastewater Disposal and the Earthquake Sequences During 2016 Near Fairview, Pawnee, and Cushing, Oklahoma

Each of the three earthquake sequences in Oklahoma in 2016—Fairview, Pawnee, and Cushing—appears to have been induced by high‐volume wastewater disposal within 10 km. The Fairview M5.1 main shock was part of a 2 year sequence of more than 150 events of M3, or greater; the main shock accounted for about half of the total moment. The foreshocks and aftershocks of the M5.8 Pawnee earthquake were too small and too few to contribute significantly to the cumulative moment; instead, nearly all of the moment induced by wastewater injection was focused on the main shock. The M5.0 Cushing event is part of a sequence that includes 48 earthquakes of M3, or greater, that are mostly foreshocks. The cumulative moment for each of the three sequences during 2016, as well as that for the 2011 Prague, Oklahoma, and nine other sequences representing a broad range of injected volume, are all limited by the total volumes of wastewater injected locally.
Identification of critical source areas under present and projected land use for effective management of diffuse pollutants in an urbanized watershed

ABSTRACT The land-use characteristics of a watershed determined the amount of pollution produced in it, which can be curtailed by implementation of best management practices (BMPs) in an identified critical source area (CSA). Present and future land-use scenarios of the watershed were produced, and the hydrological simulation program FORTRAN model was used to model the hydrology and in-stream pollutants’ concentrations. The validated model was utilized to identify the CSA for diffuse total nitrogen, total phosphorus, sediment, and biochemical oxygen demand considering the two land-use scenarios. The results showed that CSA and the water quality index produced by the two land-use scenarios varied in each of the diffuse pollutants considered. It was observed that some portion of the identified CSA remains unchanged despite the changes in the land use and this was attributed to consistent urban development in these areas. The results in this study illustrate that BMPs can be included in the design and planning of future urban expansion based on the identified CSA derived from the expected future land-use changes. As anticipated, this approach will provide resilience on the effects of urbanization on the diffuse pollutants loads in a rapidly urbanized watershed.
Species-Specific Transcription Factors Associated with Long Terminal Repeat Promoters of Endogenous Retroviruses: A Comprehensive Review

Endogenous retroviruses (ERVs) became a part of the eukaryotic genome through endogenization millions of years ago. Moreover, they have lost their innate capability of virulence or replication. Nevertheless, in eukaryotic cells, they actively engage in various activities that may be advantageous or disadvantageous to the cells. The mechanisms by which transcription is triggered and implicated in cellular processes are complex. Owing to the diversity in the expression of transcription factors (TFs) in cells and the TF-binding motifs of viruses, the comprehensibility of ERV initiation and its impact on cellular functions are unclear. Currently, several factors are known to be related to their initiation. TFs that bind to the viral long-terminal repeat (LTR) are critical initiators. This review discusses the TFs shown to actively associate with ERV stimulation across species such as humans, mice, pigs, monkeys, zebrafish, Drosophila, and yeast. A comprehensive summary of the expression of previously reported TFs may aid in identifying similarities between animal species and endogenous viruses. Moreover, an in-depth understanding of ERV expression will assist in elucidating their physiological roles in eukaryotic cell development and in clarifying their relationship with endogenous retrovirus-associated diseases.
Phytochemicals, Health-Promoting Effects, and Enzyme Inhibition Traits of Phlomis stewartii Extracts

Phlomis stewartii is a wild, perennial woody plant used for diverse therapeutic targets. The present work evaluated the influence of independent variables such as extraction time, solvent concentration, and speed in the range of (100 mL, 150 mL, and 200 mL), (2 h, 5 h, and 8 h), and (100 rpm, 150 rpm, and 200 rpm), respectively, on extraction yields, phytochemical components, total phenolic contents (TPC), and total flavonoid contents (TFC) of P. stewartii extract. In the present work, response surface methodology (RSM) was applied to optimize the extraction yield. High-performance liquid chromatography (HPLC) was performed to detect the bioactive constituents of the extracts. The potent extracts were analyzed to study α-amylase and α-glucosidase inhibitory activities. Under the optimized conditions of solvent concentration (200 mL), extraction time (8 h), and speed (150 rpm), the whole plant methanol extract (WPME) showed a maximum extraction yield of 13.5%, while the leaves methanol extract (LME) showed a maximum TPC of 19.5 ± 44 mg of gallic acid equivalent (GAE) per gram of extract and a maximum TFC of 4.78 ± 0.34 mg of quercetin equivalent (QE) per gram of extract. HPLC analysis showed the presence of p-coumaric, gallic acid, quercetin, salicylic acid, sinapic acid, and vanillic acid. LME showed the highest α-amylase inhibitory activity (IC50 = 46.86 ± 0.21 µg/mL) and α-glucosidase inhibitory activity (IC50 value of 45.81 ± 0.17 µg/mL). Therefore, in conclusion, LME could be considered to fix the α-amylase and α-glucosidase-mediated disorders in the human body to develop herbal phytomedicine.
Determinants influencing the effectiveness of internal auditing and the responsibility of auditors in fraud detection in an emerging country

Internal audit has become an essential part of firms in the age of Industry 4.0 due to its ability to identify errors or violations leading to fraud in firms (Alzeban & Gwilliam, 2014; Cohen & Sayag, 2010). This study is conducted to investigate the relationship between variables such as the quality of internal audit, the capability of the internal audit team, the independence of internal audit, and the support of leadership on the effectiveness of internal audit (EIA). Specifically, the study also examines the relationship between the EIA and the responsibility of auditors in detecting fraud. Data were gathered through a survey of 325 questionnaires from joint stock firms in the context of Vietnam, using SPSS 22 software and SmartPLS 3.0 software to analyze the regression of influencing determinants. The results reveal that: 1) the quality of the internal audit, the capability of the internal audit team, the independence of the internal audit, and the support of leadership have an impact on the internal audit effectiveness; 2) the EIA, the responsibility of auditors, and auditor training have a positive and significant impact on fraud detection. Therefore, the importance of internal audit in identifying accounting fraud and the need for firms to design internal audit processes and training to improve the effectiveness of their operations are highlighted.
Coconut (Cocos nucifera (L.)) Water Improves Glucose Uptake with Concomitant Modulation of Antioxidant and Purinergic Activities in Isolated Rat Psoas Muscles

The present study investigated the effect of coconut water on glucose uptake and utilization, and metabolic activities linked to hyperglycemia in isolated rat psoas muscles. Coconut water was subjected to in vitro antioxidant and antidiabetic assays, which cover 2,2′-diphenyl-1-picrylhydrazyl (DPPH) scavenging activity, ferric reducing antioxidant power (FRAP), and inhibition of α-glucosidase and α-amylase activities. Psoas muscles were isolated from male Sprague Dawley rats and incubated with coconut water in the presence of glucose. Control consisted of muscles incubated with glucose only, while normal control consisted of muscles not incubated in coconut water and/or glucose. The standard antidiabetic drug was metformin. Incubation with coconut water led to a significant increase in muscle glucose uptake, with concomitant exacerbation of glutathione level, and SOD and catalase activities, while suppressing malondialdehyde level, and ATPase and E-NTDase activities. Coconut water showed significant scavenging activity against DPPH, and significantly inhibited α-glucosidase and α-amylase activities. LC-MS analysis of coconut water revealed the presence of ellagic acid, butin, quercetin, protocatechuic acid, baicalin, and silibinin. Molecular docking analysis revealed potent molecular interactions between the LC-MS-identified compounds, and AKT-2 serine and PI-3 kinase. These results indicate the potential of coconut water to enhance glucose uptake, while concomitantly improving antioxidative and purinergic activities. They also indicate the potential of coconut water to suppress postprandial hyperglycemia. These activities may be attributed to the synergistic effects of the LC-MS-identified compounds.
Pergularia tomentosa, from Traditional Uses to Ecology and Phytochemistry

Background: Pergularia tomentosa L. has several traditional uses and biological activities which need to be more investigated. Objective: In this study, some ecological and phytochemical properties of Pergularia tomentosa were assessed. The flavonoids and cardenolides in the aqueous extract of leaves were identified with LCMS techniques. Methods: Some quantity characteristics of P. tomentosa were measured. Extraction was performed according to the traditional method (aqueous extract at room temperature). Metabolite profiling was conducted using untargeted liquid chromatography coupled to high resolution mass spectrometry (LCeESI-OrbitrapeMS). Data were analyzed by Xcalibur version 2.1. Results: Results showed that length of roots, leaves and fruits and the height of aerial parts were 23-30 cm, 2.5-3.2 cm, 4.95. 4 cm and 40-58.00 cm, respectively. Physiography and canopy cover were two important variables on these properties. We also identified 19 natural products in aqueous extract, 15 phenolics and flavonoids and 4 cardenolides. Three flavonoids (8, 11 and 14) and one cardenolide (17) had been previously isolated in P. tomentosa and 3 cardenolides (5, 18 and 19) has been reported for the first time in this plant. Conclusion: Results indicated that the leaves of P. tomentosa are a rich source of flavonoids and cardenolides. These findings suggest that leaves of this plant can be a good source of natural antioxidant and antitumor compounds. However, this plant needs special conservational efforts. Therefore, planting this plant under the canopy or along the borders of waterways can help in an appropriate habitat for its conservation and reclamation.
COVID-19 Vaccine Breakthrough Infections Among Healthcare Workers in A Tertiary Care Hospital in Coastal Karnataka, India – A Prospective Cohort Study

Background: The COVID-19 pandemic resulted in significant mortality and morbidity globally. The introduction of various COVID-19 vaccines at record time generated hope among people and doubts and apprehension regarding their safety. The study was done to estimate the incidence of COVID-19 vaccine breakthrough infections among Health Care Workers working in a tertiary care hospital and evaluate the outcome of these infections. Methods: A prospective Cohort study was conducted among 6342 healthcare workers in a tertiary care hospital having received at least one dose of any COVID-19 vaccine. They were followed up for COVID-19 vaccine breakthrough infection for one year by epidemiological investigations. Data on COVID-19-positive healthcare workers was obtained through personal interviews and case records. Results: 490 (7.9%) developed COVID-19 vaccine breakthrough infection during the study period. The majority of them (96.7%) were asymptomatic, and 16 (3.3%) of them developed complications needing hospitalization with 2 deaths. A statistically significant association was found in sex, healthcare worker categories, co-morbidities, and Blood groups. Conclusion: COVID-19 vaccination among healthcare workers reduces the incidence, severity, and complications of COVID-19 vaccine breakthrough infections. The risk of acquiring COVID-19 vaccine breakthrough infections was higher among males, partially vaccinated individuals, people with co-morbidities, and those involved in the regular care of COVID-19 patients. COVID-19-appropriate behaviour and receiving all the primary doses of vaccine will be instrumental in COVID-19 control.
Perceived Social Stigma of Cutaneous Leishmaniasis in Hubuna, Saudi Arabia

Background Cutaneous Leishmaniasis (CL) is a vector-borne skin infection that remains prevalent in regions with poor socioeconomic conditions. Stigmatization occurs when individuals with physical or psychological disorders interact with societal stereotypes. The aim of this study was to explore the perceived social stigma surrounding CL among people residing in Hubuna, Saudi Arabia. Methods This cross-sectional community-based survey recruited 618 individuals aged 18 years and above using the snowball sampling technique to reach hidden cases within the target population. Data was collected using a self-administered questionnaire and the Explanatory Model Interview Catalogue for Perceived Social Stigma (EMIC-SS-12) was used to assess the level of perceived social stigma. It includes questions on demographic variables, behaviors, and experiences. The analysis was performed using SPSS. Results The study included 618 participants, the majority of whom were women and girls (54.2%), with a mean age of 28 ± 12.7 years. The median score for perceived social stigma was 26.0. Only 2.1% (n = 13) of participants had the highest EMIC-SS-12 score of 36, while 7.6% (n = 47) scored zero. The mean score for overall perceived social stigma was 1.89 ± 0.91, while the mean score for experienced stigma was 1.99 ± 1.02. Univariate analysis showed that sex, employment, location of lesions, and number of lesions were insignificantly associated with stigmatization (P-value < 0.05), because these associations were uncertain because the CI includes or very close to 1. Conclusion The study reveals insights into stigmatization associated with CL in the Habuna area of Saudi Arabia. It found that the median of perceived social stigma was 26. Factors such as sex, employment status, and location of the lesion are uncertainly associated with stigma. It is crucial to explore negative behaviors and perceptions and develop suitable health education programs.
Solving a Class of Nonsmooth Nonconvex Optimization Problems Via Proximal Alternating Linearization Scheme with Inexact Information

For optimization problems minimizing the sum of two nonconvex and nonsmooth functions, we propose an alternate linearization method with inexact data. In many practical optimization applications, only the inexact information of the function can be obtained. The core idea of this method is to add a quadratic function term to the nonconvex function(called local convexification of nonconvex function), and then to construct an approximate proximal point model. In each iteration, a series of iteration points are obtained by solving subproblems alternately. It can be proved that, in the sense of inexact oracles, these iteration points converge to the stable point of the original problem, and theoretically show that the algorithm has good convergent properties.
A MOM-based ensemble method for robustness, subsampling and hyperparameter tuning

Hyperparameters tuning and model selection are important steps in machine learning. Unfortunately, classical hyperparameter calibration and model selection procedures are sensitive to outliers and heavy-tailed data. In this work, we construct a selection procedure which can be seen as a robust alternative to cross-validation and is based on a median-of-means principle. Using this procedure, we also build an ensemble method which, trained with algorithms and corrupted heavy-tailed data, selects an algorithm, trains it with a large uncorrupted subsample and automatically tune its hyperparameters. The construction relies on a divide-and-conquer methodology, making this method easily scalable for autoML given a corrupted database. This method is tested with the LASSO which is known to be highly sensitive to outliers.
Task Decomposition and Synchronization for Semantic Biomedical Image Segmentation

Semantic segmentation is essentially important to biomedical image analysis. Many recent works mainly focus on integrating the Fully Convolutional Network (FCN) architecture with sophisticated convolution implementation and deep supervision. Such complex networks need large training datasets, a requirement which is challenging for medical image analysis. In this paper, we propose to decompose the single segmentation task into three subsequent sub-tasks, including (1) pixel-wise image semantic segmentation, (2) prediction of the instance class labels of the objects within the image, and (3) classification of the scene the image belonging to. While these three sub-tasks are trained to optimize their individual loss functions at different perceptual levels, we propose to allow their interaction within the task-task context ensemble. Moreover, we propose a novel sync-regularization to penalize the deviation between the outputs of the pixel-wise semantic segmentation and the instance class prediction tasks. These effective regularizations help FCN utilize context information comprehensively and attain accurate segmentation, even though the number of images for training may be limited in many biomedical applications. We have successfully applied our framework to three diverse 2D/3D medical image datasets, including Robotic Scene Segmentation Challenge 18 (ROBOT18), Brain Tumor Segmentation Challenge 18 (BRATS18), and Retinal Fundus Glaucoma Challenge (REFUGE18). We have achieved outperformed or comparable performance in all the three challenges. Our code, typical data and trained models are available at https://github.com/xuhuaren/TDSNet.
Improved description of perovskite oxide crystal structure and electronic properties using self-consistent Hubbard 
U
 corrections from ACBN0

The wide variety of complex physical behavior exhibited in transition metal oxides, particularly the perovskites A$B$O$_3$, makes them a material family of interest in many research areas, but the drastically different electronic structures possible in these oxides raises challenges in describing them accurately within density functional theory (DFT) and related methods. Here we evaluate the ability of the ACBN0, a recently developed first-principles approach to computing the Hubbard $U$ correction self-consistently, to describe the structural and electronic properties of the first-row transition metal perovskites with $\left(B=\textrm{V}-\textrm{Ni} \right)$. ACBN0 performs competitively with hybrid functional approaches such as the Heyd-Scuseria-Ernzerhof (HSE) functional even when they are optimized empirically, at a fraction of the computational cost. ACBN0 also describes both the structure and band gap of the oxides more accurately than a conventional Hubbard $U$ correction performed by using $U$ values taken from the literature.
Four‐term progression free sets with three‐term progressions in all large subsets

This paper is mainly concerned with sets which do not contain four‐term arithmetic progressions, but are still very rich in three‐term arithmetic progressions, in the sense that all sufficiently large subsets contain at least one such progression. We prove that there exists a positive constant c and a set A⊂𝔽qn which does not contain a four‐term arithmetic progression, with the property that for every subset A′⊂A with |A′|≥|A|1−c , A′ contains a nontrivial three term arithmetic progression. We derive this from a more general quantitative Roth‐type theorem in random subsets of 𝔽qn , which improves a result of Kohayakawa–Łuczak–Rödl/Tao–Vu. We also discuss a similar phenomenon over the integers. Finally, we include another application of our methods, showing that for sets in 𝔽qn or ℤ the property of “having nontrivial three‐term progressions in all large subsets” is almost entirely uncorrelated with the property of “having large additive energy. ”