The problem of child labor has long been studied by economists, and most of it focuses on the microeconomic perspective. For this study, the researchers have decided to shift their focus to macroeconomic analysis. This study focuses on the effects of globalization and economic growth on the prevalence of child labor in the Philippines, mainly focusing on globalization, by using time-series analysis. Studies suggested that there is an inverted U-shaped relationship between globalization and child labor in developing countries, while other studies have determined a U-shaped relationship. The findings of this study reveal that there is no U-shape relationship between the variables but instead follows a linear relationship between globalization and child labor in the Philippine context. However, the lack of data and research publication on a national scale could influence the empirical results. Furthermore, this research can be used as literature in future studies.
SARS-CoV-2 surveillance is crucial to identify variants with altered epidemiological properties. Wastewater-based epidemiology (WBE) provides an unbiased and complementary approach to sequencing individual cases. Yet, national WBE surveillance programs have not been widely implemented and data analyses remain challenging. We deep-sequenced 2,093 wastewater samples representing 95 municipal catchments, covering >57% of Austria's population, from December 2020 to September 2021. Our Variant Quantification in Sewage pipeline designed for Robustness (VaQuERo) enabled us to deduce variant abundance from complex wastewater samples and delineate the spatiotemporal dynamics of the dominant Alpha and Delta variants as well as regional clusters of other variants of concern. These results were cross validated by epidemiological records of >130,000 individual cases. Finally, we provide a framework to predict emerging variants de novo and infer variant-specific reproduction numbers from wastewater. This study demonstrates the power of national-scale WBE to support public health and promises particular value for countries without dense individual monitoring.
The article is a monitoring of the main events concerning the Muslim community of the Republic of Bashkortostan in 2020 based on content analysis of text materials from government reports, press releases, websites and social media pages of Islamic organizations. The pandemic that began at the end of 2019 and the lockdown introduced in Russia demanded that Islamic communities adapt to new conditions. The danger of the spread of COVID-19 has reduced the internal confl ict in the Islamic environment and the public’s attention to the problems of radicalism. The drop in mosques attendance was off set by the development and use of digital resources in religious activities. At the same time, limited contacts, reduced offl ine events and meetings contributed to strengthening the new course of the Spiritual Administration of Muslims of the Republic of Bashkortostan towards decentralization and regionalisation. The activities of the Central Spiritual Administration of Russia, located in Ufa, remained without change on a national scale.
This study explores the application of long short-term memory (LSTM) networks to simulate runoff at the national scale of Denmark using data from 301 catchments. This is the first LSTM application on Danish data. The results were benchmarked against the Danish national water resources model (DK-model), a physically based hydrological model. The median Kling-Gupta Efficiency (KGE), a common metric to assess performance of runoff predictions (optimum of 1), increased from 0.7 (DK-model) to 0.8 (LSTM) when trained against all catchments. Overall, the LSTM outperformed the DK-model in 80% of catchments. Despite the compelling KGE evaluation, the water balance closure was modelled less accurately by the LSTM. The applicability of LSTM networks for modelling ungauged catchments was assessed via a spatial split-sample experiment. A 20% spatial hold-out showed poorer performance of the LSTM with respect to the DK model. However, after pre-training, that is, weight initialisation obtained from training against simulated data from the DK-model, the performance of the LSTM was effectively improved. This formed a convincing argument supporting the knowledge-guided machine learning (ML) paradigm to integrate physically based models and ML to train robust models that generalise well.
AbstractTidal wetlands provide myriad ecosystem services across local to global scales. With their uncertain vulnerability or resilience to rising sea levels, there is a need for mapping flooding drivers and vulnerability proxies for these ecosystems at a national scale. However, tidal wetlands in the conterminous USA are diverse with differing elevation gradients, and tidal amplitudes, making broad geographic comparisons difficult. To address this, a national-scale map of relative tidal elevation (Z*MHW), a physical metric that normalizes elevation to tidal amplitude at mean high water (MHW), was constructed for the first time at 30 × 30-m resolution spanning the conterminous USA. Contrary to two study hypotheses, watershed-level median Z*MHW and its variability generally increased from north to south as a function of tidal amplitude and relative sea-level rise. These trends were also observed in a reanalysis of ground elevation data from the Pacific Coast by Janousek et al. (Estuaries and Coasts 42 (1): 85–98, 2019). Supporting a third hypothesis, propagated uncertainty in Z*MHW increased from north to south as light detection and ranging (LiDAR) errors had an outsized effect under narrowing tidal amplitudes. The drivers of Z*MHW and its variability are difficult to determine because several potential causal variables are correlated with latitude, but future studies could investigate highest astronomical tide and diurnal high tide inequality as drivers of median Z*MHW and Z*MHW variability, respectively. Watersheds of the Gulf Coast often had propagated Z*MHW uncertainty greater than the tidal amplitude itself emphasizing the diminished practicality of applying Z*MHW as a flooding proxy to microtidal wetlands. Future studies could focus on validating and improving these physical map products and using them for synoptic modeling of tidal wetland carbon dynamics and sea-level rise vulnerability analyses.
The Saudi Arabian tourism sector is growing, and its economy has flourished over the last decades. This has resulted in numerous coastal developments close to large economic centers, while many more are proposed or planned. The coastal developments have influenced the behavior of the shoreline in the past. Here we undertake a national assessment on the state of the coast of Saudi Arabia based on recent data sets on historic and future shoreline positions. While at national scale the shoreline is found to be stable over the last three decades, the Red Sea coast shows a regional-mean retreat rate while the Gulf coast shows a regional-mean prograding behavior. Detailed analysis of the temporal evolution of shoreline position at selected locations show that human interventions may have accelerated shoreline retreat along adjacent shorelines, some of which are Marine Protected Areas. Furthermore, reef-fronted coastal sections have a mean accretive shoreline change rate, while the open coast shows a mean retreat rate. Future shoreline projections under RCP 4.5 and RCP 8.5 show that large parts of the shoreline may experience an accelerated retreat or a change in its regime from either stable or sprograding to retreating. Under the high emission RCP 8.5 scenario, the length of coastline projected to retreat more than doubles along the Red Sea coast, and approximately triples along the Gulf coast in 2100. At national scale, the Saudi Arabian coastline is projected to experience regional-mean retreats of ~30 m and of ~130 m by 2050 and 2100 under both RCPs considered in this study. These results indicate that effective adaptation strategies will be required to protect areas of ecological and economic value, and that climate resilience should be a key consideration in planned or proposed coastal interventions.
The present study provides a simplified framework verifying the degree of coverage and completeness of settlement maps derived from the OpenStreetMap (OSM) database at the national scale, with a possible use in official statistics. Measuring the completeness of the objects (i.e., buildings) derived from OpenStreetMap database supports its potential use in building/population censuses and other diachronic surveys, as well as administrative sources such as the register of building permits and land-use cadasters. A series of measurements at different scales are proposed and tested for Italy, in line with earlier studies. While recognizing the potential of the OpenStreetMap database for official statistics, the present work underlines the urgent need of an additional (spatially explicit) analysis overcoming the data heterogeneity and sub-optimal coverage of the OSM information source.