Optimal Selection of Number and Location of Meteo-Hydrological Monitoring Networks on Vu Gia – Thu Bon River Basin using GIS

Meteorological data play a particularly important role in hydrologic research because the climate and weather of an area exert a profound influence on most hydrologic processes. Meanwhile, hydrological data are critical for performing a range of purposes, including water resources assessment, impacts of climate change and flood forecasting and warning. It can be said that the prevention of disasters caused by floods and droughts would be impossible without rational forecasting technology based on an understanding of the rainfall-runoff phenomenon and statistical analysis of past hydrological data, which cannot be achieved without meteo-hydrological observations. The lack of adequate meteo-hydrological data affects the ability to model, predict and plan for catastrophic events such as floods and droughts which have obvious negative impacts on public health and socio-economic aspects. The accurate estimation of the spatial distribution of meteorological and hydrological parameters requires a dense network of instruments, which entails large installation and operational costs. It is thus necessary to optimize the number and location of meteo-hydrological stations. This paper presents a GIS-based approach to establishing an optimal meteo-hydrological station network on Vu Gia- Thu Bon river basin for developing an up-to-date real time flood warning system. Based on statistical analysis of the annual rainfall total data at 9 existing gauges in the study area from 1980 to 2013, it showed that the error of the existing network was about 7.47%. Considering 9 rain gauges as a standard representative of rainfall over the region, if the error decreases from 7.47% to 5%, the number of additional rain gauges should be 20. For adequate and economical network design, these additional rain gauges were spatially distributed between the different isohyetals after considering the relative distances between rain gauges, their accessibility, personnel required for making observations using multi-layers analysis and spatial interpolation. For hydrological stations, based on consideration existing network with the requirements set out by the flood warning system, the number of stations should be five. In terms of spatial distribution, three stations were distributed across two main tributaries of Vu Gia- Thu Bon river basin, behind the dams for water discharge calibration and the others were located on downstream for water stage calibration. The results of the study provided a scientific approach can be applied to optimizing the meteo-hydrological station network over the river basin

The Relationship between Public Debt, Budget Deficit, and Sustainable Economic Development in Developing Countries: The Role of Corruption Control

This study investigates the effects of public debt and budget deficits on the sustainable economic development of developing countries, taking into account the role of control of corruption. The two-step GMM method was applied for unbalanced panel data of 59 developing countries from 2004 to 2015. The study found that public debt and the budget deficit had negative effects on sustainable development, while the effect of control of corruption was positive. Moreover, using interaction terms between control of corruption and public debt and budget deficit, respectively, empirical results showed that controlling corruption limited these adverse effects. Thus, if the objective is to achieve sustainable economic development, developing countries should not see raising public debt or maintaining budget deficits as a strategy for economic development. The study contributes empirical evidence to the theory of debt overhang, crowded effects, and institutional theory in the context of developing countries. The implications are also discussed in this paper

Blended Learning for Secondary Schools in Nam Dinh Province to Satisfy New Standards: The Current Situation and Proposed Models

We offered blended learning models for high schools in Nam Dinh province to satisfy Vietnam's new criteria. These models were based on general approaches to issues, theoretical research, and field research based on surveys and anket questionnaires conducted throughout the area. The results of a survey demonstrate that high school teachers in Nam Dinh have gained a fundamental grasp of blended learning and have, in practice, embraced both online and face-to-face instruction, particularly during the height of the Covid-19 outbreak. However, there was not a standard model for blended learning, therefore it was only used by a few persons. In other contexts, the concept of "blended learning" referred to what was effectively a face-to-face session that was broadcast over the Internet without the necessary adjustments being made to the content, methodology, or evaluation. As a result, we offer a number of different ways to blended learning for high schools in Nam Dinh in order to improve the quality of education provided throughout the province

Bit-Vector Model Counting using Statistical Estimation

Approximate model counting for bit-vector SMT formulas (generalizing \#SAT) has many applications such as probabilistic inference and quantitative information-flow security, but it is computationally difficult. Adding random parity constraints (XOR streamlining) and then checking satisfiability is an effective approximation technique, but it requires a prior hypothesis about the model count to produce useful results. We propose an approach inspired by statistical estimation to continually refine a probabilistic estimate of the model count for a formula, so that each XOR-streamlined query yields as much information as possible. We implement this approach, with an approximate probability model, as a wrapper around an off-the-shelf SMT solver or SAT solver. Experimental results show that the implementation is faster than the most similar previous approaches which used simpler refinement strategies. The technique also lets us model count formulas over floating-point constraints, which we demonstrate with an application to a vulnerability in differential privacy mechanisms

Control of bacterial virulence through the peptide signature of the habitat

To optimize fitness, pathogens selectively activate their virulence program upon host entry. Here, we report that the facultative intracellular bacterium Listeria monocytogenes exploits exogenous oligopeptides, a ubiquitous organic N source, to sense the environment and control the activity of its virulence transcriptional activator, PrfA. Using a genetic screen in adsorbent- treated ( PrfA-inducing) medium, we found that PrfA is functionally regulated by the balance between activating and inhibitory nutritional peptides scavenged via the Opp transport system. Activating peptides provide essential cysteine precursor for the PrfA-inducing cofactor glutathione ( GSH). Non-cysteine-containing peptides cause promiscuous PrfA inhibition. Biophysical and co-crystallization studies reveal that peptides inhibit PrfA through steric blockade of the GSH binding site, a regulation mechanism directly linking bacterial virulence and metabolism. L. monocytogenes mutant analysis in macrophages and our functional data support a model in which changes in the balance of antagonistic Oppimported oligopeptides promote PrfA induction intra-cellularly and PrfA repression outside the host

Classification of current anticancer immunotherapies

© 2014. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.During the past decades, anticancer immunotherapy has evolved from a promising therapeutic option to a robust clinical reality. Many immunotherapeutic regimens are now approved by the US Food and Drug Administration and the European Medicines Agency for use in cancer patients, and many others are being investigated as standalone therapeutic interventions or combined with conventional treatments in clinical studies. Immunotherapies may be subdivided into "passive" and "active" based on their ability to engage the host immune system against cancer. Since the anticancer activity of most passive immunotherapeutics (including tumor-targeting monoclonal antibodies) also relies on the host immune system, this classification does not properly reflect the complexity of the drug-host-tumor interaction. Alternatively, anticancer immunotherapeutics can be classified according to their antigen specificity. While some immunotherapies specifically target one (or a few) defined tumor-associated antigen(s), others operate in a relatively non-specific manner and boost natural or therapy-elicited anticancer immune responses of unknown and often broad specificity. Here, we propose a critical, integrated classification of anticancer immunotherapies and discuss the clinical relevance of these approaches.info:eu-repo/semantics/publishedVersio