MODIFICATION OF TURC METHOD TO DETERMINE THE WATER YIELDS OF SUB-BASINS IN THRACE REGION OF TURKEY

In this research, directly measured flow values in three different basins in Thrace region, that is located in the Northwest Part of Turkey, were compared to the computed ones applying the Turc method to these basins. It is concluded that Turc method with original coefficients, 300 and 0.9, could not be used because of huge discrepancies between the measured and calculated values. Instead, coefficients of 601 and 0.65, respectively, were used after the correction using long term measured flow rates. Employing the modified Turc method for these research basins, reservoirs volume are reduced by 50.7 %. This may decrease the total cost of the reservoirs by about 20-30 % through reducing occupied surface area, embankment and crest height

ROCK MASS DISCONTINUITY DETERMINATION WITH TRANSFER LEARNING

Rock mass discontinuity and orientation are among the important rock mass features. They are conventionally determined with scan-line surveys by engineering geologists in field, which can be difficult or impossible depending on site accessibility. Photogrammetry and computer vision techniques can aid to automatically perform these measurements, although variations in size, shape and appearance of rock masses make the task challenging. Here we propose an automated approach for the detection of rock mass discontinuities using deep learning and photogrammetric image processing methods. Two deep convolutional neural network (DCNNs) were implemented for this purpose and applied to basalts in Kizilcahamam Guvem Geosite near Ankara, Türkiye. Red-green-blue (RGB) band images of the site were taken from an off-the-shelf camera with 1.7 mm resolution and a 3D digital surface model and orthophotos were produced by using photogrammetric software. The discontinuities were delineated manually on the orthophoto and converted to masks. The first DCNN model was based on the open-source crack dataset consisting of a total of 11,298 road and pavement images, which were used to train the Resnet-18 model (Model-1). The second model (Model-2) was based on fine-tuning of Model-1 using the study data from Kizilcahamam. After fine-tuning, Model-2 was able to achieve high performance with a Jaccard Score of 88% on the test data. The results show high potential of the methodology for transfer learning with fine-tuning of a small amount of data that can be applied to other sites and rock mass types as well

Data from an International Multi-Centre Study of Statistics and Mathematics Anxieties and Related Variables in University Students (the SMARVUS Dataset)

This large, international dataset contains survey responses from N = 12,570 students from 100 universities in 35 countries, collected in 21 languages. We measured anxieties (statistics, mathematics, test, trait, social interaction, performance, creativity, intolerance of uncertainty, and fear of negative evaluation), self-efficacy, persistence, and the cognitive reflection test, and collected demographics, previous mathematics grades, self-reported and official statistics grades, and statistics module details. Data reuse potential is broad, including testing links between anxieties and statistics/mathematics education factors, and examining instruments’ psychometric properties across different languages and contexts. Data and metadata are stored on the Open Science Framework website [https://osf.io/mhg94/]

Gut microbiota and diabetes: from pathogenesis to therapeutic perspective

More than several hundreds of millions of people will be diabetic and obese over the next decades in front of which the actual therapeutic approaches aim at treating the consequences rather than causes of the impaired metabolism. This strategy is not efficient and new paradigms should be found. The wide analysis of the genome cannot predict or explain more than 10–20% of the disease, whereas changes in feeding and social behavior have certainly a major impact. However, the molecular mechanisms linking environmental factors and genetic susceptibility were so far not envisioned until the recent discovery of a hidden source of genomic diversity, i.e., the metagenome. More than 3 million genes from several hundreds of species constitute our intestinal microbiome. First key experiments have demonstrated that this biome can by itself transfer metabolic disease. The mechanisms are unknown but could be involved in the modulation of energy harvesting capacity by the host as well as the low-grade inflammation and the corresponding immune response on adipose tissue plasticity, hepatic steatosis, insulin resistance and even the secondary cardiovascular events. Secreted bacterial factors reach the circulating blood, and even full bacteria from intestinal microbiota can reach tissues where inflammation is triggered. The last 5 years have demonstrated that intestinal microbiota, at its molecular level, is a causal factor early in the development of the diseases. Nonetheless, much more need to be uncovered in order to identify first, new predictive biomarkers so that preventive strategies based on pre- and probiotics, and second, new therapeutic strategies against the cause rather than the consequence of hyperglycemia and body weight gain

The DUNE Far Detector Interim Design Report, Volume 3: Dual-Phase Module

The DUNE IDR describes the proposed physics program and technical designs of the DUNE far detector modules in preparation for the full TDR to be published in 2019. It is intended as an intermediate milestone on the path to a full TDR, justifying the technical choices that flow down from the high-level physics goals through requirements at all levels of the Project. These design choices will enable the DUNE experiment to make the ground-breaking discoveries that will help to answer fundamental physics questions. Volume 3 describes the dual-phase module's subsystems, the technical coordination required for its design, construction, installation, and integration, and its organizational structure

Data from an International Multi-Centre Study of Statistics and Mathematics Anxieties and Related Variables in University Students (the SMARVUS Dataset)

This large, international dataset contains survey responses from N = 12,570 students from 100 universities in 35 countries, collected in 21 languages. We measured anxieties (statistics, mathematics, test, trait, social interaction, performance, creativity, intolerance of uncertainty, and fear of negative evaluation), self-efficacy, persistence, and the cognitive reflection test, and collected demographics, previous mathematics grades, self-reported and official statistics grades, and statistics module details. Data reuse potential is broad, including testing links between anxieties and statistics/mathematics education factors, and examining instruments’ psychometric properties across different languages and contexts

Data from an International Multi-Centre Study of Statistics and Mathematics Anxieties and Related Variables in University Students (the SMARVUS Dataset)

This large, international dataset contains survey responses from N = 12,570 students from 100 universities in 35 countries, collected in 21 languages. We measured anxieties (statistics, mathematics, test, trait, social interaction, performance, creativity, intolerance of uncertainty, and fear of negative evaluation), self-efficacy, persistence, and the cognitive reflection test, and collected demographics, previous mathematics grades, self-reported and official statistics grades, and statistics module details. Data reuse potential is broad, including testing links between anxieties and statistics/mathematics education factors, and examining instruments’ psychometric properties across different languages and contexts. Data and metadata are stored on the Open Science Framework website (https://osf.io/mhg94/).</p&gt

The DUNE Far Detector Interim Design Report, Volume 3: Dual-Phase Module

The DUNE IDR describes the proposed physics program and technical designs of the DUNE far detector modules in preparation for the full TDR to be published in 2019. It is intended as an intermediate milestone on the path to a full TDR, justifying the technical choices that flow down from the high-level physics goals through requirements at all levels of the Project. These design choices will enable the DUNE experiment to make the ground-breaking discoveries that will help to answer fundamental physics questions. Volume 3 describes the dual-phase module's subsystems, the technical coordination required for its design, construction, installation, and integration, and its organizational structure