Propuesta metodológica para la caracterización y tipificación de las costas españolas. Aplicación a las costas de Galicia.

There are several coastal classifications. Most of them have been elaborated worldwide using tectonic, climatic, topographic, or oceanographic criteria. Other classifications have been generated on a larger scale and focused on classifying the coastal forms, as cliffs, beaches, estuaries, lagoons, or dune complexes in different places.This project analyzes the types of coastlines, understanding as such each sector that presents certain topographic conditions marked by the elevation and slope, and that was modeled on a concrete type of rock in a specific climatic and marine environment. This paper describes a methodological approach for a detailed scale classification. This approach based on the delimitation of the different coastal systems, exemplified in cliffs and boulder beaches, sandy beaches, and dunes. In this case the shore platforms, marshes and lagoons have not been considered for the technical problems derived from the LiDAR data source, from which the 2 m spatial resolution digital terrain models (DTM) are derived.The first step in the classification was a manual delimitation combining DTMs and orthophotographs. Subsequently, other typification has been carried out through the automatic creation of Coastal Topographic Units (CTU). This index is the combination of two variables: coastal elevation and slope. The possible integration of others, such as orientation or lithology, is possible, but generate a very high number of units and make it difficult to interpret. For this reason, this study did not consider more variables.In this project 30 CTUs was generated, and then selecting only those that appear in the cliffs, boulder beaches, sandy beaches, and coastal dunes sectors. The possibility of viewing one or several CTUs in any sector of the coast allows to know more accurately the conditions of each sector and these categories could be improve the coastal management plans.

Effect of problem-based learning on severity classification agreement by triage nurses

Background Differences in the classification results among triage nurses in the emergency room can be improved by training or applying an algorithm. This study aimed to confirm whether the agreement among triage nurses could be improved through learner-led problem-based learning. Methods This study had a single-group time series design to investigate the effect of problem-based learning led by triage nurses on the agreement of Korean Triage and Acuity Scale classification results for patients who visited the emergency department. We extracted 300 patients each in May and August 2018 before learning began and 300 patients each in May and August 2019 after learning. Results After problem-based learning was applied, the self-efficacy of triage nurses for emergency patient classification increased statistically significantly compared to before learning (7.88 ± 0.96, p < .001), and the weighted kappa coefficient was also found to be almost perfectly agreement (0.835, p < .001). Conclusions In this study, problem-based learning improved the inter-rater agreement of Korean Triage and Acuity Scale classification results and self-efficacy of triage nurses. Therefore, problem-based learning can contribute to patient safety in the emergency department by enhancing the expertise of triage nurses and increasing the accuracy of triage classification.

A vegetation classification and description of white-winged flufftail ( Sarothrura ayresi ) habitat at selected high-altitude peatlands in South Africa

The white-winged flufftail is listed as critically endangered, and limited knowledge about the species' ecology has been identified as a limiting factor to effectively conserving the bird. Little is known about the vegetation inhabited by the white-winged flufftail, which hampers the identification and management of its habitat. This study presents a fine-scale classification and description of the vegetation of wetland sites where the bird is known to be present. A plant phytosociological study was conducted to describe the plant communities and vegetation structure of the habitat. Three sites were selected at Verloren Valei Nature Reserve and two at Middelpunt Wetland, Mpumalanga, South Africa, shortly after the white-winged flufftail breeding season. A total of 60 sample plots were placed within the study sites, where all plant species present were recorded and identified. Other aspects such as plant height, water depth and anthropogenic influences were also documented. A modified TWINSPAN analysis resulted in the identification of three sub-communities that can be grouped into one major community. The Cyperaceae, Asteraceae and Poaceae families dominate the vegetation, with the sedges Carex austro-africana and Cyperus denudatus being dominant, and the grasses Leersia hexandra and Arundinella nepalensis co-dominant. The broad habitat structure consisted of medium to tall herbaceous plants (0.5–0.7 m) with shallow slow-flowing water.

Effect of Problem-based Learning on Agreement of Severity Classification Results of Triage Nurses in the Emergency Department

BackgroundProblem-based learning is a learner-led learning method that helps improve critical thinking, problem solving skills, and knowledge. It is necessary to confirm whether it can help to agree the severity classification results among nurses through problem-based learning.MethodsThis study had a single-group time series design to investigate the effect of problem-based learning led by triage nurses on the agreement of Korean Triage and Acuity Scale classification results for patients who visited the emergency department. We extracted 300 patients each in May and August 2018 before problem-based learning began and 300 patients each in May and August 2019 after problem-based learning. ResultsAfter problem-based learning, the length of emergency department stay decreased about 30 minutes, although the decrease was not statistically significant (p=.172). However, self-efficacy for the classification of emergency patients in triage nurses and weighted kappa coefficients were improved (p<.001).ConclusionIn this study, problem-based learning led by triage nurses improved the inter-rater agreement of Korean Triage and Acuity Scale classification results and self-efficacy of triage nurses. Therefore, problem-based learning led by triage nurses can contribute to patient safety in the emergency department by enhancing the expertise of triage nurses and increasing the accuracy of triage classification.

Multiscale Characterization of Fracture Patterns: A Case Study of the Noble Hills Range (Death Valley, CA, USA), Application to Geothermal Reservoirs

In the basement fractured reservoirs, geometric parameters of fractures constitute the main properties for modeling and prediction of reservoir behavior and then fluid flow. This study aims to propose geometric description and quantify the multiscale network organization and its effect on connectivity using a wide-ranging scale analysis and orders scale classification. This work takes place in the Noble Hills (NH) range, located in the Death Valley (DV, USA). The statistical analyses were performed from regional maps to thin sections. The combination of the length datasets has led to compute a power law exponent around −2, meaning that the connectivity is ruled by the small and the large fractures. Three domains have been highlighted in the NH: (1) domain A is characterized by a dominance of the NW/SE direction at the fourth order scale; (2) domain B is characterized by a dominance of the E/W and the NW/SE directions at respectively the fourth and third order scales; (3) domain C is also marked by the E/W direction dominance followed by the NW/SE direction respectively at the fourth and third order scale. The numerical simulations should consider that the orientation depends on scale observation, while the length is independent of scale observation.

Analisis Deskriptif tentang Tampilan Kinerja Aparatur Sipil Negara di Sekretariat Pemerintah Kota Kupang Nusa Tenggara Timur Indonesia

<p><em>The purpose of this research was to determine the performance of state civil servants at the Secretariat of the Government of Kupang City, East Nusa Tenggara, Indonesia through the effectiveness of performance indicators, namely quantity, quality, timeliness, cooperation, and self-quality. The research was conducted for 6 months, namely September 2019-February 2020. The total population and research sample were 370 ASN which were determined by purposive stratified proportional sampling based on the position, class, and rank of the ASN. The type of data used is primary data and secondary data obtained by means of questionnaires, observation, and documentation. Data analysis was carried out on performance indicators using descriptive analysis based on the average value of the Likert scale classification. The results showed that in general, the performance of ASN in the Regional Secretariat of the Kupang City Government was effective as indicated by the average performance score of 3.71 or high. This result is because the performance indicators show the results of high-value categories which include quantity (3.79), quality (3.71), timeliness (3.67), cooperation (3.70), and self-quality (3.69).</em></p><p><strong><em>Keywords: </em></strong><em>quantity, quality, timeliness, cooperation, self-quality, performance</em></p>

Repository scale classification and decomposition of tandem mass spectral data

Various studies have shown associations between molecular features and phenotypes of biological samples. These studies, however, focus on a single phenotype per study and are not applicable to repository scale metabolomics data. Here we report MetSummarizer, a method for predicting (i) the biological phenotypes of environmental and host-oriented samples, and (ii) the raw ingredient composition of complex mixtures. We show that the aggregation of various metabolomic datasets can improve the accuracy of predictions. Since these datasets have been collected using different standards at various laboratories, in order to get unbiased results it is crucial to detect and discard standard-specific features during the classification step. We further report high accuracy in prediction of the raw ingredient composition of complex foods from the Global Foodomics Project.

Pothole Region Extraction Based On Similarity Evaluation Scale Classification Using Image Processing

Pavement deterioration and abnormal climate induced by global warming lead to a constant rise in the number of potholes. Accordingly, the loss cost for maintenance and accidents also increases. Therefore, it is necessary to develop a method of classifying pavement potholes and detecting their locations. This study proposes the pothole region extraction based on similarity evaluation scale classification using image processing. The proposed technique sets up a classification threshold appropriately by considering the structure, brightness, and other factors of the grayscale-converted image through SSIM (Structural Similarity Index Measure). It binarizes porthole images classified according to the threshold, and then extracts pothole regions through the threshold based segmentation. A conventional image classification method utilizes the rules found in objects or the label selected by a user. The proposed method can take into account detailed factors by comparing image similarity in the unit of pixel. According to the performance evaluation, the proposed classification method’s F1-score is 0.83, and its accuracy of pothole region extraction is 0.851. Therefore, with the proposed technique, it is possible to make classification in consideration of similarity between images. In addition, the proposed method makes it possible to detect the regions similar to actual potholes accurately.