Search Results (4)

Elucidating the trade-offs and synergistic relationships between different ecosystem services is essential to optimize the benefits of ecosystem services and ensure their proper management for human well-being and ecosystem health. However, previous studies have focused only on quantitative analysis based on statistical relationships to explore ecosystem service trade-offs and synergistic relationships as a whole; additionally, some of them lack scientific expression of spatial and temporal differences within regions. Therefore, here, we explored the trade-offs and synergies among ecosystem services in the Taihang Mountains region and conducted ecological service zoning based on the findings to support ecological conservation and high-quality development in the Taihang Mountains and North China Plain. We employed yield spatialization, the InVEST model, and ArcGIS kernel density analysis to assess the interactions among ecosystem services: provisioning (food supply), regulating (water yield and carbon density), supporting (soil retention and habitat quality), and cultural services (leisure and recreation) in the study area. Linear Pearson correlation coefficients and non-linear bagplots were utilized to analyze the interrelationships among these services. Based on the bagplot results, the geographic patterns of ecosystem service trade-offs/synergies and the distribution of dominant services were identified. The results revealed considerable trade-offs between food supply and both regulating and supporting services, with most of the latter exhibiting synergistic relationships with one another. In contrast, leisure and recreation services showed a neutral relationship with other services. Among ecosystem services, carbon density services demonstrated the highest synergistic effects, whereas food supply services exhibited the most conflicts. The various ecosystem trade-off/synergy zones and dominant service distributions generated through bagplot mappings may optimize management methods for multiple ecosystem services. Overall, these findings provide significant insights for improving ecological service zoning and natural resource management. Full article

Air pollution affects human health and is one of the main problems in the world, including in coastal cities with industrial seaports. In this sense, the city of Gijón (northern Spain) stands out as one of the 20 Spanish cities with the worst air quality. The study aims to identify outliers in air quality observations near the El Musel seaport, resulting from the emissions of six pollutants over an eight-year period (2014–2021). It compares methods based on the functional data analysis (FDA) approach and vector methods to determine the optimal approach for detecting outliers and supporting air quality control. Our approach involves analyzing air pollutant observations as a set of curves rather than vectors. Therefore, in the FDA approach, curves are constructed to provide the best fit to isolated data points, resulting in a collection of continuous functions. These functions capture the behavior of the data in a continuous domain. Two FDA approach methodologies were used here: the functional bagplot and the high-density region (HDR) boxplot. Finally, outlier detection using the FDA approach was found to be more powerful than the vector methods and the functional bagplot method detected more outliers than the HDR boxplot. Full article

Outliers in the training or test set used to fit and evaluate a classifier on transcriptomics data can considerably change the estimated performance of the model. Hence, an either too weak or a too optimistic accuracy is then reported and the estimated model performance cannot be reproduced on independent data. It is then also doubtful whether a classifier qualifies for clinical usage. We estimate classifier performances in simulated gene expression data with artificial outliers and in two real-world datasets. As a new approach, we use two outlier detection methods within a bootstrap procedure to estimate the outlier probability for each sample and evaluate classifiers before and after outlier removal by means of cross-validation. We found that the removal of outliers changed the classification performance notably. For the most part, removing outliers improved the classification results. Taking into account the fact that there are various, sometimes unclear reasons for a sample to be an outlier, we strongly advocate to always report the performance of a transcriptomics classifier with and without outliers in training and test data. This provides a more diverse picture of a classifier’s performance and prevents reporting models that later turn out to be not applicable for clinical diagnoses. Full article

The present research uses two different functional data analysis methods called functional high-density region (HDR) boxplot and functional bagplot. Both methodologies were applied for the outlier detection in the time pollutant emissions curves that were built using as inputs the discrete information available from an air quality monitoring data record station. Although the record of pollutant emissions is made in a discrete way, these methodologies consider pollutant emissions over time as curves, with outliers obtained by a comparison of curves instead of vectors. Then the concept of outlier passes from been a point to a curve that employed the functional depth as the indicator of curve distances. In this study, the referred methodologies are applied to the detection of outliers in pollutant emissions from the Soto de Ribera coal-fired plant which is in the nearby of the city of Oviedo, located in the Principality of Asturias, Spain. Finally, the advantages of the functional method are reported. Full article