Staying Small and Beautiful: Enhancing Sustainability in the Small Island Developing States

Spread over the ocean regions of the Caribbean, the Pacific and Atlantic, the Indian Ocean, the Mediterranean, and the South China Sea, the small island developing states (SIDS) are a distinct group of developing countries often known for their rich biological diversity, oceans, tourism, and fisheries. The pressures on these and other natural resources is most immediate in the islands where the high vulnerability to the impacts of climate change, limited land and water resources, often unsustainable natural resource use, and other particular economic vulnerabilities are disrupting livelihoods. The COVID-19 pandemic has further exacerbated the SIDS economies and livelihoods. Over the past 25 years the Global Environment Facility (GEF) has supported interventions in SIDS through $578 million in financing, in critical areas such as biodiversity protection, climate resilience, and energy access through renewable energy. But how effective and sustainable have these interventions been? What factors influencing the sustainability of GEF interventions can provide insights for future project design and implementation? This chapter draws on findings from a recent country cluster evaluation on SIDS conducted by the Independent Evaluation Office (IEO) of the GEF. It presents the main environmental challenges in SIDS, the evidence on the relevance and effectiveness of GEF interventions in addressing these challenges, and the main risks to sustainability of outcomes. Important contextual factors that affect sustainability in SIDS include good policies and legal and regulatory frameworks, national ownership of projects, environmental awareness, institutional capacity, and strategic institutional partnerships. Project-related factors including good project design and adaptive project management, scaling-up and replication based on lessons learned, and a good exit strategy are also important for sustainability.

ANALYSIS OF THE APPLICABILITY CRITERION FOR K MEANS CLUSTERING ALGORITHM RUN TEN NUMBER OF TIMES ON THE FIRST 25 NUMBERS OF THE FIBONACCI SERIES

In this research investigation Analysis Of The Applicability Criterion For K Means Clustering Algorithm Run Ten Number Of Times On The First 25 Numbers Of The Fibonacci Series is performed. For this analysis RCB Model Of Applicability Criterion For K Means Clustering Algorithm is used. K-means is one of the simplest unsupervised learning algorithms that solve the well-known clustering problem. K- Means clustering algorithm is a scheme for clustering continuous and numeric data. As K-Means algorithm consists of scheme of random initialization of centroids, every time it is run, it gives different or slightly different results because it may reach some local optima. Quantification of such aforementioned variation is of some importance as this sheds light on the nature of the Discrete K-Means Objective function with regards its maxima and minima. The K-Means Clustering algorithm aims at minimizing the aforementioned Objective function. The RCB Model Of Applicability Criterion for K-Means Clustering aims at telling us if we can use the K-Means Clustering Algorithm on a given set of data within acceptable variation limits of the results of the K-Means Clustering Algorithm when it is run several times. KEY WORDS: K-means clustering algorithm, RCB model and Cluster evaluation.

Research on music teaching practice based on fuzzy intelligent interactive tablet

With the development of economy and the progress of science and technology, China, as a powerful country in science and technology and education, has been constantly improving the level of educational informatization and perfecting the basic teaching equipment, which has realized informatization and modernization to a certain extent. This research takes intelligent interactive tablet as interactive means and combines the theories of aesthetics, psychology, pedagogy and fuzzy teaching to research the appreciation, singing and creation aspects of music teaching practice. After three months of teaching practice, the students are evaluated and construct fuzzy cluster evaluation model, investigation and analysis and other methods, which verified that the fuzzy intelligent interactive tablet can provide rich content for music teaching, improve the teaching efficiency of students’ participation, and prove that the fuzzy intelligent interactive tablet has a great auxiliary effect on music teaching.

Optimasi Jumlah Cluster pada Algoritme K-Means untuk Evaluasi Kinerja Dosen

Lecturers are one of the main actors in universities. Lecturer performance can affect the quality of a college. Because the quality of higher education is strongly influenced by the lecturers, the performance of the lecturers needs to be assessed. Lecturer performance can be assessed by evaluating the lecturer's performance in teaching. Lecturer performance can be evaluated by classifying student assessments of lecturers. Lecturers are assessed based on how the lecturer mastered the material, the lecturer's discipline in teaching, and the presentation of the material. The process of grouping lecturer scores can be done using the K-Means algorithm. K-Means is a popular clustering algorithm that performs well. K-Means requires a parameter that is K or the number of clusters. The importance of the number of clusters so that there is a need for optimization in determining the number of K. In this study, optimization was carried out using the Elbow method so as to produce the ideal number of groups of 4 groups. The results of the clustering evaluation calculated using the SSE were 54.4% which showed that the results were not optimal. The results of the cluster evaluation are not optimal due to the lack of data for the K-Means application.

The threat classification and evaluation of the economic security of the cluster

Subject. The article focuses on a set of external and internal factors that may cause risks and threats to clusters of business entities. We also discuss methodological aspects in evaluating the economic security of the cluster. Objectives. We construct the tiered classification of external and internal threats to the economic security of the cluster, articulate its evaluation technique based on the comprehensive approach to considering possible risks and threats arising from operations of cluster components. Methods. The study is based on general and special methods, such as the generalization, synthesis and analysis of scientific findings on the economic security of organizations, including clusters of such organizations. Unfolding the comprehensive approach to evaluating the economic security of clusters, we applied some techniques and methods, such as typification, identification, rating and scoring, indicative method, general analysis techniques. Results. We commented on the existing proceedings by foreign and Russian researchers on the evaluation of the economic security of business entities and relevant issues. The article classifies external and internal threats to the economic security of the cluster and points out their levels, groups and types. We summarized and examined the existing cluster evaluation techniques. The techniques were found to overlook the complex inner design of the cluster management. Conclusions and Relevance. As a result of the study, we formulated scientific and methodological clauses that would be interesting for the cluster management and help timely detect real economic threats, predict possible ones, find how their consequences can be eliminated, decide on administrative actions and perform them so as to ensure the sustainable development and the high level of the economic security of the cluster.

Cell Layers: Uncovering clustering structure and knowledge in unsupervised single-cell transcriptomic analysis

MotivationUnsupervised clustering of single-cell transcriptomics is a powerful method for identifying cell populations. Static visualization techniques for single-cell clustering only display results for a single resolution parameter. Analysts will often evaluate more than one resolution parameter, but then only report one.ResultsWe developed Cell Layers, an interactive Sankey tool for the quantitative investigation of gene expression, coexpression, biological processes, and cluster integrity across clustering resolutions. Cell Layers enhances the interpretability of single-cell clustering by linking molecular data and cluster evaluation metrics, to provide novel insight into cell populations.Availability and implementationUpon request