This study examines the spatial distribution of socioeconomic conditions in Colombia, using Moran’s Index as a tool for spatial autocorrelation analysis. Key indicators related to education, health, infrastructure, access to basic services, employment, and housing conditions are addressed, allowing the identification of inequalities and structural barriers. The research reveals patterns of positive autocorrelation in several socioeconomic dimensions, suggesting a concentration of poverty and underdevelopment in certain geographic areas of the country. The results show that municipalities with more unfavorable conditions tend to cluster spatially, particularly in the northern, northwestern, western, eastern, and southern regions of the country, while the central areas exhibit better conditions. Permutation analyses are employed to validate the statistical significance of the findings, and LISA cluster maps highlight the regions with the highest concentration of poverty and social vulnerability. This work contributes to the literature on inequality and regional development in emerging economies, demonstrating that public policies should prioritize intervention in territories that exhibit significant spatial clustering of poverty. The methodology and findings provide a foundation for future studies on spatial correlation and economic planning in both local and international contexts.

Static atomic charges affect key ground-state parameters of quasi-planar boron clusters B_n, n ≤ 20, which serve as building blocks of borophenes and other two-dimensional boron-based materials promising for various advanced applications. Assuming that the outer valence shells partial electron density of the constituent B atoms are shared between them proportionally to their coordination numbers, the static atomic charges in small planar boron clusters in the electrically neutral and positively and negatively singly charged states are estimated to be in the ranges of –0.750e (B₇⁰) to +0.535e (B₂₀⁰), –0.500e (B₇⁺, B₈⁺, and B₉⁺) to +0.556e (B₁₇⁺), and –1.000e (B₇^–) to +0.512e (B₂₀^–), respectively.

In order to scientifically evaluate the germplasm resources of Momordica charantia in southern China, the diversity, correlation and cluster analysis were carried out on the main botanical characters of 56 Momordica charantia varieties, such as melon length, melon transverse diameter, single melon weight, internode length, stem diameter, leaf length and leaf width. The results showed that the variation coefficients of 7 agronomic characters of 56 Momordica charantia varieties ranged from 8.81% to 19.44%, the average variation coefficient was 14.21%, the maximum variation coefficient of single melon weight was 19.44%, and the minimum variation coefficient of melon cross diameter was 8.81%. The correlation analysis showed that there were correlations among the agronomic traits. The positive correlation coefficient between leaf length and leaf width was up to 0.978, and the negative correlation coefficient between single melon weight and internode length was up to 0.451. The 56 varieties were divided into 3 groups by cluster analysis, of which 92.86% of the materials were concentrated in the first and second groups, and there were only 4 materials in the third group. The results can provide a reference for the cultivation, utilization and genetic improvement of Momordica charantia resources in southern China.

In recent years, the rapid development of technologies such as virtual reality, augmented reality, and mixed reality, along with the significant increase in publications related to the Metaverse, demonstrates a sustained growth in interest in this field. Some scholars have already performed bibliometric analyses of this emerging field. However, previous analyses have not been comprehensive due to limitations such as the volume of literature, particularly lacking in co-citation analysis, which is crucial for understanding the interconnectedness and impact of research works. In this study, we used the Web of Science as a database to search for topics related to the Metaverse from 1995 to 2023. Subsequently, we employed CiteSpace for co-citation network analysis to supplement previous research. Through our analysis at the journal, author, and literature levels, we identified core journals and key authors in the Metaverse field. We discovered that Extended Reality (XR), education, user privacy, and terminologies related to the Metaverse are significant research themes within the field. This study provides clear and actionable research directions for future papers in the Metaverse field.

Theoretically, within the diatomic model, the relative stability of most abundant boron clusters B₁₁, B₁₂, and B₁₃ with planar structures in neutral, positive and negative charged-states is studied. According to the specific (per atom) binding energy criterion, B₁₂⁺ (6.49 eV) is found to be the most stable boron cluster, while B₁₁^– + B₁₃⁺ (5.83 eV) neutral pair is expected to present the preferable ablation channel for boron-rich solids. Obtained results would be applicable in production of boron-clusters-based nanostructured coating materials with super-properties such as lightness, hardness, conductivity, chemical inertness, neutron-absorption, etc., making them especially effective for protection against cracking, wear, corrosion, neutron- and electromagnetic-radiations, etc.

This article describes a classification tool to cluster SARAL/AltiKa waveforms. The tool was made using Python scripts. Radar altimetry systems (e.g., SARAL/AltiKa) measures the distance from the satellite centre to a target surface by calculating the satellite-to-surface round-trip time of a radar pulse. An altimeter waveform represents the energy reflected by the earth’s surface to the satellite antenna with respect to time. The tool clusters the altimetric waveforms data into desired groups. For the clustering, we used evolutionary minimize indexing function (EMIF) with k-means cluster mechanism. The idea was to develop a simple interface which takes the altimetry waveforms data from a folder as inputs and provides single value (using EMIF algorithm) for each waveform. These values are further used for clustering. This is a simple light weighted tool and user can easily interact with it.