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.

Rural tourism plays a crucial role in rural development in Indonesia by providing employment opportunities, livelihood, infrastructure, cultural preservation, and environmental preservation. However, it is prone to external shocks such as natural disasters, public health events, and volatility in the national and global economy. This study measures the resilience of rural tourism to external shocks caused by the COVID-19 pandemic in 24 rural tourism destinations in Indonesia covering four years from 2019 to 2022. A synthetic composite index of the Adjusted Mazziotta-Pareto index (AMPI) is used to measure rural tourism resilience followed by clustering analysis to determine the typology of the resilience. The AMPI measure is also compared with the conventional Mazziotta-Pareto index (MPI) method. The resilience index is composed of capacity and performance components related to resilience. The results show that in the first year of COVID-19, most tourism villages in Indonesia were severely affected by the pandemic, yet they were able to recover afterward, as indicated by positive differences in the AMPI index before and after COVID-19. Thus, rural tourism villages in Indonesia have a strong capacity and performance to recover from pandemic shock. Lessons learned from this analysis can be applied to policies related to rural tourism resilience in developing countries.

The world has changed to a massive degree in the past thousands of years. Most of the time, the amount of carbon dioxide in the atmosphere remains constant. In the late 18th century, according to the sources of CDIAC and NOOA, the level of carbon dioxide began to rise, and then in the 20th century, it went through the roof, reaching levels that had not been seen in nature for millions of years. The increase in carbon in the atmosphere is the major contributing factor to climate change. The key to reversing the damage is restoring the earth’s delicate, balanced carbon cycle. As carbon cycle depicts the way carbon moves around the earth. It consists of sources that emit the carbon component into the atmosphere. The biological side of the carbon cycle is well balanced due to respiration, where carbon dioxide is released into the atmosphere, then plants, bacteria, and algae take carbon dioxide out of the atmosphere during photosynthesis and the process they use to generate chemical energy. On the other hand, oceans are the best sources and sinks; carbon dioxide is endlessly being absorbed into the ocean and released from the oceans almost exactly at the same rate, which is rapidly influencing the carbon cycle. Similarity is a methodology that has many applications in the real world. The current research article is destined to study how statistics of carbon emission metrics are alike and belong to one cluster. In the current study, the research is destined to derive a similarity analysis of several countries’ carbon emission metrics that are alike and often fall in the range of [0, 1]. And deriving the proximity of the carbon emission metrics leading to similarity or dissimilarity. In the current context of data matrices of numerical data, an Euclidian measure of distance between two data elements will yield a degree of similarity. The current research article is destined to study the similarity analysis of carbon emission metrics through fuzzy entropy clustering.

This paper highlights the complex relationship between entrepreneurship, sustainable development, and economic growth in 41 European countries, using a reliable K-Means cluster analysis. The research thoroughly evaluates three key factors: the SDG Index for sustainable development, GDP per capita for economic well-being, and the New Business Density Rate for entrepreneurial activity. Our methodology reveals three distinct narratives that embody varying degrees of economic vitality and sustainability. Cluster 1 comprises the financially stable and sustainability-oriented countries of Western and Northern Europe. Cluster 2 showcases the variegated economic and sustainability initiatives in Central and Southern Europe. Cluster 3 envelopes the economic titans with noteworthy business expansion but with the potential for better sustainable practices. The analysis reveals a favourable association between economic prosperity and sustainable development within clusters, although with nonlinear intricacies. The research concludes with a series of strategic imperatives specifically crafted for each cluster, promoting economic variation, increased sustainability, invention, and worldwide collaboration. The resulting findings highlight the crucial need for policy-making that considers the specific context and the potential for combined European resilience and sustainability.

This paper investigates the evolving clustering and historical progression of “Asian regionalisms” concerning their involvement in multilateral treaties deposited in the United Nations system. We employ criteria such as geographic proximity, historical connections, cultural affinities, and economic interdependencies to identify twenty-eight candidate countries from East Asia, Southeast Asia, South Asia, and Central Asia for this empirical testing. Using a social network analysis approach, we model the network of these twenty-eight Asian state actors alongside 600 major treaties from the United Nations system, identifying clusters among Asian states by assessing similarities in their treaty participation behavior. Specifically, we observe dynamic changes in these clusters across three key historical eras: Post-war reconstruction and transformation (1945–1968), Cold War tensions and global transformations (1969–1989), and post-Cold War era and globalization (1990–present). Employing the Louvain cluster detection algorithm, the results reveal the evolution in cluster numbers and changes in membership status throughout the world timeline. The results also identify the current situation of six distinct Asian clusters based on states’ inclinations to engage or abstain from multilateral treaties across six policy domains. These findings provide a foundation for further research on the trajectories of Asian regionalisms amidst evolving global dynamics and offer insights into potential alliances, cooperation, or conflicts within the region.

Regional differentiation in the Russian Federation is considered to be high in terms of gross regional product (GRP) per capita level, growth rate, and other indicators. Inefficient use of region-specific spaces entails redistribution processes in order to maximize positive agglomeration effects throughout the country. These encompass economic restructuring based on production value-added chain extension and expanding inter-regional collaborative linkages. Besides, it is vital to assess the opportunities of individual Russian territories for participation therein. The research goal is to develop a scientifically based methodology to determine promising sectoral composition of the regional economies and that of spatial interactions. Such methodology would consider the feasibility of combining “smart” industrial specializations, regional resource potential, prevailing contradictions in the economic, innovative, and technological development of the country’s internal space. The proposed methodological approach opens the way to exploit the existing regional economic potential to the full, firstly, via establishing sectoral priorities of the region regarding the regulatory factors for the territorial capital to have a major effect on the increased potential GRP level; secondly, through benchmarking performance of the available development reserves within leading regions from homogeneous groups having similar characteristics and factor potentials; thirdly, via developing inter-regional integration prospects in terms of regional potential redistribution to ensure growth in potential gross domestic product. An extensive analytical and applied investigation of the proposed methodological approach was carried out from 2014 to 2020. Diversified estimates were obtained for a wide range of indicators due to evidences from 85 Russian regions and 13 types of economic activity. Such an integrated approach allows revealing actual imbalances and barriers that impede regional development, ensures the efficient use of production factors, and enables to trace ways to implement transformation policies and design effective regulatory mechanisms. The results provide arguments in favor of strengthening inter-regional connectivity and supporting inter-regional cooperation. This insight not only contributes to the academic discourse on complex development of a territory but also holds practical implications for policymakers and regional planners aimed at ensuring comprehensiveness and robustness of the evaluation supporting the decision-making process.