This study aimed to determine the socio-economic poverty status of those living in rural areas using data surveys obtained from household expenditure and income. Machine learning-based classification and clustering models were proven to provide an overview of efforts to determine similarities in poverty characteristics. Efforts to address poverty classification and clustering typically involve comprehensive strategies that aim to improve socio-economic conditions in the affected areas. This research focuses on the combined application of machine learning classification and clustering techniques to analyze poverty. It aims to investigate whether the integration of classification and clustering algorithms can enhance the accuracy of poverty analysis by identifying distinct poverty classes or clusters based on multidimensional indicators. The results showed the superiority of machine learning in mapping poverty in rural areas; therefore, it can be adopted in the private sector and government domains. It is important to have access to relevant and reliable data to apply these machine learning techniques effectively. Data sources may include household surveys, census data, administrative records, satellite imagery, and other socioeconomic indicators. Machine learning classification and clustering analyses are used as a decision support tool to gain an understanding of poverty data from each village. These strategies are also used to describe the profile of poverty clusters in the community in terms of significant socio-economic indicators present in the data. Village clusters based on an analysis of existing poverty indicators are grouped into high, moderate, and low poverty levels. Machine learning can be a valuable tool for analyzing and understanding poverty by classifying individuals or households into different poverty categories and identifying patterns and clusters of poverty. These insights can inform targeted interventions, policy decisions, and resource allocation for poverty reduction programs.

Young people are a traditional risk group for radicalization and involvement in protest and extremist activities. The relevance of this topic is due to the growing threat of youth radicalization, the expansion of the activities of extremist organizations, and the need to organize high-quality preventive work in educational organizations at various levels. The article provides an overview of research on the topic under consideration and also presents the results of a series of surveys in general educational institutions and organizations of secondary vocational education (n = 11,052), universities (n = 3966) located in the Arctic zone of the Russian Federation. The results of the study on aspects of students’ ideas about extremism are presented in terms of assessing their own knowledge about extremism, the presence/absence of radically minded people around them, determining the degree of threat from the activities of extremist groups for themselves and their social environment, and identifying approaches to preventing the growth of extremism in society. Conclusions are drawn about the need to improve preventive work models in educational organizations towards a targeted (group) approach.

Real estate appraisal standards provide guidelines for the preparation of reliable valuations. These standards emphasize the central role of market data collection in market-oriented valuation methodologies such as the Market Comparison Approach (MCA), which is the most commonly used. The objective of this study is to highlight the difficulties in data finding, as well as the gap between the standards and the actual appraisal practices in Italy. Thus, a detailed comparison was made between the real estate data considered necessary by the standards and those ones reasonably detectable by appraisers, showing that some important market information is not reachable due to legal, technical and economic factors. Finally, a case study is presented in which the actual appraisal of a residential property is schematically described to support what is claimed with the research question and thus the degree of uncertainty around an estimate judgment.

In today’s digital education landscape, safeguarding the privacy and security of educational data, particularly the distribution of grades, is paramount. This research presents the “secure grade distribution scheme (SGDS)”, a comprehensive solution designed to address critical aspects of key management, encryption, secure communication, and data privacy. The scheme’s heart lies in its careful key management strategy, offering a structured approach to key generation, rotation, and secure storage. Hardware security modules (HSMs) are central to fortifying encryption keys and ensuring the highest security standards. The advanced encryption standard (AES) is employed to encrypt graded data, guaranteeing the confidentiality and integrity of information during transmission and storage. The scheme integrates the Diffie-Hellman key exchange protocol to establish secure communication, enabling users to securely exchange encryption keys without vulnerability to eavesdropping or interception. Secure communication channels further fortify graded data protection, ensuring data integrity in transit. The research findings underscore the SGDS’s efficacy in achieving the goals of secure grade distribution and data privacy. The scheme provides a holistic approach to safeguarding educational data, ensuring the confidentiality of sensitive information, and protecting against unauthorized access. Future research opportunities may centre on enhancing the scheme’s robustness and scalability in diverse educational settings.

While the notion of the smart city has grown in popularity, the backlash against smart urban infrastructure in the context of changing state-public relations has seldom been examined. This article draws on the case of Hong Kong’s smart lampposts to analyse the emergence of networked dissent against smart urban infrastructure during a period of unrest. Deriving insights from critical data studies, dissentworks theory, and relevant work on networked activism, the article illustrates how a smart urban infrastructure was turned into both a source and a target of popular dissent through digital mediation and politicisation. Drawing on an interpretive analysis of qualitative data collected from multiple digital platforms, the analysis explicates the citizen curation of socio-technic counter-imaginaries that constituted a consent of dissent in the digital realm, and the creation and diffusion of networked action repertoires in response to a changing political opportunity structure. In addition to explicating the words and deeds employed in this networked dissent, this article also discusses the technopolitical repercussions of this dissent for the city’s later attempts at data-based urban governance, which have unfolded at the intersections of urban techno-politics and local contentious politics. Moving beyond the common focus on neoliberal governmentality and its limits, this article reveals the underexplored pitfalls of smart urban infrastructure vis-à-vis the shifting socio-political landscape of Hong Kong, particularly in the digital age.

This systematic literature review examines data saturation in qualitative research within the context of entrepreneurship studies from 2004 to 2024. Data saturation, a critical concept in ensuring the rigor of qualitative research, remains inadequately defined in terms of sample size and assessment criteria across various studies. This review synthesizes 11 empirical studies, focusing on strategies such as stopping criterion, code frequency counts, and comparative methods for determining saturation. It identifies sample sizes ranging from 7 to 39 interviews, with an average saturation occurring between 10 and 12 interviews. Furthermore, the study explores the influence of different sampling methods and homogeneity of study populations on saturation outcomes. Despite the reliability of existing methods, the findings underscore the need for greater transparency and consistency in reporting saturation criteria. The review offers valuable insights for entrepreneurial researchers aiming to design qualitative studies, emphasizing the importance of tailored saturation standards based on research objectives and methodologies. This research contributes to a clearer understanding of data saturation in entrepreneurial studies and highlights the necessity for further empirical investigation into saturation across diverse qualitative methods.