Search Results (47)

Various scientific disciplines (economics, geography, sociology, urban planning, and environmental sciences) have analysed industrialization processes in peri-urban environments. This has given rise to a wide and diverse bibliography on which this bibliometric study, using the most advanced computer tools, offers a comprehensive overview that helps to structure existing knowledge. To this end, the Web of Science and Scopus databases were used, which, after applying inclusion and exclusion criteria and detecting duplicate works, identified a total of 626 documents involving 1484 authors. The results identify two basic lines of research, each relating to the processes of urbanization and industrialization. They also show that, since the approval of the SDGs by the UN in 2015, studies on industrialization in peri-urban environments have been growing significantly. Chinese scientific output stands out among the proliferation of these works. This study also offers a dynamic view of the lines of work that could experience greater future development and that are associated with the challenges inherent in the processes of urbanization and industrialization. Among the former are problems arising from migration or access to housing; among the latter are the challenges of land use transformation, environmental problems, and those linked to inequality. Full article

The Generalized Inner Product SCALing (GIPSCAL) model is a specialized tool for analyzing square asymmetric tables within asymmetric multidimensional scaling (MDS), with applications in sociology (e.g., social mobility tables) and marketing (e.g., brand switching data). This paper presents the development of an efficient numerical algorithm for solving the three-way GIPSCAL problem. We focus on vector $\epsilon$-algorithm-accelerated fixed-point iterations, detailing the underlying acceleration principles. Extensive numerical experiments show that the proposed method achieves acceleration performance comparable to polynomial extrapolation and Anderson acceleration. Furthermore, compared to continuous-time projected gradient flow methods and first- and second-order Riemannian optimization algorithms from the Manopt toolbox, our approach demonstrates superior computational efficiency and scalability. Full article

The escalating frequency and complexity of natural disasters highlight the urgent need for deeper insights into how individuals and communities perceive and respond to risk information. Yet, conventional research methods—such as surveys, laboratory experiments, and field observations—often struggle with limited sample sizes, external validity concerns, and difficulties in controlling for confounding variables. These constraints hinder our ability to develop comprehensive models that capture the dynamic, context-sensitive nature of disaster decision-making. To address these challenges, we present a novel multi-stage simulation framework that integrates Large Language Model (LLM)-driven social–cognitive agents with well-established theoretical perspectives from psychology, sociology, and decision science. This framework enables the simulation of three critical phases—information perception, cognitive processing, and decision-making—providing a granular analysis of how demographic attributes, situational factors, and social influences interact to shape behavior under uncertain and evolving disaster conditions. A case study focusing on pre-disaster preventive measures demonstrates its effectiveness. By aligning agent demographics with real-world survey data across 5864 simulated scenarios, we reveal nuanced behavioral patterns closely mirroring human responses, underscoring the potential to overcome longstanding methodological limitations and offer improved ecological validity and flexibility to explore diverse disaster environments and policy interventions. While acknowledging the current constraints, such as the need for enhanced emotional modeling and multimodal inputs, our framework lays a foundation for more nuanced, empirically grounded analyses of risk perception and response patterns. By seamlessly blending theory, advanced LLM capabilities, and empirical alignment strategies, this research not only advances the state of computational social simulation but also provides valuable guidance for developing more context-sensitive and targeted disaster management strategies. Full article

US college students are typically required to take at least one mathematics or statistics course, either as part their major area of study, or as a general education requirement. College mathematics requirements are an obstacle for many college students. Active learning, a pedagogical approach that places emphasis on students’ collaborative work, has been shown to increase student learning and course success in STEM fields of study and in mathematics courses. Active learning has also been shown to be adaptable to courses involving computer software and programming, such as introductory statistics. This case study is based on the author’s experience implementing an active learning model in an introductory statistics course for students majoring in sociology and related social sciences. Results indicate that the active learning approach was adaptable to the structure and particular learning goals of the course. Students perceived greater learning relative to other courses despite doing less work outside of class, and attributed this to the active learning structure of the course. These findings align with more systematic studies on the impacts of active learning in science and mathematics courses. Full article

The aim of this study is to reveal in an interpretive way how computer-mediated communication, the Internet, and social media can be grasped by authority models and how these new types of authority influence religious communities that are (also) present on online platforms. In some cases, computer-mediated communication weakened and made traditional church authorities porous, but in other cases, it specifically helped and strengthened them. In other words, the impact of digital media is not uniform or unidirectional in this respect. Although there is no doubt that the Internet has multiplied it, made it optional, and personalized it from the user’s point of view, it has made religious authority customizable. The power of choice means that, in the digital sphere, the user decides when, what form of network authority they will submit to, for how long, and why they do so. In the classics of the sociology of religion, the concept of authority appears in a hierarchical representation under the concepts of (social) order and rationality. In other words, it cannot be thought of in a way that is contrary to rationality and contrary to social order. In network communication, the concept of authority is subordinated to technology, or as Castells puts it, power can only be interpreted with the logic of the network. Of course, the technological network and its contents are under external (legal) control, but it is precisely the power of the symbolic struggles taking place here that shows how important this issue is in the 21st century. The concept of authority classified under technology will no longer be linked to order or rationality, but to the processes of control, datafication, and attention management on the part of the owners of the platforms, while from the users’ side to concepts such as identity, authenticity, choice, and voluntariness. Its boundaries will be malleable, and the phenomenon itself will multiply. In summary, we cannot talk about one single online religious authority but more types of religious authorities, which are continuously and discursively formed, change, and occasionally hybridize. Full article

Artificial Intelligence (AI) is one of the science fields with huge potential to create a cognitive and tech-leaping type of future smart city design/development. However, extant studies lag behind recent applications, potential growth areas, and the challenges associated with AI implementation. This study examines AI’s current role, trend, and future potential impacts in enhancing smart city drivers. The methodology entails conducting a Systematic Literature Review (SLR) of publications from 2022 onwards. The approach involves qualitative deductive coding methods, descriptive statistical analysis, and thematic analysis. The findings revealed the impacts of AI in (i) public services and connectivity, (ii) improving accessibility and efficiency, (iii) quality healthcare, (iv) education, and (v) public safety. Likewise, strategies, such as collaborative ecosystems, digital infrastructure, capacity building, and clear guidelines and ethical framework, were proposed for fostering the integration of AI in potential future smart cities. This research fills a notable gap in the current understanding of AI’s specific contributions to smart cities, offering insights for stakeholders in urban planning, computer science, sociology, economics, environmental science, and smart city initiatives. It serves as a strategic guideline and scholarly research output for enhancing smart city design. It also underscores the potential of AI in creating dynamic, sustainable, and efficient urban environments. Full article

This research provides a comprehensive overview of micro-influence marketing, analyzing the characteristics of influencers and the mechanisms of their impact. A systematic review was conducted, encompassing 2091 citing articles and references across 74 studies involving 95 research institutions and over 12,000 samples. Employing an interdisciplinary approach that integrates insights from computer science, information science, communication, culture, psychology, sociology, education, business, and management, this study outlines the distinct features of micro-influencers. These features include performable authenticity, affinity expressed through consistency and transparency, musical and artistic media talent, and competitive individual traits. The research synthesizes antecedents of trust and attachment mechanisms commonly employed in influencer theory, taking an objective standpoint and minimizing emphasis on audience engagement and perception to trigger influence. The findings highlight that followers’ pursuit of self-branding, driven by self-consciousness, social consciousness, credibility, and social presence, significantly influences the impact of self-expressive products on the audience’s purchase intention. The research contributes to micro-influence marketing theory by integrating mechanics, offering practical implications for micro-influencers, and suggesting future research agendas. Full article

Multiple studies have demonstrated that the conventional method of learning is suboptimal when our goal is to enhance individuals’ genuine privacy behavior. This study introduces a framework for transforming privacy behavior, with the objective of enhancing individuals’ privacy practices to a higher level of confidentiality. We performed an experiment on a limited number of people to validate the efficacy of our suggested transformation framework. This framework combined determining aspects of privacy behavior with experiential behavior modification methodologies such as neutral stimuli (e.g., cognitive behavioral transformation—CBTx), practical assessments and motivational interviews from other disciplines. While these methods have proven effective in fields like psychology and sociology, they have not yet been applied to the realm of Information Computer and Technology (ICT). In this study, we have effectively demonstrated the efficacy of the proposed framework through a five-phase experiment. The suggested framework has the potential to be advantageous for educational institutions, including both public and private schools as well as universities, to construct new frameworks or develop new methodologies regarding individuals’ privacy behavior transformation to a more protective one. Furthermore, our framework offers a conducive environment for further investigation into privacy behavior transformation methodologies. Full article

The health crisis triggered by COVID-19 has exerted a profound influence on both conventional communication methods and the manifestations of interaction within the virtual sphere. Gradually, studies on digital communication have taken on an increasingly prominent role in various social science disciplines that address determinants such as the crisis of misinformation or digital interaction in contemporary societies. This study aims to analyze the key research topics that sociology has addressed in relation to the pandemic, along with the level of innovation in the utilization of digital sources and analytical methodology. The analysis is grounded in the hypothesis that the effects of the pandemic have led the discipline of sociology to reassess and more fully integrate studies on digital communication. On this premise, a systematic review of studies sourced from the Web of Science (WoS) and Scopus databases was executed. Innovative computational methodologies were employed for the categorization of articles and the elucidation of principal research topics. Furthermore, this research scrutinized the principal digital platforms utilized in these investigations and assessed the extent of methodological innovation applied to data analysis. The outcomes unveiled a pronounced ascendancy in the prominence of communication studies during the pandemic. Nevertheless, it is noteworthy that the utilization of digital data sources in research remains surprisingly limited. This observation highlights a potential avenue for further exploration within the domain of sociological research, promising a more profound and contemporaneous comprehension of social phenomena amid times of crisis. Full article

Inland water bodies play a crucial role in both ecological and sociological contexts. The distribution of these water bodies can change over time due to natural or human-induced factors. Monitoring the extent of surface water is vital to understand extreme events such as floods and droughts. The availability of dense temporal Earth observation data from sensors like Landsat and Sentinel, coupled with advancements in cloud computing, has enabled the analysis of surface water extent over extended periods. In this study, automated thresholding approaches were applied within the Google Earth Engine platform to extract the surface water extent of the Chembarampakkam reservoir in Tamil Nadu, India. Sentinel-2 data spanning from 2019 to 2023 were used to derive two key indices, namely, the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI). These indices were then thresholded to determine the presence of water. The performance of two different global thresholding techniques, namely, the deterministic thresholding and Otsu thresholding methods, was compared to achieve better results. To enhance the accuracy of the deterministic technique, an iterative method was implemented. While the threshold values were generally similar for both techniques, the Otsu algorithm slightly outperformed the iterated deterministic technique in water classification. Furthermore, a surface water dynamics image was obtained using temporal images, providing insights into the temporal surface dynamism of the reservoir. Overall, this study highlights the significance of surface water monitoring using remote sensing and cloud computing techniques. Full article

The German labor market relies heavily on vocational training, retraining, and continuing education. In order to match training seekers with training offers and to make the available data interoperable, we present a novel approach to automatically detect access to education and training in German training offers and advertisements and identify open research questions and areas for further research. In particular, we focus on (a) general education and school leaving certificates, (b) work experience, (c) previous apprenticeship, and (d) a list of skills provided by the German Federal Employment Agency. This novel approach combines several methods: First, we provide technical terms and classes of the education system that are used synonymously, combining different qualifications and adding obsolete terms. Second, we provide rule-based matching to identify the need for work experience or education. However, not all qualification requirements can be matched due to incompatible data schemas or non-standardized requirements such as initial tests or interviews. Although there are several shortcomings, the presented approach shows promising results for two data sets: training and retraining advertisements. Full article

The analysis of network structure is essential to many scientific areas ranging from biology to sociology. As the computational task of clustering these networks into partitions, i.e., solving the community detection problem, is generally NP-hard, heuristic solutions are indispensable. The exploration of expedient heuristics has led to the development of particularly promising approaches in the emerging technology of quantum computing. Motivated by the substantial hardware demands for all established quantum community detection approaches, we introduce a novel QUBO-based approach that only needs number-of-nodes qubits and is represented by a QUBO matrix as sparse as the input graph’s adjacency matrix. The substantial improvement in the sparsity of the QUBO matrix, which is typically very dense in related work, is achieved through the novel concept of separation nodes. Instead of assigning every node to a community directly, this approach relies on the identification of a separation-node set, which, upon its removal from the graph, yields a set of connected components, representing the core components of the communities. Employing a greedy heuristic to assign the nodes from the separation-node sets to the identified community cores, subsequent experimental results yield a proof of concept by achieving an up to 95% optimal solution quality on three established real-world benchmark datasets. This work hence displays a promising approach to NISQ-ready quantum community detection, catalyzing the application of quantum computers for the network structure analysis of large-scale, real-world problem instances. Full article

Understanding the process of reaching consensus or disagreement between the members of a team is critical in many situations. Consensus and disagreement can refer to various aspects, such as requirements that are collectively perceived to be important, shared goals, and solutions that are jointly considered to be realistic and effective. Getting insight on how the end result of the interaction process is influenced by parameters such as the similarity of the participants’ experience and behavior (e.g., their available concepts, the produced responses and their utility, the preferred response generation method, and so on) is important for optimizing team performance and for devising novel applications, i.e., systems for tutoring or self-improvement and smart human computer interfaces. However, understanding the process of reaching consensus or disagreement in teams raises a number of challenges as participants interact with each other through verbal communications that express new ideas created based on their experience, goals, and input from other participants. Social and emotional cues during interaction are important too. This paper presents a new model, called Learning and Response Generating Agents, for studying the interaction process during problem solving in small teams. As compared to similar work, the model, grounded in work in psychology and sociology, studies consensus and disagreement formation when agents interact with each other through symbolic, dynamically-produced responses with clauses of different types, ambiguity, multiple abstraction levels, and associated emotional intensity and utility. Full article

The scientific literature is abundant regarding control charts in multivariate environments for numerical and mixed data; however, there are few publications for qualitative data. Qualitative variables provide valuable information on processes in various industrial, productive, technological, and health contexts. Social processes are no exception. There are multiple nominal and ordinal categorical variables used in economics, psychology, law, sociology, and education, whose analysis adds value to decision-making; therefore, their representation in control charts would be useful. When there are many variables, there is a risk of redundant or excessive information, so the application of multivariate methods for dimension reduction to retain a few latent variables, i.e., a recombination of the original and synthesizing of most of the information, is viable. In this context, the T2Qv control chart is presented as a multivariate statistical process control technique that performs an analysis of qualitative data through Multiple Correspondence Analysis (MCA), and the Hotelling T2 chart. The interpretation of out-of-control points is carried out by comparing MCA charts and analyzing the ${\chi }^2$ distance between the categories of the concatenated table and those that represent out-of-control points. Sensitivity analysis determined that the T2Qv control chart performs well when working with high dimensions. To test the methodology, an analysis was performed with simulated data and with a real case applied to the graduate follow-up process in the context of higher education. To facilitate the dissemination and application of the proposal, a reproducible computational package was developed in R, called T2Qv, and is available on the Comprehensive R Archive Network (CRAN). Full article

Modern management requires the highest level of analytics and the optimisation of business processes with a low risk of poor management decisions. These are essential since rapid changes in the financial world and the external environment can have critical effects. The direction of a company’s growth and the effectiveness of its management systems depend directly on the quality of intellectualisation. This study aims to develop a new methodology for studying the criteria and results of the intellectualisation processes to achieve the highest efficiency in company management systems. This study used sociological and empirical methods to find intellectualisation efficiency criteria. These criteria were then used to analyse the intellectualisation process in ten Russian companies. The correlation analysis method revealed a close relationship between the intellectualisation integral indicator and company performance over time. The results showed that the intellectualisation efficiency criteria are intellectualisation indicators in human resource management systems as well as computer-aided and automated management systems. In addition, it was found that company performance depends on the intellectualisation integral indicator, the human intelligence and artificial intelligence synergy, as well as on the efficiency of using artificial intelligence in business processes. Full article