Religion is alive and well in the modern world, and the social-scientific study of religion is undergoing a renaissance. For much of this century, respected social theorists predicted the death of religion as inevitable consequence of science, education, and modern economics. But they were wrong. Stark and Bainbridge set out to explain the survival of religion. Using information derived from numerous surveys, censuses, historical case studies, and ethnographic field expeditions, they chart the full sweep of contemporary religion from the traditional denominations to the most fervent cults. This wealth of information is located within a coherent theoretical framework that examines religion as a social response to human needs, both the general needs shared by all and the desires specific to those who are denied the economic rewards or prestige enjoyed by the privileged. By explaining the forms taken by religions today, Stark and Bainbridge allow us to understand its persistence in a secular age and its prospects for the future.
Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis. Sentiment analysis systems are being applied in almost every business and social domain because opinions are central to almost all human activities and are key influencers of our behaviors. Our beliefs and perceptions of reality, and the choices we make, are largely conditioned on how others see and evaluate the world. For this reason, when we need to make a decision we often seek out the opinions of others. This is true not only for individuals but also for organizations. This book is a comprehensive introductory and survey text. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular. Lecturers can readily use it in class for courses on natural language processing, social media analysis, text mining, and data mining. Lecture slides are also available online.
In World-Systems Analysis , Immanuel Wallerstein provides a concise and accessible introduction to the comprehensive approach that he pioneered thirty years ago to understanding the history and development of the modern world. Since Wallerstein first developed world-systems analysis, it has become a widely utilized methodology within the historical social sciences and a common point of reference in discussions of globalization. Now, for the first time in one volume, Wallerstein offers a succinct summary of world-systems analysis and a clear outline of the modern world-system, describing the structures of knowledge upon which it is based, its mechanisms, and its future. Wallerstein explains the defining characteristics of world-systems analysis: its emphasis on world-systems rather than nation-states, on the need to consider historical processes as they unfold over long periods of time, and on combining within a single analytical framework bodies of knowledge usually viewed as distinct from one another—such as history, political science, economics, and sociology. He describes the world-system as a social reality comprised of interconnected nations, firms, households, classes, and identity groups of all kinds. He identifies and highlights the significance of the key moments in the evolution of the modern world-system: the development of a capitalist world-economy in the sixteenth-century, the beginning of two centuries of liberal centrism in the French Revolution of 1789, and the undermining of that centrism in the global revolts of 1968. Intended for general readers, students, and experienced practitioners alike, this book presents a complete overview of world-systems analysis by its original architect.
We review the recent rapid progress in the statistical physics of evolving networks. Interest has focused mainly on the structural properties of complex networks in communications, biology, social sciences and economics. A number of giant artificial networks of this kind have recently been created, which opens a wide field for the study of their topology, evolution, and the complex processes which occur in them. Such networks possess a rich set of scaling properties. A number of them are scale-free and show striking resilience against random breakdowns. In spite of the large sizes of these networks, the distances between most of their vertices are short - a feature known as the 'small-world' effect. We discuss how growing networks self-organize into scale-free structures, and investigate the role of the mechanism of preferential linking. We consider the topological and structural properties of evolving networks, and percolation and disease spread on these networks. We present a number of models demonstrating the main features of evolving networks and discuss current approaches for their simulation and analytical study. Applications of the general results to particular networks in nature are discussed. We demonstrate the generic connections of the network growth processes with the general problems of non-equilibrium physics, econophysics, evolutionary biology, and so on.
Risk-taking is a critical driver of sustainable development and financial performance for firms, especially under environmental degradation constraints. Despite the increasing implementation of green credit policies, their impact on corporate risk-taking remains underexplored in the existing literature. This study investigates the effects and underlying mechanisms of green credit policies on risk-taking behaviors among Chinese listed companies from 2009 to 2019. Utilizing econometric methodologies, including Difference-in-Differences, mediation analysis, and moderation analysis, the findings reveal that green credit policies significantly enhance the risk-taking activities of polluting enterprises. These results are robust across various sensitivity tests. Additionally, the relationship between green credit policies and corporate risk-taking is mediated by debt maturity mismatch and moderated by ESG and executive compensation. Subgroup analyses indicate that large and state-owned polluting enterprises experience greater increases in risk-taking compared to their small, medium-sized, and private counterparts. Furthermore, executive remuneration notably amplifies risk-taking in private firms. This research provides essential micro-level insights to optimize the effectiveness of green credit policies in promoting corporate risk-taking and advancing sustainable development.
Salih Gülen, İsmail Dönmez, Fatma Betül Şengönül
et al.
STEM education aims to develop 21st-century skills, support economic growth and promote gender equality in STEM fields. It is known that gender stereotypes play a significant role in the formation of STEM identity. The most important factor preventing some high school-level female students from pursuing STEM careers is their lack of participation in STEM activities. Female students in high schools have limited opportunities to explore or learn about STEM careers due to the emphasis on verbal and religious courses in their curriculum. However, it is known that women can work more autonomously in scientific activities compared to men. The current study examines the effect of robotics and coding education on the development of girls’ STEM careers. The study was conducted at an all-girls high school in Turkey, where the curriculum is predominantly centered on verbal and religious subjects. In the study, a pre-test and post-test experimental design with control group was used. A total of 76 volunteer female students (34 in the experimental group and 42 in the control group) participated in robotics and coding education over a period of 12 weeks. The data were collected using the validated STEM career, motivation and attitude scales and analyzed using t -tests, ANOVA and Pearson correlation. The findings revealed that robotics and coding training significantly improved the participants’ STEM career aspirations, attitudes and motivations. A strong positive correlation was found between career interest, attitude and motivation. The study also showed that STEM career scores are significantly higher among students who wish to become teachers compared to those considering a career in the fields of health or engineering. However, no significant correlation was found between the participants’ parents’ education levels, family income and STEM career aspirations.
History of scholarship and learning. The humanities, Social Sciences
Mekdes Demissie, Charlotte Hanlon, Lauren C. Ng
et al.
Abstract Background There is a very large unmet need for appropriate psychological interventions for bipolar disorder (BD) for use in low- and middle-income countries. We developed a psychological intervention for BD in a primary health care (PHC) setting in Ethiopia using the Medical Research Council’s framework for the Development and Evaluation of Complex Interventions. The aim of this study is to investigate the feasibility, acceptability, and fidelity of this newly developed psychological intervention for BD in a PHC setting in south-central Ethiopia. Method A total of 12 euthymic people with bipolar disorder and five caregivers participated in five 20-min weekly sessions of the psychological intervention. We conducted a mixed-method evaluation, including in-depth qualitative interviews, fidelity ratings of a random selection of 25% of the audio recorded intervention sessions, and self-reported change in symptom severity. We used thematic analysis for qualitative data and descriptive analysis for quantitative data. Results Except for one caregiver, all participants completed all five sessions. Intervention providers and recipients expressed satisfaction with the intervention. Intervention providers reported that the intervention can be feasibly delivered in the PHC setting, although 20 min was considered insufficient. While participants acknowledged the importance of involving caregivers in the intervention, they raised privacy concerns. Intervention providers’ adherence to the manual was moderate. Preliminary findings indicate a reduction in depressive symptoms post-intervention and improvement in providers’ perceived knowledge and skills. Conclusions This contextually developed psychological intervention for bipolar disorder has promising feasibility, acceptability, and potential utility. Further studies should evaluate time considerations and effectiveness. Trial registration The trial was registered on 16 August 2024, retrospectively on the Pan African Clinical Trial Registry database [PACTR202408896160144], https://pactr.samrc.ac.za/TrialDisplay.aspx?TrialID=31727 .
James M. Berzuk, Lauren Corcoran, Brannen McKenzie-Lefurgey
et al.
Contemporary robots are increasingly mimicking human social behaviours to facilitate interaction, such as smiling to signal approachability, or hesitating before taking an action to allow people time to react. Such techniques can activate a person's entrenched social instincts, triggering emotional responses as though they are interacting with a fellow human, and can prompt them to treat a robot as if it truly possesses the underlying life-like processes it outwardly presents, raising significant ethical questions. We engage these issues through the lens of informed consent: drawing upon prevailing legal principles and ethics, we examine how social robots can influence user behaviour in novel ways, and whether under those circumstances users can be appropriately informed to consent to these heightened interactions. We explore the complex circumstances of human-robot interaction and highlight how it differs from more familiar interaction contexts, and we apply legal principles relating to informed consent to social robots in order to reconceptualize the current ethical debates surrounding the field. From this investigation, we synthesize design goals for robot developers to achieve more ethical and informed human-robot interaction.
While recent research highlights the potential of social robots to support mood regulation, little is known about how prospective users view their integration into everyday life. To explore this, we conducted an exploratory case study that used a speculative robot concept "Mora" to provoke reflection and facilitate meaningful discussion about using social robots to manage subtle, day-to-day emotional experiences. We focused on the "Sunday Blues," a common dip in mood that occurs at the end of the weekend, as a relatable context in which to explore individuals' insights. Using a video prototype and a co-constructing stories method, we engaged 15 participants in imagining interactions with Mora and discussing their expectations, doubts, and concerns. The study surfaced a range of nuanced reflections around the attributes of social robots like empathy, intervention effectiveness, and ethical boundaries, which we translated into design considerations for future research and development in human-robot interaction.
This study examines the structural dynamics of Truth Social, a politically aligned social media platform, during two major political events: the U.S. Supreme Court's overturning of Roe v. Wade and the FBI's search of Mar-a-Lago. Using a large-scale dataset of user interactions based on re-truths (platform-native reposts), we analyze how the network evolves in relation to fragmentation, polarization, and user influence. Our findings reveal a segmented and ideologically homogenous structure dominated by a small number of central figures. Political events prompt temporary consolidation around shared narratives, followed by rapid returns to fragmented, echo-chambered clusters. Centrality metrics highlight the disproportionate role of key influencers, particularly @realDonaldTrump, in shaping visibility and directing discourse. These results contribute to research on alternative platforms, political communication, and online network behavior, demonstrating how infrastructure and community dynamics together reinforce ideological boundaries and limit cross-cutting engagement.
The article introduces a new concept designed to describe the social and sociotechnical processes that were triggered by digital transformation and, in their turn, resulted in Industry 5.0. The author described and modelled egocentric, communicative, convergent, and cause-and-effect rational-semantic networks, in which rationality relies on the semantic model of the system and forms some rational concern for its values and meanings to be implemented in behavior. The theoretical side of the research could be represented as a pyramid of conceptual levels that concentrate from philosophy and general science to particular research with statistical, network, and other methods. The author introduced the term of rational-semantic system to study the network contexts of behavior, including that of social network users. The term was also applied to the phenomena and effects of network interaction, e.g. the legitimacy of network power and its effect on user behavior patterns. The methodological character of this research allows for a broader study of social and political networks. The network context revealed some congruence issues, i.e., compatibility of rational-semantic systems. The author believes that eventually all systems can be combined into a single whole.
Nil Patil, Rupal Dhariwal, Arifullah Mohammed
et al.
Alzheimer's disease (AD) is increasingly becoming a major public health concern in our society. While many studies have explored the use of natural polyketides, alkaloids, and other chemical components in AD treatment, there is an urgent need to clarify the concept of multi-target treatment for AD. This study focuses on using network pharmacology approach to elucidate how secondary metabolites from Dictyostelium discoideum affect AD through multi-target or indirect mechanisms. The secondary metabolites produced by D. discoideum during their development were obtained from literature sources and PubChem. Disease targets were selected using GeneCards, DisGeNET, and CTD databases, while compound-based targets were identified through Swiss target prediction and Venn diagrams were used to find intersections between these targets. A network depicting the interplay among disease, drugs, active ingredients, and key target proteins (PPI network) was formed utilizing the STRING (Protein-Protein Interaction Networks Functional Enrichment Analysis) database. To anticipate the function and mechanism of the screened compounds, GO and KEGG enrichment analyses were conducted and visually presented using graphs and bubble charts. After the screening phase, the top interacting targets in the PPI network and the compound with the most active target were chosen for subsequent molecular docking and molecular dynamic simulation studies. This study identified nearly 50 potential targeting genes for each of the screened compounds and revealed multiple signaling pathways. Among these pathways, the inflammatory pathway stood out. COX-2, a receptor associated with neuroinflammation, showed differential expression in various stages of AD, particularly in pyramidal neurons during the early stages of the disease. This increase in COX-2 expression is likely induce by higher levels of IL-1, which is associated with neuritic plaques and microglial cells in AD. Molecular docking investigations demonstrated a strong binding interaction between the terpene compound PQA-11 and the neuroinflammatory receptor COX2, with a substantial binding affinity of −8.4 kcal/mol. Subsequently, a thorough analysis of the docked complex (COX2-PQA11) through Molecular Dynamics Simulation showed lower RMSD, minimal RMSF fluctuations, and a reduced total energy of −291.35 kJ/mol compared to the standard drug. These findings suggest that the therapeutic effect of PQA-11 operates through the inflammatory pathway, laying the groundwork for further in-depth research into the role of secondary metabolites in AD treatment.
Masoud Gazizadeh, Masoumeh Foroutan Koudehi, Hossein Fasihi
et al.
In this work, an innovative ratiometric sensing platform was developed for the determination of methotrexate (MTX), an antifolate drug, a chemotherapy agent, and an immune system suppressant based on blue emission graphene quantum dots/Rhodamine B doped gold nanostars (B-GQDs/Au NSt-RB). The developed sensor was a dual-emission fluorescent probe with two major emission peaks at 440 nm (B-GQDs) and 580 nm (Au NSt-RB) by exciting at 330 nm. Based on the inhibiting effect of MTX on the system's fluorescence density, the stable ratiometric fluorescent probe was used for the rapid determination of MTX in aquatic solutions and spiked human serum samples. The results indicated good linear correlations over the logarithmic concentration range of 0.3 nM–50.0 μM. In addition, B-GQDs/Au NSt-RB can further realize highly sensitive detection of MTX with a low LOD value of 2.28 × 10−10 M. The RSD% values obtained for the intra-day and inter-day precision were 0.63–3.86 %. With recoveries of 98.2–100.1 % and 98.7–100.5 %, respectively. The short-term temperature and freeze-thaw tests confirmed the higher stability of the developed sensor. In addition, the calculated recoveries for MTX recognition in real samples were in the range of 98–102 %. These findings suggested the excellent potential of the ratiometric fluorescence B-GQDs/Au NSt-RB sensor for detecting MTX in real plasma samples.
Forest park tourism ecological security is the cornerstone of ensuring ecological tourism safety. Delineating the ecological carrying capacity within forest parks is crucial for enhancing the security of forest tourism resources. This study utilizes statistical data from China’s forest parks spanning 2004 to 2019, employing methodologies to comprehensively depict the spatiotemporal dynamic characteristics of forest park tourism ecology in China. Subsequently, this research forecasts the prospective trajectory of forest park tourism ecology in China from 2020 to 2029. The research findings reveal that China’s forest park tourism ecological footprint exhibits oscillating characteristics, while the overall touristic ecological carrying capacity shows a sustained upward trend. However, a significant portion of regions experience deficits in tourism ecology. Notably, the coldspot regions with ecological security features demonstrate relative stability, while the hotspot areas gradually transition from inland to eastern coastal regions. Spatially and temporally, the disparities in touristic ecological profit and deficit depict a “U”-shaped distribution, more pronounced along the east–west axis than the north–south orientation. The migratory shift in the touristic ecological surplus and deficit center gravitates towards the southwest, demonstrating a fluctuating trend characterized by varying migration speeds. The discernible difference between the east and west concerning touristic ecological profit and deficit amplifies the likelihood of imbalance, surpassing disparities between the north and south. Projections suggest a deepening forest park tourism ecological deficit in China from 2020 to 2029, particularly accentuating the unsustainable development of forest park resources in economically developed regions. Through this study, a more comprehensive understanding of the current status and changing trends in the ecological carrying capacity of forest park tourism can be obtained. This research provides theoretical and practical support to promote sustainable tourism development and establishes a solid foundation for the ecological security of future forest park tourism.