Search Results (8,438)

Sloman and Rabb found support for the existence of the community of knowledge (CK) effect, which occurs when individuals are more likely to report understanding and being able to explain even fake scientific information when told that an expert understands the information. To date, no studies have been conducted that attempted to replicate original findings, let alone test the presence of the CK effect in realistic, legal scenarios. Therefore, Study One replicated original CK effect studies in a jury-eligible M-Turk sample (N = 291) using both Sloman and Rabb’s experimental stimuli as well as new stimuli. Study Two then tested the presence of the CK effect using scientific testimony in a mock court hearing from a forensic evaluator (N = 396). Not only did the CK effect improve laypeople’s perceptions of the scientific information in court, but it also improved their perceptions of the expert witness’s credibility, increased the weight assigned to the scientific information, and increased the weight assigned to the expert testimony. This effect was mediated by participants’ perceived similarity to the expert, supporting the theory behind the CK effect. These studies have important implications for the use of scientific information in court, which are discussed. Full article

The Evidential K-Nearest Neighbor (EK-NN) classifier has demonstrated robustness in handling incomplete and uncertain data; however, its application in high-dimensional big data for feature selection, such as genomic datasets with tens of thousands of gene features, remains underexplored. Our proposed Granular--Elastic Evidential K-Nearest Neighbor (GEK-NN) approach addresses this gap. In the context of big data, GEK-NN integrates an Elastic Net within the Genetic Algorithm’s fitness function to efficiently sift through vast amounts of data, identifying relevant feature subsets. This process mimics human cognitive behavior of filtering and refining information, similar to concepts in cognitive computing. A granularity metric is further employed to optimize subset size, maximizing its impact. GEK-NN consists of two crucial phases. Initially, an Elastic Net-based feature evaluation is conducted to pinpoint relevant features from the high-dimensional data. Subsequently, granularity-based optimization refines the subset size, adapting to the complexity of big data. Before applying to genomic big data, experiments on UCI datasets demonstrated the feasibility and effectiveness of GEK-NN. By using an Evidence Theory framework, GEK-NN overcomes feature-selection challenges in both low-dimensional UCI datasets and high-dimensional genomic big data, significantly enhancing pattern recognition and classification accuracy. Comparative analyses with existing EK-NN feature-selection methods, using both UCI and high-dimensional gene datasets, underscore GEK-NN’s superiority in handling big data for feature selection and classification. These results indicate that GEK-NN not only enriches EK-NN applications but also offers a cognitive-inspired solution for complex gene data analysis, effectively tackling high-dimensional feature-selection challenges in the realm of big data. Full article

Employee turnover remains a critical challenge for organizations, prompting an examination of how leadership approaches influence employees’ intentions to leave. This study investigates the impact of transformational leadership on turnover intention, focusing on emotional exhaustion and creativity as potential mediators. The study employs a quantitative design grounded in leadership and organizational psychology theory and surveys 182 professionals working in the information technology sector across Bosnia and Herzegovina, Croatia, Serbia, and Montenegro. Structural equation modeling reveals that transformational leadership reduces turnover intention by alleviating emotional exhaustion, highlighting the importance of psychological well-being in employee retention. While transformational leadership enhances employee creativity, creativity did not significantly mediate turnover intention in this context. These findings suggest that strategies that foster engagement and reduce burnout in knowledge-intensive industries can strengthen organizational commitment and improve retention. This study contributes to the understanding of behavioral mechanisms linking leadership to employee outcomes and offers actionable insights for modern organizations aiming to address turnover through supportive, empowering leadership practices. Additional mediators and contextual variables should be explored in further research. Full article

This study intends to provide insights and challenges for the shape of the B movement, an emerging paradigm that fosters cross-sectoral partnerships and encourages ethical business practices through so-called purpose-driven businesses. Focusing on Italy, the first European country to adopt this managerial model, the research investigates Italian Benefit Corporations, known as Società Benefit (SB), and their most appealing sustainability claims from a consumer perspective. The analysis intends to inform theory development by assuming the cultural and creative industry (CCI) as a field of interest, utilizing a within-subjects experimental design to analyze data from a diverse consumer sample across various contexts. The results indicate that messaging centered on economic sustainability emerged as the most effective in generating positive consumer responses, highlighting a prevailing inclination toward pragmatic factors such as affordability, economic accessibility, and tangible benefits rather than social issues. While sustainable behaviors are not yet widespread, latent ethical sensitivity for authentic, value-driven businesses suggests that economic and ethical dimensions can be strategically synthesized to enhance consumer engagement. This insight highlights the role of BCs in catalyzing a shift in consumption patterns within ethical-based and creative-driven sectors. Full article

Urban sustainability challenges demand integrated frameworks capable of addressing the dynamic, non-linear nature of cities. This study explores how the principles of the circular economy and complexity theory intersect to support systemic transformation in sustainable urban planning. Through a systematic literature review of 71 peer-reviewed articles published between 2015 and 2025, we analyze conceptual, methodological, and practical articulations across multiple thematic axes, including circular governance, urban metabolism, regenerative design, adaptive planning, digital integration, and environmental justice. Bibliometric and content analyses were conducted using Scopus metadata, VOSviewer for thematic clustering, and the StArt software (Version 3.4) to structure article selection. The findings reveal that circular economy provides practical tools for resource efficiency and regeneration, while complexity theory offers an adaptive framework to navigate uncertainty, emergent behaviors, and feedback dynamics. The synthesis suggests that their integration enables a more holistic and resilient approach to urban transformation. However, gaps remain in social inclusivity, long-term assessment, and the operationalization of complexity-informed planning. This study contributes to advancing a transdisciplinary agenda for circular and adaptive urban futures, offering insights for scholars, planners, and policymakers aiming to reconfigure cities within planetary boundaries. Full article

While existing studies document physical and economic impacts of new-build gentrification, the temporally protracted trauma of indirect displacement in communities adjacent to redeveloped areas remains understudied. Employing constructivist grounded theory, this study asks the following question: how do residents experience place attachment erosion during prolonged policy suspension in Shanghai’s new-build gentrification? Through iterative analysis of 25 interviews, we reveal a temporal vicious cycle of waiting triggered by uneven redevelopment and policy inertia. This cycle systematically dismantles belonging through several mechanisms: (1) chronic place-identity deterioration; (2) progressive social network fragmentation; (3) the collapse of imagined futures; and (4) the ambiguous loss of place attachment—where physical presence coexists with psychological disengagement. Crucially, we redefine indirect displacement as a temporal erosion of place identity and attachment, revealing a paradoxical state of physical presence coexisting with psychological disengagement. This paper provides a new perspective for better understanding the different dimensions of indirect displacement in new-build gentrification, which will help inform equitable development efforts that are more inclusive and just. Full article

This systematic review examines how machine learning (ML) and generative AI (GenAI) have been integrated into learning analytics (LA) in higher education (2018–2025). Following PRISMA 2020, we screened 9590 records and included 101 English-language, peer-reviewed empirical studies that applied ML or GenAI within LA contexts. Records came from 12 databases (last search 15 March 2025), and the results were synthesized via thematic clustering. ML approaches dominate LA tasks, such as engagement prediction, dropout-risk modelling, and academic-performance forecasting, whereas GenAI—mainly transformer models like GPT-4 and BERT—is emerging in real-time feedback, adaptive learning, and sentiment analysis. Studies spanned world regions. Most ML papers (n = 75) examined engagement or dropout, while GenAI papers (n = 26) focused on adaptive feedback and sentiment analysis. No formal risk-of-bias assessment was conducted due to heterogeneity. While ML methods are well-established, GenAI applications remain experimental and face challenges related to transparency, pedagogical grounding, and implementation feasibility. This review offers a comparative synthesis of paradigms and outlines future directions for responsible, inclusive, theory-informed AI use in education. Full article

In this work, the structures of the isolated anion and anhydrous and monohydrated sodium salts of alizarin red S (ARS) have been theoretically investigated within the density functional theory framework (B3LYP/6-311++G** calculations). The combination of calculations with the scaled quantum mechanics force field (SQMFF) methodology has allowed the assignment of the experimental infrared spectrum of ARS in the solid phase and the determination of the corresponding force constants. The structural analysis also included the investigation of the NMR and UV-visible spectra of the compound in solution in light of the undertaken quantum chemical calculations, the obtained theoretical data being in good agreement with the corresponding experimental ones. The impact of the presence of the Na⁺ counterion and hydration water on the properties of the organic ARS⁻ fragment was evaluated. Atoms in molecules theory (AIM) analysis was also undertaken to obtain further details on the electronic structure of the investigated species, and the HOMO-LUMO gap was determined to evaluate their relative reactivity. Globally, the results obtained in this work extend the available information on alizarin red S and may also be used for the fast identification of the three studied species of the compound investigated (anhydrous and monohydrated sodium salts and isolated anion). Full article

This article reports on a Swedish study investigating how children aged 2–3 years experience mathematical concepts through representations in play-responsive teaching. Drawing on the semiotic–cultural theory of learning, this study examines how representations, such as spoken language, bodily action, and artifacts, are mediated. Video-recorded teaching sessions are analyzed to identify semiotic means of objectification and semiotic nodes at which these representations converge. The analysis distinguishes between children encountering concepts expressed by others and expressing concepts themselves. The results indicate that play-responsive teaching creates varied opportunities for experiencing mathematical concepts, with distinct modes of sensuous cognition linked to whether a concept is encountered or expressed. This study underscores the role of teachers’ choices in shaping these experiences and highlights bodily action as a significant form of representation. These findings aim to inform the use of representations in teaching mathematics to the youngest children in preschool. Full article

In this study, we present a comprehensive theoretical analysis of modifications in the physical and chemical properties of MoSe₂ upon the introduction of substitutional transition metal impurities, specifically, Ti, V, Cr, Fe, Co, Ni, Cu, W, Pd, and Pt. Wet systematically calculated the adsorption enthalpies for various representative analytes, including O₂, H₂, CO, CO₂, H₂O, NO₂, formaldehyde, and ethanol, and further evaluated their free energies across a range of temperatures. By employing the formula for probabilities, we accounted for the competition among molecules for active adsorption sites during simultaneous adsorption events. Our findings underscore the importance of integrating temperature effects and competitive adsorption dynamics to predict the performance of highly selective sensors accurately. Additionally, we investigated the influence of temperature and analyte concentration on sensor performance by analyzing the saturation of active sites for specific scenarios using Langmuir sorption theory. Building on our calculated adsorption energies, we screened the catalytic potential of doped MoSe₂ for CO₂-to-methanol conversion reactions. This paper also examines the correlations between the electronic structure of active sites and their associated sensing and catalytic capabilities, offering insights that can inform the design of advanced materials for sensors and catalytic applications. Full article

The green renovation of existing residential buildings is a key way for the construction industry to achieve sustainable development and the dual carbon goals of China, which makes it urgent to make collaborative decisions among multiple stakeholders. However, because of divergent interests and risk perceptions among governments, energy service companies (ESCOs), and owners, the implementation of green renovation is hindered by numerous obstacles. In this study, we integrated prospect theory and evolutionary game theory by incorporating core prospect-theory parameters such as loss aversion and perceived value sensitivity, and developed a psychologically informed tripartite evolutionary game model. The objective was to provide a theoretical foundation and analytical framework for collaborative governance among stakeholders. Numerical simulations were conducted to validate the model’s effectiveness and explore how government regulation intensity, subsidy policies, market competition, and individual psychological factors influence the system’s evolutionary dynamics. The findings indicate that (1) government regulation and subsidy policies play central guiding roles in the early stages of green renovation, but the effectiveness has clear limitations; (2) ESCOs are most sensitive to policy incentives and market competition, and moderately increasing their risk costs can effectively deter opportunistic behavior associated with low-quality renovation; (3) owners’ willingness to participate is primarily influenced by expected returns and perceived renovation risks, while economic incentives alone have limited impact; and (4) the evolutionary outcomes are highly sensitive to parameters from prospect theory, The system’s evolutionary outcomes are highly sensitive to prospect theory parameters. High levels of loss aversion (λ) and loss sensitivity (β) tend to drive the system into a suboptimal equilibrium characterized by insufficient demand, while high gain sensitivity (α) serves as a key driving force for the system’s evolution toward the ideal equilibrium. This study offers theoretical support for optimizing green renovation policies for existing residential buildings in China and provides practical recommendations for improving market competition mechanisms, thereby promoting the healthy development of the green renovation market. Full article

CareCoach seeks to enhance self-efficacy in family caregivers of people living with dementia and has been feasibility tested in a multicentre randomised controlled trial. The intervention offers two face-to-face sessions with a trained coach and access to an online platform with nine modules. This paper reports findings from an embedded qualitative process evaluation assessing implementation from the implementer’s (‘coach’s’) (n = 8) perspective using individual interviews and implementer group discussions. Qualitative data were transcribed verbatim, inductively coded and analysed using Normalisation Process Theory. Implementers demonstrated (1) ‘Coherence’ by seeking to understand how CareCoach compared to current practice, highlighting the importance of supporting coaches to differentiate and identify boundaries between their new ‘coach role’ and usual practice; (2) ‘Cognitive Participation’ by reviewing training and resources to understand their role own responsibilities and facilitate delivery of coaching sessions; group supervision and peer support were also emphasised; (3) ‘Collective Action’ through interactions with carers to deliver key behavioural aspects such as goal setting, problem solving, and providing feedback; and (4) ‘Reflexive Monitoring’ by appraising the intervention to gain useful insights that could facilitate refinement of CareCoach training and delivery. This study provides a theoretically informed understanding of the implementation of CareCoach for caregivers of people living with dementia and provides recommendations to enhance training for coaches, intervention delivery and carer engagement. Full article

This theoretical study rigorously investigates how generative artificial intelligence reshapes decision-making in social entrepreneurship by modeling the opportunity search process through the lens of optimal stopping theory. Social entrepreneurs often face high uncertainty and resource constraints, requiring them to strategically balance the cost of continued searching with the chance of identifying socially impactful opportunities. This study develops a formal model that captures two core mechanisms of generative AI: reducing search costs and increasing the probability of mission-aligned opportunity success. The theoretical analysis yields three key findings. First, generative AI accelerates the optimal stopping point, allowing social entrepreneurs to act more quickly on high-potential opportunities by lowering cognitive and resource burdens. Second, the influence of increased success probability outweighs that of reduced search costs, underscoring the strategic importance of insight quality over efficiency in socially embedded contexts. Third, the benefits of generative AI are amplified in uncertain environments, where it helps navigate complexity and mitigate information asymmetry. These insights contribute to a deeper conceptual understanding of how intelligent technologies transform the cognitive and strategic dimensions of social entrepreneurship, and they offer empirically testable propositions for future research at the intersection of AI, innovation, and mission-driven opportunity pursuit. Full article

This article presents a novel mathematical formalism for advanced manifold–metric pairs, enhancing the frameworks of geometry and topology. We construct various D-dimensional manifolds and their associated metric spaces using functional methods, with a focus on integrating concepts from mathematical physics, field theory, topology, algebra, probability, and statistics. Our methodology employs rigorous mathematical construction proofs and logical foundations to develop generalized manifold–metric pairs, including homogeneous and isotropic expanding manifolds, as well as probabilistic and entropic variants. Key results include the establishment of metrizability for topological manifolds via the Urysohn Metrization Theorem, the formulation of higher-rank tensor metrics, and the exploration of complex and quaternionic codomains with applications to cosmological models like the expanding spacetime. By combining spacetime generalized sets with information-theoretic and probabilistic approaches, we achieve a unified framework that advances the understanding of manifold–metric interactions and their physical implications. Full article

Background: The progression of Alzheimer’s Disease (AD) deeply affects not only the diagnosed person but also their close relatives, who are often called to take on the role of informal caregivers. This transition is frequently unplanned and emotionally complex, yet poorly understood in its deeper processual dimensions. This study aims to explore and theorize the transition experienced by a family member becoming the primary informal caregiver for a person with advanced AD. Methods: A qualitative study based on the Constructivist Grounded Theory according to Charmaz’s approach (2006) was conducted. In-depth interviews were carried out with 10 participants who had become informal caregivers for a loved one with advanced AD. Data were analyzed using initial coding, focused coding, the constant comparative method, and theoretical coding. Results: Ten caregivers (mean age 39 years, range 35–54; nine females) of patients with advanced AD participated in the study. The analysis revealed a complex, emotionally intense caregiving experience marked by sacrifice, feelings of powerlessness, identity loss, and the necessity of sharing caregiving responsibilities. A core category emerged: A Silent and Certain Willingness to Care, representing the caregivers’ deep, often unconscious commitment to prioritize the care of their loved ones above their own needs. Four interconnected phases characterized the caregiving process: (1) The Changing Daily Life—involving significant sacrifices in personal and social life; (2) Feeling Powerless—confronting the inevitable decline without means to alter the course; (3) Losing Oneself—experiencing physical and psychological exhaustion and a sense of identity loss; and (4) Sharing with Others—seeking external support to sustain caregiving. These findings highlight the evolving nature of becoming a caregiver and the enduring dedication that sustains this role despite the challenges. Conclusions: The progression of AD deeply transforms the lives of caregivers, who become co-sufferers and active participants in the disease’s management. The results underscore the urgency of designing integrative care strategies—including psychological, social, and potentially technological support—that can enhance both patient outcomes and caregiver resilience. Grounded in real-world experiences, this study contributes to the broader neurodegeneration discourse by emphasizing caregiving as a critical factor in long-term disease management and therapeutic success. Full article