Search Results (25,170)

This article explores Daniel Kahneman’s concept of system thinking from his book Thinking Fast and Slow (2013) in the context of contemporary spiritual and theological reflection. The question studied here is: What does the intentional use of emotions, dreams and intuition described by Daniel Kahneman as System 1 thinking look like in contemporary spiritual and theological reflection? According to Kanheman, System 1 thinking includes emotions, dreams and intuition. The method for answering the research question is hermeneutical. This means gathering texts that fit Kahneman’s description of System 1 thinking and integrating these concepts into some form of spiritual and theological reflection. Hermeneutical research is text-based. Fifty-three (53) texts were found in a search of various databases. These texts are analyzed noting the impact of System 1 thinking on spiritual and theological reflection. Findings include the following: First, there is a rise in the number of texts using System 1 thinking in spiritual and theological reflection. Second, disciplines outside of theology are practicing spiritual reflection as part of their spiritual care. Third, these non-theological disciplines are also using System 1 thinking in their spiritual reflections. Fourth, there is an awareness and utilization of diverse cultures and faith experiences in spiritual reflection. Fifth, these texts indicate the growth of the demographic of people who are spiritual but not religious and a connection to dreams, emotions and intuition in spiritual and theological reflection. Sixth, there is also a developing overlap between spiritual and theological reflection. Cautions and gaps in the textual analysis are noted as well as future applications. Full article

Post-harvest losses of fruits and vegetables are an important bottleneck in food systems of countries around the world, with 30–50% of perishable food items lost between farm and consumer, smallholder farmers in low-and-middle income countries (LMICs) with poor cold chain infrastructures facing a disproportionate burden. Evaporative cooling (EC) is a low-cost and energy-efficient alternative to mechanical refrigeration; however, traditional systems are operated in one position and are dependent on climate, which restricts its performance. The combination of Internet of Things (IoT) sensing, machine learning (ML), and the advanced control theory has made intelligent evaporative cooling systems (IECS) adaptive, data-driven platforms that can regulate the environment in real-time and optimise autonomously. This is a systematic literature review that was carried out according to PRISMA 2020, summarising 94 peer-reviewed articles published in 2018–2025 to map the technological landscape, performance indicators, and research directions of the field of post-harvest fruit and vegetable preservation using IECS. Findings indicate that IECS can considerably lower the storage temperatures, increase the shelf life by 50–200%, and reduce energy consumption by 75–90% compared to traditional refrigeration, and the payback period is as short as 1.2 years. In arid conditions, ML models are accurate in prediction with an R² of 0.98. The gaps in the research identified are a lack of validation in wet climatic conditions, non-existent standardised Ag-IoT protocols, inadequate Food–Energy–Water (FEW) nexus calculation, and no explainable AI (XAI) interfaces. An example of a conceptual framework of four layers synthesised is proposed to direct next-generation research and implementation of the IECS. Full article

The production and leasing of electric construction machinery play a critical role in the low-carbon transition. However, from a multi-cycle dynamic perspective, there is a lack of targeted research on how to enhance electric goodwill and AI-enabled maintenance service levels while maximizing enterprise profits. To fill this gap, this study incorporates AI-enabled O&M effort, R&D technology, AI-enabled maintenance effort, and advertising effort into a long-term dynamic framework to examine optimal decisions for the manufacturer and the lessor. We assume that the information in the leasing supply chain is symmetric, that the marginal profits of the manufacturer and the lessor are fixed parameters, and that the AI-enabled maintenance service effort level and the electric goodwill are taken as state variables. We develop differential game models across four decision cases: centralized (Case C), decentralized (Case D), unilateral cost-sharing contract (Case U), and bilateral cost-sharing contract (Case B). Results demonstrate monotonic state variable trajectories. Both Case U and Case B can achieve supply chain coordination, with the profit-sharing mechanism in Case B proving superior. In addition, the optimal cost-sharing proportion depends on the relative sizes of the manufacturer’s and the lessor’s marginal profits in both Case U and Case B. The AI-enabled maintenance service plays a significant role in enhancing equipment reliability and supply chain resilience. In addition, the impacts of key parameters on optimal decision variables, state variables, profits, and coordination of the leasing supply chain are comprehensively discussed. Full article

Generative Artificial Intelligence (GenAI) is reshaping how students engage with learning both within and beyond traditional classroom settings. In a time when the development of transferable skills is essential for enabling students to thrive in varied and rapidly evolving environments, the potential of GenAI to enhance learning engagement remains insufficiently understood. Despite rising interest in interactive, personalised learning companions that enable deep engagement and ongoing skills development, scholarly research remains limited. This gap constrains effective institutional use of GenAI, reinforces black-box thinking, and restricts understanding of meaningful student engagement and skills acquisition. This paper investigates how a GenAI-enabled AI teacher supports student learning engagement, focusing on behavioral engagement as evidenced by learner interaction and participation patterns across diverse international higher education institutions. Using a combination of quantitative engagement metrics and qualitative learner reflections, the study examines how GenAI supports personalised learning, sustained interaction, autonomy, and cognitive engagement among students with varying educational backgrounds. The findings demonstrate that GenAI-based teaching systems can promote meaningful learning engagement, enhance motivation, and strengthen the development of transferable and employability skills. The study contributes empirical evidence to current debates on GenAI integration, teacher practices, and student engagement, offering implications for curriculum design and institutional adoption of GenAI-enabled learning tools. Full article

Escalating environmental challenges have increased interest in understanding pro-sustainability behaviours (PSBs) within the built environment. Grounded in the Theory of Planned Behaviour (TPB) and the Value–Belief–Norm (VBN) Theory, this study maps the global PSB research landscape and examines how cognitive, normative, and moral behavioural determinants are conceptualised. Employing the PRISMA framework and scientometric analysis using VOSviewer, the study analysed 22 key publications sourced from multiple academic databases. The findings indicate a steady growth in PSB research since 2017, with substantial contributions from Asia, particularly Malaysia and China. However, the literature remains theoretically fragmented, with limited integration of established behavioural frameworks. Eight categories of PSBs were identified, demonstrating how TPB constructs (attitudes, subjective norms, perceived behavioural control) and VBN constructs (values, beliefs, personal norms) are reflected in stakeholder practices across the built environment lifecycle. The findings highlight conceptual gaps, notably the underutilisation of hybrid behavioural models, and emphasise the need for future research that enhances theoretical integration, interdisciplinarity, and geographical diversity. The study provides evidence-based insights to support policy, education, and industry efforts to strengthen PSBs in the built environment. Full article

Soil salinity poses a major challenge to agricultural productivity, especially threatening food security in arid and semi-arid areas. Traditional soil reclamation methods, such as leaching, chemical amendments, and drainage engineering, usually need large amounts of water, involve high costs, and can lead to environmental problems. This review compiles existing knowledge on innovative strategies for managing saline soils, focusing on buried interlayer systems that use materials like straw, sand, gravel–sand mixtures, and biochar. These interlayers improve soil hydraulic properties by preventing capillary rise, encouraging salt leaching, and reducing surface salt buildup. Biochar stands out as a particularly useful material because of its stability, large surface area, porosity, and high cation exchange capacity. These features help improve soil structure, increase water retention, and effectively retain sodium. Evidence from lab and field tests shows that buried biochar layers can stop salt from moving upward, aid in desalinating the root zone, and boost crop yields. While straw and sand interlayers show potential in reducing salinity, biochar is noted for its multifunctionality and long-term effectiveness in addressing salinity problems. The success of buried biochar systems depends on several factors, including the properties of the biochar, how much is used, how deep it is buried, and the specific soil and climate conditions. This review highlights how these systems work, compares their performance, and points out research gaps, advocating for their potential as a sustainable, resource-efficient way to manage salinity and improve soil health over the long term. A substantial proportion of the existing evidence is derived from controlled laboratory studies, and the buried biochar layer approach remains an emerging technique that requires further validation under field conditions. Still, significant knowledge gaps persist regarding long-term performance and water-salt dynamics, while site-specific soil variability and scalability challenges may limit the effective implementation of biochar interlayer systems under field conditions. Full article

Urban socio-economic resilience to climate change has emerged as a central research theme as cities increasingly confront interconnected environmental, economic, and social risks. Despite the rapidly expanding body of literature, the conceptual boundaries, thematic evolution, and analytical priorities of this field remain fragmented across disciplines, and no prior study has systematically mapped the socio-economic dimension of urban resilience through a combined bibliometric and thematic analysis over a multi-decadal horizon. This study addresses that gap by providing a systematic review of global research on urban socio-economic resilience to climate change, integrating bibliometric and thematic analyses of peer-reviewed publications from 1990 to 2025. Following the PRISMA 2020 guidelines, records were retrieved from the Web of Science Core Collection and subjected to a multi-stage screening procedure that combined automated relevance scoring with mandatory manual validation of the socio-economic dimension, resulting in a final dataset of 5076 publications. The analysis examines conceptual interpretations of socio-economic resilience, dominant climate hazards affecting urban systems, methodological approaches and assessment indicators, adaptation strategies and governance responses, and emerging research gaps. The results reveal a marked acceleration of scientific output after 2015, driven by the Paris Agreement and the IPCC Special Report on Global Warming of 1.5 °C (2018). The bibliometric network analyses identify adaptation, vulnerability, flooding, and sustainability transitions as the core thematic clusters. The findings trace a paradigmatic trajectory from equilibrist recovery frameworks toward transformative, socio-economically grounded resilience models and reveal persistent gaps in the operationalization of governance, equity measurement, and geographic representation. By synthesizing three-and-a-half decades of scholarship, this review clarifies the intellectual structure of the field and proposes four specific post-2026 research pathways that emphasize longitudinal cross-city comparisons, mixed-methods assessments, sector-specific compound hazard analyses, and governance mechanism studies. Full article

Background: Multibody models are essential for studying knee joint mechanics, but their reliability and subsequent clinical utility are limited by uncertainties in ligament and contact parameters. Currently, no consensus exists on which parameters to prioritize or which statistical distributions best establish model credibility. Objectives: This scoping review aims to systematize reported uncertainty values for ligament and contact parameters in multibody models of the natural knee to identify trends and research gaps. Methods: Following PRISMA-ScR guidelines, a systematic search was conducted across PubMed, Scopus, and Web of Science. Methodological quality was assessed using a customized 13-item checklist, and the data were synthesized via a narrative approach by charting parameter types, quantification methods, and model structures. Results: In total, 19 articles were included (out of 494 identified), showing a wide variability in uncertain parameter types, values, and modeling approaches. Ligaments were typically represented as deformable cables with quadratic–linear behavior, while articular contact utilized elastic foundation formulations or mechanisms. Standard deviations of 30% of the mean for ligament stiffness and 0.02 for reference strain (typically modeled within Gaussian distributions) were the most frequently quantified uncertain parameters. Geometric uncertainties for ligament attachment points varied widely, ranging from 1.0 to 5.0 mm. Idealized contact geometry also varied within 2.5 mm for linear coordinates and 15° for angular coordinates. Conclusions: Wide variability and inconsistent reports highlight a need for standardized definitions of parameter uncertainty in multibody knee modeling to improve reproducibility of musculoskeletal knee simulations and ensure a reliable transition of these models into clinical practice. Full article

BIM is a key technology for the digital transformation and sustainable development of the construction industry through enhanced productivity, transparency, and fostered innovation. Although scholars have investigated the constructs driving BIM adoption intention, a comprehensive framework has seldom been adopted, and thus some vital factors have been overlooked, such as collaboration partner pressure. Meanwhile, the targeted group is usually practitioners of a certain type of company while a construction project requires the participation of multiple types of companies. To address these research gaps, the aim of this study is to explore the factors driving various stakeholders’ intention to adopt BIM by applying the TOE framework, considering nine factors across three dimensions. A total of 512 valid responses from owners, consulting firms, design firms, construction companies, suppliers, engineering surveying firms, and universities or research institutes were collected and analyzed through the structural equation modeling (SEM) method. The SEM results indicated that six factors were positively related to the intention to employ BIM, among which management commitment (β = 0.182, p < 0.001) and perceived ease of use (β = 0.180, p < 0.001) exhibited the strongest effects. However, three factors (perceived usefulness, supporting technical facilities, and mimetic pressure) exerted no significant influence. The findings of this study may provide a valuable reference for promoting the application of BIM technology in the construction industry. Full article

Research on information and communication technologies (ICTs) in sustainable agriculture has largely been technocentric, focusing on effectiveness, efficiency, and adoption, with limited consideration of end-user perceptions in practice. This study addresses this gap by examining perceptions of mobile gardening apps as accessible ICT tools that may support sustainable behaviours. Based on over 180,000 user reviews from Google Play and the Apple App Store, Contextualized Topic Modeling (CTM) was used to identify key themes and interpret them within the Theory of Consumption Value (TCV) framework. This approach allows for the analysis of functional, emotional, and epistemic dimensions of user experiences based on large-scale, real-world data. The results indicate that functional aspects, such as reliability and usability, dominate app evaluation, but emotional engagement and knowledge acquisition also play a significant role. By combining a data-driven approach with a well-established behavioural framework, this study bridges the gap between technological and user perspectives. It simultaneously extends the application of the TCV to the field of ICT solutions supporting sustainable development and provides practical guidance for designing more effective gardening apps. Full article

Our community-based participatory research (CBPR) partnership convened an in-person, bilingual empowerment theory-based community forum to disseminate and translate findings from our trial of Nuestra Comunidad Saludable (Our Healthy Community), a multilevel intervention designed to improve uptake of COVID-19 testing and vaccination among Spanish-speaking Latine communities in North Carolina. The forum brought together community members, healthcare providers, organizational representatives, and academic researchers from across North Carolina. Drawing on findings from the intervention trial, participants engaged in facilitated, structured dialogue to identify community priorities and generate recommendations to advance health equity among Latine communities. Thirty-six participants identified eight priorities: (1) reducing health service gaps and inequities exposed by COVID-19; (2) expanding access to bilingual, culturally responsive mental health services; (3) improving understanding of HIV prevention and treatment; (4) strengthening services for children with disabilities; (5) protecting immigrant rights and ensuring safe access to services; (6) increasing political and social support for Latine health; (7) improving access to trusted, culturally responsive providers and community organizations; and (8) addressing social determinants of health, including employment, housing, and food security. The empowerment-based forum identified community priorities, challenges, and recommendations that can inform practice, intervention, policy, and research, and advance health equity for Spanish-speaking Latine communities. Full article

A business model is an essential concept for securing long-term success and competitive advantage, where technologies take on an enabling role. Without the respective technology, the business model becomes unviable. Digital technologies are well known to enable business models, and various definitions of the term digital business models and their characteristics are discussed in the existing literature. However, scholars sometimes use this term interchangeably with the term technology-based business models, although not all technologies are digital in nature. In fact, many different types of technologies other than digital ones exist. A strong theoretical foundation for digital business models is available, while the research on technology-based business models is fragmented and incomplete. This term has been used without further defining its meaning or the definitions provided are too narrow and decisive characteristics are missing. Although authors have used these two terms interchangeably, they cannot be seen as equivalent, and the definitions and characteristics of digital business models cannot be simply transferred to the concept of technology-based ones. Therefore, the absence of a holistic and comprehensive definition of the term technology-based business models in the existing literature represents a significant gap that this research seeks to address. The aim of our study was to examine how business models are based on a particular technology. We use the results of a systematic literature review and semi-structured expert interviews to construct a comprehensive definition for the term technology-based business models, and we identify the major and minor characteristics of both terms. In addition to adding to the business model literature, these findings help practitioners understand technology-based business models in more detail and how technology can enable business models to meet customers’ needs. Full article

Social networks such as Twitter have grown rapidly and are now flooded with sarcastic comments, both in text and in images. Detecting sarcasm in multimodal data has significant social value and is attracting increasing research attention. However, most studies overlook the role of sentiment, even though sentiment information in text is closely linked to clues of sarcasm. Additionally, few consider how text and images align semantically. To address these issues, we propose a sentiment-guided mutual learning network (SGMLN) for multimodal sarcasm detection. SGMLN utilizes sentiment information to inform the combination of text and image features, and employs mutual learning to facilitate knowledge sharing among classifiers. We design a sentiment-guided attention layer that injects sentiment into both modalities, producing features that capture sarcasm more effectively. Sentic-BERT extracts sentiment-aware vectors from text, using word-level sentiment as a mask. In mutual learning, a logistic distribution function measures differences between classifiers, improving knowledge transfer between modalities. This step boosts multimodal understanding and model performance. By introducing sentiment-aware representations and semantic alignment, SGMLN bridges the gap between text and images, making them more consistent. Experiments on public datasets demonstrate that our model is effective and outperforms alternatives. Full article

School-based neuromuscular training interventions have the potential to mitigate dynapenia in the paediatric population and enhance movement skill outcomes; however, translating research into practice in primary school settings has been slow due to the expertise and professional learning required for implementation. This review describes the new teacher-supported intervention ‘Kids Innovative Neuromuscular Enhancement & Teacher-supported Instructional Coaching with AI’ (Kinetic AI) and presents evidence supporting its use in primary school settings. The Scale for the Assessment of Narrative Review Articles (SANRA) was used to guide the narrative and conceptual review methodology employed to synthesise peer-reviewed literature on paediatric dynapenia, school-based neuromuscular training, and AI technology-supported instructional models. This synthesis informed the development of a conceptual approach to neuromuscular training delivery in primary schools. The newly developed Kinetic AI conceptual model provides a pathway to embed neuromuscular training within active class breaks, offering adaptive feedback and targeted teacher support to facilitate implementation. This approach has the potential to bridge gaps between research, access, and practice. The Kinetic AI application is designed to support children’s muscular fitness and movement skills through school-based neuromuscular training, while addressing barriers to research translation and teacher expertise. When applied during school breaks, this approach has the potential to reduce the risk of dynapenia and contribute to scalable improvements in paediatric health and wellbeing. Full article

This study explores how board characteristics impact corporate cash flow risk in an emerging market setting. While previous research has examined firm risk, crash risk, and earnings quality, there is limited evidence on cash flow risk and its governance factors, especially in developing economies. To fill this gap, this study differentiates between volatility-based and distortion-based measures of cash flow risk and assesses how board attributes influence these aspects. Using a balanced panel of 327 non-financial firms listed in Vietnam from 2013 to 2023, cash flow risk is measured by the rolling five-year volatility of operating cash flows and short-term distortions shown in earnings–cash flow mismatches. To address endogeneity and dynamic persistence, the analysis uses the system generalized method of moments estimator, along with fixed-effects and feasible generalized least squares models for robustness. The findings suggest that board independence, gender diversity, and financial expertise are linked to lower cash flow risk, highlighting the importance of effective monitoring. Conversely, board meeting frequency is positively linked to risk, suggesting that boards tend to increase meeting frequency as a reactive response to heightened uncertainty. Board size and CEO duality do not show consistent effects. Focusing on Vietnam’s institutional context, this study provides evidence that governance mechanisms influence different dimensions of cash flow risk through separate channels, offering valuable insights for enhancing board effectiveness in emerging markets. Full article