Search Results (548)

Digital Twin (DTw) technology is a cornerstone of Industry 4.0, enabling real-time monitoring, predictive maintenance, and performance optimization across diverse industries. A key requirement for effective DTw implementation is high geometric fidelity—ensuring the digital model accurately represents the physical counterpart. Fidelity metrics provide a quantitative means to assess this alignment in terms of geometry, behavior, and performance. Among these, 3D shape descriptors play a central role in evaluating geometric fidelity, offering computational tools to measure shape similarity between physical and digital entities. This paper presents a comprehensive review of 3D shape descriptor methods and their applicability to geometric fidelity assessment in DTw systems. We introduce a structured taxonomy encompassing classical, structural, texture-based, and deep learning-based descriptors, and evaluate each in terms of transformation invariance, robustness to noise, computational efficiency, and suitability for various DTw applications. Building upon this analysis, we propose a conceptual fidelity metric that maps descriptor properties to the specific fidelity requirements of different application domains. This metric serves as a foundational framework for shape-based fidelity evaluation and supports the selection of appropriate descriptors based on system needs. Importantly, this work aligns with and contributes to the emerging ISO 30138 standardization initiative by offering a descriptor-driven approach to fidelity assessment. Through this integration of taxonomy, metric design, and standardization insight, this paper provides a roadmap for more consistent, scalable, and interoperable fidelity measurement in digital twin environments—particularly those demanding high precision and reliability. Full article

With persistent racial inequities, cultural adaptations of health programs can promote equitable maternal mental health. Despite the intersecting identities of Black mothers with preterm birth combined with racial discrimination, previous mental health programs in the neonatal intensive care unit (NICU) are void of the sociocultural context that perpetuates racially motivated care. This study uses a two-round Delphi process to gather knowledge on the cultural appropriateness and content validity of the adapted Mothers and Babies Online Course, a United States Prevention Service Task Force-recommended intervention for managing mental health symptoms in pregnant and new mothers for Black mothers with preterm birth. The Black feminist theory and cultural adaptation strategies were used to conceptualize the adaptation process. Opinions were solicited from Black mothers and professionals based on predetermined criteria. Eleven participants, comprising eight mothers and three professionals, participated in Round One. Of these, only one professional did not participate in Round Two, totaling 10 participants who participated in Round Two. The participants rated the adapted program—eMB 4 Blackmamas, positively, and four themes emerged, substantiating the importance of culturally responsive mental health. The themes also offered additional feedback related to improving the program delivery. Including diverse stakeholders in mental health research offers unique and balanced insights into designing culturally appropriate programs to promote and protect Black maternal mental health in the NICU. Full article

This systematic review explores the number and role of the UNO^® card game and UNO-type learning media across different levels of mathematics education. UNO^®-inspired games have gained recognition as innovative pedagogical tools that promote engagement, motivation, and active learning among students in various mathematics topics in the last few years. This review synthesises existing research on the application of these games in preschool, primary, secondary, and higher education settings. We employed a hybrid approach, combining database searches and the snowball method, to ensure a comprehensive and thorough selection of the relevant literature for our systematic review. Published research studies between January 1980 and June 2025 that employed UNO^® or UNO-type card games in education were collected. Forty-one research studies met the inclusion criteria for this review. We sought answers to three research questions. At first, we made a time analysis to organise the collected records. Based on the collection, we identified the mathematical topics for which teachers and researchers used the original UNO^® game or developed UNO-type games at various educational levels, as well as the role of UNO^® and UNO-type games in mathematics education. In approximately 68.29% of the cases, the authors introduce their own educational games modelled after UNO^®. In contrast, just less than one-third of the records (31.71%) utilise the original UNO^® playing cards in the teaching–learning process. The findings indicate that UNO-type educational maths games can enhance conceptual understanding, foster collaborative skills, and improve learning outcomes when appropriately integrated into curricula. This review also offers educators recommendations for effectively introducing these games at various educational levels. We aim to provide an evidence-based, well-structured insight into the potential of UNO^® and UNO-type learning tools. Full article

To assist stakeholders in selecting appropriate social media influencers (SMIs), this study proposes a multi-attribute decision-making framework for influencer evaluation based on their key performance metrics and engagement characteristics. This study introduces a new modification of the Evaluation Based on Distance from Average Solution (EDAS) under an interval-valued Fermatean fuzzy (IVFF) environment, addressing the limitations of the conventional EDAS method. In addition, a conceptual framework for the static and dynamic evaluation of SMIs is developed, integrating various crisp and fuzzy multi-criteria decision-making (MCDM) approaches. Empirical validation through two practical case studies demonstrates the effectiveness and applicability of the proposed framework, resulting in recommendations for marketers seeking to optimize their influencer-based marketing strategies. Full article

Hospital in the Home (HITH) programs are emerging as a key pillar of smart city healthcare infrastructure, leveraging technology to extend care beyond traditional hospital walls. The global healthcare sector has been conceptualizing the notion of a care without walls hospital, also called HITH, where virtual care takes precedence to address the multifaceted needs of an increasingly aging population grappling with a substantial burden of chronic disease. HITH programs have the potential to significantly reduce hospital bed occupancy, enabling hospitals to better manage the ever-increasing demand for inpatient care. Although many health providers and hospitals have established their own HITH programs, there is a lack of research that provides healthcare executives and HITH program managers with management models and frameworks for such initiatives. There is also a lack of research that provides strategies for improving HITH management in the health sector. To fill this gap, the current study ran a systematic literature review to explore state-of-the-art with regard to this topic. Out of 2631 articles in the pool of this systematic review, 20 articles were deemed to meet the eligibility criteria for the study. After analyzing these studies, nine management models were extracted, which were then categorized into three categories, namely, governance models, general models, and virtual models. Moreover, this study found 23 strategies and categorized them into five groups, namely, referral support, external support, care model support, technical support, and clinical team support. Finally, implications of findings for practitioners are carefully provided. These findings provide healthcare executives and HITH managers with practical frameworks for selecting appropriate management models and implementing evidence-based strategies to optimize program effectiveness, reduce costs, and improve patient outcomes while addressing the growing demand for home-based care. Full article

Art therapy serves as a crucial intervention modality for children with autism spectrum disorder (ASD), demonstrating unique value in emotional expression, sensory integration, and social communication. However, current practice presents critical challenges, including the disconnect between design expertise and clinical needs, unclear mechanisms of environmental factors’ impact on therapeutic outcomes, and insufficient evidence-based support for technology integration. Purpose: This study aimed to construct an evidence-based theoretical framework for art therapy environment design for children with autism, clarifying the relationship between environmental design elements and therapeutic effectiveness. Methodology: Based on the Web of Science database, this study employed a dual-track approach comprising bibliometric analysis and micro-qualitative content analysis to systematically examine the knowledge structure and developmental trends. Research hotspots were identified through keyword co-occurrence network analysis using CiteSpace, while 24 representative design cases were analyzed to gain insights into design concepts, emerging technologies, and implementation principles. Key Findings: Through keyword network visualization analysis, this study identified ten primary research clusters that were systematically categorized into four core design elements: sensory feedback design, behavioral guidance design, emotional resonance design, and therapeutic support design. A responsive therapeutic environment conceptual framework was proposed, encompassing four interconnected components based on the ABC model from positive psychology: emotional, sensory, environmental, and behavioral dimensions. Evidence-based design principles were established emphasizing child-centeredness, the promotion of multisensory expression, the achievement of dynamic feedback, and appropriate technology integration. Research Contribution: This research establishes theoretical connections between environmental design elements and art therapy effectiveness, providing a systematic design guidance framework for interdisciplinary teams, including environmental designers, clinical practitioners, technology developers, and healthcare administrators. The framework positions technology as a therapeutic mediator rather than a driver, ensuring technological integration supports rather than interferes with children’s natural creative impulses. This contributes to creating more effective environmental spaces for art therapy activities for children with autism while aligning with SDG3 goals for promoting mental health and reducing inequalities in therapeutic access. Full article

Redwood (Sequoia sempervirens (Lamb. ex D. Don) Endl.) is a fast-growing, long-lived conifer native to a narrow coastal zone along the western seaboard of the United States. Redwood can accumulate very high amounts of carbon in plantation settings and continuous cover forestry (CCF) represents a highly profitable option, particularly for small-scale forest growers in the North Island of New Zealand. We evaluated the profitability of conceptual CCF regimes using two case study forests: Blue Mountain (109 ha, Taranaki Region, New Zealand) and Spring Creek (467 ha, Manawatu-Whanganui Region, New Zealand). We ran a strategic harvest scheduling model for both properties and used its results to guide a tactical-spatially explicit model harvesting small 0.7 ha units over a period that spanned 35 to 95 years after planting. The internal rates of return (IRRs) were 9.16 and 10.40% for Blue Mountain and Spring Creek, respectively, exceeding those considered robust for other forest species in New Zealand. The study showed that small owners could benefit from carbon revenue during the first 35 years after planting and then switch to a steady annual income from timber, maintaining a relatively constant carbon stock under a continuous cover forestry regime. Implementing adjacency constraints with a minimum green-up period of five years proved feasible. Although small coupes posed operational problems, which were linked to roading and harvesting, these issues were not insurmountable and could be managed with appropriate operational planning. Full article

Drought is one of the most complex natural hazards, characterized by its slow onset, persistent nature, diverse sectoral impacts (e.g., agriculture, water resources, ecosystems), and dependence on meteorological, hydrological, and socioeconomic factors. Over the years, significant scientific effort has been devoted to developing methodologies that address its multifaceted nature, reflecting the interdisciplinary challenges of drought analysis. However, previous reviews have typically focused on individual methods, while this study presents a unified, multidisciplinary framework that integrates multiple drought analysis methods and links them to key factors guiding method selection. To address this gap, five widely used methods—index-based, remote sensing, threshold-level methods (TLM), impact-based methods, and the storyline approach—are critically evaluated from a multidisciplinary perspective. In addition, the study examines spatial and temporal trends in scientific publications, illustrating how the application of these methods has evolved over time and across regions. The primary objective of this review is twofold: (1) to provide a holistic, state-of-the-art synthesis of these methods, their applications, and their limitations; and (2) to evaluate and prioritize the critical decision-making factors, including drought type, data type/availability, study scale, and management objectives that influence method selection. By bridging this gap, the paper offers a conceptual decision-support framework for selecting context-appropriate drought analysis methods. However, challenges remain, including the vast diversity of methods beyond the scope of this review and the limited consideration of less influential factors such as user expertise, computational resources, and policy context. The paper concludes with insights and recommendations for optimizing method selection under varying circumstances, aiming to support both drought research and effective policy implementation. Full article

Formative research is an essential component of higher education, aimed at developing research competencies in students, with an emphasis on critical thinking, academic autonomy, and analytical capacity. Its purpose is not the production of original knowledge but the systematic preparation for research activity. Within this framework, the objective of this study is to analyze how conceptual appropriation, which encompasses theoretical, methodological, procedural, and normative knowledge, is related to students’ perceived research skills. This study is grounded in the imperative of fostering higher education that cultivates critical, autonomous, and ethically responsible researchers. For this purpose, a quantitative methodology was used, with a non-experimental and correlational design, applying a census sampling to 10,536 students from a higher education institution. Data were collected through a structured survey on conceptual appropriation and perceived research skills. After the removal of inconsistent records, the data were processed statistically using non-parametric tests, particularly Spearman’s correlation, due to the non-normal distribution of the variables. The results reveal strong and significant correlations between conceptual appropriation and key research skills such as hypothesis formulation, critical thinking, and motivation for research, demonstrating that greater conceptual mastery promotes a more solid and engaged research training. These findings reinforce the need to systematically and progressively integrate research content into the university curriculum, fostering an authentic, reflective, and contextualized education. Full article

Background: The emergency department (ED) often represents the entry point to care for patients that require urgent medical attention or have no alternative for medical treatment. This has implications on scope of practice and how quality of care is measured. A diverse array of methodologies has been developed to evaluate the quality of clinical care and broadly includes quality improvement (QI), quality assurance (QA), observational research (OR) and clinical quality registries (CQRs). Considering the overlap between QI, QA, OR and CQRs, we conceptualized a modular framework for TECOR to effectively and efficiently streamline clinical quality evaluations. Streamlining is both appropriate and justified as it reduces redundancy, enhances clarity and optimizes resource utilization, thereby allowing clinicians to focus on delivering high-quality patient care without being overwhelmed by excessive data and procedural complexities. The objective of this study is to describe the process for designing a modular framework for ED CQRs using TECOR as a case study. Methods: We performed a scoping audit of all quality projects performed in our ED over a 1-year period (1 January 2021 to 31 December 2021) as well as data mapping and categorical formulation of key themes from the TECOR dataset with clinical data sources. Both these processes then informed the design of TECOR. Results: For the audit of quality projects, we identified 29 projects. The quality evaluation methodologies for these projects included 12 QI projects, 5 CQRs and 12 OR projects. Data mapping identified that clinical information was fragmented across 11 distinct data sources. Through thematic analysis during data mapping, we identified three extraction techniques: self-extractable, manual entry and on request. Conclusions: The modular framework for TECOR aims to enable an efficient streamlined approach that caters to all aspects of clinical quality evaluation to enable higher throughput of clinician-led quality evaluations and improvements. TECOR is also an essential component in the development of a learning health system to drive evidence-based practice and the subject of future research. Full article

Miami, Florida, renowned for its cultural richness and coastal beauty, also faces the concerning challenges created by urban heat islands (UHIs). As one of the hottest cities of the United States, Miami is facing escalating temperatures and threatening heat-related vulnerabilities due to urbanization and climate change. Our study addresses the critical issue of mapping and investigating UHIs in complex urban settings. This study leveraged Planet satellite data and Landsat data to conceptualize and develop appropriate mitigation strategies for UHIs in Miami. Utilizing the Planet satellite imagery and Landsat data, we conducted a combined study of land cover and land surface temperature variations within the city. This approach fuses remotely sensed data to identify the UHI hotspots. This study aims for dynamic approaches for UHI mitigation. This includes studying the status of green spaces present in the city, possible expansion of urban green spaces, the propagation of cool roof initiatives, and exploring the recent climatic trend of the city. The research revealed that built-up areas consistently showed higher land surface temperatures while zones with dense vegetation have lower surface temperatures, supporting the role of urban green spaces in surface temperature reduction. This research can also set a robust model for addressing UHIs in other cities facing rapid urbanization and experiencing mounting temperatures each passing year by helping in assessing LST, land cover, and related spectral indices as well. Full article

Agent-based models are increasingly used to study supply chain systems due to their capacity to capture decentralised behaviour, heterogeneity, and emergent dynamics. However, the mere use of agent-based simulation platforms does not necessarily imply that the models fully exploit the agent-based paradigm. Although this concern, namely, the potential misuse of agent-based models, has been frequently raised in the literature, no previous study has precisely quantified how frequently this issue occurs, especially in the supply chain domain. To address this gap, a systematic review of 58 academic contributions was conducted to evaluate the extent to which agent-based models applied to supply chain contexts adhere to the fundamental principles of agent-based simulation. Specifically, the reviewed works were classified into two categories: Green Flag models, representing coherent and appropriate implementations of agent-based models, and Red Flag models, which fail to capture the essential characteristics of agent-based simulation. The classification was based on key discriminating factors such as the type and number of simulated entities or agents, the nature of agent interactions, and the incorporation of system-level dynamics. Further nuance is provided by two subtypes of Green Flag models: those featuring intelligent agents, and those based on responsive or reactive entities, which might generate emergent dynamics. Our results reveal that almost 64% of the analysed agent-based contributions lack key characteristics to justify the use of agent-based models. Hence, the paper also provides conceptual tools to aid in distinguishing between different agent-based approaches. In conclusion, the present work offers both a theoretical framework and a practical evaluation guide to support the development of future models and to foster critical analysis within the field. Full article

The residential sector has the potential to significantly impact overall energy consumption and emissions due to the long lifespan of residential buildings and the difficulty of outsourcing residential emissions to other nations. This research primarily aimed to investigate the impact of government expenditure on environmental protection in reducing household energy consumption, thus contributing to Canada’s environmental quality. We utilized panel data from ten Canadian provinces covering the period from 1995 to 2020. To estimate household energy demand, both conceptual and empirical models were developed. Advanced second-generation econometric techniques were applied, including appropriate unit root and co-integration tests. Long-term relationships were analyzed using the fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) methodologies. The long-term estimates indicate that all explanatory variables align with theoretical expectations and are highly significant. The findings reveal that government funding for environmental protection, along with province-specific environmental policies, contributes to reducing per capita residential energy consumption, which is essential for enhancing energy efficiency. Additionally, factors such as weather, income, demographic shifts, and energy prices are found to influence household energy consumption trends. These outcomes highlight the importance of increased government funding for environmental protection and underscore the need for provinces to implement context-specific environmental policies. This approach is essential in the effort to reduce household energy consumption across Canada. Full article

Identifying and classifying mathematical errors is crucial to improving the teaching and learning process, particularly for first-year engineering students who often struggle with foundational mathematical competencies. This study aims to select the most appropriate theoretical framework for error classification by applying the Analytic Hierarchy Process (AHP), a multicriteria decision-making method. Five established classification models—Newman, Kastolan, Watson, Hadar, and Polya—were evaluated using six pedagogical criteria: precision in error identification, ease of application, focus on conceptual and procedural errors, response validation, and viability in improvement strategies. Expert judgment was incorporated through pairwise comparisons to compute priority weights for each criterion. The results reveal that the Newman framework offers the highest overall performance, primarily due to its structured approach to error analysis and its applicability in formative assessment contexts. Newman’s focus on reading, comprehension, transformation, and encoding addresses the most common errors encountered in the early stages of mathematical learning. The study demonstrates the utility of the AHP as a transparent and replicable methodology for educational model selection. It addresses a gap in the literature regarding evidence-based criteria for designing diagnostic instruments. These findings support the development of targeted pedagogical interventions in mathematics education for engineering programs. Full article

Differential evolution (DE) has emerged as a numerical optimization technique due to its conceptual simplicity and demonstrated effectiveness across diverse problem domains. However, the algorithm’s performance remains critically dependent on appropriate control parameter settings. This paper introduces a novel diversity-based parameter adaptation (div) mechanism, generates two sets of symmetrical parameters, F and CR, adaptively first, and then dynamically selects the final parameters based on individual diversity rankings. It employs a straightforward approach to identify the more effective option from two sets of symmetrical parameters. Comprehensive experimental evaluation demonstrated that the div mechanism significantly enhanced the performance of the DE algorithm. Furthermore, by incorporating div, our enhanced algorithm exhibited superior optimization capability compared to five state-of-the-art DE variants. The results show that, among the 145 cases studied, DTDE-div outperformed others in 92 cases and underperformed in 32 cases, with the lowest performance ranking of 2.59. Consequently, DTDE-div demonstrated superior performance compared to other advanced DE variants. The results highlight the effectiveness of div in enhancing solution precision while preventing premature convergence. Full article