Search Results (14,173)

The COVID-19 pandemic accelerated telehealth adoption, but disparities in digital access hinder its potential, especially for older adults in nonmetropolitan areas. This study examined associations between sociodemographic factors and access to select digital resources among nonmetropolitan Medicare beneficiaries. This cross-sectional study used the 2022 Medicare Current Beneficiary Survey Public Use File, including 1732 Medicare beneficiaries aged ≥65 in nonmetropolitan areas. The dependent variable of digital access was categorized as (1) access to both a computer/tablet and the internet, (2) access to either, and (3) access to neither. A survey-weighted multinomial logit model was conducted to examine associations between sociodemographic factors and digital access, with no access to either a computer/tablet or the internet as the reference category. Approximately 71.7% of nonmetropolitan beneficiaries had both computer/tablet and internet access, 14.4% had one or the other, and 13.9% had neither. About one-third of study beneficiaries lacked full digital access. Older age, male, minority race/ethnicity, lower education, and lower income were associated with reduced digital access among nonmetropolitan beneficiaries. Targeted interventions to expand digital access for these at-risk populations are needed. Full article

Grocery shopping remains a heavily car-dependent activity in urban areas, even for short-distance trips within residential neighbourhoods. A primary barrier to shifting toward active travel (walking or cycling) is the physical burden of carrying heavy or bulky goods. This study investigates whether a retailer-managed home delivery service could encourage consumers who currently rely on motorised modes for grocery shopping to shift towards active travel while preserving the in-store shopping experience. The analysis focuses on urban Italian consumers who currently use motorised modes for grocery shopping. Using a Stated Preference (SP) experiment and a Mixed Logit (MMNL) model (n = 88), we analyse the conditions under which such a service may encourage the adoption of active travel modes and support proximity-based shopping patterns. Given the exploratory nature of the study and the small, non-representative sample, the findings should be interpreted as preliminary evidence for urban motorised grocery shoppers rather than as representative of the Italian population. The results indicate a substantial willingness among respondents to adopt the proposed service configuration. Delivery time, service cost, and the availability of delivery time-window selection emerge as critical factors influencing consumers’ choices. Acceptance of the service is also influenced by perceptions of walking and cycling infrastructure quality, trust in the integrity of delivered groceries, preferences for local products, and concerns regarding the working conditions of delivery personnel. Additionally, the model reveals significant heterogeneity in preferences regarding delivery by drone/autonomous vehicle and a 100% reduction in greenhouse gas emissions relative to conventional motorised transport. Younger respondents exhibit a more favourable attitude towards automated delivery technologies, while differences in the valuation of environmental benefits emerge between male and female respondents. The findings suggest that retailer-managed home delivery may represent a promising mechanism for encouraging active travel among current motorised grocery shoppers, while maintaining consumers’ relationship with neighbourhood retail services. These results provide retailers and urban policymakers with valuable insights, suggesting that appropriately designed delivery services may support more sustainable and proximity-oriented shopping behaviours. Such services could potentially contribute to maintaining the accessibility and vitality of neighbourhood retail activities, particularly in ageing urban contexts. Full article

Building information modeling (BIM) and artificial intelligence (AI), through their deep integration, are ushering in a new era for the construction industry. However, due to insufficient practical experience and exploration, construction companies still face significant challenges when adopting integrated BIM and AI technologies. This study aims to explore the integration of BIM and AI in depth by identifying and analyzing key barriers. Through systematic literature review and expert consultation, 16 major obstacles were identified. On this basis, a radial basis function neural network-enhanced weighted-influence non-linear gauge system (RBF-WINGS) model was constructed to calculate the weights of each barrier and deconstruct its non-linear interaction structure. The results indicate that data-related factors constitute the core category of barriers, while organizational culture and technological factors are the key secondary barriers. This study provides a theoretical basis for the development of China’s construction industry and serves as a decision-making reference for government departments. Full article

Background/Objectives: Special populations, including pediatric, geriatric, and organ-impaired patients, are consistently underrepresented in randomized controlled trials (RCTs), resulting in limited evidence for safe and effective dosing. Off-label use is common, and variability in drug exposure and response increases the risk of adverse drug reactions (ADRs). This review aims to examine how integrating pharmacometrics (PMX) with real-world data (RWD) can address evidence gaps by supporting dose optimization, population expansion, and safety evaluation in these vulnerable groups. Methods: A narrative literature review was conducted using PubMed, Embase, and Web of Science (January 2000–November 2025). Using Boolean combinations of PMX and RWD-related search terms, approximately 200–300 records were identified across the three databases; approximately 30 full-text articles were reviewed, and representative case studies were selected based on population diversity, methodological variation, and regulatory or clinical impact. Results: RWD–PMX integration has been applied across three domains: (i) dosing optimization through therapeutic drug monitoring (TDM)-informed PopPK modeling and model external validation in pediatric and neonatal populations; (ii) population expansion supporting dose extrapolation and regulatory decision-making for unapproved groups; and (iii) safety evaluation enabling identification of exposure–toxicity risk factors in vulnerable cohorts. Conclusions: Integrating PMX with RWD provides a practical and mechanistically grounded framework for evaluating dosing, treatment eligibility, and safety in populations insufficiently represented in clinical trials. Accumulating evidence indicates that RWD–PMX methodologies can complement traditional clinical research and inform regulatory decision-making. Continued refinement of data quality standards, validation practices, and guidance frameworks will be essential for broader adoption. Full article

Background/Objectives: As a key health information and communication technology, mobile health information services (MHISs) play a critical role in delivering health information, enabling remote monitoring, and supporting patient well-being. However, widespread resistance among older adults hinders their access to these information services and undermines these benefits. Employing the technology–personal–environment (TPE) framework, this study constructed and verified a comprehensive model to explain older adults’ resistance to MHIS use. Methods: Quantitative data from 430 elderly individuals aged 65 and above from China who participated in the free health check-up basic public health program were analyzed using structural equation modeling. Results: Technology access barriers, technology usage barriers, declining physiological conditions, and resistance to change were positively related to technology anxiety. Declining physiological conditions, resistance to change, social legitimacy power, and perceived institutional effort were negatively related to perceived autonomy. Additionally, technology anxiety was positively related to resistance to MHIS use, while perceived autonomy was negatively related to resistance to MHIS use. Conclusions: The findings clarify the mechanisms linking technological barriers, individual characteristics, and environmental factors to older adults’ resistance to MHIS use. Therefore, relevant health information service providers should adopt systematic actions that simultaneously alleviate technology anxiety through user-centric design and supportive training while fostering perceived autonomy by respecting older adults’ choices and enabling meaningful participation. These findings offer actionable insights for healthcare information system designers and providers to reduce older adults’ exclusion from digital health information ecosystems, thereby enhancing patient well-being among aging populations. Full article

Transbronchial lung cryobiopsy (TBLC) is increasingly used as a minimally invasive approach for tissue acquisition in the evaluation of interstitial lung disease (ILD), serving as an alternative to surgical lung biopsy (SLB) within multidisciplinary diagnostic pathways. Despite its growing adoption, variability in diagnostic yield and complication rates highlight the importance of procedural technique, probe selection, and freezing parameters. This narrative review summarizes the current landscape of TBLC, with emphasis on factors that influence diagnostic performance and safety, including procedural considerations involving endobronchial balloon blockade (EBB), radial probe endobronchial ultrasound (RP-EBUS), and cone-beam computed tomography (CBCT) for biopsy localization and airway management. Much of the existing experience is based on conventional cryoprobes, including 2.4 mm and 1.9 mm devices, typically used with freezing times of several seconds. While these approaches have defined the current role of TBLC in ILD, outcomes remain variable across centers, prompting continued refinement of procedural strategies to improve consistency. More recently, attention has expanded to include a broader range of smaller cryoprobe sizes—1.7 mm and 1.1 mm. Overall, this review provides a framework for understanding contemporary TBLC practice and highlights key areas where further study is needed to better define optimal technique and improve consistency in clinical outcomes. Full article

Activity-based learning (ABL), as a student-centered approach, has gained significant attention for improving student achievement in mathematics worldwide. However, its practical use in different mathematics classrooms remains less explored. This review examines empirical evidence on how teachers’ pedagogical skills affect students’ learning outcomes in mathematics classrooms. A systematic literature review was conducted, focusing on peer-reviewed original studies published in academic journals. Two prominent databases, Scopus and Web of Science (WOS), were used to investigate various aspects of ABL, including teachers’ ability to effectively implement ABL, challenges they face, its impact on students’ math learning outcomes, and how ABL influences students’ understanding, engagement, and academic achievement. The findings highlight that teachers’ pedagogical skills are crucial for successful ABL implementation in middle and high school mathematics classes. Additionally, effective ABL use depends on several other factors, such as teachers’ professional development and training, available resources, infrastructure, technological tools, school culture, curriculum, and leadership. The results suggest that ABL has the potential to improve students’ learning, engagement, and academic performance in mathematics when implemented effectively. The study recommends that educators, school leaders, and policymakers consider local conditions and professional factors when adopting ABL strategies. Full article

Contemporary art is part of societal transformations reflecting socio-economic challenges and needs within specific geographical environments. This exploratory visitor-centered assessment case study aims to investigate how the National Museum of Contemporary Art (MNAC) leverages temporary and permanent exhibitions not only as a form of artistic expression but also as a strategic tool for cultural accessibility. Interviews and field research were used to collect both visitor answers and observational data, which were further processed in the study with the help of word clouds, correlations, and statistical tests, particularly used to analyze nominal and categorical data. The main results show an important attractiveness of MNAC for educated and informed audiences, both residents and international visitors, who perceive the museum as moderately accessible, with further gaps to be addressed for people with different types of impairments. The main results of the exploratory factor analysis (EFA) (significant loads of underlying factors for internal physical and cognitive accessibility) underscore the relevance of adopting a holistic accessibility paradigm in the design and optimization of museum products. Full article

Achieving the coordinated development of the digital economy (DE), the tourism industry (TI) and the ecological environment (EE) is of great significance for regional sustainable development. This paper constructs a comprehensive evaluation index system for the digital economy–tourism industry–ecological environment (DTE) complex system. Indicator weights are determined via the entropy method, and the comprehensive development levels of the three subsystems in the Yangtze River Delta (YRD) region from 2010 to 2023 are systematically assessed. Based on this, the coupling coordination degree model is applied to measure the coordination of the DTE system, and the obstacle degree model is employed to identify the key factors restricting its coupling coordinated development. The results show the following: (1) From 2010 to 2023, the overall level of comprehensive development of the DE and EE in the YRD showed an upward trend, while the TI declined significantly during 2020–2022 due to the COVID-19 pandemic. (2) In terms of temporal evolution, the coupling coordination degree rose from 0.434 to 0.676 between 2010 and 2019, steadily improving from near disorder to primary coordination; although there were fluctuations between 2020 and 2023, it remained stable at a primary coordination level. Spatially, the region exhibited a “higher in the east, lower in the west” pattern. (3) From 2010 to 2019, the primary bottleneck in coordinated development stemmed from the DE subsystem; after 2020, the degree of constraints in the TI rose rapidly, creating a dual-system constraint pattern where the DE and the TI coexist. This study provides theoretical insights and practical recommendations for fostering positive DTE interactions in the YRD and offers valuable experience for other regions. This study has limitations regarding its research scale and indicator system, and it does not account for external influencing factors. Future research could adopt municipal or county-level analyses, apply causal inference methods such as panel Granger causality and system GMM, refine the evaluation index system, integrate internal and external factors, and thoroughly analyze the underlying mechanisms governing interactions within the DTE system. Full article

Equity in science education remains a pressing global challenge, as persistent gender disparities continue to shape students’ participation, motivation, and career aspirations in STEM fields. This study examines whether a curriculum-integrated STEAM (Science, Technology, Engineering, Arts and Mathematics) subject, implemented in a Portuguese private school with 82 lower secondary students (seventh and eighth grade), is associated with equitable engagement among boys and girls. A mixed-methods design was adopted, combining Likert-scale questionnaires with classroom observations. While qualitative data provided contextual insight into students’ overall participation and engagement in hands-on and creative activities, gender-related comparisons were based exclusively on quantitative questionnaire responses. Inferential analyses revealed no significant differences between boys and girls in their perceptions of the STEAM subject, their engagement with the activities, or their perceived skill development. These findings suggest that the design of practical, student-centred STEAM experiences may support comparable participation and engagement among involvement of both boys and girls. The results highlight the importance of curriculum design and pedagogical approaches as key factors in promoting more inclusive science education, with implications for policy and practice aimed at engaging all learners in science. Full article

This study, based on phase equilibrium theory, uses reservoir crude oil systems as the research object and adopts the Perturbed Chain-Statistical Associating Fluid Theory (PC-SAFT) equation of state. By combining the Panuganti characterization method with the three-phase Rachford–Rice algorithm, an integrated RRPC-SAFT engineering workflow is established, which effectively addresses the drawbacks of traditional PC-SAFT models, including low computational efficiency and poor convergence under extreme working conditions. On this basis, systematic performance comparisons are conducted between the RRPC-SAFT workflow and classical cubic equations of state (PR and SRK). Furthermore, the asphaltene phase behavior under gas injection development conditions is simulated, and the quantitative effects of the four SARA fractions on the critical precipitation conditions and precipitation intensity of asphaltenes are determined, clarifying the evolution rules and main controlling factors of asphaltene phase instability under various development scenarios. The research results reveal that the average relative errors of bubble point pressure and asphaltene onset precipitation pressure (AOP) for the three crude oil samples are all less than or equal to 5%. Compared with the PR and SRK models, the average prediction errors are reduced by 1.27% and 2.01%, respectively. Gas injection simulation results demonstrate that nitrogen poses the highest risk of triggering asphaltene precipitation under equimolar injection, with the asphaltene onset precipitation pressure increasing up to 114.94%. Single-factor analysis of SARA fractions verifies that saturates and asphaltenes aggravate precipitation, while aromatics and resins suppress asphaltene destabilization. In terms of computational efficiency, the computational speed of the RRPC-SAFT algorithm is four times higher than that of the traditional Gibbs free energy minimization algorithm. This model can be applied to calculate the thermodynamic critical equilibrium conditions of asphaltene precipitation, providing a thermodynamic basis for early screening of asphaltene deposition risks, optimization of gas injection schemes, and design of deposition prevention and control technologies. Full article

Artificial intelligence (AI) serves as a vanguard technology in the modern epoch, playing an essential part in fostering ecological and sustainable progress. By utilizing longitudinal data from Chinese A-share corporations between 2014 and 2023, this inquiry empirically explores how AI integration affects the green innovation bubbles of firms along with the governing mechanisms. Our evidence reveals that AI adoption exerts a significant inhibitory effect on such bubbles; for every one-standard-deviation uptick in AI utilization, there is a corresponding decline in green innovation bubbles of approximately 0.108 standard deviations. This finding remains robust across multiple robustness checks. Mechanism analysis shows that AI mitigates green innovation bubbles by enhancing green total factor productivity and reducing excessive managerial expenses. Furthermore, the expansion of the digital financial landscape and the exploitation of information assets bolster the repressive influence of artificial intelligence. Analytical tests for heterogeneity demonstrate that this influence is more significant for state-controlled corporations, businesses operating in non-polluting industries, and those headquartered within the eastern regions of China. Overall, the findings provide robust empirical evidence that AI adoption contributes to the governance of inefficient and inflated green innovation activities, while the causal interpretation of the results should remain cautious given the observational nature of the data and the limitations of the identification strategy. Full article

As the core automated execution components of blockchain technology, smart contracts enable programmatic control over digital assets; however, their immutable characteristics and inherent logical vulnerabilities give rise to substantial security risks. Although smart contract vulnerability detection methods based on large language models (LLMs) have exhibited certain potential in vulnerability detection and explanation, the coarse-grained modeling of traditional binary preference optimization paradigms hinders the model ability to learn the priority of domain-specific requirements, frequently leading to extreme optimization at the cost of detection accuracy. Furthermore, existing approaches fail to consider non-ideal factors in real-world application scenarios and overlook noise interference induced by missing prompts, which results in inadequate detection stability and reliability, making them challenging to adapt to complex practical scenarios. To address these critical issues, this study proposes a Noise-Aware Preference Reinforcement Large Language Model for Smart Contract Vulnerability Detection (NAPO-SCVD). This method adopts a four-stage framework consisting of data construction, continuous pre-training, supervised fine-tuning, and noise-aware preference optimization. Specifically, it enhances the model’s comprehension of contract syntax and semantics through domain-specific pre-training, improves its detection and explanation capabilities using high-quality datasets, constructs deliberately guided biased explanations to simulate noisy samples, refines preference gradients, and strengthens the model’s anti-interference ability. Consequently, this approach achieves high-precision and high-reliability smart contract vulnerability detection, along with fine-grained explanations. Full article

This comprehensive review investigates hybrid propulsion technologies as a pathway to decarbonization and improved energy efficiency in the maritime sector. Through a review of the recent literature, this study synthesizes current knowledge on energy management strategies and capacity sizing approaches for hybrid ship propulsion systems. Reported results indicate that optimized energy management can reduce fuel consumption and greenhouse gas emissions while minimizing total operational costs. Among real-time strategies, the Equivalent Consumption Minimization Strategy emerges as particularly suitable for maritime use due to its low computational demand and independence from full voyage profile knowledge, yet its maritime application remains far less developed than in the automotive domain. Capacity sizing and energy management are usually treated as separate optimization problems, limiting the achievability of truly optimal solutions. Only a few studies adopt integrated co-optimization frameworks, and these are typically built around simplified or fixed operational profiles. Moreover, the coupling between energy management parameters, such as the ECMS equivalence factor, and hardware sizing remains insufficiently explored. To address this, the review contributes a ship-specific classification of energy management strategies, a consolidated treatment of battery sizing methods with explicit attention to degradation, and a generalized two-loop framework that couples component sizing with ECMS-based energy management. The findings suggest that future research should prioritize adaptive energy management formulations calibrated for stochastic maritime duty cycles, the incorporation of battery degradation models into co-optimization, and validation against stochastic, real-world operating conditions. Full article

Background: Analysis of digital images of histopathological sections is increasing due to widespread adoption of fully digitised workflows and the greater availability of whole-slide scanners. Currently, hormone receptor status in breast carcinoma is assessed by pathologists scoring separate immunohistochemically stained sections. Methods: In this study, we employed pathologist-verified pixel-level annotations to train nested pixel classifiers capable of making case-level predictions of oestrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status directly from H&E-stained sections using biopsy cases alone. The model was evaluated on both an internal test set and an external international evaluation set from an institution in a different continent using different scanner hardware without the need for image normalisation. Results: In the internal test set, the models achieved AUCs of 0.8030, 0.7956 and 0.7488 for ER, PR, and HER2, respectively, with AUCs of 0.7008 and 0.7488 for ER and PR using an external cohort from an institution from which no cases were used for training. Conclusions: Our data highlight a potential strategy by which a pixel-based classifier, typically developed to quantify histological features within individual cases, could be used to make case/slide-level predictions but illustrate the challenges associated with this approach. Full article