Search Results (12,149)

Digitalisation is transforming organisational practices, making digital readiness essential for strategic planning. However, customised digital maturity tools for the Irish Architecture, Engineering, Construction, and Operations (AECO) sector remain limited. This paper presents the development and validation of a Digital Maturity Gap Analysis Toolkit (DMGAT) for the Irish AECO sector. The toolkit assesses digital maturity across three dimensions—people, process and culture; technology; and policy and governance—covering 16 sub-dimensions and 69 assessment questions. Unlike existing tools such as the BIM Maturity Matrix, VDC BIM Scorecard, and Maturity Scan, the DMGAT uniquely integrates ISO 19650 maturity stages with a comprehensive maturity level matrix across three key dimensions, offering a customised, industry-specific assessment for the Irish AECO sector that combines structured benchmarking with actionable gap analysis. The toolkit supports gap analysis by comparing an organisation’s current maturity profile with the detailed descriptors of higher maturity levels (maturity level matrix), thereby enabling prioritised and context-specific improvement planning rather than pursuit of a uniform maximum level. The study uses a mixed-methods approach within a Design Science Research (DSR) framework, developing the tool across six phases: literature review, defining dimensions and key performance indicators (KPIs), prototype development, testing, refining and finalisation, and deployment for practical application and empirical evaluation within real organisational contexts in the Irish AECO sector, demonstrating its use as an operational diagnostic and learning tool. Alpha testing by the organisational research team refined structural enhancements including maturity stages, KPIs, and maturity matrix. Beta testing with 20 Irish AECO organisations confirmed the toolkit’s relevance, scope, and coverage. Participants highlighted its clarity and industry alignment, while suggesting minor improvements in wording, visuals, and support materials. This study concludes that DMGAT is a useful resource for informed decision-making and digital innovation in the Irish AECO sector. Full article

Background/Objectives: Schools, as structured social environments, are important settings for shaping lifelong eating habits, and teachers play a mediating role in nutrition education. This study aimed to assess nutrition knowledge among public school teachers, examine their perceptions of the school food environment, and identify factors associated with knowledge scores. Methods: A stratified cross-sectional survey was conducted among 736 teachers from 12 public schools during the fall of 2025. A structured questionnaire based on the Knowledge–Attitudes–Practice model was used to evaluate nutrition knowledge, teaching practices, perceived school food environment, and teachers’ observations of student food-related behaviors. Group differences were examined using t-tests and ANOVA, and multivariable logistic regression was applied to identify factors associated with low nutrition knowledge. Results: The mean knowledge score was 6.26 ± 2.64 out of 12, with 23.6% of teachers classified as having low knowledge, 59.9% satisfactory, and 16.4% good. Primary school teachers scored significantly higher than subject teachers (7.27 vs. 5.64; p < 0.001). Regular conduct of nutrition classes was associated with lower odds of low knowledge (adjusted OR 0.10, 95% CI 0.05–0.23, p < 0.001). A sensitivity analysis using continuous knowledge scores confirmed this pattern, with the absence of nutrition teaching predicting a 1.40-point reduction in scores (95% CI −1.86 to −0.94, p < 0.001). Teachers rated school meal quality moderately high, and frequently observed student refusal of canteen food and purchase of sweets or fast food outside school. Conclusions: This study found that public school teachers in Karaganda, Kazakhstan showed satisfactory overall nutrition knowledge, with gaps in applied understanding and common dietary myths. Primary teachers and those actively teaching nutrition had higher knowledge scores, indicating an association between pedagogical engagement and content knowledge. To help optimize the school food environment, educational policies would benefit from the development of cross-curricular instructional materials fitted specifically for secondary school subject teachers. Full article

The Air-front Smart City (ASC) concept is proposed to address the stagnation of industries in developed countries and stimulate economic growth in developing countries while maintaining a higher quality of life for people and contributing to decarbonization and overall United Nations SDGs in an existing study. However, no studies have been conducted to assess ASC policies. Therefore, this study integrates the integrated accessibility index into the quality of life (QOL) and quality of business (QOB) evaluation models to assess the startup ecosystem in Aichi, Singapore, and Munich within the ASC concept. The study uses survey data conducted in Aichi to estimate monetary values of QOL and QOB component indicators, calculates the integrated accessibility indices, and estimates QOL and QOB. Furthermore, the study sets scenarios to assess the impacts of living and business urban policies in Aichi. Additionally, the study using Aichi parameters compares the startup ecosystem in Singapore and Munich. The result shows that the key drivers of startup attraction are corporate tax rate, economic growth, and safety; enhancing these indicators directly increases startups’ QOB, business partners, and residents’ QOL. It was found that QOB in Singapore is comparatively higher, whereas QOL is higher in Aichi. Full article

Against the backdrop of dietary structure evolution and the “big food concept” strategy, there has been a shift from the traditional grain-centric perspective toward a diversified supply system. Taking Hubei Province—a major grain-producing region in China—as a case study, this research establishes a multi-criteria evaluation system and conducts analysis using statistical yearbooks and land survey data. By integrating natural conditions, economic benefits, and production capacity, the suitability of cultivated land for growing grain crops, cash crops, and forage crops is assessed. Concurrently, landscape pattern indices were applied to quantify the degree of farmland fragmentation. Employing a self-organizing mapping (SOM) neural network model, we synthesized suitability and fragmentation data to delineate differentiated farmland conservation zones. The results revealed significant spatial heterogeneity in crop suitability and fragmentation levels. High-suitability zones for grain crops were concentrated in the Jianghan Plain, while forage crops exhibited higher suitability in northeastern and southeastern Hubei. Farmland fragmentation showed a spatial pattern of lower levels in central Jianghan Plain, gradually increasing toward surrounding hilly and mountainous areas. SOM clustering effectively partitioned farmland into six functional zones: multifunctional agricultural zones, mixed farming zones, grain crop zones, cash crop zones, forage crop zones, and production improvement zones. This multi-source geographic and statistical data-driven zoning framework provides scientific basis for targeted policy interventions. It enables the quantitative management, quality enhancement, and spatial optimization of farmland resources, thereby operationalizing the big food concept to strengthen regional food security. Full article

Background/Objectives: In recent years, the migrant population has been increasing. Migrants are at risk for malnutrition, mental disorders and related health problems. In this study, we aimed to examine the relationships among diet quality, eating behavior, posttraumatic stress disorders and sleep quality in Syrian migrants. Methods: This study included 78 female and 72 male Syrian adult migrants. The participants completed a questionnaire including demographic information, the Pittsburgh Sleep Quality Index, the Posttraumatic Stress Disorder Short Scale, and a three-factor eating questionnaire. For the diet quality calculation, a 24 h retrospective food consumption record was taken. Results: A total of 94.0% of the participants had mild to moderate risk of posttraumatic stress disorder. It was determined that 90.7% of Syrian migrants had low or moderate diet quality. There was a positive correlation between the PTSD score and age (r = 0.244) and the Pittsburgh sleep quality index score (r = 0.244) and between the Pittsburgh sleep quality index score and uncontrolled eating (r = 0.171) (p < 0.05). The probability of impaired sleep quality increased in individuals with PTSD scores in the T2 (11–19 points) (OR: 1.342; 95% CI: 1.073–1.678) and T3 (20–31 points) (OR: 1.485; 95% CI:1.157–1.905) groups, whereas the probability of improved diet quality increased in individuals in the T2 (11–19 points) (OR: 1.042; 95% CI: 1.000–1.086) group. Conclusions: Poor diet quality, risk of posttraumatic stress disorder and poor sleep quality are very common among Syrian migrants. In this respect, evaluating Syrian migrants and taking necessary precautions may help reduce the risk of chronic diseases related to nutrient deficiencies and mental problems. There is a need for policies and programs to manage PTSD among Syrian refugees. Full article

Enhancing urban ecological resilience (UER) is crucial for mitigating soil erosion, improving land use efficiency, and preventing ecological degradation. The digital–real economy integration (DRI) plays a pivotal role in strengthening UER, offering a vital pathway for modernizing ecological governance systems and capabilities in the Yellow River Basin (YRB). Based on ecological resilience theory, this study establishes a three-dimensional evaluation framework centered on “resistance–recovery–adaptation”. Using panel data from 78 cities in the YRB from 2011 to 2023, we empirically examine the impact of DRI on UER. The results indicate that DRI significantly improves UER in the YRB, with notably strong positive effects on recovery and adaptation capacities, although there is no significant effect on resistance capacity. Mechanism analysis reveals that DRI promotes UER primarily through three channels: upgrading the industrial structure, strengthening government governance, and spurring green technological innovation. Heterogeneity analysis further shows that the positive impact of DRI on UER is more pronounced in downstream cities, urban agglomerations, non-resource-based cities, key environmental protection cities, green data center pilot cities, and informatization–industrialization integration pilot cities. Spatial analysis confirms DRI generating positive spatial spillover effects on the UER of neighboring cities. This study provides a theoretical basis for understanding the ecological governance potential of DRI and offers policy insights to support coordinated digital and green transformation in the YRB. Full article

Background: Many SCOR performance measures rely on internal operational data, which limits empirical work using public information. Methods: This study evaluates a small set of publicly auditable, SCOR-linked ratios (SCOR_E) in a panel of 12 publicly traded firms across four sectors from 2000 to 2022. Using firm- and year-fixed-effects panel models, the paper examines whether these candidate proxies show pre-specified directional associations within firms and whether the same ratios are associated with operating margin in parallel models. Instrumental-variable (IV) specifications are reported only as sensitivity analyses, and nearly all are weak by the paper’s reported first-stage diagnostics. Results: Accordingly, most findings are interpreted as associative rather than causal. After false-discovery-rate adjustment and weak-instrument-robust inference, only four firm–proxy pairs meet the paper’s detection criterion; all remaining estimates are treated as non-robust. Conclusions: The contribution is therefore narrow: this is a constrained exploratory screening exercise showing which candidate mappings survive the paper’s inferential filters in this sample and which do not. The results do not establish a validated cross-industry scorecard, a scalable benchmarking framework, or a basis for policy claims. Full article

The establishment of AI ethics bylaws in academia is needed for teaching, learning, and assessment. The adaptive parameters of these bylaws define the ethical, pedagogical, and operational standards for the use of artificial intelligence tools within academia. The main aim is to ensure that AI tools are used to enhance educational practices while preserving human judgment, safeguarding academic integrity, and promoting critical thinking. Specifically, these are intended to mentor all domains of academia to uphold the core values of fairness and transparency while adapting to the advent of modern technologies. While many are enthused by the support provided by large language models, it is also important to prevent over-reliance or misuse of AI technologies. This establishes clear responsibility for faculty, students, and administration. These significant bylaws pay more attention to these issues to provide a foundation for good governance, evaluation, and amendment of AI-related practices. To provide normative insight into the anticipated reception of these bylaws, we conducted a small exploratory pilot study with STEM faculty. The resulting observations offer preliminary indications of the feasibility of the proposed method for future research and policy development. Full article

Disaster resilience has become a key focus of risk reduction efforts, but measuring it remains complex due to differences in hazards, development paths, and data systems. This study modifies the Baseline Resilience Indicators for Communities (BRIC) approach, based on the Disaster Resilience of Place (DROP) framework, to evaluate community resilience in Serbia and highlight regional differences. An initial list of 186 indicators was created from international BRIC studies and resilience research, then tailored to Serbian conditions through contextual review and data checks. Indicators were normalized using min–max scaling (0–1), and indicators with negative orientation were inverted to ensure that higher values indicate greater resilience. Scores for each dimension were calculated as equally weighted averages across six areas: social, economic, social capital, institutional, infrastructural, and environmental. The overall BRIC index was derived as the average of these dimension scores. Z-scores facilitated the classification of resilience levels and the comparison between regions. The results show clear regional disparities: in the complete model, Belgrade has the highest resilience (BRIC = 0.557), while Southern and Eastern Serbia have the lowest (BRIC = 0.414). Patterns across dimensions show that Belgrade excels in social and economic capacity but lags in environmental indicators; Vojvodina has the strongest institutional and infrastructural capacity; and Šumadija and Western Serbia perform best in environmental indicators. Correlation analysis revealed multicollinearity, leading to the removal of 14 redundant indicators and the refinement to a set of 57. After this reduction, regional rankings change, with Vojvodina (BRIC = 0.530) and Šumadija and Western Serbia (BRIC = 0.522) emerging as higher-resilience regions, while Southern and Eastern Serbia remain the least resilient (BRIC = 0.456). The adapted BRIC-DROP model offers a clear, locally relevant tool for mapping resilience and guiding targeted policies in Serbia, enabling region-specific efforts to address structural resilience gaps. Full article

Due to the rapid process of urbanization and the threat of environmental hazards, the need to enhance the intelligence and resilience of urban infrastructure has emerged as a pre-eminent demand of sustainable urban development. This paper evaluates the smart-resilience of urban infrastructure in Kunming by creating a well-developed evaluation framework with reference to the DPSIR (Driving Force–Pressure–State–Impact–Response) model and using the Entropy Weight TOPSIS technique to measure infrastructure performance during the years 2020–2024. The study fills an existing gap in the literature regarding the integration of intelligence and resilience evaluation, as well as the dynamic obstacle diagnosis based on causal logic. It provides a transferable analytical framework and empirical evidence for the “smart-resilience” development of similar cities. The findings suggest that there is steady progress in infrastructure smart-resilience in Kunming, whereby the composite index grew from 0.330 to 0.597, which is equivalent to an average growth rate of about 16.0 per annum. In spite of this favorable tendency, there are a number of structural issues that remain unsolved. The driving force dimension is unstable with regard to long-term mechanisms of investment, and the responding dimension is lagging behind, indicating weaknesses in the governance capacity and inter-departmental coordination. Moreover, extreme weather events have become the major threat to infrastructure systems in the city, superseding traditional social and operational risks; consequently, the city has changed its risk profile. Obstacle factor analysis shows that state and response dimensions make up almost 60% of the total constraint level, which shows the significance of enhancing the effectiveness of management. The research findings are based on the proposal of specific policy actions, such as the creation of special infrastructure resilience funds, the enhancement of mechanisms relating to cross-departmental emergency responses, the implementation of risk-based engineering standards, and the creation of an integrated infrastructure data platform to facilitate efficient, resilient, and sustainable urban governance. Full article

Global progress towards the 2030 Sustainable Development Goals (SDGs) remains critically off track, with current trends indicating that only 17% of targets will be met by the deadline. As sustainability transitions increasingly depend on regional and institutional capacity, understanding heterogeneous transition pathways and resilience across territorial contexts is essential. This study investigates whether observed divergence in SDG performance reflects temporary setbacks or persistent structural regimes characterised by distinct institutional and technological configurations. Using panel data from over 160 countries (2019–2024), we employ annual latent class analysis to identify hidden structures in SDG performance across 15 goals, introducing intertemporal volatility as a dimension of development dynamics. We complement this with ordered logistic regression to examine structural determinants of regime membership, including governance quality, digital infrastructure, health investment, and macroeconomic indicators. Our analysis identifies three temporally stable development regimes—lagging, transitional, and leading—with fewer than 15% of countries transitioning between classes over the observation period. ANOVA results reveal that internet access and government effectiveness exhibit the most substantial between-regime differences. Ordered logit models indicate that governance quality and digital connectivity are the strongest correlates of regime membership (government effectiveness: β = 0.943, p < 0.001; internet penetration: β = 0.049, p < 0.001), whereas short-term GDP growth exerts negligible influence (p > 0.10). These findings challenge assumptions of linear convergence in sustainable development and provide a data-driven framework for evaluating transition dynamics across diverse territorial contexts. The results suggest that achieving the SDGs requires that deep structural constraints be addressed—particularly digital divides and institutional quality—through regionally targeted policy design rather than relying solely on incremental adjustments or economic growth. The identified regimes provide a basis for place-based targeting by distinguishing contexts where governance and digital capacity constraints are binding. Full article

Energy security represents a fundamental pillar of economic sustainability, being defined as a state’s ability to ensure continuous, reliable, and affordable access to energy resources. In the context of recent geopolitical shifts, such as worldwide military conflicts, the vulnerabilities of energy systems have become evident, highlighting the interdependence between energy security and economic stability. Analyzing energy security involves assessing the diversification of sources, supply routes, critical infrastructure, and the degree of dependence on imports. The transition to renewable sources, in line with the objectives established by the European Union, contributes to reducing the risks associated with fossil market volatility and to strengthening economic resilience. At the same time, the integration of digital technologies and the development of storage capacities increase the flexibility of energy systems. Evaluating energy security must include indicators regarding price accessibility, environmental sustainability, and institutional capacity for crisis management. By aligning energy policies with macroeconomic and climate strategies, states can prevent major energy crises, mitigate the impact of external shocks, and ensure long-term sustainable economic development. The study highlights and brings to light Romania’s energy security situation by conducting an in-depth analysis of the Romanian Power System and assessing the most severe vulnerabilities and risks that could jeopardize the proper functioning of the system and the supply to electricity consumers. Based on these findings, various strategies for the safety, security, and resilience of the Romanian Power System have been developed. Full article

Background/Objectives: Liver fat represents an early metabolic lesion in the development of diabetes and its cardiometabolic complications. Diets high in free sugars, particularly from sugar-sweetened beverages (SSBs), are associated with abdominal obesity and increased cardiometabolic risk, prompting global guidelines to limit SSBs as a major public health strategy. Low-fat cow’s milk is promoted as the preferred caloric replacement strategy for SSBs due to its high nutritional value and cardiometabolic advantages. Fortified soymilk is a plant-based alternative with approved health claims for cholesterol and coronary heart disease risk reduction that offers an equivalent nutritional value to cow’s milk. However, given concerns about its classification as an ultra-processed food (UPF), it is unclear whether soymilk offers comparable metabolic health benefits to milk as part of clinical and public health strategies to reduce SSB intake. The Soy Treatment Evaluation for Metabolic (STEM) health trial seeks to evaluate the impact of replacing SSBs with either 2% soymilk or 2% cow’s milk on liver fat and other cardiometabolic risk factors in habitual adult consumers of SSBs with obesity. Methods: The STEM trial is a 24-week, pragmatic, 3-arm, parallel, randomized trial. We recruited adults with obesity (high BMI plus high waist circumference based on ethnic specific cut-offs) consuming ≥1 SSB/day. Participants were randomized to one of three groups based on their usual SSB intake at baseline (servings/day): continued SSB (355 mL can) intake; replacement with fortified, sweetened 2% soymilk (250 mL); or replacement with 2% cow’s milk (250 mL). The primary outcome is the change in intrahepatocellular lipid (IHCL) measured by ¹H-MRS at 24 weeks. Hierarchical testing will be done to reduce the familywise error rate. The superiority of cow’s milk to SSBs will be assessed first to establish assay sensitivity. If superiority is established, then the non-inferiority of soymilk to cow’s milk will be assessed using a pre-specified non-inferiority margin of 1.5% IHCL units (assessed by difference of means using a 90% confidence interval [CI]). Analyses will be conducted according to the intention-to-treat (ITT) principle using inverse probability weighting (IPW) for superiority testing and per-protocol analyses for non-inferiority testing, using ANCOVA adjusted for age, sex, metabolic dysfunction-associated steatotic liver disease (MASLD) status, medication use, intervention dose, and baseline levels. We hypothesize that soymilk will be non-inferior to cow’s milk (Clinicaltrials.gov NCT05191160). Results: Recruitment began in November 2021. A total of 3050 individuals were screened. We randomized 186 participants (62 per group) between 19 April 2022 and 16 April 2024. Participants are 57% male; with a mean [SD] age of 39.9 [11.8] years; BMI of 34.6 [6.1] kg/m², waist circumference of 112.6 [13.8] cm; IHCL of 10.0 [8.2] % with 64.1% meeting the criteria for MASLD; and SSBs intake of 2.3 [1.3] servings/day. Conclusions: Baseline characteristics were balanced across the study arms, with participants representing adults with a high-risk metabolic phenotype, and 64.1% meeting the criteria for MASLD. Findings will contribute to evidence on the cardiometabolic benefits of soymilk, informing clinical practice guidelines and public health policy. Full article

Decision Transformers (DTs) reformulate reinforcement learning as a conditional sequence modeling problem and have demonstrated competitive performance in offline Reinforcement Learning (RL) scenarios. However, their behavior in card games, specifically partially observable imperfect-information, trick-taking games remains underexplored. In parallel, general-purpose card-game toolkits have shown the value of unified environments and standardized evaluation protocols for accelerating research in imperfect-information games. Motivated by the goal of creating a general card-game-playing framework, we present a unified RL pipeline for trick-taking card games using DTs. While classical learning methods have demonstrated strong performance in card games, transformer-based reinforcement learning remains comparatively underexplored in this domain. This paper studies the applicability of DTs to the core play-phase of trick-taking games and evaluates whether a single, reusable pipeline can be transferred across multiple games in this class with minimal game-specific engineering. We propose a unified framework integrating offline pretraining, online selective expert iteration, and inference-time legal-action filtering. Crucially, our proposed approach demonstrates two key advantages over standard implementations. First, the model successfully internalizes complex game rules (e.g., follow-suit constraints) implicitly from the empirical data distribution, completely eliminating the need for explicit action masking during training. Second, we introduce a selective expert iteration mechanism equipped with strict acceptance filtering, which effectively prevents distribution collapse and enables safe, monotonic offline-to-online policy refinement. Ultimately, we show that this single, reusable transformer-based pipeline achieves competitive performance across multiple trick-taking domains (Hearts, Whist, and Spades) with minimal game-specific engineering. Full article

Environmental protection and the reduction of carbon dioxide (CO₂) emissions are central priorities within European climate policy. This study analyses and forecasts annual CO₂ emissions in Greece using a univariate time-series framework. Annual data from 1960 to 2024, sourced from Our World in Data, enable the analysis to capture both the historical expansion of emissions and the recent decarbonization phase of the Greek energy system. Using the Box–Jenkins methodology, multiple ARIMA specifications were evaluated based on information criteria and diagnostic tests. To examine the stationarity properties of the series, the Augmented Dickey–Fuller (ADF) unit root test is applied. The findings indicate that the ARIMA (1,1,1) model most accurately represents the stochastic dynamics of the emissions series. The estimated autoregressive and moving-average coefficients, 0.9404 and −0.7165, respectively, are statistically significant at the 1% level. Residual diagnostics confirm the absence of serial correlation, approximate normality, and no significant heteroskedasticity. Forecast evaluation for the 2020–2024 holdout period demonstrates satisfactory predictive performance, with a mean absolute percentage error (MAPE) of approximately 6%. Dynamic forecasts for 2025 to 2030 indicate a gradual decline in national CO₂ emissions, reaching an estimated 45.5 million tonnes by 2030. Overall, the study demonstrates that parsimonious ARIMA models offer a transparent and empirically reliable benchmark for national emissions forecasting. These models provide a reproducible tool for monitoring climate policy outcomes and for supporting evidence-based environmental decision-making. This study contributes to the environmental forecasting literature by providing an updated, diagnostically rigorous univariate benchmark model for Greece’s CO₂ emissions that encompasses both the pre- and post-decarbonization phases of the national energy transition. Full article