Search Results (15,049)

Background: Stroke remains a leading cause of mortality, disability, and health-system burden in Korea’s rapidly aging population. We aimed to describe national stroke incidence trends from 2011 to 2023, characterize ecological associations between stroke incidence and health-screening indicators, and generate model-based projections through 2028 to support health-system planning. Methods: This retrospective ecological longitudinal analysis used three publicly available aggregate national data sources: (1) NHIS annual aggregate statistics on crude and age-standardized stroke incidence, stroke case counts, first-onset vs. recurrent stroke, and case-fatality rates (2011–2023); (2) regional standardized health-awareness survey rates for stroke symptoms, myocardial infarction symptoms, blood pressure, and blood glucose (2017–2025); and (3) national cancer-screening outcome tallies for breast and cervical cancer (2010–2024). All analyses used pre-aggregated annual summary data; individual-level NHIS records were not used. Annual trends were modeled with ordinary least-squares linear regression (n = 13 annual observations). Pearson correlations were computed only for overlapping observation windows. Model-based projections are presented with 95% prediction intervals and are explicitly distinguished from observed NHIS values. This study is purely descriptive and ecological; no causal inference is made. Results: Crude stroke incidence increased from 199.2 to 221.1 per 100,000 (2011–2023; slope +2.32/year, R² = 0.83), whereas age-standardized incidence declined from 158.3 to 113.2 per 100,000 (slope −3.41/year, R² = 0.96), a pattern consistent with demographic aging as a contributing factor to growing absolute burden, though formal age-decomposition analysis would be required to confirm this inference. Total cases increased from 99,837 to 113,098; the 30-day case-fatality rate declined from 8.5% to 7.5%. Ecological correlations showed that blood glucose awareness was strongly negatively correlated with age-standardized incidence (r = −0.944, p = 0.001, n = 7), though these are ecological associations and must not be interpreted as individual-level causal relationships. Model-based projections estimate crude incidence near 230.7 (95%PI 219.2–242.2) and age-standardized incidence near 103.2 (95%PI 95.7–110.8) per 100,000 by 2026. Conclusions: Concurrent increase in crude burden and decline in age-standardized incidence reflects demographic aging as the primary driver of Korea’s stroke burden. Projections support integrated cardiovascular prevention, public health education, and age-sensitive service planning. All projections are short-horizon statistical extrapolations intended for policy scenario planning only and must not be interpreted as observed future NHIS outcomes. Full article

Radiation use efficiency (RUE) is closely associated with cotton biomass and yield, yet the synergistic regulation of phenotypic structure and physiological potential remains unclear. A field experiment (2024–2025) in Anyang, China, utilized three independent trials: six sowing dates (from 12 April to 12 May at 6-day intervals, S1–S6), six planting densities (1.5, 3.3, 5.1, 6.9, 8.7, and 10.5 × 10⁴ plants·ha⁻¹, D1–D6), and ten cultivars with distinct architectures (V1–V10). Feature importance and structural relationships were quantified via random forest (RF) and partial least squares structural equation modeling (PLS-SEM). Results indicated that delaying sowing reduced true leaf number (TLN) and plant height (PH), with the April 24 sowing (S3) optimizing leaf area index (LAI, 2.57) and light interception rate (iPAR, 0.61). Increasing density significantly enhanced population-level LAI, above-ground biomass, and RUE, despite a progressive decline in TLN. Among cultivars, CCRI 60 (V6) exhibited superior structural traits (PH: 72.94 cm; iPAR: 0.61), while CCRI 113 (V8) exhibited the highest maximum carboxylation rate (V_cmax, 88.9 μmol·m⁻²·s⁻¹) and RUE (4.88 g·MJ⁻¹). Across the comprehensive dataset (integrating the density, sowing date, and cultivar trials), iPAR exhibited the highest relative importance (42.01%) for RUE variation, while associated structural traits (PH, LAI, TLN) yielded a cumulative relative importance of 41.69%. RUE was strongly associated with biomass accumulation (path coefficient > 0.97), which subsequently optimized yield components. Conversely, within the cultivar-comparison subset, the relative importance of iPAR decreased to 17.95%, while V_cmax rose significantly to 19.20%. PLS-SEM indicated that canopy structure exerted a significant negative association with photosynthetic potential (V_cmax, J_max) within this cultivar subset (path coefficient ≈ −0.51), whereas enhanced physiological potential was positively associated with resource allocation to yield components (path coefficient ≈ 0.57). Consequently, mitigating the inherent trade-off between canopy structure and leaf photosynthetic capacity is critical for further improving RUE and cotton yield under similar production environments. Full article

This paper addresses bearing-only three-dimensional target localization using three cooperative UAVs under observation inconsistency and degraded geometry. A weighted point-to-line least-squares localization model is established to fuse multiple line-of-sight (LOS) observations derived from image measurements, camera calibration, and UAV poses. To handle unreliable measurements without ground truth, a reliability assessment mechanism is developed by combining geometric stability indicators with observation consistency metrics, enabling weak geometry and abnormal observations to be identified online. Based on this assessment, an adaptive optimization framework is introduced to perform residual-driven adaptive weighting and configuration optimization, thereby suppressing unreliable LOS measurements and improving the conditioning of cooperative geometry. Simulation results under four representative scenarios show that the proposed method consistently improves localization accuracy and robustness. The mean localization error is reduced from 0.545 m to 0.260 m under abnormal observations, from 0.355 m to 0.081 m under degraded geometry, and from 0.711 m to 0.280 m when both effects occur simultaneously. Statistical evaluations including RMSE, standard deviation, maximum error, confidence intervals, and box-plot analysis further demonstrate that the proposed framework effectively reduces error dispersion and improves robustness. Full article

Construction management, from the contractor’s perspective, is led by the Construction Manager (CM). The work motivation and leadership style of the CM are critical variables for the successful execution of construction projects. The scientific literature identifies participative leadership as the most effective style for mitigating conflicts among various stakeholders. However, analyzing the specific variables that influence a CM’s conflict resolution capacity remains an underexplored area. Furthermore, while the CM must act as a leader for their team (subcontractors, suppliers, etc.), they remain accountable to the contractor’s senior management. Therefore, this study aims to analyze the mediating role of CM motivation in the relationship between leadership and conflict resolution capacity using Partial Least Squares Structural Equation Modeling (PLS-SEM). In the construction industry, conflict resolution is not merely a situational fix but a critical process of capturing and externalizing tacit knowledge. Knowledge management and the ability to resolve conflicts in the construction sector are directly linked, critical, and strategic in nature. Construction is an industry characterized by fragmentation, the temporary nature of its projects, diversity of stakeholders (developers, builders, subcontractors, engineering firms) and a high level of uncertainty. In this environment, conflict is virtually inevitable. However, the way in which a CM handles a conflict determines whether it becomes a destructive dispute or an opportunity for improvement. Full article

With intensifying measures related to investor and policy requirements, corporate governance and sectoral environmental performance became a focal point for sustainability disclosure, especially in energy-intensive industries with high environmental externalities. This study evaluates whether corporate environmental governance practices in key sectors correspond to their pollution intensity and economic output, analysing a panel dataset across EU member states, for the 2000–2021 period. The empirical methodology includes ordinary least squares (OLS), fixed- and random-effects models, and dynamic system generalised method of moments (GMM) panel estimation to account for sectoral heterogeneity. Results prove that sectoral value added is an influential factor of greenhouse gas emissions, with carbon dioxide exhibiting the highest elasticity to economic activity, followed by methane emissions, and nitrous oxide displaying cross-country variations due to structural and regulatory differences. While services and manufacturing sectors partially decouple via cleaner technologies, overall growth positively correlates with emissions, and renewable energy offers limited mitigation due to scale and integration challenges. Conclusions emphasise robust governance frameworks in high-value energy sectors to meet EU climate-neutrality goals, as stronger environmental accountability attracts capital and supports sustainable development, underscoring the needs for targeted decarbonisation, regulatory coordination, and accelerated technological innovation within persistent industry disparities. Full article

This study discusses the influence of Digital Leadership (DL) on Sustainable Innovation Performance (SIP) in telecommunications companies. In addition to examining the direct effect of Digital Leadership, the study focuses on the mediating roles of Knowledge Sharing (KS) and Employee Engagement (EE). A sample of 412 employees was collected by a simple cross-sectional survey. A partial least squares structural equation modeling (PLS-SEM) approach was used for analyzing results. The study reveals that Digital Leadership directly and positively enhanced Knowledge Sharing but did not lead to a significant direct influence on Employee Engagement and Sustainable Innovation Performance. Moreover, Knowledge Sharing did not significantly influencing Sustainable Innovation Performance, a condition that was the strongest predictor of Sustainable Innovation Performance emerging from Employee Engagement. The mediation analysis shows that neither Knowledge Sharing nor Employee Engagement mediates the relationship between Digital Leadership and Sustainable Innovation Performance. The objective contribution of this study is to shed light on the idea that Digital Leadership and Sustainable Innovation Performance are not directly related but may instead reflect other circumstances or contextual conditions. The research offers practice advice in showing that Employee Engagement benefits organizational sustainable innovation results by urging companies to consider not only Digital Leadership strategies but also alternative strategies that foster employee involvement. Full article

While the operational benefits of Artificial Intelligence (AI) are well-documented, the mechanisms through which firms leverage AI for strategic exploration and radical innovation remain under-theorized. This study addresses the “black box” of AI value creation by integrating the Technology–Organization–Environment (TOE) framework with the Dynamic Capabilities View (DCV). We propose that AI adoption is not a direct antecedent to performance but a multi-stage process wherein technological, organizational, and environmental factors enable the development of sensing capability, which in turn fosters a novel capability we term “AI-Enabled Exploration.” Analyzing survey data from 245 senior executives in Saudi Arabia, a high-growth economy undergoing state-led digital transformation, we employed Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the model. The results confirm a serial mediation chain: organizational readiness and technology compatibility drive sensing capability, which subsequently powers AI-enabled exploration to enhance innovation performance. Contrary to expectations, government support was not a significant predictor of sensing capability, suggesting that in resource-rich environments, external incentives are necessary but insufficient for capability building. Furthermore, competitive pressure was found to positively moderate the relationship between organizational readiness and exploration, acting as a critical catalyst that converts latent resources into active experimentation. These findings offer a theoretical roadmap for firms attempting to transition from AI-driven efficiency to AI-driven ambidexterity. Full article

Understanding the impact of neighborhood-level factors on stroke prevalence is crucial for addressing existing disparities. However, there is a distinct lack of ecological studies at the census tract level that investigate the social determinants of health (SDOH) influencing stroke prevalence within the U.S. Health and Human Services Region 3 (HHS Region 3: Delaware, Maryland, Pennsylvania, Virginia, West Virginia, and the District of Columbia). This study adopted a multivariate modeling approach to investigate the association between the 13 indicators of the Health Opportunity Index (HOI) and stroke prevalence at the census tract level in HHS Region 3 using four HOI indicator profiles and to highlight the specific SDOHs that are most associated with stroke prevalence. The four HOI indicator profiles include: (a) neighborhood and built environment profile, (b) social and community context profile, (c) resource profile, and (d) economic profile. The methodological approach was quantitative, using secondary data. The sample size was 8021 census tracts. The HOI was estimated for each census tract in the study area. Ordinary least squares regression (OLS) analysis and spatial lag model (SLM) were run to examine whether the 13 indicators of the HOI (categorized into four profiles) reliably predict stroke prevalence and to determine the most appropriate model that best identifies the strongest predictors of stroke prevalence. The results show that affordability, education, spatial segregation, and income inequality indicators were the strongest predictors of stroke prevalence in HHS Region 3. This granular research identifies the neighborhood-level SDOH most strongly linked to stroke prevalence, which can be leveraged to guide the development of targeted public health programs, quality improvement initiatives, resource allocation, and policy creation to combat stroke-related morbidity and mortality across census tracts in HHS Region 3. For example, the built environment, encompassing factors like employment access, affordable housing, and walkability, profoundly influences stroke prevalence and provides urban planners with practical insights for developing healthier, more equitable communities, such as creating neighborhood parks to encourage physical activity, a key factor in stroke prevention. This study also provides neighborhood organizations with the evidence needed to pursue grant funding and raise awareness about the socio-structural influences on stroke outcomes in their respective neighborhoods. Lastly, the insights generated from our study can facilitate collaborative decision-making processes with communities in HHS Region 3 regarding the prioritization of neighborhood-level SDOH for targeted public health interventions. This prioritization should focus on addressing predictors of stroke prevalence that are congruent with the community’s established priorities, thereby maximizing cost savings. Full article

This study analyzes the determinants of self-reported behaviours and perceptions associated with pesticide toxicity risk in children using the Social Practice Theory framework, linking individual factors and agricultural practices to understand vulnerability and prevention opportunities. This research was conducted in Pattapang Village, Tinggimoncong District, Gowa Regency, South Sulawesi Province, Indonesia. To examine the relationship between pesticide use patterns, social norms, competence, material, and individual aspects and the risk of sensitive toxicity in children, data were analyzed using structural equation modeling-partial least squares (SEM-PLS) with bootstrapping resampling. Pesticide use patterns had a significant negative effect on toxicity risk. Competence was the strongest predictor of pesticide use patterns, followed by materials and short-term goals. Personal values dominate personal norms and long-term goals, while social norms only influence personal norms. Self-efficacy, personal norms, and long-term goals showed no significant effects. The novelty of this research lies in the integration of a socio-ecological approach with individual psychological factors in a comprehensive structural model that explains the complex mechanisms of children’s protective behavior formation from pesticide toxicity, identifying that personal values—not personal norms or self-efficacy—are the most effective leverage points for farmer behavior change interventions. Full article

The dense tree canopy in the complex orchard environment obstructs wireless positioning signals and generates NLOS interference, which reduces the positioning accuracy of agricultural mobile robots. This study investigates a localization method for agricultural mobile robots based on two UWB tags and an electronic compass. By analyzing the NLOS interference factors and error sources of UWB, a method for NLOS interference suppression and positioning correction employing two UWB tags tightly coupled with heading angle was proposed. The construction of the heading angle L2IB system and its comprehensive process were also introduced as follows. The proposed method constructs candidate localization domains for dual UWB tags based on multilateration and integrates the inter-tag distance and heading-angle constraints within an L2IB framework to suppress NLOS-induced errors and estimate the robot center position. Experiments were performed under four simulated scenarios, namely line-of-sight (LOS), single-anchor occlusion, multi-anchors occlusion, and single-tag occlusion. The proposed method was compared with the centroid and least-squares methods. The results demonstrate that the L2IB method effectively improves localization accuracy under NLOS conditions. Specifically, in the single-tag NLOS interference scenario, the MAE, RMSE, and maximum localization error were 3.7, 4.0, and 6 cm, respectively. These results indicated that the system could meet the positioning needs of most NLOS environments in the orchard. Therefore, the proposed method exhibits feasibility and provides a new alternative for high-precision localization of mobile robots in orchards under NLOS conditions. Full article

While Zanthoxylum bungeanum Maxim. (Z. bungeanum) pericarps are a globally prized spice, their leaves are frequently discarded as agricultural waste. This study systematically characterizes the aromatic potential of leaf by-products compared with traditional pericarps under diverse extraction strategies, utilizing an integrated flavoromics and sensomics approach. Qualitative GC-MS-O analysis revealed that leaf-derived fractions possess superior aromatic diversity: leaf essential oil and volatile solvent extract yielded 71 and 68 odorants, respectively, significantly surpassing pericarp counterparts (65 and 43 compounds). Concurrently, HS-GC-IMS profiling confirmed that targeted extraction allows leaf-derived flavors to replicate and exceed traditional spice complexity. Specifically, the leaf solvent extract achieved aromatic parity with pericarps by effectively mirroring the core spicy–citrus profile through cuminaldehyde and limonene retention. Conversely, distilled leaf essential oil unlocked a distinctive herbal–woody sensory innovation, driven by eucalyptol and a broader variety of aldehydes and ketones. Sensomics validation, incorporating aroma recombination, omission experiments, and partial least-squares regression modeling, conclusively identified β-myrcene, limonene, caryophyllene, and humulene as core molecular markers dictating these perceptual shifts. Ultimately, this research provides a robust theoretical foundation for upcycling Z. bungeanum leaves into valuable flavoring resources, facilitating circular bio-economy practices by delivering functional equivalence and entirely novel sensory experiences for the global food industry. Full article

Green hydrogen—hydrogen produced from renewable electricity—is central to global decarbonization strategies. However, despite their fragile governance, damaged infrastructure, water scarcity, and limited investment security, conflict-affected developing economies remain largely absent from hydrogen research. This study addresses that gap by developing and validating a multi-evidence strategic framework for green-hydrogen (GH₂) adoption in fragile institutional environments, using Palestine as a challenging test case. Methodologically speaking, the framework integrates four evidence streams—barrier prioritization by 45 Palestinian experts using the Analytic Hierarchy Process (AHP); structural modeling of barrier–adoption–sustainability relationships using partial least squares structural equation modeling (PLS-SEM); strategic-pathway ranking using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS); and an original Sustainable Development Goal (SDG) Contribution Index—externally validated by an independent panel of 120 energy experts across 18 Middle East and North Africa (MENA) countries. Three findings stand out. Firstly, expert perception and structural evidence diverge: technical barriers receive the highest expert weight (56.2%) yet show the weakest structural effect on adoption (β = −0.230), whereas social barriers, weighted lowest by experts (4.8%), rank second in predictive power (β = −0.310). Secondly, Small-Scale Community Production is the most robust deployment pathway, ranked first under every weighting scenario tested. Thirdly, government policy quality acts as a governance multiplier, raising the sustainability returns of adoption by 20.2%, with benefits concentrated in SDGs 7, 13, 8, and 9. Practically speaking, the framework yields seven strategic goals and a phased 2026–2040 roadmap for fragile developing economies. Full article

This study examines the role of artificial intelligence (AI) in marine insurance within the Moroccan maritime sector. Drawing on Dynamic Capabilities Theory, the study investigates the relationships among AI Adoption, Risk Assessment Accuracy, Fraud Detection Capability, Claim Processing Efficiency, and Customer Trust, while also examining the mediating role of these operational capabilities. A quantitative survey was conducted among maritime and insurance professionals operating within the Tangier Med and Casablanca port ecosystems, and the data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicate that AI Adoption is positively associated with Risk Assessment Accuracy, Fraud Detection Capability, and Claim Processing Efficiency. These operational capabilities are also positively associated with Customer Trust and function as significant mediating pathways between AI Adoption and stakeholder confidence. The study contributes to the emerging literature on AI applications in marine insurance by providing empirical evidence from an emerging maritime economy and offers theoretical and practical implications for insurers, maritime operators, and policymakers. Full article

This study examines the association between corporate governance and financial performance and investigates whether real earnings management (REM) mediates this relationship in an emerging-market context. Using a balanced panel of 434 nonfinancial listed firms in Vietnam from 2020 to 2024, yielding 2170 firm-year observations, the study employs feasible generalized least squares (FGLS) after diagnostic tests indicate heteroskedasticity and autocorrelation. The Durbin–Wu–Hausman test does not indicate significant endogeneity in the current model specification. REM is measured using the Roychowdhury-based approach, and mediation effects are examined through sequential regressions. Tobin’s Q is used for robustness testing, and a two-step System GMM is used as an additional robustness test. The results show that board size, institutional ownership, and state ownership are positively associated with financial performance, while board independence is negatively associated with performance. Board financial expertise has no significant direct relationship with performance. REM is negatively associated with financial performance and serves as a mediating channel in the governance–performance relationship. The study contributes to the corporate governance literature by showing that REM can transmit governance effects to firm performance in an emerging market characterized by evolving enforcement, state ownership, and potential gaps between formal and substantive governance mechanisms. Full article

Accurate prediction of overpressured formations in deepwater is important for drilling safety and reservoir evaluation. However, conventional two-step inversion workflows are affected by cumulative errors and parameter crosstalk, which limits their ability to characterize the sharp pressure-transition interfaces at the top of overpressured zones. In this study, we propose a direct prestack nonlinear inversion method for the formation pressure coefficient (λ), a dimensionless and drilling-relevant indicator of overpressure intensity. Unlike previous exact-Zoeppritz direct inversions that target effective stress or elastic moduli, here a single formation pressure coefficient drives the pressure-sensitive rock-physics chain—linking pore pressure, effective stress, and pore-space stiffness to the seismic response—thereby reducing the number of free inversion variables. This single-parameter mapping is then coupled with the exact Zoeppritz equation to build a nonlinear prestack forward operator, helping to reduce the parameter coupling and error propagation associated with conventional multiparameter inversion workflows. To describe the typical blocky structural features of overpressured strata, a nonconvex Lp-norm (0 < p < 1) regularization is introduced as a structural prior, and a decoupled optimization strategy combining the alternating direction method of multipliers (ADMM) and iteratively reweighted least squares (IRLS) is developed for a stable solution. In a single pseudo-well synthetic test, the proposed method achieved a higher correlation coefficient and lower root mean square error (RMSE) than the indirect workflow, indicating improved agreement with the reference formation-pressure-coefficient profile. Application to field seismic data from the Yinggehai Basin, South China Sea, shows that the method produces clearer pressure-transition boundaries and pressure-coefficient profiles more consistent with the available well constraints. These results suggest that, under the tested conditions, the proposed method can provide useful geophysical support for pressure prediction and the characterization of deepwater overpressured reservoirs. Full article