Search Results (52,374)

This short communication synthesizes evidence on how the Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) altimetry data are used by private sector actors and the implications for economic value creation. Using secondary research that collected and summarized information from existing data from reports, journals, websites, and databases, the work identifies 54 companies across 9 sectors leveraging ICESat-2-derived elevation, canopy height, bathymetry, and surface measurements to inform decision-making, risk assessment, and new business models. The analysis situates ICESat-2 within a broader context where freely available Earth observation data can generate substantial private- and public-sector value, potentially exceeding hundreds of billions in aggregate when scaled across industries such as geospatial services, climate management, real estate, and insurance. The paper uses a four-pillar conceptual model to guide valuation of data-driven impacts: Data Utility (intrinsic information value of altimetry and related metrics), Decision Impact (tangible economic benefits from improved models and operations), Strategic Integration (emergence of new business models and market opportunities), and Data Ecosystem Exclusivity (development of proprietary datasets and workflows that enable competitive differentiation). Empirical findings illustrate how these pillars manifest in practice. The paper seeks to connect private-sector uptake to NASA’s Earth Science to Action framework and related capacity-building efforts, highlighting pathways for broader utilization through training, tutorials, and accessible interfaces. Limitations of the study include partial sector coverage and reliance on publicly reported use cases. Future work should quantify economic returns with standardized metrics and extend the dataset to capture dynamic shifts in data products, governance, and IP development within the evolving data ecosystem. Full article

Modeling and forecasting nonstationary and nonlinear economic time series remain fundamentally challenging due to structural breaks, volatility clustering, and noise contamination that distort the intrinsic stochastic structure. To address these limitations, this study proposes a novel three-stage hybrid statistical framework that systematically integrates multi-level signal decomposition with structured parametric modeling to enhance predictive accuracy. The proposed hybrid architectures—EMD–EEMD–ARIMA, EMD–EEMD–GMDH, and EMD–EEMD–ETS—employ a hierarchical decomposition–reconstruction strategy before forecasting. In the first stage, Empirical Mode Decomposition (EMD) decomposes the observed series into intrinsic mode functions (IMFs) and a residual component. In the second stage, Ensemble Empirical Mode Decomposition (EEMD) is applied to further refine the extracted components, mitigating mode mixing and improving signal separability. In the final stage, each reconstructed component is modeled using ARIMA, Exponential Smoothing State Space (ETS), and Group Method of Data Handling (GMDH) frameworks, and the individual forecasts are aggregated to obtain the final prediction. Empirical evaluation based on a recursive one-step-ahead forecasting scheme demonstrates consistent numerical improvements across all standard accuracy measures. In particular, the proposed EMD–EEMD–ARIMA model achieves the lowest forecasting error, reducing the root-mean-square error (RMSE) by approximately 6–7% relative to the best-performing single-stage model and by about 3–4% relative to the two-stage EMD-based hybrids. Similar improvements are observed in mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), indicating enhanced stability and robustness of the three-stage architecture. The results provide strong numerical evidence that multi-level decomposition combined with structured statistical modeling yields superior predictive performance for complex nonlinear and nonstationary time series. The proposed framework offers a mathematically coherent, computationally tractable, and systematically structured hybrid modeling strategy that effectively integrates noise-assisted decomposition with parametric and data-driven forecasting techniques. Full article

Eucalyptus plantations are expanding rapidly in southern China, delivering economic benefits but also posing ecological risks, which creates a pressing need for precise, municipal-scale monitoring. Mapping eucalyptus with sub-meter resolution imagery, however, is confronted by two main challenges: (1) the pronounced multi-scale heterogeneity of fragmented stands, and (2) the difficulty in achieving precise boundary delineation due to shadowed and complex canopy edges. To address these, this study makes two primary contributions. First, we present the Eucalyptus Semantic Segmentation Dataset (ESSD)—a high-quality, pixel-level annotated dataset that includes geographic coordinates to support reproducible research. Second, we propose SDCNet, a task-specific deep learning network optimized for eucalyptus mapping. SDCNet incorporates a redesigned SD-ASPP module that leverages Deep Over-parameterized Convolution (DO-Conv) to capture multi-scale features, alongside a novel Coordinated Self-Attention Mechanism (CSAM) to enhance the accuracy of canopy boundary detection. Ablation studies confirm the effectiveness of each component. In benchmark tests against seven state-of-the-art semantic segmentation models, SDCNet achieves superior performance, obtaining a per-class Intersection over Union (IoU) of 88.83% and an F1-score of 93.81% for eucalyptus—an improvement of +2.24% in IoU and +1.71% in F1-score over the strongest baseline. Applied to Nanning City, SDCNet produces the first 0.3 m resolution eucalyptus distribution map for the region. This map reveals a critical finding: within the watershed of the Xiyunjiang Reservoir—Nanning’s primary drinking water source—eucalyptus plantations cover more than 50% of the forested area. This result provides the first quantitative, high-resolution evidence of potential hydrological risk at a municipal scale. Our work establishes an integrated framework that bridges advanced remote sensing with actionable forest governance, offering scientifically grounded support for ecological risk assessment and sustainable land-use policy. Full article

Chronic inflammation contributes to bladder cancer (BC) development and progression through dysregulated cytokine signaling and tumor–immune interactions. This case–control study investigated associations between IL6 rs1800795, TNF rs1800629, CCL2 rs1024611, and VEGFA rs699947 polymorphisms, circulating cytokine levels, clinicopathological characteristics, and autonomic nervous system balance assessed by heart rate variability (HRV) in 73 BC patients and 88 controls. Genotyping was performed using PCR–RFLP, serum cytokine levels were measured by ELISA, and associations were evaluated using logistic, linear regression, and survival analyses. No significant associations with BC risk were observed for IL6, TNF, or VEGFA variants. However, the CCL2 rs1024611 GG genotype was associated with increased BC risk (recessive model: OR = 5.82, p = 0.026). Stratified analyses showed a lower frequency of the IL6 rs1800795 C allele and TNF rs1800629 GA genotype in high-grade and muscle-invasive tumors, suggesting potential associations with reduced tumor aggressiveness. No polymorphism was associated with serum cytokine levels or disease-free survival. In BC patients, the TNF rs1800629 A allele was associated with higher parasympathetic-related HRV indices and lower sympathetic parameters, whereas no such associations were observed in controls. These findings indicate that genetic variation within inflammatory pathways may contribute to BC susceptibility and tumor phenotype and may also modulate neuroimmune interactions. Full article

Background/Objective: Cognitive frailty, the coexistence of physical frailty and cognitive impairment, is a potentially reversible and high-risk state for dementia. This study examined the association between Color Kanji Pick-out Test (CKPT) app performance and cognitive frailty independent of Mini-Mental State Examination (MMSE) scores in community-dwelling older women. Methods: In this cross-sectional study, the participants were 102 community-dwelling older women without dementia and with MMSE scores ≥ 27 (73.6 ± 6.0 years). Reversible cognitive frailty was defined as subjective cognitive decline (≥1 point in the cognitive domain of the Kihon Checklist) plus physical frailty or prefrailty, according to the Japanese Cardiovascular Health Study (J-CHS) criteria. Firth’s penalized logistic regression using three prespecified models, adjusted for age and education, was used to examine the independent associations between CKPT app performance and MMSE scores with reversible cognitive frailty. Results: Fourteen participants (13.7%) met the criteria for cognitive frailty. In separate models, higher CKPT app and MMSE scores were significantly associated with lower odds of cognitive frailty (CKPT: odds ratio [OR] 0.470, p = 0.019; MMSE: OR 0.548, p = 0.020). In a multivariable model including both measures, the CKPT app (OR 0.499, p = 0.031) and MMSE scores (OR 0.553, p = 0.031) remained independently associated with cognitive frailty, and this model had the lowest Akaike information criterion. Conclusions: The CKPT app performance was independently associated with cognitive frailty beyond global cognition. The CKPT app may detect subtle executive and attentional vulnerabilities not captured by the MMSE, supporting practical, objective, early screening and risk stratification of cognitive frailty. Full article

Environmental and financial datasets often display complex distributional characteristics, including heavy tails, high skewness and the presence of extreme observations. Traditional probability models such as the exponential, gamma or log-normal distributions may not adequately capture these behaviours particularly when modelling extreme events such as rainfall, pollution levels, stock returns or loss severities. By integrating the characteristics of Pareto and exponential distributions into an exponentiated framework that can describe datasets arising from environmental and finance fields, this study presents a novel three-parameter exponentiated Pareto exponential distributions using the exponentiated Pareto family of distributions with classical exponential distribution as the baseline model. This novel model extends the classical exponential distribution with the addition of extra shape parameters which simultaneously regulate the centre and tail behaviours of the new model. The statistical and mathematical characteristics of the proposed distribution are determined and studied. The maximum likelihood estimate approach is used in a conducted simulation exercise, and the estimator’s efficiency is evaluated as seen from the results. The practical applicability of the model is illustrated with four real-life datasets utilising model adequacy and goodness-of-fit measurements such as log–likelihood, Akaike information criteria and Bayesian information criteria. The data reveal that the proposed model gives a better fit than the models chosen as comparators, making the EPE distribution useful and robust in environmental and financial fields of study. Full article

The Associated Press (AP) Stylebook, endearingly called “the journalist’s bible,” contains thousands of entries outlining style rules and situational guidance. Designed initially for practitioners, the AP stylebook is a seminal resource at many journalism education programs. Its density and complexity as a learning material inherently poses cognitive load risks for novices—and yet—it remains notably under researched. This explanatory sequential mixed methods study explored journalism instructor axiology, pedagogy, and perceptions of teaching effectiveness when introducing AP Style to novice students. Findings revealed that while AP Style remains a pillar of U.S. journalism curriculum, experienced instructors sometimes feel uncertain about the effectiveness of their introductory pedagogy. They described a hodgepodge of methods and design constraints often incongruous with knowledge of human cognitive architecture. We problematize these findings through cognitive load research, recommend Cognitive Apprenticeship Model principles to reduce load-inducing strategies, and suggest directions for future research. Full article

Background: Respiratory syncytial virus (RSV) is a leading cause of hospitalization for acute respiratory tract infection (ARTI) in young children. Respiratory viral coinfections are frequently identified in RSV-related ARTIs, yet their impact on disease severity remains controversial and may vary according to the co-pathogen involved. In the context of evolving RSV prevention strategies, a clearer understanding of RSV coinfection phenotypes is needed. Methods: We conducted a multicenter retrospective cohort study of children aged ≤ 5 years hospitalized for ARTI at two Italian tertiary-care pediatric hospitals between 1 September 2022 and 30 April 2025. Children with laboratory-confirmed RSV infection detected by multiplex polymerase chain reaction were included. Patients were classified as having RSV monoinfection, RSV–rhinovirus coinfection, or RSV–non-rhinovirus coinfection. Severe disease was defined as a composite outcome including intensive care unit (ICU) admission, need for respiratory or hemodynamic support, or death. Association between infection status and severe disease was evaluated using a Poisson regression model with robust variance, adjusted for age, sex, and comorbidities. Results: Among 231 RSV-related hospitalizations, 118 (51.1%) were classified as RSV monoinfection, 65 (28.1%) as RSV–rhinovirus coinfection, and 48 (20.8%) as RSV–non-rhinovirus coinfection. Children with RSV–rhinovirus coinfection were older and had shorter hospital stays. Severe disease occurred in 80.5% of RSV monoinfections, 70.8% of RSV–rhinovirus coinfections, and 75.0% of RSV–non-rhinovirus coinfections. After adjustment, neither RSV–rhinovirus coinfection (adjusted risk ratio [aRR]: 0.93; 95% confidence interval [95% CI]: 0.80–1.13) nor RSV–non-rhinovirus coinfection (aRR: 0.99; 95% CI: 0.83–1.18) was associated with increased disease severity compared with RSV monoinfection. Conclusions: RSV–rhinovirus and RSV–non-rhinovirus coinfections were not associated with greater disease severity compared with RSV monoinfection in hospitalized children. These findings support pathogen-specific interpretation of multiplex diagnostic results and inform clinical risk stratification in the era of expanding RSV prevention strategies. Full article

This study examines the predictability of monthly excess returns for the “Magnificent Seven” U.S. technology firms using machine learning and economically motivated thematic feature grouping. Framed as a focused study of the most systemically consequential equity panel in modern markets—seven firms representing over 30% of the S&P 500—the analysis confronts a small-N, large-P environment where economically structured dimensionality reduction is essential. Using 154 firm-level characteristics categorized into 13 economic themes, we evaluate linear, penalized, tree-based, and neural network models in a small-N, large-P setting. Unrestricted models suffer substantial overfitting and fail to outperform the historical average benchmark out-of-sample. In contrast, theme-based models generate economically meaningful and regime-dependent predictive gains. Short-Term Reversal and seasonality exhibit stronger expansion-period predictability, while size and profitability perform better during recessions. Regularized linear models provide the most stable performance in limited-data environments, whereas nonlinear ensemble methods improve only when training windows are extended. The findings underscore the importance of economically structured dimensionality reduction and adaptive factor allocation in managing concentration risk among systemically important mega-cap firms. Full article

Recent findings in nutritional epidemiology report an association between high dietary phosphorus intake and increased cancer risk. Building on the author’s analysis of breast cancer incidence in the Study of Women’s Health Across the Nation (SWAN), this paper presents a theoretical–conceptual model and a hypothesis to guide further population-study replication. To strengthen the initial SWAN analysis signal, a sensitivity analysis increased the number of controls in the nested case–control design from four to five per case. This adjustment modestly raised the relative risk (RR) of breast cancer incidence among middle-aged women consuming >1800 mg/day of dietary phosphorus (compared with 800–1000 mg/day) from RR: 2.30 to 2.38 (95% CI: 0.95–5.95; p = 0.06), improving statistical precision from the original p = 0.07. However, the result remains an exploratory pilot signal, not a confirmed association. Because clinical trials cannot ethically expose participants to potential harm from phosphate toxicity, a confirmed association relies on observational research. As in historical tobacco–cancer investigations, secondary analyses are needed across large cohort studies to examine dietary phosphorus intake and incidence of major cancer types. Relevant cohorts include the Nurses’ Health Study, Women’s Health Initiative, Health Professionals Follow-Up Study, National Health and Nutrition Examination Survey (NHANES) Epidemiologic Follow-Up Study, European Prospective Investigation into Cancer and Nutrition (EPIC), and the Canadian Study of Diet, Lifestyle and Health. Effect estimates can be synthesized using meta-analytic methods following PRISMA-P 2015 guidelines. Dietary phosphate modification may offer a cancer prevention strategy with substantial public health impact and clinical implications. Full article

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

This study reconstructs annual streamflow variability in the Yenice Stream Basin (northwestern Türkiye) for the period 1809–2020 using tree-ring data, substantially extending the short instrumental record (1979–2020). Three moisture-sensitive conifer chronologies were integrated using principal component analysis (PCA), and the first two principal components were employed as predictors in a multiple linear regression model calibrated against observed streamflow. The model explains a significant proportion of interannual variability (R² = 0.39; adjusted R² = 0.36; p < 0.001). Temporal stability was assessed using a 30-year moving-window correlation analysis, which reveals consistently positive and statistically significant relationships across all subperiods, indicating a stable and persistent calibration relationship through time. Years exceeding ± 1 standard deviation account for approximately 24% of the record, while extreme events (±2 standard deviations) represent about 5%. The reconstruction identified several extreme events, including severe drought years (e.g., 1840, 1887, and 1907) and extremely wet years (e.g., 1896 and 1936). Among these, 1887 stands out as one of the most severe drought years, while the period 1927–1928 represents a persistent low-flow episode. The reconstruction provides a long-term perspective on streamflow variability and contributes baseline information for regional water resource planning and hydroclimatic risk assessment. Full article

Background/Objectives: Patients involved in high-energy accidents (HEAs) are frequently admitted for inpatient surveillance despite normal clinical examination and imaging, although the yield of this practice is uncertain. This study evaluated the frequency and nature of clinical events, interventions during surveillance and missed injuries in such low-risk patients and explored potential predictors. Methods: Retrospective study at a Level I trauma center including patients ≥18 years admitted between January 2022 and September 2023 solely due to HEA mechanism, without apparent injury requiring inpatient treatment. Baseline characteristics, clinical presentation, imaging findings, and laboratory values were extracted. Outcomes included additional diagnostics, new diagnoses, therapeutic interventions, and missed injuries. Patients with eventful and uneventful stays were compared using univariate statistical tests. Results: Among 363 included patients, 86.0% experienced an uneventful stay. Fifty-one patients (14.0%) had an eventful stay, most commonly requiring additional radiological examinations (8.5%) or blood tests (6.9%). New diagnoses occurred in 6.6%, and 6.1% received additional therapeutic interventions. Missed injuries were detected in 3.9%, including two potentially life-threatening injuries (0.6%). No robust predictors for missed injuries were identified. Established predictors of missed injuries from broader trauma populations were absent in this selected low-risk cohort. However, individuals after bicycle accidents were significantly more likely to experience any event during their stay (p = 0.009). Conclusions: Inpatient surveillance of patients without apparent injury after HEAs has a low overall yield but occasionally identifies clinically significant conditions. As no reliable predictors for adverse events were found, selective admission remains challenging. Hybrid models combining short-term observation with structured outpatient reassessment may represent a resource-efficient alternative for low-risk patients. Full article

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