Search Results (587)

Research on the coupling coordination relationship between land–use carbon emissions (LUCE) and ecosystem service value (ESV) is vital for achieving China’s “dual carbon” goals and advancing ecological civilization. Applying the carbon emission coefficient method, equivalent factor method, coupling coordination degree model, and Geographically and Temporally Weighted Regression (GTWR) model, this study examined spatiotemporal patterns of land–use changes, carbon emissions per unit area (ACE), and ecosystem service value per unit area (AESV) across China (excluding Xizang, Hong Kong, Macao, and Taiwan) from 2000 to 2020, along with their coupling relationship and driving forces. Results showed that grassland, forest, and cropland accounted for more than 70% of China’s land area, with a total land–use conversion area of 1.42 × 106 km². ACE increased while AESV generally decreased, both displaying strong spatial heterogeneity and agglomeration. The average Dagum Gini coefficients were approximately 0.70 for ACE and 0.58 for AESV. Coupling coordination degree mostly fluctuated around 0.20, rising initially and then falling, with distinct spatial variations in coordinated development types. Population density, Normalized Difference Vegetation Index (NDVI), and average elevation were negatively correlated with the synergistic effect between LUCE and ESV, whereas GDP density, annual precipitation, and average slope showed positive correlations. These findings provided theoretical support for formulating scientific land–use policies, optimizing carbon emission management, and implementing effective ecological protection measures. Full article

Background: This retrospective study evaluated pretreatment masseter muscle index (MMI), masseter radiodensity (HUAC), and Global Immune–Nutrition–Inflammation Index (GINI) as prognostic markers in patients with laryngeal squamous cell carcinoma treated with definitive radiotherapy. Methods: MMI and HUAC were measured on simulation computed tomography, and GINI was calculated from pretreatment laboratory values. Biomarkers were analyzed continuously using Firth penalized Cox models adjusted for age, performance status, tumor site, and T and N categories. Nonlinearity was assessed using restricted cubic splines. Joint models and multiple imputation assessed robustness. HUAC associations were assessed over time. Exploratory cutoffs were derived using censoring-aware three-year time-dependent receiver operating characteristic analysis, with radiotherapy initiation as time zero. Results: Among 146 patients, 30 died and 48 experienced progression or death; GINI was available in 116. Each standard-deviation decrease in MMI was associated with worse overall survival (OS; adjusted hazard ratio 1.92, 95% confidence interval 1.21–3.05; p = 0.002) and progression-free survival (PFS; 1.91, 1.35–2.71; p < 0.001). These associations weakened after further adjustment for body mass index and concurrent systemic therapy. GINI findings were not robust in adjusted, joint, and missing-data analyses. Lower HUAC showed an exploratory association with poorer outcomes later in follow-up, based on few late events. Three-year OS/PFS estimates were 92.5%/81.1% versus 68.9%/59.5% for MMI > 2.830 versus ≤2.830 cm²/m², and 90.2%/81.9% versus 45.1%/38.0% for GINI < 139.67 versus ≥139.67. The GINI cutoff was unstable, with a 95% bootstrap interval of 20.104–180.985. Conclusions: Lower MMI showed the most consistent prognostic association. This initial evaluation of GINI in laryngeal cancer supports further investigation of its prognostic potential. HUAC findings and cohort-derived cutoffs remain exploratory and require external validation. Full article

Aggregate livability indices consistently rank Fargo, North Dakota, among the most livable small-to-mid-sized U.S. cities, yet city-level scores obscure whether favorable average performance reflects equitable neighborhood-level access to essential amenities. However, evidence on whether favorable citywide livability rankings correspond to equitable neighborhood-level access in small- and mid-sized U.S. cities remains limited. To address this gap, this study conducts a tract-level spatial accessibility analysis in Fargo, examining access to grocery stores, healthcare facilities, and parks across 38 census tracts using road network-based nearest-facility assignment, a Composite Accessibility Index (CAI), and Gini coefficients. Results reveal that despite strong aggregate rankings, approximately 9700 residents (8.0% of the study-area population of 121,219) reside in the bottom composite accessibility quartile. Standard Gini coefficients ranged from 0.081 to 0.093, while population-weighted Gini coefficients were lower (grocery = 0.040; healthcare = 0.042; parks = 0.029; CAI = 0.029), indicating modest citywide inequality. Nevertheless, the worst-served peripheral tract recorded a CAI of 0, with network distances ranging from 6.9 to 7.8 miles across the three amenity types. A sensitivity analysis confirms robustness across threshold choices. These findings indicate that modest citywide inequality can coexist with localized accessibility disadvantages in peripheral tracts, supporting spatially disaggregated, equity-oriented livability assessment. Full article

MicroRNAs are frequently dysregulated in cancer, yet how their tissue specificity is remodeled during malignant transformation remains poorly characterized. Here, we systematically quantified the tissue specificity of miRNAs across normal (GTEx) and cancer (TCGA) tissues using the Tau index as the primary metric and the Gini index for independent validation. Our analysis revealed a negative association between tissue specificity in healthy tissues and changes in specificity during malignant transformation, indicating that miRNAs with higher tissue specificity in normal tissues tend to undergo greater loss of specificity in cancer. To robustly define dysregulation, we combined two independent analyses: a binomial test over per-project differential expression across 17 matched normal tissues within the TCGA cohort, and a TCGA–GTEx pan-tissue comparison of mean expression. The change in specificity (ΔTau) separated up- from downregulated miRNAs, showing moderate agreement with the binomial signal and a strong correlation with the expression-based contrast. Finally, we identified six miRNAs that lose tissue specificity upon transformation while remaining consistently upregulated (miR-519a-5p, miR-512-3p, miR-522-3p, miR-105-5p, miR-935, miR-1269a). Functional analysis of their experimentally validated and predicted targets showed significant enrichment for converging on core oncogenic programs. Collectively, integrating specificity dynamics with dysregulation evidence pinpoints candidate miRNAs with coordinated, cancer-relevant regulatory roles and highlights those with favorable tissue-specificity profiles that may warrant further investigation as potential therapeutic candidates. Full article

Beautiful City construction is a core initiative to realize the Beautiful China vision and advance China’s progress toward the Sustainable Development Goals (SDGs). This study develops a unified evaluation framework aligned with official Chinese assessment guidelines and global sustainability objectives. Based on a 2015–2022 dataset of 126 prefecture-level cities in the Yangtze River Economic Belt (YREB), we adopt Dagum’s Gini coefficient decomposition, kernel density estimation (KDE), and Moran’s I to investigate the spatiotemporal characteristics of beautiful city construction. The results indicate that the regional Beautiful City Construction Index (BI) increased steadily at both the regional and subregional levels, with all sample cities achieving continuous improvement. A consistent development gradient of lower reach > middle reach > upper reach was identified, with gradually narrowing inter-regional gaps. Importantly, the primary source of regional disparity shifted from inter-subregional differences to intra-subregional differences around 2021. Statistically significant positive spatial clustering was observed, with high-value clusters concentrated in downstream areas and low-value clusters in upstream areas, while the overall clustering effect gradually weakened. This study provides empirical evidence for targeted urban governance and regional coordinated development, offering a typical Chinese practice for global SDG research. Full article

Internationally, rising income inequality has become a critical policy concern, as social disparities often translate into economic and financial vulnerabilities. While economic freedom is generally associated with market efficiency and growth, its distributional consequences remain contested. This study investigates the interplay between economic freedom, financial development, and income inequality across 26 economies from 2002 to 2024, employing panel data techniques, mainly the system generalised method of moments (GMM). In addition to the core variables, the analysis incorporates inflation, economic development, and education to control broader macroeconomic influences. The results indicate that both economic freedom and financial development exacerbate income inequality, particularly when access to financial resources is skewed toward higher-income groups. These findings contribute to the growing empirical literature on economic freedom and the finance–inequality nexus and underscore the importance of inclusive financial policies in the Global South. Policymakers are urged to design interventions that expand equitable access to financial services, thereby ensuring that economic freedom and financial development foster fair income distribution rather than reinforce existing disparities. Full article

Against the backdrop of China’s Dual Carbon Initiative and national transportation empowerment strategy, accelerating the low-carbon green transition of the transport sector has emerged as an imperative developmental priority. Incorporating carbon emissions as undesirable outputs into the efficiency evaluation framework, this study adopts a multi-method analytical paradigm encompassing the super-efficiency SBM model, Malmquist–Luenberger index, kernel density estimation, Dagum Gini coefficient, geographical detector model, and Geographically and Temporally Weighted Regression (GTWR). Based on panel data covering 30 provincial administrative regions in China from 2004 to 2022, this paper systematically investigates the spatio-temporal evolutionary patterns and intrinsic driving mechanisms of carbon total factor productivity (CTFP) within the transportation industry. The main findings are as follows: (1) The static efficiency results reveal that the national mean CTFP is below unity, indicating overall inefficiency. Nevertheless, it exhibits a fluctuating upward trend after 2009. Regionally, CTFP follows this pattern: Eastern China > Central China > national mean > Northeastern China ≈ Western China. (2) Dynamic productivity analysis shows that the annual average ML index is close to 1, demonstrating an overall upward trend in CTFP, and productivity growth is primarily driven by technological progress. (3) In terms of spatio-temporal patterns, inter-regional disparities constitute the principal source of overall spatial gaps in CTFP, with considerable contribution from transvariation density. (4) Geographical detector analysis suggests that energy intensity (EI) and economic development level are the two factors most strongly correlated with the spatial differentiation of CTFP, and interaction effects exist between them. The results from geographically weighted regression (GWR) further confirm that the strength of the correlation between each driving factor and CTFP presents pronounced regional heterogeneity. Based on these findings, differentiated regional policies for emission reduction and efficiency improvement should be formulated in accordance with the law of diminishing marginal returns. Full article

Objective: To analyze the association between income inequality and age-standardized mortality from diabetes mellitus and hypertensive diseases among adults aged 30–69 across Brazilian Federative Units (2012–2024). Methods: Ecological longitudinal study using balanced panel data from the 27 Federative Units. Data were retrieved from the Mortality Information System and IBGE/IPEA databases. Mortality rates were standardized using the direct method (WHO population). Trends were evaluated via Joinpoint regression, and associations were analyzed using mixed linear models with random intercepts and first-order autoregressive covariance structure. Results: Inequality decreased in 25 Federative Units, accompanied by rising income and declining poverty, whereas national aggregated analysis showed a linear increase in inequality (APC = 0.514%; p < 0.001). Overall mortality trends were stationary for diabetes (AAPC = −1.507%; p = 0.102) and hypertension (AAPC = −0.881%; p = 0.524), despite significant declines from 2021 to 2024. In adjusted mixed models, higher per capita household income was independently associated with lower diabetes mortality (β = −0.012; p = 0.031), while hypertension displayed an upward adjusted linear trend (β = 0.289; p = 0.001). Neither the Gini Index nor poverty rates maintained significant independent associations with mortality. Conclusions: After structural adjustment, only absolute per capita household income maintained an independent protective association with diabetes mortality, highlighting the relevance of material living conditions beyond inequality measures alone. Full article

AI-driven credit scoring is supervised as a high-risk application in banking and insurance, yet unfairness is rarely operationalized as a measurable category of model, conduct, legal, and reputational risk. Using 20,000 anonymized applications from a Southern European digital lender (15.2% twelve-month default rate), we estimate three model families—a regularized logistic regression, a gradient-boosting machine, and a multi-layer perceptron—under a fully crossed design in which each family is evaluated without mitigation and under pre-processing (reweighing), in-processing (an exponentiated-gradient reduction, applicable to any base learner, together with adversarial debiasing where gradient-based training permits it), and post-processing (reject-option) interventions, so that the mitigation effect is no longer confounded with the choice of estimator. No sensitive-group field enters any estimated specification; group membership is used exclusively for auditing. Predictive performance (AUC-ROC, Brier score and Brier skill score relative to the base-rate forecast, F1 on the default class, Gini, and the Kolmogorov–Smirnov statistic) is reported jointly with group fairness (demographic-parity and equal-opportunity differences, disparate-impact ratio, Theil index) and with group-conditional calibration, at an explicitly stated and economically justified decision threshold. Every fairness quantity is accompanied by stratified-bootstrap confidence intervals and, for stochastic learners, by seed-level dispersion. The interpretable benchmark attains an AUC of 0.780 and a Brier score of 0.104 against 0.129 for the constant base-rate forecast, and the high-capacity models improve on it by under one AUC point. Disparity is present but is located geographically rather than in the composite group label: the disparate-impact ratio is 0.724 [0.693, 0.754] for the lowest socio-economic neighborhood cluster, excluding the four-fifths screening value, against 0.809 [0.776, 0.840] for the ethno-socioeconomic proxy, whose interval contains it, and no measurable gender disparity. Group membership is recoverable from the neutral feature set at an AUC of 0.654, and 42% of the group gap in predicted risk travels through the bureau credit score alone, so feature deletion cannot close the channel. Feature attributions and an auxiliary group-recoverability test locate the proxy pathways through which disparity arises, and a misclassification-sensitivity analysis bounds the effect of error in the group proxy, which attenuates measured disparity toward parity. We map the results onto Regulation (EU) 2024/1689 as amended by Regulation (EU) 2026/1744, the GDPR as interpreted in SCHUFA Holding, Directive (EU) 2023/2225, EBA loan-origination guidance, and Solvency II, EIOPA, and IAIS expectations, and propose fairness-risk controls organized around impact assessment, independent validation, and three lines of defense governance. Because the evidence comes from credit origination at a single lender, the insurance argument is developed at the level of regulatory and governance architecture rather than as an empirical transfer of estimates. Full article

Background/Objectives: Cutaneous squamous cell carcinoma (cSCC) is a highly prevalent malignancy. While Cemiplimab has transformed the treatment landscape across various disease stages, reliable pretreatment biomarkers for risk stratification remain lacking.. The Global Immune–Nutrition–Inflammation Index (GINI) is a composite biomarker integrating systemic inflammation and nutritional status into a single score. This study aimed to evaluate the prognostic value of the pretreatment GINI in patients with cSCC receiving Cemiplimab across all treatment settings. Methods: This retrospective cohort study evaluated 73 patients with unresectable, locally advanced, or metastatic cSCC treated with Cemiplimab between 2020 and 2023. Pretreatment laboratory parameters were extracted to calculate the GINI score. Receiver operating characteristic (ROC) curve analysis determined the optimal GINI cutoff to stratify patients into low- and high-GINI groups. Survival outcomes, including overall survival (OS) and progression-free survival (PFS), were analyzed using Kaplan–Meier curves and multivariable Cox proportional hazards models. Results: An optimal GINI cutoff of 74.4 divided the cohort into low-GINI (38%) and high-GINI (62%) groups. In the multivariable Cox regression models, a high pretreatment GINI remained independently associated with both inferior OS (adjusted HR 2.51, 95% CI 1.05–6.01, p = 0.039) and inferior PFS (adjusted HR 3.22, 95% CI 1.45–7.14, p = 0.004). When compared against five established inflammatory biomarkers (NLR, PLR, LMR, PNI, and CAR), the GINI consistently demonstrated comparable prognostic performance across multiple statistical approaches. Conclusions: The pretreatment GINI is a candidate prognostic marker for patients with cSCC undergoing Cemiplimab therapy. Given its cost-effectiveness and ready availability from routine clinical laboratory workups, the GINI score may improve prognostic risk stratification and could potentially inform risk assessment and clinical monitoring in real-world oncological practice. Full article

Alzheimer’s disease and related dementias are projected to affect more than 150 million people worldwide by 2050. Early staging with validated instruments such as the Clinical Dementia Rating (CDR) scale is essential for timely intervention, yet access to clinician-administered CDR assessment remains constrained by workforce, time, and geographic barriers. This study complements a previously published machine learning pipeline for Alzheimer’s disease prediction by addressing the downstream task of dementia staging. Because the global CDR score is already derived from the six sub-domain ratings through an established rule-based procedure, the contribution reported here lies not in discovering that mapping but in encoding it in a transparent, deployable form: an explainable decision tree classifier embedded in DiAbot, a large-language-model-fronted conversational system that supports self-administered CDR-style assessment. We extracted 13,453 CDR records from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), removed administrative variables, invalid entries, and missing rows (final n = 13,290), and trained decision tree classifiers under two impurity criteria, Information Gain and Gini Index, using a 70/30 stratified record-level hold-out and ten-fold stratified record-level cross-validation. This classifier-level evaluation uses the six domain scores as recorded during ADNI’s clinician-administered assessment, not scores elicited by the DiAbot chatbot; the trained classifier was separately embedded in a web application in which a prompt-engineered large language model conducts a CDR-style interview and normalizes responses to ordinal domain scores, but the end-to-end accuracy of that full conversational pipeline (chatbot elicitation through to final CDGLOBAL) has not yet been measured, and is not what the headline accuracy figures below report. The Information Gain Decision Tree reproduced the established mapping from the six CDR sub-domain scores to the CDGLOBAL with 99.86% accuracy under the record-level hold-out protocol (matching macro-averaged precision, recall, and F1-score), with a ten-fold record-level cross-validated mean of 99.81% (SD 0.07); this result represents fidelity to the established CDR scoring rule rather than independent dementia-diagnosis accuracy. Gini-based trees performed almost identically (99.79% hold-out, 99.74% cross-validated). Memory dominated feature importance, consistent with its role as the primary domain in the official CDR scoring algorithm. Residual misclassifications were confined to adjacent CDR stages. Because the CDGLOBAL is deterministically derived from the six sub-domain scores, these figures should be read throughout as evidence of high-fidelity reconstruction of the established CDR scoring relationship, not as general dementia-diagnosis accuracy comparable to imaging- or biomarker-based classifiers; further, participant-independent generalization remains unverified under the record-level protocol evaluated here. An interpretable classifier embedded in a conversational front-end can nonetheless make standardized CDR-style staging more widely accessible while preserving clinical inspectability; the resulting system is positioned as a screening-stage adjunct to, and not a replacement for, clinician-administered CDR assessment. Full article

In this paper, we propose a new flexible modification of the Lomax distribution within the Bilal-G family generated through the T-X framework, referred to as the Lomax–Bilal distribution. The proposed model offers greater flexibility for modeling-skewed and heavy-tailed phenomena that frequently arise in survival and reliability studies. A comprehensive set of statistical properties is derived, as moments, order statistics, reliability measures, the quantile function, stochastic ordering, and maximum likelihood estimation. Furthermore, several information measures are obtained to characterize the uncertainty structure of the distribution, namely Shannon entropy, Rényi entropy, extropy, cumulative residual extropy, and generalized weighted extropy. Also, the Lorenz, Bonferroni, Zenga curves and Gini index are presented. The practical applicability and effectiveness of the proposed distribution are illustrated through analyses of two real datasets: survival times of patients with head and neck cancer treated with chemotherapy and radiation therapy, and repair times of an airborne communication transceiver. The empirical results showed that the Lomax–Bilal distribution consistently provides a better fit than several well-established lifetime distributions according to goodness-of-fit statistics and information criteria, particularly in modeling tail behavior. These findings suggest that the Lomax–Bilal distribution constitutes a flexible and competitive alternative for analyzing complex lifetime data in reliability engineering and medical survival studies. Full article

Femicide is one of the most extreme manifestations of gender-based violence and remains a persistent challenge across Latin America. This study examines the association between socioeconomic and contextual factors and femicide rates across 12 Latin American countries during 2014–2024 using an unbalanced panel dataset. The dependent variable is the femicide rate per 100,000 women, while the explanatory variables include the Gini index, GDP per capita, female labor force participation, and intentional homicide rates. Owing to heteroskedasticity, serial correlation, and contemporaneous dependence across panels, the model was estimated using Panel-Corrected Standard Errors (PCSE) with a common AR(1) disturbance structure. The Gini index was negatively and statistically significantly associated with femicide rates (β = −0.057, p = 0.004), whereas intentional homicide rates showed a positive and statistically significant association (β = 0.059, p < 0.001). GDP per capita retained a negative but non-significant coefficient (β = −0.371, p = 0.076), while female labor force participation was also negative and non-significant (β = −0.008, p = 0.556). These findings indicate that the associations between femicide and socioeconomic conditions are sensitive to model specification, while income inequality and generalized lethal violence remain statistically significant correlates. The results underscore the multidimensional nature of femicide and the need for cautious interpretation of aggregate cross-national associations. Full article

Urban green space supply (UGSS) is a core component of territorial spatial planning, and the advancement of the park city initiative has been temporally associated with a systematic transformation. Differentiation in UGSS is a comprehensive issue concerning ecological environment, public welfare, and high-quality urban development, and it is closely related to the achievement of United Nations Sustainable Development Goal (SDG) 11.7. This study employs a comprehensive approach combining spatiotemporal dynamic analysis (Mann–Kendall trend test and Theil–Sen slope estimation), differentiation measures (Gini coefficient and Theil index), and the explainable machine learning SHAP model to conduct a large-sample empirical analysis of 1760 towns in China from 2015 to 2024. The findings show the following: First, park city construction corresponds to notable spatiotemporal evolution of UGSS in China’s towns, with approximately 85% of towns showing a significant rise. Second, park city construction coincides with a reduction in differentiation in both the outcomes and processes of UGSS in China’s towns, with both the Gini coefficient and Theil index declining to varying degrees. An analysis of the Theil index further confirms that the observed changes in the Theil index are more pronounced in disadvantaged towns, and the differentiation in UGSS originates more from intra-regional disparities than from inter-regional gaps. Third, the differentiation in UGSS shows deep structural correlates, with the associations of socio-economic and natural ecological factors exhibiting various complex nonlinear associations such as inverted U-shape, arc shape, U-shape, and wave shape. These associations are specifically manifested as mixed directionality of associations, hierarchical intensity of associations, threshold-based evolutionary pathways, geographic spatial heterogeneity, and interactive relationships among factors. This study recommends that the policy design of park city construction and green space system planning should promptly establish a new model combining situational response, threshold management, and collaborative governance. The nonlinear and interpretable analytical paradigm constructed in this study holds significant value for achieving precise supply and equitable sharing of green space resources. Full article

Background: Accurate assessment of extubation readiness in mechanically ventilated children remains difficult because successful sustained breathing depends on the interaction of respiratory, metabolic, inflammatory, neurological, and cardiovascular factors. This study developed and internally validated machine-learning models for predicting 48 h extubation success using routinely available pre-extubation data. Methods: Of 1097 total PICU admissions, 341 mechanically ventilated children treated between 2021 and 2026 constituted the final analytic cohort. The primary outcome was survival without reintubation during the first 48 h after planned extubation. Demographic, clinical, laboratory, blood gas, illness severity, and ventilator variables were evaluated. Logistic Regression, Random Forest, Gradient Boosting, and Support Vector Machine models were assessed using stratified 5-fold cross-validation. Unsupervised k-means clustering was performed to identify physiological phenotypes. Results: Extubation was successful in 298 children (87.4%) and failed in 43 (12.6%). Failure was associated with higher oxygenation index, lactate, procalcitonin, C-reactive protein, PaCO₂, pSOFA, PEEP, and rapid shallow breathing index, together with lower arterial pH, bicarbonate, ionized calcium, hemoglobin, albumin, sodium, and Glasgow Coma Scale scores. Random Forest yielded the numerically highest discrimination, with an AUC of 0.983 (95% CI, 0.971–0.993), a sensitivity of 0.980, a specificity of 0.814, an accuracy of 0.959, and a Brier score of 0.032. Arterial pH, the oxygenation index, bicarbonate, ionized calcium, procalcitonin, lactate, and PaCO₂ showed the highest Random Forest Gini importance scores. Clustering identified a low-severity phenotype (n = 302; 97% success; 0% mortality) and a high-severity phenotype (n = 39; 10% success; 100% mortality). Conclusions: Multidimensional machine-learning models predicted 48 h pediatric extubation success with strong internal discrimination. Prospective multicenter external validation is required before clinical implementation. Full article