The impact of DRG payment reform on hospitalization costs and length of stay: A multicenter study across different hospital levels and departments.

Accurately identifying the spatiotemporal evolution and spatial differentiation of carbon emission intensity in the transport sector is essential for formulating region-specific carbon reduction policies. This study develops an analytical framework that integrates both static and dynamic perspectives to examine spatial disparities in transport sector carbon emission intensity. From a static perspective, the Dagum Gini coefficient is employed to quantify spatial differences and their sources of transport carbon emission intensity. From a dynamic perspective, kernel density estimation is applied to depict the evolution trajectories of transport carbon emission intensity. Furthermore, the traditional Markov chain model is refined to construct a spatial Markov chain model that accounts for spatial adjacency, enabling identification of persistence and spatial spillover effects. The empirical results indicate that (1) The carbon emission intensity of the transport sector in China presents an overall declining trend with significant spatial heterogeneity among provinces. Regional disparities have expanded, with the largest gap between the eastern and western regions, where inter-regional differences contribute an average of 47.374% to total disparity, representing the main source of variation. (2) The carbon emission intensity in the national, eastern, and central regions tends to converge gradually, while the western region shows a pattern of initial convergence followed by renewed divergence. Within each region, several provinces maintain carbon emission intensity levels significantly higher than the average, forming a clear spatial gradient structure. (3) The traditional Markov chain analysis reveals evident persistence and club convergence in transport carbon emission intensity. The spatial Markov chain analysis further shows that neighboring regions strongly influence local transition probabilities, demonstrating spatial spillover and path dependence effects. Hypothesis testing confirms the necessity of incorporating spatial dependence into the analysis. Based on these findings, this study proposes that carbon reduction strategies in the transport sector should be tailored to regional disparities and spatial interdependencies, aiming to enhance overall mitigation efficiency and foster coordinated governance.

Efficient workforce management is critical for ensuring the quality, safety, and sustainability of hospital operations. Traditional human resource management (HRM) approaches often rely on manual processes that are prone to errors, lack adaptability, and fail to adequately balance staff preferences with patient care requirements. To address these challenges, this research proposes an AI-driven HRM framework for hospitals that integrates forecasting, optimization, and performance evaluation to enhance workforce planning, staff scheduling, and continuous assessment. The framework comprises three core modules: (i) workforce demand forecasting, leveraging machine learning models such as LSTM, XGBoost, and Random Forest to predict patient admissions and staffing needs; (ii) intelligent staff scheduling, employing optimization models under legal, contractual, skill-based, and preference-aware constraints to generate equitable and efficient rosters; and (iii) performance evaluation, combining structured metrics (task completion, attendance, punctuality) with unstructured feedback (patient surveys, peer reviews) analyzed using natural language processing. Extensive experiments were conducted using both synthetic and real hospital datasets. Results show that the proposed approach outperforms conventional methods, with LSTM achieving the highest forecasting accuracy (MAE = 6.1, R2 = 0.91), and the scheduling module reducing conflicts by 41% while improving fairness (Gini coefficient = 0.08). The performance evaluation framework further revealed 74% positive patient feedback and highlighted actionable insights for administrators. Stress tests confirmed scalability, with solver times remaining under 95s for 1000 staff members. Pilot deployments demonstrated tangible benefits, including an 18% reduction in patient waiting times and a 14% improvement in satisfaction scores. Overall, the framework demonstrates strong potential for advancing hospital workforce management by improving efficiency, fairness, and quality of care.

Purpose The purpose of this study is to empirically examine the financial structure, legal foundations and social role of Ottoman cash waqfs using a rare fully audited ledger from Kocaeli (1872). Moving beyond the predominantly normative and jurisprudential orientation of the existing literature, this study offers evidence-based insights into how these institutions operated as community-based financial intermediaries, mobilized endowed capital through Sharia-compliant mechanisms such as istiglal and broadened access to finance in local communities. Design/methodology/approach The analysis uses a streamlined mixed-methods framework that combines qualitative institutional assessments with transparent, descriptive quantitative tools. The audited ledger was examined using historical accounting methods and analyzed using descriptive statistics, concentration measures (Gini coefficients and Lorenz curves) and correlation analysis to assess the distributions of capital, financial sustainability and borrower characteristics. All techniques were selected to match the structure and limitations of the historical data. Findings The audited ledger records 39 cash waqfs administering a combined principal of 1,093,620 gurus. More than 97% of this capital was allocated through Sharia-compliant istiglal contracts, which generated returns in the range of 10%–20%. The distribution of credit reveals a wide socioeconomic and cross-communal outreach: peasants constituted the largest category of borrowers (41%), followed by artisans and traders (26%). Although larger mosque waqfs controlled the bulk of the total capital, smaller neighborhood waqfs exhibited markedly higher lending efficiency. The resulting surpluses were systematically reinvested in educational, infrastructural and welfare-oriented initiatives. Practical implications The operational features of Ottoman cash waqfs reflect principles that are central to contemporary Islamic microfinance and ethical finance, including local resource mobilization, risk sharing, community-based governance and the integration of financial activity with social welfare provision. These historical insights offer meaningful design implications for modern Islamic microfinance institutions, waqf-based social investment frameworks and community-oriented fintech initiatives. Originality/value Empirical studies that reconstruct the financial operations of Ottoman cash waqfs remain scarce. Most existing research relies on foundation deeds or court records rather than audited financial data. By analyzing a fully audited ledger, this study provides rare quantitative evidence on operational performance, capital allocation and socioeconomic outreach. It thereby offers a more robust empirical assessment of historical Islamic financial institutions and contributes new insights to both Ottoman economic history and modern Islamic finance literature.

Abstract Spectral amplitude modulation (SAM) can effectively enhance the features of different signal components due to its superior spectral line editing capabilities. However, noise and interference cannot be separated by the SAM equalization effect. Therefore, this paper proposes a method named variable differential spectrum amplitude modulation (VDSAM) to optimize the amplitude modulation process. By designing differential spectral amplitude modulation assisted by the modulation Gini index, the optimal differential demodulation order is selected, and bearing fault information within the frequency band is enhanced by frequency correlation. Constructing a variational filtering framework based on spectral envelope and an adaptive resonant band identification method can increase the accuracy of feature extraction under multi-band unknown strong interference. The feasibility of the proposed method is verified using simulation data and experimental signals, with comparisons against other widely used methods confirming its prominent superiority.

Based on panel data from China's 13 major grain - producing provinces between 2014 and 2023, this study constructs a multidimensional grain security evaluation index system, incorporating the dimensions of quantity, quality, ecology, and capacity. Using the entropy method, kernel density estimation, Dagum Gini coefficient, and convergence model, we analyze the regional differences, dynamic evolution, and spatial convergence of grain security levels. The results show that: (1) grain security level is generally increasing; there are obvious spatial differences in grain security level among; (2) There are obvious spatial differences in grain security levels among regions, showing the trend of "Northeast> central> eastern> western"; (3) α Convergence analysis shows that the central region converges, while the northeast, east and west regions diverge; both absolute β convergence and conditional β convergence exist in the whole region and each region. Compared with previous studies, this paper provides new insights into the dynamic evolution, spatial distribution and regional convergence of multidimensional grain security in China's major grain producing areas by integrating a multi-dimensional framework including quantity, quality, ecology and capacity. The research has enriched the evaluation system of grain security and laid a solid foundation for policy optimization.

Abstract Fairness is a critical yet underexplored dimension of tournament scheduling, where fixture asymmetries can systematically distort competition outcomes. This paper develops a Monte Carlo simulation framework as a general decision-support tool for assessing procedural fairness in league formats, isolating distortions arising from fixture structure rather than team strength or ranking rules. The approach integrates probabilistic match outcome modelling with two complementary fairness indicators: total fairness bias, capturing the magnitude of schedule-induced distortions, and the Gini index, measuring their distribution across participants. By incorporating competition-specific importance weights, the framework enables a context-aware, two-dimensional assessment applicable to any round-robin or multi-phase format, and can be embedded as a modular component into optimization, heuristics, or machine learning–based scheduling systems. To illustrate its applicability, we apply the framework to two European football leagues with structurally asymmetric designs. The results show how corrective mechanisms – such as home/away balancing phases – can significantly reduce fairness distortions compared to uncorrected triple round-robin structures. Beyond the case studies, the contribution is methodological: the framework provides a reproducible tool for fairness assessment and a foundation for fairness-aware optimization and scheduling methods applicable across sports and competition formats. Although demonstrated on football, the methodology is domain-independent and transferable to any context where fairness and equity are of relevance.

Abstract Random walks in a finite Abelian group G are studied. They use Markov chains with doubly stochastic transition matrices, in a Birkhoff subpolytope B ( G ) associated with the group G . It is shown that all future probability vectors belong to a polytope which does not depend on the transition matrices, and which shrinks during time evolution. Various quantities are used to describe the probability vectors: the majorization preorder, Lorenz values and the Gini index, entropic quantities, and the total variation distance. The general results are applied to the additive group Z ( d ) , and to the Heisenberg–Weyl group H W ( d ) / Z ( d ) . A physical implementation of random walks in Z ( d ) that involves a sequence of non-selective projective measurements, is discussed. A physical implementation of random walks in the Heisenberg–Weyl group H W ( d ) / Z ( d ) using a sequence of non-selective positive operator-valued measure measurements with coherent states, is also presented.

Although interest in value-added dairy products is increasing, empirical evidence regarding their economic viability remains limited. Specifically, there is a lack of research on the economic viability and cost structure of traditional fermented dairy products, such as sweet curd (Mishti Doi), within regional value chains. This study aims to assess the economic viability of sweet curd production across four blocks in the Terai District of West Bengal, India, by analysing cost structures, profitability, market concentration, and seasonal production dynamics. Primary data were collected from 31 purposively selected dairy processing units over one year (April 2024 to March 2025) using structured interview schedules. Data were analyzed using benefit-cost ratio analysis, market concentration indices (CR4, HHI, Gini coefficient), and the seasonal index. The results indicate that variable costs constitute 98.6% of total production costs, with fluid milk accounting for 59–62% (Rs 44–51/kg). Regional production costs range from Rs 71.84/kg in Coochbehar-II to Rs 86.35/kg in Alipurduar-I, driven largely by milk procurement price differentials (Rs 36.61–42.91/L). Benefit-cost ratios range from 1.44 to 1.72, confirming economic viability across all blocks, with Coochbehar-II recording the highest profit margin at 71.9% (net profit: Rs 51.68/kg). Low Gini coefficients (0.07–0.15) indicate equitable distribution of market shares. Seasonal indices range from 86% during the monsoon (July–September) to 112.57% during the festive season (September–December). Labor costs account for 9.24% of total production expenses, combining family and hired Labor. Spoilage rates are effectively managed below 0.64%. The study concludes that sweet curd production is economically promising across the Terai region, with an average benefit-cost ratio of 1.61 and weighted average profit margins of 37.9%. Improving milk procurement strategies and adopting adaptive seasonal management are critical to enhancing long-term profitability and sustainability within the dairy value chain.

ABSTRACT This study analysed the main changes in the concentration of daily precipitation totals in the Atlantic‐European region in 1961–1990 and 1991–2020. Precipitation concentration was characterised by the Gini index (GI), which measures unevenness in precipitation temporal distribution. GI was calculated from ERA5 reanalysis for individual seasons of the year. A comparison of the mean GI distribution and its trends in two 30‐year periods showed a gradual expansion of areas with increasing precipitation concentration over Europe since 1961. Areas with statistically significant GI increases occurred more frequently in the Mediterranean (winter, summer) and western and central Europe (spring). Trend analysis revealed a significant GI increase in parts of central (spring, summer) and eastern (all seasons except winter) Europe in 1991–2020. Significant rising trends appeared in coastal areas near the Iberian Peninsula (autumn) and along the coast of northern Scandinavia (winter). Although areas with significant trends did not exceed 10% of the Atlantic‐European region, these areas show apparent seasonality and dependence on the studied period. Precipitation concentration shows prevailing significant negative relationships with precipitation totals, and especially with the number of precipitation days, for much of mainland Europe. While the areas of significant correlations decrease in the case of precipitation totals, they increase in the case of the number of precipitation days. Changes in precipitation concentration showed no clear link to orography (altitude, slope orientation) and the maritime/continental precipitation regime. Comparison of changes in precipitation concentration derived from ERA5 with those calculated from the E‐OBS database shows generally higher GI mean and lower GI variability for E‐OBS. However, areas with significant changes between the study periods and significant trends are in excellent agreement for both datasets.

The relation between socioeconomic status and societal development with cardiovascular disease - A systematic review and meta-analysis on global data.

This study investigated intraoccupational income and wage-rate distributions across 8 health care professions: physicians and surgeons, dentists, physician assistants, optometrists, pharmacists, nurse practitioners and nurse midwives, physical therapists, and registered nurses. The research was based on a sample of 142,527 U.S. practitioners from the 2019 to 2022 American Community Survey (ACS) and focused on 4 inequality indicators: the coefficient of variation, lower median share, 90-10 decile ratio, and Gini coefficient. Findings revealed substantial income dispersion, with dentists and physicians/surgeons displaying the highest levels of inequality, while pharmacists, registered nurses, physical therapists, and nurse practitioners and nurse midwives exhibited more even distributions. The occupations' degree of inequality was correlated with average annual income and wage-rate levels. Gender disparities were significant across all professions, with male practitioners consistently earning more than their female counterparts. The degree of inequality was greater for professions in which there were more male than female practitioners. Annual income and wage-rate inequality also was identified within genders. This research contributes to understanding income disparities within health care professions and suggests that further exploration is needed to identify the determinants of these inequalities and their long-term evolution.

ABSTRACT Amid the global climate and energy crises, developing green buildings has become the key to the low-carbon transition of the construction industry. However, academic attention to the collaborative development of China’s green building industry (GBISD) remains relatively limited, and the exploration of its spatiotemporal heterogeneity laws and the spatiotemporal effects of core influencing factors is still insufficient. To fill this research gap, this study constructs a multi-dimensional analytical framework of “subject-factor-path-goal” based on synergy theory. Combining the AHP-CRITIC combined weighting method, composite system synergy degree model, Dagum Gini coefficient, and Spatio-temporal geographically weighted regression (GTWR) model, it conducts an empirical study on the GBISD level of 27 provinces in China from 2012 to 2022. Its core goal is to reveal the characteristics of spatio-temporal heterogeneity, spatial correlation laws, and spatio-temporal effects of influencing factors, so as to provide scientific support for industrial synergy optimization and regional green development. The findings reveal that: (1) Provincial levels of GBISD exhibit sustained growth with enhanced resilience while demonstrating emerging regional polarization and spatial diffusion effects. (2) The three major regions (Eastern, Central, and Western China) show differentiated development trajectories with significant spatial differences. (3) The overall GBISD distribution displays clustering patterns, with pronounced agglomeration effects in central and eastern China, whereas the spatial correlations remain weak in most provinces. (4) The GBISD performance in Northwest and Northeast China is suboptimal, which is related to insufficient attention to low-carbon development and restricted construction industry scale. The impacts of factors such as technological innovation, government attention to green and low-carbon development, and green finance exhibit significant spatiotemporal heterogeneity. This study enriches the theoretical system and quantitative methods of GBISD, and provides targeted practical strategies for the low-carbon transition of the construction industry and regional synergistic governance.

Agricultural Green Development (AGD) across 13 major grain-producing provinces in China from 2006 to 2022. Drawing on the entropy method, Dagum Gini coefficient, and obstacle degree model, we track spatiotemporal dynamics while identifying the barriers that most constrain AGD. The results show that AGD has improved steadily over time; against this upward backdrop, the Yangtze River Basin and North China Region display a marked catch-up effect, eventually surpassing the Northeast Region. At the same time, spatial disparities do not diminish accordingly, but instead follow a fluctuating upward trend. Decomposition results further suggest a shift in what drives inequality: inter-regional disparity still contributes the most, yet its dominance is weakening, whereas intra-regional inequality and transvariation density are gaining influence. This pattern implies that the green development gap is increasingly shaped by polarization within regions, rather than being explained mainly by the North–South divide. When the constraints are examined more closely, Ecological Conservation stands out as the most binding barrier at the criterion level. At the indicator level, the Proportion of Waterlogging Control Area, the Development Level of Green Food-Labeled Products, and the Proportion of Soil and Water Loss Control Area repeatedly emerge as the key obstacle factors limiting further improvement. These findings indicate that future interventions should shift from uniform guidance toward precision-based governance, with localized ecological restoration prioritized and the supply structure of green agricultural products further optimized.

Agricultural water footprint is an important indicator for assessing water-use efficiency and resource carrying capacity in agricultural systems, especially in arid regions. From the perspective of ecological function zones, this study examines the spatiotemporal dynamics of the agricultural water footprint in Xinjiang, China, and evaluates its adaptability to socioeconomic factors. The blue and green water footprints of crop production and the water footprint of animal products during 2000–2020 were quantified to estimate the total agricultural water footprint. The Gini coefficient and imbalance index were used to quantitatively evaluate the spatial adaptability between the agricultural water footprint and socioeconomic factors, including sown area, population, and agricultural Gross Domestic Product (GDP) across different ecological function zones. The results indicate that the agricultural water footprint increased from 2.54 × 1010 m3 to 5.85 × 1010 m3, with a clear spatial gradient characterized by higher values in southwestern Xinjiang and lower values in the northeastern region. Crop production accounted for more than 85% of the total footprint, with cotton as the dominant contributor, while beef consumption drove the growth in the animal product water footprint. The adaptability between the agricultural water footprint and sown area improved overall, whereas coordination with population distribution remained weak, and notable regional differences were observed in water footprint intensity relative to agricultural GDP. These findings indicate that the spatiotemporal differentiation of the agricultural water footprint is closely linked to resource endowments and development characteristics across ecological function zones, providing support for region-specific agricultural water management in arid areas.

Abstract Choquet integrals are used for the analysis of annual reports of companies, with a focus on their use of positive words. It is shown that the Choquet integral is able to distinguish one report from another by taking into account of the potential latent relationships of different sections of the annual reports. The robustness of the results in the presence of random noise in the data is discussed. The uniformity of use of positive tone in annual reports is also analysed using entropy and the Gini index. The inverse problem of calculating the capacities from known values of the Choquet integral linked to a certain set of data is also studied (and called ‘training’ of the Choquet integral). These capacities can then be used for the calculation of the Choquet integral for different sets of ‘similar data’. This is exemplified with data related to solar flares.

ABSTRACT Nature-based solutions, which integrate natural processes into urban environments to address societal challenges, are increasingly promoted for enhancing urban sustainability. However, their distribution and accessibility remain uneven, particularly in marginalised communities. While most research emphasises their ecological performance, less attention has been given to how nature-based solutions relate to patterns of social vulnerability, especially when considering both their quantity and quality. This study investigates the intersection of nature-based solutions characteristics and social vulnerability in Oslo, Helsinki, and Stockholm, applying a composite social vulnerability index, cluster analysis, and comparative methods. The findings reveal that socially vulnerable communities frequently coincide with areas of reduced nature-based solutions coverage and lower-quality green spaces. High Gini coefficients and significant spatial clusters indicate a persistent inequality in both the quantity and quality of nature-based solutions across urban landscapes, even within cities known for strong welfare and environmental planning systems. These findings suggest that current urban greening strategies often remain insufficiently attuned to social vulnerability and may unintentionally reinforce existing spatial inequalities. The study highlights the importance of integrating social vulnerability into nature-based solutions planning and evaluation, moving beyond generalised greening toward approaches that explicitly consider where, how, and for whom urban nature is delivered.

Abstract Developing regions face critical water security challenges driven by rapid urban growth, economic development, and climate change. In India, these issues are particularly evident in Pune, the country's 9th most populated city. It is evolving into a sprawling urban agglomeration expected to grow from 7 to 11 million residents by mid‐century. The city's aging water‐supply system is ill‐equipped to ensure water access during droughts lasting 2–3 years, particularly for residents in informal settlements. We present a policy‐evaluation model to assess options for addressing future urban freshwater insecurity. The model uses a coupled multi‐agent systems approach that integrates human‐environment interactions and responses to future drought, population, and economic conditions. Under business‐as‐usual for a mid‐century, multi‐year drought, major reservoirs dry up and groundwater levels decrease dramatically. The water use Gini coefficient exceeds 0.5, indicating severe inequality where most low‐income individuals face: (a) unaffordable water costs (10%–18% of income), (b) vulnerability (<40 L daily), and (c) prolonged shortages (>6 continuous months). Comprehensive interventions, combining supply‐ and demand‐side measures, cut the water use Gini coefficient in half and lower water costs by two‐thirds. Implementing a strategic subset of interventions creates synergies that significantly enhance water security, yet remains insufficient for the low‐income population. This study highlights how growing inequalities in urban water access exacerbate water security challenges, even under a suite of mitigating measures. In all scenarios, additional drought emergency supply will be required to address water insecurity of the lowest 10% income population.

Unraveling the nonlinear effects of urban blue-green spaces on residents' health in the United States (2000-2019).

Single-cell RNA sequencing data enables analysis of transcript levels of single cells across different cell types and conditions. Recent work has highlighted the value of measuring gene-specific transcriptional variability, or noise, within a genetically identical population of cells in addition to mean expression, given that these differences contribute to biological processes including development and disease. However, measuring transcriptional noise remains a challenge. Here, we systematically compared statistical methods by simulating single-cell data by varying both dispersion and count size to assess the relative responsiveness to noise of several commonly used statistical metrics: the Gini index, variance-to-mean ratio, variance, coefficient of variance (CV), CV2, and Shannon entropy. We found that the variance-to-mean ratio scales approximately linearly with increasing dispersion and is independent of dataset size. In contrast, the Gini index displayed paradoxical behavior in that it increases as dispersion decreases, and Shannon entropy was not scale-invariant. Next, we applied the variance-to-mean ratio (Fano factor) to measure transcriptional variability in single-cell datasets representing different complex systems and cross-platform measurements. Our data show that many genes display transcriptional variability within the same cell type, and that while variation does not correlate with gene characteristics such as transcript level, promoter GC content, or evolutionary gene age, variable genes are often correlated with specific biological processes. Notably, most genes and pathways with highest transcriptional variability as identified by the Fano factor were largely independent of differentially expressed genes and have also been implicated in biological processes related to the system. Thus, our data highlight that choice and application of appropriate models for measuring transcriptional variation in scRNA-seq data can reveal biologically relevant information beyond what is observed from mean expression alone.