DEA-GAO: A two-stage approach optimal controller placement in software-defined networks using data envelopment analysis

Male infertility is a major yet understudied global health concern. This study aims to analyze the temporal trends in the global and regional burden of male infertility from 1990 to 2021, explore influencing factors, assess the relationship with sociodemographic development, analyze health inequalities, and forecast future trends. Using epidemiological data on male infertility from the Global Burden of Disease Study database for 1990-2021, we calculated prevalence, disability-adjusted life years (DALYs), and other indicators. Joinpoint regression, age-period-cohort analysis, and decomposition analysis were used to examine temporal trends and influencing factors. Data envelopment analysis was used to assess the relationship between male infertility and the sociodemographic index (SDI). The slope index of inequality (SII) and concentration index were used to analyze health inequalities. Autoregressive integrated moving average (ARIMA) and Bayesian age-period-cohort (BAPC) models were used to forecast prevalence from 2022 to 2036. The global crude prevalence rate of male infertility was 1389.1 per 100,000 population in 2021, and the age-standardized prevalence rate was 1354.8 per 100,000 population. Eastern Europe had the highest prevalence, while Australasia had the lowest prevalence. Overall, prevalence showed a decreasing-then-increasing trend from 1990 to 2021, with the fastest growth occurring from 2010 to 2014. The trend of DALY changes is basically consistent with the incidence rate, with a rapid increase after 2010. The risk of infertility increased with age up to approximately 37.5 years and subsequently declined, with an overall downward trend observed after 1994. Population growth was the main driver of increasing prevalence. The increase in DALY rates is also mainly driven by population growth, contributing up to 68.06% globally, and the impact of population aging in high-SDI regions on DALY rates initially shows a significant negative effect. As SDI increased, the DALY rate generally decreased, but there was room for improvement in some countries. The inequality between high and low-SDI regions increased. Prevalence was predicted to rise in the future. The global burden of male infertility is increasing overall, with lower prevalence in high-SDI regions and higher DALYs in low-SDI regions. Attention should be paid to rapidly growing populations, improving reproductive health services, and equitable access. Screening and interventions for high-risk populations should be strengthened to curb the rising prevalence trend.

This paper develops a pessimistic two-stage network data envelopment analysis (DEA) model that integrates interval-valued data and endogenous weight restrictions within a unified linear programming framework. The proposed approach explicitly captures internal network structures while addressing bounded data uncertainty through an interval-to-deterministic transformation that preserves linearity and avoids probabilistic assumptions. Robustness is interpreted in the pessimistic interval DEA sense, where efficiency is evaluated under worst-case realizations of observed bounds rather than through explicit uncertainty-set optimization. To mitigate weight degeneracy and enhance discrimination power, data-driven proportional weight restrictions are introduced; these endogenous bounds are constructed solely from observed data and regularize the multiplier space without relying on subjective preferences or tuning parameters, while maintaining scale invariance and the nonparametric nature of DEA. The model admits equivalent multiplier and envelopment formulations and enables meaningful decomposition of overall efficiency into stage-specific components. Fundamental theoretical properties—including feasibility, boundedness, monotonicity, efficiency decomposition, and special case consistency—are rigorously established. An empirical application to OECD macroeconomic data, accompanied by sensitivity evaluation, demonstrates the stability and discriminatory capability of the proposed framework under bounded variability. Computational analysis confirms that the model retains linear programming structure and exhibits linear growth in problem size with respect to the number of decision-making units, thereby preserving the scalability characteristics of classical two-stage network DEA formulations. The proposed framework provides a theoretically grounded and computationally tractable approach for network efficiency analysis under bounded interval uncertainty.

Although higher technical efficiency is theoretically expected to enhance farm sustainability, empirical evidence in livestock systems remains ambiguous. This study investigates the interplay between technical efficiency and sustainability using data from 72 farms in Tokat, Türkiye, selected via stratified random sampling. Technical efficiency was calculated using Data Envelopment Analysis (DEA), while a multidimensional Sustainability Index was constructed using the analytic hierarchy process (AHP) for weighting dimensions. Determinants of inefficiency were estimated using a Tobit model. Results revealed an average technical efficiency of 0.717 and a Composite Sustainability Index of 0.41, classifying the sector as "moderate" but fragile. Crucially, the Kruskal-Wallis test showed no statistically significant difference in sustainability scores across efficiency groups (p > 0.05). This finding confirms a "structural disconnect," demonstrating that high technical efficiency does not guarantee sustainability because of systemic bottlenecks such as dysfunctional organizations and infrastructure deficits. Furthermore, Tobit results showed that non-farm income and internet access were positively associated with technical efficiency, whereas indebtedness was negatively associated. Consequently, achieving lasting sustainability requires a shift from simple productivity support to structural modernization policies, including the integration of sustainability-oriented criteria such as institutional strengthening, environmental management, and financial capacity into existing support schemes.

The objective of healthcare institutions is to utilize available resources efficiently without compromising the quality of service provided. Training and research hospitals differ from other healthcare institutions as they have training and research activities in addition to providing healthcare services. Therefore, quality and efficiency studies are important for these hospitals. This study aims to determine the effect of quality on efficiency by examining the efficiency of training and research hospitals in Türkiye. Using the data of 59 training and research hospitals for the period 2016-2020, efficiency is examined with Data Envelopment Analysis (DEA) models. Then, we apply panel regression analyses to examine the effects of quality on efficiency. Although the hospital efficiency levels showed a stable horizontal course over the years, there was seen to be a dramatic decrease in 2020 with the effect of the COVID-19 pandemic. The rate of inefficient training and research at hospitals was high in all the years. There was no clear effect of quality on efficiency. In the 4 models established, there was determined to be a negative effect of patient satisfaction on efficiency in only one model. Our research advocates for the development of tailored efficiency and quality measurement methods for these unique healthcare institutions. It is also one of the limited studies examining the relationship between quality and efficiency.

ABSTRACT This paper develops a framework to benchmark public water sector efficiency across Europe, addressing the lack of a relevant comparative assessment tool. Using dynamic network data envelopment analysis (DEA) and statistical analysis, it evaluates water provision, wastewater treatment and overall performance in 26 countries from 2013 to 2020. Results show that modern infrastructure, advanced treatment technologies and effective resource management are associated with higher efficiency, while structural and operational constraints hinder performance. Improving efficiency requires targeted infrastructure upgrades, enhanced resource recovery and broader sector restructuring. The paper provides the first comprehensive, comparative pan‑European water sector efficiency study and offers decision‑support insights for integrated, context‑sensitive governance.

Health information exchanges (HIEs) facilitate the secure, electronic sharing of patient medical information/records across providers, enabling healthcare professionals to access timely, comprehensive data and thereby improve care coordination and quality. Yet, despite these expected benefits, empirical evidence shows that healthcare professionals often spend substantial time and effort interacting with HIE platforms without discernible productivity gains. Moreover, although HIEs are promoted as a potential solution for addressing geospatial disparities in healthcare, their impact on healthcare professionals’ productivity across urban and rural hospitals remains unclear. Using data envelopment analysis (DEA) to construct a measure of healthcare professionals’ productivity and applying the difference-in-differences (DiD) approach, we investigate the impact of HIE adoption on healthcare professionals’ productivity in urban and rural hospitals in the United States (U.S.). Our findings show that hospitals that have adopted HIE experience a significant increase in healthcare professionals’ productivity. However, this effect is more pronounced in urban hospitals than in rural hospitals. We attribute this result to urban healthcare professionals having workflows with greater information intensity and higher technology capabilities than healthcare professionals in rural hospitals. Furthermore, our study reveals that HIE adoption improves communication and the quality of clinical decision-making among urban healthcare professionals, but not among those in rural hospitals. We also find that the productivity gains from HIE adoption are greater for nurses than for physicians. We discuss the theoretical and practical implications of these findings.

Selecting urban mobility hub locations using data envelopment analysis and geographic information systems

Productivity change with bad outputs: Data envelopment analysis aggregate joint production vs. data envelopment analysis input-output models

Brazil faces a persistent penitentiary crisis marked by institutional fragility, social inequality, and chronic underinvestment, while empirical evidence on the efficiency of its prisons remains limited. This study addresses this gap by evaluating the performance of 55 Penitentiary Units (PUs) in the Brazilian state of Minas Gerais using a dual-model framework based on Data Envelopment Analysis (DEA). Two dimensions of institutional performance are examined: Custodial Services Efficiency (CSE), which captures each PU’s ability to ensure secure accommodation, supervision, and administrative control; and Social Services Efficiency (SSE), which reflects the provision of staff-based services related to inmate well-being, including healthcare, education, and psychosocial support. These two dimensions are integrated into a composite index, the Prison System Efficiency Index (PSEI), which provides a global efficiency score for each PU. The findings reveal that nearly 90% of PUs operate with low or very low efficiency, and that small PUs tend to outperform larger ones. Paradoxically, PUs in less urbanized areas often show higher SSE scores despite custodial constraints. By revealing structural asymmetries and scale-related disadvantages, the results contribute to international debates on prison governance and demonstrate the need for more equitable and evidence-based penal policy in the Global South. • Proposes the Prison System Efficiency Index (PSEI) to assess efficiency. • DEA-based findings reveal urgent need for equity-driven prison reform. • 90% of prisons operate inefficiently in Brazil’s second most populous state. • Small prisons outperform large ones despite limited resources. • Penal efficiency is disconnected from rights, reintegration, and equity.

ABSTRACT Farmer households employ various coping strategies to address climate and non‐climate shocks, which are primarily categorized into production‐focused and consumption‐focused approaches. While the former has been extensively remarked on in recent studies, the latter has received little attention in the literature. In this study, we developed consumption‐focused coping strategies by balancing the likelihood of household shock exposure with welfare outcomes for 1369 farmer households in Senegal. The outcomes are estimated using a discrete choice model regression. We then modeled the challenges faced by Senegalese farmer households using a Data Envelopment Analysis (DEA) approach, enabling us to measure household efficiency in achieving balanced gains. The household shock exposure regression equation results confirmed the role of food and non‐food factors in controlling the probability of shock; however, outcomes may vary across households, particularly in expenditure items linked to lifestyle transformations. The DEA results revealed a non‐linear relationship between shock exposure probability and efficiency scores, explaining the variability observed in household expenditure outcomes. Excitingly, the nonlinear impact of shock efficiency was rarely observed for food expenditure items, highlighting an unexpected resilience in this crucial area. These results indicate that policies aimed at improving consumption‐based welfare should be differentiated rather than uniformly applied. The empirical findings show relatively consistent responses in food consumption across households, supporting the use of broadly implemented food‐related assistance and nutrition security programs. In contrast, nonfood consumption responses exhibit substantial heterogeneity, particularly for health, education, and mobility‐related expenditures. Accordingly, interventions targeting these domains should be tailored to household characteristics and efficiency levels to more effectively buffer welfare losses under economic and environmental shocks.

Evaluating safety performance in manufacturing sector: An enhanced super-efficiency data envelopment analysis approach

Integrating dynamic data envelopment analysis with long short-term memory for performance prediction with integer inputs and undesirable outputs

Employing a hierarchical data envelopment analysis to evaluate the competitiveness of listed companies in Indonesia

Generalizing inverse data envelopment analysis through directional distance function

This study examines the financial performance and relative efficiency of the Civil Society Organization (CSO)–accredited agricultural cooperatives in the Cordillera Administrative Region, Philippines. Using secondary financial data obtained from the Cooperative Development Authority–CAR, the study analyzes 88 cooperatives employing correlation analysis, multiple regression models, and Data Envelopment Analysis (DEA) under a Variable Returns to Scale (VRS), input-oriented framework. Results reveal substantial heterogeneity in cooperative size, revenues, and net surplus. Correlation analysis shows that total assets are very strongly and positively associated with total revenues (r = 0.964, p < 0.01) and strongly related to net surplus (r = 0.759, p < 0.01), highlighting the central role of asset base in cooperative financial performance. Regression results further confirm that total assets exert a significant positive influence on both total revenues and net surplus, while total operating expenses exhibit a statistically significant negative effect, indicating inefficiencies arising from excessive input utilization. DEA findings indicate a low mean efficiency score of 0.279, suggesting that cooperatives could achieve current output levels while reducing inputs by approximately 72.1%. Only six cooperatives (7%) were found to be fully efficient, while the majority operated well below the efficiency frontier.

ABSTRACT This study applies a non-radial slacks-based measure data envelopment analysis (SBM-DEA) model to evaluate the carbon emission efficiency of 81 listed high-emission firms during the period from 2020 to 2023. The analysis further investigates heterogeneity in efficiency across industry sectors, geographic regions, and ownership structures. The results reveal several important findings: (1) carbon emission efficiency initially improved following the introduction of the dual carbon goals, but subsequently declined, indicating that although progress has been made, substantial potential for further enhancement remains; (2) significant inter-industry disparities in efficiency are observed; (3) foreign-controlled firms and central state-owned firms exhibit higher average efficiency than local state-owned firms and private firms. Based on these findings, the study offers policy recommendations for improving overall carbon emission efficiency and supporting China’s transition to a low-carbon economy.

The advancement of the digital economy has transformed productivity patterns and created opportunities to support sustainable development, with Green Total Factor Productivity (GTFP) serving as a key indicator that incorporates resource efficiency and environmental considerations. This study aims to examine the effect of the digital economy on GTFP. This study used a multi-year panel dataset, where GTFP is measured through the Malmquist–Luenberger Productivity Index with a Slack-Based Measurement Data Envelopment Analysis (SBM-DEA) approach, and the impact of the digital economy is analyzed using a Two-Way Fixed Effect regression model. The findings show that the digital economy has a significant positive effect on GTFP, with stronger impacts in more developed regions. These results indicate that digital economic development can enhance green productivity by improving human capital and supporting environmentally oriented economic activities. Therefore, policies that integrate digital transformation, human development, and environmental regulation are essential to maximize sustainable productivity and strengthen progress toward long-term sustainable development goals.

This study has three objectives: (i) to evaluate the efficiency of the Operations Strategy Matrix in healthcare systems of OECD (38) countries using Data Envelopment Analysis (DEA), (ii) to evaluate the efficiency of the Operations Strategy Matrix in healthcare systems of OECD (38) countries using the integration of Artificial Neural Network (ANN) and Data Envelopment Analysis (DEA-ANN Model), (iii) to classify OECD (38) countries divided into four groups Super Stars, Potential Stars, Question Marks and To Be Governed Effectively based on Data Envelopment Analysis Matrix (DEAM). The findings indicated that there are 27 countries (Australia, Austria, Canada, Colombia, Costa Rica, Czech Republic, Denmark, Estonia, Germany, Greece, Hungary, Iceland, Ireland, Israel, Latvia, Lithuania, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Slovak Republic, Slovenia, Sweden, Switzerland, Türkiye, and the United States) in the Super Star Group, 1 (one) country (United Kingdom) in the Potential Star Group, 2 countries (Belgium and Finland) in the To Be Governed Effectively Group and 8 countries (Chile, France, Italy, Japan, Korea, Poland, Portugal and Spain) in the Question Mark Group. At the end of the study, several managerial implications based on the findings of this study are presented for healthcare managers of OECD countries.

The Impact of Size Heterogeneity on Performance Analysis in Data Envelopment Analysis Approach: An Application in Banking Sector