Search Results (2,005)

Corporate failure prediction remains a major topic in the literature. Numerous methodologies have been established for its assessment, while data envelopment analysis (DEA) has received particular attention. This study contributes to the literature, establishing a new approach in the construction process of prediction models based on the combination of logistic LASSO and an advanced version of data envelopment analysis (DEA). We adopt the modified slacks-based super-efficiency measure (modified super-SBM-DEA), following the “Worst practice frontier” approach, and focus on the selection process of predictive variables, implementing the logistic LASSO regression. A balanced sample with one-to-one matching between forty-five firms that filed for reorganization under U.S. bankruptcy law during the period 2014–2020 and forty-five non-failed firms of a similar size from the U.S. energy economic sector has been used for the empirical analysis. The proposed methodology offers superior results in terms of corporate failure prediction accuracy. For the dynamic assessment of failure, Malmquist DEA has been implemented during the five fiscal years prior to the event of failure, offering insights into financial distress before the event of a default. The model outperforms alternatives by achieving higher overall prediction accuracy (85.6%), the better identification of failed firms (91.1%), and the improved classification of non-failed firms (80%). Compared to prior DEA-based models, it demonstrates superior predictive performance with lower Type I and Type II errors and higher sensitivity as well as specificity. These results highlight the model’s effectiveness as a reliable early warning tool for bankruptcy prediction. Full article

Alkanolamine additives play a critical role in enhancing the early process performance of cement slurries, thereby improving construction efficiency and structural durability. This study systematically evaluates the effects of ethanolamine (EA), diethanolamine (DEA), and triethanolamine (TEA) on cement slurry properties, including the thickening time, rheology, density, shrinkage, and hydration kinetics. Clear structure–activity relationships are established based on the findings. The experimental analysis demonstrated that increasing the hydroxyl group count in the alkanolamines significantly accelerated cement hydration. At a dosage of 1.0%, the thickening time of the cement slurry was significantly shortened to 125 min (EA), 15 min (DEA), and 12 min (TEA), respectively. Concomitantly, a reduction in fluidity was observed, with flow diameters measuring 15.8 cm (EA), 14.6 cm (DEA), and 14.1 cm (TEA). The rheological analysis revealed that the alkanolamine additives significantly increased the consistency coefficient (K) and decreased the flowability index (n) of the slurry, with TEA exhibiting the most pronounced effect. The density measurements confirmed the enhanced settlement stability, as the density differences diminished to 0.1 g/cm³ at the optimal dosages (0.6% TEA and 0.8% DEA). The hydration degree analysis indicated a hydration rate acceleration of up to 32% relative to plain slurry, attributed to the hydroxyl-facilitated promotion of Ca(OH)₂ formation and C₃S dissolution. The XRD analysis confirmed that the alkanolamines modified the reaction kinetics without inducing phase transformation in the hydration products. Crucially, the hydroxyl group count governed the performance: a higher hydroxyl density intensified Ca²⁺/Al³⁺ complexation, thereby reducing ion mobility and accelerating setting. These findings establish a molecular design framework for alkanolamine-based additives that balances early process performance development with practical workability. The study advances sustainable cement technology by enabling targeted optimization of rheological and mechanical properties in high-demand engineering applications. Full article

In the present era where digitalization, FinTech, and blockchain technologies are reshaping the global financial landscape, traditional measures of bank performance need to evolve. This paper introduces a hybrid approach that combines multi-criteria efficiency assessment and econometric modeling to evaluate bank performance within the context of digital transformation in emerging economies. Focusing on a panel of banks across selected emerging markets, this study first applies a multi-criteria decision-making technique (Data Envelopment Analysis) to assess operational efficiency using both conventional indicators and digitalization-driven metrics, such as mobile banking penetration and blockchain adoption. We then employ a panel econometric model to investigate the factors that shape efficiency outcomes, with special attention to FinTech and blockchain innovations as potential drivers. The results reveal a nuanced picture of how digital technologies can influence bank performance, highlighting both opportunities and constraints for financial institutions in less developed markets. The findings offer actionable insights for bank managers, regulators, and policymakers striving to balance traditional operational priorities with the demands of digital transformation. By linking efficiency measurement with an examination of the digitalization process, this paper provides a timely contribution to the literature on banking and financial innovation, serving as a foundation for future research and strategic decision-making in the FinTech and blockchain era. Full article

The reduction of substantial agricultural greenhouse gases (GHGs) emissions can make a significant contribution to climate change mitigation and regional sustainable development. Given that most of the current studies about eco-efficiency only considers CO₂, while ignoring other GHGs, such as CH₄ and N₂O, this study analyzes the spatiotemporal characteristics of CO₂, CH₄, and N₂O, and considers them as undesirable outputs to assess the agricultural eco-efficiency (AEE) in the North China Plain from 2004 to 2022, respectively, including AEECO₂, AEECH₄, AEEN₂O, and AEEGHG. The results show that (1) Agricultural GHGs emissions increased significantly before 2018 and slightly decreased after 2018, due to the enforcement of energy-saving and emission-reducing policies. Spatially, GHG emissions are higher in the north but lower in the south. (2) The study demonstrated that incorporating CH₄ and N₂O significantly affects efficiency (p < 0.01). AEECH₄ and AEEN₂O are higher than AEEGHG, while AEECO₂ is lower than AEEGHG, indicating that only considering a single emission will result in an inefficient outcome. (3) With significant regional heterogeneity, AEEGHG is higher in Henan, Beijing, and Tianjin, while it is the lowest in Hebei. Specific suggestions are proposed to promote sustainable agricultural development. This study presents a novel perspective for comprehensively assessing AEE and offers scientific evidences for agricultural policy formulation to promote climate mitigation. Full article

This research evaluates the carbon dioxide emission-reduction efficiency of new energy vehicles (NEVs) in China from 2018 to 2023 by applying a three-stage super-SBM data envelopment analysis (DEA) model that incorporates undesirable outputs. This model offers significant advantages over traditional DEA models, as it effectively disentangles the influences of external environmental factors and stochastic noise, thereby providing a more accurate and robust assessment of true efficiency. Its super-efficiency characteristic also allows for effective ranking of all decision-making units (DMUs) on the efficiency frontier. The empirical findings reveal several key insights. (1) The NEV industry’s carbon-reduction efficiency in China between 2018 and 2023 displayed an upward trend accompanied by pronounced fluctuations. Its mean super-efficiency score was 0.353, indicating substantial scope for improvements in scale efficiency. (2) Significant interprovincial disparities in efficiency appear. Unbalanced coordination between production and consumption in provinces such as Shaanxi, Beijing, and Liaoning has produced correspondingly high or low efficiency values. (3) Although accelerated urbanization has reduced the capital and labor inputs required by the NEV industry and has raised energy consumption, the net effect enhances carbon-reduction efficiency. Household consumption levels and technological advancement exerts divergent effects on efficiency. The former negatively relates to efficiency, whereas the latter is positively associated. Full article

Innovation-driven labor markets play a pivotal role in economic development, yet significant disparities exist in how efficiently countries transform innovation inputs into labor market outcomes. This study addresses the critical gap in benchmarking multi-stage innovation efficiency by developing an integrated framework combining Data Envelopment Analysis (DEA) Super Slack-Based Measure (Super-SBM) for static efficiency evaluation and the Malmquist Productivity Index (MPI) for dynamic productivity decomposition, enhanced with cooperative game theory for robustness testing. Focusing on the top 20 innovative economies over a 5-year period, we analyze key inputs (Innovation Index, GDP, trade openness) and outputs (labor force, unemployment rates), revealing stark efficiency contrasts: China, Luxembourg, and the U.S. demonstrate optimal performance (mean scores > 1.9), while Singapore and the Netherlands show significant underutilization (scores < 0.4). Our results identify a critical productivity shift period (average MPI = 1.325) driven primarily by technological advancements. This study contributes a replicable, data-driven model for cross-domain efficiency assessment and provides empirical evidence for policymakers to optimize innovation-labor market conversion. The methodological framework offers scalable applications for future research in computational economics and productivity analysis. Full article

Drag embedment anchors (DEAs) play a vital role in maintaining the stability and safety of offshore structures, including floating wind turbines, oil rigs, and marine renewable energy systems. Accurate prediction of anchor performance is essential for optimizing mooring system designs, reducing costs, and minimizing risks in challenging marine environments. By leveraging advanced machine learning techniques, this research provides innovative solutions to longstanding challenges in geotechnical engineering, paving the way for more efficient and reliable offshore operations. The findings contribute significantly to developing sustainable marine infrastructure while addressing the growing global demand for renewable energy solutions in coastal and deep-water environments. This current study evaluated tree-based machine learning algorithms, e.g., decision tree regression (DTR) and random forest regression (RFR), to predict the holding capacity and efficiency of DEAs in sand seabed. To train and validate the results of machine learning models, the K-fold cross-validation method, with K = 5, was utilized. Eleven geotechnical and geometric parameters, including sand friction angle (φ), fluke-shank angle (α), and anchor dimensions, were analyzed using 23 model configurations. Results demonstrated that RFR outperformed DTR, achieving the highest accuracy for capacity prediction (R = 0.985, RMSE = 344.577 KN) and for efficiency (R = 0.977, RMSE = 0.821 KN). Key findings revealed that soil strength dominated capacity, while fluke-shank angle critically influenced efficiency. Single-parameter models failed to capture complex soil-anchor interactions, underscoring the necessity of multivariate analysis. The ensemble approach of RFR provided superior generalization across diverse seabed conditions, maintaining errors within ±10% for capacity and ±5% for efficiency. Full article

Nitrosamine impurities have provoked numerous global medicine recalls due to their possible presence during drug manufacturing or storage. Regarding formulation of nitrosamine impurities, a key risk involves reactions between nitrosating agents (nitrite) in excipients and vulnerable amines as impurities or degradants. Rapid detection across various sample types is essential to support pharmaceutical manufacturing. In this study, two methods were developed to detect nitrite in excipients and crucial secondary amines in active ingredient metformin hydrochloride at trace levels, respectively. The former method was developed based on the reaction of nitrite ions with 2,3-diaminonaphthalene to form 1-[H]-naphthotriazole (NAT), whereas the latter was based on amine tosylation. Mass spectrometric conditions were optimized using electrospray ionization in the positive mode. Multiple reaction monitoring transitions were determined at m/z 170 → 115 for NAT, and m/z 200.1 → 91 for dimethylamine (DMA) and 228.1 → 91 for diethylamine (DEA). These methods were validated using selected eight excipients or metformin hydrochloride in terms of specificity, linearity, accuracy, precision, robustness, limit of quantification (LOQ), and limit of determination according to the ICH guidelines. The results of the validation were within the acceptable criteria. Applicability of the methods was evaluated using 170 pharmaceutical samples donated by industries. The nitrite content in the excipients ranged from <LOQ to 4.74 ppm, with observed levels 1.3 to 6 times higher than the average (0.8 ppm) in the samples. The DMA levels in the metformin hydrochloride were within the limit (500 ppm) but varied significantly (0.2–209.2 ppm) among manufacturers. DEA was detected at lower levels (0.7–0.9 ppm). To mitigate the nitrosamine content in the metformin products, the excipient compositions were investigated by selecting those with low nitrite levels. As the types of impurities detected have become increasingly diverse and detection cycles have become more frequent, the requirement for preemptive safety management to relieve public anxiety is essential for regulatory aspects. Nitrite and secondary amines are crucial precursors to N-nitrosamine, and the suggesting approach could be a means to mitigate N-nitrosamine contamination. Full article

Forage sorghum (Sorghum bicolor (L.)) is a cereal native to Africa and belongs to the family Poaceae. It is a forage with a C4 photosynthetic pathway that stands out for its ability to adapt to different environments; it is able to produce even in unfavorable circumstances. The objective of this study was to analyze the attenuating effect of the brassinosteroid hormone in the form of 24-epibrassinolide on forage sorghum plants subjected to water deficit and rehydration. A completely randomized design (CRD) was used in the experiment. A 2 × 3 × 5 factorial arrangement was used, with two water conditions (water deficit and rehydration), three brassinosteroid doses (0 nM, 50 nM, and 100 nM as 24-epibrassinolide), and five replicates. The experiment was conducted in a greenhouse. Sorghum seeds were sown in pots with a capacity of 3 kg of substrate. Analyses were performed on the roots and leaves of sorghum plants at different growth stages. The macronutrients (N, P, K, Ca, and Mg) were analyzed in the soil physics laboratory. As a result, the content of N, P, K, Ca, and Mg decreased under a water deficit and was then restored by the hormone 24-epibrassinolide, which was able to restore these nutrients. The effect of the hormone under rehydration had a positive effect, increasing the levels of nutrients. Given the above, it was possible to conclude that there were no significant divergences between the treatments during the period of irrigation suspension. Among the tested concentrations, 50 nM of 24-epibrassinolide showed the most consistent improvements in nutrient concentrations under water-deficit conditions, suggesting a potential role in mitigating nutritional imbalance during stress. Rehydrated plants maintained nutrient levels similar to the controls regardless of 24-epibrassinolide application. However, it is important to note that nutritional quality indices such as crude protein and total digestible nutrients (TDN) were not evaluated in this study, which limits direct conclusions about the forage nutritional value. Full article

Coal plays a significant role in the economies of many countries and serves as an energy source for numerous societies. However, its combustion causes various environmental problems and contributes to climate change. This article examines the efficiency of coal consumption in 26 European Union countries and its changes from 2014 to 2022. Data Envelopment Analysis (DEA) methodology was applied to measure the extent of overall technical, pure technical, and scale technical efficiency, based on data concerning three production factors (labour, fixed assets, and energy), with GDP as a desirable output and CO₂ emissions as an undesirable output. The empirical findings revealed that Cyprus, Denmark, Luxembourg, and Poland were efficiency leaders throughout the entire study period. France, Germany, Italy, and the Netherlands managed energy and non-energy resources efficiently but were found inefficient in terms of operational scale. Countries that do not use their resources at optimal levels in the production of goods and services should provide greater technical and financial support to their production processes and improve the organisation and structure of labour. Full article

Biodegradable hydroretentive polymers, such as UPDT^®, have emerged as promising alternatives to synthetic hydrogels, particularly in pasture systems where sustainable water management is crucial. These materials enhance subsurface drip irrigation by maintaining soil moisture, which supports germination and early root development until roots access deeper water reserves. However, their degradation dynamics in tropical forage systems remain poorly characterized, posing a challenge to long-term application strategies. This study aimed to evaluate the effects of different UPDT^® doses (0, 7.5, 15, 22.5, and 30 kg ha⁻¹) on the morphological and agronomic traits of Mombaça grass under controlled conditions. After a uniformity cycle, treatments were evaluated across four cultivation cycles with monitored irrigation to avoid water deficits. Morphogenetic traits such as number of live leaves (NLL), final number of emerging leaves (NEmL), leaf appearance rate (LAR), and stem elongation rate (SER), as well as shoot dry mass (SDM), were analyzed. Results showed that morphological variables responded quadratically to polymer doses during the initial and intermediate cycles. In the final cycle, reductions in these traits and in water productivity suggested the onset of polymer degradation and loss of hydroretentive capacity. Agronomic traits were influenced throughout all cycles, with the fourth cycle showing the highest SDM due to elevated temperatures. These findings highlight the need to better understand the degradation kinetics of biodegradable hydrogels such as UPDT^® in tropical pastures. Field trials are recommended to define optimal reapplication intervals and integrate degradation monitoring into irrigation planning, ensuring long-term sustainability in pasture management. Full article

Primary healthcare is vital to achieving universal health coverage, as emphasized by Sustainable Development Goal 3 (SDG 3). However, energy poverty remains a critical yet overlooked barrier to the efficiency of primary healthcare services in rural China—precisely the focus of this study. It employs panel regression models and threshold analysis methods using data from 31 Chinese provinces for the period 2014–2021, sourced from the EPSDATA data platform. Robustness checks are performed using bootstrap procedures, accompanied by detailed mechanism analyses. The empirical results demonstrate that rural energy poverty significantly reduces primary healthcare efficiency, particularly in provinces initially characterized by lower healthcare performance. The mechanism analysis identifies four critical transmission channels—off-farm employment, internet intensity, food safety, and health education—through which rural energy poverty undermines healthcare outcomes. Furthermore, threshold regressions uncover nonlinear relationships, indicating that the negative impacts of rural energy poverty intensify when household medical expenditures exceed 10.9%, the old-age dependency ratio surpasses 22.61%, and the rural energy poverty index is higher than 0.641. In theoretical terms, this study identifies rural energy poverty as a critical determinant of primary healthcare efficiency, thereby addressing an important gap in the existing literature. At the policy level, the findings emphasize the necessity for integrated measures targeting both rural energy poverty and primary healthcare inefficiencies to achieve SDG 3 and sustainably promote equitable, high-quality healthcare access in rural China. Full article

This study investigates the production and economic feasibility of biochar for smallholder and family farms in Central Amazonia, with potential implications for other tropical regions. The costs of construction of a prototype mobile kiln and biochar production were evaluated, using small-sized biomass from acai (Euterpe oleracea Mart.) agro-industrial residues as feedstock. The biochar produced was characterised in terms of its liming capacity (calcium carbonate equivalence, CaCO_3eq), nutrient content via organic fertilisation methods, and ash analysis by ICP-OES. Field trials with cowpea assessed economic outcomes, as well scenarios of fractional biochar application and cost comparison between biochar production in the prototype kiln and a traditional earth-brick kiln. The prototype kiln showed production costs of USD 0.87–2.06 kg⁻¹, whereas traditional kiln significantly reduced costs (USD 0.03–0.08 kg⁻¹). Biochar application alone increased cowpea revenue by 34%, while combining biochar and lime raised cowpea revenues by up to 84.6%. Owing to high input costs and the low value of the crop, the control treatment generated greater net revenue compared to treatments using lime alone. Moreover, biochar produced in traditional kilns provided a 94% increase in net revenue compared to liming. The estimated externalities indicated that carbon credits represented the most significant potential source of income (USD 2217 ha⁻¹). Finally, fractional biochar application in ten years can retain over 97% of soil carbon content, demonstrating potential for sustainable agriculture and carbon sequestration and a potential further motivation for farmers if integrated into carbon markets. Public policies and technological adaptations are essential for facilitating biochar adoption by small-scale tropical farmers. Full article

The objective of this study is to create an integrated and sustainability-centered framework to identify optimal locations for wind energy projects in Indonesia. This research employs a novel two-phase multi-criteria decision-making (MCDM) framework that combines the strengths of Data Envelopment Analysis (DEA), Fuzzy Analytic Hierarchy Process (FAHP), and Fuzzy Combined Compromise Solution (F-CoCoSo). Initially, DEA is utilized to pinpoint the most promising sites based on a variety of quantitative factors. Subsequently, these sites are evaluated against qualitative criteria such as technical, economic, environmental, and socio-political considerations using FAHP for criteria weighting and F-CoCoSo for ranking the sites. Comprehensive sensitivity analysis of the criteria weights and a comparative assessment of methodologies substantiate the robustness of the proposed framework. The results converge on consistent rankings across methods, highlighting the effectiveness of the integrated approach. Notably, the results consistently identify Lampung, Aceh, and Riau as the top-ranked provinces, showcasing their strategic suitability for wind plant development. This framework provides a systematic approach for enhancing resource efficiency and strategic planning in Indonesia’s renewable energy sector. Full article

Although organizational resilience is well established, refining the systematic quantitative evaluation of health systems resilience (HSR) remains an ongoing opportunity for advancement. Research either focuses on individual HSR indicators, such as social welfare policy, public expenditure, health insurance, healthcare quality, and technology, or broadly examines socio-economic factors, highlighting the need for a more comprehensive methodological approach. This study employed the Slacks-Based Measure (SBM) within Data Envelopment Analysis (DEA) to analyze efficiency by maximizing outputs. It systematically examined key HSR factors across countries, providing insights for improved policymaking and resource allocation. Taking a five-year (2016–2020) dataset that covered 55 to 56 countries and evaluating 17 indicators across governance, health systems, and economic aspects, the paper presents that all sixteen top-ranked countries with a perfect efficiency score of 1 belonged to the high-income group, with ten in Europe, highlighting regional HSR differences. This paper concludes that adequate economic resources form the foundation of HSR and ensure stability and sustained progress. A properly supported healthcare workforce is essential for significantly enhancing health systems and delivering quality care. Last, effective governance and the equitable allocation of resources are crucial for fostering sustainable development and strengthening HSR. Full article