Search Results (304)

Ports are the major gateways of cities. Background: Sustainable growth requires ports to prioritize efficiency while balancing economic, social, and environmental goals. There is limited synthesized evidence on the sustainability evaluation of ports, including those of North America. In this paper, we propose a multi-step approach based on fuzzy DEA to evaluate the environmental performance of ports. Methods: In the first step, we identify indicators for environmental performance evaluation. The second step involves application of fuzzy DEA using the identified indicators to measure the environmental efficiency of ports. In the third step, a numerical illustration is provided using open data. The proposed model incorporates undesirable outputs and employs one set of constraints to make a production frontier. Results: The findings show wide differences in performance, ports reach higher scores when they use resources wisely plus keep emissions low, not merely when they expand. Conclusions: The proposed methodology provides a robust and comparable measurement of port environmental efficiency under uncertainty. Full article

Mobility-as-a-Service (MaaS) has emerged as a sustainable solution that integrates multiple transport services through digital platforms. Across different cities, MaaS development exhibits variation in terms of economic support, infrastructure capacity, service integration level, and long-term sustainability orientation. The complexity of multistakeholder interactions and functional components in MaaS ecosystems calls for a more comprehensive performance evaluation framework. To address this, this study proposes a holistic four-dimensional indicator system covering economic, infrastructure, integration and sustainability aspects. To address the hybrid uncertainties arising from the heterogeneous information aggregated by the proposed framework, encompassing both quantitative statistics and qualitative expert judgements, a novel rough–fuzzy best–worst method (BWM) and rough–fuzzy data envelopment analysis (DEA) approach is developed. The empirical application to six representative core cities in China reveals that high performance in “Integration” and “Economic” dimensions plays a pivotal role in determining overall MaaS performance, and coordinated enhancement across dimensions is also important. Comparative and sensitivity analyses validate the framework’s robustness, offering policymakers a reliable tool for benchmarking MaaS maturity. Full article

Background: Midwives, despite their health-promoting role, face factors that may disrupt eating behaviours and weight regulation. Little is known about their body weight perception or disordered eating attitudes (DEAs). This study assessed body weight perception and eating attitudes across BMI categories among Polish midwives. Methods: A cross-sectional survey of 568 midwives was conducted. BMI was calculated from self-reported measures and classified according to WHO criteria. Body weight perception was assessed using discrepancies between actual and ideal body weight and between self-perceived ideal body weight and ideal body weight. Long-term weight variability was additionally evaluated using the difference between maximum and minimum adult body weight. Eating attitudes were examined using the Polish version of the EAT-26. Group differences were analysed with the Kruskal–Wallis and χ² tests. Results: Among the participants, 62.9% had normal weight, 23.4% were overweight, and 13.7% were obese. Perceived ideal body weight increased with BMI (p < 0.001). Midwives with overweight and obesity demonstrated higher EAT-26 scores than those with normal BMI, with EAT-26 > 20 observed in 8.3% of overweight and 14.1% of obese participants (p = 0.010). Overweight and obese midwives also showed larger discrepancies between actual and ideal body weight and greater lifetime weight variability, and these groups simultaneously presented higher levels of disturbed eating attitudes. Emotional eating, binge-type episodes, and dieting behaviours were more common among overweight and obese participants, while calorie awareness remained consistently high across groups. Conclusions: Midwives with excess body weight often misperceive their body size and show an elevated risk of DEA. Weight perception appears more strongly related to maladaptive eating patterns than BMI alone. These findings highlight the need for targeted, non-stigmatising interventions addressing weight perception, eating attitudes, and occupational stressors, which may support both midwives’ well-being and their professional effectiveness in delivering nutrition and lifestyle counselling. Full article

With increasing emphasis on ecological conservation and food security, cultivated land issues have become more prominent. This study focuses on Jilin Province and uses nine prefecture-level administrative units and prefectures as the basic analytical units. Using continuous data for 2000–2023, this study analyzes the spatiotemporal evolution of cultivated land use efficiency (CLUE). By 2023, most regions had achieved ecological safety (ES), examined through their coupling and coordination. The Super-Efficiency SBM-DEA model and the Malmquist–Luenberger (ML) index were used to evaluate the static and dynamic changes in CLUE. A DPSIR–PLS-SEM integrated framework was applied to identify causal mechanisms influencing ES, while the TOPSIS method was employed to assess overall evolutionary trends. In addition, the coupling coordination degree (CCD) model combined with kernel density estimation (KDE) was used to characterize the interaction between CLUE and ES and their spatial evolution. Results indicated the following: (1) From 2000 to 2023, overall CLUE in Jilin Province showed an upward trend with fluctuations, while regional disparities narrowed and spatial distribution became more balanced. (2) The composite ES index increased from 0.3009 to 0.7900, accompanied by a marked expansion of areas classified as secure. (3) The CCD improved from a basic level to a high-quality coordination level, indicating enhanced synergistic development. Higher coordination was observed in central and eastern regions, whereas western and peripheral areas lagged. This study integrates multi-dimensional modeling approaches to systematically assess the coupled dynamics on cultivated land use efficiency and ecological safety, providing insights for land management and policy formulation. Full article

This study investigates the carbon footprint (CF) and eco-efficiency of rice cultivation in the Thessaloniki Plain, with the objective of identifying sustainable practices that mitigate greenhouse gas emissions while safeguarding productivity and farm income. Primary data were collected through structured questionnaires, and two complementary methods were employed: Life Cycle Assessment (LCA) for the quantification of CO₂e emissions and Data Envelopment Analysis (DEA) for the evaluation of technical and environmental efficiency. Results indicated a CF ranging from 6532 to 13,263 kg CO₂e/ha, largely shaped by residue management practices. Overall, the findings underline the importance of rational input use and the adoption of best practices to enhance sustainability. Full article

Nowadays, optimization methods are widely used to adjust controller parameters and tune their optimal values in order to enhance the efficiency and performance of dynamic systems. In this study, the parameters of a linear Proportional–Integral (PI) controller were optimized by using five different optimization algorithms, such as Artificial Tree Algorithm (ATA), Particle Swarm Optimization (PSO), Differential Evolution Algorithm (DEA), Constrained Multi-Objective State Transition Algorithm (CMOSTA), and Adaptive Fire Forest Optimization (AFFO). The optimized controllers were implemented in real time for temperature control of a Heat-flow System (HFS) under various step and time-varying reference signals. In addition, the Ziegler–Nichols (Z–N) method was also applied to the system as a benchmark to compare the temperature tracking performance of the proposed optimization methods. To further evaluate the performance of each optimization algorithm, Mean Absolute Error (MAE) values were calculated, and improvement ratios were obtained. The experimental results showed that the proposed optimization methods provided more successful reference tracking and enhanced controller performance as well. Full article

The digital economy, driven by data-enabled innovation, has become a critical engine for advancing agricultural modernization and promoting inclusive and sustainable rural revitalization in China. This study conceptualizes the rural revitalization system as an integrated system comprising five interconnected subsystems. A global parallel Data Envelopment Analysis (DEA) model with shared inputs is developed to evaluate the total system and subsystem efficiencies of rural revitalization. In addition, quantification of the digital economy’s development level is achieved through the joint application of the entropy weight method and TOPSIS. Finally, based on a 2013–2022 panel of 31 provincial-level units in China, this paper identifies the impact and underlying mechanisms of the digital economy on the total system and subsystem efficiencies of rural revitalization. The findings reveal that (i) the digital economy significantly enhances rural revitalization efficiency, and this conclusion remains robust after addressing endogeneity and conducting multiple robustness tests. (ii) Heterogeneity analyses indicate that the digital economy contributes more in regions with lower rural revitalization efficiency, medium economic development, larger labor forces, or lower levels of Internet development. Furthermore, although digital economy does not have a significant impact on the subsystem efficiency of social etiquette and civility, its impacts on the remaining subsystem efficiencies are all significant. (iii) The impact of the digital economy on improving rural revitalization efficiency is mediated by technological innovation, and the expansion of the scale of non-agricultural employment enhances the promoting effect of the digital economy on rural revitalization efficiency. Full article

(1) Background: Ecuador is the fourth largest milk producer in Latin America, where ap-proximately 80% of production originates from small family farms located in the Andean region. Despite their socioeconomic importance, these farms face challenges related to low technical efficiency. While there are specific studies on efficiency in dairy systems from other regions, a knowledge gap persists regarding the temporal evolution of technical efficiency (TE) in Ecuadorian Andean dairy farms, especially during crisis periods such as the COVID-19 pandemic. The objective of this study was to evaluate the evolution of TE of family dairy farms in the Ecuadorian Andean region during the period 2018–2024 and to analyze the impact of the pandemic on said efficiency. (2) Methods: Data Envelopment Analysis (DEA) with input orientation and bootstrap simulation was employed to estimate TE, using data from a representative sample that included between 2370 and 2987 farms per year (approximately 25% of the national database of the Ministry of Agriculture and Livestock). Farms were selected based on the availability of complete information on key variables: number of milking cows, area dedicated to forage, family and hired labor (annual hours), and total annual milk production. Statistical analysis included ANOVA to compare mean TE values between years, post-hoc tests to identify specific differences between periods, and the identification of factors related to the TE. (3) Results: The mean TE of Andean dairy farms increased significantly from 0.37 in 2018 to 0.44 in 2024 (p < 0.10), evidencing sustained improvement, although the mean is still distant from the efficiency frontier. The analysis revealed a notable decrease in TE during 2020–2021, coinciding with the period of greatest impact of the COVID-19 pandemic, followed by progressive recovery in subsequent years. The TE distribution showed that between 70% and 75% of farms remained below 0.50 throughout the analyzed period, while only 8–12% achieved levels above 0.70. The main sources of technical inefficiency identified were relative excesses of labor and forage area in relation to milk production obtained. When compared with international studies, Ecuadorian farms present TE levels substantially lower than those reported in the European Union (>0.80) and similar to or slightly lower than those found in Turkey (0.61–0.71). (4) Conclusions: Family dairy farms in the Ecuadorian Andean region operate with technical efficiency levels considerably below their potential and international standards, suggesting substantial scope for improvement through the optimization of productive resource use, particularly labor and land. The COVID-19 pandemic impacted the sector’s efficiency negatively but temporarily, demonstrating resilience and recovery capacity. These findings are relevant to the design of public policies and technical assistance programs aimed at sustainable intensification of family dairy production in the Andes, with an emphasis on improving labor productivity and the efficient use of forage area. Full article

Each maritime country produces annual reports on its maritime safety policy. The annual report details the implementation of established policies, plans, and regulations concerning the supervision and protection of rights and interests at sea. By analyzing the Annual Reports for the Republic of Croatia from 2017 to 2024, maritime traffic and activities at sea were examined. The data include the number of available inspection vessels, the nautical miles traveled, fuel consumption, and similar metrics. All this information is related to the total number of inspected vessels, which is a key performance indicator for maritime traffic control. The aim of the analysis is to determine the correlation between fuel consumption, distance traveled, number of voyages, and number of inspected vessels over eight consecutive years. Data Envelopment Analysis (DEA) is used to assess the relationship between inputs and outputs to identify which years were efficient. Additionally, the multi-criteria decision-making method PROMETHEE (Preference Ranking Organization METHod for Enrichment of Evaluations) is used to interpret and validate the DEA results, particularly the efficiency ranking. The proposed DEA–PROMETHEE hybrid model enables decision-makers to better understand DEA results, especially when efficiency scores are very similar. In terms of practical applications, the results based on the DEA input and output analysis, extended with the PROMETHEE method, show that the optimized use of available resources contributes to increased overall maritime safety. Full article

To address the limitations of unimodal visual detection in complex scenarios involving low illumination, occlusion, and texture-sparse environments, this paper proposes an improved YOLOv11-based dual-branch RGB-D fusion framework. The symmetric architecture processes RGB images and depth maps in parallel, integrating a Dual-Encoder Cross-Attention (DECA) module for cross-modal feature weighting and a Dual-Encoder Feature Aggregation (DEPA) module for hierarchical fusion—where the RGB branch captures texture semantics while the depth branch extracts geometric priors. To comprehensively validate the effectiveness and generalization capability of the proposed framework, we designed a multi-stage evaluation strategy leveraging complementary benchmark datasets. On the M³FD dataset, the model was evaluated under both RGB-depth and RGB-infrared configurations to verify core fusion performance and extensibility to diverse modalities. Additionally, the VOC2007 dataset was augmented with pseudo-depth maps generated by Depth Anything, assessing adaptability under monocular input constraints. Experimental results demonstrate that our method achieves mAP50 scores of 82.59% on VOC2007 and 81.14% on M3FD in RGB-infrared mode, outperforming the baseline YOLOv11 by 5.06% and 9.15%, respectively. Notably, in the RGB-depth configuration on M³FD, the model attains a mAP50 of 77.37% with precision of 88.91%, highlighting its robustness in geometric-aware detection tasks. Ablation studies confirm the critical roles of the Dynamic Branch Enhancement (DBE) module in adaptive feature calibration and the Dual-Encoder Attention (DEA) mechanism in multi-scale fusion, significantly enhancing detection stability under challenging conditions. With only 2.47M parameters, the framework provides an efficient and scalable solution for high-precision spatial perception in autonomous driving and robotics applications. Full article

Golden pompano (Trachinotus ovatus) is the largest-scale marine aquaculture fish species in China, with a significant economic and nutritional value as a high-quality seafood product. The recent outbreak of an epidemic caused by a novel Carpione rhabdovirus (CAPRV) occurred in cultured golden pompano. To address it, a CAPRV enzyme-mediated one-step sample processing–reverse transcription–enzyme-mediated duplex exponential amplification (EmOSP-RT-EmDEA) detection system was developed. This innovative molecular diagnostic tool integrates enzyme-mediated one-step sample processing (EmOSP) with enzyme-mediated duplex exponential amplification (EmDEA) technology. Unlike traditional RPA-Cas12a detection methods, this system directly incorporates fluorophores into RNA components, eliminating the need for exogenous fluorescent probes while maintaining high sensitivity. It enables rapid, sensitive, and specific detection of CAPRV2023 across various sample types, including clinical, invasive, minimally invasive, and environmental specimens. Performance evaluation of the CAPRV2023 EmOSP-RT-EmDEA detection system against conventional diagnostic methods, such as TaqMan qPCR and traditional PCR, demonstrated superior sensitivity, with a detection limit as low as 4 copies/μL, and exceptional specificity. The optimized EmOSP protocol for nucleic acid extraction from fecal, hepatic, and water samples provided robust and reproducible results. The EmOSP-RT-EmDEA system achieved a detection rate of 68.14% in fecal samples, matching the performance of the gold-standard TaqMan qPCR assay. Full article

The world is being orchestrated by dramatic changes caused by technological and innovative disruptions. Accordingly, Industry X.0 terminology was coined because the revolutionary numbers could not represent this industrial disruption. Coping with these technological disruptions is essential for an organization’s sustainability and resilience. Therefore, defining the technological gaps, as well as mapping the potential innovative disruptions for industrial systems, becomes compulsory. Technology Readiness Levels is a standardized method widely adopted to evaluate the maturity of a technology, using a scale from 1 (concept) to 9 (commercialized solution). This framework helps stakeholders to benchmark different industrial systems from a technology innovation perspective. However, TRL sometimes fails to capture the maturity of breakthrough innovations and lacks quantification. In this paper, a comprehensive framework for assessing technological readiness levels is proposed. The automotive industry was selected as one of the top technology-related industries to validate this framework. This framework maps the technological readiness levels of the following three main industry components: product, engineering, and operations. A tailored Data Envelopment Analysis (DEA) model has been employed as a benchmarking approach to evaluate the technological readiness gaps and map the technological footprint position of a selected automotive company across the best practices in the automotive industry. Full article

The existence of fixed-sum output constraints in real-world situations is widespread, such as market share and carbon dioxide emissions, etc. However, existing fixed-sum output data envelopment analysis (DEA) methods mostly focus on individual decision-making units (DMUs) and ignore the interactions between groups. Therefore, this study first establishes a systematic framework to quantify group performance by the average criterion, and constructs the equilibrium efficient frontier (EEF) to evaluate all groups on a common platform. To address the non-uniqueness issue of EEF, we further introduce the aggressive cross-efficiency mechanism, ultimately proposing a novel group cross-EEF methodology that explicitly accounts for competitive intergroup dynamics. The proposed method is applied in the assessment of carbon emission efficiency in the industrial sector for 30 provinces in China, and the validity of the method is verified. The result shows that (1) even though the average industrial carbon efficiency stands at 1.2015, half of the provinces exhibit values below 1; (2) significant regional heterogeneity is observed, with North China and East China exhibiting higher efficiency levels, while the Northeast and Northwest regions lag behind; (3) provinces such as Beijing, Guangdong, and Zhejiang demonstrate superior performance, in contrast to Ningxia, Hebei, and Qinghai, which remain at relatively low efficiency levels. This study provides theoretical and policy insights to support the advancement of low-carbon development in China’s industrial sector. Full article

Quality assurance (QA) in higher education has become increasingly vital in response to global competition, digital transformation, and evolving sustainability demands. This study examines the leading QA frameworks—namely the European Standards and Guidelines (ESG), the EFQM Excellence Model, and ISO 9001—while integrating advanced analytical methodologies, including Data Envelopment Analysis (DEA), the Analytic Hierarchy Process (AHP), and Bayesian modeling, to propose a comprehensive framework for assessing university performance. Through empirical analysis and comparative case studies of internationally ranked universities, this study demonstrates that combining objective indicators with quantitative methods significantly improves institutional efficiency, transparency, and competitiveness. Additionally, the role of digital education, ESG-driven sustainability strategies, and AI-based student feedback systems emerge as being crucial to the effectiveness of QA practices. The results suggest that hybrid evaluation models—blending traditional QA principles with data-driven analytics—promote continuous improvement, optimize resource management, and enhance educational outcomes. This research ultimately highlights the growing relevance of advanced quantitative frameworks in modernizing QA systems and supporting universities in addressing dynamic global challenges. Full article

Effective assessment of power grid investment efficiency is crucial for optimizing resource allocation and improving operational performance. However, existing evaluation methods typically fail to account for two critical factors: inherent uncertainties in input–output data and temporal delays in investment returns. To address these limitations, this study introduces an integrated evaluation framework combining robust optimization techniques for uncertain variables with a time-lag Data Envelopment Analysis (DEA) approach to capture the multi-period dynamics and ensure resilience against external shocks and data perturbations. An empirical analysis conducted on panel data from 31 provincial power grid enterprises in China (2015–2023) reveals significant regional disparities in efficiency, particularly between coastal and resource-rich provinces. The findings highlight that excluding time-lag effects leads to systematic underestimation of efficiency and employing robust optimization yields more resilient efficiency scores amidst data uncertainties. The study contributes methodologically by advancing DEA frameworks to better reflect the complexities of power grid investments and empirically provides valuable insights for policymakers seeking to enhance investment strategies and achieve sustainable development goals. Full article