Search Results (1,074)

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

Platform-enabled governance is reshaping destination management, yet subnational destinations still lack replicable dashboards that combine key performance indicators (KPIs) with efficiency analysis. This study examines whether a compact KPI stack coupled with longitudinal Data Envelopment Analysis (DEA) can provide actionable targets for destination development management and marketing organizations (DDMMOs). Using 2020–2024 administrative data for five regional units of the Peloponnese, an output-oriented CRS DEA model is specified with one input (room capacity) and two outputs (tourism revenue and overnight stays), complemented by a VRS specification that decomposes Overall Technical Efficiency into Pure Technical and Scale Efficiency. The results show a clear differentiation in trajectories: one regional unit remains consistently on the efficiency frontier, and others exhibit gradual convergence towards best practice, while at least one unit displays persistent underperformance that is driven primarily by scale rather than managerial inefficiency. These distances to frontier are transformed into proportional, output-specific targets and dynamically updated peer sets, which are integrated into a KPI dashboard to support a continuous measure–act–learn loop on pricing, promotion, and capacity allocation. Overall, the article proposes a transparent, reproducible template that links destination competitiveness frameworks with a multi-input, multi-output efficiency lens and embeds KPIs and dynamic DEA insights in a continuous governance loop for destination management. Full article

Traditional dwellings in southern Xinjiang, exemplified by the Suohema House, have evolved as adaptive responses to the region’s cold and arid climatic conditions, providing thermally comfortable living environments with relatively low energy consumption. Learning from these climate-responsive design strategies offers an effective approach to reconciling the conflict between energy efficiency and indoor comfort. Such exploration is of great significance for preserving regional architectural identity and promoting the development of low-carbon buildings. This study establishes a performance-driven morphological multi-objective optimization framework for traditional dwellings, taking building energy consumption, thermal comfort, and indoor temperature as the primary optimization objectives. The framework integrates parametric modeling, performance simulation, and multi-objective optimization within the Rhino & Grasshopper platform, employing a genetic algorithm to achieve performance-oriented design exploration. Key design variables were identified through data analysis, and the influence weights and prioritization of morphological parameters were quantified. The results reveal that the room depth in residential dwellings (4.57–4.73 m), room width (3.97–6.75 m), room clear height (2.33–2.42 m), wall thickness (lower wall thickness ranging from 1.14 to 1.22 m, upper wall thickness at 0.76 m), and building orientation (true south) have significant impacts on both energy consumption and indoor thermal performance. Based on these findings, adaptive optimization strategies were proposed from three perspectives: scale optimization, spatial hierarchy refinement, and enhancing the performance of building envelopes. The proposed framework provides methodological guidance for the conservation and adaptive renewal of traditional dwellings, as well as for the design of new, green, and low-carbon residential buildings suited to the climatic conditions of southern Xinjiang. Full article

Several Data Envelopment Analysis (DEA)-based benchmarking approaches have been proposed to enable step-by-step performance improvement; however, they frequently overlook the practical aspects necessary to ensure the feasibility and success of the benchmarking process for real-world operations. Simply, these approaches allow inefficient Decision Making Units (DMUs) to reach their targets step by step, thus facilitating gradual performance improvement. Most of the relevant studies in the literature have focused on stratifying DMUs into multiple efficiency layers. This paper introduces a new, practical DEA-based benchmarking framework tailored to the maritime port industry. Specifically, it proposes a feasibility-weighted multi-layer benchmarking path optimization (FW-MLBP) model that determines optimal stepwise benchmarking targets for inefficient ports by minimizing the total amount of controllable resource adjustment required at each stage. This model enables an inefficient port to select a series of manageable intermediate benchmarking targets from a set of efficient ports before ultimately reaching the best-performing one. To validate the effectiveness and practicality of our proposed methodology, we applied it to 30 international port terminals, successfully identifying their optimal stepwise benchmarking targets and demonstrating a viable, incremental path toward enhanced efficiency. Full article

This work addresses the critical need for documentation and validation of structural flight loads for Medium-Altitude Long-Endurance (MALE) Unmanned Aerial Systems (UAS). Despite the increasing prevalence of these aircraft, the industrial and research landscape still exhibits a significant data gap regarding loads under extreme operating conditions, particularly for unconventional geometric configurations. This study presents a rigorous and comprehensive load analysis for the certification of a fixed-wing MALE UAS, which is distinguished by its unique V-Tail configuration, characteristic of platforms such as the Elbit Hermes series. The entire investigation was conducted in strict adherence to the requirements of the NATO airworthiness standard STANAG 4671, aiming to precisely define the aerodynamic behavior and structural integrity of the airframe under an exhaustive set of critical flight conditions. The implemented methodology relies on the use of high-fidelity Computational Fluid Dynamics (CFD) data, derived from RANS simulations to create a complete aerodynamic database. This advanced approach is crucial for the accurate modeling of forces and moments, especially those generated by the coupled control surfaces, known as the ruddervators of the V-Tail. The results obtained include the precise derivation of the operational envelope, which defines the maximum load factors for both maneuver and atmospheric gust conditions. A detailed analysis of balancing and specific loads on the control surfaces was performed, leading to the definition of structural load distributions essential for subsequent stress analysis. Notably, the analysis identified the Unchecked Pitch-Up maneuver performed at the maximum load factor as the dimensioning design condition, particularly for the empennage structure. This work not only provides fundamental data for demonstrating compliance with applicable airworthiness criteria but also establishes a robust and repeatable methodology for the evaluation of flight loads in structurally complex UAS configurations. Full article

To support national carbon neutrality goals, enhancing the thermal insulation of building envelopes has emerged as a crucial strategy in reducing building energy consumption. This study conducted a detailed quantitative analysis of energy performance improvements achieved through enhanced insulation levels in four representative non-residential building types: office, accommodation, educational, and sales facilities. Based on four scenarios—Baseline (2019), Insulation Reinforced, Passive House, and Zero Energy Building (ZEB)—EnergyPlus simulations were performed to calculate end-use energy demand and consumption. The results revealed that office buildings achieved the highest improvement, with up to 34.7% energy reduction, while educational and sales facilities showed moderate and limited improvements, respectively. These findings provide quantitative evidence for prioritizing insulation-based policies and differentiated ZEB strategies tailored to each building type. The proposed RB models and scenario-based methodology offer a robust foundation for establishing future ZEB regulations and performance-based energy policies in South Korea. To ensure clarity, the study explicitly referenced verified data sources and field measurements. The IdealLoadsAirSystem used in the simulation assumes 100% system efficiency; thus, the reported outcomes represent building system loads rather than final energy consumption. The ZEB-level scenario analyzed in this study focuses on envelope and lighting improvements only, not on HVAC system optimization. Full article

This study presents a novel low-cost workflow integrating smartphone-based photogrammetry, Building Information Modeling (BIM), infrared thermography, and real-time interactive visualization to create digital twins for comprehensive energy assessment of existing buildings. Unlike conventional approaches requiring expensive laser scanning equipment and specialized software, this methodology democratizes advanced building diagnostics through accessible technologies and academic licenses. The research aims to develop and validate a replicable workflow that enables architects, engineers, and educators to conduct detailed energy assessments without high-end equipment, while establishing technical criteria for accurate geometric reconstruction, thermal data integration, and interactive visualization. The workflow combines terrestrial photogrammetry using smartphone cameras for 3D reconstruction, BIM modeling in Autodesk Revit for semantic building representation, infrared thermography for thermal performance documentation, and Unreal Engine for immersive real-time visualization. The approach is validated through application to the historic control tower of the former Rabassa aerodrome at the University of Alicante, documenting data capture protocols, processing workflows, and integration criteria to ensure methodological replicability. Results demonstrate that functional digital twins can be generated using consumer-grade devices (high-end smartphones) and academically licensed software, achieving geometric accuracy sufficient for energy assessment purposes. The integrated platform enables systematic identification of thermal anomalies, heat loss patterns, and envelope deficiencies through intuitive three-dimensional interfaces, providing a robust foundation for evidence-based energy assessment and renovation planning. The validated workflow offers a viable, economical, and scalable solution for building energy analysis, particularly valuable in resource-constrained academic and professional contexts, advancing both scientific understanding of accessible digital twin methodologies and practical applications in building energy assessment. Full article

This study assesses the technical efficiency of dairy farms in the Region of Thessaly in Greece and explores the impact of cooperative participation on farm performance. The research is motivated by the need to enhance the competitiveness and sustainability of the livestock sector, particularly considering evolving economic and environmental challenges. The analysis is based on primary data collected at the farm level from 43 dairy cattle farms, of which 20 are formally registered members of recognized dairy cooperatives and 23 operate independently as non-cooperative farms. Technical efficiency was estimated using an output-oriented Data Envelopment Analysis (DEA) model with bootstrap correction. The findings indicate that cooperative farms demonstrate significantly higher technical efficiency, with an average score of 0.92, compared to 0.76 for non-cooperative farms. This efficiency gap is likely attributable to improved access to infrastructure, specialized knowledge, and broader market integration among cooperative members. The results underscore the importance of organizational structures in shaping farm-level productivity and support the case for targeted policies that reinforce cooperative frameworks. Overall, the study contributes to the literature on agricultural efficiency and offers actionable insights for policymakers and practitioners in the primary sector. Full article

The increasing challenges of healthcare systems demand innovative approaches to resource optimization, particularly for hospitals operating under economic and operational constraints. This study investigates the organizational and managerial factors influencing scale efficiency in 127 Italian hospitals, leveraging advanced machine learning (ML) techniques to identify key determinants of efficiency. A multi-level framework was developed, integrating Principal Component Analysis (PCA), Data Envelopment Analysis (DEA), and K-Means clustering to assess the interplay between energy costs, staff composition, and medical equipment across hospital levels. To enhance predictive capabilities, a classification model based on K-Nearest Neighbors (K-NN) was implemented, demonstrating high performance in distinguishing efficiency classes and confirming the importance of targeted resource management strategies. Additionally, the use of LIME (Local Interpretable Model-agnostic Explanations) provided actionable insights into the contribution of individual variables, enabling a deeper understanding of their impact on operational efficiency. In conclusion, this research highlights the importance of an integrated approach to support decision-makers in managing hospital resources, offering innovative tools to optimize efficiency and ensure the economic and operational sustainability of the healthcare system. Full article

This study explores the intellectual landscape and methodological evolution of Data Envelopment Analysis (DEA) in the context of renewable energy and carbon emission efficiency. Using bibliometric techniques and data extracted from the Web of Science Core Collection (2389 publications from 2000 to 2024), the research identifies influential authors, institutions, and thematic clusters shaping the field. The results reveal that DEA has evolved from a traditional efficiency assessment tool into a comprehensive analytical framework supporting sustainable energy transition and carbon mitigation policies. Six major research clusters were identified, encompassing carbon emission measurement, efficiency benchmarking, methodological innovations, industrial applications, circular economy perspectives, and international productivity comparisons. Notably, Asian scholars, particularly from China and Taiwan, dominate the research landscape, reflecting strong regional leadership in empirical and methodological advancements. The findings demonstrate that recent studies increasingly adopt advanced models such as network DEA, dynamic DEA, DEA–Malmquist, and hybrid DEA–machine learning approaches to address complex energy systems. Comparative insights highlight DEA’s advantages over Stochastic Frontier Analysis (SFA) in handling multi-dimensional, non-parametric data, while emphasizing the need for hybrid frameworks to improve robustness. This study contributes to the ongoing discourse on energy sustainability by mapping knowledge structures, revealing methodological trajectories, and providing guidance for future research on efficiency and carbon reduction strategies. Full article

Urban Building Energy Modeling (UBEM) plays a critical role in supporting data-driven strategies for the energy transition of cities. However, its application is often hindered by the lack of harmonized, high-quality input data representing the building stock. This paper presents the methodology and outputs of a national research project to construct an Italian library of reference buildings suitable for UBEM applications described with scorecards. The methodological workflow included six key phases: definition of a national data classification framework, acquisition and integration of heterogeneous data sources, data harmonization, statistical analysis and clustering, archetype formalization, and dissemination. The result is a library of 380 scorecards covering residential, educational, office, commercial, and catering buildings across multiple climate zones and construction periods. Each scorecard is based on empirical data from public databases, field surveys, or technical standards, and includes detailed descriptions of geometry, envelope characteristics, HVAC systems, internal gains, and ventilation. The scorecards are shared openly on the project’s website and were built to work with different UBEM platforms. Overall, both the method and the results help bring more consistency to UBEM practice and support better, data-driven urban energy planning. 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

Campaigns significantly impact overall company performance, making the measurement and prediction of campaign efficiency essential. This study proposes an integrated methodology that combines efficiency measurement with efficiency prediction for airline email campaigns. In the first part of the methodology, Data Envelopment Analysis (DEA) was applied to real airline campaign data to evaluate efficiency; this is the first study to analyze email campaign efficiency in this context. In the second part of the methodology, decision tree algorithms were employed to classify historical campaign data based on DEA scores, with the aim of predicting the efficiency of future campaigns—a novel approach in this context. A core dataset of 76 airline email campaigns with six inputs and two outputs was analyzed using output-oriented CCR (Charnes, Cooper, Rhodes) and BCC (Banker, Charnes, Cooper) models; 26 and 46 campaigns were identified as efficient, respectively. The analysis was further segmented by group size, seasonality, and route type. Efficient campaigns were then ranked via super-efficiency, and sensitivity analysis assessed variable and campaign effects. For prediction, decision tree algorithms (J48 (C4.5), C5.0, and CART (Classification and Regression Trees)) were employed to classify campaigns as efficient or inefficient, using DEA efficiency scores as the target variable and DEA inputs as attributes, with classification performed for both BCC and CCR core models. Class imbalance was addressed with SMOTE, and models were evaluated under stratified 10-fold cross-validation. After balancing, the BCC core model (BCC_C) yielded the most reliable predictions (overall accuracy 76.3%), with J48 providing the most balanced results, whereas the CCR core model (CCR_C) remained weak across algorithms. Full article

This study aims to evaluate the operational efficiency of electric vehicle (EV) charging stations and explore optimization strategies to enhance resource utilization and service performance. A systematic review approach was first applied to identify the main evaluation indicators and influencing factors from existing studies. Subsequently, a super-efficiency Data Envelopment Analysis (DEA) model was used to assess the efficiency of six EV charging stations in a certain City, China. The robustness analysis was carried out, and the output variables were replaced, and the evaluation results did not change. The results show substantial disparities in efficiency across stations: C1 exhibits the highest operational efficiency, while C3 performs the lowest. The inefficiencies primarily result from supply–demand mismatches and redundant capacity investment. Based on these findings, the study proposes both overall and localized optimization strategies to improve operational performance. The results provide valuable insights for urban energy infrastructure planning and contribute to the enhancement of high-quality, low-carbon transportation development in China. Full article

In geosynchronous orbit satellite synthetic aperture radar (GEO SAR) maritime surveillance, imaging of ship targets with complex motion is a hard task. The difficulty lies in how to process the received signal with an extremely low signal-to-noise ratio (SNR), long synthetic aperture time, high-order phase term and range migration. To address the issue, this paper proposes a GEO SAR refocusing algorithm for ship targets with complex motion via ISAR technique, which is based on complex fast-time slow-time frequency distribution (CFSFD). First, the received signal of ship targets with complex motion is derived and modeled as a multicomponent 2-D joint sine-series-polynomial phase signal (2-D JSPS), where 2-D signal is used to describe the signal with range migration induced by complex motion. To deal with the signal, a joint envelope-phase estimation named CFSFD is proposed. Even under low SNR and long synthetic aperture time, CFSFD can achieve directly instantaneous frequency analysis for 2-D JSPS accurately. Finally, a hybrid SAR/ISAR refocusing algorithm is proposed, in which CFSFD-based ISAR technique replaces the range migration correction followed by time–frequency transform approach, yielding clear refocused results of ship targets with complex motion. Simulation and real data experiments validate the effectiveness of the proposed algorithm. Full article