Search Results (344)

This study presents a robust framework for optimizing the site selection of Electric Vehicle Charging Stations (EVCS) in Qatar by integrating a Geographic Information System (GIS) with a Multi-Criteria Decision-Making (MCDM) model. The core innovation lies in the enhancement of the conventional Analytic Hierarchy Process (AHP) with a Removal Sensitivity Analysis (RSA). This unique integration moves beyond traditional, subjective expert-based weighting by introducing a transparent, data-driven methodology to quantify the influence of each criterion and generate objective weights. The Analytic Hierarchy Process (AHP) was used to evaluate fourteen criteria related to accessibility, economic and environmental factors that influence EVCS site suitability. To enhance robustness and minimize subjectivity, a Removal Sensitivity Analysis (RSA) was applied to quantify the influence of each criterion and generate objective, data-driven weights. The results reveal that accessibility factors, particularly proximity to road networks and parking areas exert the highest influence, while environmental variables such as slope, CO concentration, and green areas have moderate but spatially significant impacts. The integration of AHP and RSA produced a more balanced and environmentally credible suitability map, reducing overestimation of urban sites and promoting sustainable spatial planning. Environmentally, the proposed framework supports Qatar’s transition toward low-carbon mobility by encouraging the expansion of clean electric transport infrastructure, reducing greenhouse gas emissions, and improving urban air quality. The findings contribute to achieving the objectives of Qatar National Vision 2030 and align with global efforts to mitigate climate change through sustainable transportation development. Full article

In the current context of the energy transition, the reuse of offshore oil and gas (O&G) structures that have reached the end of their operational life presents new engineering challenges. Many projects aim to adapt existing facilities for a range of alternative uses. This paper outlines guidelines for identifying the most suitable conversion options aligned with the goals of the ongoing energy transition, focusing on the Italian offshore area. The study promotes the reuse—instead of partial or full removal—of existing offshore platforms originally built for the exploitation of hydrocarbon reservoirs. From an engineering perspective, the project describes the development of guidelines based on an innovative methodology to identify new uses for both offshore oil and gas platforms and the depleted reservoirs, with a focus on safety and environmental impact. The guidelines identify the most suitable and effective conversion option for the platform–reservoir system under consideration. To ensure a realistic approach, the developed methodology allows one to identify the preferable conversion option even when some piece of information is missing or incomplete, as often happens in the early stages of a feasibility study. The screening process provides an associated level of uncertainty related to the degree of data incompleteness. The outcome is a complete evaluation procedure divided into five phases: definition of criteria; assignment of an importance scale to determine how critical each criterion is; connection of indices and weights to each criterion; and analysis of the relationships between them. The guidelines are implemented in a software tool that supports and simplifies the decision-making process. The results are very promising. The developed methodology and the related guidelines applied to a case study have proven to be an effective decision-support for analysts. The study shows that it is possible to identify the most suitable conversion option from a technical, engineering, and operational point of view while also considering its environmental impact and safety implications. Full article

The impact and solidification of multiple molten droplets on a cold substrate critically influence the quality and performance of thermally sprayed coatings. We present a Smoothed Particle Hydrodynamics (SPH) model that couples fluid-solid interaction, wetting, heat transfer and phase change to simulate multi-droplet impact and freezing. The model is validated against benchmark cases, including the Young–Laplace relation, wetting dynamics, single-droplet impact and the Stefan solidification problem, showing good agreement. Using the validated model, we investigate two droplets—either centrally or off-centrally—impacting on a cold surface. Simulations reveal two distinct solidification patterns: convex pattern (CVP), which results in a mountain-like splat morphology, and concave pattern (CCP), which leads to a valley-like shape. The criterion for the two patterns is explored with two dimensionless numbers, the Reynolds number $Re$ and the Stefan number $Ste$. When $Re\le 17.8$, droplets tend to solidify in CVP; at higher Reynolds numbers $Re\ge 18.8$, they tend to solidify in CCP. The transition between the two patterns is primarily governed by $Re$, with $Ste$ exerting a secondary influence. For example, when droplets have $Re=9.9$ and $Ste=5.9$, they tend to solidify in a convex pattern, whereas at $Re=19.8$ and $Ste=5.9$, they tend to solidify in a concave pattern. Also, the solidification state of the first droplet greatly influences the subsequent spreading and solidification of the second droplet. A parametric study on CCP cases with varying vertical and horizontal offsets shows that larger vertical offsets accelerate solidification and reduce the maximum spreading factor. For small vertical distances, the solidification time increases with horizontal offset by more than 29%; for large vertical distances the change is minor. These results clarify how droplet interactions govern coating morphology and thermal evolution during thermal spraying. Full article

The energy transition in Ibero-American countries demands significant diversification, yet the vast potential of offshore renewable energies (ORE) remains largely untapped. Slow adoption is often attributed to the hostile marine environment, high investment costs, and a lack of institutional, regulatory, and industrial readiness. A critical barrier for policymakers is the absence of methodologically robust tools to assess national preparedness. Existing indices typically rely on simplistic weighting schemes or are susceptible to known flaws, such as the rank reversal phenomenon, which undermines their credibility for strategic decision-making. This study addresses this gap by developing a multi-criteria decision-making (MCDM) framework based on a problem-specific synthesis of established optimization principles to construct a comprehensive Offshore Readiness Index (ORI) for 13 Ibero-American countries. The framework moves beyond traditional methods by employing an advanced weight-elicitation model rooted in the Robust Ordinal Regression (ROR) paradigm to analyze 42 sub-criteria across five domains: Regulation, Planning, Resource, Industry, and Grid. Its methodological core is a non-linear objective function that synergistically combines a Shannon entropy term to promote a maximally unbiased weight distribution and to prevent criterion exclusion, with an epistemic regularization penalty that anchors the solution to expert-derived priorities within each domain. The model is guided by high-level hierarchical constraints that reflect overarching policy assumptions, such as the primacy of Regulation and Planning, thereby ensuring strategic alignment. The resulting ORI ranks Spain first, followed by Mexico and Costa Rica. Spain’s leadership is underpinned by its exceptional performance in key domains, supported by specific enablers, such as a dedicated renewable energy roadmap. The optimized block weights validate the model’s structure, with Regulation (0.272) and Electric Grid (0.272) receiving the highest importance. In contrast, lower-ranked countries exhibit systemic deficiencies across multiple domains. This research offers a dual contribution: methodological innovation in readiness assessment and an actionable tool for policy instruments. The primary policy conclusion is clear: robust regulatory frameworks and strategic planning are the pivotal enabling conditions for ORE development, while industrial capacity and infrastructure are consequent steps that must follow, not precede, a solid policy foundation. Full article

This work investigated the comparative performance of two fluidized bed agglomeration systems for producing instant coconut milk powder: a commercially manufactured unit and a hybrid dryer previously modified into a fluidized bed agglomerator. Three binder solutions, distilled water, xanthan gum, and xyloglucan polysaccharide, were employed to examine how equipment configuration and binder type influence key powder properties. The aim was to evaluate the effects of fluidized bed agglomerator design and binder selection on coconut milk powder characteristics, including moisture content, bulk density, solubility, and glass transition temperature. All samples, including the non-agglomerated control, exhibited moisture contents ranging from 2.1% and 2.6% (w.b.), meeting the criterion for safe long-term storage. Powders produced with hydrocolloid binders (xanthan gum and xyloglucan) possessed lower bulk densities than those agglomerated with water, reflecting the formation of more open particle structures. When identical binders were applied, the two agglomerators produced comparable solubility outcomes, although water-based agglomerates consistently dissolved the fastest. Differential scanning calorimetry indicated a substantial increase in glass transition temperature after agglomeration, confirming improved structural stability. Overall, the results demonstrate that both agglomeration systems effectively enhanced the physicochemical and functional characteristics of coconut milk powder, with only minor variations that were attributable to equipment design. Full article

Amid fossil-fuel depletion and worsening environmental impacts, battery electric vehicles (BEVs) are pivotal to the energy transition. Energy management in BEVs relies on accurate motor efficiency maps, yet real-time onboard control demands models that balance fidelity with computational cost. To address map inaccuracy under real driving and the high runtime cost of 2-D interpolation, we propose a driving-cycle-aware, physically interpretable quadratic polynomial-surface framework. We extract priority operating regions on the speed–torque plane from typical driving cycles and model electrical power $P_e$ as a function of motor speed $n$ and mechanical power $P_m$. A nested model family (M3–M6) and three fitting strategies—global, local, and region-weighted—are assessed using $R^2$, RMSE, a computational complexity index (CCI), and an Integrated Criterion for accuracy–complexity and stability (ICS). Simulations on the Worldwide Harmonized Light Vehicles Test Cycle, the China Light-Duty Vehicle Test Cycle, and the Urban Dynamometer Driving Schedule show that region-weighted fitting consistently achieves the best or near-best ICS; relative to Global fitting, mean ICS decreases by 49.0%, 46.4%, and 90.6%, with the smallest variance. Regarding model order, the four-term M4 $\left(+P_m^2\right)$ offers the best accuracy–complexity trade-off. Finally, the region-weighted fitting M4 $\left(+P_m^2\right)$ polynomial model was integrated into the vehicle-level economic speed planning model based on the dynamic programming algorithm. In simulations covering a 27 km driving distance, this model reduced computational time by approximately 87% compared to a linear interpolation method based on a two-dimensional lookup table, while achieving an energy consumption deviation of about 0.01% relative to the lookup table approach. Results demonstrate that the proposed model significantly alleviates computational burden while maintaining high energy consumption prediction accuracy, thereby providing robust support for real-time in-vehicle applications in whole-vehicle energy management. Full article

This paper presents the Circle-Mapping Transition and Weighted Red-Billed Blue Magpie Optimizer (CTWRBMO), designed to address significant challenges in 3D path planning for drones. Although the original Red-Billed Blue Magpie Optimizer (RBMO) algorithm features a simple structure, few parameters, and strong local search capability, making it well-suited for UAV path optimization, it suffers from insufficient population diversity, limited global search ability, and a tendency to fall into local optima in complex high-dimensional scenarios. To overcome these limitations, four enhancement strategies are introduced. Firstly, the Circle chaotic mapping strategy leverages the randomness and ergodicity of chaotic sequences to generate an initial population that is uniformly distributed. This enhancement improves population diversity from the beginning and provides a solid foundation for global optimization. Secondly, the ε parameter is dynamically adjusted to prioritize local refinement during the early stages of optimization. This adjustment enables rapid convergence toward potentially optimal areas. This parameter increases to enhance global search capabilities as the algorithm progresses, thereby broadening the optimization space and achieving a dynamic equilibrium. Additionally, a nonlinear dynamic weighting factor (wd) is incorporated into the position update formula. The algorithm’s ability to escape local optima is significantly improved by dynamically altering the weight ratio between historical optimal positions and the current position. Furthermore, an elite perturbation mechanism based on individual neighborhoods is implemented to generate candidate solutions using local information. This mechanism enhances the algorithm’s local exploration capabilities and improves the stability of preserving optimal solutions, supported by a greedy criterion for optimal retention. Experimental results show that the CTWRBMO algorithm significantly outperforms comparison algorithms in terms of optimization accuracy and convergence speed, demonstrating exceptional global optimization capabilities. Additional applications in UAV 3D path planning simulations evaluated paths based on length, threat avoidance efficiency, and smoothness. The results indicate that paths planned using CTWRBMO are shorter, safer, and smoother compared to those generated by the Harrier Hawks Optimization (HHO), African Vulture Optimization Algorithm (AVOA), Artificial Bee Colony (ABC) Algorithm, and the traditional Magpie Algorithm, effectively meeting practical engineering requirements for UAV 3D path planning. Full article

Background/Objectives: The transition to higher education is a critical phase of human development that makes adolescents and young adults particularly vulnerable to mental health problems, such as depression, anxiety, and stress. This study aimed to evaluate the psychometric properties of the Portuguese version of the Depression, Anxiety and Stress Scale-21 Items (DASS-21) among first-year undergraduate nursing students. Methods: A methodological study was conducted with 225 undergraduate nursing students, aged 17 to 18 years, from a higher education institution in central Portugal. Data were collected using the Google Forms platform. Confirmatory factor analysis was conducted to test three competing models: a single-factor model, a three-factor correlated model, and a second-order factor model. Reliability was assessed using composite reliability, and validity was evaluated using average variance extracted and the Fornell–Larcker criterion for discriminant validity. Results: Factor analyses revealed that the three-factor correlated model fit the data best overall, showing superior fit indices compared to the competing models (χ²/df = 2.37; CFI = 0.90; and RMSEA = 0.08; TLI = 0.88 and SRMR = 0.04). Composite reliability was high across all tested models, ranging from 0.84 to 0.94. The analysis of score distributions by category revealed a high prevalence of severe or extremely severe symptoms of anxiety, stress, and, to a lesser extent, depression. A statistically significant association was found between higher symptom severity and prior familiarity with mental illness. Conclusions: The DASS-21 proved to be a valid and reliable instrument for assessing psychological distress in higher education students. These findings underscore the urgent need for mental health programs in higher education institutions that focus on early detection and intervention, particularly for students initiating their studies and those with a history of mental health problems. Full article

The transition to environmentally friendly mobility inevitably requires users to use sustainable modes of transport. Rapid urbanization, along with the growing demand for efficient, inclusive, and ecological transport systems, has highlighted the urgent need for research and analysis into the acceptability and experiences of transitioning to sustainable modes of transport. This article proposes a six-step procedure to support the selection of vehicles for car-sharing fleets in cities. The analysis utilizes the Analytic Hierarchy Process method, which allows for the comparison and evaluation of five vehicle variants with different powertrains, taking into account various evaluation criteria: ecological and economic. To refine the research, criterion weights were determined based on original surveys among six car-sharing operators and eighty-seven experts in the field of decarbonization of urban transport. The results indicated that plug-in hybrid vehicles are the most advantageous option for car-sharing fleets, providing a balance between emissions, cost-effectiveness and operational flexibility. The solution obtained is in line with expectations, confirming that the proposed analytical approach is a reliable decision support tool that reduces the risk of making the wrong decision regarding the choice of powertrains. Full article

Greenhouse gas emissions from the energy sector are the main driver of global warming, which has led to an increase in the average surface temperature of the Earth by more than 1 °C above pre-industrial levels. Responding to the urgent need for energy transition, the countries of the European Union have set themselves the goal of achieving climate neutrality by 2050. The main objective of this article is to comprehensively assess the progress of decarbonization in the 27 European Union countries between 2004 and 2024, using an advanced multi-criteria model. The study used the quantitative Analytical Hierarchy Process (AHP) method to construct a multidimensional decision-making model. Eight energy technologies were evaluated through the prism of 13 criteria grouped into three pillars of sustainable development: economic (including technical), environmental, and social. Based on the weights of each criterion, estimated by a group of experts, a synthetic decarbonization index (DI) was calculated for each technology. In the next stage, a cumulative decarbonization index (CDI) was formulated for each country, reflecting the structure of its energy mix. The analysis revealed a fundamental divergence between conventional and zero-emission technologies. Renewable sources and nuclear energy have the highest positive impact on decarbonization (highest DI): hydropower (27.5), nuclear (20.7), wind (20.3). The lowest, unfavorable values of the index are characteristic of fossil fuels: oil (3.6), coal (3.9), and gas (4.8). The average cumulative decarbonization index (CDI) for the EU-27 rose from 14.0 in 2004 to 26.4 in 2024, demonstrating the effectiveness of the EU’s common policy. The leaders of the transition are countries with diversified, green mixes, such as Luxembourg (CDI = 40.4), Lithuania (CDI = 39.6), Portugal (38.5), Austria (36.9), and Spain (33.6). Despite starting from the lowest level in 2004 (CDI = 5.2), Poland recorded one of the most dynamic increases in 2024 (CDI = 17.7), mainly due to a reduction in the share of coal from 93% to 53.5%. The analysis confirms the effectiveness of the EU’s common climate and energy policy and demonstrates the usefulness of the methodology presented for a comprehensive assessment of the decarbonization process. The results indicate the need to further increase the share of zero-emission energy sources in the energy mix in order to achieve the objectives of the European Green Deal. The varying pace of transformation among Member States requires an individualized approach and support for countries with a historical dependence on fossil fuels. Full article

In response to the issues of low synergy efficiency between gas extraction and water injection, unclear procedural connections, and high costs in coal mine gas disaster prevention, this paper proposes a collaborative prevention technology for coal mine gas disasters termed “pump–injection–lock.” First, based on the kinetics of gas desorption in gas-bearing coal under different water-bearing conditions, an optimization model for the sequence of gas extraction and high-pressure water injection was developed. This model reduced the gas desorption rate in the experimental area by 32.5% and increased the effective extraction radius of boreholes by 18.7%. Second, based on the coupling relationship between water lock formation pressure, interfacial tension, and pore structure, a criterion model for process transition was constructed, enabling quantifiable identification of the transition node between “pump–injection.” The water lock’s inhibition of gas release duration was improved by over 25% compared to conventional water injection. Finally, by integrating the multiple effects of high-pressure water injection—enhancing permeability, softening, displacement, and flow limitation—a “multi-purpose” synergistic pathway was established. This increased the pre-drainage gas concentration in the test working face by 40%, the pure gas extraction volume by 28%, and reduced gas over-limit incidents by over 50%. Experiments and industrial trials demonstrated that the application of this technology in the 15^# coal seam of Yixin Coal Mine shortened gas extraction by 36%, reduced borehole engineering by 72.8%, eliminated gas over-limit incidents during mining, and cumulatively generated economic benefits exceeding 425 million yuan in the same year, significantly improving the efficiency and cost-effectiveness of gas disaster prevention. Full article

The integration of large-scale offshore wind power, facilitated by Flexible Fractional Frequency Transmission Systems (FFFTS), presents significant challenges for traditional transmission line protection. The fault current fed by the Modular Multilevel Matrix Converter (M3C) exhibits weak-infeed and controlled characteristics during faults, severely degrading the sensitivity of conventional current differential protection. Moreover, the stringent synchronization requirement for data from both line ends further compromises reliability. To address this issue, this paper proposes a novel differential protection scheme based on the Dynamic Time Warping (DTW) algorithm. The method leverages the DTW algorithm to quantify and compare the variation trends of current waveforms on both sides of the line before and after a fault. By utilizing the pre-fault current as a reference sequence, the scheme constructs a protection criterion that is inherently insensitive to synchronization errors. A key innovation is its capability for fault identification and phase selection under weak synchronization conditions. Simulation results demonstrate that the proposed scheme operates correctly within 0.5 ms, exhibits high sensitivity with a DTW ratio significantly greater than 2.0 during internal faults, and remains stable during external faults. It also shows strong robustness against high transition resistance, noise interference, and current transformer sampling errors. Full article

Irreversible erosion damage of the hearth lining determines the campaign life of a blast furnace (BF). Among the factors involved, structural thermal stress resulting from both internal and external temperature differences and external constraints is a key mechanism in the damage to the hearth lining. Based on a thermal–fluid–structural coupling model that accounts for molten iron flow and solidification, this study, building on thermal stress analysis of the hearth lining, proposes a method to determine the critical strength-based erosion boundary of the lining, using the compressive strength of carbon bricks as the criterion. It also investigates the influence of factors such as dead iron layer depth, tapping productivity, and molten iron temperature on the thermal stress-driven erosion boundary. The findings reveal that the depth of the dead iron layer determines the morphology of the hearth lining’s erosion. With increasing depth, the erosion pattern transitions from an elephant foot profile to a wide-face profile, while the radial erosion depth first increases and then decreases. Both increased tapping productivity and elevated molten iron temperature do not change the erosion shape but aggravate the erosion degree and induce axial displacement of the erosion zone. The research findings are of great significance for deepening the understanding of thermal stress damage in the hearth lining and provide an effective reference for long-term hearth design. Subsequent validation with a large amount of industrial data will further enhance the practical applicability of the proposed method. Full article

This paper addresses the problem of decision-making support for the modernization of distributed automated control systems (ACS) in power engineering by proposing an integral quality criterion that combines similarity-driven Markov process modeling with geometric programming. The methodology transforms the transition rate matrix of a continuous-time Markov chain (CTMC) into a matrix polynomial, enabling the derivation of normalized similarity indices and the development of a criterion-based model to quantify relative variations in system quality without requiring global optimization. The proposed approach yields a generalized criterion model that facilitates the ranking of modernization alternatives and the evaluation of the sensitivity of optimal decisions to parameter variations. The practical implementation is demonstrated through updated state transition graphs, quality functions, and UML-based architectures of diagnostic-ready evaluation modules. The scientific contribution of this work lies in the integration of similarity-based Markov modeling with the mathematical framework of geometric programming into a unified criterion model for the quantitative assessment of functional readiness under multistate conditions and probabilistic failures. The methodology enables the comparison of modernization scenarios using a unified integral indicator, assessment of sensitivity to structural and parametric changes, and seamless integration of quality evaluation into SCADA/Smart Grid environments as part of real-time diagnostics. The accuracy of the assessment depends on the adequacy of transition rate identification and the validity of the Markovian assumption. Future extensions include the real-time estimation of transition rates from event streams, generalization to semi-Markov processes, and multicriteria optimization considering cost, risk, and readiness. Full article

The construction industry faces urgent challenges in reducing its carbon footprint, particularly in geotechnical engineering where conventional methods often involve high-emission materials. Artificial Ground Freezing (AGF) presents a sustainable, material-saving alternative for stabilizing water-rich strata, but its efficiency relies on accurate characterization of frozen soil behavior under in situ conditions. This study advances the understanding of AGF’s sustainability by investigating the directional shear behavior of pressurized frozen saturated medium sand (Fujian ISO standard sand) at −10 °C using a novel hollow cylinder apparatus. Through systematic testing under varying mean principal stresses (p = 0.5–6 MPa) with fixed intermediate principal stress coefficient (b = 0.5) and principal stress direction (α = 30°), we demonstrate that pressurized freezing creates a fundamentally different soil–ice composite compared to conventional unpressurized freezing. Key findings reveal (1) a linear strength increase described by the failure criterion q_f = 1.17p + 3.77 (R² = 0.98) without pressure melting effects within the tested range; (2) a distinct brittle-to-ductile transition at p ≈ 4 MPa, with associated failure mode changes from localized shear bands to homogeneous plastic flow; (3) a stable peak stress ratio (q/p ≈ 1.8) for p ≥ 4 MPa. These findings enable more reliable and potentially less conservative frozen wall design, directly contributing to reduced energy consumption in AGF operations. The research provides mechanical insights and practical parameters that enhance AGF’s viability as a low-carbon ground stabilization technology, supporting the construction industry’s transition toward sustainable underground development. Full article