Search Results (319)

The growing dominance of low-cost carriers (LCCs) in global air transport has intensified the need for airport terminal facilities that reflect their simplified, efficiency-driven operating principles. Traditional Level of Service (LOS) standards, based on International Air Transport Association’s Airport Development Reference Manual (IATA ADRM), were primarily designed for traditional air carriers or full-service network carriers (FSNCs) and may lead to over-dimensioned or misaligned airport terminal facilities when applied to airports with dominance of LCCs. This study presents the first newly developed computational tool called Airport Terminal Facilities Software (ATFS) as a methodological and conceptual advance in airport terminal planning, that integrates LOS guidelines differentiated by airline business models. The methodology integrates spatial–temporal LOS parameters, specific facility capacity formulas, and peak-hour demand calculations of airport terminal facilities. Results from the case study conducted at Pula Airport show substantial differences between IATA and LCC LOS outcomes, i.e., applying LCC LOS guidelines can significantly reduce required areas for the several airport terminal facilities. Findings confirm that new LCC LOS guidelines and the ATFS tool can optimize airport terminal facilities, reduce or reconfigure excessive or empty space, and improve passenger flow efficiency at LCC-dominant airports. Full article

The increasing popularity of electric vehicles (EVs) as green alternatives to traditional internal combustion engine cars has highlighted the need for sustainable and environmentally friendly supply chain models. In particular, the handling of EV batteries, which are environmentally unfriendly and logistically critical due to their hazardous nature and short life cycle, requires a well-designed closed-loop supply chain (CLSC). This study proposes a new multi-objective optimization model of the CLSC, explicitly tailored to EV batteries under demand and return rate uncertainty. The proposed model incorporates three primary objectives that are typically in conflict with one another: minimizing the total cost, reducing carbon emissions throughout the entire supply chain network, and maximizing the recycling and reuse of batteries. The model employs a neutrosophic goal programming (NGP) methodology to address the uncertainties associated with demand and battery return quantities. The NGP model translates multiple objectives into non-monolithic goals with crisp aspiration levels (i.e., prescribed ideal levels for achieving the best of each goal) and thresholds that capture tolerances, thereby accounting for uncertainty. The efficiency of the proposed method is illustrated by a numerical example, solved using a IBM ILOG CPLEX Optimization Studio 22.1.2 solver. The findings demonstrate that the NGP can offer cost-effective, low-impact, and environmentally friendly solutions, thereby enhancing system robustness and flexibility to adapt to uncertainties. This study contributes to the emerging literature on sustainable operations research by developing a decision-making tool for EV-HV battery supply chain management. It also offers relevant suggestions for policymakers and industrialists who seek to co-optimize economic benefits, ecological sustainability, and logical feasibility in the emerging green society. Full article

Openings are often introduced in continuous reinforced concrete (RC) deep beams to accommodate utility services, which can compromise their structural capacity. This paper presents a numerical investigation—via nonlinear finite element (FE) modeling—into the effects of post-construction rectangular openings in continuous high-strength concrete (HSC) deep beams. A previously tested two-span continuous HSC deep beam with rectangular openings was used for model validation and subsequently adopted in a parametric study, maintaining consistent beam and opening dimensions. The study focuses on the influence of opening location, both symmetric and asymmetric, at mid-depth within critical shear and flexural zones of the two-span continuous deep beam. Key parameters analyzed include load-carrying capacity, support reactions, initial and post-cracking stiffness, reinforcement stresses, and concrete stress distribution. Results indicate that mid-depth openings located in flexure-critical regions have minimal impact, causing only a 3–5% reduction in load-carrying capacity and negligible changes in stress behavior. However, when openings intersect the primary strut paths, reductions in capacity ranged from 17% to 53%, depending on the number and location of the openings (i.e., crossing external or internal struts). Furthermore, symmetric placement of openings was found to significantly mitigate performance degradation compared to asymmetric configurations. These findings provide design insights that enable safe incorporation of service openings without excessive material use, thereby promoting more sustainable and resource-efficient concrete construction. Full article

High-volume fly ash (FA) mitigates the expansion of magnesium oxide (MgO), and the uneven regional distributions of high-quality FA collectively limit the application of roller-compacted concrete admixed with MgO (M-RCC). This study evaluated the autoclave expansion and compressive strength of MgO-admixed cement paste and mortar, wherein phosphorus slag (PS) was used to partially or fully replace FA. The expansion mechanism within the MgO-FA-PS system was explored. Results show that the autoclave expansion of the mortar increased as the proportion of PS replacing FA rose. At a replacement ratio of 33% (i.e., 20% of the total mass of cementitious materials), the mortar maintained the same ultimate MgO dosage (8%) as the control specimen, yet exhibited a 12.7% increase in expansion and an 8.8% decrease in strength. The mechanism is that PS is less efficient than FA in reducing the pore solution alkalinity, thereby promoting the formation of more brucite. The growth pressure of brucite crystals expands the internal gaps in the matrix and coarsens the pore size, resulting in greater expansion and reduced compressive strength. The results of this study can provide theoretical and technical insights for the application of PS in M-RCC. Full article

Steel surface defect detection is critical for ensuring industrial product quality and safety. Although deep learning-based detectors like the YOLO series have demonstrated considerable promise, they often struggle with three key challenges under computational constraints: the anisotropic morphology (i.e., direction-variant shapes) of defects, insufficient modeling of long-range dependencies, and the confusion between signal and noise in feature representation. To address these issues, this paper proposes PSC-YOLO, an enhanced model based on YOLOv11n. Our core design philosophy leverages symmetry principles to guide feature representation and fusion. First, we introduce Pinwheel-shaped Convolution (PConv), whose set of rotationally symmetric kernels explicitly captures multi-directional features to effectively represent anisotropic defects. Second, a Pyramid Sparse Transformer (PST) module is integrated to capture global context via its efficient cross-scale sparse attention, which reduces computational complexity by dynamically focusing on the most relevant features across different scales, leveraging a symmetrical pyramid architecture for balanced multi-scale fusion, thereby overcoming the bottleneck in long-range dependency modeling. Finally, a Channel-Prior Convolutional Attention (CPCA) mechanism is embedded to perform dynamic feature recalibration, which leverages internal structural symmetry—through symmetric pooling pathways and parallel multi-scale convolutions—to suppress background noise and highlight salient defects. Comprehensive experiments on the public NEU-DET dataset show that PSC-YOLO achieves superior performance, obtaining a mAP@0.5 of 78.3% and a mAP@0.5:0.95 of 48.3%, while maintaining a real-time inference speed of 2.8 ms per image. This demonstrates the model’s strong potential for deployment on industrial production lines, enabling high-precision, real-time quality inspection. Full article

Energy efficiency in buildings is a vital subject within sustainable construction and climate change mitigation, yet comprehensive bibliometric analyses mapping the complete evolution of this domain remain limited. This study provides a comprehensive four-decade analysis (1981–2025) of building energy efficiency research using data from the Web of Science database, employing VOSviewer (1.6.20), Bibliometrix (4.3.0), and custom Python (3.12.3) scripts with automated terminology normalization through TF-IDF vectorization (n-grams 2–3) and cosine similarity algorithms (threshold = 0.75). Two critical methodological innovations distinguish this investigation: first, Pruned Exact Linear Time changepoint detection statistically validated 2011 as the field’s statistically validated transition point (Mann–Whitney U test, p < 0.000001, effect size = 2.48), replacing arbitrary decade-based periodization; second, computational keyword harmonization enabled precise thematic evolution mapping across inconsistent terminology. The analysis reveals marked increase in research post-2011, with median annual output increasing from 15 articles (1981–2011) to 840.5 articles (2012–2024), and China emerging as the preeminent research center with 2978 publications. Thematic evolution analysis demonstrates fundamental transformation from seven specialized research themes (i.e., behavior, heat-transfer, simulation, impact, performance, consumption, optimization) in the foundational period to dramatic consolidation into two dominant themes (i.e., performance and simulation) in the contemporary period, reflecting maturation from fragmented, component-focused investigations toward holistic, integrated frameworks. International collaboration network analysis identifies four distinct geographic clusters with China, United States, United Kingdom, and Italy serving as central hubs. These findings provide actionable intelligence for researchers, policymakers, and industry stakeholders, while the computationally enhanced framework offers a replicable methodology for bibliometric analysis in other rapidly evolving interdisciplinary domains. Full article

Battery health monitoring is essential for ensuring the safety, longevity, and efficiency of energy storage systems, particularly in critical applications where reliability is important. Traditional methods for assessing battery degradation, such as Electrochemical Impedance Spectroscopy (EIS), are effective but impractical for large-scale deployment due to their time-intensive nature. This study introduces a novel model-based approach for estimating a critical indicator of battery aging, the internal resistance. Using the NASA battery dataset, specifically focusing on battery numbers 5 and 7 with NCA chemistry, a comprehensive framework that integrates advanced predictive models, i.e., the Random Forest Regressor (RF), the XGBoost Regressor (XGBR), the Gated Recurrent Unit (GRU), and the Long Short-Term Memory (LSTM) networks, was developed. The models were evaluated using common regression metrics, while hyperparameter tuning was performed to optimize performance. The results demonstrated that recurrent neural networks, particularly GRU and LSTM, effectively capture the temporal dependencies inherent in battery aging, offering more accurate state-of-health (SOH) predictions. This approach significantly improves computational efficiency and prediction accuracy, paving the way for practical applications in Battery Management Systems (BMSs). Full article

Rapid evolution in electrified transportation and, in general, sustainability of electrical and electronic assets is turning the traditional power supply and utilization into something more complex and less known. This transition involves increasing operating voltage and specific power, as well as various types of power supply sources, from AC sinusoidal to DC and power electronics. This revolution, beneficial for asset efficiency and resilience, does come at the cost of increased risk of failure for electrical insulation systems. Intrinsic and extrinsic aging mechanisms are not completely known under DC and power electronics, and the risk of inception of partial discharges, PD, which is the most harmful extrinsic aging factor for electrical insulation, is as high, or even higher, compared with AC. To complicate the picture, electrical and electronic components can be used at different pressure levels, such as in aerospace, and it is known that partial discharge inception voltage, PDIV, drops down, and PD magnitude increases, lowering pressure. Models to predict PDIV for surface and internal discharges, as function of pressure, have been proposed recently, but they cannot be applied straightforwardly on practical asset components where type and locations of defects generating PD is unknown. This paper wants to close this application gap. Derivation and validation of an approximate, heuristic model able to predict PDIV at various pressure levels below and above the standard atmospheric pressure, SAP, are dealt with in this paper, referring to typical asset components such as cables, motors, printed circuit-boards, PCB, and under sinusoidal AC voltage. The good capability of the model to predict PDIV and any investigated pressure, from 3 to 0.05 bar, is validated by PD measurements performed using an innovative, automatic PD analytics software able to identify the typology of defect generating PD, i.e., whether surface or internal. Full article

The subject of this paper is an analysis of the use of wall heating and cooling modules with heat pipes for efficient space heating and cooling. The modules under consideration constitute a structural element installed in the room’s partition structure and consist of heat pipes embedded in a several-centimeter layer of concrete. Water-based central heating and chilled water systems were used as the heat and cooling source. The heat pipes are filled with a thermodynamic medium that changes state in repeated gas–liquid cycles. The advantage of this solution is the use of heat pipes as a heating and cooling element built into the wall, instead of a traditional water system. This solution offers many operational benefits, such as reduced costs for pumping the heat medium. This paper presents an analysis of the potential of using heat pipe modules for heating and cooling in real-world buildings in Poland. Taking into account the structural characteristics of the rooms under consideration (i.e., internal wall area, window area), an analysis was conducted to determine the potential use of the modules for space heating and cooling. The analysis was based on rooms where, according to the authors, the largest possible use of internal and external wall surfaces is possible, such as hotels and schools. Based on the simulations and calculations, it can be concluded that the modules can be effectively used in Poland as a real heating and cooling element: standalone, covering the entire heating and cooling demand of a room, e.g., a hotel room, or as a component working with an additional system, e.g., air cooling and heating in school buildings. The changes in outdoor air temperature, during the year analyzed in the article, were in the range of −24/+32 °C. Full article

Species of Trichoderma are currently in high demand as eco-friendly and commercial biocontrol agents due to the proliferation of organic farming methods. This study focused on the potential biocontrol agents of Trichoderma against plant-pathogenic fungi. Trichoderma strains were isolated from different sources (soil, grapevine tissues, lemon fruit, and maize seeds), and were characterized morphologically on two culture media, i.e., Potato Dextrose Agar and Malt Extract Agar, and molecularly using two gene regions: translation elongation factor 1 (TEF) and nuclear ribosomal internal transcribed spacer (ITS). Phylogenetic trees were constructed. As a result, two Trichoderma species were identified, i.e., T. afroharzianum and T. longibrachiatum. The biocontrol effects of all isolated strains of Trichoderma on Fusarium plant damping-off and the promotion of plant growth were evaluated. Additionally, the antagonistic efficiency of Trichoderma spp. against F. proliferatum using the dual-culture method was evaluated. Under greenhouse conditions, T. afroharzianum strains AEMCTa3 and AEMCTa6 were used to treat maize plants infected with Fusarium. The application of Trichoderma significantly reduced the disease index to 15.6% and 0%, respectively. Additionally, maize seedlings showed significant improvements in shoot and root lengths and fresh and dry weights and increased photosynthetic pigment contents compared to Fusarium-infected plants and the untreated control. The gas chromatography–mass spectrometry (GC-MS) analysis of T. afroharzianum extracts identified a variety of bioactive compounds. These compounds included antifungal substances like N-ethyl-1,3-dithioisoindoline, as well as plant growth-promoting hormones like 6-pentyl-α-pyrone and gibberellic acid. Interestingly, the analysis also revealed new phenylacetic acid derivatives that may play important roles in both plant health and disease resistance. From a practical perspective, developing diverse application methods for Trichoderma is essential to optimize its role as a biocontrol agent and a plant growth promoter, thereby supporting sustainable agriculture through improved adaptability and effectiveness across different farming systems. Full article

As infrastructure construction expands, the massive consumption of traditional concrete materials has led to resource shortages and environmental pollution. Utilizing industrial wastes such as coal gangue and fly ash to produce high-performance concrete is an important pathway toward a greener construction industry. However, concrete incorporating high volumes of fly ash and coal gangue (i.e., high-volume fly-ash coal-gangue self-compacting concrete, CGSC) suffers from low strength and high brittleness due to the inherent deficiencies of its constituents. This study proposes using glass fiber-reinforced polymer (GFRP) tubes for external confinement to improve the axial compressive capacity and deformability of CGSC. A total of 27 concrete cylinders were prepared and tested under axial compression, with real-time acoustic emission (AE) monitoring. The variables examined include the coarse aggregate type (coal-gangue and natural gravel), GFRP tube thickness (5 mm and 8 mm), and fly-ash content (80%, 85%, 90%). The stress–strain response of each specimen and the failure evolution of internal cracks were recorded throughout the loading process. The results show that GFRP tube confinement markedly increases the axial strength and ductility of CGSC. AE features exhibited staged behavior that closely mirrored the stress–strain curves. This correspondence reveals the progression of internal cracks under confinement and indicates that AE is an effective tool for damage monitoring in such composites. The findings provide a new technical approach for the efficient reuse of solid waste in concrete and offer a theoretical and practical basis for applying FRP composite structures in underground support engineering. Full article

The significance of children’s mathematical competence during the early years is well established; however, the methods for developing such competencies remain less understood. Specifically, there is a need to identify what constitutes high-quality educational environments and effective instruction. Both the study and promotion of high-quality educational environments and teaching, through coaching and other professional development initiatives, necessitate the use of observational instruments that are reliable, efficient, and valid, including content, internal, external, and consequential validity. Moreover, domain-specific measures are essential, as general quality measures often fail to adequately assess curriculum content, scope, or sequence, and they do not reliably predict improvements in children’s learning outcomes. This study employed innovative analytical techniques to evaluate the scoring and interpretation of an existing domain-specific observational measure: the Classroom Observation of Early Mathematics Environment and Teaching (COEMET). We applied non-linear modeling approaches (i.e., Random Forest [RF] and Generalized Additive Models [GAMs]) to investigate and provide a comprehensive overview of the relationships between COEMET’s measures—at both the scale and item levels—of teachers’ practices and children’s mathematical competencies. The study first employed the RF machine learning method to identify the most important COEMET items for prediction, followed by the use of GAMs to depict the non-linear relationships between COEMET predictors and the outcome variable. The analysis revealed that certain teaching practices, as indicated by the COEMET items, exhibited non-linear and even non-monotonic associations with children’s mathematical competencies. Full article

The study presented in this paper is intended to be a contribution to the practical implementation of the “sustainability” concept, often misunderstood at times and incorrectly applied. The first sections describe a systematic procedure for a rigorous definition of “sustainability” and of “sustainable development” based on thermodynamics. A concept tightly connected with “sustainability” is “resource thriftiness”, i.e., the reduction in the anthropic extraction of irreplaceable supplies of fossil ores and fuels contained in the Earth’ crust and the reduction in the load posed on the environment by discharges, collectively referred to as “environmental conservation”: this is another concept that must be embedded in the definition of sustainability. An environmentally friendly society ought to concentrate on minimising such consumption by implementing an efficient and rational conversion of primary resources to final commodities while maintaining acceptable life standards. A thermodynamics-based approach can help identify the boundaries of the “sustainable region”: if sustainable development depends on a balance between primary input and final consumption, the internal allocation of the latter among citizens becomes a relevant parameter. The study presented in this paper introduces a direct link between demographics and pro capite final exergy use, showing how the age distribution of a society strongly impacts primary consumption. The paper presents some considerations about the quantitative link between the so-called “demographic pyramids” and the exergy budget of a country, with specific examples based on currently available data. Full article

We investigate power indices for simple games with precoalitions which distribute power among players in an external and an internal step. We extend an existing approach which uses the Public Good index both on the external level in the quotient game as well as on the internal level for measuring the leverage of players to threaten their peers through departing the precoalition. We replace the Public Good index in that model by five other efficient power indices, i.e., the Shapley–Shubik index, the Deegan–Packel index, the Johnston index and two indices based on null player free winning coalitions. Axiomatizations of the novel power indices with threat partitions are presented. We also propose a slight modification to the existing framework for threat power indices which guarantees that null players are always assigned zero power. Numerical results for all power indices combined with different threat partitions are presented and discussed. Full article

The present study applied a GIS-based methodology for assessing soil diversity in a protected mountain area of Italy. Using QGIS, morphological (i.e., altitude and slope), lithological, climatic, and land use layers were intersected to delineate 16 land units (LUs), each representing relatively homogeneous conditions for soil formation, according to Jenny’s equation. To obtain the soil map units, a total of 112 soil profiles were analyzed, including 79 from previous studies and 33 that were newly excavated during 2023–2024 to fill gaps in underrepresented LU types. Most soils were classified as Inceptisols/Cambisols, occurring in both Dystric and Eutric variants, mainly in relation to lithology (i.e., arenaceous or pelitic facies). Alfisols, Umbrisols, and hydromorphic soils were also identified. The physicochemical properties showed marked variability among LUs, with sand content ranging from 39 to 798 g kg⁻¹, pH from 4.4 to 7.9, and organic carbon content from 1.6 to 6.1%. This LU-based framework allowed efficient field sampling, if compared to grid-based surveys, while retaining information on fine-scale pedodiversity. No quantitative accuracy assessment (e.g., boundary precision, internal homogeneity metrics) was conducted, even if the spatial coherence of the delineated LUs was supported by the distribution of soil profiles, which provided empirical validation of the LU framework. Full article