Search Results (278)

UML class diagram modeling is a fundamental task in software engineering, yet the application of large language models (LLMs) to this domain remains underexplored. Existing studies predominantly focus on single closed-source models with simple prompting strategies, lacking systematic comparisons across model types, prompt engineering techniques, and iterative refinement approaches. In this paper, we construct a difficulty-stratified dataset of 30 UML class diagram exercises and propose an automated weighted evaluation metric over generated PlantUML code—both of which are rarely constructed and systematically applied in existing LLM-driven UML modeling research. We present a preliminary empirical evaluation comparing open-source and closed-source LLMs across multiple scales and types, diverse prompting strategies, and varying requirement complexity levels. Beyond the single-round static paradigm of prior work, we further introduce and evaluate iterative prompting schemes that continuously improve model outputs through structured feedback. Our findings reveal that chain-of-thought prompting has different effects on improving the quality of different models, that relationship modeling is the persistent bottleneck under increasing complexity, and attribute extraction remains a largely unsolved technical challenge across all tested LLMs. Further, automated feedback-driven iterative refinement yields varied improvements: it brings notable performance gains for reasoning-oriented thinking models while delivering only marginal promotion for high-performance general chat models. These results provide actionable guidance for practitioners and researchers applying LLMs to UML modeling tasks. Full article

Visual programming using blocks and diagrams facilitates understanding of fundamental programming concepts, which is particularly important for children with special educational needs because it reduces their cognitive load and encourages interactive learning. This study aimed to develop and apply a hybrid multi-criteria framework to evaluate, rank, and select visual programming software solutions intended for children with special educational needs. Based on an analysis of the educational context and the target population’s needs, a set of criteria was defined to evaluate and select the most suitable software solution. Data for the analysis were collected using a structured questionnaire, from which a decision matrix was developed. Within the proposed hybrid multi-criteria decision-making (MCDM) framework, criterion weights were determined using the Rank Order Centroid (ROC) method, and the ranking of alternatives was performed using the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE II). Additionally, a sensitivity analysis was conducted to assess the stability and robustness of the obtained rankings in relation to changes in the criterion weights. The results indicate a stable ranking of alternatives and the identification of the most favorable solution in the majority of scenarios. The projection quality of 91.1% in the Geometrical Analysis for Interactive Aid (GAIA) plane confirmed the reliability of the visual interpretation of the results. The proposed framework improves the decision-making process and provides a foundation for further research in educational software evaluation. Full article

Vehicle restrictions on urban expressways are widely used to relieve traffic congestion and reduce traffic emissions. However, the effects of such restrictions should be assessed over the wider urban road network rather than on expressways alone, and over both short-run and longer-run periods. This study empirically investigates the impacts of vehicle restriction policies on network-level emissions in Shanghai. The network-level vehicle emissions are dynamically estimated using a carbon-emissions macroscopic fundamental diagram (CE-MFD) model based on taxi trajectory data and loop detector data. The effects are then identified using a spatial difference-in-differences (SDID) framework, while geographically weighted regression (GWR) is used to examine spatial heterogeneity in the associated factors. The results show that extending the restriction periods reduced carbon emissions across the urban road network by 9.96% after one month and by 17.93% after one year. The effects are spatially heterogeneous and are associated with population, road-network characteristics, parking supply, and ramp configuration. These findings suggest that the sustainability impacts depend not only on the restrictions themselves, but also on traffic redistribution and local network conditions. Findings provide empirical evidence for designing sustainability-oriented traffic strategies, underscoring the importance of evaluating emissions outcomes across the urban road network over both short-run and longer-run horizons. Full article

The work is a continuation of studies , focused on the development of fundamental principles of aluminum activation by low-melting metals forming eutectic alloys with fine-grained structure and limited solid solubility. The aim of this work is to investigate the mechanism and kinetics of the interaction of aluminum-based eutectic alloys with water and aqueous electrolytes. Analysis of phase diagrams of binary systems (Al–Ga, Al–In, In–Ga, Al–Sn, Sn–Ga, Al–Zn, Zn–Ga) shows that alloy composition governs surface heterogeneity and reactivity. Ternary and quaternary systems (Al–In–Ga, Al–Sn–Ga, Al–In–Sn–Ga) exhibit enhanced interaction with water due to increased heterogeneity, leading to the formation of numerous microgalvanic couples and accelerated aluminum dissolution. The process is characterized by the stationary potential of aluminum and involves coupled chemical, electrochemical, and topochemical stages described by the Avrami–Erofeev equation, with n ≈ 1.27–2.07. An increase in the In–Ga or In–Sn–Ga fraction reduces the activation energy: 9.1 kcal/mol (82% Al–9% Ga–9% Sn), 11.4 kcal/mol (92% Al–4% Ga–4% In), and 15.5 kcal/mol (91% Al–3% Ga–3% In–3% Sn). Full article

Conventional standards for evaluating the Korean Highway Capacity Manual (HCM) and U.S. HCM often inadequately represent the localized macroscopic traffic dynamics inherent in complex urban networks. To address this limitation, this study proposes an adaptive branching framework for level of service (LOS) reclassification, guided by the empirical identifiability of fundamental diagrams (FDs) and vehicular density distribution patterns. The methodology classifies traffic states into four categories: (a) FD-based LOS, (b) segmented FD-based LOS, (c) single-state LOS, and (d) empirical free-flow speed-based LOS. These categories redefine LOS criteria based on the temporal and spatial conditions prevalent in urban environments. The proposed reclassified LOS framework, applied to twenty-eight urban corridors across four distinct urban typologies using a reference free-flow speed, effectively captures region-specific performance variations. Ultimately, this research establishes a robust, data-driven methodological framework for localized LOS recalibration, thereby significantly enhancing the realism of urban traffic evaluation. Full article

With the occurrence of global climate change and the depletion of agricultural water resources, there is a growing need to develop rapid, non-destructive, and autonomous plant health monitoring systems. As an economically valuable crop, Ocimum basilicum L. (basil) is sensitive to changes in water availability and may exhibit stress-related morphological variations under drought and over-irrigation conditions. However, due to the visual similarity of leaf symptoms under drought stress, waterlogging stress, and optimal irrigation conditions, accurately distinguishing these conditions remains challenging in practical applications. To address this challenge, this paper presents an attention-based dual-branch deep learning framework designed to extract both subtle leaf details and channel-related features from high-resolution plant images. By combining the Convolutional Block Attention Module (CBAM) and Squeeze-and-Excitation (SE) mechanism in a parallel structure, the proposed network improves the analysis of high-resolution images with an input size of 720 × 720 pixels. Under controlled environmental conditions, with ground-truth labels obtained using soil moisture sensor measurements, the proposed model was compared with eight deep learning architectures, including DenseNet121, InceptionV3, and VGG16. The proposed model achieved a hold-out evaluation accuracy of 99.54%, outperforming the second-best model, DenseNet121, which achieved 96.43%. In addition, the proposed model reached a class-specific precision value of 100% for the Drought Stress category and achieved an area under the receiver operating characteristic curve of 1.00 under the controlled experimental setting. Taylor Diagram analysis also indicated that the model closely preserved the variability pattern of the reference data. These results suggest that the proposed application-specific framework may support non-destructive basil water-stress detection under controlled conditions. After further validation with larger datasets, different cultivars, variable environmental conditions, and real-world agricultural scenarios, the proposed approach may contribute to precision irrigation management and sustainable agricultural production. The contribution of this study should be interpreted as an application-specific implementation and evaluation of complementary attention mechanisms for controlled-environment basil water-stress classification, rather than as the introduction of a fundamentally new deep learning methodology. Full article

Whether copper fundamentally alters Mo-centered redox thermodynamics or mainly tunes hydrogen adsorption in Ni–Mo electrocatalysts under alkaline hydrogen evolution reaction (HER) conditions remains unresolved. Density functional theory calculations combined with a field-corrected computational hydrogen electrode framework are used to evaluate the thermodynamic stability of H₃Mo, H₃MoOH, H₂Mo(OH)₂, and MoO(OH)₃ on Cu(111) and Ni(111) and to construct surface Pourbaix diagrams under electrochemical conditions. The results show that substrate identity reorganizes the redox stabilization hierarchy of these Mo intermediates. Across the examined conditions, at least one of H₃Mo, H₃MoOH, or MoO(OH)₃ is thermodynamically favored over H₂Mo(OH)₂ on both surfaces. However, only Cu(111) exhibits measurable pH-dependent free-energy shifts, reaching 0.25 eV on the reversible hydrogen electrode scale. The magnitude of this electrostatic modulation is comparable to the intrinsic substrate-dependent relative Gibbs free-energy differences, suggesting that Cu reshapes Mo redox thermodynamics rather than merely weakening hydrogen binding strength. Electronic structure and vibrational analyses further show that Cu(111) preferentially weakens Mo–O interactions, whereas Ni(111) more strongly perturbs Mo–H bonding in hydrogen-rich complexes. Overall, these results establish that substrate identity governs the electrostatic modulation of Mo redox thermodynamics under alkaline HER conditions and provide mechanistic insight into substrate effects relevant to Cu-containing Ni–Mo systems. Full article

Phantom congestion in highway tunnels reduces operational efficiency and destabilizes traffic flow. In this study, the effects of a pacemaker system (PMS) on traffic operation in the Geumnam Tunnel on the Seoul–Yangyang Expressway were evaluated using a before–after analysis based on long-term vehicle detection system (VDS) data. Unlike past studies, this study provides an integrated empirical evaluation by jointly examining changes in spatiotemporal traffic flow, traffic capacity, and speed improvement at different level of service. The analyses were conducted using data from five VDS detectors installed upstream and downstream from the tunnel. After PMS installation, (i) increased average and 25th-percentile speeds at most detector locations and decreased speed standard deviation were observed near the tunnel exit and downstream sections, (ii) the maximum traffic volume increased from 1661 to 1765 veh/h/lane, and (iii) the mean speed and 25th-percentile speed increased by 6.5%, indicating speed-reduction alleviation among low-speed vehicles. Thus, the PMS increases vehicle speed, reduces speed variability, and enhances traffic flow stability and processing capability. These findings provide empirical evidence for the operational effectiveness of a PMS as a practical tool for mitigating phantom congestion in highway tunnel sections, reducing speed differences between vehicles, and improving traffic stream stability. Full article

While multilane roundabouts follow geometric design standards, they often overlook motorcycle-dominated traffic behavior. This study evaluates lane-reduction strategies to create safer and more inclusive urban corridors in mixed-traffic conditions, focusing on a case study in Southern Thailand. High-resolution unmanned aerial vehicle (UAV) trajectory data were analyzed using the Macroscopic Fundamental Diagram (MFD), Cell Transmission Model (CTM), and Time-To-Collision (TTC) frameworks under three configurations: full lane availability, partial inner-lane closure, and full inner-lane closure. Results indicate progressive deterioration in performance under restricted-lane conditions. Under full closure, total flow decreased by 31%, and average travel time increased by 43%. The MFD curve shifted toward higher critical densities, indicating earlier congestion onset, while CTM results revealed longer discharge times, queue spillback, and increased merging friction. Conversely, safety outcomes (TTC) improved significantly: extreme rear-end conflicts were reduced by 48%, and severe lane-change conflicts were nearly eliminated (99%). Behavioral evidence suggests that full closure constrains motorcycles to a single circulating path, reducing erratic filtering and promoting more stable interactions. Overall, this study identifies a systemic trade-off between safety and efficiency, highlighting how geometric interventions catalyze behavioral adaptation. The findings highlight how geometric constraints shape collective behavior in motorcycle-dominated roundabouts and demonstrate the value of an integrated UAV-based framework as a vital tool for inclusive urban management, providing the granular data needed to balance safety and mobility in complex traffic landscapes. Full article

In the classical Stochastic Subspace Identification (SSI) method, mode selection is primarily based on frequency stability, damping stability, and mode shape similarity using the Modal Assurance Criterion (MAC). However, these criteria are often insufficient for reliable modal identification in high-noise environments. This study advances beyond the classical approach by introducing a multi-criteria optimization framework for mode evaluation. In addition to the conventional frequency and damping assessments utilized in the classical SSI method, the proposed approach incorporates a range of supplementary structural metrics. These include Density, Cosine Similarity Difference (CSD), Damping Stability (DS), Spatial Roughness (SR), Mode Shape Complexity (MSC), Signal Energy Coherence (SEC), and Normalized Modal Difference (NMD). These metrics are computed within specifically optimized windows on the stabilization diagram. By integrating spatial, phase, and energy-based characteristics of mode shapes alongside traditional metrics such as the MAC, the method enables a more comprehensive and robust mode selection process that surpasses the limitations of relying solely on frequency and damping stability. Compared to the classical SSI, the optimized window approach provides a significant advantage by enabling the reliable selection of consistent modes by considering the continuity and multi-criteria coherence of modes across window transitions. As a result, the elimination of noise modes and the reliable separation of structural modes are established on a more systematic basis. To achieve this, a two-stage optimization strategy is implemented: the first stage determines the optimal frequency window width and minimum mode count threshold, while the second stage utilizes a Multi-Criteria Decision Making (MCDM) framework based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm to assign optimized weights to the structural metrics and rank the candidate windows accordingly. As a result, the ideal frequency window is identified based on its TOPSIS score and subsequently validated using the MAC, confirming that the selected window corresponds to reliable structural modes. The framework is validated using long-term in situ measurements from a Roller Compacted Concrete (RCC) dam operating under significant environmental and operational noise. The dataset comprises continuous, high-resolution (200 Hz) vibration recordings collected between 1 July 2023 and 30 October 2024. While the calendar duration is limited to several weeks, the uninterrupted 24 h measurements yield a high-density time-series dataset with substantial information content, enabling a statistically meaningful and robust evaluation of modal identification performance under real-world and noisy conditions. The results reveal that relying solely on traditional selection criteria such as pole density and the MAC can often lead to the identification of spurious modes, particularly in noisy environments. In contrast, the proposed TOPSIS-based multi-criteria decision-making framework incorporates a broader range of structural indicators, balancing frequency, damping, spatial, and energy-related metrics to enhance the consistency and reliability of mode selection. This approach proved effective even under high-noise conditions, successfully distinguishing true structural modes from artificial ones. Application of the TOPSIS method to RCC dam data revealed consistent fundamental frequencies at approximately 5–10 Hz, 10 Hz, and 15 Hz, confirming its robustness and suitability for complex structural monitoring tasks. Full article

Design for excellence (DfX) comprises a broad family of product design methodologies aimed at addressing the often-competing requirements of multiple stakeholders throughout the product life cycle. Among the most pressing contemporary challenges in DfX is integrating environmental considerations into product design systematically and comprehensively. The circular economy (CE) is implemented through various R strategies (e.g., 3R: reduce, reuse, recycle), providing a normative framework intended to guide pro-environmental design decisions and life cycle thinking. This study conducts a systematic literature review focused on product development and its alignment with DfX methods in the context of circular economy practices (CEPs) to identify prevailing research trends and structural gaps in this area. Both quantitative and qualitative analyses of the literature are performed and interpreted through the lens of the circular economy butterfly diagram proposed by the Ellen MacArthur Foundation. The results reveal a pronounced asymmetry in the distribution of CE practices within product design research. Recycling and remanufacturing dominate the literature despite being positioned in the CE framework as less desirable than resource-preserving strategies such as reuse, repair, and life extension. This imbalance suggests that current research and design practices remain largely anchored in end-of-life (EoL) and resource-intensive solutions rather than prioritizing strategies that slow material and product flows. These findings highlight a critical need to intensify research on product circularity at the design stage, with greater emphasis on life-extension-oriented strategies and their integration within DfX methodologies and practices. The study provides a structured foundation for future research on circular product design and contributes to ongoing discussions on aligning design practice more closely with the fundamental principles of the circular economy. Full article

A proper understanding of hydrogen–trap interactions in materials is of considerable significance, as it holds the potential to provide promising solutions to the long-standing issue of hydrogen embrittlement. In the present study, we employed a novel integrated approach combining electro-oxidation (EO) technique and thermal desorption spectroscopy (TDS) to characterize both reversible and irreversible deuterium in Fe samples. The samples were deuterium-charged at 500 kPa and temperatures ranged from 25 °C to 500 °C. Deuterium retention was measured by TDS for samples with and without EO treatment. Experimental findings demonstrate that the EO technique not only accelerates the expulsion of spontaneously releasable deuterium but also efficiently removes the majority of non-spontaneously releasable deuterium. It is evidenced that the proportion of reversible deuterium in the non-spontaneously releasable deuterium fraction reaches as high as 70%. Furthermore, an illustrative energy level diagram concerning the different barriers depending on the trap sites was devised to elucidate the trapping and diffusion behaviors of deuterium. Correspondingly, the microstructural trap sites associated with reversible or irreversible states were discussed in detail. This work enhances our understanding of hydrogen-Fe material interactions, thereby strengthening the fundamental theories underlying hydrogen embrittlement. Full article

Rail infrastructure plays an important role in freight and passenger mobility, and the assessment of rail track structure depends critically on understanding how the rail interacts with the supporting foundation. When rail support degrades (e.g., due to ballast fouling, settlement, etc.), the rail exhibits greater localized deformation that can lead to serious deleterious conditions. Track modulus represents a fundamental diagnostic measure of rail support, encompassing the vertical stiffness characteristics of the foundation and its resistance against downward rail movement. Existing track modulus characterization methodologies typically comprise deflection measurements of railway track (e.g., tie deflections) under known loads. Track modulus estimations result from analyzing deflection and load under assumptions of a traditional Winkler foundation, which can oversimplify mechanic relationships. Specifically, in the context of rail–ballast–subgrade interaction, a tensionless foundation permits gap development which can occur as track structure separates from the supporting ballast; additionally, track modulus may vary along the track length as conditions vary spatially. This paper presents a general analytical solution of ballasted track support characterization based on an iterative algorithm for the static response of a finite beam resting on a tensionless Winkler foundation. The method relates to multiple loads (e.g., concentrated axle loads and distributed self-weight), deflection along the track, and track condition through singularity functions, superposition of discrete support springs, and moment–curvature relationships. The model estimates rail deflections, lift-off points and shear and moment diagrams along the track. The technique permits: (1) validations against benchmark solutions and previously published results, (2) estimations of track modulus from known loads and measured deflections, and ultimately, (3) a framework for designing and processing sensor data streams for use in analyses and evaluations of railway track structure. Full article

We explore a phenomenological extension of the Polyakov–Nambu–Jona-Lasinio (PNJL) model by introducing a curvature-sensitive effective contribution to the Polyakov-loop potential, motivated by the hypothesis that the non-perturbative QCD vacuum in the confined phase may retain a residual sensitivity to cosmic expansion. In a spatially flat FLRW background, this modification reduces to a term proportional to $\alpha {(H/H_0)}^{d}f(\Phi ,{\Phi }^{*})$, which naturally vanishes in the deconfined regime and behaves as an effective dynamical vacuum component at late times, without invoking a fundamental cosmological constant. The construction provides an effective thermodynamic description of the QCD sector within an adiabatic framework and introduces a minimal phenomenological extension characterized by the exponent d and the amplitude parameter $\alpha$. We analyze the cosmological implications at the background level and compare the model with low-redshift observations, including cosmic chronometers, Type Ia supernovae, HII galaxies, and quasars. Using Bayesian Monte Carlo techniques, we constrain the model parameters and compare its performance with the ΛCDM. Our results indicate that the modified PNJL cosmology provides a statistically competitive fit to current data while allowing small departures from the ΛCDM within observational uncertainties. We also investigate the impact of the coupling on the QCD phase diagram and the critical end point. The framework offers a tractable effective approach to connect confinement physics with late-time cosmology and suggests directions for further theoretical development in QCD under curved backgrounds. Full article

The article discusses the methods for classifying processes for testing and processing metals by plastic deformation, based on the characteristics of their stress–strain state. The basic methods for determining the stress and strain states using fundamental scalar quantities representing the stress and strain tensors are discussed. Equations have been derived for the quantitative determination of the type of stress–strain state through a combination of principal stresses, represented as the strain rigidity of the deformation mode. A deformable work-hardening alloy, AA7075, from the database Quantor Form 8.2.4 software product, is used, which is deformed at room temperature with an analysis of elastic–plastic deformations. A classification of deformation processes for testing and processing metals by plastic deformation is proposed, using the stress triaxiality parameter and the strain rigidity coefficient. Some 2D and 3D diagrams have been created based on simulation modeling of plastic deformation processes using virtual tools, allowing the grouping of processes according to the measured principal stresses and their combinations, which represent the stress triaxiality and strain rigidity of the deformation mode. By determining the type of grouping in these diagrams and the change in the stress–strain state with increasing strain levels, the characteristic features of the deformation processes used in materials testing and in the processing metals by plastic deformation of metals/alloys have been confirmed. Full article