Search Results (1,474)

Aluminum alloys under long-term service or repetitive stress are prone to small fatigue cracks (FCs) with arbitrary orientations, necessitating eddy current probes with focused magnetic fields and directional selectivity for reliable detection. This study presents a flexible printed circuit board (FPCB) probe with a double-layer planar excitation coil and a double-layer differential receiving coil. The excitation coil employs a reverse-wound design to enhance magnetic field directionality and focusing, while the differential receiving coil improves sensitivity and suppresses common-mode noise. The probe is optimized by adjusting the excitation coil overlap and the excitation–receiving coil angles to maximize eddy current concentration and detection signals. Finite element simulations and experiments confirm the system’s effectiveness in detecting surface cracks of varying sizes and orientations. To further characterize these defects, two time-domain features are extracted: the peak-to-peak value ($\Delta$P), reflecting amplitude variations associated with defect size and orientation, and the signal width ($\Delta$W), primarily correlated with defect angle. However, substantial overlap in their value ranges for defects with different parameters means that these features alone cannot identify which specific parameter has changed, making prior defect classification using a Transformer-based approach necessary for accurate quantitative analysis. The proposed method demonstrates reliable performance and clear interpretability for defect evaluation in aluminum components. Full article

Malay songket motifs are a vital component of Malaysia’s intangible cultural heritage, characterized by intricate visual designs and deep cultural symbolism. However, the practical digital preservation and retrieval of these motifs present challenges, particularly due to the rotational variations typical in textile imagery. This study introduces a novel Content-Based Image Retrieval (CBIR) model that integrates Principal Component Analysis (PCA) for feature extraction and Quadratic Geometric Distance (QGD) for measuring similarity. To evaluate the model’s performance, a curated dataset comprising 413 original images and 4956 synthetically rotated songket motif images was utilized. The retrieval system featured metadata-driven preprocessing, dimensionality reduction, and multi-angle similarity assessment to address the issue of rotational invariance comprehensively. Quantitative evaluations using precision, recall, and F-measure metrics demonstrated that the proposed PCAQGD + Rotation technique achieved a mean F-measure of 59.72%, surpassing four benchmark retrieval methods. These findings confirm the model’s capability to accurately retrieve relevant motifs across varying orientations, thus supporting cultural heritage preservation efforts. The integration of PCA and QGD techniques effectively narrows the semantic gap between machine perception and human interpretation of motif designs. Future research should focus on expanding motif datasets and incorporating deep learning approaches to enhance retrieval precision, scalability, and applicability within larger national heritage repositories. Full article

In recent years, convolutional neural network-based object detectors have achieved extensive applications in remote sensing (RS) image interpretation. While multi-scale feature modeling optimization remains a persistent research focus, existing methods frequently overlook the symmetrical balance between feature granularity and morphological diversity, particularly when handling high-aspect-ratio RS targets with anisotropic geometries. This oversight leads to suboptimal feature representations characterized by spatial sparsity and directional bias. To address this challenge, we propose the Parallel Interleaved Convolutional Kernel Network (PICK-Net), a rotation-aware detection framework that embodies symmetry principles through dual-path feature modulation and geometrically balanced operator design. The core innovation lies in the synergistic integration of cascaded dynamic sparse sampling and symmetrically decoupled feature modulation, enabling adaptive morphological modeling of RS targets. Specifically, the Parallel Interleaved Convolution (PIC) module establishes symmetric computation patterns through mirrored kernel arrangements, effectively reducing computational redundancy while preserving directional completeness through rotational symmetry-enhanced receptive field optimization. Complementing this, the Global Complementary Attention Mechanism (GCAM) introduces bidirectional symmetry in feature recalibration, decoupling channel-wise and spatial-wise adaptations through orthogonal attention pathways that maintain equilibrium in gradient propagation. Extensive experiments on RSOD and NWPU-VHR-10 datasets demonstrate our superior performance, achieving 92.2% and 84.90% mAP, respectively, outperforming state-of-the-art methods including EfficientNet and YOLOv8. With only 12.5 M parameters, the framework achieves symmetrical optimization of accuracy-efficiency trade-offs. Ablation studies confirm that the symmetric interaction between PIC and GCAM enhances detection performance by 2.75%, particularly excelling in scenarios requiring geometric symmetry preservation, such as dense target clusters and extreme scale variations. Cross-domain validation on agricultural pest datasets further verifies its rotational symmetry generalization capability, demonstrating 84.90% accuracy in fine-grained orientation-sensitive detection tasks. Full article

As a clean energy carrier, hydrogen is essential for global low-carbon energy transitions due to its unique combination of safe transport properties and energy density. This investigation employs computational fluid dynamics (ANSYS Fluent) to systematically characterize hydrogen dispersion through soil media from buried pipelines. The research reveals three fundamental insights: First, leakage orifices smaller than 2 mm demonstrate restricted hydrogen migration regardless of directional orientation. Second, dispersion patterns remain stable under both low-pressure conditions (below 1 MPa) and minimal thermal gradients, with pipeline temperature variations limited to 63 K and soil fluctuations under 40 K. Third, dispersion intensity increases proportionally with higher leakage pressures (exceeding 1 MPa), greater soil porosity, and larger particle sizes, while inversely correlating with burial depth. The study develops a predictive model through Sequential Quadratic Programming (SQP) optimization, demonstrating exceptional accuracy (mean absolute error below 10%) for modeling continuous hydrogen flow through moderate-porosity soils under medium-to-high pressure conditions with weak inertial effects. These findings provide critical scientific foundations for designing safer hydrogen transmission infrastructure, establishing robust risk quantification frameworks, and developing effective early-warning systems, thereby facilitating the practical implementation of hydrogen energy systems. Full article

To address the challenge of effective tracking of weather-induced power fluctuation trends in daytime PV power forecasting, this paper proposes a joint forecasting framework oriented to weather classification. For the weather classification module, a spectral clustering method incorporating multivariate feature fusion-based evaluation is introduced to address the limitation that conventional clustering models fail to effectively identify power fluctuations caused by dynamic weather variations. Simultaneously, to tackle non-stationary fluctuations and local abrupt changes in PV power forecasting, a non-stationary Transformer-BiLSTM model optimised using the Differentiated Creative Search (DCS) algorithm (DCS-NsT-BiLSTM)is proposed. This model enables the co-optimisation of global and local features under diverse weather patterns. The proposed method takes into consideration the climatic typology of PV power plants, thereby overcoming the insensitivity of traditional clustering models to high-dimensional non-stationary data. Furthermore, the approach utilises the novel intelligent optimisation algorithm DCS to update the key hyperparameters of the forecasting model, which in turn enhances the accuracy of day-ahead PV power generation forecasting. Applied to a photovoltaic power station in Jilin Province, China, this method reduced the mean root mean square error by 4.63% across various weather conditions, effectively validating the proposed methodology. Full article

Stomata play a crucial role in plant immune responses, with their morphological characteristics closely linked to disease resistance. Accurate detection and analysis of stomatal phenotypic parameters are essential for soybean disease resistance research and variety breeding. However, traditional stoma detection methods are challenged by complex backgrounds and leaf vein structures in soybean images. To address these issues, we proposed a Soybean Stoma-YOLO (You Only Look Once) model (SS-YOLO) by incorporating large separable kernel attention (LSKA) in the Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv8 and Deformable Large Kernel Attention (DLKA) in the Neck part. These architectural modifications enhanced YOLOV8′s ability to extract multi-scale and irregular stomatal features, thus improving detection accuracy. Experimental results showed that SS-YOLO achieved a detection accuracy of 98.7%. SS-YOLO can effectively extract the stomatal features (e.g., length, width, area, and orientation) and calculate related indices (e.g., density, area ratio, variance, and distribution). Across different soybean rust disease stages, the variety Dandou21 (DD21) exhibited less variation in length, width, area, and orientation compared with Fudou9 (FD9) and Huaixian5 (HX5). Furthermore, DD21 demonstrated greater uniformity in stomatal distribution (SEve: 1.02–1.08) and a stable stomatal area ratio (0.06–0.09). The analysis results indicate that DD21 maintained stable stomatal morphology with rust disease resistance. This study demonstrates that SS-YOLO significantly improved stoma detection and provided valuable insights into the relationship between stomatal characteristics and soybean disease resistance, offering a novel approach for breeding and plant disease resistance research. Full article

Under the accelerating demographic aging trend, the rational allocation of elderly care facilities has emerged as a critical challenge. Although existing studies have investigated elderly care facilities planning using conventional methods, they frequently overlook the nonlinear interactions between built environment factors and heterogeneous demands across different elderly care facility types. This study addresses these gaps by proposing a data-driven framework that integrates machine learning with spatial analysis to optimize elderly care facility distribution in Xi’an City central area, Shaanxi Province, China. Leveraging multi-source datasets encompassing points of interest (POIs), road networks, and demographic statistics, we classify facilities into three categories (service-oriented, activity-oriented, and care-oriented) and employ an XGBoost model with SHAP interpretability to evaluate spatial distributions and influencing factors. The results demonstrate that the XGBoost model outperforms comparative algorithms (Random Forest, CatBoost, LightGBM) with superior performance metrics (accuracy rate of 97%, precision of 95%, and F1-score of 90%), effectively capturing nonlinear thresholds effects. Key findings reveal the following: (1) Accessibility and road density exert threshold effects on care-oriented facilities, with facility attractiveness saturating when these values exceed 6; (2) Land use intensity and medical resources positively correlate with activity-oriented facilities, while excessive retail density inhibits their distribution; (3) Service-oriented facilities thrive in areas with balanced accessibility and moderate commercial diversity. Spatial analysis identifies clustered distribution patterns in urban core areas contrasted with peripheral deficiencies, indicating need for targeted interventions. This research contributes a scalable methodology for equitable facility planning, emphasizing the integration of dynamic built environment variations with model interpretability. The framework provides significant implications for formulating age-friendly urban policies applicable to global cities undergoing rapid urbanization and population aging. Full article

Reliable tools for cultivar discrimination and ripening stage evaluation are critical to optimize harvest timing and support milling process focused on olive oil quality. This research examines the spectral properties of olive drupes throughout different maturation stages, ranging from green to full purple-black pigmentation, across 29 distinct cultivars. High-resolution spectrometric analysis was conducted within the 380–1080 nm wavelength range. Multiple analytical approaches were employed to optimize wavelength selection from hyperspectral reflectance data to obtain discriminating tools for olive classification. A Biologically Informed Wavelength Extraction method (BIWE) was developed, focusing on cultivar and ripening stages identification, and pivoted on biologically informed single wavelengths and Vegetation Indices (VIs) selection. The methodology integrated multi-scale spectral analysis with biochemically weighted scoring and a multi-criteria evaluation framework, employing a two-iteration refinement process to identify optimal spectral features with high discriminatory power and biological relevance. Analysis revealed spectral variations associated with maturation. A characteristic reflectance peak at approximately 550 nm observed during early ripening stages underwent a notable shift, developing into distinct spectral behavior within the 700–780 nm range in intermediate and advanced ripening stages and reaching a plateau for all the samples between 800 and 950 nm. The BIWE method achieved exceptional efficiency in olive classification, utilizing only 25 single wavelengths compared to 114 required by Principal Component Analysis (PCA) and 131 by Recursive Feature Elimination (RFE), representing 4.6-fold and 5.2-fold reductions, respectively. Despite this reduction, BIWE’s overall accuracy (0.5634) remained competitive compared to RFE (−10%) and PCA (−8%) alternative approaches requiring larger wavelengths dataset acquisition. The integration of biochemically relevant VIs enhanced accuracy across all methodologies, with BIWE demonstrating notable improvement (+19.2%). BIWE demonstrated effective olive identification capacity with a reduction in required wavelengths and VIs dataset, affecting the technological needs (spectrometer offset and real-time classification applications) for a tool oriented to olive cultivars and ripening stage discrimination. Full article

This paper presents an extended investigation into the role of environmental assumptions in the emergence and management of Requirements Technical Debt (RTD). Building on earlier work that identified environmental assumptions as a critical yet often overlooked source of RTD, we provide a comparative evaluation of seven representative modeling frameworks: KAOS, i*, Obstacle Analysis, Failure Frames, Claims, SysML, and RDAL. The analysis is structured around five dimensions: modeling focus, tracing support, validation capability, integration readiness, and assumption evolution. The results show substantial variation in support for assumption management. Among the frameworks, KAOS and Obstacle Analysis stand out for explicitly modeling assumptions and their violations. SysML, on the other hand, is particularly strong in its ability to integrate with industrial toolchains. RDAL demonstrates its greatest strength in tracing, where assumptions, requirements, and verification conditions are systematically linked, though its explicit modeling support remains more limited compared to goal-oriented approaches. i* and Claims capture assumptions more implicitly, with weaker validation and evolution capabilities, while Failure Frames focus on assumption violations but lack integration with broader MBSE workflows. The paper’s main contribution is a synthesis of trade-offs across assumption-aware modeling frameworks, highlighting both their strengths and remaining gaps. This provides actionable insights for researchers and practitioners in selecting, combining, or extending modeling approaches to better manage environmental assumptions and mitigate assumption-related technical debt. Full article

This paper investigates the thermal performance of an office floor within the Faculty of Engineering at the British University in Egypt (BUE), located in Cairo, a city characterized by a hot arid climate. The study focuses on understanding the building’s thermal behavior by comparing two identical office rooms: Room 212 (north-facing) and Room 201 (south-facing). Utilizing dynamic thermal simulations with TRNSYS 18 for a full year, the research specifically analyzes the impact of these opposite orientations on indoor space temperature, total cooling loads, the monthly heat absorbed by various building surfaces, and the heat absorbed per unit area for each surface. The findings reveal significant disparities in thermal performance, particularly in terms of heat gain and cooling demand, directly attributable to orientation. This research highlights the critical role of facade orientation in mitigating radiative heat absorption and reducing energy consumption in educational buildings within hot climates, providing valuable insights for optimizing building design strategies to enhance thermal comfort and energy efficiency. Full article

The aim of this article is to assess the digital maturity of Polish enterprises and to identify the most and least developed dimensions of maturity within these organizations in the context of their potential to achieve sustainable development goals. The authors pose research questions regarding the overall level of digital maturity in Polish enterprises, its variation depending on the type of business activity, and the specific dimensions of digital maturity that were rated the highest and lowest. The main thesis of the article assumes that the level of digital maturity determines a company’s sustainable orientation. The article presents the results of empirical research conducted among 697 Polish enterprises operating in the manufacturing, trade, and service sectors. The study employed the seven-dimensional Digitalcheck Mittelstand model for assessing digital maturity. The average scores of digital maturity, both by industry and by specific dimensions, were mapped to six levels of digital maturity adapted for Polish enterprises. The findings confirm that Polish enterprises demonstrate a moderate level of digital maturity. Among the analyzed sectors, manufacturing enterprises exhibit the highest level of maturity. The study also confirmed that the highest maturity levels are observed in the areas of organization and processes. Conversely, the lowest level of digital advancement is found in the environmental dimension, indicating a gap in aligning corporate strategies with green funding programs and eco-initiatives. Future research should take into account causal mechanisms and disruptive factors affecting digital transformation in organizations. Full article

First examples of mononuclear homoleptic zinc aminopyridinates have been isolated by reacting the sterically bulky deprotonated 2-aminopyridine ligands, N-(2,6-diisopropylphenyl)-[6-(2,6-dimethylphenyl)-pyridine-2-yl]-amine (1) and N-(2,6-diisopropylphenyl)-[6-(2,4,6-triisopropylphenyl)-pyridine-2-yl]-amine (2) with [Zn{N(SiMe₃)₂}₂]. Single crystal X-ray analyses of the zinc bis(aminopyridinate) complexes (3 and 4) reveal two different orientations of the coordinated ligands most probably due to the steric variation of the of the applied ligands. For 3 not only the two ligands show rare head to head arrangement but also one of the ligand exhibit localized and the other ligand delocalized mode of coordination. In 4 the two ligands adopt the head to tail arrangement for the two coordinated aminopyridinato ligands with anionic function localized at the amido nitrogen atom of both the ligands. NMR tube reactions between equimolar ratios of 1 or 2 and [Zn{N(SiMe₃)₂}₂] show the possible synthesis of the mono(aminopyridnate) Zn amide complexes (5 and 6, respectively) in solution phase, however, the corresponding bis(aminopyridinate) Zn complexes are the selective products. Hirshfeld surface analysis and the two-dimensional fingerprint plots indicate that intermolecular H⋯H contacts and H⋯C/C⋯H π-interactions dominate the crystal packing. Full article

Enhanced lubrication is critical for improving gear wear resistance. Current research on surface textures has overlooked the fundamental role of structural connectivity. Inspired by biological scales, a biomimetic hexagonal texture (BHT) was innovatively designed for tooth flanks, featuring dual-orientation grooves (perpendicular and inclined to the rolling-sliding direction) with bidirectional interconnectivity. This design synergistically combines hydrodynamic effects and directional lubrication to achieve tribological breakthroughs. A lubrication model for line contact conditions was established. Subsequently, the texture parameters were then optimized using response surface methodology and numerical simulations. FZG gear tests demonstrated the superior performance of the optimized BHT, which achieved a substantial 82.83% reduction in the average wear area ratio and a 25.35% decrease in tooth profile deviation variation. This indicated that the biomimetic texture can effectively mitigate tooth surface wear, thereby extending the service life of gears. Furthermore, it significantly improves thermal management by enhancing convective heat transfer and lubricant distribution, as evidenced by a 7–11 °C rise in bulk lubricant temperature. This work elucidates the dual-mechanism coupling effect of bio-inspired textures in tribological enhancement, thus establishing a new paradigm for gear surface engineering. Full article

This qualitative study investigates how academic faculty in a UK university conceptualise and implement alternative assessment practices aimed at fostering critical 21st-century skills—such as problem-solving, collaboration, and creativity—in an increasingly diverse higher education context. Drawing on in-depth interviews with six academic faculty members, the study explores the extent to which inclusive and alternative assessment practices are embedded in teaching and examines the institutional and cultural barriers that shape these practices. Thematic analysis reveals that while staff broadly value critical skills, there is considerable variation in how these skills are understood and operationalised in assessment. Many staff face structural constraints, including rigid assessment policies and market-driven accountability frameworks, that limit their ability to innovate. Furthermore, the findings highlight a disjunction between staff awareness of inclusive pedagogies and their capacity to enact them systematically in assessment design. The study contributes to the literature by foregrounding the complex interplay between institutional logics, assessment practices, and inclusive pedagogical aims. It argues that advancing genuinely inclusive and skills-oriented assessment requires systemic change at both institutional and policy levels. Full article

The visual performance and photobiological effects of white LED systems based on spectral compensation are discussed, specifically focusing on the total optical power, the ratio of scotopic vision luminous flux to photopic vision luminous flux (S/P), the blue light hazard (BLH), and the circadian action factor (CAF). Theoretical models are established by integrating the spectral power distribution (SPD) with spectral sensitivity functions associated with the human visual system, and meanwhile, the impacts of LEDs’ electro-thermal characteristics on the mixed spectral structure and optical properties are analyzed. As experimental results demonstrate, an excellent agreement is shown between the calculated and measured values of the total optical power, S/P, BLH, and CAF, in terms of both values and variation trends. These proposed models are expected to serve as effective tools for understanding the visual perception and non-visual biological effects in specific illumination environments. Moreover, they can offer valuable reference frameworks for the development of lighting solutions that are more human-centered and health-oriented. Full article