Search Results (39,600)

Composite aircraft panels are subjected to combined mechanical and thermal loading conditions requiring assessment approaches beyond conventional single-load stiffness evaluation. A design-oriented finite element framework is presented for the comparative assessment of symmetric composite laminate configurations under compression, pressure, and thermal loading. Normalized stiffness metrics derived from displacement-based responses are combined with Pareto-based comparative interpretation, stiffness anisotropy indices, and robustness evaluation to identify laminate configurations exhibiting either specialized or balanced structural behavior. The results highlight the importance of trade-off-driven laminate selection during preliminary aircraft structural design. The proposed methodology provides a computationally efficient framework suitable for early-stage composite structural assessment and future extension toward stiffened panel applications. Full article

Single-image 3D reconstruction of stylized side-face characters remains challenging because profile-view inputs contain severe self-occlusion, missing frontal geometry, and stylized appearance cues that differ from the assumptions of generic reconstruction models. Because the unseen facial geometry cannot be uniquely determined from a single profile-view input, this study focuses on generating plausible and visually consistent 3D completions rather than uniquely recovering the unobserved geometry. When CRM is directly applied to stylized profile inputs, the outputs often exhibit unstable facial completion, local mesh collapse, UV misalignment, texture discontinuity, and other reconstruction artifacts. Rather than introducing a new reconstruction backbone, this study first diagnoses the task-specific limitations of CRM in this setting. We identify eight characteristic failure modes that occur when CRM is directly applied to stylized profile inputs and use this diagnosis to guide a retraining-free inference-time intervention strategy. The proposed strategy combines reconstruction-compatible auxiliary-view generation with failure-mode-oriented CRM refinement, including candidate verification, adaptive facial cropping, geometric stabilization, local detail enhancement, normal correction, UV repair, and texture continuity improvement. Experiments on a rendered stylized-character dataset and a cross-style adaptation set show that the proposed intervention improves frontal-view plausibility, mesh usability, texture continuity, and rendered appearance compared with direct reconstruction baselines. The seven-configuration progressive ablation and parameter sensitivity analyses further support the complementary role of the main intervention stages and the stability of the selected settings. These findings suggest that systematic failure-mode diagnosis, followed by task-specific inference-time intervention, provides a practical way to extend public image-to-3D models to stylized profile reconstruction scenarios, within the scope of the evaluated stylized-character datasets, while extreme viewpoints and highly abstract styles remain challenging. Full article

This study examines the processes of technology adoption and appropriation among older adults participating in two community-based digital inclusion workshops (LAB65+) in Madrid, exploring how digital technologies are appropriated within community learning environments and identifying the social, relational, and pedagogical factors that shape this process, with particular attention to the role of mutual support, warm experts, and community learning dynamics. Drawing on a series of workshops and group interaction recordings conducted with regular attendees, the research identifies a set of factors that consistently shape participants’ engagement with digital tools. Particular attention is given to socio-educational background, previous work experience, and prior exposure to technology, as well as to the everyday motivations associated with the use of mobile phones for communication through WhatsApp, online purchasing, access to health services, and routine banking procedures. Across both labs, the findings reveal that successful and sustained engagement with technology among older adults depends less on technical training per se than on elements related to motivation, self-efficacy, meaningful instruction, and the creation or reinforcement of social ties in familiar environments. Although minor differences emerge between the two settings, the evidence consistently underscores the centrality of these relational and contextual factors over purely operational or skill-based considerations. The study highlights the need for community-oriented approaches that recognize and build upon the social dimensions of learning and using technology in later life. Full article

Growing up without parental care may negatively affect adolescents’ behavioral and emotional development, particularly among adolescents in conflict with the law. In a selected secure care center in the Vhembe District, limited research has explored the lived experiences and behavioral impact of growing up without parents. This study aimed to explore and describe the behavioral and emotional impact of growing up without parents among adolescents in conflict with the law in a child and adolescent secure care center in Limpopo Province. A qualitative, explorative, descriptive, and contextual research design was employed. Purposive sampling was used to recruit twelve (12) adolescents aged 15–17 years admitted to a secure care center in the Vhembe District. Data was collected through individual semi-structured interviews conducted in Xitsonga or Tshivenda, depending on participants’ preferred language. Interviews were audio-recorded, transcribed, translated into English, and analyzed using Tesch’s eight steps of data analysis. Ethical considerations and measures to ensure trustworthiness were observed throughout the study. The findings revealed that adolescents experienced low self-esteem, diminished self-confidence, early initiation of substance use, poor educational engagement, survival-oriented delinquent behavior, and feelings of neglect. These findings highlight the need for an integrated intervention approach to ensure coordinated psychosocial, educational, behavioral, and socio-economic support for this population. Full article

Artificial intelligence (AI) is rapidly transforming higher education. Notably, institutions are increasingly deploying AI-enabled chatbots across administrative, teaching and learning, and research functions. Despite the proliferation of these tools, most conversational systems remain generic and disconnected from subject-level context, curriculum design, and academic integrity requirements. A design-oriented framework is proposed in this paper to improve the effectiveness of AI-enabled chatbots used in teaching and learning, embedded within curriculum. The framework is grounded in systems thinking for context-aware AI-enabled learning systems that operate within bounded pedagogical and disciplinary environments. Adopting a design science approach, the study synthesises expert-informed insights from pedagogical, programmatic, and subject-level perspectives to develop a framework that integrates context alignment, instructional control, and integrity-preserving guardrails. The resulting artefact was verified through artefact-focused testing against the proposed design objectives using a structured non-human evaluation protocol, including requirement-based testing, baseline comparison with generic AI systems, academic integrity stress testing, and robustness analysis. The study proposes and verifies a framework intended to support curriculum alignment, instructional control, and academic integrity preservation within AI-enabled learning systems. The paper contributes a systems-oriented framework for embedding AI within educational systems while preserving pedagogical intent and governance requirements. Implications for scalable deployment of AI in higher education and future human-centred evaluation are discussed. Full article

Natural bioactive compounds play an increasingly important role in modern cosmetic formulations, particularly in the context of efficacy, safety, and regulatory compliance. Among them, vitamin A-related compounds have attracted significant attention due to their well-documented effects on skin renewal, collagen synthesis, and photoaging prevention. This review provides a comprehensive overview of natural vitamin A derivatives and structurally related carotenoids used in cosmetic applications, including retinol, retinal, retinyl esters, β-carotene, lycopene, zeaxanthin, astaxanthin, lutein, and fucoxanthin. The article critically examines their intracellular mechanisms of action, distinguishing between canonical retinoid signaling pathways mediated by nuclear receptors (RAR/RXR) and the predominantly antioxidant activities of carotenoids. Particular attention is given to the metabolic conversion of provitamin A compounds and their relevance to biological activity in the skin. Furthermore, recent regulatory restrictions on the use of retinol and retinyl esters, especially within the European Union, are discussed in the context of formulation challenges and the growing interest in naturally derived alternatives. By integrating mechanistic insights with regulatory and application-oriented perspectives, this review highlights the potential and limitations of natural vitamin A-related compounds and outlines future directions for their use in safe and effective cosmetic products. Full article

This study analyses the professional development provision in artificial intelligence (AI) offered to non-university teachers in Andalusia during the 2023–2024 academic year. The research follows a descriptive documentary design based on the systematic review of training activities provided by the Teacher Training Centres (CEPs) in the eight Andalusian provinces. The initial corpus comprised 1832 training activities. After screening course titles and, when available, objectives, descriptions, metadata and content lists, 26 AI-related activities were identified; after excluding repeated editions and activities whose information was not accessible, the final analysable corpus consisted of 15 distinct and visible courses. The analysis combined quantitative description of distribution by province with qualitative content analysis supported by Atlas.ti. The results show that AI-related training represented only 1.42% of the overall offer and that the final analysable corpus represented 0.82% of all training activities. The provision was also unevenly distributed across provinces and tended to privilege specific tools, generative AI applications and productivity-oriented uses, with comparatively limited attention to ethical, pedagogical, assessment-related and interdisciplinary dimensions. The findings suggest the need for a more coherent regional strategy based on common minimum AI literacy modules, differentiated pathways by educational stage, blended and mentored formats, classroom-based projects and systematic evaluation of training impact. Full article

Low-frequency mobile bands remain essential for wide-area and penetration-limited wireless coverage, but their limited channel bandwidth constrains the achievable capacity. This paper presents a measurement-supported extrapolation framework for assessing how lowband MIMO and future AAS-assisted operation can enhance coverage and single-user throughput-oriented capacity in wireless networks. The motivation is to evaluate whether such deployments can strengthen the lower-frequency layer as a robust coverage-and-capacity support layer for general traffic and reduce the load on midband and higher-frequency resources. Controlled radiated SISO and $2\times 2$ MIMO measurements were performed with a base-station simulator and commercial user equipment in representative lowband and midband frequency bands. Measured RSRP, CQI, BLER, MAC-layer throughput, and IP-layer throughput thresholds for a 25 Mbit/s downlink target were used for coverage estimation and conditional extrapolation. Under the Extended Hata model, the measured $2\times 2$ MIMO thresholds yielded a 43% larger estimated radius at 800 MHz than at 1800 MHz, while the same model indicated a 93% radius increase for a representative 10 dB AAS-related beamforming gain scenario. Conditional $4\times 4$ MIMO extrapolations indicated data rates above 100 Mbit/s in 10 MHz and above 200 Mbit/s with 10 MHz two-component-carrier aggregation under ideal high-CQI conditions. The results support the potential of future lowband AAS deployments. The AAS and higher-order MIMO results are scenario-based estimates rather than direct field validation. Full article

The rapid expansion of microwave and millimeter-wave telecommunication systems has intensified the need for precise dielectric material characterization at high frequencies. As operating frequencies increase, small uncertainties in permittivity and loss tangent significantly degrade resonance stability, bandwidth control, and quality factor, directly affecting RF system reliability and performance. However, the growing diversity of resonator architectures and extraction methodologies has led to fragmentation in the literature, making it difficult to identify optimal solutions for telecommunication-oriented applications. This review provides a structured and application-driven assessment of high-frequency resonator-based dielectric characterization techniques relevant to modern telecommunication systems. Resonator topologies—including cavity, planar, substrate-integrated, metamaterial-inspireds—are systematically classified and critically compared. Their sensing mechanisms and parameter-extraction approaches are analyzed in terms of frequency-shift sensitivity, Q-factor performance, scalability toward millimeter-wave bands, integration capability, and measurement robustness. By synthesizing performance trade-offs, practical limitations, and emerging research directions, this review establishes clear design guidelines and a forward-looking framework for advancing dielectric metrology in next-generation high-frequency telecommunication technologies. Full article

Software defect prediction (SDP) aims to identify defect-prone modules before testing, but severe class imbalance and redundant software metrics often limit prediction performance. Many conventional feature selection methods estimate feature relevance with the original imbalanced empirical distribution and mainly emphasize marginal relevance or global classifier-oriented criteria, which may under-prioritize features that are informative for the minority defective class. To address this issue, this paper proposes SCAR-CMB, a simplified class-reweighted and interaction-aware feature selection method for imbalanced SDP. SCAR-CMB estimates feature-label dependency with a class-balanced empirical distribution, controls redundancy using weighted conditional dependency information, and incorporates an interaction-aware conditional-gain term as an auxiliary re-prioritization signal within a relevance-screened feature pool. Rather than performing full causal structure discovery or formal synergy estimation, SCAR-CMB adopts a Markov-blanket-inspired conditional dependency design as a practical guide for feature selection. The final configuration excludes both hardness-aware weighting and false discovery rate filtering. SCAR-CMB is evaluated on ten public NASA and PROMISE defect datasets under a leakage-free cross-validation protocol. Compared with seven representative baselines, SCAR-CMB achieves competitive overall performance and obtains the highest average defective-class recall, G-mean, and balanced accuracy. However, it is not uniformly superior across all metrics, and the recall advantage is not confirmed by the omnibus Friedman test. Additional mechanism-level, stability, and sensitivity analyses show that class reweighting changes feature prioritization, the selected feature subsets are relatively stable across folds, and the interaction-aware term provides limited and dataset-dependent auxiliary effects. Sensitivity analyses further indicate that the main conclusions are not solely determined by a specific feature budget, discretization-bin setting, or downstream classifier. Overall, SCAR-CMB should be interpreted as a practical minority-class-oriented feature selection method that provides a trade-off among defective-class detection, feature subset control, and computational cost. Full article

Reliable state estimation is essential for the secure and efficient operation of sustainable energy systems, especially under the increasing integration of renewable energy, distributed resources, and heterogeneous sensing devices. However, in practical power systems, SCADA, PMU, and AMI measurements often have different sampling rates, accuracies, communication delays, and availability levels, which makes reliable data completion and multi-source fusion difficult. This paper focuses on the state estimation problem of renewable-integrated distribution networks under multi-source heterogeneous measurement conditions. In such distribution networks, the increasing penetration of distributed renewable energy resources and the joint deployment of multiple measurement devices, including SCADA, PMU, and AMI, may lead to incomplete measurements, asynchronous sampling, differences in measurement accuracy, and reduced system observability. To address these issues, this paper proposes a model-based digital twin reference-guided physics-constrained DDPM framework to improve the quality of missing-measurement completion and the reliability of state estimation in distribution-network scenarios. A four-layer simulation-oriented cyber–physical framework is first constructed to integrate physical sensing, model-based digital twin reference mapping, AI-based measurement completion, and state estimation feedback. Within this framework, a physics-constrained self-supervised denoising diffusion probabilistic model is developed to recover missing measurements by combining observed data, digital twin reference measurements, real-time topology information, and power system operational constraints. The completed pseudo-measurements and physical measurements are then fused through a credibility-aware weighting strategy that considers timeliness, data integrity, measurement accuracy, and virtual–real consistency verification under simulation settings. Simulation results on the IEEE 14-bus system show that the proposed method improves pseudo-measurement completion and supports more reliable voltage magnitude and phase angle estimation under different measurement configurations. Under the tested simulation settings and multi-source measurement configurations, the results indicate that the proposed method can improve pseudo-measurement completion and support more reliable voltage magnitude and phase angle estimation. However, its performance under frequent topology switching, high missing-data ratios, and complex abnormal data conditions remains to be further evaluated. Full article

As AI-generated humanoid characters are increasingly used in virtual, augmented, and mixed reality applications, evaluating the Uncanny Valley Effect (UVE) is crucial for immersive user experience. Existing evaluation methods map visual features to affective scores, offering limited interpretability regarding which visual cues are associated with affinity judgments. Among the theoretical perspectives proposed to explain the UVE, perceptual conflict provides a visual-cue-oriented perspective for analyzing whether local-feature realism supports a coherent overall human-likeness impression and how this is reflected in affinity judgments, yet this perspective is rarely incorporated into interpretable UVE assessment. Thus, we propose UVE-Perception Chain-of-Thought (UVE-PCoT), a vision-language framework for interpretable UVE evaluation from a perceptual-conflict-oriented perspective. UVE-PCoT organizes assessment through a structured perceptual decomposition, including assessments of overall human-likeness, local-feature realism, perceptual conflict, and affinity. To provide supervision, we construct UVE-R, a structured rationale dataset with image-grounded, rating-consistent rationales linking visual cue observations, cue-level inconsistency analysis, and affinity judgments. Results show that UVE-PCoT improves affinity prediction and cue-level explanation over general-purpose multimodal large language models and ablations. Our approach operationalizes this perceptual-conflict-oriented perspective into an interpretable framework, advancing UVE evaluation from black-box scoring to explanatory analysis and providing cue-level insights for XR character assessment and revision. Full article

Surface deformation of a phased array antenna (PAA) induced by external loads can degrade its radiation performance. To restore the beam of a deformed PAA, this paper proposes a new strain–excitation mapping model (SEMM) capable of rapidly calculating excitation adjustments based on measured structural strains. In the derivation of the SEMM, an analytical formula establishing the relationship between antenna excitations and the element positions and orientations for a PAA with an arbitrary surface shape is derived using the projection principle. Subsequently, the positions and orientations of the elements are expressed as functions of a limited number of strain measurements from the deformed antenna structure. An X-band PAA experimental system, equipped with a deformable mechanism and strain measurement capabilities, was developed. Two typical deformations were taken as examples to validate the proposed SEMM. Experimental results demonstrate that the SEMM can effectively recover the distorted pattern across the observation region. Compared with existing models, the proposed model achieves better sidelobe recovery. The rapid computation capability and analytical formulation of the SEMM make it highly suitable for developing an adaptive PAA that can autonomously preserve radiation beam quality under in-service deformations. Full article

With the rapid development of social media and online interactive platforms, online public opinion has become a vital information source for public emotional expression, social risk perception, and decision support. However, public opinion texts are typically characterized by short length, obscure semantics, complex emotional expressions, and strong context dependence, making it difficult for traditional lexicon-based or shallow neural network methods to achieve stable and robust performance in sentiment discrimination tasks. To address these issues, this paper proposes BERT-BiLSTM-MHSA-Capsule (BBMC), hereafter referred to as BBMC, an online public opinion sentiment analysis model based on multi-level semantic feature fusion. The model first utilizes the pretrained language model BERT to extract dynamic semantic representations with context-aware capabilities; subsequently, a Bidirectional Long Short-Term Memory (BiLSTM) network is employed to model the bidirectional temporal dependencies within the texts, while a Multi-Head Self-Attention (MHSA) mechanism is introduced to achieve adaptive focusing on key emotional information. Building upon this, a three-layer cascaded capsule network is constructed to achieve structured modeling of high-order emotional attributes through vector neurons and dynamic routing mechanisms, effectively mitigating the loss of spatial feature information caused by traditional pooling and fully connected structures. Experimental results on a manually annotated online public opinion dataset show that BBMC achieves better performance than the evaluated baseline models in terms of accuracy, recall, and F1-score. These results indicate the empirical effectiveness of the proposed task-oriented feature-integration strategy and capsule-based classification head for online public opinion sentiment analysis. Full article

Farm-scale expansion is widely viewed as a means of improving agricultural efficiency and linking smallholders to modern agriculture. Yet whether it improves the conditions under which smallholders participate in grain production remains unclear. Using panel data from the China Rural Revitalization Survey (CRRS) for 2020 and 2022, this study examines how village-level farm-scale expansion affects smallholder grain production. The results show that a 0.1 increase in village-level farm-scale expansion intensity is associated with a 0.81-percentage-point higher grain-sown share, but without corresponding improvements in production conditions. Farm-scale expansion is also associated with lower mechanization, a lower share of spending on purchased agricultural services, greater reliance on household-owned machinery, and higher family labor input. We describe this pattern as constrained grain-oriented adjustment: an increase in grain-sown share without corresponding improvements in mechanization or external service support, leaving production more dependent on household-based resources. Cooperative membership is associated with less severe mechanization and cost pressures. Overall, a higher grain-sown share under farm-scale expansion does not necessarily imply improved conditions for smallholder grain production. To promote inclusive agricultural modernization, policy efforts should focus not only on farm-scale operations, but also on strengthening smallholders’ access to mechanized, service-based, and organizational support. Full article