Search Results (1,129)

Galaxy morphology combines measurable structural properties with subjective visual interpretation, limiting strictly hard-label classifications. This study proposes a framework designed to compare physically derived and human-based galaxy classifications while explicitly accounting for uncertainty and interpretability. Using photometric and structural features from the Sloan Digital Sky Survey (SDSS), physical groupings are obtained through Fuzzy C-Means clustering, enabling gradual transitions via soft memberships. Human clusters are constructed from Galaxy Zoo 2 debiased vote fractions, capturing aggregated perceptual judgments. Supervised models are trained to predict both physical and human cluster assignments from the same set of physical variables, providing a quantitative assessment of structural coherence and perceptual–physical alignment. SHAP-based explainability identifies the relative influence of color and concentration parameters in each scheme. Results show that physical clustering is driven by structural concentration and bulge dominance, while human classification exhibits smoother decision boundaries and greater sensitivity to photometric appearance. Discrepancies concentrate in transitional and orientation-sensitive systems. An interactive visualization layer supports traceable qualitative inspection. The framework provides a reproducible methodology for analyzing classification consistency, uncertainty, and human–model alignment. Full article

This study advances a configurational perspective on domestic tourism in protected areas by introducing the Applied Cultural Proximity Model (ACPM). While dominant tourism frameworks rely on causal relationships grounded in cultural distance, novelty, and outcome-based evaluation, domestic tourism remains theoretically underdeveloped despite being embedded in shared symbolic systems and cultural familiarity. To address this gap, the study conceptualizes tourism experience as a multidimensional configuration of interrelated dimensions, emphasizing patterns of covariance rather than causal relationships. The ACPM specifies six experiential domains—natural, cultural, administrative, accessibility, complementary, and communication—modeled as a system of covarying latent constructs within culturally proximate contexts. A sequential exploratory mixed-methods design was employed. The qualitative phase supported construct specification, and the quantitative phase analyzed data from 1113 domestic tourists visiting Cotopaxi National Park using Confirmatory Factor Analysis and covariance-based Structural Equation Modeling. Results support a six-dimensional measurement model with satisfactory reliability and validity (CFI = 0.95; RMSEA = 0.064). Significant positive associations among all dimensions indicate a coherent covariance structure. Natural attributes exhibit higher perceptual salience within the covariance structure, while cultural and communication dimensions occupy a central position within the experiential configuration. The study contributes by modeling tourism experience as a relational system and positioning cultural proximity as an interpretive condition, providing a non-causal framework for understanding experiential organization in domestic tourism. Full article

Urban housing rents are central to socioeconomic dynamics and urban sustainability, shaping affordability and quality of life. Existing research largely relies on linear models and focuses on economic, demographic, and locational factors, often neglecting complex nonlinear interactions and the impact of human perceptions. This study introduces a comprehensive, multi-perspective framework that integrates housing attributes, living convenience, competition, location, accessibility, and quantified perceptual metrics using multimodal machine learning. Advanced techniques, including XGBoost, SHAP, Partial Dependence Plots (PDPs), Interpretative Structural Modeling (ISM), and Bayesian Network (BN), capture nonlinearities, interactions, and hierarchical dependencies among rent determinants. Housing attributes and living convenience indicators exert the strongest cumulative influence on rents, while perceptual variables rank third, providing significant, threshold-dependent contributions and explaining up to 21.66% of rent variation. Notable interactions are identified between accessibility, facility density, and perceptual quality. The ISM–BN analysis uncovers multi-level pathways, demonstrating how both environmental features and human perceptions jointly influence rents. This framework offers actionable insights for equitable housing and urban planning policies, supporting data-driven decisions in complex urban rental markets. Full article

Corner detection is a fundamental task in computer vision that plays a critical role in applications such as image registration, 3D reconstruction, and object tracking. In biological visual systems, simple cells in the primary visual cortex exhibit high selectivity to edge stimuli of specific orientations, while end-stopped cells can detect geometric singular structures such as line segment endpoints and corners. Existing corner detection methods based on visual neural computation typically employ a strategy of densely distributed end-stopped cells for corner localization, which suffers from significant localization deviation under small angle conditions due to mutual interference between responses of adjacent neurons. To address this problem, this paper proposes a bionic corner detection method based on cooperative processing of simple cells and end-stopped cells. The method constructs a two-stage cooperative processing framework: the edge filtering stage employs a Gabor filter bank to simulate the orientation selectivity of simple cells, extracting edge positions and orientation information; the dynamic construction stage builds unilateral end-stopped cells only at filtered edge positions based on local orientation information, fundamentally avoiding computational redundancy and response interference caused by global dense distribution; the corner localization stage determines precise corner coordinates through hierarchical clustering and dual-cluster centroid fusion strategies. Experimental results demonstrate that, in the 15° acute-angle regime where dense end-stopped schemes are most severely affected by response interference, the proposed method reduces the mean localization error from 8.76 to 2.34 pixels, corresponding to a 73.3% improvement; averaged across the eight tested angle levels from 15° to 165°, the improvement is approximately 40.9%, and all per-angle differences are statistically significant (paired t-test, p < 0.01 or below, N = 10 independent runs). On standard test images, the method attains the lowest mean localization error among the eight compared detectors (1.58 pixels, versus 1.68–3.42 pixels for Harris, FAST, COSFIRE, KAZE, SuperPoint, Deep Corner, and Wei et al.), while maintaining competitive detection rate, false-alarm rate, and runtime. Physiological plausibility validation experiments show that the correlation coefficient between the detection deviation of this method and human perceptual deviation reaches 0.923, indicating that the output of the framework aligns with previously reported human perceptual bias patterns and supporting its biological plausibility as a biologically inspired—rather than mechanistic—model of corner perception. The source code, dataset, and experimental results are publicly available (see Data Availability Statement). Full article

The construction sector in Saudi Arabia is under increasing pressure to enhance productivity and technological capability in line with Vision 2030, yet the adoption of artificial intelligence (AI) remains uneven. This study investigates the multi-level barriers affecting AI adoption in the Saudi construction industry using a sequential explanatory design that combines large-scale survey analysis with Interpretive Structural Modeling (ISM) and MICMAC classification. Data were collected from 181 construction professionals through a structured questionnaire covering eight constructs and 50 measurement items. Descriptive statistics reveal moderate AI utilization with a clear preference for analytics-driven applications over physical automation technologies. Perceptual rankings identify trust deficits and workforce capability gaps as prominent concerns. However, the ISM hierarchy uncovers a different structural reality: limited government support emerges as the root driver, cascading through cost and leadership constraints into workforce deficiencies, attitudinal resistance, and ultimately data ecosystem challenges. This perception–structure divergence highlights the risk of prioritizing visible symptoms over foundational causes. The MICMAC analysis further confirms the dominance of policy and strategic drivers within the adoption system. The study contributes by providing one of the first hierarchical mappings of AI adoption barriers in the Saudi construction context and offers a phased intervention roadmap for policymakers and industry leaders. The findings emphasize that sustainable AI diffusion in government-influenced construction ecosystems requires coordinated action across regulatory, organizational, and human capital dimensions rather than isolated technical investments. Full article

Historical rubbing images often suffer from stroke breakage, material loss, uneven background interference, and age-related degradation, which make broken-stroke restoration in masked regions difficult. This challenge is particularly severe for oracle bone rubbings, where sparse strokes and damaged radicals require both structural continuity and local texture realism. To address this problem, we propose a three-stage generative adversarial image inpainting framework and evaluate it on oracle bone rubbing images as a focused case study. Stage I employs an LBP-guided coarse completion network to recover local binary texture priors in missing regions. Stage II introduces spatial-attention refinement and a dual-discriminator strategy to improve stroke continuity and local realism. Stage III uses a Swin-based refinement network to model long-range dependencies and enhance global consistency. A composite optimization objective combining reconstruction, weighted hole, perceptual, style, total-variation, and adversarial terms is used to coordinate the three stages. Experiments on oracle bone rubbing images with masking ratios from 10% to 40% show that the proposed framework produces visually coherent restorations and competitive quantitative results, reaching up to 35.18 dB PSNR and 0.9906 SSIM under the 10–20% masking setting. Because oracle bone glyph morphology is highly specialized, the current validation is intentionally restricted to this domain rather than overstating cross-domain generalization. The results show that the proposed framework can support digital conservation and recognition-oriented analysis of damaged oracle bone rubbing images. Full article

Background/Objectives: As artificial intelligence systems become increasingly embedded in everyday cognitive tasks, human–AI interaction is no longer limited to tool use but evolves into a dynamic process of mutual adaptation. While extensive research has examined algorithmic learning, far less attention has been given to how users progressively adapt to AI systems. This paper introduces the concept of algorithmic habituation, defined as the gradual accommodation of users to the regularities and predictive patterns of AI systems. The objective is to provide a neurocognitive and systems-based framework that explains this phenomenon. Methods: The study develops a conceptual and integrative framework grounded in classical theories of habituation, neuroplasticity, predictive processing, and systems theory. Building on these foundations, we propose a mechanistic model of human–AI co-adaptation, conceptualized as a recursive feedback loop involving repeated interaction, pattern recognition, expectation stabilization, and cognitive economy. In addition, a typology of algorithmic habituation is advanced, alongside proposed empirical pathways for future validation, including scale development, experimental paradigms, and longitudinal designs. Results: The proposed framework suggests that repeated interaction with AI systems leads to stabilization of cognitive expectations, reduced cognitive effort, and increased behavioral standardization. This process extends beyond perceptual habituation into higher-order domains, including decision-making, creativity, and moral judgment. The typology identifies four primary forms of algorithmic habituation: cognitive, decisional, creative, and moral. The model predicts both adaptive outcomes (efficiency, reduced cognitive load) and maladaptive consequences (reduced reflexivity, automation bias, and potential erosion of critical thinking). Conclusions: Algorithmic habituation represents a novel construct at the intersection of neuroscience, cognitive psychology, and human–AI interaction. By framing user adaptation as a form of neurocognitively grounded habituation within recursive systems, this paper contributes a new perspective to understanding AI integration in human cognition. The framework has implications for digital wellbeing, education, and AI ethics, and opens multiple avenues for empirical research. Full article

Background/Objectives: While human epidermal growth factor receptor 2 (HER2) immunohistochemistry (IHC) is pivotal for breast cancer management, its reliance on additional tissue processing beyond routine H&E staining remains a clinical burden. Although virtual staining offers a potential solution, current methods often fail to explicitly account for HER2 score-specific expression patterns. To address this gap, we developed a score-aware framework designed for the precise generation of virtual HER2 IHC images. Methods: We introduce the non-contrastive multi-task (NCMT) framework, which integrates negative-free patch alignment, style–content constraints, and auxiliary HER2 score supervision for high-fidelity H&E-to-IHC translation. For rigorous evaluation, the model was validated on the BCI dataset, utilizing an official split of 3896 training and 977 independent test images derived from 51 whole-slide images. Results: NCMT demonstrated superior virtual staining performance, achieving a Fréchet Inception Distance (FID) of 38.8, a Kernel Inception Distance (KID) of 5.6, and an average Perceptual Hash Value (PHV) of 0.439. In downstream HER2 scoring tasks, while virtual IHC images alone yielded an accuracy of 83.01%, the fusion of H&E and virtual IHC further elevated performance to 97.85% accuracy and a 98.23% F1 score. These findings suggest that our framework effectively preserves diagnostic features while providing complementary information to H&E-based morphological analysis. Conclusions: NCMT enables HER2 score-aware virtual IHC generation from H&E and can serve as a complementary tool for HER2 assessment in digital pathology. Full article

Background: The development of robust artificial intelligence (AI) models for diagnosing Temporomandibular Disorders (TMDs) is severely constrained by data scarcity and patient privacy regulations. Cone-beam computed tomography (CBCT), the gold standard for assessing osseous changes in the temporomandibular joint (TMJ), inherently contains sensitive biometric facial features, making de-identification difficult without losing critical anatomical information. This study aims to develop and evaluate TMJCTGenerator, a specialized latent diffusion model (LDM) framework designed to synthesize high-fidelity, diverse, and anonymous TMJ CBCT images. We hypothesize that this LDM approach can achieve superior anatomical fidelity and diversity compared to traditional generative adversarial network (GAN)- and variational autoencoder (VAE)-based methods, specifically in capturing fine osseous details within sagittal and coronal views of the mandibular condyle. Methods: A training dataset comprising 348 anonymized CBCT volumes was obtained in this retrospective comparative study to extract high-resolution sagittal and coronal regions of interest of the mandibular condyle. An independent test set of 39 anonymized CBCT volumes was further included. We developed a class-conditional LDM that integrates a pre-trained VAE for perceptual compression with a conditional U-Net for iterative denoising in the latent space. Performance was evaluated via qualitative anatomical fidelity assessment, Fréchet Inception Distance (FID), and a blinded Visual Turing test conducted by experienced clinicians to determine the distinguishability of synthetic images from real data. Results: Qualitative analysis revealed that TMJCTGenerator produced images with superior sharpness and anatomical consistency compared to baseline models, successfully reconstructing fine bone structures essential for diagnosing degenerative joint disease. TMJCTGenerator achieved lower FID scores than both VAE and GAN baselines. In the visual Turing test, clinicians were unable to reliably distinguish the generated images from real scans, and non-inferiority analysis confirmed that the synthetic data were statistically non-inferior to real data. Furthermore, TMJCTGenerator demonstrated the capability to generate diverse pathological conditions, ranging from normal anatomy to severe osteoarthritic changes. Conclusions: The proposed LDM framework effectively addresses the data scarcity and privacy bottlenecks in TMJ AI research by generating realistic, fully anonymous medical imaging data. TMJCTGenerator outperforms traditional generative methods in both visual fidelity and diversity, offering a viable solution for training downstream diagnostic algorithms. The source code and pre-trained models of TMJCTGenerator have been made open-source. Full article

Remote sensing images are susceptible to atmospheric scattering, imaging conditions, and post-processing strategies during actual acquisition, resulting in issues such as low contrast, insufficient color saturation, and overall poor visual quality. These problems significantly degrade the color quality and expressiveness of the imagery. To address these issues, a prior-guided conditional diffusion enhancement framework (PGCDE) is proposed in this paper. First, an unconditional diffusion model is built upon large-scale natural images to extract stable color priors. Then, these prior features are dynamically injected into the conditional enhancement network through an adaptive hierarchical feature fusion (AHFF) module, with a multi-domain joint loss introduced during training to constrain structural consistency. Finally, at the inference stage, a luminance-decoupled multi-scale fusion strategy is employed to recombine the generated low-frequency color tones with the high-frequency textures of the original image. Experiments on the GID-5 and LoveDA datasets demonstrate that the proposed method outperforms existing representative approaches, providing a practical solution for remote sensing image color quality enhancement that balances perceptual improvement with structure preservation. Full article

Cognitive models of depression posit a core role for attentional biases, though empirical evidence remains inconsistent, likely due to variations in task demands. This study utilized eye-tracking to assess attentional patterns in individuals with depressive symptoms during a goal-directed visual search task, specifically dissociating early orienting and late disengagement. Seventy-seven participants, classified into high (HD) and low (LD) depressive-symptom groups based on PHQ-9 scores, completed a “face-in-the-crowd” (FITC) task. The set size (4, 8, or 12 faces) was varied to examine the role of perceptual load. The task involved searching for a single emotional target among neutral distractors (assessing early orienting) and searching for a single neutral target among emotional distractors (assessing late disengagement). Contrary to the negativity-bias hypothesis, the HD group demonstrated what might be interpreted as adaptive attentional regulation. During early orienting (8-face condition), the HD group showed reduced total dwell time on happy targets, suggesting accelerated identification. An attentional bias index (sad minus happy dwell time) correlated positively with depression severity. During late disengagement (8-face condition), the HD group exhibited shorter target fixation latency specifically with sad distractors, indicating facilitated disengagement from negative information. The corresponding bias index correlated negatively with depression levels. Under explicit goal-directed demands, individuals with high depressive symptoms displayed facilitated processing of happy faces and accelerated disengagement from sad faces, rather than an enhanced negativity bias. This pattern tentatively suggests a possible adaptive attentional regulatory mechanism in early depression, although the findings were limited to the 8-face condition and no significant group differences emerged at set sizes 4 or 12. Replication is required before firm conclusions can be drawn. The result underscores the critical influence of task demands and highlights the value of early identification and targeted intervention. Full article

Underwater images often suffer from mixed degradations, including motion blur, which reduce structural clarity and adversely affect downstream vision tasks. To address this problem, we propose a physically guided Transformer framework for underwater motion deblurring. The proposed method combines two-stage cepstrum-based blur estimation with a point spread function (PSF)-guided self-attention mechanism. Specifically, blur parameters are first robustly estimated through cepstrum analysis, ellipse fitting, and negative-peak refinement, and the resulting PSF is then embedded into the Transformer attention module to guide feature aggregation. On the real underwater benchmark datasets UIEB Challenge-60 and EUVP330, the proposed method achieves UIQM/UCIQE scores of 4.09/0.56 and 3.40/0.58, respectively, significantly outperforming UFPNet and Phaseformer, thereby demonstrating superior perceptual restoration in terms of sharpness, contrast, and color consistency. On the synthetic test set, the proposed method attains 24.23 dB PSNR and 0.918 SSIM, outperforming both recent deep models and classical non-blind deconvolution methods, which confirms its strong restoration fidelity and structural consistency. In the controlled water-tank experiments, the proposed method consistently achieves the best performance under different camera motion speeds, demonstrating excellent robustness and practical applicability. Overall, the proposed framework provides an effective and physically interpretable solution for underwater motion deblurring. Full article

Driven by the iteration of digital technologies and the upgrading of residents’ health consumption demands, smart food packaging has developed rapidly and is widely applied across various food categories. However, issues such as consumer cognitive biases and insufficient acceptance hinder its market penetration. This paper constructs a chained mediation model based on the Theory of Planned Behavior (TPB) and Perceived Value Theory, employing PLS-SEM and IPMA methods to validate multiple research hypotheses. It innovatively integrates multiple theories to establish an interdisciplinary research framework, overcoming the limitations of single theories. The analysis, combined with IPMA, clarifies the priority of each variable, addressing existing research gaps. The results indicate that the four perceptual factors of smart food packaging significantly and positively influence the three core constructs of TPB, with experiential factors exerting the strongest drive on individual needs. The TPB constructs significantly and positively affect perceived value, perceived trust, and self-efficacy, with the drive of individual needs being most prominent. Perceived trust has the strongest influence on healthy eating behavior. IPMA analysis reveals that perceived value (PV) is a key area for improvement, while individual needs (IN) and self-efficacy (SEHB) are key areas of strength. This study elucidates the internal mechanisms through which smart food packaging influences consumers’ healthy eating behaviors, providing theoretical and practical support for enterprises to optimize design and guide healthy consumption. Full article

The deployment of grid-forming microgrids has attracted growing attention as a pathway toward improving energy system resilience and supporting low-carbon transitions in decentralized power systems. However, the relative influence of distinct stakeholder groups on microgrid development performance remains inadequately understood in the extant literature. Grounded in stakeholder theory and informed by behavioral economics, this study develops and empirically tests a stakeholder-based framework that examines the effects of government support, investor participation, user acceptance, and utility participation on microgrid development performance. Survey data were collected from 200 stakeholders engaged in microgrid-related activities and analyzed using consistent Partial Least Squares Structural Equation Modeling (PLS-SEM). The structural model accounts for a substantial proportion of the variance in microgrid development performance (R² = 0.647). The quantitative results indicate that all four stakeholder constructs exert statistically significant positive effects on microgrid development performance. Investor participation emerges as the strongest driver (β = 0.399, p < 0.001), followed by user acceptance (β = 0.190, p < 0.001), government support (β = 0.175, p = 0.015), and utility participation (β = 0.170, p = 0.003). Interpreted through a behavioral economics lens, these findings demonstrate that development performance is governed primarily by behavioral and perceptual factors, namely capital confidence, risk tolerance, and demand-side acceptance, rather than by technical preparedness alone. Conventional assumptions of linear adoption driven by technical superiority are therefore insufficient to account for observed development outcomes in complex, decentralized energy systems. This study advances a stakeholder-centered and behaviorally grounded understanding of grid-forming microgrid development and offers empirical guidance for designing governance frameworks that align regulatory structures with market and user behavioral dynamics. Full article

Leaf Area Index is a crucial biophysical variable, and its accurate estimation is essential for understanding vegetation dynamics. However, cloud cover significantly restricts optical remote sensing, hindering the generation of spatially continuous Leaf Area Index products. Remote sensing foundation models offer novel solutions to this challenge. This study presents an end-to-end framework based on the fine-tuned Prithvi foundation model for direct LAI retrieval from cloud-contaminated 30 m Harmonized Landsat and Sentinel-2 imagery. By mapping inputs directly to Hi-GLASS reference labels, the proposed architecture processes cloud contamination and vegetation signals simultaneously and circumvents the error propagation inherent in cascaded retrieval pipelines. Results demonstrate that the end-to-end LAI retrieval model significantly outperforms cascaded variants, achieving a superior R² (0.78) and lower RMSE (0.57). Furthermore, predictive accuracy exhibits a distinct U-shaped trajectory relative to the temporal mean cloud fraction, reaching an inflection point at 50–60% occlusion, which highlights the model’s implicit regularization capacity under severe atmospheric interference. This work establishes that direct feature learning with foundation models offers a more robust and streamlined pathway for generating continuous biophysical products from imperfect optical observations, prioritizing quantitative fidelity over artificial perceptual sharpness. Full article