Search Results (1,125)

Artistic style transfer aims to transfer the style of an artwork to a photograph while maintaining its original overall content. Although current style-transfer methods have achieved promising results when processing photorealistic images, they often struggle with brushstroke preservation in artworks, especially in styles such as oil painting and pointillism. In such cases, the extracted style and content features tend to include redundant information, leading to issues such as blurred edges and a loss of fine details in the transferred images. To address this problem, this paper proposes a multi-scale general style-transfer network based on diffusion models. The proposed network consists of a coarse style-transfer module and a refined style-transfer module. First, the coarse style-transfer module is designed to perform mainstream style-transfer tasks more efficiently by operating on downsampled images, enabling faster processing with satisfactory results. Next, to further enhance edge fidelity, a refined style-transfer module is introduced. This module utilizes a segmentation component to generate a mask of the main subject in the image and performs edge-aware refinement. This enhances the fusion between the subject’s edges and the target style while preserving more detailed features. To improve overall image quality and better integrate the style along the content boundaries, the output from the coarse module is upsampled by a factor of two and combined with the subject mask. With the assistance of ControlNet and Stable Diffusion, the model performs content-aware edge redrawing to enhance the overall visual quality of the stylized image. Compared with state-of-the-art style-transfer methods, the proposed model preserves more edge details and achieves more natural fusion between style and content. Full article

The design and planning of Western-style constructions during the early Qing Dynasty in China constituted a significant multicultural encounter that fused technological advancement with aesthetic innovation. This cultural interplay is particularly evident in the imperial garden and pavilion projects commissioned by the Qing court, which served as physical and symbolic sites of cross-cultural dialogue. Influenced by the intellectual and artistic movements of the European Renaissance, Western architectural concepts gradually found their way into the spatial and visual language of Chinese architecture, especially within the royal gardens and aristocratic buildings of the time. These structures were not simply imitative but rather represented a selective adaptation of Western ideas to suit Chinese imperial tastes and principles. This article examines the architectural language that emerged from this encounter between Chinese and European cultures, analysing symbolic motifs, spatial design, ornamental aesthetics, the application of linear perspective, and the integration of foreign architectural forms. These elements collectively functioned as tools to construct a unique visual discourse that communicated both political authority and cultural hybridity. The findings underscore that this architectural phenomenon was not merely stylistic imitation, but rather a dynamic convergence of technological knowledge and artistic vision across cultural boundaries. Full article

The increasing reliance on Medical Internet of Things (MIoT) devices introduces critical cybersecurity vulnerabilities, necessitating advanced, adaptive defense mechanisms. Recent cyber incidents—such as compromised critical care systems, modified therapeutic device outputs, and fraudulent clinical data inputs—demonstrate that these threats now directly impact life-critical aspects of patient security. In this paper, we introduce a machine learning-enabled Cognitive Cyber-Physical System (ML-CCPS), which is designed to identify and respond to cyber threats in MIoT environments through a layered cognitive architecture. The system is constructed on a feedback-looped architecture integrating hybrid feature modeling, physical behavioral analysis, and Extreme Learning Machine (ELM)-based classification to provide adaptive access control, continuous monitoring, and reliable intrusion detection. ML-CCPS is capable of outperforming benchmark classifiers with an acceptable computational cost, as evidenced by its macro F1-score of 97.8% and an AUC of 99.1% when evaluated with the ToN-IoT dataset. Alongside classification accuracy, the framework has demonstrated reliable behaviour under noisy telemetry, maintained strong efficiency in resource-constrained settings, and scaled effectively with larger numbers of connected devices. Comparative evaluations, radar-style synthesis, and ablation studies further validate its effectiveness in real-time MIoT environments and its ability to detect novel attack types with high reliability. Full article

In the context of Industry 4.0, user demands for industrial robots have shifted toward diversification and experience-orientation. Effectively integrating users’ affective imagery requirements into industrial-robot form design remains a critical challenge. Traditional methods rely heavily on designers’ subjective judgments and lack objective data on user cognition. To address these limitations, this study develops a comprehensive methodology grounded in Kansei engineering that combines Extenics-based semantic analysis, eye-tracking experiments, and user imagery evaluation. First, we used web crawlers to harvest user-generated descriptors for industrial floor-cleaning robots and applied Extenics theory to quantify and filter key perceptual imagery features. Second, eye-tracking experiments captured users’ visual-attention patterns during robot observation, allowing us to identify pivotal design elements and assemble a sample repository. Finally, the semantic differential method collected users’ evaluations of these design elements, and correlation analysis mapped emotional needs onto stylistic features. Our findings reveal strong positive correlations between four core imagery preferences—“dignified,” “technological,” “agile,” and “minimalist”—and their corresponding styling elements. By integrating qualitative semantic data with quantitative eye-tracking metrics, this research provides a scientific foundation and novel insights for emotion-driven design in industrial floor-cleaning robots. Full article

Background/Objectives: Chronic inflammation and Western-style diets elevate colorectal cancer (CRC) risk, particularly in individuals with colitis, a feature of inflammatory bowel disease (IBD). Diets rich in polyphenol-containing functional foods, such as cocoa, may reduce gut inflammation and modulate the gut microbiome. This study investigated the impact of cocoa polyphenol (CP) supplementation on inflammation and microbiome composition in mice with colitis, fed either a healthy or Western diet, before, during, and after the onset of disease. We hypothesized that CPs would attenuate inflammation and promote distinct shifts in the microbiome, especially in the context of a Western diet. Methods: A 2 × 2 factorial design tested the effects of the basal diet (AIN93G vs. total Western diet [TWD]) and CP supplementation (2.6% w/w CocoaVia™ Cardio Health Powder). Inflammation was induced using the AOM/DSS model of colitis. Results: CP supplementation did not reduce the severity of colitis, as measured by disease activity index or histopathology. CPs did not alter gene expression in healthy tissue or suppress the colitis-associated pro-inflammatory transcriptional profile in either of the two diet groups. However, fecal microbiome composition shifted significantly with CPs before colitis induction, with persistent effects on several rare taxa during colitis and recovery. Conclusions: CP supplementation did not mitigate inflammation or mucosal injury at the tissue level, nor did it affect the expression of immune-related genes. While CPs altered microbiome composition, most notably in healthy mice before colitis, these shifts did not correspond to changes in inflammatory signaling. Basal diet remained the primary determinant of inflammation, mucosal damage, and colitis severity in this model. Full article

In advanced Complementary Metal-Oxide-Semiconductor (CMOS) technologies, where diminished feature sizes amplify radiation-induced soft errors, the optimization of fault-tolerant circuit designs requires detailed transistor-level analysis of reliability–performance trade-offs. As a fundamental building block in digital systems and critical data paths, the 2:1 multiplexer, widely used in data-path routing, clock networks, and reconfigurable systems, provides a critical benchmark for assessing radiation-hardened design methodologies. In this context, this work aims to analyze the power consumption, area overhead, and delay of 2:1 multiplexer designs under transient fault conditions, employing the CMOS and Differential Cascode Voltage Switch Logic (DCVSL) logic styles and mitigation strategies. Electrical simulations were conducted using 32 nm high-performance predictive technology, evaluating both the original circuit versions and modified variants incorporating three mitigation strategies: transistor sizing, D-Cells, and C-Elements. Key metrics, including power consumption, delay, area, and radiation robustness, were analyzed. The C-Element and transistor sizing techniques ensure satisfactory robustness for all the circuits analyzed, with a significant impact on delay, power consumption, and area. Although the D-Cell technique alone provides significant improvements, it is not enough to achieve adequate levels of robustness. Full article

Educational institutions are facing a crisis characterized by the need to address diverse learning styles and vocational aspirations, exacerbated by ongoing financial pressures. To navigate these challenges effectively, there is an urgent need to innovate educational practices and learning environments, ensuring they are adaptable and responsive to the evolving needs of students and the workforce. The adoption of the Industry 5.0 framework offers a promising solution, providing a holistic approach that emphasizes the integration of human creativity and advanced technologies to transform educational institutions into resilient, human-centric, and sustainable learning environments. In this context, this article presents a transdisciplinary methodology that integrates Asset Management (AM) with Social Innovation (SI) through Design Thinking (DT) to co-design Educational Facilities 5.0 with stakeholders. The application of the proposed approach in an AgroLab case study—a food and agricultural laboratory—demonstrates how the methodology enables the definition of an Educational Facility 5.0 and generates AM Design Knowledge to support informed decision-making in the subsequent design, implementation, and operation phases. Following DT principles—where knowledge emerges through iterative experimentation and insights from practical applications—this article also discusses the role of SI and DT in AM, the role of Large Language Models in convergent processes, and a vision for Educational Facilities 5.0. Full article

The research was initiated by the Plečnik House gallery (Ljubljana, Slovenia) and focuses on the 3D architectural reconstruction of the unrealised monument of the Czech military leader Jan Žižka, designed by the Slovenian architect Jože Plečnik. In addition, the experience with the 3D reconstructed monument in the exhibition “Plečnik and the Sacred” was analysed. Using the available references and interpretative approaches, a digital and 3D-printed reconstruction was created that retains Plečnik’s architectural style. The experimental phase included a detailed interpretation of the studied references, 3D modelling, 3D printing, exhibition and experience analysis. The dimensions of the finished 3D-printed model are 52.80 × 55.21 × 44.60 cm. It was produced using stereolithography (SLA) for figurative elements and fused deposition modelling (FDM) for architectural components. The reconstruction was evaluated using participant testing, including semantic differential analysis, comparative studies, and knowledge-based questionnaires. The results showed that architectural elements were reconstructed with an average similarity score of 1.97 out of 5. Statues followed with a score of 1.81, and props, though detailed, met audience expectations, scoring 1.61. Clothing received the lowest score of 1.40. This research emphasises the importance of a hypothetical digital 3D reconstruction of never constructed monument for broader understanding of Plečnik’s legacy. Full article

VisRep (Visualisation Report) is an AI-powered system for capturing and structuring the early stages of the visualisation design process. It addresses a critical gap in predesign: the lack of tools that can naturally record, organise, and transform raw ideation, spoken thoughts, sketches, and evolving concepts into polished, shareable outputs. Users engage in talk-aloud sessions through a terminal-style interface supported by intelligent transcription and eleven structured questions that frame intent, audience, and output goals. These inputs are then processed by a large language model (LLM) guided by markdown-based output templates for reports, posters, and slides. The system aligns free-form ideas with structured communication using prompt engineering to ensure clarity, coherence, and visual consistency. VisRep not only automates the generation of professional deliverables but also enhances reflective practice by bridging spontaneous ideation and structured documentation. This paper introduces VisRep’s methodology, interface design, and AI-driven workflow, demonstrating how it improves the fidelity and transparency of the visualisation design process across academic, professional, and creative domains. Full article

Heterogeneous remote sensing images, acquired from different sensors, exhibit significant variations in data structure, resolution, and radiometric characteristics. These inherent heterogeneities present substantial challenges for change detection, a task that involves identifying changes in a target area by analyzing multi-temporal images. To address this issue, we propose the Multi-Head Graph Attention Mechanism (MHGAN), designed to achieve accurate detection of surface changes in heterogeneous remote sensing images. The MHGAN employs a bidirectional adversarial convolutional autoencoder network to reconstruct and perform style transformation of heterogeneous images. Unlike existing unidirectional translation frameworks (e.g., CycleGAN), our approach simultaneously aligns features in both domains through multi-head graph attention and dynamic kernel width estimation, effectively reducing false changes caused by sensor heterogeneity. The network training is constrained by four loss functions: reconstruction loss, code correlation loss, graph attention loss, and adversarial loss, which together guide the alignment of heterogeneous images into a unified data domain. The code correlation loss enforces consistency in feature representations at the encoding layer, while a density-based kernel width estimation method enhances the capture of both local and global changes. The graph attention loss models the relationships between features and images, improving the representation of consistent regions across bitemporal images. Additionally, adversarial loss promotes style consistency within the shared domain. Our bidirectional adversarial convolutional autoencoder simultaneously aligns features across both domains. This bilateral structure mitigates the information loss associated with one-way mappings, enabling more accurate style transformation and reducing false change detections caused by sensor heterogeneity, which represents a key advantage over existing unidirectional methods. Compared with state-of-the-art methods for heterogeneous change detection, the MHGAN demonstrates superior performance in both qualitative and quantitative evaluations across four benchmark heterogeneous remote sensing datasets. Full article

Federated learning (FL) struggles under non-IID client data when local models drift toward conflicting optima, impairing global convergence and performance. We introduce entropy-regularized federated optimization (ERFO), a lightweight client-side modification that augments each local objective with a Shannon entropy penalty on the per-parameter update distribution. ERFO requires no additional communication, adds a single-scalar hyperparameter $\lambda$, and integrates seamlessly into any FedAvg-style training loop. We derive a closed-form gradient for the entropy regularizer and provide convergence guarantees: under $\mu$-strong convexity and L-smoothness, ERFO achieves the same $O(1/T)$ (or linear) rates as FedAvg (with only $O\left(\lambda \right)$ bias for fixed $\lambda$ and exact convergence when ${\lambda }_t\to 0$); in the non-convex case, we prove stationary-point convergence at $O(1/T)$. Empirically, on five-client non-IID splits of the UNSW-NB15 intrusion-detection dataset, ERFO yields a +1.6 pp gain in accuracy and +0.008 in macro-F1 over FedAvg with markedly smoother dynamics. On a three-of-five split of PneumoniaMNIST, a fixed $\lambda$ matches or exceeds FedAvg, FedProx, and SCAFFOLD—achieving 90.3% accuracy and 0.878 macro-F1—while preserving rapid, stable learning. ERFO’s gradient-only design is model-agnostic, making it broadly applicable across tasks. Full article

In novels, readers encounter a variety of characters with distinct personalities, and their satisfaction tends to increase when each character’s utterances consistently reflect their unique traits. Recent advances in large language model (LLM) technology have made it possible to perform complex tasks such as generating long-form narratives and adapting writing styles. However, research on generating character utterances that reflect individual personalities remains limited. In this paper, we identify a key challenge in this task, namely the unconscious influence of the author’s writing style, and propose a novel clustering-based method to mitigate this problem by tuning large language models. We manually annotated Big Five personality trait scores for characters appearing in selected novels and designed prompts to generate examples for instruction-tuning. Experimental results demonstrate that language models trained using our proposed method produce utterances that more consistently reflect character personalities compared to untuned models. Full article

Throughout history, communities have struggled to build homes in places actively hostile to their presence, a challenge long faced by African descendants in the American diaspora. In cities across the U.S., including Washington, D.C., efforts have often been made to erase Black cultural identity. D.C., once a hub of Black culture, saw its urban fabric devastated during the 1968 riots following Dr. Martin Luther King Jr.’s assassination. Since then, redevelopment has been slow and, more recently, marked by gentrification, which has further displaced Black communities. Amid this context, Black architects such as Michael Marshall, FAIA, and Sean Pichon, AIA, have emerged as visionary leaders. Their work exemplifies Value-Inclusive Design and aligns with Roberto Verganti’s Design-Driven Innovation by embedding cultural relevance and community needs into development projects. These architects propose an intentional approach that centers Black identity and brings culturally meaningful businesses into urban redevelopment, shifting the paradigm of design practice in D.C. This collective case study (methodology) argues that their work represents a distinct architectural style, Black Modernism, characterized by cultural preservation, community engagement, and spatial justice. This research examines two central questions: Where does Black Modernism begin, and where does it end? How does it fit within and expand beyond the broader American Modernist architectural movement? It explores the consequences of the destruction of Black communities, the lived experiences of Black architects, and how those experiences are reflected in their designs. Additionally, the research suggests that the work of Black architects aligns with heutagogical pedagogy, which views community stakeholders not just as beneficiaries, but as educators and knowledge-holders in architectural preservation. Findings reveal that Black Modernism, therefore, is not only a design style but a method of reclaiming identity, telling untold histories, and building more inclusive cities. Full article

Indoor radon constitutes a public health issue in various regions across the United States as the second leading cause of lung cancer following tobacco smoke. The U.S. Environmental Protection Agency advises radon mitigation interventions for residential buildings with indoor radon concentrations exceeding the threshold level of 4 pCi/L. Despite considerable research assessing the technical effectiveness of radon mitigation systems, there remains a gap in understanding their broader influence on occupant behavior and preferences in residential design. This study aims to investigate the impact of residing in radon-mitigated homes within the Commonwealth of Kentucky—an area known for elevated radon concentrations—on occupants’ preferences regarding healthy home design attributes. The objectives of this research are twofold: firstly to determine if living in radon-mitigated homes enhances occupant awareness and consequently influences their preferences toward health-related home attributes and secondly to quantitatively evaluate and compare the relative significance homeowners assign to health-related attributes such as indoor air quality, thermal comfort, and water quality relative to conventional attributes including home size, architectural style, and neighborhood quality. The overarching purpose is to explore the potential role radon mitigation initiatives may play in motivating occupants towards healthier home construction and renovation practices. Using choice-based conjoint (CBC) analysis, this paper compares preferences reported by homeowners from radon-mitigated homes against those from non-mitigated homes. While the findings suggest a relationship between radon mitigation and increased preference for indoor air quality, the cross-sectional design limits causal interpretation, and the possibility of reverse causation—where health-conscious individuals are more likely to seek mitigation—must be considered. The results provide novel insights into how radon mitigation efforts might effectively influence occupant priorities towards integrating healthier design elements in residential environments. Full article

Grief reactions among relatives of palliative care patients are often overlooked, with most interventions targeting Prolonged Grief Disorder (PGD) rather than its prevention. Few interventions have been developed for individuals at risk. This study aimed to evaluate the efficacy of Empower-Grief, a selective intervention designed to address early problematic grief reactions and to explore predictors of its effectiveness. This exploratory randomized controlled trial (RCT) compared Empower-Grief with Treatment as Usual (TAU) among relatives or caregivers of palliative and oncological patients at risk of developing PGD. A total of 46 participants were assessed at baseline, post-intervention, and six months later. The primary outcome was PGD symptoms, with additional measures including anxiety, depression, coping strategies, attachment style, psychological flexibility, post-traumatic growth, social support, and therapeutic alliance. The final analyses indicate equivalence between Empower-Grief and TAU, suggesting that both interventions yielded comparable outcomes in reducing PGD symptoms and associated psychological distress. The initial symptoms and therapeutic alliance were predictors of the results in both post- and follow-up moments. This study contributes to the evidence on grief interventions in palliative care, highlighting the importance of structured support for bereaved caregivers. While Empower-Grief demonstrated comparable effectiveness to TAU, its lower intensity, ease of training, and application make it a promising treatment option. Full article