Search Results (332)

The study presents the experiences of emerging artists from the Republic of Moldova in the field of comic strips between 2000 and 2025. Comic strip creation has undergone various periods of development, as well as periods of stagnation. The evolution of current technologies, with their implications for the visual arts, alongside contemporary societal imperatives and challenges, is shaping a new attitude amongst young readers towards documentation, information and knowledge. Drawing on current global trends regarding the relevance of publications with sequential graphic content, the aim of this article is to map the new visual discourses of emerging comic strip creators in the Republic of Moldova, documenting their stylistic and thematic contributions, as well as the role of digital technologies in their creative process. The objectives focus on analysing the artistic development, visual language and messages of key figures such as Octavian Curoșu, Cristina Grati, Alex Buretz, Inna Sacali, Călin Enachi, Valentin Enachi and others. The methodology combines case studies with stylistic and semiotic analysis, examining the structure of the comics, their layout and the relationship between text and image. The results reveal significant diversity. Octavian Curoșu combines the sensitivity of traditional techniques with digital media, addressing historical and socio-cultural themes. The artist Alex Buretz uses caricature and the grotesque as tools for civic activism and social criticism. Comic strip artists Cristiana Grati and Inna Sacali explore the world of family life and human relationships, using an expressive digital style and colour palettes that are either evocative or decorative. Graphic designers Călin and Valentin Enachi bring a youthful and adventurous touch to the narrative images, with experimental and original page layouts. All the artists featured make full use of specialised software (Adobe Photoshop, Clip Studio Paint, Procreate and others) and graphics tablets, modernising the creative process. The use of these tools has enabled the emerging artists to emulate traditional techniques, as well as create complex visual effects, optimising work on layers and facilitating publication on online platforms. Thus, through stylistic diversity and digital adaptation, these artists are strengthening the presence of comic strips within the cultural landscape, confirming a vibrant and modern revival of comic art in the Republic of Moldova. Full article

Architectural colour in public libraries significantly influences indoor atmospheric quality and occupant emotional well-being, yet palette selection in design practice remains predominantly reliant on subjective intuition, precedents, and vendor catalogues. This study develops a computational framework that links multimodal image colour extraction, circular clustering, human perceptual evaluation, and multi-criteria decision weighting into a structured analysis pipeline. Using a corpus of 500 curated library interior images across diverse geographic regions, a multimodal large language model extracted dominant architectural colour records and visual weights, which were deterministically converted to HSV space and clustered using weighted circular k-means. A semantic differential experiment involving fifty design students across five bipolar scales provided empirical perceptual ratings, evaluated through repeated nested ten-fold cross-validation across five machine learning algorithms. The analysis identified five recurrent colour paradigms, demonstrating that spatial hue distributions form distinct chromatic clusters across modern library architecture. Perceptual models revealed that average colour features reliably predict perceived warmth and quietness, whereas modernity and naturalness depend more heavily on non-chromatic spatial cues. By integrating these predictive models with analytic hierarchy process and Delphi expert weights, the framework establishes a transparent decision-support protocol for early schematic design. This allows architects and clients to quantitatively compare alternative colour schemes against functional zoning requirements, providing an objective, accountable foundation for evidence-based interior design. Full article

Urban waterfront spaces that are ecologically sensitive often remain visually prominent yet inaccessible, posing a management challenge: how to generate public engagement and economic value without physical access. This paper proposes a four-stage socio-ecological framework integrating sensory restoration, behavioral transformation, economic generation, and ecological reinvestment. Using Xianyang Lake in China as a demonstrative case, the study applies six years of phenological records from 36 plant species. Analytic hierarchy process and k-means clustering derive four seasonal sensory palettes, ensuring year-round visual, olfactory, and auditory engagement. A proposed WeChat-based guidance system illustrates how passive sensory exposure could be converted into active behavioral engagement across walking, bridge viewing, and boating. The framework links enhanced experience to a dynamic revenue model and closes the loop by reinvesting proceeds into maintenance and sediment reuse for greenbelt expansion. The empirical component consists of phenological data analysis and seasonal palette derivation, while the socio-economic pathways are presented as testable hypotheses requiring future empirical validation. The findings shift the paradigm from physical to sensory accessibility, offering a conceptually transferable framework for managing inaccessible urban blue-green spaces. Full article

Fuel design based on distillation fractions is crucial for advancing fuel development and optimizing combustion systems. However, the chemical diversity and broad boiling-point distribution of full-range fuels pose significant challenges for kinetic modeling. In this study, a volatility-aware, multi-component kinetic mechanism was developed for simulating the combustion of various distillation fractions from an FRF in diesel engines. The mechanism comprises 261 species and 860 reactions. Unlike conventional surrogate mechanisms designed primarily for a single fuel or narrow distillation range, the proposed framework simultaneously represents the major hydrocarbon classes, ignition quality, and boiling-point distribution of FRF. The surrogate palette includes n-pentane, n-heptane, n-decane, n-dodecane, n-hexadecane, heptamethylnonane, 1-methylnaphthalene, iso-octane, methylcyclohexane, decalin, toluene, tetralin, and 1,2,4-trimethylbenzene. These components were selected to reproduce the molecular structures, ignition characteristics, and distillation behavior of the target fuel fractions. The mechanism was further refined through targeted replacement of the toluene sub-mechanism using updated hydrogen-abstraction and benzyl-radical oxidation reactions, followed by a fuel-oriented five-stage reduction strategy involving reaction-pathway-based pruning, DRGEP reduction, isomer lumping, sensitivity/ROP refinement, and targeted rate optimization. The resulting mechanism provides reasonable predictions of ignition delay, laminar flame speed, and species profiles for pure components, surrogate fuels, and real gasoline, jet, and diesel fuels. Coupled with a three-dimensional CFD model, the reduced mechanism also reproduces the main combustion phasing, pressure-rise process, and peak in-cylinder pressure of a diesel engine at 500 and 800 r/min over the investigated intake-temperature range. Although discrepancies remain in the low-temperature/negative-temperature-coefficient regime and in the quantitative prediction of the peak apparent heat-release rate, the mechanism provides a unified and practical framework for linking FRF distillation characteristics with chemical reactivity and engine-level combustion behavior. It therefore offers a foundation for designing tailored fuels from distillation fractions for operation in extreme environments. Full article

Recognizing visual artworks is difficult because images of art combine large within-class diversity, ambiguous boundaries, and non-photographic textures with intertwined cues related to the artist, genre, style, and historical period. Standard convolutional or transformer models are commonly inherited from natural-image classification, and such backbones may fail to emphasize the visual statistics that distinguish paintings, drawings, prints, and other artistic media. This paper presents ArtNAS-Style, a differentiable neural architecture search framework that embeds artistic style priors into the architecture search stage for visual art recognition. Rather than optimizing architecture choices only through classification loss, the framework employs a style-prior-aware bilevel objective that jointly accounts for recognition accuracy, diversity of style-sensitive features, preservation of brushstroke-like texture, and robustness across stylistic domains. The proposed Style Prior Encoding Module derives Gram-statistic, spectral, and palette-distribution cues from each artwork, and the Prior-Conditioned Search Cell uses these cues to adaptively modulate candidate operations during the search. A Style-Balanced Architecture Regularizer further discourages the discovered architecture from specializing to dominant artistic categories and promotes discriminative features across heterogeneous art domains. The search model is compact, trainable end to end, and transferable to multiple recognition tasks. Our evaluation protocol covers style, artist, school, and object-type recognition, in addition to parameter-matched ablations, robustness, statistical testing, and computational costs. Full article

Transforming concepts into architectural designs is challenging, as it requires clear ideas and the ability to visualise them. AI-powered text-to-image tools help designers quickly convert concepts into visual representations, enabling faster exploration of design alternatives. This study introduces a proposed ten-step numerical procedure for assessing the richness of textual prompts submitted to text-to-image generative AI tools within an architectural design studio. Twenty-three architecture students were enrolled in the design studio; twenty-two submitted analyzable text prompts as part of a design assignment requiring AI-assisted conceptual visualisation. Each prompt was scored across seven weighted dimensions (subject specificity, style and medium, composition and framing, lighting and atmosphere, colour and palette, quality modifiers, and negative clauses) to produce a composite Prompt Richness Index (R, scale 0–100). Corresponding AI-generated images were independently scored using a parallel Output Richness Index (O, scale 0–100). Pearson’s r between per-student average R and O yielded r = 0.940 (p < 0.001, 95% confidence interval [0.86, 0.98]), confirming a nearly perfect positive linear relationship. Rich-tier prompts were produced by two students and yielded the most architecturally coherent and visually distinctive outputs. Two students produced Sparse-tier prompts (average R < 30) and consistently received undifferentiated, generically rendered outputs. Two further students scored just above the Sparse threshold but showed similarly limited output differentiation. Class-wide deficits were identified in lighting/atmosphere description and negative clause usage. Eight pedagogical recommendations are derived from the findings to guide prompt learning instruction in AI-integrated design studios. Full article

This study proposes a methodology for developing nature-inspired color palettes for timeless residential interiors based on classical principles of beauty and chromatic harmony. The research employed a literature review of color theory, a survey, and a research-through-design approach. To investigate residential color preferences, a survey was conducted among older adults and undergraduate architecture students using examples of living room interiors representing different color palettes. Based on the survey findings, the color palette positively evaluated by both participant groups was identified. The results indicated a preference for a balanced palette characterized by muted tones, subtle tonal transitions, and harmonious chromatic relationships. Building on these findings, an original design methodology was developed, drawing on the observed color interactions and compositional patterns of the natural environment. The proposed methodology was subsequently applied within an AI-assisted design workflow to generate and evaluate residential interior concepts through a text-to-image process. In addition, two approaches to residential color design were compared: a traditional approach based on color theory and color-wheel harmonies, and a nature-inspired approach informed by the questionnaire findings and supported by the proposed AI-assisted design workflow. The study demonstrates how nature-inspired color relationships, classical color harmony principles, and AI-assisted visualization can be integrated into a structured methodology supporting the design of coherent and timeless residential interiors. Full article

A CMY palette of ceramic pigments was synthesized using both microwave-assisted firing (800 W, 30 min) and conventional electric firing (1000 °C for 3 h). For the allochromatic vanadium-zircon system (including the non-mineralized green and halide-mineralized blue compositions), as well as the chromium-doped scheelite yellow pigment, microwave firing does not outperform conventional calcination. Although comparable reactions occur during synthesis, microwave firing produces powders with lower colour performance. Nevertheless, these differences become visually negligible after incorporation into glazes. This behaviour can be attributed to the low dopant concentration and the localized, selective heating characteristic of microwave irradiation. In the vanadium-zircon system, the microwave-mineralized sample exhibits features similar to those of the non-mineralized compositions, including lower reactivity, smaller crystallite size, and enhanced blue colour development when applied in glazes. In contrast, for the idiochromatic Zn(Al_1.3Fe_0.5Cr_0.2)O₄ spinel red-brown pigment, microwave firing yields superior colour performance compared with conventional electric firing, producing higher chroma and greater colour intensity. In idiochromatic pigments, the relatively high proportion of chromophore components promotes more homogeneous microwave absorption and heating throughout the precursor mixture. The enhanced colour properties achieved through microwave synthesis may indicate the presence of a beneficial non-thermal microwave effect, leading to improved chromatic performance. Full article

The choice of suitable color palettes is a central task in thematic cartography. Established palette libraries such as ColorBrewer 2.0 provide cartographically proven color schemes, but they account only to a limited extent for the specific visual context of a background map. Particularly in web-based mapping environments, basemaps vary widely in lightness, colorfulness, texture, and semantic density. This paper introduces CartoPalette v4.2, a basemap-dependent tool for generating thematic color palettes. The system combines a trained Conditional Variational Autoencoder model (CVAE) with explicit scoring, minimum domain checks, constrained reranking, and optional CIELAB-color-space-based repair. The goal is not the automation of cartographic design, but a transparent, traceable decision aid. The benchmark comprised 40 basemaps from held-out test locations, drawn from the ten map styles included in the training data, together with two palette types and four class counts. In this setting, 90% of CartoPalette’s suggestions scored higher than the corresponding best available ColorBrewer palette. In about 83% of cases, the visual contrast to the background map was greater than with ColorBrewer. The results show that explicitly incorporating the basemap context offers a clear added value over classical palette libraries. These findings therefore refer to previously unseen locations within basemap styles represented during training. Transfer to entirely new styles has not yet been evaluated. Full article

Natural disaster emergency cartography requires high semantic accuracy in color design and efficient visual communication. However, existing studies still lack systematic palette analysis and knowledge organization methods based on real-world emergency maps. To address this gap, this study proposes a framework for palette analysis and knowledge organization that uses publicly available Emergency Response Coordination Centre (ERCC)’s emergency maps as the primary data source. The framework extracts disaster types and thematic mapping indicators. It performs palette identification, matching, and statistical analysis using color information from legend regions in the RGB, HSV, and CIELab color spaces, together with the ColorBrewer palette system. Based on the statistical matching results, we constructed a structured knowledge graph that links disaster types, thematic mapping indicators, and palettes, enabling organized retrieval of palette knowledge. Results show that color extraction from legend regions effectively reduces interference from non-thematic elements and improves the accuracy of palette identification. In addition, palette usage in ERCC emergency maps exhibits clear statistical associations and shared and differentiated patterns, indicating stable yet non-unique associations among disaster themes, thematic mapping indicators, and color palettes. The proposed knowledge graph provides a structured framework for organizing palette knowledge and analyzing semantic relationships in ERCC emergency cartography. Full article

Human skin tone is an important consideration in pulse oximetry and evaluating skin health conditions. Various tools are available to measure skin tone, including visual scales, palette based estimations, and optical sensor based devices. The objective of this experimental study is to compare common skin tone measurement methods. We used Fitzpatrick Skin Type (self- and observer-reported), Pantone SkinTone Guide, Delfin SkinColorCatch, and Nix Spectro 2 devices to measure the forehead skin tone of 52 participants. We compared measurements in L*a*b color space and in individual typology angles (ITA). We observed statistically significant correlation between methods, including in specific color space components (L* and ITA). Between the device measurements, we observed discrepancies in ITA values. Such discrepancies, together with a lack of standard or ground truth method, show a need for further research into skin tone measurement. We suggest in the interim that research and clinical work adopt protocols that take current methodological limitations into consideration, for example through the parallel use of different methods if applicable. Further work is needed to develop validated, reliable and consistent skin tone measurement tools. Full article

Deep networks dominate photovoltaic (PV) thermographic fault detection but need large annotated datasets and resist interpretation. We present a lightweight, interpretable infrared workflow combining support-vector-machine (SVM) module/background segmentation from four handcrafted features with an adaptive grid analysis labelling regions as nominal-intensity, high-intensity anomaly or low-intensity anomaly relative to a module-internal reference; the anomaly classes are inspection candidates, not confirmed faults. Evaluation used 21 close-range images of one 20 W module—recorded with the camera’s visible-light edge fusion active, so they are fused infrared/visible frames—and all 596 of a public five-sector UAV dataset. Segmentation against manual masks reached a mean intersection-over-union of 0.64; a feature ablation shows intensity statistics dominate, and an end-to-end Otsu pipeline gives almost the same high-intensity share (4.54% versus 4.50%): the SVM contributes reproducibility—removing the manual segmentation threshold, though not the empirical +48/−60 offsets—not accuracy. High-intensity regions concentrated in the module’s lower half, co-locating with a bus-bar defect known from hardware inspection—suggestive, not validated. The single-module, image-level close-range evaluation is optimistic, and the UAV shares, from a separately trained SVM, illustrate cross-domain application only. Segmentation runs at about 15 images per second on CPU. The method is a relative-intensity thermal screening workflow, not a validated defect-diagnosis or plant-health assessment method, and applies only where acquisition is controlled and the offsets are recalibrated for the target camera and palette. Full article

Visual art and graphic design tasks, such as generating a harmonious color palette or arranging the elements of a page, can be naturally formulated as mathematical optimization problems whose objective functions are non-convex, multimodal, and non-differentiable. Metaheuristic algorithms are well-suited to such problems. The secretary bird optimization algorithm (SBOA) is a recently proposed bio-inspired metaheuristic that mimics the hunting and predator-escaping behaviors of secretary birds, and it has shown competitive performance. However, SBOA still suffers from insufficient population diversity, premature convergence, and an unbalanced transition between exploration and exploitation, which limits its accuracy on complex design problems. To overcome these limitations, this paper proposes a multi-strategy secretary bird optimization algorithm (MSSBOA) that integrates three improvement strategies. First, a good point set initialization is employed to generate a low-discrepancy initial population that covers the search space more uniformly and enriches population diversity. Second, a lens opposition-based learning strategy is applied to the inferior individuals to help the population escape local optima while preserving the elite. Third, an adaptive Cauchy–Gaussian mutation is imposed on the best individual to balance global exploration and local exploitation throughout the search. The performance of MSSBOA is comprehensively examined on the CEC2017 benchmark suite in 10, 30, 50, and 100 dimensions and compared with the basic SBOA and nine state-of-the-art algorithms; the results are analyzed by the Friedman test, the Nemenyi post hoc test and the Wilcoxon rank-sum test. MSSBOA is then applied to two representative visual-design optimization problems: aesthetic color-harmony palette generation and graphic-layout aesthetics optimization. In all cases, MSSBOA outperforms the basic SBOA and the competing algorithms in terms of convergence speed, stability, and solution quality, confirming its effectiveness for computational-aesthetics applications in art design. Full article

The study of ancient sculptural polychromy increasingly relies on non-invasive analytical approaches capable of identifying pigments and reconstructing original decorative schemes while preserving the integrity of archaeological objects. This paper presents the investigation of the polychromy, extraordinarily preserved, on two Roman marble statues discovered in the forum of Formia (southern Latium) and currently housed in the National Archaeological Museum of Formia: a togate statue (inv. 147614) and a headless draped female figure (inv. 147680). Both sculptures retain exceptionally well-preserved traces of pigments, offering a rare opportunity to investigate materials and painting techniques applied to Roman marble statuary. The analytical protocol combined multiband imaging (Visible-Induced Luminescence and Ultraviolet Luminescence), optical microscopy, Fiber Optic Reflectance Spectroscopy (FORS), portable X-ray Fluorescence (XRF), and Surface Enhanced Raman Spectroscopy (Raman-SERS) applied to two micro-samples. The analyses allowed the identification of Egyptian blue, iron-based pigments, gilding, and an organic red lake on the palettes used for the statues. Raman-SERS measurements provided additional information on the composition of the organic lake detected on the female statue’s himation, supporting its attribution to a natural vegetal-derived dye, as madder lake. The results highlight the success of integrated non-destructive methodologies for the study of Roman sculptural polychromy and contribute to the reconstruction of complex decorative schemes on marble statuary. Full article

This study examines how the chromatic characteristics of the Min River urban waterfront are represented in publicly circulated social media images to support place-based biophilic landscape design. Dominant-color records were extracted through pixel-level filtering and per-image K-means clustering in CIELAB space, with image-level lighting-condition interpretation and record-level landscape-element labeling assisted by a multimodal large language model and subsequently reviewed and corrected by the author. Hue distributions and five record-proportion-weighted metrics of saturation, value, vividness, chromatic dissonance, and complexity were examined through an analytical framework integrating temporal scenarios, landscape elements, and lighting conditions. The results reveal a recurrent blue–orange orientation produced by the complementary positioning of multiple landscape elements rather than by any single category. Nighttime imagery showed the highest saturation but the lowest value, whereas dawn and dusk combined relatively high saturation and value and produced the highest vividness and chromatic dissonance. Transitional illumination brought built surfaces closer to natural elements in saturation–value space, while Ward hierarchical clustering identified Color-Affinity Groups that crossed temporal, lighting-condition, and landscape-element boundaries. These findings support a relational interpretation of biophilic color as a condition-dependent configuration rather than a fixed set of element-bound hues. Weighted representative palettes provide scenario-sensitive design references. However, the findings characterize publicly circulated visual representations of the waterfront rather than calibrated physical-color measurements or direct evidence of restorative effects. Full article