Search Results (272)

Lactiplantibacillus species have been historically used for food applications. Although several species are regarded as safe according to their regulatory status, the safety issues and functional roles of these lactic acid bacteria have been given attention. A selected Lactiplantibacillus strain TBRC 20328, with probiotic properties isolated from fermented Isan-style pork sausage (Mam), was evaluated for its safety through whole-genome sequencing and analysis using integrative bioinformatics tools. The metabolic genes were assessed through comparative genome analysis among Lactiplantibacillus species. The genome of the strain TBRC 20328 consisted of one circular chromosome (3.49 Mb) and five plasmids (totaling 0.25 Mb), encoding 3056 and 284 protein-coding genes, respectively. It exhibited an average nucleotide identity (ANI) with other Lactiplantibacillus pentosus strains of over 95%. Whole-genome analysis confirmed the absence of virulence and antimicrobial resistance genes, supporting its safety for food applications. Functional annotation revealed clusters for bacteriocins (plantaricin EF and pediocin) and polyketides, indicating potential roles in biopreservation and host interactions. Genes involved in the biosynthesis of some short-chain fatty acids and exopolysaccharides were also identified. Comparative genomic analysis across 33 other Lactiplantibacillus strains identified 2380 orthogroups, with 94 unique to the Lp. pentosus group. These included gene clusters involved in malonate decarboxylation, leucine biosynthesis, and 5-oxoprolinase activity. Such distinct genomic features emphasize the sustainable biotechnological potential and safety of Lp. pentosus TBRC 23028. Together, the findings highlight its promise as a safe and functional probiotic candidate with broad applications in functional food development and precision fermentation technologies. Full article

Exhibition design in museum environments serves as a vital mechanism for enhancing cultural engagement, enriching visitor experience, and promoting heritage preservation. Despite the growing number of museums, improvements in exhibition quality remain limited. In this context, understanding exhibition visual content becomes fundamental to shaping visitor experiences in cultural heritage settings, as it directly influences how individuals perceive, interpret, and engage with displayed information. However, due to individual differences in cognitive processing, standardized visualization strategies may not effectively support all users, potentially resulting in unequal levels of knowledge acquisition and engagement. This study presents a quasi-experimental eye-tracking investigation examining how visualizer–verbalizer (V–V) cognitive styles influence content comprehension in a historical museum context. Participants were classified as visualizers or verbalizers via standardized questionnaires and explored six artifacts displayed through varying information modalities while their eye movements—including fixation durations and transition patterns—were recorded to assess visual processing behavior. The results revealed that participants’ comprehension performance was strongly associated with their visual attention patterns, which differed systematically between visualizers and verbalizers. These differences reflect distinct visual exploration strategies, with cognitive style influencing how individuals allocate attention and process multimodal exhibition content. Eye movement data indicated that visualizers engaged in broader cross-modal integration, whereas verbalizers exhibited more linear, text-oriented strategies. The findings provide empirical evidence for the role of cognitive style in shaping visual behavior and interpretive outcomes in museum environments, underscoring the need for cognitively adaptive exhibition design. Full article

Ancient Chinese scene text detection, as an emerging interdisciplinary topic between computer vision and cultural heritage preservation, presents unique technical challenges. Compared with modern scene text, ancient Chinese text is characterized by complex backgrounds, diverse fonts, extreme aspect ratios, and a scarcity of annotated data. Existing detection methods often perform poorly under these conditions. To address these challenges, this paper proposes a novel detection network based on dynamic feature fusion upsampling and text-region focus, named DRA-Net. The core innovations of the proposed method include (1) a dynamic fusion upsampling module, which adaptively assigns weights to effectively fuse multi-scale features while preserving critical information during feature propagation; (2) an adaptive text-region focus module that incorporates axial attention mechanisms to enhance the model’s ability to locate text regions and suppress background interference; and (3) the integration of deformable convolution, which improves the network’s capacity to model irregular text shapes and extreme aspect ratios. To tackle the issue of data scarcity, we construct a dataset named ACST, specifically for ancient Chinese text detection. This dataset includes a wide range of scene types, such as stone inscriptions, calligraphy works, couplets, and other historical media, covering various font styles from different historical periods, thus offering strong data support for related research. Experimental results demonstrate that DRA-Net achieves significantly higher detection accuracy on the ACST dataset compared to existing methods and performs robustly in scenarios with complex backgrounds and extreme text aspect ratios. It achieves an F1-score of 72.9%, a precision of 82.8%, and a recall of 77.5%. This study provides an effective technical solution for the digitization of ancient documents and the intelligent preservation of cultural heritage, with strong theoretical significance and practical potential. Full article

Artificial intelligence technologies, particularly machine learning and computer vision, are being increasingly utilized to preserve, restore, and create immersive virtual experiences with cultural artifacts and sites, thus aiding in conserving cultural heritage and making it accessible to a global audience. This paper examines the performance of Generative Adversarial Networks (GAN), especially Style-Based Generator Architecture (StyleGAN), as a deep learning approach for producing realistic images of Egyptian monuments. We used Sigmoid loss for Language–Image Pre-training (SigLIP) as a unique image–text alignment system to guide monument generation through semantic elements. We also studied truncation methods to regulate the generated image noise and identify the most effective parameter settings based on architectural representation versus diverse output creation. An improved discriminator design that combined noise addition with squeeze-and-excitation blocks and a modified MinibatchStdLayer produced 27.5% better Fréchet Inception Distance performance than the original discriminator models. Moreover, differential evolution for latent-space optimization reduced alignment mistakes during specific monument construction tasks by about 15%. We checked a wide range of truncation values from 0.1 to 1.0 and found that somewhere between 0.4 and 0.7 was the best range because it allowed for good accuracy while retaining many different architectural elements. Our findings indicate that specific model optimization strategies produce superior outcomes by creating better-quality and historically correct representations of diverse Egyptian monuments. Thus, the developed technology may be instrumental in generating educational and archaeological visualization assets while adding virtual tourism capabilities. Full article

This study examines the evolution and integration of Buddhist architecture in East Asia and emphasizes the preservation of indigenous building traditions by adapting pre-Buddhist architectural typologies, vernacular construction techniques, and localized worship practices. In addition, this study highlights the adaptive transformation of Indian Buddhist structures as they incorporate regional architectural forms, resulting in distinct monumental styles that had a profound symbolic significance. By introducing the concept of a cosmopolitan attitude, it underscores the dynamic coexistence and reciprocal influence of universalized and vernacular architectural traditions. The findings highlight the interplay between cultural universality and particularity, illustrating how architectural meaning and intention define the uniqueness of structures beyond their stylistic similarities. This study demonstrates that even when architectural forms appear similar, their function and underlying intent must be considered to fully comprehend their historical and cultural significance. Full article

Value-at-Risk (VaR) is a key metric widely applied in market risk assessment and regulatory compliance under the Basel III framework. This study compares two Monte Carlo-based VaR models using publicly available equity data: a return-based model calibrated to historical portfolio volatility, and a CAPM-style factor-based model that simulates risk via systematic factor exposures. The two models are applied to a technology-sector portfolio and evaluated under historical and rolling backtesting frameworks. Under the Basel III backtesting framework, both initially fall into the red zone, with 13 VaR violations. With rolling-window estimation, the return-based model shows modest improvement but remains in the red zone (11 exceptions), while the factor-based model reduces exceptions to eight, placing it into the yellow zone. These results demonstrate the advantages of incorporating factor structures for more stable exception behavior and improved regulatory performance. The proposed framework, fully transparent and reproducible, offers practical relevance for internal validation, educational use, and model benchmarking. Full article

As an integral component of humanity’s cultural heritage, traditional villages universally confront challenges such as population loss and cultural discontinuity amid rapid urbanization. Cluster-based protection models have increasingly become the international consensus for addressing the survival crisis of such settlements. This study selects the Central Yunnan region of Southwest China—characterized by its complex geography and multi-ethnic habitation—as the research area. Employing ArcGIS spatial analysis techniques alongside clustering algorithms, we examine the spatial distribution characteristics and clustering patterns of 251 traditional villages within this region. The findings are as follows. In terms of spatial distribution, traditional villages in Central Yunnan are unevenly dispersed, predominantly aggregating on mid-elevation gentle slopes; their locations are chiefly influenced by rivers and historical courier routes, albeit with only indirect dependence on waterways. Regarding single-cluster attributes, the spatial and geomorphological features exhibit a composite “band-and-group” pattern shaped by river valleys; culturally, two dominant modes emerge—“ancient-route-dependent” and “ethnic-symbiosis”—reflecting an economy-driven cultural mechanism alongside latent marginalization risks. Concerning construction characteristics, the “Qionglong-Ganlan” and Han-style “One-seal” residential features stand out, illustrating both adaptation to mountainous environments and the cumulative effects of historical culture. Based on these insights, we propose a three-tiered clustering classification framework—“comprehensive-element coordination”, “feature-led”, and “potential-cultivation”—to inform the development of contiguous and typological protection strategies for traditional villages in highland, multi-ethnic regions. Full article

Sacred choral music continues to hold a significant place in contemporary concert settings, with historical and newly composed works featuring in today’s choral programmes. Contemporary choral composers have continued to engage with the longstanding tradition of setting sacred texts to music, bringing fresh interpretations through their innovative compositional techniques and fusion of styles. Irish composer Rhona Clarke’s (b. 1958) expansive choral oeuvre includes a wealth of both sacred and secular compositions but reveals a notable propensity for the setting of sacred texts in Latin. Her synthesis of archaic and contemporary techniques within her work demonstrates both the solemn and visceral aspects of these texts, as well as a clear nod to tradition. This article focuses on Clarke’s choral work O Vis Aeternitatis (2020), a setting of a text by the medieval musician and saint Hildegard of Bingen (c. 1150). Through critical score analysis, we investigate the piece’s melodic, harmonic, and textural frameworks; the influence of Hildegard’s original chant; and the use of extended vocal techniques and contrasting vocal timbres as we articulate core characteristics of Clarke’s compositional style and underline her foregrounding of the more visceral aspects of Hildegard’s words. Clarke’s fusion of creative practices from past and present spotlights moments of dramatic escalation and spiritual importance, and exhibits the composer’s distinctive compositional voice as she reimagines Hildegard’s text for the twenty-first century. Full article

Riverbed dynamics are natural processes that are strongly driven by human and climatic factors. In the last two centuries, the anthropogenic influence and impact of climate change on European rivers has resulted in significant degradation of riverbeds. This research paper aims to determine the historical evolution (1856–2021) and future trends of the Ialomița riverbed (Romania) under the influence of anthropogenic impact and climate change. The case study is a reach of 66 km between the confluences with the Ialomicioara and Pâscov rivers. The localisation in a contact zone between the Curvature Subcarpathians and the Târgoviște Plain, the active recent tectonic uplift of the area, and the intense anthropogenic intervention gives to this river reach favourable conditions for pronounced riverbed dynamics over time. To achieve the aim of the study, we developed a complex methodology which involves the use of Geographical Information System (GIS) techniques, hierarchical cluster analysis (HCA), the Mann–Kendall test (MK), and R programming. The results indicate that the evolution of the Ialomița River aligns with the general trends observed across Europe and within Romania, characterised by a reduction in riverbed geomorphological complexity and a general transition from a braided, multi-thread into a sinuous, single-thread fluvial style. The main processes consist of channel narrowing and incision alternating with intense meandering. However, specific temporal and spatial evolution patterns were identified, mainly influenced by the increasingly anthropogenic local influences and confirmed climate changes in the study area since the second half of the 20th century. Future evolutionary trends suggest that, in the absence of river restoration interventions, the Ialomița riverbed is expected to continue degrading on a short-term horizon, following both climatic and anthropogenic signals. The findings of this study may contribute to a better understanding of recent river behaviours and serve as a valuable tool for the management of the Ialomița River. Full article

This research aims to explore the role of architecture in creating an identifiable brand for Amman. It seeks to put forward a vision through which Amman’s city can formulate a clear model for implementing a successful branding strategy. In doing so, this research studies the concepts associated with the ideas of branding, city image and identity, and the extent to which such ideas are to be implemented in Amman. The study adopted an inductive approach using in-depth, semi-structured interviews with 35 experts with central roles in stating the city’s key values that best reflect the city’s identity. A thematic analysis was conducted in line with theoretical aspects, including the city’s message, strategies for formulating the brand, and communication via architecture. The image of Amman shows an obvious distinction between its historical character and modern global styles as it suffers from disorder within its architectural landscape. Amman needs to rethink its identity in order to create a new brand that keeps pace with time without losing the originality of the place. This calls for re-evaluating the role of the iconic buildings and their associations with the surroundings, enabling them to become of significant presence, both symbolically and operationally, in expressing the city’s personality and promoting its message. Full article

Processing ancient text images presents significant challenges due to severe visual degradation, missing glyph structures, and various types of noise caused by aging. These issues are particularly prominent in Chinese historical documents and stone inscriptions, where diverse writing styles, multi-angle capturing, uneven lighting, and low contrast further hinder the performance of traditional OCR techniques. In this paper, we propose a unified neural framework, UniText, for the detection, recognition, and glyph restoration of Chinese characters in images of historical documents and inscriptions. UniText operates at the character level and processes full-page inputs, making it robust to multi-scale, multi-oriented, and noise-corrupted text. The model adopts a multi-task architecture that integrates spatial localization, semantic recognition, and visual restoration through stroke-aware supervision and multi-scale feature aggregation. Experimental results on our curated dataset of ancient Chinese texts demonstrate that UniText achieves a competitive performance in detection and recognition while producing visually faithful restorations under challenging conditions. This work provides a technically scalable and generalizable framework for image-based document analysis, with potential applications in historical document processing, digital archiving, and broader tasks in text image understanding. Full article

The stylistic emulation of historical building facades presents significant challenges for artificial intelligence (AI), particularly for complex and data-scarce styles like Krakow’s Eclecticism. This study aims to develop a methodological framework for a “typological transcoding” of style that moves beyond mere visual mimicry, which is crucial for heritage preservation and urban renewal. The proposed methodology integrates architectural typology with Low-Rank Adaptation (LoRA) for fine-tuning a Stable Diffusion (SD) model. This process involves a typology-guided preparation of a curated dataset (150 images) and precise control of training parameters. The resulting typologically guided LoRA-tuned model demonstrates significant performance improvements over baseline models. Quantitative analysis shows a 24.6% improvement in Fréchet Inception Distance (FID) and a 7.0% improvement in Learned Perceptual Image Patch Similarity (LPIPS). Furthermore, qualitative evaluations by 68 experts confirm superior realism and stylistic accuracy. The findings indicate that this synergy enables data-efficient, typology-grounded stylistic emulation, highlighting AI’s potential as a creative partner for nuanced reinterpretation. However, achieving deeper semantic understanding and robust 3D inference remains an ongoing challenge. Full article

Preserving cultural heritage can be efficiently supported by structured and semantic representation of historical artifacts. Bookbinding, a critical aspect of book history, provides valuable insights into past craftsmanship, material use, and conservation practices. However, existing bibliographic records often lack the depth needed to analyze bookbinding techniques, provenance, and preservation status. This paper presents a proof-of-concept system that explores how Large Language Models (LLMs) can support knowledge graph engineering within the context of 19th-century Greek bookbinding (1830–1900), and as a result, generate a domain-specific ontology and a knowledge graph. Our ontology encapsulates materials, binding techniques, artistic styles, and conservation history, integrating metadata standards like MARC and Dublin Core to ensure interoperability with existing library and archival systems. To validate its effectiveness, we construct a Neo4j knowledge graph, based on the generated ontology and utilize Cypher Queries—including LLM-generated queries—to extract insights about bookbinding practices and trends. This study also explores how semantic reasoning over the knowledge graph can identify historical binding patterns, assess book conservation needs, and infer relationships between bookbinding workshops. Unlike previous bibliographic ontologies, our approach provides a comprehensive, semantically rich representation of bookbinding history, methods and techniques, supporting scholars, conservators, and cultural heritage institutions. By demonstrating how LLMs can assist in ontology/KG creation and query generation, we introduce and evaluate a semi-automated pipeline as a methodological demonstration for studying historical bookbinding, contributing to digital humanities, book conservation, and cultural informatics. Finally, the proposed approach can be used in other domains, thus, being generally applicable in knowledge engineering. Full article

The present research aims to assess, from both ecological and economic perspectives, a strategic solution applied to the building sector that can contribute to mitigating the planetary tragedy of the overconsumption of global fossil energy (coal, oil, and gas) and, thus, climate change, along with its dramatic negative impacts on the planet, humanity, and the world’s economy. Buildings are the largest consumers of fossil fuel energy, significantly contributing to Greenhouse Gas (GHG) emissions and, consequently, to climate change. Reducing their environmental impact is therefore crucial for achieving global sustainability goals. Existing buildings, mostly the historical ones, represent a significant part of the global building stocks, which, for the most part, consist of buildings built more than 70 years ago, which are aged, in a state of deterioration, and in need of intervention. Recovering, renovating, and redeveloping existing and historical buildings could be a formidable instrument for improving the energy quality of the international and national building stocks. When selecting the type of possible interventions to be applied, there are two choices: simple and unsustainable ordinary maintenance versus ecological retrofitting, i.e., a quality increase in the indoor environment and building energy savings using local bio-natural materials. The success of the “Ecological Retrofitting” Strategy strongly relies on its economic and financial sustainability; therefore, the goal of this research is to underline and demonstrate the economic and ecological benefits of the ecological transition at the building level through an integrated valuation applied in a case study, located in Southern Italy. First, in order to demonstrate the ecological benefits of the proposed strategy, the latter was tested through a new energy assessment tool in an updated BIM platform; subsequently, an economic valuation was conducted, clearly demonstrating the cost-effectiveness of the building’s ecological transition. The real-world experiment through the proposed case study achieved important results and reached the goals of the “Ecological Retrofitting” Strategy in existing (but not preserved) liberty-style constructions. First of all, a significant improvement in the buildings’ thermal performance was achieved after some targeted interventions, resulting in energy savings; most importantly, the economic feasibility of the proposed strategy was demonstrated. Full article

Identifying the scriptwriter in historical manuscripts is crucial for historians, providing valuable insights into historical contexts and aiding in solving historical mysteries. This research presents a robust deep learning system designed for classifying historical manuscripts by writer, employing intelligent feature selection and vision transformers. Our methodology meticulously investigates the efficacy of both handcrafted techniques for feature identification and deep learning architectures for classification tasks in writer identification. The initial preprocessing phase involves thorough document refinement using bilateral filtering for denoising and Otsu thresholding for binarization, ensuring document clarity and consistency for subsequent feature detection. We utilize the FAST detector for feature detection, extracting keypoints representing handwriting styles, followed by clustering with the k-means algorithm to obtain meaningful patches of uniform size. This strategic clustering minimizes redundancy and creates a comprehensive dataset ideal for deep learning classification tasks. Leveraging vision transformer models, our methodology effectively learns complex patterns and features from extracted patches, enabling precise identification of writers across historical manuscripts. This study pioneers the application of vision transformers in historical document analysis, showcasing superior performance on the “ICDAR 2017” dataset compared to state-of-the-art methods and affirming our approach as a robust tool for historical manuscript analysis. Full article