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The article describes new challenges for North Rhine-Westphalia's State Libraries since the 2022 cultural code NRW was decreed, focusing on aspects of indexing, preserving, and digitizing written cultural heritage in North Rhine-Westphalia. [ABSTRACT FROM AUTHOR]

The German National Library (DNB) is Germany's central archival library and has the statutory mandate to collect and document all media works issued in Germany. This encompasses both physical and online publications from printed books, journals to e-books, e-journals, and, more recently, also digital musical works. The collection comprises well over 51 million works at present, with a key focus on automated collection and long-term preservation of digital media works. For efficient processing and storage, DNB has relied on established metadata standards and developed its technical infrastructure step by step. Web archiving is an important part for the preservation of digitized cultural heritage, and ongoing development and adjustment is envisaged. In future the DNB will further develop its digital collection and increasingly seek cooperation with other institutions to provide access to as many users as possible. [ABSTRACT FROM AUTHOR]

This research paper presents a novel algorithmic approach for character recognition and contextual analysis of temple inscriptions, specifically focusing on Tamil ancient script. The methodology combines advanced preprocessing techniques, deep learning models, and contextual analysis to address the challenges posed by noisy images, script variations, and historical context understanding. We compiled a dataset of 100 high-resolution images of temple inscriptions from various regions and periods. The preprocessing phase involves Noise Reduction, Contrast Enhancement, Orientation Correction, and Adaptive Binarization algorithms to enhance the quality of the inscription images. The character recognition stage employs Convolutional Neural Networks with Transfer Learning, further enhanced by the Multi-head Attention mechanism in Vision Transformers (ViT). The character segmentation algorithm used was the Stroke Width Transform. Transfer Learning was incorporated to adapt the pre-trained ViT model to our specific task. This approach significantly improves the model's ability to recognize characters from diverse scripts and languages. The results demonstrate the effectiveness of the proposed methodology. The character recognition accuracy metrics include a precision of 97.25%, a recall of 95.05%, and an F1-score of 95.17%. Additionally, the model achieved a recognition rate of 98.92% for key terms related to historical events, deities, and rulers. It also demonstrated a 94% recognition rate for context-specific phrases and a 95% recognition rate for historical dates. Contextual analysis results indicate that the model successfully identifies specific terms, phrases, and historical references, contributing to a deeper understanding of the inscriptions. The model's ability to recognize characters from multiple scripts underscores its adaptability to diverse inscriptions. In conclusion, this research provides a comprehensive and efficient solution for character recognition and contextual analysis of temple inscriptions in Tamil ancient script. It advances the field of epigraphy and historical research, facilitating the digital preservation and exploration of historical and cultural heritage contained within these inscriptions. [ABSTRACT FROM AUTHOR]

Purpose: This article investigates the construction of risk within trustworthy digital repository audits. It contends that risk is a social construct, and social factors influence how stakeholders in digital preservation processes comprehend and react to risk. Design/methodology/approach: This research employs a qualitative research design involving in-depth semi-structured interviews with stakeholders in the Trustworthy Digital Repository Audit and Certification (TRAC) process, and document analysis of the TRAC checklist and audit reports. I apply an analytic framework based on the Model for the Social Construction of Risk in Digital Preservation to this data. Findings: The findings validate the argument that risk in digital preservation is indeed socially constructed and demonstrate that the eight factors in the Model for the Social Construction of Risk in Digital Preservation do indeed influence how stakeholders constructed their understanding of risk. Of the eight factors in the model, communication, expertise, uncertainty and vulnerability were found to be the most influential in the construction of risk during the TRAC audit process. The influence of complexity, organizations political culture, were more limited. Originality/value: This article brings new insights to digital preservation by demonstrating the importance of understanding risk as a social construct. I argue that risk identification and/or assessment is only the first step in the long-term preservation of digital information and show that perceptions of risk in digital preservation are shaped by social factors by applying theories of social construction and risk perception to an analysis of the TRAC process. [ABSTRACT FROM AUTHOR]

Purpose: This article reports on findings from interviews with ICT-based content creators whose work focuses on documenting and curating queer history and culture. The research specifically examines how as amateur historians, the participant's embodied knowledge plays a central role in how they engage with discourse about queer historical figures, methods of queer historiography and community accountability. Design/methodology/approach: The research deploys a queer constructivist framework to qualitatively gather and analyzes the semi-structured interviews of 31 North American content creators who curate digital project related to queer history and culture. The interviews were gathered between August 2022 and August 2023. Findings: The research highlights how the subjectivity of queer embodiment aids, rather than hinders, participants' ability to collaborate with LGBTQIA+ communities while also addressing more significant ethical questions around intersectionality and inclusive historiographic work. Research limitations/implications: The content creators' own positionality and commitments to community accountability and queer inclusivity fostered richer stories and historical documentation, while also helping make visible queer identity as affirming and valuable within queer culture. Additionally, practical implications include highlighting the value of ICT-based content within the distribution of educational and informational resources related to queer history. Originality/value: This research offers an underexamined intersection of historiography and queer embodiment. While extensive scholarship on institutional and community-based historiography work exist the content creators interviewed within this study exist within the space of both, often using a combination of embodied knowledge and traditional curatorial work to translate between such spaces, inviting, in turn, new ways of thinking about queer archival knowledge. [ABSTRACT FROM AUTHOR]

This research paper introduces a novel approach in the realm of digital preservation and inheritance of Intangible Cultural Heritage (ICH) through a Customized 3D Convolutional Neural Network (CNN). The core of this study lies in the development of an advanced image processing algorithm tailored to accurately recognize, categorize, and archive diverse forms of ICH, which include traditional performances, ceremonies, oral traditions, and crafts. Utilizing a volumetric 3D CNN, this paper demonstrates how complex ICH elements can be effectively captured and analyzed, overcoming the limitations of traditional 2D image processing methods. The network is trained on a comprehensive dataset of ICH imagery, ensuring sensitivity to the subtle nuances and dynamic nature of these cultural expressions. This paper highlights the algorithm's capability in not only safeguarding the visual aspects of ICH but also in providing an interactive, digital medium for education and cultural dissemination. The proposed method shows significant promise in aiding the efforts of cultural preservationists and educators, offering a technologically advanced pathway for the protection and inheritance of the world's rich, yet vulnerable, cultural heritage. This study sets a precedent in the interdisciplinary field of cultural heritage conservation, digital technology, and artificial intelligence, providing a scalable and effective solution for global ICH preservation initiatives. [ABSTRACT FROM AUTHOR]

AbstractThis quarter’s report starts with a review of the one-day Potomac Technical Processing Librarians Annual Meeting, with topics focused on the status of linked data. Next is an American Association of Law Libraries Meeting and Conference session on format migration that, while given from a law library perspective, provides useful information for all professionals working through print to electronic format changes. The remainder of the conference reports are from the 2022 Charleston Conference and start with a virtual session on electronic resource licenses that is an excellent summary for those responsible for negotiating licenses. The in-person conference reports cover sessions on an assessment framework for negotiating transformative agreements, an overview of a new cooperative collections project that will be standards based, and one on mis- and dis-information with specific guidance for publishers and librarians’ collection development efforts. The final reports cover two panel sessions on preservation – one on the importance of digital preservation and the other on a preservation policy from NASIG. [ABSTRACT FROM AUTHOR]

The revelation of the internal structure of objects through computed tomography (CT scan) contributes to a more comprehensive understanding of their creation, the assessment of their preservation status, and the prediction of their future behavior. Consequently, in the case of Yiannis Pappas' collection, this knowledge aids in the perpetuation of the models it hosts, which are made from malleable materials, such as wax, plasticine, and mazut, on metallic armature. This publication presents the complete methodology for extracting three-dimensional (3D) models (reconstructions) of the individual construction materials of the figurines, with the aim of subsequently utilizing them in research, as well as in their digital preservation and restoration. The 3D reconstructions were obtained by automatic segmentation algorithms based on the absorption measurements of the materials of the specific figurines, and were furthered edited (post-processing) to obtain the final models. [ABSTRACT FROM AUTHOR]

In the face of escalating threats to Armenian cultural heritage in Artsakh due to armed conflicts and destruction, urgent measures are imperative to safeguard this invaluable legacy. The Digital Humanities Institute at the EPFL (École polytechnique fédérale de Lausanne, Switzerland) is leading an effort to digitize the Armenian epigraphic heritage in Artsakh, where inscriptions and toponyms serve as cultural identifiers. Documenting endangered inscriptions and using the , and tags safeguard historical integrity. Integrating GeoNames and Pleiades enhances accessibility and accuracy, bridging perspectives and preserving heritage. The project's bilingual nature amplifies complexity, as English and Armenian representations are layered with intricate grammar nuances. Lemmatization and regularization ensure consistent proper names, particularly across diverse grammatical cases. In a landscape where cultural heritage preservation is needed, the marriage of external resources, Armenian toponyms, lemmatization and regularization ensures historical accuracy and accessibility. This academic set-up clarifies the methodologies and standards used, emphasizing the preservation of Armenian cultural heritage through digital epigraphy. The set-up of the corpus allows for future enrichment by incorporating additional inscriptions, more detailed historical background information and etymological clarifications about the toponyms. This flexibility ensures that the corpus can continue to grow and provide comprehensive insights into the cultural heritage of Artsakh. [ABSTRACT FROM AUTHOR]

With the current proliferation of training opportunities available in digital preservation, this study asks: what are the most in demand digital preservation instruction topics? To answer this question, we did a qualitative content analysis of 168 Professional Development Support applications received by the Digital Preservation Outreach and Education Network (DPOE-N) between September 2020 and December 2023. The study finds that the management of digital records and metadata/cataloging standards were the most requested training topics, and that general and broadly applicable skills tend to be the most sought after. This indicates that there is a continuing need to provide education focusing on the core elements of digital preservation and knowledge, and that we have not moved on yet to a place where cultural heritage professionals are solely seeking skills in more advanced or specialized digital preservation topics. [ABSTRACT FROM AUTHOR]

Films deteriorate over time and archival methods are still being developed to prevent this. Digital technology enables the conversion of films, but there are various issues with digital approaches that present Indian archivists with the dilemma of choosing the appropriate one to film preservation. It is perceived that digital technology is inferior to film and, thus, archivists in India are struggling to preserve their film heritage for future generations. Film archivists, curators and restorers in India were interviewed as part of this study, with a thematic analysis conducted to understand the perceived insufficiency of digital preservation. Film archivists dislike digital technology but recognise its benefits, however, digital film preservation faces issues of originality, obsolescence, cost, resolution and legal aspects. Archivists suggested that digital archiving is not an alternative to film archiving but rather a complement and the study finds that the problems in digital approaches can be minimised through measures such as diligence, technological advancement, multiple copies and raising awareness which can prolong the film artefact in a digital archive. Both methods must work together to preserve the unpreservable. [ABSTRACT FROM AUTHOR]

Image denoising is crucial for enhancing image quality, especially in medical applications where noise can significantly impact the accuracy of analysis and interpretation. This paper presents the development of an adaptive Gaussian filter-based denoising technique that effectively enhances images corrupted by various types of noise. By incorporating the adaptive adjustment of filter parameters based on local image characteristics, the proposed method achieves superior denoising performance. The algorithm analyzes the noisy image to estimate the noise characteristics, dynamically adjusting the Gaussian filter parameters to ensure optimal preservation of image details while effectively suppressing noise artifacts. Optimized strategies for parameter selection and filtering operations are employed to ensure computational efficiency. A comparative analysis demonstrates that the adaptive Gaussian filter outperforms traditional methods, achieving a higher Peak Signal-to-Noise Ratio (PSNR) and a lower Root Mean Square Error (RMSE). The technique also exhibits robustness against different noise distributions, making it a versatile solution for various image enhancement applications. These findings highlight the potential of the adaptive Gaussian filter to significantly improve image quality, facilitating more accurate and reliable analysis across diverse domains. [ABSTRACT FROM AUTHOR]

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Earthen architecture holds deep historical, cultural, and ecological value, forming an essential component of our global cultural heritage. However, these structures face numerous threats, including climate change, socio-economic shifts, and, in many cases, neglection, which accelerate their deterioration. This study introduces a photogrammetry-based methodology adapted for the digital documentation and preservation of earthen architecture within the context of developing countries. We focus on the Ex-Hotel Oasis Rouge in Timimoun, an iconic earthen building in southwestern Algeria and the current headquarters of CAPTERRE (Algerian Centre for Earthen Built Cultural Heritage). This paper details our approach to using photogrammetry to capture both the interior and exterior of the building, produce detailed orthophotos for archiving the unique earthen bas-reliefs, and conduct a condition assessment. Our findings demonstrate the effectiveness of photogrammetry as a cost-effective tool for heritage documentation, highlighting its potential to assist in the ongoing preservation and informed restoration of earthen architecture. [ABSTRACT FROM AUTHOR]

The synthesis of facial sketch-photo has important applications in practical life, such as crime investigation. Many convolutional neural networks (CNNs) based methods have been proposed to address this issue. However, due to the substantial modal differences between sketch and photo, the CNN's insensitivity to global information, and insufficient utilization of hierarchical features, synthesized photos struggle to balance both identity preservation and image quality. Recently, State Space Sequence Models (SSMs) have achieved exciting results in computer vision (CV) tasks. Inspired by SSMs, we design a hybrid CNN–SSM model called FaceMamba for the Face Sketch-Photo Synthesis (FSPS) task. It includes an original Face Vision Mamba Attention for modeling in latent space using SSM. Additionally, it incorporates a general auxiliary method called Attention Feature Injection that combines encoding features, decoding features, and external auxiliary features using attention mechanisms. FaceMamba combines Mamba's modeling ability for long-range dependencies with CNN's powerful local feature extraction ability, and utilizes hierarchical features at the appropriate position. Adequate experimental and evaluation results reveal that FaceMamba has strong competitiveness in FSPS task, achieving the best balance between identity preservation and image quality. [ABSTRACT FROM AUTHOR]

AbstractEastgate Systems Inc. published influential works of electronic literature through the 1990s and 2000s, many of which are only compatible with obsolete computing environments. This research has identified over 1,000 copies of Eastgate titles in hundreds of libraries, archives, and special collections around the world, but issues with media obsolescence and the poor representation of these titles in library catalogs inhibit the ongoing preservation and circulation of this literary corpus. Representative of collections of legacy media more generally, Eastgate titles held in libraries suggest new collaborative approaches for both preserving and providing access to historic software. [ABSTRACT FROM AUTHOR]

As digital crime continues to rise, the preservation of digital evidence has become a critical phase in digital forensic investigations. This phase focuses on securing and maintaining the integrity of evidence for legal proceedings. Existing solutions for evidence preservation, such as centralized storage systems and cloud frameworks, present challenges related to security and collaboration. In this paper, we propose a novel framework that addresses these challenges in the preservation phase of forensics. Our framework employs a combination of advanced technologies, including the following: (1) Segmenting evidence into smaller components for improved security and manageability, (2) Utilizing steganography for covert evidence preservation, and (3) Implementing blockchain to ensure the integrity and immutability of evidence. Additionally, we incorporate Long Short-Term Memory (LSTM) networks to enhance steganography in the evidence preservation process. This approach aims to provide a secure, scalable, and reliable solution for preserving digital evidence, contributing to the effectiveness of digital forensic investigations. An experiment using linguistic steganography showed that the LSTM autoencoder effectively generates coherent text from bit streams, with low perplexity and high accuracy. Our solution outperforms existing methods across multiple datasets, providing a secure and scalable approach for digital evidence preservation. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions Low‐dose computed tomography (LDCT) can mitigate potential health risks to the public. However, the severe noise and artifacts in LDCT images can impede subsequent clinical diagnosis and analysis. Convolutional neural networks (CNNs) and Transformers stand out as the two most popular backbones in LDCT denoising. Nonetheless, CNNs suffer from a lack of long‐range modeling capabilities, while Transformers are hindered by high computational complexity.In this study, our main goal is to develop a simple and efficient model that can both focus on local spatial context and model long‐range dependencies with linear computational complexity for LDCT denoising.In this study, we make the first attempt to apply the State Space Model to LDCT denoising and propose a novel LDCT denoising model named Visual Mamba Encoder‐Decoder Network (ViMEDnet). To efficiently and effectively capture both the local and global features, we propose the Mixed State Space Module (MSSM), where the depth‐wise convolution, max‐pooling, and 2D Selective Scan Module (2DSSM) are coupled together through a partial channel splitting mechanism. 2DSSM is capable of capturing global information with linear computational complexity, while convolution and max‐pooling can effectively learn local signals to facilitate detail restoration. Furthermore, the network uses a weighted gradient‐sensitive hybrid loss function to facilitate the preservation of image details, improving the overall denoising performance.The performance of our proposed ViMEDnet is compared to five state‐of‐the‐art LDCT denoising methods, including an iterative algorithm, two CNN‐based methods, and two Transformer‐based methods. The comparative experimental results demonstrate that the proposed ViMEDnet can achieve better visual quality and quantitative assessment outcomes. In visual evaluation, ViMEDnet effectively removes noise and artifacts, while exhibiting superior performance in restoring fine structures and low‐contrast structural edges, resulting in minimal deviation of denoised images from NDCT. In quantitative assessment, ViMEDnet obtains the lowest RMSE and the highest PSNR, SSIM, and FSIM scores, further substantiating the superiority of ViMEDnet.The proposed ViMEDnet possesses excellent LDCT denoising performance and provides a new alternative to LDCT denoising models beyond the existing CNN and Transformer options. [ABSTRACT FROM AUTHOR]

The MuralDH dataset is an invaluable digital resource developed for the conservation and restoration of Dunhuang murals, which are critical components of global cultural heritage facing threats from degradation. This dataset comprises over 5000 high-resolution images tailored to 512 × 512 pixels, emphasizing the preservation of mural integrity and detail. It includes 1000 images with pixel-level damage annotations for segmentation research and 500 images specially processed for super-resolution studies, catering to a wide range of digital restoration needs. While the primary focus of this work is the dataset itself, we also introduce a supportive digital restoration framework. This framework, which encompasses damage segmentation, inpainting, and super-resolution techniques, serves as a secondary validation of MuralDH's utility and versatility. Through MuralDH, technology revives ancient art, embodying the essence of interdisciplinary innovation. By facilitating advanced research in computer vision and artificial intelligence, MuralDH aims to revolutionize the digital preservation practices for murals and other cultural artifacts, demonstrating the critical role of interdisciplinary collaboration in safeguarding our cultural legacy. [ABSTRACT FROM AUTHOR]

The absence of clear protocols in the conservation of new media art has led to a situation of extreme precariousness of digital artworks. The irruption of blockchain culture and NFTs seems to offer an opportunity to overcome this situation. This paper aims to clarify whether or not the structure of data and metadata associated with transactions in the blockchain solves the challenging obsolescent and unstable condition of new media art. The disturbing reality is that the so-called 'cryptoart' is not always stored and encapsulated on the blockchain itself. In a high number of cases, assets just point to artworks living outside, stored on off-chain servers or platforms. This paper analyzes the possibilities for the preservation of art hosted on the chain. Despite some limitations, this method is understood as the more coherent and robust for long-term life and availability of digital art associated with Web3 technology. [ABSTRACT FROM AUTHOR]

Purpose: Community-generated digital content (CGDC) is one of the UK's prime cultural assets. However, CGDC is currently "critically endangered" (Digital Preservation Coalition, 2021) due to technological and organisational barriers and has proven resistant to traditional methods of linking and integration. The challenge of integrating CGDC into larger archives has effectively silenced diverse community voices within our national collection. Our Heritage, Our Stories (OHOS), funded by the UK's AHRC programme Towards a National Collection, responds to these urgent challenges by bringing together cutting-edge approaches from cultural heritage, humanities and computer science. Design/methodology/approach: Existing solutions to CGDC integration, involving bespoke interventionist activities, are expensive, time-consuming and unsustainable at scale, while unsophisticated computational integration erases the meaning and purpose of both CGDC and its creators. Using innovative multidisciplinary methods, AI tools and a co-design process, previously unfindable and unlinkable CGDC will be made discoverable in our virtual national collection. Findings: There currently exists a range of disconnected, fragile and under-represented community-generated heritage which is at increasing risk of loss. Therefore, OHOS will work to ensure the survival and preservation of these nationally important resources, for the future and for our shared national collection. Originality/value: As we dissolve barriers to create meaningful new links across CGDC collections and develop new methods of engagement, OHOS will also make this content accessible to new and diverse audiences. This will facilitate a wealth of fresh research while also embedding new strategies for future management of CGDC into heritage practice and training and fostering newly enriching, robust connections between communities and archival institutions. [ABSTRACT FROM AUTHOR]

The present study focuses on the activities and efforts of the National Mission for Manuscripts (NMM) in the Uttar Pradesh region, which is known for its vast area, population, and rich cultural heritage. The aim is to examine the digitization work carried out by the NMM in this area, as digitization plays a crucial role in preserving our country’s rich ancient heritage. The importance of safeguarding cultural heritage is universally acknowledged, and digitization serves as a vital tool in this endeavor. Through digitization, we can protect and preserve our heritage for future generations. The government has implemented several commendable initiatives for manuscript digitization, and the NMM stands as a prominent organization dedicated to the conservation of cultural heritage. The NMM possesses a diverse range of cultural heritage resources, including photographic slides, photographs, digital images, photo-negatives, motion pictures, audio spools, microfiche, LP records, endangered manuscripts, audio and videotapes, digital images, microfilms, digital audio and video files, and more. The mission has undertaken extensive digitization efforts to conserve and provide access to a significant portion of its collection. This study is unique as it explores the digital conservation and digitization practices of a premier institute working in the field of art and cultural heritage in Uttar Pradesh. With its extensive network of institutions, the mission aims to cover all manuscripts, digitize them, and consolidate them on a common platform for easy access and utilization. [ABSTRACT FROM AUTHOR]

Conservation science, also known as heritage science or cultural heritage science, is a field that uses analytical methods to study art, archaeology, architecture, and archives. It combines concepts from various technical fields such as anthropology, chemistry, physics, and computer science. Advances in analytical methods have allowed for non-destructive analysis of art objects, the development of portable equipment for in-situ analysis, and collaborations with scientists from other fields. Challenges in the field include addressing global issues like climate change and sustainable tourism, integrating artificial intelligence into analytical techniques, and finding sustainable solutions for the display and storage of artwork. Collaboration and access to state-of-the-art instrumentation are crucial in cultural heritage research. This special issue aims to highlight recent advances in analytical methods for cultural heritage and address gaps in the field. [Extracted from the article]

Cultural heritage crowdsourcing has emerged as a promising approach to address the challenges of digitizing and preserving cultural heritage, contributing to the sustainable development goals of cultural preservation and digital inclusivity. However, the long-term sustainability of these projects faces numerous obstacles. This study explores the key configurational determinants and dynamic evolutionary mechanisms driving the sustainable development of cultural heritage crowdsourcing projects, aiming to enhance their longevity and impact. An innovative integration of fuzzy-set qualitative comparative analysis (fsQCA) and system dynamics (SD) is employed, drawing upon a "resource coordination–stakeholder interaction–value co-creation" analytical framework. Through a multi-case comparison of 18 cultural heritage crowdsourcing projects, we identify necessary conditions for project sustainability, including platform support, data resources, knowledge capital, and digitalization performance. The study reveals multiple sufficient pathways to sustainability through configurational combinations of participant motivation, innovation drive, social capital, and social impact. Our system dynamics analysis demonstrates that crowdsourcing project sustainability exhibits significant nonlinear dynamic characteristics, influenced by the interaction and emergent effects of the resource–participation–performance chain. This research offers both theoretical insights and practical guidance for optimizing crowdsourcing mechanisms and sustainable project operations, contributing to the broader goals of sustainable cultural heritage preservation and digital humanities development. The findings provide a roadmap for policymakers and project managers to design and implement more sustainable and impactful cultural heritage crowdsourcing initiatives, aligning with global sustainability objectives in the digital age. [ABSTRACT FROM AUTHOR]

The notion ‘Convexity’ is applied in various areas of mathematics particularly in optimization techniques. It is known that this concept is applied in fuzzy sets, which is studied by many authors. This article deals with convexity utilized for neutrosophic and interval valued neutrosophic sets which is a generalization of intuitionistic fuzzy sets. Also some interesting preservation properties of convexity under intersection operators in interval valued neutrosophic sets are discussed. Adding to the discussion, preservation property of digital convexity under intersection using deformation and other techniques are touched upon. Eventually the application of convexity to decision making are illustrated using examples. [ABSTRACT FROM AUTHOR]

Image denoising has gained in relevance as a component of image preprocessing due to the increased use of digital images in a range of applications, as well as the degradation of image quality caused by noise introduced by unavoidable occurrences. This work suggests a novel two-stage filter to remove salt and pepper noise from the images. It operates in two stages, the first stage uses an enhanced adaptive type-2 fuzzy noise identifier to identify the corrupted pixel, and the second stage uses a simple linear regression-based approach filter to denoise the corrupted pixel. We first identify a pixel as corrupted or uncorrupted using an improved adaptive type-2 fuzzy-based Gaussian membership function with variables both mean and variance for a specific corrupted image frame. The second step is denoising the damaged pixel using a linear regression-based technique. Herein, we propose a novel co-design method that uses the Gaussian membership function for detection and a linear regression-based denoising technique without any parameter tuning, resulting in better time efficiency. We validate the proposed improved adaptive type-2 fuzzy detection and linear regression-based filter (IAFDLRBF) on a variety of standard images and real-time images with varying noise density. We compare the simulation results with various state-of-the-art methods in terms of various assessment metrics. The results demonstrate the effectiveness of the proposed filter even at high noise densities by providing better detail and edge preservation of an image. [ABSTRACT FROM AUTHOR]

This persuasive position paper aims to discuss the public policy gaps in digital preservation identified by UNESCO and persuade the development of the current digital public policies. To evidence these gaps, the principal topics of digital public policies (digital strategy) from four countries were analyzed: Brazil, France, Spain, and the United Kingdom. The analysis evidence that these digital public policies are focused on the modernization of public administration and cybersecurity; they do not contemplate digital preservation. To support the position, current legislation in record management is indicated to connect the preservation of physical and digital documents. The 2030 Agenda for Sustainable Development is also used to persuade because digital preservation is an indispensable component of sustainable development. That is because today, large amounts of cultural heritage are created and only available digitally, and never "fixed" in a physical form, museums, archives, and libraries, also embarking on an epic journey to digitization and "no development is sustainable without considering culture." Digital culture heritage must be available for future generations, and only digital preservation can guarantee that; therefore, digital preservation must be treated as a public policy. [ABSTRACT FROM AUTHOR]

La preservación digital sistémica representa un enfoque archivístico y estratégico necesario para su comprensión integral y para garantizar la continuidad del patrimonio digital, el acceso y la disponibilidad de los documentos electrónicos y objetos digitales a largo plazo. La propuesta metodológica de preservación digital que se presenta se basa en la sistematización de experiencias de consultoría y asesoría desarrolladas por el Centro de Investigaciones en Ciencia de la Información (CICINF) de la Escuela Interamericana de Bibliotecología de la Universidad de Antioquia (Medellín, Colombia). Esta sistematización presenta la metodología de consultoría, bajo un enfoque cualitativo, el cual se caracteriza por la interacción universidad-sector-estado, el trabajo interdisciplinario, la sustentación teórica, la valoración y estimación de propuestas acorde con los contextos institucionales. Algunos de los resultados enmarcan la importancia del diagnóstico, la política, el plan, los proyectos, el programa y los procesos de preservación digital y la necesidad de documentar las prácticas, en tanto este es un tema especializado que requiere un nivel de profundidad adecuado en relación con los contextos sociales y tecnologías que cambian y se transforman permanentemente = Systemic digital preservation represents an archival and strategic approach necessary for its comprehensive understanding and to ensure the continuity of digital heritage, access and availability of electronic records and digital objects in the long term. The digital preservation methodological proposal presented here is based on the systematization of consulting and advisory experiences developed by the Information Science Research Center (CICINF) of the Inter-American School of Librarianship of the University of Antioquia (Medellin, Colombia). This systematization presents the consulting methodology, under a qualitative approach, which is characterized by the interaction between university-sector-state, interdisciplinary work, theoretical support, evaluation and estimation of proposals according to the institutional contexts. Some of the results frame the importance of the diagnosis, policy, plan, projects, program and processes of digital preservation and the need to document the practices, as this is a specialized subject that requires an adequate level of depth in relation to the social contexts and technologies that are constantly changing and transforming.

Digital preservation is widely recognized as a critical necessity for organizations worldwide. Archival professionals consider it a pressing issue, recognizing its central role in contemporary information management practices. This study aims to illuminate the challenges and opportunities in this field, specifically focusing on the accessibility and preservation of institutional archives in the Sultanate of Oman. Utilizing a mixed-methods research design, it includes an extensive review of relevant literature and semi-structured interviews with administrative personnel from various public organizations in Oman. A purposive sampling strategy was employed to select 10 individuals, each possessing the required expertise and experience in this field, from different government agencies to participate in these interviews. The findings indicate that professionals in Omani institutions highly value digital preservation, citing reasons such as enhanced document accessibility, improved organizational efficiency and security, and compliance with national archival regulations. Despite this, the methods used for digital preservation vary across institutions. The study identifies several obstacles, including threats to digital security, the complexity of managing vast amounts of data, the rapid outdatedness of technology, and challenges related to human resource management. [ABSTRACT FROM AUTHOR]

Point cloud registration technology, by precisely aligning repair components with the original artifacts, can accurately record the geometric shape of cultural heritage objects and generate three-dimensional models, thereby providing reliable data support for the digital preservation, virtual exhibition, and restoration of cultural relics. However, traditional point cloud registration methods face challenges when dealing with cultural heritage data, including complex morphological and structural variations, sparsity and irregularity, and cross-dataset generalization. To address these challenges, this paper introduces an innovative method called Enhancing Point Cloud Registration with Transformer (EPCRT). Firstly, we utilize local geometric perception for positional encoding and combine it with a dynamic adjustment mechanism based on local density information and geometric angle encoding, enhancing the flexibility and adaptability of positional encoding to better characterize the complex local morphology and structural variations of artifacts. Additionally, we introduce a convolutional-Transformer hybrid module to facilitate interactive learning of artifact point cloud features, effectively achieving local–global feature fusion and enhancing detail capture capabilities, thus effectively handling the sparsity and irregularity of artifact point cloud data. We conduct extensive evaluations on the 3DMatch, ModelNet, KITTI, and MVP-RG datasets, and validate our method on the Terracotta Warriors cultural heritage dataset. The results demonstrate that our method has significant performance advantages in handling the complexity of morphological and structural variations, sparsity and irregularity of relic data, and cross-dataset generalization. [ABSTRACT FROM AUTHOR]

As an important representative of ancient Chinese cultural heritage, the classification of Terracotta Warriors point cloud data aids in cultural heritage preservation and digital reconstruction. However, these data face challenges such as complex morphological and structural variations, sparsity, and irregularity. This paper proposes a method named Multi-scale Local Geometric Transformer-Mamba (MLGTM) to improve the accuracy and robustness of Terracotta Warriors point cloud classification tasks. To effectively capture the geometric information of point clouds, we introduce local geometric encoding, including local coordinates and feature information, effectively capturing the complex local morphology and structural variations of the Terracotta Warriors and extracting representative local features. Additionally, we propose a multi-scale Transformer-Mamba information aggregation module, which employs a dual-branch Transformer with a Mamba structure and finally aggregates them on multiple scales to effectively handle the sparsity and irregularity of the Terracotta Warriors point cloud data. We conducted experiments on several datasets, including the ModelNet40, ScanObjectNN, ShapeNetPart, ETH, and 3D Terracotta Warriors fragment datasets. The results show that our method significantly improves the classification task of Terracotta Warriors point clouds, demonstrating strong accuracy. [ABSTRACT FROM AUTHOR]

Painted cultural relics hold significant historical value and are crucial in transmitting human culture. However, mold is a common issue for paper or silk-based relics, which not only affects their preservation and longevity but also conceals the texture, patterns, and color information, hindering cultural value and heritage. Currently, the virtual restoration of painting relics primarily involves filling in the RGB based on neighborhood information, which might cause color distortion and other problems. Another approach considers mold as noise and employs maximum noise separation for its removal; however, eliminating the mold components and implementing the inverse transformation often leads to more loss of information. To effectively acquire virtual restoration for mold removal from ancient paintings, the spectral characteristics of mold were analyzed. Based on the spectral features of mold and the cultural relic restoration philosophy of maintaining originality, a 3D CNN artifact restoration network was proposed. This network is capable of learning features in the near-infrared spectrum (NIR) and spatial dimensions to reconstruct the reflectance of visible spectrum, achieving the virtual restoration for mold removal of calligraphic and art relics. Using an ancient painting from the Qing Dynasty as a test subject, the proposed method was compared with the Inpainting, Criminisi, and inverse MNF transformation methods across three regions. Visual analysis, quantitative evaluation (the root mean squared error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MEA), and a classification application were used to assess the restoration accuracy. The visual results and quantitative analyses demonstrated that the proposed 3D CNN method effectively removes or mitigates mold while restoring the artwork to its authentic color in various backgrounds. Furthermore, the color classification results indicated that the images restored with 3D CNN had the highest classification accuracy, with overall accuracies of 89.51%, 92.24%, and 93.63%, and Kappa coefficients of 0.88, 0.91, and 0.93, respectively. This research provides technological support for the digitalization and restoration of cultural artifacts, thereby contributing to the preservation and transmission of cultural heritage. [ABSTRACT FROM AUTHOR]

Preservation and accessibility of cultural heritage have become essential with the rapid advancements of globalization and modernization endeavors constantly threatening historical artifacts and sites across the world. The advent of the metaverse, with its immersive and interactive capabilities, presents a novel approach to safeguarding and promoting cultural heritage. The current study aims to explore the potential of metaverse applications in digitalizing cultural heritage, encompassing virtual reconstructions, educational outreach, global accessibility, and sustainability. It conducts a systematic literature review using PRISMA guidelines to collect, screen, and evaluate existing research enquiries into investigating current practices and technologies in the digitalization of heritage preservation. The study also conducts case study research to investigate the challenges and opportunities associated with the virtual preservation of tangible and intangible cultural assets, including issues of authenticity, representation, and sustainability. Furthermore, it evaluates the impact of metaverse applications on public engagement and cultural education. The findings provide valuable insights into developing innovative strategies for preserving cultural heritage in the digital age while highlighting best practices and guidelines for optimizing metaverse applications in cultural heritage preservation. [ABSTRACT FROM AUTHOR]

Cancers arising from the gingivobuccal complex are one of the most common types of cancer in the oral cavity and are associated with poor prognosis. Among the various prognostic factors, positive surgical margin is the most important one that can be controlled by the operating surgeon. The deep surgical margins for buccal mucosa cancers is normally assessed by palpating the skin for induration and skin pinchability. The present study evaluates the role of imaging in assessing the deep surgical margin and its efficacy for skin preservation in buccal mucosa/ gingivobuccal carcinomas. The patients of gingivobuccal complex squamous cell carcinomas after histopathological confirmation were selected for the study. In imaging, the distance between the base of the tumour and skin (epidermis) of the cheek was measured by a senior radiologist preoperatively. The frozen section findings were confirmed by histopathological examination and the depth of invasion of the tumour was measured and the clearance of the deep surgical margin was confirmed. The correlation between imaging, skin pinch test and histopathological examination of the specimen was assessed. The sensitivity and specificity of imaging to predict the skin preservation (deep surgical margin more than 5 mm) is 100% and 75% respectively compared to sensitivity and specificity of skin pinch test of 82.6% and 50% respectively. Imaging is an effective tool in predicting the skin preservation and skin excision compared to skin pinch test. Compared to the skin pinch test, imaging appears to be a useful tool for advising surgeons on skin preservation vs excision. [ABSTRACT FROM AUTHOR]

Oil paintings represent significant cultural heritage, as they embody human creativity and historical narratives. The preservation of these invaluable artifacts requires effective maintenance practices to ensure their longevity and integrity. Despite their inherent durability, oil paintings are susceptible to mechanical damage and chemical deterioration, necessitating rigorous conservation efforts. Traditional preservation techniques that have been developed over centuries involve surface treatment, structural stabilization, and gel-based cleaning to maintain both the integrity and aesthetic appeal of these artworks. Recent advances in artificial intelligence (AI)-powered predictive maintenance techniques offer innovative solutions to predict and prevent deterioration. By integrating image analysis and environmental monitoring, AI-based models provide valuable insights into painting preservation. This review comprehensively analyzes traditional and AI-based techniques for oil painting maintenance, highlighting the importance of adopting innovative approaches. By integrating traditional expertise with AI technology, conservators can enhance their capacity to maintain and preserve these cultural treasures for future generations. [ABSTRACT FROM AUTHOR]

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Reducing the initial bacteria number from meat and extending its shelf life are crucial factors for ensuring product safety and enhancing economic benefits for enterprises. Currently, controlling enzyme activity and the microbial survival environment is a common approach to reducing the rate of deterioration in raw meat materials, thereby achieving the goal of bacteria reduction during storage and preservation. This review summarizes the commonly used technologies for reducing bacteria in meat, including slightly acidic electrolyzed water (SAEW), organic acids, ozone (O3), ultrasound, irradiation, ultraviolet (UV), cold plasma, high-pressure processing (HPP), and biological bacterial reduction agents. This review outlines the mechanisms and main features of these technologies for reducing bacteria in meat processing. Additionally, it discusses the status of these technologies in meat storage and preservation applications while analyzing associated problems and proposing solutions. The aim is to provide valuable references for research on meat preservation technology. [ABSTRACT FROM AUTHOR]

In computer vision tasks, the ability to remove rain from a single image is a crucial element to enhance the effectiveness of subsequent high-level tasks in rainy conditions. Recently, numerous data-driven single-image deraining techniques have emerged, primarily relying on paired images (i.e., in a supervised manner). However, when dealing with real deraining tasks, it is common to encounter unpaired images. In such scenarios, removing rain streaks in an unsupervised manner becomes a challenging task, as there are no constraints between images, resulting in suboptimal restoration results. In this paper, we introduce a new unsupervised single-image deraining method called SE-RRACycleGAN, which does not require a paired dataset for training and can effectively leverage the constrained transfer learning capability and cyclic structures inherent in CycleGAN. Since rain removal is closely associated with the analysis of texture features in an input image, we proposed a novel recurrent rain attentive module (RRAM) to enhance rain-related information detection by simultaneously considering both rainy and rain-free images. We also utilize the squeeze-and-excitation enhancement technique to the generator network to effectively capture spatial contextual information among channels. Finally, content loss is introduced to enhance the visual similarity between the input and generated images. Our method excels at removing numerous rain streaks, preserving a smooth background, and closely resembling the ground truth compared to other approaches, based on both quantitative and qualitative results, without the need for paired training images. Extensive experiments on synthetic and real-world datasets demonstrate that our approach shows superiority over most unsupervised state-of-the-art techniques, particularly on the Rain12 dataset (achieving a PSNR of 34.60 and an SSIM of 0.954) and real rainy images (achieving a PSNR of 34.17 and an SSIM of 0.953), and is highly competitive when compared to supervised methods. Moreover, the performance of our model is evaluated using RMSE, FSIM, MAE, and the correlation coefficient, achieving remarkable results that indicate a high degree of accuracy in rain removal and strong preservation of the original image's structural details. [ABSTRACT FROM AUTHOR]

AbstractThe purpose of this paper was to discuss the collaborative initiative between the Mona Library (ML) and the Library and Information Association of Jamaica (LIAJA) focused on digitizing the Association’s peer-reviewed publications, specifically the Jamaica Library Association (JLA) Bulletin and the LIAJA Bulletin, for enhanced access and preservation. The paper also addressed the compilation of a second index for the LIAJA Bulletin, covering the period from 2007 to its electronic publication in 2014. This second index was intended to complement the first index, which was compiled from 1950 to 2006. This collaborative project presented numerous challenges and benefits, offering valuable insights for future initiatives. Key benefits for the library included the enhancement of staff competencies, strengthening of institutional reputation, increased growth and visibility, and the preservation of Jamaican and Caribbean librarians’ scholarly output for future generations. [ABSTRACT FROM AUTHOR]