Search Results (106)

The last decade has seen a transformative advancement in computational technologies, enabling the precise creation, evaluation, visualization, and reproduction of high-fidelity three-dimensional (3D) models of archaeological sites and artefacts. With the advent of 3D printing, both small- and large-scale objects can now be reproduced with remarkable accuracy and at customizable scales. Artefacts composed of organic materials—such as wood—are inherently susceptible to biological degradation and thus require extensive, long-term conservation employing costly methodologies. These procedures often raise environmental concerns and lead to irreversible alterations in the wood’s chemical composition, dimensional properties, and the intangible essence of the original artefact. In the context of public education and the dissemination of knowledge about historical technologies and objects, 3D replicas can effectively fulfill the same purpose as original artefacts, without compromising interpretative value or cultural significance. Furthermore, the digital data embedded in 3D surface and object models provides a wealth of supplementary information that cannot be captured, preserved, or documented through conventional techniques. Waterlogged wooden objects can now be thoroughly documented in 3D, enabling ongoing, non-invasive scientific analysis. Given these capabilities, it is imperative to revisit the philosophical and ethical foundations of preserving waterlogged wood and to adopt innovative strategies for the conservation and presentation of wooden artefacts. These new paradigms can serve educational, research, and outreach purposes—core functions of contemporary museums. Full article

Objective: To test whether hypervitaminosis B12 is useful for prognosis of all-cause mortality. Methods: Meta-analysis of longitudinal, observational, epidemiologic studies. PubMed, Scopus, Web of Science, Google Scholar, and ProQuest One Academic were searched from inception to 30 June 2024. Studies including humans aged ≥18 years with hypervitaminosis B12, and in whom the outcome variable was all-cause mortality, were included. Two reviewers screened, abstracted (using standardized data collection sheet), and appraised articles (ROBINS-E framework) independently. Frequentist and Bayesian approaches were used for the meta-analysis. Results: A total of 28 studies were included in the meta-analysis (among the 69,610 participants, 15,815 all-cause deaths were reported). High serum levels of B12 increased marginally the risk of all-cause mortality specifically among chronic diseases (RR = 1.40; 95% IC = 1.05 to 1.85) and hospitalized (RR = 1.57; 95% IC = 1.19 to 2.07). In the meta-regression, these results were not statistically significant. The Bayesian analysis confirmed the risks of the mentioned groups; however, it was limited by the number of studies that contained the necessary information. The methodology applied and the clinical heterogeneity of each included study bring up the idea that artefacts might be involved when mortality is found to be high for hypervitaminosis B12. Conclusions: This meta-analysis did not show that hypervitaminosis B12 represents a higher risk of all-cause mortality in adults. Full article

This study examines the architectural ceramic corpus—comprising azulejos (tiles) and alicatados (tiling mosaics)—preserved in the Alhambra Museum, with the aim of elucidating its historical, artistic, and technical significance. Through a systematic methodology combining visual analysis, documentary research, and typological classification, a representative selection of ceramic artefacts was assessed. This article explores the artistic characteristics and technological principles of pieces produced using painted, relief, metallic lustre, incrustación, alicatado, cuerda seca, and arista techniques and reconstructs the historical trajectory of these decorative practices, tracing their origins in the pre-Islamic world to their adaptation within the Alhambra Palatine City. This diachronic perspective contextualises the innovations observed in the citadel, where production strategies reflect both inherited traditions and local adaptations across different historical phases. The findings highlight the richness and diversity of the Nasrid (mediaeval era) and Christian (modern era) ceramic legacy in the Alhambra and contribute to a more nuanced understanding of manufacturing processes and conservation challenges associated with these architectural elements. This preliminary characterisation establishes a basis for future material analysis and supports broader initiatives in documentation and heritage management. Full article

The current research presents an extension of the Pit-Stop Manufacturing framework. It addresses the challenges of managing complexity and uncertainty in the production ramp-up phase of manufacturing systems, bridging the gap in existing approaches that lack comprehensive, quantitative, and system-level solutions. This research integrates state-of-the-art methodologies, utilising such metrics as Overall Equipment Effectiveness and Effective Throughput Loss to enhance ramp-up management. The developed framework is represented by a conceptual model, which is translated into a digital product combining multiple artefacts for comprehensive ramp-up research, namely a digital twin of the production system, a Custom Experiment Manager for multiple simulation runs, and a Graph Solver that uses the stochastic dynamic programming approach to address the decision-making issues during the production system ramp-up evolution. This work provides a robust decision-support tool to optimise production transitions under dynamic conditions by combining stochastic dynamic programming and discrete event simulation. The framework enables manufacturers to model, simulate, and optimise system evolution, reducing throughput losses, improving equipment efficiency, and enhancing decision-making precision. This paper demonstrates the framework’s potential to streamline ramp-up processes and boost competitiveness in volatile manufacturing environments. Full article

In March 2022, an auction house in Zurich sold two female figurines made from mammoth ivory, along with other prehistoric artefacts. This is a rare occurrence because the scarcity and value of Paleolithic figurines have limited their presence in the international art market. Researchers from the Archaeological Museum Hamburg and the University of Tübingen subsequently undertook in situ and non-destructive investigations to illuminate the authenticity of the two figurines. We conducted a comprehensive analytical study that included detailed microscopic optical observations and spectroscopic investigations. This methodological approach, combined with a thorough comparison to contemporary ivory figurine replicas, proved effective and clearly demonstrated that the specimens were forgeries. Research efforts of this kind are crucial, as they significantly help reduce the spread of intentional fakes posing as genuine artefacts in the art market. By doing so, we foster collaboration between academic institutions and the art market to preserve and protect the integrity and value of authentic archaeological and cultural heritage. Full article

The proliferation of Internet of Things (IoT) devices presents significant challenges for cybersecurity and digital forensics, particularly as these devices have become increasingly weaponised for malicious activities. This research focuses on the forensic analysis capabilities of Raspberry Pi devices configured with Kali Linux, comparing their forensic capabilities to conventional PC-based forensic investigations. The study identifies key gaps in existing IoT forensic methodologies, including limited tool compatibility, constrained data retention, and difficulties in live memory analysis due to architectural differences. The research employs a testbed-based approach to simulate cyberattacks on both platforms, capturing and analysing forensic artefacts such as system logs, memory dumps, and network traffic. The research findings reveal that while traditional PCs offer extensive forensic capabilities due to superior storage, tool support, and system logging, Raspberry Pi devices present significant forensic challenges, primarily due to their ARM architecture and limited forensic readiness. The study emphasises the need for specialised forensic tools tailored to IoT environments and suggests best practices to enhance forensic investigation capabilities in weaponised IoT scenarios. This research contributes to the field by bridging the gap between theoretical frameworks and real-world forensic investigations, offering insights into the evolving landscape of IoT forensics and its implications for digital evidence collection, analysis, and forensic readiness. Full article

The paper explores the correlation between the concepts of territoriality and social practices in the context of urban and rural (re-)production of space. It traces the degree of “habitualisation” of certain actions and the behaviour of stakeholders, identifying those defined as practices, and revealing their role in the (dis-)continuation of territoriality of a region. It takes a German region Ostwestfalen-Lippe (OWL) as a case study. The research methodology is based on the practice theory of Andreas Reckwitz and his “praxeological quadrat of cultural analysis”, which is applied in this study. The research process includes (a) semi-structured interviews with the representatives of several institutions from the region, (b) narrative analysis and thematic content structuring of the interviews and (c) synthesis analysis. The study clarifies relations between the artefacts and discourses mentioned by the interviewees, and their impact on the practices of the institutions and others contributing to the (dis-)continuation of territoriality and identity of the region. The main findings are related to the (1) methodological contribution—operationalisation of the “praxeological quadrat of cultural analysis”, and (2) substantive contribution—revealing the role of social practices on the continuation of territoriality of the region. The article presents cultural patterns in the perception of and orientation towards long-past territorialities by the interviewees and makes clear what significance these historical and historicising spatial references have for the spatial planning of the present. Full article

Understanding the deterioration processes in wooden artefacts is essential for accurately assessing their conservation status and developing effective preservation strategies. Advanced imaging techniques are currently being explored to study the impact of chemical changes on the structural and mechanical properties of wood. Nonlinear optical modalities, including second harmonic generation (SHG) and two-photon excited fluorescence (TPEF), combined with fluorescence lifetime imaging microscopy (FLIM), offer a promising non-destructive diagnostic method for evaluating lignocellulose-based materials. In this study, we employed a nonlinear multimodal approach to examine the effects of artificially induced delignification on samples of Norway spruce (Picea abies) and European beech (Fagus sylvatica) subjected to increasing treatment durations. The integration of SHG/TPEF imaging and multi-component fluorescence lifetime analysis enabled the detection of localized variations in nonlinear signals and τ-phase of key biopolymers within wood cell walls. This methodology provides a powerful tool for early detection of wood deterioration, facilitating proactive conservation efforts of wooden artefacts. Full article

Microscopic image segmentation (MIS) is a fundamental task in medical imaging and biological research, essential for precise analysis of cellular structures and tissues. Despite its importance, the segmentation process encounters significant challenges, including variability in imaging conditions, complex biological structures, and artefacts (e.g., noise), which can compromise the accuracy of traditional methods. The emergence of deep learning (DL) has catalyzed substantial advancements in addressing these issues. This systematic literature review (SLR) provides a comprehensive overview of state-of-the-art DL methods developed over the past six years for the segmentation of microscopic images. We critically analyze key contributions, emphasizing how these methods specifically tackle challenges in cell, nucleus, and tissue segmentation. Additionally, we evaluate the datasets and performance metrics employed in these studies. By synthesizing current advancements and identifying gaps in existing approaches, this review not only highlights the transformative potential of DL in enhancing diagnostic accuracy and research efficiency but also suggests directions for future research. The findings of this study have significant implications for improving methodologies in medical and biological applications, ultimately fostering better patient outcomes and advancing scientific understanding. Full article

In the evolving landscape of computer vision, the integration of machine learning algorithms with cutting-edge hardware platforms is increasingly pivotal, especially in the context of disruptive healthcare systems. This study introduces an optimized implementation of a Convolutional Neural Network (CNN) on the Basys3 FPGA, designed specifically for accelerating the classification of cytotoxicity in human kidney cells. Addressing the challenges posed by constrained dataset sizes, compute-intensive AI algorithms, and hardware limitations, the approach presented in this paper leverages efficient image augmentation and pre-processing techniques to enhance both prediction accuracy and the training efficiency. The CNN, quantized to 8-bit precision and tailored for the FPGA’s resource constraints, significantly accelerates training by a factor of three while consuming only 1.33% of the power compared to a traditional software-based CNN running on an NVIDIA K80 GPU. The network architecture, composed of seven layers with excessive hyperparameters, processes downscale grayscale images, achieving notable gains in speed and energy efficiency. A cornerstone of our methodology is the emphasis on parallel processing, data type optimization, and reduced logic space usage through 8-bit integer operations. We conducted extensive image pre-processing, including histogram equalization and artefact removal, to maximize feature extraction from the augmented dataset. Achieving an accuracy of approximately 91% on unseen images, this FPGA-implemented CNN demonstrates the potential for rapid, low-power medical diagnostics within a broader IoT ecosystem where data could be assessed online. This work underscores the feasibility of deploying resource-efficient AI models in environments where traditional high-performance computing resources are unavailable, typically in healthcare settings, paving the way for and contributing to advanced computer vision techniques in embedded systems. Full article

It is difficult to achieve high-precision color reproduction using traditional color reproduction methods when the angle is changed, and, for large-sized artefacts, it is also significantly difficult to collect a large amount of data and reproduce the colors. In this paper, we use three Bidirectional Reflectance Distribution Function (BRDF) modeling methods based on spectral imaging techniques, namely, the five-parameter model, the Cook–Torrance model and the segmented linear interpolation model. We investigated the color reproduction of color chips with matte surfaces and Chinese paintings with rough surfaces under unknown illumination angles. Experiments have shown that all three models can effectively perform image reconstruction under small illumination angle intervals. The segmented linear interpolation model exhibits a higher stability and accuracy in color reconstruction under small and large illumination angle intervals; it can not only reconstruct color chips and Chinese painting images under any illumination angle, but also achieve high-quality image color reconstruction standards in terms of objective data and intuitive perception. The best test model (segmented linear interpolation) performs well in reconstruction, reconstructing Chinese paintings at 65° and 125° with an illumination angle interval of 10°. The average RMSE of the selected reference color blocks is 0.0450 and 0.0589, the average CIEDE2000 color difference is 1.07 and 1.50, and the SSIM values are 0.9227 and 0.9736, respectively. This research can provide a theoretical basis and methodological support for accurate color reproduction as well as the large-sized scientific prediction of artifacts at any angle, and has potential applications in cultural relic protection, art reproduction, and other fields. Full article

This study explores the enactment of critical thinking policies in Chinese senior high schools through the lens of Ball et al.’s policy enactment theory and within the broader context of Chinese education reform aimed at enhancing students’ thinking abilities. Employing a case study methodology with diverse data types, the research assessed current school-level practices and the effectiveness of Ball et al.’s framework in capturing interactions among objective contexts, policy actors, and cultural artefacts. Findings indicate that the framework captures these complexities when the policy is actively enacted. In one school, a systematic enactment mechanism facilitated diverse policy roles and external connections, thereby promoting schoolwide critical thinking development; however, another school exhibited fragmented practices due to the lack of key policy roles, despite students’ interest in deeper engagement. Challenges were also identified, including deficiencies in the mid-level education bureau and conflicts between a collective-oriented educational paradigm and the promotion of independent thinking. The study unravelled the nuances of the enactment of critical thinking policies in Chinese senior high schools. Future research could test the framework’s applicability for guiding the construction of policy mechanisms across different settings. Full article

Background: Older adults in residential aged care facilities (RACFs) experience disproportionate levels of poor oral health relative to other groups in the general population, affecting their physical and mental wellbeing. The Oral Health Assessment Tool (OHAT) is a validated and widely used dental assessment tool; however, recent systematic reviews have identified shortcomings with respect to its measurement properties. Objective: The objective of this protocol is to provide a detailed overview of a multidisciplinary qualitative study that aims to (a) co-design and develop a modified OHAT for RACFs and (b) inform the development of an OHAT training package and implementation strategies. Methods: This study will utilize a co-design methodology with aged care residents, caregivers, staff members, and health professionals. The co-design workshops will: (1) investigate the barriers to and enablers of optimal oral healthcare in RACFs; and (2) co-design a modified version of the Oral Health Assessment Tool and a referral to treatment pathway that is appropriate for use in RACFs. The co-design workshops will facilitate group discussion and involve interactive activities using, for example, mind mapping and Sticky Notes. Qualitative data (transcripts and artefacts from co-design activities) will be analyzed in NVivo using an inductive codebook thematic analysis, specifically a template analysis. Conclusion: The findings of this study will inform a modified OHAT (M-OHAT), as well as future study phases regarding training and implementation strategies. It is expected that the M-OHAT will have enhanced usability and relevance to RACFs, facilitating the identification of poor oral health and timely referral to dental treatment. Full article

Vernacular architecture offers valuable technological and typological solutions rooted in knowledge systems that connect environmental, social, economic, and cultural contexts. This paper introduces the Heritage for People Platform, a collaborative digital tool developed under the VerSus+ project (Creative Europe Program), aimed at organising and disseminating vernacular architectural knowledge with a focus on sustainable practices that are both culturally and environmentally conscious. The platform builds upon lessons learned from similar projects, offering key improvements such as the adoption of a Case-Based Reasoning methodology, which organises examples by geographic location, materials, and intervention types. A structured classification system based on sustainability principles and strategies enables cross-disciplinary research and comparison. The design process was highly participatory, incorporating user feedback at every stage to ensure the platform is accessible to a wide range of users, including designers, scholars, craftspeople, and the general public. A cross-device, user-centered approach further broadens its accessibility. The platform provides a public, web-based geospatial repository where users can create, update, and explore a dynamic collection of artefacts, design models, people, and institutions from across the globe. This tool marks a significant advance in promoting and preserving both physical and intangible heritage, with strong educational and practical applications for sustainable architecture. The paper details the theoretical framework and real-world potential of the platform as a critical resource for promoting sustainability in the built environment. Full article

The complexity and interconnection within the financial ecosystem demand innovative solutions to improve transparency, security, and efficiency in financial reporting and liquidity management, while also reducing accounting fraud. This paper presents Blockchain Financial Statements (BFS), an innovative accounting system designed to address accounting fraud, reduce data manipulation, and misrepresentation of company financial claims, by enhancing availability of the real-time and tamper-proof accounting data, underpinned by a verifiable approach to financial transactions and reporting. The primary goal of this research is to design, develop, and validate a blockchain-based accounting prototype—the BFS system—that can automate transformation of transactional data, generated by traditional business activity into comprehensive financial statements. Incorporating a Design Science Research Methodology with Domain-Driven Design, this study constructs a BFS artefact that harmonises accounting standards with blockchain technology and business orchestration. The resulting Java implementation of the BFS system demonstrates successful integration of blockchain technology into accounting practices, showing potential in real-time validation of transactions, immutable record-keeping, and enhancement of transparency and efficiency of financial reporting. The BFS framework and implementation signify an advancement in the application of blockchain technology in accounting. It offers a functional solution that enhances transparency, accuracy, and efficiency of financial transactions between banks and businesses. This research underlines the necessity for further exploration into blockchain’s potential within accounting systems, suggesting a promising direction for future innovations in tamper-evident financial reporting and liquidity management. Full article