Search Results (18)

Artificial intelligence (AI) is a cutting-edge and revolutionary technology in computer science that has the potential to completely transform a wide range of disciplines, including the social sciences, the arts, and the humanities. Therefore, since its significance has been recognized in engineering and medicine, history, literature, paleography, and archaeology have recently embraced AI as new opportunities have arisen for preserving ancient manuscripts. Acknowledging the importance of digitizing archival documents, this paper explores the use of advanced technologies during this process, showing how these are employed at each stage and how the unique challenges inherent in past scripts are addressed. Our study is based on Cyril’s Lexicon, a Byzantine-era dictionary of great historical and linguistic significance in Greek territory. Full article

Historical archives hold untapped potential for understanding long-term biodiversity change. This study introduces computational approaches to historical ecology, combining archival research, text analysis, and spatial mapping to reconstruct past biodiversity patterns. Using the 1845 Bavarian Animal Observation Dataset (AOD1845), a comprehensive survey of vertebrate species across 119 districts, we transform 5400 prose records into structured ecological data. Our analyses reveal how species distributions, habitat associations, and human–wildlife interactions were shaped by land use and environmental pressures in pre-industrial Bavaria. Beyond documenting ecological baselines, the study captures early perceptions of habitat loss and species decline. We emphasise the critical role of historical expertise in interpreting archival sources and avoiding anachronisms when integrating historical data with modern biodiversity frameworks. By bridging the humanities and environmental sciences, this work shows how digitised archives and computational methods can open new frontiers for conservation science, restoration ecology, and Anthropocene studies. The findings advocate for the systematic mobilisation of historical datasets to better understand biodiversity change over time. Full article

Background/Objectives: The presence of enamel on the tooth surface is crucial for the long-term success of minimally invasive adhesive restorations such as dental veneers. Our study aims to evaluate the enamel thickness in the incisal, middle, and cervical portions of the labial surface of the upper central incisors using cone beam computed tomography (CBCT). This imaging method provides detailed and accurate three-dimensional images with a low radiation dose, allowing an accurate assessment of enamel thickness. The analysis aims to identify variations in enamel thickness depending on the age and different levels of the labial tooth surface. Methods: 800 CBCT scans performed for diagnostic or therapeutic purposes on patients aged 18–60 years were analyzed. The data were gathered from the imaging archives of private practitioners from Targu Mures and the “George Emil Palade” University of Medicine, Pharmacy, Science, and Technology of Targu Mures. Enamel thickness measurements were conducted using the OnDemand3D Communicator CBCT evaluation program, with subsequent statistical analysis performed using GraphPad Instat Prism software. Results: Results showed significant variation in enamel thickness between the incisal, middle, and cervical segments of the labial surface of the upper central incisors. A decrease in enamel thickness with age has been observed. In patients aged 18–40, mean values of enamel thickness 1 mm and 3 mm above the cementoenamel junction (CEJ) were 0.48 ± 0.092, respectively, 0.819 ± 0.158. In patients over 40, the mean values were 0.454 ± 0.116 and 0.751 ± 0.067 at 1 mm, respectively, 3 mm above the CEJ. Statistically significant differences were found between the two age groups at 1 mm and 3 mm above the CEJ, with p < 0.0001 and p = 0.0214. Conclusions: A statistically significant decrease can be observed in enamel thickness in almost the entire labial surface of the upper central incisors with aging. The varied thickness of the enamel at different tooth levels requires individualized planning for each patient to maximize the long-term aesthetic and functional results. Full article

This paper explores the concept of zip quines, which are zip files that contain themselves upon extraction, extending the idea of computational self-reference. While only two individuals, Russ Cox and Erling Ellingsen, have created such entities, this study focuses on Cox’s method to develop a generator for these files. Overcoming the initial limitations, the generator allows for the inclusion of additional files within the zip quine. Additionally, this research explores the concept of looped zip files, wherein a zip archive contains another archive. This archive then contains the initial zip file. By offering practical methodologies and insights, this study advances the understanding and application of quines in computer science. Full article

This paper presents the results of an empirical research study of Croatian scientists’ use and management of research data. This research study was carried out from 28 June 2023 until 31 August 2023 using an online questionnaire consisting of 28 questions. The answers of 584 respondents working in science were filtered out for further analysis. About three-quarters of the respondents used the research data of other scientists successfully. Research data were mostly acquired from colleagues from the same department or institution. Roughly half of the respondents did not ask other scientists directly for their research data. Research data are important to the respondents mostly for raising the quality of research. Repeating someone else’s research by using their research data is still a problem. Less than one-third of the respondents provided full access to their research data mostly due to their fear of misuse. The benefits of research data sharing were recognized but few of the respondents received any reward for it. Archiving research data is a significant problem for the respondents as they dominantly use their own computers prone to failure for that activity and do not think about long-term preservation. Finally, the respondents lacked deeper knowledge of research data management. Full article

This paper summarizes the lessons learned after several decades of exploring and applying Structural Health Monitoring (SHM) in operating bridge structures. The challenges in real-time imaging and processing of large amounts of sensor data at various bandwidths, synchronization, quality check and archival, and most importantly, the interpretation of the structural condition, performance, and health are necessary for effective applications of SHM to major bridges and other infrastructures. Writers note that such SHM applications have served as the forerunners of cyber infrastructures, which are now recognized as the key to smart infrastructures and smart cities. Continued explorations of SHM in conjunction with control, therefore, remain vital for assuring satisfactory infrastructure system performance at the operational, damageability, and safety limit-states in the future. Researchers in the SHM of actually constructed systems, given their experience in monitoring major structures in the field, are well positioned to contribute to these vital needs. Especially, SHM researchers who have learned how to integrate the contributions from various disciplines such as civil, electrical, mechanical, and materials engineering; computer and social sciences; and architecture and urban planning would appear to be well equipped and could become instrumental in assessing the health and performance of urban regions, which today must function by optimizing and balancing the needs of Livability, Sustainability, and Resilience (LSR). Full article

Artificial intelligence and emerging data science techniques are being leveraged to interpret medical image scans. Traditional image analysis relies on visual interpretation by a trained radiologist, which is time-consuming and can, to some degree, be subjective. The development of reliable, automated diagnostic tools is a key goal of radiomics, a fast-growing research field which combines medical imaging with personalized medicine. Radiomic studies have demonstrated potential for accurate lung cancer diagnoses and prognostications. The practice of delineating the tumor region of interest, known as segmentation, is a key bottleneck in the development of generalized classification models. In this study, the incremental multiple resolution residual network (iMRRN), a publicly available and trained deep learning segmentation model, was applied to automatically segment CT images collected from 355 lung cancer patients included in the dataset “Lung-PET-CT-Dx”, obtained from The Cancer Imaging Archive (TCIA), an open-access source for radiological images. We report a failure rate of 4.35% when using the iMRRN to segment tumor lesions within plain CT images in the lung cancer CT dataset. Seven classification algorithms were trained on the extracted radiomic features and tested for their ability to classify different lung cancer subtypes. Over-sampling was used to handle unbalanced data. Chi-square tests revealed the higher order texture features to be the most predictive when classifying lung cancers by subtype. The support vector machine showed the highest accuracy, 92.7% (0.97 AUC), when classifying three histological subtypes of lung cancer: adenocarcinoma, small cell carcinoma, and squamous cell carcinoma. The results demonstrate the potential of AI-based computer-aided diagnostic tools to automatically diagnose subtypes of lung cancer by coupling deep learning image segmentation with supervised classification. Our study demonstrated the integrated application of existing AI techniques in the non-invasive and effective diagnosis of lung cancer subtypes, and also shed light on several practical issues concerning the application of AI in biomedicine. Full article

Background: Minimal change disease (MCD), a major cause of nephrotic syndrome, is usually treated by corticosteroid administration. MCD unresponsiveness to therapy and recurrences are nonetheless frequently observed, particularly in adults. To explore MCD-related pathogenetic mechanisms and to identify novel drug targets ultimately contributing to novel therapeutic avenues with a certain specificity for MCD, we compared glomerular transcriptomes from MCD with membranous nephropathy (MN) patients and healthy controls. Methods: Renal biopsies from adult patients with MCD (n = 14) or MN (n = 12), and non-diseased controls (n = 8) were selected from the Norwegian Kidney Biopsy Registry. RNA for 75 base-pair paired-end RNASeq were obtained from laser capture micro-dissected (LCM) glomeruli from FFPE sections. Transcriptional landscapes were computed by combining pathway-centered analyses and network science methodologies that integrate multiple bioinformatics resources. Results: Compared to normal glomeruli, cells from MCD displayed an inflammatory signature apparently governed by the IL1 and IL7 systems. While enrichment of IL1 production and secretion was a shared feature of MCD and MN compared to normal tissue, responses involving IL7 pathway activation were unique to MCD. Indeed, IL7R expressed by glomeruli was the most upregulated gene of the interleukin family in MCD versus normal controls. IL7 pathway activation was paralleled by significant enrichment in adaptive immune system processes and transcriptional regulation and depletion in pathways related to energy metabolism and transcription. Downregulation of these organ function-related themes again occurred predominately in MCD and was significantly less pronounced in MN. Immunofluorescence and immunohistochemistry, respectively, confirmed the expression of phosphorylated IL-7 receptor alpha (IL7RA, CD127) and IL12 receptor beta 1 (IL12RB1) proteins. Conclusions: Gene expression profiling of archival FFPE-biopsies identifies MCD-specific signatures with IL7RA and IL12RB1 as novel targets for MCD treatment. Full article

Malignant melanomas resembling seborrheic keratosis (SK-like MMs) are atypical, challenging to diagnose melanoma cases that carry the risk of delayed diagnosis and inadequate treatment. On the other hand, SK may mimic melanoma, producing a ‘false positive’ with unnecessary lesion excisions. The present study proposes a computer-based approach using dermoscopy images for the characterization of SΚ-like MMs. Dermoscopic images were retrieved from the International Skin Imaging Collaboration archive. Exploiting image embeddings from pretrained convolutional network VGG16, we trained a support vector machine (SVM) classification model on a data set of 667 images. SVM optimal hyperparameter selection was carried out using the Bayesian optimization method. The classifier was tested on an independent data set of 311 images with atypical appearance: MMs had an absence of pigmented network and had an existence of milia-like cysts. SK lacked milia-like cysts and had a pigmented network. Atypical MMs were characterized with a sensitivity and specificity of 78.6% and 84.5%, respectively. The advent of deep learning in image recognition has attracted the interest of computer science towards improved skin lesion diagnosis. Open-source, public access archives of skin images empower further the implementation and validation of computer-based systems that might contribute significantly to complex clinical diagnostic problems such as the characterization of SK-like MMs. Full article

Distributed ledger technologies (DLT), including blockchains, combine the use of cryptography and distributed networks to achieve a novel form of records creation and keeping designed for tamper-resistance and immutability. Over the past several years, these capabilities have made DLTs, including blockchains, increasingly popular as a general-purpose technology used for recordkeeping in a variety of sectors and industry domains, yet many open challenges and issues, both theoretical and applied, remain. This editorial introduces the Special Issue of Computers focusing on exploring the frontiers of blockchain/distributed ledger technology and recordkeeping. Full article

With uninterrupted space-based data collection since 1972, Landsat plays a key role in systematic monitoring of the Earth’s surface, enabled by an extensive and free, radiometrically consistent, global archive of imagery. Governments and international organizations rely on Landsat time series for monitoring and deriving a systematic understanding of the dynamics of the Earth’s surface at a spatial scale relevant to management, scientific inquiry, and policy development. In this study, we identify trends in Landsat-informed change detection studies by surveying 50 years of published applications, processing, and change detection methods. Specifically, a representative database was created resulting in 490 relevant journal articles derived from the Web of Science and Scopus. From these articles, we provide a review of recent developments, opportunities, and trends in Landsat change detection studies. The impact of the Landsat free and open data policy in 2008 is evident in the literature as a turning point in the number and nature of change detection studies. Based upon the search terms used and articles included, average number of Landsat images used in studies increased from 10 images before 2008 to 100,000 images in 2020. The 2008 opening of the Landsat archive resulted in a marked increase in the number of images used per study, typically providing the basis for the other trends in evidence. These key trends include an increase in automated processing, use of analysis-ready data (especially those with atmospheric correction), and use of cloud computing platforms, all over increasing large areas. The nature of change methods has evolved from representative bi-temporal pairs to time series of images capturing dynamics and trends, capable of revealing both gradual and abrupt changes. The result also revealed a greater use of nonparametric classifiers for Landsat change detection analysis. Landsat-9, to be launched in September 2021, in combination with the continued operation of Landsat-8 and integration with Sentinel-2, enhances opportunities for improved monitoring of change over increasingly larger areas with greater intra- and interannual frequency. Full article

In the field of cultural heritage, three-dimensional (3D) reconstruction of monuments is a usual activity for many professionals. The aim in this paper focuses on the new technology educational application combining science, history, and archaeology. Being involved in almost all stages of implementation steps and assessing the level of participation, university students use tools of computer gaming platform and participate in ways of planning the virtual environment which improves their education through e-Learning. The virtual 3D environment is made with different imaging methods (helium-filled balloon, Structure for motion, 3D repository models) and a developmental plan has been designed for use in many future applications. Digital tools were used with 3D reconstructed buildings from the museum archive to Unity 3D for the design. The pilot study of Information Technology work has been employed to introduce cultural heritage and archaeology to university syllabuses. It included students with a questionnaire which has been evaluated accordingly. As a result, the university students were inspired to immerse themselves into the virtual lab, aiming to increasing the level of interaction. The results show a satisfactory learning outcome by an easy to use and real 3D environment, a step forward to fill in needs of contemporary online sustainable learning demands. Full article

Life sciences (LS) are advanced in research data management, since LS have established disciplinary tools for data archiving as well as metadata standards for data reuse. However, there is a lack of tools supporting the active research process in terms of data management and data analytics. This leads to tedious and demanding work to ensure that research data before and after publication are FAIR (findable, accessible, interoperable and reusable) and that analyses are reproducible. The initiative CyVerse US from the University of Arizona, US, supports all processes from data generation, management, sharing and collaboration to analytics. Within the presented project, we deployed an independent instance of CyVerse in Graz, Austria (CAT) in frame of the BioTechMed association. CAT helped to enhance and simplify collaborations between the three main universities in Graz. Presuming steps were (i) creating a distributed computational and data management architecture (iRODS-based), (ii) identifying and incorporating relevant data from researchers in LS and (iii) identifying and hosting relevant tools, including analytics software to ensure reproducible analytics using Docker technology for the researchers taking part in the initiative. This initiative supports research-related processes, including data management and analytics for LS researchers. It also holds the potential to serve other disciplines and provides potential for Austrian universities to integrate their infrastructure in the European Open Science Cloud. Full article

From cells to organisms, every living system is three-dimensional (3D), but the performance of fluorescence microscopy has been largely limited when attempting to obtain an overview of systems’ dynamic processes in three dimensions. Recently, advanced light-sheet illumination technologies, allowing drastic improvement in spatial discrimination, volumetric imaging times, and phototoxicity/photobleaching, have been making live imaging to collect precise and reliable 3D information increasingly feasible. In particular, lattice light-sheet microscopy (LLSM), using an ultrathin light-sheet, enables whole-cell 3D live imaging of cellular processes, including mitosis, at unprecedented spatiotemporal resolution for extended periods of time. This technology produces immense and complex data, including a significant amount of information, raising new challenges for big image data analysis and new possibilities for data utilization. Once the data are digitally archived in a computer, the data can be reused for various purposes by anyone at any time. Such an information science approach has the potential to revolutionize the use of bioimage data, and provides an alternative method for cell biology research in a data-driven manner. In this article, we introduce examples of analyzing digital mitotic spindles and discuss future perspectives in cell biology. Full article

The article describes the development and implementation of a complex monitoring system for measuring the concentration of carbon dioxide, ambient temperature, relative humidity and atmospheric pressure. The presented system was installed at two locations. The first was in the rooms at the Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VŠB-Technical University of Ostrava. The second was in the classrooms of the Grammar School and Secondary School of Electrical Engineering and Computer Science in Frenštát pod Radhoštěm. The article contains a detailed description of the entire measurement network, whose basic component was a device for measuring carbon dioxide concentration, temperature and relative humidity in ambient air and atmospheric pressure via wireless data transmission using IQRF^® technology. Measurements were conducted continuously for several months. The data were archived in a database. The article also describes the methods for processing the data with statistical analysis. Carbon dioxide concentration was selected for data analysis. Data were selected from at least two different rooms at each location. The processed results represent the time periods for the given carbon dioxide concentrations. The graphs display in percent how much of the time students or employees spent exposed to safe or dangerous concentrations of carbon dioxide. The collected data were used for the future improvement of air quality in the rooms. Full article