Iskanje po katalogu digitalne knjižnice

Opis: The goal of this research was to investigate the level of digital divide among selected European countries according to the big data usage among their enterprises. For that purpose, we apply the K-means clustering methodology on the Eurostat data about the big data usage in European enterprises. The results indicate that there is a significant difference between selected European countries according to the overall usage of big data in their enterprises. Moreover, the enterprises that use internal experts also used diverse big data sources. Since the usage of diverse big data sources allows enterprises to gather more relevant information about their customers and competitors, this indicates that enterprises with stronger internal big data expertise also have a better chance of building strong competitiveness based on big data utilization. Finally, the substantial differences among the industries were found according to the level of big data usage.
Ključne besede: big data, cluster analysis, digital divide, k-means, enterprise, industry, Europe, quality
Objavljeno v DKUM: 14.01.2025; Ogledov: 0; Prenosov: 2
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Opis: Background: The estimates of attention-deficit hyperactivity disorder (ADHD) prevalence across various studies are significantly variable, contributing to uncertainty in ADHD prevalence estimation. Previous systematic reviews and meta-analyses have attributed this variability primarily to the methodological characteristics of the studies, including the diagnostic criteria, source of information, and impairment requirement for the diagnosis. Methods: Review identified studies reporting ADHD prevalence in representative samples of children and adults in Europe and worldwide. Studies that were conducted in the general population were included. We focused on studies that report ADHD prevalence based on clinical diagnosis (clinical diagnostic criteria based on the Diagnostic and Statistical Manual of Mental Disorders and International Classification of Diseases criteria, other diagnostic tools, such as various scales or interviews based on clinical diagnostic criteria). PubMed/Medline was searched to identify relevant articles published until 2024/2/01. The study was registered in PROSPERO (CRD42020200220) and followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines for systematic review and meta-analysis. Results: In total, 117 studies were subjected to full evaluation. In the meta-analysis, 103 studies representing 159 independent datapoints were included. The overall prevalence of ADHD in register studies was 1.6%, 95% CI [0.9; 3.0], in survey studies 5.0%, 95% CI [2.9; 8.6], in one-stage clinical studies 4.2%, 95% CI [2.9; 6.0], and in two-stage clinical studies 4.8%, 95% CI [4.0; 5.8]. Conclusions: Exact comparisons among studies with different diagnostic criteria and types of sampling can impact prevalence estimates. When comparing data from methodologically different studies, these factors need to be considered.
Ključne besede: attention-deficit hyperactivity disorder (ADHD), epidemiology, prevalence, systematic review, meta-analysis
Objavljeno v DKUM: 18.12.2024; Ogledov: 0; Prenosov: 5
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Opis: The lack of reliable biomarkers in response to anti-TNFα biologicals hinders personalized therapy for Crohn’s disease (CD) patients. The motivation behind our study is to shift the paradigm of anti-TNFα biomarker discovery toward specific immune cell sub-populations using single-cell RNA sequencing and an innovative approach designed to uncover PBMCs gene expression signals, which may be masked due to the treatment or ongoing inflammation; Methods: The singlecell RNA sequencing was performed on PBMC samples from CD patients either naïve to biological therapy, in remission while on adalimumab, or while on ustekinumab but previously non-responsive to adalimumab. Sieves for stringent downstream gene selection consisted of gene ontology and independent cohort genomic profiling. Replication and meta-analyses were performed using publicly available raw RNA sequencing files of sorted immune cells and an association analysis summary. Machine learning, Mendelian randomization, and oligogenic risk score methods were deployed to validate DEGs highly relevant to anti-TNFα therapy response; Results: This study found PLCB1 in CD4+ T cells and CRTAM in double-negative T cells, which met the stringent statistical thresholds throughout the analyses. An additional assessment proved causal inference of both genes in response to anti-TNFα therapy; Conclusions: This study, jointly with an innovative design, uncovered novel candidate genes in the anti-TNFα response landscape of CD, potentially obscured by therapy or inflammation.
Ključne besede: inflammatory bowel diseases, Crohn’s disease, tumor necrosis factor alpha, adalimumab, single-cell gene expression analysis
Objavljeno v DKUM: 10.12.2024; Ogledov: 0; Prenosov: 3
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Opis: This paper introduces a new method for the region segmentation of images. The approach is based on the raster-scan paradigm and builds the segments incrementally. The pixels are processed in the raster-scan order, while the construction of the segments is based on a distance metric in regard to the already segmented pixels in the neighbourhood. The segmentation procedure operates in linear time according to the total number of pixels. The proposed method, named the RSM (raster-scan segmentation method), was tested on selected images from the popular benchmark datasets MS COCO and DIV2K. The experimental results indicate that our method successfully extracts regions with similar pixel values. Furthermore, a comparison with two of the well-known segmentation methods—Watershed and DBSCAN—demonstrates that the proposed approach is superior in regard to efficiency while yielding visually similar results.
Ključne besede: segment, image analysis, distance metric, Watershed, DBSCAN
Objavljeno v DKUM: 05.12.2024; Ogledov: 0; Prenosov: 2
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Opis: The content of this paper presents the research results of a three-year research project in which a multi-criteria evaluation model (according to the DEX methodology) was developed for the evaluation of three different food sectors (represented by a cattle breeding chain, a pig farming chain, and a milk production chain) with added value in Slovenia. Indicators for the assessment of the economically, socially, and environmentally sustainable development of food chains were taken into account. The data for the analysis, such as prices and costs of food, wage levels by sector, food miles and others, were obtained from various public services between 2020 and 2023. The final qualitative assessment of the food sectors was uniform (“average”), while the longest analysis of the results using the plus-minus-1 analysis method showed the reasons for such an assessment in individual sectors (such as the ratio between the price of agricultural products and the price of agricultural inputs is poor, the ratio between average gross salary in the individual food sector and gross salary in the agricultural sector is poor, etc.). In addition to the results already mentioned, recommendations or suggestions for building a sustainable food chain were made using the results of the modelling. The research results contributed to a better understanding of the importance of stable relationships between different groups of indicators and later showed their importance for improving the functioning of agri-food chains. The results of the research will help various stakeholders (such as the agricultural advisory service, decision-makers at the level of agricultural policy, researchers in further analyses, and especially the international professional public interested in various case studies from EU countries) to further analyse and plan for the organisation of the agricultural sector.
Ključne besede: food sectors, value added, DEX methodology, plus-minus-1 analysis, Slovenia
Objavljeno v DKUM: 29.11.2024; Ogledov: 0; Prenosov: 8
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