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In pattern mining from tabular data using fuzzy logic, a common task involves computing triangular norms (t-norms) to represent conjunctions of fuzzy predicates and summing the resulting truth values …

In pattern mining from tabular data using fuzzy logic, a common task involves computing triangular norms (t-norms) to represent conjunctions of fuzzy predicates and summing the resulting truth values to evaluate rule support or other pattern quality measures. Building on previous work, this paper presents an approach that packs multiple fuzzy truth values into a single integer and performs t-norm computations directly on this compact representation. By using 4-, 8-, or 16-bit precision, the method substantially reduces memory consumption and improves computational efficiency. For example, with 8-bit precision—offering two decimal places of accuracy—it requires only one-quarter of the memory and achieves 3–16× speedup compared to conventional floating-point-based method of computation. The proposed method is also compared with a traditional computation approach optimized using advanced Single-Instruction/Multiple-Data (SIMD) CPU operations, demonstrating its superior performance on modern architectures. 

In this article, we analyse the digitalisation process in social services in Catalonia from the perspective of social workers' demands, establishing a set of strategies for better incorporation of dig…

In this article, we analyse the digitalisation process in social services in Catalonia from the perspective of social workers' demands, establishing a set of strategies for better incorporation of digital technologies in public administrations. Co-design and co-creation methodologies allow us to evaluate our organisations more effectively, giving social workers a voice so that they can highlight the positive and negative effects of their organisations' digitalisation model, actively participating in the redesign of the digital social services model from the outset. Through a participatory process involving 109 social workers from social services in Catalonia, using methodologies such as the customer journey and impact maps, this article presents some strategies for strengthening inclusive digitalisation focused on the well-being of social service workers and users.

This article examines the role of digital skills in the health sector, where healthcare social workers perform their professional work, using the Delphi method in two rounds: 2020 (pre-Covid-19 contex…

This article examines the role of digital skills in the health sector, where healthcare social workers perform their professional work, using the Delphi method in two rounds: 2020 (pre-Covid-19 context) and 2021 (Covid-19 context). Experts point out three major transformations in organisations resulting from digitalisation: (i) the growing importance of digital skills; (ii) the relevance of developing a leadership adapted to a digitalisated context, and (iii) the key role of health managers and their organisations. Based on the results obtained, a conceptual approach based on the Shaw et al. (2017) model is proposed to face the challenges of e-Health, explicitly centred on fostering both organisational and individual wellbeing. This leadership model aims to improve the wellbeing of workers in the healthcare sector, including healthcare social workers.

This repository contains data collected for Work Package 1.3: Short-term impacts of ICT use on adult wellbeing, part of the DigiWELL project (Research of Excellence on Digital Technologies and Wellbei…

This repository contains data collected for Work Package 1.3: Short-term impacts of ICT use on adult wellbeing, part of the DigiWELL project (Research of Excellence on Digital Technologies and Wellbeing, CZ.02.01.01/00/22_008/0004583). This study was conducted as a technical pilot (EMA1) to verify the functionality of the research applications used for subsequent EMA studies: Health React (for survey collection) and eBehave (for passive smartphone trace data collection), both developed by the University of Hradec Králové. The seven-day EMA study involved 58 Masaryk University students (58% female; age range: 19–30 years; M = 21.5; SD = 2.3). KeywordsEcological Momentary Assessment, Experience Sampling Method, Technical pilot   For additional information or questions, please contact the contact person: Martin Tancoš (tancos@fss.muni.cz).

DXAlyzer is a desktop tool for extracting, reviewing, validating, and exporting DXA measurement data from Hologic DXA PDF reports and OCR text exports. The application runs locally, stores data in a l…

DXAlyzer is a desktop tool for extracting, reviewing, validating, and exporting DXA measurement data from Hologic DXA PDF reports and OCR text exports. The application runs locally, stores data in a local SQLite database, and prepares cohort-level CSV tables and SVG figures for research and publication workflows. Features Import DXA reports from .pdf, .txt, .text, and .ocr files. Extract patient identifiers, scan dates, BMD, BMC, T-score, Z-score, fat mass, lean mass, and total mass values. Review and edit extracted records before saving them. Validate measurements with QC status and notes. Compare repeated measurements for the same patient. Export full measurement data to CSV. Generate publication summary tables and subject-level change tables. Export cohort figures as SVG. Manage parser templates for different report formats. Requirements For running from source: Python 3.11 or newer Windows, macOS, or Linux with Tkinter support Python dependencies are listed in requirements.txt. Run From Source python -m pip install -r requirements.txt python main.py   Application data is stored locally in: ~/DXAlyzer/dxa.db ~/DXAlyzer/templates/   Windows Executable The Windows executable is built with PyInstaller and published through GitHub Actions. To download it: Open the repository on GitHub. Go to Releases. Download DXAlyzer.exe from the latest release. If there is no release yet, open Actions, run the Windows Release workflow manually, and download the windows-DXAlyzer artifact. Build On Windows From a Windows command prompt in the project folder: build.bat Expected output: dist\DXAlyzer.exe   More release and code-signing details are documented in WINDOWS_RELEASE.md. Basic Workflow Import PDF reports or OCR text exports. Review extracted records before saving. Check measurement quality in the QC tab. Run cohort analysis. Export CSV tables and SVG figures.   Acknowledgements This software was developed as part of the project Research of Excellence on Digital Technologies and Wellbeing, CZ.02.01.01/00/22_008/0004583, co-funded by the European Union. License This project is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). 

This dataset contains aggregated prevalence estimates regarding cigarette smoking among adolescents in the Czech Republic, derived from three waves of the Health Behaviour in School-aged Children (HBS…

This dataset contains aggregated prevalence estimates regarding cigarette smoking among adolescents in the Czech Republic, derived from three waves of the Health Behaviour in School-aged Children (HBSC) study conducted in 2014, 2018, and 2022. The data represents a total sample of 29,525 respondents (14,761 boys and 14,764 girls) across three target age groups: 11, 13, and 15 years old.

This dataset contains aggregated prevalence estimates regarding alcohol consumption among adolescents in the Czech Republic, based on three waves of the Health Behaviour in School-aged Children (HBSC)…

This dataset contains aggregated prevalence estimates regarding alcohol consumption among adolescents in the Czech Republic, based on three waves of the Health Behaviour in School-aged Children (HBSC) study conducted in 2014, 2018, and 2022. The data represents a total sample of 29,525 respondents (14,761 boys and 13,764 girls) across three target age groups: 11, 13, and 15 years old.

Abstract Background Sensory Processing Sensitivity (SPS) is a trait linked to deeper processing of stimuli and heightened emotional reactivity. These characteristics suggest a potential link to more …

Abstract Background Sensory Processing Sensitivity (SPS) is a trait linked to deeper processing of stimuli and heightened emotional reactivity. These characteristics suggest a potential link to more intense self-conscious emotions. The objective of this study was to investigate the associations between SPS, feelings of guilt, shame, and self-esteem, and to test whether these relationships are independent of the influence of neuroticism. Methods We conducted a cross-sectional study using data from an online survey of Czech adults (n =&thinsp;1012; 49.3 ± 16.7 years, 50.4% female). Participants completed measures of SPS (Sensory Processing Sensitivity Questionnaire, SPSQ; Highly Sensitive Person Scale), feelings of guilt and shame (Guilt and Shame Experience Scale), self-esteem (Rosenberg Self-Esteem Scale), and neuroticism (the neuroticism subscale of the Big Five Inventory). The associations were examined using linear and logistic regression models, with adjustments for neuroticism and key demographic variables. Results Linear regression analyses showed that higher SPS was significantly associated with increased feelings of guilt and shame, and with lower self-esteem. After adjustment for neuroticism, the association between SPS and self-esteem was no longer significant (β ≈ 0.03, p > 0.05), whereas the β-coefficients for feelings of guilt and shame were reduced but remained significant. Logistic regression analyses comparing low, medium, and high SPS groups and, separately, the equivalent levels on the Sensory Sensitivity subscale of the SPSQ, indicated that highly sensitive individuals were more likely to report feelings of guilt alone and combined guilt and shame, with odds ratios (ORs) ranging from 4.42 (95% CI 2.17–8.99, p < 0.001) to 6.38 (95% CI 3.08–13.25, p < 0.001). No significant associations emerged between SPS and feelings of shame alone or low self-esteem. Analyses using alternative SPS measures yielded broadly similar results, with the Highly Sensitive Person Scale showing even stronger associations with feelings of guilt and shame, while again no effect was found for self-esteem. Conclusions Highly sensitive individuals appear to be more prone to experiencing heightened feelings of guilt and, to a lesser degree, shame. However, the initially observed negative association between SPS and self-esteem was no longer significant after neuroticism was included in the model.  

Abstract Digital Behaviour Change Interventions (DBCIs) aim at improving individual health by engaging various means of Information and Communication Technology (ICT), including mobile apps and weara…

Abstract Digital Behaviour Change Interventions (DBCIs) aim at improving individual health by engaging various means of Information and Communication Technology (ICT), including mobile apps and wearables. Participant intervention fatigue may happen when DBCIs become too frequent, repetitive, demanding, or lack perceived relevance, and this may result in participants’ reduced motivation and adherence over time. Advancing technology-supported engagement mechanisms is therefore of utmost importance. To address this problem, we present a reference and solution architecture based on open-source technologies and open Application Programming Interfaces (Open APIs). First, we integrated a Large Language Model (LLM) component into the DBCI design. Second, to support context-awareness, we enhanced this integration by adding a Geographic Information Systems (GIS) element. Our pilot implemented AI4Motion platform targets both personalization and contextualization aspects of DBCIs. Our work contributes to the emerging discussion on LLM/GIS-related system design patterns for digital platforms supporting Ecological Momentary Assessment (EMA), Experience Sampling Method (ESM), and Just-in-Time Adaptive Interventions (JITAIs).  

ABSTRACT This study illustrates how organizations reconcile their information processing capabilities with uncertainty within the supply chain (SC) through generative artificial intelligence (GAI) to…

ABSTRACT This study illustrates how organizations reconcile their information processing capabilities with uncertainty within the supply chain (SC) through generative artificial intelligence (GAI) to achieve carbon performance (CP). A quantitative research methodology is applied, and 155 responses from manufacturing firms are analyzed through structural equation modeling (SEM) for hypothesis testing. The findings suggest that GAI for process automation and cognitive engagement has a positive influence on business intelligence (BI), whereas end-of-life (EOL) product management mediates the relationship between green product innovation (GPI) and CP. This study contributes to the SC context, focusing on GAI and BI in mitigating uncertainties within SCs to foster GPI and improve CP. This study highlights actionable frameworks for leveraging digital technologies in sustainable SCs by addressing technological challenges and integrating green innovation practices