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Query: "work type" (1) AND "fulltext" AND "organization" (Jožef Stefan Institute) .

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How does day-to-day stress appraisal relate to coping among office workers in academia? : an ecological momentary assessment study
Stephanie Hulin, Larissa Bolliger, Junoš Lukan, Anneleen Caluwaerts, Rosalie De Neve, Mitja Luštrek, Dirk De Bacquer, Els Clays, 2023, original scientific article

Abstract: Existing literature indicates that academic staff experience increasing levels of work stress. This study investigated associations between day-to-day threat and challenge appraisal and day-to-day problem-focused coping, emotion-focused coping, and seeking social support among academic office workers. This study is based on an Ecological Momentary Assessment (EMA) design with a 15-working day data collection period utilising our self-developed STRAW smartphone application. A total of 55 office workers from academic institutions in Belgium (n = 29) and Slovenia (n = 26) were included and 3665 item measurements were analysed. Participants were asked approximately every 90 min about their appraisal of stressful events (experienced during the working day) and their coping styles. For data analysis, we used an unstructured covariance matrix in our linear mixed models. Challenge appraisal predicted problem-focused coping and threat appraisal predicted emotion-focused coping. Our findings suggest an association between threat appraisal as well as challenge appraisal and seeking social support. Younger and female workers chose social support more often as a coping style. While working from home, participants were less likely to seek social support. The findings of our EMA study confirm previous research on the relationship between stress appraisal and coping with stress. Participants reported seeking social support less while working from home compared to working at the office, making the work location an aspect that deserves further research.
Keywords: academic setting, coping, work stress
Published in DiRROS: 25.03.2024; Views: 31; Downloads: 8
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Hardware–software co-design of an audio feature extraction pipeline for machine learning applications
Jure Vreča, Ratko Pilipović, Anton Biasizzo, 2024, original scientific article

Abstract: Keyword spotting is an important part of modern speech recognition pipelines. Typical contemporary keyword-spotting systems are based on Mel-Frequency Cepstral Coefficient (MFCC) audio features, which are relatively complex to compute. Considering the always-on nature of many keyword-spotting systems, it is prudent to optimize this part of the detection pipeline. We explore the simplifications of the MFCC audio features and derive a simplified version that can be more easily used in embedded applications. Additionally, we implement a hardware generator that generates an appropriate hardware pipeline for the simplified audio feature extraction. Using Chisel4ml framework, we integrate hardware generators into Python-based Keras framework, which facilitates the training process of the machine learning models using our simplified audio features.
Keywords: FPGA, MFCC, keyword spotting, chisel
Published in DiRROS: 25.03.2024; Views: 30; Downloads: 17
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3.
Comparing algorithm selection approaches on black-box optimization problems
Ana Kostovska, Anja Janković, Diederick Vermetten, Sašo Džeroski, Tome Eftimov, 2023, published scientific conference contribution

Published in DiRROS: 25.03.2024; Views: 28; Downloads: 10
.pdf Full text (582,18 KB)

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GPU adding-doubling algorithm for analysis of optical spectral images
Matija Milanič, Rok Hren, 2024, original scientific article

Keywords: medical imaging, medical optics, adding-doubling algorithm
Published in DiRROS: 12.03.2024; Views: 79; Downloads: 38
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Impact assessment of the Gajke and Brstje landfills on groundwater status using stable and radioactive isotopes
Sonja Cerar, Luka Serianz, Polona Vreča, Marko Štrok, Tjaša Kanduč, 2023, original scientific article

Abstract: Waste disposal in landfills represents a severe threat to aquatic environments on the local, regional, and global levels. In Slovenia, there are 69 registered landfills where groundwater is regularly monitored. However, isotope techniques are not regularly employed. Therefore, we employed isotope analysis of hydrogen, carbon, and oxygen in combination with total alkalinity to assess the impact of the selected landfill on groundwater and to evaluate the biogeochemical processes at work. The δ18O, δ2H, δ13CDIC, 3H activity and total alkalinity were determined in October 2020 at 12 sampling points from the surrounding area of the Gajke and Brstje landfills and leachate from the Gajke landfill. The δ18O (-9.24 ± 0.3 ‰) and δ2H (-64.9 ± 2.7 ‰) in groundwater indicate that the main water source consists in direct infiltration of precipitation, with no significant isotopic fractionation. Total alkalinity in the investigated area ranges from 5.45 to 73 mM and δ13CDIC from –14.9 to +6.1 ‰, respectively. Higher values of total alkalinity (up to 73 mM), δ13CDIC (up to +6.1 ‰), δ18O (-7.64 ‰) and 3H (209.8 TU) are detected in the leachate, indicating biogeochemical process related to CO2 reduction or methanogenesis. Methanogenesis could be present at locations GAP-10/13 (Brstje landfill) and G-2 (Gajke landfill) with δ13CDIC values ranging from –8.2 to –7.6 ‰ and with dissolved oxygen values around 0 % and elevated 3H values (from 16 to 18 TU). This study demonstrates the effectiveness of isotopic analysis as a valuable tool for monitoring landfills, revealing shifts in biogeochemical processes within the groundwater there.
Keywords: groundwater, monitoring, landfill, stable isotopes, tritium, Gajke, Brstje
Published in DiRROS: 16.01.2024; Views: 158; Downloads: 54
.pdf Full text (8,52 MB)

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SONAR, a nursing activity dataset with inertial sensors
Orhan Konak, Lucas Liebe, Kirill Postnov, Franz Sauerwald, Hristijan Gjoreski, Mitja Luštrek, Bert Arnrich, 2023, short scientific article

Abstract: Accurate and comprehensive nursing documentation is essential to ensure quality patient care. To streamline this process, we present SONAR, a publicly available dataset of nursing activities recorded using inertial sensors in a nursing home. The dataset includes 14 sensor streams, such as acceleration and angular velocity, and 23 activities recorded by 14 caregivers using five sensors for 61.7 hours. The caregivers wore the sensors as they performed their daily tasks, allowing for continuous monitoring of their activities. We additionally provide machine learning models that recognize the nursing activities given the sensor data. In particular, we present benchmarks for three deep learning model architectures and evaluate their performance using different metrics and sensor locations. Our dataset, which can be used for research on sensor-based human activity recognition in real-world settings, has the potential to improve nursing care by providing valuable insights that can identify areas for improvement, facilitate accurate documentation, and tailor care to specific patient conditions.
Keywords: nursing documentation, nursing activities, SONAR, sensors
Published in DiRROS: 11.12.2023; Views: 200; Downloads: 102
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