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Iskalni niz: "vrsta gradiva" (1) AND "polno besedilo" AND "organizacija" (Institut Jožef Stefan) .

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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, izvirni znanstveni članek

Povzetek: 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.
Ključne besede: groundwater, monitoring, landfill, stable isotopes, tritium, Gajke, Brstje
Objavljeno v DiRROS: 16.01.2024; Ogledov: 123; Prenosov: 48
.pdf Celotno besedilo (8,52 MB)

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

Povzetek: 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.
Ključne besede: nursing documentation, nursing activities, SONAR, sensors
Objavljeno v DiRROS: 11.12.2023; Ogledov: 172; Prenosov: 89
.pdf Celotno besedilo (1,62 MB)
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HARE : unifying the human activity recognition engineering workflow
Orhan Konak, Lucas Liebe, Kirill Postnov, Franz Sauerwald, Hristijan Gjoreski, Mitja Luštrek, Bert Arnrich, 2023, izvirni znanstveni članek

Povzetek: Sensor-based human activity recognition is becoming ever more prevalent. The increasing importance of distinguishing human movements, particularly in healthcare, coincides with the advent of increasingly compact sensors. A complex sequence of individual steps currently characterizes the activity recognition pipeline. It involves separate data collection, preparation, and processing steps, resulting in a heterogeneous and fragmented process. To address these challenges, we present a comprehensive framework, HARE, which seamlessly integrates all necessary steps. HARE offers synchronized data collection and labeling, integrated pose estimation for data anonymization, a multimodal classification approach, and a novel method for determining optimal sensor placement to enhance classification results. Additionally, our framework incorporates real-time activity recognition with on-device model adaptation capabilities. To validate the effectiveness of our framework, we conducted extensive evaluations using diverse datasets, including our own collected dataset focusing on nursing activities. Our results show that HARE’s multimodal and on-device trained model outperforms conventional single-modal and offline variants. Furthermore, our vision-based approach for optimal sensor placement yields comparable results to the trained model. Our work advances the field of sensor-based human activity recognition by introducing a comprehensive framework that streamlines data collection and classification while offering a novel method for determining optimal sensor placement.
Ključne besede: human activity recognition, multimodal classification, privacy preservation, real-time classification, sensor placement
Objavljeno v DiRROS: 11.12.2023; Ogledov: 161; Prenosov: 63
.pdf Celotno besedilo (6,40 MB)
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Sensitivity analysis of RF+clust for leave-one-problem-out performance prediction
Ana Nikolikj, Michal Pluhacek, Carola Doerr, Peter Korošec, Tome Eftimov, 2023, objavljeni znanstveni prispevek na konferenci

Ključne besede: automated performance prediction, autoML, single-objective black-box optimization, zero-shot learning
Objavljeno v DiRROS: 13.11.2023; Ogledov: 254; Prenosov: 154
.pdf Celotno besedilo (4,94 MB)
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Evolution of surface functional groups and aromatic ring degradation upon treatment of polystyrene with hydroxyl radicals
Alenka Vesel, Rok Zaplotnik, Gregor Primc, Miran Mozetič, 2023, izvirni znanstveni članek

Povzetek: The surface properties of hydrocarbon polymers are inadequate for numerous applications. Hence, they require alteration via functionalisation with desired functional groups. Hydroxyl groups are often preferred, since they enable appropriate polarity for the irreversible grafting of desired molecules. In this study, the surface kinetics resulting from the treatment of polystyrene with hydroxyl (OH) radicals from the gas phase was fundamentally investigated through a precisely-designed experiment. Polystyrene samples were exposed to various known fluences of OH radicals, and the evolution of surface functional groups versus the OH fluence was monitored using high-resolution X-ray photoelectron spectroscopy (XPS). The fluences of OH radicals varied between 1 × 1018 and 4 × 1023 m−2 in the process of finding a threshold fluence for the formation of specific groups. The surface concentration of carbonyl (C=O) groups could be measured using XPS at a fluence of approximately 5 × 1020 m−2. The C=O groups became measurable at a fluence of approximately 1.5 × 1021 m−2, and carboxyl (COOH)/ester groups at approximately 4 × 1021 m−2. As deduced from the XPS, a concentration of C=O groups at approximately 5 % occurred before the degradation of the aromatic ring. The formation of other oxygen-functional groups required opening of the aromatic ring. The results have been explained using a two-step process, considering available theories vis-a-vis initial stages in the functionalisation of PS with polar functional groups.
Ključne besede: polistiren, kinetika površinske funkcionalizacije, OH radikali, vpliv doze radikalov, časovni razvoj, polystyrene, surface functionalisation kinetics, OH radicals
Objavljeno v DiRROS: 09.11.2023; Ogledov: 181; Prenosov: 92
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AFM study of roughness development during ToF-SIMS depth profiling of multilayers with a ▫$Cs^+$▫ ion beam in a ▫$H_2$▫ atmosphere
Jernej Ekar, Janez Kovač, 2022, izvirni znanstveni članek

Objavljeno v DiRROS: 18.10.2023; Ogledov: 318; Prenosov: 126
.pdf Celotno besedilo (5,77 MB)
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Models for forecasting the traffic flow within the city of Ljubljana
Gašper Petelin, Rok Hribar, Gregor Papa, 2023, izvirni znanstveni članek

Povzetek: Efficient traffic management is essential in modern urban areas. The development of intelligent traffic flow prediction systems can help to reduce travel times and maximize road capacity utilization. However, accurately modeling complex spatiotemporal dependencies can be a difficult task, especially when real-time data collection is not possible. This study aims to tackle this challenge by proposing a solution that incorporates extensive feature engineering to combine historical traffic patterns with covariates such as weather data and public holidays. The proposed approach is assessed using a new real-world data set of traffic patterns collected in Ljubljana, Slovenia. The constructed models are evaluated for their accuracy and hyperparameter sensitivity, providing insights into their performance. By providing practical solutions for real-world scenarios, the proposed approach offers an effective means to improve traffic flow prediction without relying on real-time data.
Ključne besede: traffic modeling, time-series forecasting, traffic-count data set, machine learning, model comparison
Objavljeno v DiRROS: 28.09.2023; Ogledov: 271; Prenosov: 116
.pdf Celotno besedilo (5,05 MB)
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