Iskanje po repozitoriju

Opis: The aim of this study is to compare the structure as well as the mechanical and thermomechanical properties of polyethylene stabilized with a petrochemical additive (Irganox 1010) and a sustainable alternative, antioxidant-rich black tea waste leaves (BTW). The influence of both stabilizers was evaluated for samples produced by compression, injection, and rotational molding (CM, INM, and RM). The polymer ma- trix used was low-density polyethylene LDPE SBC 818 I’m Green® (Braskem), modified with 5 wt% BTW and an equiva- lent amount of Irganox 1010 based on the total phenolic con- tent in BTW. Stabilization efficiency was assessed using Fourier transform infrared spectroscopy (FTIR) and calori- metric techniques, including oxidation induction time (OIT) and oxidation onset temperature (OOT), both measured by differential scanning calorimetry (DSC). Additionally, porosity, mechanical performance in static tensile testing, and Vicat softening temperature (VST) were determined to provide a comprehensive comparison of the stabilization systems.
Ključne besede: polyethylene, thermoplastics, antioxidants, rotational molding, stabilization
Objavljeno v RUP: 02.06.2026; Ogledov: 136; Prenosov: 7
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Opis: Recently, there has been a growing interest in scenarios that employ a few (imbalanced) power traces for deep learning based side-channel analysis (DL-SCA). Finding a lightweight DL-SCA architecture that is more generalizable and faster to learn is of great importance when handling such situations. In this work, an initial analysis is conducted on the capability of learning and the susceptibility to attacks of prevalent DL-SCA models, focusing on cases that culminate in an unsuccessful attack. Subsequently, a meta-learning technique, known as prototypical networks, is delineated for the construction of a lightweight deep learning framework. In contrast to conventional DL-SCA models, the proposed architecture, designated as PN-SCA, does not predict the probability of a power trace belonging to an intermediate value in classification. Instead, it facilitates the learning of an encoder capable of embedding power consumption data within a latent space, while also establishing templates, or prototypes, for diverse categories. Moreover, we developed a metric that is specifically intended for the selection of hyperparameters due to the unique training phase of PN-SCA. Finally, four distinct scenarios are established with few (including one imbalanced) power traces to evaluate the correctness of our architecture. The results clearly illustrate the advantages of our PN-SCA in terms of generalization, reduced training costs (with a decrease in profiling traces by over 50%), and significantly enhanced attack effect (with a reduction of power traces requirements by over 90%), thus demonstrating notable improvements over existing methodologies
Ključne besede: side-channel analysis, deep learning based SCA, prototypical networks, few-shot learning
Objavljeno v RUP: 20.05.2026; Ogledov: 193; Prenosov: 8
Celotno besedilo (3,78 MB)
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Opis: A $\Gamma$-distance magic labeling of a graph $G = (V, E)$ with $|V| = n$ is a bijection $\ell$ from $V$ to an Abelian group $\Gamma$ of order $n$, for which there exists $\mu \in \Gamma$, such that the weight $w(x) =\sum_{y\in N(x)}\ell(y)$ of every vertex $x \in V$ is equal to $\mu$. In this case, the element $\mu$ is called the magic constant of $G$. A graph $G$ is called a group distance magic if there exists a $\Gamma$-distance magic labeling of $G$ for every Abelian group $\Gamma$ of order $n$. In this paper, we focused on cubic $\Gamma$-distance magic graphs as well as some properties of such graphs.
Ključne besede: group distance magic labeling, Kotzig array, generalized Petersen graph
Objavljeno v RUP: 06.05.2026; Ogledov: 324; Prenosov: 6
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Opis: Motivation Advances in hardware have made molecular dynamics (MD) simulations of protein structures faster and more accessible to the scientific community. However, accurately estimating protein flexibility using MD remains computationally demanding, especially for large systems and long time scales. Several MD-based resources—including MdMD, the DynamD database, and more recently ATLAS and mdCATH—now provide MD trajectories for thousands of proteins, enabling the development of predictive models. Results Here, the Graphlet Degree Vector (GDV) is introduced as a lightweight, fast, and easy-to-implement linear model for predicting protein flexibility directly from atom coordinates. GDV is a 15-dimensional feature vector that captures local packing and the spatial connectivity of each atom with its nearby neighbors. Trained on a subset of globular-like proteins from the ATLAS database, the GDV model achieves a Spearman correlation of 0.828 compared to MD data. The model trained on ATLAS dataset was further evaluated on independent Nuclear Magnetic Resonance and cryo-electron microscopy datasets, demonstrating the robustness and generalizability of the GDV-based approach. A key advantage of the GDV model is that it requires no additional external or experimental data and can be applied in near real time (on the order of 10 seconds) even for large proteins with 20,000 atoms on a standard desktop or laptop. Overall, the results show that a lightweight, fast, and purely coordinate-based model can provide accurate and generalizable predictions of protein flexibility across diverse folds and sizes.
Ključne besede: protein flexibility, graphlets, predictive model
Objavljeno v RUP: 16.04.2026; Ogledov: 348; Prenosov: 12
Celotno besedilo (10,69 MB)
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Opis: Background: Physical activity, sedentary behaviour, and sleep were shown to be independently associated with low back pain (LBP). The aim of this cross-sectional study was to explore the associations between 24-hour movement behaviour compositions and the occurrence, severity, and estimated level of LBP impact on an individual’s life. Methods: A convenience sample of 197 adults (40% females, 37 ± 11 years of age) were asked to wear an activPAL accelerometer for at least 7 consecutive days to assess their time-use composition consisting of moderate- to vigorous-intensity physical activity (MVPA), light- intensity physical activity (LPA), sedentary behaviour (SB), and sleep and to complete a questionnaire on LBP and sociodemographic characteristics. Compositional isotemporal substitution analyses were conducted separately for the non-domain-specific and domain- specific (including occupational and non-occupational domains) movement behaviour compositions. Results: Reallocating time from MVPA to any other movement behaviour or from sleep to LPA was associated with a higher LBP impact score. For example, reallocating 60 min/day from MVPA to LPA was associated with on average 17 points (95% CI: 6 to 28) higher LBP impact score (on a 0-70 scale). We did not find significant associations between the domain- specific time-use composition and LBP impact score (p = 0.060). We also did not find significant associations of the time-use compositions with occurrence and severity of LBP (p- value range: 0.067 to 0.649). Conclusion: Our study suggests that LBP sufferers with higher MVPA and sleep better cope with LBP. The differences in the LBP impact scores associated with theoretical reallocations between movement behaviours may be deemed clinically important. Future longitudinal and experimental studies in population-representative samples are needed to confirm our findings.
Ključne besede: musculoskeletal health, physical behaviours, time-use epidemiology
Objavljeno v RUP: 30.03.2026; Ogledov: 426; Prenosov: 9
Celotno besedilo (1,05 MB)
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