VSB-TUO Repository

Communities in DSpace

Select a community to browse its collections.

Now showing 1 - 10 out of 11 results

Recent Submissions

Validation of the Tena pregnancy phantom and fetal dose assessment in proton scanning beam therapy
(Elsevier, 2026) Mojżeszek, Natalia; Brkić, Hrvoje; Foltyńska, Gabriela; Van Hoey, Olivier; Jabłoński, Hubert; Kasabasic, Mladen; Kopeć, Renata; Krzempek, Katarzyna; Lipa, Monika; Matamoros, Andrea; Radolińska, Monika; Rydygier, Marzena; Skóra, Tomasz; Granja, Carlos; Stolarczyk, Liliana; Krzempek, Dawid; De Saint-Hubert, Marijke
Background: Intensity modulated proton therapy (IMPT) is the preferred option during pregnancy, as it reduces out-of-field doses compared to photon techniques. A physical pregnancy phantom was validated for in-field proton dosimetry and used to assess fetal dose across four IMPT plans. Methods: The 18-week pregnancy Tena phantom was composed of bone, soft tissue, and lung substitutes. Proton relative stopping power (RSP) for Tena tissues was measured and compared with treatment planning system (TPS) and Monte Carlo (MC) calculations. Experimental TPS dose verification was performed using gamma index (GI). Fetal dose was measured for IMPT of glioma, Hodgkin lymphoma without (HL) and with a range shifter (HL-RS), and submandibular gland (neck) cancer using a Timepix and bubble detectors. Results: Differences between TPS-assigned and MC-simulated relative to the measured RSP values were up to -7.4 %. GI(3 %/3 mm) values were above 93.38 %. The neutron dose equivalent in the fetus position ranged between 2.5 and 49.4 mu Sv/Gy(RBE) for glioma and HL-RS plans, respectively. The HL plan reduced neutron dose equivalent to 15.8 mu Sv/Gy(RBE), while for the neck 20 mu Sv/Gy(RBE) was measured. Neutrons were dominant with similar to 80 % contribution to the total dose equivalent. A summed fetal dose was calculated considering the prescribed dose per treatment and ranged between 0.17 mSv and 1.89 mSv for glioma and HL-RS, respectively. Conclusions: The Tena phantom is suitable for proton dosimetry and enables accurate TPS calculations. The use of a range shifter increased the fetal dose by more than threefold. Fetal doses for all IMPT plans remained below 2 mSv.
Lunar regolith simulant-based triboelectric nanogenerators: Toward sustainable energy harvesting from resources on the moon
(Elsevier, 2026) Yohannan, Alex; Vaghasiya, Jayraj V.; Sonigara, Keval K.; Pumera, Martin
The exploration of extraterrestrial materials for energy harvesting, generation and storage is important for futuristic material evolution and use. Thus, study and use of extraterrestrial materials simulants becomes straightforward way to identify potential of those materials. Such as Lunar Regolith Simulants are tested as reference material to explore suitability for construction, solar cell components and beyond. However, aiming futuristic space exploration, on-site energy generator development from Lunar regolith materials is unexplored and necessary to unveil it. In this work, we introduce a lightweight, flexible triboelectric nanogenerator (TENG) that uses lunar regolith simulant particles (LRPs) embedded in polydimethoxysilane (PDMS) to harvest mechanical energy as first proof-of-concept. Under cyclic contact-separation, the optimized device containing 30 wt % of <= 45 mu m LRPs yields an open-circuit voltage V-oc of similar to 10.5 V, a short-circuit current I-sc of similar to 2.2 mu A, and a peak power density reached its maximum at 3.0 mu W cm(-)(2) under a force of 2.5 N at 10 Hz. Systematic optimization of grain size and weight fraction of LRPs in PDMS film is analyzed and resulted in the voltage output of 1.6 times and current density by 2.1 times compared to the bare PDMS material. Furthermore, the device shows 95 % performance retention of its output after 36,000 operation cycles, underscoring its good stability and potential for sustainable energy harvesting in ambient environments. These results demonstrate that utilizing extraterrestrial fillers, such as LRPs, is a useful approach for enhancing TENG performance in future terrestrial settings, offering insight for future space materials employed in composite design for TENG devices.
Fractional analysis for multiple solutions of thermodynamic model of Casson fluid under hydrodynamic and non-hydrodynamic optimization
(Elsevier, 2026) Abro, Shahnila Yaseen; Souayeh, Basma; Flah, Aymen; Hamdi, Monia; Abro, Kashif Ali; Faizan, Muhammad
This study investigates the flow behavior of a non-Newtonian Casson fluid influenced by hydromagnetic and non-hydromagnetic effects over an oscillating plate, subject to combined gradients of temperature and mass concentration. The analysis is framed within the context of linear fractional differential equations incorporating the Caputo-Fabrizio fractional derivative with a non-singular kernel. A mathematical model is developed, employing a linear boundary condition to characterize the temperature distribution, mass concentration, and velocity profiles. The governing equations are first non-dimensionalized and then extended into their fractional forms. An analytical solution is obtained using integral transform techniques, specifically the Laplace transform with its inversion and the Fourier sine transform with inversion. The break down the data analysis process under rheological variation for temperature and concentration is explored through which generalization and comparison is investigated. The key findings are focused on the flow and heat transfer characteristics, examining the influence of key dimensionless parameters. Moreover, the comparison between fractional and classical approaches are found in excellent agreement.
Dynamic graph learning for bus passenger profiling in urban transportation networks
(IEEE, 2026) Hou, Mingliang; Tahir, Muhammad; Frnda, Jaroslav; Zheng, Xiaoa; Anwar, Muhammad Shahid; Tang, Yongwei; Hussain, Imtiaz
Bus passenger profiling is a critical task for optimizing urban transportation, but it is hindered by three key challenges: the heterogeneity of passenger behaviors, complex station-level interactions, and the prevalence of sparse, noisy transit data. Conventional end-to-end models that operate on aggregated traffic flow often fail to address these issues systematically. To overcome these limitations, this paper proposes GRASP, a novel two-stage paradigm for passenger profiling and flow prediction. In the first stage, GRASP acts as a disentangling module, constructing a passenger-centric graph to cluster individuals into distinct behavioral profiles based on their co-occurrence patterns. In the second stage, it performs profile-aware forecasting by learning group-specific, dynamic spatio-temporal dependencies using an adaptive station graph. This station-level model is further enhanced by a contrastive learning objective to ensure robustness against data imperfections. Extensive experiments on three real-world datasets demonstrate that GRASP not only achieves significantly superior flow prediction accuracy but also uncovers actionable passenger profiles. By structurally decoupling passenger behavior from station-level dynamics, GRASP offers a more interpretable and effective solution for data-driven public transportation management.
Optimizing feature selection with random reversal and adaptive Gaussian based Dung beetle optimizer for intrusion detection system in IoT
(Springer Nature, 2025) Vurubindi, Padmavathi; Frnda, Jaroslav; Sujatha, Canavoy Narahari; Divakarachari, Parameshachari Bidare; Nijaguna, G. S.; Mahendar, A.
The Internet of Things (IoT) is an emerging, promising technology developed with the objective of establishing global connectivity among devices. IoT is highly susceptible to malicious attacks, owing to its resource-constrained architecture, insecure wireless communication, diverse device ecosystems, and the vast volume of sensor data transmitted over networks. An effective Intrusion Detection System (IDS) is essential to address these security concerns. However, challenges such as irrelevant features and poor class separability complicate its development. This research proposes a novel IDS by introducing an Improved Random Reversal Learning (IRRL) and Dimensional Adaptive Gaussian Variation (DAGV)-based Dung Beetle Optimizer (RGDBO) for optimal feature selection, enhancing exploration, and avoiding premature convergence. For classification, a Convolutional Neural Network (CNN) integrated with CosFace and ArcFace loss functions, termed CACNN, is employed to enhance intrusion classification through more efficient discrimination among classes. The combined RGDBO-CACNN framework is evaluated on three benchmark datasets: UNSW-NB15, NSL-KDD, and CICIDS-2017, using accuracy, recall, precision, and F1-score as performance metrics. A comparative analysis of existing methods, including GA-FR-CNN, GTO-BSA, and BMRF-RF, demonstrates the superiority of the proposed model, with RGDBO-CACNN achieving an accuracy of 99.999% on the UNSW-NB15 dataset.