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Item type:Item, Access status: Open Access , Ultrasound-Based Assessment of Subcutaneous Adipose Tissue Changes During a 7-Day Ultramarathon: Association with Anthropometric Indices, Not Body Mass(2025-12-01) Chlíbková, Daniela; Knechtle, Beat; Weiss, Katja; Kovacova, Ingrid; Rosemann, ThomasBackground: Accurately tracking body-composition changes in endurance field settings remains methodologically challenging. This study aimed to evaluate whether changes in subcutaneous adipose tissue (SAT) across a 7-day ultramarathon are better reflected by anthropometric indices than by body mass (BM) alone. Methods: Twenty ultrarunners were assessed using both anthropometric indices and ultrasound measurements of SAT thickness, applying a novel method that distinguishes layers including (D-I) versus excluding (D-E) embedded fibrous structures. Measurements were obtained before the race and after Stages 4 and 7. Indices included body mass index (BMI), mass index (MII), and waist-to-height ratio (WHtR). Results: Total SAT thickness decreased significantly for both D-I (p = 0.001) and D-E (p < 0.001). BM, BMI, MII, and WHtR also declined significantly post-race (p < 0.001). SAT reduction was most pronounced at the abdominal and thigh sites. Additionally, ultrarunners with lower D-E values exhibited lower fat at the abdomen and distal triceps. BMI was significantly related to D-E at the upper and lower abdomen and erector spinae; MII was significantly associated with D-E at the upper and lower abdomen; and WHtR correlated with both D-E and D-I at abdominal and erector spinae sites. BM showed no significant association with any SAT parameter. Conclusions: Ultrasound-derived SAT thickness, in combination with BMI, MII, and WHtR, offers a field-feasible approach to evaluate body-composition change during multistage ultramarathons. In contrast, BM alone does not reliably reflect SAT distribution or loss.Item type:Item, Access status: Open Access , Compact Wideband 5G SIW Bandpass Filter with Enhanced Selectivity Using Mixed-Coupling Butterfly CSRRs(Radioengineering Society, 2026-04) Hamrioui, F. Z.; Touhami, R.; Al Sabbagh, M.; Yagoub, M. C. E.In this work, a novel integrated waveguide bandpass filter for 5G applications is presented. The proposed compact filter is loaded by mixed coupling butterfly shaped complementary split ring resonators (B-CSRRs). Initially, the design exhibited only one transmission zero at higher stop-band. Thus, an asymmetric etched slot has been inserted between the split of each face-to-face B-CSRRs to produce an induced mixed coupling, thereby allowing the emergence of an additional transmission zero at lower stop-band. To further improve the selectivity factor, a second-order SIW filter using two-mixed coupling face-to-face B-CSRRs, separated by a Plus shaped Ring Slot Resonator was designed. The measured results show good performance, including low insertion loss of 1.46 dB, high selectivity factor of 55.75%, compact size of 0.107 〖λ_g〗^2 (with g the guided wave at the center frequency fc = 4.60 GHz), and high fractional bandwidth of 13.70% (i.e., 4.28-4.91 GHz). By covering the N79 5G band with a minimum attenuation of 40 dB from dc to 3.74 GHz and a minimum attenuation of 20 dB from 5.15 to 9.43 GHz, the proposed filter can be used for sub-6GHz 5G applications, as it prevents the interference between the N79 band and WiFi 5 GHz.Item type:Item, Access status: Open Access , A Neural Network-Enabled OTFS-PAPR Reduction with Low Computational Complexity(Radioengineering Society, 2026-04) Al-Rayif, M. I.; Eldukhri, E. E.This study proposes a new solution to overcome the high peak-to-average power ratio (PAPR) in Orthogonal Time Frequency Space (OTFS) by using an Artificial Neural Network (ANN) algorithm. The algorithm checks the magnitude (power) of each element in the matrix of the first stage of the inverse symplectic finite Fourier transform (ISFFT) process against a pre-specified threshold and, consequently adjusts the elements whose magnitudes exceed the threshold. This is achieved by using the ANN algorithm to apply fractional shifts to the elements of the original delay-Doppler (DD) data matrix without changing their orientation. The simulation results demonstrated a significant PAPR reduction while maintaining the system performance in terms of the Bit Error Rate (BER), with almost the same computational complexity of the conventional OTFS system.Item type:Item, Access status: Open Access , A Pre-impact Fall Algorithm Based on a Lightweight Re-Parameters-Parallel Convolutional-TCN(Radioengineering Society, 2026-04) Pan, J.; Wang, H.; Xu, J.; Xu, H.With the aging society intensifying, the problem of elderly falls has become a key issue of social concern. Research on fall prediction based on Internet of Things (IoT) technology has received widespread attention. To effectively predict fall events, a lightweight IoT-based fall prediction model called lwRPPC-TCN (lightweight Re-Parameters-Parallel-Convolutional Temporal Convolutional Network) is proposed. The model utilizes the temporal data collected by IoT sensors in the input stage and achieves efficient decoupled extraction of temporal and spatial features through lwRPPC blocks. The subsequent Temporal Convolutional Networks (TCNs) further strengthens the ability of modeling the global temporal dependency, thus optimizing the processing capability of sensor time-series data. To validate the generalization ability of the model and mitigate fall data scarcity, two public datasets, SisFall and KFall, are fused, and the performance of the model is evaluated by five-fold cross-validation. In addition, a homogeneous (models belong to the same model family) knowledge distillation technique is introduced to improve the performance of the model. Experimental results demonstrate that the proposed lwRPPC-TCN achieves an accuracy of 98.88% on the fused dataset, outperforming existing fall prediction models, with a fall prediction lead time (interval between the fall prediction time and the collision time) of 250ms, and a compact model size of 60 KB, which makes it suitable and possible to deploy in a resource-constrained wearable device.Item type:Item, Access status: Open Access , Asymmetric Coupled-Resonator Bandpass Filter for Ultra-Wide Stopband(Radioengineering Society, 2026-04) Pritha, N.; Maheswari, S.The research presents a novel combination of an asymmetric coupling with a stepped impedance resonator (ACSIR) to realize high performance, especially an ultra-wide stopband rejection. A basic second-order bandpass filter was developed and subsequently extended to a fourth-order configuration to improve key performance metrics, including wide stopband characteristics and compact size, without compromising insertion loss. The use of asymmetric coupling enables optimization of the filter response while preserving a simple configuration. Furthermore, the resonance conditions associated with the asymmetric coupling were examined through mathematical analysis. The proposed ACSIR filter achieved an enhanced fractional bandwidth (FBW) of 15.4%, a low insertion loss of 0.64 dB, and wide stopband rejection extending up to 7.25 f₀ tailored for WLAN applications operating at 2.45 GHz.