This paper presents a measurement-based comparison between distributed and concentrated massive multiple-input multiple-output (MIMO) systems, which are called D-mMIMO and C-mMIMO systems, in an indoor environment considering a 400 MHz bandwidth centered at 3.5 GHz. In both cases, we have considered an array of 64 antennas in the base station and eight simultaneously active users. The work focuses on the characterization of both schemes in the up-link, considering the analysis of the sum capacity, the total spectral efficiency (SE) achievable by using the zero forcing (ZF) combining method, as well as the user fairness. The effect of the power imbalance between the different transmitters or user terminal (UT) locations, and thus, the benefits of performing an adequate power control are also investigated. The differences between the C-mMIMO and D-mMIMO channel performances are explained through the observation of the structure of their respective measured channel matrices through parameters such as the condition number or the power imbalance between the channels established by each UT. The channel measurements have been performed in the frequency domain, emulating a massive MIMO system in the framework of a time-domain duplex orthogonal frequency multiple access network (TDD-OFDM-MIMO). The characterization of the MIMO channel is based on the virtual array technique for both C-mMIMO and D-mMIMO systems. The deployment of the C-mMIMO and D-MIMO systems, as well as the distribution of users in the measurement environment, has been arranged as realistically as possible, avoiding the movement of people or machines.

Offset quadrature amplitude modulation-based filter bank multicarrier (FBMC/OQAM) is among the promising waveforms for future wireless communication systems. This is due to its flexible spectrum usage and high spectral efficiency compared with the conventional multicarrier schemes. However, with OQAM modulation, the FBMC/OQAM signals are not orthogonal in the imaginary field. This causes a significant intrinsic interference, which is an obstacle to apply multiple input multiple output (MIMO) technology with FBMC/OQAM. In this paper, we propose a deep neural network (DNN)-based approach to deal with the imaginary interference, and enable the application of MIMO technique with FBMC/OQAM. We show, by simulations, that the proposed approach provides good performance in terms of bit error rate (BER).

Background/Objectives: Accurate drug-target affinity (DTA) prediction supports virtual screening, lead optimization, and drug repurposing. This study investigates whether replacing the conventional post-aggregation multilayer perceptron within a Graph Isomorphism Network drug encoder with a Kolmogorov-Arnold Network (KAN) transformation improves DTA prediction. Methods: The resulting KAN-GIN framework combines molecular-graph representations with a convolutional protein-sequence encoder. It was evaluated on the DAVIS, KIBA, and METZ benchmarks against a matched GIN baseline and representative multiscale and graph-pretraining-based DTA architectures under a common split and evaluation protocol. Complementary analyses examined latent-space organization, gradient-based molecular attribution, and an enhancer of zeste homolog 2 (EZH2) candidate-ranking case study. Results: KAN-GIN consistently reduced prediction error relative to the matched GIN baseline and achieved competitive performance against the additional architectures, including the strongest overall results on METZ. The latent-space findings were dataset-dependent, while gradient-based attribution identified molecular substructures contributing to individual predictions. The EZH2 case study illustrated how KAN-GIN can support candidate ranking before structure-based evaluation. Conclusions: KAN-based post-aggregation transformations represent an emerging design option for graph-based DTA prediction. However, the latent-space and structure-based findings remain computational and should not be interpreted as evidence of mechanistic biological relevance without prospective experimental validation.

Background : Automated segmentation of the left and right ventricles (LV) and (RV) in cine cardiac MRI (CMR) underpins reliable volumetry and mass estimation. However, papillary muscles and trabeculae (PM/T) introduce clinically meaningful variability and exacerbate cross-dataset domain shift. Methods : We present VentrEX, an anatomically guided pipeline. The core segmenter, VentrEX-Seg is a 3D encoder-decoder with {parallel channel-spatial attention} and a {Transformer} bottleneck. Training is performed exclusively on ACDC. A lightweight PM/T module automatically extracts papillary and trabecular burden and standardizes cavity volumes. External evaluation is {zero-shot} (no fine-tuning) on Sunnybrook (LV) and MM-WHS MRI (RV). We report Dice, HD95 (mm); for volumetry we use Bland-Altman analyses (LV and RV volumes). Attention/Grad-CAM visualizations support interpretability. Results : On ACDC, VentrEX achieved higher Dice and lower boundary error than U-Net, nnU-Net, CBAM, and VentrEX-Seg. Zero-shot performance was preserved externally (e.g., Sunnybrook LV Dice~0.9053, HD95~4.95\,mm; MM-WHS RV Dice~0.9236, HD95~6.61\,mm). PM/T standardization reduced volumetric bias and narrowed limits of agreement in Bland-Altman analyses. Qualitative overlays and 3D reconstructions showed fewer PM/T "leaks" and anatomically plausible borders across ED/ES. Conclusions : Single-source training with dual zero-shot external tests demonstrates robustness under domain shift, while explicit PM/T modeling reduces volume bias and improves reproducible volumetry. The combination of parallel attention and a Transformer bottleneck enables accurate, transparent cine-CMR segmentation across datasets.