Graph Neural Networks (GNNs) have established themselves as powerful tools for learning from graph-structured data. However, their reliance on local message-passing mechanisms leads to over-squashing-the compression of exponentially growing neighborhood information into fixed-size vectors-which severely limits long-range dependency modeling. We introduce the Quantum-Inspired Graph Neural Network (QGNN) with a novel Quantum Entanglement Loss (QEL) function that addresses this challenge through a fundamentally different mechanism than existing approaches. Unlike spectral regularization (which enforces smoothness) or maximum entropy methods (which encourage representation diversity), QEL minimizes the von Neumann entropy of the node embedding correlation matrix, thereby concentrating eigenvalues in dominant eigenmodes that preserve global structural patterns. This entropy minimization creates direct information pathways between distant but functionally related nodes, effectively bypassing multi-hop bottlenecks. We evaluate QGNN on both standard benchmarks (Cora, Citeseer, PPI, Electronic Circuits) and the Long Range Graph Benchmark (LRGB) suite, which features graphs with average diameters up to 56.99 (Peptides). On LRGB datasets, QGNN achieves substantial improvements: 37.6% relative MAE reduction on Peptides-struct compared to GCN, 4.0% improvement over Graph Transformers (GraphGPS), and notably, 97% better performance than GCN on node pairs separated by 7+ hops. Despite these gains, QGNN requires only 20-30% additional computational overhead compared to standard GCN, while being 5-6 × faster than Graph Transformer approaches. Our results establish entropy-based regularization as a principled and efficient approach for long-range dependency modeling in graphs.

The statistics highlights that physical rehabilitation are required nowadays by increased number of people that are affected by motor impairments caused by accidents or aging. Among the most common causes of disability in adults are strokes or cerebral palsy. To reduce the costs preserving the quality of services new solutions based on current technologies in the area of physiotherapy are emerging. The remote monitoring of physical training sessions could facilitate for physicians and physical therapists' information about training outcome that may be useful to personalize the exercises helping the patients to achieve better rehabilitation results in short period of time process. This research work aims to apply physical rehabilitation monitoring combining Virtual Reality serious games and Wearable Sensor Network to improve the patient engagement during physical rehabilitation and evaluate their evolution. Serious games based on different scenarios of Virtual Reality, allows a patient with motor difficulties to perform exercises in a highly interactive and non-intrusive way, using a set of wearable devices, contributing to their motivational process of rehabilitation. The system implementation, system validation and experimental results are included in the paper.

Existing routing protocols for multi-sink wireless sensor networks (WSNs) attempt to optimize energy efficiency and routing survivability from the perspective of the network itself, without considering the impact of external environment, making them unable to respond to environmental changes in a timely manner. Therefore, the routing survivability of these routing protocols in harsh environments is questionable. To solve this problem, we design a sustainable multipath routing protocol SMRP, in which the routing decisions are made according to a mixed potential field in terms of depth, residual energy and environment. The basic idea of SMRP is to instruct messages to select paths with a tradeoff among delivery latency, energy balance and routing survivability. As the environmental field is constructed and updated using the sensing capability of WSN itself, the constructed multipath can be secured by avoiding passing through the dangerous areas. We explore the impact of the number of sink nodes and the number of paths on routing performance, and compare SMRP with commonly used routing protocols (i.e., EDGR and IPF). Through extensive experiments, SMRP has proved to have better performance in terms of packet delivery ratio and network lifetime under harsh environments.

Seriouslgames based therapeutic approaches create environments that increase the motivation of patients to achieve successful completion of therapy programs that can be demanding. The therapeutic games should be designed and configured by therapists, who should also monitor and evaluate, on a regular basis, the therapy exercises masked by the games to measure the patient's progress in treatment. Thus, there is still a demand for customizable tools by therapists toward specific groups of patients. We are developing a solution that combines serious games, NUI, and sensors, such as Kinect and Leap Motion, to address both physiotherapy and cognitive stimulation therapy. This paper presents the Web-based platform that provides the gaming solution, detailing the work in progress on the components responsible for the therapists' supervision.