Abstract

Abstract
This systematic review synthesizes contemporary research on the psychological profile of elite athletes, with a focus on self-confidence, anxiety/negativity, attentional control, motivation, imagery, positivity, and competitive attitude. Drawing on 48 empirical studies across diverse sports and global contexts, the review evaluates the dynamic relationships among these constructs, benchmarks psychological training interventions, and critically examines the unique roles of positivity and competitive attitude in elite performance. Findings show that self-confidence inversely correlates with anxiety/negativity, while attentional control and motivation are pivotal for sustaining resilience and competitive drive. Psychological skills training, encompassing imagery, relaxation, and cognitive restructuring, effectively enhance self-confidence and reduce anxiety/negativity, although intervention heterogeneity limits definitive conclusions. Persistent challenges include a lack of standardized measurement tools, a predominance of cross-sectional studies, and insufficient longitudinal and culturally adapted research. The review underscores the need for rigorous, longitudinal investigations and tailored interventions to advance theoretical models and optimize psychological preparation in elite sport.

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Device-free Human Activity Recognition (HAR) presents a significant challenge, offering a privacy-preserving alternative to vision-based systems. This work proposes a novel methodology that leverages the rich motion dynamics captured in Doppler traces derived from Channel State Information (CSI). We introduce a hybrid deep learning architecture, InceptionBiLSTM, specifically engineered to process these traces. The Inception module excels at extracting salient, multi-scale local features from the Doppler data, while the Bidirectional Long Short-Term Memory (BiLSTM) network subsequently models the long-range temporal dependencies inherent in complex human activities. To further enhance classification performance, a Support Vector Machine (SVM) with a non-linear kernel is integrated as a post-processing stage. This step refines the decision boundaries learned by the deep neural network, significantly improving generalization. The proposed methodology achieves outstanding accuracy rates approaching 99 % in identifying distinct human movements. These results are validated through comprehensive performance metrics, including confusion matrices, confirming the robustness and high efficacy of this hybrid approach for CSI-based HAR.

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To address the challenge of detecting stealthy port scans in high-speed networks, this paper introduces p4SD, a lightweight anomaly detection system that identifies reconnaissance activities directly within programmable data planes. Leveraging the P4 language, p4SD uses a cyclic fingerprint buffer and frequency analysis to monitor for anomalous traffic without relying on attack signatures. The system is designed to detect both fast and slow port scans, as its method of measuring relative changes in distinct fingerprints between cycles effectively identifies both the rapid spikes from fast scans and the gradual increases from slow scans. The proof-of-concept demonstrates resource efficiency, achieving throughput close to the hardware's theoretical limits, detecting scan activity in near real-time, and enabling timely responses to potential threats. With over 99% detection accuracy for slow scans, these findings establish p4SD as a practical and scalable solution for real-time, in-network threat detection in modern SDN environments.

Abstract
Objectives: Social isolation and loneliness among older people are widespread, with an impact on physical and mental health. Cycling Without Age (CWA) is an international cycling programme developed to minimise social isolation and loneliness in older people. It involves trishaw (electric bicycle) rides in the open air, led by volunteer riders. This study aimed to analyse the effects of CWA intervention on loneliness and social isolation among older people living in Porto, Portugal. Methods: Older adults (aged 55 years or older) living in the community or a nursing home were included. The intervention comprised at least four bicycle rides, with a duration between 30 and 60 min. A research protocol was applied before and after the intervention, which included the UCLA Loneliness Scale and the Abbreviated Lubben Social Network Scale. Results: A total of 47 participants (median age = 85 years) completed the intervention. Participants were mostly female (81%), widowed (66%) and living in nursing homes (72%). A statistically significant decrease in loneliness was found after the intervention (Median [IQR]_after = 24.0 [16.0] vs. before = 17.0 [6.0]; p < 0.05). Discussion: This preliminary work highlights the positive effect the CWA intervention may have on loneliness among older adults, which is consistent with other CWA programme studies. However, future research is required to evaluate whether these effects persist over time.

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This paper proposes a stochastic optimization model for industrial hubs to enable their participation in energy markets. The model aims to leverage the resources of multi-energy systems to minimize energy costs in the day-ahead market. It accounts for uncertainties in photovoltaic generation, electrical and heat demand, and outdoor temperatures. A comparison is made with a deterministic approach, along with an analysis of the impact of thermal storage and reserve market participation on costs and bidding strategies. The results show that the stochastic approach is more conservative than the deterministic, while the integration of thermal storage and reserve services help decrease costs.