Abstract
Executing Artificial Intelligence (AI) at the edge is challenging due to tight energy and computational constraints. Heterogeneous platforms, particularly those incorporating Coarse-Grained Reconfigurable Arrays (CGRAs), offer a compelling trade-off between hardware specialization and programmability, supporting spatially distributed and energy-efficient computation. Despite their potential, the deployment of applications on CGRA accelerators remains limited by the lack of practical toolchains and methodologies. In this work, we propose a compilation flow based on MLIR to enable the seamless integration of both C/C++ kernels and ONNX-based AI models into a RISC-V system augmented with a CGRA accelerator. Our approach extracts the underlying Data Flow Graph (DFG) from the high-level representation. It maps it onto the CGRA using an Integer Linear Programming (ILP) mapper that accounts for the accelerator's architectural constraints. A custom backend completes the toolchain by generating the necessary binaries for coordinated execution across the RISC-V processor and the CGRA. This framework enables the practical deployment of heterogeneous edge workloads, combining the flexibility of software execution with the efficiency of hardware acceleration.

Abstract
High-performance sport has evolved into an arena where psychological excellence is as decisive as physical prowess. The ability to effectively regulate emotions, particularly pre-competitive anxiety/negativity (PCA), consistently distinguishes elite performance. This article presents a critical and in-depth analysis of the contemporary literature on emotional management in sport. Beginning with a deconstruction of the athlete’s complex emotional landscape, it delves into the neurocognitive mechanisms and multifactorial antecedents of these states, identifying them as a central obstacle to optimal performance. The core of the work focuses on evaluating a spectrum of evidence-based psychological interventions, ranging from physiological regulation strategies and pre-competitive routines to third-wave approaches such as Acceptance and Commitment Therapy (ACT) and mindfulness training. The analysis reveals a paradigm shift from models aimed at eliminating anxiety/negativity to approaches that promote psychological flexibility and acceptance. It is concluded that the most effective intervention resides in an integrated, periodized, and personalized model—a “toolbox” of psychological skills—adapted to the athlete’s individual needs and the phases of their training cycle. This work argues that the future of sport psychology lies in promoting the athlete’s holistic well-being as the fundamental pillar for sustainable, high-level performance.

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.

Abstract
The increasing global burden of lung diseases necessitates the development of improved diagnostic tools. According to the WHO, hundreds of millions of individuals worldwide are currently affected by various forms of lung disease. The rapid advancement of artificial neural networks has revolutionized lung disease diagnosis, enabling the development of highly effective detection and classification systems. This article presents dual channel neural networks in image feature extraction based on classical CNN and vision transformers for multi-label lung disease diagnosis. Two separate subnetworks are employed to capture both global and local feature representations, thereby facilitating the extraction of more informative and discriminative image features. The global network analyzes all-organ regions, while the local network simultaneously focuses on multiple single-organ regions. We then apply a novel feature fusion operation, leveraging a multi-head attention mechanism to weight global features according to the significance of localized features. Through this multi-channel approach, the framework is designed to identify complicated and subtle features within images, which often go unnoticed by the human eye. Evaluation on the ChestX-ray14 benchmark dataset demonstrates that our hybrid model consistently outperforms established state-of-the-art architectures, including ResNet-50, DenseNet-121, and CheXNet, by achieving significantly higher AUC scores across multiple thoracic disease classification tasks. By incorporating test-time augmentation, the model achieved an average accuracy of 95.7% and a specificity of 99%. The experimental findings indicated that our model attained an average testing AUC of 87%. In addition, our method tackles a more practical clinical problem, and preliminary results suggest its feasibility and effectiveness. It could assist clinicians in making timely decisions about lung diseases.

Abstract
This study examines the social factors influencing the acceptance of autonomous buses, with a focus on per-ceived benefits, safety, and comfort. It also explores whether these factors differ among residents of cities with varying sizes and urban mobility solutions. A survey was conducted in three Polish cities, collecting data from 1,160 respondents. Structural Equation Modelling (SEM) was used to analyse relationships between perceived benefits, safety, comfort, and future intentions to use autonomous buses. Results indicate that safety and comfort positively influence future intentions to use autonomous buses. However, the effect of perceived benefits varies across cities, suggesting that urban mobility conditions shape public acceptance. The study focuses on Polish cities, which may limit generalizability. Future research should examine other geo-graphical contexts. Findings provide insights for policymakers and manufacturers on enhancing public trust and promoting autonomous bus adoption. Improving public awareness and addressing safety concerns may increase societal acceptance of autonomous mobility. The study uniquely assesses how city characteristics influence social acceptance of autonomous buses.

Abstract
This study explores the context of blood donation in Portugal, specifically aiming to understand how communication strategies can effectively recruit young blood donors aged 18 to 24. The research addresses the following question: What is the impact of communication efforts on the recruitment of young blood donors in Portugal? To answer this question, four specific objectives were set: (1) To evaluate the level of awareness among young individuals in this age group regarding blood donation; (2) to analyze and assess the communication strategies employed by the Portuguese Institute of Blood and Transplantation (IPST) to promote blood donation; (3) to investigate the motivations and barriers related to blood donation; and (4) to identify effective communication strategies for encouraging blood donation. To achieve the first objective, which is the primary focus of this article, a content analysis of 14 blood donation campaigns was conducted. For the second objective, an exploratory interview was held with a specialist from the IPST. The third objective is being addressed through a survey involving 390 young individuals, which has already been administered and revealed that over half of the respondents are not blood donors. The findings suggest that future campaigns should adopt more targeted segmentation strategies based on behavioral criteria and make greater use of integrated marketing communication to enhance effectiveness.