Abstract
Heterogeneous CPU-FPGA C/C++ applications may rely on High-level Synthesis (HLS) tools to generate hardware for critical code regions. As typical HLS tools have several restrictions in terms of supported language features, to increase the size and variety of offloaded regions, we propose several code transformations to improve synthesizability. Such code transformations include: struct and array flattening; moving dynamic memory allocations out of a region; transforming dynamic memory allocations into static; and asynchronously executing host functions, e.g., printf(). We evaluate the impact of these transformations on code region size using three realworld applications whose critical regions are limited by nonsynthesizable C/C++ language features.

Abstract
Among transportation researchers, pedestrian issues are highly significant, and various solutions have been proposed to address these challenges. These approaches include Multi-Criteria Decision Analysis (MCDA) and machine learning (ML) techniques, often categorized into two primary types. While previous studies have addressed diverse methods and transportation issues, this research integrates pedestrian modeling with MCDA and ML approaches. This paper examines how MCDA and ML can be combined to enhance decision-making in pedestrian dynamics. Drawing on a review of 1574 papers published from 1999 to 2023, this study identifies prevalent themes and methodologies in MCDA, ML, and pedestrian modeling. The MCDA methods are categorized into weighting and ranking techniques, with an emphasis on their application to complex transportation challenges involving both qualitative and quantitative criteria. The findings suggest that hybrid MCDA algorithms can effectively evaluate ML performance, addressing the limitations of traditional methods. By synthesizing the insights from the existing literature, this review outlines key methodologies and provides a roadmap for future research in integrating MCDA and ML in pedestrian dynamics. This research aims to deepen the understanding of how informed decision-making can enhance urban environments and improve pedestrian safety.

Abstract
The rapid evolution of Artificial Intelligence (AI) is reshaping Human Resource Management (HRM), with growing interest in its role in talent identification. While AI has demonstrated effectiveness in analysing structured data, its limitations in assessing qualitative attributes such as creativity, adaptability, and emotional intelligence remain underexplored. This study addresses these gaps through an exploratory mixed-methods design, combining a global survey (n = 240) with semi-structured interviews of HR professionals. Quantitative analysis highlights patterns of association between key competencies, while qualitative findings provide contextual insights into perceptions of fairness, bias, and cultural resistance. The results suggest that AI can complement, but not replace, human judgement, supporting a Hybrid Evaluative Model that integrates algorithmic efficiency with human interpretation. The study contributes rare empirical evidence to a nascent field, highlights the ethical imperatives of bias mitigation and transparency, and underscores the importance of cultural context (collectivist versus individualist orientations) in shaping the acceptance and effectiveness of AI-enabled HR practices. These findings offer practical guidance for organisations and advance theory-building at the intersection of AI and HRM.

Abstract
Objective: This scoping review synthesized and mapped the breadth of the existing literature on technological resources used to support individuals undergoing hemodialysis treatment. Methods: Following the methodological guidelines of the Joanna Briggs Institute (JBI) for scoping reviews and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist, comprehensive searches were conducted across the following databases: MEDLINE, Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsycINFO, Scopus, Scientific Electronic Library Online (SciELO), MedicLatina, and the Cochrane Central Register of Controlled Trials, with no time restrictions. Results: Thirty-nine studies conducted between 2003 and 2023 met the inclusion criteria. These studies covered a range of technological innovations developed specifically for hemodialysis treatment, including virtual reality, exergames, websites, and mobile applications. These technologies were designed with diverse objectives: to facilitate physical exercise, optimize dietary and medication management, improve disease adherence and management, and promote self-efficacy and self-care in patients. Conclusions: The review revealed a wide range of technological resources available to hemodialysis patients. These digital solutions show great potential to transform care by promoting more engaged and personalized health practices. Although this study did not directly assess the impact of these technologies, it provides a solid foundation for future investigations that can explore in-depth how such innovations contribute to effective disease management and improvement in clinical outcomes.

Abstract
This paper presents ElderMind, a mobile application designed to promote cognitive stimulation and engagement among older adults. Developed using a User-Centered Design (UCD) approach, the application incorporates gamified elements to enhance usability. ElderMind features three cognitive games-memory, puzzle, and maze-solving-each with adjustable difficulty levels, ensuring accessibility for diverse user needs. Key functionalities include performance tracking, customizable font sizes, and multilingual support, making it a versatile tool for aging populations. Accessibility and usability assessments were conducted to refine the application iteratively, addressing issues such as visual contrast and touch target sizes. Preliminary usability testing with participants aged 50-64 demonstrated ease of use, with most tasks rated as not difficult at all. Feedback highlighted the application's simplicity and accessibility while identifying areas for improvement, such as interface aesthetics and game variety. ElderMind represents a preliminary solution toward inclusive digital solutions for cognitive health and user engagement.

Abstract
Human Activity Recognition (HAR) is the identification and classification of static and dynamic human activities, which find applicability in domains like healthcare, entertainment, security, and cyber-physical systems. Traditional HAR approaches rely on wearable sensors, vision-based systems, or ambient sensing, each with inherent limitations such as privacy concerns or restricted sensing conditions. Instead, Radio Frequency (RF)-based HAR relies on the interaction of RF signals with people to infer activities. Reconfigurable Intelligent Surfaces (RISs) are significant for this use-case by allowing dynamic control over the wireless environment, enhancing the information extracted from RF signals. We present an Hand Gesture Recognition (HGR) approach using our own 6.5GHz RIS design, which we use to gather a dataset for HGR classification for three different hand gestures. By employing two Convolutional Neural Networks (CNNs) models trained on data gathered under random and optimized RIS configuration sequences, we achieved classification accuracies exceeding 90%.