Abstract
Critical performance regions of software applications are often accelerated by offloading them onto an FPGA. An efficient end result requires the judicious application of two processes: hardware/software (hw/sw) partitioning, which identifies the regions for offloading, and the optimization of those regions for efficient High-level Synthesis (HLS). Both processes are commonly applied separately, not relying on any potential interplay between them, and not revealing how the decisions made in one process could positively influence the other. This paper describes our primary efforts and contributions made so far, and our work-in-progress, in an approach that combines both hw/sw partitioning and optimization into a unified, holistic process, automated using source-to-source compilation. By using an Extended Task Graph (ETG) representation of a C/C++ application, and expanding the synthesizable code regions, our approach aims at creating clusters of tasks for offloading by a) maximizing the potential optimizations applied to the cluster, b) minimizing the global communication cost, and c) grouping tasks that share data in the same cluster.

Abstract

Abstract
BackgroundIndividuals with spinal cord injury (SCI) face complex and ongoing rehabilitation needs. In this context, mobile health applications have emerged as promising tools to support self-management and rehabilitation.ObjectiveTo map and characterize mobile applications specifically developed to support rehabilitation of individuals with SCI.MethodsA scoping review was conducted in accordance with PRISMA-ScR guidelines. A systematic search was performed across five electronic databases (PubMed, Scopus, Web of Science, and CINAHL). Studies published between 2015 and 2024 describing the use of mobile applications in the rehabilitation of adults with SCI were included.ResultsA total of 24 studies were included. We synthesized the identified applications descriptively into four domains: self-management and health education; gamification and motivation for physical rehabilitation; monitoring and prevention of secondary complications; and assistive technology and advanced rehabilitation. A consistent adoption of user-centered design principles was observed. Despite high levels of reported usability, challenges remain regarding long-term engagement, technological complexity, and sustained adherence.ConclusionMobile applications represent a promising complementary resource to support rehabilitation and health management in individuals with SCI. However, more robust longitudinal studies with larger sample sizes are required to assess the clinical impact and long-term feasibility of these interventions.

Abstract
Artificial intelligence is gaining attraction in more ways than ever before. The popularity of language models and AI-based businesses has soared since ChatGPT was made available to the public via the OpenAI web platform. It gains popularity in a very short period because of its real-world problem-solving capability. Considering the widespread use of ChatGPT and the people relying on it, this study determined how reliable ChatGPT can be used for learning in the medical domain. The capability of ChatGPT was evaluated using the questions of Harvard University gross anatomy and the United States Medical Licensing Examination (USMLE). The outcome of the ChatGPT was analyzed using a 2-way ANOVA and post-hoc analysis. Both tests showed systematic covariation between format and prompt. Furthermore, the physician adjudicators independently rated the outcome’s accuracy, concordance, and insight into the answers given by ChatGPT. As a result of the analysis, ChatGPT-generated answers were more context-oriented and represented a better model for deductive reasoning than regular Google search results. Furthermore, ChatGPT obtained 58.8% on logical questions and 60% on ethical questions. This means that the ChatGPT is approaching the passing range for logical questions and has crossed the threshold for ethical questions. These results indicate that ChatGPT and other language-learning models can be invaluable tools for e-learners. © 2025 Elsevier B.V., All rights reserved.

Abstract
Wi-Fi 6E will enable dense communications with low latency and high throughput, meeting the demands of ever growing network traffic and supporting emergent services such as ultra HD or multi-video streaming, and augmented or virtual reality. However, the 6GHz band suffers from higher path loss and signal attenuation, and poor performance in NLoS conditions. Reconfigurable Intelligent Surfaces (RISs) can address these challenges by providing low-cost directional communications with increased spectral and energy efficiency. However, RIS designs for the WiFi-6E range are under-explored in literature. We present the implementation of an 8x8 RIS tuned for 6.5GHz designed for scalability. We characterize the response of the unit cell, and evaluate the RIS in an anechoic chamber, measuring the far field radiation patterns for several digital beamsteering configurations in a horizontal plane, demonstrating effective signal steering.

Abstract
TCP is the default transport protocol of choice, namely for message-oriented middleware protocols (e.g., ZMTP, AMQP, MQTT) or distributed language runtimes (e.g., distributed Erlang), where exactly-once (EO) messaging is paramount. However, EO is only guaranteed within the TCP session, since reality shows that TCP connections can fail under many circumstances. Ensuring EO delivery ends up at the middleware layer, at the cost of higher complexity and lack of obliviouness - due to the use of permanent per-peer state. Moreover, using TCP at scale in highly concurrent systems leads to the need for TCP connection multiplexing, and possibly drastic performance loss due to head-of-line blocking. This paper introduces Exon, an oblivious exactly-once messaging protocol, and a corresponding lightweight (requiring no persistent storage, minimal memory, and low computation) library implementation over UDP. Exon uses a novel strategy of a per-message four-way protocol to ensure oblivious exactly-once messaging, with on-demand protocol-level "soft half-connections", established when needed and safely discarded. Obliviousness here refers to the protocol's ability to discard connection-specific state between incarnations, although some global information is retained. Exon achieves simultaneously: correctness with no timing assumptions, obliviousness, and performance through merging and pipelining basic protocol messages. Exon also employs a reliable delegation technique to handover the sending responsibility to a mediating node, without violating EO, when the sender the receiver are directly unreachable to each other and even if the message had already been delivered. The empirical evaluation of Exon demonstrates significant improvements (40%) over TCP in throughput and latency under packet loss, while maintaining a negligible (8%) overhead in healthy networks.