Search Results (6)

Real-time media streaming over publish–subscribe platforms is increasingly vital in scenarios that demand the scalability of event-driven architectures while ensuring timely media delivery. This is especially true in multi-modal and resource-constrained environments, such as IoT, Physical Activity Recognition and Measure (PARM), and Internet of Video Things (IoVT), where integrating sensor data with media streams often leads to complex hybrid setups that compromise consistency and maintainability. Publish–subscribe (pub/sub) platforms like Kafka and MQTT offer scalability and decoupled communication but fall short in supporting real-time video streaming due to platform-dependent design, rigid optimization, and poor sub-second media handling. This paper presents FrameMQ, a layered, platform-agnostic architecture designed to overcome these limitations by decoupling application logic from platform-specific configurations and enabling dynamic real-time optimization. FrameMQ exposes tunable parameters such as compression and segmentation, allowing integration with external optimizers. Using Particle Swarm Optimization (PSO) as an exemplary optimizer, FrameMQ reduces total latency from over 2300 ms to below 400ms under stable conditions (over an $80\%$ improvement) and maintains up to a $52\%$ reduction under adverse network conditions. These results demonstrate FrameMQ’s ability to meet the demands of latency-sensitive applications, such as real-time streaming, IoT, and surveillance, while offering portability, extensibility, and platform independence without modifying the core application logic. Full article

With advancements in gravitational wave detection technology, an increasing number of binary neutron star (BNS) merger events are expected to be detected. Due to the narrow opening angle of jet cores, many BNS merger events occur off-axis, resulting in numerous gamma-ray bursts (GRBs) going undetected. Models suggest that kilonovae, which can be observed off-axis, offer more opportunities to be detected in the optical/near-infrared band as electromagnetic counterparts of BNS merger events. In this study, we calculate kilonova emission using a three-dimensional semi-analytical code and model the GRB afterglow emission with the open-source Python package afterglowpy at various inclination angles. Our results show that it is possible to identify the kilonova signal from the observed color evolution of BNS merger events. We also deduce the optimal observing window for SVOM/VT and JWST/NIRCam, which depends on the viewing angle, jet opening angle, and circumburst density. These parameters can be cross-checked with the multi-band afterglow fitting. We suggest that kilonovae are more likely to be identified at larger inclination angles, which can also help determine whether the observed signals without accompanying GRBs originate from BNS mergers. Full article

This study presents a systematic review of the literature on individuals’ health-related quality of life (HRQoL) following radical cystectomy for muscle-invasive bladder cancer (MIBC), utilizing the Short Form-36 Health Survey (SF-36) as a primary assessment tool. The review was designed as an exhaustive literature search across three major databases including PubMed, Scopus, and Embase up to December 2023, using the PRISMA guidelines. The selection process refined 2281 identified articles down to 11 studies meeting our inclusion criteria. These studies encompassed a diverse demographic and clinical profile of 774 participants, with follow-up durations ranging from 3 to 130 months, thereby offering insights into both short-term and long-term HRQoL outcomes. The results highlighted significant alterations in individuals’ HRQoL across various domains post-radical cystectomy. Notably, the Physical Functioning (PF) and Bodily Pain (BP) domains generally scored higher, indicating a moderate to high perceived physical health status. However, the Role Physical (RP) and Role Emotional (RE) domains showed variability, reflecting the challenges in daily role fulfillment and emotional adjustment post-surgery. A marked variability in physical recovery was observed, with studies reporting significant differences in PF and RP scores between patient groups. The General Health (GH) and Vitality (VT) domains sometimes reflected perceived deteriorations, whereas the Mental Health (MH) scores suggested that many patients maintained or achieved high levels of well-being post-operatively. The conclusions drawn from this systematic review underscore the profound and multi-faceted impact of radical cystectomy on HRQoL, varying widely between studies, being influenced by geographic factors, surgical methods, and the time of evaluation. The findings emphasize the necessity for holistic patient care approaches that address both physical and emotional rehabilitation, aiming to improve HRQoL outcomes. Full article

In this paper, the innovations in device design of the gate-all-around (GAA) nanosheet FET are reviewed. These innovations span enablement of multiple threshold voltages and bottom dielectric isolation in addition to impact of channel geometry on the overall device performance. Current scaling challenges for GAA nanosheet FETs are reviewed and discussed. Finally, an analysis of future innovations required to continue scaling nanosheet FETs and future technologies is discussed. Full article

Maize (Zea mays L.) is one of the most consumed grains in the world. Within the context of continuous climate change and the reduced availability of arable land, it is urgent to breed new maize varieties and screen for the desired traits, e.g., high yield and strong stress tolerance. Traditional phenotyping methods relying on manual assessment are time-consuming and prone to human errors. Recently, the application of uncrewed aerial vehicles (UAVs) has gained increasing attention in plant phenotyping due to their efficiency in data collection. Moreover, hyperspectral sensors integrated with UAVs can offer data streams with high spectral and spatial resolutions, which are valuable for estimating plant traits. In this study, we collected UAV hyperspectral imagery over a maize breeding field biweekly across the growing season, resulting in 11 data collections in total. Multiple machine learning models were developed to estimate the grain yield and flowering time of the maize breeding lines using the hyperspectral imagery. The performance of the machine learning models and the efficacy of different hyperspectral features were evaluated. The results showed that the models with the multi-temporal imagery outperformed those with imagery from single data collections, and the ridge regression using the full band reflectance achieved the best estimation accuracies, with the correlation coefficients (r) between the estimates and ground truth of 0.54 for grain yield, 0.91 for days to silking, and 0.92 for days to anthesis. In addition, we assessed the estimation performance with data acquired at different growth stages to identify the good periods for the UAV survey. The best estimation results were achieved using the data collected around the tasseling stage (VT) for the grain yield estimation and around the reproductive stages (R1 or R4) for the flowering time estimation. Our results showed that the robust phenotyping framework proposed in this study has great potential to help breeders efficiently estimate key agronomic traits at early growth stages. Full article

Airborne LiDAR technology is widely used in archaeology and over the past decade has emerged as an accurate tool to describe anthropomorphic landforms. Archaeological features are traditionally emphasised on a LiDAR-derived Digital Terrain Model (DTM) using multiple Visualisation Techniques (VTs), and occasionally aided by automated feature detection or classification techniques. Such an approach offers limited results when applied to heterogeneous structures (different sizes, morphologies), which is often the case for archaeological remains that have been altered throughout the ages. This study proposes to overcome these limitations by developing a multi-scale analysis of topographic position combined with supervised machine learning algorithms (Random Forest). Rather than highlighting individual topographic anomalies, the multi-scalar approach allows archaeological features to be examined not only as individual objects, but within their broader spatial context. This innovative and straightforward method provides two levels of results: a composite image of topographic surface structure and a probability map of the presence of archaeological structures. The method was developed to detect and characterise megalithic funeral structures in the region of Carnac, the Bay of Quiberon, and the Gulf of Morbihan (France), which is currently considered for inclusion on the UNESCO World Heritage List. As a result, known archaeological sites have successfully been geo-referenced with a greater accuracy than before (even when located under dense vegetation) and a ground-check confirmed the identification of a previously unknown Neolithic burial mound in the commune of Carnac. Full article