Abstract
The Constant Bandwidth Server (CBS) is a mechanism used in real-time systems to enable aperiodic soft realtime tasks with unknown execution parameters to run under a dynamic scheduling policy such as Earliest Deadline First (EDF), while still ensuring schedulability by using a bandwidth reservation strategy. This paper proposes an approach to extend the Zephyr open-source real-time operating system, currently maintained by the Linux Foundation, to support aperiodic tasks with CBS. The paper provides the proposed architecture and the design and implementation of the CBS mechanisms in the operating system, which are then evaluated in two test cases in an embedded platform. © 2025 Elsevier B.V., All rights reserved.

Abstract

Abstract
PurposeFresh food products remain one of the most challenging product categories for e-commerce managers. The literature emphasizes the importance of perceived freshness in explaining their purchase behavior. However, studies on online purchases of fresh food products are scarce, especially regarding repurchase intentions, and the role of perceived freshness in online settings has so far been disregarded. This research addresses this gap by examining the role of perceived freshness in the intention to repurchase fresh food products online.Design/methodology/approachGuided by the expectation confirmation theory (ECT) and the perceived risk theory, this study defined a set of hypotheses tested through structural equation modeling. Participants were consumers with previous experience in purchasing fresh food products online.FindingsThe findings indicate that the importance of sensory attributes negatively affected the perceived freshness of fresh food products purchased online, while the importance of non-sensory attributes had a non-significant impact. Expectations of freshness positively affected perceived freshness and confirmation of freshness, as suggested by ECT. The hypothesized positive effects of confirmation on satisfaction and of satisfaction on intention to repurchase fresh food products online were also supported. Finally, it was found that repurchase intention was negatively affected by perceived performance risk and financial risk.Originality/valueThis article contributes to the limited literature on online purchase of fresh food by focusing on perceived freshness as a determinant of repurchase intention.

Abstract

Abstract
This study investigates the impact of class imbalance and its potential interplay with other factors on machine learning models for transportation mode classification, utilising two real-world GPS trajectory datasets. A Random Forest model serves as the baseline, demonstrating strong performance on the relatively balanced dataset but experiencing significant degradation on the imbalanced one. To mitigate this effect, we explore various state-of-the-art class imbalance learning techniques, finding only marginal improvements. Resampling the fairly balanced dataset to replicate the imbalanced distribution suggests that factors beyond class imbalance are at play. We hypothesise and provide preliminary evidence for class overlap as a potential contributing factor, underscoring the need for further investigation into the broader range of classification difficulty factors. Our findings highlight the importance of balanced class distributions and a deeper understanding of factors such as class overlap in developing robust and generalisable models for transportation mode detection.

Abstract
Due to space and energy restrictions, lightweight autonomous underwater vehicles (AUVs) are usually fitted with low-power processing units, which limits the ability to run demanding applications in real time during the mission. However, several robotic perception tasks reveal a parallel nature, where the same processing routine is applied for multiple independent inputs. In such cases, leveraging parallel execution by offloading tasks to a GPU can greatly enhance processing speed. This article presents a collection of generic matrix manipulation kernels, which can be combined to develop parallelized perception applications. Taking advantage of those building blocks, we report a parallel implementation for the 3DupIC algorithm-a probabilistic scan matching method for sonar scan registration. Tests demonstrate the algorithm's real-time performance, enabling 3D sonar scan matching to be executed in real time onboard the EVA AUV.