Robert Gordon's College

This Work in Progress paper presents an innovative, cross-university software development initiative using the Collaborative Online International Learning (COIL) framework. Driven by local businesses seeking new revenue streams, the project engages undergraduate students in international, industry-oriented collaboration to enhance both learning outcomes and business innovation. Student teams developed interactive software prototypes in collaboration with business partners. Each project included a student Product Owner to ensure alignment with business needs. Completed prototypes were then transferred to a partner institution for full-stack implementation. This two-phase model blended distributed teamwork, cultural exchange, and authentic industry input. Data collection includes student reflections, lecturer insights, and industry feedback. Early results highlight growth in agile practices, cross-cultural communication, and stakeholder collaboration. Conference participants will gain insight into designing international, industry-led collaborations that build technical and cross-border skills. Future work will focus on improving engagement and expanding partnerships with more industry stakeholders.

Abstract In recent years, there has been a significant increase in research focused on Human Activity Analysis (HAA). This field has progressed from basic activity recognition tasks to addressing more challenging ones, such as predicting future human actions based on partially observed videos and even predicting actions before they happen. The evolution of HAA has been driven by recent advancements in attention-based models like Transformers, along with a wide range of applications from security surveillance to advanced monitoring systems, behaviour analysis, and more. A comprehensive review of HAA literature from 2017 to 2025, with a novel taxonomy emphasising activity recognition, prediction, and anticipation, is presented. We critically review and examine recognition methods from trimmed and untrimmed videos, context-aware and trajectory-based prediction, and short-term and long-term anticipation. Through a comprehensive analysis, we review and evaluate key aspects of this domain, including attention-based contextual comprehension, temporal dynamics modelling, and multi-model fusion methods. Furthermore, we critically examine and assess the public datasets utilised in driving this research forward, pinpointing limitations and primary challenges within this domain. Finally, the paper provides a summary of recent developments in HAA and suggests future directions, with the hope that it will serve as a valuable reference for researchers in the field.

In underwater subsea environments light attenuation, water turbidity, and limitations of the optical devices make the captured images suffer from poor contrast and quality, proportional degradation, low visibility, and low color richness. In recent years, various image enhancement techniques have been applied to improve the image quality, resulting in a new challenge, i.e., the quality assessment of the underwater images. In this study, we introduce an innovative and versatile blind quality assessment method for underwater images without using any references. Our approach leverages structural and contour-based metrics, combined with dispersion rate analysis, to quantify image degradation and color richness within an opponent color space. Specifically, we measure the proportional degradation by computing the edge magnitude using the directional Kirsch kernels, strengthened by image contour and saliency maps. To assess the color quality, chrominance dispersion rates and the overall saturation and hue are used to capture color distortions introduced by enhancement methods. The final quality score is obtained via a multiple linear regression model trained on extensive data sets. Experiments on three benchmark data sets have demonstrated the superior accuracy, consistency, and computational efficiency of the proposed method for both raw and enhanced underwater images.

Power transformers play a critical role within the electrical power system, making their health assessment and the prediction of their remaining lifespan paramount for the purpose of ensuring efficient operation and facilitating effective maintenance planning. This paper undertakes a comprehensive examination of existent literature, with a primary focus on both conventional and cutting-edge techniques employed within this domain. The merits and demerits of recent methodologies and techniques are subjected to meticulous scrutiny and explication. Furthermore, this paper expounds upon intelligent fault diagnosis methodologies and delves into the most widely utilized intelligent algorithms for the assessment of transformer conditions. Diverse Artificial Intelligence (AI) approaches, including Artificial Neural Networks (ANN) and Convolutional Neural Network (CNN), Support Vector Machine (SVM), Random Forest (RF), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), are elucidated offering pragmatic solutions for enhancing the performance of transformer fault diagnosis. The amalgamation of multiple AI methodologies and the exploration of time-series analysis further contribute to the augmentation of diagnostic precision and the early detection of faults in transformers. By furnishing a comprehensive panorama of AI applications in the field of transformer fault diagnosis, this study lays the groundwork for future research endeavors and the progression of this critical area of study.

An abstract is not available for this content. As you have access to this content, full HTML content is provided on this page. A PDF of this content is also available in through the ‘Save PDF’ action button.

Theresa Fyffe explains why conflicting positions do not necessarily mean that work-based negotiations are fruitless.

A. Bell, Spectacular Power in the Greek and Roman City. Oxford: Oxford University Press, 2004. Pp. 289. ISBN 9-780-19924-234-4 (bound); 9-780-19929-827-3 (paper). £67.00 (bound); £24.00 (paper). - Volume 98

Line current has become the favoured parameter for the purpose of detecting rotor faults in squirrel-cage induction motors and is being used increasingly as a basis on which to monitor the occurrence of stator insulation damage. Line current can be monitored noninvasively via clip-on current transducers; hence no interruption to the machine operation is required and the system can be made versatile and portable. Recent work has seen the development of a technique to monitor the current of the motor while under transient conditions during start-up. The author discusses how this can, under certain conditions, offer distinct advantages over steady-state monitoring.

The effect of the ATM network usage parameter control function on VBR coded MPEG video is investigated. A video sequence is coded using MPEG and packetised into ATM cells. A simulated generic rate control algorithm (GRCA) function monitors the stream of cells and cells which exceed the connection parameters are dropped. The effect of these lost cells on the decoded video data is evaluated. Cells lost in this way severely degrade the quality of the decoded video data. We conclude that it is important to avoid UPC cell loss effects on video traffic.