Abstract

Power quality (PQ) issues, including weak grids, voltage transients, harmonics, notches, current imbalance, and voltage sags, are critical challenges in the textile industry. Even a brief power interruption can halt industrial processes, leading to substantial financial losses. This paper proposes a Model Predictive Control (MPC)-based Unified Power Quality Conditioner (UPQC) as a robust solution to mitigate these PQ disturbances in textile industry-integrated distribution grids. The proposed UPQC is designed to enhance voltage stability, suppress harmonics, regulate reactive power, and correct current imbalance, ensuring uninterrupted industrial operation. A key contribution of this work is the realistic modeling of a textile industry’s electrical network, replicating actual industry ratings to evaluate system performance. The proposed MPC-based UPQC is assessed through five case studies, addressing weak vs. strong grids, voltage transients, current imbalance, and voltage sags—the most significant PQ challenges in textile applications. Simulation results demonstrate that the UPQC significantly improves voltage profiles, reduces harmonic distortion, and effectively compensates for current imbalance. Compared to conventional Proportional-Integral (PI) controllers, the MPC-based UPQC exhibits superior performance in dynamic PQ disturbance mitigation and grid stabilization. These findings underscore the proposed system’s suitability for large-scale industrial deployment, offering a cost-effective and robust solution to enhance operational efficiency and grid reliability in the textile sector.

Abstract
Wikipedia is the largest and most globally well-known online encyclopedia, but its collaborative nature leads to a significant disparity in article quality. In this work, we explore real-time and automatic quality assessment within Wikipedia through machine-learning. We first constructed a dataset of 36,000 English articles and 145 features, then compared the performance of multiple classification and regression algorithms and studied how the number of classes and features affects the model’s performance. The six-class experiments achieved a classifier accuracy of 64% and a mean absolute error of 0.09 in regression methods, which matches or beats most state-of-the-art approaches. Our model produces similar results on some non-English Wikipedias, but the error is slightly higher on other versions. We have also determined that the features measuring the article’s content and revision history bring the largest performance boost. © 2025 Elsevier B.V., All rights reserved.

Abstract
Events like Valentine's Day and Christmas can influence user intent when interacting with search engines. For example, a user searching for gift around Valentine's Day is likely to be looking for Valentine's-themed options, whereas the same query close to Christmas would more likely suggest an interest in Holiday-themed gifts. These shifts in user intent, driven by temporal factors, are often implicit but important to determine the relevance of search results. In this demo, we explore how incorporating temporal awareness can enhance search relevance in an e-commerce setting. We constructed a database of 2K events and, using historical purchase data, developed a temporal model that estimates each event's importance on a specific date. The most relevant events on the date the query was issued are then used to enrich search results with event-specific items. Our demo illustrates how this approach enables a search system to better adapt to temporal nuances, ultimately delivering more contextually relevant products.

Abstract
Traditional approaches in the competitive recruitment landscape frequently encounter difficulties in effectively identifying exceptional applicants, resulting in delays, increased expenses, and biases. This study proposes the utilisation of contemporary technologies such as Large Language Models (LLMs) and chatbots to automate the process of resume screening, thereby diminishing prejudices and enhancing communication between recruiters and candidates. Algorithms based on LLM can greatly transform the process of screening by improving both its speed and accuracy. By integrating chatbots, it becomes possible to have personalised interactions with candidates and streamline the process of scheduling interviews. This strategy accelerates the hiring process while maintaining principles of justice and ethics. Its objective is to improve algorithms and procedures to meet changing requirements and enhance the competitive advantage of talent acquisition within organisations.

Abstract
The Eighth International Workshop on Narrative Extraction from Texts (Text2Story'25) was held on April 10 th , 2025, in conjunction with the 47 th European Conference on Information Retrieval (ECIR 2025) in Lucca, Italy. During this half-day event, more than 30 attendees engaged in discussions and presentations focused on recent advancements in narrative representation, extraction, and generation. The workshop featured a keynote address and a mix of oral presentations and poster sessions covering nineteen papers. The workshop proceedings are available online 1 . Date: 10 April 2025. Website: https://text2story25.inesctec.pt/.

Abstract
The Maintenance, Repair and Overhaul (MRO) activities in the aviation industry face constant challenges due to the uncertainty and variability of their operations. Aircraft engine maintenance, which is fundamental to the safety of aircraft operations, is particularly challenging due to its job-shop nature. Each engine requires a specific intervention process, based on its condition and the needs identified. The inherent uncertainty in task duration, resource availability, and the scope of required repairs adds complexity to capacity planning. Traditional capacity planning methods often fall short in accounting for these uncertainties, leading to potential inefficiencies and bottlenecks. Discrete Event Simulation (DES) emerges as a powerful tool to address these challenges. By modelling the entire MRO process, DES can consider various scenarios, incorporating the stochastic nature of task times, machine downtimes, and labour availability. This study explores the application of DES to evaluate capacity planning and quantify the impact of uncertainty on operational efficiency. The proposed methodology enables the anticipation of delays and enhances resource management. The primary contribution of this work is the ability to predict delays and quantify their impact. The future application of this tool in real-world MRO operations has the potential to enhance operational efficiency and reliability. © 2025 Elsevier B.V., All rights reserved.