Abstract
The textile industry is a complex and dynamic system where structured decision-making processes are essential for efficient supply chain management. In this context, mathematical programming models offer a powerful tool for modeling and optimizing the textile supply chain. This systematic review explores the application of mathematical programming models, including linear programming, nonlinear programming, stochastic programming, robust optimization, fuzzy programming, and multi-objective programming, in optimizing the textile supply chain. The review categorizes and analyzes 163 studies across the textile manufacturing stages, from fiber production to integrated supply chains. Key results reveal the utility of these models in solving a wide range of decision-making problems, such as blending fibers, production planning, scheduling orders, cutting patterns, transportation optimization, network design, and supplier selection, considering the challenges found in the textile sector. Analyzing those models, we point out that sustainability considerations, such as environmental and social aspects, remain underexplored and present significant opportunities for future research. In addition, this study emphasizes the importance of incorporating multi-objective approaches and addressing uncertainties in decision-making to advance sustainable and efficient textile supply chain management.

Abstract
The rise of environmental crimes has become a major concern globally as they cause significant damage to ecosystems, public health and result in economic losses. The availability of vast sensor data provides an opportunity to analyze environmental data proactively. This helps to detect irregularities and uncover potential criminal activities. This paper highlights the critical role played by machine learning (ML) and remote sensing technologies in the continuously evolving scenarios of environmental crime. By examining some case studies on detecting illegal fishing, illegal oil spills, illegal landfills, and illegal logging, we delve into the practical implementation of data-driven approaches for environmental crime detection. Our goal with this study is to provide an overview of the existing research in this area and foster the use of ML and data science techniques to enhance environmental crime detection.

Abstract
Electronic Health Records store extensive patient health data, playing a crucial role in healthcare management. Extracting information from these text-heavy records is difficult due to their domain-specific vocabulary, which challenges applying general-domain techniques. Recent advancements in Large Language Models (LLMs) and an increasing interest in the field have sparked considerable progress in solving Clinical Information Extraction (IE) tasks. We review these applications in Clinical IE, highlighting the most common tasks, most successful methods, and most used datasets and evaluation criteria. Examining 85 studies, we synthesize and organize the current research trends, highlighting common points between papers. The presence of LLMs can be felt in the most common tasks, with novel approaches being attempted and showing promising results. However, breakthroughs are still necessary in designing reliable end-to-end systems that can perform all the Clinical IE tasks within a single system.

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
Recently, there has been growing attention on the decarbonisation of maritime transport, particularly regarding the landside operations at ports. This has spurred the development and implementation of strategies and policies aimed at enhancing the environmental performance of port activities. Among these strategies, the electrification of port infrastructure is emerging as a potential industry standard for the future. However, there remains a significant gap in understanding the patterns of electricity consumption in ports and how to forecast them accurately. To address this gap, this paper provides a review of the current literature on electricity demand in ports, examining practical applications, methodologies employed, and their key limitations. The findings indicate that, despite its importance in supporting the electrification process, electricity demand forecasting in ports has not received substantial attention in either industry or academic research, and there are no clearly established policies to support port authorities in obtaining the necessary data. Finally, the paper outlines potential directions for future research and how port authorities or local government agencies can contribute to these efforts.