Abstract
Water is essential for maintaining plant health and optimal growth in agriculture. While some crops depend on irrigation, others can rely on rainfed water, depending on regional climatic conditions. This is exemplified by grapevines, which have specific water level requirements, and irrigation systems are needed. However, these systems can be susceptible to damage or leaks, which are not always easy to detect, requiring meticulous and time-consuming inspection. This study presents a methodology for identifying potential damage or leaks in vineyard irrigation systems using RGB and thermal infrared (TIR) imagery acquired by unmanned aerial vehicles (UAVs). The RGB imagery was used to distinguish between grapevine and non-grapevine pixels, enabling the division of TIR data into three raster products: temperature from grapevines, from non-grapevine areas, and from the entire evaluated vineyard plot. By analyzing the mean temperature values from equally spaced row sections, different threshold values were calculated to estimate and map potential leaks. These thresholds included the lower quintile value, the mean temperature minus the standard deviation (Tmean-sigma), and the mean temperature minus two times the standard deviation (Tmean-2 sigma). The lower quintile threshold showed the best performance in identifying known leak areas and highlighting the closest rows that need inspection in the field. This approach presents a promising solution for inspecting vineyard irrigation systems. By using UAVs, larger areas can be covered on-demand, improving the efficiency and scope of the inspection process. This not only reduces water wastage in viticulture and eases grapevine water stress but also optimizes viticulture practices.

Abstract
Continuous improvement (CI) have been recognised as one of the most effective ways to improve organisational performance. However, there is a lack of research on this topic from a food service perspective. Thus, the aim of this work is to explore the adoption of CI-focused methodologies in food services and to understand how they contribute to improving the performance of these services. Critical success factors and barriers to the implementation of CI are also analysed. This systematic review was conducted using the PRISMA methodology and a total of 43 studies were included in the analysis. This review shows that CI is effective in improving operations and performance, as well as increasing stakeholder satisfaction in the food service sector. Additionally, the review reveals that CI-focused tools are mainly used in problem identification, waste identification, planning, operations, and logistics. Human-related issues are the most frequently mentioned when it comes to the factors determining the success or failure of CI in food services.

Abstract
The concept of patterns and pattern languages, although very common in software nowadays, was first approached by Christopher Alexander, in the area of architecture, in the book A pattern language: towns, buildings, construction. However, it was only in 1980 that the term was adapted for software development, gaining its popularity in 1994. Despite the fact that the concept of patterns has been used in the area of software development for more than 40 years, there is still no consensus on the best method to validate patterns and patterns languages, and the existing methods are scattered in several different papers and research across the scientific community. As such, in this paper, we conduct a systematic literature review about the existing methods in the scientific community to validate patterns and pattern languages. © 2024 Copyright held by the owner/author(s).

Abstract

Abstract
Inherited retinal diseases such as Retinitis Pigmentosa and Stargardt’s disease are genetic conditions that cause the photoreceptors in the retina to deteriorate over time. This can lead to vision symptoms such as tubular vision, loss of central vision, and nyctalopia (difficulty seeing in low light) or photophobia (high light). Timely healthcare intervention is critical, as most forms of these conditions are currently untreatable and usually focused on minimizing further vision loss. Machine learning (ML) algorithms can play a crucial role in the detection of retinal diseases, especially considering the recent advancements in retinal imaging devices and the limited availability of public datasets on these diseases. These algorithms have the potential to help researchers gain new insights into disease progression from previous classified eye scans and genetic profiles of patients. In this work, multi-class identification between the retinal diseases Retinitis Pigmentosa, Stargardt Disease, and Cone-Rod Dystrophy was performed using three pretrained models, ResNet101, ResNet50, and VGG19 as baseline models, after shown to be effective in our computer vision task. These models were trained and validated on two datasets of autofluorescent retinal images, the first containing raw data, and the second dataset was improved with cropping to obtain better results. The best results were achieved using the ResNet101 model on the improved dataset with an Accuracy (Acc) of 0.903, an Area under the ROC Curve (AUC) of 0.976, an F1-Score of 0.897, a Recall (REC) of 0.903, and a Precision (PRE) of 0.910. To further assess the reliability of these models for future data, an Explainable AI (XAI) analysis was conducted, employing Grad-Cam. Overall, the study showed promising capabilities of Deep Learning for the diagnosis of retinal diseases using medical imaging. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

Abstract
This study evaluates a new solution approach for the Resource -Constrained Project Scheduling with Alternative Subgraphs (RCPSP-AS) in case that complex relations (i.e. nested and linked alternatives) are considered. In the RCPSP-AS, the project activity structure is extended with alternative activity sequences. This implies that only a subset of all activities should be scheduled, which corresponds with a set of activities in the project network that model an alternative execution mode for a work package. Since only the selected activities should be scheduled, the RCPSP-AS comes down to a traditional RCPSP problem when the selection subproblem is solved. It is known that the RCPSP and, hence, its extension to the RCPSP-AS is NP -hard. Since similar scheduling and selection subproblems have already been successfully solved by satisfiability (SAT) solvers in the existing literature, we aim to test the performance of a GA -SAT approach that is derived from the literature and adjusted to be able to deal with the problem -specific constraints of the RCPSP-AS. Computational results on smalland large-scale instances (both artificial and empirical) show that the algorithm can compete with existing metaheuristic algorithms from the literature. Also, the performance is compared with an exact mathematical solver and learning behaviour is observed and analysed. This research again validates the broad applicability of SAT solvers as well as the need to search for better and more suited algorithms for the RCPSP-AS and its extensions.