Abstract
Glaucoma is one of the leading causes of irreversible blindness in the world. Early diagnosis and treatment increase the chances of preserving vision. However, despite advances in techniques for the functional and structural assessment of the retina, specialists still encounter many challenges, in part due to the different presentations of the standard optic nerve head (ONH) in the population, the lack of explicit references that define the limits of glaucomatous optic neuropathy (GON), specialist experience, and the quality of patients' responses to some ancillary exams. Computer vision uses deep learning (DL) methodologies, successfully applied to assist in the diagnosis and progression of GON, with the potential to provide objective references for classification, avoiding possible biases in experts' decisions. To this end, studies have used color fundus photographs (CFPs), functional exams such as visual field (VF), and structural exams such as optical coherence tomography (OCT). However, it is still necessary to know the minimum limits of detection of GON characteristics performed through these methodologies. This study analyzes the use of deep learning (DL) methodologies in the various stages of glaucoma screening compared to the clinic to reduce the costs of GON assessment and the work carried out by specialists, to improve the speed of diagnosis, and to homogenize opinions. It concludes that the DL methodologies used in automated glaucoma screening can bring more robust results closer to reality.

Abstract
This paper presents a methodology for assessing the long-term economic feasibility of renewable energy-based systems for green hydrogen and ammonia production. A key innovation of this approach is the incorporation of a predictive algorithm that optimizes day-ahead system operation on an hourly basis, aiming to maximize profit. By integrating this feature, the methodology accounts for forecasting errors, leading to a more realistic economic evaluation. The selected case study integrates wind and PV as renewable energy sources, supplying an electrolyser and a Haber-Bosch ammonia production plant. Additionally, all supporting equipment, including an air separation unit for nitrogen production, compressors, and hydrogen / nitrogen / ammonia storage devices, is also considered. Furthermore, an electrochemical battery is included, allowing for an increased electrolyser load factor and smoother operating regimes. The results demonstrate the effectiveness of the proposed methodology, providing valuable insights and performance indicators for this type of energy systems, enabling informed decision-making by investors and stakeholders.

Abstract
Customer value is influenced by several factors, which impose major challenges to global Supply Chains (SCs) and their management. This study aims to understand how companies tackle these challenges by focusing their global SC management on major strategies and supporting practices. Based on customer value theory, and recognising major trends affecting what end consumers value, we identify four global SC strategies: customer-driven, service-driven, resource-efficient, and closed-loop. A multiple case study carried out in eleven companies in the consumer goods industry explores the practices adopted per each SC strategy in managing global sourcing, production, and distribution networks. Results show the key requirement of selecting tailored practices for SC management that align with the context and the value expected by customers. Operational SC practices entail managing collaborative actions both up and downstream and competing with other SCs and can benefit from the implementation of appropriate digital technologies for customer value creation and delivery, as well as for continuous learning about customer needs.

Abstract

Abstract
Learning environments unquestionably enable learners to develop their pedagogical and scientific processes efficiently and effectively. Thus, considering the impossibility of not having conditions of autonomy over the routine underlying the studies and, consequently, not having guarantees of the learning carried out makes the learners experience gaps in the domain of materials adequate to their actual needs. The paper's objective is to present the relevance of the applicability of Artificial Intelligence in Recommendation Systems, reinforced through the Assurance of Learning, oriented towards adaptive-personalized practice in corporate e-learning contexts. The research methodology underlying the work fell on Design Science Research, as it is considered adequate to support the research, given the need to carry out the design phases, development, construction, evaluation, validation of the artefact and, finally, communication of the results. The main results instigate the development of an Adaptive-Personalized Learning framework for corporate e-learning, provided with models of Artificial Intelligence and guided using the Assurance of Learning process. It becomes central that learners can enjoy adequate academic development. In this sense, the framework has an implicit structure that promotes the definition of personalized attributes, which involves recommendations and customizations of content per profile, including training content that will be suggested and learning activity content that will be continuously monitored, given the specific needs of learners.

Abstract
Hybrid storage systems that combine high energy density and high power density technologies can enhance the flexibility and stability of microgrids and local energy communities under high renewable energy shares. This work introduces a novel approach integrating rule-based (RB) methods with evolutionary strategies (ES)-based reinforcement learning. Unlike conventional RB methods, this approach involves encoding rules in a domain-specific language and leveraging ES to evolve the symbolic model via data-driven interactions between the control agent and the environment. The results of a case study with Liion and redox flow batteries show that the method effectively extracted rules that minimize the energy exchanged between the community and the grid.