Abstract
O processo de revisão sistemática de literatura em investigação continua a apresentar-se como um processo com um elevado custo de recursos humanos e de tempo. Com vista em otimizar este processo pretende-se estudar a performance da ferramenta de pesquisa Cognitive Search da Microsoft que contem funcionalidades de inteligência artificial (IA). Neste trabalho foi implementada uma solução de pesquisa, i.e., parametrização do serviço de pesquisa, que produz uma classificação de relevância dos artigos científicos. Uma análise qualitativa aos artigos científicos foi efetuada para analisar a performance da solução de pesquisa e habilidades de inteligência artificial da ferramenta. O tema da revisão sistemática é “how is artificial intelligence (AI) being used in Higher Education (HE) today, involving tree dimensions: learning with AI, learning about AI and learning for AI”.;The systematic review process of research literature continues to be a very time and human resource expensive process. With the objective of optimizing this process we intend to study the performance of Microsoft Cognitive Search service which contains artificial intelligence capabilities. In this work the search service tool was configured and parameterized (search solution) to produce a classification ranking of the research articles. These were manually analysed to infer on the performance of the search solution. The topic of the systematic review is “how is artificial intelligence (AI) being used in Higher Education (HE) today, involving tree dimensions: learning with AI, learning about AI and learning for AI”.

Abstract
Decarbonization of energy systems is one of the main tracks in the energy sector, and in this transition, green hydrogen assumes an important role. Considering the variability of renewable energy sources (RES), the flexibility of the hydrogen production could help dealing with imbalances. However, to truly contribute to a greener energy mix, a principle of additivity must be obeyed. In other words, to produce green hydrogen, the energy supplied to the electrolyzers must be renewable and must not entail a decrease in the RES consumed by other loads according to the energy strategic plans. This study integrates power flow tracing (PFT) technique within an optimal power flow (OPF) to determine and maximize the physical flow between the energy from RES generators and the electrolyzer through the existing grid. The proposed method was tested on both radial and meshed IEEE test grids. Simulation results showed that the electrolyzer green supply can be increased by controlling the dispatch of the distributed generators (e.g., CHP) according to the location of the electrolyzer. In addition, installing storage systems nearby load buses allows increasing the amount of green supply by using the RES-based electricity stored. © 2022 IEEE.

Abstract
The truth is that competitivity has gained a strong growth in business, and it is important that companies pay attention to the practice of their relational marketing strategies. Technology, innovation, and digital have been transforming the way society operates in the market. Organizations must look for current opportunities in order to add value of their business and negotiation process and of course in the way they act and interact with the target. This way, the current research demonstrates the importance of CRM in relational marketing practices, particularly in non-profit organizations. Regarding the methodology, a case study was developed using a qualitative methodology through semi-structured interviews in a convenience sample, with the aim of retaining the opinion fundraising and marketing responsible department between the different organizations under study. The main result of this exploratory study appears to prove the importance of using CRM for the good practice of relational marketing strategies in order to attract, retain, and build trust with their stakeholders.

Abstract
Tide refers to a phenomenon that causes the change of water level in oceans. Tidal level forecasting plays an important role in many real-world applications especially those related to oceanic and coastal areas. For instance, accurate forecasting of tidal level can significantly increase the vessels' safety as an excessive level of tidal makes serious problems in the movement of vessels. In this work, we propose a deep learning-based prediction interval framework in order to model the forecasting uncertainties of tidal current datasets. The proposed model develops optimum prediction intervals (PIs) focused on the deep learning-based CNN-LSTM model (CLSTM), and nonparametric approach termed as the lower upper bound estimation (LUBE) model. Moreover, we develop a novel deep neuroevolution algorithm based on a two-stage modification of the gaining-sharing knowledge optimization algorithm to optimize the architecture of the CLSTM automatically without the procedure of trial and error. This leads to a decline in the complexity raises in designing manually the deep learning architectures, as well as an enhancement in the performance of the PIs. We also utilize coverage width criterion to establish an excellent correlation appropriately between both the PI coverage probability and PI normalized average width. We indicate the searching efficiency and high accuracy of our proposed framework named as MGSK-CLSTM-LUBE by examining over the practical collected tidal current datasets from the Bay of Fundy, NS, Canada.

Abstract
Modern large-scale I/O applications that run on HPC infrastructures are increasingly becoming metadata-intensive. Unfortunately, having multiple concurrent applications submitting massive amounts of metadata operations can easily saturate the shared parallel file system's metadata resources, leading to unresponsiveness of the storage backend and overall performance degradation. To address these challenges, we present PADLL, a storage middleware that enables system administrators to proactively control and ensure QoS over metadata workflows in HPC storage systems. We demonstrate its performance and feasibility by controlling the rate of both synthetic and realistic I/O workloads. Results show that PADLL can dynamically control metadata-aggressive workloads, prevent I/O burstiness, and ensure I/O fairness and prioritization.

Abstract
Color comparison is a key aspect in many areas of application, including industrial applications, and different metrics have been proposed. In many applications, this comparison is required to be closely related to human perception of color differences, thus adding complexity to the process. To tackle this, different approaches were proposed through the years, culminating in the CIEDE2000 formulation. In our previous work, we showed that simple color properties could be used to reduce the computational time of a color similarity decision process that employed this metric, which is recognized as having high computational complexity. In this paper, we show mathematically and experimentally that these findings can be adapted and extended to the recently proposed CIEDE2000 PF metric, which has been recommended by the CIE for industrial applications. Moreover, we propose new efficient models that not only achieve lower error rates, but also outperform the results obtained for the CIEDE2000 metric.