Abstract
Cloud native database systems provide highly available and scalable services as part of cloud platforms by transparently replicating and partitioning data across automatically managed resources. Some systems, such as Google Spanner, are designed and implemented from scratch. Others, such as Amazon Aurora, derive from traditional database systems for better compatibility but disaggregate storage to cloud services. Unfortunately, because they follow an open-box approach and fork the original code base, they are difficult to implement and maintain. We address this problem with Loom, a replicated and partitioned database system built on top of PostgreSQL that delegates durable storage to a distributed log native to the cloud. Unlike previous disaggregation proposals, Loom is a closed-box approach that uses the original server through existing interfaces to simplify implementation and improve robustness and maintainability. Experimental evaluation achieves 6x higher throughput and 5x lower response time than standard replication and competes with the state of the art in cloud and HPC hardware.

Abstract
The emergence of the Industry 4.0 trend brings automation and data exchange to industrial manufacturing. Using computational systems and IoT devices allows businesses to collect and deal with vast volumes of sensorial and business process data. The growing and proliferation of big data and machine learning technologies enable strategic decisions based on the analyzed data. This study suggests a data-driven predictive maintenance framework for the air production unit (APU) system of a train of Metro do Porto. The proposed method assists in detecting failures and errors in machinery before they reach critical stages. We present an anomaly detection model following an unsupervised approach, combining the Half-Space-trees method with One Class K Nearest Neighbor, adapted to deal with data streams. We evaluate and compare our approach with the Half-Space-Trees method applied without the One Class K Nearest Neighbor combination. Our model produced few type-I errors, significantly increasing the value of precision when compared to the Half-Space-Trees model. Our proposal achieved high anomaly detection performance, predicting most of the catastrophic failures of the APU train system.

Abstract
Predicting taxi demands in large cities can help in better traffic management as well as ensure better commuter satisfaction for an intelligent transportation system. However, the traffic demands across different locations have varying spatio-temporal correlations that are difficult to model. Despite the ability of the existing Deep Neural Network (DNN) models to capture the non-linearity in spatial and temporal characteristics of the demand time-series, capturing spatio-temporal characteristics in different real-world scenarios like varying historic and prediction time frame, spatio-temporal variations due to noise or missing data, etc. still remain a big challenge for the state-of-the-art models. In this paper, we introduce Encoder-ApproXimator (EnAppX), an encoder-decoder DNN-based model that uses Chebyshev function approximation in the decoding stage for taxi demand times-series prediction and can better estimate the time-series in the presence of large spatio-temporal variations. Opposed to any existing state-of-the-art model, the proposed model approximates complete spatiotemporal characteristics in the frequency domain which in turn enables the model to make a robust and improved prediction in different scenarios. Validation over two real-world taxi datasets from different cities shows a considerable improvement of around 23% in RMSE scores compared to the state-of-the-art baseline model. Unlike several existing state-of-the-art models, EnAppX also produces improved prediction accuracy across two regions for both to and fro demands.

Abstract
The ocean has a huge impact on our way of life; therefore, there is a need to monitor and protect its biodiversity [...].

Abstract
The relevance of the tourism industry to the overall sustainability of rural territories grows along with the demand for rural tourism destinations. Likewise, as the digital transition of rural tour operators takes place, their marketing initiatives also evolve towards a digital nature, which is why it is crucial to comprehend how the overall calibre of these activities might affect the perception of rural places, while also motivating tourists' travel intentions and, as a result, promoting the general sustainability of the destination. Thus, in this paper, we propose a novel conceptual model based on Delone and McLean's representative model of Information Systems Success Model, on Tan and Wu's arguments on tourism destinations' image relationship with tourists' visit intentions, and also on Verma's tourism destination brand equity concept. To validate the proposed model, an online focus group was developed involving several specialists whose opinions and perspectives corroborated the potential adequacy of the proposed artefact and, consequently, assumed its contribution and value. From this validation process, it was possible to highlight that digital marketing initiatives' overall quality influences both rural destinations' image and tourists' intention to visit these territories, that a positive image will trigger tourists' visit behaviour, and that these behaviours represent a valuable asset to rural destinations' global sustainability.

Abstract
This paper investigates the impact of different immersive Virtual Reality (iVR) technological approaches in teaching and learning English as a Foreign Language (EFL). Specifically, this paper explores the passive iVR and interactive iVR in a real authentic learning context as didactic possibilities compared to the conventional method of listening, consisting of audio-only listening exercises. The study was conducted using university students of B1 level EFL classes. The dependent variables considered in the study were Knowledge Retention, Presence, User Satisfaction, Cybersickness, and Preferred Technology. Results indicated that users showed significant satisfaction and preference for using this technology for learning, revealing enjoyment and motivation which are vital factors when learning a foreign language. However, no significant differences were found between learning via traditional listening exercises or the virtual system. Correlation tests were conducted between the questionnaire subscales to understand better which elements can influence learning. The study concludes that using iVR-based learning tools to learn a foreign language as an alternative to audio listening can only produce a broader positive impact and greater motivation. The results also suggest that iVR can be a valuable tool in the education field for knowledge transfer and motivation when learning foreign languages.