Abstract
Implementing Artificial Reefs (AR) is seen as a worldwide strategy to overcome the problematic environmental impacts due to climate change, overfishing and other activities that damage natural habitats and ecosystems. The production and deployment of ARs involve a series of technical challenges and this work explores a possible alternative solution to overcome some of these challenges. This work addresses a new system for building underwater modular structures integrating monitoring sensors, exploring the potential that Flat Knitted textiles have when specifically designed and used as Knitted Textile Preforms (KTPs) for concrete filling. The casting of AR modules directly in their final position, underwater, brings new paradigms to the design and production of ARs clusters with more complex geometries and geometrical continuity between modules. This manuscript is dedicated to the analysis of variables such as injection pressure and its variations during filling and curing, respectively, as well as the textile structure and geometrical design. It also addresses the possibilities of the technology to embed environmental sensors, which will add functions to the AR structure.

Abstract
An isolation effort focused on sporogenous Actinomycetota from the Tagus estuary in Alcochete, Portugal, yielded a novel actinomycetal strain, designated MTZ3.1T, which was subjected to a polyphasic taxonomic study. MTZ3.1T is characterised by morphology typical of members of the genus Streptomyces, with light beige coloured substrate mycelium, which does not release pigments to the culture medium and with helicoidal aerial hyphae that differentiate into spores with a light- grey colour. The phylogeny of MTZ3.1T, based on the full 16S rRNA gene sequence, indicated that its closest relatives were Streptomyces alkaliterrae OF1T (98.48 %), Streptomyces chumphonensis KK1-2T (98.41 %), Streptomyces albofaciens JCM 4342T (98.34 %), Streoptomyces paromomycinus NBRC 15454T (98.34 %) and Streptomyces chrestomyceticus NRBC 13444T (98.34 %). Moreover, average nucleotide identity (ANI), average amino acid identity (AAI) and digital DNA-DNA hybridisation (dDDH) are below the species cutoff values (ANI 67.70 and 68.35 %, AAI 77.06 and 76.71 % and dDDH 22.10 and 21.50 % for S. alkaliterrae OF1T and S. chumphonensis KK1-2T, respectively). Whole genome sequencing revealed that MTZ3.1T has a genome of 5 644 485 bp with a DNA G+C content of 71.29 mol% and 5044 coding sequences. Physiologically, MTZ3.1T is strictly aerobic, able to grow at 15-37 & DEG;C, optimally at 25 & DEG;C and between pH5 and 8 and showed high salinity tolerance, growing with 0-10 %(w/v) NaCl. Major cellular fatty acids are C15:0, iso-C15:0, anteiso-C15: 0 and iso- C16:0. Furthermore, it was able to utilise a variety of nitrogen and carbon sources. Antimicrobial screening indicated that MTZ3.1T has potent anti- Staphylococcus aureus activity. On the basis of the polyphasic data, MTZ3.1T is proposed to represent a novel species, Streptomyces meridianus sp. nov. (= CECT 30416T = DSM 114037T=LMG 32463T).

Abstract
Introduction: Emergency department visits have increased substantially, leading to a significant rise in waiting time for patients. Several kiosk-based solutions have been introduced to reduce waiting times in healthcare facilities and to increase efficacy and user satisfaction. Purpose of the Study: This systematic review aims to identify the most effective self-service kiosk features for collecting patients' health information and to evaluate their acceptability among elderly and less educated populations, despite not being the focus, there is pontencial in the development of the system interface to facilitate the perception and understanding of those with less digital literacy. Methods: We conducted a systematic review of studies on diagnosis, replacement of face-to-face consultation, and triage kiosks published between January 2009 and March 2023 in the databases PubMed, IEEE Xplore, Web of Science, Cochrane Library, ScienceDirect, and Scopus. Results: The eight analyzed studies included 2,298 participants in total, with participants aged between 16 and 94 years. Most studies provided kiosk assistance. Elderly patients demonstrated the capability and willingness to participate in technological interventions. Conclusion: User interface elements were the most critical features in health kiosk design, followed by clear communication and patients' understanding of the benefits associated with kiosk use. The high levels of kiosk acceptance and satisfaction observed indicate a significant opportunity for the introduction of self-service kiosks in various healthcare contexts.

Abstract
In recent years, the use of remote sensing data obtained from satellite or unmanned aerial vehicle (UAV) imagery has grown in popularity for crop classification tasks such as yield prediction, soil classification or crop mapping. The ready availability of information, with improved temporal, radiometric, and spatial resolution, has resulted in the accumulation of vast amounts of data. Meeting the demands of analysing this data requires innovative solutions, and artificial intelligence techniques offer the necessary support. This systematic review aims to evaluate the effectiveness of deep learning techniques for crop classification using remote sensing data from aerial imagery. The reviewed papers focus on a variety of deep learning architectures, including convolutional neural networks (CNNs), long short-term memory networks, transformers, and hybrid CNN-recurrent neural network models, and incorporate techniques such as data augmentation, transfer learning, and multimodal fusion to improve model performance. The review analyses the use of these techniques to boost crop classification accuracy by developing new deep learning architectures or by combining various types of remote sensing data. Additionally, it assesses the impact of factors like spatial and spectral resolution, image annotation, and sample quality on crop classification. Ensembling models or integrating multiple data sources tends to enhance the classification accuracy of deep learning models. Satellite imagery is the most commonly used data source due to its accessibility and typically free availability. The study highlights the requirement for large amounts of training data and the incorporation of non-crop classes to enhance accuracy and provide valuable insights into the current state of deep learning models and datasets for crop classification tasks.

Abstract
The main objective of this article is to investigate the factors that influence customers' intention to reuse chatbot-based services. The study employs a combination of the technology acceptance model (TAM) with other contributions in the literature to develop a theoretical model that predicts and explains customers' intention to reuse chatbots. The research uses structural equation modeling (PLS-SEM) to test the proposed hypotheses. Data collected from 201 chatbot users among Portuguese consumers were analyzed, and the results showed that user satisfaction, perceived usefulness, and subjective norm are significant predictors of chatbot reuse intentions. Additionally, the findings indicated that perceived usefulness, perceived ease of use, and trust have a positive impact on attitudes toward using chatbots. Trust was found to have a significant impact on perceived usefulness, user satisfaction, and attitudes toward using chatbots. However, there was no significant effect of attitude toward using chatbots, perceived ease of use, trust, and perceived social presence on reuse intentions. The article concludes with theoretical contributions and recommendations for managers.

Abstract
The oceans are abundant in natural diversity, minerals and energy resources, and there is an urgent need for a better understanding of its ecosystems and dynamics. The Synchronous Oceanic and Atmospheric Data Acquisition (SONDA) Project intends to contribute to better atmospheric and oceanic modelling and monitoring by launching High-Altitude Balloons (HAB) equipped with atmospheric and deep-sea probes to be released in oceanic areas of interest. This work reports the development and validation of three different probes: 1) atmospheric monitoring with APRS communications to be launched by HAB; 2) oceanographic monitoring; and 3) deep-sea monitoring with satellite communications. All probes were preliminarily tested in a semi-controlled fluvial environment, and posteriorly in real field conditions in the Azores Islands, Portugal. During the campaign, the Atmospheric probe was launched by HAB and its communications were tested with fixed and mobile ground stations, the oceanographic probe was deployed for three days to monitor the effect of a geothermal spring in the sea and the deep-sea probe was released into the Atlantic Ocean.