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.

Abstract
Formal verification techniques aim at formally proving the correctness of a computer program with respect to a formal specification, but the expertise and effort required for applying formal specification and verification techniques and scalability issues have limited their practical application. In recent years, the tremendous progress with SAT and SMT solvers enabled the construction of a new generation of tools that promise to make formal verification more accessible for software engineers, by automating most if not all of the verification process. The Dafny system is a prominent example of that trend. However, little evidence exists yet about its accessibility. To help fill this gap, we conducted a set of 10 case studies of developing verified implementations in Dafny of some real-world algorithms and data structures, to determine its accessibility for software engineers. We found that, on average, the amount of code written for specification and verification purposes is of the same order of magnitude as the traditional code written for implementation and testing purposes (ratio of 1.14) – an “overhead” that certainly pays off for high-integrity software. The performance of the Dafny verifier was impressive, with 2.4 proof obligations generated per line of code written, and 24 ms spent per proof obligation generated and verified, on average. However, we also found that the manual work needed in writing auxiliary verification code may be significant and difficult to predict and master. Hence, further automation and systematization of verification tasks are possible directions for future advances in the field. © 2023, IFIP International Federation for Information Processing.

Abstract
Clinical decision support systems have been increasingly utilized in the healthcare industry to improve patient outcomes and enhance clinical decision-making, taking advantage of the growing digital medical data. Despite their potential, there are still obstacles in an extensive adoption of these systems, such as low usability and human factors. In this systematic review, several articles describing clinical decision support systems with clinical validation are used to address some of the gaps, as well as to map the current academic landscape for the given context. The selected articles are observed through a Human-Computer Interaction perspective, aiming to identify the state-of-the-art, as well as barriers to the application of these principles. From an initial database search resulting in 121 articles, 16 articles were selected that fulfilled the chosen criteria: (1) article must be available and written in English, (2) article must report experimental work, (3) the reported system must be clinically validated. The research strategy followed the PRISMA framework. We highlight the need for clinical validation, a standardized clinical decision support taxonomy and the evaluation of these tools across multiple variables. Based on the found results, a list of recommendations can be formed to aid the development of future CDSS, or the improvement of current ones.