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
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.

Abstract
The effectiveness of digital marketing relies on the seamless integration of intelligent technology, enabling encounters that closely resemble those experienced with physical vendors in the real world. Thus, the importance of scalable artificial intelligence (AI) systems guided by a multimodal approach cannot be overstated, as they can be used to gain a deeper understanding of user preferences and engagement behaviors. The investigation conducted concerning multimodal learning in this review uncovers a variety of benefits and limitations on the available data, presenting consistency in finding the relationship between modalities. The results suggest multimodality as a topic with a noticeable dearth of research, yet a promising path to reduce uncertainty and develop innovative perspectives on decision-making for Digital Marketing improvement tasks. The complexity inherent in data processes like analysis, processing, and granular modulation requires a lot of effort for researchers to build accurate multimodal representations while trying to suppress imprecision in these new elements. Therefore, our approach aims to explore how theoretical foundations are successfully applied to learning operational procedures, considering real-life case comprehension, the technical challenges of the learning process, and the importance given to each feature. Even so, comparing the restrictions found in the state-of-the-art made possible the reformulation of limitations to this particular type of technology and encouraged the search for more guidelines on the entire process.

Abstract
This study aims to examine consumer intention to purchase eco-friendly, handcrafted fashion products made from upcycled clothing and traditional Indonesian batik fabric. Data were collected via an online questionnaire with 289 participants, including both Indonesian and non-Indonesian consumers. The hypotheses were tested using structural equation modeling in SmartPLS 3. The results showed that fashion motivation and perceived value positively impacted the intention to purchase this type of product for personal use and for gifting. The perceived price had a positive effect on purchase intention for gifting. Altruistic motivations affected attitudes but not purchase intentions. Differences were identified between national and foreign consumers regarding the impact of price perception on attitudes and personal purchase intentions. The study provides practical implications for small businesses, artisan crafts, and entrepreneurs.

Abstract
In every critical infrastructure system, unexpected events and outages have the potential to cause massive impacts, affecting people and the economy, such as in the power power grid blackouts. To avoid similar incidents in the future, extensive research is necessary to improve resilience and reliability of power grids. This work presents a Transmission Expansion Planning (TEP) model that confronts the largely adopted deterministic security criteria N-1 versus an AC-Cascade Failure Model (AC-CFM) analysis. The main goal is to highlight the importance of cascade failure analysis to increase power system resilience. Tests over the NREL-118 system verify the AC-CFM coupling in TEP models, demonstrating its benefits for assuming a risky proneness behavior for reaching long-term power grid resilience.