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
The Industry 4.0 paradigm (I4.0) supports the improvement of industrial processes through Information and Communication Technologies (ICT), with information systems providing real-time information to humans and machines, in order to make the production process more flexible and efficient. In this context, Virtual Assistants (VA) collect and process production data and provide contextualized and real-time information to the workers in the production environment. This paper presents a prototype of a VA developed to collect production data from heterogeneous sources in the factory, process them based on contextual information, and provide workers with useful information to assist them in taking informed decisions. In that context, VA can represent a valuable aid to improve overall productivity and efficiency in the I4.0 factories.

Abstract
Human-Computer Interaction (HCI) research has illuminated how technology can influence users’ awareness of their environmental impact and the potential for mitigating these impacts. From hot water saving to food waste reduction, researchers have systematically and widely tried to find pathways to speed up achieving sustainable development goals through persuasive technology interventions. However, motivating users to adopt sustainable behaviors through interactive technologies presents significant psychological, cultural, and technical challenges in creating engaging and long-lasting experiences. Aligned with this perspective, there is a dearth of research and design solutions addressing the use of persuasive technology to promote sustainable recycling behavior. Guided by a participatory design approach, this investigation focuses on the design opportunities for leveraging persuasive and human-centered Internet of Things (IoT) applications to enhance user engagement in recycling activities. The assumption is that one pathway to achieve this goal is to adopt persuasive strategies that may be incorporated into the design of sustainable applications. The insights gained from this process can then be applied to various sustainable HCI scenarios and therefore contribute to HCI’s limited understanding in this area by providing a series of design-oriented research recommendations for informing the development of persuasive and socially engaged recycling platforms. In particular, we advocate for the inclusion of educational content, real-time interactive feedback, and intuitive interfaces to actively engage users in recycling activities. Moreover, recognizing the cultural context in which the technology is socially situated becomes imperative for the effective implementation of smart devices to foster sustainable recycling practices. To this end, we present a case study that seeks to involve children and adolescents in pro-recycling activities within the school environment.

Abstract
Diversity is crucial in systems that tolerate Byzantine faults. Traditionally, system builders have relied on standardized interfaces (e.g., POSIX for operating systems) to obtain off-the-shelf components or on n-version programming for custom functionality. Unfortunately, standardized alternatives are rare, and the independent development of multiple versions of the same software is costly and justified only on the most critical applications. In this paper, we show that a limited and focused use of LLMs for translation opens up the possibility of leveraging the existing diversity in functionally equivalent but non-standardized components. Specifically, we show that LLMs can produce functionally correct database query translations with minimal guidance and adapt to diverse data models and query contexts, enabling the use of radically different database models, both SQL and NoSQL, together in a Byzantine fault-tolerant replicated system. We outline an approach to achieve this in practice and discuss future research directions.

Abstract
This paper presents an overview of the FGPE (Framework for Gamified Programming Education), a set of three Erasmus+ projects aimed at providing a framework for applying gamification to programming education. The overview will encompass all three phases of the framework development, emphasizing the gamification elements embedded in the design and implementation of the outputs of each phase. These outputs will be presented as a unified narrative, including the gamification framework for programming exercises, a format for defining gamification details for programming exercises and courses, the authoring tool for the gamification layer, a gamification Web service, a tutorial on gamifying programming exercises (guidance material), and a tool that automatically generates gamified programming exercises.

Abstract
Food waste generated throughout the food supply chain raises several environmental, social, and economic issues. Quantitative methods can aid in managing food waste by describing current contexts, predicting future scenarios, and improving related operations. However, a literature review on the use of quantitative methods, specifically the descriptive, predictive, and prescriptive dimensions, to assess and prevent food waste is lacking. This paper aims to explore and categorize quantitative studies that perform descriptive, predictive, and prescriptive analysis concerning food waste, to identify gaps and inform future research. For this purpose, we developed a systematic literature review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement methodology, which resulted in the inclusion of 65 relevant studies. We identified the key features of each data analytics approach, with a particular focus on (i) food waste quantification methods, (ii) demand, food waste, and shelf-life forecasting algorithms, and (iii) optimization approaches. Additionally, the context in which each of these studies is focused is also explored. We found that predictive analysis is the most prominent among the data analytics approaches, followed by descriptive and prescriptive systems, respectively. Moreover, the most explored setting is the hospitality sector, and it is the only context in which all descriptive, predictive, and prescriptive approaches can be found. The algorithms and models adopted in the studies vary, and there is still room for adopting more recent or advanced methods. This paper establishes a foundation for advancing focused and systematic quantitative research in the field of food waste. © 2025 Elsevier B.V., All rights reserved.