Abstract
Municipal solid waste (MSW) management has become a critical issue today, posing substantial economic, environmental, and social challenges. Identifying and analyzing dominant themes in this field is essential for advancing research and policies towards sustainable MSW management practices. This study aims to explore the key issues related to MSW management that have been addressed by both the scientific community and policymakers through funded projects. By doing so, the study seeks to guide the scientific community as a knowledge producer and the EU as a key funder. Two Latent Dirichlet Allocation (LDA) models were applied to analyze the themes from two corpora: one representing scientific literature and another focusing on EU-funded projects. Additionally, this analysis was complemented by a quantitative estimation of the similarity between the two corpora, providing a measure of alignment between the scientific community and policymakers. The results generally indicate that the two spheres are aligned and highlight the diversity of topics explored by the scientific community. Nevertheless, it is concluded that there are opportunities for further research on specific topics, such as leaching and the extraction of heavy metals. Additionally, the popularity of topics identified in European Union-funded projects has fluctuated considerably over time, focusing primarily on waste management rather than its prevention. In light of these findings, waste prevention emerges as a promising avenue for future EU-funded research initiatives.

Abstract
The retention of public transport users is widely acknowledged as a paramount challenge in the path towards the establishment of more sustainable cities and societies. In this setting, in which no contractual relationship with customers exists, an early and accurate prediction of whether a customer will remain with the company or leave, assumes great significance for businesses to develop effective retention strategies. This work focuses on this topic by identifying potential churners based on their past travel behavior. To achieve this, we developed a set of classification models using various machine learning techniques. These models were then employed as base learners within a stacking ensemble. All classifiers were developed with a profit-driven approach, optimizing for expected maximum profit. Finally, we calculated Shapley Additive Explanation values to enhance the interpretability of the proposed classifiers. The performance of the predictive models was evaluated using the data of taxi services recorded in a Portuguese city for 52 months. A broad range of predictors is proposed, including recency and frequency measures of taxi usage as well as others related to customers' satisfaction level. The predictive power of the models was also assessed for specific proportions of higher risk customers. All models have shown the capability to identify churners accurately. This study innovates in evaluating the one-to-one service provider company-customer relationship in the context of taxi industry. Retention actions to promote customers loyalty and enhance retention are also suggested. © 2025 The Author(s)

Abstract
The growing interest in learning more about consumer behaviors through analytical techniques requires the integration of innovative approaches that relate their needs to strategic marketing procedures. Multimodality and Affective Computing combined a series of robust optimizations for this challenge, implying the complexity of each application. However, the entanglement of different modalities demands new and tailored refinements to enhance adaptability and accuracy in the field. This paper outlines the implementation of a Multimodal Artificial Intelligence methodology with Affective Computing to enhance consumer insights and marketing strategies. The application combines different data modalities, such as textual, visual, and audio inputs, to tackle complex issues in dealing with consumer sentiment. The proposed approach uses advanced preprocessing techniques, including word embeddings, neural networks, and recurrent models, to extract information from diverse modalities. Fusion strategies, such as attention-based and late fusion procedures, are utilized to combine knowledge, facilitating robust sentiment detection. The implementation includes the analysis of real-time customer feedback on social media and product assessments, demonstrating improvements in predicting engagement and shaping consumer behavior. The results underscore the practical viability of the suggested method, promoting progress in multimodal sentiment analysis to extract actionable consumer insights in marketing.

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
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.

Abstract
IntroductionThe economic development, well-being of the population, and environmental protection are all strongly linked to a sustainable transportation network. In this sense, in order to ensure a high level of sustainability, it is crucial to have a comprehensive understanding of this sector. As an integrated element of these transportation systems, the efficiency assessment of taxis' operations is essential in setting managerial strategies for leveraging the sustainability of taxi system.MethodologyThis study employs a two-stage bootstrap Data Envelopment Analysis approach to assess the efficiency of taxis' operations, with a focus on minimizing service time and distance traveled. Additionally, this study innovates on investigating the impact of distinct contextual factors on efficiency scores attained to uncover the determinants of taxi operations' efficiency. The methodology is validated using real data collected from onboard devices of a fleet operating in a Portuguese city over a one-year period.ResultsThe results obtained show that taxis of the fleet can significantly reduce service time and distance traveled, without affecting output levels. Moreover, the decisive role of the stands where taxis queue on the efficiency of their operation is also verified.ConclusionsThe findings can support practitioners in reaching a more suitable and efficient allocation of resources, leading to a more sustainable transportation combined with improved business results. Furthermore, this study contributes to the current literature by suggesting recommendations to assist managers and public administrators in defining improvement actions for the taxi sector.