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
Adaptive protocols enable the construction of more efficient state preparation circuits in variational quantum algorithms (VQAs) by utilizing data obtained from the quantum processor during the execution of the algorithm. This idea originated with Adaptive Derivative-Assembled Problem-Tailored variational quantum eigensolver (ADAPT-VQE), an algorithm that iteratively grows the state preparation circuit operator by operator, with each new operator accompanied by a new variational parameter, and where all parameters acquired thus far are optimized in each iteration. In ADAPT-VQE and other adaptive VQAs that followed it, it has been shown that initializing parameters to their optimal values from the previous iteration speeds up convergence and avoids shallow local traps in the parameter landscape. However, no other data from the optimization performed at one iteration is carried over to the next. In this work, we propose an improved quasi-Newton optimization protocol specifically tailored to adaptive VQAs. The distinctive feature in our proposal is that approximate second derivatives of the cost function are recycled across iterations in addition to optimal parameter values. We implement a quasi-Newton optimizer where an approximation to the inverse Hessian matrix is continuously built and grown across the iterations of an adaptive VQA. The resulting algorithm has the flavor of a continuous optimization where the dimension of the search space is augmented when the gradient norm falls below a given threshold. We show that this inter-optimization exchange of second-order information leads the approximate Hessian in the state of the optimizer to be consistently closer to the exact Hessian. As a result, our method achieves a superlinear convergence rate even in situations where the typical implementation of a quasi-Newton optimizer converges only linearly. Our protocol decreases the measurement costs in implementing adaptive VQAs on quantum hardware as well as the runtime of their classical simulation.

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.

Abstract
This special issue showcases state-of-the-art research at the intersection of analytics and retail operations. As the retail landscape becomes increasingly complex – driven by omnichannel strategies, evolving customer expectations, and a surge in data availability – analytics has emerged as a critical enabler of operational efficiency, customer experience, responsiveness, and sustainability and ethics. Collectively, these contributions demonstrate how advanced analytics can support retailers in navigating uncertainty, personalizing services, and scaling up innovation across formats and channels. The articles featured in this issue address a diverse set of decision domains, including warehousing, inventory and assortment planning, and distribution and last-mile delivery. Methodologically, they span descriptive, prescriptive, and hybrid approaches, leveraging tools such as machine learning, stochastic modeling, and dynamic optimization. By grounding models in real-world data and focusing on practical implementation, the issue provides actionable insights for both scholars and practitioners. It also highlights emerging opportunities for future research on behavioral integration, human-machine collaboration, and the ethical dimensions of retail analytics. © 2025 Elsevier B.V.

Abstract
Energy communities (EC) and collective self-consumption (CSC) systems can make a significant contribution to reducing dependence on fossil fuels and energy costs. They create mechanisms for the active participation of end-consumers in the energy system by becoming self-producers of renewable electricity and adapting their energy behavior to the needs of the system. CSC also alleviates energy poverty by reducing the energy costs of vulnerable members. The CSC is still in its early stages, and regulation is being developed in several countries along with pilot projects to test different rules and incentives. This chapter discusses the most relevant common definitions of CSC and EC so far, as well as the main challenges in relation to energy sharing rules and the management of EC and CSC. © 2025 Elsevier B.V., All rights reserved.