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.

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Abstract
Agriculture needs to produce more with fewer resources to satisfy the world's demands. Labor shortages, especially during harvest seasons, emphasize the need for agricultural automation. However, the high cost of commercially available robotic manipulators, ranging from EUR 3000 to EUR 500,000, is a significant barrier. This research addresses the challenges posed by low-cost manipulators, such as inaccuracy, limited sensor feedback, and dynamic uncertainties. Three control strategies for a low-cost agricultural SCARA manipulator were developed and benchmarked: a Sliding Mode Controller (SMC), a Reinforcement Learning (RL) Controller, and a novel Proportional-Integral (PI) controller with a self-tuning feedforward element (PIFF). The results show the best response time was obtained using the SMC, but with joint movement jitter. The RL controller showed sudden breaks and overshot upon reaching the setpoint. Finally, the PIFF controller showed the smoothest reference tracking but was more susceptible to changes in system dynamics.

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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
The increase in complexity of deep learning models demands explanations that can be obtained with methods like Grad-CAM. This method computes an importance map for the last convolutional layer relative to a specific class, which is then upsampled to match the size of the input. However, this final step assumes that there is a spatial correspondence between the last feature map and the input, which may not be the case. We hypothesize that, for models with large receptive fields, the feature spatial organization is not kept during the forward pass, which may render the explanations devoid of meaning. To test this hypothesis, common architectures were applied to a medical scenario on the public VinDr-CXR dataset, to a subset of ImageNet and to datasets derived from MNIST. The results show a significant dispersion of the spatial information, which goes against the assumption of Grad-CAM, and that explainability maps are affected by this dispersion. Furthermore, we discuss several other caveats regarding Grad-CAM, such as feature map rectification, empty maps and the impact of global average pooling or flatten layers. Altogether, this work addresses some key limitations of Grad-CAM which may go unnoticed for common users, taking one step further in the pursuit for more reliable explainability methods.