Abstract
Amidst an urgent need for sustainability, novel approaches are required to address environmental challenges. In this context, biomimicry offers a promising logic for catalyzing nature’s wisdom to address this complexity. The purpose of this research is to (1) establish a biomimetic understanding and vocabulary for sustainability and (2) apply biomimicry to upframe service ecosystems as a foundation for sustainability. Our research question is: How can the principles of natural ecosystems inform and enhance the sustainability of service ecosystems? The findings highlight upframed service ecosystems as embodying a set of practices that (1) promote mutualistic interactions, (2) build on local biotic and abiotic components supporting emergence processes, (3) leverage (bio)diversity to build resilience, (4) foster resource sharing for regeneration, and (5) bridge individual roles to optimize the community rather than individual well-being. Our upframed definition of a service ecosystem is a system of resource-integrating biotic actors and abiotic resources functioning according to ecocentric principles for mutualistic and regenerative value creation. The discussion emphasizes the implications of this upframed definition for sustainability practices, advocating for a shift in understanding and interacting with service ecosystems. It emphasizes the potential for immediate mutualistic benefits and long-term regenerative impacts. © Academy of Marketing Science 2025.

Abstract
Gastric intestinal metaplasia (GIM) characterization is challenging for humans and AI models. Deep learning solutions for this task are sensitive to training data, which is particularly concerning given the wide range of acquisition conditions, sampling biases, and overall scarcity of high-quality datasets. In this paper, we set forth the GIM self-similarity hypothesis where we assume that an underlying stationary self-similar process governs the structural changes observed in the mucosa. To validate this hypothesis we show that a deep learning model can map an adequately placed patch to the endoscopic grading of GIM (EGGIM) of the entire still frame. To evaluate our approach, we collected and annotated both retrospective and prospective datasets with EGGIM scores. Our results are promising: using leave-one-patient-out cross-validation, the predictions from a ResNet-50 model can be used to correctly stratify the risk for 57 out of 65 patients with perfect sensitivity on an extremely biased dataset.

Abstract
Indoor Positioning Systems (IPSs) are essential for applications requiring accurate location awareness in indoor environments. However, achieving high precision remains challenging due to signal interference and environmental variability. This study proposes a multimodal IPS that integrates Bluetooth Received Signal Strength Indicator (RSSI) measurements and video imagery using machine learning (ML) and ensemble learning techniques. The system was implemented and deployed in the Hall of Biodiversity at the Natural History and Science Museum of the University of Porto. The venue presented significant deployment issues, namely restrictions on beacon placement and lighting conditions. We trained independent ML models on RSSI and video datasets, and combined them through ensemble learning methods. The experimental results from test scenarios, which included simulated visitor trajectories, showed that ensemble models consistently outperformed the RSSI-based and video-based models. These findings demonstrate that the use of multimodal data can significantly improve IPS accuracy despite constraints such as multipath interference, low lighting, and limited beacon infrastructure. Overall, they highlight the potential of multimodal data for deployments in complex indoor environments.

Abstract
Recent verification tools aim to make formal verification more accessible for software engineers by automating most of the verification process. However, the manual work and expertise required to write verification helper code, such as loop invariants and auxiliary lemmas and assertions, remains a barrier. This paper explores the use of Large Language Models (LLMs) to automate the generation of loop invariants for programs in Dafny. We tested the approach on a curated dataset of 100 programs in Dafny involving arrays, strings, and numeric types. Using a multimodel approach that combines GPT-4o and Claude 3.5 Sonnet, correct loop invariants (passing the Dafny verifier) were generated at the first attempt for 92% of the programs, and in at most five attempts for 95% of the programs. Additionally, we developed an extension to the Dafny plugin for Visual Studio Code to incorporate automatic loop invariant generation into the IDE. Our work stands out from related approaches by handling a broader class of problems and offering IDE integration.

Abstract
Decision Trees (DTs) remain one of the most important algorithms in machine learning for their simplicity, interpretability, and often satisfactory performance. Furthermore, they are critical foundational components for more performant models such as Random Forests (RFs) and Gradient Boosted Trees. Central to DTs is the splitting process, where data is partitioned according to criteria traditionally based on information-theoretic measures such as Shannon entropy or Gini index. In this article, we propose a novel parametric entropy-based information gain criterion designed to generalize and extend classical entropic measures to improve classification performance in DTs and RFs. We introduce a five-parameter entropy formulation capable of replicating and extending known entropy measures. This new criterion was incorporated into DT and RF classifiers and evaluated on a collection of 18 benchmarking datasets, including both synthetic and real-world data retrieved from publicly available repositories. Performance was assessed using 5-fold cross-validation and optimized via Bayesian hyperparameter search, with weighted F1-score as the primary metric. Compared to splitting criteria based on existing entropy/purity measures (e.g., Gini, Shannon, R & eacute;nyi, and Tsallis), our method yielded statistically significant improvements in classification performance across most datasets. On multiclass and imbalanced datasets, such as the Wine Quality dataset, F1-score improvements exceeded 40% using RF algorithms. Bayesian signed-rank tests confirmed the robustness of our method, which never underperformed relative to standard approaches. The proposed entropy-based splitting criterion offers a flexible and effective alternative to classical information-theoretic measures, delivering improvements in classification performance.

Abstract
[No abstract available]