Abstract

Abstract
The growing importance of sustainability in consumer markets in recent years has attractedthe attention of researchers in industry-related fields. Large-scale product networks, which comprisemillions of connections between consumers and green products, facilitate the identificationof consumer-driven relationships between products and enable an understanding of the dynamicsof sustainable consumption. It is well-known that understanding the dynamics of these networks does not require observing the entire network. In this work, we propose an efficient way to analyze the dynamics between consumers and products through an adaptive sampling process that combines the Random Walk and a novel Neighborhood Expansion Framework. Theproposed hybrid framework uses a bipartite network projection, and applies adaptive samplingusing weighted random walks, and neighborhood expansion to enable efficient and representativenetwork exploration. Networks are first projected onto product–product similarity networks,where clusters of co-consumed items may reveal sustainability-oriented market segments. Then,adaptive weighted random walks dynamically balance the representation of popular and nichesustainable products, while neighborhood expansion preserves local structural context. We applythis novel methodology to consumer-product networks of sustainability-related attributesthat influence purchasing decisions, such as rankings and discounts, since the size and heterogeneityof such networks make direct analysis computationally challenging. Comparative resultsshow that the proposed approach outperforms uniform sampling in terms of efficiency, structuralfidelity, and retention of sustainability-related diversity. Empirical applications demonstrate itspotential to identify sustainability clusters, uncover links between eco-labeled and conventionalproducts, and support data-driven strategies for sustainable market transitions.

Abstract
Energy Storage Systems (ESSs) are an important source of flexibility in power systems with high penetration of Renewable Energy Sources (RESs). When installed at transmission-distribution interface nodes, shared ESSs can support both Transmission System Operators (TSOs) and Distribution System Operators (DSOs), but their long-term planning remains challenging because investment decisions depend on coordinated operation under uncertainty and battery degradation over time. This paper proposes a degradation-aware planning framework for shared battery ESSs in coordinated TSO-DSO operation. The problem is formulated as a bi-level stochastic optimization model in which the upper level determines siting, sizing, and staged investment decisions under investment-cost uncertainty, while the lower level evaluates these decisions through coordinated system operation. To preserve tractability, the framework combines Benders' decomposition for long-term planning with an Alternating Direction Method of Multipliers (ADMM)-based decentralized coordination mechanism for short-term operation. The framework is evaluated on integrated IEEE transmission-distribution test systems over a 15-year planning horizon. Relative to uncoordinated operation, coordinated operation with shared ESSs reduces operating costs by up to 18.25% and RES curtailment by up to 92.16% in the later years of the planning horizon, while eliminating voltage violations. The results also show that degradation materially affects ESS valuation and that temporal discretization can influence siting and sizing decisions.

Abstract
The use of gamified crowdsourcing mechanisms through serious games and games with a purpose (GWAPs) has emerged as an effective motivational strategy for enhancing performance in human intelligence tasks (HITs). In this systematic literature review, we examine the underlying characteristics of competitive and cooperative player-oriented GWAPs and how they can be leveraged to optimize crowdsourcing performance in completing batches of HITs. By exploring gamified crowdsourcing elements in GWAPs, we can evaluate the impact of these two types of player behaviors (i.e., competition and cooperation) on motivation and performance. We reviewed 27 publications and grouped them into five categories: player orientation, game elements and motivation, crowd work optimization, gamified knowledge collection, and comparative studies and best practices. Our research pinpoints the significance of intuitive task instructions, alignment of game elements with player motivations, and the role of competitive and cooperative dynamics in enhancing engagement and performance.

Abstract
The integration of students with intellectual and developmental disabilities into STEAM education presents ongoing challenges, particularly in engineering disciplines where both technical and social competencies are essential. Robotics and active learning methodologies have emerged as promising solutions to address these challenges by offering adaptive, interactive, and student-centered learning environments. This study conducts a systematic literature review to examine how these technologies and methodologies are applied to support students with Intellectual and Developmental Disabilities. A total of 34 high-quality studies published over the past ten years were selected through a rigorous process of database searching, inclusion/exclusion filtering, and quality assessment. The analysis reveals that robotics is particularly effective in fostering academic development, cognitive skills, social-behavioral interaction, and emotional regulation, while active learning promotes social responding, role understanding, and collaborative skills. Together, these approaches not only enhance individual learning outcomes but also facilitate the broader inclusion of students with disabilities within engineering education.

Abstract
Decision trees are a cornerstone of interpretable machine learning and are widely used for their simplicity and effectiveness in classification tasks. To address the growing need for models that can operate on continuous, unbounded data, decision trees have been reinvented for the data stream setting, where they must learn incrementally under constraints such as limited memory, evolving distributions, and delayed supervision. A critical component of these tree-based models, particularly those based on the Hoeffding Trees, is the split criterion, which determines how the input space is partitioned. This study introduces a new split criterion for stream-based Hoeffding trees, based on a unified five-parameter entropic formulation that generalizes several well-known measures, including Shannon, Gini, Tsallis, and Rényi entropies. While such formulations have been explored in batch learning, their application to streaming scenarios has not been made. By incorporating this criterion into a variety of established streaming classifiers and evaluating performance on standard benchmark datasets, we demonstrate consistent and statistically significant improvements over existing methods, including those implemented in the River library. Notably, we report gains of up to 40% in immediate evaluation metrics, along with consistent wins and some draws on the prequential Macro-F1, with no observed losses against baseline criteria. The generality of the approach introduces additional computational overhead and also enables greater expressiveness and adaptability in handling uncertainty and nonstationary data. This work advances the integration of information-theoretic principles into online learning and highlights the importance of efficient hyperparameter tuning and adaptive entropy selection in streaming environments.