Abstract
The Manufacturer's Pallet Loading Problem involves optimising the packing of a maximal number of identical rectangular boxes onto a single rectangular pallet. This problem arises in various logistic operations that involve the storage and transportation of boxed products, where efficient packing can result in substantial cost reductions and improved operational efficiency. Logistics managers anticipate that some boxes can be damaged during handling and transport, so the stability of the pallet load is essential to avoid such damage. The interlocking method is commonly used in practice to improve stability when loading pallets, minimising product damage and reducing the risk of injury to personnel handling the pallet. This study introduces a Mixed Integer Linear Programming model that addresses the Manufacturer's Pallet Loading Problem, promoting static stability through interlocking. Stability is evaluated with respect to the relationship between successive layers of the loading plan, with three types of interlocking incorporated into the mathematical model. Computational experiments with real-world instances were conducted to assess the model's performance using different objective functions and post-optimisation heuristics that target real-world requirements. Three stability metrics were used to evaluate the load plans generated by the mathematical model. The results show the interlocking method's benefits on the pallet loads' stability while maximising the pallet volume usage.

Abstract

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.