Abstract
Data augmentation is a crucial tool in time series forecasting, especially for deep learning architectures that require a large training sample size to generalize effectively. However, extensive datasets are not always available in real-world scenarios. Although many data augmentation methods exist, their limitations include the use of transformations that do not adequately preserve data properties. This paper introduces Grasynda, a novel graph-based approach for synthetic time series generation that: (1) converts univariate time series into a network structure using a graph representation, where each state is a node and each transition is represented as a directed edge; and (2) encodes their temporal dynamics in a transition probability matrix. We performed an extensive evaluation of Grasynda as a data augmentation method for time series forecasting. We use three neural network variations on six benchmark datasets. The results indicate that Grasynda consistently outperforms other time series data augmentation methods, including ones used in state-of-the-art time series foundation models. The method and all experiments are publicly available. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
As competitive and cooperative dynamics gain prominence in games, they present unique opportunities to study player behavior. This paper explores the orientations of different player types, as categorized by Bartles Taxonomy, through the lens of a Game With A Purpose (GWAP) called BartleZ. Bartle's Taxonomy identifies four distinct player types Achievers, Explorers, Socializers, and Killers. This study delves into how these different types approach competitive and cooperative gameplay, through structured dilemmas in BartleZ. Results with 45 participants, reveal that player orientations significantly influence engagement and decision-making. Achievers balanced both strategies; Explorers favored cooperation; Socializers consistently chose cooperation; and Killers preferred competition but adapted in some contexts. Overall, players leaned toward cooperation early on, with a shift toward competition as complexity increased. Our findings pinpoint the importance of tailoring GWAP mechanics with diverse player motivations, enhancing both engagement and problem-solving effectiveness.

Abstract
Counterfactual explanations (CFs) help users understand and act on black-box machine learning decisions by suggesting minimal changes to achieve a desired outcome. However, existing methods often ignore individual feasibility, leading to unrealistic or unactionable recommendations. We propose a personalized CF generation method based on cluster-specific fine-tuning of Generative Adversarial Networks (GANs). By grouping users with similar behavior and constraints, we adapt immutable features and cost weights per cluster, allowing GANs to generate more actionable and user-aligned counterfactuals. Experiments on the German Credit dataset show that our approach achieves a 6x improvement in prediction gain and a 30% reduction in sparsity compared to a baseline CounterGAN, while maintaining plausibility and acceptable latency for online use.

Abstract
Effective warehouse management plays a pivotal role in optimizing supply chain performance, particularly in high-demand, time-sensitive environments. This study introduces a simulation-based decision support system designed to improve the management of Picking-By-Line (PBL) operations in cross-docking distribution centres. Developed in FlexSim and calibrated with empirical data from an industrial case study, the model replicates real-world warehouse conditions and is validated against observed operational performance. The tool supports warehouse managers in evaluating and comparing operational strategies, such as dynamic storage allocation policies and picker routing constraints, with the goal of reducing operator travel distances, mitigating congestion, and enhancing overall efficiency. A key contribution of this work is the integration of congestion-sensitive performance indicators that allow for a detailed analysis of the trade-offs between travel efficiency and localized congestion-an aspect often overlooked in traditional optimization methods. This study demonstrates the value of simulation as a scalable and realistic decision-support tool for optimizing PBL operations in complex and variable environments where human movement is a major cost and performance driver. The proposed tool bridges the gap between theoretical modelling and practical implementation, offering actionable insights for warehouse layout, space utilization, and resource allocation.

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