Abstract
This paper contributes to developing a Method for Creating Persona Templates (MCPT), addressing a significant gap in user-centred design methodologies. Utilising qualitative data collection and analysis techniques, MCPT offers a systematic approach to developing robust and context-oriented persona templates. MCPT was created by applying the Design Science Research (DSR) methodology, and it incorporates multiple iterations for template refinement and validation among project stakeholders; all of the proposed steps of this method were based on theoretical contributions. Furthermore, MCPT was tested and refined within a real-life R&D project focusing on developing a digital platform e-marketplace for short agrifood supply chains in two iteration cycles. MCPT fills a critical void in persona research by providing detailed instructions for each step of template development. By involving the target audience, users, and project stakeholders, MCPT adds rigour to the persona creation process, enhancing the quality and relevance of personae casts. This paper contributes to the body of knowledge by offering an initial proposal of a comprehensive method for creating persona templates within diverse projects and contexts. Further research should explore MCPT's adaptability to different settings and projects, thus refining its effectiveness and extending its utility in user-centred design practices.

Abstract
Missing data is characterized by the presence of absent values in data (i.e., missing values) and it is currently categorized into three different mechanisms: Missing Completely at Random, Missing At Random, and Missing Not At Random. When performing missing data experiments and evaluating techniques to handle absent values, these mechanisms are often artificially generated (a process referred to as data amputation) to assess the robustness and behavior of the used methods. Due to the lack of a standard benchmark for data amputation, different implementations of the mechanisms are used in related research (some are often not disclaimed), preventing the reproducibility of results and leading to an unfair or inaccurate comparison between existing and new methods. Moreover, for users outside the field, experimenting with missing data or simulating the appearance of missing values in real-world domains is unfeasible, impairing stress testing in machine learning systems. This work introduces mdatagen, an open source Python library for the generation of missing data mechanisms across 20 distinct scenarios, following different univariate and multivariate implementations of the established missing mechanisms. The package therefore fosters reproducible results across missing data experiments and enables the simulation of artificial missing data under flexible configurations, making it very versatile to mimic several real-world applications involving missing data. The source code and detailed documentation for mdatagen are available at https://github.com/ArthurMangussi/pymdatagen.

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
Image inpainting is a crucial technique in computer vision, particularly for reconstructing corrupted images. In medical imaging, it addresses issues from instrumental errors, artifacts, or human factors. The development of deep learning techniques has revolutionized image inpainting, allowing for the generation of high-level semantic information to ensure structural and textural consistency in restored images. This article presents a comprehensive review of 53 studies on deep image inpainting in medical imaging, analyzing its evolution, impact, and limitations. The findings highlight the significance of deep image inpainting in artifact removal and enhancing the performance of multi-task approaches by localizing and inpainting regions of interest. Furthermore, the study identifies magnetic resonance imaging and computed tomography as the predominant modalities and highlights generative adversarial networks and U-Net as preferred architectures. Future research directions include the development of blind inpainting techniques, the exploration of techniques suitable for 3D/4D images, multiple artifacts, and multi-task applications, and the improvement of architectures.

Abstract
Water stress is a significant issue in many countries, including Portugal, which has seen a 20% reduction in water availability over the last 20 years, with a further 10-25% reduction expected by the end of the century. To address potable water consumption, this study aims to identify the optimal rainwater harvesting (RWH) system for a commercial building under various non-potable water use scenarios. This research involved qualitative and quantitative methods, utilizing the Rippl method for storage reservoir sizing and ETA 0701 version 11 guidelines. Various scenarios of non-potable water use were considered, including their budgets and economic feasibility. The best scenario was determined through cash flow analysis, considering the initial investment (RWH construction), income (water bill savings), and expenses (energy costs from hydraulic pumps), and evaluating the net present value (NPV), payback period (PB), and internal rate of return (IRR). The energy savings obtained were calculated by sizing a hybrid system with an RWH system and a photovoltaic (PV) system to supply the energy needs of each of the proposed scenarios and the water pump, making the system independent of the electricity grid. The results show that the best scenario resulted in energy savings of 92.11% for a 7-month period of regularization. These results also demonstrate the possibility for reducing potable water consumption in non-essential situations supported by renewable energy systems, thus helping to mitigate water stress while simultaneously reducing dependence on the grid.

Abstract
Car sharing, car clubs and short-term rentals could support the transition toward net zero but their success depends on them being financially sustainable for service providers and attractive to end users. Dynamic pricing could support this by incentivizing users while balancing supply and demand. We describe the usage of a round trip car sharing fleet by a continuous time Markov chain model, which reduces to a multi-server queuing model where hire duration is assumed independent of the hourly rental price. We present analytical and simulation optimization models that allow the development of dynamic pricing strategies for round trip car sharing systems; in particular identifying the optimal hourly rental price. The analytical tractability of the queuing model enables fast optimization to maximize expected hourly revenue for either a single fare system or a system where the fare depends on the number of cars on hire, while accounting for stochasticity in customer arrival times and durations of hire. Simulation optimization is used to optimize prices where the fare depends on the time of day or hire duration depends on price. We present optimal prices for a given customer population and show how the expected revenue and car availability depend on the customer arrival rate, willingness-to-pay distribution, dependence of the hire duration on price, and size of the customer population. The results provide optimal strategies for pricing of car sharing and inform strategic managerial decisions such as whether to use time- or state-dependent pricing and optimizing the fleet size. © 2025 The Authors