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
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 paper 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

Abstract
Scientific research in digital transformation is expanding in scope, quantity, and relevance, bringing forth diverse perspectives on which factors and specific dimensions-such as organizational structure, culture, and technological readiness-affect the success of digital transformation initiatives. Numerous studies have proposed mechanisms to assess an organization's maturity through digital transformation across various models. Some of these models focus on external influences, others on internal factors, or both. Although these assessments provide valuable insights into a company's transformation state, they often lack consistency, and recent research highlights key gaps. Specifically, many models primarily reflect the views of senior management on the general progress of digital transformation rather than on measurable outcomes. Moreover, these models tend to target large enterprises, overlooking small and medium enterprises (SMEs), which are crucial to economic growth yet face unique challenges, such as limited resources and expertise. Our study addresses these gaps by concentrating on SMEs and introducing a novel approach to assessing digital transformation readiness-a metric that reflects how prepared an organization is to optimize transformation outcomes. Following design science research methodology, we develop a model that centers on the perspectives of general employees, offering companies an in-depth view of their readiness across 20 dimensions. Each dimension is evaluated through behaviors indicative of the highest level of digital transformation readiness, helping companies identify areas to maximize potential benefits. Our model focuses not on technological quality but on the degree to which behaviors essential for leveraging technology and innovative business models are integrated within the organization.

Abstract
PurposeThis paper aims to identify the critical success factors (CSFs) for managing remote project teams (RPT) within project environments. In other words, it focuses on identifying the crucial elements for the success of projects executed by RPT.Design/methodology/approachAn exploratory mixed-method was used combining a case study approach with the application of surveys. Document analysis and direct observation were also applied. The analyzed company is a well-known project-based company acting in the coffee industry and is justified due to its multilocation and multicultural perspectives.FindingsThrough an initial literature review, 93 CSFs were identified and then organized into 7 categories. The subsequent phase involved the relevance evaluation of the identified CSFs through surveys conducted in an international company. The first results analysis identified 20 CSFs. A deeper analysis identified the most relevant factors for each category (Project Managers, 33 factors; Team Leaders, 15; and Team Members, 29). Combining these results, 11 CSFs were identified.Originality/valueWith the trend of remote work that is being kept after the pandemic, this study contributes to identify the most relevant issues that must be taken into account in managing remote teams. By identifying those issues, or CSFs, managers and team members might focus on the most relevant factors.