Abstract
This work presents a taxonomic proposition that integrates six views of business process (BP), allowing the identification of different BP perspectives as well as their elements used in the BP modelling activity. A literature review (LR) was conducted to identify the theoretical elements of the BP construct. Based on the LR findings, a taxonomic proposition of BP is presented. Finally, interviews were conducted with practitioners to validate it. The taxonomy contributes to the systematisation of knowledge around the theoretical construct BP and offers the practitioner a broad spectrum of points of view for the analysis of a given BP.

Abstract
Background: Pelvic floor muscle training can cure or alleviate stress urinary incontinence. This study aimed to evaluate maximum voluntary contractions of the pelvic floor muscle in sportswomen and verify the association with leg stiffness and muscle power, both maximal and submaximal. Methods: The sample consisted of 41 sportswomen between 18 and 42 years of age. Pelvic floor muscle strength was measured by the manometer. The sportswomen were instructed to perform 3 maximum voluntary contractions of the perineum, held for 3 seconds. Maximal and submaximal leg stiffness and muscle power were measured with a force platform, in two conditions: 1st condition was the sub-maximal, double leg hop test, which was performed allowing sportswomen to self-select their preferred frequency and 2nd condition was the maximal double leg hop test, which was performed asking athletes to maximize hop height and minimize contact time on the top of the force platform for 6 consecutive hops. Findings: Maximal and submaximal leg stiffness values increase with increasing age, weight, height, and body mass index, showing positive and significant (p <0.05) or close correlations. There are strong positive correlations between maximal and submaximal leg stiffness (r = 0.759) and between maximal and submaximal muscle power. Maximum voluntary contractions values decrease with increasing leg stiffness: the correlation is significant with maximal leg stiffness. Interpretation: Maximum voluntary contractions values decrease with increasing leg stiffness and increase with increasing muscle power values. If the training program aims to increase muscle power, it may also increase maximum voluntary contractions.

Abstract
Social media represents a large part of the content available on the Web. While the accessibility of the UIs of existing social media platforms has been improving, the same cannot be said about the accessibility of the content authored by their users. Specifically, the accessibility of multimedia content that is increasingly available given the ease of access to mobile devices with cameras. User research has revealed that accessible authoring practices are a foreign concept to most social media users, but also that they are motivated to adopt inclusive practices. Our work focuses on promoting awareness to accessible social media authoring practices and in assisting the authoring process. We have prototyped a Google Chrome extension and an Android application that can identify when a Twitter or a Facebook user is authoring content with images and suggests a text alternative for the image. By suggesting the alternative, we raise awareness to the accessible authoring process and make it easier for the user to include it in the tweet or post. Text alternatives may be suggested from different sources: descriptions entered by other users for the same image, analysis of the main concepts present in the image, or text present in the image, for instance. Our prototypes can also provide text alternatives on demand for images on any web page or Android application, not just social media. In this paper, we highlight some of the challenges faced to offer this support in different technological platforms (web and mobile), but also ones that are raised by the domain characteristics (e.g. detecting the same image, supporting different languages) and that can be addressed through AI based technologies.

Abstract
Through technological advents from Industry 4.0 and the Internet of Things, as well as new Big Data solutions, predictive maintenance begins to play a strategic role in the increasing operational performance of any industrial facility. Equipment failures can be very costly and have catastrophic consequences. In its basic concept, Predictive maintenance allows minimizing equipment faults or service disruptions, presenting promising cost savings. This paper presents a data-driven approach, based on multiple-instance learning, to predict malfunctions in End-of-Line Testing Systems through the extraction of operational logs, which, while not designed to predict failures, contains valid information regarding their operational mode over time. For the case study performed, a real-life dataset was used containing thousands of log messages, collected in a real automotive industry environment. The insights gained from mining this type of data will be shared in this paper, highlighting the main challenges and benefits, as well as good recommendations, and best practices for the appropriate usage of machine learning techniques and analytics tools that can be implemented in similar industrial environments. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Abstract
Green energy has become a media issue due to climate changes, and consequently, the population has become more aware of pollution. Wind farms are an essential energy production alternative to fossil energy. The incentive to produce wind energy was a government policy some decades ago to decrease carbon emissions. In recent decades, wind farms were formed by a substation and a couple of turbines. Nowadays, wind farms are designed with hundreds of turbines requiring more than one substation. This paper formulates an integer linear programming model to design wind farms' cable layout with several turbines. The proposed model obtains the optimal solution considering different cable types, infrastructure costs, and energy losses. An additional constraint was considered to limit the number of cables that cross a walkway, i.e., the number of connections between a set of wind turbines and the remaining wind farm. Furthermore, considering a discrete set of possible turbine locations, the model allows identifying those that should be present in the optimal solution, thereby addressing the optimal location of the substation(s) in the wind farm. The paper illustrates solutions and the associated costs of two wind farms, with up to 102 turbines and three substations in the optimal solution, selected among sixteen possible places. The optimal solutions are obtained in a short time.

Abstract
This paper presents a novel iterative three-level optimization framework for the optimal resilient operational scheduling of active multi-carrier energy generation and distribution systems. The main contribution of this paper is that the proposed framework simulates the day-ahead and real-time pre-event preventive and post-event corrective actions for external shocks and explores the effectiveness of risk-averse operational strategies on the system's costs. The solution methodology is another contribution of this paper that finds the optimal scheduling of distributed energy resources and switching of electrical switches and district heating and cooling control valves. At the first stage, the optimal day-ahead scheduling of distributed energy resources and the initial value of the risk control parameter are determined using robust optimization. At the second stage, the optimal realtime market scheduling of distributed energy resources is performed. Finally, at the third stage, different extreme shock scenarios are considered, the effectiveness of corrective actions are investigated, and the value of risk control parameter is modified. The proposed method was successfully applied to the modified 123-bus test system and 600 scenarios of external shocks were considered. The proposed process successfully reduced the expected cost of the. 123-bus system by about 74.59% for the worst-case external shock. Further, the algorithm reduced the aggregated expected values of operational and interruption costs by about 57.73% for all of the 600 cases of the considered external shocks.