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

Abstract
This paper presents a self-scheduling framework, using a risk-constrained optimization model for the home energy management system (HEMS), considering fixed, controllable, and interruptible loads, as a new contribution to earlier studies. The objectives are reducing the electricity bill and managing the risk of purchasing energy over on-peak hours and prosumer's discomfort index (DI) due to shifting load to undesired hours. In this regard, the problem formulation is represented as a mixed-integer linear programming (MILP) model. Afterward, the proposed HEMS is promoted to a conditional value-at-risk (CVaR) model. The prosumer is equipped with an energy storage system and a solar photovoltaic (PV) panel. A substantial fraction of the load demand is controllable, and there is an inverter-based heating, ventilation, and air conditioning (HVAC), where HVAC is modeled as a variable-capacity interruptible load. The optimal scheduling of the loads is supposed to be done by the proposed HEMS, and the time-of-use (TOU) mechanism is utilized, including three price steps over the day. The results, obtained from thoroughly simulating the problem using household data, validate the performance of the presented HEMS in mitigating the amount of the electricity bill, while keeping the discomfort index of the prosumer at a desired level.

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.