Abstract
Fault current limiters (FCLs) are getting high-degree of attention since they can properly overcome the transient conditions of modern power systems. FCLs have the ability to limit the short-circuit currents before reaching to its maximum value so that they can be cut off by the available switches. In fact, FCLs show negligible resistance in the normal operation, but their resistance suddenly increases with a short-circuit and prevents it from rising. The technical and economic benefits of FCLs in power systems depend on their number, locations and optimal structural parameters. In this paper, a two-stage approach is proposed for determining the number, location and impedance of FCLs in the transmission network with high penetration of distributed generations (DGs). The suggested algorithm determines the number and locations of FCLs in the first step using a sensitivity based technique and the value of FCL impedance is chosen in the second step utilizing an optimization objective function. The improved grey wolf optimizer (IGWO) is developed to solve the optimization problem which its main objective is reducing the short-circuit level of the network. The proposed approach is assessed on the IEEE-30 bus transmission network considering the effects of different kind of DGs. The results shown that FCLs can significantly reduce the short-circuit level of the network along with other advantages.

Abstract
This paper describes the several steps to build an elaborate flight simulator cockpit, where the hardware is designed based on Mechatronic principles and the proposed software was developed using agile methodologies to create a Cyber-Physical System (CPS). Furthermore, this research attempts to simulate the real environment from an aircraft as close as possible with a real scale developed cockpit. Based on this, the presented paper contributions include: (1) The implementation of a complex dynamic system such as a CPS, where the Mechatronic system is part of it; (2) The deployment of a scale model of an Airbus A32x aircraft (one of the most used), integrating into a mathematical model adapted to the operation of an aircraft flight simulation system, regarding the physical forces involved. This project is also used to captivate the students' motivation to the areas of technology such as electronics and programming and permits its development as a student project and thesis. Results allow validating the proposed cockpit.

Abstract
Purpose The purpose of this paper is to introduce the concept of meaning as a relevant but missing link in understanding the building blocks of service innovation informed by service-dominant (S-D) logic. In exploring the role of meaning in service innovation, especially related to new value propositions, resource integration and new value cocreation, the authors suggest using the conceptualization of meaning within human-centered design, which has an established body of knowledge on addressing how actors engage and interact. Design/methodology/approach The paper builds an actionable conceptual framework that relates meaning to central tenets of service innovation, such as resource integration, value propositions and cocreation of value. It delineates the central building blocks of service innovation and conceptually integrates them with meaning to explain the underlying mechanisms of service innovation related both to its development and adoption. Findings The findings highlight how and why meaning precedes value creation and directs resource integration. Indicating that meaning is driven by experience of earlier interactions it delineates its relationships with new value formation and positions resource interpretation as a driver of this process. Originality/value This paper extends the understanding of service innovation in relation to S-D logic, with meaning as a conceptual link to aspects of S-D logic that claim a phenomenological nature. Meaning contributes to S-D logic by providing an understanding of how beneficiaries form intentions to engage in value creation and resource integration. Additionally, by integrating service and design research domains, this paper suggests possibilities for multidisciplinary contributions in future research.

Abstract
One of the most widely used methods in multi-objective optimization problems is the weighted sum method. However, in this method, defining the weights of objectives is always a challenge. Various methods have been suggested to achieve the weights, one of which is Shannon’s entropy method. In this study, a bi-objective model is introduced to solve the sectorization problem. As a solution method, the model is transformed into two single-objective ones. Also, the bi-objective model is solved for the case where the weights are equal to one. The gained three results from a benchmark are supposed as alternatives in a decision matrix. After the limitation of this approach appears, solutions from different benchmarks are added to the matrix. With Shannon’s entropy method, the weights of the objective functions are got from the decision matrix. The limitations of the approach and possible causes are discussed.

Abstract
Cardiovascular diseases are one of the leading causes of death in the world. In low income countries, heart auscultation is of capital importance since it is an efficient and low cost method to monitor the heart. In this paper, we propose a multi-spot system that aims to detect cardiac anomalies and to support a diagnosis in remote areas with limited heath care response. Our proposed solutions exploits data collected from the four main auscultation spots: Mitral, Pulmonary, Tricuspid and Aorta in a asynchronous way. From the several multi-spot systems implemented, the best results were obtained using a bi-modal system that only processes the Mitral and the Pulmonary spot simultaneously. Using these two spots we have achieved an accuracy between 85.7% (smallest value, using ANN) and the best value of 91.4% (obtained with a logistic regression algorithm). Taking into a account the pediatric population and the incident cardiac pathologies, it happens to be the spots where the observed murmurs were most audible. We have also find out that when using four auscultation spots, the choice of the algorithm is of secondary priority, which does not seem to be the case for a single auscultation spot system. With one single auscultation we have an average of 4% of difference between the results obtained with the algorithms and with four auscultation spots we have a smaller average of 2.1%.

Abstract
Background: The American Academy of Sleep Medicine guidelines suggest that clinical prediction algorithms can be used in patients with obstructive sleep apnea (OSA) without replacing polysomnography, which is the gold standard. Objective: This study aims to develop a clinical decision support system for OSA diagnosis according to its standard definition (apnea-hypopnea index plus symptoms), identifying individuals with high pretest probability based on risk and diagnostic factors. Methods: A total of 47 predictive variables were extracted from a cohort of patients who underwent polysomnography. A total of 14 variables that were univariately significant were then used to compute the distance between patients with OSA, defining a hierarchical clustering structure from which patient phenotypes were derived and described. Affinity from individuals at risk of OSA phenotypes was later computed, and cluster membership was used as an additional predictor in a Bayesian network classifier (model B). Results: A total of 318 patients at risk were included, of whom 207 (65.1%) individuals were diagnosed with OSA (111, 53.6% with mild; 50, 24.2% with moderate; and 46, 22.2% with severe). On the basis of predictive variables, 3 phenotypes were defined (74/207, 35.7% low; 104/207, 50.2% medium; and 29/207, 14.1% high), with an increasing prevalence of symptoms and comorbidities, the latter describing older and obese patients, and a substantial increase in some comorbidities, suggesting their beneficial use as combined predictors (median apnea-hypopnea indices of 10, 14, and 31, respectively). Cross-validation results demonstrated that the inclusion of OSA phenotypes as an adjusting predictor in a Bayesian classifier improved screening specificity (26%, 95% CI 24-29, to 38%, 95% CI 35-40) while maintaining a high sensitivity (93%, 95% CI 91-95), with model B doubling the diagnostic model effectiveness (diagnostic odds ratio of 8.14). Conclusions: Defined OSA phenotypes are a sensitive tool that enhances our understanding of the disease and allows the derivation of a predictive algorithm that can clearly outperform symptom-based guideline recommendations as a rule-out approach for screening.