Abstract
Uncontrolled operation of distributed generation (DG) can cause interference with the operation of other equipment such as tap-changers, and non-optimal use of their capability. Thus, having an appropriate scheduling and control on DGs is a crucial issue for distribution system operators. In this paper, a linear multi-objective model for power distribution system scheduling that coordinates tap-changers, photovoltaics (PVs) and battery energy storage operation is proposed. Accordingly, tap-changers experience lower stress, batteries' state of charge is kept in suitable range and DGs are used more effectively. The objective functions of the proposed model encompass improving voltage profile, minimizing losses and peak load. Epsilon-constraint method is employed for solving the multi-objective problem, generating the Pareto set. A new decision-making method is proposed to select the preferred solution from the Pareto set. The 33-bus IEEE test system is used to test the performance of the model. Conclusions are duly drawn.

Abstract
Breast cancer is one of the most common malignanciesaffecting women worldwide. However, despite its incidence trends have increased, the mortality rate has significantly decreased. The primary concern in any cancer treatment is the oncological outcome but, in the case of breast cancer, the surgery aesthetic result has become an important quality indicator for breast cancer patients. In this sense, an adequate surgical planning and prediction tool would empower the patient regarding the treatment decision process, enabling a better communication between the surgeon and the patient and a better understanding of the impact of each surgical option. To develop such tool, it is necessary to create complete 3D model of the breast, integrating both inner and outer breast data. In this review, we thoroughly explore and review the major existing works that address, directly or not, the technical challenges involved in the development of a 3D software planning tool in the field of breast conserving surgery. © 2018 by Begell House, Inc.

Abstract
Lung cancer is one of the most common causes of death in the world. The early detection of lung nodules allows an appropriate follow-up, timely treatment and potentially can avoid greater damage in the patient health. The texture is one of the nodule characteristics that is correlated with the malignancy. We developed convolutional neural network architectures to classify automatically the texture of nodules into the non-solid, part-solid and solid classes. The different architectures were tested to determine if the context, the number of slices considered as input and the relation between slices influence on the texture classification performance. The architecture that obtained better performance took into account different scales, different rotations and the context of the nodule, obtaining an accuracy of 0.833 ± 0.041. © Springer Nature Switzerland AG 2019.

Abstract

Abstract
Performance assessment is critical in today's competitive environments, where companies need to establish trade-offs between key competitive dimensions. The complexity of these environments calls for new approaches to performance assessment. Thus, in this work, we propose a novel conceptual framework for performance assessment in manufacturing environments combining different production strategies. Focus is laid on MTO/ETO combined environments and a three-stage problem analysis is considered. Firstly, a hybrid SD-DES-ABS model approach addresses the needs of a system that handles different types of orders, processes and workforce allocation requirements; secondly, the model results for different demand scenarios are assessed using a one-way ANOVA analysis followed by a Tukey - Kramer's test, with pairwise comparisons for assessment of significant performance variations under different system operating policies. A full factorial Design of Experiments (DOE) analysis follows, for determining the relevant process parameters influencing the system performance. As an example of application of the proposed framework, we consider the case of an advanced manufacturing company, whose manufacturing environment encompasses combined MTO/ETO production strategies.

Abstract
The visually impaired must face several well-known difficulties on their daily life. The use of technology in assistive systems can greatly improve their lives by helping with navigation and orientation, for which several approaches and technologies have been proposed. Lately, it has been introduced powerful online image processing services, based on machine learning and deep learning, promising truly cognitive assessment capacities. Google and Microsoft are two of these main players. In this work we built a device to be used by the blind in order to test the usage of the Google and Microsoft services to assist the blind. The online services were tested by researchers in a laboratory environment and by blind users on a large meeting room, familiar to them. This work reports on our findings regarding the online services effectiveness, the user interface and system latency. © Springer International Publishing AG, part of Springer Nature 2018.