Abstract
One of the main difficulties in the use of deep learning strategies in medical contexts is the training set size. While these methods need large annotated training sets, these datasets are costly to obtain in medical contexts and suffer from intra and inter-subject variability. In the present work, two new pre-processing techniques are introduced to improve a deep classifier performance. First, data augmentation based on co-registration is suggested. Then, multi-scale enhancement based on Difference of Gaussians is proposed. Results are accessed in a public mammogram database, the InBreast, in the context of an ordinal problem, the BI-RADS classification. Moreover, a pre-trained Convolutional Neural Network with the AlexNet architecture was used as a base classifier. The multi-class classification experiments show that the proposed pipeline with the Difference of Gaussians and the data augmentation technique outperforms using the original dataset only and using the original dataset augmented by mirroring the images.

Abstract
The smart grid paradigm has provided great opportunities to decrease energy consumption and electricity bills of end users. Among a wide variety of end users, electrical railway systems with their huge installed power capacity should be considered as a vital option in order to avoid wasted energy, provided that an energy management system is utilized. In this study, a mixed-integer linear programming model of a railway station energy management (RSEM) system is formulated by a stochastic approach, aiming to utilize the emerged regenerative braking energy (RBE) during the braking mode in order to supply station loads. Furthermore, the proposed RSEM model is composed of an energy storage system (ESS), RBE utilization, photovoltaic (PV) generation units, and an external grid in this paper. The passengers' impact on RBE as well as the stochastic behaviour of the initial state-of-energy of ESS along with uncertainty of PV generation by the RSEM model are also evaluated. The model is tested under a bunch of case studies formed considering several combinations of the cases that an ESS or PV are available or not and using RBE is possible or not.

Abstract
The large-scale integration of wind generation in power systems increases the need for reserve procurement in order to accommodate its highly uncertain nature, a fact that may overshadow its environmental and economic benefits. For this reason, the design of reserve procurement mechanisms should be reconsidered in order to embed resources that are capable of providing reserve services in an economically optimal way. In this study, a joint energy and reserve day-ahead market structure based on two-stage stochastic programming is presented. The developed model incorporates explicitly the participation of demand side resources in the provision of load following reserves. Since a load that incurs a demand reduction may need to recover this energy in other periods, different types of load recovery requirements are modeled. Furthermore, in order to evaluate the risk associated with the decisions of the system operator and to assess the effect of procuring and compensating load reductions, the Conditional Value-at-Risk metric is employed. In order to solve the resulting multi-objective optimization problem, a new approach based on an improved variant of the epsilon-constraint method is adopted. This study demonstrates that the proposed approach to risk management presents conceptual advantages over the commonly used weighted sum method.

Abstract
An optical fiber probe was developed for viscosity measurements. The sensor acts as a two-wave interferometer, sensible to the position of the fluid inside the cavity. Viscosity is measured through the fluid evacuation velocity. © OSA 2018 © 2018 The Author(s)

Abstract
Drawing on the concept of strategic fit, this conceptual paper seeks to clarify the relationship between innovation strategies and supply chain management strategies. This work seeks to propose a conceptual framework to help advance research in this area. A literature review was conducted as a basis for developing a unified framework which best reflects the relationship and fit between the different strategies in each area, something which has been clearly under researched from the strategic fit perspective. The findings can be used to guide the decision making of managers in the areas of innovation and supply chain. Additionally, they can serve as a reference for helping coordinate with other areas of the business, in order to ensure the correct fit between activities and strategies.

Abstract
ABSTRACTTumors subvert immune cell function to evade immune responses, yet the complex mechanisms driving immune evasion remain poorly understood. Here we show that tumors induce de novo steroidogenesis in T lymphocytes to evade anti-tumor immunity. Using a novel transgenic steroidogenesis-reporter mouse line we identify and characterize de novo steroidogenic immune cells. Genetic ablation of T cell steroidogenesis restricts primary tumor growth and metastatic dissemination in mouse models. Steroidogenic T cells dysregulate anti-tumor immunity, and inhibition of the steroidogenesis pathway was sufficient to restore anti-tumor immunity. This study demonstrates T cell de novo steroidogenesis as a mechanism of anti-tumor immunosuppression and a potential druggable target.