Abstract
Supplier selection problem is a multi-criteria decision-making problem that involves both quantitative and qualitative criteria. Typically, supplier selection decisions require a preliminary stage where pool of suppliers are initially screened to select potential set of suppliers for further evaluation and select the optimal supplier. This preliminary stage is heavily dependent on non-scientific approaches and do not consider any criteria during the screening process. Furthermore, quantifying the qualitative criteria has always relied quite considerably on subjective judgments, which render the supplier selection process ineffective. Therefore, this paper addresses these problems by proposing an easy going two-phase supplier selection decision model, based on fuzzy set theory that uses a scientific approach and incorporates performance criteria in screening and selecting the potential suppliers for further optimal supplier selection. To illustrate the applicability and validate the proposed model, a case study of a beverage producing company located in Ghana, the Sub-Saharan Africa is proposed.

Abstract
Despite the existence of patterns able to discriminate between consensual and non-consensual intercourse, the relevance of genital lesions in the corroboration of a legal rape complaint is currently under debate in many countries. The testimony of the physicians when assessing these lesions has been questioned in court due to several factors (e.g., a lack of comprehensive knowledge of lesions, wide spectrum of background area, among others). Therefore, it is relevant to provide automated tools to support the decision process in an objective manner. In this work, we evaluate the performance of state-of-the-art deep learning architectures for the forensic assessment of sexual assault. We propose a deep architecture and learning strategy to tackle the class imbalance on deep learning using ranking. The proposed methodologies achieved the best results when compared with handcrafted feature engineering and with other deep architectures .

Abstract
This paper extensively models the interactions of emerging players in future power systems to analyze their impacts on electricity markets. To this end, renewable energy resources are modeled in such a way that wind power poses uncertainty on the supply side, and rooftop photovoltaics add uncertainty to the demand side. Moreover, both uncontrolled and controlled behaviors of individual electric vehicles (EV) in electricity markets arc addressed through a new EV model. Further, a comprehensive demand response model considering several customer-driven constraints is developed to undertake the practical constraints of customers. A stochastic market clearing formulation is presented to comprehensively account for the unique features of the given resources while evaluating their impacts. The numerical results clearly show the importance of such modeling in electricity markets to investigate the mutual impacts of emerging resources.

Abstract
This article is devoted to the problem of training cyclists from a system approach. Technologies were used to monitor and evaluate in an integrated way the physical form, load parameters and level of functional capabilities of the athlete's body. For correlation between the physiological indices and performance, data from cardiac activity (ECG), muscle activity (EMG and temperature), respiratory processes (oximetry), as well as data from the environment where this athlete is inserted (ambient temperature, pressure, humidity).

Abstract
This study approaches the internationalization-performance (I-P) relationship following an innovative strategy, using DEA to calculate a financial performance metric that considers several financial indicators. We then apply a truncated regression to evaluate the relationship between financial performance and internationalization for a sample of firms in the footwear Portuguese industry for the period 2010-2013, using several controls, while exploring potential non-linear effects. Results tend to support the conclusion that export participation leads to increased efficiency, eventually through the so-called learning effects. For our case, the relationship is U-shaped. So, beyond a certain level the degree of international engagement might compromise efficiency.

Abstract
The purpose of this study is to investigate Second Generation Ethanol’s (SGE) production cycle in order to understand the level of SGE’s technological intensity in the integrated cycle. The suggested methodology comprises of a review of literature surrounding the requirements and indexes of technological intensity. A wide selection of database and review of specialized literature have been described to demonstrate the proposed discussion and conclusions. It has been observed that SGE puts forward a higher level of technological intensity in relation to First Generation Ethanol (FGE).