Abstract
Background: While health needs and expenditure in the Occupied Palestinian Territories (OPT) are growing, the international donations are declining and the economic situation is worsening. The purpose of this paper is twofold, to evaluate the productive efficiency of public hospitals in West Bank and to study contextual factors contributing to efficiency differences. Methods: This study examined technical efficiency among 11 public hospitals in West Bank from 2010 through 2015 targeting a total of 66 observations. Nationally representative data were extracted from the official annual health reports. We applied input-oriented Data Envelopment Analysis (DEA) models to estimate efficiency scores. To elaborate further on performance, we used Tobit regression to identify contextual factors whose impact on inefficient performance is statistically significant. Results: Despite the increase in efficiency mean scores by 4% from 2010 to 2015, findings show potential savings of 14.5% of resource consumption without reducing the volume of the provided services. The significant Tobit model showed four predictors explaining the inefficient performance of a hospital (p < 0.01) are: bed occupancy rate (BOR); the outpatient-inpatient ratio (OPIPR); hospital's size (SIZE); and the availability of primary healthcare centers within the hospital's catchment area (PRC). There is a strong effect of OPIPR on efficiency differences between hospitals: A one unit increase in OPIPR will lead a decrease of 19.7% in the predicted inefficiency level holding all other factors constant. Conclusion: To date, no previous studies have examined the efficiency of public hospitals in the OPT. Our work identified their efficiency levels for potential improvements and the determinants of efficient performance. Based on the measurement of efficiency, the generated information may guide hospitals' managers, policymakers, and international donors improving the performance of the main national healthcare provider. The scope of this study is limited to public hospitals in West Bank. For a better understanding of the Palestinian market, further research on private hospitals and hospitals in Gaza Strip will be useful. © 2018 The Author(s).

Abstract
dado o interesse premente, não só da comunidade académica como do mundo empresarial, sobre a internacionalização das empresas, esta investigação faz uma revisão da literatura empírica sobre a internacionalização de Pequenas e Médias Empresas (PME) no continente americano, a partir das principais bases de dados académicas. Partindo de um conjunto de 329 artigos, este estudo analisa em pormenor 39 destes, entre 1999 e 2014, contribuindo significativamente para o desenvolvimento do conhecimento e da compreensão da investigação feita até à data nesta área. Com esta análise, foram identificadas tendências e padrões, podendo concluir-se que a investigação feita sobre a internacionalização de PME centra-se nos Estados Unidos e no Canadá, influenciada sobretudo por quatro grandes tópicos: teorias da internacionalização, estratégia, desempenho e perspetiva internacional. Conclui-se igualmente que há uma predominância quantitativa. As conclusões e interpretações sobre oportunidades para futuras investigações são de relevância tanto para a comunidade académica como para a empresarial.

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Abstract
In medical image analysis applications, the availability of the large amounts of annotated data is becoming increasingly critical. However, annotated medical data is often scarce and costly to obtain. In this paper, we address the problem of synthesizing retinal color images by applying recent techniques based on adversarial learning. In this setting, a generative model is trained to maximize a loss function provided by a second model attempting to classify its output into real or synthetic. In particular, we propose to implement an adversarial autoencoder for the task of retinal vessel network synthesis. We use the generated vessel trees as an intermediate stage for the generation of color retinal images, which is accomplished with a generative adversarial network. Both models require the optimization of almost everywhere differentiable loss functions, which allows us to train them jointly. The resulting model offers an end-to-end retinal image synthesis system capable of generating as many retinal images as the user requires, with their corresponding vessel networks, by sampling from a simple probability distribution that we impose to the associated latent space. We show that the learned latent space contains a well-defined semantic structure, implying that we can perform calculations in the space of retinal images, e.g., smoothly interpolating new data points between two retinal images. Visual and quantitative results demonstrate that the synthesized images are substantially different from those in the training set, while being also anatomically consistent and displaying a reasonable visual quality.

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.