Abstract

Abstract
Visual emotion perception is the ability of recognizing and identifying emotions through the visual interpretation of a situation or environment. In this paper we propose an innovative environment for supporting this type of studies, aimed at replacing current pencil-and-paper approaches. Besides automatizing the whole process, this environment provides new features that can enrich the study of emotion perception. These new features are especially interesting for the field of Human-Compute Interaction and Affective computing as they quantify the effects of experiencing different emotional dimensions on the individual's interaction with the computer.

Abstract

Abstract
In this study, hyperspectral reflectance (HySR) data derived from a handheld spectroradiometer were used to assess the water status of three grapevine cultivars in two sub-regions of Douro wine region during two consecutive years. A large set of potential predictors derived from the HySR data were considered for modelling/predicting the predawn leaf water potential (Psi(pd)) through different statistical and machine learning techniques. Three HySR vegetation indices were selected as final predictors for the computation of the models and the in-season time trend was removed from data by using a time predictor. The vegetation indices selected were the Normalized Reflectance Index for the wavelengths 554 nm and 561 nm (NRI554;561), the water index (WI) for the wavelengths 900 nm and 970 nm, and the D1 index which is associated with the rate of reflectance increase in the wavelengths of 706 nm and 730 nm. These vegetation indices covered the green, red edge and the near infrared domains of the electromagnetic spectrum. A large set of state-of-the-art analysis and statistical and machine-learning modelling techniques were tested. Predictive Modelling techniques based on generalized boosted model (GBM), bagged multivariate adaptive regression splines (B-MARS), generalized additive model (GAM), and Bayesian regularized neural networks (BRNN) showed the best performance for predicting Psi(pd), with an average determination coefficient (R-2) ranging between 0.78 and 0.80 and RMSE varying between 0.11 and 0.12 MPa. When cultivar Touriga Nacional was used for training the models and the cultivars Touriga Franca and Tinta Barroca for testing (independent validation), the models performance was good, particularly for GBM (R-2 = 0.85; RMSE = 0.09 MPa). Additionally, the comparison of Psi(pd) observed and predicted showed an equitable dispersion of data from the various cultivars. The results achieved show a good potential of these predictive models based on vegetation indices to support irrigation scheduling in vineyard.

Abstract
This paper proposes a conceptual model that analyses the factors influencing the export performance of small and medium-sized enterprises (SMEs) and integrates international entrepreneurship theory and international strategies. Rooted in an extensive bibliography that provides the basis for key constructs, the proposed model brings together the fundamentals of SMEs' internationalisation processes that influence these enterprises' export performance. The model highlights three important factors: industry-, environment- and firm-related aspects affecting firms' entrepreneurial orientation. The model contributes to a better understanding of the key factors affecting the export performance of SMEs, providing a simple structure that can be strategically used by entrepreneurs when launching their firms into international markets. The model complements previous approaches but offers a more integrative approach based on research that ventures further into a little explored area of the literature on internationalisation theories. Copyright © 2017 Inderscience Enterprises Ltd.

Abstract
Optimum operation of the energy consumption of end-users gains more importance to reduce total electricity bills and in order to more efficiently use energy resources thanks to smart grid concept. Electrical railway stations are one of the best places to take into account for in this manner. This study proposes a railway station energy management (RSEM) model. As the main contribution to the literature, regenerative braking energy (RBE) recovered during the operation of a metro line is assumed to meet the station load demand in daily operation. The RSEM model composed of RBE usage, energy storage system (ESS), and grid support is formulated as a mixed-integer linear programming (MILP) framework. RSEM model is tested by introducing the impact of passengers change on RBE for such cases that whether RBE and ESS are considered or neglected.