Abstract
Predicting whether a student will pass or fail is one of the most important actions to take while giving lectures. Usually, the experienced teacher is able to detect problematic situations at early stages. However, this is only true for classes up to a hundred students. For bigger ones, automatic methods are needed. In this paper, we present a predictive system based on three criteria retrieved and computed from the logs of the learning management system. We built fast frugal decision trees to help predict and prevent student failures, using data retrieved from their resource usage patterns. Evaluation of the decision system shows that the system's accuracy is very high both in train and test phases, surpassing logistic regression and CART. © 2017 IEEE.

Abstract
A manufacturing system often consists of multiple units as workcells with complex work systems to achieve the desired outcomes in an efficient and effective manner. Uncertain events such as machine down time or scheduled maintenance are unavoidable in any manufacturing unit. In this paper, we are trying to find the maximum workload of the remaining machines to fulfill the production requirements. To achieve this, a dynamic workload adjustment strategy has been proposed with dynamic upgradation of residual life distribution model. With parallel configurations and different benchmark instances the simulation experiments has been conducted to evaluate the degradation rate of different units. Results show that the proposed method is effective for finding the residual life of multi-unit systems.

Abstract
Reading room conditions such as illumination, ambient light, human factors and display luminance, play an important role on how radiologists analyze and interpret images. Indeed, serious diagnostic errors can appear when observing images through everyday monitors. Typically, these occur whenever professionals are ill-positioned with respect to the display or visualize images under improper light and luminance conditions. In this work, we show that virtual reality can assist radiodiagnostics by considerably diminishing or cancel out the effects of unsuitable ambient conditions. Our approach combines immersive head-mounted displays with interactive surfaces to support professional radiologists in analyzing medical images and formulating diagnostics. We evaluated our prototype with two senior medical doctors and four seasoned radiology fellows. Results indicate that our approach constitutes a viable, flexible, portable and cost-efficient option to traditional radiology reading rooms.

Abstract
Olive trees are one of the most economically important perennial crops in Portugal. During the last decade, the Alentejo olive-growing region has suffered a significantly change in the crop production system, with the regional pollen index (RPI) and olive fruit production registering a significant growth. The aim of this study was to ascertain the utility of this highly variable production and pollen data in crop forecasting modeling. Airborne pollen was sampled using a Cour-type trap from 1999 to 2015. A linear regression model fitted with the regional pollen index as the independent variable showed an accuracy of 87% in estimating olives fruit production in Alentejo. However, the average deviation between observed and modeled production was 32% with half of the tested years presenting deviations between 36 and 66%. The low accuracy of this model is a consequence of the great overall variation and significant upward trend observed in both the production and the RPI dataset that conceal the true association between these variables. In order to overcome this problem, a detrend procedure was applied to both time series to remove the trend observed. The regression model fitted with the fruit production and the RPI detrended data showed a lowest forecasting accuracy of 63% but the average deviation between observed and modeled production decrease to 14% with a maximum deviation value of 33%. This procedure allows focusing the analysis on the production fluctuations related to the biological response of the trees rather than with the changes in the production system.

Abstract
We observe the high-mass X-ray binary (HMXB) BP Cru using interferometry in the near-infrared K band with VLTI/GRAVITY. Continuum visibilities are at most partially resolved, consistent with the predicted size of the hypergiant. Differential visibility amplitude (Delta|V| similar to 5%) and phase (Delta phi similar to 2 degrees) signatures are observed across the He I 2.059 mu m and Br gamma lines, the latter seen strongly in emission, unusual for the donor star's spectral type. For a baseline B similar to 100 m, the differential phase rms similar to 0 degrees 2 corresponds to an astrometric precision of similar to 2 mu as. We generalize expressions for image centroid displacements and variances in the marginally resolved limit of interferometry to spectrally resolved data, and use them to derive model-independent properties of the emission such as its asymmetry, extension, and strong wavelength dependence. We propose geometric models based on an extended and distorted wind and/or a high-density gas stream, which has long been predicted to be present in this system. The observations show that optical interferometry is now able to resolve HMXBs at the spatial scale where accretion takes place, and therefore to probe the effects of the gravitational and radiation fields of the compact object on its environment.

Abstract
Under specific conditions, there is a formal analogy between the fundamental equations of electromagnetism and relativistic gravitation, described by the Einstein field equations of general relativity. In this paper, we report on how we have used this analogy to implement a solver of the Einstein equations adapting algorithms initially developed for electromagnetism, combined with methods of heterogeneous supercomputing, in GPU that can achieve fast computing and exhibit good performance. We also present the results of the simulations used to validate our solver. © 2017 SPIE.