ALBERT

Description: The aged properties of Reclaimed Asphalt (RA) binders are one of the main factors working against their utilisation in high-RA content (〉30%) mixes for surface courses. Fatigue cracking is the main distress of surface courses that are manufactured with a high percentage of RA. This investigation presents results of the rheological and fatigue results of different asphalt mixtures and their recovered binders. The binders were recovered from asphalt mixtures that had been manufactured in asphalt plants using different amounts of RA with contents up to 60% with and without rejuvenators. Two different sources of RA were used, representing a moderately aged RA and an extremely aged RA. The Dynamic Shear Rheometer (DSR) was used to assess the fatigue-characteristics of the binders using time sweep tests while the fatigue characteristics of their mixtures were assessed using the Indirect Tensile Fatigue Test (ITFT). The fatigue data was analysed based on the cumulative dissipated energy approach in addition to traditional fatigue analysis. Results have shown that the ageing condition of RA significantly affects the fatigue properties of recovered binders. Binder and asphalt mixture fatigue results showed that RA contents up to 60% can produce comparable fatigue performance compared to lower percentages of RA in road surface course if the aged RA binder is sufficiently rejuvenated.

Description: Peak current-mode control is widely used in power converters and involves the use of an external compensation ramp to suppress undesired behaviors and to enhance the stability range of the Period-1 orbit. A boost converter uses an analytical expression to find a compensation ramp; however, other more complex converters do not use such an expression, and the corresponding compensation ramp must be computed using complex mechanisms. A boost-flyback converter is a power converter with coupled inductors. In addition to its high efficiency and high voltage gains, this converter reduces voltage stress acting on semiconductor devices and thus offers many benefits as a converter. This paper presents an analytical expression for computing the value of a compensation ramp for a peak current-mode controlled boost-flyback converter using its simplified model. Formula results are compared to analytical results based on a monodromy matrix with numerical results using bifurcations diagrams and with experimental results using a lab prototype of 100 W.

Description: Lippia graveolens, commonly known as Mexican oregano, is an aromatic plant of great industrial, nutritional, and medicinal value, principally for its essential oils. Regeneration via axillary buds was established in MS medium supplemented with 6-benzyladenine (BA) (0.5 mgL−1) as a growth regulator. Three genotypes and three stages of cultivation were considered in the study. On average, 3.5, 4.2, and 6.4 shoots induced per explant were obtained for genotypes B, C, and D, respectively. Several doses (0.1, 0.3, and 0.5%) of ethyl methane sulfonate (EMS) and different exposure times (1, 2, and 3 h) were applied to investigate the effect of the chemical mutagen on the formation of axillary buds. Genetic variation among the collected plants, the micro-propagated plants during three sub-cultivations, and the plants regenerated in the presence of the mutagen was evaluated by means of randomly amplified microsatellite polymorphism (RAMP) markers. A high genetic stability was observed in the micro-propagation of Mexican oregano for the three genotypes and three sub-cultivations, presenting 100% of monomorphic bands. The genetic variation observed in the different collections of wild populations (A, R, and V) and after treatment with EMS regarded 34 and 35% of polymorphic loci, respectively.

Description: The work presented in this paper is focused on the use of spectroscopy to identify the type of tissue of human brain samples employing support vector machine classifiers. Two different spectrometers were used to acquire infrared spectroscopic signatures in the wavenumber range between 1200–3500 cm−1. An extensive analysis was performed to find the optimal configuration for a support vector machine classifier and determine the most relevant regions of the spectra for this particular application. The results demonstrate that the developed algorithm is robust enough to classify the infrared spectroscopic data of human brain tissue at three different discrimination levels.

Description: Our research focuses on refining the ability to discriminate two petrogenic oil-slick categories: the sea surface expression of naturally-occurring oil seeps and man-made oil spills. For that, a long-term RADARSAT-2 dataset (244 scenes imaged between 2008 and 2012) is analyzed to investigate oil slicks (4562) observed in the Gulf of Mexico (Campeche Bay, Mexico). As the scientific literature on the use of satellite-derived measurements to discriminate the oil-slick category is sparse, our research addresses this gap by extending our previous investigations aimed at discriminating seeps from spills. To reveal hidden traits of the available satellite information and to evaluate an existing Oil-Slick Discrimination Algorithm, distinct processing segments methodically inspect the data at several levels: input data repository, data transformation, attribute selection, and multivariate data analysis. Different attribute selection strategies similarly excel at the seep-spill differentiation. The combination of different Oil-Slick Information Descriptors presents comparable discrimination accuracies. Among 8 non-linear transformations, the Logarithm and Cube Root normalizations disclose the most effective discrimination power of almost 70%. Our refined analysis corroborates and consolidates our earlier findings, providing a firmer basis and useful accuracies of the seep-spill discrimination practice using information acquired with space-borne surveillance systems based on Synthetic Aperture Radars.

Description: Research findings concerning burnout prevalence rate among nurses from the medical area are contradictory. The aim of this study was to analyse associated factors, to determine nurse burnout levels and to meta-analyse the prevalence rate of each burnout dimension. A systematic review, with meta-analysis, was conducted in February 2018, consulting the next scientific databases: PubMed, CUIDEN, CINAHL, Scopus, LILACS, PsycINFO and ProQuest Health & Medical Complete. In total, 38 articles were extracted, using a double-blinded procedure. The studies were classified by the level of evidence and degrees of recommendation. The 63.15% (n = 24) of the studies used the MBI. High emotional exhaustion was found in the 31% of the nurses, 24% of high depersonalisation and low personal accomplishment was found in the 38%. Factors related to burnout included professional experience, psychological factors and marital status. High emotional exhaustion prevalence rates, high depersonalisation and inadequate personal accomplishment are present among medical area nurses. The risk profile could be a single nurse, with multiple employments, who suffers work overload and with relatively little experience in this field. The problem addressed in this study influence the quality of care provided, on patients’ well-being and on the occupational health of nurses.

Description: Evapotranspiration (ET) is an important component of the hydrological cycle. Understanding the ET process has become of fundamental importance given the scenario of global change and increasing water use, especially in the agricultural sector. Determining ET over large agricultural areas is a limiting factor due to observational data availability. In this regard, remote sensing data has been used to estimate ET. In this study, we evaluated the Moderate-Resolution Imaging Spectroradiometer (MODIS) land surface ET product estimates (hereafter MOD16 ET – MODIS Global Terrestrial Evapotranspiration Product) over two rice paddy areas in Southern Brazil, through the ET measured using the eddy covariance technique (hereafter EC). The energy balance components were evaluated during fallow and flooded seasons showing latent heat flux dominates in both seasons. The results showed that MOD16 ET underestimated EC measurements. Overall, the RMSE (root mean square error) ranged between 13.40 and 16.35 mm 8-day−1 and percent bias (PBIAS) ranged between −33.7% and −38.7%. We also assessed the ET (measured and estimated) main drivers, with EC yielding higher correlation against observed net radiation (Rn) and global radiation (Rg), followed by air temperature (Temp) and vapor pressure deficit (VPD), whilst MOD16 ET estimates yielded higher correlation against leaf area index (LAI) and fraction of photosynthetically active radiation (fPAR). The MOD16 algorithm was forced with meteorological measurements but the results did not improve as expected, suggesting a low sensitivity to meteorological inputs. Our results indicated when a water layer was present over the soil surface without vegetation (LAI around zero), the largest differences between EC measurements and MOD16 ET were found. In this period, the expected domain of soil evaporation was not observed in MOD16 ET physical processes partition, indicating the algorithm was not able to detect areas with high soil moisture. In general, the MOD16 ET product presented low accuracy when compared against experimental measurements over flooded rice paddy, suggesting more studies are necessary, in order to reduce uncertainties associated to the land cover conditions.

Description: Industry is constantly seeking ways to avoid corrective maintenance so as to reduce costs. Performing regular scheduled maintenance can help to mitigate this problem, but not necessarily in the most efficient way. In the context of condition-based maintenance, the main contributions of this work were to propose a methodology to treat and transform the collected data from a vibration system that simulated a motor and to build a dataset to train and test an Artificial Neural Network capable of predicting the future condition of the equipment, pointing out when a failure can happen. To achieve this goal, a device model was built to simulate typical motor vibrations, consisting of a computer cooler fan and several magnets. Measurements were made using an accelerometer, and the data were collected and processed to produce a structured dataset. The neural network training with this dataset converged quickly and stably, while the tests performed, k-fold cross-validation and model generalization, presented excellent performance. The same tests were performed with other machine learning techniques, to demonstrate the effectiveness of neural networks mainly in their generalizability. The results of the work confirm that it is possible to use neural networks to perform predictive tasks in relation to the conditions of industrial equipment. This is an important area of study that helps to support the growth of smart industries.

Description: Recent advances in semantic web and deep learning technologies enable new means for the computational analysis of vast amounts of information from the field of digital humanities. We discuss how some of the techniques can be used to identify historical and cultural symmetries between different characters, locations, events or venues, and how these can be harnessed to develop new strategies to promote intercultural and cross-border aspects that support the teaching and learning of history and heritage. The strategies have been put to the test in the context of the European project CrossCult, revealing enormous potential to encourage curiosity to discover new information and increase retention of learned information.

Description: It is well recognized that exposure to fine particulate matter (PM2.5) affects health adversely, yet few studies from South America have documented such associations due to the sparsity of PM2.5 measurements. Lima’s topography and aging vehicular fleet results in severe air pollution with limited amounts of monitors to effectively quantify PM2.5 levels for epidemiologic studies. We developed an advanced machine learning model to estimate daily PM2.5 concentrations at a 1 km2 spatial resolution in Lima, Peru from 2010 to 2016. We combined aerosol optical depth (AOD), meteorological fields from the European Centre for Medium-Range Weather Forecasts (ECMWF), parameters from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), and land use variables to fit a random forest model against ground measurements from 16 monitoring stations. Overall cross-validation R2 (and root mean square prediction error, RMSE) for the random forest model was 0.70 (5.97 μg/m3). Mean PM2.5 for ground measurements was 24.7 μg/m3 while mean estimated PM2.5 was 24.9 μg/m3 in the cross-validation dataset. The mean difference between ground and predicted measurements was −0.09 μg/m3 (Std.Dev. = 5.97 μg/m3), with 94.5% of observations falling within 2 standard deviations of the difference indicating good agreement between ground measurements and predicted estimates. Surface downwards solar radiation, temperature, relative humidity, and AOD were the most important predictors, while percent urbanization, albedo, and cloud fraction were the least important predictors. Comparison of monthly mean measurements between ground and predicted PM2.5 shows good precision and accuracy from our model. Furthermore, mean annual maps of PM2.5 show consistent lower concentrations in the coast and higher concentrations in the mountains, resulting from prevailing coastal winds blown from the Pacific Ocean in the west. Our model allows for construction of long-term historical daily PM2.5 measurements at 1 km2 spatial resolution to support future epidemiological studies.