Proposal of visual strength grading rules for Uruguayan pine timber

A previous database with bending and density data of 261 structural size specimens of currently produced Uruguayan slash pine and loblolly pine timber was adjusted to meet testing and characterization requirements of European standards. Visual parameters and mechanical properties for each specimen were evaluated and their relationship was statistically analyzed. Results suggested that all specimens can be grouped in one visual grade with engineered properties similar to those of European C14 strength class. Modulus of elasticity and characteristic bending strength were the defining properties for class assignation.     

Desiccant Cooling Cycle Tuning for Variable Environmental Conditions

The performance of desiccant cooling systems has been increasingly addressed, with applications spanning from thermal comfort to gas-turbine air cooling. Desiccant systems are particularly suitable regarding the environmental impact, due to the absence of refrigerants with ozone-depleting properties. Moreover, the use of low-grade waste heat as the primary energy source also characterizes a low global warming potential, when compared to vapor compression systems. Under this scenario, this study demonstrates how desiccant ventilation cycles can be tuned for environmental conditions while maintaining the conditioned space within acceptable thermal comfort conditions. The analysis is based on a simple numerical procedure for desiccant cooling simulation in which the overall system operation is calculated from individual cycle components’ characteristics. With the employed methodology, the conditioned space state is calculated for different environmental conditions and compared to a standard, previously set, comfort zone. The results show that, in addition to desiccant wheel performance, the effectiveness of evaporative coolers and the regenerator is of prime importance for achieving acceptable thermal comfort conditions.     

Ozone as an Alternative for Disinfection of Explants during In Vitro Mass Plant Propagation

Ozone was used as an alternative for replacing sodium hypochlorite in the disinfection of yam nodal segments during the establishment stage for in vitro mass plant propagation. The purpose of this work was to evaluate the effect of ozone on disinfection of yam nodal segments in gaseous and aqueous phase in comparison to sodium hypochlorite. The immersion of yam nodal segments in water containing dissolved ozone with an ozone concentration of 1.0, 1.5 and 2.0 ppm during for 5 to 10 min gave the best results; 100% of yam nodal segments were free of microbial contamination. In terms of sprouting initiation (7.5 days), sprouting percentage (88%), and sprouts growth (4.81 cm), all were improved in comparison to the conventionally used sodium hypochlorite.     

Thiophene‐ and silarylene‐containing polyesters. Resonance effect on conductivity after polarization in an external electric field

Polyesters were synthesized by direct polycondensation of thiophene‐2,5‐dicarboxylic acid and five different silarylene‐containing diphenols using a tosyl chloride/pyridine/N,N‐dimethylformamide system as a condensing agent. Polymers were obtained in good yields and were characterized using Fourier transform infrared and NMR (¹H, ¹³C, 135‐DEPT and ²⁹Si) spectroscopy and elemental analysis. All polymers were completely soluble in aprotic organic polar solvents such as dimethylformamide, dimethylsulfoxide and N‐methyl‐2‐pyrrolidone. The range of effective mass of the polymers (m/z) was 1 × 10⁵–2 × 10⁵, determined using electrospray ionization mass spectrometry. Asymmetry and steric hindrance prevented dense packing of the polymeric chains, showing glass transition temperatures between ? 78 and ? 51 °C and loss of thermal stability at 177–199 °C (10% weight loss). Additionally, the melting points of the polyesters were found to be in the range 62–67 °C. Because of this, the samples were semi‐solid at room temperature. The optical band gaps of the polymers were observed between 4.54 and 4.48 eV, corresponding in all cases to insulator behavior. The molecular structure of the samples was studied using X‐ray diffraction, showing a degree of order that was associated with two monoclinic lattices. Additionally, the conductivity was studied using a two‐point method with contacts on top of polymer films. Prior to the electrical measurement, the samples were polarized in an external electric field of 0.8 to 6.4 V cm^?1, and the alignment of the dipoles increased the electrical conductivity. Copyright © 2012 Society of Chemical Industry     

Hierarchical feature selection based on relative dependency for gear fault diagnosis

Feature selection is an important aspect under study in machine learning based diagnosis, that aims to remove irrelevant features for reaching good performance in the diagnostic systems. The behaviour of diagnostic models could be sensitive with regard to the amount of features, and significant features can represent the problem better than the entire set. Consequently, algorithms to identify these features are valuable contributions. This work deals with the feature selection problem through attribute clustering. The proposed algorithm is inspired by existing approaches, where the relative dependency between attributes is used to calculate dissimilarity values. The centroids of the created clusters are selected as representative attributes. The selection algorithm uses a random process for proposing centroid candidates, in this way, the inherent exploration in random search is included. A hierarchical procedure is proposed for implementing this algorithm. In each level of the hierarchy, the entire set of available attributes is split in disjoint sets and the selection process is applied on each subset. Once the significant attributes are proposed for each subset, a new set of available attributes is created and the selection process runs again in the next level. The hierarchical implementation aims to refine the search space in each level on a reduced set of selected attributes, while the computational time-consumption is improved also. The approach is tested with real data collected from a test bed, results show that the diagnosis precision by using a Random Forest based classifier is over 98 % with only 12 % of the attributes from the available set.     

Every team deserves a second chance: an extended study on predicting team performance

Voting among different agents is a powerful tool in problem solving, and it has been widely applied to improve the performance in finding the correct answer to complex problems. We present a novel benefit of voting, that has not been observed before: we can use the voting patterns to assess the performance of a team and predict their final outcome. This prediction can be executed at any moment during problem-solving and it is completely domain independent. Hence, it can be used to identify when a team is failing, allowing an operator to take remedial procedures (such as changing team members, the voting rule, or increasing the allocation of resources). We present three main theoretical results: (1) we show a theoretical explanation of why our prediction method works; (2) contrary to what would be expected based on a simpler explanation using classical voting models, we show that we can make accurate predictions irrespective of the strength (i.e., performance) of the teams, and that in fact, the prediction can work better for diverse teams composed of different agents than uniform teams made of copies of the best agent; (3) we show that the quality of our prediction increases with the size of the action space. We perform extensive experimentation in two different domains: Computer Go and Ensemble Learning. In Computer Go, we obtain high quality predictions about the final outcome of games. We analyze the prediction accuracy for three different teams with different levels of diversity and strength, and show that the prediction works significantly better for a diverse team. Additionally, we show that our method still works well when trained with games against one adversary, but tested with games against another, showing the generality of the learned functions. Moreover, we evaluate four different board sizes, and experimentally confirm better predictions in larger board sizes. We analyze in detail the learned prediction functions, and how they change according to each team and action space size. In order to show that our method is domain independent, we also present results in Ensemble Learning, where we make online predictions about the performance of a team of classifiers, while they are voting to classify sets of items. We study a set of classical classification algorithms from machine learning, in a data-set of hand-written digits, and we are able to make high-quality predictions about the final performance of two different teams. Since our approach is domain independent, it can be easily applied to a variety of other domains.     

An overview of the nutritional value of beef and lamb meat from South America

The southern region of South America, a subtropical and temperate zone, is an important region for the production of beef and lamb meat, which is mainly produced in extensive pasture-based systems. Because of its content in highly valuable nutrients such as iron, zinc, selenium, fatty acids, and vitamins, meat is a unique and necessary food for the human diet in order to secure a long and healthy life, without nutritional deficiencies. Beef and lamb production systems based on temperate or tropical grasslands show interesting and, in some cases, a differential content in minerals, fatty acids and vitamins. This review deals with the distinctive aspects of the nutritional quality of beef and lamb meat produced in this region in terms of nutritional composition and the bioavailability of key nutrients related to its contribution for a healthy diet for all ages.     

Modeling energy consumption for master–slave applications

With energy costs now accounting for nearly 30 % of a datacenter’s operating expenses, energy consumption has become an important issue when designing and executing a parallel algorithm. This paper analyzes the energy consumption of MPI applications following the master–slave paradigm. The analytical model is derived for this paradigm and is validated over a master–slave matrix-multiplication. This analytical model is parameterized through architectural and algorithmic parameters, and it is capable of predicting the energy consumption for a given instance of the problem over a given architecture. We use an external, metered, power distribution unit that allows to easily measure the power consumption of computing nodes without the needing of dedicated hardware.