Agricultural use of three organic residues: effect on orange production and on properties of a soil of the ‘Comarca Costa de Huelva’ (SW Spain)

Disposal of urban, agricultural and industrial organic residues impliesan increasing problem because of all the economic and environmentalrepercussions involved. One of the most adequate ways of managing this problemis the agricultural use of these wastes as organic amendments. Three organicresidues (AC, olive mill waste water sludge compost; MWC, municipal solid wastecompost; and PS, paper mill sludge) were used in a 3-year field experimentinvolving orange production. The effect of their application on crop productionand on soil quality was investigated. Soil samples (0–20 cm depth)collected 11 months after the last soil amendment were analysed for: pH and EC,Kjeldahl-N, available-P, available-K, total organic carbon, humic substances,dehydrogenase, phosphatase, -glucosidase, urease andbenzoyl-argininamidehydrolysing protease (BAA-protease) activities. Generally, the application of the MWC and PSincreased orange yield when compared to control. Moreover, total organic carbonand humic substances significantly increased in soils treated with all theorganic amendments. Organic fertilisation increased the Kjeldahl-N andavailable-P contents of the soil. The application of the organic residues also causedsignificant increases in dehydrogenase, -glucosidase, urease andBAA-protease activities of the soil. Significant positive correlations (p <0.01) between these enzymatic activities and total organic carbon were foundforall treatments. Significant positive correlation between dehydrogenase, urease,-glucosidase, and BAA-protease and orange yield was also found. However,a clear inhibition of phosphatase activity was observed in soils treated withPS. The results indicate that the repeated application to the soil of moderateamounts of organic amendments has positive effects on the chemical andbiochemical properties of the soil, as well as on the orange yield.     

A Fluorometric Enzymatic Assay for Quantification of Steryl Glucosides in Biodiesel

Steryl glucosides (SG) are common contaminants in biodiesel that form precipitates, which form and cause problems due to fouling during transport and storage. Therefore, their quantification is necessary to assess the quality of this fuel. The methods currently available for SG analysis require expensive instrumentation, need a previous concentration step by solid‐phase extraction (SPE) or are of limited use for the quantitative assessment. We developed an enzymatic method for SG quantification in biodiesel samples based on the hydrolysis of the glucoside catalyzed by a broadly specific beta glucosidase and the subsequent determination of the glucose released by the reaction. The method is non‐expensive, sensitive and was adapted to 96‐well format fluorescence plate reader, making it useful for the parallel assay of multiple samples. The enzymatic assay presented here represent a valuable tool for both quality control and the development of improved biodiesel production and purification procedures.     

An approach to reduce energy consumption and performance losses on heterogeneous servers using power capping

Journal of Scheduling - Rapid growth of demand for remote computational power, along with high energy costs and infrastructure limits, has led to treating power usage as a primary constraint in...     

A geostatistical algorithm to reproduce lateral gradual facies transitions: Description and implementation

Valid representations of geological heterogeneity are fundamental inputs for quantitative models used in managing subsurface activities. Consequently, the simulation of realistic facies distributions is a significant aim. Realistic facies distributions are typically obtained by pixel-based, object-based or process-based methods. This work presents a pixel-based geostatistical algorithm suitable for reproducing lateral gradual facies transitions (LGFT) between two adjacent sedimentary bodies. Lateral contact (i.e. interfingering) between distinct depositional facies is a widespread geometric relationship that occurs at different scales in any depositional system. The algorithm is based on the truncation of the sum of a linear expectation trend and a random Gaussian field, and can be conditioned to well data. The implementation introduced herein also includes subroutines to clean and geometrically characterize the obtained LGFT. The cleaned sedimentary body transition provides a more appropriate and realistic facies distribution for some depositional settings. The geometric measures of the LGFT yield an intuitive measure of the morphology of the sedimentary body boundary, which can be compared to analogue data. An example of a LGFT obtained by the algorithm presented herein is also flow simulated, quantitatively demonstrating the importance of realistically reproducing them in subsurface models, if further flow-related accurate predictions are to be made.     

Scenario-based testing from UML/OCL behavioral models

We present in this article a way to produce test suites applied to the POSIX mini-challenge based on a behavioral model of a file system manager written in UML/OCL. We illustrate the limitations of a fully automated test generation approach, which justifies the use of test scenarios as a complement to a functional testing approach. Scenarios are expressed through regular expressions describing sequences of operations, possibly punctuated by intermediate states that have to be reached by the execution of the model. Scenarios are unfolded into extended sequences of operations that are played on the model using symbolic animation techniques. We experimented our approach by testing the conformance of two different file systems w.r.t. the POSIX standard: a recent Linux distribution and a customized Java implementation of POSIX used to evaluate the relevance of our approach and its complementarity with a structural test generation approach.     

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.     

An efficient deconvolution algorithm for estimating oxygen consumption during muscle activities

The reconstruction of an unknown input function from noisy measurements in a biological system is an ill-posed inverse problem. Any computational algorithm for its solution must use some kind of regularization technique to neutralize the disastrous effects of amplified noise components on the computed solution. In this paper, following a hierarchical Bayesian statistical inversion approach, we seek estimates for the input function and regularization parameter (hyperparameter) that maximize the posterior probability density function. We solve the maximization problem simultaneously for all unknowns, hyperparameter included, by a suitably chosen quasi-Newton method. The optimization approach is compared to the sampling-based Bayesian approach. We demonstrate the efficiency and robustness of the deconvolution algorithm by applying it to reconstructing the time courses of mitochondrial oxygen consumption during muscle state transitions (e.g., from resting state to contraction and recovery), from the simulated noisy output of oxygen concentration dynamics on the muscle surface. The model of oxygen transport and metabolism in skeletal muscle assumes an in vitro cylindrical structure of the muscle in which the oxygen from the surrounding oxygenated solution diffuses into the muscle and is then consumed by the muscle mitochondria. The algorithm can be applied to other deconvolution problems by suitably replacing the forward model of the system.     

Performance assessment through bootstrap

A new performance evaluation paradigm for computer vision systems is proposed. In real situation, the complexity of the input data and/or of the computational procedure can make traditional error propagation methods infeasible. The new approach exploits a resampling technique recently introduced in statistics, the bootstrap. Distributions for the output variables are obtained by perturbing the nuisance properties of the input, i.e., properties with no relevance for the output under ideal conditions. From these bootstrap distributions, the confidence in the adequacy of the assumptions embedded into the computational procedure for the given input is derived. As an example, the new paradigm is applied to the task of edge detection. The performance of several edge detection methods is compared both for synthetic data and real images. The confidence in the output can be used to obtain an edgemap independent of the gradient magnitude     

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.