Production of FAME from acid oil,a by-product of vegetable oil refining

Simple alkyl FA esters have numerous uses, including serving as biodiesel, a fuel for compression ignition (diesel) engines. The use of acid-catalyzed esterification for the synthesis of FAME from acid oil, a by-product of edible vegetable oil refining that is produced from soapstock, was investigated. Soybean acid oil contained 59.3 wt% FFA, 28.0 wt% TAG, 4.4 wt% DAG, and less than 1% MAG. Maximum esterification occurred at 65°C and 26 h reaction at a molar ratio of total FA/methanol/sulfuric acid of 1∶15∶1.5. Residual unreacted species under these conditions, as a fraction of their content in unesterified acid oil, were FFA, 6.6%; TAG, 5.8%; and DAG, 2.6%. This corresponds to estimated concentrations of FFA, 3.2%; TAG, 1.3%; and DAG, 0.2%, on a mass basis, in the ester product. In an alternative approach, the acylglycerol species in soapstock were saponified prior to acidulation. High-acid (HA) acid oil made from this saponified soapstock had an FFA content of 96.2 wt% and no detectable TAG, DAG, or MAG. Optimal esterification conditions for HA acid oil at 65°C were a mole ratio of FFA/methanol/acid of 1∶1.8∶0.17, and 14 h incubation. FAME recovery under these conditions was 89% of theoretical, and the residual unesterified FFA content was approximately 20 mg/g. This was reduced to 3.5 mg/g, below the maximum FFA level allowed for biodiesel, by washing with NaCl, NaHCO₃, and Ca(OH)₂ solutions. Alternatively, by subjecting the unwashed ester layer to a second esterification, the FFA level was reduced to less than 2 mg/g. The acid value of this material exceeded the maximum allowed for biodiesel, but was reduced to an acceptable value by a brief wash with 0.5 N NaOH.     

On the Relation of Job Insecurity,Job Autonomy,Innovative Work Behaviour and the Mediating Effect of Work Engagement

European policy is focusing on innovation as a way out of the economic crisis. At the same time, job insecurity is rising as Europe is still in crisis. In this paper, we examine whether job insecurity affects the innovative work behaviour of employees by focusing on the relation between job insecurity, job autonomy, work engagement and innovative work behaviour (IWB). Using employee level survey data, we use structural equation modelling to disentangle the relations between these variables. The partially mediated model shows the best fit with the data. This model shows that job insecurity and autonomy are both directly and indirectly, through work engagement, related with IWB. For autonomy these relations are positive, while they are negative (and smaller) for job insecurity. Moreover, a negative covariance is observed between job insecurity and autonomy.     

Classification of semantic relations between nominals

The NLP community has shown a renewed interest in deeper semantic analyses, among them automatic recognition of semantic relations in text. We present the development and evaluation of a semantic analysis task: automatic recognition of relations between pairs of nominals in a sentence. The task was part of SemEval-2007, the fourth edition of the semantic evaluation event previously known as SensEval. Apart from the observations we have made, the long-lasting effect of this task may be a framework for comparing approaches to the task. We introduce the problem of recognizing relations between nominals, and in particular the process of drafting and refining the definitions of the semantic relations. We show how we created the training and test data, list and briefly describe the 15 participating systems, discuss the results, and conclude with the lessons learned in the course of this exercise.     

Iris segmentation in non-ideal images using graph cuts

A non-ideal iris image segmentation approach based on graph cuts is presented that uses both the appearance and eye geometry information. A texture measure based on gradients is computed to discriminate between eyelash and non-eyelash regions, combined with image intensity differences between the iris, pupil, and the background (region surrounding the iris) are utilized as cues for segmentation. The texture and intensity distributions for the various regions are learned from histogramming and explicit sampling of the pixels estimated to belong to the corresponding regions. The image is modeled as a Markov Random Field and the energy minimization is achieved via graph cuts to assign each image pixel one of the four possible labels: iris, pupil, background, and eyelash. Furthermore, the iris region is modeled as an ellipse, and the best fitting ellipse to the initial pixel based iris segmentation is computed to further refine the segmented region. As a result, the iris region mask and the parameterized iris shape form the outputs of the proposed approach that allow subsequent iris recognition steps to be performed for the segmented irises. The algorithm is unsupervised and can deal with non-ideality in the iris images due to out-of-plane rotation of the eye, iris occlusion by the eyelids and the eyelashes, multi-modal iris grayscale intensity distribution, and various illumination effects. The proposed segmentation approach is tested on several publicly available non-ideal near infra red (NIR) iris image databases. We compare both the segmentation error and the resulting recognition error with several leading techniques, demonstrating significantly improved results with the proposed technique.     

A fast and noise resilient cluster-based anomaly detection

Clustering, while systematically applied in anomaly detection, has a direct impact on the accuracy of the detection methods. Existing cluster-based anomaly detection methods are mainly based on spherical shape clustering. In this paper, we focus on arbitrary shape clustering methods to increase the accuracy of the anomaly detection. However, since the main drawback of arbitrary shape clustering is its high memory complexity, we propose to summarize clusters first. For this, we design an algorithm, called Summarization based on Gaussian Mixture Model (SGMM), to summarize clusters and represent them as Gaussian Mixture Models (GMMs). After GMMs are constructed, incoming new samples are presented to the GMMs, and their membership values are calculated, based on which the new samples are labeled as “normal” or “anomaly.” Additionally, to address the issue of noise in the data, instead of labeling samples individually, they are clustered first, and then each cluster is labeled collectively. For this, we present a new approach, called Collective Probabilistic Anomaly Detection (CPAD), in which, the distance of the incoming new samples and the existing SGMMs is calculated, and then the new cluster is labeled the same as of the closest cluster. To measure the distance of two GMM-based clusters, we propose a modified version of the Kullback–Libner measure. We run several experiments to evaluate the performances of the proposed SGMM and CPAD methods and compare them against some of the well-known algorithms including ABACUS, local outlier factor (LOF), and one-class support vector machine (SVM). The performance of SGMM is compared with ABACUS using Dunn and DB metrics, and the results indicate that the SGMM performs superior in terms of summarizing clusters. Moreover, the proposed CPAD method is compared with the LOF and one-class SVM considering the performance criteria of (a) false alarm rate, (b) detection rate, and (c) memory efficiency. The experimental results show that the CPAD method is noise resilient, memory efficient, and its accuracy is higher than the other methods.     

A local linearized least squares algorithm for training feedforwardneural networks

In training the weights of a feedforward neural network, it is well known that the global extended Kalman filter (GEKF) algorithm has much better performance than the popular gradient descent with error backpropagation in terms of convergence and quality of solution. However, the GEKF is very computationally intensive, which has led to the development of efficient algorithms such as the multiple extended Kalman algorithm (MEKA) and the decoupled extended Kalman filter algorithm (DEKF), that are based on dimensional reduction and/or partitioning of the global problem. In this paper we present a new training algorithm, called local linearized least squares (LLLS), that is based on viewing the local system identification subproblems at the neuron level as recursive linearized least squares problems. The objective function of the least squares problems for each neuron is the sum of the squares of the linearized backpropagated error signals. The new algorithm is shown to give better convergence results for three benchmark problems in comparison to MEKA, and in comparison to DEKF for highly coupled applications. The performance of the LLLS algorithm approaches that of the GEKF algorithm in the experiments.     

A symbolic summation approach to Feynman integral calculus

Given a Feynman parameter integral, depending on a single discrete variable N$N$ and a real parameter ε$\epsilon$, we discuss a new algorithmic framework to compute the first coefficients of its Laurent series expansion in ε$\epsilon$. In a first step, the integrals are expressed by hypergeometric multi-sums by means of symbolic transformations. Given this sum format, we develop new summation tools to extract the first coefficients of its series expansion whenever they are expressible in terms of indefinite nested product–sum expressions. In particular, we enhance the known multi-sum algorithms to derive recurrences for sums with complicated boundary conditions, and we present new algorithms to find formal Laurent series solutions of a given recurrence relation.     

A framework for heading-guided recognition of human activity

A combined 2D, 3D approach is presented that allows for robust tracking of moving people and recognition of actions. It is assumed that the system observes multiple moving objects via a single, uncalibrated video camera. Low-level features are often insufficient for detection, segmentation, and tracking of non-rigid moving objects. Therefore, an improved mechanism is proposed that integrates low-level (image processing), mid-level (recursive 3D trajectory estimation), and high-level (action recognition) processes. A novel extended Kalman filter formulation is used in estimating the relative 3D motion trajectories up to a scale factor. The recursive estimation process provides a prediction and error measure that is exploited in higher-level stages of action recognition. Conversely, higher-level mechanisms provide feedback that allows the system to reliably segment and maintain the tracking of moving objects before, during, and after occlusion. Heading-guided recognition (HGR) is proposed as an efficient method for adaptive classification of activity. The HGR approach is demonstrated using “motion history images” that are then recognized via a mixture-of-Gaussians classifier. The system is tested in recognizing various dynamic human outdoor activities: running, walking, roller blading, and cycling. In addition, experiments with real and synthetic data sets are used to evaluate stability of the trajectory estimator with respect to noise.     

Two-point higher-order BVPs with singularities in phase variables

The existence of solutions for singular higher-order differential equations with the Lidstone or the (n,p) boundary conditions is proved. The right-hand sides of differential equations can have singularities in the zero value of their phase variables and so higher derivatives of solutions changing their signs can pass through these singularities. Proofs are based on the method of a priori estimates, the degree theory arguments and on Vitali's convergence theorem.