Polar Lipids Reduce In Vitro Duodenal Lipolysis Rate of Oat Oil and Liquid Oat Base Products

Alternative ways for increased appetite control are today widely sought for due to the growing global health issues connected to obesity. In in vivo studies, oat has been proven an attractive candidate for inducing satiety. Oat is rich in polar lipids, of which the galactolipids are especially interesting, and a hypothesis is that these lipids play an important role for the ileal brake mechanism. In this study, the aim is to investigate the role of polar oat lipids on pancreatic lipolysis rate, using a pH-stat based in vitro digestion model of the duodenum. Lipolysis of oat oil, a mix of oat oil/rapeseed oil (RSO), as well as a liquid oat base (OB) simulating an oat drink with different polar lipid content are investigated, and compared with RSO as control. Increasing the polar lipid content of the product digested leads to a significantly decreased lipolysis rate, and this effect is even observed when mixing RSO with a low amount of oat oil (10%). The results support the hypothesis that polar lipids can delay lipolysis also in a complex, natural system like the liquid OB, and even a minor amount of oat lipids can have large effect on lipolysis rates. Practical applications: The number of studies connecting galactolipids with a decreasing effect on duodenal lipolysis is growing; however, the mechanism behind this phenomenon is still not clarified. Here, the same effect is seen in a complex, natural food system. These findings open up for interesting future food products, where inclusion of oat oil, even at low concentrations, can have a prolonging effect on satiety. Oat for human consumption is an increasing market, thanks to the positive health benefits oat has been connected to, in combination with the current trend toward climate-friendly plant-based options for meat and dairy products. It is believed that oat oil can be attractive as an ingredient in various food products, for example, protein bars and spreads. More studies are needed to confirm the results in vivo. However, a great potential is seen for the use of oat oil to enhance appetite control.     

Photoinitiated synthesis of poly(poly(ethylene glycol) methacrylate-co-diethyl amino ethyl methacrylate) superabsorbent hydrogels for dye adsorption

Random copolymers of poly(ethylene glycol) methacrylate (PEGM), and diethyl amino ethyl methacrylate (DEAEM) were synthesized at low, and high conversions by photoinitiation. Crosslinked poly(PEGM-co-DEAEM) samples were obtained, and characterized by FTIR, SEM, DSC, TGA, and elemental analyses. Swelling behavior of the copolymers revealed that the copolymers acted as superabsorbent hydrogels. The monomer reactivity ratios were calculated using Fineman Ross, Extended Kelen Tüdøs , and Mayo Lewis methods that gave r₁(PEGM) = 0.90, r₂(DEAEM) = 0.14 at low conversions. At high conversions r₁ and r₂ values were calculated as 1.01 and 0.40, respectively. Adsorption isotherms of methyl orange (MO) onto hydrogels were studied using Langmuir, Freundlich, and Temkin models. The experimental data fitted well with the Langmuir equation. The maximum adsorption capacity for MO was 212.7 mg g⁻¹ at pH = 3. The adsorption data gave best fit with the pseudo-second order kinetic model. Thermodynamic evaluation showed spontaneous nature for MO adsorption onto poly(PEGM-co-DEAEM) hydrogels. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 47707.     

Note-against-note two-voice counterpoint by means of fuzzy logic

In this study, we propose a fuzzy logic based approach for the ‘harmonization with constraints’ problem in music. After the mathematical modeling of the harmonization problem, the solution is carried out by means of proper fuzzy membership functions depending on the rules imposed by the music theory. To demonstrate the applicability of the proposed technique, particular problems of note-against-note two-voice counterpoint are considered. The method is flexible, adaptable and simple in terms of implementation. Moreover, from the constraint satisfaction perspective, the solutions generated by the method satisfy ‘arc-consistency’; which could not have been achieved by majority of the previous studies existing in the literature. The method also provides a gateway for the arranger/composer to incorporate his/her own stylistic preferences to the solution by simply adjusting the shapes of the membership functions. Additional features (such as providing variability in the final solutions at different executions) increase the power of the method in terms of creativity. This approach can be extended for the solution of more complicated problems in music such as orchestration, improvisation, and even composition.     

Competitive Randomized Nonlinear Prediction Under Additive Noise

We consider sequential nonlinear prediction of a bounded, real-valued and deterministic signal from its noise-corrupted past samples in a competitive algorithm framework. We introduce a randomized algorithm based on context-trees . The introduced algorithm asymptotically achieves the performance of the best piecewise affine model that can both select the best partition of the past observations space (from a doubly exponential number of possible partitions) and the affine model parameters based on the desired clean signal in hindsight. Although the performance measure including the loss function is defined with respect to the noise-free clean signal, the clean signal, its past samples or prediction errors are not available for training or constructing predictions. We demonstrate the performance of the introduced algorithm when applied to certain chaotic signals.      

Evaluation of chitosan tripolyphosphate gel beads as bioadsorbents for iron in aqueous solution and in human blood in vitro

Nonimprinted and Fe³⁺ imprinted chitosan tripolyphosphate gel beads were prepared via physical gel formation. A method based on in situ crosslinking using ethylene glycol diglycidyl ether was developed to imprint the chitosan tripolyphosphate gels with Fe³⁺ ion without deteriorating the gel beads. The beads were characterized by FTIR, SEM, XRD, and DSC with respect to the chemical structure, surface morphology, crystallinity, and thermal behavior. Swelling kinetics and Fe³⁺ ion adsorption behavior from aqueous solution were studied. The Fe³⁺ imprinted and in situ crosslinked beads proved to be durable and effective adsorbents for Fe³⁺ in solution. The bead prepared by in situ crosslinking and in the presence of 10 mM template ion had an equilibrium iron adsorption capacity of 53.9 mg/g after 3-hour contact with 5 mM Fe³⁺ solution. The pros and cons of the beads as biomedical iron adsorbents were tested by evaluating their serum iron removal capacities from human blood. The preliminary tests carried out showed that Fe³⁺ imprinted beads were more effective in decreasing serum iron in human blood when compared to the nonimprinted beads. The decrease in serum iron level accompanied a parallel decrease in the hemoglobin level. The calcium level was also affected upon contact with the beads. The Fe³⁺ imprinted beads were less effective than the nonimprinted ones in decreasing the calcium level indicating selectivity towards iron containing species. © 2012 Wiley Periodicals, Inc. J Appl Polym Sci, 2012     

Pitch angle control in wind turbines above the rated wind speed by multi-layer perceptron and radial basis function neural networks

In wind energy conversion systems, one of the operational problems is the changeability and discontinuity of wind. In most cases, wind speed can fluctuate rapidly. Hence, quality of produced energy becomes an important problem in wind energy conversion plants. Several control techniques have been applied to improve the quality of power generated from wind turbines. Pitch control is the most efficient and popular power control method, especially for variable-speed wind turbines. It is a useful method for power regulation above the rated wind speed. This paper proposes an artificial neural network-based pitch angle controller for wind turbines. In the simulations, a variable-speed wind turbine is modeled, and its operation is observed by using two types of artificial neural network controllers. These are multi-layer perceptrons with back propagation learning algorithm and radial basis function network. It is shown that the power output was successfully regulated during high wind speed, and as a result overloading or outage of the wind turbine was prevented.     

Silhouette-based 3-D model reconstruction from multiple images

The goal of this study is to investigate the reconstruction of three-dimensional (3-D) graphical models of real objects in a controlled imaging environment and present the work done in our group based on silhouette-based reconstruction. Although many parts of the whole system have been well-known in the literature and in practice, the main contribution of the paper is that it describes a complete, end-to-end system explained in detail. Based on a multi-image calibration method, an algorithm to extract the rotation axis of a turn-table has been developed. Furthermore, this can be extended to estimate robustly the initial bounding volume of the object to be modeled. The disadvantages of the silhouette-based reconstruction can be removed by an algorithm using photoconsistency. This algorithm has a simpler visibility check, and it eliminates the selection of threshold existing in similar algorithms. Besides, in order to construct the appearance, we use the concept of particles. The reconstruction results are shown both on real world and synthetic objects.     

Planar shape representation and matching under projective transformation

This paper introduces a new representation for planar objects which is invariant to projective transformation. Proposed representation relies on a new shape basis which we refer to as the conic basis. The conic basis takes conic-section coefficients as its dimensions and represents the object as a convex combination of conic-sections. Pairs of conic-sections in this new basis and their projective invariants provides the proposed view invariant representation. We hypothesize that two projectively transformed versions of an object result in the same representation. We show that our hypothesis provides promising recognition performance when we use the nearest neighbor rule to match projectively deformed objects.     

Neural computing models for prediction of permeability coefficient of coarse-grained soils

Correlations are very significant from the earliest days; in some cases, it is essential as it is difficult to measure the amount directly, and in other cases it is desirable to ascertain the results with other tests through correlations. Soft computing techniques are now being used as alternate statistical tool, and new techniques such as artificial neural networks, fuzzy inference systems, genetic algorithms, and their hybrids were employed for developing the predictive models to estimate the needed parameters, in the recent years. Determination of permeability coefficient (k) of soils is very important for the definition of hydraulic conductivity and is difficult, expensive, time-consuming, and involves destructive tests. In this paper, use of some soft computing techniques such as ANNs (MLP, RBF, etc.) and ANFIS (adaptive neuro-fuzzy inference system) for prediction of permeability of coarse-grained soils was described and compared. As a result of this paper, it was obtained that the all constructed soft computing models exhibited high performance for predicting k. In order to predict the permeability coefficient, ANN models having three inputs, one output were applied successfully and exhibited reliable predictions. However, all four different algorithms of ANN have almost the same prediction capability, and accuracy of MLP was relatively higher than RBF models. The ANFIS model for prediction of permeability coefficient revealed the most reliable prediction when compared with the ANN models, and the use of soft computing techniques will provide new approaches and methodologies in prediction of some parameters in soil mechanics.     

Exploring the Belief Systems of Software Development Professionals

It is commonly accepted that an individual's beliefs and actions are based on his or her assessment and perceptions of the world. In order to determine what practices an individual is likely to follow at any given time, it is necessary to understand the individual's behavioral intention in a given circumstance. From an Information Technology perspective, a software development professional's belief systems are potentially the basis for the adoption and implementation of new and innovative work practices and processes. In this article, we explore the belief systems of software development professionals in order to understand the beliefs underlying intention and practice, and we seek answers about how they adopt or reject new and innovative software development processes and practices. The results point out a strong influence of past experiences, personality types, and repeated behavior on current software development processes and practices in industrial settings.