Solvent fractionation of hard palm stearin to increase the concentration of tripalmitoylglycerol and dipalmitoyl-stearoyl-glycerol as substrates for synthesis of human milk fat substitute

This research aimed to produce triacylglycerols fraction rich in palmitic acid (PA) at the sn-2 position from hard palm stearin (HPS) through acetone fractionation. Fractionations were done at HPS-to-acetone ratios of 1:3, 1:5 and 1:7 (w/v) and at temperatures of 25, 30 and 35 °C for 3 h. Triacylglycerols with PA at the sn-2 position, such as tripalmitoylglycerol (PPP) and dipalmitoyl-stearoyl-glycerol (PPS), were concentrated in the solid fraction, obtained after separation by filtration. The optimal fractionation conditions were at a HPS-to-acetone ratio of 1:5 (w/v) and 30 °C. The total PPP and PPS content was more than 91%, with a corresponding yield of more than 59%. Triacylglycerols obtained were composed of more than 91% PA in which 88% were at the sn-2 position. Conclusively, the solvent fractionation is a suited alternative method for the preparation of triacylglycerols used for the enzymatic synthesis of 1,3-dioleoyl-2-palmitoylglycerol which is the main triacylglycerol for human milk fat substitute.     

Multichromic polymers based on pyrene clicked thienylpyrrole

In this study, the effect of pendant pyrene on the optical and electronic properties of poly(2,5‐dithienylpyrrole)s was studied. For this purpose a new pyrene coupled 2,5‐dithienylpyrrole derivative (SNS‐pyrene) was synthesized through click reaction. SNS‐pyrene was electrochemically polymerized and its electrochemical and optical properties were investigated by electrochemical and optical techniques. The polymer had a band gap of 3.36 eV and displayed light green to blue color variation upon oxidation in less than 2.48 s. Additionally, electrochemical copolymerization of SNS‐pyrene with 3,4‐ethylenedioxythiophene was achieved whilst a detailed investigation was performed on the effect of electrochemical polymerization conditions on the optoelectronic properties of the copolymers. Studies revealed that the copolymers exhibit multichromic reversible redox behavior with lower band gaps and shorter switching times than their parent polymer, P(SNS‐pyrene) © 2014 Society of Chemical Industry.     

Cellular neural network with trapezoidal activation function

This paper presents a cellular neural network (CNN) scheme employing a new non‐linear activation function, called trapezoidal activation function (TAF). The new CNN structure can classify linearly non‐separable data points and realize Boolean operations (including eXclusive OR) by using only a single‐layer CNN. In order to simplify the stability analysis, a feedback matrix W is defined as a function of the feedback template A and 2D equations are converted to 1D equations. The stability conditions of CNN with TAF are investigated and a sufficient condition for the existence of a unique equilibrium and global asymptotic stability is derived. By processing several examples of synthetic images, the analytically derived stability condition is also confirmed. Copyright © 2005 John Wiley & Sons, Ltd.     

Enhancing electrochromic properties of polypyrrole by silsesquioxane nanocages

A new monomer, octa(thiophenephenyl)silsesquioxane (OThiophenePS) was synthesized via click chemistry. The chemical structure of OThiophenePS was characterized by nuclear magnetic resonance (¹H NMR) and Fourier transform infrared (FT-IR) spectroscopies. Electrochemical polymerization of pyrrole with OThiophenePS was performed resulting in polypyrrole-attached, polyhedral oligomeric silsesquioxane (OPS–PPy). The spectroelectrochemical studies show that the electrochromic properties of (OPS–PPy) are superior to those of polypyrrole (PPy). This great improvement can be attributed to the more accessible doping sites and the facile ion movement during the redox switching brought by the loose packing of the PPy chains.     

Global structure constrained local shape prior estimation for medical image segmentation

Organ shape plays an important role in clinical diagnosis, surgical planning and treatment evaluation. Shape modeling is a critical factor affecting the performance of deformable model based segmentation methods for organ shape extraction. In most existing works, shape modeling is completed in the original shape space, with the presence of outliers. In addition, the specificity of the patient was not taken into account. This paper proposes a novel target-oriented shape prior model to deal with these two problems in a unified framework. The proposed method measures the intrinsic similarity between the target shape and the training shapes on an embedded manifold by manifold learning techniques. With this approach, shapes in the training set can be selected according to their intrinsic similarity to the target image. With more accurate shape guidance, an optimized search is performed by a deformable model to minimize an energy functional for image segmentation, which is efficiently achieved by using dynamic programming. Our method has been validated on 2D prostate localization and 3D prostate segmentation in MRI scans. Compared to other existing methods, our proposed method exhibits better performance in both studies.     

Symbol Detection Based on Back Tracking Search Algorithm in MIMO-NOMA Systems

One of the most important methods used to cope with multipath fading effects, which cause the symbol to be received incorrectly in wireless communication systems, is the use of multiple transceiver antenna structures. By combining the multi-input multi-output (MIMO) antenna structure with non-orthogonal multiple access (NOMA), which is a new multiplexing method, the fading effects of the channels are not only reduced but also high data rate transmission is ensured. However, when the maximum likelihood (ML) algorithm that has high performance on coherent detection, is used as a symbol detector in MIMO NOMA systems, the computational complexity of the system increases due to higher-order constellations and antenna sizes. As a result, the implementation of this algorithm will be impractical. In this study, the backtracking search algorithm (BSA) is proposed to reduce the computational complexity of the symbol detection and have a good bit error performance for MIMO-NOMA systems. To emphasize the efficiency of the proposed algorithm, simulations have been made for the system with various antenna sizes. As can be seen from the obtained results, a considerable reduction in complexity has occurred using BSA compared to the ML algorithm, also the bit error performance of the system is increased compared to other algorithms.     

Deformation of Mesoporous Titania Nanostructures in Contact with D2O Vapor

For many applications, mesoporous titania nanostructures are exposed to water or need to be backfilled via infiltration with an aqueous solution, which can cause deformations of the nanostructure by capillary forces. In this work, the degree of deformation caused by water infiltration in two types of mesoporous, nanostructured titania films exposed to water vapor is compared. The different types of nanostructured titania films are prepared via a polymer template assisted sol–gel synthesis in conjunction with a polymer‐template removal at high‐temperatures under ambient conditions versus nitrogen atmosphere. Information about surface and inner morphology is extracted by scanning electron microscopy and grazing incidence small‐angle neutron scattering (GISANS) measurements, respectively. Furthermore, complementary information on thin film composition and porosity are probed via X‐ray reflectivity. The backfilling induced deformation of near surface structures and structures inside the mesoporous titania films is determined by GISANS before and after D₂O infiltration. The respective atmosphere used for template removal influences the details of the titania nanostructure and strongly impacts the degree of water induced deformation. Drying of the films shows reversibility of the deformation.     

TORC: test plan optimization by requirements clustering

Acceptance testing is a time-consuming task for complex software systems that have to fulfill a large number of requirements. To reduce this effort, we have developed a widely automated method for deriving test plans from requirements that are expressed in natural language. It consists of three stages: annotation, clustering, and test plan specification. The general idea is to exploit redundancies and implicit relationships in requirements specifications. Multi-viewpoint techniques based on RM-ODP (Reference Model for Open Distributed Processing) are employed for specifying the requirements. We then use linguistic analysis techniques, requirements clustering algorithms, and pattern-based requirements collection to reduce the total effort of testing against the requirements specification. In particular, we use linguistic analysis for extracting and annotating the actor, process and object of a requirements statement. During clustering, a similarity function is computed as a measure for the overlap of requirements. In the test plan specification stage, our approach provides capabilities for semi-automatically deriving test plans and acceptance criteria from the clustered informal textual requirements. Two patterns are applied to compute a suitable order of test activities. The generated test plans consist of a sequence of test steps and asserts that are executed or checked in the given order. We also present the supporting prototype tool TORC, which is available open source. For the evaluation of the approach, we have conducted a case study in the field of acceptance testing of a national electronic identification system. In summary, we report on lessons learned how linguistic analysis and clustering techniques can help testers in understanding the relations between requirements and for improving test planning.     

Interface feature prioritization for web services: Case of online flight reservations

Acceptance, utility, and usability of system designs have become a focal interest in human–computer interaction (HCI) research, yet at present there is a lack detailed understandings of which system design features influence them. The purpose of the study is to examine the effects of five product design features; customization, adaptive behavior, memory load, content density, and speed on user preference through an experimental study by using conjoint analysis. In experimental study, instead of classical conjoint cards, prototypes were generated for products. Besides, desirability and market segments of product prototypes were identified. In line with the results, among the five product design features, speed is the most and customization is the least important features that affect user preference. Contrary to the expectations, customization has a relatively small importance value in this research. Subsequent design features that influence user preference after speed are minimal memory load, adaptive behavior, and content density, respectively. According to findings, interfaces that have high-speed, minimal memory load, adaptive behavior, low content density, and customization features are more preferable than those that do not.     

Simultaneously learning actions and goals from demonstration

Our research aim is to develop interactions and algorithms for learning from naïve human teachers through demonstration. We introduce a novel approach to leverage the goal-oriented nature of human teachers by learning an action model and a goal model simultaneously from the same set of demonstrations. We use robot motion data to learn an action model for executing the skill. We use a generic set of perceptual features to learn a goal model and use it to monitor the executed action model. We evaluate our approach with data from 8 naïve teachers demonstrating two skills to the robot. We show that the goal models in the perceptual feature space are consistent across users and correctly recognize demonstrations in cross-validation tests. We additionally observe that a subset of users were not able to teach a successful action model whereas all of them were able to teach a mostly successful goal model. When the learned action models are executed on the robot, the success was on average 66.25 %. Whereas the goal models were on average 90 % correct at deciding on success/failure of the executed action, which we call monitoring.