Parameter-Dependent Lyapunov Functions for Linear Systems With Constant Uncertainties

Robust stability of linear time-invariant systems with respect to structured uncertainties is considered. The small gain condition is sufficient to prove robust stability and scalings are typically used to reduce the conservatism of this condition. It is known that if the small gain condition is satisfied with constant scalings then there is a single quadratic Lyapunov function which proves robust stability with respect to all allowable time-varying perturbations. In this technical note we show that if the small gain condition is satisfied with frequency-varying scalings then an explicit parameter dependent Lyapunov function can be constructed to prove robust stability with respect to constant uncertainties. This Lyapunov function has a rational quadratic dependence on the uncertainties.     

Analysis of autocatalytic networks in biology

Autocatalytic networks, in particular the glycolytic pathway, constitute an important part of the cell metabolism. Changes in the concentration of metabolites and catalyzing enzymes during the lifetime of the cell can lead to perturbations from its nominal operating condition. We investigate the effects of such perturbations on stability properties, e.g., the extent of regions of attraction, of a particular family of autocatalytic network models. Numerical experiments demonstrate that systems that are robust with respect to perturbations in the parameter space have an easily “verifiable” (in terms of proof complexity) region of attraction properties. Motivated by the computational complexity of optimization-based formulations, we take a compositional approach and exploit a natural decomposition of the system, induced by the underlying biological structure, into a feedback interconnection of two input–output subsystems: a small subsystem with complicating nonlinearities and a large subsystem with simple dynamics. This decomposition simplifies the analysis of large pathways by assembling region of attraction certificates based on the input–output properties of the subsystems. It enables numerical as well as analytical construction of block-diagonal Lyapunov functions for a large family of autocatalytic pathways.     

A New Mobile Robot Toolbox for Matlab

In this study, a wheeled mobile robot navigation toolbox for Matlab is presented. The toolbox includes algorithms for 3D map design, static and dynamic path planning, point stabilization, localization, gap detection and collision avoidance. One can use the toolbox as a test platform for developing custom mobile robot navigation algorithms. The toolbox allows users to insert/remove obstacles to/from the robot’s workspace, upload/save a customized map and configure simulation parameters such as robot size, virtual sensor position, Kalman filter parameters for localization, speed controller and collision avoidance settings. It is possible to simulate data from a virtual laser imaging detection and ranging (LIDAR) sensor providing a map of the mobile robot’s immediate surroundings. Differential drive forward kinematic equations and extended Kalman filter (EKF) based localization scheme is used to determine where the robot will be located at each simulation step. The LIDAR data and the navigation process are visualized on the developed virtual reality interface. During the navigation of the robot, gap detection, dynamic path planning, collision avoidance and point stabilization procedures are implemented. Simulation results prove the efficacy of the algorithms implemented in the toolbox.     

TPS3: A privacy preserving data collection protocol for smart grids

Smart grid (SG) allows for two-way communication between the utility and its consumers and hence they are considered as an inevitable future of the traditional grid. Since consumers are the key component of SGs, providing security and privacy to their personal data is a critical problem. In this paper, a security protocol, namely TPS3, is based on Temporal Perturbation and Shamir’s Secret Sharing (SSS) schemes that are proposed to ensure the privacy of SG consumer’s data. Temporal perturbation is employed to provide temporal privacy, while the SSS scheme is used to ensure data confidentiality. Temporal perturbation adds random delays to the data collected by smart meters, whereas the SSS scheme fragments these data before transmitting them to the data collection server. Joint employment of both schemes makes it hard for attackers to obtain consumer data collected in the SG. The proposed protocol TPS3 is evaluated in terms of privacy, reliability, and communication cost using two different SG topologies. The performance evaluation results clearly show that the TPS3 protocol ensures the privacy and reliability of consumer data in SGs. The results also show that the tradeoff between the communication cost and security of TPS3 is negligible.     

Raman Spectroscopic Barcode Use for Differentiation of Vegetable Oils and Determination of Their Major Fatty Acid Composition

In this study, differentiation of vegetable oils and determination of their major fatty acid (FA) composition were performed using Raman spectral barcoding approach. Samples from seven different sources (sunflower, corn, olive, canola, mustard, soybean and palm) were analyzed using Raman spectroscopy. Second derivative of the spectral data was utilized to generate unique barcodes of oils. Chemometric analyses, namely, principal component analysis (PCA) and partial least square (PLS) methods were used for data analysis. PCA was applied for classification of the samples according to the differences in their levels arising from their barcode data. A successful differentiation based on second derivative barcodes of Raman spectra (2D‐BRS) of vegetable oils was obtained. In addition, PLS method was applied on 2D‐BRS in order to determine the major FA composition of these samples. Coefficient of determination values for palmitic, stearic, oleic, linoleic, α‐linolenic, cis‐11 eicosenoic, erucic and nervonic acids were in the range of 0.970–0.989. Limit of detection and limit of quantification values were found to be satisfactory (0.09–8.09 and 0.30–26.95 % in oil) for these fatty acids . Advantages of both chemometric analysis and spectral barcoding approach have been utilized in the present study. Taking the second derivative of the Raman spectra has minimized background variability and sensitivity to intensity fluctuations. Spectral conversion to the barcodes has further increased the quality of information obtained from Raman spectra and also made it possible to improve the visualization of the data. Converting Raman spectra of oils into barcodes enables simpler presentation of the valuable information, and still allows further analysis such as classification of vegetable oils and prediction of their major fatty acids with high accuracy.     

Bioactive components and antioxidant capacities of different miniature tomato cultivars grown by altered fertilizer applications

This study investigated the organic acid and phenolic compound levels, total phenolic (TP) and antioxidant capacity (TEAC) of three miniature tomato cultivars grown on a farmer’s field with three different fertilizer applications. Analysis of phenolic compound (protocatechuic, vanillic, gallic, chlorogenic, caffeic, syringic, p-coumaric and ferulic acid, rutin, quercetin, catechin and phloridzine) organic acid (citric, tartaric, malic, succinic and fumaric acid), TP and TEAC levels in fruit samples showed statistically significant (p?<?0.05) differences between tomato cultivars and fertilizer applications. Rutin and chlorogenic acid were the predominant phenolic compounds found in all three cultivars. The highest rutin value (50.48 mg kg^?1 FW) was found in the Black Zebra fertilized with DAP plus organic and biochemical fertilizers and the lowest (20.52 mg kg^?1 FW) in the Black Zebra fertilized with DAP only. The highest chlorogenic acid value (63.31 mg kg^?1 FW) was found in the sweet pea currant fertilized with DAP only and the lowest (21.06 mg kg^?1 FW) in the Black Zebra fertilized with DAP plus chemical fertilizer. Citric acid content was the dominant organic acid in all three cultivars, with the highest citric acid value (6439.50 mg kg^?1 FW) found in the Sweet Pea Currant cherry tomato fertilized with DAP plus organic and biochemical fertilizers and the lowest (2435.20 mg kg^?1 FW) in the Window Box Yellow fertilized with DAP plus chemical fertilizer. Total phenolic and antioxidant levels in the window box yellow were significantly lower as compared to the Black Zebra and sweet pea currant varieties for all three fertilizer applications.     

Obtaining Butter Oil Triacylglycerols Free from β-Carotene and α-Tocopherol via Activated Carbon Adsorption and Alumina-Column Chromatography Treatments

It is difficult to remove β-carotene from oils with alumina-column chromatography, because β-carotene is even less-polar than triacylglycerols (TAGs) are. The objective of this study was to obtain butter oil TAGs free from β-carotene and antioxidants via sequential treatments with activated carbon (AC) adsorption and alumina column chromatography. The AC used was prepared from waste apricots. The effects of AC dosages, temperatures and time courses on β-carotene adsorption were studied. The Langmuir and Freundlich isotherms were used to describe the adsorption of β-carotene onto AC, and it was found to be more consistent with the Freundlich isotherm with a higher R ² value (0.9784). Adsorption kinetics of β-carotene was analyzed by pseudo-first order and pseudo-second order models. The pseudo-second order model was found to explain the kinetics of β-carotene adsorption more effectively (R ² = 0.9882). The highest β-carotene reduction was achieved (from 31.9 to 1.84 mg/kg) at an AC dosage of 10 wt%, temperature of 50 °C, and adsorption time of 240 min. A considerable amount of α-tocopherol was also adsorbed during the AC treatment. Remaining portions of α-tocopherol were completely removed with alumina adsorption chromatography. The method described may be used for purification of vegetable oil TAGs, which will be used as model compounds in model oxidation studies.     

Flexible poly(styrene-ethylene-butadiene-styrene) hybrid nanofibers for bioengineering and water filtration applications

Among the thermoplastic elastomers that play important roles in the polymer industry due to their superior properties, styrene-based species and polyurethane block copolymers are of great interest. Poly(styrene-ethylene-butadiene-styrene) (SEBS) as a triblock copolymer seems to have the potential to meet many demands in different applications due to various industrial requirements where durability, biocompatibility, breaking elongation, and interfacial adhesion are important. In this study, the SEBS triblock copolymer was functionalized with natural (Satureja hortensis, SH) and synthetic (nanopowder, TiO₂) agents to obtain composite nanofibers by electrospinning and electrospraying methods for use in biomedical and water filtration applications. The results were compared with thermoplastic polyurethane (TPU) composite nanofibers, which are commonly used in these fields. Here, functionalized SEBS nanofibers exhibited antibacterial effect while at the same time improving cell viability. In addition, because of successful water filtration by using the SEBS composite nanofibers, the material may have a good potential to be used comparably to TPU for the application.     

Stochastic resonance in binary composite hypothesis-testing problems in the Neyman–Pearson framework

Performance of some suboptimal detectors can be enhanced by adding independent noise to their inputs via the stochastic resonance (SR) effect. In this paper, the effects of SR are studied for binary composite hypothesis-testing problems. A Neyman–Pearson framework is considered, and the maximization of detection performance under a constraint on the maximum probability of false-alarm is studied. The detection performance is quantified in terms of the sum, the minimum, and the maximum of the detection probabilities corresponding to possible parameter values under the alternative hypothesis. Sufficient conditions under which detection performance can or cannot be improved are derived for each case. Also, statistical characterization of optimal additive noise is provided, and the resulting false-alarm probabilities and bounds on detection performance are investigated. In addition, optimization theoretic approaches to obtaining the probability distribution of optimal additive noise are discussed. Finally, a detection example is presented to investigate the theoretical results.