Formulation of apple juice beverages containing whey protein isolate or whey protein hydrolysate based on sensory and physicochemical analysis

Whey protein isolate (WPI) or its bioactive hydrolysate (WPH) was mixed with apple juice along with sweetener, obtaining a series of beverages with various pH values. Sedimentation of WPI‐apple juice and WPH‐apple juice beverages was inhibited at pH values of 3.15 and 3.47, respectively. The higher the whey protein content, the more undesirable was the taste of samples. A clearer appearance with smaller particle size was obtained with WPH‐apple juice formulations compared to WPI‐apple juice formulations at pH values closer to the pI of the whey proteins. Intrinsic viscosity measurements revealed the weaker associations of peptides compared with protein molecules.     

Application of surrogate artificial intelligent models for real‐time flood routing

Developing a robust flood forecasting and warning system (FFWS) is essential in flood‐prone areas. Hydrodynamic models, which are a major part of such systems, usually suffer from computational instabilities and long runtime problems, which are particularly important in real‐time applications. In this study, two artificial intelligence models, namely artificial neural network (ANN) and adaptive neuro‐fuzzy inference system (ANFIS), were used for flood routing in an FFWS in Madarsoo river basin, Iran. For this purpose, different rainfall patterns were transformed to run‐off hydrographs using the Hydrologic Engineering Center (HEC)‐1 hydrological model and routed along the river using HEC river analysis system RAS hydrodynamic model. Then, the simulated hydrographs with different lag times were used as inputs for training of ANN and ANFIS models to simulate flood hydrograph at the basin outlet. Results showed that the simulations obtained from ANN and ANFIS coincided with the results simulated by the HEC‐RAS, and application of such models is strongly suggested as a backup tool for flood routing in FFWSs.     

Bidding strategies in dynamic electricity markets

In this paper the problem of developing bidding strategies for the participants of dynamic oligopolistic electricity markets is studied. Attention is given to strategic bidding of load serving entities (LSE) in these markets. We model oligopolistic electricity markets as non-linear dynamical systems and use discrete-time Nash bidding strategies. We assume a Cournot model for our game, where the LSEs decide on demand quantities and the market price is the marginal cost of producing electricity.Attention is given to a problem, where the objective functions are quadratic in the deviations of trajectories from desired trajectories and quadratic in the control deviations from the nominal controls. It is assumed that each power marketer can estimate his/her competitors' benefit function coefficients.The optimal bidding strategies are developed mathematically using dynamic game theory. We deal with games that are non-linear in the state equations. We linearize these equations for complex non-linear oligopolistic electricity multi-markets and use discrete-time Nash strategies. We show that the actual dynamic excursions from the operating point where we linearize are small so that the linearization is valid. The developed algorithm is applied to an IEEE 14-bus power system. We show that the LSEs' expected profits are higher for our method than those for other methods in the literature (F. Wen, A.K. David, Optimal bidding strategies and modeling of imperfect information among competitive generators. IEEE Transactions on Power Systems, Vol. 16, No. 1, pp.15–21, Feb. 2001.     

Improved Two-Dimensional Millimeter-Wave Imaging for Concealed Weapon Detection Through Partial Fourier Sampling

Active millimeter-wave imaging based on synthetic aperture focusing offers certain unique and practical advantages in nondestructive testing applications. Traditionally, the imaging for this purpose is performed through a long procedure of raster scanning with a single antenna across a two-dimensional grid, leading to a slow, bulky, and expensive scanning platform. In this paper, an improved bistatic structure based on radial compressive sensing is proposed, where one fixed transmitter antenna and a linear array of receiving antennas are used. The main contributions of this paper are (a) reducing the scanning time, (b) improving the output quality, and (c) designing an inexpensive setup. These improvements are the result of the underlying proposed simpler scanning structure and faster reconstruction process.     

Application of Codes Performance‐Based Design Procedures for a Tall Building Using Recorded and Spectrally Modified Ground Motions

There is a growing tendency toward the performance‐based design of tall buildings, where any assessment using response history analysis requires a set of ground motion (GM) records. This paper considering a tall building as a case study investigates how judgment on the seismic safety of the structure is affected by the use of recorded or spectrally matched GMs. Three model structures are developed: (a) using conventional design procedure of Chapter 12 of ASCE 7‐16; (b) adopting linear analysis requirements of Chapter 16 of ASCE 7‐16; (c) designing for service‐level design earthquake of Los Angles Tall Building Structural Design Council (LATBSDC) procedure. It is shown that all of the structures give acceptable performance when subjected to simulated GMs, although this is not the case for amplitude‐scaled GMs based on ASCE 7‐16 and LATBSDC. Finally, to have an objective assessment of performance, independent of GM types, incremental dynamic analysis is employed to derive fragility and mean annual rate of exceeding (MAR). Results show that for anticipated drifts at Maximum Considered Earthquake (MCE) level, the structures provide acceptable MAR at the fundamental period. However, for the higher modes including the second and third periods, MAR values become acceptable only at drifts as large as 0.085.     

An attempt to cast light into starch nanocrystals preparation and cross-linking

Potato starch was hydrolyzed with 2.2 or 3.7 M hydrochloric acid in order to obtain the nanocrystals which afterwards were chemically cross-linked with sodium hexametaphosphate. The stronger acidity resulted in smaller nanocrystals with mean size of 48 nm in a shorter time. X-ray diffraction confirmed the dominant crystalline nature of particles and Fourier transform infrared spectroscopy suggested the presence of lower number of free hydroxyl groups in nanocrystals after cross-linking. Starch nanocrystals showed two distinctive differential scaning colorimetry endotherms at 26 and 125 °C, attributed to destruction of nanocrystals lattice and moblizing of each nanocrystal’s structure, respectively. Cross-linking resulted in a tenacious spatial arrangement of nanocrystals, strengthening the crystals lattice against phase transitions induced by heating. Scanning electron microscopy images confirmed the particle size measured for nanocrystals by light scattering. Atomic force microscopy topographic images suggested that starch nanocrystals were originated from small amylopectin blocklets in granular assembly of starch.     

An artificial neural network for predicting the physiochemical properties of fish oil microcapsules obtained by spray drying

The aim of this work was to develop an artificial neural network (ANN) to predict the physiochemical properties of fish oil microcapsules obtained by spray drying method. The relation amongst inlet-drying air temperature, outlet-drying air temperature, aspirator rate, peristaltic pump rate, and spraying air flow rate with 5 performance indices, namely capsules’ residual moisture content, particle size, bulk density, encapsulation efficiency, and peroxide value was bridged by using ANN. A multilayer perceptron ANN was developed to predict the performance indices based on the input variables. The optimal ANN model was found to be a 5-10-5 structure with tangent sigmoid transfer function, Levenberg-Marquardt error minimization algorithm, and 1,000 training epochs. This optimal network was capable to predict the outputs with R² values higher than 0.87. It was concluded that ANN is a useful tool to investigate, approximate, and predict the encapsulation characteristics of fish oil.