Textural,Rheological and Sensory Properties and Oxidative Stability of Nut Spreads—A Review

Tree nuts are rich in macro and micronutrients, phytochemicals, tocopherols and phenolic compounds. The development of nut spreads would potentially increase the food uses of nuts and introduce consumers with a healthier, non-animal breakfast snack food. Nut spreads are spreadable products made from nuts that are ground into paste. Roasting and milling (particle size reduction) are two important stages for the production of nut spreads that affected the textural, rheological characteristic and overall quality of the nut spread. Textural, color, and flavor properties of nut spreads play a major role in consumer appeal, buying decisions and eventual consumption. Stability of nut spreads is influenced by its particle size. Proper combination of ingredients (nut paste, sweetener, vegetable oil and protein sources) is also required to ensure a stable nut spread product is produced. Most of the nut spreads behaved like a non-Newtonian pseudo-plastic fluid under yield stress which help the producers how to start pumping and stirring of the nut spreads. Similar to other high oil content products, nut spreads are susceptible to autoxidation. Their oxidation can be controlled by application of antioxidants, using processing techniques that minimize tocopherol and other natural antioxidant losses.     

Multi-reservoir Operation Rules: Multi-swarm PSO-based Optimization Approach

Reservoir operation rules are intended to help an operator so that water releases and storage capacities are in the best interests of the system objectives. In multi-reservoir systems, a large number of feasible operation policies may exist. System engineering and optimization techniques can assist in identifying the most desirable of those feasible operation policies. This paper presents and tests a set of operation rules for a multi-reservoir system, employing a multi-swarm version of particle swarm optimization (MSPSO) in connection with the well-known HEC-ResPRM simulation model in a parameterization–simulation–optimization (parameterization SO) approach. To improve the performance of the standard particle swarm optimization algorithm, this paper incorporates a new strategic mechanism called multi-swarm into the algorithm. Parameters of the rule are estimated by employing a parameterization–simulation–optimization approach, in which a full-scale simulation model evaluates the objective function value for each trial set of parameter values proposed with an efficient version of the particle swarm optimization algorithm. The usefulness of the MSPSO in developing reservoir operation policies is examined by using the existing three-reservoir system of Mica, Libby, and Grand Coulee as part of the Columbia River Basin development. Results of the rule-based reservoir operation are compared with those of HEC-ResPRM. It is shown that the real-time operation of the three reservoir system with the proposed approach may significantly outperform the common implicit stochastic optimization approach.     

A smart multi-user massive MIMO system for next G Wireless communications using evolutionary optimized antenna selection

Recently multiserver queues with setup times have been extensively studied because they have applications in power-saving data centers. A challenging model is the M/M/c/Setup queue where a server is turned off when it is idle and is turned on if there are some waiting jobs. Recently, Gandhi et al. (in: Proceedings of the ACM SIGMETRICS, pp. 153–166, ACM, 2013; Queueing Syst. 77(2):177–209, 2014) obtain the generating function of the number of jobs in the system, as well as the Laplace transform of the response time using the recursive renewal reward approach and the distributional Little’s law (Keilson and Servi in Oper Res Lett 7(5):223– 227, 1988). In this paper, we derive exact solutions for the joint stationary queue length distribution of the same model using two alternative methodologies: generating function approach and matrix analytic method. The generating function approach yields exact closed form expressions for the joint stationary queue length distribution and the conditional decomposition formula. On the other hand, the matrix analytic approach leads to an exact recursive algorithm to calculate the joint stationary distribution and performance measures so as to provide some application insights.     

Ligand‐Free Synthesis of Aluminum‐Doped Zinc Oxide Nanocrystals and their Use as Optical Spacers in Color‐Tuned Highly Efficient Organic Solar Cells

The color of polymer solar cells using an opaque electrode is given by the reflected light, which depends on the composition and thickness of each layer of the device. Metal‐oxide‐based optical spacers are intensively studied in polymer solar cells aiming to optimize the light absorption. However, the low conductivity of materials such as ZnO and TiO₂ limits the thickness of such optical spacers to tenths of nanometers. A novel synthesis route of cluster‐free Al‐doped ZnO (AZO) nanocrystals (NCs) is presented for solution processing of highly conductive layers without the need of temperature annealing, including thick optical spacers on top of polymer blends. The processing of 80 nm thick optical spacers based on AZO nanocrystal solutions on top of 200 nm thick polymer blend layer is demonstrated leading to improved photocurrent density of 17% compared to solar cells using standard active layers of 90 nm in combination with thin ZnO‐based optical spacers. These AZO NCs also open new opportunities for the processing of high‐efficiency color tuned solar cells. For the first time, it is shown that applying solution‐processed thick optical spacer with polymer blends of different thicknesses can process solar cells of similar efficiency over 7% but of different colors.     

An integrated supervised learning solution for monitoring process mean vector

When an out-of-control condition is detected by a control chart, a search begins to identify and eliminate the source(s) of the signal. Identification of the time when a process first changed is an important step in root cause analysis which helps a process engineer to eliminate the source(s) of assignable cause effectively. The time when a change takes place in the process is referred to as the change point. In multivariate environment, since there is more than one variable involved, then root cause analysis is relatively harder compared to the case of univariate because it is not clear exactly which variable has contributed to the out-of-control condition and in what direction its mean has shifted. Hence, a procedure that identifies the change point, performs diagnostic analysis, and specifies the direction of the shift in the mean of the contributing variable(s) all simultaneously could help to conduct root cause analysis effectively. Although different multivariate methods exist in the literature that allow to either estimate change point in the process mean vector or identify the contributing variables leading to the out-of-control condition, but in this research, an integrated supervised learning solution is proposed, which helps to (1) detect of an out-of-control condition, (2) identify the change point leading to shift in the mean vector, (3) specify the variable(s) contributing to the out-of-condition, and (4) identify the direction of the shift in the mean of each contributing variable simultaneously. A real case study is used to evaluate and compare the performance of the proposed integrated approach to existing methods in the literature.     

Simultaneous polarographic determination of isoniazid and rifampicin by differential pulse polarography method and support vector regression

A differential pulse polarography (DPP) for the simultaneous determination of isoniazid and rifampicin was proposed. Under optimum experimental conditions (pH = 7, scan rate = 10 mV/s, pulse amplitude = −50 mV), serious overlapping polarographic peaks were observed in the mixture of these compounds. In this study, support vector regression (SVR) was applied to modeling the overlapped polarograms. Furthermore, a comparison was made between the performance of SVR and partial least square (PLS) on data set. The experimental calibration matrix was designed with 30 mixtures of these compounds. Calibration graphs were linear in the range of 6 × 10⁻⁸-10⁻⁴ and 10⁻⁷-10⁻⁴ M for isoniazid and rifampicin, respectively. The results demonstrated that SVR is a good well-performing alternative for the analysis and modeling of DPP data than the commonly applied PLS technique.     

Structure,texture and surface acidity studies of a series of mixed zinc–aluminum (60–90 molar % Al) phosphate catalysts

A series of mixed zinc–aluminum phosphate (ZnAlP) catalysts containing 40–90 aluminum molar % were synthesized by a coprecipitation method and characterized by nitrogen adsorption–desorption, X‐ray diffraction, FTIR spectroscopy, thermogravimetric analysis (TGA), differential thermal analysis (DTA), diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) and temperature programmed desorption (TPD) of ammonia. The presence of aluminum greatly affected the surface properties of Zn₃(PO₄)₂ by delaying the crystallization process of Zn₃(PO₄)₂. All amorphous samples were shown to be mesoporous and they contained two types of aluminum surface hydroxyl groups and one type of phosphorus hydroxyl group, as shown by DRIFT spectra. The specific surface area and the acidity of ZnAlP increased on increasing the aluminum content. On the other hand, a great difference in the texture and the concentration of surface acid sites was found by changing the precipitating agent and calcination temperature. Thus these factors also play an important role in the final properties of these catalysts. © 2001 Society of Chemical Industry