Antioxidant activity of ginger extract in sunflower oil

The antioxidant activity of dichloromethane extract from ginger was evaluated during 6 months of storage of refined sunflower oil at 25 and 45 °C. Free fatty acid (FFA) content, peroxide value (POV) and iodine value (IV) were used as criteria to assess ginger extract as an antioxidant. After 6 months of storage at 45 °C, sunflower oil containing 1600 and 2400 ppm ginger extract showed lower FFA contents (0.083 and 0.080%) and POVs (24.5 and 24.0 meq kg^?1) than the control sample (FFA contents 0.380%, POV 198.0 meq kg^?1). Sunflower oil containing 200 ppm butylated hydroxyanisole (BHA) and butylated hydroxytoluene (BHT) showed FFA contents of 0.089 and 0.072% and POVs of 26.5 and 24.7 meq kg^?1 respectively after 6 months of storage at 45 °C. Similarly, after 6 months of storage at 45 °C, IVs of sunflower oil containing 1600 and 2400 ppm ginger extract were 80 and 92 respectively, higher than that of the control sample (53). However, IVs of sunflower oil treated with 200 ppm BHA and BHT were 94 and 96 respectively after 6 months of storage at 45 °C. These results illustrate that ginger extract at various concentrations exhibited very strong antioxidant activity, almost equal to that of synthetic antioxidants (BHA and BHT). Ginger extract also showed good thermal stability and exhibited 85.2% inhibition of peroxidation of linoleic acid when heated at 185 °C for 120 min. Therefore the use of ginger extract in foods is recommended as a natural antioxidant to suppress lipid oxidation. © 2003 Society of Chemical Industry     

Effect of fortification on physico-chemical and microbiological stability of whole wheat flour

Stability of fortified whole wheat flour (WWF) was evaluated using NaFeEDTA, elemental iron, ZnSO₄ and ZnO as fortificants. Fortified WWF was stored in tin boxes and polypropylene bags for 60 days under ambient storage condition (ASC) and controlled storage condition (CSC). Fortification significantly (p ? 0.05) decreased moisture and protein content and increased ash content to 5.44%, 6% and 23%, as compared to control. Fortified WWF, assayed periodically for mould contamination manifested a significant inhibition (∼1 log reduction) in flours containing elemental iron. Low storage temperature and relative humidity (RH) indicated lower level of mould count during extended storage time. Tin boxes, as storage material, exhibited a better protection against mould attack, acting as an effective barrier for moisture. Fortificants exerted a slight deteriorative effect on texture characteristics of the chapattis made of these flours but chapattis were still accepted by the judges. Zinc fortificants seemed like having little or no effect on the quality of the flours and chapattis, made of such flours. Shelf life of fortified flour may be extended by using elemental iron as fortificant and storing the product in tin boxes under relatively low temperature and RH.     

新疆1949—2019年洪水灾害调查与特征分析

洪水灾害是新疆最严重的自然灾害,调查并研究洪水灾害的特征与规律对防洪减灾有重要意义。本文通过调查新疆洪水灾害相关的年鉴、公报、简报及文献资料等,统计分析了新疆1949-2019年近71年主要洪水灾害,给出了洪水灾害的时间及地域分布规律、灾害类型特征。对洪水灾害造成的直接经济损失及占国民生产总值的比例进行了统计分析,并重点对2006年以后的受灾人口与死亡人口、直接经济损失进行了分析。结果表明,虽然洪水频次与洪灾直接经济损失有增加趋势,但因灾死亡人口及直接经济损失所占GDP比重不断减少,并趋于平稳,防洪减灾效益明显。本文研究为新疆洪水灾害风险评估、防御应对与决策支持提供了借鉴参考。     

Estimation of global solar radiation using artificial neural networks

This paper introduces a neural network technique for the estimation of global solar radiation. There are 41 radiation data collection stations spread all over the kingdom of Saudi Arabia where the radiation data and sunshine duration information are being collected since 1971. The available data from 31 locations is used for training the neural networks and the data from the other 10 locations is used for testing. The testing data was not used in the modeling to give an indication of the performance of the system in unknown locations. Results indicate the viability of this approach for spatial modeling of solar radiation.     

Solar radiation over Saudi Arabia and comparisons with empirical models

We present a comparison between models developed by the present authors and 16 other models for different geographical and varied meteorological conditions. The comparisons are made using the mean bias error (MBE), root mean square error (RMSE), mean percentage error (MPE), and mean absolute bias error (MABE). These errors are calculated using monthly-mean, measured daily and estimated values of total solar radiation for 41 locations in Saudi Arabia. We find that our latitude, longitude, altitude, and sunshine-duration-dependent model given in Eq. (1)produced the best estimates for global solar radiation. The second- and third-best estimates were obtained from our linear model and other models given in Eq. (2)and Eq. (11), respectively.     

A survey on resource allocation in high performance distributed computing systems

An efficient resource allocation is a fundamental requirement in high performance computing (HPC) systems. Many projects are dedicated to large-scale distributed computing systems that have designed and developed resource allocation mechanisms with a variety of architectures and services. In our study, through analysis, a comprehensive survey for describing resource allocation in various HPCs is reported. The aim of the work is to aggregate under a joint framework, the existing solutions for HPC to provide a thorough analysis and characteristics of the resource management and allocation strategies. Resource allocation mechanisms and strategies play a vital role towards the performance improvement of all the HPCs classifications. Therefore, a comprehensive discussion of widely used resource allocation strategies deployed in HPC environment is required, which is one of the motivations of this survey. Moreover, we have classified the HPC systems into three broad categories, namely: (a) cluster, (b) grid, and (c) cloud systems and define the characteristics of each class by extracting sets of common attributes. All of the aforementioned systems are cataloged into pure software and hybrid/hardware solutions. The system classification is used to identify approaches followed by the implementation of existing resource allocation strategies that are widely presented in the literature.     

Evolving Recurrent Neural Network using Cartesian Genetic Programming to Predict The Trend in Foreign Currency Exchange Rates

Forecasting the foreign exchange rate is an uphill task. Numerous methods have been used over the years to develop an efficient and reliable network for forecasting the foreign exchange rate. This study utilizes recurrent neural networks (RNNs) for forecasting the foreign currency exchange rates. Cartesian genetic programming (CGP) is used for evolving the artificial neural network (ANN) to produce the prediction model. RNNs that are evolved through CGP have shown great promise in time series forecasting. The proposed approach utilizes the trends present in the historical data for its training purpose. Thirteen different currencies along with the trade-weighted index (TWI) and special drawing rights (SDR) is used for the performance analysis of recurrent Cartesian genetic programming-based artificial neural networks (RCGPANN) in comparison with various other prediction models proposed to date. The experimental results show that RCGPANN is not only capable of obtaining an accurate but also a computationally efficient prediction model for the foreign currency exchange rates. The results demonstrated a prediction accuracy of 98.872 percent (using 6 neurons only) for a single-day prediction in advance and, on average, 92% for predicting a 1000 days’ exchange rate in advance based on ten days of data history. The results prove RCGPANN to be the ultimate choice for any time series data prediction, and its capabilities can be explored in a range of other fields.     

Uncertainty modeling and robust minimax LQR control of multivariable nonlinear systems with application to hypersonic flight

For a class of multi‐input and multi‐output nonlinear uncertainty systems, a novel approach to design a nonlinear controller using minimax linear quadratic regulator (LQR) control is proposed. The proposed method combines a feedback linearization method with the robust minimax LQR approach in the presence of time‐varying uncertain parameters. The uncertainties, which are assumed to satisfy a certain integral quadratic constraint condition, do not necessarily satisfy a generalized matching condition. The procedure consists of feedback linearization of the nominal model and linearization of the remaining nonlinear uncertain terms with respect to each individual uncertainty at a local operating point. This two‐stage linearization process, followed by a robust minimax LQR control design, provides a robustly stable closed loop system. To demonstrate the effectiveness of the proposed approach, an application study is provided for a flight control problem of an air‐breathing hypersonic flight vehicle (AHFV), where the outputs to be controlled are the longitudinal velocity and altitude, and the control variables are the throttle setting and elevator deflection. The proposed method is used to derive a linearized uncertainty model for the longitudinal motion dynamics of the AHFV first, and then a robust minimax LQR controller is designed, which is based on this uncertainty model. The controller is synthesized considering seven uncertain aerodynamic and inertial parameters. The stability and performance of the synthesized controller is evaluated numerically via single scenario simulations for particular cruise conditions as well as a Monte‐Carlo type simulation based on numerous cases. It is observed that the control scheme proposed in this paper performs better, especially from the aspect of robustness to large ranges of uncertainties, than some controller design schemes previously published in the literature. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Geometrical model to calculate the consumption of sewing thread for 504 over-edge stitch

Sewing thread is one of the most important components of a sewn product that contributes significantly in the useful life of a product. Stitch class 504 is the one which is used in all types of sewn products. Its thread consumption is higher than class 300 and class 400. A mathematical model to predict the sewing thread consumption of stitch class 504 has been proposed in this paper. The model is based on the geometry of the stitch. The proposed model takes into account material thickness and stitch density. The model was validated by using 24 samples (with different material thickness and stitch densities). The accuracy of the model was found to be 99%. Sensitivity analysis revealed that stitch density has 62% effect and material thickness has 38% effect on thread consumption. The proposed model can predict the thread consumption accurately; therefore, it can be used for better estimation of required thread and encourage its better utilization in sewn product industry.