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.     

Weibull parameters for wind speed distribution in Saudi Arabia

The shape and scale parameters of a Weibull density distribution function are calculated for 10 locations in Saudi Arabia. The daily mean wind speed data from 1970 to mid-1990 are used for this purpose. It is found that the numerical values of the shape parameter vary between 1.7 and 2.7, whereas the value of the scale parameter is found to vary between 3 and 6. It is also concluded from this study that wind data are very well represented by the Weibull distribution function.     

Forced migration of soluble and suspended materials by freezing front in aqueous systems

Migration of soluble and suspended materials by directional freezing of aqueous systems has been studied qualitatively. Slow freezing was employed vertically as well as horizontally through solutions and suspensions. In all cases, the impurities (soluble salts as well as suspended materials) were appreciably forced out by dynamic freezing front. The phenomenon worked for concentrating/separating inorganic ions, soluble organic compounds and dyes in synthetic solutions as well as in natural streams. Various analytical techniques were employed to monitor the migrating species through the freezing media. It was found that separation efficiency depends on different factors like rate of cooling, pH and concentration. Model experiments were designed and exercised successfully to employ the technique for treatment of dye-polluted water.     

Chronic growth faltering amongst a birth cohort of Indian children begins prior to weaning and is highly prevalent at three years of age

Background  

Poor growth of children in developing countries is a major public health problem associated with mortality, morbidity and developmental delay. We describe growth up to three years of age and investigate factors related to stunting (low height-for-age) at three years of age in a birth cohort from an urban slum.     

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。     

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.     

Optimal Automatic Generation Control of Asynchronous Power Systems Using Output Feedback Control Strategy with Dynamic Participation of Wind Turbines

Abstract—This article presents the design of optimal output feedback automatic generation control regulators for an interconnected power system with dynamic participation of doubly fed induction generator based wind turbines. The power systems consist of plants with hydro-thermal turbines and are interconnected via parallel AC/DC links. Efforts have been made to propose optimal automatic generation control regulators based on feedback of output state variables, which are easily accessible and available for the measurement. The designed optimal output feedback automatic generation control regulators are implemented, and the system dynamic responses for various system states are obtained considering 1% load perturbation in one of the areas. The dynamic performance is compared with that obtained with optimal automatic generation control regulators designed using full state vector feedback. The pattern of closed-loop eigenvalues is also determined to test the system stability.     

Study of the Enhancement of the Efficiency of the Monocrystalline Silicon Solar Cell by Optimizing Effective Parameters Using PC1D Simulation

Silicon - In this paper, simulation of a monocrystalline silicon solar cell was done using PC1D software. The impact of different solar cell parameters, with their effects on power and efficiency,...     

Effect of transition metal doping on the sintering and electrochemical properties of GDC buffer layer in SOFCs

A dense Ce_0.9Gd_0.1O_2−d (GDC) interlayer is an essential component of the SOFCs to inhibit interfacial elemental diffusion between zirconia-based electrolytes (eg YSZ) and cathodes. However, the characteristic high sintering temperature of GDC (>1400°C) makes it challenging to fabricate an effective highly dense interlayer owing to the formation of more resistive (Zr,Ce)O₂ interfacial solid solutions with YSZ at those temperatures. To fabricate a useful GDC interlayer, we studied the influence of transition metal (TM) (Co, Cu, Fe, Mn, & Zn) doping on the sintering and electrochemical properties of GDC. Dilatometry data showed dramatic drops in the necking and final sintering temperatures for the TM-doped GDCs, improving the densification of the GDC in the order of Fe > Co > Mn > Cu > Zn. However, the electrochemical impedance data showed that among various transition metal dopants, Mn doping resulted in the best electrochemical properties. Anode supported SOFCs with Mn-doped, nano, and commercial-micron GDC interlayers were compared with regard to their performance and stability levels. Although all of the SOFCs showed stable performance, the SOFC with the Mn-doped GDC interlayer showed the highest power density of 1.14 W cm⁻² at 750°C. Hence, Mn-doped GDC is suggested for application as an effective diffusion barrier layer in SOFCs.