Insight into Structural and Optical Properties of Pristine and Sr2+ Doped La2NiMnO6

Double perovskites oxide (DPO) multiferroics La2-xSrxNiMnO6(x=0.0, 0.1, 0.2, 0.4, 0.6) are synthesized by sol-gel technique. The structural, optical and electrical (both DC and AC) properties of La2-xSrxNiMnO6 have been investigated by XRD and FTIR spectroscopy and two-probe resistivity and dielectric measurements as a function of temperature, respectively. The effect of doping of Strontium at A-site in double perovskites is discussed. XRD has revealed the formation of monoclinic structure of La2-xSrxNiMnO6 with space group P21 / n for x=0.0 and P21 for x=0.1, 0.2, 0.4, 0.6. The average crystallite size has been calculated to be in the range 31 to 46 nm as determined by Debye Scherrer equation. Infrared active optical phonons observed from reflectivity spectra have been analysed fitting the theoretical oscillators using Lorentz oscillator model. We have observed several well-resolved phonon modes in La2-xSrxNiMnO6 with increasing dopant concentration. Activation energy calculated using Arrhenius Plot is in the range of 0.31 to 0.18 eV, confirming the semiconducting nature of all samples. The dielectric constant and tangent loss as a function of temperature and frequency are also discussed for these multiferroics.

A New Hybrid Exponentially Weighted Moving Average control chart using Mixture Ratio Estimator of Mean

In this paper, we propose a Hybrid Exponentially Weighted Moving Average (HEWMA) control chart based on a mixture ratio estimator of mean using a single auxiliary variable and a single auxiliary attribute (Moeen et al., [1]). We call it as Z- HEWMA control chart. The proposed control chart performance is evaluated using outof- control-Average Run Length (ARL1). The control limits of the proposed chart is based on estimator, its mean square errors. A simulated example is used to compare the proposed Z-HEWMA, traditional/simple EWMA chart and CUSUM control chart. From this study the fact is revealed that Z-HEWMA control chart shows more efficient results as compared to traditional/simple EWMA and CUSUM control charts. The Z-HEWMA chart can be used for efficient monitoring of the production process in manufacturing industries where auxiliary information about a numerical variable and an attribute is available.

Mass Spectrum and Decay Constants of Heavy Quarkonia

In this paper, a non-relativistic potential model is used to find the solution of radial Schrodinger wave equation by using Crank Nicolson discretization for heavy quarkonia ( ̅, ̅). After solving the Schrodinger radial wave equation, the mass spectrum and hyperfine splitting of heavy quarkonia are calculated with and without relativistic corrections. The root means square radii and decay constants for S and P states of c ̅ and ̅ mesons by using the realistic and simple harmonic oscillator wave functions. The calculated results of mass, hyperfine splitting, root means square radii and decay constants agreed with experimental and theoretically calculated results in the literature.

Body Weight Prediction of Thalli Sheep Reared in Southern Punjab Using Different Data Mining Algorithms

This study is conducted to predict the body weight (BW) for Thalli sheep of southern Punjab from different body measurements. In the BW prediction, several body measurements viz., withers height, body length, head length, head width, ear length, ear width, neck length, neck width, heart girth, rump length, rump width, tail length, barrel depth and sacral pelvic width are used as predictors. The data mining algorithms such as Chi-square Automatic Interaction Detector (CHAID), Exhaustive CHAID, Classification and Regression Tree (CART) and Artificial Neural Network (ANN) are used to predict the BW for a total of 85 female Thalli sheep. The data set is partitioned into training (80 %) and test (20 %) sets before the algorithms are used. The minimum number of parent (4) and child nodes (2) are set in order to ensure their predictive ability. The R2 % and RMSE values for CHAID, Exhaustive CHAID, ANN and CART algorithms are 67.38(1.003), 64.37(1.049), 61.45(1.093) and 59.02(1.125), respectively. The mostsignificant predictor is BL in the BW prediction of Thalli sheep. The heaviest BW average of 9.596 kg is obtained from the subgroup of those having BL > 25.000 inches. On behalf of the several goodness of fit criteria, we conclude that the CHAID algorithm performance is better in order to predict the BW of Thalli sheep and more suitable decision tree diagram visually. Also, the obtained CHAID results may help to determine body measurements positively associated with BW for developing better selection strategies with the scope of indirect selection criteria.

The Reve’s Puzzle Revisited

The Reve’s puzzle, introduced by the English puzzlist, H.E. Dudeney, is a mathematical puzzle with 10 discs of different sizes and four pegs, designated as S, P1, P2 and D. Initially, the n (  1) discs rest on the source peg, S, in a tower (with the largest disc at the bottom and the smallest disc at the top). The objective is to move the tower from the peg S to the destination peg D, in a minimum number of moves, under the condition that each move can transfer only one disc from one peg to another such that no disc can ever be placed on top of a smaller one. This paper considers the solution of the dynamic programming equation corresponding to the Reve’s puzzle.

Automated Corn Seed Fusarium Disease Classification System Using Hybrid Feature Space and Conventional Machine Learning Techniques

The purpose of this learning is to detect the Corn Seed Fusarium Disease using Hybrid Feature Space and Conventional machine learning (ML) approaches. A novel machine learning approach is employed for the classification of a total of six types of corn seed are collected which contain Infected Fusarium (moniliforme, graminearum, gibberella, verticillioides, kernel) as well as healthy corn seed, based on a multi-feature dataset, which is the grouping of geometric, texture and histogram features extracted from digital images. For each corn seed image, a total of twenty-five multi-features have been developed on every area of interest (AOI), sizes (50 × 50), (100 × 100), (150 × 150), and (200 × 200). A total of seven optimized features were selected by using a machine learning-based algorithm named “Correlation-based Feature Selection”. For experimentation, “Random forest”, “BayesNet” and “LogitBoost” have been employed using an optimized multi-feature user-supplied dataset divided with 70% training and 30 % testing. A comparative analysis of three ML classifiers RF, BN, and LB have been used and a considerably very high classification ratio of 96.67 %, 97.22 %, and 97.78 % have been achieved respectively when the AOI size (200×200) have been deployed to the classifiers.

The Parametric Estimation of Tidal Potential Power Density using Modeling Strategies at Hajambro Creek of Indus Delta, Pakistan

There are many accessible resources for electricity generation using renewable energy, like, solar, wind, tidal and wave etc. The output of all these resources depend on weather conditions, force of gravity or rotation of the Earth, but tidal energy has a major advantage over many other forms of renewable generation as it is predictable over a long period of time. Pakistan has about 1000 km long coastline with complex network of creeks in the Indus delta region which include 17 major creeks and further divide into a number of estuaries with considerable tidal ranges and tidal current. This research study is carried out at one of these major creeks namely Hajambro (Hajambro river) and extends from Hajambro 24ᵒ 08’N 67ᵒ 22’E (sea mouth) to Keti Bander 24ᵒ 09’N 67ᵒ 27’E (mouth of river Indus). Study area is targeted within creek region where there is a large shortfall of electricity observed and this situation has threaten the community socioeconomically. In this research study, available tidal energy resources of Hajambro creek are assessed, tidal power density models and bathymetry model are developed in Arc-GIS (geographical informationsystem) environment, for the first time. A comprehensive tidal turbine technology review is conducted and based on up-to-date tidal turbine technology review and results achieved from assessment of tidal energy resources, deployment of a turbine at Hajambro creek is proposed. With effective area of 9.46 km2 mean spring estimated power (seasonally) is observed as 14 MW in winter, 12.9 MW in Pre-Monsoon, 13.6 MW in Monsoon and 13.1 MW in Post-Monsoon.

Deprived of Second Derivative Iterated Method for Solving Nonlinear Equations

Non-linear equations are one of the most important and useful problems, which arises in a varied collection of practical applications in engineering and applied sciences. For this purpose, in this paper has been developed an iterative method with deprived of second derivative for the solution of non-linear problems. The developed deprived of second derivative iterative method is convergent quadratically, and which is derived from Newton Raphson Method and Taylor series. The numerical results of the developed method are compared with the Newton Raphson Method and Modified Newton Raphson Method. From graphical representation and numerical results, it has been observed that the deprived of second derivative iterative method is more appropriate and suitable as accuracy and iteration perception by the valuation of Newton Raphson Method and Modified Newton Raphson Method for estimating a non-linear problem.

Design and Simulation “Ha”-Slot Patch Array Microstrip Antenna for WLAN 2.4 GHz

This paper presents a linear 1 × 2 “Ha ( )”–slot patch array microstrip antenna. The proposed design of an array microstrip antenna is intended for Wireless Local Area Network (WLAN) 2.4 GHz devices. From the previous research concerning the single patch “Ha ( )”–slot microstrip antenna, the gain that can be achieved is 5.77 dBi in simulation. This value is considered too small for an antenna to accommodate WLAN devices if compare to a Hertzian antenna. To enhance the gain of microstrip antenna, some methods can be considered using linear 1 × 2 patch array and T-Junction power divider circuit to have matching antenna impedance. The distances between two patches are one of the important steps to be considered in designing the patch array microstrip antenna. Thus, the minimum distance between the patch elements are calculated should be greater than λ/2 of the resonance frequency antenna. If the distance less than λ/2 electromagnetically coupled will occur, vice versa when it is to widen the dimension of the antenna will less efficient. Epoxy substrate Flame Resistant 4 (FR4) with dielectric constant 4.3 is used as the platform designed for the array antenna and it is analyzed using simulation software Computational Simulation Technology (CST) studio suite by which return loss, Voltage Standing Wave Ratio (VSWR), and gain are calculated. The simulation result showed that the designed antenna achieve return loss (S11) -25.363 dB with VSWR 1.1 at the frequency 2.4 GHz, and the gain obtained from simulation is 8.96 dBi, which is greater than 64.4 % if compared to the previous one. The proposed antenna design shows that increasing the number of patches in the array can technically improve the gain of a microstrip antenna, which can cover a wider area if applied to WLAN devices

Resolving Economic Dispatch with Uncertainty Effect in Microgrids Using Hybrid Incremental Particle Swarm Optimization and Deep Learning Method

Microgrids are one example of a low voltage distributed generation pattern that can cover a variety of energy, such as conventional generators and renewable energy. Economic dispatch (ED) is an important function and a key of a power system operation in microgrids. There are several procedures to find the optimum generation. The first step is to find every feasible state (FS) for thermal generator ED. The second step is to find optimum generation based on FS using incremental particle swarm optimization (IPSO), FS is assumed that all units are activated. The third step is to train the input and output of the IPSO into deep learning (DL). And the last step is to compare DL output with IPSO. The microgrids system in this paper considered 10 thermal units and a wind plant with power generation based on probabilistic data. IPSO shows good results by being capable to generate a total generation as the load requirement every hour for 24 h. However, IPSO has a weakness in execution times, from 10 experiments the average IPSO process takes 30 min. DL based on IPSO can make the execution time of its ED function faster with an 11 input and 10 output architecture. From the same experiments with IPSO, DL can produce the same output as IPSO but with a faster execution time. From the total cost side, wind energy is affecting to reduce total cost until USD 22.86 million from IPSO and USD 22.89 million from DL.