DIVIDEND POLICY ATTRIBUTES AND ITS IMPACT ON FIRM PROFITABILITY

The objective of the study was to analyze the impact of dividend policy on firm profitability. Dividend paying firms from three industrial sectors namely cement, automobile and sugar were selected for the period 2010-2020. Out of the total 74 dividend paying firms in these three industrial sectors only 33 were selected for which data was available for the entire study period. Fixed effects model is used to measure the impact of dividend policy from firm profitability. Findings of the study revealed that both attributes of dividend policy namely DPR and DY has a significant positive impact on all three proxies of firm profitability (ROA, ROE, EPS) except for DPR impact on EPS in Model III where the impact is also positive but insignificant

Determination of Yearly Wind Energy Potential and Extraction of Wind Energy Using Wind Turbine for Coastal Cities of Baluchistan, Pakistan

04 March, 2019 Accepted: 24 April, 2019Abstract: Wind energy assessment of Ormara, Gwadar and Lasbela wind sites which are located in provinceBaluchistan is presented. The daily averaged wind speed data for the three sites is recorded for a period of four yearsfrom 2010-2013 at mast heights 7 m, 9.6 m and 23 m. Measured wind data are extrapolated to heights 60 m (Ormara),80 m (Gwadar) and 60 m (Lasbela). Yearly averaged wind speeds are modeled using a two parameters Weibullfunction whose shape (k) and scale (c) parameters are computed using seven well known numerical iterative methods.Reliability of the fitting process is assessed by employing three goodness-of-fit test statistics, namely, RMSE, R2 and χ2tests. Tests indicate that MLE, MLM and EPFM outperformed other Weibull parameter estimation methods for a betterfit behavior. Yearly Weibull pdf and cdf are obtained and Weibull wind characteristics are determined. Wind turbinesEcotecnia 60/1.67 MW and Nordex S77 1500 kW are used to extract wind energy on yearly basis. Estimated yearlyWeibull power densities are in the range 623.00 - 700.13 W/m2, 276.04 – 307.55 W/m2 and 66.85 – 75.93 W/m2 forOrmara, Gwadar and Lasbela respectively. Extracted wind energy values for Ormara and Gwadar using wind turbinesare reported as ca. 8623 kWh and ca. 4622 kWh, respectively

JUXTAFACET CYST OF CERVICAL SPINE: A RARE NEURAL FORAMINAL LESION

Juxtafacet cysts (JFC) are well recognized entities occurring in the spine in relation to facet joints. These lesions include synovial cysts and ganglion cysts. Lumbar spine is more common location amongst other regions of the spine. Rarely these cysts can be seen in cervical spine. Only 29 cases of cervical synovial cysts has been reported so far to the best of our knowledge.2 The cervicothoracic junction is the preferential site of involvement. Usual location is in the dorsolateral spinal canal. Foraminal juxtafacet cyst, as seen in our case, are extremely rare

Design and Development of MIMO Antennas for WiGig Terminals

This article presents a design for high-gain MIMO antennas with compact geometry. The proposed design is composed of four antennas in MIMO configuration, wherein, each antenna is made up of small units of microstrip patches. The overall geometry is printed on the top layer of the substrate, i.e., Rogers RT-5880 with permittivity of 2.2, permeability of 1.0, dielectric loss of 0.0009, and depth of 0.508 mm. The proposed design covers an area of 29.5 × 61.4 mm2, wherein each antenna covers an area of 11.82 × 25.28 mm2. The dimensions of the microstrip lines in each MIMO element were optimized to achieve a good impedance matching. The design is resonating at 61 GHz, with a wide practical bandwidth of more than 7 GHz, thereby covering IEEE 802.11ad WiGig (58–65 GHz). The average value of gain ranges from 9.45 to 13.6 dBi over the entire frequency bandwidth whereas, the average value of efficiency ranges from 55.5% to 84.3%. The proposed design attains a compact volume, wide bandwidth, and good gain and efficiency performances, which makes it suitable for WiGig terminals

Comparison of Weibull and Gaussian Mixture Models for Wind Speed Data Analysis

In order to have a reliable estimate of wind energy potential of a site, high frequency wind speed and direction data recorded for an extended period of time is required. Weibull distribution function is commonly used to approximate the recorded data distribution for estimation of wind energy. In the present study a comparison of Weibull function and Gaussian mixture model (GMM) as theoretical functions are used. The data set used for the study consists of hourly wind speeds and wind directions of 54 years duration recorded at Ijmuiden wind site located in north of Holland. The entire hourly data set of 54 years is reduced to 12 sets of hourly averaged data corresponding to 12 months. Authenticity of data is assessed by computing descriptive statistics on the entire data set without average and on monthly 12 data sets. Additionally, descriptive statistics show that wind speeds are positively skewed and most of the wind data points are observed to be blowing in south-west direction. Cumulative distribution and probability density function for all data sets are determined for both Weibull function and GMM. Wind power densities on monthly as well as for the entire set are determined from both models using probability density functions of Weibull function and GMM. In order to assess the goodness-of-fit of the fitted Weibull function and GMM, coefficient of determination (R2) and Kolmogorov-Smirnov (K-S) tests are also determined. Although R2 test values for Weibull function are much closer to ‘1’ compared to its values for GMM. Nevertheless, overall performance of GMM is superior to Weibull function in terms of estimated wind power densities using GMM which are in good agreement with the power densities estimated using wind data for the same duration. It is reported that wind power densities for the entire wind data set are 307 W/m2 and 403.96 W/m2 estimated using GMM and Weibull function, respectively

Performance Evaluation of Models Established for the Estimation of Diffused Solar Radiation: Case Study Lahore, Pakistan

A suitable design of solar power project requires accurate measurements of solar radiation for the site ofinvestigation. Such measurements play a pivotal role in the installation of PV systems. While conducting such studies,in general, global solar radiation (GSR) is recorded, whereas diffuse component of solar radiation on a horizontalsurface is seldom recorded. The objective of the present study is to assess diffuse solar radiation (DSR) on horizontalsurfaces by using polynomial models for Lahore, Pakistan (27.89 N, 78.08 E) and by correlating clearness index withdiffuse fraction. The established models are compared with some of the existing models from the literature.Performance of models is evaluated by employing five goodness-of-fit (GoF) tests that are, mean bias error (MBE),root mean square (RMSE), Coefficient of Determination (R2), Mean Absolute Percentage Error (MAPE) and Akaike’sInformation Criterion (AIC). The comparison of the results of goodness-of-fit tests with those of existing modelsindicate that the models established in the present study are performed better as compared to the existing models. Thevalues of statistical error analysis further suggested that a cubic model with a good accuracy of 97.5% and AIC of -22.8is relatively more suitable for this climatic region for estimating diffuse solar radiation. The study shows that the modeldeveloped is in good agreement with Elhadidy and Nabi model with an accuracy of 96.1% and AIC of 4.4 andsatisfactory results are obtained for Lahore. The findings can help to give a generous understanding of solar radiation inorder to optimize the solar energy conversion systems. The results of this study provide a better understanding of theassociations between global solar radiation, clearness index and diffused fraction for the region under study

Multi-scale image denoising based on goodness of fit (GOF) tests

A novel image denoising method based on discrete wavelet transform (DWT) and goodness of fit (GOF) statistical tests employing empirical distribution function (EDF) statistics is proposed. We formulate the denoising problem into a hypothesis testing problem with a null hypothesis corresponding to the presence of noise, and alternate hypothesis representing the presence of only desired signal in the image samples being tested. The decision process involves GOF tests, employing statistics based on EDF, being applied directly on multiple image scales obtained from DWT. We evaluate the performance of the proposed method against the state of the art in wavelet image denoising through extensive experiments performed on standard images