Organizational Commitment of Teachers and Role of Their Employment Traits in the Context of Higher Education Institutions of Pakistan

Employment traits have been studied having an impact over the organization commitment of the teachers. However, the scholars have inconsistent views regarding the relative strength of different traits groups such as Experience, Education, Type of Organizations, Chairpersonship, Salary and Designation over the commitment. In existing study, Meyer and Allen (1984-1997) “Three Component Model” was employed for collection of commitment profile of 312 both Public and Private faculty members of Institute of Management Sciences of Pakistan. Test of significance both t and ANOVA was applied and results of the statistical test divulge that most of the demographic variable like (experience, education, Salary etc.) causes a variation in the mean of commitment of the faculty members of Higher Education Institutions of Pakistan

Determinants of Exports of Pakistan: A Country-wise Disaggregated Analysis.

Given the importance of international trade and export performance in economic growth, this study attempts to examine the determinants of exports of Pakistan, using a time series data over the period 1975-2008. A simultaneous equation approach is followed and the demand and supply side equations are specified with appropriate variables. This is a country-wise disaggregated analysis of Pakistan versus its trade partners and the estimation strategy is based on two approaches. First we employ the Generalised Methods of Moments (GMM), which is followed by the Empirical Bayesian technique to get consistent estimates. The GMM technique is believed to be efficient for time series data provided the sample size is sufficiently large. In case of small samples, the estimates might not be precise and might appear with unbelievable sign and insignificant magnitudes. To avoid the sample bias and other problems, we employ the Empirical Bayesian technique which provides much precise estimates. The factual results obtained via the GMM technique are a little bit mixed, although most of the coefficients are found to be statistically significant and carry their expected signs. In order to compare and validate these results, the Empirical Bayesian technique is employed. This offers considerable improvement over the previous results and all the variables are found to be highly significant with correct sign across the countries concerned with the exception of a few cases. The price and income elasticities in both the demand and supply side equations carry their expected signs and significant magnitudes for the trading partners. The findings suggest that exports of Pakistan are much sensitive to changes in the world demand and world prices. This establishes the importance of demand side factors like world GDP, Real exchange rate, and world prices to determine the exports of Pakistan. On the supply side, we find relatively small price and income elasiticities. The results reveal that demand for exports is relatively higher for countries in NAFTA, European Union and Middle East regions. The study recommends particular concentration on the trade partners in these regions to improve the export performance of Pakistan. Keywords: Exports, GMM, Empirical Bayesian Method, Pakista

Secure Cluster-Head Selection Algorithm Using Pattern for Wireless Mobile Sensor Networks

Selecting a cluster head (CH) in wireless mobile sensor network is a crucial task. Numerous algorithms have been presented for this purpose in recent literature. In these algorithms, all nodes are permitted to contend in CH selection process regardless of their less resource. This meaningless participation of ineligible nodes in the CH selection process causes unnecessary communication cost. Similarly the use of real data for CH selection increases communication cost. Additionally, no one algorithm has focus in security aspect of CH selection process. In this article Pattern Based secure CH Selection algorithm has been presented. This algorithm filters the ineligible nodes, puts them to sleep mode thereby restricting them from not participating in the CH selection process. Additionally the uses of pattern instead of real data in CH selection decrease communication cost and increase security of CH selection process. The simulation results show the improvement in lifetime and enhancement in security of CH selection

ADAPTIVE TRIMMED MEAN AUTOREGRESSIVE MODEL FOR REDUCTION OF POISSON NOISE IN SCINTIGRAPHIC IMAGES

A 2-D Adaptive Trimmed Mean Autoregressive (ATMAR) model has been proposed for denoising of medical images corrupted with poisson noise. Unfiltered images are divided into smaller chunks and ATMAR model is applied on each chunk separately. In this paper, two 5x5 windows with 40% overlapping are used to predict the center pixel value of the central row. The AR coefficients are updated by sliding both windows forward with 60% shift. The same process is repeated to scan the entire image for prediction of a new denoised image. The Adaptive Trimmed Mean Filter (ATMF) eradicates the lowest and highest variations in pixel values of the ATMAR model denoised image and also average out the remaining neighborhood pixel values. Finally, power-law transformation is applied on the resultant image of the ATMAR model for contrast stretching. Image quality is judged in terms of correlation, Mean Squared Error (MSE), Structural Similarity Index Measure (SSIM) and Peak Signal to Noise Ratio (PSNR) of the image with latest denoising techniques. The proposed technique showed an efficient way to scale down poisson noise in scintigraphic images on a pixel-by-pixel basis

Noise Characterization in Web Cameras using Independent Component Analysis

An image captured by a web camera contains stationary and nonstationary noise patterns. These noise patterns are of three types i.e. Fixed Pattern Noise (FPN), Interactive Nose (IN) and Temporal Noise (TN). TN is an independent noise pattern and needs an algorithm that does exploit its higher-order dependencies. Previously, these noise patterns have been characterized using Principal Component Analysis (PCA). PCA is restricted to second order dependencies. In this paper Independent Component Analysis (ICA) has been investigated for actual TN noise. The experimental results demonstrates the effectiveness of the proposed method

PHYTOCHEMISTRY AND PHARMACOLOGICAL ASPECTS OF LEUCAS URTICIFOLIA (VAHL) BENTH

Medicinal plants have attracted increased attention because of their beneficial effects on human health. Many medicinal plants are used as traditional medicine in various countries for long time. A large number of secondary metabolites with various biological activities have been discovered from various medicinal plants and some bioactive substances derived from plants have diverse functional roles as secondary metabolites and these properties can be applied to the developments of novel pharmaceuticals. Leucas Urticifolia (family- Lamiaceae) is an annual herbaceous plant and has various activities. Chemical studies have underlined the presence of various classes of compounds, the main being triterpenes, diterpene, flavonoids and fatty acids. The extract of this plant as well as pure compounds isolated from this plant, have been demonstrated to posses multiple pharmacological activities. In this review, we have explored the phytochemistry and pharmacological activites of Leucas Urticifolia in order to collate existing information on this plant as well as highlight its multi-activity properties as a medicinal agent

A new approach of weighted gradient filter for denoising of medical images in the presence of Poisson noise

Predlažemo ponderirani stupnjevani filtar za otklanjanje Poissonova šuma na rendgenskim slikama. U unaprijed definiranom prozoru izračunat je gradijent središnjeg piksela. Za izračunavanje vrijednosti gradijenta primijenjen je Gaussov ponderirani filtar. Predložena metoda je primijenjena na biomedicinske rendgenske slike, a zatim na različite uobičajene slike LENE i paprika. Rezultati pokazuju učinkovitost i bolju jasnoću slika uz primjenu ponderiranog stupnjevanog filtra. Uz to, predložena metoda je računalno vrlo učinkovita i brža od Non Local Mean (NLM) filtra koji predstavlja unaprijeđenu metodu za otklanjanje Poissonova šuma. Rezultati predložene metode su također bolji u odnosu na parametre za mjerenje performanse t.j. korelacije, Peak Signal-to-Noise Ratio (PSNR), Maximum Structural Similarity Index Measure (MSSIM) i Mean Square Error (MSE) nego uobičajeni Median, Wiener i NLM filter.We propose a Weighted Gradient Filter for denoising of Poisson noise in medical images. In a predefined window, gradient of the centre pixel is averaged out. Gaussian Weighted filter is used on all calculated gradient values. Proposed method is applied on biomedical images X-Rays and then on different general images of LENA and Peppers. Recovery results show that the proposed weighted gradient filter is efficient and has better visual appearance. Moreover, proposed method is computationally very efficient and faster than Non Local Mean (NLM) filter which is an advanced technique for Poisson noise removal. Proposed method results are also better in terms of performance measures parameters i.e. correlation, Peak Signal-to-Noise Ratio (PSNR), Maximum Structural Similarity Index Measure (MSSIM) and Mean Square Error (MSE) than the conventional Median, Wiener and NLM filter