Bovine tuberculosis is one of the important diseases of dairy and wild animals. The disease is prevalent all over the world, though developed countries have tremendously reduced the prevalence through eradication campaigns. The prevalence of disease in Pakistan on the basis of tuberculin testing or culture isolation of the organism has been reported previously. It is, however, important to use the latest diagnostic tools, i.e. PCR to confirm the type of Mycobacterium infecting the animals in Pakistan. Therefore, the present study was carried out to assess the utility of direct PCR on milk samples and nasal swabs to confirm the type of Mycobacterium infecting the animals. This study was carried out on 215 cattle and buffaloes of more than 2 years of age present at two livestock farms. The tuberculin results showed 22.5% prevalence at one farm and 25.9% at the other with an overall prevalence of 24.7%. The 92.5% of milk samples and/or nasal swabs showed positive PCR for Mycobacterium genus, 86.8% for Mycobacterium tuberculosis complex and 77.4% for Mycobacterium bovis. The M. bovis by PCR was detected in 13.2% of milk samples, 24.5% of nasal swabs and 39.6% of both milk samples + nasal swabs. The results suggested that there are 60% higher chance for a nasal swab to yield a positive PCR for M. bovis than the milk sample. It can be concluded from the present study that tuberculin testing is a useful method in studying the prevalence of disease as the PCR for Mycobacterium genus was positive in 92.5%, M. tuberculosis complex in 86.8% and Mycobacterium bovis in 77.4% cases.

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The study was planned to determine the comparative efficacy of leaf extract of Azadirachta indica, Cichorium intybus and Moringa oleifera, on haematology, intestinal histomorphology and nutrient digestibility in broilers. Day old broiler chicks (n=180) purchased from a commercial hatchery were reared in a group for one week (adaptation period). At day 8, these birds were individually weighed and 120 birds of middle weight range were randomly selected and distributed into 12 replicates (10 chicks/replicate). These replicates were further allotted to four treatment groups designated as A, B, C and D. Group A was offered water without any supplementation and served as a control. Whereas, group B, C and D were offered water supplemented with leaf extract of Azadirachta indica (4%), Cichorium intybus (2%) and Moringa oleifera (6%), @ 50 ml/l, @ 10 ml/l and @ 30 ml/l, respectively. Results of the study revealed that supplementation of Azadirachta indica leaf extract showed better nutrient digestibility of crude protein and ether extract as compared to that of control. However, digestibility of crude fiber due to the treatments remained unaffected. Stabilization of blood metabolites resulted in improved intestinal histo-morphology. The birds using Azadirachta indica fetched the highest profit as compare to the other treatment groups. Therefore, use of Azadirachta indica leaf extracts in broiler is recommended as inexpensive and efficient growth promoting agent without residual effects like antibiotic growth promoter.

Efficient utilization of limited radio frequency spectrum is only possible to use smart/adaptive antenna system. Smart antenna radiates not only narrow beam towards desired users exploiting signal processing capability but also places null towards interferers, thus optimizing the signal quality and enhancing capacity. Least mean square (LMS) and normalized least mean square (NLMS) are two adaptive beamforming algorithms which are presented in this paper. Smart antenna incorporates these algorithms in coded form which calculates complex weights according to the signal environment. The efficiency of LMS and NLMS algorithms is compared on the basis of normalized array factor and mean square error (MSE) for mobile communication. Simulation results reveal that both algorithms have high resolution for beam formation. However LMS has good performance to minimize MSE as compared to NLMS. Therefore, LMS is found more efficient algorithm to implement in the mobile communication environment to minimize MSE and enhancing capacity.

Classification of brain neoplasm images is one of the most challenging research areas in the field of medical image processing. The main objective of this study is to design a brain neoplasm classification system that can be trained using multiple various sized MR images of different institutions. The proposed method is a generalized classification system; it can be used in a single institute or in a number of institutions at the same time, without any restriction on image size. The generalization and unbiased capability of the proposed method can bring researchers on a single platform to work on some standard forms of computer aided diagnosis system with more efficient diagnostic capabilities. In this study, a suitable size of moveable rectangular window is used between segmentation and feature extraction stages. A semiautomatic, localized region based active contour method is used for segmentation of brain neoplasm region. Discrete wavelet transform for feature extraction, principal component analysis for feature selection and support vector machine is used as classifier. For the first time MR images of 2 sizes and from different institutions are used in training and testing of brain neoplasm classifier. Three glioma grades were classified using 92 MR images. The proposed method achieved the highest accuracy of 88.26%, the highest sensitivity of 92.23% and the maximum specificity of 93.93%. In addition, the proposed method is computationally less complex, requires shorter processing time and is more efficient in terms of storage capacity.

The adoption and implementation of green supply chain management (GSCM) initiatives within an organization have been claimed to create specific ecological, competitiveness and monetary benefits. This study explores the drivers of GSCM initiatives and their effect on the economic performance of manufacturing companies in three sectors (textile, chemical, and pharmaceutical) of Pakistan's economy. The three driver social pressure, competitive pressure, and institutional pressure were chosen as the enablers of GSCM initiatives in Pakistan's manufacturing sector. The data was collected through a questionnaire distributed among managerial level employees working in the manufacturing industry of Pakistan. The study was based on the evaluation of 263 responses through partial least squares structural equation modeling (PLS-SEM) method. The results show that institutional pressures are the most significant enablers towards competitiveness and instituting GSCM initiatives, while social pressures and competitive pressures also have a substantial impact. The effect of GSCM initiatives on firm competitiveness and economic performance was also analyzed and found to be significant. The presented results may be seen as useful for managers, practitioners and policy makers, as well as for society in general in various ways.

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