Economic growth coupled with equitable distribution of income and low poverty levels are the prime objective of economists and policy makers. Industrial sector has been the ‘engine of growth’ in the process of growth and development of the developed economies of today. Pakistan economy is the sixth largest economy of the world. About 48 percent of population, in Pakistan, is living under multidimensional poverty. The industrial sector of the Pakistan economy contributes about one-fifth of shares in the GDP. It employs a large share of labor force. So these facts provided the aspirations to explore the impact of manufacturing sector employment on multidimensional poverty in Pakistan. Crosssectional data of 34 districts of Punjab province is used for the analysis. Multidimensional poverty head count index is regressed on manufacturing sector employment, healthcare, and education service. The standard OLS method is used to estimate the poverty equation. The study confirms the poverty alleviating impact of manufacturing sector employment and human capital (healthcare and education). The estimated model qualifies the diagnostic, specification error and stability tests. The study also suggests some policy recommendations for the improvement of the human capital and manufacturing sector.

The aim of this paper is to investigate the lead-lag effect on the predictability of returns. This analysis is applied to daily and one-minute interval data on the TAIWAN stock market. The results indicate evidence of predictability between indices with different degrees of liquidity and when considering one-minute interval data.

More and more companies are adopting Agile methods as a flexible way to introduce new software products. An important part of any software project is testing. Agile testing may have similar aims as traditional software testing, but the structure of the team is different, testers need to support quality infusion through entire team. Test automation and selection of test tool can help project teams deliver more effectively, and in shorter timescales. The challenges in testing of cloud are visible also in the tools for automatic test case execution. This paper addresses some of these challenges and also highlights every aspect of software testing process in Agile development.

This paper uses cross country regression analysis to try to explain the variation in cross country cross country intelligence within the framework of an intelligence production function model. It proposes that average country intelligence is positively related to food security, healthy security, and education, but negatively related to income inequality. The empirical findings of the paper tend to provide statistical verification for each of these contentions. Intellectual ability is critical not just for the operation of modern technologically sophisticated economy, but is also essential for rapid economic advancement through innovation, creativity, the development of new and improved products, and the introduction of new means of production. Intellectual ability, as measured by average country IQ scores, varies substantially across countries. As a consequence, countries with lower average level of intelligence are at a distinct disadvantage with regard to economic growth and development relative to other countries. This paper assumes that the level of intelligence is a product of a society that can be changed through appropriate changes in environmental, institutional, and cultural conditions. If this is the case, then it is a potentially highly profitable to understand the intelligence production process, to identify important variables in intelligence production function, in society. If the variables can be identified, then policy can be designed to promote favorable factors and to downplay unfavorable ones so as to enhance average societal intelligence. The central hypothesis of the paper is that conditions, some of which if made known could actually be subject to conscious policy control, matter, are critical, for the development of the modern intelligence of the people of a country. Four potential environmentally conditioning variables are considered and are empirically investigated for possible influence on the production of national intelligence. They are f

Knowledge of kidney character is important for clinical assessments of renal diseases. The aims of this study were to establish a normal range of values for kidney length and volume in normal Sudanese adults with no known history of renal disease and to determine the usefulness of body mass index (BMI), Body surface area (BSA), Glomerular filtration rate (GFR), Total body water (TBW),Creatinine Clearance(Crcl), Serum Creatinine Level(Scr) for prediction of kidney characters. 98 consecutive patients (43 females; 55 males) who had undergone axial T1, T2 weighted abdominal MRI images, were obtained during the period from June 2012 to June 2013 for indications other than renal diseases. Excluded patients were those who had renal cysts, hydronephrosis, and congenital kidney diseases .Detailed demographic information of the sample wererecorded. The kidneys volume and length were measured using Disc Summation Method and the relations between the variables were studied. The study showed that the kidneys length measured for normal Sudanese subjects were 10.08±0.46, 10.67±0.47 and the volumes were 101.6±12.98, 104.0±12.99 for right and left kidneys respectively, and it differed from other population. There were significant differences between males and females measurements and the correlation was significant between kidneys length and volume with BMI, TBW and subjects height. New equations were established to measure the kidneys length and volume. Our study confirmed that there was significant relation between the CrCl, GFR, and serum creatinine level with BSA, BMI, TBW, weight, gender and age and revealed that the kidney volume predicted the renal function significantly at p=0.005, for SCr p-value=0.056, 0.007, CrCl pvalue= 0.054, 0.043 and GFR p value= 0.051, 0.59 for right and left kidneys volume. MRI measurements using disc summation method for renal volume and length were accurate and a reference values were established for adult Sudanese subjects and were well corre

This study is focussed on statistical optimization of foam mat drying conditions of potato (kufri chandramukhi) based on functional properties of dried powder. During the preparation of the mat maximum foam expansion of 25% was found with interactive effect of 10 minutes of magnetic stirring and 2% concentration of glycerol monostearate (GMS). The range of factors employed for optimization of drying was different concentrations of GMS, temperature, time. The optimum drying condition obtained was at 600C for 135 minutes with 2% GMS. The value of percentage of final moisture content, coefficient of reconstitution, browning index, and percentage gelatinized starch was 1.89 ± 0.551, 0.914 ± 0.025, 0.013 ± 0.00175 and 70.00 ± 1.645, respectively was found at optimum drying condition. Time and interaction of temperature with time have significant effect on moisture content at p<0.05 level.

Many empirical findings have shown that local communities can be improved if at least one altruistic person engages in cooperative behavior for the benefit of the community, as in the case of “The 100 charisma ambassadors of tourism” in Japan. This paper conducted a multilevel analysis to elucidate the psychological variables underlying such cooperative behavior (CB). A questionnaire survey was conducted using items previously proposed for measuring determinant factors of CB. The respondents were: “the 100 charisma ambassadors of tourism” (n = 95), residents living in the same region as the charismas (n = 400), and residents living in other regions (n = 500). By comparing different groups, personality and environmental factors promoting CB were examined. The results indicate that Schwartz’s norm-activation factors contribute to the personality characteristics of the charismas, and that feelings of sympathy among residents contribute to the environmental characteristics of the locality of the charismas.

Medical science industry has huge amount of data, but unfortunately most of this data is not mined to find out hidden information in data. Advanced data mining techniques can be used to discover hidden pattern in data. Models developed from these techniques will be useful for medical practitioners to take effective decision. In this research work, we have analyzed the performance of the classification rule algorithms namely PART based on K-Means Clustering algorithms. The k-means is the simplest, most commonly and good behavior clustering algorithm used in many applications. Firstly the preprocessed heart disease dataset is grouped using the K-means algorithm with the K =2 values on classes to cluster evaluation testing mode. After that data mining classification rule algorithms namely Projective Adaptive Resonance Theory are analyzed on clustered relevant dataset. In our studies 10-fold cross validation method was used to measure the unbiased estimate of the prediction model. Accuracy of K-Means Clustering, PART and PART based on K-Means Clustering are 81.08%, 79.05% and 84.12% respectively. Our analysis shows that out of these three classification models Classification based on Clustering predicts cardiovascular disease with improved accuracy.

Mininghasasignificantimpactontheeconomic,socialandenvironmentalfabricofadjoiningareas.Althoughminingactivitiesbringabouteconomicdevelopmentinthearea, at the same time the land degradation causes ecologicalandsocio-economicproblems.Miningadverselyaffectstheeco-systemasawhole.Itisimportanttoconductsuitableassessmentstudiestolearnthepotentialadverseimpactofminingonfloraandfauna.Toovercomefromtheproblemsoneshouldhaveknowledgeaboutthevariousactivitiesofenvironmentalconcern.Aswehaveseenearliertimethateveryminemanagerskeepchecklistgivinginformationonenvironmental controls, as envisaged in various mining leaseconditionsoftheGovernmentofIndiaandEnvironmentmanagementplan.Frequentreviewofthisinformationmayenableidentificationofthesite-specificenvironmentalissuesat the mine. Poor environmental performance may acceleratethedemandsformerestringentregulatoryconditions.Therefore,thetaskistomakecontinuouseffortstowardsenvironmentalimprovementbyeachmineauthority.Thepresent paper discusses on various risks involved around coalminesactivitiesinIndia.Italsodealswiththerightsofthepeople to live in clean environment.

The main objective of this paper is to design a generalized architecture for polyphase code identification used in RADAR signal processing applications. The proposed VLSI architecture will identify the type of a given polyphase code, amount of phase change and number of phase changes. RADAR signal processing applications require a set of sequences with individually peaky autocorrelation and pair wise cross correlation. Obtaining such sequences is a combinatorial problem. This paper aims at implementation of an efficient VLSI system for the design of polyphase codes identification useful for RADAR applications. The VLSI system is implemented on the field programmable gate array as it provides the flexibility of reconfigurability and reprogrammability and it is a real time signal processing solution which identifies the polyphase codes. The simulation results and the FPGA implementation shows the successful code identification, amount of phase, number of phase changes for a given input sequence.