The social construction of Muslim minority groups in Canada

This dissertation explores Canadian mainstream print media's discourse on Muslim minority groups in Canada in the pre- and post-9/11 timeframe. By using critical discourse analysis (CDA) of three Canadian English newspapers as well as focus group discussions, and individual interviews, this study explores the issues of racialization, Islamophobia, and the role of Canadian mainstream print media in the construction of stereotypical images of the Muslim minority groups in Canada. The data reveal the frequent usage of stereotypical racialized terms in three Canadian newspapers (two national and one local) directed toward Muslims in the post-9/11 period cultivated moral panics in Canadian society. Although Muslim were negatively portrayed in these newspapers before 9/11, the situation escalated in the post-9/11 era. Participants in the focus group discussion and individual interviews also stated that the post-9/11 moral panics augmented and perpetuated the negative feelings towards Muslims. An increased trend in racial and religious discrimination against Muslims was observed throughout Canada after the 9/11 incident in New York. The negative portrayal of Muslims stemmed from a lack of understanding of Muslim minority groups, their culture and/or international distortion of Muslims by media personnel, who did not differentiate between the small number of Muslims who engaged in or support terrorist acts, and the majority of Muslims who do not. By neglecting to recognize this conflation of the different perspectives of Muslims on these matters, the selected newspapers contributed to the escalation of moral panics in Canadian society, which resulted in increased negative attitudes towards Muslims

The effect of Foreign Direct Investment on economic growth of Pakistan

This study assesses the relationship between foreign direct investment and economic growth of Pakistan. The method of ordinary least square (OLS) has been used, utilizing time series data for the period 1980-2013. Our study found that, in case of Pakistan the results are significant showing that there is important relationship between economic growth and foreign direct investment. The study also give some recommendations including; making law & order situation better, provision of electricity, political stability and establishment of separate bench on the ministry of finance to attract foreign investment. Key words: Economic growth, foreign direct investment, developing country

Pakistan's Relations with the United States and China in the Post-9/11 Era

This paper intends to explore the key elements that contribute to reshaping Pakistan's relations with the United States (US) and China in the post-9/11 era. In addition, the reasons contributing to how the US and China are perceived in Pakistan have been examined. The US and China both use different approaches in their relations with Pakistan. The US makes unilateral decisions, poses economic and security threats to Pakistan in pursuing its objectives, while China makes bilateral decisions and asserts itself as a soft power in its relations with Pakistan. The Pew Research Center's public opinion poll data shows that the US has become increasingly unpopular in Pakistan even after its substantial economic and military aid to Pakistan. China, on the other hand, has been able to win the hearts and minds of Pakistanis even though its economic and military aid to Pakistan in comparison to the US aid package is not nearly as significant. The study also reveals that Pakistan has grave concerns about future developments in the region after the US withdraws its forces from Afghanistan

Forecasting selectivity of Au-based partial oxidation catalysts via temperature programmed desorption studies on the Au(111) model catalyst

Includes bibliographical references (leaves 81-86).Cataloged from PDF version of thesis.Thesis (M.S.): Bilkent University, The Department of Chemistry and the Graduate School of Engineering and Science of Bilkent University, 2014.Gold-based heterogeneous catalysts have attracted significant attention due to their selective partial oxidation capabilities which are comparable to that of the industrial homogeneous benchmark catalysts. In the current study, a planar Au(111) single crystal model catalyst surface was utilized to understand the behavior of different organic compounds (alcohols, aldehydes, esters etc.) in conjunction to the partial oxidation reactions. Stability of different organic compounds were investigated on the Clean Au(III) surface. The stability of a particular organic compound on the Au(III) model catalyst surface was found to be closely related to the variety of generated products. Surface sensitive analytical techniques such as Temperature Programmed Desorption (TPD) and Low Energy Electron Diffraction (LEED) were used to investigate the interaction of organic compounds with the clean Au(111) single crystal surfaces under ultrahigh vacuum (UHV) conditions. Organic compounds were dosed onto atomically clean Au(III) surfaces at the liquid nitrogen temperature. All organic compounds desorbed non-dissociatively on the clean Au(111) surface. All organic compounds reveal monolayer and multilayer desorption signals but in the case of aldehydes, desorption is quite different, as they lead to polymerization on the surface with high desorption temperatures. Zeroth order desorption kinetics was observed for multilayers, while 1st order desorption was seen for the monolayer. In most cases, the multilayer feature can be observed with two distinct desorption peaks associated with amorphous and crystalline phases. In this work, it is confirmed that majority of the studied compounds have relatively low adsorption energies on Au(111). The species with lower desorption energies on Au(111) tend to undergo partial oxidation rather than total oxidation. Thus, desorption energy appears as an important descriptor for predicting the extent of oxidation in partial/total oxidation in oxidative coupling reactions.by Shah, Syed Asad Ali.M.S

Inventory Management System for a General Items Warehouse of the Textile Industry

This research is based on Inventory Management System for a General Items Warehouse of the Textile Industry. The overall inventory is managed by applying classification tools such as ABC, FSN & HML that categorize inventory based on consumption value, issuance rate and unit price respectively. Also, it helps to appropriately position the items on the desired rack and position. The optimized layout is designed that reduces the retrieval time, uplift the storage capacity, and have cross aisles that reduce the retrieval time of any item from the warehouse. The system for proper traceability & tracking of the items is also studied that is based on the 1D Barcode. This whole study improves the overall operation of the Supply Chain

Rice Seedling Characteristics of Various Genotypes Influenced by Different Sowing Dates in Swat-Pakistan

To study the effect of different sowing dates on rice nursery rising and to evaluate their effect on yield and yield components of rice genotypes, an experiment was conducted at Agriculture Research Institute (N) Mingora Swat, Pakistan, during summer 2011. The experiment was laid out in randomized complete block design with four replications. Seven genotypes (PARC 403, OM5627,IR64,IR8225-9-3-2-3, CIBOGO,GA-5015, and FakhreMalakand) and 5 sowing dates (D1= 25th April, D2= 10th May, D3= 25th May, D4= 9th June, and D5= 24th June) were used. Each genotype was sown in six rows in dry bed nursery. The germination percentage was above 90%. The nursery reached its optimum size up to 30 days and was ready for transplantation.Sowing on either D5 (24th June) or D4(9th June)gave maximum leaf area(9.6 and 9.1 cm2) followed by D3 (7.2 cm2), maximum leaves seedling-1 produced by D3 (5.3) followed by D2 (5.0). Maximum seedling height was gave by sowing on D5 (23.5 cm) followed by D4 (19.9 cm), maximum biomass gave by sowing on D4 (15.3 gm) and D5 (13.8 gm) followed by D2 and D3 (12.8 and 12.2 gm), highest root number gave by D4 (13.0) followed by D2 and D3 (11.3 and 11.5) and highest root length gave by D1 and D2 (10.7 and 10.7 cm) followed by D3 (9.6 cm). Among the rice genotypes FakhreMalakand produced highest seedling height (21.9 cm), highest biomass (14.0 gm), highest root length (12.9 cm) followed by GA-5015 while maximum leaf area gave by genotype PARC 403 (9.8 cm2) and number of leaves (4.9) followed by FakhreMalakand (8.3 cm2 and 4.6) respectively. Later data showed that highest paddy yield (6.49 t ha-1) was produced by FakhreMalakand sown on either D2 or D3 while the other genotypes were at par valued in this order. On the basis of the above results, it is recommended that rice nursery rising should be either D2 or D3May10th or 25th in the agro-ecological conditions of swat valley. Keywords: Rice (Oryza sativa L.), genotypes, sowing dates, biomass, seedlin

A meta-heuristic approach for developing PROAFTN with decision tree

© 2016 IEEE. Machine learning algorithms known for their performance in using historical data and examples to predict and classify unknown instances. Decision tree is an efficient machine learning approach that can use data only without the involvement of decision maker to improve the decision making process. Multi-Criteria Decision Analysis (MCDA)is another paradigm used for data classification. In this paper, we propose a new fuzzy classification method based on MCDA called PROAFTN. To use PROAFTN, a set of parameters need to be established from data. The proposed approach uses data pre-processing and canonical genetic algorithm (GA) for obtaining these parameters from data. The generated models have been applied on popular data selected from several application domain, health, economy, etc. According to our experimental study, the new model performs significantly better than decision trees according in terms of accuracy and the interpretation of the decision rules

Improving M-Learners\u27 Performance through Deep Learning Techniques by Leveraging Features Weights

© 2013 IEEE. Mobile learning (M-learning) has gained tremendous attention in the educational environment in the past decade. For effective M-learning, it is important to create an efficient M-learning model that can identify the exact requirements of mobile learners (M-learners). M-learning model is composed of features that are generated during M-learners\u27 interaction with mobile devices. For an adaptive M-learning model, not only learning features are required, but it is also important to determine how they differ for various M-learners, their weights, and interrelationship. This study proposes a robust and adaptive M-learning model that is based on machine learning and deep learning (ML/DL) techniques. The proposed M-learning model dynamically explores learning features, their corresponding weights, and association for M-learners. Based on learning features, the M-learning model categorizes M-learners into different performance groups. The M-learning model then provides adaptive content, suggestions, and recommendations to M-learners in order to make learning adaptive and stimulating. For comparative analysis, the prediction accuracy of five baseline ML models was compared with the deep Artificial Neural Network (deep ANN). The results demonstrated that deep ANN and Random Forest (RF) models exhibited better prediction accuracy. Subsequently, both models were selected for developing the M-learning model which included the performance categorization of M-learners under a five-level classification scheme and assigning weights to various features for providing adaptive help and support to M-learners. Our explanatory analysis has shown that behavioral features besides contextual features also influence the learning performance of M-learners. As a direct outcome of this research, more efficient, interactive, and useful mobile learning applications can be developed that accurately predict learning objectives and requirements of diverse M-learners thus helping M-learners in enhancing their study behavior

Semantic Orientation of Crosslingual Sentiments: Employment of Lexicon and Dictionaries

Sentiment Analysis is a modern discipline at the crossroads of data mining and natural language processing. It is concerned with the computational treatment of public moods shared in the form of text over social networking websites. Social media users express their feelings in conversations through cross-lingual terms, intensifiers, enhancers, reducers, symbols, and Net Lingo. However, the generic Sentiment Analysis (SA) research lacks comprehensive coverage about such abstruseness. In particular, they are inapt in the semantic orientation of Crosslingual based code switching, capitalization and accentuation of opinionative text due to the lack of annotated corpora, computational resources, linguistic processing and inefficient machine translation. This study proposes a Heuristic Framework for Crosslingual Sentiment Analysis (HF-CSA) and takes into consideration the NetLingua, code switching, opinion intensifiers, enhancers and reducers in order to cope with intrinsic linguistic peculiarities. The performance of proposed HF-CSA is examined on Twitter dataset and robustness of system is assessed on SemEval-2020 task9. The results show that HF-CSA outperformed the existing systems and reached to 71.6% and 76.18% of average accuracy on Clift and SemEval-2020 datasets respectively

EFFECTS OF FUND ATTRIBUTES ON FUND RETURN: AN ANALYSIS OF CLOSE-END MUTUAL FUNDS OF PAKISTAN

Mutual funds have become an attractive investment option particularly for small investors to diversify their portfolio. The aim of this study is to explore the determinants of close ended mutual funds return in Pakistan. For this purpose secondary data is used from 2007-2013. Multiple regression analysis is carried out to measure the determinants (fund size, liquidity, expense ratio and fund turnover) of fund return. The findings of the study revealed that expense ratio and fund turnover significantly influences the return of the fund. Moreover, fund size is positively related to fund return whereas expense ratio, fund turnover and liquidity are inversely related to fund return