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حکیم حافظ خواجہ شمس الدین لکھنوی

حکیم حافظ خواجہ شمس الدین لکھنوی
افسوس ہے کہ گذشتہ مہینہ دو ممتاز اہل علم نے وفات پائی، حکیم حافظ خواجہ شمس الدین لکھنوی اور سید اختر علی صاحب تلہری، حکیم صاحب تنہا حاذق طبیب ہی نہیں تھے، بلکہ عربی زبان اور اسلامی علوم کے فاضل بھی تھے اور شعر و ادب کا بڑا ستھرا ذوق رکھتے تھے طب یونانی کے تو ماہر ہی تھے، اور اب لکھنؤ میں اس کی عظمت انہی کے دم سے قائم تھی، طب کی کتابوں کا درس بھی دیتے تھے جن کے پڑھانے والے اب کم رہ گئے ہیں، آداب و اخلاق میں لکھنؤ کی پرانی تہذیب اور وضعداری کا نمونہ تھے، لکھنؤ کے متعدد قومی ملی اداروں کے رکن تھے اور ان کے کاموں میں بڑی دلچسپی سے حصہ لیتے تھے، ندوہ سے خاص تعلق تھا، اور اس کی مجلس منتظمہ کے جلسوں میں بڑی پابندی سے شریک ہوتے تھے، مولانا عبدالباریؒ فرنگی محل کے شاگرد بھی تھے اور مرید بھی، اس تعلق سے ان سے بہت پرانی شناسائی تھی آخر میں تصوف کی طرف زیادہ رجحان ہوگیا تھا، اب طب یونانی کے ماہر اٹھتے جارہے ہیں، طبی درسگاہوں سے طبیب کے بجائے ’’ڈاکٹر‘‘ پیدا ہونے لگے ہیں اور خالص فن طب ختم ہوتا جاتا ہے، مرحوم لکھنؤ میں اس کی آخری یادگار تھے، ان کی موت سے فن طب اور پرانی تہذیب و شرافت کی ایک بڑی یادگار مٹ گئی۔ اﷲ تعالیٰ ان کی مغفرت فرمائے۔ (شاہ معین الدین ندوی،مئی ۱۹۷۱ء)

 

Impact of Institutional Quality on Trade Performance of Small and Medium Enterprises in Pakistan

The trade economy is dependent upon the institutional quality of the country. It affects the ease of doing business in the economy. It is plausible to think that, how institutional quality can affect the trading performance of Pakistan. Small & medium enterprises (SMEs) are playing the role of the backbone of the trade sector in Pakistan. Contribution SMEs can be significantly improved, by improving the supporting macroeconomic indicators. This paper studies the short-run and the long-run association between SME trade growth and cost of production, relative prices, and Institutional quality in Pakistan. It also examines the Environmental Kuznets curve (EKC) hypothesis, between SME trade growth and institutional quality in Pakistan. This study utilizes secondary data, which is taken from multiple secondary sources, including the SMEDA, Pakistan Economic Survey, and world development indicators. The biannual data is assembled up for 38 observations from (2000 to 2019). This study uses Auto Regressive Distributive Lag (ARDL) bound testing method to examine the short run and long run connections between SMEs’ trade growth and macro-economic variables, like; relative prices, Cost of production. Gross Domestic Product, exchange rate, and institutional quality. These variables are selected from the available literature. The study finds that the short-run response of SMEs trade is not significant, but it significantly responds to macro-economic indicators in long run. The institutional quality has a non-linear relationship with SMEs trade growth. This indicates that the pollution heaven hypothesis holds valid even for the case of institutional quality and SMEs trading performance. The study focuses on the optimality of institutional quality for the optimal performance level of SMEs in Pakistan.

Multi-Label Classification of Computer Science Research Papers Using Paper Metadata

In scientific literature, a publication is deemed to be a way of expression regarding scientific contribution in a specific context of a discipline. It can be further substantiated through a well-known quote that “Communication in science is realized through research publications”. Over the decades, the tremendous increase has been witnessed in the production of documents available in the digital form. The increased production of documents has gained so much momentum that their rate of production jumps two-fold every five years. The large chunk of these documents comprises of research publications due to the subsequent discoveries and inventions in science. This incessant process of research publications has never been interrupted on the contrary, it has gained significant momentum. Almost 28,100 active scholarly journals are publishing almost 2.5 million articles per year. These articles are searched over the Internet via search engines, digital libraries, and citation indexes. However, retrieval of relevant research papers for user queries is still a pipedream. This is due to the fact that scientific documents are not indexed based on some subject classification hierarchies such as ACM classification system for Computer Science. This has motivated researchers to propose innovative approaches for research papers classification. This is not only beneficial for relevant retrieval of research papers but also is helpful in many other application scenarios such as when: (1) journal/conference editors want to identify reviewers; (2) research scholar wishes to identify the suitable supervisor; (3) authors intend to submit their research papers; and (4) one seeks to analyze trends, find experts and to recommend relevant papers etc. In this dissertation, author has critically reviewed the literature on research papers classification and identified the following research deficiencies which have been focused in this dissertation: (1) The existing research papers’ classification schemes utilize content of papers and most of the time, non-availability of content make those schemes non-applicable. There is a need to explore some alternative features to classify research articles that could produce results closer to content based approaches. (2) Majority of state-of-the-art approaches focus on single-label classification, while experiments on comprehensive dataset revealed that a research article may belong to multiple categories. There is a need of such multi-label classification system that utilizes best possible alternate of the content based approaches with closer or improved accuracy. (3) The existing multi-label classification schemes classify citations into limited number of categories, In Computer Science domain; ACM classificationsystem contains 11 classes at its root level. An approach that could classify research articles at least to the root level of ACM classification system is a need of the hour. The objective of this dissertation is to use freely available metadata in the best possible way to perform multi-label classification and to evaluate that; to what extent metadata based features can perform similar to content-based approaches? We have proposed, developed and evaluated techniques on metadata such as Title , Keywords, Title & Keywords, References of the research papers and have reported the achieved results. For classification of research articles based on metadata and into multi-labels, we have harnessed metadata in diverse ways for example: (1) Multi-label Document Classification using Papers’ Metadata (Title & Keywords); and (2) Multi-label Document Classification based on Research Articles’ References. These techniques have been evaluated for two different and diversified datasets. One dataset is from online journal known as Journal of Universal Computer Science (J.UCS) and other is benchmark dataset comprises of research papers published by the ACM. These techniques yield encouraging results (i.e. 88% of accuracy) by using only freely available metadata as compared to the state-of-the-art techniques on both datasets.
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