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آیات منہمات کی تشریح اردو تفاسیر کی روشنی میں

Thesis Info

Author

فیصل شریف

Supervisor

نبیلہ اسحاق

Program

Mphil

Institute

Minhaj University Lahore

City

لاہور

Degree Starting Year

2015

Degree End Year

2017

Language

Urdu

Keywords

تفسیر , متفرق آیات

Added

2023-02-16 17:15:59

Modified

2023-02-19 12:20:59

ARI ID

1676733451885

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پروفیسر محب الحسن

پروفیسر محب الحسن مرحوم
گزشتہ مہینے ملک کے ممتاز مورخ اور مشہور معلم جناب پروفیسر محب الحسن کا انتقال ۹۰ برس کی عمر میں ہوگیا۔ اناﷲ وانا الیہ راجعون۔
مرحوم نے تاریخ ٹیپو سلطان کے مصنف کی حیثیت سے بڑی شہرت حاصل کی وہ اس موضوع پر سند کا درجہ رکھتے تھے، ان کی کتاب ’’کشمیر سلاطین کے عہد میں‘‘ بھی کشمیرکی تاریخ میں بڑی وقیع خیال کی جاتی ہے۔ انہوں نے اگرچہ کم لکھا تاہم اپنی بلند پایہ کتابوں اور اہم تحریروں کی وجہ سے وہ نامور اور اچھے مصنفوں میں شمار کیے جاتے ہیں۔
پروفیسر محب الحسن نے لکھنؤ میں تعلیم حاصل کرنے کے بعد لندن یونیورسٹی سے تاریخ میں بی اے آنرز کیا، وہاں سے واپسی کے بعد ان کی طویل زندگی کا آغاز کلکتہ یونیورسٹی سے ہوا جہاں انہوں نے ۴۲؁ء سے ۵۶؁ء تک اسلامی تاریخ و تہذیب کا درس دیا۔ ۵۶؁ء سے ۶۳؁ءتک وہ مسلم یونیورسٹی کے شعبہ تاریخ کے ریڈر رہے۔ پھر جامعہ ملیہ اسلامیہ میں پروفیسر اور شعبہ تاریخ کے صدر کی حیثیت سے ۷۰؁ء تک سرگرم عمل رہے اور آخر میں وہ کشمیر یونیورسٹی کے شعبہ تاریخ کے صدر مقرر ہوئے اور ۷۴؁ء تک وہاں درس و تدریس میں مشغول رہے۔
ان کے وسیع علمی و تعلیمی تجربات سے مختلف اداروں اور تنظیموں کو بڑا فائدہ پہنچا۔ بنگال کی ریجنل ریکارڈ سروے کمیٹی کے وہ اہم رکن تھے۔ آثار قدیمہ کی ایک اہم کمیٹی سے بھی ان کا تعلق رہا۔ حکومت ہند نے ایک وفد امریکہ اور برطانیہ میں تعلیم عامہ کے جائزہ کے لیے روانہ کیا تھا، اس کے آٹھ رکنی وفد میں بھی شامل تھے۔ انہوں نے انڈین ہسٹری کانگریس اور پنجاب ہسٹری کانگریس کے شعبہ قرون وسطیٰ کی صدرارت بھی کی۔ کلکتہ کی ایران سوسائٹی کے وہ اساسی رکن تھے، اس کے نائب صدر اور سوسائٹی...

Understanding the anti-Mughal Struggle of Khushal Khan Khattak

Khushal Khan Khattak, a seventeenth century Pakhtun writer, poet and swordsman, and his forefathers had served the Mughal for a long time. However, his fortune took a sudden twist when Mughal Emperor Aurangzeb imprisoned him in 1664, and kept him in solitary confinement at Ranthambore fort. After his release from prison, Khushal Khan was a different person. He remained no more a loyal Mughal official afterwards. Although, Aurangzeb Alamgir and a number of Mughal governors of Kabul tempted him several time to accept a position in the frontier areas but he out-rightly declined. This transformation is clearly visible in his poetry. He took up arms against the Mughals in 1673 and declared a war against them despite the fact that some of his family members even his son had sided with the Mughals. He continued his anti-Mughal struggle till his death in 1689. Some of the critics look at the antiMughal role of Khushal Khan with suspicion and have raised a few queries in this connection. This study looks into the circumstances that saw transformation in his outlook towards the Mughals. Then it explores, whether it was a personal vendetta or the start of a collective anti-Mughal Pakhtun struggle. The article looks into various dimensions, nature and direction of his struggle. This research paper is an attempt to evaluate objectively as to why and how Khushal Khan joined the anti-Mughal camp in the borderland area. Some more related questions are also discussed in details in this article.

Improved Inference for Panel Data Model With Unknown Heteroscedasticity

In econometrics, three types of data are studied like cross-sectional, timeseries and panel data. Panel data is based on various observations, collected from same individuals over several time periods. It is combination of crosssectional and time-series data. The regression model formulated for such data is called panel data model (PDM). Heteroscedasticity is a usual problem in the PDM and it is desirable to concentrate on it for making robust inference. The ordinary techniques used for estimation of PDM do not lead e cient estimation and correct inference in the presence of heteroscedasticity. Moreover, presence of the high leverage points in given dataset may also lead to incorrect inference. Therefore, focus of this study is to bring improvement in inference of linear PDM su ering from heteroscedasticity of both known and unknown form. For linear regression model, White (1980) consistent estimator can be applied in the presence of heteroscedasticity in order to get correct inference. In context of the PDM, the amended version of White''s estimator has been presented by Arellano (1987). The variant of White''s estimator like HC5, the HCCME for true generalised least square (TGLS) and feasible GLS (FGLS) and some adaptive version of the HCCMEs have been proposed in this thesis for the PDM which are not available in the existing literature. Besides, Efron (1979), Freedman (1981) and Wu (1986) bootstrap estimators, another estimator is also available in the literature. This estimator is known as kernel estimator. The kernel bootstrap estimator of Racine and MacKinnon (2007) has been proposed for the PDM which is previously given only for linear regression model. In this work, improved inferences via kernel smoothing is also presented and compared with conventional approaches. For novelty of the approach, kernel version of Wu''s bootstrap estimator has been improved. In this work, heteroscedasticity related to unit speci c is studied as considered by Roy (2002), Aslam (2006) and Aslam and Pasha (2007). Roy''s adaptive estimator is studied for e cient estimation of the PDM. Adaptive based consistent estimators are given by Aslam and Pasha (2007). For this study, new versions of adaptive based consistent estimators are presented for the PDM. Empirical results are based on the Monte Carlo study as used by Roy (2002) and Aslam (2006). The results indicate that kernel based bootstrap show better performance than other considered estimators. The new versions of adaptive based consistent estimators perform better than the previous ones. Performance of estimators are justi ed in terms of interval estimation, size and power of test. Illustrative examples are also given in the thesis.