Search or add a thesis

Advanced Search (Beta)
Home > Studies on Comparative Analysis of Lactate Dehydrogenase Isoenzymes in Breast Cancer

Studies on Comparative Analysis of Lactate Dehydrogenase Isoenzymes in Breast Cancer

Thesis Info

Author

Saira Javed

Department

Deptt. of Biological Sciences, QAU.

Program

Mphil

Institute

Quaid-i-Azam University

Institute Type

Public

City

Islamabad

Province

Islamabad

Country

Pakistan

Thesis Completing Year

2001

Thesis Completion Status

Completed

Page

96

Subject

Biological Sciences

Language

English

Other

Call No: DISS/M.Phil BIO/952

Added

2021-02-17 19:49:13

Modified

2023-02-19 12:33:56

ARI ID

1676718347078

Similar


Loading...
Loading...

Similar Books

Loading...

Similar Chapters

Loading...

Similar News

Loading...

Similar Articles

Loading...

Similar Article Headings

Loading...

اسرار خودی

اسرار خودی
یہ پہلی بار 12 ستمبر 1915 ء کو منظر عام پر آئی۔ اقبال نے خواجہ حافظ شیرازی کے صوفیانہ خیالات سے خبر دار کیا تھا۔ مثنوی پڑھنے کے بعداکبر الہ آبادی نے اقبال کی تائید کی۔دراصل اقبال نے غلام قوم کی نفسیات پر روشنی ڈالتے ہوئے اسے بدلنے کے پہلو بھی اس مثنوی میں واضح کیے ہیں۔ اقبال نے فلسفہ خودی کو ملک کی غلامی کے پس منظر میں پیش کیا۔ اس طرح جب بھی خودی کا لفظ آتا ہے تو ذہن میں اقبال کا نام ہی آتا ہے۔ یہ مثنوی کی طرز پر لکھی گئی۔ مثنوی میں قصے، کہانیاں، حکایتیں، واقعات ہوا کرتے ہیں مگر مثنوی اسرار خودی اس سے بالکل مختلف ہے۔ اس میں جتنے بھی موضوعات ہیں وہ فکر و فلسفہ کا آہنگ لیے ہوئے ہیں جن کا مقصد غلام قوم کو خواب غفلت سے بیدار کرنا ہے۔
خودی کے تین مراحل اطاعت ، ضبط نفس اور نیابت الہی ہے۔ باطل قوتوں سے ڈرنے کی بجائے ڈٹ کر ان کا مقابلہ کرنا چاہیے اور ان کا خوف دل سے نکال دینا چاہیے۔ بھیڑ بکریوں کی طرح کمزور بن کر زندگی نہیں گزارنی چاہیے۔ اس طرح طاقت ور لوگ کمزوروں پر حکمران بن جاتے ہیں۔ حکمران چاہے تعداد میں کم ہی کیوں نہ ہوں اور رعایا چاہے تعداد میں جتنی بھی زیادہ ہو، اسے حکمران قوت کے سامنے سجدہ ریز ہونا پڑتا ہے۔

مشاكل اللغة العربية في نيجيريا

The Arabic language faces many hurdles in its expansion and progress in the non-Arab world internationally. This time our focus is the multi-lingual community Nigeria. Nigeria is a country of languages, where there are hordes of mother tongues (dialects) , an official language and then there is a religious language as well. It is very tough to focus on the Arabic language in this myriad of languages though; it enjoys a historic status and prestige there. A few reasons for this lack of focus on Arabic language Nigeria are as follow: 1. The British colonialism and its treacherous role to downplay the Arabic language. 2. To segregate Arabic from Muslim Ummah in Nigeria, the colonialists thus promoted English and French as official administrative languages. 3. Dearth of text books in Arabic at primary and secondary level. Moreover there are no well equipped language laboratories to develop Arabic in Nigeria. 4. The stranded economic state of the country. 5. The scarcity of Arabic press houses in Nigeria and lack of interest in the Arabic language by the general public. A few suggestions to promulgate and develop the Arabic language in Nigeria are as follow: a. Students’ attention needs to be drawn towards the Arabic language as a modern and rich language. For this all the available resources need to be exhausted. b. The Arabic language centres should be developed where proficient teachers should teach Arabic. The present faculty for Arabic should do refresher courses in Arabic to enhance their capabilities. c. The availability of Arabic text books to cater the various levels of the students is made possible. A committee should monitor the overall process and progress of Arabic language in Nigeria. d. The Nigerian government should play an active role in the development and progress of the Arabic language in Nigeria

Performance Improvement of Parallel Sparse Matrix-Vector Product on Pc Cluster

The efficient parallelization of sparse matrix-vector product (SMVP) is of prime importance in scientific computing. To achieve this on a distributed memory computers, we concentrate on minimizing the inter-processor communication, achieving a good balance of workload, overlapping communication with computation along with optimizing single processor performance. The thesis consists of two parts presenting the optimization and improvement of sparse matrix-vector multiplication performance on single as well as multi processors. For the performance improvement of SMVP on a single scalar processor, we propose two sparse storage formats, namely the grouped compressed row storage with permutation (GCRSP) and the blocked compressed row storage with permutation (BCRSP). The proposed formats are designed to efficiently exploit the benefits of blocking such as reduced indirect addressing, increased spatial and temporal locality along with eliminating the corresponding overheads. For the good load balancing and low communication cost, reordering of sparse matrices according to their sparsity structure is highly important. For this purpose we proposed reordering based partitioning strategies that tend to exploit sparsity of input matrix presenting the balanced load distribution along with the reduced communication cost. It has been observed that GCRSP improves the performance over simple compressed row storage (CRS) and compressed row storage with permutation (CRSP) with an average of 16% and 25%, respectively. Moreover, due to blocking in BCRSP, the performance improvements of an average of 32%, 41% and 20% are observed over CRS, CRSP and GCRSP respectively. Likewise, the proposed partitioning models permuted row column matrix produce an average of 49% better load balancing and 14% better communication than the corresponding naïve row/column and checker board models. Moreover, they produce same level of balanced load and an average of 78% better communication than the corresponding balanced naïve partitioning i.e. row/column block and balanced checker board (BCH) models. On the whole an average of 30% performance gain for parallel SMVP is achieved by using BCRSP format along with permuted row partitioning over the implementation using CRS format with naïve row partitioning using cluster of eight processors.