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مولانا اشرف علی تھانوی

آہ! حکیم الامت
اِنَّکَ مَیِّت’‘ وَّاِنَّھُمْ مَیِّتُوْنْ
یوں تو موت اس عالمِ آب وگل کی ہراس چیزکے لیے ہی مقدر ہے جو زندگی کاعاریتی لباس پہن کر بساطِ ہستی پرنمودار ہوئی ہے۔لیکن جس طرح زندگی زندگی میں فرق ہوتا ہے۔اسی طرح ہرایک کی موت بھی یکساں نہیں ہوتی۔ کبھی کبھی ایسی اموات بھی واقع ہوتی ہیں جوصرف افراد واشخاص کی اموات نہیں ہوتیں بلکہ ان ہزاروں لاکھوں انسانوں کی عمارتِ حیات بھی اس سے متزلزل ہو جاتی ہے جومرنے والے کے دامانِ عقیدت وارادت سے وابستہ ہوتے ہیں۔ پھراس کی موت کاماتم آنکھوں کے چند قطرہ ہائے اشک سے نہیں ہوتا۔بلکہ ہزاروں دلوں کی پرسکون آبادیاں ایک مستقل غم کدۂ آمال دامانی بن کر رہ جاتی ہیں۔ امیدوں اور ولولوں کے چراغ بجھ جاتے ہیں ۔نشاط وکامرانی ٔحیات کے آتش کدے سرد ہوجاتے ہیں اورایسا محسوس ہوتاہے کہ اس حادثہِ جان کاہ نے کائناتِ عالم کی ہرہر چیز کواداس اورغمگین بنادیا ہے۔اسی قسم کی ایک موت پرعربی شاعر نے کہاتھا۔
وماکان قیس’‘ ھلکہٗ ھلک واحد
ولکنَّہ بنیانُ قومٍ تَھَدَّمَا
قیس کامرنا صرف ایک شخص کامرنا نہیں ہے
بلکہ وہ ایک قوم کی بنیاد تھا جومنہدم ہوگئی
گذشتہ ماہِ جولائی کی تاریخ ۱۹؍ ۲۰؍کی درمیانی شب کو تقریباً دس بجے حکیم الامت حضرت مولانا اشرف علی صاحب تھانوی کاجو سانحۂ ارتحال پیش آیاوہ اسی قسم کاسانحہ تھا۔حضرت مولانا جس طرح شریعت کے عالم متبحر تھے طریقت اور سلوک میں بھی مقامِ رفیع کے مالک تھے۔ان کی ذات علومِ ظاہری وباطنی کا مخزن تھی۔علمِ سفینہ سے زیادہ علم سینہ ان کااصلی جوہر اور زیور تھا۔تحریریں علم و فضل کامعدن ہوتی تھیں اور تقریر بھی بلاکی اثر انگیز تھی، وہ جس بات کوحق سمجھتے تھے اسے برملا کہتے اور کرتے تھے اوراس میں انھیں کسی لومۃ لائم کی پروا نہیں ہوتی تھی۔خودایک درویش گوشہ نشین تھے۔مگران کاآستانہ بڑے بڑے...

The Extent to Which Pupils Have Adopted the Values of Middle School English Book Year Three

The present study aims at determining the extent to which the values, derived from the second-generation curriculum and introduced in the third-year middle school English book, are adopted by teenagers to help them overcome their identity crisis, in Algeria. To achieve this aim, two questionnaires applied to 70 third year teenage pupils were carried out. One prior to the study with the third year English book, the other one after the study.The results of the two questionnaires were compared and showed that the values total average has increased from 16.06 in the first questionnaire to 21.33 in the second. An increase has also been noticed in the averages of the four value dimensions: national identity (from 4.76 to 6.10), national awareness (from 3.63 to 5.36), citizenship (from 4.13 to 5.31) and openness to the world (from 3.50 to 4.50). The previous results confirm that there is an improvement in the values of pupils yet the commitment to the values of « openness to the world » dimension is still weak compared to the other ones and needs further research to be improved.

Prediction of Membrane Proteins Using Machine Learning Approaches

Membrane proteins are the basic constituent of a cell that manage intra and extracellular processes of a cell. About 20-30% of genes of eukaryotic organisms are encoded from membrane proteins. In addition, almost 50% of drugs are directly targeted against membrane proteins. Owing to the significant role of membrane proteins in living organisms, the identification of membrane proteins with substantial accuracy is essential. However, the annotation of membrane proteins through conventional methods is difficult, sometimes even impossible. Therefore, membrane proteins are predicted from topogenic sequences using computational intelligence techniques. In this study, we conducted our research in two phases regarding the prediction of membrane protein types and structures. In Phase-I, regarding the prediction of membrane protein types, four different ways are explored in order to enhance true prediction. In the first part of phase-I, membrane protein types are predicted using Composite protein sequence representation followed by the application of principal component analysis in conjunction with individual classifiers. In the second part, the notion of ensemble classification is utilized. In part three, an error correction code is incorporated with Support Vector Machine using evolutionary profiles (Position Specific Scoring Matrix) and SAAC based features. Finally, in part four, a two-layer web predictor Mem- PHybrid is developed. Mem-PHybrid accomplishes the prediction in two steps. First, a protein query is identified as a membrane or a non-membrane protein. In case of membrane protein, then its type is predicted. In the second phase of this research, the structure of membrane protein is recognized as alpha-helix transmembrane or outer membrane proteins. In case of alpha- helix transmembrane proteins, features are explored from protein sequences by two feature extraction schemes of distinct natures; including physicochemical properties and compositional index of amino acids. Singular value decomposition is employed to extract high variation features. A hybrid feature vector is formed by combining the different types of features. Weighted Random Forest is then used as a classification algorithm. On the other hand, in case of outer membrane proteins, protein sequences are represented by Amino acid composition, PseAA composition, and SAAC along with their hybrid models. Genetic programming, K-nearest neighbor, and fuzzy K-nearest neighbor are adopted as classification algorithms. Through the simulation study, we observed that the prediction performance of our proposed approaches in case of both types and structures prediction is better compared to existing state of the arts/approaches. Finally, we conclude that our proposed approach for membrane proteins might play a significant role in Computational Biology, Molecular Biology, Bioinformatics, and thus might help in applications related to drug discovery. In addition, the related web predictors provide sufficient information to researchers and academicians in future research.
Asian Research Index Whatsapp Chanel
Asian Research Index Whatsapp Chanel

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