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اقبال شناسی کے تناظر میں فیض اور جابر علی سید کا اختصاصی مطالعہ

اقبال شناسی کےتناظر میں فیض اور جابر علی سید کا اختصاصی مطالعہ

اردو ادب میں اقبال شناسی ایک بلند مقام و مرتبہ رکھتی ہے۔ سیالکوٹ کے مشاہیر کی بڑی تعداد نے اقبا ل شناسی پر نمایاں کام کیا ہے۔میجر اقبا ل ڈار ،نعیم اللہ ملک ،ٖڈاکٹر نظیر صوفی،امان اللہ  خاں،آسی ضیائی رامپوری،خالد نظیر صوفی،ڈاکٹر الحمید عرفانی ،مولوی الف دین  نفیس،یوسف سلیم چشتی ،مولانا ظفر علی خاں،چودھری محمد حسین ،فیض احمد فیض   جابر علی سید اور   سیالکوٹ سے تعلق رکھنے والے دیگر مشاہیر نے فکر اقبال پر قابلِ قد ر کام کیا ہے۔البتہ پیش نظر آرٹیکل میں دو معروف مشاہیر فیض اور جابر علی سید کا تنقیدی و جائزہ پیش کیا گیا ہے ۔  
فیض احمد فیض ایک شاعر ،نثر نگار کے ساتھ ساتھ اقبال شناس بھی ہیں۔ فیض احمد  بین الاقوامی شہرت یافتہ شاعر ہیں۔ فیضؔ عظیم مفکر اقبال  کے فکروفلسفہ سے خاص نسبت رکھتے تھے ۔ ان دونوں کے کئی اساتذہ  اور تعلیمی درسگاہیں بھی مشترک تھیں۔فیض اور اقبال دونوں  کاجائے پیدائش سیالکوٹ ہےعلاوہ ازیں دونوں کے والد بھی آپس میں گہرے دوست تھے۔ فیض کی  طرح جابر علی سیدایک شاعر ،نقاد ،ماہر لسانیات وعر وض کے ساتھ ساتھ اقبال شناس بھی ہیں۔ جابر نے کئی ادبی مشاہیر کو اپنی تنقید کا موضوع بنایا ہے۔ انھوں نے علامہ اقبال کے کلام اور فن و فکر کو اپنی خصوصی توجہ کا محور بنایا ہے۔ تحقیق وتنقید کے سلسلے میں اقبال جابر کا پسندیدہ موضوع تھا۔ انھوں نے فکر ِاقبال پر بھرپور انداز اور دل وجان سے سپرد قلم کیا۔ اس سلسلے میں انھوں نے اقبال پر باقاعدہ کتب بھی تصنیف کی ہیں۔
فیض احمد فیض ایک شاعر اورنثر نگار کے ساتھ ساتھ اقبال شناس بھی ہیں۔ علامہ اقبال پر لکھے ہوئے مضامین پر مشتمل فیضؔ کی کتاب’’اقبال‘‘ ۱۹۸۷ء میں شائع ہوئی۔ اس کتاب کا ناشر...

Unveiling Self-Cursing Patterns in Sensitive Individuals: A Qualitative Inquiry from a Pakistani Context

Being sensitive means being overwhelmed and easily affected by little things happening in the environment. Sensitivity is usually considered negative trait in humans and sensitive people are being treated negatively and harshly in society, such individuals are usually observed to curse themselves. This qualitative study was designed to explore the meanings and perception of sensitivity. Triangulation of data sources was selected to find the results. This study recruited 21 participants for interview and 56 documents for data collection. The participants in this study were inducted purposefully having ages between 18 to 29 years. WhatsApp interviews, aligned best with the objectives of the research study that lasted from 20- 30 minutes. Data collection was started after the formal approval of research protocol from the departmental research committee. Three veterans of qualitative research were involved in the recording and transcription of data; memos and code book were also incorporated along with the data. Phenomenological analysis for interviews and content analysis for documents were used to analyze the data through the lens of Rubin (2021). Methodological Integrity was maintained through triangulation of data sources. The findings exhibit six main themes; intensive feelings, personality traits, expectations, prioritizing relations, emotions and extreme reactions. Researchers faced an increment in sensitivity during data collection. To deal with this problem few pauses were taken during data collection, so it took longer than usual to collect data. Implications of the study have been discussed.

Investigating Protein Semantic Similarity Measurement and its Correlation With Sequence Similarity

Protein sequence similarity is commonly used to compare proteins, and to search for proteins similar to a query protein. With the growing use of biomedical ontologies, especially Gene Ontology (GO), semantic similarity between ontology terms, proteins and genes is getting attention of researchers. Protein semantic similarity measurement has many applications in bioinformatics, including protein function prediction and protein-protein interactions. Semantic similarity measures were proposed by Resnik, Jiang and Conrath, and Lin. Recent measures include Wang and AIC. The question whether the semantic similarity has a strong correlation with sequence similarity, has been addressed by some authors. It has been reported that such correlation exists, and it has been used for the evaluation of semantic similarity computation methods as well as for protein function prediction. We investigate the correlation between semantic similarity and sequence similarity using graphs, Pearson''s correlation coe cient and example proteins. Wend that there is no strong correlation between the two similarity measures. Pearson''s correlation coef- cient is not su cient to explain the nature of this relationship, if not accompanied by graph analysis. Wend that there are several pairs with low sequence similarity and high semantic similarity, but very few pairs with high sequence similarity and low semantic similarity. Interestingly, the correlation coe cient depends only on the number of common GO terms in proteins under comparison. We propose a novel method SemSim for semantic similarity measurement. It addresses the limitations of existing methods, and computes similarity in two steps. In therst step, SimGIC like approach is used where contribution of common ancestors is divided by contribution of all ancestors. In the second step, we use two new factors: Speci city computed from ontology based information content, and Uniqueness computed from annotation based information content. Thenal result, after applying these two factors, makes clear distinction between the generalized and specialized terms. We conducted experiments on protein pairs having evidence of high similarity, and the ones having evidence of low similarity. Experiments show that SemSim performs better than the previous measures in both cases. When semantic similarity is used for searching proteins from large databases, the speed issue becomes signi cant. To search for proteins similar to a query protein having m annotations, from the database of p proteins, p m n g comparisons would be required. Here n is the average annotations per protein, g is the complexity of GO term similarity computation algorithm, and it is assumed that each term of one protein is compared with each term of the other. We propose a method SimExact that is suitable for high speed searching of semantically similar proteins. Although SimExact works on common terms only, our experiments show that it gives correct results required for protein semantic searching. SimExact can be used as a pre processor, generating candidate list for the existing methods, which proceed for further computation. Such arrangement will gain high speed while retaining the accuracy of the given method. We provide online tool that generates a ranked list of the proteins similar to a query protein, with a response time of less than 8 seconds in our setup. We use SimExact to search for protein pairs having high disparity between semantic similarity and sequence similarity. SimExact makes such searches possible, which would be NP-hard otherwise.
Asian Research Index Whatsapp Chanel
Asian Research Index Whatsapp Chanel

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