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ڈاکٹر مختار احمد انصاری

ڈاکٹر مختار احمدانصاری مرحوم
۹؍ مئی ۱۹۳۶؁ء کی شام کو سات بجے کے قریب میں ڈیرہ دون کی ایک سڑک سے گزر رہا تھا کہ پیچھے سے ایک موٹر تیزی سے آئی اور نکل گئی، میں نے دیکھا کہ اس پر ڈاکٹر انصاری بیٹھے ہیں، سرکھلا تھا اور چہرہ سے بے حد تکان معلوم ہوتا تھا، رات گزر گئی اور صبح کو ان کی قیام گاہ کی تلاش کی، معلوم ہوا کہ وہ رات ہی دلّی چلے گئے، لیکن جب شام ہوئی تو معلوم ہوا کہ وہ رات دلّی نہیں گئے، راستہ سے سیدھے جنت کو سدھارے، دل دھڑکا آنکھیں پرنم ہوئیں اور سینہ سے آہ کا ایک شعلہ اٹھا، جس نے صبر و تمکین کی متاع کو جلا کر خاکستر بنادیا۔
ڈاکٹر مختار احمد انصاری گو نسب و وطن کے لحاظ سے ضلع غازی پور کے ایک ممتاز قصبہ یوسف پور کے ایک نہایت شریف خاندان سے تھے، مگر در حقیقت ان کا تعلق پورے ہندوستان سے تھا، اس یوسف کا کنعان، وہ محدود مقام نہ تھا، جس کو یوسف پور کہتے ہیں، بلکہ پورا ہندوستان تھا، اسی لئے آج پورے ہندوستان نے ان کی موت کا ماتم کیا، کیا مسلمان، کیا ہندو، کیا سکھ، کیا عیسائی سب نے یہی جانا کہ آج ان کا حقیقی بھائی اس دنیا سے چل بسا۔
میں نے ڈاکٹر انصاری کو سب سے پہلے ۱۹۱۲؁ء میں اس وقت دیکھا جب وہ بلقان کی جنگ میں طبی وفد لے کر ترکی جارہے تھے اور اس تقریب سے لکھنؤ اسٹیشن سے گزر رہے تھے، مولانا شبلی اور بہت سے لوگ لکھنؤ اسٹیشن پر ڈاکٹر صاحب کو الوداع کہنے گئے تھے، اس وقت ڈاکٹر صاحب کی عمر ۳۰، ۳۲ برس کی تھی، کھلتا ہوا رنگ، دُبلا پتلا چھریرا بدن کشیدہ قامت، ہنستا چہرہ، انوری یا قیصری مونچھیں، جسم پر چست خاکی وردی،...

Pengaruh Kompensasi dan Lingkungan Kerja Terhadap Kinerja Karyawan pada PT. Enseval Putera Megatrading cabang Pekanbaru menurut Perspektif Ekonomi Syariah

This research is motivated by the ups and downs in the quality of employee performance at PT Enseval Putera Megatrading Pekanbaru Branch from 2020 to 2022. Based on the formulation of the problems that have been made, this study aims to determine the effect of compensation and work environment on employee performance at PT Enseval Putera Megatrading Pekanbaru Branch This research is a type of quantitative research. The sample used in this study were employees of PT Enseval Putera Megatrading Pekanbaru Branch as many as approximately 52 people. Sampling technique by means of Simple Random Sampling. With data collection through observation, interviews, questionnaires and literature studies. The analysis technique in this study uses Multiple Linear Regression Analysis. This study uses multiple linear regression with the help of the SPSS Ver program. 21.00. The results of this study, compensation has a positive effect on employee performance. This is evident from the results of the t test which obtained t count X1 greater than t table, namely the value of t count (7.467) greater than t table (2.010) accepted at a significance level of 5%. The work environment has a positive effect on employee performance. This is evident from the results of the t test which obtained the t count X2 greater than the t table, namely the t value (7.484) greater than the t table (2.010), accepted at the 5% significance level. Compensation and work environment together have a positive effect on employee performance. This is evident from the results of the F test which shows the results of the F count of (415.672) greater than the F table of (3.19) accepted at the 5% significance level. Islamic economic review of the effect of compensation and work environment on employee performance at PT Enseval Putera Megatrading Pekanbaru branch can be categorized as sharia based on ijarah contract.

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.
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