مفتی عبدالقادر
افسوس ہے کہ گذشتہ مہینہ ۲۴؍ اگست کو فرنگی محل کے نامور عالم مفتی عبدالقادر صاحب نے وفات پائی، مرحوم علم و عمل میں اپنے اسلاف کرام کا نمونہ اور طبعاً نہایت خاموش اور عزلت پسند تھے، پوری زندگی خاموشی اور قناعت کے ساتھ درس و تدریس اور علم و افتاد کی خدمت میں گذاری، ان کی موت سے فرنگی محل کی ایک اہم یادگار مٹ گئی، نئی نسل جدید تعلیم یافتہ ہے، اس کو اپنے اسلاف کے علوم اور روایات سے بہت کم علاقہ رہ گیا ہے اس لئے جو ایک دو پرانے بزرگ باقی رہ گئے ہیں ان کے بعد فرنگی محل میں سناٹا نظر آتا ہے۔
اس خاندان میں جتنی طویل مدت تک علم رہا اور اس سے پورے ہندوستان کو جو فیض پہنچا اس کی مثال دوسرے علمی خاندانوں میں کم ملے گی، عموماً دو چار پشتوں سے زیادہ کسی خاندان میں علم نہیں چلتا، مگر فرنگی محل تقریباً تین صدیوں تک دینی علوم اور اس کی تعلیم کا مرکز رہا اور اس مدت میں ملا نظام الدین بانی درس نظامیہ ، ملا حیدر ، ملا حسن، مولانا بحرالعلوم، مولانا عبدالحئی اور مولانا عبدالباری رحہم اﷲ جیسے بڑے بڑے علماء پیدا ہوئے مگر اب بظاہر اس سلسلۃ الذہب کا خاتمہ نظر آتا ہے۔
مفتی صاحب مرحوم علم و عمل کے ساتھ اخلاق فاضلہ اور اوصاف حمیدہ سے بھی آراستہ نہایت خاموش متواضع، نرم خور، خندہ جبیں، شگفتہ مزاج اور خوش خلق تھے، ملنے والوں پر ان کے علم سے زیادہ ان کے اخلاق کا اثر پڑتا تھا، ان اوصاف کی بنا پر وہ ہر طبقے میں بڑے مقبول تھے۔ راقم نے ان سے مختصر المعانی پڑھی تھی، اس زمانہ میں ان کے اخلاق اور مہرومحبت کا جو نقش دل پر قائم ہوا تھا وہ اب تک باقی ہے، اﷲ تعالیٰ اس...
Gender roles not only keep men and women in different spheres of family and social life but they also promote gender segregation in the education sector and professional life. There is a lot of research being conducted on women working in male dominated professions but there is scarcity of research regarding males working in female dominated professions. This study was conducted to explore the experiences of men working in female dominated professions. The first phase of this study collected quantitative data about the type female-dominated professions in Pakistan. Based on this data in-depth qualitative interviews were done with 5 professionals using snowball sampling: Nurse, Psychologist, Montessori teacher, Makeup artist/ Beautician, and Bus host. Thematic analysis was used to identify sub-themes presented in this study: (i) Reasons or motivation for joining nontraditional profession; (ii) Reaction of near and dear ones; (iii) Positive aspects of female dominated profession; (iv) Challenges of female dominated profession; (v) Professional journey; (vi) Being a minority in female majority; (vii) Struggle to maintain masculinity and (viii) Future aspirations. Study findings can be used to support male entry and retention in female dominated professions.
The advancement in microscopic imaging techniques results in the generation of a large amount of high quality data in no time. The accurate, real time and autonomous analysis of this data is crucial for the theoretical biomedical research and clinical diagnosis. A lot of vigorous attempts are dedicated for the evolution of computer aided techniques, which improve human diagnosis by increasing efficiency, decreasing variability in the observations and reducing the human effort on labelling and classifying images. Among such determined attempts, histology image classification is one of the most significant fields due to its extensive application in pathological diagnosis such as tumor/cancer diagnosis. Inherent heterogeneous nature and random spatial intensity differences of histopathology images make the histology tissue classification a complex task. In this thesis four novel, robust and adaptive frameworks are proposed for automated and correct classification of histopathology images. The goal of this research is to achieve the expert pathologists’s diagnosis by catering inherent complexity and prevailing the variations in the opinion of different pathologists. The key contributions of this reseach are: First, a histopathological classification problem is explored from all the perspectives by performing pattern analysis at image level and cell level individually and collectively. The first proposed framework is an abstract feature based framework which performs image-level analysis to capture global texture information. The second framework performs cell-level analysis to get nuclei structure and texture. The third and fourth frameworks perform cell-level and image-level analysis to get nuclei structure and image global texture. Second, the exploration of RGB colour space is preferred to mimic the pathologists’ diagnosis process. The imperative role of RGB color channels is investigated in histopathology image classification by extracting nuclear and global image features across these color channels. Third, instead of analyzing various colour spaces a number of feature measures are explored from spatial and frequency domain to encounter maximum diversity. The individual and combined effect of a large number of statistical, structural and spectral feature measures are analyzed including co-occurrence matrices, run-length matrices, local binary patterns with Fourier transforms, morphology features and intensity features. Fourth, an extensive investigation of rank-based feature selection schemes is performed and proved that elitism is not an optimal strategy for feature selection in histopathology image classification. An abstract feature i.e. an optimal combination of functionally collaborating features having implicit linkages is identified based on classification accuracy through evolutionary search process. Fifth, a number of classifiers are explored and an automatic selection of parameters of classification model (classifier and classifier’s parameters) is performed through Genetic Algorithm based evolutionary technique. The experimentation is performed on images of grade-I benign meningioma four subtypes (meningothelial, fibroblastic, transitional and psammomatous) and pre-invasive breast lesions four classes (usual ductal hyperplasia (UDH) and three nuclear grades of ductal carcinoma in situ (DCIS)). The proposed frameworks achieved the promising classification results for four meningioma subtypes and breast lesions grades. In most of the cases, optimal sets of features obtained from the combination of three color channels and classified through linear support vector machine classifier presented the highest classification accuracy. The extraction of nuclear texture in spatial and frequency domain presented promising classification results.