مولانا محمد یوسف
افسوس ہے مولانا محمدیوسف صاحب امیر تبلیغی جماعت اس خاک دان عالم کوالوداع کہہ کر رہ گزاے عالمِ جاودانی ہوگئے۔ اُن کی زندگی کاہرلمحہ تبلیغ و ارشاد اور دعوت الی الحق کے لیے وقف تھا،اس لیے موت بھی اسی حالت میں آئی۔ یکم اپریل کومغرب کے بعد لاہور میں ایک مجمع کوخطاب کررہے تھے کہ تقریر کرتے کرتے اچانک غشی طاری ہوئی۔رات بھر یہی کیفیت رہی ۔صبح کوچند منٹ کے لیے ہوش آیاتوصرف اتنا فرمایا ’’بھائیو! اب میراوقت پورا ہوچکا ہے آپ سب میرے لیے دعا کریں‘‘ اتناکہہ کر جوبے ہوش ہوئے تو پھر ہوش نہ آیا اور ۲؍اپریل کوجمعہ کی نماز کے بعد جان جان آفریں کے سپردکردی۔اِنَّا لِلّٰہِ وَاِنَّااِلَیْہِ رَاجِعُوْنَ ۔جنازہ ہوائی جہاز کے ذریعہ دہلی لایا گیا اور۳؍ اپریل کوبستی نظام الدّین میں جہاں زندگی گزاری تھی تدفین عمل میں آئی۔
مولانا ہندو پاک کے اکابر علماء میں سے تھے ۔مطالعہ اورتحریر وتصنیف کا ذوق فطری تھا۔ ہزار مصروفیتوں کے باوجود روزانہ چندگھنٹے مطالعہ ضرور کرتے اور لکھتے تھے۔ چنانچہ ’حیات الصحابہ‘ کے نام سے عربی زبان میں ایک ضخیم کتاب دو جلدوں میں دائرۃ المعارف حیدرآباد دکن کی طرف سے شائع ہوچکی ہے۔ علم و عمل، اخلاق وعادات اورتقویٰ وطہارت میں علمائے سلف کانمونہ تھے ۔لیکن اُن کا نہایت عظیم الشان کارنامہ جو مسلمانوں کے موجودہ حالات میں ایک نہایت اہم موڑ کی حیثیت رکھتا ہے یہ ہے کہ اُنھوں نے اپنے والدماجد مولانا محمدالیاس صاحب ؒ کی وفات کے بعد اُن کے جاری کیے ہوئے تبلیغی کام اوراس کے نظم و نسق کوباقی رکھا بلکہ اُسے ترقی دے کر کہیں سے کہیں پہنچادیا اور پھر اُسی شان اور اسی وضع کے ساتھ ، چنانچہ اس جماعت کانہ کہیں دفتر ہے نہ اس کے لیے عہدہ دار اور نہ اُن کا انتخاب، نہ صدر نہ سکریٹری، نہ خازن اور...
The Patient Safety Goals (SKP) drive specific improvements in patient safety. These objectives highlight problematic areas of health care in a system implemented in hospitals to make patient care safer. This study aims to analyze the implementation of patient safety goals in Makassar City Hospital. This type of research is mixed methods research. The research uses a sequential explanatory strategy. The results showed that the implementation of patient safety targets based on the Hospital Patient Safety Target Standards (SNARS) at Makassar City Hospital has a good implementation of patient safety targets. The implementation of patient safety targets in terms of leadership in the Makassar City Regional General Hospital (RSUD), namely the awarding of awards has never been done, and supervision is carried out by looking at patient safety reports. In terms of human resources, training related to patient safety is still lacking and only during accreditation. Regarding policies, there are SOPs related to patient safety incidents and there is no clear sanction, only a warning. For teamwork, there is no availability of a patient safety team in the treatment room, only KMKP has a patient safety team. In addition, the implementation of patient safety goals in terms of communication, namely the existence of positive feedback given and followed up by the Patient Safety and Quality Committee (KMKP), as well as lack of socialization by KMKP, only at the time of accreditation.
The non-destructive analysis of a Solid Pharmaceutical Product (SPP) is essential to verify the quality without destroying the product. This analysis may be performed using various image processing and signal processing techniques on images and multispectral data. Based on this analysis, an SPP may be classified as defective or non-defective. The SPP (categorized as defective) are exposed to three different environmental factors (humidity, temperature and moisture) over different time periods and the variations in data are analyzed to judge the effects of these factors on classification of an SPP. In this research, we have proposed two non-destructive methods to identify defective and non-defective SPPs using their surface morphology. In first approach, multiple textural features are extracted using microscopic images of the surface of the defective and non-defective SPPs. These textural features are Gray Level Co-occurrence Matrix, Run Length Matrix, Histogram, Auto Regressive Model and HAAR Wavelet. Total textural features extracted from microscopic images are 281. The features are reduced using three feature reduction techniques; Chi-square, Gain Ratio and Relief-F. We have formulated three feature sets, through experimentation, with 281, 15 and 2 features. We have used four classifiers namely Support Vector Machine, K-Nearest Neighbors, Naïve Bayes and Ensemble of Classifiers, to calculate the accuracy of proposed approach. The classifiers are implemented using leave-one-out cross validation and holdout validation methods. We tested each classifier against all feature sets and the results were compared. The results showed that in most of the cases, Support Vector Machine performed better than the other classifiers. In second approach, we have used multispectral data and applied wavelet transformations in conjunction with various machine learning techniques for the classification. The results showed that the spectrum extracted from Ultra Violet x wavelength range is more suitable for the classification between defective and non-defective SPPs. Furthermore, results also described that K-Nearest Neighbors classifier or Ensemble of Classifiers is a more appropriate classifier. In the last, the hybrid of the both approaches was tested. The analysis of the results showed that the hybrid approach is better than the individual ones. An accuracy of 94% is achieved using K-Nearest Neighbors when a combined dataset of SPPs affected by all of the three environmental factors is used.