Milk is generally considered as highly nutritious and useful food for all age groups as well as inexpensive and widely available. However, its quality and microbial content are the features which have to be observed. The quality of milk depends on its biochemical composition and hygienic conditions during the collection of milk and its distribution. Milk itself acts as best medium for enhancing the growth and development of different types of microbes because of its composition and presence of water in large quantity. Milk sold in Bahawalpur district was suspected to be contaminated because of repeated outbreaks of gastroenteritis. In this context, the research was designed to rule out molecular detection and quantitative analysis of Escherichia coli (E. coli) in the milk samples obtained from various tehsils of Bahawalpur district. E. coli were considered as the most prevalent bacteria of milk and could be the most probable microbe causing this disease. In this research, 100 raw milk samples were gathered in a way that 10 samples each were collected from Jamalpur, Hasilpur, Khairpur, Qaimpur, Lal Sohanra, Lal Sohanra National Park, Yazman Mandi, Head Rajkan, Ahmedpur East and Uch Sharif. Then the samples of milk were cultured on different culture media for bacterial segregation. Identification of bacterial specie was done through gram?s staining and properties of bacterial culture on different selective media. Biochemical tests were also performed which include catalase and coagulase tests. Final identification was performed through PCR and resolution of PCR products by gel electrophoresis. Antibiotic sensitivity test was also performed so as to confirm the susceptibility of E. coli regarding multiple antibiotics. Out of 10 samples collected each from Jamalpur, Hasilpur, Khairpur, Qaimpur, Badar Sher, Lal Sohanra National Park, Yazman Mandi, Head Rajkan, Ahmedpur East and Uch Sharif, results had shown the presence of E.coli in 4(40%), 6(60%), 3(30%), 2(20%), 4(40%), 5(50%), 3(30%), 4(40%), 6(60%) and 5(50%) samples respectively. E. coli isolates were amplified by PCR based on 16S rRNA gene. Results of antibiotic sensitivity test revealed that E. coli isolates had shown resistance to amoxicillin (85%) and erythromycin (72%). However, they were found to be affected by azithromycin (53%), ciprofloxacin (86%), gentamicin (86%), norfloxacin (80%) and streptomycin (66%). Resistant pattern in relation to broad spectrum antibiotic (i.e., amoxycillin) points to a situation which should be considered carefully and suggests that indiscriminate use of antibiotics for precautionary or therapeutic purposes should be avoided as it could be the cause of increasing antimicrobial resistance.
ڈاکٹر سعید انصاری افسوس ہے کہ ۲۶؍ جنوری کو ڈاکٹر سعید انصاری کا دہلی میں کینسر کے موذی مرض میں انتقال ہوگیا، اﷲ تعالیٰ ان کی مغفرت فرمائے، وہ دارالمصنفین کی مجلس انتظامیہ کے قدیم رکن تھے۔ (صباح الدین عبدالرحمن، فروری ۱۹۸۴ء)
ڈاکٹر سعید انصاری قارئین معارف کو گزشتہ شمارہ سے جناب سعید انصاری کے انتقال کی خبر معلوم ہوچکی ہے، ان کا وطن اعظم گڑھ ہی تھا، اپنے محلہ اور شہر کے قدیم مدرسہ اسلامیہ میں ابتدائی تعلیم حاصل کرنے کے بعد انھوں نے مشن اسکول میں داخلہ لیا، یہ بڑا پرآشوب دور تھا، ملک کے گوشہ گوشہ میں خلافت، اور ترک موالات کی تحریک کے اثر سے انگریزوں کے خلاف ہیجان برپا تھا۔ تحریک کے پروگرام میں سرکاری اسکولوں اور کالجوں کا مقاطعہ بھی تھا، سعید انصاری صاحب نے اس سے متاثر ہوکر اسکول چھوڑ دیا اور بنارس جاکر کاشی ودیا پیٹھ سے فرسٹ ڈویژن میں میڑک کیا، انٹرمیڈیٹ میں بارہ روپے ماہوار وظیفہ ملا مگر جامعہ اسلامیہ کی کشش انھیں علی گڑھ کھینچ لائی، ۱۹۲۴ء میں وہ طلبہ کی انجمن اتحاد کے سکریٹری اور ان کے ہم سبق ڈاکٹر یوسف حسین خان مرحوم نائب صدر ہوئے، ۱۹۲۵ء میں بی۔اے کیا اور ۱۹۲۶ء میں جامعہ میں استاد کی حیثیت سے ان کا تقرر ہوا، اس زمانہ میں اس کی مالی حالت نہایت خراب تھی، کئی کئی مہینے تک استادوں کو تنخواہیں نہیں ملتی تھیں، جامعہ کے امنا (ٹرسٹیز) اسے بند کردینے کے لئے آمادہ ہوگئے تھے مگر انجمن تعلیم ملی کے ارکان نے بیس برس تک جامعہ کی خدمت کرتے رہنے اور ڈیڑھ سو سے زیادہ مشاہرہ نہ لینے کا عہد کیا تھا، ابتداء میں گیارہ استاد اس کے حباتی رکن تھے جن میں سعید انصاری مرحوم بھی تھے۔ وہ اپنی علمی و تعلیمی استعداد بڑھانے...
Social anxiety is the fear of interaction with other people that brings on self-consciousness, feelings of being negatively judged and evaluated, and, as a result, leads to avoidance. Social anxiety is the fear of being judged and evaluated negatively by other people, leading to feelings of inadequacy, inferiority, embarrassment, humiliation, and depression. The major causes of Social anxiety are Rights abuses, Provocation, corruption, murder, Law-lessens, nepotism, Prejudices, grouping, Propaganda and carelessness. All of these things are causes of the destruction of a prosperous society. Because of these each individual of society remains restive and society become victims of violence. In this paper the social anxiety conditions and its solution will be described in detail in the light of seerah.
The availability of low-cost video cameras and digital media storage has invited huge investments in developing state-of-the-art algorithms that automatically evaluate and understand video datasets. One such class of algorithm is object tracking which analyzes the data and automatically determines the location of the object in a video sequence. As these algorithms are a prelude to a higher level decision making algorithms, therefore estimation of the trajectory of the object must be accurate and robust under many challenging constraints. A very popular class of object tracking algorithm is the hybrid object tracking category based on integrating Meanshift (MS) and Particle Filter (PF) (MSPF). The purpose of this integration was to overcome the limitation of the PF methods that required a large number of samples/particles PF method to approximate the object state. Consequently, this integration uses the MS optimization procedure to move fewer particles, in the direction of gradient ascent, which represents the dynamics of the target more accurately. The existing methods employ a pre-determined combination of features, inherently assuming that the background would not change. However in uncontrolled environment, it is difficult to specify the background of the object in advance as it moves around the field of view of the camera and thereof this assumption may not often hold. Moreover, hybrid tracking systems based on the MSPF methodology are very compute intensive and it is desirable to reduce this complexity. In the first part of this research, the dissertation aims to investigate an adaptive multi-feature framework that is implemented on top of the MSPF methodology that tracks the object in the local perspective. Essentially that takes care of the dynamic and changing characteristic of the background, which is one of the most important challenges in the object tracking domain. In this research work, an Adaptive Multi-Feature framework is proposed and implemented on top of the MSPF methodology (AMF-MSPF). An adaptive ranking module is proposed that is triggered after a certain criteria is violated, in which case a new set of features are selected for tracking the object. The top ranked features are selected to represent the object, which gives the tracker the ability to adapt to locate the object with an upgraded set of feature. Consequently, this improved local discrimination of the target from its immediate neighboring pixels. In most applications a small portion of computational resources are dedicated to trackers and rest is reserved for higher level decision making tasks, which mandate trackers to be efficient and less complex. Thereby, the second part of the dissertation looks into the complexity of the MSPF methodology. As the MSPF methodology is already a computationally intensive processing task, implementing a feature ranking module on top of it might complicate matters. The feature ranking module also requires a significant portion of the power, thereby a novel MS technique is proposed to free some resources for the ranking module. This novelty comes from an observation that only a fraction of random samples were required by the MS optimization to approximate the similarity metric without inducing significant error. This computational reduction would be advantageous given the complex integration of the MS and PF, because the MS procedure is directly proportional to the number of particles that would take many MS iterations to converge. The proposed novelty in the MS method has reduced its complexity that has greatly impacting the overall complexity of the proposed AMF-MSPF. The proposed AMF-MSPF framework is tested on sequences from the CAVIAR datasets such as Browse and Walkbyshop1and an s8 sequence was taken from the PET dataset. These datasets are known for a number of challenging constraints such as abrupt intensity variations, full occlusions, cluttered background etc. Qualitative results have shown robust and accurate tracking under stringent constraints. In the quantitative analysis, a comparison with the existing methods has been carried out. The proposed framework has shown significant improvements in terms of root mean square error (RMSE), false alarm rate (FAR), and F-SCORE. The average RMSE, FAR, and F_SCORE, over all the video sets, of the proposed AMF-MSPF are 8.68, 0.15, and 0.92, which has improved manifold as compared to the chosen reference methods. Experimental results have proved the effectiveness of the proposed framework.