ﷺ
صفاتِ حسنِ مطلق سے بشر کی آشنائی ہو
’’پسِ فکر و تعمّل جب جمالِ مُصطفائی ہو‘‘
یہی پُر نور منظر مظہرِ حسنِ حقیقت ہے
رُخِ تاباں کی رعنائی سے حق کی رونمائی ہو
اگر شامل درودوں کی صدائیں اِس میں ہو جائیں
بھلا حرفِ دعا کو کیوں ملالِ نارسائی ہو
گدایانِ درِ آلِ نبیؐ کیسے نہ نازاں ہوں
فزوں تر تختِ شاہی سے جب اِس در کی گدائی ہو
یہ وہ کوچہ ہے جس کوچے پہ جنّت ناز کرتی ہے
یہی وہ در ہے جس در پر دو عالم کی بھلائی ہو
سرِ شاخِ تمنّا غنچہ ہائے شوق رقصاں ہوں
صبا گلزارِ طیبہ سے کوئی پیغام لائی ہو
عطائے حرفِ مدحت ہو پسِ خاموشیِ خلوت
سرِ بزمِ سخن ہم کو عطا پھر لب کشائی ہو
وہاں عرفانؔ سا عاجز جھکائے کیوں نہ سر اپنا
جہاں پر سرنگوں سارے جہاں کی پارسائی ہو
Pseudomonas aeruginosa is a widespread organism, caused severe nosocomial infection in human and associated with multiple drug resistance (MDR)Objective: The present study was carried out to observe current antimicrobial resistant pattern of Pseudomonas aeruginosa in Lahore and to detect the Metallo-beta-lactamase (MBL) gene in carbapenem resistantPseudomonas aeruginosaMethods: By screening 360 samples total 123 Pseudomonas aeruginosa was identified by standard microbiology techniques such as microscopy and biochemical testing. The isolated Pseudomonas aeruginosa was evaluated for drug resistance by disc diffusion method and polymerase chain reaction(PCR) was used to identify the carbapenem resistance causing gene (bla-VIM and bla-IMP) Results: Following antibiotic resistant pattern was observed, Gentamycin (59.00%), Ceftazidime(58.7%), Ceftriaxone (58.00%), Cefotazime (57.0%) and Ciprofloxacin (55.00%). Resistance rates to carbapenem group of antibiotics is Doripenem (30.5%) Meropenem(31.0%) and Imipenem (28.0%). Out of 123 samples of Pseudomonas aeruginosa, 28 isolates were found resistant to carbapenem group of antibiotic which was supposed to be highly sensitive for this bacterium. Molecular based identification of resistance genes showed that bla-IMP gene was present in 32.1% (09) and bla-VIM was found positive in 17.8% (04) samples. Metallo-beta-lactamasesproducing genes (bla-VIM and bla-IMP), amongcarbapenem resistant Pseudomonas aeruginosa were detectedin 28.1% of samples. If other carbapenem resistant gene were also included this number might be higherConclusions: PCRbased test should be included in routine laboratory examination for quick detection of the resistancecausing genes.
The amount of data has been increasing over the last few years due to the emergence of various
end-user applications. These applications utilize cloud computing infrastructure in the data
centers. Apart from the increasing volume of data, there are other factors such as variety,
velocity, and veracity of the data which result in the problem of big data. Traditional database
management systems are not efficient to handle big data. The use of big data platform is
necessary to resolve the big data problem. Hadoop is one of the platforms which resolve the
problem of big data. Hadoop uses a distributed storage system. Hive and HBase are some of the
big data tools for storing big data in Hadoop. They run on top of Hadoop distributed file system
(HDFS). Hive is a data warehouse framework for querying and analysis of data that is stored in
HDFS.?Hive?is an open-source software that lets programmers analyze large data sets on Hadoop.
HBase is a column-oriented, distributed and high fault-tolerant database. It is used to store and
manage big data. It can store billions of rows at a time. Both Hive and HBase can be used to store
the big data in Hadoop. When the data comes from multiple sources, it is stored into multiple
tables in Hive and HBase. As a result, its performance degrades when there is a need to perform
join operations.
In this thesis, we propose an architecture which stores data from multiple sources into a single
HBase table. A new table schema with a unique row key is designed which integrates
multi-source data in a table. There is no need to perform join operation in the proposed technique
as the data is integrated into a single HBase table. We evaluated the proposed technique using a
real testbed by considering a dataset of two publishers. We compare the performance by storing
data into Hive and also in the proposed HBase table. Results show improved query performance
of the proposed technique as compared to the traditional approach of using join operations in
multiple tables in Hive.