آنکھ سے دُور سہی، دل کے قریں رہنے دے
میری ہر سانس میں تو خود کو مکیں رہنے دے
میں کہ اک عکس ہوں گمنام سا پس منظر ہوں
کب کہاں کیسے کسی طور کہیں رہنے دے
اک نظر مجھ پہ مرے ماہِ منیر ایسی ہو
کب طلب میں نے کیا زر یا نگیں، رہنے دے
میں ہوں اس قافلۂ عشق سے بچھڑا راہی
میرا کب ٹھور ٹھکانہ ہے کہیں، رہنے دے
تجھ سے منسوب ہوئی، تجھ سے ہی منسوب رہوں
غیر کے آگے جھکے گی یہ جبیں، رہنے دے
دل میں یا آنکھ میں یا دستِ حنائی میں فضاؔ
تیری مرضی ہے جہاں چاہے، وہیں رہنے دے
Background: Women with polycystic ovarian syndrome (PCOS) have insulin resistance and hyperinsulinemia that may play a key role in the pathogenesis of PCOS. Objectives: To determine and compare glucose-insulin ratio in hyper-insulinemic women with the polycystic ovarian syndrome and healthy controls. Materials & Methods: A cross-sectional comparative study was conducted at Lahore General Hospital. A total of 80 women 24-35 years of age were recruited from Lahore General Hospital. 50 women had PCOS, and 30 were healthy controls. PCOS was diagnosed by using the Rotterdam criteria. Height, weight, and waist circumference were measured. Glucose and insulin were estimated by the glucose oxidase method and ELISA, respectively. HOMA-IR was calculated to determine insulin resistance (IR). HOMA- β was calculated to assess the β-cell function. Fasting glucose and insulin ratio were also calculated. Results: Mean age of the women with PCOS and healthy controls was 29.89±3.54 and 28.60±1.12 years, respectively (p>0.54). BMI and waist circumference of women with PCOS were higher compared to healthy controls (p>0.45). Fasting glucose, fasting insulin, HOMA- β, and IR were significantly higher in women with PCOS compared to healthy controls (p<0.001). Conclusion: In addition to HOMA IR, the glucose-insulin ratio may be considered to assess hyperinsulinemia in women with polycystic ovary syndrome.
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.