Ischemic heart disease (IHD) is the leading cause of both mortality and forgone healthy years of life among working -age adults (15-69 years) in South Asia. It is the leading cause of death in India and worldwide. For noncommunicable diseases (NCDs), common, modifiable and easily measurable risk factors could be reliably used to predict the future burden of the diseases and to measure the effectiveness of public health interventions. A casecontrol study was undertaken to examine the socio-demographic profile of IHD patients and to identify the risk factors in already diagnosed cases of IHD admitted in three tertiary care hospitals of Ahmedabad, India. We have included 100 cases and 100 controls who were group matched with the cases. The association of various risk factors with IHD was assessed. On univariate analysis it was found that 7 out of 8 risk factors were significantly associated with IHD. They are alcohol consumption (OR; 14.6, 95% CI; 6.4-33.3), smoking (OR; 13.6, 95% CI; 6.6-27.8), tobacco consumption in non-smoking form (OR;2.3, 95% CI; 0.78-7.02), hypertension (OR; 6.5, 95% CI; 3.4-12.3), Type 2 diabetes (OR; 4.5, 95% CI; 2.4-8.7), obesity (OR; 9.7, 95% CI; 4.9-19.1), sedentary lifestyle (OR; 3.8, CI; 1.8-8.4 ) and family history (OR; 5.3, 95% CI; 2.8-9.9). This study identified the significance of alcohol, smoking, obesity, Type 2 diabetes, hypertension, sedentary lifestyle and family history in the outcome of IHD. This suggests that the increased cardiovascular risk among the urban population of Ahmedabad city may be preventable through lifestyle interventions along with the judicious use of medicines to attain optimal levels of blood pressure, lipids and glucose among the high risk population. A total of 57 million deaths occurred in the world during 2008; 36 million (63%) were due to non-communicable diseases (NCDs), principally cardiovascular diseases (CVD), diabetes, cancer and chronic respiratory diseases. 1NCDs are the most frequent causes of death in most countries in the Americas, the Eastern Mediterranean, Europe, South-East Asia and the Western Pacific.2 The leading causes of NCD deaths in 2008 were CVD (17 million deaths, or 48% of NCD deaths) -over 80% of cardiovascular and diabetes deaths occurred in low-and middleincome countries.3 NCD deaths are projected to increase by 15% globally between 2010 and 2020 (to 44 million deaths). The greatest increases will be in the WHO regions of Africa, South-East Asia and the Eastern Mediterranean, where they will increase by over 20%. The regions that are projected to have the greatest total number of NCD deaths in 2020 are South-East Asia (10.4 million deaths) and the Western Pacific (12.3 million deaths). 4 Most NCDs are strongly associated and causally linked with four particular behaviors: tobacco use, physical inactivity, unhealthy diet and the harmful use of alcohol. 5These behaviors lead to four key metabolic/physiological changes: raised blood pressure, overweight/obesity, hyperglycemia and hyperlipidemia. In terms of attributable

This paper deals with application of evolutionary algorithm (EA) to solve optimal power flow problem in an efficient manner. In this paper a new approach using cuckoo search (CS) method is proposed for solving OPF problem by optimal setting of control variables. Cuckoo search method is a bio-inspired algorithm based on brooding behaviour of cuckoo birds. This algorithm can search for a global solution using multiple paths. Different objective functions as fuel cost minimization and power loss minimization has been considered for optimal active & reactive power dispatch respectively. The proposed method is implemented and evaluated on the IEEE 30-bus system. The simulation results of the proposed approach are compared to others those reported in the literature. The results demonstrate the potential of the proposed approach and show its effectiveness and robustness to solve the OPF problem.

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This paper representing a study of supply chain operation data that was used on 100 different store items from 10 stores using 5 years history of sales through open sources contest to compare the performance of time-series forecasting model mainly, decomposition, Auto-Regressive Integrated Moving Average(ARIMA), Prophet, Box-Cox transformation. Here data is collected from 2013 to 2018 were used in real-time transaction at different store, initially model was applied on 2013 to 2017 data and based on the that predicted for 2018 then again cross checked with actual 2018 with proceed predicted data of 2018. To improve the performance and evaluation of the supply chain management system, scrutiny 3 metrices that will help to make decision on the model selection. The accuracy of the Machine learning model in forecasting future sales of supply chain store. Although the result on comparison indicates that there is no single method gives better and superior result. But present study indicates that prophet and ARIMA hybrid model gives better result compare to individual model.