Chip placement has been one of the most time consuming task in any semi conductor area, Due to this negligence, many projects are pushed and chips availability in real markets get delayed. An engineer placing macros on a chip also needs to place it optimally to reduce the three important factors like power, performance and time. Looking at these prior problems we wanted to introduce a new method using Reinforcement Learning where we train the model to place the nodes of a chip netlist onto a chip canvas. We want to build a neural architecture that will accurately reward the agent across a wide variety of input netlist correctly.

Osteoarticular tuberculosis is a relatively uncommon type of extrapulmonary tuberculosis. It is an important cause of mortality and morbidity and accounts for approximately 10-15% of all extrapulmonary forms of tuberculosis. The diagnosis is difficult and hence often late. The disability resulting from osteoarticular tuberculosis is directly related to the time of detection of disease and initiation of treatment. A prospective study of 36 patients suspected with osteoarticular tuberculosis was done from August 2010 to December 2012 was done at St. stephens hospital Delhi. For the purpose of this study, a diagnosis of osteoarticular TB was based on a combination of suggestive clinical features, in conjunction with typical radiological findings associated with osteoarticular TB. The specimens were subjected to ZN staining, Real Time PCR, Bac-T alert culture & Accuprobe. The sensitivity of real time PCR was 100%, specificity was 58.8%, positive predictive value was 73%, negative predictive value was 100%, efficiency was 80.5% considering culture as gold standard. As shown by the study, each diagnostic test including Real time PCR has its own disadvantages and shortcomings and does not provide 100% accuracy in diagnosing osteoarticular tuberculosis, therefore, strong clinical suspicion and correlation along with radiological and laboratory evidence is a must in establishing a diagnosis.

Introduction: The presence of thrombus within deep veins of the extremity is termed as deep vein thrombosis (DVT). If not recognized deep venous thrombi can embolize to pulmonary arterial circulation which can be fatal within few hours of onset. The thrombosis occurs due to inappropriate activation of the process of normal hemostasis as a response of the blood to injury. Study was done to find out the prevalence of DVT in patients with surgeries around knee. Material and Methods: The study was a non analytical cohort study. The study was conducted in the department of Orthopedics, St Stephen's Hospital Tis Hazari,New Delhi between oct '12 to Dec '14. A detailed history, clinical and radiological examination along with investigations was carried out in all 74 patients. 74 cases were included in study out of which 51 fracture patients and 23 arthroplasty patients. 16 cases of VTE were seen with highest prevalence in patients with fracture around knee. Results: The overall prevalence of VTE was found to be 22%. Clinical DVT was 8% and subclinical DVT was 14%. In other words,62% of DVT was subclinical while 38% was clinical. Among the patients with DVT, 10 cases i.e 71% cases had proximal DVT and rest of 4 cases ie 29% cases had distal DVT. Conclusion: Conservatively treated cases shows statistically significant more risk for VTE than operated cases.In our study, 63% cases of diagnosed VTE were clinically asymptomatic as compared to 37% of clinically symptomatic cases.Among the operative cases, use of tourniquet with DVT was assessed. But since the number of patients were too less, no adequate correlation can be made out.

The National Football League (NFL) and Amazon Web Services (9) (AWS) teamed up to develop the best sports injury surveillance and mitigation program via the Kaggle competition. Through which the NFL wants to assign specific players to each helmet, which would help accurately identify each player's "exposures" throughout a football play. We are trying to implement a computer vision based ML algorithms capable of assigning detected helmet impacts to correct players via tracking information. Our paper will explain the approach to automatically track player helmets and their collisions. This will also allow them to review previous plays and explore the trends in exposure over time.