Design and Development of an Airblast Atomiser for the KAVERI engine and the sectoral combustor tests

This report deals with the design and development of an airblast atomiser for application in the KAVERI engine. Five atomisers of the chosen design were fabricated and tested at ambient conditions to determine the fuel spray SMD, patternation, cone angle and atomiser flow number. The atomiser performance parameters specified were achieved and hot tests carried out in the 90° combustor sector. The combustor pressure loss, exit temperature distribution, ignition and stability limits were evaluate

Visualize and Correlate changes to network device to anomalies in network

NOM products enable users to manage their network by enforcing controlled changes to configuration of network devices apart from monitoring performance, fault and compliance of all devices in their network. While monitoring a network, it is always been a challenge to deduce and represent correlation, among anomalies across thousands of devices, in a format that users can consume, validate and act on. This paper proposes a method to deduce and visualize, possibly related anomalies on connected devices in the network based on user’s selection of an anomaly on a device of interest. While this paper takes network domain as an example to demonstrate the idea, it could be applied to any of the infrastructure and application management solutions in a datacenter. This paper proposes a unique approach to solve this problem by leveraging ML and network operator’s deployment/domain knowledge

A STUDY ON THE CONSUMER BEHAVIOUR DURING FESTIVE SEASON IN MALLS

The aim of the study is to find out how the customers behave during festive seasons Christmas, Diwali and New Year in malls. In today’s world there are a lot of promotions and strategies to attract customers. The buying pattern of customers, generally, changes during festive seasons. This study focuses on finding how the customer’s buying pattern varies from normal days to festive days. The conclusion is that further importance has to be given towards improvement of quality of service during festival seasons

The prevalence and determinants of pregnancy-related anxiety amongst pregnant women at less than 24 weeks of pregnancy in Bangalore, Southern India.

Background: A pregnant woman undergoes physiological as well as psychological changes during this phase of life during which anxiety is a commonly faced mental condition. There is sufficient evidence on the association of pregnancy specific anxiety with adverse pregnancy outcomes. Studies on anxiety during pregnancy from low and middle income countries are limited. Methods: This study included 380 pregnant women, having a confirmed pregnancy of less than 24 weeks without any obstetric complication, who were availing of antenatal care at a public sector hospital in Bangalore city. Pregnancy-related thoughts (PRT) scale was used to screen for anxiety. Details pertaining to sociodemographic data, obstetric history, psychosocial factors including social support, marital discord, domestic violence, consanguinity, history of catastrophic events, history of mental illness, current presence of depression and anxiety was obtained by means of electronic data capture using an Android-based App. Results: Out of 380 pregnant women, 195 (55.7%) were found to have pregnancy-related anxiety. Lower socioeconomic status, low social support and depression emerged as significant determinants of anxiety. Conclusion: The prevalence of anxiety was fairly high in the study population and isp therefore an important public health concern. Pregnancy-related anxiety must be identified early during routine antenatal care to prevent any untoward pregnancy outcomes

Effunet-spagen: An efficient and spatial generative approach to glaucoma detection

Current research in automated disease detection focuses on making algorithms “slimmer” reducing the need for large training datasets and accelerating recalibration for new data while achieving high accuracy. The development of slimmer models has become a hot research topic in medical imaging. In this work, we develop a two-phase model for glaucoma detection, identifying and exploiting a redundancy in fundus image data relating particularly to the geometry. We propose a novel algorithm for the cup and disc segmentation “EffUnet” with an efficient convolution block and combine this with an extended spatial generative approach for geometry modelling and classification, termed “SpaGen” We demonstrate the high accuracy achievable by EffUnet in detecting the optic disc and cup boundaries and show how our algorithm can be quickly trained with new data by recalibrating the EffUnet layer only. Our resulting glaucoma detection algorithm, “EffUnet-SpaGen”, is optimized to significantly reduce the computational burden while at the same time surpassing the current state-of-art in glaucoma detection algorithms with AUROC 0.997 and 0.969 in the benchmark online datasets ORIGA and DRISHTI, respectively. Our algorithm also allows deformed areas of the optic rim to be displayed and investigated, providing explainability, which is crucial to successful adoption and implementation in clinical settings

Prenatal Depression and Its Associated Risk Factors Among Pregnant Women in Bangalore: A Hospital Based Prevalence Study

BACKGROUND The most common psychological problem that affects a woman during her perinatal period worldwide is depression. The risk of prenatal depression increases significantly as pregnancy progresses and clinically significant depressive symptoms are common in mid and late trimesters. Studies from various countries around the world have shown a prevalence rate ranging from as low as 4% to as high as 81%. The prevalence of depression in India is shown to vary from 9.18% in one study to 36.75 % reported in another. There is paucity of research on depression during the prenatal period, especially in India. Given this background, the present study aimed to assess the prevalence of prenatal depression and its associated risk factors among pregnant women in Bangalore, Southern India. METHODS: The study was nested within an on-going cohort study. The study participants comprised of pregnant women attending the antenatal clinic at Jaya Nagar General Hospital (Sanjay Gandhi Hospital) in Bangalore. The data was collected using standardised questionnaires. Edinburgh Postnatal Depression Scale (EPDS), Multidimensional Scale of Perceived Social Support Scale (MSPSS), Revised Dyadic Adjustment Scale, The Modified Conflict Tactics scale, Modified Kuppuswamy socio economic scale, Pregnancy related anxiety Scales were used. RESULTS: Of the 280 pregnant mothers, the proportion of them who screened positive for prenatal depression was 35.7%. Presence of domestic violence was found to impose a five times higher and highly significant risk of developing prenatal depression among the respondents. Pregnancy related anxiety was also found to be a positive predictor of prenatal depression. Presence of catastrophic events in the past one year was found to impose a two times higher and significant risk of developing prenatal depression among the respondents. CONCLUSION: The present study showed a higher prevalence of prenatal depression among the study participants which is suggestive of the public health importance in the study region. Health care plans therefore can include screening and diagnosis of prenatal depression in the antenatal care along with other health care facilities provided

Prediction by Promoter Logic in Bacterial Quorum Sensing

Quorum-sensing systems mediate chemical communication between bacterial cells, coordinating cell-density-dependent processes like biofilm formation and virulence-factor expression. In the proteobacterial LuxI/LuxR quorum sensing paradigm, a signaling molecule generated by an enzyme (LuxI) diffuses between cells and allosterically stimulates a transcriptional regulator (LuxR) to activate its cognate promoter (pR). By expressing either LuxI or LuxR in positive feedback from pR, these versatile systems can generate smooth (monostable) or abrupt (bistable) density-dependent responses to suit the ecological context. Here we combine theory and experiment to demonstrate that the promoter logic of pR – its measured activity as a function of LuxI and LuxR levels – contains all the biochemical information required to quantitatively predict the responses of such feedback loops. The interplay of promoter logic with feedback topology underlies the versatility of the LuxI/LuxR paradigm: LuxR and LuxI positive-feedback systems show dramatically different responses, while a dual positive/negative-feedback system displays synchronized oscillations. These results highlight the dual utility of promoter logic: to probe microscopic parameters and predict macroscopic phenotype