Commemorative Issue of Defence Science Journal on Golden Jubilee of DRDO

Defence Research and Development Organisation(DRDO), Ministry of Defence, is dedicatedly working towards enhancing self-reliance in Defence systems. DRDO undertakes design and development leading to production of world class weapon systems and equipment in accordance withthe expressed needs and the qualitative requirements laid  down by the three Services. The vision of DRDO is tomake India prosperous by establishing world class science and technology base and provide the Defence Services a decisive edge by equipping them with internationally competitive systems and solutions.Defence Science Journal, 2010, 60(2), pp.121-123, DOI:http://dx.doi.org/10.14429/dsj.60.34

Defence Science Journal: Sixty Successful Years of Publication

Its objective is to stimulate study and research in science fundamental and appliedin relation to the problems of Defence. It serves to bring to the notice of the scientists in universities and other research institutions the basic problems in Defence science, the work that is being done in this field and its importance and also the role of Defence Science in the progress of science generally. A properly conducted Defence Science Journal will go a long way in creating and sustaining interest amongst the research workers in universities and civil institutions, in Defence Science and Technology.Defence Science Journal, 2009, 59(4), pp.321-325, DOI:http://dx.doi.org/10.14429/dsj.59.152

Spindle cell sarcoma of sphenoid bone

Primary bone tumors involving skull are extremely rare and they constitute 0.8% of all bone tumors. The common tumors that are seen in skull base include fibrous dysplasia, giant cell tumor, chordoma, ossifying fibroma, angiosarcoma. We report a rare case of spindle cell sarcoma arising from right sphenoid bone in a 70-year-old male which presented as unilateral defective vision with mild proptosis

A method of beam–couch intersection detection

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134785/1/mp8509.pd

Comorbidities and Treatments in United States Youth with Chronic Musculoskeletal Pain

Introduction: Chronic musculoskeletal (MSK) pain has been associated with chronic illnesses and high rates of pain medication use, often in referral centers, European populations, or studies focused on single drug classes. We aimed to characterize patterns of comorbidities and treatments associated with chronic MSK pain in a nationally-representative sample of US youth. Methods: We used the National Ambulatory Medical Care Survey (2002-2015) and National Hospital Ambulatory Medical Care Survey (2002-2011), which contain cross-sectional data for US outpatient visits. The study included all visits for youth age 8-24, excluding those with malignancy or sickle cell disease. We compared comorbidities and drugs ordered in visits for chronic MSK pain with (1) visits for any reason besides MSK pain and (2) visits for acute MSK pain, using chi-square tests and logistic regression, adjusting for several covariates. Results: Chronic non-psychiatric diseases were more common among visits for chronic MSK pain (32.0%) in comparison to both visits for acute MSK pain (17.9%) and visits for other reasons (18.8%). Nonsteroidal anti-inflammatories were less commonly ordered at visits for chronic MSK pain in comparison to acute MSK pain (adjusted odds ratio [aOR]: 0.63, 95% CI 0.50-0.80). Opioids, gabapentinoids, and alternative medicine were each ordered more commonly at visits for chronic MSK pain in comparison to visits for acute MSK pain and other visits. Conclusion: US youth with chronic MSK were more likely to have chronic non-psychiatric medical conditions compared to youth without pain. Additionally, opioids, gabapentinoids, and alternative medicine were ordered more often in chronic MSK visits, which warrants further study

Crowdsourcing EO datasets to improve cloud detection algorithms and land cover change

Involving citizens in science is gaining considerable traction of late. With positive examples (e.g. Geo-Wiki, FotoQuest Austria), a number of projects are exploring the options to engage the public in contributing to scientific research, often by asking participants to collect some data or validate some results. The International Institute for Applied Systems Analysis (IIASA), with extensive experience in crowdsourcing and gamification, has joined Sinergise, Copernicus Masters 2016 winners, to engage the public in an initiative involving ESA’s Sentinel-2 satellite imagery. Sentinel-2 imagery offers high revisit times and sufficient resolution for land change detection applications. Unfortunately, simple (but fast) algorithms often fail due to many false-positives: changes in clouds are perceived as land changes. The ability to discriminate of cloudy pixels is thus crucial for any automatic or semi-automatic solutions that detect land change. A plethora of algorithms to distinguish clouds in Sentinel-2 data are available. However, there is a need for better data on where and when clouds occur to help improve these algorithms. To overcome this current gap in the data, we are engaging the public in this task. Using a number of tools, developed at IIASA, and Sentinel Hub services, which provide fast access to the entire global archive of Sentinel-2 data, the aim is to obtain a large data resource of curated cloud classifications. The resulting dataset will be published as open data and made available through Geopedia platform. The gamified process will start by asking users if there are clouds on a small image (e.g. 8x8 pixels at the highest Sentinel-2 resolution of 10 m/px), which will provide us with a screening process to pinpoint cloudy areas, employing Picture Pile crowdsourcing game from IIASA. The next step will involve a more detailed workflow, as users will get a slightly larger image (e.g. 64x64 pixels) and will then be asked to delineate different types of clouds: opaque clouds (nothing is seen through the clouds), thick clouds (where the surface is still discernible through the clouds), and thin clouds (where the surface is unequivocally covered by a cloud); the rest of the image will be implicitly cloud-free. The resulting data will be made available through the Geopedia portal, both for exploring and downloading. This paper will demonstrate this process and show some results from a crowdsourcing campaign. The approach will also allow us to collect other datasets in a rapid and efficient manner. For example, using a slightly modified configuration, a similar workflow could be used to obtain a manually curated land cover classification data set, which could be used as training data for machine learning algorithms