Geochemical characteristics of charnockite and high grade gneisses from Southern Peninsular Shield and their significance in crustal evolution

Presented here are the results of detailed investigations encompassing externsive structural mapping in the charnockite-high grade gneiss terrain of North Arcot district and the type area in Pallavaram in Tamil Nadu supported by petrography, mineral chemistry, major, minor and REE distribution patterns in various lithounits. This has helped in understanding the evolutionary history of the southern peninsular shield. A possible tectonic model is also suggested. The results of these studies are compared with similar rock types from parts of Andhra Pradesh, Kerala, Sri Lanka, Lapland and Nigeria which has brought about a well defined correlation in geochemical characteristics. The area investigated has an interbanded sequence of thick pile of charnockite and a supracrustal succession of shelf type sediments, layered igneous complex, basic and ultrabasic rocks involved in a complex structural, tectonic, igneous and metamorphic events

Effects of Formaldehyde Fumigation and Fytolan Drench on VAM fungi and nodulation in some Leguminous forest Tree Seedlings in India

Seedlings of 12 legume tree species (Acacia caesia, A. catechu, A. farnesiana, A. holosericea, A. leucocephala, A. nilotica, Albizia lebbeck, Dichrostachys cinerea, Leucaena latisiliqua, Prosopis cineraria, Dalbergia latifolia and Pterocarpus marsupium) were raised informaldehyde-fumigatedf Fytol and renched beds in a nursery. Seedlings in the formaldehyde fumigated beds had stunted growth and were chlorotic; had poor VAM root colonization (18-25.3%) and spore density (3.1 - 10.6 g. soil-1 ) and lower nodule number (3 - 8 plant-1 ) and nodular biomass (100 - 870 mg plant-1 ); the total biomass (15.5 - 72 g plant-1 ) and field survival rate (31.2 - 40.4%) of the seedlings were very low. The mycorrhizal species isolated were Acaulospora bireticulata, Glomus fasciculatum and G. geosporum. In contrast, seedlings form Fytolan-drenched beds showed normal growth, enhanced biomass (18 - 83.21g plant-1 ) and higher field survival rate (71 - 86%); intense VAM root colonization (53.4-100%) and higher spore density (36 - 82.8 g soil-1 ) and higher module number (7.4 - 17.6 plant-1 ) and nodular biomass (195 - 950 mg plant ) compared with the control seedlings. Roots of these plants exhibited extensively developed arbuscular and vesicular structures. Ofthe seven VAMF species recordedfrom the rhizosphere soils of control and Fytolan-drenched beds, A. bireticulata, G. fasciculatum and G. geosporum were the dominant species. The differences between treatments were statistically significant (P < 0.05)

Creating psychological connections between intervention recipients: development and focus group evaluation of a group singing session for people with aphasia

This is the final version of the article. Available from BMJ Publishing Group via the DOI in this record.Objectives The study sought to identify key design features that could be used to create a new framework for group-based health interventions. We designed and tested the first session of a group intervention for stroke survivors with aphasia which was aimed at nurturing new psychological connections between group members. Setting The intervention session, a participant focus group and interviews with intervention facilitators were held in a local community music centre in the South West of England. Participants A convenience sample of 10 community-dwelling people with poststroke aphasia participated in the session. Severity of aphasia was not considered for inclusion. Intervention Participants took part in a 90-min group singing session which involved singing songs from a specially prepared song book. Musical accompaniment was provided by the facilitators. Primary and secondary outcome measures Participants and group facilitators reported their experiences of participating in the session, with a focus on activities within the session related to the intervention aims. Researcher observations of the session were also made. Results Two themes emerged from the analysis, concerning experiences of the session (‘developing a sense of group belonging’) and perceptions of its design and delivery (‘creating the conditions for engagement’). Participants described an emerging sense of shared social identity as a member of the intervention group and identified fixed (eg, group size, session breaks) and flexible (eg, facilitator responsiveness) features of the session which contributed to this emergence. Facilitator interviews and researcher observations corroborated and expanded participant reports. Conclusions Engagement with health intervention content may be enhanced in group settings when intervention participants begin to establish positive and meaningful psychological connections with other group members. Understanding and actively nurturing these connections should be a core feature of a general framework for the design and delivery of group interventions.This research was funded by the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care South West Peninsula. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health

International Space Station Future Correlation Analysis Improvements

Ongoing modal analyses and model correlation are performed on different configurations of the International Space Station (ISS). These analyses utilize on-orbit dynamic measurements collected using four main ISS instrumentation systems: External Wireless Instrumentation System (EWIS), Internal Wireless Instrumentation System (IWIS), Space Acceleration Measurement System (SAMS), and Structural Dynamic Measurement System (SDMS). Remote Sensor Units (RSUs) are network relay stations that acquire flight data from sensors. Measured data is stored in the Remote Sensor Unit (RSU) until it receives a command to download data via RF to the Network Control Unit (NCU). Since each RSU has its own clock, it is necessary to synchronize measurements before analysis. Imprecise synchronization impacts analysis results. A study was performed to evaluate three different synchronization techniques: (i) measurements visually aligned to analytical time-response data using model comparison, (ii) Frequency Domain Decomposition (FDD), and (iii) lag from cross-correlation to align measurements. This paper presents the results of this study

International Space Station Modal Correction Analysis

This paper summarizes the on-orbit modal test and the related modal analysis, model validation and correlation performed for the ISS Stage ULF4, DTF S4-1A, October 11,2010, GMT 284/06:13:00.00. The objective of this analysis is to validate and correlate analytical models with the intent to verify the ISS critical interface dynamic loads and improve fatigue life prediction. For the ISS configurations under consideration, on-orbit dynamic responses were collected with Russian vehicles attached and without the Orbiter attached to the ISS. ISS instrumentation systems that were used to collect the dynamic responses during the DTF S4-1A included the Internal Wireless Instrumentation System (IWIS), External Wireless Instrumentation System (EWIS), Structural Dynamic Measurement System (SDMS), Space Acceleration Measurement System (SAMS), Inertial Measurement Unit (IMU) and ISS External Cameras. Experimental modal analyses were performed on the measured data to extract modal parameters including frequency, damping and mode shape information. Correlation and comparisons between test and analytical modal parameters were performed to assess the accuracy of models for the ISS configuration under consideration. Based on the frequency comparisons, the accuracy of the mathematical models is assessed and model refinement recommendations are given. Section 2.0 of this report presents the math model used in the analysis. This section also describes the ISS configuration under consideration and summarizes the associated primary modes of interest along with the fundamental appendage modes. Section 3.0 discusses the details of the ISS Stage ULF4 DTF S4-1A test. Section 4.0 discusses the on-orbit instrumentation systems that were used in the collection of the data analyzed in this paper. The modal analysis approach and results used in the analysis of the collected data are summarized in Section 5.0. The model correlation and validation effort is reported in Section 6.0. Conclusions and recommendations drawn from this analysis are included in Section 7.0

Sparse Signal Models for Data Augmentation in Deep Learning ATR

Automatic Target Recognition (ATR) algorithms classify a given Synthetic Aperture Radar (SAR) image into one of the known target classes using a set of training images available for each class. Recently, learning methods have shown to achieve state-of-the-art classification accuracy if abundant training data is available, sampled uniformly over the classes, and their poses. In this paper, we consider the task of ATR with a limited set of training images. We propose a data augmentation approach to incorporate domain knowledge and improve the generalization power of a data-intensive learning algorithm, such as a Convolutional neural network (CNN). The proposed data augmentation method employs a limited persistence sparse modeling approach, capitalizing on commonly observed characteristics of wide-angle synthetic aperture radar (SAR) imagery. Specifically, we exploit the sparsity of the scattering centers in the spatial domain and the smoothly-varying structure of the scattering coefficients in the azimuthal domain to solve the ill-posed problem of over-parametrized model fitting. Using this estimated model, we synthesize new images at poses and sub-pixel translations not available in the given data to augment CNN's training data. The experimental results show that for the training data starved region, the proposed method provides a significant gain in the resulting ATR algorithm's generalization performance.Comment: 12 pages, 5 figures, to be submitted to IEEE Transactions on Geoscience and Remote Sensin

Microwave lymphedema assessment using deep learning with contour assisted backprojection

We present a method for early detection of lymphatic fluid accumulation in lymphedema patients based on microwave imaging of the limb volume across an air gap. The proposed algorithm uses contour information of the imaged limb surface to approximate the wave propagation velocity locally to solve the eikonal equation for implementing the adjoint imaging operator. This modified backprojection algorithm results in focused imagery close to the limb surface where lymphatic fluid accumulation presents itself. Next, a deep neural network based on U-Net architecture is employed to identify the location and extent of the lymphatic fluid. Simulation studies with various upper and lower arm profiles compare the focusing performance of the proposed contour assisted backprojection imaging with the baseline imaging approach that assumes homogeneous media. The empirical results of the simulation experiments show that the proposed imaging method significantly improves the ability of the deepnet model to identify the location and the volume of the excess fluid in the limb.Comment: 6 pages, 6 figures, accepted RadarCon