Cooperative Planning System for Self-Separation in En-route Airspace

The increase in flight density and the need to integrate Unmanned Areal Vehicles into the National Airspace demands higher flexibility. Distributing the conflict detection and resolution (CD&R) functions among the aircraft ensures a greater flexibility in the flight plans for the aircraft. A co-operative planning system is proposed for separation assurance by distributing the CD&R in the en-route airspace among a fully-connected network of aircraft. The aircraft cooperate to achieve the common goal of conflict-free trajectories, while attempting to reduce the disruptions from their original flight plans. A pairwise CD&R algorithm is developed through heuristics which is then implemented iteratively to obtain the solution. Coordination of the aircraft maneuvers in the distributed CD&R algorithm is ensured implicitly through geometric criteria and explicitly through communication for multiple conflicts. Furthermore, a novel robust aircraft trajectory model using cubic Bezier parametric curves is developed, which gives an accurate, minimalistic representation of flight paths for the algorithm to act on and modify for new resolutions. The algorithm is validated by sweeping through different parameters for a two aircraft configuration and also compared with a benchmark tactical CD&R algorithm. Furthermore, the planning system is shown to be feasible for implementation with the current ADS-B surveillance technology

The Moderating Role of Bank Performance Indicators on Credit Risk of Indian Public Sector Banks

Credit risk is inherent in banking. With its pervasive impact, it poses significant threat to the existence, stability and growth of the banking industry. The present study investigates the moderating role of various bank performance indicators on the relationship between lending and credit risk, i.e., Non Performing Assets (NPA) during the period 2000-01 to 2011-12. The study concentrates on Indian Public Sector Banks. Basically, NPA results from advances. This relationship is often more complex because it is modified by the changes in both bank performance indicators and macroeconomic indicators. The bank performance indicators moderate the relationship between advances and NPA. In order to achieve the stated objectives, the study utilized correlation, regression and ANOVA with moderation effect. The study revealed that the selected bank performance variables exercise a moderating role in the relationship between advances and NPA. The conclusion derived from the analysis can be utilized to improve the credit risk management in banks. Keywords: Non Performing Assets, Advances, Moderation, Performance Indicator

Octaband antenna for mobile applications

An octaband micro strip antenna is designed that integrates the current wireless technologies with some of the older and upcoming technologies of the mobile communication. It provides backward compatibility to the technologies like 2G, 3G. It operates in all the three Wi-Fi bands, the proposed 5G band at 3.3 to 3.4 GHz (lower 5G bands) and the Wi-Max band. It is designed with copper as the conductive material and FR-4 as the substrate. Defective ground structure (DGS) has been implemented to improve the return losses. This antenna radiates at 8 different frequencies which are: 2G (1.8 GHz), 3G (2.1 GHz), 4G (2.3 GHz), 5G (3.3GHz), Wi-Fi (2.4 GHz,5.0GHz,5.9 GHz), and Wi-Max (4.2 GHz)

Hypothermia for moderate or severe neonatal encephalopathy in low-income and middle-income countries (HELIX): a randomised controlled trial in India, Sri Lanka, and Bangladesh

Background: Although therapeutic hypothermia reduces death or disability after neonatal encephalopathy in high-income countries, its safety and efficacy in low-income and middle-income countries is unclear. We aimed to examine whether therapeutic hypothermia alongside optimal supportive intensive care reduces death or moderate or severe disability after neonatal encephalopathy in south Asia. Methods: We did a multicountry open-label, randomised controlled trial in seven tertiary neonatal intensive care units in India, Sri Lanka, and Bangladesh. We enrolled infants born at or after 36 weeks of gestation with moderate or severe neonatal encephalopathy and a need for continued resuscitation at 5 min of age or an Apgar score of less than 6 at 5 min of age (for babies born in a hospital), or both, or an absence of crying by 5 min of age (for babies born at home). Using a web-based randomisation system, we allocated infants into a group receiving whole body hypothermia (33·5°C) for 72 h using a servo-controlled cooling device, or to usual care (control group), within 6 h of birth. All recruiting sites had facilities for invasive ventilation, cardiovascular support, and access to 3 Tesla MRI scanners and spectroscopy. Masking of the intervention was not possible, but those involved in the magnetic resonance biomarker analysis and neurodevelopmental outcome assessments were masked to the allocation. The primary outcome was a combined endpoint of death or moderate or severe disability at 18–22 months, assessed by the Bayley Scales of Infant and Toddler Development (third edition) and a detailed neurological examination. Analysis was by intention to treat. This trial is registered with ClinicalTrials.gov, NCT02387385. Findings: We screened 2296 infants between Aug 15, 2015, and Feb 15, 2019, of whom 576 infants were eligible for inclusion. After exclusions, we recruited 408 eligible infants and we assigned 202 to the hypothermia group and 206 to the control group. Primary outcome data were available for 195 (97%) of the 202 infants in the hypothermia group and 199 (97%) of the 206 control group infants. 98 (50%) infants in the hypothermia group and 94 (47%) infants in the control group died or had a moderate or severe disability (risk ratio 1·06; 95% CI 0·87–1·30; p=0·55). 84 infants (42%) in the hypothermia group and 63 (31%; p=0·022) infants in the control group died, of whom 72 (36%) and 49 (24%; p=0·0087) died during neonatal hospitalisation. Five serious adverse events were reported: three in the hypothermia group (one hospital readmission relating to pneumonia, one septic arthritis, and one suspected venous thrombosis), and two in the control group (one related to desaturations during MRI and other because of endotracheal tube displacement during transport for MRI). No adverse events were considered causally related to the study intervention. Interpretation: Therapeutic hypothermia did not reduce the combined outcome of death or disability at 18 months after neonatal encephalopathy in low-income and middle-income countries, but significantly increased death alone. Therapeutic hypothermia should not be offered as treatment for neonatal encephalopathy in low-income and middle-income countries, even when tertiary neonatal intensive care facilities are available. Funding: National Institute for Health Research, Garfield Weston Foundation, and Bill & Melinda Gates Foundation. Translations: For the Hindi, Malayalam, Telugu, Kannada, Singhalese, Tamil, Marathi and Bangla translations of the abstract see Supplementary Materials section

Anisotropic diffusion of water molecules in hydroxyapatite nanopores

Funded by EPSRC Grant EP/K000128/1

Erratum to: 36th International Symposium on Intensive Care and Emergency Medicine

[This corrects the article DOI: 10.1186/s13054-016-1208-6.]

Deep Learning Framework for Trajectory Prediction and In-time Prognostics in the Terminal Airspace

Terminal airspace around an airport is the biggest bottleneck for commercial operations in the National Airspace System (NAS). In order to prognosticate the safety status of the terminal airspace, effective prediction of the airspace evolution is necessary. While there are fixed procedural structures for managing operations at an airport, the confluence of a large number of aircraft and the complex interactions between the pilots and air traffic controllers make it challenging to predict its evolution. Modeling the high-dimensional spatio-temporal interactions in the airspace given different environmental and infrastructural constraints is necessary for effective predictions of future aircraft trajectories that characterize the airspace state at any given moment. A novel deep learning architecture using Graph Neural Networks is proposed to predict trajectories of aircraft 10 minutes into the future and estimate prog?nostic metrics for the airspace. The uncertainty in the future is quantified by predicting distributions of future trajectories instead of point estimates. The framework’s viability for trajectory prediction and prognosis is demonstrated with terminal airspace data from Dallas Fort Worth International Airport (DFW). </p