Effect of track structure and radioprotectors on the induction of oncogenic transformation in murine fibroblasts by heavy ions

The oncogenic potential of high-energy ⁵⁶Fe particles (1 GeV/nucleon) accelerated with the Alternating Gradient Synchrotron at the Brookhaven National Laboratory was examined utilizing the mouse C3H 10T1/2 cell model. The dose-averaged LET for high-energy ⁵⁶Fe is estimated to be 143 keV/μm with the exposure conditions used in this study. For ⁵⁶Fe ions, the maximum relative biological effectiveness (RBE_max) values for cell survival and oncogenic transformation were 7.71 and 16.5 respectively. Compared to 150 keV/μm ⁴He nuclei, high-energy ⁵⁶Fe nuclei were significantly less effective in cell killing and oncogenic induction. The prostaglandin E₁ analog misoprostol, an effective oncoprotector of C3H 10T1/2 cells exposed to X rays, was evaluated for its potential as a radioprotector of oncogenic transformation with high-energy ⁵⁶Fe. Exposure of cells to misoprostol did not alter ⁵⁶Fe cytotoxicity or the rate of ⁵⁶Fe-induced oncogenic transformation.     

Ares I–X Flight Test Vehicle similitude to the Ares I Crew Launch Vehicle

The Ares I–X Flight Test Vehicle is the first in a series of flight test vehicles that will take the Ares I Crew Launch Vehicle design from development to operational capability. Ares I–X is scheduled for a 2009 flight date, early enough in the Ares I design and development process so that data obtained from the flight can impact the design of Ares I before its Critical Design Review. Decisions on Ares I–X scope, flight test objectives, and FTV fidelity were made prior to the Ares I systems requirements being baselined. This was necessary in order to achieve a development flight test to impact the Ares I design. Differences between the Ares I–X and the Ares I configurations are artifacts of formulating this experimental project at an early stage and the natural maturation of the Ares I design process. This paper describes the similarities and differences between the Ares I–X Flight Test Vehicle and the Ares I Crew Launch Vehicle. Areas of comparison include the outer mold line geometry, aerosciences, trajectory, structural modes, flight control architecture, separation sequence, and relevant element differences. Most of the outer mold line differences present between Ares I and Ares I–X are minor and will not have a significant effect on overall vehicle performance. The most significant impacts are related to the geometric differences in Orion Crew Exploration Vehicle at the forward end of the stack. These physical differences will cause differences in the flow physics in these areas. Even with these differences, the Ares I–X flight test is poised to meet all five primary objectives and six secondary objectives. Knowledge of what the Ares I–X flight test will provide in similitude to Ares I—as well as what the test will not provide—is important in the continued execution of the Ares I–X mission leading to its flight and the continued design and development of Ares I.     

Fourier spectroscopy in planetary research

The application of Fourier Transform Spectroscopy (FTS) to planetary research is reviewed. The survey includes FTS observations of the Sun, all the planets except Uranus and Pluto, the Galilean satellites and Saturn's rings. Instrumentation and scientific results are considered. The prospects and limitations of FTS for planetary research in the forthcoming years are discussed.     

A new distributed constant false alarm rate detector

A new constant false alarm rate (CFAR) test termed signal-plus-order statistic CFAR (S+OS) using distributed sensors is developed. The sensor modeling assumes that the returns of the test cells of different sensors are all independent and identically distributed In the S+OS scheme, each sensor transmits its test sample and a designated order statistic of its surrounding observations to the fusion center. At the fusion center, the sum of the samples of the test cells is compared with a constant multiplied by a function of the order statistics. For a two-sensor network, the functions considered are the minimum of the order statistics (mOS) and the maximum of the order statistics (MOS). For detecting a Rayleigh fluctuating target in Gaussian noise, closed-form expressions for the false alarm and detection probabilities are obtained. The numerical results indicate that the performance of the MOS detector is very close to that of a centralized OS-CFAR and it performs considerably better than the OS-CFAR detector with the AND or the OR fusion rule. Extension to an N-sensor network is also considered, and general equations for the false alarm probabilities under homogeneous and nonhomogeneous background noise are presented.     

Application of SVM on satellite images to detect hotspots in Jharia coal field region of India

The present paper deals with the application of Support Vector Machine (SVM) and image analysis techniques on NOAA/AVHRR satellite image to detect hotspots on the Jharia coal field region of India. One of the major advantages of using these satellite data is that the data are free with very good temporal resolution; while, one drawback is that these have low spatial resolution (i.e., approximately 1.1 km at nadir). Therefore, it is important to do research by applying some efficient optimization techniques along with the image analysis techniques to rectify these drawbacks and use satellite images for efficient hotspot detection and monitoring. For this purpose, SVM and multi-threshold techniques are explored for hotspot detection. The multi-threshold algorithm is developed to remove the cloud coverage from the land coverage. This algorithm also highlights the hotspots or fire spots in the suspected regions. SVM has the advantage over multi-thresholding technique that it can learn patterns from the examples and therefore is used to optimize the performance by removing the false points which are highlighted in the threshold technique. Both approaches can be used separately or in combination depending on the size of the image. The RBF (Radial Basis Function) kernel is used in training of three sets of inputs: brightness temperature of channel 3, Normalized Difference Vegetation Index (NDVI) and Global Environment Monitoring Index (GEMI), respectively. This makes a classified image in the output that highlights the hotspot and non-hotspot pixels. The performance of the SVM is also compared with the performance obtained from the neural networks and SVM appears to detect hotspots more accurately (greater than 91% classification accuracy) with lesser false alarm rate. The results obtained are found to be in good agreement with the ground based observations of the hotspots. This type of work will be quite helpful in the near future to develop a hotspots monitoring system using these operational satellites data.     

A novel framework for liquid propellant engine’s cooling system design by sensitivity analysis based on RSM and multi-objective optimization using PSO

One of the challenges of combustion chamber and nozzle design in a Liquid Propellant Engine (LPE) is to predict the behavior and performance of the cooling system. Therefore, while designing, the optimization of the cooling system is always of great importance. This paper presents the multi-objective optimization of the LPE’s cooling system. To this end, a novel framework has been developed, resulting from the application of the Response Surface Method (RSM) and the correlation coefficients matrix, sensitivity analysis and the The Particle Swarm Optimization (PSO). based on this method, the input variables, constraints, objective functions, and their surfaces were identified. In terms of multi-optimization algorithms, RSM and PSO are utilized to get global optimum. In conclusion, the methodology capability is to optimize the LPE’s cooling system, 6 percentage increase in total heat transfer and 7 bar decrease cooling system pressure loss, which resulted in a 1.2-seconds increase in the specific impulse of the engine.