Synthesis and antimicrobial activity of some hybrid 2-aryl-4-(1-phenyl-3-(p-tolyl)-1H-pyrazol-4-yl)-2,3-dihydro-1H-benzo[b][1,4]diazepine derivatives

238-246Some new hybrid 2-aryl-4-(1-phenyl-3-(p-tolyl)-1H-pyrazol-4-yl)-2,3-dihydro-1H-benzo[b][1,4]diazepines have been synthesised which possessed the pyrazole and benzodiazepine heterocycles 2a-t. Antimicrobial evaluation of the synthesized compounds have been carried out against different strains of bacteria like E. coli, P. aeruginosa, S. aureus and S. pyogenes and fungal strains like C. albicans, A. niger and A. clavatus using serial dilution method. The newly synthesized compounds 2i, 2j, 2k, 2l, 2m, 2q and 2t have shown significant activity against the above mentioned strains. The reported compounds in the present paper are supported by IR, 1H and 13C NMR, and LC-MS spectral analysis

Reliable dual-redundant sensor failure detection and identification for the NASA F-8 DFBW aircraft

A technique was developed which provides reliable failure detection and identification (FDI) for a dual redundant subset of the flight control sensors onboard the NASA F-8 digital fly by wire (DFBW) aircraft. The technique was successfully applied to simulated sensor failures on the real time F-8 digital simulator and to sensor failures injected on telemetry data from a test flight of the F-8 DFBW aircraft. For failure identification the technique utilized the analytic redundancy which exists as functional and kinematic relationships among the various quantities being measured by the different control sensor types. The technique can be used not only in a dual redundant sensor system, but also in a more highly redundant system after FDI by conventional voting techniques reduced to two the number of unfailed sensors of a particular type. In addition the technique can be easily extended to the case in which only one sensor of a particular type is available

Mars Exploration Rovers Entry, Descent, and Landing Trajectory Analysis

In this study we present a novel method of land surface classification using surface-reflected GPS signals in combination with digital imagery. Two GPS-derived classification features are merged with visible image data to create terrain-moisture (TM) classes, defined here as visibly identifiable terrain or landcover classes containing a surface/soil moisture component. As compared to using surface imagery alone, classification accuracy is significantly improved for a number of visible classes when adding the GPS-based signal features. Since the strength of the reflected GPS signal is proportional to the amount of moisture in the surface, use of these GPS features provides information about the surface that is not obtainable using visible wavelengths alone. Application areas include hydrology, precision agriculture, and wetlands mapping

Genetic risk for Huntington Disease and reproductive decision-making: A systematic review

Huntington Disease (HD) is an incurable autosomal dominant single gene neurodegenerative disorder. Typical onset is between 30 and 40 years and characterised by motor difficulties, cognitive impairment, and behavioural and personality changes. The availability of reproductive testing means that affected and at-risk individuals can make reproductive decisions with genetic risk in mind. We aimed to summarise the literature on reproductive decision-making in the context of HD risk in terms of outcomes and the subjective experiences of at-risk individuals. Five databases were searched. Findings were synthesised using Framework analysis to identify common factors across results of quantitative and qualitative studies. Twenty five studies met inclusion criteria. Framework analysis identified the following key areas: ‘The relationship between reproductive intentions and HD genetic risk’, ‘Views on assistive options’, ‘Complexity and challenges in reproductive decision-making’, ‘Actual reproductive outcomes’, and ‘Other factors influencing reproductive decision-making’. Quality of included studies was mixed. Reproductive decision making in the context of HD risk was found to be a complex and emotionally challenging process. Further research is required into reproductive decision-making and outcomes among those not utilising assistive options, and in developing a model of reproductive decision-making in HD

Stellar Mass to Halo Mass Scaling Relation for X-ray Selected Low Mass Galaxy Clusters and Groups out to Redshift z≈1z\approx1

We present the stellar mass-halo mass scaling relation for 46 X-ray selected low-mass clusters or groups detected in the XMM-BCS survey with masses $2\times10^{13}M_{\odot}\lesssim M_{500}\lesssim2.5\times10^{14}M_{\odot}$ at redshift $0.1\le z \le1.02$. The cluster binding masses $M_{500}$ are inferred from the measured X-ray luminosities \Lx, while the stellar masses $M_{\star}$ of the galaxy populations are estimated using near-infrared imaging from the SSDF survey and optical imaging from the BCS survey. With the measured \Lx\ and stellar mass $M_{\star}$, we determine the best fit stellar mass-halo mass relation, accounting for selection effects, measurement uncertainties and the intrinsic scatter in the scaling relation. The resulting mass trend is $M_{\star}\propto M_{500}^{0.69\pm0.15}$, the intrinsic (log-normal) scatter is $\sigma_{\ln M_{\star}|M_{500}}=0.36^{+0.07}_{-0.06}$, and there is no significant redshift trend $M_{\star}\propto (1+z)^{-0.04\pm0.47}$, although the uncertainties are still large. We also examine $M_{\star}$ within a fixed projected radius of $0.5$~Mpc, showing that it provides a cluster binding mass proxy with intrinsic scatter of $\approx93\%$ (1$\sigma$ in $M_{500}$). We compare our $M_{\star}=M_{\star}(M_{500}, z)$ scaling relation from the XMM-BCS clusters with samples of massive, SZE-selected clusters ($M_{500}\approx6\times10^{14}M_{\odot}$) and low mass NIR-selected clusters ($M_{500}\approx10^{14}M_{\odot}$) at redshift $0.6\lesssim z \lesssim1.3$. After correcting for the known mass measurement systematics in the compared samples, we find that the scaling relation is in good agreement with the high redshift samples, suggesting that for both groups and clusters the stellar content of the galaxy populations within $R_{500}$ depends strongly on mass but only weakly on redshift out to $z\approx1$.Comment: 15 pages, 10 figures. Accepted for publication in MNRA

'Big Data' informed drug development: a case for acceptability

Data, which help inform various stages of drug product development, are increasingly being collected using newer, more novel platforms, such as mobile applications, and analysed computationally as much larger 'Big Data' data sets, revealing patterns relating to human behaviour and interactions. Medicine acceptability gauges the ability and willingness of patients to take their dosage forms. It has become a crucial human component of drug product design. Vouching for the age appropriateness of medicinal products, acceptability related data are now expected by regulatory bodies. Shifting from traditional paper-based to electronic data-gathering platforms will allow the pharmaceutical industry to collect real-world, real-time, clinically relevant data, capable of informing current and future drug product development, reducing time and cost, and setting foundations for patient-centric drug product design

Fermion masses and mixings in gauge theories

The recent evidence for neutrino oscillations stimulate us to discuss again the problem of fermion masses and mixings in gauge theories. In the standard model, several forms for quark mass matrices are equivalent. They become ansatze within most extensions of the standard model, where also relations between quark and lepton sectors may hold. In a seesaw framework, these relations can constrain the scale of heavy neutrino mass, which is often related to the scale of intermediate or unification gauge symmetry. As a consequence, two main scenarios arise. Hierarchies of masses and mixings may be explained by broken horizontal symmetries.Comment: 25 pages, RevTex, no figures. Few misprints corrected and two references adde

Packet Size Optimization for Cognitive Radio Sensor Networks Aided Internet of Things

Cognitive Radio Sensor Networks (CRSN) is state of the art communication paradigm for power constrained short range data communication. It is one of the potential technology adopted for Internet of Things (IoT) and other futuristic Machine to Machine (M2M) based applications. Many of these applications are power constrained and delay sensitive. Therefore, CRSN architecture must be coupled with different adaptive and robust communication schemes to take care of the delay and energy-efficiency at the same time. Considering the tradeoff that exists in terms of energy efficiency and overhead delay for a given data packet length, it is proposed to transmit the physical layer payload with an optimal packet size (OPS) depending on the network condition. Furthermore, due to the cognitive feature of CRSN architecture overhead energy consumption due to channel sensing and channel handoff plays a critical role. Based on the above premises, in this paper we propose a heuristic exhaustive search based Algorithm-1 and a computationally efficient suboptimal low complexity Karuh-Kuhn- Tucker (KKT) condition based Algorithm-2 to determine the optimal packet size in CRSN architecture using variable rate m-QAM modulation. The proposed algorithms are implemented along with two main cognitive radio assisted channel access strategies based on Distributed Time Slotted-Cognitive Medium Access Control (DTS-CMAC) and Centralized Common Control Channel based Cognitive Medium Access Control (CC-CMAC) and their performances are compared. The simulation results reveals that proposed Algorithm-2 outperforms Algorithm-1 by a significant margin in terms of its implementation time. For the exhaustive search based Algorithm-1 the average time consumed to determine OPS for a given number of cognitive users is 1.2 seconds while for KKT based Algorithm-2 it is of the order of 5 to 10 ms. CC-CMAC with OPS is most efficient in terms of overall energy consumption but incurs more delay as compared to DTS-CMAC with OPS scheme

Packet Size Optimization for Multiple Input Multiple Output Cognitive Radio Sensor Networks aided Internet of Things

The determination of Optimal Packet Size (OPS) for Cognitive Radio assisted Sensor Networks (CRSNs) architecture is non-trivial. State of the art in this area describes various complex techniques to determine OPS for CRSNs. However, it is observed that under high interference from the surrounding users, it is not possible to determine a feasible optimal packet size of data transmission under the simple point-to-point CRSN network topology. This is contributed primarily due to the peak transmit power constraint of the cognitive nodes. To address this specific challenge, this paper proposes a Multiple Input Multiple Output based Cognitive Radio Sensor Networks (MIMO-CRSNs) architecture for futuristic technologies like Internet of Things (IoT) and machine-to-machine (M2M) communications. A joint optimization problem is formulated taking into account network constraints like the overall end to end latency, interference duration caused to the non-cognitive users, average BER and transmit power.We propose our Algorithm-1 based on generic exhaustive search technique blue to solve the optimization problem. Furthermore, a low complexity suboptimal Algorithm-2 based on solving classical Karush-Kuhn-Tucker (KKT) conditions is proposed. These algorithms for MIMO-CRSNs are implemented in conjunction with two different channel access schemes. These channel access schemes are Time Slotted Distributed Cognitive Medium Access Control denoted as MIMO-DTS-CMAC and CSMA/CA assisted Centralized Common Control Channel based Cognitive Medium Access Control denoted as MIMO-CC-CMAC. Simulations reveal that the proposed MIMO based CRSN network outperforms the conventional point-to-point CRSN network in terms of overall energy consumption. Moreover, the proposed Algorithm-1 and Algorithm2 shows perfect match and the implementation complexity of Algorithm-2 is much lesser than Algorithm-1. Algorithm-1 takes almost 680 ms to execute and provides OPS value for a given number of users while Algorithm- 2 takes 4 to 5 ms on an average to find the optimal packet size for the proposed MIMO-CRSN framework

Atomic data from the IRON Project. I. Electron-impact scattering of Fe17+ using <I>R</I>-matrix theory with intermediate coupling

We present results for electron-impact excitation of F-like Fe calculated using R-matrix theory where an intermediate-coupling frame transformation (ICFT) is used to obtain level-resolved collision strengths. Two such calculations are performed, the first expands the target using 2s2 2p5, 2s 2p6, 2s2 2p4 3l, 2s 2p5 3l, and 2p6 3l configurations while the second calculation includes the 2s2 2p4 4l, 2s 2p5 4l, and 2p6 4l configurations as well. The effect of the additional structure in the latter calculation on the n=3 resonances is explored and compared with previous calculations. We find strong resonant enhancement of the effective collision strengths to the 2s2 2p4 3s levels. A comparison with a Chandra X-ray observation of Capella shows that the n=4 R-matrix calculation leads to good agreement with observation</p