Air motion determination by tracking humidity patterns in isentropic layers

Determining air motions by tracking humidity patterns in isentropic layers was investigated. Upper-air rawinsonde data from the NSSL network and from the AVE-II pilot experiment were used to simulate temperature and humidity profile data that will eventually be available from geosynchronous satellites. Polynomial surfaces that move with time were fitted to the mixing-ratio values of the different isentropic layers. The velocity components of the polynomial surfaces are part of the coefficients that are determined in order to give an optimum fitting of the data. In the mid-troposphere, the derived humidity motions were in good agreement with the winds measured by rawinsondes so long as there were few or no clouds and the lapse rate was relatively stable. In the lower troposphere, the humidity motions were unreliable primarily because of nonadiabatic processes and unstable lapse rates. In the upper troposphere, the humidity amounts were too low to be measured with sufficient accuracy to give reliable results. However, it appears that humidity motions could be used to provide mid-tropospheric wind data over large regions of the globe

Whither discrete time model predictive control?

This note proposes an efficient computational procedure for the continuous time, input constrained, infinite horizon, linear quadratic regulator problem (CLQR). To ensure satisfaction of the constraints, the input is approximated as a piecewise linear function on a finite time discretization. The solution of this approximate problem is a standard quadratic program. A novel lower bound on the infinite dimensional CLQR problem is developed, and the discretization is adaptively refined until a user supplied error tolerance on the CLQR cost is achieved. The offline storage of the required quadrature matrices at several levels of discretization tailors the method for online use as required in model predictive control (MPC). The performance of the proposed algorithm is then compared with the standard discrete time MPC algorithms. The proposed method is shown to be significantly more efficient than standard discrete time MPC that uses a sample time short enough to generate a cost close to the CLQR solution

The ν\nu-cleus experiment: A gram-scale fiducial-volume cryogenic detector for the first detection of coherent neutrino-nucleus scattering

We discuss a small-scale experiment, called $\nu$-cleus, for the first detection of coherent neutrino-nucleus scattering by probing nuclear-recoil energies down to the 10 eV-regime. The detector consists of low-threshold CaWO$_4$ and Al$_2$O$_3$ calorimeter arrays with a total mass of about 10 g and several cryogenic veto detectors operated at millikelvin temperatures. Realizing a fiducial volume and a multi-element target, the detector enables active discrimination of $\gamma$, neutron and surface backgrounds. A first prototype Al$_2$O$_3$ device, operated above ground in a setup without shielding, has achieved an energy threshold of ${\sim20}$ eV and further improvements are in reach. A sensitivity study for the detection of coherent neutrino scattering at nuclear power plants shows a unique discovery potential (5$\sigma$) within a measuring time of ${\lesssim2}$ weeks. Furthermore, a site at a thermal research reactor and the use of a radioactive neutrino source are investigated. With this technology, real-time monitoring of nuclear power plants is feasible.Comment: 14 pages, 19 figure

Flying Solo

This article is designed to assist solo librarians in thinking through their libraries, responsibilities,clientele, support personnel, and work habits in order to determine best practices to maximize their professional and personal effectiveness

Exploring the selective gray matter profile of autism spectrum disorder through Bayes Factor Modeling

INTRODUCTION: Despite decades of brain MRI research demonstrating atypical neuroanatomical substrate in patients with autism spectrum disorder (ASD), it remains unclear whether and to what extent disorder-selective neuroanatomical abnormalities occur in this spectrum. This, and the fact that multiple brain disorders report a common neuroanatomical substrate, makes transference and the application of neuroimaging findings into the clinical setting an open challenge. OBJECTIVES: To investigate the selective neuroanatomical alteration profile of the ASD brain, we employed a meta-analytic, data-driven, and reverse inference-based approach (i.e.; Bayes fACtor mOdeliNg). METHODS: Eligible voxel-based morphometry data were extracted by a standardized search on BrainMap and MEDLINE databases (849 published experiments, 131 brain disorders, 22747 clinical subjects, 16572 x-y-z coordinates). Two distinct datasets were generated: the ASD dataset, composed of ASD-related data; and the non-ASD dataset, composed of all other clinical conditions data. Starting from the two unthresholded activation likelihood estimation (ALE) maps, the calculus of the Bayes fACtor mOdeliNg was performed. This allowed us to obtain posterior probability distributions on the evidence of brain alteration specificity in ASD. RESULTS: We revealed both cortical and cerebellar areas of neuroanatomical alteration selectivity in ASD. Eight clusters showed a selectivity value ≥ 90%, namely the bilateral precuneus, the right inferior occipital gyrus, left lobule IX, left Crus II, right Crus I, and the right lobule VIIIA (Fig. 1). CONCLUSIONS: The identification of this neuroanatomical pattern provides new insights into the complex pathophysiology of ASD, opening attractive prospects for future neuroimaging-based interventions. DISCLOSURE: No significant relationships