Marginalized Students\u27 Perspectives of School Consolidation: A Case Study in Rural West Virginia

While prior research provides evidence that school consolidation impacts student achievement, the economic efficiency at state and district levels, dropout rates, participation in extracurricular activities, curriculum offerings, and the length of bus rides, “little is known about what happens in consolidated schools to impact student learning,” (Blake, 2003, p. 21) and little attention has been given to studying students’ lived experiences of consolidation. This qualitative case study explored these issues by attempting to understand students’ transition to a consolidated high school, as well as their current experiences with and perceptions of consolidation in a rural community in West Virginia. The data collected included observations of and interviews with six students, along with reviews of pertinent student documents. Data collected also included interviews with seven teachers and one administrator who were identified by the students for inclusion in this study. The purpose of this case study was to add to the body of knowledge concerning the ways economically marginalized students, who are perceived as at risk of school failure, experienced and perceived school consolidation in a rural community. Through an analysis of the data, factors that enabled and/or constrained students’ success were identified. Three themes emerged: supportive relationships with principals, teachers, and others who had high expectations; expanded curricular opportunities; and participation in extracurricular activities

Resonantly enhanced tunneling of Bose-Einstein condensates in periodic potentials

We report on measurements of resonantly enhanced tunneling of Bose-Einstein condensates loaded into an optical lattice. By controlling the initial conditions of our system we were able to observe resonant tunneling in the ground and the first two excited states of the lattice wells. We also investigated the effect of the intrinsic nonlinearity of the condensate on the tunneling resonances.Comment: accepted for publication in Phys. Rev. Letter

Dynamical control of matter-wave tunneling in periodic potentials

We report on measurements of dynamical suppression of inter-well tunneling of a Bose-Einstein condensate (BEC) in a strongly driven optical lattice. The strong driving is a sinusoidal shaking of the lattice corresponding to a time-varying linear potential, and the tunneling is measured by letting the BEC freely expand in the lattice. The measured tunneling rate is reduced and, for certain values of the shaking parameter, completely suppressed. Our results are in excellent agreement with theoretical predictions. Furthermore, we have verified that in general the strong shaking does not destroy the phase coherence of the BEC, opening up the possibility of realizing quantum phase transitions by using the shaking strength as the control parameter.Comment: 5 pages, 3 figure

Observation of photon-assisted tunneling in optical lattices

We have observed tunneling suppression and photon-assisted tunneling of Bose-Einstein condensates in an optical lattice subjected to a constant force plus a sinusoidal shaking. For a sufficiently large constant force, the ground energy levels of the lattice are shifted out of resonance and tunneling is suppressed; when the shaking is switched on, the levels are coupled by low-frequency photons and tunneling resumes. Our results agree well with theoretical predictions and demonstrate the usefulness of optical lattices for studying solid-state phenomena.Comment: 5 pages, 3 figure

A real time bolometer tomographic reconstruction algorithm in nuclear fusion reactors

In tokamak nuclear fusion reactors, one of the main issues is to know the total emission of radiation, which is mandatory to understand the plasma physics and is very useful to monitor and control the plasma evolution. This radiation can be measured by means of a bolometer system that consists in a certain number of elements sensitive to the integral of the radiation along straight lines crossing the plasma. By placing the sensors in such a way to have families of crossing lines, sophisticated tomographic inversion algorithms allow to reconstruct the radiation tomography in the 2D poloidal cross-section of the plasma. In tokamaks, the number of projection cameras is often quite limited resulting in an inversion mathematic problem very ill conditioned so that, usually, it is solved by means of a grid-based, iterative constrained optimization procedure, whose convergence time is not suitable for the real time requirements. In this paper, to illustrate the method, an assumption not valid in general is made on the correlation among the grid elements, based on the statistical distribution of the radiation emissivity over a set of tomographic reconstructions, performed off-line. Then, a regularization procedure is carried out, which merge highly correlated grid elements providing a squared coefficients matrix with an enough low condition number. This matrix, which is inverted offline once for all, can be multiplied by the actual bolometer measures returning the tomographic reconstruction, with calculations suitable for real time application. The proposed algorithm is applied, in this paper, to a synthetic case study

Forecasting-Aided Monitoring for the Distribution System State Estimation

In this paper, an innovative approach based on an artificial neural network (ANN) load forecasting model to improve the distribution system state estimation accuracy is proposed. High-quality pseudomeasurements are produced by a neural model fed with both exogenous and historical load information and applied in a realistic measurement scenario. Aggregated active and reactive powers of small or medium enterprises and residential loads are simultaneously predicted by a one-step ahead forecast. The correlation between the forecasted real and reactive power errors is duly kept into account in the definition of the estimator together with the uncertainty of the overall measurement chain. The beneficial effects of the ANN-based pseudomeasurements on the quality of the state estimation are demonstrated by simulations carried out on a small medium-voltage distribution grid

Dressed matter waves

We suggest to view ultracold atoms in a time-periodically shifted optical lattice as a "dressed matter wave", analogous to a dressed atom in an electromagnetic field. A possible effect lending support to this concept is a transition of ultracold bosonic atoms from a superfluid to a Mott-insulating state in response to appropriate "dressing" achieved through time-periodic lattice modulation. In order to observe this effect in a laboratory experiment, one has to identify conditions allowing for effectively adiabatic motion of a many-body Floquet state.Comment: 9 pages, 4 figures, to be published in: J. Phys.: Conference Serie

Automatic disruption classification in JET with the ITER-like wall

The new full-metal ITER-like wall at JET was found to have a deep impact on the physics of disruptions at JET. In order to develop disruption classification, the 10D operational space of JET with the new ITER-like wall has been explored using the generative topographic mapping method. The 2D map has been exploited to develop an automatic disruption classification of several disruption classes manually identified. In particular, all the non-intentional disruptions have been considered, that occurred in JET from 2011 to 2013 with the new wall. A statistical analysis of the plasma parameters describing the operational spaces of JET with carbon wall and JET ITER-like wall has been performed and some physical considerations have been made on the difference between these two operational spaces and the disruption classes which can be identified. The performance of the JET- ITER-like wall classifier is tested in realtime in conjunction with a disruption predictor presently operating at JET with good results. Moreover, to validate and analyse the results, another reference classifier has been developed, based on the k-nearest neighbour technique. Finally, in order to verify the reliability of the performed classification, a conformal predictor based on non-conformity measures has been developed

Convolutional Neural Network for Seizure Detection of Nocturnal Frontal Lobe Epilepsy

The Nocturnal Frontal Lobe Epilepsy (NFLE) is a form of epilepsy in which seizures occur predominantly during sleep. In other forms of epilepsy, the commonly used clinical approach mainly involves manual inspection of encephalography (EEG) signals, a laborious and time-consuming process which often requires the contribution of more than one experienced neurologist. In the last decades, numerous approaches to automate this detection have been proposed and, more recently, machine learning has shown very promising performance. In this paper, an original Convolutional Neural Network (CNN) architecture is proposed to develop patient-specific seizure detection models for three patients affected by NFLE. The performances, in terms of accuracy, sensitivity, and specificity, exceed by several percentage points those in the most recent literature. The capability of the patient-specific models has been also tested to compare the obtained seizure onset times with those provided by the neurologists, with encouraging results. Moreover, the same CNN architecture has been used to develop a cross-patient seizure detection system, resorting to the transfer-learning paradigm. Starting from a patient-specific model, few data from a new patient are enough to customize his model. This contribution aims to alleviate the task of neurologists, who may have a robust indication to corroborate their clinical conclusions