On improving the iterative convergence properties of an implicit approximate-factorization finite difference algorithm

The iterative convergence properties of an approximate-factorization implicit finite-difference algorithm are analyzed both theoretically and numerically. Modifications to the base algorithm were made to remove the inconsistency in the original implementation of artificial dissipation. In this way, the steady-state solution became independent of the time-step, and much larger time-steps can be used stably. To accelerate the iterative convergence, large time-steps and a cyclic sequence of time-steps were used. For a model transonic flow problem governed by the Euler equations, convergence was achieved with 10 times fewer time-steps using the modified differencing scheme. A particular form of instability due to variable coefficients is also analyzed

On applications of chimera grid schemes to store separation

A finite difference scheme which uses multiple overset meshes to simulate the aerodynamics of aircraft/store interaction and store separation is described. In this chimera, or multiple mesh, scheme, a complex configuration is mapped using a major grid about the main component of the configuration, and minor overset meshes are used to map each additional component such as a store. As a first step in modeling the aerodynamics of store separation, two dimensional inviscid flow calculations were carried out in which one of the minor meshes is allowed to move with respect to the major grid. Solutions of calibrated two dimensional problems indicate that allowing one mesh to move with respect to another does not adversely affect the time accuracy of an unsteady solution. Steady, inviscid three dimensional computations demonstrate the capability to simulate complex configurations, including closely packed multiple bodies

FoxK1 and FoxK2 in insulin regulation of cellular and mitochondrial metabolism

A major target of insulin signaling is the FoxO family of Forkhead transcription factors, which translocate from the nucleus to the cytoplasm following insulin-stimulated phosphorylation. Here we show that the Forkhead transcription factors FoxK1 and FoxK2 are also downstream targets of insulin action, but that following insulin stimulation, they translocate from the cytoplasm to nucleus, reciprocal to the translocation of FoxO1. FoxK1/FoxK2 translocation to the nucleus is dependent on the Akt-mTOR pathway, while its localization to the cytoplasm in the basal state is dependent on GSK3. Knockdown of FoxK1 and FoxK2 in liver cells results in upregulation of genes related to apoptosis and down-regulation of genes involved in cell cycle and lipid metabolism. This is associated with decreased cell proliferation and altered mitochondrial fatty acid metabolism. Thus, FoxK1/K2 are reciprocally regulated to FoxO1 following insulin stimulation and play a critical role in the control of apoptosis, metabolism and mitochondrial function

Exploring functional regression for dynamic modeling of shallow landslides in South Tyrol, Italy

Shallow landslides are ubiquitous hazards in mountainous regions worldwide that arise from an interplay of static predisposing factors and dynamic preparatory and triggering conditions. Modeling shallow landslides at regional scales has leveraged data-driven approaches to separately investigate purely spatial landslide susceptibility and temporally varying conditions. Yet, the joint assessment of shallow landslides in space and time using data-driven methods remains challenging. Furthermore, dynamic factors have been typically included in data-driven landslide models as scalar predictors by employing aggregated descriptors over time (e.g., mean, maximum, or total precipitation over a defined time window), where many choices are possible for the considered time scales and aggregation operators. Therefore, incorporating the time-varying behavior of dynamic factors remains difficult.This study addresses these challenges by exploring Functional Generalized Additive Models (FGAMs) to predict the occurrence of shallow landslides in space and time within the Italian province of South Tyrol (7,400 km²). In contrast to conventional techniques, we test the benefits of using functional predictors to describe dynamic factors (e.g., precipitation and temperature) leading to landslide events. In other words, we evaluate dynamic factors as collections of measurements over time (i.e., time series). To do so, our approach uses a binomial FGAM to analyze the statistical associations between the static factors (scalar predictors), the dynamic weather conditions prior to a potential landslide occurrence (functional predictors), and the occurrence of shallow landslides in space and time.Potential outcomes of this novel approach show an overview of the added value of using functional predictors for space and time shallow landslide modeling. These research findings are positioned within the context of the PROSLIDE project, which has received financial support from the Research Südtirol/Alto Adige 2019 research program of the Autonomous Province of Bozen/Bolzano – Südtirol/Alto Adige

Time-frequency detection of Gravitational Waves

We present a time-frequency method to detect gravitational wave signals in interferometric data. This robust method can detect signals from poorly modeled and unmodeled sources. We evaluate the method on simulated data containing noise and signal components. The noise component approximates initial LIGO interferometer noise. The signal components have the time and frequency characteristics postulated by Flanagan and Hughes for binary black hole coalescence. The signals correspond to binaries with total masses between $45 M_\odot$ to $70 M_\odot$ and with (optimal filter) signal-to-noise ratios of 7 to 12. The method is implementable in real time, and achieves a coincident false alarm rate for two detectors $\approx$ 1 per 475 years. At this false alarm rate, the single detector false dismissal rate for our signal model is as low as 5.3% at an SNR of 10. We expect to obtain similar or better detection rates with this method for any signal of similar power that satisfies certain adiabaticity criteria. Because optimal filtering requires knowledge of the signal waveform to high precision, we argue that this method is likely to detect signals that are undetectable by optimal filtering, which is at present the best developed detection method for transient sources of gravitational waves.Comment: 24 pages, 5 figures, uses REVTE

Ultrafast control of strong light-matter coupling

We dynamically modulate strong light–matter coupling in a GaAs/AlGaAs microcavity using intense ultrashort laser pulses tuned below the interband exciton energy, which induce a transient Stark shift of the cavity polaritons. For 225-fs pulses, shorter than the cavity Rabi cycle period of 1000 fs, this shift decouples excitons and cavity photons for the duration of the pulse, interrupting the periodic energy exchange between photonic and electronic states. For 1500-fs pulses, longer than the Rabi cycle period, however, the Stark shift does not affect the strong coupling. The two regimes are marked by distinctly different line shapes in ultrafast reflectivity measurements—regardless of the Stark field intensity. The crossover marks the transition from adiabatic to diabatic switching of strong light–matter coupling

Segmentation of Loops from Coronal EUV Images

We present a procedure which extracts bright loop features from solar EUV images. In terms of image intensities, these features are elongated ridge-like intensity maxima. To discriminate the maxima, we need information about the spatial derivatives of the image intensity. Commonly, the derivative estimates are strongly affected by image noise. We therefore use a regularized estimation of the derivative which is then used to interpolate a discrete vector field of ridge points ``ridgels'' which are positioned on the ridge center and have the intrinsic orientation of the local ridge direction. A scheme is proposed to connect ridgels to smooth, spline-represented curves which fit the observed loops. Finally, a half-automated user interface allows one to merge or split, eliminate or select loop fits obtained form the above procedure. In this paper we apply our tool to one of the first EUV images observed by the SECCHI instrument onboard the recently launched STEREO spacecraft. We compare the extracted loops with projected field lines computed from almost-simultaneously-taken magnetograms measured by the SOHO/MDI Doppler imager. The field lines were calculated using a linear force-free field model. This comparison allows one to verify faint and spurious loop connections produced by our segmentation tool and it also helps to prove the quality of the magnetic-field model where well-identified loop structures comply with field-line projections. We also discuss further potential applications of our tool such as loop oscillations and stereoscopy.Comment: 13 pages, 9 figures, Solar Physics, online firs

A Revised Design for Microarray Experiments to Account for Experimental Noise and Uncertainty of Probe Response

Background Although microarrays are analysis tools in biomedical research, they are known to yield noisy output that usually requires experimental confirmation. To tackle this problem, many studies have developed rules for optimizing probe design and devised complex statistical tools to analyze the output. However, less emphasis has been placed on systematically identifying the noise component as part of the experimental procedure. One source of noise is the variance in probe binding, which can be assessed by replicating array probes. The second source is poor probe performance, which can be assessed by calibrating the array based on a dilution series of target molecules. Using model experiments for copy number variation and gene expression measurements, we investigate here a revised design for microarray experiments that addresses both of these sources of variance. Results Two custom arrays were used to evaluate the revised design: one based on 25 mer probes from an Affymetrix design and the other based on 60 mer probes from an Agilent design. To assess experimental variance in probe binding, all probes were replicated ten times. To assess probe performance, the probes were calibrated using a dilution series of target molecules and the signal response was fitted to an adsorption model. We found that significant variance of the signal could be controlled by averaging across probes and removing probes that are nonresponsive or poorly responsive in the calibration experiment. Taking this into account, one can obtain a more reliable signal with the added option of obtaining absolute rather than relative measurements. Conclusion The assessment of technical variance within the experiments, combined with the calibration of probes allows to remove poorly responding probes and yields more reliable signals for the remaining ones. Once an array is properly calibrated, absolute quantification of signals becomes straight forward, alleviating the need for normalization and reference hybridizations