Miracle-dz/UniFAR-FTUs huggingface dataset:
Dependence of the GRB Lag-Luminosity Relation on Redshift in the Source Frame

The lag-luminosity relation for gamma-ray bursts (GRBs) is an anti-correlation between the time lag, ? lag, which represents the delay between the arrival of hard and soft photons, and the isotropic peak luminosity, L. In this paper, we use a sample of 43 Swift bursts, which was taken from Ukwatta et al., to investigate whether this relation depends on redshift. Both the z-correction and the k-correction are taken into account. Our analysis consists of binning the data in redshift, z, then applying a fit of the form: for each bin, where ? lag0 is the time-lag in the burst’s source frame, and is the corresponding mean value for the entire sample. The goal is to see whether the two fitting parameters, A and B, evolve in a systematic way with z. Our results indicate that both the normalization, A, and the slope, B, seem to vary in a systematic way with redshift. We note that although good best-fits were obtained, with reasonable values for both the linear regression coefficient, r, and the reduced chi-squared, the data showed large scatter. Also, the number of GRBs in the sample studied is not large, and thus our conclusions are only tentative at this point. A flat universe with M = 0.27, ? = 0.73, and a Hubble constant, H0 = 70 km.s-1.Mpc-1 is assumed.
A ZVS-PWM Scheme for Three-Phase Active-Clamping T-Type Inverters

For increasing power density and other performance of T-type three-level inverters, the soft-switching technique is studied. This article proposes a novel zero-voltage switching (ZVS) PWM scheme for active clamping three-phase T-type inverters, which can realize the ZVS operation of all switches and reduce the switching loss. The proposed ZVS PWM scheme is evolved from discontinuous PWM and modified for the requirements of T-type inverter. It enables all switches, including main switches and auxiliary switches, operate at a fixed frequency. However, a larger current on the power semiconductor devices occurs at 60° zone transition. To suppress this current surge, the carriers used in the ZVS PWM scheme are phase shifted. Finally, the novel ZVS PWM scheme is verified on a 10 kW/150 kHz SiC-based ZVS T-type inverter prototype.
The role of relevancy in native advertising on social media

Abstract Native advertising has grown in popularity in recent years, and advertisers continue to look for ways to increase its effectiveness. Beyond the emphases of prior research on ad disclosure and format relevancy of native advertising, the current research contributes to the literature by first testing the relationship between the native ad and social media content by varying the thematic content relevancy of the ad (irrelevant vs. relevant) and second, adding media context effects to the equation by changing the thematic media content consistency (low vs. high) of the surrounding social media posts in the feed. Across three experiments, the ad relevancy effect was significant. The ad relevant to the surrounding content posts decreased ad intrusiveness and increased positive ad or brand attitude compared to an irrelevant ad in high media content consistency settings. In low media content consistency settings, the ad’s relevancy did not matter as much. Moderated mediation found that higher ad intrusiveness drove lower ad attitude and brand attitude for the irrelevant ad only in the high content consistency condition. This relationship did not occur for the low content consistency condition. The findings highlight the importance of testing thematic ad-content relevancy in digital media settings that present novel content presentation formats. Theoretical implications are provided regarding the psychological appeal of relevancy and the different media contexts where schema theory can be more or less applicable. Practical implications on how to place ‘in-feed’ native ads and how social media platforms may facilitate contextual ad targeting, given the different media content consistency backdrop, are discussed.
Fully Printed and Flexible Schottky Diodes Based on Carbon Nanomaterials Operating Up to 5 MHz

The capability to fabricate printed and flexible energy harvesting and conditioning circuits, over a large area and with low-cost techniques, is crucial to enable flexible and wearable electronics. Carbon nanomaterials show excellent electrical, optical, chemical, and mechanical properties for printed electronics, offering low-cost, and large-area functionality on flexible substrates. In the past few years, many efforts have been dedicated to the fabrication of printed transistors made with carbon nanomaterials, but very few works have been dedicated to diodes, which are fundamental circuit elements, often employed in energy harvesting systems. Here we report a simple and cost-effective approach for the fabrication of fully inkjet-printed Schottky diodes on Kapton and paper substrates using carbon nanotubes (CNT) and graphene. We demonstrate that both ohmic and Schottky contacts between printed nanotubes and graphene can be obtained with post-printing thermal treatments at different temperatures. The diodes thus fabricated work with low supply voltages, exhibit excellent mechanical stability and have a maximum operating frequency in the order of 5 MHz.
Time and Phase Resolved Optical Spectra of Potentially Hazardous Asteroid 2014 JO25

The asteroid 2014 JO25, considered to be potentially hazardous by the Minor Planet Center, was spectroscopically followed during its close-Earth encounter on 2017 April 19 and 20. The spectra of the asteroid were taken with the low-resolution spectrograph (LISA), mounted on the 1.2 m telescope at the Mount Abu Infrared Observatory, India. Coming from a region close to the Hungaria population of asteroids, this asteroid follows a comet-like orbit with a relatively high inclination and large eccentricity. Hence, we carried out optical spectroscopic observations of the asteroid to look for comet-like molecular emissions or outbursts. However, the asteroid showed a featureless spectrum, devoid of any comet-like features. The light curve of the asteroid was analyzed using V-band magnitudes derived from the spectra and the most likely solution for the rotation of the asteroid was obtained. The absolute magnitude H and the slope parameter G were determined for the asteroid in the V filter band using the IAU accepted standard two-parameter H–G model. A peculiar, rarely found result from these observations is its phase bluing trend. The relative B–V color index seems to decrease with increasing phase angle, which indicates a phase bluing trend. Such trends have seldom been reported in the literature. However, phase reddening in asteroids is very common. The asymmetry parameter g and the single-scattering albedo w were estimated for the asteroid by fitting the Hapke phase function to the observed data. The asteroid shows a relatively large value for the single-scattering albedo and a highly back-scattering surface.
Quantum probes for quantum wells

We seek for the optimal strategy to infer the width $a$ of an infinite potential wells by performing measurements on the particle(s) contained in the well. In particular, we address quantum estimation theory as the proper framework to formulate the problem and find the optimal quantum measurement, as well as to evaluate the ultimate bounds to precision. Our results show that in a static framework the best strategy is to measure position on a delocalized particle, corresponding to a width-independent quantum signal-to-noise ratio (QSNR), which increases with delocalisation. Upon considering time-evolution inside the well, we find that QSNR increases as $t^2$. On the other hand, it decreases with $a$ and thus time-evolution is a metrological resource only when the width is not too large compared to the available time evolution. Finally, we consider entangled probes placed into the well and observe super-additivity of the QSNR: it is the sum of the single-particle QSNRs, plus a positive definite term, which depends on their preparation and may increase with the number of entangled particles. Overall, entanglement represents a resource for the precise characterization of potential wells.
A Practical Switch Condition Monitoring Solution for SiC Traction Inverters

As automotive manufacturers move toward silicon carbide (SiC) MOSFET-based traction inverters, practical online switch condition monitoring solutions are crucial to address potential reliability concerns. In this article, an end-to-end practical online condition monitoring (OCM) solution is proposed. An online sensing circuit is proposed, which enables online ON-state resistance (<inline-formula> <tex-math notation="LaTeX">$R_{\text {ds-}\mathrm{\scriptscriptstyle ON}}$ </tex-math></inline-formula>) measurement for all six switches of the inverter. To address the challenge of periodic data acquisition alongside higher priority motor control tasks, a fast, code-efficient out-of-order equivalent time sampling (ETS) technique is also proposed. The obtained periodic, high-resolution <inline-formula> <tex-math notation="LaTeX">$R_{\text {ds-}\mathrm{\scriptscriptstyle ON}}$ </tex-math></inline-formula> data are filtered by a Kalman filter stage. With the proposed measurement solution, <inline-formula> <tex-math notation="LaTeX">$R_{\text {ds-}\mathrm{\scriptscriptstyle ON}}$ </tex-math></inline-formula> obtained at the motor current peak has an error of <inline-formula> <tex-math notation="LaTeX">${< }1.5\%$ </tex-math></inline-formula>. Furthermore, the symmetrical nature of the inverter’s operation is exploited to propose a Bayesian inference solution for independent online state-of-health (SoH) estimation for all six switches. This technique isolates aging-related <inline-formula> <tex-math notation="LaTeX">$R_{\text {ds-}\mathrm{\scriptscriptstyle ON}}$ </tex-math></inline-formula> change from operating conditions-related changes. In particular, by automatically accounting for device- and system-level variations in the model, the proposed Bayesian SoH estimation solution eliminates the need for extensive system/device specific calibration. The efficacy and robustness of the proposed solution are tested by inducing bond-wire failure in several decapsulated discrete SiC MOSFETs.
Molecular dynamic simulation of wet water vapour transport in porous medium

Studies of the transport of wet water vapour are relevant for various areas of human activity, including the construction and production of building materials, mining, agriculture, environmental safety of technological processes, scientific research. In particular, one of the methods for extracting highly viscous bitumen grades of oil from the subterranean depths is based on the dilution of the contents of the porous medium by means of pumping coolants. The simplest and environmentally friendly coolant is wet steam containing small drops of water. In addition, with the help of heated water vapour, the filtering elements of collectors are cleaned from sediments of the solid phase (for example, paraffins, gas hydrates and ice floes) on the walls of the porous medium. For realistic modeling of the processes of filtration and heat and mass transfer during the injection of wet steam into a porous medium, it is necessary to investigate the characteristics of the interaction of saturated water vapour with individual through-type pores. In this paper, a study was carried out through mathematical modelling of the dependence of the diffusion rate of wet water vapour on the pressure difference outside the pore that occurs when water vapour is injected into a porous medium. The dependencies of the diffusion rate on the pore cross section, the magnitude of vapour adsorption on the pore walls, as well as the effect of water vapour temperature on all these processes were also investigated. Practical interest is the study of the influence of the rate of cooling of water vapour on the diffusion rate and the adsorption of water vapour on the wall of the pores. The calculations were carried out using a hybrid type model that combines molecular dynamics and macro-diffusion approaches to describe the interaction of water vapour with individual pores.
VG-GAN: Conditional GAN Framework for Graphical Design Generation

This paper introduces VG-GAN, a novel conditional GAN for graphical design generation tasks with applications in background design, layout and scene generation. VG-GAN utilizes vector-based methods to achieve scale invariance of the generated layouts. Concretely, the GAN model outputs only relevant vector layout information, and the final layout image is rendered in a post-processing step to allow layouts to be scaled arbitrarily. In contrast to existing vector-based generation models that require a choice of initial class relationships, VG-GAN proposes a selection module to automatically learn the class relationships in target applications, presenting a novel application of vector-based generation not addressed by existing literature. VG-GAN is applied on three generation tasks given optional design conditions, namely, banner background generation, document layout generation and cli-part scene generation. The results demonstrate the model’s effectiveness in learning and generating conditional graphical design.
A low-flux state in IRAS 00521−7054 seen withNuSTARandXMM–Newton: relativistic reflection and an ultrafast outflow

We present results from a deep, coordinated XMM–Newton + NuSTARobservation of the Seyfert 2 galaxy IRAS 00521−7054. The NuSTAR data provide the first detection of this source in high-energy X-rays (E > 10 keV), and the broad-band data show this to be a highly complex source which exhibits relativistic reflection from the inner accretion disc, further reprocessing by more distant material, neutral absorption, and evidence for ionized absorption in an extreme, ultrafast outflow (⁠v_(out) ∼ 0.4c). Based on lamppost disc reflection models, we find evidence that the central supermassive black hole is rapidly rotating (a > 0.77), consistent with previous estimates from the profile of the relativistic iron line, and that the accretion disc is viewed at a fairly high inclination (i ∼ 59°). Based on extensive simulations, we find the ultrafast outflow is detected at ∼4σ significance (or greater). We also estimate that the extreme outflow should be sufficient to power galaxy-scale feedback, and may even dominate the energetics of the total output from the system.
Coil combination of multichannel MRSI data at 7 T: MUSICAL

The goal of this study was to evaluate a new method of combining multi-channel 1 H MRSI data by direct use of a matching imaging scan as a reference, rather than computing sensitivity maps. Seven healthy volunteers were measured on a 7-T MR scanner using a head coil with a 32-channel array coil for receive-only and a volume coil for receive/transmit. The accuracy of prediction of the phase of the 1 H MRSI data with a fast imaging pre-scan was investigated with the volume coil. The array coil 1 H MRSI data were combined using matching imaging data as coil combination weights. The signal-to-noise ratio (SNR), spectral quality, metabolic map quality and Cramér – Rao lower bounds were then compared with the data obtained by two standard methods, i.e. using sensitivity maps and the ﬁ rst free induction decay (FID) data point. Additional noise decorrelation was performed to further optimize the SNR gain. The new combination method improved signi ﬁ cantly the SNR (+29%), overall spectral quality and visual appearance of metabolic maps, and lowered the Cramér – Rao lower bounds ( (cid:1) 34%), compared with the combination method based on the ﬁ rst FID data point. The results were similar to those obtained by the combination method using sensitivity maps, but the new method increased the SNR slightly (+1.7%), decreased the algorithm complexity, required no reference coil and pre-phased all spectra correctly prior to spectral processing. Noise decorrelation further increased the SNR by 13%. The proposed method is a fast, robust and simple way to improve the coil combination in 1 H MRSI of the human brain at 7 T, and could be extended to other 1 H MRSI techniques. © 2013 The Authors. NMR in Biomedicine published by John Wiley & Sons, Ltd. MPRAGE, Image Calibration
Prevention and Detection of Coordinated False Data Injection Attacks on Integrated Power and Gas Systems

The growing reliance of integrated power and gas systems (IPGSs) on information and communication technology has left them prone to cyber-attacks. To protect IPGSs against such attacks, having comprehensive knowledge about possible attack strategies and their impacts is essential. On this basis, this paper (i) presents a new family of coordinated stealthy false data injection attacks (FDIAs) whose aims are to disconnect gas-fired power plants (GFPPs) by targeting IPGS measurements, (ii) develops a preventive defense strategy against the introduced family of attacks by improving the security of high-risk measurements, and (iii) proposes an online learning-based detection scheme using secured measurements. Initially, a two-stage attack model is developed to construct the FDIA vector. In the first stage, power system measurements are manipulated to maximize the grid reliance on the targeted GFPPs, while bypassing bad data detection schemes. In the second stage, the measurements of gas networks (GNs) are falsified to portray a fake leakage, so misleading the operator to disconnect under-attack pipelines and interrupting the supply of fuel to targeted GFPPs. Each stage is formulated as an attacker-operator bi-level optimization problem, which is solved as mixed-integer linear programming (MILP) by converting the operator level to its corresponding Karush-Kuhn-Tucker (KKT) conditions. Afterwards, based on possible attack scenarios, high-risk measurements are identified to be secured as a preventive defense strategy. Finally, a multilayer perceptron (MLP) model is trained to detect cyber-attacks against IPGS. The performance of the proposed method is corroborated through simulations on IEEE 24-Bus 20-Node IPGS and IEEE 118-bus 135-node IPGS.
CellularFlow: Memristive Circuits of Gyrator Neurons Toward Brain Circuits

Now is the age of neuromorphic computing that creates brain circuits. The analog and digital circuit theory changes because the values of the basic conductance elements can be made variable by learning. The computer structure changes to in-memory computing technology in cooperation with a von Neumann architecture. In this paper, we propose a gyrator neuron (GN) that enables analog computer operations by nodal equation. The GN is constructed based on memristor elements. The GN executes learning by back propagation processing and association by forward propagation processing.
Modulation of the Work Function by the Atomic Structure of Strong Organic Electron Acceptors on H‐Si(111)

Advances in hybrid organic/inorganic architectures for optoelectronics can be achieved by understanding how the atomic and electronic degrees of freedom cooperate or compete to yield the desired functional properties. Here, how work function changes are modulated by the structure of the organic components in model hybrid systems is shown. Two cyano‐quinodimethane derivatives (F4‐TCNQ and F6‐TCNNQ), which are strong electron‐acceptor molecules, adsorbed on H‐Si(111) are considered. From systematic structure searches employing the range‐separated hybrid HSE06 functional including many‐body van der Waals (vdW) contributions, it is predicted that, despite their similar composition, these molecules adsorb with significantly different densely packed geometries in the first layer, due to strong intermolecular interaction. F6‐TCNNQ shows a much stronger intralayer interaction (primarily due to vdW contributions) than F4‐TCNQ in multilayered structures. The densely packed geometries induce a large interface‐charge rearrangement that results in a work function increase of 1.11 and 1.76 eV for F4‐TCNQ and F6‐TCNNQ, respectively. Nuclear fluctuations at room temperature produce a wide distribution of work function values, well‐modeled by a normal distribution with σ = 0.17 eV. These findings are corroborated with experimental evidence of pronounced island formation for F6‐TCNNQ on H‐Si(111) and with the agreement of trends between predicted and measured work function changes.
Analysis of correlation-based biomolecular networks from different omics data by fitting stochastic block models

Background: Biological entities such as genes, promoters, mRNA, metabolites or proteins do not act alone, but in concert in their network context. Modules, i.e., groups of nodes with similar topological properties in these networks characterize important biological functions of the underlying biomolecular system. Edges in such molecular networks represent regulatory and physical interactions, and comparing them between conditions provides valuable information on differential molecular mechanisms. However, biological data is inherently noisy and network reduction techniques can propagate errors particularly to the level of edges. We aim to improve the analysis of networks of biological molecules by deriving modules together with edge relevance estimations that are based on global network characteristics. Methods: The key challenge we address here is investigating the capability of stochastic block models (SBMs) for representing and analyzing different types of biomolecular networks. Fitting them to SBMs both delivers modules of the networks and enables the derivation of edge confidence scores, and it has not yet been investigated for analyzing biomolecular networks. We apply SBM-based analysis independently to three correlation-based networks of breast cancer data originating from high-throughput measurements of different molecular layers: either transcriptomics, proteomics, or metabolomics. The networks were reduced by thresholding for correlation significance or by requirements on scale-freeness. Results and discussion: We find that the networks are best represented by the hierarchical version of the SBM, and many of the predicted blocks have a biologically and phenotypically relevant functional annotation. The edge confidence scores are overall in concordance with the biological evidence given by the measurements. We conclude that biomolecular networks can be appropriately represented and analyzed by fitting SBMs. As the SBM-derived edge confidence scores are based on global network connectivity characteristics and potential hierarchies within the biomolecular networks are considered, they could be used as additional, integrated features in network-based data comparisons.
Trust More, Fear Less: The Role of Social Support in Fully Automated Vehicle Choice

In this article, we reveal how mental support from family, friends, and local authorities—social support (SP)—contributes to inclusivity in the context of fully autonomous vehicles (FAVs). We focus on disaster victims who have trouble driving and test how the information on traumatic experiences relates to their purchase decisions. To this end, we create a unique survey that asks about respondents' purchasing decisions before/after we share information with them on FAV traffic accidents that led to a driver's death. Our results show that SP is crucial for encouraging people to choose FAVs, even after fatal news concerning FAVs is shared, regardless of disaster experience. We also find that other factors, such as safety and convenience, can encourage people to “substitute” choosing FAVs in place of not choosing FAVs. By adopting integrated choice and latent variable models considering individual heterogeneity over 60 000 respondents, our results indicate possible policy paths through which to utilize SP in creating FAVs as an inclusive transportation mode.