Miracle-dz/UniFAR-FTUs huggingface dataset:
A model of the planetary nebula NGC 2392 determined from velocity observations.

High-resolution slit spectra are combined with published photometric and velocity data to develop a model of the double-ring planetary nebula NGC 2392. The inner ring is due to an incomplete, prolate spheroid shell with most of the material concentrated near the minor axis circumference. The spheroid is seen almost pole-on, open at the sharp ends, and the formal velocity of expansion (93 km/s) at the ends is a record high for planetary nebulae. The outer ring is a nearly round spheroid of approximately 16 km/s expansion velocity and a different tilt angle than the inner shell. It is surrounded by rapidly expanding material with velocities of at least 75 km/s. A high-velocity stream of material is formed at the open ends of the inner shell and is accelerated by the central star's stellar wind to velocities of at least + or - 190 km/s, providing the first direct evidence for the kinematic role of a strong stellar wind. The inner shell age is approximately 800 yr, arguing that the shell material was preferentially ejected from the equator of the parent star, in contradiction with the usual predictions of theory.
A SemiContinuous Solution of the Boltzmann Equation for Carrier‐Carrier and Carrier‐Phonon Interaction

An accurate description of the carrier transport in modern bipolar semiconductor devices requires the consideration of carrier‐carrier interactions. A semicontinuous version of the Boltzmann equations governing the temporal evolution of a coupled system of electrons, holes, and phonons is presented. The accuracy of the developed numerical method is investigated by simulating the relaxation of carriers in bulk GaAs.
A maximum likelihood method for obtaining integrated attenuation from ultrasound transmission mode measurements

In photoacoustic imaging applications an image is reconstructed of the optical absorption distribution the imaged object. The photoacoustic measurements however are also dependent on acoustic properties of the imaged object. By estimating the acoustic properties from photoacoustic measurements, we can improve the performance of the optical absorption reconstruction and allow for the imaging of separate acoustic modalities. We derive and evaluate a maximum likelihood estimator for the measurements of integrated acoustic attenuation. This estimator is applicable to media like soft tissue. In this kind of media, the attenuation due to dissipative effects obeys a frequency power law. By measuring the propagation of ultrasound signals through such a medium, the parameters that describe the attenuation can be estimated. In this paper a new method is introduced for estimating the attenuation of ultrasound media by means of transmission mode measurements. The method is based on analyzing the noise characteristics.
Iron availability affects the function of mitochondria in Fe-deficient cucumber roots

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Sustainable Cities and Society

The emergence of COVID-19 pandemic is causing tremendous impact on our daily lives, including the way people interact with buildings. Leveraging the advances in machine learning and other supporting digital technologies, recent attempts have been sought to establish exciting smart building applications that facilitates better facility management and higher energy efficiency. However, relying on the historical data collected prior to the pandemic, the resulting smart building applications are not necessarily effective under the current ever- changing situation due to the drifts of data distribution. This paper investigates the bidirectional interaction between human and buildings that leads to dramatic change of building performance data distributions post- pandemic, and evaluates the applicability of typical facility management and energy management applications against these changes. According to the evaluation, this paper recommends three mitigation measures to rescue the applications and embedded machine learning algorithms from the data inconsistency issue in the post-pandemic era. Among these measures, incorporating occupancy and behavioural parameters as independent variables in machine learning algorithms is highlighted. Taking a Bayesian perspective, the value of data is exploited, historical or recent, pre- and post-pandemic, under a people-focused view.
Time series analysis of real-time music perception: approaches to the assessment of individual and expertise differences in perception of expressed affect

We use time series analysis methods to detect differences between individuals and expertise groups in continuous perceptions of the arousal expressed by Wishart's electroacoustic piece Red Bird. The study is part of a project in which we characterise dynamic perception of the structure and affective expression of music. We find that individual series of perceptions of expressed arousal often show considerable periods of stasis. This may challenge conventional time series methodologies, so we test their validity by application of a general linear autoregressive moving average (GLARMA) approach, which supports it. Acoustic intensity is a dominant predictor of perceived arousal in this piece. We show that responses are time-variant and that animate sounds influence the conditional variance of perceived arousal. Using vector autoregression and cross-sectional time series analysis (which preserves the integrity of each individual response series), we find differences between musical expertise groups (non-musicians, musicians, and electroacoustic musicians). Individual differences within each group are greater than those between expertise groups. The companion paper applies the developed methods to all four pieces in our overall project (Dean, R.T., F. Bailes, and W.T.M. Dunsmuir. 2014. “Shared and Distinct Mechanisms of Individual and Expertise-Group Perception of Expressed Arousal in Four Works.” Journal of Mathematics and Music 8 (3): 207–223). An Online Supplement is available at http://dx.doi. org/10.1080/17459737.2014. 928752.
Hierarchical Neural Networks (HNN): Using TensorFlow to build Hierarchical Neural Networks (HNN): Using TensorFlow to build HNN HNN

This research demonstrates the use of TensorFlow to build a Hierarchical Neural Network (HNN). Constructing and engineering neural networks to maximize accuracy and efficiency is an active field of research in machine learning. HNN, along with several other applications of split networks have been developed as recently as 2017. However, implementations thus far have required custom-built and coded HNNs. The research conducted here uses TensorFlow to validate this structure by building entirely separate neural nets with logical relations between the output of one net and the inputs of the nets that are downstream. Research has shown that Hierarchical Neural Networks can increase training speed and reduce compute resources. The validation results of HNN using the Fashion-MNIST dataset demonstrate a prediction accuracy of 99.49%, 95.96%, and 88.84% for coarse, medium, and fine level classification respectively, in which the fine level classification accuracy is greater than the baseline model.
A Reexamination of Riming on Thin Ice Plates

Abstract The investigation of Pitter and Prappacher (1974) was reexamined including the effect of changing drag on the accelerating water drops and the effect of the drop flow field on the ice crystal. The present results indicate that drops greater than 10 µm collide with ice crystals at higher efficiencies than found earlier. The annular collision domain near the small drop cutoff size for any given ice crystal size was not significantly changed.
Nonsymmetric Gravitational Theories in Hamiltonian Form

The dynamics of a class of nonsymmetric gravitational theories are presented in Hamiltonian form. The derivation begins with the first-order action, treating the generalized connection coefficients as the canonical coordinates and the densitized components of the inverse of the fundamental tensor as conjugate momenta. The phase space of the symmetric sector is enlarged over that of conventional treatments of general relativity by a canonical pair that represents the metric density and its conjugate, removable by imposing strongly an associated pair of second-class constraints and introducing Dirac brackets. In the antisymmetric sector, all six components of the fundamental tensor contribute conjugate pairs for the massive theory, and the absence of additional constraints gives six configuration space degrees of freedom per space–time point.
An Improved Difference Method Based on Three Asymmetric Frames and its Applications in the Vehicle Detection

Original difference method based on three sequential frames is a common method in the moving objective detection. After the introduction of the original method, the formation cause of the internal cavity in vehicle region was analyzed. Then, an improved difference method based on three asymmetric frames, which were abstracted according to the judgment rule of optimal frame interval, has been proposed in this paper. The area of the moving vehicle region is assumed as the criterion to calculate the best frame interval. In the improved method, the best benchmark frame and the best auxiliary frame were firstly abstracted out and used to subtract the current frame separately. Subsequently, the two difference images were transformed to binary images separately. Lastly, the final moving vehicles were detected in the current frame after logical "and" operation was carried out between the two binary images. The experimental results showed that the improve difference method based on three asymmetric frames can effectively reduce the internal cavities in the vehicle region, comparing with the original one.
Unusual spin-glass phase in icosahedral Tb-Mg-Zn quasicrystals

The nature of the spin state below the spin freezing temperature was studied in an icosahedral Tb-Mg-Zn quasicrystal by thermoremanent dc magnetization time decay as a function of aging time and magnetic field. The system does not exhibit a highly degenerate free-energy landscape obeying an ultrametric organization of metastable states, which makes it essentially different from canonical spin glasses. The Tb-Mg-Zn system can be considered as a special class of spin glass, which possesses some local order, but still preserves the notion of spin freezing.
Radiation hydrodynamics simulation of high-Z doped ICF targets

We conducted a comparative study among the Bi-CG family with incomplete LU factorization employed for solving diffusion-type radiative transfer equation in radiation hydrodynamics (RHD) simulations for laser-produced plasmas. The result suggests that Bi-CGSTAB is the most suitable method thanks to its high convergence stability and low computational cost. RHD simulations were performed for Rayleigh–Taylor instability experiments of an inertial confinement fusion target. We found that higher harmonics of static pressure perturbation become important at an intermediate wavelength.
A Stator Fault Diagnosis of an Induction Motor based on the Phase Angle of Park's Vector Approach

Abstract: In this paper, we propose a fault diagnosis method based on Park’s Vector Approach using the Euler’s theorem. If we interpreted it as Euler’s theorem, it is possible to easily find the phase angle difference between the healthy condition and the fault condition. And, we analyzed the variation of the phase angle and performed the diagnostic method of the induction motor using feature vectors that were obtained by using a Fourier transform. The analysis of time and speed variation of the motor was performed and, as a result, we could find more soft variations than rough variations. In particular, the analysis of the distortion through each phase shows that two-turn and four-turn shorted motors are linearly separable. In this experiment, we know that the maximum breakdown threshold value for determining steady-state fault detection is 49.0788. Simulation and experimental results show the more detectable than conventional method. Keywords: Park’s vector approach, induction machine, fault detection, euler’s theorem, phase angle
Maximum margin sparse representation discriminative mapping with application to face recognition

Abstract. Sparse subspace learning has drawn more and more attention recently. We propose a novel sparse subspace learning algorithm called maximum margin sparse representation discriminative mapping (MSRDM), which adds the discriminative information into sparse neighborhood preservation. Based on combination of maximum margin discriminant criterion and sparse representation, MSRDM can preserve both local geometry structure and classification information. MSRDM can avoid the small sample size problem in face recognition naturally and the computation is efficient. To improve face recognition performance, we propose to integrate Gabor-like complex wavelet and natural image features by complex vectors as input features of MSRDM. Experimental results on ORL, UMIST, Yale, and PIE face databases demonstrate the effectiveness of the proposed face recognition method.
A subnational family planning estimation tool (SFPET) for estimating contraceptive use and unmet need by subnational areas and demographic subgroups

Background Access to safe, effective, and voluntary family planning (FP) is a fundamental human right and is an important step toward achieving gender equality and economic autonomy. Global organizations have set ambitious goals to create universal access to FP services but assessing progress can be challenging, especially at the local geographic scale. Here, we present a quantitative visualization tool called the subnational family planning estimation tool (SFPET) that can be used by policy makers to monitor programmatic success at subnational levels and gain insight into trends within demographic subgroups. Methods The visualization tool builds on the statistical methods and analyses presented in earlier research to generate model-based estimates of FP indicators for 25 sub-Saharan countries at administrative units one and two. Here, we have extended this modeling approach to more demographic and geographic subgroups, integrated recently released survey data, and constructed a web-based dashboard to navigate survey and model-based estimates. Findings Significant heterogeneity in the levels and trends of FP indicators exist at subnational levels and across demographic subgroups. This tool provides the ability to efficiently navigate these model-based estimates across local regions within countries and across demographic subgroups. The tool also helps highlight regions and groups that may be falling behind the national trends as well as identify exemplars that exceed the national average. Interpretation SFPET is an interactive dashboard created to aid policymakers in assessing progress towards family planning goals by visualizing both model-based and direct survey estimates of family planning indicators at the subnational level. This framework provides insight into locations and population segments experiencing the greatest unmet need, enabling targeting and allocation of resources to achieve family planning goals.
Bayesian uncertainty quantification to identify population level vaccine hesitancy behaviours

When effective vaccines are available, vaccination programs are typically one of the best defences against the spread of an infectious disease. Unfortunately, vaccination rates may be suboptimal for a prolonged duration as a result of slow uptake of vaccines by the public. Key factors driving slow vaccination uptake can be a complex interaction of vaccine roll-out policies and logistics, and vaccine hesitancy behaviours potentially caused by an inflated sense of risk in adverse reactions in some populations or community complacency in communities that have not yet experienced a large outbreak. In the recent COVID-19 pandemic, public health responses around the world began to include vaccination programs from late 2020 to early 2021 with an aim of relaxing non-pharmaceutical interventions such as lockdowns and travel restrictions. For many jurisdictions there have been challenges in getting vaccination rates high enough to enable the relaxation of restrictions based on non-pharmaceutical interventions. A key concern during this time was vaccine hestitancy behaviours potentially caused by vaccine safety concerns fuelled by misinformation and community complacency in jurisdictions that had seen very low COVID-19 case numbers throughout 2020, such as Australia and New Zealand. We develop a novel stochastic epidemiological model of COVID-19 transmission that incorporates changes in population behaviour relating to responses based on non-pharmaceutical interventions and community vaccine uptake as functions of the reported COVID-19 cases, deaths, and vaccination rates. Through a simulation study, we develop a Bayesian analysis approach to demonstrate that different factors inhibiting the uptake of vaccines by the population can be isolated despite key model parameters being subject to substantial uncertainty. In particular, we are able to identify the presence of vaccine hesitancy in a population using reported case, death and vaccination count data alone. Furthermore, our approach provides insight as to whether the dominant concerns driving hesitancy are related to vaccine safety or complacency. While our simulation study is inspired by the COVID-19 pandemic, our tools and techniques are general and could be enable vaccination programs of various infectious diseases to be adapted rapidly in response to community behaviours moving forward into the future.
Type 1 interferon auto-antibodies are elevated in patients with decompensated liver cirrhosis

Understanding the biological basis of clinical risk factors for severe COVID-19 is required to ensure at-risk patient populations receive appropriate clinical care. Patients with decompensated liver cirrhosis, in particular those classified as Childs-Pugh class B and C, are at increased risk of severe COVID-19 upon infection with SARS-CoV-2. The biological mechanisms underlying this are unknown. We hypothesised this may be due to changes in expression levels of intrinsic antiviral proteins within the serum as well as alterations in the innate immune response to SARS-CoV-2 infection. We identified significant alterations in the serum proteome of patients with more severe liver disease and an increased frequency of auto-antibodies capable of neutralising type I interferons. No difference in SARS-CoV-2 pseudoparticle infection or live SARS-CoV-2 virus infection was observed with serum from decompensated cirrhotic patients. Principal component analysis of the serum proteome identified two main clinical parameters associated with serum proteome changes - aetiology and MELD-Na score. Among patients with MELD-Na scores >20 we detected significant inhibition of IFN-2b and IFN-8 signalling but not IFN-{beta}1a, mediated by auto-antibodies. Our results suggest pre-existing neutralising auto-antibodies targeting type I IFN may increase the likelihood of severe COVID-19 in chronic liver disease patients upon SARS-CoV-2 infection and may also be of relevance to other viral infections in this patient population.
Induced currents on vibrating perfect surfaces under the illumination of electromagnetic waves: One-dimensional simulation

One-dimensional numerical results of the induced currents on traveling and/or vibrating perfect conductor surfaces are presented in this report. The computational data are obtained using the method of characteristics with the application of the characteristic variable boundary conditions and the relativistic boundary conditions. The perfect plane may travel at a constant speed and/or vibrate sinusoidally or in zigzag with a constant frequency and a constant amplitude. The Doppler effects on both magnitude and frequency of induced currents were investigated by comparing computational results with the theoretical double-Doppler shift values. Good agreements were found. It is also found that when conductor travels and vibrates simultaneously, modulation on the magnitude of induced current is dependent on the combined instantaneous velocity while that on frequency is on the translational velocity. The scattering of electromagnetic waves from perfectly conducting objects in motion has been attracting significant attentions of many researchers since the 60s. This arouse from the need of knowledge for such as target identification. Kleinman and Mack (1), Cooper (2), and Harfoush et al. (3) developed analytical expressions for the electromagnetic wave scattering by either moving or vibrating bodies with the following conclusions. Object undergoing translational motion results in double-Doppler shifts in the reflected fields. An oscillating perfect electric conductor changes both phase and magnitude of the scattered fields. Though theoretical studies are available, the objective of the present effort is to develop an accurate numerical approach for electromagnetic scattering problems. The validation of the present numerical technique is carried out by the comparison of computational results with analytical values. Among variety of computational methods that provide numerical approximations to the solutions of the scattering of electromagnetic waves, the method of moment (MoM) and the finite-difference time-domain (FDTD) technique are the two most popular approaches. The method of characteristics is a recently developed numerical solver for like purpose. Whitfield and Janus applied the method of characteristics to solve fluid dynamic problems in the early 80s (4). By incorporating the explicit centered finite-difference scheme into the method of characteristics, Shang directly approximates the time-domain Maxwell curl equations (5) in the early 90s. In reference (6), the method of characteristics was formulated as an implicit numerical solver and found to have good agreement with the FDTD method. In contrast to the FDTD method where fields are allocated at node points, in the method of characteristics all field variables
Separating between‐ and within‐cluster covariate effects by using conditional and partitioning methods

Summary. We consider the situation where the random effects in a generalized linear mixed model may be correlated with one of the predictors, which leads to inconsistent estimators. We show that conditional maximum likelihood can eliminate this bias. Conditional likelihood leads naturally to the partitioning of the covariate into between‐ and within‐cluster components and models that include separate terms for these components also eliminate the source of the bias. Another viewpoint that we develop is the idea that many violations of the assumptions (including correlation between the random effects and a covariate) in a generalized linear mixed model may be cast as misspecified mixing distributions. We illustrate the results with two examples and simulations.