Miracle-dz/UniFAR-FTUs huggingface dataset:
New Insights into Aristarchus Crater with LRO LAMP Far-ultraviolet Observations

Aristarchus crater is a Copernican-age impact crater well known for its high-albedo ejecta blanket and nearby flow features rich in pyroclastic materials. The Lyman Alpha Mapping Project (LAMP) is a far-ultraviolet (FUV) spectrograph (57–197 nm) on board the Lunar Reconnaissance Orbiter (LRO). LAMP data identified Aristarchus crater as having a high Off-band (155–184 nm) to On-band (130–155 nm) albedo (Off/On) ratio, providing new insight into the mineral composition of the area. Previous LAMP observations of bright crater rays and lunar swirls show that regions of high Off/On-band ratios may indicate highly feldspathic compositions. In this study we use LAMP data to understand FUV compositional signatures at Aristarchus. We investigate four well-characterized regions of interest around Aristarchus crater, and we compare Off/On-band ratios at Aristarchus crater to laboratory-derived ratios of several endmembers such as anorthite and olivine. We further analyze LAMP FUV spectra alongside near-infrared spectra from the Moon Mineral Mapper (M3) on board Chandrayaan-1 to characterize the mineralogy in several regions of interest. We find that LAMP Off/On-band ratios are able to distinguish between plagioclase feldspars and minerals such as quartz and mafic-dominated compositions. The LAMP Off/On-band ratios at Aristarchus are higher than previously reported ratios for plagioclase-rich regions, suggesting that the composition is unique to Aristarchus. Spectra from LAMP and M3 both show that the central peak and high-albedo ejecta around Aristarchus contain shocked, possibly alkalic, plagioclase feldspar.
Ultrasonic guided wave imaging of pipelines based on physics embedded inversion neural network

Pipeline corrosion quantification plays a vital role in guaranteeing the safety of critical industrial structures and thus significant work has been carried out to address such an issue. Although quantitative imaging is crucial for non-destructive testing, research in guided wave pipeline testing has primarily centered on qualitative approaches. Here, we propose a deep neural network built upon physical model to reconstruct pipe wall thickness from ultrasonic guided wave (UGW) signals. The workflow of reconstruction contains three layers, where each layer consists of a fixed forward network and a residual inversion network. The forward model is represented by an agent convolutional neural network which would be embedded into the entire inversion network. The residuals between data from the forward model and real signals are then mapped into velocity profile differences through sub-inversion network. Numerical experiments were conducted to verify the inversion performance of the deep neural network using thickness maps obtained from guided wave frequency domain information. Results show that inversion images are capable to reveal the positions, shapes, and depths of corrosion with high resolution and precision, yielding an average inversion of 87.37% in the test set. In addition, by utilizing the periodicity of the pipeline, the inversion accuracy of eight pairs of transducers were improved from 67.7% to 89.43% with high-order helical guided wave. Compared with traditional high-precision inversion methods such as full waveform inversion, the proposed method achieved approximately 300 times faster inversion speed at the cost of some accuracy. The research demonstrates that real-time quantitative imaging of defects on pipes can be achieved accurately by physics embedded network. Furthermore, an experimental verification of the method was carried out through UGW pipeline testing, demonstrating its feasibility. The mean squared error of wall thickness reconstruction was 0.0070, achieving a high level of precision.
Optical fluorescent spider silk electrospun nanofibers with embedded cerium oxide nanoparticles

Abstract. The work demonstrates an electrospun nanocomposite of recombinant spider silk protein (rSSp) nanofibers with embedded cerium oxide (ceria) nanoparticles. RSSP (MaSp1) has been produced, extracted from goat milk, and fabricated into nanofibers using an electrospinning process. The resulting electrospun nanofibers have a mean diameter of ∼50 nm. Furthermore, ceria nanoparticles of mean diameter <10 nm were added in the spinning dope to be embedded within the generated nanofibers. These nanoparticles show certain optical activity due to optical trivaliant cerium ions, associated with formed oxygen vacancies. The formed nanocomposite shows promising mechanical properties such as the Young’s modulus, elasticity (or elongation at break), and toughness. In addition, the electrospun mat becomes fluorescent with 520-nm emission upon exposure to UV light, due to excitation of the optically active ceria nanoparticles. Also, the formed nanocomposite shows a decay of its electric resistance over time upon exposure to cyclic loads at different humidity conditions. The synthesized nanocomposite can be utilized in different biomedical, textile, and sensing applications.
Total economic costs of climate change at different discount rates for market and non-market values

What will be the aggregated cost of climate change in achieving the Paris Agreement, including mitigation, adaptation, and residual impacts? Several studies estimated the aggregated cost but did not always consider the critical issues. Some do not address non-market values such as biodiversity and human health, and most do not address differentiating discount rates. In this study, we estimate the aggregated cost of climate change using an integrated assessment model linked with detailed-process-based climate impact models and different discount rates for market and non-market values. The analysis reveals that a climate policy with minimal aggregated cost is sensitive to socioeconomic scenarios and the way discount rates are applied. The results elucidate that a lower discount rate to non-market value—that is, a higher estimate of future value—makes the aggregated cost of achieving the Paris Agreement economically reasonable.
Gibt es eine neuropsychologische R ü ckfallpr ä vention bei Alkoholabh ä ngigen? Is There a Case in Neuropsychological Relapse Prevention in Alcoholic Patients?

▼ Advances in neurosciences have pointed to the handicaps in autonomy of alcohol patients in the moment of relapse. Research has lead to the concept of a permanent addiction memory which is characterized by a momentary restraint of rational self control by automatic information processing bias and appetence reactions to alcohol triggers in high risk situations. Because alcohol patients are not necessarily aware of these automatic processes, the latter can not be overcome by traditional methods of cognitive-behavioral relapse prevention. The paper will discuss to what extent a neuropsychological perspective is helpful to improve assessment tools and speci ﬁ c interventions for relapse prevention in alcoholic patients.
Measurement of the Laser-Induced Energy Modulation Amplitude at the Short-Pulse Facility at DELTA

The short-pulse facility at the synchrotron light source DELTA operated by the TU Dortmund University employs coherent harmonic generation (CHG) to provide ultrashort pulses in the vacuum ultraviolet and terahertz regime. Here, a laser-electron interaction results in a modulation of the electron energy which is transformed into a density modulation by a magnetic chicane. Measurements of the energy modulation amplitude with different techniques including an RF phase modulation are presented. A combination of the results allow to estimate the energy spread of the electron beam.
Adaptive convolutional layer selection based on historical retrospect for visual tracking

Visual tracking has recently gained a great advance with the use of the convolutional neural network (CNN). Usually, existing CNN-based trackers exploit the features from a single layer or a certain combination of multiple layers. However, these features only characterise an object from an invariable aspect and cannot adapt to scene variation, which limits the performance of such trackers. To overcome this limitation, the authors study the problem from a new perspective and propose a novel convolutional layer selection method. To obtain robust appearance representation, they investigate the advantages of features extracted from different convolutional layers. To determine the correctness of the tracking prediction and updated model, they design a verification mechanism based on historical retrospect, which can estimate the deviation for each layer by bidirectionally locating the target. Meanwhile, the deviation works as the layer-wise selection criteria. Extensive evaluations on the OTB-2013, visual object tracking (VOT)-2016 and VOT-2017 benchmarks demonstrate that the proposed tracker performs favourably against several state-of-the-art trackers.
Robust Tracking Control of a Class of Switched Nonlinear Systems with Input Delay under Asynchronous Switching

This paper concerns the problems of stability and robust model reference tracking control for a class of switched nonlinear systems with input delay under asynchronous switching. By proposing a new Lyapunov-Krasovskii functional, and using free-weighting matrices and average dwell time technique, new input-to-state stability conditions are derived in terms of linear matrix inequalities under a certain delay bound. Then, robust model reference tracking control problem is studied based on the proposed Lyapunov-Krasovskii functional; Finally a kind of state feedback control law which guarantees robust model reference tracking performance is proposed. Illustrative examples are presented to demonstrate the efficacy and feasibility of results.
Models of energy loss from internal waves breaking in the ocean

The supply of energy to the internal wave field in the ocean is, in total, sufficient to support the mixing required to maintain the stratification of the ocean, but can the required rates of turbulent dissipation in mid-water be sustained by breaking internal waves? It is assumed that turbulence occurs in regions where the field of motion can be represented by an exact solution of the equations that describe waves propagating through a uniformly stratified fluid and becoming unstable. Two instabilities leading to wave breaking are examined, convective instability and shear-induced Kelvin–Helmholtz instability. Models are constrained by data representative of the mid-water ocean. Calculations of turbulent dissipation are first made on the assumption that all the waves representing local breaking have the same steepness, $s$ , and frequency, $\unicode[STIX]{x1D70E}$ . For some ranges of $s$ and $\unicode[STIX]{x1D70E}$ , breaking can support the required transfer of energy to turbulence. For convective instability this proves possible for sufficiently large $s$ , typically exceeding 2.0, over a range of $\unicode[STIX]{x1D70E}$ , while for shear-induced instability near-inertial waves are required. Relaxation of the constraint that the model waves all have the same $s$ and $\unicode[STIX]{x1D70E}$ requires new assumptions about the nature and consequences of wave breaking. Examples predict an overall dissipation consistent with the observed rates. Further observations are, however, required to test the validity of the assumptions made in the models and, in particular, to determine the nature and frequency of internal wave breaking in the mid-water ocean.
Metabolic State of the Rat I n Viuo 3 1 P h o ~ p h o ~ ~ Spectroscopy with Liver with Ethanol: Comparison of Nuclear Magnetic Resonance Freeze Clamp Assessment

I n uiuo "phosphorus nuclear magnetic resonance spectroscopy was uaed to measure the hepatic metabolic state in various groups of rats given ethanol, a control liquid diet or a solid chow diet. The use of selective presaturation pulses applied to the broad phosphorus reaonancea of immobile phospholipids permitted reliable debrmination of ATP/ADP ratios by quantitation of the ATP-/3 and ATP-7 pea% areas. ATP/ADP ratios were d e p d by both techniques in rats chronically ingesting ethanol compared to pair-fed animals consuming the control liquid diet. These differences were observed regardless of whether ethanol feeding was continued up to the time of investigation or whether it was discontinued for 24 hr prior to study. Acute alcohol administration in chow-fed rats, not previously ingesting ethanol, did not lower hepatic ATP/ADP ratios by either methodology. In all case6, liver ATP/ADP ratios 888889ed by *'phosphorus nuclear magnetic resonance spectroscopy were higher than those measured by high-performance liquid chromatography. However, parallel decreases in hepatic ATP/ADP ratios were observed with chronic ethanol consumption by both "phosphorus nuclear magnetic resonance spectroscopy and the biochemical method, confirming the utility of in uiuo slphosphorus nuclear magnetic resonance spectroscopy for aseessment of the hepatic bioenergetic status. The difference in absolute ATP/ADP ratios by the two methods may to some degree be explained by binding
A SLIDING WINDOW METHOD FOR DETECTING CORNERS OF OPENINGS FROM TERRESTRIAL LIDAR DATA

Abstract. Architectural building models (LoD3) consist of detailed wall and roof structures including openings, such as doors and windows. Openings are usually identified through corner and edge detection, based on terrestrial LiDAR point clouds. However, singular boundary points are mostly detected by analysing their neighbourhoods within a small search area, which is highly sensitive to noise. In this paper, we present a global-wide sliding window method on a projected façade to reduce the influence of noise. We formulate the gradient of point density for the sliding window to inspect the change of façade elements. With derived symmetry information from statistical analysis, border lines of the changes are extracted and intersected generating corner points of openings. We demonstrate the performance of the proposed approach on the static and mobile terrestrial LiDAR data with inhomogeneous point density. The algorithm detects the corners of repetitive and neatly arranged openings and also recovers angular points within slightly missing data areas. In the future we will extend the algorithm to detect disordered openings and assist to façade modelling, semantic labelling and procedural modelling.
Local kernel mapping based piecewise constant model for medical image segmentation

Intensity inhomogeneity and noise are two major obstacles for segmenting medical images. The global kernel mapping based piecewise constant model (PCM) has superior performance on resisting noise, though it fails to cope with intensity inhomogeneity. In order to overcome the difficulty caused by intensity inhomogeneity, we first establish an energy based on kernel mapping in a neighborhood of a pixel. Then such energies for all pixels in an image are integrated to formulate the energy of the local kernel mapping based PCM. Energy minimization has been implemented in level set framework. Comparative experimental results show that our proposed model has higher segmentation accuracy in the presence of intensity inhomogeneity and noise.
Parametric amplification of Rydberg six- and eight-wave mixing processes

We study the parametric amplification of electromagnetically induced transparency-assisted Rydberg six- and eight-wave mixing signals through a cascaded nonlinear optical process in a hot rubidium atomic ensemble both theoretically and experimentally. The shift of the resonant frequency (induced by the Rydberg–Rydberg interaction) of parametrically amplified six-wave mixing signal is observed. Moreover, the interplays between the dressing effects and Rydberg–Rydberg interactions in parametrically amplified multiwave mixing signals are investigated. The linear amplification of Rydberg multiwave mixing processes with multichannel nature acts against the suppression caused by Rydberg–Rydberg interaction and dressing effect.
Convergence analysis of virtual element methods for semilinear parabolic problems on polygonal meshes

In this article, we discuss and analyze new conforming virtual element methods (VEMs) for the approximation of semilinear parabolic problems on convex polygonal meshes in two spatial dimension. The spatial discretization is based on polynomial and suitable nonpolynomial functions, and a Euler backward scheme is employed for time discretization. The discrete formulation of both the proposed schemes—semidiscrete and fully discrete (with time discretization) is discussed in detail, and the unique solvability of the resulted schemes is discussed. A priori error estimates for the proposed schemes (semidiscrete and fully discrete) in H1‐ and L2‐norms are derived under the assumption that the source term f is Lipschitz continuous. Some numerical experiments are conducted to illustrate the performance of the proposed scheme and to confirm the theoretical convergence rates.
Astronomical image restoration using variational Bayesian blind deconvolution#br#

: An algorithm is presented for image prior combinations based blind deconvolution and applied to astronomical images. Using a hierarchical Bayesian framework, the unknown original image and all required algorithmic parameters are estimated simultaneously. Through utilization of variational Bayesian analysis, approximations of the posterior distributions on each unknown are obtained by minimizing the Kullback-Leibler (KL) distance, thus providing uncertainties of the estimates during the restoration process. Experimental results on both synthetic images and real astronomical images demonstrate that the proposed approaches compare favorably to other state-of-the-art reconstruction methods.
Extreme Te nanowires encapsulated within ultra-narrow single-walled carbon nanotubes

Extreme nanowires (ENs) represent the ultimate class of crystalline materials: They are the smallest possible periodic materials. With atom-wide motifs repeated in 1D, they offer a unique perspective into the Physics and Chemistry of low-dimensional systems. Single-walled carbon nanotubes (SWCNTs) provide ideal environments for the creation of such materials. Here we report the observation of Te ENs grown inside ultra-narrow SWCNTs with diameters between 0.7nm and 1.1nm. Through state-of-the-art imaging techniques and high-precision, high-throughput ab initio calculations, we unambiguously determine the competing structures of encapsulated Te as a function of the encapsulating diameters. From 1-atom-wide Peierls-distorted linear chains -- the ultimate ENs, Te morphs into zigzag chains and then gives rise to helical structures that are the 1D analogues of bulk Te. The pitch of the encapsulated Te coils varies non-monotonically with the diameter of the encapsulating SWCNTs.
A Godunov type scheme for a class of scalar conservation laws with non-local flux

We present a Godunov type numerical scheme for a class of scalar conservation laws with non-local flux arising for example in traffic flow models. The proposed scheme delivers more accurate solutions than the widely used Lax-Friedrichs type scheme. In contrast to other approaches, we consider a non-local mean velocity instead of a mean density and provide $L^\infty$ and bounded variation estimates for the sequence of approximate solutions. Together with a discrete entropy inequality, we also show the well-posedness of the considered class of scalar conservation laws. The better accuracy of the Godunov type scheme in comparison to Lax-Friedrichs is proved by a variety of numerical examples.
Fitting three parameter growth curves using a nonlinear mixed effects modelling approach

A nonlinear mixed modelling approach was applied to model individual tree diameter increment using three nonlinear growth functions for Eucalyptus tree plantations. The objective of the study is to develop a stem radial increment model for two clones of Eucalyptus tree, and compare their growth potential. Three nonlinear growth curves (Gompertz, logistic and asymptotic regression) were fitted to stem radius data. Estimations of parameters were made using the approximate likelihood functions. The estimators obtained from these approximate likelihood function are a combination of least squares estimators for nonlinear mixed effects models and maximum likelihood estimators from linear mixed effects models. The asymptotic regression model with three random effects for f1, f2 and f3 appears well suited to represent the random effect covariance structure. The heterogeneous variance model that varies with tree age is found to be suitable model that characterize the within tree error variability. Clone has a significant effect on the asymptote of the asymptotic regression curve. The analysis suggests that GU clone on the average has a larger stem radial measurement than the GC clone during the entire juvenile stage.
A Bayesian multinomial regression model for palaeoclimate reconstruction with time uncertainty

Estimating chronological uncertainty is a critical but often neglected prerequisite in palaeoclimatological reconstructions and core comparisons. We describe a hierarchical Bayesian multinomial regression model for temperature reconstruction that allows uncertainty in the sediment core sample dates. Climate model output is used as pseudodata to provide prior information about the overall variability of past temperatures. Including time uncertainty in the model can make usual summaries such as the posterior mean temperature less useful. We therefore recommend considering also fixed chronology reconstructions and exploration of individual temperature history realizations. This can be particularly relevant for tracking short‐lived events. We demonstrate the proposed model by reconstructing the past temperature from fossil pollen data obtained from three cores in Estonia and southern Sweden. Copyright © 2016 John Wiley & Sons, Ltd.
Analysis of Innovations in the European Union Via Ensemble Symbolic Density Clustering

Abstract Innovations play a very important role in the modern economy. They are the key to a higher quality of life, better jobs and economy and sustainable development. The innovation policy is a key element of both national and European Union strategy. The main aim of this paper is to present an ensemble clustering of European Union countries (member states) considering their innovativeness. In the empirical section, symbolic density-based ensemble clustering is used to obtain the co-occurrence matrix. The paper uses symbolicDA, clusterSim and dbscan packages of R software for all calculations. Four different clusters where obtained in the result of clustering. Cluster 1 contains highinnovative countries (innovation leaders). This cluster is also the least homogenous. Cluster 2 contains post-communist countries mainly from central Europe. These countries can be seen as rather mid-low innovative (they try to “catch up” with innovation leaders). Cluster 3 contains moderate innovators. Cluster 4 contains two countries that are also mid-innovative.
Diagonally implicit Runge–Kutta schemes: Discrete energy-balance laws and compactness properties

Abstract We study diagonally implicit Runge–Kutta (DIRK) schemes when applied to abstract evolution problems that fit into the Gelfand-triple framework. We introduce novel stability notions that are well-suited to this setting and provide simple, necessary and sufficient, conditions to verify that a DIRK scheme is stable in our sense and in Bochner-type norms. We use several popular DIRK schemes in order to illustrate cases that satisfy the required structural stability properties and cases that do not. In addition, under some mild structural conditions on the problem we can guarantee compactness of families of discrete solutions with respect to time discretization.