Miracle-dz/UniFAR-FTUs huggingface dataset:
On discretization of continuous-time state-space models: a stable-normal approach

The problem of developing discrete-time models for continuous-time systems from some closed-form discretizing transformations is discussed. Stable forms of normal interpolators and integrating operators, which are of key issue in an associated normal class of transformations, are given, and their approximating properties are shown in the frequency domain. Generic forms of state-space models obtained by a simple piece-wise-normal rule (time invariance constrained to one sampling period) and by direct and indirect stable-normal approximation methods (originally resulting in a discrete-time extended state-space model) are developed, and numerical examples are given. >
Fabrication of Electroceramic Components by Layered Manufacturing (LM)

Layered Manufacturing (LM) has been developed and utilized to provide a cost-effective route to rapidly turn new designs into net-shape components. In LM, components are built layer-by-layer via a computer-controlled dispensing system, directly from a CAD file. Fused Deposition of Ceramics (FDC), which is used for single material fabrication, and Fused Deposition of Multi-Materials (FDMM), which is used to fabricate prototypes composed of up to four materials, are two examples of techniques employed to produce a variety of electroceramic components at Rutgers University. Novel piezoelectric and electrostrictive actuators and sensors with single and multi-materials have been prototyped via LM such as spiral and telescoping actuators, volume fraction gradient composites, and oriented structures. The concept of Templated Grain Growth (TGG) has been combined with LM to initiate the fabrication of net-shape grain-oriented and single crystal components.
Robust Superpixel Segmentation for Hyperspectral-Image Restoration

Hyperspectral-image (HSI) restoration plays an essential role in remote sensing image processing. Recently, superpixel segmentation-based the low-rank regularized methods for HSI restoration have shown outstanding performance. However, most of them simply segment the HSI according to its first principal component, which is suboptimal. In this paper, integrating the superpixel segmentation with principal component analysis, we propose a robust superpixel segmentation strategy to better divide the HSI, which can further enhance the low-rank attribute of the HSI. To better employ the low-rank attribute, the weighted nuclear norm by three types of weighting is proposed to efficiently remove the mixed noise in degraded HSI. Experiments conducted on simulated and real HSI data verify the performance of the proposed method for HSI restoration.
Evaluating Northern Hemisphere Growing Season Net Carbon Flux in Climate Models Using Aircraft Observations

Understanding terrestrial ecosystems and their response to anthropogenic climate change requires quantification of land‐atmosphere carbon exchange. However, top‐down and bottom‐up estimates of large‐scale land‐atmosphere fluxes, including the northern extratropical growing season net flux (GSNF), show significant discrepancies. We developed a data‐driven metric for the GSNF using atmospheric carbon dioxide concentration observations collected during the High‐Performance Instrumented Airborne Platform for Environmental Research Pole‐to‐Pole Observations and Atmospheric Tomography Mission flight campaigns. This aircraft‐derived metric is bias‐corrected using three independent atmospheric inversion systems. We estimate the northern extratropical GSNF to be 5.7 ± 0.3 Pg C and use it to evaluate net biosphere productivity from the Coupled Model Intercomparison Project phase 5 and 6 (CMIP5 and CMIP6) models. While the model‐to‐model spread in the GSNF has decreased in the CMIP6 models relative to that of the CMIP5 models, there is still disagreement on the magnitude and timing of seasonal carbon uptake with most models underestimating the GSNF and overestimating the length of the growing season relative to the observations. We also use an emergent constraint approach to estimate annual northern extratropical gross primary productivity to be 56 ± 17 Pg C, heterotrophic respiration to be 25 ± 13 Pg C, and net primary productivity to be 28 ± 12 Pg C. The flux inferred from these aircraft observations provides an additional constraint on large‐scale gross fluxes in prognostic Earth system models that may ultimately improve our ability to accurately predict carbon‐climate feedbacks.
Patent Text Classification based on Deep Learning and Vocabulary Network

—Patent documents are a special long text format, and traditional deep learning methods have insufficient feature extraction ability, which results in a weaker classification effect than ordinary text. Based on this, this paper constructs a text feature extraction method based on the lexical network, according to the inner relation between words and classification. Firstly, the inner relationship between words and classification was obtained from linear and probability dimensions and the lexical network were constructed. Secondly, the lexical network is fused with the features extracted from the deep learning model. Finally, the fusion features are trained in the original model to get the final classification result. T This method is a classification enhancement method that can classify patent text alone or enhance the accuracy of various types of neural networks in patent text classification. Experimental results demonstrate that the accuracy of BERT combined with lexical network method is as high as 82.73%, and the accuracy of lexical network method combined with CNN and LSTM is increased by 2.19% and 2.25% respectively. In addition, it was demonstrated that the lexical network feature extraction method accelerated the convergence speed of the model during training and improved the classification ability of the model in Chinese patent texts.
Surgical and Bioengineering Integration in the Anatomy Course of Medicine and Surgery High Technology: Knowledge and Perception of Anatomy

The Locomotor System Anatomy (LSA) course, placed in the first semester of the first year of the new Master’s degree in Medicine and Surgery High Technology (MSHT) at the Sapienza University of Rome, was integrated with surgical and bioengineering content. This study investigated the educational value and the students’ perceptions of the effectiveness of these two types of integration, comparing surgical integration (SI) with engineering integration (EI). Anatomy knowledge and students’ opinions attending the LSA course in MSHT degree (n = 30) were compared with those of students (n = 32) attending another medical and surgery course not comprising EI. Data show that students in the MSHT course like in-depth SI much more than in-depth EI. However, those who like in-depth SI also like in-depth EI. Significant differences were in anatomy knowledge between the two groups in the three sections of the test. There was no significant correlation between the three test scores and the levels of liking, while there was a significant correlation between students liking SI and those liking EI. A statistically significant correlation was also found in students who correctly responded to questions on the head and trunk, with students responding correctly to questions on the upper limbs. This study will be important in optimizing the deepening of SI and EI in the LSA course.
Tropospheric infrared mapping spectrometers (TIMS) for air quality measurements

We are currently developing grating mapping spectrometers (GMS) with very high spectral resolution, very low noise, and very wide field of view. These also would be very compact facilitating deployment in either a leo or geo application. The measurement set could be very comprehensive, addressing air quality, climate change and meteorology, or subsets of these. For this presentation we'll focus on potential applications of these GMS for air quality measurements of the species ozone O3, formaldehyde HCHO and carbon monoxide CO. We will discuss these applications at various levels of complexity and the commensurate value for application to understanding and forecasting air quality. At lowest complexity we would utilize a single GMS operating in the solar reflective infrared region for column measurements of O3 and HCHO. A more complex approach would utilize a second and/or third GMS for thermal emissive O3 measurements that provide improved vertical resolution, and for CO profile. Our major emphasis is the lowest tropospheric air layer 0-2 km. For realistic models of these GMS we'll present retrieval performance as predicted by a linear error analysis. In a polar leo orbit the most complex approach could provide twice daily global mapping with some footprints as small as 1.6 km at nadir. We'll present results from an in house lab demonstration GMS. This demo is a predecessor to an advanced design that we are currently developing with support of the NASA ESTO Instrument Incubator Program (IIP).
Snekmer: a scalable pipeline for protein sequence fingerprinting based on amino acid recoding

Abstract Motivation The vast expansion of sequence data generated from single organisms and microbiomes has precipitated the need for faster and more sensitive methods to assess evolutionary and functional relationships between proteins. Representing proteins as sets of short peptide sequences (kmers) has been used for rapid, accurate classification of proteins into functional categories; however, this approach employs an exact-match methodology and thus may be limited in terms of sensitivity and coverage. We have previously used similarity groupings, based on the chemical properties of amino acids, to form reduced character sets and recode proteins. This amino acid recoding (AAR) approach simplifies the construction of protein representations in the form of kmer vectors, which can link sequences with distant sequence similarity and provide accurate classification of problematic protein families. Results Here, we describe Snekmer, a software tool for recoding proteins into AAR kmer vectors and performing either (i) construction of supervised classification models trained on input protein families or (ii) clustering for de novo determination of protein families. We provide examples of the operation of the tool against a set of nitrogen cycling families originally collected using both standard hidden Markov models and a larger set of proteins from Uniprot and demonstrate that our method accurately differentiates these sequences in both operation modes. Availability and implementation Snekmer is written in Python using Snakemake. Code and data used in this article, along with tutorial notebooks, are available at http://github.com/PNNL-CompBio/Snekmer under an open-source BSD-3 license. Supplementary information Supplementary data are available at Bioinformatics Advances online.
A Classification Method for Workers’ Physical Risk

In Industry 4.0 scenarios, wearable sensing allows the development of monitoring solutions for workers’ risk prevention. Current approaches aim to identify the presence of a risky event, such as falls, when it has already occurred. However, there is a need to develop methods capable of identifying the presence of a risk condition in order to prevent the occurrence of the damage itself. The measurement of vital and non-vital physiological parameters enables the worker’s complex state estimation to identify risk conditions preventing falls, slips and fainting, as a result of physical overexertion and heat stress exposure. This paper aims at investigating classification approaches to identify risk conditions with respect to normal physical activity by exploiting physiological measurements in different conditions of physical exertion and heat stress. Moreover, the role played in the risk identification by specific sensors and features was investigated. The obtained results evidenced that k-Nearest Neighbors is the best performing algorithm in all the experimental conditions exploiting only information coming from cardiorespiratory monitoring (mean accuracy 88.7±7.3% for the model trained with max(HR), std(RR) and std(HR)).
High-Sensitivity Transverse-Load and Axial-Strain Sensor Based on Air-Bubble Fabry–Pérot Cavity and Fiber Sagnac Loop Cascaded

We have designed a highly sensitive optical fiber transverse load and axial strain sensor based on the cascaded Fabry–Perot (F–P) cavity and fiber Sagnac loop. F–P cavity is composed of an air-bubble formed by a Section of quartz capillary discharge. Fiber Sagnac loop is made by winding a Section of panda fiber (PF) through a 3 dB coupler. When the free spectral ranges (FSRs) of two interferometers are similar, they are cascaded to form the optical Vernier effect, which amplifies the sensitivity of the sensor. The experimental results show that the sensitivities of transverse load and axial strain of the sensor are up to 19.1 nm/N and 28.9 pm/ $\mu \varepsilon $ , respectively. The sensitivities of a single F–P cavity for measuring the transverse load and the axial strain are 1.5 nm/N and 2.4 pm/ $\mu \varepsilon $ , respectively. It can be seen that by using the optical Vernier effect, the sensitivity of the transverse load and axial strain of the sensor is improved by 12.7 and 12 times, respectively. The measurement matrix is constructed by using the sensitivities of a single F–P cavity and the cascaded structure to realize the simultaneous measurement of the transverse load and axial strain and eliminate the cross-sensitivity between them. The designed sensor has a certain attraction in small load and small strain monitoring.
Intermetallic compounds of rare-earth and 3d transition metals

A survey is given of the physical properties, composition and crystal structure of intermetallic compounds formed between rare-earth elements and 3d transition elements. Apart from binary compounds the results of pseudobinary series are also considered. The magnetic properties determined by the exchange interactions involving 4f as well as 3d electrons, are discussed together with experimental results available on magnetovolume effects and various resonance techniques such as NMR and the Mossbauer effect.
On multigrid solution of the implicit equations of hydrodynamics - Experiments for the compressible Euler equations in general coordinates

Aims. We describe and study a family of new multigrid iterative solvers for the multidimensional, implicitly discretized equations of hydrodynamics. Schemes of this class are free of the Courant-Friedrichs-Lewy condition. They are intended for simulations in which widely differing wave propagation timescales are present. A preferred solver in this class is identified. Applications to some simple stiff test problems that are governed by the compressible Euler equations, are presented to evaluate the convergence behavior, and the stability properties of this solver. Algorithmic areas are determined where further work is required to make the method sufficiently efficient and robust for future application to difficult astrophysical flow problems. Methods. The basic equations are formulated and discretized on non-orthogonal, structured curvilinear meshes. Roe’s approximate Riemann solver and a second-order accurate reconstruction scheme are used for spatial discretization. Implicit Runge-Kutta (ESDIRK) schemes are employed for temporal discretization. The resulting discrete equations are solved with a full-coarsening, nonlinear multigrid method. Smoothing is performed with multistage-implicit smoothers. These are applied here to the time-dependent equations by means of dual time stepping. Results. For steady-state problems, our results show that the efficiency of the present approach is comparable to the best implicit solvers for conservative discretizations of the compressible Euler equations that can be found in the literature. The use of red-black as opposed to symmetric Gauss-Seidel iteration in the multistage-smoother is found to have only a minor impact on multigrid convergence. This should enable scalable parallelization without having to seriously compromise the method’s algorithmic efficiency. For time-dependent test problems, our results reveal that the multigrid convergence rate degrades with increasing Courant numbers (i.e. time step sizes). Beyond a Courant number of nine thousand, even complete multigrid breakdown is observed. Local Fourier analysis indicates that the degradation of the convergence rate is associated with the coarse-grid correction algorithm. An implicit scheme for the Euler equations that makes use of the present method was, nevertheless, able to outperform a standard explicit scheme on a time-dependent problem with a Courant number of order 1000. Conclusions. For steady-state problems, the described approach enables the construction of parallelizable, efficient, and robust implicit hydrodynamics solvers. The applicability of the method to time-dependent problems is presently restricted to cases with moderately high Courant numbers. This is due to an insufficient coarse-grid correction of the employed multigrid algorithm for large time steps. Further research will be required to help us to understand and overcome the observed multigrid convergence difficulties for time-dependent problems.
The off-resonant aspects of decoherence and a critique of the two-level approximation

Conditions in favour of a realistic multilevelled description of a decohering quantum system are examined. In this regard the first crucial observation is that the thermal effects, contrary to the conventional belief, play a minor role at low temperatures in the decoherence properties. The system–environment coupling and the environmental energy spectrum dominantly affect the decoherence. In particular, zero temperature quantum fluctuations or non-equilibrium sources can be present and influential on the decoherence rates in a wide energy range allowed by the spectrum of the environment. A crucial observation against the validity of the two-level approximation is that the decoherence rates are found to be dominated not by the long time resonant but the short time off-resonant processes. This observation is demonstrated in two stages. Firstly, our zero temperature numerical results reveal that the calculated short time decoherence rates are Gaussian-like (the time dependence of the density matrix is led by the second time derivative at t = 0). Exact analytical results are also permitted in the short time limit, which, consistent with our numerical results, reveal that this specific Gaussian-like behaviour is a property of the non-Markovian correlations in the environment. These Gaussian-like rates have no dependence on any spectral parameter (position and the width of the spectrum) except, in totality, the spectral area itself. The dependence on the spectral area is a power law. Furthermore, the Gaussian-like character at short times is independent of the number of levels (N), but the numerical value of the decoherence rates is a monotonic function of N. In this context, we demonstrate that leakage, as a characteristic multilevel effect, is dominated by the non-resonant processes. The long time behaviour of decoherence is also examined. Since our spectral model allows Markovian environmental correlations at long times, the decoherence rates in this regime become exponential independently from the number of levels. The latter and the coupling strengths play the major role in the quantitative values of the rates and the rates are independent of the other spectral parameters. The validity of the presented results is restricted only by their reliance on the Born–Oppenheimer approximation. This approximation is strongly dependent on the external observational time and its reliability depends on an additional timescale. In the rest of the work, the crossover between the short and the long time behaviour of the density matrix of the multilevelled system is examined using an intuitive argument. It is shown that the Born approximation weakens as the resonant couplings become more effective at long times. This implies that, in calculations made with this approximation in the long time regime, a need for a justification arises for the reliability of the results. This justification is made for the present work.
Assessing User Interest in Web API Recommendation using Deep Learning Probabilistic Matrix Factorization

— Internet 2.0 Things connected to the Internet not only manage data supply through devices but also control the commands that flow through it. The communication technology created by the desired sensor is used by a new computing model so that the collected data appears in Web 2.0 for management. In addition to enhancing Sense efficiency through the simple IoT computing process, it is used in many cases for example video surveillance, and improved and intelligent manufacturing. Every fragment of the system is carefully continued and supervised in this process by software collection using a large number of recurs. An important process for this is to access web APIs from various public platforms in an efficient way. The use of different APIs by developers for the integration of different IoT devices and the deployment process required for this is unnecessary. Obtaining configured target APIs makes it easy to know where and how to get started with the workflow approach. Rapid industrial development can be achieved through this powerful API approach. But finding adequately powerful APIs from a large number of APIs has become a great challenge. However, due to the massive spike in the count of APIs, combining the two APIs has now become a major challenge. In this paper, for the time being, only the relationships between users and the API are considered. In this case, they had to face difficulties in extracting contextual value from their interpretation. So better accuracy could not be obtained due to this. The consequence of the user's time aspect on the cryptographic properties concerning the information collected from the API contextual description can be enhanced by the Deep Learning Probabilistic Matrix Factorization (DL-PMF) method, which improves the accuracy of the API recommendation in considering the cryptographic features of the user in the API recommendation. In this paper, we have used CNN (Convulsive Neural Network) for web elements such as APIs, and LSTM (Long-Term and Short-Term Memory) Network, which works with a diligent mechanism to find hidden features, to find hidden features that suit the tastes of the users. In conclusion, the combination of PMF (Probabilistic Matrix Factorization) evaluation of the recommended results was obtained as described above. The combination of DL-PMF method experimental results was found to be better than previous PMF, ConvMF, and other methods, thus improving the recommended accuracy.