Miracle-dz/UniFAR-FTUs huggingface dataset:
Infrared thermography inspection of glass reinforced plastic (GRP) wind turbine blades and the concept of an automated scanning device

Infrared thermography techniques have been used for many years in the non-destructive testing and evaluation (NDT and E) of materials and structures. The main advantage of thermography over classical NDT techniques resides in the possibility of inspecting large areas in a fast and safe manner without needing to have access to both sides of the component. Nonetheless infrared thermography is limited to the detection of relatively shallow defects (a few millimetres under the surface), since it is affected by 3D heat diffusion. However, the most common types of anomalies found on composites, such as GRP wind turbine blades are delaminations, disbonds, water ingress, node failure and core crushing, and can be effectively detected and sometimes quantified using active thermographic techniques. This research work presents the use of infrared thermography on glass reinforced plastic (GRP) wind turbine blades assessment. Finally, the development of an autonomous, novel and lightweight multi-axis scanning system, as a concept, deploying in situ thermography NDT is also presented, with the intention of developing maximisation of the blade area coverage in a single run, at a known sensitivity, with the utilisation of the minimum number of system degrees of freedom and the maximum repeatability, as well as positional accuracy possible.
Scale Invariance in Liquid Water Distributions in Marine Stratocumulus. Part II: Multifractal Properties and Intermittency Issues

Abstract This is the second of two papers analyzing the internal liquid water content (LWC) structure of marine stratocumulus (Sc) based on observations taken during the First ICCP (International Commission on Cloud Physics) Regional Experiment (FIRE) 1987 and Atlantic Stratocumulus Transition Experiment (ASTEX) 1992 field programs. Part I examined wavenumber spectra and the three-decade scale range (tens of meters to tens of kilometers) over which scale invariance holds; the inability of spectral analysis to distinguish between different random processes was also underscored. This indetermination is removed in this part by applying multifractal analysis techniques to the LWC fields, leading to a characterization of the role of intermittency in marine Sc. Two multiscaling statistics are computed and associated nonincreasing hierarchies of exponents are obtained: structure functions and H(q), singular measures and D(q). The real variable q is the order of a statistical moment (e.g., q = 1.0 yields a mean);.
Existence of Value and Randomized Strategies in Zero-Sum Discrete-Time Stochastic Dynamic Games

Two players with conflicting objectives are simultaneously controlling a discrete-time stochastic system. The goal of this paper is to analyze such zero-sum, discrete-time, stochastic systems when the two players are allowed to use randomized strategies.Previous results have been restricted to systems with finite or compact state spaces. Such restrictions are usually untenable from the point of view of applications, since many applications frequently use either the integers or $\mathbb{R}^n $ as a state space. Our results are proved for complete, separable, metric spaces which are very useful for applications.All previously known results emerge as special cases of our results. In addition, a variety of conjectures and open problems are resolved regarding the existence of a value function, its properties such as Borel measurability or continuity, and the existence for either or both players of optimal or $\varepsilon $-optimal stationary strategies.
Enzyme Kinetics and the Michaelis-Menten Equation

Abstract The concepts presented in this article represent the cornerstone of classical mathematical biology. The central problem of the article relates to enzyme kinetics, which is a biochemical system. However, the theoretical underpinnings that lead to the formation of systems of time-dependent ordinary differential equations have been applied widely to any biological system that involves modeling of populations. In this project, students first learn about the general balance equation, which is a statement of conservation within a system. They then learn how to simplify the balance equation for several specific cases involving chemically reacting systems. Derivations are reinforced with a concrete experiment in which enzyme kinetics are illustrated with pennies. While a working knowledge of differential equations and numerical techniques is helpful as a prerequisite for this set of activities, all of the requisite mathematical skills are introduced in the project, so the methods would also serve as an introduction to these techniques. It is also helpful if students have some basic understanding of chemical concepts such as concentration and reaction rate, as typically covered in high school or college freshman chemistry courses. [Supplementary materials are available for this article. Go to the publisher's online edition of PRIMUS for the following free supplemental resource(s): Appendices and Sample Solution]
Short-term quantitative precipitation forecast experiments based on blending of nowcasting with numerical weather prediction.

In order to overcome the deficiency of the quantitative precipitation forecast(QPF) by a mesoscale numerical weather prediction(NWP) model for the very short range at convective scales and the vanishingly low skill of radar-based rainfall now-castings beyond the first couple of hours,a QPF scheme to blend these two sources has been developed.It consists of the following three major steps.Firstly,a phase correction technique is applied to the NWP model QPF to correct their systematic timing or location errors by comparing to the corresponding quantitative precipitation estimation(QPE) derived from the radar reflectivity and automatic raingauge data.Secondly,an intensity calibration technique is applied to the phase-corrected model QPF to adjust its intensity so that the calibrated intensity distribution can match the actual distribution calculated from QPE. Thirdly,the blended QPF is obtained as a weighted average between the radar-based nowcast and the phase-corrected intensity-adjusted model QPF with the weights at the different lead times assigned according to a parameterized hyperbolic tangent function. Such parameters can vary dynamically according to an index that could quantify the skill of extrapolative nowcast relative to the model QPF and,such an index is found to be different for the different precipitation types.In general,higher weights are given to nowcast in the couple of hours.At about 3 hours and beyond,higher weights are given to model QPF as the now-cast skill drops.This blending scheme was applied to five typical heavy rain cases in 2011 and two in 2012 over the Beijing-Tianjin -Hebei region with about eighty tests done.The QPF verification results indicated that the performance of the blended QPF was significantly improved in the 0-6 forecast range with the overall forecast skill generally higher than either radar-based nowcast or model QPF taken individually.Given such encouraging results,the present blending scheme is expected to be promising as an effective tool when deployed for operation.
A Combinatorial Proof of an Alternating Convolution Identity for Multichoose Numbers

Abstract We give a combinatorial proof for an identity involving an alternating sum of products of multichoose numbers. In our proof, we construct a sign-reversing involution on certain ordered pairs of words, then count the leftovers. We also outline two alternative proofs, one using hypergeometric functions and the other using mechanical summation.
A METHOD OF SOLUTION TO SELECTIVE HARMONIC ELIMINATED PWM SWITCHING ANGLES FOR NPC INVERTERS

This paper analyzes the selected harmonic eliminated PWM technique for three-level NPC Inverter-Fed AC Drives. For the difficulty of determining the initial values when solving the SHE-PWM nonlinear equations, this paper proposes a method that using the differentiation of tbe solutions to predict the initial values. The initial values equations when the modulation index m is equal to zero are also given. The calculation results proved that numerical algorithm using this initial value method will convergent to the desired solutions fast.Through the calculation results, the maxim modulation index of the SHE-PWM for the NPC inverter is analyzed. The calculation results are confirmed by test results.
Building an accurate 3D model of a circular feature for robot vision

In this paper, an accurate 3D model analysis of a circular feature is built with error compensation for robot vision. We propose an efficient method of fitting ellipses to data points by minimizing the algebraic distance subject to the constraint that a conic should be an ellipse and solving the ellipse parameters through a direct ellipse fitting method by analysing the 3D geometrical representation in a perspective projection scheme, the 3D position of a circular feature with known radius can be obtained. A set of identical circles, machined on a calibration board whose centres were known, was calibrated with a camera and did the model analysis that our method developed. Experimental results show that our method is more accurate than other methods.
On the Continuous-Discrete Nonlinear Filters by Using Divided Difference Approximation

This paper considers a continuous-discrete (C-D) nonlinear filtering problem by using the divided difference method, and develops C-D versions of the divided difference first-order (DD1) filter and the divided difference second-order (DD2) filter by extending the results due to Schei [6], Norgaard, et al [7] and Simandl, et al [8]. Since Jacobians are not needed in designing C-D DD1 and DD2 filters, these filters can be applied to the filtering problem for any C-D nonlinear systems, to which the C-D extended Kalman filter (EKF) is difficult to apply. Simulation results show that the performance of the proposed C-D DD2 algorithm is comparable to that of the C-D unscented Kalman filter(UKF), though the performance of the C-D DD1 filter is slightly inferior to them.
Soaring Energy Prices: Understanding Public Engagement on Twitter Using Sentiment Analysis and Topic Modeling With Transformers

Energy prices have gone up gradually since last year, but a drastic hike has been observed recently in the past couple of months, affecting people’s thrift. This, coupled with the load shedding and energy shortages in some parts of the world, led many to show anger and bitterness on the streets and on social media. Despite subsidies offered by many Governments to their citizens to compensate for high energy bills, the energy price hike is a trending topic on Twitter. However, not much attention is paid to opinion mining on social media posts on this topic. Therefore, in this study, we propose a solution that takes advantage of both a transformer-based sentiment analysis method and topic modeling to explore public engagement on Twitter regarding energy prices rising. The former method is employed to annotate the valence of the collected tweets as positive, neutral and negative, whereas the latter is used to discover hidden topics/themes related to energy prices for which people have expressed positive or negative sentiments. The proposed solution is tested on a dataset composed of 366,031 tweets collected from 01 January 2021 to 18 June 2022. The findings show that people have discussed a variety of topics which directly or indirectly affect energy prices. Moreover, the findings reveal that the public sentiment towards these topics has changed over time, in particular, in 2022 when negative sentiment was dominant.
Estimation of Frictional Parameters and Initial Values of Simulation Variables Using an Adjoint Data Assimilation Method with Synthetic Afterslip Data

Since variations of slip on plate boundaries depend on frictional properties, it is essential to know frictional parameters on the fault, as well as initial values of simulation variables for earthquake generation prediction. In this study, an adjoint data assimilation method is introduced to a simpli.ed fault model with a rate-and state-dependent friction law as a . rst step toward the goal of estimating the frictional parameters and initial values of simulation variables in a realistic situation. The method is applied to the simpli.ed model which mimics the 2003 Tokachi-oki afterslip. We make synthetic data set, slip velocities on the fault surface by assigning the “true” frictional parameters and initial values artificially. We investigate the feasibility of estimating frictional parameters and initial values through the adjoint data assimilation method on the assumption of knowing “background” values and observations. It is confirmed that the adjoint data assimilation method is computationally efficient to estimate control variables such as frictional parameters and initial values by time-trajectory fitting of observation data, compared to the grid search method. Also, we examine the sensitivity of each frictional parameter and initial value to the observed afterslip velocity data. In the range of our search using afterslip data, we . nd that 1) the initial value of velocity can be constrained, 2) the initial value of state variable cannot be constrained, 3) the frictional parameter value of a-b is well retrieved in the region where aftreslip velocity is observed, and 4) the value of characteristic length L can be retrieved only from the early portion of afterslip velocity data, where the velocity is rapidly changing.
A New Aggregate Method for Multiple Attribute Decision Making with Intervals

With respect to multiple attribute decision-making problems in which the attribute weights are interval numbers,after the quality of interval numbers being assayed,a new aggregate method for obtaining synthetic attribute intervals for alternatives is presented.At first,the decision predilection is giving by attribute intervals.Then,using the comprehensive appraisal value of maximum deviation,the decision making problems with preference information is solved.At last,an example is used to illustrate the useless of the proposed method.
Unsupervised learning in hyperspectral classifiers using hidden Markov models

Hyperspectral data represents a mixture of several component spectra from many classifiable sources. The knowledge of the contributions of the underlying sources to the recorded spectra is valuable in many remote sensing applications. Traditional Hyperspectral classification and segmentation algorithms have used Markov random field (MRF) based estimation in recent investigations. Although, this method reflects plausible local, spatial correlation in the true scene, it is limited to using supervised learning schemes for parameter estimation. Besides, the expectation-maximization (EM) for the hidden MRF is considerably more difficult to realize due to the absence of a closed form formulation. In this paper, we propose a hidden Markov model (HMM) based probability density function (PDF) classifier for reduced dimensional feature space. Our approach uses an unsupervised learning scheme for maximum-likelihood (ML) parameter estimation that combines both model selection and estimation in a single algorithm. The proposed method accurately models and synthesizes the approximate observations of the true data in a reduced dimensional feature space.
Frequency- and Pattern-Reconfigurable Antenna Array With Broadband Tuning and Wide Scanning Angles

A frequency- and pattern-reconfigurable $1\times4$ array is analyzed and designed, which consists of a reconfigurable feed network, 12 rectangular parasitic patches, and four octagonal patch antennas. Characteristic mode analysis (CMA) is used to study the radiation characteristics of the proposed element antenna. By changing the ON–OFF states of 18 p-i-n diodes loaded in the feed network, different combinations of the initial current phase are obtained to realize the reconfiguration of five radiation states. By loading varactor diodes and parasitic patches, frequency reconfigurability is achieved. The measured result shows that the array pattern can be discretely switched in the directions of 0°, ±15°, ±25°, ±30°, and ±55°. The tunable overlapped −10-dB impedance bandwidth is 3.54–4.46 GHz for the five radiation patterns. The maximum gain reaches 7.67 dBi and the overlapped relative bandwidth of five radiation states is more than 22%.
THE COMPUTATION OF FUNCTIONS OF MATRICES BY TRUNCATED FABER SERIES

In this paper we consider a method based on Faber polynomials for the approximation of functions of real nonsymmetric matrices. Particular attention is devoted to some functions that occur in practical problem, such as exp (z), . Finally we give some numerical results on a test matrix arising from the discretization of a second order partial differential operator.
Sensitivity of functional near-infrared spectroscopy to optical properties in brain imaging

Functional near-infrared spectroscopy (fNIRS) is a noninvasive optical technique for assessing spatial brain activation by determining the relative changes in the concentrations of oxy- and deoxyhemoglobin (HbO and HbR) in different regions of the brain. The modified Beer-Lambert Law (mBLL) is essential for calculating the relative concentrations of HbO and HbR, and the differential pathlength factor (DPF) is a key component of the mBLL. In this paper, we investigate how error in DPF estimates translates to relative concentration calculations for HbO and HbR. To do this, we generate various amounts of error in DPF values and calculated the error in the resulting concentration data. We then use a two-regression fit to generate a model of concentration error as a function of DPF error. We also compare different reported DPF values to assess their impact on concentration error and to verify our findings with the concentration-error model. We find that age appears to be a greater contributing factor to concentration error than same-subject anatomical differences, and we also find that DPF over- and underestimation in equal amounts will produce unequal amounts of concentration error.
Prompt gamma-ray burst emission from gradual energy dissipation

I describe the main aspects of a model for the gamma-ray burst (GRB) emission in which energy is dissipated gradually in a Poynting-flux-dominated flow. In this picture, the energy of the radiating particles is determined by heating and cooling balance. Detailed radiative transfer calculations show that, at Thomson optical depths of order of unity, the dominant radiative process is inverse Compton scattering. Synchrotron-self-absorbed emission and inverse Compton dominate in the Thomson thin parts of the flow. The electrons stay thermal throughout the dissipation region because of Coulomb collisions (Thomson thick part of the flow) and exchange of synchrotron photons (Thomson thin part). The resulting spectrum naturally explains the observed sub-MeV break of the GRB emission and the spectral slopes above and below the break. In this model, the Amati relation indicates a tendency for the more luminous bursts to have more energy per baryon. If this tendency also holds for individual GRB pulses, the model predicts the observed narrowing of the width of pulses with increasing photon energy. The model also predicts that the γ-ray power-law tail has a high-energy cutoff typically in the ~GeV energy range that should be observable with FERMI. A prompt emission component in the optical and UV is predicted to be associated with the GeV emission.
White Matter Hyperintensities in Young Patients from a Neurological Outpatient Clinic: Prevalence, Risk Factors, and Correlation with Enlarged Perivascular Spaces

(1) Background: to investigate the prevalence of white matter hyperintensities (WMH), risk factors, and correlation with enlarged perivascular spaces (ePVS) among young patients (age, 16–45 years) in a neurological outpatient clinic. (2) Methods: a total of 887 young patients who underwent a head magnetic resonance imaging (MRI)examination between 1 June 2021, and 30 November 2021, were included in this study. Paraventricular WMH (PWMH), deep WMH (DWMH), ePVS in the centrum semiovale (CSO-ePVS), and basal ganglia (BG-ePVS) were rated. Logistic regression analysis was used to identify the best predictors for the presence of WMH and, for the association of the severity of ePVS with the presence of WMH. Goodman–Kruskal gamma test was used to assess the correlation between the severity of ePVS and WMH. (3) Results: the prevalence of WMH was 37.0%, with low severity. Age, hypertension (p < 0.001), headache (p = 0.031), syncope (p = 0.012), and sleep disturbance (p = 0.003) were associated with the presence of DWMH. Age, sex (p = 0.032), hypertension (p = 0.004) and sleep disturbance (p < 0.001) were associated with the presence of PWMH. The severity of CSO-ePVS was associated with the presence and the severities of DWMH. The severity of BG-ePVS was associated with the presence and severities of DWMH and PWMH. (4) Conclusions: the prevalence of WMH was 37% and mild in young patients without specific causes. Older age, female, hypertension, headache, syncope, and sleep disturbance were associated with WMH. The severity of ePVS had an impact on the presence and severity of WMH in the corresponding brain regions.
Exploring the Allen mouse connectivity experiments with new neuroinformatic tools for neurophotonics, diffusion MRI and tractography applications

The Allen Mouse Brain Connectivity Atlas (AMBCA) offers a high-resolution map of neural connections detailing axonal projections labeled by viral tracers. It is a unique tool for studying structural connectivity and better understanding the white matter pathways of the gene mouse brain. But, the analysis and comparison of these data are limited to a simple visualization on the Allen website and have no direct relationship with specific User data. Here, we propose a series of python-based tools to operate with AMBCA data in the User’s data space. Our method is based on ”back and forth” actions between Allen and User data using the Allen Software Development Kit (AllenSDK) to import data from the Allen Institute and the Python package ANTsPyX for registration. A transformation matrix is calculated with ANTsPyX to overlay, for instance, Allen’s projection density maps with a diffusion MRI-based tractography in the User space. Conversely, applying the inverse transformation to a specific location along a white matter bundle within the User space allows us to recover which experiments were done at this particular location in the Allen Mouse brain Common Coordinate Framework (CCFv3). Thus, both data can be used in a natural interaction, e.g., by inspecting them in a visualization tool such as the MI-Brain software. This series of tools will offer an attractive solution for researchers with neural tracing and/or tractography data to be combined with the AMBCA. The code is available at: https: //github.com/linum-uqam/m2m.
Constructal complex-objective optimization of electromagnet based on magnetic induction and maximum temperature difference

The good performance of an electromagnet requires high magnetic induction and a low temperature. A new complex-objective function reflected magnetic induction and maximum temperature difference is set up, and the electromagnet is optimized using the new complexobjective function. The optimization results show that the performance of the electromagnet is improved as the number of high thermal conductivity cooling discs inserted increases. When the performance of the electromagnet achieves its best level, the solenoid becomes longer and thinner as the number of high thermal conductivity cooling discs increases. Simultaneously, the magnetic induction becomes higher and the maximum temperature difference becomes lower. The optimized performance of the electromagnet also improves as the volume of solenoid increases; simultaneously, the magnetic induction first increases and then decreases, and the maximum temperature difference decreases all along.