Miracle-dz/UniFAR-FTUs huggingface dataset:
Synthesis and applicable condition of vector wave envelopes in layered random elastic media with anisotropic autocorrelation function based on the Markov approximation

SUMMARY The Markov approximation is a powerful stochastic method for the direct synthesis of wave envelopes in random velocity fluctuated media when the wavelength is shorter than their correlation distance. To apply the Markov approximation to realistic cases, we consider horizontal layered random media characterized by an anisotropic autocorrelation function (ACF), where different layers have different randomnesses and different background velocities having step-like changes. Solving the parabolic master equation for the two frequency mutual coherence function (TFMCF) in random elastic media for the vertical incidence of a plane wavelet from the bottom, we calculate the angular spectrum just before the first velocity boundary. Multiplying transmission or PS conversion coefficients of the boundary by the angular spectrum, we calculate the angular spectrum and TFMCF on the other side of the boundary. Then we solve the master equation for the forward propagating wavelet in the second layer. Taking the same procedure for each layer boundary, we finally obtain the mean square (MS) vector envelopes on the free surface on the top layer. For the practical simulation, we use 2-D random media characterized by a Gaussian ACF. We numerically confirm the validity of the envelope synthesis for a specific case of layered random media with anisotropic ACF by comparing with finite difference (FD) simulations of elastic waves. Considering the Earth structure, the horizontal correlation distance is larger than the vertical one and the velocity fractional fluctuation becomes weak as depth increases, the Markov approximation is good for modelling the primary wavelet and also the converted wavelet for the vertical incident wavelet. We derive an applicable range of the Markov approximation for random media with anisotropic ACF by comparing with FD simulations. The results show that the Markov approximation is accurate when the wavelength is comparable or shorter than the both of vertical and horizontal correlation distances and the MS fractional fluctuation is much smaller than the ratio of squared horizontal correlation distance to the product of the vertical correlation distance and propagation distance.
Reuniting Humanity and the Cosmos in Barth's Theology: Natural Disasters and The Fall

Using literature, this article argues that Karl Barth's (1886 –1968 CE) concept of an "assumed fall" could be helpful if applied to the cosmos and humanity. Barth's conception of the created order is that it is perfect exactly the way it is, natural disasters included. Further, the fall did not affect the creation. Barth does however argue for fallen humanity. Nevertheless, the fall is assumed in the Election of Jesus Christ. There was never a time in which humans did not need a Saviour. Barth, therefore, places a separation between humanity and the created order. A close reading of Romans 8 and a look at Christian interpretative tradition, argue for an inseparability between humankind and the cosmos. In other words, fallen humans live in a fallen cosmos, and redeemed humans will live in a redeemed universe. If Barth's concept of the assumed fall of humanity could be applied to the cosmos as well, then the concept could fit well within the traditional reading of Romans 8 and could be beneficial for scientific/theological engagement.
1030 nm multilayer oxide aperture VCSELs with 25 GHz modulation bandwidth and 40 Gb/s NRZ transmission

We present the design, fabrication, and performance of high-speed oxide-confined 1030 nm vertical-cavity surfaceemitting lasers (VCSELs) with a short optical cavity, and multiple oxide apertures. High-speed modulation was facilitated by using the shortest possible cavity of a half-wavelength length and multiple oxide apertures to enhance the confinement of optical fields and reduce capacitance. We carefully optimized the multiple quantum wells and the doping profile in order to achieve a high-speed operation. The developed VCSELs exhibit a modulation bandwidth exceeding 25.1 GHz at 25°C, supporting back-to-back data rate up to 40 Gb/s under binary non-return-to-zero (NRZ) modulation.
Natural Convection in a Square Cavity with Spatial Side-Wall Temperature Variation

ABSTRACT Laminar natural convection in a two-dimensional square cavity filled with a pure air (Pr = 0.71) is studied numerically in the present article with nonuniform side-wall temperature. The heated vertical wall is assumed to have spatial sinusoidal temperature variations about a constant mean value, which is higher than the cold side-wall temperature, while the top and the bottom walls are adiabatic. A finite-volume method is used to solve numerically the nondimensional governing equations in the vorticity–stream function formulation. The effects of the amplitude and the wave number of the heated side-wall temperature variation on the natural convection in the cavity are investigated. It is found that the average Nusselt number varies based on the hot-wall temperature. It increases with an increase in the amplitude, while the maximum average Nusselt number occurs at the wave number of k = 0.7 for Rayleigh number range 103 ≤ Ra ≤ 106. It is found that the values of maximum fluid circulation occur at a similar wave number, which produces maximum heat transfer for small values of Ra, while it occurs at higher values of wave number at high Ra.
Difference-similitude set theory

In this paper, Difference-Similitude Set Theory is proposed which is fundamental different from the known algorithms. Two new concepts in this subject, “Available Member Set” and “Upper-approximate Set” are defined. In the framework of Difference Similitude Set Theory (DSST), the knowledge reduction on any information system is equivalent to the serials calculations on difference set and similitude set of both the whole information system and each object. It is concluded and demonstrated that: 1. The attribute reduction process is just to find an upper-approximate set of the available set of difference set with minimal cardinality as possible. 2. The rule construction of certain object is just to find an upper-approximate set of available set of the object’s difference set with minimal cardinality as possible. The algorithms about these two jobs are also described. The first process can be separated into two steps: a) to find the base attribute. b) to find an upper-approximate set of available member set of no-base set. The union of this two steps’ results is the reserved attribute. The second process can be also separated into two steps: a) to find the object’s base attribute. b) to find an upper-approximate set of the object’s no-base set. By comparing the overlapness of each rule and rejecting the object one by one which the reserved rules suit, the synthesized rules as less as possible should be found.
Openness of political structures and gender gaps in protest behaviour in Africa

Abstract Protest behaviour has been conceptualized as a high-risk form of political engagement, and it tends to elicit a relatively lower engagement rate than other forms of political participation. In Africa, the risky nature of protests is often complicated by the predominant socio-cultural bias and masculine political norms that hinder women’s political agency. Many of these political systems in Africa are emerging democracies, where women are likely to be marginalized in the civic and political sphere. Using the Afrobarometer data of 2014/2015, this study seeks to examine the impact of the political context on the gender gap in protest behaviour. The study finds that the gender gap in protest behaviour is lower in countries that are politically free and higher in countries with more years of military regimes. These findings offer valuable insights into the political and institutional contexts in which women’s protest behaviour is accentuated and diminished.
A simulation study of ridge and other regression estimators

We consider a number of estimators of regression coefficients, all of generalized ridge, or 'shrinkage' type. Results of a simulation study indicate that with respect to two commonly used mean square error criteria, two ordinary ridge estimators, one proposed by Hoerl, Kennard and Baldwin, and the other introduced here, perform substantially better than both least squares and the other estimators discussed here
Anisotropies in the cosmic neutrino background after Wilkinson Microwave Anisotropy Probe five-year data

We search for the presence of cosmological neutrino background (CNB) anisotropies in recent Wilkinson Microwave Anisotropy Probe (WMAP) five-year data using their signature imprinted on modifications to the cosmic microwave background (CMB) anisotropy power spectrum. By parameterizing the neutrino background anisotropies with the speed viscosity parameter cvis, we find that the WMAP five-year data alone provide only a weak indication for CNB anisotropies with cvis2>0.06 at the 95% confidence level. When we combine CMB anisotropy data with measurements of galaxy clustering, the SN-Ia Hubble diagram, and other cosmological information, the detection increases to cvis2>0.16 at the same 95% confidence level. Future data from Planck, combined with a weak lensing survey such as the one expected with DUNE from space, will be able to measure the CNB anisotropy parameter at about 10% accuracy. We discuss the degeneracy between neutrino background anisotropies and other cosmological parameters such as the number of effective neutrinos species and the dark energy equation of state.
Acoustic detection and identification of insects in soil

There is considerable practical need for user‐friendly, inexpensive devices that detect and quantify insect populations in environments hidden from visual observation. One approach that has been used with varying success has been to detect the insects through the sounds or vibrations they generate for communication or through noises that are produced incidentally during feeding and general movement. The ability to precisely monitor soil insect populations is limited by interference from background noise and the high rate of attenuation of sound in soil. This paper describes experiments using different sensors and analysis techniques for detection of insects in soil in an agricultural environment. Results from different sensors are compared, and the spectral and temporal patterns that can be used to distinguish the target insects from noise and nontarget soil organisms are discussed. The use of accelerometers attached to 20–30‐cm nails appears to be a low‐cost, user‐friendly method to locate and monitor th.
How arterial pressures affect the consideration of internal carotid artery angle as a risk factor for carotid artherosclerotic disease

Patient–specific geometric factors together with traditional risk factors may aid the early identification of patients at high risk of developing carotid artery disease requiring surgical intervention. Recent studies have linked aspects of carotid geometry to the pathogenesis of internal carotid artery (ICA) stenosis. Abnormal wall shear stress (WSS) is found for large ICA angles. Low WSS is believed to correspond to plaque formation whereas high WSS may result in plaque rupture and clotting. Here, the meshless method, smoothed particle hydrodynamics, is used to simulate Newtonian flow through a clinical, rigid walled, carotid bifurcation. The resulting flow field and WSS are reported for a range of different ICA angles. Varying the angle without changing boundary pressure conditions produces minimal change in flow and WSS. Greater ICA downstream pressures appear important for maintaining well–behaved flow through the bifurcation by suppressing flow separation downstream of the stenosis resulting in more uniform wall stress.
On-Chip All-Optical Switching and Memory by Silicon Photonic Crystal Nanocavities

We review our recent studies on all-optical switching and memory operations based on thermo-optic and carrier-plasma nonlinearities both induced by two-photon absorption in silicon photonic crystal nanocavities. Owing to high-Q and small volume of these photonic crystal cavities, we have demonstrated that the switching power can be largely reduced. In addition, we demonstrate that the switching time is also reduced in nanocavity devices because of their short diffusion time. These features are important for all-optical nonlinear processing in silicon photonics technologies, since silicon is not an efficient optical nonlinear material. We discuss the effect of the carrier diffusion process in our devices, and demonstrate improvement in terms of the response speed by employing ion-implantation process. Finally, we show that coupled bistable devices lead to all-optical logic, such as flip-flop operation. These results indicate that a nanocavity-based photonic crystal platform on a silicon chip may be a promising candidate for future on-chip all-optical information processing in a largely integrated fashion.
Role of Data Mining in Education Sector

Abstract : Data mining is a method used for categorizing and predicting the performance of a student and a teacher as well. It helps both student and teacher in developing the teaching method and the entire system. Every student who uses this method get a huge favor from this. This helps students to choose the right career option. Currently, the tendency of providing immense importance to data mining techniques in the educational sector is increasing as these procedures have a huge necessity in bringing efficiency in both learning and teaching procedures. Each and every person is gathering a large amount of data every day, in case these data are not further examined; only the large amount of data will remain. With the latest systems and technologies, people can utilise these data and examine those and benefit from it. The best technique for this issue is the data mining process. Data mining is a method of bringing out the useful and disclosed data and information from big data sets. Educational data mining is a process from which teachers and students get a lot of help. Teachers are able to observe every student’s performance. On the other hand, students can choose a perfect and accurate career option by this process. This process utilises several techniques and methods such as statistics, machine learning, data analysis and data mining. Educational data mining is a method of raw information converting from a huge educational database to meaningful and effective information.
Multi-contrast photoacoustic microscopy

We developed multi-contrast photoacoustic microscopy (PAM) for in vivo anatomical, functional, metabolic, and molecular imaging. This technical innovation enables comprehensive understanding of the tumor microenvironment. With multi-contrast PAM, we longitudinally determined tumor vascular anatomy, blood flow, oxygen saturation of hemoglobin, and oxygen extraction fraction.
Examination of Effective Dielectric Constants of Nonspherical Mixed-Phase Hydrometeors

AbstractThe validity of the effective dielectric constant ɛeff for nonspherical mixed-phase particles is tested by comparing the scattering parameters of ice–water mixtures for oblate and prolate spheroids obtained from the conjugate-gradient and fast Fourier transform (CGFFT) numerical scheme with those computed from the T matrix for a homogeneous particle with the derived ɛeff with the same size, shape, and orientation as that of the mixed-phase particle. The accuracy of the effective dielectric constant is evaluated by examining whether the scattering parameters of interest can reproduce those of the direct computations, that is, the CGFFT results. Computations have been run over a range of prolate and oblate spheroids of different axial ratios up to size parameters of 4. It is found that the effective dielectric constant, obtained from realizations of small particles, can be applied to a class of particle types if the fractional water content remains the same. Analysis of the results indicates that th.
Bootstrapping confidence bands for the instantaneous frequency

Data produced by a reproducible source contains redundant information which allows seismic inversion to simultaneously determine the high-frequency fluctuation in the p-wave velocity (or reflectivity) as well as the input energy source. The seismogram model is the plane-wave convolutional model derived from the constant density, variable sound velocity acoustic wave equation. The first step is to analyze this linearized model when the background velocity is constant. Then perturbations in the seismic data stably determine corresponding perturbations in the source and reflectivity. The stability of this determination improves as the slowness aperture over which the data is defined increases. Further, the normal operator for the convolutional seismogram model is continuous with respect to velocity. Thus the stability result for constant background velocities may be extended to more realistic background velocity models which vary slowly and smoothly with depth. The theory above is illustrated with four synthetic numerical examples derived from marine data. The examples indicate that for a wide slowness aperture, inversion is very effective in establishing the true shape of the reflectivity and the shape and location of the compactly supported energy source. As this aperture window narrows, the corresponding inversion-estimated model still describes the data quite accurately, but the inversion is not able to recover the original two distinct parameters.
Three-Dimensional Human Pose Estimation from Sparse IMUs through Temporal Encoder and Regression Decoder

Three-dimensional (3D) pose estimation has been widely used in many three-dimensional human motion analysis applications, where inertia-based path estimation is gradually being adopted. Systems based on commercial inertial measurement units (IMUs) usually rely on dense and complex wearable sensors and time-consuming calibration, causing intrusions to the subject and hindering free body movement. The sparse IMUs-based method has drawn research attention recently. Existing sparse IMUs-based three-dimensional pose estimation methods use neural networks to obtain human poses from temporal feature information. However, these methods still suffer from issues, such as body shaking, body tilt, and movement ambiguity. This paper presents an approach to improve three-dimensional human pose estimation by fusing temporal and spatial features. Based on a multistage encoder–decoder network, a temporal convolutional encoder and human kinematics regression decoder were designed. The final three-dimensional pose was predicted from the temporal feature information and human kinematic feature information. Extensive experiments were conducted on two benchmark datasets for three-dimensional human pose estimation. Compared to state-of-the-art methods, the mean per joint position error was decreased by 13.6% and 19.4% on the total capture and DIP-IMU datasets, respectively. The quantitative comparison demonstrates that the proposed temporal information and human kinematic topology can improve pose accuracy.
Research on LSTM multi-factor quantitative stock selection strategy based on attention mechanism

In this paper, monthly frequency multi-factor data on valuation, momentum, turnover rate and technology of A-share listed companies in Shanghai and Shenzhen markets from January 2012 to July 2022 are selected and input to LSTM and LSTM model with fused attention mechanism respectively for training after data pre-processing. The sector-neutral layered-portfolios and the sector-neutral stock selection portfolios were constructed based on the model output, respectively. In the model evaluation section, it is confirmed that the Attention-LSTM model outperforms the LSTM model in predicting stock ups and downs. The single-factor layered back test under monthly position adjustment and stock selection strategy back test confirmed that the Attention-LSTM model significantly outperformed the LSTM model in terms of annualized return, sharpe ratio, and maximum retracement, and also significantly outperformed the CSI 300 and CSI 500.
A Numerical Experiment of Airmass Transformation Processes Over Warmer Sea: Part II: Interaction Between Small-Scale Convections and Large-Scale Flow@@@第2部小規模な対流と大規模な流れの相互作用

Utilisation d'un modele numerique pour l'etude de l'interaction entre la convection de petite echelle et l'ecoulement de grande echelle au sein d'une transformation de masse d'air. Conditions: vent de grande echelle suppose monodirectionnel; resolution fine dans les directions verticale et transversale a l'ecoulement, en vue de traiter explicitement la convection transportant la chaleur, l'humidite, et la quantite de mouvement dans la couche transformee; resolution grossiere dans la direction de l'ecoulement. Resultats: les profils moyennes de t potentielle, d'humidite specifique et de v montrent que la couche transformee forme une couche melangee convectivement, humide, caracterisee par une structure de couche multiple; lorsque la t de la surface marine croit dans la direction de l'ecoulement, une chute de p y est provoquee par le rechauffement convectif; en outre, l'acceleration due au gradient de p, est equilibree par la deceleration due au frottement induit par le flux convectif de quantite de mouvement
A first-principles phase stability study on the Au-Ni system

We present a first-principles statistical mechanics theory that incorporates the calculation of electronic total energies in the local density approximation, configurational entropies, vibrational modes and relaxation effects in disordered systems. Applications of the theory to the binary Au-Ni system are given using the linear muffin-tin orbital method for the total energy calculations, the cluster variation method for the description of the configurational entropy and the Debye-Gruneisen approximation for the vibrational modes. The solubilities of both ends of the phase diagram, calculated with no adjustable parameters, are compared with experiment.
The expansion of clusters of particles in the atmosphere

In this paper, the problem of the spatial expansion of a cluster of particles under the action of turbulence is investigated. The basic difficulty, that this form of diffusion depends on the Lagrangian properties of the turbulent field, is met by the assumption that the Lagrangian and Eulerian time-correlograms are functionally similar. In consequence, it is possible to solve the equation governing the cluster's expansion to give prediction laws expressed only in terms of easily measured parameters of the turbulence. The application of the full solution is lengthy but a simple working approximation follows from the fact that over an important part of the expansion at which the size of the cluster is of the same order of magnitude as the Eulerian length-scale, the rate of expansion is approximately constant and is proportional to the square of the intensity of turbulence whilst independent of the length scale itself. Experiments to test these formulae have been carried out on the diffusion of clusters of Lycopodium spores over a few hundred metres downwind travel near the ground, and the diffusion of long crosswind clouds of aircraft-released fluorescent particles, sampled along the vertical, after several kilometres travel in the free atmosphere. Whilst the experiments are few in number and the conditions often did not strictly conform to the conditions of isotropy and homogeneity required by the theory, the results are satisfactorily consistent with the prediction formulae and imply an effectively constant ratio between the scales of the Lagrangian and Eulerian time-correlograms.