Miracle-dz/UniFAR-FTUs huggingface dataset:
LOGARITHMIC SOBOLEV INEQUALITIES FOR FINITE MARKOV CHAINS

This is an expository paper on the use of logarithmic Sobolev inequalities for bounding rates of convergence of Markov chains on finite state spaces to their stationary distributions. Logarithmic Sobolev inequalities complement eigenvalue techniques and work for nonreversible chains in continuous time. Some aspects of the theory simplify considerably with finite state spaces and we are able to give a self-contained development. Examples of applications include the study of a Metropolis chain for the binomial distribution, sharp results for natural chains on the box of side n in d dimensions and improved rates for exclusion processes. We also show that for most r-regular graphs the log-Sobolev constant is of smaller order than the spectral gap. The log-Sobolev constant of the asymmetric twopoint space is computed exactly as well as the log-Sobolev constant of the complete graph on n points.
A simple algorithm for localized construction of non‐matching structural interfaces

A simple and effective algorithm for the modular construction of non‐matched interfaces is presented for the partitioned solution of large‐scale structural problems. The formulation is based on a recently developed four‐field variational principle, which introduces a connection frame between the interfaced partitions. A key result of the present study is a frame nodal placement criterion that uniquely determines the frame discretization into piecewise linear elements so that the interface patch test condition is satisfied a priori. The method is demonstrated with several 2D and 3D example problems. Copyright © 2002 John Wiley & Sons, Ltd.
Parameter expansion to accelerate EM: The PX-EM algorithm

SUMMARY The EM algorithm and its extensions are popular tools for modal estimation but ar-e often criticised for their slow convergence. We propose a new method that can often make EM much faster. The intuitive idea is to use a 'covariance adjustment' to correct the analysis of the M step, capitalising on extra information captured in the imputed complete data. The way we accomplish this is by parameter expansion; we expand the complete-data model while preserving the observed-data model and use the expanded complete-data model to generate EM. This parameter-expanded Ei M, PX-EM, algorithm shares the simplicity and stability of ordinary EM, but has a faster rate of convergence since its M step performs a more efficient analysis. The PX-EM algorithm is illustrated for the multivariate t distribution, a random effects model, factor analysis, probit regression and a Poisson imaging model.
Coupling Schemes in Terahertz Planar Metamaterials

We present a review of the different coupling schemes in a planar array of terahertz metamaterials. The gap-to-gap near-field capacitive coupling between split-ring resonators in a unit cell leads to either blue shift or red shift of the fundamental inductive-capacitive (LC) resonance, depending on the position of the split gap. The inductive coupling is enhanced by decreasing the inter resonator distance resulting in strong blue shifts of the LC resonance. We observe the LC resonance tuning only when the split-ring resonators are in close proximity of each other; otherwise, they appear to be uncoupled. Conversely, the higher-order resonances are sensitive to the smallest change in the inter particle distance or split-ring resonator orientation and undergo tremendous resonance line reshaping giving rise to a sharp subradiant resonance mode which produces hot spots useful for sensing applications. Most of the coupling schemes in a metamaterial are based on a near-field effect, though there also exists a mechanism to couple the resonators through the excitation of lowest-order lattice mode which facilitates the long-range radiative or diffractive coupling in the split-ring resonator plane leading to resonance line narrowing of the fundamental as well as the higher order resonance modes.
A Novel Deep Belief Network with Butterfly Optimization Algorithm for the Classification of Paddy Leaf Disease Detection

The widespread presence of a wide variety of diseases during paddy farming is one of the most significant elements that annually contributes to enormous economic losses. These losses occur as a direct result of the widespread prevalence of these diseases. In this paper, a deep learning algorithm using Deep Belief Network (DBN) and a meta-heuristic optimization using Butterfly optimization algorithm (BOA) is used to classify the images to detect the diseases in a Plant Leaf. The steps of classification involve three different process that includes pre-processing, feature extraction and classification. The simulation is conducted in python to test the efficacy of the classifier. The result of simulation shows that the proposed method has obtained higher classification rate than the existing machine learning classifiers.
Comparison of the Micro-Shear Bond Strength of Resin Cements to CAD/CAM Glass Ceramics with Various Surface Treatments

This study aimed to compare the effect of acid etching, sandblasting, or silica coating on the micro-shear bond strength of dual-cured resin cements to computer-aided design and computer-aided manufacturing (CAD/CAM) glass ceramic materials. Feldspathic, lithium disilicate, and zirconia-reinforced CAD/CAM ceramics were divided into four groups: control group (C), no surface treatment; hydrofluoric (HF) group, 5% HF acid-etched; sandblasting (SB) group, abraded with 50 µm aluminum oxide (Al2O3) particles; silica-coated (CJ) group, abraded with 30 µm silica-modified Al2O3 particles. Roughness values were obtained by using a profilometer. The cements were condensed on the surface-treated specimens and a micro-shear bond test was conducted. The ceramic material (p < 0.001) and surface treatment type (p < 0.001) significantly affected the micro-shear bond strength values. HF acid etching can be recommended for the surface pretreatment of feldspathic, lithium disilicate, and zirconia-reinforced CAD/CAM ceramics. Better bond strengths can be obtained with HF acid etching than with sandblasting and silica coating.
The study of system-interferences in ship UWB communications

The Ultra-wideband transmission techniques have wide foreground in martial communications because of having so many advantages such as high band width, high bit rate, low power consumption and low cost. With the character of ship-communications a net-structure for UWB was present in the paper. The interference when two asynchronous UWB communication systems work together was study and simulated. Such intra-system interference can be restrained by increasing the length of pseudo noise (PN).
Oxylipins and Reactive Carbonyls as Regulators of the Plant Redox and Reactive Oxygen Species Network under Stress

Reactive oxygen species (ROS), and in particular H2O2, serve as essential second messengers at low concentrations. However, excessive ROS accumulation leads to severe and irreversible cell damage. Hence, control of ROS levels is needed, especially under non-optimal growth conditions caused by abiotic or biotic stresses, which at least initially stimulate ROS synthesis. A complex network of thiol-sensitive proteins is instrumental in realizing tight ROS control; this is called the redox regulatory network. It consists of sensors, input elements, transmitters, and targets. Recent evidence revealed that the interplay of the redox network and oxylipins–molecules derived from oxygenation of polyunsaturated fatty acids, especially under high ROS levels–plays a decisive role in coupling ROS generation and subsequent stress defense signaling pathways in plants. This review aims to provide a broad overview of the current knowledge on the interaction of distinct oxylipins generated enzymatically (12-OPDA, 4-HNE, phytoprostanes) or non-enzymatically (MDA, acrolein) and components of the redox network. Further, recent findings on the contribution of oxylipins to environmental acclimatization will be discussed using flooding, herbivory, and establishment of thermotolerance as prime examples of relevant biotic and abiotic stresses.
Testing for Granger Causality in Moments

In this paper, we consider a generalized approach which is flexibly applicable to testing Granger causality in various moments and in both the full-sample and out-of-sample contexts. We further use this approach to establish a class of cross-correlation tests for financial time series analysis, and show the advantages of this class of tests in unifying and generalizing Box–Pierce-type Granger causality tests. We also conduct a Monte Carlo simulation to show the validity of our tests, and provide an empirical example to demonstrate the flexibility of our tests in exploring various types of Granger causality.
Application of machine learning algorithm in porosity calculation

Reservoir porosity is an important parameter to reflect reservoir performance. At present, the logging data with high correlation with core analysis porosity and porosity are usually selected to build a multiple linear regression model to calculate reservoir porosity. Some formation porosity information may be lost due to the ignorance of logging data with low correlation, and the multicollinearity of the variables may lead to the instability of the regression model when using logging data for multivariate comprehensive analysis and increase the calculation error . In view of the above problem, this paper comprehensive reaction formation acoustic, electric, properties of well logging data, using principal component regression (PCR) algorithm, Levenberg-Marquardt (LM) algorithm to optimize the BP neural network and multiple linear regression, respectively, on the porosity in Q4 target calculation, the results showed that LM - BP neural network, PCR and the relative error of the multiple linear regression were 7.10%, 10.63% and 14.99% respectively.
Comparative Study of Inertia Support for Networks with High Renewable Energy Penetration

Low inertia has become a great concern for the power grid with high inverter-based resources (IBR), such as solar and wind power generation systems. Therefore, synchronous condensers have been used to enhance the inertia level of the system. However, only a limited number of synchronous condensers can be installed due to their high costs. Thus, grid-forming (GFM) inverters have been proposed as a promising alternative method to provide frequency support with simulated inertia to achieve 100% renewable penetration in the future. In order to provide a cost-effective adoption strategy for frequency support, a comparative study between synchronous condensers and GFM inverters has been performed through extensive simulation studies. The simulation results show that both strategies can reduce the rate-of-change-of-frequency (RoCoF) and enhance the frequency stability, and the combination methods with both synchronous condenser and GFM inverters can achieve a cost-effective transition plan for the bulk grid.
A powerful transformation trimmed mean method for one-way fixed effects ANOVA model under non-normality and inequality of variances

The present study proposes a transformation trimmed mean method for the one-way e xed effects ANOVA model to deal with non-normality and inequality of variances. First, the data are trimmed on both sides. Then the test statistic is transformed by Johnson’ s method for each group to deal with non-normality. Finally, an approximate test such as the Alexander‐ Govern test, the James second-order test or the Welch test is applied to test the equality of population trimmed means. Monte Carlo simulation results show that, under non-normal cases, the proposed method can control Type I error rates, and has greater power than other competitors, such as the original Alexander‐ Govern method, the Alexander‐ Govern trimmed mean method, the James second-order trimmed mean method, the Welch trimmed mean method, as well as the corresponding bootstrapping methods. The Johnson’ s transformation trimmed mean method can be programmed easily and is highly recommended as an alternative for the one-way e xed effects ANOVA model if normality or equality of variances cannot be assumed.
A classification of second‐order raising operators for Hamiltonians in two variables

We develop a group theoretic method based on results of Winternitz et al. to compute and classify all first‐ and second‐order raising and lowering operators admitted by Hamiltonians of the form H¯=−(1/2)Δ2+V(x,y). The key to our results, which generalize to higher dimensions, is a proof that H¯ admits a second‐order raising operator only if the Schrodinger equation separates in Cartesian, polar, or elliptic coordinates.
Fast Multilevel Mental Stress Identification From Bispectrum-Based Heart Rate Variability Feature

Fast and accurate identification of mental stress is critical to reducing its risk to human health and the related consequences in industry. This study proposed a bispectrum-based feature to improve the accuracy and reduce the latency of multilevel mental stress identification from heart rate variability (HRV). The performance was evaluated by two stress induction protocols, i.e., Stroop color-word interference test and mental arithmetic calculation, and the results demonstrated that the bispectrum-based methods significantly outperformed traditional HRV features with windows as short as 15 s. The high-order components found by an established nonlinear test, and the strong feature correlations detected between short and long windows, further validated the effectiveness and the feasibility of the proposed methods on mental stress identification from supershort windows. The results had the potential to promote the applications of HRV-based mental stress management across industries with the Internet of Medical Things devices.
Gravitational instabilities in a dust-gas layer and formation of planetesimals in the solar nebula

Dust particles which are initially distributed homogeneously in the solar nebula aggregate and settle towards the equatorial plane. As a result, the solar nebula separate into three layers: an equatorial thin dust-gas layer and two residual gas layers. We study gravitational in­ stabilities in the dust-gas layer on the assumption of axial symmetry, taking account of motions perpendicular to as well as parallel to the equatorial plane and also making use of proper boundary conditions between the dust-gas layer and the gas layers. As far as radii of dust particles are sufficiently small (:S 1 cm at 1 au), a dust fluid and a gas fluid are firmly coupled owing to the drag force; consequently, they behave like one fluid. It is also found that the dust-gas layer behaves like an incompressible fluid owing to high pressure of the gas layers exerted on the dust-gas layer. Taking these conditions into account, a mode of gravitational instability accompanied with a motion perpendicular to the equatorial plane is newly found. If unstable wavelengths of the largest growing rate for non-axisymmetric and axisymmetric perturbations are the same, typical masses of planetesimals are 4 x 10 17
Design of Free-Space Optical Wavelength-Division Demultiplexers with a Phase-Only SLM

In this paper, a free-space wavelength-division demultiplexer utilizing single hologram displayed on a phase-only liquid crystal spatial light modulator is designed. The method combines different independently calculated 2π-holograms of each wavelength into a multi-level hologram utilizing the extended phase range of the spatial light modulator. The algorithm has low complexity, and can be employed to generate holograms that guide laser at different wavelengths to corresponding target devices with high speed. In combination with tracking technology, this method can be future applied in free space optics (FSO) link with wavelength division multiplexed (WDM) scheme for solving crosstalk problem.
Overview of the Blanket Comparison and Selection Study

A comprehensive Blanket Comparison and Selection Study was conducted to evaluate proposed D-T fusion reactor blanket concepts and to identify those concepts that offer the greatest potential for fusion reactor applications. The multilaboratory study was led by Argonne National Laboratory and included support from thirteen industrial, national and university laboratories; six primary subcontractors and seven specialized contributors. The primary objectives of the program were (1) to identify a small number (approx. 3) of the blanket concepts that should be the focus of the blanket R and D program, (2) to define and prioritize the critical issues for the leading blanket concepts, and (3) to provide technical input for development of blanket R and D programs. A blanket concept is generally defined by the selection of the component materials, viz., breeder, coolant, structure, and neutron multiplier, and specification of the geometrical configuration. Blanket concepts were evaluated for both the tokamak and tandem mirror reactor configurations using the STARFIRE and MARS reactor designs as a basis, with appropriate modifications to reflect recent advances in technology.
Ultrafast (370 GHz bandwidth) p-i-n traveling wave photodetector using low-temperature-grown GaAs

The authors demonstrate p-i-n traveling wave photodetectors utilizing low-temperature-grown GaAs as the absorption layer. The electro-optically measured impulse response was found to exhibit a pulsewidth of 1.1 ps full width at half maximum, corresponding to a −3 dB bandwidth of 370 GHz with an external quantum efficiency of 8% at 800 nm.
Antiviral Lipid Nanocarrier Loaded with Remdesivir Effective Against SARS-CoV-2 in vitro Model

Introduction The ongoing SARS-CoV-2 pandemic has affected public health, the economy, and society. This study reported a nanotechnology-based strategy to enhance the antiviral efficacy of the antiviral agent remdesivir (RDS). Results We developed a nanosized spherical RDS-NLC in which the RDS was encapsulated in an amorphous form. The RDS-NLC significantly potentiated the antiviral efficacy of RDS against SARS-CoV-2 and its variants (alpha, beta, and delta). Our study revealed that NLC technology improved the antiviral effect of RDS against SARS-CoV-2 by enhancing the cellular uptake of RDS and reducing SARS-CoV-2 entry in cells. These improvements resulted in a 211% increase in the bioavailability of RDS. Conclusion Thus, the application of NLC against SARS-CoV-2 may be a beneficial strategy to improve the antiviral effects of antiviral agents.
Analysis of geomagnetic hourly ranges

In an attempt to develop better forecasts of geomagnetic activity, hourly ranges of geomagnetic data are analyzed with a focus on how the data are distributed. A lognormal distribution is found to be able to characterize the magnetic data for all observatories up to moderate disturbances with each distribution controlled by the mean of the logarithm of the hourly range. In the subauroral zone, the distribution deviates from the lognormal, which is interpreted as motion of the auroral electrojet toward the equator. For most observatories, a substantial deviation from the lognormal distribution was noted at the higher values and is best modeled with a power law extrapolation, which gives estimates of the extreme values that may occur at observatories which contribute to the disturbance storm time (Dst) index and in Canada.
Robust Evaluating Approach of Distributed Photovoltaic Hosting Capacity in Uncertain Environment

Large scale distributed photovoltaic(DPV) integration will bring new challenges to dispatching due to its uncertainty, and further increase the regulating capacity requirement of power system. As a result, a robust evaluating approach of DPV hosting capacity in uncertain environment is proposed. Based on the relationship between DPV hosting capacity and its conditional value at risk(CVaR), a robust optimization model is constructed, which considering the probability distribution characteristics of DPV. The base point and regulating capacity of automatic generation control(AGC) units are coordinated optimized to accurately evaluate the DPV hosting capacity The original model is transformed into a deterministic linear programming problem using piecewise linear approximation and uncertain variables eliminating. The effectiveness of the proposed approach is verified through a 6-bus system.
VR for automobile customization and its feedback analysis

One of the main agendas behind any business is understanding their customer's views about their products. In the proposed model text analysis is performed on the feedback which is given on the VR model of a car customization application. This application is built in Unity with help of its XR toolkit. The text analysis is done using NLTK and Scikit learn. This text analysis will be based on multiple options provided by the VR model. This analysis will help automobile companies to know better about their customer's opinions. Customers will also be able to customize their cars with the help of the features provided in the application. Text analysis covers the summarization of the feedback form that is provided to the customers.
A COMPARISON OF THE PROPERTIES OF IRT PARAMETER ESTIMATES USING TWO DIFFERENT CALIBRATION DESIGNS

The purpose of this study was to compare two different methods of obtaining 3PL IRT pretest item parameter estimates for the Graduate Management Admissions Testing Program. The first method consisted of calibrating pretest and operational items simultaneously in a LOGIST run, that is, a concurrent calibration design. The second approach entailed analyzing the pretest items separately from the operational items holding examinee ability scores constant from a previous operational items run, that is, using a two-stage calibration design. Results show that the means of the item difficulty (b-parameter) estimates were very similar, regardless of the method employed. However, the higher b-parameter values using the two-stage calibration run method (i.e. holding ability fixed excluding the studied items from the criterion) were slightly overestimated and the lower b-parameter values were slightly underestimated. The a-parameters were consistently underestimated using the two-stage estimation procedure. Finally, the slopes of the item-ability regressions using a concurrent calibration (including the studied items) are steeper for nearly all of the items. These preliminary results are consistent with those reported in past studies (Stocking & Eignor, 1986) and suggest that non-operational (pretest) items should be calibrated concurrently with operational items for item banking purposes.
Prediction of Positive Cloud-To-Ground Lightning Striking Zones for Tilted Thundercloud Based on Line Charge Model

Bushfire is known as one of the ascendant factors to create pyrocumulus thundercloud that causes the ignition of new fires by pyrocumulonimbus (pyroCb) lightning strikes mostly of positive polarity, and causes massive damage to nature and infrastructure. A conceptual model-based risk planning would be beneficial to predict the lightning striking zones on the surface of the earth underneath the pyroCb thundercloud. In this paper, a simple line charge structured thundercloud model is constructed in 2-D coordinates using the method of images to predict the probable +CG (positive cloud-to-ground) lightning striking zones on the earth's surface for tilted dipole thundercloud charge configuration. The electric potential distribution and ground surface charge density for tilted dipole thundercloud is investigated via continuously adjusting the position and charge density of its charge regions. Simulation results confirm the initiation of negative charged density for the wind shear extension of upper positive charge region by 2 to 8 km, and would expect +CG lightning to strike within 7.88 to 20 km around the earth periphery particularly in the direction of the cloud's forward flank. The proposed model would serve as the foundation to identify the probable lightning affected area as well as can also be extended to analyze the hazardous situation appears in wind energy farms or agricultural fencing situated nearby the power grid during pyroCb events.