Spectral Coefficients Research Articles

Raman spectroscopy as a tool for monitoring osteoporosis therapy in postmenopausal osteoporosis

AbstractBisphosphonate‐based pharmacological therapy of osteoporosis reduces risk of fracture, but modulation of bone mineral density does not solely explain this. Additional bone quality aspects affecting fragility need to be better understood, alongside methods to monitor them. Systemic factors that influence bone collagen remodelling also remodel keratin in parallel processes. In this study, human fingernail clippings from subjects with and without bone active pharmaceutical intervention are compared. A discriminant model was able to distinguish between the nails from patients that were treatment‐naive and treated with an area under the curve of 71% in the test set. A time series of changes relative to baseline revealed that after 1 year, the scores of the treated group (95 confidence interval 37% to 377% change) differed from both the 12 week measurements (−198% to 34%) and scores from untreated subjects (−92% to −674%). Analysis of the spectral differences and model coefficients revealed features that were inverse to those observed in three previous osteoporosis models, indicating that treatment was reverting damaged protein structure. This study provides preliminary evidence that bone active medication systemically influences keratin structure in humans and provides some discussion on the underlying mechanism. The study demonstrates that bisphosphonates have a direct influence on protein structure, warranting further investigation of these effects.

Fuzzy inference system model for the spectrum site coefficients of the Türkiye Building Earthquake Code (2018)

The response spectra in the seismic design codes guide the seismic design force calculations that are crucial for structural safety and construction costs. In general, they are determined by crisp logic-based numerical classifications of seismic parameters, which primarily affect the shape and values of the spectrum. Crisp logic response spectra generation may have limitations in the accurate determination of relevant parameters. In this paper, a fuzzy inference system (FIS) rule-based model is proposed to address this issue. The proposed model uses spectral acceleration coefficients and shear wave velocity as inputs to generate the site coefficients of the response spectra according to the Türkiye Building Earthquake Code (TBEC 2018). The FIS model spectra generation is compared with the crisp logic model for thirty-five examples, and the model performance is evaluated. The study demonstrates significant differences between the two models in terms of the response spectra' shape and acceleration spectrum intensity values, but the FIS model provides comparatively better accuracy in response spectra generation. The proposed model can be modified and applied to various parameters for generating response spectra in different seismic design codes with the exposition of fuzzy logic rules that show the concerned problem's internal working mechanism.

A study on teaching English in higher education based on an improved deep belief network

Abstract This paper combines machine learning with acoustic features to design an automatic pronunciation error correction system. The article first adopts Meier’s inverse spectral coefficients and random forest algorithm to classify and detect learners’ pronunciation errors and clarify learners’ pronunciation problems, from which the MFCC-RF model is proposed. Then, using the feature self-learning capability of deep belief networks and the OneClass idea of SVM, we proposed a DBN-SVM model to overcome the shortcomings of the MFCC-RF model in pronunciation classification and error detection due to unbalanced samples and missing data, which resulted in low error detection rate and poor coverage of error types. By comparing the model’s performance for pronunciation error detection, the DBN-SVM model was more accurate than the other two algorithms in detecting the three error types with a stable accuracy of around 80%. Finally, when the experimental class was taught with the automatic pronunciation error correction system, the experimental class improved by 19.5 points after one semester of study, while the control class only improved by 6.8 points. Hence, the DBN-SVM model-based pronunciation mistake correction system has significantly impacted the speed of change and advancement in English teaching techniques while substantially enhancing the quality of oral pronunciation and learning efficiency of English learners.

A space-time pseudospectral method for solving multi-dimensional quasi-linear parabolic partial differential (Burgers') equations

The viscous Burgers' equation is a kind of quasi-linear important partial differential equation appeared in many fields. The nonlinear term contained in this equation results in difficulty in finding its high-precision numerical solution. In this paper, a space-time pseudospectral method for solving multi-dimensional Burgers' equations is proposed based on the Lagrange polynomials. The method is employed in time and space both at Chebyshev- Gauss- Lobbato(CGL) points. The spectral coefficients are found in such a way that the residual becomes minimum. The given problem is reduced to a system of nonlinear algebraic equations, which is solved by Newton-Raphson's method. Error bounds on discrete L2-norm and Sobolev norm (Hp) are presented. The computational experiments are carried out to corroborate the theoretical results and to compare the present method with existing methods in the literature. Moreover, some model examples of multi-dimensional Burgers' equations are tested and a detailed comparison of the proposed method with various other methods is also given. When using the proposed method, some interesting phenomenon appears with the coefficient of kinematic viscous.

Analysis of corticomuscular-cortical functional network based on time-delayed maximal information spectral coefficient

Objective. The study of brain networks has become an influential tool for investigating post-stroke brain function. However, studies on the dynamics of cortical networks associated with muscle activity are limited. This is crucial for elucidating the altered coordination patterns in the post-stroke motor control system. Approach. In this study, we introduced the time-delayed maximal information spectral coefficient (TDMISC) method to assess the local frequency band characteristics (alpha, beta, and gamma bands) of functional corticomuscular coupling (FCMC) and cortico-cortical network parameters. We validated the effectiveness of TDMISC using a unidirectionally coupled Hénon maps model and a neural mass model. Main result. A grip task with 25% of maximum voluntary contraction was designed, and simulation results demonstrated that TDMISC accurately characterizes signals’ local frequency band and directional properties. In the gamma band, the affected side showed significantly strong FCMC in the ascending direction. However, in the beta band, the affected side exhibited significantly weak FCMC in all directions. For the cortico-cortical network parameters, the affected side showed a lower clustering coefficient than the unaffected side in all frequency bands. Additionally, the affected side exhibited a longer shortest path length than the unaffected side in all frequency bands. In all frequency bands, the unaffected motor cortex in the stroke group exerted inhibitory effects on the affected motor cortex, the parietal associative areas, and the somatosensory cortices. Significance. These results provide meaningful insights into neural mechanisms underlying motor dysfunction.

Modelling the spectral influence on photovoltaic device performance using the average photon energy and the depth of a water absorption band for improved forecasting

Accurate forecasts of solar panel performance can improve grid penetration, enable cost evaluation prior to project implementation, and improve fault detection during operation. Spectral correction functions (SCFs) used to model the influence of the solar spectrum in such forecasting models are typically based on either proxy representations of the spectrum, using parameters such as air mass, or parameters derived directly from the spectrum, such as the average photon energy (APE). Although the latter is more accurate, the APE is argued in some studies not to be a unique characteristic of the spectrum and to suffer from increased uncertainty when analysing spectra at longer wavelengths. This study first derives APE spectral correction function coefficients for three PV technologies — multicrystalline (mSi), triple junction amorphous silicon (aSi-T), and cadmium telluride (CdTe). Based on an analysis of uncertainty in the SCF for each of the three devices, this study proposes a new spectral correction function based on the average photon energy, φ, and the depth of a water absorption band, ɛ. The additional index enables spectra to be characterised by unique combinations of φ and ɛ. Several water absorption bands are tested and the 650–670 nm band is found to yield the most accurate SCF for all three PV devices. An optimal parameterisation of the SCF for each PV device, as well as a cost-accuracy-balanced parameterisation, is presented. Improvements in the prediction accuracy of up to 60% for both the mSi and aSi-T modules, and around 20% for the CdTe module, are achieved by the proposed model with respect to a comparable two-variable proxy SCF, namely the air mass and precipitable water function. Compared with the single-variable APE SCF, f(φ), the proposed model improves the prediction accuracy by around 10% for the aSi-T and mSi modules, and by around 2% for the CdTe module. No new data are required for the proposed model compared with f(φ) as the same spectra used to calculate the APE are used to calculate ɛ. The proposed spectral correction function can easily be integrated into wider photovoltaic performance models for improved forecasting.

Light Absorption by Optically Active Components in the Arctic Region (August 2020) and the Possibility of Application to Satellite Products for Water Quality Assessment

In August 2020, during the 80th cruise of the R/V “Akademik Mstislav Keldysh”, the chlorophyll a concentration (Chl-a) and spectral coefficients of light absorption by phytoplankton pigments, non-algal particles (NAP) and colored dissolved organic matter (CDOM) were measured in the Norwegian Sea, the Barents Sea and the adjacent area of the Arctic Ocean. It was shown that the spatial distribution of the three light-absorbing components in the explored Arctic region was non-homogenous. It was revealed that CDOM contributed largely to the total non-water light absorption (atot(λ) = aph(λ) + aNAP(λ) + aCDOM(λ)) in the blue spectral range in the Arctic Ocean and the Barents Sea. The fraction of NAP in the total non-water absorption was low (less than 20%). The depth of the euphotic zone depended on atot(λ) in the surface water layer, which was described by a power equation. The Arctic Ocean, the Norwegian Sea and the Barents Sea did not differ in the Chl-a-specific light absorption coefficients of phytoplankton. In the blue maximum of phytoplankton absorption spectra, Chl-a-specific light absorption coefficients of phytoplankton in the upper mixed layer (UML) were higher than those below the UML. Relationships between phytoplankton absorption coefficients and Chl-a were derived by least squares fitting to power functions for the whole visible domain with a 1 nm interval. The OCI, OC3 and GIOP algorithms were validated using a database of co-located results (day-to-day) of in situ measurements (n = 63) and the ocean color scanner data: the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra (EOS AM) and Aqua (EOS PM) satellites, the Visible and Infrared Imager/Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (S-NPP) and JPSS-1 satellites (also known as NOAA-20), and the Ocean and the Land Color Imager (OLCI) onboard the Sentinel-3A and Sentinel-3B satellites. The comparison showed that despite the technological progress in optical scanners and the algorithms refinement, the considered standard products (chlor_a, chl_ocx, aph_443, adg_443) carried little information about inherent optical properties in Arctic waters. Based on the statistic metrics (Bias, MdAD, MAE and RMSE), it was concluded that refinement of the algorithm for retrieval of water bio-optical properties based on remote sensing data was required for the Arctic region.

Investigation of Plasma Parameters Using OES and Spectra Simulations

The radiation from the arc plasma ignited between the flat ends of copper electrodes in atmospheric pressure air at a direct current of 3.5 A is analyzed to determine the plasma temperature and composition. Side-on spectra in the range (430 – 650) nm are recorded using a CMOS camera connected to a spectrograph. The spectral emission coefficient is evaluated from the spectral radiance, which provides the radiator number density. The excitation temperature is obtained by applying the Boltzmann plot technique to at least two spectral lines and a series of radial positions in the midplane of the arc. Subsequently, the plasma composition is determined. The equation of radiative transfer is solved along lines of sight considered in the experiment. The spectral intensity of Cu I lines is computed and compared with the experimental ones. The arc properties are obtained by assuming axial symmetry and mapping the evaluated values in the midplane.

Spectral Heat Transfer Coefficient for convection

The present work is aimed at developing a methodology to predict convective heat transfer of flow bounded by a non-isothermal wall, which has been a long-standing issue of interest for Newton's law of cooling. A framework is proposed to centre around the Spectral Heat Transfer Coefficients (SHTC). They are effectively global-to-local temperature-heat flux influence coefficients for a small number of low order spectral modes of wall temperature disturbances for a range of physically relevant and numerically resolvable length scales. The framework is implemented with a unified Fourier spectral method applicable to both spatially periodic and non-periodic wall temperature distributions. The Fourier spectral formulation also leads to the zeroth harmonic base mode corresponding to exactly the standard form of Newton's law of cooling, thus the method is directly applicable to both non-isothermal and isothermal cases, as intended. The method formulations in both 2D and 3D settings and the working procedure are presented. All computational case studies for 2D and 3D configurations clearly and consistently demonstrate the validity and effectiveness of the proposed framework methodology and method implementation.

Quantitative analysis of pattern formation in a multistable model of glycolysis with diffusion

Motivated by the problem of uncertainty quantification in self-organization, we study a spatially extended Sel’kov–Strogatz model of glycolysis. A variety of coexisting patterns induced by the Turing instability is studied in the parametric zones where the original local model without diffusion exhibits stable equilibria or self-oscillations. A phenomenon of the suppression of homogeneous self-oscillations by diffusion with formation of stationary non-homogeneous patterns-attractors is revealed. To quantify the uncertainty in the number and modality of patterns-attractors and to perform an advanced parametric analysis, we use the spectral coefficients technique and Shannon entropy.

A structural model of teachers’ teaching competencies based on multimodal affective features of support vector machines

Abstract Based on big data technology, this paper first proposes to study and analyze the structural model of teachers’ teaching ability based on multimodal sentiment features of support vector machines. Then the Mel inverse spectral coefficients, wavelet packet coefficients, Fourier coefficient features, dynamic features, and global features are extracted respectively, and the features are dimensionalized using linear support vector machine and Lagrangian function, and the dimensionalized feature parameters are sent to the classifier for emotion recognition. Finally, the structural model evaluation index system of teachers’ teaching ability is constructed, and the current teaching ability development of college teachers in a province is studied and analyzed from two perspectives of teachers’ self-assessment and students’ evaluation based on multimodal emotional features of support vector machine. The results showed that in terms of teaching ability dimensions, teachers had the highest expectation of their teaching ability (4.38) in each dimension, followed in order by teachers’ self-evaluation of teaching ability (4.15), students’ expectation (4.05), and students’ evaluation of teachers’ teaching ability (3.56) was the lowest. This study reveals that teachers’ perceptions of the teaching competencies they should have are biased, and their awareness of self-development is insufficient from the emotional characteristics.

Global distribution of the spectral power coefficient of particulate backscattering coefficient obtained by a neural network scheme

The spectral power coefficient (η) of the particulate backscattering coefficient (bbp(λ)) is directly estimated from the remote sensing reflectance (Rrs(λ)) with a neural network-based scheme (NNη) in this study. Evaluations with both synthetic dataset and in-situ measurements show that NNη could significantly improve the accuracy of estimated η compared to several conventional schemes reported in the literature that are based on chlorophyll-a concentration (Chl), band ratios of Rrs(λ), or remotely sensed bbp(λ). Demonstrations with measurements from MODerate resolution Imaging Spectroradiometer (MODIS) Aqua further confirm the robustness of NNη, where reasonable spatial distribution and seasonality of η in the global oceans can be acquired by NNη. High and low η values are observed in the oligotrophic gyres and the coastal zones, respectively, which are consistent with the current understanding of η distribution concluded from theoretic analysis and repeated field measurements in the global ocean. Implementation of NNη to 19-year MODIS monthly composite measurements from 2003 to 2021 reveals strong seasonal variations of η in most of the global ocean, but the decadal changes of η are insignificant in the majority (∼82.2%) of the global ocean. Similar to any empirical algorithms, the performance of NNη is dependent on the training dataset, particularly its range, here a proper upper limit of η for natural waters is provided.

Solving nonlinear equations with the convex combination of two positive spectral coefficients

We present a spectral algorithm based on the convex combination of two modified spectral coefficients for solving systems of nonlinear equations. The proposed algorithm does not require the exact or approximated directional derivative for its implementation. By employing a derivative-free line search, the global convergence of the sequence generated by the algorithm is supported. Numerical experiments are given to demonstrate the performance of the algorithm compared with a similar algorithm in the literature for solving nonlinear equations problems.

Determination of the polarization plane specific rotation in gyrotropic crystals of the middle category by the spectrophotometric method

A large number of modern functional single crystals of the middle category belong to gyrotropic media. In these crystals, when light propagates along the optical axis, rotation of the plane of its polarization is observed. In this work a spectrophotometric method was used to obtain the dispersion dependences of the rotation angle of the polarization plane. This method is based on measuring the intensity of light passing through the polarizer–crystal–analyzer system, the crystal is a polished plane-parallel plate of a uniaxial gyrotropic crystal cut perpendicular to the optical axis. Measurements were carried out on a UV-Vis-NIR spectrophotometer Cary-5000 in the wavelength range of 200—1200 nm using polarizers — Glan–Taylor prisms. Polished plane-parallel plates of known SiO2 and α-LiIO3 crystals were used as samples. The obtained dispersion dependences of the spectral transmission coefficients are oscillating. Discrete values of the specific angles of rotation of the plane of polarization of light are calculated from the extremes on these dependencies. These discrete values can be approximated by the formulas Drude, Chandrasekhar and Vyshina, depending on what determines the nature of the rotational ability of the plane of polarization of light in each particular material. For the studied crystals, dependences of the modified Drude formula of the form 1/ρ = f(λ2) are plotted, these dependences should have a linear character in the case of an ideal crystal. The obtained experimental results correlate well with the available literature data. The advantages of this method are efficiency, the possibility of obtaining dispersion dependences of the specific rotation angle of the polarization plane, the need for a single sample, the possibility of assessing the nature of the rotational ability of specific crystals, the possibility of evaluating the structural perfection of the studied crystals.

Overlapping peak separation algorithm for ion mobility spectra based on multistrategy JAYA.

In the field of separation science, ion mobility spectrometry (IMS) plays an important role as an analytical tool. However, the lack of sufficient structural resolution is a common problem in qualitative and quantitative analysis using IMS. A method is needed to solve the problem of overlapping peaks caused by insufficient resolution. The method uses multiple strategies to more effectively use population information to balance exploration and exploitation capabilities, prevent local optimization, accurately resolve overlapping peaks, quickly obtain optimal spectral peak model coefficients, and accurately identify compounds. Multistrategy JAYA algorithm's (MSJAYA) performance is compared with improved particle swarm optimization (IPSO), dynamic inertia weight particle swarm optimization (DIWPSO), and multiobjective dynamic teaching-learning-based optimization (MDTLBO). The analysis shows that MSJAYA's maximum separation error is within 0.6%, a level of accuracy not guaranteed by the other algorithms. In addition, the separation error fluctuates within a much smaller range, demonstrating MSJAYA's superior robustness. Compared with other overlapping peak separation algorithms, MSJAYA is more applicable because no special parameters are used. The method allows fast deconvolution analysis of strong overlapping peaks with multiple components, which greatly improves the resolution of IMS.

Temperature Evaluation of a Building Facade with a Thin Plaster Layer under Various Degrees of Cloudiness

In this paper, we investigate the surface temperature of a wall with a facade heat-insulating composite system (FHIC), which has a thin plaster layer, taking into account solar radiation exposure at different degrees of cloudiness during the month. The object of study is a wall with FHIC, on the outer surface of which temperature sensors were mounted and measurements were taken. Air temperatures were also measured for one month of the warm period of the year. The coefficient of absorption of solar radiation by the surface of the facade is calculated based on the measurement of the spectral reflection coefficient. Measurements of direct and scattered solar radiation arriving on a horizontal surface were carried out, and the cloudiness of the sky was also recorded. The calculation of direct and scattered solar radiation was carried out, taking into account the shading of surrounding buildings using the authors’ novel methods. The experimental days were divided into three groups according to the degree of cloudiness; statistically significant differences between the groups for the studied parameters were demonstrated. The temperature of the outer surface of the wall was calculated according to A.M. Shklover’s formula. The measured values of the temperature of the outer surface of the wall were compared with the calculated ones. It was shown that there is a good correlation between the measured and calculated temperatures for different degrees of cloudiness. At the same time, for days with no or slight cloudiness (Group I), when direct solar radiation predominates, the differences reach 1.7 °C; smaller differences are observed for days with average cloudiness (Group II) during daytime hours, with a maximum difference of 0.5 °C; and on days with continuous cloudiness (Group III), when only scattered radiation is present for daytime hours, the maximum difference is 0.3 °C. Statistically significant differences were found between the measured and calculated temperatures for groups of days, divided by the degree of cloudiness, for the experimental period of a day from 10 a.m. to 5 p.m., which indicates the possibility of considering amendments to A.M. Shklover’s formula for sunny days. The results of comparing the measured and calculated heating temperatures of the facade surface also indirectly confirm the correctness of the author’s calculations of the incoming solar radiation, taking into account the effect of the surrounding buildings. The results obtained can be used to study the inertia and durability of building structures under solar radiation.

Application of informative textural Law’s masks methods for processing space images

<span lang="EN-US">Image processing systems are currently used to solve many applied problems. The article is devoted to the identification of negative factors affecting the growth of grain in different periods of harvesting, using a program implemented in the MATLAB software environment, based on aerial photographs. The program is based on the Law’s textural mask method and successive clustering. This paper presents the algorithm of the program and shows the results of image processing by highlighting the uniformity of the image. To solve the problem, the spectral luminance coefficient (SBC), normalized difference vegetation index (NDVI), Law’s textural mask method, and clustering are used. This approach is general and has great potential for identifying objects and territories with different boundary properties on controlled aerial photographs using groups of images of the same surface taken at different vegetation periods. That is, the applicability of sets of Laws texture masks with original image enhancement for the analysis of experimental data on the identification of pest outbreaks is being investigated.</span>

Radial dependence diagnosis of inductively coupled Ar plasma based upon optical emission spectroscopic measurement with spectral reflectance compensation

This study proposed a simple method to evaluate the spectral reflectance of the inner wall of a vacuum chamber. A method for calculating spectral emission coefficients by taking the spectral reflectance of the chamber inner wall into account was proposed. Furthermore, plasma diagnosis based on optical emission spectroscopic (OES) measurement was performed so as to obtain radial dependence of electron temperature Te and density Ne of a radio frequency inductively coupled Argon (Ar) plasma by applying a collisional-radiative model to radially resolved emission spectra of the Ar plasma assuming axial symmetry. In addition, Langmuir probe measurement and electromagnetic simulation were performed and compared with the OES-based plasma diagnosis results. The spectral radiance compensation improved the diagnostic result by 0.6% and 3.1% for Te and Ne, respectively.

A Filter-Based Approach for the Measurement of Mass Absorption Coefficient of OC and EC Components in Soot

ABSTRACT There is a general consensus that soot produced by hydrocarbon combustion consists of two components, elemental carbon associated with the graphitic nanostructure and organic carbon. Despite the relevance of evaluating their radiative properties, particularly for organic carbon, the knowledge of the spectral absorption behavior remains limited. In this work, a dual-beam five wavelengths absorption photometer is used to estimate the optical properties of particulate matter generated by a laminar premixed ethylene-air flame at different residence times along the flame. The wavelength dependence of the light absorption of aerosols collected on filters is analyzed in terms of both the optical band gap and the absorption Ångström exponent, AAE. The aerosols are then analyzed in terms of the organic and elemental carbon content by a thermal-optical OC-EC analyzer. Combining the results of light absorption and OC-EC analysis, the spectral mass absorption coefficient of both the organic carbon and elemental carbon components in soot are obtained.

Assessing the Performance of a Speech Recognition System Embedded in Low-Cost Devices

The main purpose of this research is to investigate how an Amazigh speech recognition system can be integrated into a low-cost minicomputer, specifically the Raspberry Pi, in order to improve the system's automatic speech recognition capabilities. The study focuses on optimizing system parameters to achieve a balance between performance and limited system resources. To achieve this, the system employs a combination of Hidden Markov Models (HMMs), Gaussian Mixture Models (GMMs), and Mel Frequency Spectral Coefficients (MFCCs) with a speaker-independent approach. The system has been developed to recognize 20 Amazigh words, comprising of 10 commands and the first ten Amazigh digits. The results indicate that the recognition rate achieved on the Raspberry Pi system is 89.16% using 3 HMMs, 16 GMMs, and 39 MFCC coefficients. These findings demonstrate that it is feasible to create effective embedded Amazigh speech recognition systems using a low-cost minicomputer such as the Raspberry Pi. Furthermore, Amazigh linguistic analysis has been implemented to ensure the accuracy of the designed embedded speech system.