Estimation Of Coefficients Research Articles

Ultrasound backscatter coefficient for fat quantification is affected by the measurement depth.

It has been reported that the estimate of ultrasound attenuation coefficient (AC) is affected by depth of measurement, with linear decrease of values with depth. It is unknown whether backscatter coefficient (BSC) has similar behavior. This retrospective study was performed with Sequoia US system equipped with ultrasound derived fat fraction (UDFF) algorithm (Siemens Healthineers, Issaquah, WA, USA) that combines BSC with AC. UDFF was obtained positioning upper edge of the region of interest at 1.5,2,3,4,5cm below liver capsule. BSC data were extracted from UDFF offline. A fractional polynomial regression, which selects the best model considering the polynomial development of the variables of interest, was used. Covariates included were age, sex, skin-to-liver-capsule distance, stiffness. Distance was included as linear factor or with a power ranging from -2 to 3, and the best fitting model was chosen according to partial F test. Body mass index (BMI) was not included because of collinearity with skin-to-liver capsule distance. 104 individuals (56 females; age: 57.9 ± 13.0years; BMI: 29.0 ± 6.5kg/m2; skin-to-liver-capsule distance: 2.3 ± 0.7cm; liver stiffness: 7.5 ± 5.5 kiloPascal) were studied. Best fitting model for BSC included a combination of depth as linear factor and square root. BSC showed a decrease of -13.98dB/cm-steradian for each logarithmic increase of 1cm depth (coefficient: -13.98; 95% CI: -21.016; -5.379; p = .001). Skin-to-liver-capsule distance and stiffness also were independent predictors of BSC. The estimation of the BSC in the liver exhibits a depth dependence that significantly affects results. A standardized acquisition protocol is needed to compare results and reliably assess changes over time.

Estimation of Pollution Export Coefficients of Tea Farms and Its Application in Watershed Management

Tea is an important economic crop worldwide, especially in Asian countries. However, tea cultivation requires substantial fertilizer use and may become a nutrient pollution source and affect water quality. This study presented two objectives: one was to estimate the pollution export coefficients of tea farms, and the other was to assess the performance of bioretention cells in terms of tea farm pollution control. This study employed a tea farm pollutant transport model (TPTM) and a watershed pollutant transport model (WPTM) to link watershed management goals and the tea farm control strategy. Field data collected for Jingualiao Creek in the Feitsui Reservoir watershed in Taipei, Taiwan, were analyzed. The resulting export coefficients for total phosphorus (TP), NH3-N, suspended solids (SS), and chemical oxygen demand (COD) were 2.55, 4.22, 768.39, and 145.71 kg/ha-y, respectively. Bioretention cells, which are low-impact development (LID) facilities and structural best management practices (BMPs), were installed and tested for their ability to reduce nonpoint source pollution. The field investigation and modeling results showed that 1 m2 of bioretention cells could reduce TP, NH3-N, SS, and COD by 18.6, 20.9, 5545.5, and 881.4 g/y, respectively. According to the WPTM results, 540 m2 of bioretention cells could achieve an 85% water quality attainment goal, and 715 m2 could reach 90% water quality attainment. Four tea farms covering 1.43 ha require 30.0 m2 of bioretention cells to achieve an 85% goal and 33.5 m2 to 90% goal. The export coefficients of tea nonpoint pollution sources presented in this study can serve as a valuable tool for estimating potential exported nutrients, and the field test results of bioretention cells are helpful information for policymakers in formulating effective watershed management measures.

Effectiveness of nonpharmaceutical policy interventions in reducing population mobility during the COVID-19 pandemic

Abstract Nonpharmaceutical policy interventions (NPIs) are intended to reduce population mobility in mitigating the spread of COVID-19. This paper evaluates their effect on population mobility during the COVID-19 pandemic. State space models are applied to estimate the time-varying effects of NPI stringency on weekly pedestrian counts from location-based sensors installed before the pandemic. Different models are developed that evaluate compliance with NPIs over time, identify the most effective NPI, and identify regional differences. An efficient parsimonious alternative is proposed for the multivariate Seemingly Unrelated Time Series Equation model if full covariance matrices are of full rank. Kalman filter estimates of the regression coefficients show that NPI stringency initially had a negative effect on population mobility. The effect weakened during the pandemic, suggesting a reduced willingness to comply with regulations. Four of nine NPIs were identified as the most effective. The multivariate model confirmed the findings across federal states. This paper highlights how combining new data sources, routinely collected administrative data, and sound methodology fosters modern policy evaluation.

Estimation of bandwidth, voltage swing and DC coefficients for VLSI interconnects: current mode technique

ABSTRACT Necessity of bandwidth requires the effective use of it for high speed data transmission and faster read/write operation in memory cells. In this paper the transfer function expressions for RC interconnect line is presented for resistive load. The line delay, bandwidth and step response for current and voltage mode signalling is formulated. The current mode and voltage mode signalling provides the 3-dB bandwidth of 6.8 GHz and 3.1 GHz, respectively. An interconnect line modelled as either RLC or RC line depending upon input signal rise time, damping, and range of interconnect length with significant inductance effect is investigated for global interconnect lines. Low swing for column bit line in memory architectures is very important and estimated in terms of dc coefficients. For RC global interconnect of 10 mm, a swing of 0.382 V is obtained for current mode signalling, when a power supply of 1.8 V is applied to the network. DC coefficient and steady state value has been obtained which is significant for power computation on column bit line. It is also analytically validated by SPICE and MATLAB simulations.

Estimation of sound absorption coefficient at modal frequencies using decay time measurements and eigenvalue model in a small room

Measuring the sound absorption coefficient in a small reverberation room below the Schroeder frequency is a challenging task and the results are subject to a high degree of uncertainty. Recent research shows that the low-frequency acoustic properties of the enclosure and the test sample should be characterized by quantities that describe the modal sound field. The aim of this paper is to present a method for estimating the absorption coefficient of a test sample at modal frequencies in the low frequency range and to demonstrate its applicability in a small room. The hybrid inverse approach is based on eigenvalue measurement data coupled with a finite element model to estimate the sound-absorbing properties of a small room and the test sample. The method was tested experimentally and the results were consistent with those of the long impedance tube method. The applicability of the proposed method was further demonstrated by the in-situ characterization of different acoustic samples in the low frequency range.

Evaluation of Genetic Variability for Yield Improvement in Bitter Gourd (Momordica charantia L.) Genotypes

The degree of genetic variability found in any particular germplasm is a major factor in determining the best breeding program or technique to be used. Exploitation of the natural genetic variability present within a crop species can aid in meeting the rising demand through the identification and modification of the adaptive and productive genes present. Breeders thus identify natural genetic variability as the key to crop improvement. The present investigation was undertaken at the Experimental field of Urban Technological Park Habbak, Srinagar, Jammu and Kashmir during kharif-2022. To investigate several aspects of genetic variability, including mean, range, PV, GV, PCV, GCV, heritability, genetic gain, and genetic advance among the genotypes, the experiment was set up in a Randomized Block Design with three replications and a plant spacing of 2×1 m for thirty genotypes. For every character under study, it was discovered that the estimates of the phenotypic coefficient of variation were marginally greater than the respective genotypic coefficient of variation, indicating a possible involvement of environment in the expression of these traits. Fruit yield hectare-1 (q) exhibited the largest genotypic and phenotypic coefficient of variation (39.788 and 38.970). For yield hectare-1 (q), high heritability and high genetic gain (0.95 and 78.62) were observed. This suggests that additive gene effects are most likely the cause of the heritability and increases the likelihood that this characteristic would be fixed by selection. For every characteristic, the estimates of broad sense heritability were high. All these factors help in selection of better parents for the development of commercial varieties/hybrids. Considering the potential nutritional and economic benefits of bitter gourd (Momordica charantia L.), there is an imperative necessity to isolate such breeding lines having desirable traits, high yield potential along with better quality.

A comparative study on hydrocarbon detection using cepstrum–based methods

Amplitude anomaly caused by a high–frequency component is attenuated more rapidly than a low–frequency component for a seismic trace in sediments susceptible to gas influence and is always used as a direct indicator of hydrocarbons. Recently, cepstrum–based hydrocarbon detection methods have become an important exploration pathway for detecting amplitude anomalies in the gas–bearing formation more sensitively and accurately. In this study, we compared three cepstrum–based hydrocarbon detection methods. We compared their fluid identification abilities in gas reservoirs and the noise robustness. They were further compared with the traditional absorption coefficient estimation method. We also indicate how the choice of the selection of the sliding–window length influences the performance of the three cepstrum–based hydrocarbon detection methods. Model test results and real data applications show that for thick gas reservoirs, the hydrocarbon detection methods using the Berthil–based cepstrum and the wavelet–based cepstrum can better discriminate the upper and lower interfaces than the hydrocarbon detection method using the Fourier–based cepstrum. For thin gas reservoirs, the hydrocarbon detection method using the wavelet–based cepstrum can identify the lower interface of the gas–bearing reservoir more obviously than the upper interface. The hydrocarbon detection method using the Berthil cepstrum cannot identify the upper and lower interfaces of the gas–bearing reservoir, but it shows higher temporal and spatial resolution compared to the hydrocarbon detection method using the Fourier–based cepstrum. For noise robustness, the hydrocarbon detection method using the wavelet–based cepstrum has the strongest noise robustness property, and the hydrocarbon detection method using the Fourier–based cepstrum is the most sensitive method to the noise. For time–consumption, the hydrocarbon detection method using the wavelet–based cepstrum is the most time–consuming, and the hydrocarbon detection method using the Fourier–based cepstrum is the fastest.

Unlocking genetic insights: Evaluating wheat RILs for physiobiochemical traits under terminal heat stress conditions

BackgroundThe increasing impacts of heat stress on wheat production due to climate change has entailed the development of heat-resilient crop varieties. To address this, two hundred recombinant inbred lines (RILs) derived from a cross between WH711/WH1021 were evaluated in a randomized block design (RBD) with two replications at CCSHAU, Hisar, during 2018-19 under heat stress and non-stress conditions. Heat stress was induced by altering the date of sowing so that the grain filling stage coincide with heat stress.ResultsHeat stress adversely affects RILs performance, as illustrated by alterations in phenotypic traits. Highest coefficients of variations were recorded for TAA, CTD 1, WUE, CTD 2, Cc and A under non-stress and heat stress conditions whereas gs, WUEi and GY under non-stress and SPAD 1, SPAD 2, GY and NDVI 2 under heat-stress conditions recorded moderate estimates of coefficient of variations. CTD 2, TAA, E, WUE and A displayed a significant occurrence of both high heritability and substantial genetic advance under non-stress. Similarly, CTD 2, NDVI 2, A, WUEi, SPAD 2, gs, E, Ci, MDA and WUE exhibited high heritability with high genetic advance under heat-stress conditions.ConclusionsComplementary and duplicate types of interactions with number of controlling genes were observed for different parameters depending on the traits and environments. RILs 41, 42, 59, 74, 75, 180 and 194 were categorized as heat tolerant RILs. Selection preferably for NDVI 1, RWC, TAA, A, E and WUEi to accumulate heat tolerance favorable alleles in the selected RILs is suggested for development of heat resilient genotypes for sustainable crop improvement. The results showed that traits such as such as NDVI, RWC, TAA, A, E, and WUEi, can be effective for developing heat-resilient wheat genotypes and ensuring sustainable crop improvement.

Meta-Analysis of Coefficient Alpha: Empirical Demonstration Using English Language Teaching Reflection Inventory

Cronbach’s alpha is a reliability coefficient commonly reported in second language (L2) and English language teaching (ELT) studies. The alpha coefficient provides information on the internal consistency of a measuring instrument. The reported alpha coefficients are obtained from, and apply only to, the research sample. However, the estimation of the alpha coefficient for the population has not received the attention of L2 and ELT researchers. This study aims to provide an overview of the alpha coefficient estimation procedure of a measuring instrument for a population with the reliability generalization method, commonly known as alpha coefficient meta-analysis. An example alpha coefficient meta-analysis study—using empirical data of the 29-item English Language Teaching Reflection Inventory (ELTRI) from 27 independent study samples—was conducted to provide an overview of the procedure for applying the method and the information that needs to be reported from the results of the analysis. The results of the study using a random-effect model show that the population alpha of ELTRI was 0.872, indicating excellent reliability; this is followed by application of a mixed-effect model that shows that article type and means of teaching experience significantly impacted ELTRI reliability. Implications for future research are discussed.

Discharge coefficient estimation of modified semi-cylindrical weirs using machine learning approaches

Discharge coefficient estimation of modified semi-cylindrical weirs using machine learning approaches

Application of Oversampling Techniques for Enhanced Transverse Dispersion Coefficient Estimation Performance Using Machine Learning Regression

The advection–dispersion equation has been widely used to analyze the intermediate field mixing of pollutants in natural streams. The dispersion coefficient, manipulating the dispersion term of the advection–dispersion equation, is a crucial parameter in predicting the transport distance and contaminated area in the water body. In this study, the transverse dispersion coefficient was estimated using machine learning regression methods applied to oversampled datasets. Previous research datasets used for this estimation were biased toward width-to-depth ratio (W/H) values ≤ 50, potentially leading to inaccuracies in estimating the transverse dispersion coefficient for datasets with W/H > 50. To address this issue, four oversampling techniques were employed to augment the dataset with W/H > 50, thereby mitigating the dataset’s imbalance. The estimation results obtained from data resampling with nonlinear regression method demonstrated improved prediction accuracy compared to the pre-oversampling results. Notably, the combination of adaptive synthetic sampling (ADASYN) and eXtreme Gradient Boosting regression (XGBoost) exhibited improved accuracy compared to other combinations of oversampling techniques and nonlinear regression methods. Through the combined ADASYN–XGBoost approach, it is possible to enhance the transverse dispersion coefficient estimation performance using only two variables, W/H and bed friction effects (U/U*), without adding channel sinuosity; this represents the effects of secondary currents.

Comparative Analysis of Bilinear Time Series Models with Time-Varying and Symmetric GARCH Coefficients: Estimation and Simulation

This paper makes a significant contribution by focusing on estimating the coefficients of a sample of non-linear time series, a subject well-established in the statistical literature, using bilinear time series. Specifically, this study delves into a subset of bilinear models where Generalized Autoregressive Conditional Heteroscedastic (GARCH) models serve as the white noise component. The methodology involves applying the Klimko–Nilsen theorem, which plays a crucial role in extracting the asymptotic behavior of the estimators. In this context, the Generalized Autoregressive Conditional Heteroscedastic model of order (1,1) noted that the GARCH (1,1) model is defined as the white noise for the coefficients of the example models. Notably, this GARCH model satisfies the condition of having time-varying coefficients. This study meticulously outlines the essential stationarity conditions required for these models. The estimation of coefficients is accomplished by applying the least squares method. One of the key contributions lies in utilizing the fundamental theorem of Klimko and Nilsen, to prove the asymptotic behavior of the estimators, particularly how they vary with changes in the sample size. This paper illuminates the impact of estimators and their approximations based on varying sample sizes. Extending our study to include the estimation of bilinear models alongside GARCH and GARCH symmetric coefficients adds depth to our analysis and provides valuable insights into modeling financial time series data. Furthermore, this study sheds light on the influence of the GARCH white noise trace on the estimation of model coefficients. The results establish a clear connection between the model characteristics and the nature of the white noise, contributing to a more profound understanding of the relationship between these elements.

Prediction of the flow resistance in non-prismatic compound channels

ABSTRACT Achieving an accurate estimation of the flow resistance in open channel flows is crucial for resolving several critical engineering difficulties. In instances when there is excessive flow on both banks of a river, it results in the breach of the primary channel, leading to the discharge of water into the adjacent floodplain. The alteration of floodplain geometry occurs as a consequence of agricultural and developmental practises, leading to the emergence of compound channels that exhibit converging, diverging, or skewed characteristics throughout the course of the flow. The efficacy of conventional equations in accurately forecasting flow resistance is limited due to their heavy reliance on empirical approaches. As a result of this phenomenon, there persists a significant need for methodologies that possess both novelty and precision. The objective of this work is to use the support vector machine (SVM) technique for the estimation of the Manning's roughness coefficient in a compound channel with converging floodplains. Statistical indicators are used to validate the constructed models in the experimental investigation, enabling the assessment of their performance and efficacy. The findings indicate a significant correlation between the Manning's roughness coefficient predicted by SVM and both experimental data and prior research outcomes.

Estimation of Multiple Contact Conductances in a Silicon-Indium-Silicon Stack

Estimation of Multiple Contact Conductances in a Silicon-Indium-Silicon Stack

Research on the economic agents' green co-evolution of China: Framework, model, and application

Based on the idea of green coordinated development and green co-evolution, the system of green co-evolution composed of government, financial institutions, enterprises, and the public is constructed, using Functional Data Analysis combined with CRITIC weighting, Dagum Gini coefficient and Kernel density estimation to measure the green co-evolution level (GCEL) and the regional disparity based on data of 31 provinces and 992 enterprises from 2010 to 2021, it finds: (1) At the turning point of 2015, the GCEL has been from declining to rising, and the poor performance of enterprises and the public is main factors. (2) The GCEL of regions shows a distinct non-balanced development trend, with the middle- and low-level provinces accounting for the largest proportion. Moreover, the rising of Gini coefficient means that provincial synergy is weaker. (3) Since 2012, the disparity between eight comprehensive economic zones has increased, mainly due to the polarization of the performance of the main players, such as the rising gap in the allocation of government and financial resources. Regional differences have always been dominant, with the largest disparity between Northwest China and the three coastal economic zones. (4) The total distribution tends to converge to lower level, but has no significant convergence. From the perspective of green co-evolution, this paper measures the green co-evolution level, temporal and spatial evolution, and regional disparities, providing a reference for promoting regional green coordination and sustainable development.

The impact of foreign participation on risk-taking in Chinese commercial banks: The co-governance role of equity checks and foreign supervision

This study examines how foreign equity participation affects the risk-taking of commercial banks using Chinese bank-level data from 2012 to 2020. The results indicate that foreign equity participation significantly reduces bank risk, and there is a U-shaped relationship between the foreign shareholding ratio and bank risk. Mechanism analysis reveals that augmenting foreign ownership strengthens shareholder equilibrium and boosts the presence of foreign directors. This structure enhances dual governance mechanisms, lowering the risk in banking activities. Considering that confounding factors affecting mediator and dependent variables can cause bias in coefficient estimation, we used machine learning methods to determine the best predictive factors affecting bank risk-taking. We indirectly verified the theoretically driven empirical model by evaluating more explanatory variables and selecting subsets of explanatory variables. In addition, we find that governance enhancements are particularly pronounced in Chinese banks, characterized by smaller scale and less income diversification. Additionally, foreign ownership significantly impacts banks' market risk more than credit and operational risks. Our results imply that China's financial regulatory authorities need to shift the focus of supervision to examining foreign shareholding ratio and corporate governance ability.

Effect of temperature sampling methods on sphere packed tube heat transfer coefficient determination

This paper investigates the heat transfer characteristics and the pressure drop of a sphere packed tube using Computational Fluid Dynamics (CFD) simulation. The turbulent kinetic energy of the fluid exhibits significant variation based on the differing accumulation structures of the particles, which is deemed to greatly influence the heat transfer characteristics. Consequently, the temperature distribution at the outer wall becomes inhomogeneous. Previous studies have overlooked the relative positioning between the thermocouples and the particles, potentially leading to inaccurate estimations of the heat transfer coefficient and discrepancies between CFD simulations and real-world experiments. To address this, two temperature sampling methods are employed in this paper to estimate the heat transfer coefficients: one method involves measuring the temperatures at various sampling positions, while the other uses the surface average temperature of the packed area as the sample temperature. Interestingly, it is found that different temperature sampling methods significantly impact the heat transfer coefficients. This study serves as a reminder for future research on sphere packed tubes to prioritize temperature sampling positions.

The neural network approach for estimation of heat transfer coefficient in heat exchangers considering the fouling formation dynamic

Routine maintenance for plate heat exchanger (PHE) cleaning improves the effectiveness of heat exchange network operation. Until recently, complex mathematical modelling was used to predict the value of the heat transfer coefficient after a certain period of operation of the heat exchanger, as well as the point in time when the coefficient reached the allowable limit. The applied mathematical tools included the systems of differential equations, matrices of heuristic coefficients, which needed a lot of computer resources. This paper offers an artificial neural network (ANN) technique for forecasting the following values: heat transfer coefficient at any time points during the operating period of PHEs; the time point, when the heat transfer coefficient reaches its lower permitted value. In this method, ANN uses the fuzzy logic techniques to expand the set of training parameters for the model, working with data of industrial measurements and data obtained from the mathematical modelling of the process. It allowed to train the developed feed-forward neural network (FFNN) with the coefficient of determination R2 equal to 0.99 and can predict the thermal resistance in PHE based on measurement data. To adequately predict the time-point to reach the limiting value of the heat transfer coefficient, it was proposed a recurrent neural network (RNN) with a hidden layer of long short-term memory (LSTM), where R2 value came up to 0.89.

Enhancing rectangular side weir discharge prediction using stacking technique

In this paper, the impact of different variables on discharge coefficients in sharp-crested rectangular side weirs was investigated utilizing 559 experimental data points. Various methodologies, including De Marchi, Traditional weir equation (TWE), Domínguez, adjusted Domínguez, and Schmidt procedures, were employed for the analysis. Novel equations were developed to predict the discharge coefficients with greater accurately, surpassing the predictive capabilities of previous studies' equations. Additionally, three supervised machine learning algorithms, namely extreme gradient boosting (XGBoost), Light gradient boosting machine (LightGBM), and support vector regression (SVR), were employed to enhance the precision of discharge coefficient estimation. Among these algorithms, the XGBoost model exhibited superior performance compared to LightGBM and SVR models for forecasting discharge coefficients. Furthermore, using a stacking technique, hybrid models were constructed by combining XGBoost, LightGBM, and SVR. The hybrid models showed enhanced performance and generalization capacities than the individual models. Notably, the HYBRID5 model, specifically designed for estimated the Schmidt coefficient, achieved outstanding statistical indices (R2 = 0.995, MSE = 1.378, MRPE = 3.73 %) for calculating discharge of rectangular side weirs.

Predicting perceived quality of life through social trust, physical activity, and sense of happiness in Iran: Moderating role of gender.

In Iran, few studies have addressed the moderating effect of gender on determinants of quality of life. This study aimed to determine the effect of social trust, physical activity, and sense of happiness on the perceived quality of life, considering the moderating role of gender. This was a population-based cross-sectional study. The statistical population were men and women aged 16 years and above in five western provinces of Iran. The final sample size was estimated to be 1268 people calculated through the correlation coefficient estimation formula, and finally 1185 questionnaires received. Data were analyzed using SPSS and AMOS software. The mean score of quality of life among women was significantly higher than that of men (p < 0.001). The direct standardized coefficient showed that the sense of happiness (β = 0.40), social trust (β = 0.20), and physical activity (β = 0.12) had a significant effect on the perceived quality of life (p < 0.001). Also, gender had a moderating role in the relationship between sense of happiness and perceived quality of life (z-score = 3.246, p < 0.001). Totally, 21% of the changes in perceived quality of life were explained by three main variables. The main variables were stronger explanatory factors in men (R = 0.26) than in women(R = 0.17). The final model showed that sense of happiness had the most direct effect on the perceived quality of life, moderated by gender. Considering that the quality of life among men was lower than that among women and the direct effect of happiness on quality of life was more among men than that among women, it is suggested that gender-based health promotion interventions with an emphasis on men be carried out to strengthen the sources of happiness to improve quality of life.