Statistical F-test Research Articles

Determining blood platelet morphology modelled by a superellipsoid from the solution of the inverse light-scattering problem

In this work, we propose a new optical model to describe the morphology of a platelet in the form of a special axisymmetric superellipsoid. This model is more general than the widely used classical oblate spheroid model of the platelet and includes the oblate spheroid as a special case. The inverse light scattering (ILS) problem within the developed optical model was solved using the light scattering profile (LSP) of individual platelets obtained by the scanning flow cytometer. Solving the ILS problem we used a hybrid method that combines the DIRECT algorithm of global optimization with the linear interpolation between the nodes of the pre-calculated regular database of superellipsoid LSPs. The statistical F-test shows that the new optical model is better than the conventional oblate spheroid model in more than 60 percent of cases. This made it possible to obtain the refined shape index distributions for platelets consisting of three fractions, namely resting, partially and fully activated platelets.

Algorithmic Pulsar Timer for Binaries

Pulsar timing is a powerful tool that, by accounting for every rotation of a pulsar, precisely measures the spin frequency, spin frequency derivatives, astrometric position, binary parameters when applicable, properties of the interstellar medium, and potentially general relativistic effects. Typically, this process demands fairly stringent scheduling requirements for monitoring observations as well as deep domain knowledge to “phase connect” the timing data. We present an algorithm that automates the pulsar-timing process for binary pulsars, whose timing solutions have an additional level of complexity, although the algorithm works for isolated pulsars as well. Using the statistical F-test and the quadratic dependence of the reduced χ 2 near a minimum, the global rotation count of a pulsar can be determined efficiently and systematically. We have used our algorithm to establish timing solutions for two newly discovered binary pulsars, PSRs J1748−2446aq and J1748−2446at, in the globular cluster Terzan 5, using ∼70 Green Bank Telescope observations from the last 13 yr.

Respiratory motion prediction based on deep artificial neural networks in CyberKnife system: A comparative study.

In external beam radiotherapy, a prediction model is required to compensate for the temporal system latency that affects the accuracy of radiation dose delivery. This study focused on a thorough comparison of seven deep artificial neural networks to propose an accurate and reliable prediction model. Seven deep predictor models are trained and tested with 800 breathing signals. In this regard, a nonsequential-correlated hyperparameter optimization algorithm is developed to find the best configuration of parameters for all models. The root mean square error (RMSE), mean absolute error, normalized RMSE, and statistical F-test are also used to evaluate network performance. Overall, tuning the hyperparameters results in a 25%-30% improvement for all models compared to previous studies. The comparison between all models also shows that the gated recurrent unit (GRU) with RMSE=0.108±0.068mm predicts respiratory signals with higher accuracy and better performance. Overall, tuning the hyperparameters in the GRU model demonstrates a better result than the hybrid predictor model used in the CyberKnife VSI system to compensate for the 115ms system latency. Additionally, it is demonstrated that the tuned parameters have a significant impact on the prediction accuracy of each model.

Pengaruh Likuiditas, Profitabilitas, Dan Solvabilitas Terhadap Kebijakan Dividen Pada Perusahaan Otomotif Yang Terdaftar Di Bursa Efek Indonesia Periode 2016-2020

Effect of Liquidity, Profitability, and Solvency on Dividend Policy in Automotive Companies Listed on the Indonesia Stock Exchange (IDX) 2016-2020. Under the guidance of Drs. Bambang Sudarsono, MM.This study aims to determine the effect of liquidity, profitability, and solvency variables on dividend policy in automotive companies listed on the Indonesia Stock Exchange (IDX) in 2015-2020.The approach used in this research is quantitative research. The number of samples in this study were 7 companies. The data analysis technique used in this research is multiple linear regression analysis, with hypothesis testing statistical t-test and statistical f-test.Based on the research that has been done, the results show that liquidity and profitability have a significant positive effect on dividend policy, while solvency has no effect on dividend policy, but liquidity, profitability and solvency have a simultaneous positive and significant effect on dividend policy.

Function-Wise Dual-Omics analysis for radiation pneumonitis prediction in lung cancer patients.

Purpose: This study investigates the impact of lung function on radiation pneumonitis prediction using a dual-omics analysis method. Methods: We retrospectively collected data of 126 stage III lung cancer patients treated with chemo-radiotherapy using intensity-modulated radiotherapy, including pre-treatment planning CT images, radiotherapy dose distribution, and contours of organs and structures. Lung perfusion functional images were generated using a previously developed deep learning method. The whole lung (WL) volume was divided into function-wise lung (FWL) regions based on the lung perfusion functional images. A total of 5,474 radiomics features and 213 dose features (including dosiomics features and dose-volume histogram factors) were extracted from the FWL and WL regions, respectively. The radiomics features (R), dose features (D), and combined dual-omics features (RD) were used for the analysis in each lung region of WL and FWL, labeled as WL-R, WL-D, WL-RD, FWL-R, FWL-D, and FWL-RD. The feature selection was carried out using ANOVA, followed by a statistical F-test and Pearson correlation test. Thirty times train-test splits were used to evaluate the predictability of each group. The overall average area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and f1-score were calculated to assess the performance of each group. Results: The FWL-RD achieved a significantly higher average AUC than the WL-RD group in the training (FWL-RD: 0.927 ± 0.031, WL-RD: 0.849 ± 0.064) and testing cohorts (FWL-RD: 0.885 ± 0.028, WL-RD: 0.762 ± 0.053, p < 0.001). When using radiomics features only, the FWL-R group yielded a better classification result than the model trained with WL-R features in the training (FWL-R: 0.919 ± 0.036, WL-R: 0.820 ± 0.052) and testing cohorts (FWL-R: 0.862 ± 0.028, WL-R: 0.750 ± 0.057, p < 0.001). The FWL-D group obtained an average AUC of 0.782 ± 0.032, obtaining a better classification performance than the WL-D feature-based model of 0.740 ± 0.028 in the training cohort, while no significant difference was observed in the testing cohort (FWL-D: 0.725 ± 0.064, WL-D: 0.710 ± 0.068, p = 0.54). Conclusion: The dual-omics features from different lung functional regions can improve the prediction of radiation pneumonitis for lung cancer patients under IMRT treatment. This function-wise dual-omics analysis method holds great promise to improve the prediction of radiation pneumonitis for lung cancer patients.

Ogden material calibration via magnetic resonance cartography, parameter sensitivity and variational system identification.

Contemporary material characterization techniques that leverage deformation fields and the weak form of the equilibrium equations face challenges in the numerical solution procedure of the inverse characterization problem. As material models and descriptions differ, so too must the approaches for identifying parameters and their corresponding mechanisms. The widely used Ogden material model can be comprised of a chosen number of terms of the same mathematical form, which presents challenges of parsimonious representation, interpretability and stability. Robust techniques for system identification of any material model are important to assess and improve experimental design, in addition to their centrality to forward computations. Using fully three-dimensional displacement fields acquired in silicone elastomers with our recently developed magnetic resonance cartography (MR-u) technique on the order of greater than [Formula: see text], we leverage partial differential equation-constrained optimization as the basis of variational system identification of our material parameters. We incorporate the statistical F-test to maintain parsimony of representation. Using a new, local deformation decomposition locally into mixtures of biaxial and uniaxial tensile states, we evaluate experiments based on an analytical sensitivity metric and discuss the implications for experimental design. This article is part of the theme issue 'The Ogden model of rubber mechanics: Fifty years of impact on nonlinear elasticity'.

Algorithmic Pulsar Timing

Abstract Pulsar timing is a process of iteratively fitting pulse arrival times to constrain the spindown, astrometric, and possibly binary parameters of a pulsar, by enforcing integer numbers of pulsar rotations between the arrival times. Phase connection is the process of unambiguously determining those rotation numbers between the times of arrival while determining a pulsar timing solution. Pulsar timing currently requires a manual process of step-by-step phase connection performed by individuals. In an effort to quantify and streamline this process, we created the Algorithmic Pulsar Timer (APT), an algorithm that can accurately phase connect and time isolate pulsars. Using the statistical F-test and knowledge of parameter uncertainties and covariances, the algorithm decides what new data to include in a fit, when to add additional timing parameters, and which model to attempt in subsequent iterations. Using these tools, the algorithm can phase-connect timing data that previously required substantial manual effort. We tested the algorithm on 100 simulated systems, with a 99% success rate. APT combines statistical tests and techniques with a logical decision-making process, very similar to the manual one used by pulsar astronomers for decades, and some computational brute force, to automate the often tricky process of isolated pulsar phase connection, setting the foundation for automated fitting of binary pulsar systems.

Assessment of Rainfall and Temperature Variability on Maize Production in Kaduna State

Rainfall and temperature are important requirements in crop production. Any variation in the distribution of these elements have marked influence on the productivity of most crops including maize. This study assessed rainfall and temperature variability on maize production from 1999 to 2016 in Kaduna State, Nigeria. Data for this study were obtained mostly from secondary sources. These data include temperature, rainfall and maize production which were collected from Nigerian Meteorological Agency (NiMet), National Bureau of Statistic (NBS) and the Kaduna State Agricultural Development Projects (KADP). Results are presented and discussed by the use of mean, mean departures, regression and correlation. To ascertain the annual trend in the meteorological parameters, a statistical t-test was carried out at the 1 and 5% levels of significance. An increasing trend in rainfall and temperature was observed with early onset and cessation dates although only temperature was significant at the 1 and 5% level. To ensure uniformity in the analysis, the average annual yield per 100,000 hectares for each of the years were determined. Thereafter a multiple linear regression (MLR) model, that include maize yield as the dependent variable and the meteorological parameters, such as annual rainfall, average annual temperature, onset and cessation of rainfall as the dependent variables was developed using least square approach. Its overall significance was tested using the statistical f-test at the 1, 5 and 10% levels of significance and found to be significant. On that basis, each of the climatic parameter was tested and temperature and rainfall were found to be significant contributors to maize production. A model for predicting maize production was then developed and it was significant at 95 and 99 % confidence level. The computed co-efficient of determination and linear correlation were 74.2% and 0.86. The study recommends the use of the model developed to determine maize yield before the production season so as to equip farmers with fore knowledge of the expected yield and make alternative adjustments were necessary.

Quantitative characterization on fatigue fracture features of A6005 aluminum alloy welded joints

Fatigue performance is important for materials and welded joints in engineering structures. A fatigue test of A6005 aluminum alloy welded joints was conducted. When confidence level was 50%, fatigue lives were reliable for reliability levels of 50% and 97.7%. Statistical F-test evaluation proved that the linear fatigue stress-life model was rational. Metallic oxides or discrete bar-like materials led to crack nucleation. Fractal dimension characterized the fracture features, initially increased and then decreased at all stress levels. However, crack propagation rates were inversely proportional to fractal dimension. Further, fractal dimension integration equalled the fatigue life. A linear relationship was established with a good R2 obtained 0.978.

Single-Drop Analysis of Epinephrine and Uric Acid on a Screen-Printed Carbon Electrode.

This work demonstrates the analysis of epinephrine (EP) and uric acid (UA) in a single drop (the volume of the test solution was only 50 µL) using a screen-printed carbon electrode (SPCE) sensor and square-wave voltammetry (SWV). The limit of detection, limit of quantification, linearity, accuracy, precision, and robustness were validated. The normality of the experimental data was tested and confirmed for both methods. Heteroscedasticity was checked by residual analysis followed by a statistical F-test. The latter was confirmed for both analytes. The low relative standard deviations (RSD) at all calibration points and repetitive slopes justified the use of a calibration curve; therefore, the standard addition methodology was avoided (the latter is common in electroanalysis, but time-consuming). Since the conditions for using an ordinary least squares (OLS) regression were not met, weighted linear regression (WLR) was used to improve the accuracy of the analytical results at low concentrations of the analytes. In this manner, the best weighted model was determined and used for the quantification. A comparison was made between the OLS and WLR methods to show the necessity of using the WLR method for EP and UA analysis. The newly developed and validated methods were also shown to be effective in the analysis of real samples. The content of EP in an EP auto-injector and UA in human urine was tested by employing the best weighted model. For EP and UA, the accuracy in terms of the average recovery value was 101.01% and 94.35%, and precision in terms of RSD was 5.65% and 2.75%, respectively. A new analytical methodology is presented that uses a low volume (a single drop), and it offers the advantage of electroanalysis for on-site analysis, where conventional chromatographic techniques cannot be easily employed. Furthermore, the developed technique has additional advantages in terms of speed, cost, and miniaturization.

Validated quantitative 19F-NMR technique for the determination of ticagrelor: A novel method for fluorochemicals detection

Accurate, simple and reproducible quantitative fluorine nuclear magnetic resonance spectroscopy (19F-NMR) method for the determination of ticagrelor was developed and validated. Some proton signals of ticagrelor in the low field split into multiple or became broad peaks for the coupling of fluorine atom, which complicates the analysis using routinely 1H NMR method. However, this phenomenon did not occur to 19F signals and 19F-NMR method was successfully applied to the content determination of ticagrelor with ciprofloxacin as internal standard (IS) in 0.5 mL mixed solutions of deuterated dimethylsulfoxide (DMSO‑d6), deuteroxide (D2O) and dimethylsulfoxide (DMSO) (1.5:1.5:2, v/v/v). 1H NMR method was also adopted to the content determination to clarify whether the multiple or broad peaks had influences on the results or not. Statistical F-test and student t-test at 95% confidence level were performed and results revealed that there were insignificant differences in terms of precision and average content between 19F-NMR and 1H NMR methods, demonstrating that both 19F-NMR and 1H NMR methods were suitable to the determination of ticagrelor so long as parameters were optimized.

ASCORBIC ACID DETERMINATION IN FRESH AND COMMERCIAL FRUIT JUICES BY DIFFERENTIAL STRIPPING VOLTAMMETRIC TECHNIQUE AT A GLASSY CARBON ELECTRODE

Ascorbic acid, also known as Vitamin C cannot be synthesized by humans. Ascorbic acid is commonly found in a variety of vegetables and fruits such as mangoes, oranges, broccolis and lettuce. Hence, vegetables and fruits become the main sources of ascorbic acid to meet dietary intake. The differential pulse anodic stripping voltammetry (DPASV) technique using glassy carbon electrode (GCE) as a working electrode and phosphate buffer at pH 4.2 as a supporting electrolyte has been proposed for ascorbic acid determination in natural and commercial fruit juices. The optimum instrumental conditions for electroanalytical determination of ascorbic acid by the proposed DPASV technique were initial potential (Ei) = 0 V, end potential (Ef)= 0.8 V, accumulation time (tacc) = 60 s, scan rate (v) = 0.125 V/s and pulse amplitude = 0.150 V. The anodic peak appeared at 0.3598 V. The curve was linear from 0.028 to 1.703 mM (R2=0.9999) with a detection limit of 0.0114 mM. The precisions in terms of relative standard deviation (RSD) were 1.30%, 0.50% and 0.06%, respectively. The ruggedness of the proposed DPASV technique was tested with statistical F-test. Satisfactory recoveries ranging from 73.65±1.70% to 101.93±1.65% were obtained for three different known concentrations of AA in the fruit juice samples. It can be concluded that the proposed technique is precise, accurate, rugged, low cost, fast and has the potential to be an alternative method for routine analysis of ascorbic acid in natural and commercial fruit juices.

Determinants of the Commercial Bank’s Efficiency in ASEAN

The purpose of this study is to analyze the influence of capitalization, bank size, bank age, and loan to asset ratio (LAR) to bank efficiency in ASEAN-5 countries (Singapore, Indonesia, Thailand, Malaysia, and the Philippines). Net interest margin (NIM) and non-net interest income (Non-NIM) were used as control variables. There was a total of 58 banks used as a sample using a purposive sampling technique. There were two stages of the analytical method used: data envelopment analysis (DEA) approach – to provide estimates of bank efficiency, and multiple regression linear – consists of the statistical F-test and t-test, coefficient of determination (R2) test and the classic assumption test. The results show that capitalization and bank age affect bank efficiency negatively, while bank size and LAR affect bank efficiency positively. The banks are suggested to consider optimizing their capital to continue to operate efficiently, increase their assets to be more efficient, the older banks are expected to be able to adjust to technological developments, and the banks are also expected to increase the amount of credit by monitoring its quality to be efficient.

A Complex Stiffness Human Impedance Model With Customizable Exoskeleton Control.

The natural impedance, or dynamic relationship between force and motion, of a human operator can determine the stability of exoskeletons that use interaction-torque feedback to amplify human strength. While human impedance is typically modelled as a linear system, our experiments on a single-joint exoskeleton testbed involving 10 human subjects show evidence of nonlinear behavior: a low-frequency asymptotic phase for the dynamic stiffness of the human that is different than the expected zero, and an unexpectedly consistent damping ratio as the stiffness and inertia vary. To explain these observations, this article considers a new frequency-domain model of the human joint dynamics featuring complex value stiffness comprising a real stiffness term and a hysteretic damping term. Using a statistical F-test we show that the hysteretic damping term is not only significant but is even more significant than the linear damping term. Further analysis reveals a linear trend linking hysteretic damping and the real part of the stiffness, which allows us to simplify the complex stiffness model down to a 1-parameter system. Then, we introduce and demonstrate a customizable fractional-order controller that exploits this hysteretic damping behavior to improve strength amplification bandwidth while maintaining stability, and explore a tuning approach which ensures that this stability property is robust to muscle co-contraction for each individual.

A Causal Model of the Sustainable Use of Resources: A Case Study on a Woodworking Process

Controlling the life cycle of natural resources, from extraction within the design and the production of products to handling waste, is crucial to green growth and is a part of advancing a resource-efficient, circular economy where everything is fully utilised. One way of using resources more efficiently for a greener economy is to design a production process that takes cost and energy savings into account. From this point of view, the goal of the article is to create a causal description of sustainable woodworking—especially using renewable and non-renewable resources—in relation to changes in the concentration levels of CO2 in the atmosphere. After estimating the partial parameters, this model can be used to predict or simulate different CO2 concentration levels in the atmosphere—for example, based on the ratio of renewable to non-renewable sources. After a theoretical description, the subsequent practical goal is to identify the optimal settings of wood-milling process parameters for either minimising energy consumption per workpiece and unit variable costs or for maximising the overall customer benefit. For this purpose, a complete factorial design was used, and based on this, the consumption energy (direct cost) optimisation of the production process was supplemented by a profitable production calculation. The effect of reducing variability was verified using a statistical F-test. The impact of minimising energy consumption (economically expressed as the mean profit) was then validated using a Student’s t-test.

Pengaruh Reliability, Responsiveness dan Emphaty Terhadap Customer Loyality Pada Parahyangan Golf Club Bandung

This research was conducted at Parahyangan Golf Club Bandung. The purpose of this study was to determine the relationship of Reliability, Responsiveness, and Emphaty on Customer Loyalty both simultaneously and partially. The method used in this research uses associative descriptive method. This study uses 99 as a research sample using multiple linear regression analysis. Testing the hypothesis in this study using the statistical F-test and t-test. The statistical analysis process uses the SPSS for Windows program. Based on the results of the study, the influence of Reliability, Responsiveness, and Emphaty on Customer Loyalty at Parahyangan Golf Club Bandung conducted with the analysis of the coefficient of determination has an influence meaning that Reliability, Responsiveness, and Emphaty have an effect on Customer Loyalty is influenced by other factors not examined by the authors.

Pengaruh Periklanan dan Harga Jual Terhadap Penjualan Produk Air Minum (Pada PT. Segara Bumi Persada)

This study aims to analyze whether advertising and selling prices have an effect on product sales. The hypothesis proposed in this study is that advertising and selling prices have a significant effect on product sales partially or simultaneously.The method used in this research is to do literature and field research. While the analysis model used is multiple regression analysis, multiple correlation coefficient analysis, multiple determination coefficient analysis, t-test statistical analysis and f-test hypothesis testing analysis and the method uses the SPSS-21 assistance program.Results Based on the research, it can be seen that the effect of Advertising and Selling Prices on Product Sales, either partially or simultaneously has a positive and significant effect. Keywords: Advertising, Selling Price, Product Sales

Boundary conditions for modeling the ground thermal profile near shallow culverts

Abstract Culverts create disturbance in the ground thermal regime in cold regions below pavement structures. The thermal disturbance caused by culverts may result in geotechnical problems such as permafrost degradation, frost heave, and differential settlement which impact road roughness and road safety. Thermal numerical modeling of shallow culverts below a pavement structure requires accurately defining boundary conditions for the road surface. This study evaluates five empirical pavement temperature methods used as the pavement surface boundary condition in a 2D numerical model that predicts the soil temperature below pavement surfaces over shallow culverts. The predicted soil temperatures from the numerical model are validated with two years of measured soil temperatures at five instrumented culvert sites. The predicted soil temperatures are evaluated through trend analysis and statistical F-test at 95% confidence. A pavement surface boundary condition obtained from each of the five pavement temperature methods is considered successful when the difference between the predicted and measured values pass the F-test for all seasons over the two-year study period. The results of the analysis show that three of the five pavement temperature methods passed the trend and F-test analysis. The three pavement temperature methods more accurately defines the boundary conditions for the road surface and are utilized in the numerical model to predicted frost penetration depth below the pavement surface and near the culvert openings. The findings from this study are expected to increase the accuracy of modeling the frost heave and the differential thaw settlement under pavement structure due to the influence of culverts.

Characterization of urease derived from Citrullus lanatus (watermelon) seeds to estimate total Kjeldahl nitrogen in human urine

ABSTRACTA urease extract prepared by decanting liquid from a suspension of finely ground Citrullus lanatus (watermelon) seeds was characterized and applied to dilute urine samples to demonstrate a low-cost field method to estimate total Kjeldahl nitrogen (TKN) concentrations in human urine. The extract exhibited a Michaelis-Menten constant, Km, of 3.00 mM urea and a specific activity of up to 12.2 U/mg protein at an optimum pH of 8.1. A statistical F-test on 54 samples demonstrated that TKN can be estimated as the total ammonium-nitrogen recovered upon addition of urease in dilute fresh and stale urine samples. The total ammonium-nitrogen in urine samples determined after treatment with watermelon seed urease was consistent with that determined using traditional acid digestion techniques. The extract retained 85% of its initial capacity after three months of refrigeration. The effectiveness of this method to assay nitrogen in unbuffered urine samples will be useful in nitrogen analyses in nutrient recovery and urine or slurry storage contexts. Accordingly, this study is useful in understanding the kinetics of a plant-derived urease acting in dilute urine.

Students’ Beliefs Regarding the Use of E-portfolio to Enhance Cognitive Skills in a Blended Learning Environment

This paper reports on a quantitative study on ICT readiness among undergraduate students in Thailand, students’ beliefs about use of e-portfolios in the Blended Learning Environment (BLE), and students’ beliefs about using e-portfolios to enhance their cognitive skills in the BLE. The sample group comprised 360 undergraduate students, divided by study fields. The data collection tool was a questionnaire of students’ beliefs. The reliability value of the questionnaire was 0.889. Data was analyzed using statistical analysis and f-test. The beliefs and needs were ranked by PNI modified. The research found that every student had a computer connected to the Internet. The analysis results of students’ beliefs about the use of e-portfolios in the BLE were positive and the top five results were: (1) learning by creating work, (2) enhancing creativity, (3) enhancing the problem solving skill, (4) enhancing critical thinking, and (5) enhancing authentic assessment. The current state of using e-portfolios to enhance cognitive skills in the BLE was at average level, while the needs were at the high level.