Search

This paper demonstrates novel application of neutron and proton based nuclear analytical techniques for the rapid evaluation of cement quality in terms of compressive strength using Ca to Si ratios. Preliminary phase characterization was performed using X-ray diffraction patterns. External PIGE method was optimized and applied for direct analysis of "as received" samples in thin mylar foils with reduced turn-around time of analysis. Moreover, Pneumatic Carrier Facility at Dhruva reactor of BARC was utilized for NAA for rapid and short irradiation of samples followed by analysis. Quality control of both NAA and PIGE methods were done using several geological Certified / Standard Reference Materials (CRMs and SRMs). EDXRF was employed as alternative analytical technique for comparative evaluation. Statistical evaluation was performed in terms of t-test and F-tests to understand the reliability of measurement and acceptable variance of the results. Eight cementitious samples were analyzed and their compressive strengths were evaluated using NAA and PIGE. [ABSTRACT FROM AUTHOR]

Abstract Background In literature are reported different analytical methods (AM) to choose the proper fit model and to fit data of the time-activity curve (TAC). On the other hand, Machine Learning algorithms (ML) are increasingly used for both classification and regression tasks. The aim of this work was to investigate the possibility of employing ML both to classify the most appropriate fit model and to predict the area under the curve (τ). Methods Two different ML systems have been developed for classifying the fit model and to predict the biokinetic parameters. The two systems were trained and tested with synthetic TACs simulating a whole-body Fraction Injected Activity for patients affected by metastatic Differentiated Thyroid Carcinoma, administered with [131I]I-NaI. Test performances, defined as classification accuracy (CA) and percentage difference between the actual and the estimated area under the curve (Δτ), were compared with those obtained using AM varying the number of points (N) of the TACs. A comparison between AM and ML were performed using data of 20 real patients. Results As N varies, CA remains constant for ML (about 98%), while it improves for F-test (from 62 to 92%) and AICc (from 50 to 92%), as N increases. With AM, $$\Delta \tau$$ Δ τ can reach down to − 67%, while using ML $$\Delta \tau$$ Δ τ ranges within ± 25%. Using real TACs, there is a good agreement between τ obtained with ML system and AM. Conclusions The employing of ML systems may be feasible, having both a better classification and a better estimation of biokinetic parameters.

Copyright of Chinese Journal of Inorganic Analytical Chemistry / Zhongguo Wuji Fenxi Huaxue is the property of Beijing Research Institute of Mining & Metallurgy Technology Group and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: In literature are reported different analytical methods (AM) to choose the proper fit model and to fit data of the time-activity curve (TAC). On the other hand, Machine Learning algorithms (ML) are increasingly used for both classification and regression tasks. The aim of this work was to investigate the possibility of employing ML both to classify the most appropriate fit model and to predict the area under the curve (τ). Methods: Two different ML systems have been developed for classifying the fit model and to predict the biokinetic parameters. The two systems were trained and tested with synthetic TACs simulating a whole-body Fraction Injected Activity for patients affected by metastatic Differentiated Thyroid Carcinoma, administered with [131I]I-NaI. Test performances, defined as classification accuracy (CA) and percentage difference between the actual and the estimated area under the curve (Δτ), were compared with those obtained using AM varying the number of points (N) of the TACs. A comparison between AM and ML were performed using data of 20 real patients. Results: As N varies, CA remains constant for ML (about 98%), while it improves for F-test (from 62 to 92%) and AICc (from 50 to 92%), as N increases. With AM, Δ τ can reach down to − 67%, while using ML Δ τ ranges within ± 25%. Using real TACs, there is a good agreement between τ obtained with ML system and AM. Conclusions: The employing of ML systems may be feasible, having both a better classification and a better estimation of biokinetic parameters. [ABSTRACT FROM AUTHOR]

Abstract.A nonparametric test is proposed for the two-sample variance equality problem based on the fact that the covariance of U=X+Y and W=X−Y is zero when X and Y have equal variances. It is shown that the test statistic has an asymptotic standard normal distribution, which is useful for obtaining critical values for moderate sample sizes. The results of a Monte Carlo simulation experiment conducted in order to study the power properties of the test are presented. [ABSTRACT FROM AUTHOR]

The application of classical statistical methods is not feasible given the presence of imprecise, fuzzy, uncertain, or undetermined observations in the underlying dataset. This is due to the existence of uncertainties pervading every aspect of real-life situations, which cannot always be accurately addressed by classical statistical approaches. In order to tackle this problem, a new methodology known as neutrosophic analysis of variance (NANOVA) has been developed as an extension of classical approaches to analyze datasets with uncertainty. The proposed approach can be applied regardless of the number of factors and replications. Moreover, NANOVA introduces a novel matrix-based approach to derive the F_N-test in an uncertain environment. To assess the effectiveness of NANOVA, various real datasets have been employed, and research findings on single- and two-factor NANOVAs with measures of indeterminacy have been presented. According to our comparisons, NANOVA provides a more informative, efficient, flexible, and reliable approach to deal with uncertainties than classical statistical methods. Therefore, there is a need to go beyond conventional statistical techniques and adopt advanced methodologies that can effectively handle uncertainties. [ABSTRACT FROM AUTHOR]

This study explores a multidimensional collaborative landscape design course, detailing its unique collaborative teaching framework within the context of Zhuhai’s transformative historical and cultural landscape. It employs a comprehensive set of metrics, including teaching objectives and methodologies, to assess the course’s effectiveness. The relevance of variations among elements was evaluated through statistical analyses, including T-tests, F-tests, and correlation studies. Further, the study examined participant satisfaction, visual appeal, and aesthetic impact of the “red cultural” landscape design. Results indicated that the average teaching evaluation scores surpassed 4 points, and satisfaction levels with the red cultural landscape design approached 80%, reflecting a beneficial influence on educational outcomes. This research contributes novel insights to the development of landscape evaluation systems in cultural tourism settings and offers theoretical advancements for implementing multidimensional collaborative teaching strategies in landscape design.

This paper analyzed the variations of two air temperature indices, diurnal temperature range (DTR) and annual temperature range (ATR), calculated based on observations at the Zagreb Grič Observatory over a period of 133 years (1887–2019). In intense climate changes strongly manifested by the increased air temperature, these two climate indices were determined to significantly impact human health and the environment. This effect is especially evident in urban areas. The Zagreb Grič Observatory is located in the center of Zagreb and has not changed its location during the observed period. It has a long homogeneous series of climatological observations, enabling a detailed study of climate variation in the city, which is strongly influenced by various urbanization processes. In 133 years, both of the analyzed indicators showed a statistically insignificant downward trend. The Rescaled Adjusted Partial Sums (RAPS) method revealed statistically significant differences in DTR's time series between three sub-periods: 1887–1953, 1954–1989, and 1990–2019. The time series of ATR during 133 years behaved statistically differently in four sub-periods: 1887–1905; 1906–1926; 1927–1964; and 1965–2019. The analysis of monthly values of DTR showed that the DTR values are the highest in the warm part of the year, from May to August, when they are twice as high as those during the cold period from November to December. With an increase in precipitation, the DTR values decrease, while they increase as the mean annual temperature increases. [ABSTRACT FROM AUTHOR]

Authors aim to analyze the requirements for the use of simulation technologies for the Air Traffic Pilot License Theory and, based on the selected, measured, and interpreted parameters, to compare the platforms used by the Czech Air Force. At the University of Defence, the simulation center is used as a support instrument. The objective of this paper is to determine the most appropriate flying platform for student-pilots when employing basic radionavigation theory. To verify the analysis, simulated student flights were carried out. The first group of approaches was performed on the simulated platform of the Zlin Z142 aircraft, and the second group on the L39 Albatros aircraft. Individual flights were statistically evaluated and compared. The parameters for comparison were the deviation of the flight altitude compared to the recommended approach altitude and the deviation of the heading compared to the final approach course. Statistical evaluation of the performed simulated flights was made by F-test and verified confirmed that to teach the theoretical subject of Radionavigation, a slower-flying Zlin Z-142 is more suitable. According to the results, the Z142 platform gives the student more time to observe a pilotage error and correct it before starting the final approach phase.

In continuation of the ongoing long term research work of the first author on the detailed mathematical and statistical analysis of the budget of the Bangalore Municipal Corporation (see, A. Sathyavathi https://ssrn.com/abstract=4403863, https://ssrn.com/abstract=4461494, https://ssrn.com/abstract=4518454 and https://ssrn. com/abstract=4568499), we carry ahead our previous study (A. Sathyavathi and L. M. Upadhyaya, Possible regression models for the municipal finances of the municipal corporations of various Indian states, Bull. Pure Appl. Sci. Sect. E Math. Stat. 42E(1), 72-93 (2023))to propose some possible regression models to account for the observed trends of the revenue receipts and the revenue expenditures of the municipal corporations of the various Indian States including the Union Territory of Chandigarh for the 2018-19 Budget Estimates, which pertains to the data of finances of municipal corporations of the various Indian States during the financial year 2018-2019. We also present the comparisons of our different models for each of the categories of our present explorations. The entire data used by us in this study is gratefully taken by us from the Report on Municipal Finances which was published by the Reserve Bank of India recently on 10 November, 2022 (see, https://m.rbi.org.in/scripts/AnnualPublications.aspx? head=Report%20on%20Municipal%20Finances, https://rbidocs.rbi.org.in/rdocs/Publications/PDFs/RMF101120223A34C4F7023A4A9E99CB7F7FEF6881D0.PDF). We find that a Polynomial Degree 4 model best describes the financial data and in addition we also give a Rational Model and an Exponential Association 2 model for describing the trend of finances for the municipal corporations of India during the year 2018-19 in the Budget Estimates category and we also present our computational results of the Polynomial Degree 4 Model and the Rational Model using MATLAB. [ABSTRACT FROM AUTHOR]

Authors aim to analyze the requirements for the use of simulation technologies for the Air Traffic Pilot License Theory and, based on the selected, measured, and interpreted parameters, to compare the platforms used by the Czech Air Force. At the University of Defence, the simulation center is used as a support instrument. The objective of this paper is to determine the most appropriate flying platform for student-pilots when employing basic radionavigation theory. To verify the analysis, simulated student flights were carried out. The first group of approaches was performed on the simulated platform of the Zlin Z142 aircraft, and the second group on the L39 Albatros aircraft. Individual flights were statistically evaluated and compared. The parameters for comparison were the deviation of the flight altitude compared to the recommended approach altitude and the deviation of the heading compared to the final approach course. Statistical evaluation of the performed simulated flights was made by F-test and verified confirmed that to teach the theoretical subject of Radionavigation, a slower-flying Zlin Z-142 is more suitable. According to the results, the Z142 platform gives the student more time to observe a pilotage error and correct it before starting the final approach phase. [ABSTRACT FROM AUTHOR]

To study the effect of exposure mixture on the continuous health outcomes, one can use the linear model with a weighted sum of multiple standardized exposure variables as an index predictor and its coefficient for the overall effect. The unknown weights typically range between zero and one, indicating contributions of individual exposures to the overall effect. Because the weight parameters present only when the parameter for overall effect is non-zero, testing hypotheses on the overall effect can be challenging, especially when the number of exposure variables is above two. This paper presents a working model based approach to estimate the parameter for overall effect and to test specific hypotheses, including two tests for detecting the overall effect and one test for detecting unequal weights when the overall effect is evident. The statistics are computationally easy and one can apply existing statistical software to perform the analysis. A simulation study shows that the proposed estimators for the parameters of interest may have better finite sample performance than some other estimators. [ABSTRACT FROM AUTHOR]

Abstract Introduction: Water-crop production functions indicate an increase or a decrease in harvest for the plant’s consumed water (evaporation-respiration). To assess production functions of wheat and barley products, linear and second-order methods are used in numerous researches. On the other hand, since the mentioned relationships for some products may not have an acceptable correlation, third-order, logarithmic, and polynomial functions are used in various deficit irrigation treatments of 50, 60, 70, 80, and 90 percent. Methods: Farm data were evaluated and validated with resulted data from discussed equations. In order to study the proposed functions, statistical indices such as root mean square error (RMSE), normalized root mean square error (NRMSE), mean absolute error (MAE), Wilmott index (d), and F-test were used via measurement data in every scenario. Findings: All statistic values of both winter wheat and barley were in permissible range, indicating properness and acceptance of the equations. All functions were acceptable at a significance level of 5 percent for both products. According to the results of studying irrigation depth in terms of winter wheat and barley yield, it can be argued that third-order functions provide more accurate prediction in comparison with logarithmic and polynomial functions in both winter wheat and barley crops, and therefore, third-order function is prioritized over other functions

The existing F-test of linearity cannot be applied when data has indeterminacy and uncertainty. The present paper introduces the F-test of testing linearity under neutrosophic statistics. We will develop F-test under neutrosophic statistics and neutrosophic analysis of the variance (NANOVA) table. The application of the proposed test will be given using the data of dry bulb temperature and relative humidity. From the analysis and comparison studies, it is found that the proposed F-test under neutrosophic statistics gives the results in indeterminate intervals and measures of indeterminacy. In addition, the proposed test is more flexible, adequate, and more informative than the F-test under classical statistics.

The existing F-test of linearity cannot be applied when data has indeterminacy and uncertainty. The present paper introduces the F-test of testing linearity under neutrosophic statistics. We will develop F-test under neutrosophic statistics and neutrosophic analysis of the variance (NANOVA) table. The application of the proposed test will be given using the data of dry bulb temperature and relative humidity. From the analysis and comparison studies, it is found that the proposed F-test under neutrosophic statistics gives the results in indeterminate intervals and measures of indeterminacy. In addition, the proposed test is more flexible, adequate, and more informative than the F-test under classical statistics. [ABSTRACT FROM AUTHOR]

We consider balanced one-way, two-way, and three-way ANOVA models to test the hypothesis that the fixed factor A has no effect. The other factors are fixed or random. We determine the noncentrality parameter for the exact F-test, describe its minimal value by a sharp lower bound, and thus we can guarantee the worst-case power for the F-test. These results allow us to compute the minimal sample size, i.e. the minimal number of experiments needed. We also provide a structural result for the minimum sample size, proving a conjecture on the optimal experimental design. [ABSTRACT FROM AUTHOR]

Experimental densities, viscosities, refractive indices, and sound speeds at temperature 298.15 K and atmospheric pressure are reported for the binary liquid mixtures of ethanol + benzene, ethanol + pyridine, and benzene + pyridine. From these experimental data, various thermodynamic excess and deviation properties were calculated and fitted by the Redlich-Kister polynomial to determine the adjustable coefficients and the standard deviations. The number of Redlich-Kister coefficients for significantly representing each thermodynamic property was optimized by applying the F-test. The variation of thermodynamic excess and deviation properties with composition has been interpreted in terms of molecular interactions between components of the mixture and structural effects. Furthermore, several theoretical and semi-empirical models were used to predict the refractive indices and sound speeds of the investigated mixtures. The predicting ability of each model was ascertained in terms of mean absolute percentage deviation between experimental and calculated data. [ABSTRACT FROM AUTHOR]

After rejecting the null hypothesis in the analysis of variance, the next step is to make the pairwise comparisons to find out differences in means. The purpose of this paper is threefold. The foremost aim is to suggest expression for calculating decision limit that enables us to collect the test and pairwise comparisons in one step. This expression is proposed as the ratio of between square for each treatment and within sum of squares for all treatments. The second aim is to obtain the sampling distribution of the proposed ratio under the null hypothesis. This sampling distribution is derived exactly as the beta distribution of the second type. The third aim is to use beta distribution of second type and adjusted p-values to create adjusted points and decision limit. Therefore, reject the null hypothesis of equal means if any adjusted point falls outside the decision limit. Simulation study is conducted to compute type I error. The results show that the proposed method controls the type I error near the nominal values using Benjamini-Hochbergadjusted p-values and it is very comparative to classical one-way analysis of variance plus pairwise comparisons. Two applications are given to show the benefits of the proposed method. [ABSTRACT FROM AUTHOR]

Noninvasive blood pressure estimation is crucial for cardiovascular and hypertension patients. Cuffless-based blood pressure estimation has received much attention recently for continuous blood pressure monitoring. This paper proposes a new methodology that combines the Gaussian process with hybrid optimal feature decision (HOFD) in cuffless blood pressure estimation. First, we can choose one of the feature selection methods: robust neighbor component analysis (RNCA), minimum redundancy, maximum relevance (MRMR), and F-test, based on the proposed hybrid optimal feature decision. After that, a filter-based RNCA algorithm uses the training dataset to obtain weighted functions by minimizing the loss function. Next, we combine the Gaussian process (GP) algorithm as the evaluation criteria, which is used to determine the best feature subset. Hence, combining GP with HOFD leads to an effective feature selection process. The proposed combining Gaussian process with the RNCA algorithm shows that the root mean square errors (RMSEs) for the SBP (10.75 mmHg) and DBP (8.02 mmHg) are lower than those of the conventional algorithms. The experimental results represent that the proposed algorithm is very effective. [ABSTRACT FROM AUTHOR]

Cuffless blood pressure (BP) monitoring is crucial for patients with cardiovascular disease and hypertension. However, conventional BP monitors provide only single-point estimates without confidence intervals. Therefore, the statistical variability in the estimates is indistinguishable from the intrinsic variability caused by physiological processes. This study introduced a novel method for improving the reliability of BP and confidence intervals (CIs) estimations using a hybrid feature selection and decision method based on a Gaussian process. F-test and robust neighbor component analysis were applied as feature selection methods for obtaining a set of highly weighted features to estimate accurate BP and CIs. Akaike's information criterion algorithm was used to select the best feature subset. The performance of the proposed algorithm was confirmed through experiments. Comparisons with conventional algorithms indicated that the proposed algorithm provided the most accurate BP and CIs estimates. To the best of the authors' knowledge, the proposed method is currently the only one that provides highly reliable BP and CIs estimates. Therefore, the proposed algorithm may be robust for concurrently estimating BP and CIs. [ABSTRACT FROM AUTHOR]

Tensile strength is an essential parameter for improving the quality of Injection-molded products. This thesis presents an experimental investigation into the impact of input variables of plastic injection molding process on the ultimate tensile strength of High-Density Polyethylene. Depending on Taguchi quality design concept, a L16 mixed-level array was implemented to evaluate the S/N ratio (dB), analysis of variance (ANOVA) and the 'F' test values for detecting dominant process variables influencing the injection molding performance and product quality. The ultimate tensile strength was improved with the optimum setting of input parameters. The input parameters were Injection Pressure, Melt Temperature, Cooling Time, Mold Temperature, Injection Speed, Holding Time, and Holding Pressure. Here, the room temperature was selected as a noise factor whereas the output response was ultimate tensile strength. Based on the experimental results and through ANOVA and 'F' test values, all the control parameters were observed to be significant, but the room temperature was insignificant (P value .054). Considering these significant parameters, verification of the improvement in the quality characteristics of the product has been made through confirmation test concerning the selected initial parameter setting. [ABSTRACT FROM AUTHOR]

The international evidence base shows that the mass communication industry is characterized by elevated levels of employee burnout while there is scant research addressing parallel situations in Africa and more specifically Ethiopia. Accordingly, the objective, of the present study was to determine the prevalence and magnitude of burnout among journalists working for Amhara TV, Oromia Broadcasting Network and Tigray TV using the Maslachburnout inventory and a sample of 123 journalists. The aim was also to determine the contribution of job control, job demands, organizational support and biographical factors to burnout using hierarchical regression methods. A two-step hierarchical regression procedure was employed to determine the best fit to the data. Results indicated that biographical predictors failed to qualify as significant explanatory factors, but the model explained an improved 32 % of the variance in burnout. ANOVA tests showed no significant scores in burnout were attributable to gender, job level, age or work experience differences except for region. Implications are discussed.

Speech frequency following responses (sFFRs) are increasingly used in translational auditory research. Statistically-based automated sFFR detection could aid response identification and provide a basis for stopping rules when recording responses in clinical and/or research applications. In this brief report, sFFRs were measured from 18 normal hearing adult listeners in quiet and speech-shaped noise. Two statistically-based automated response detection methods, the F-test and Hotelling’s T2 (HT2) test, were compared based on detection accuracy and test time. Similar detection accuracy across statistical tests and conditions was observed, although the HT2 test time was less variable. These findings suggest that automated sFFR detection is robust for responses recorded in quiet and speech-shaped noise using either the F-test or HT2 test. Future studies evaluating test performance with different stimuli and maskers are warranted to determine if the interchangeability of test performance extends to these conditions.

This paper analyzed the variations of two air temperature indices, diurnal temperature range (DTR) and annual temperature range (ATR), calculated based on observations at the Zagreb Grič Observatory over a period of 133 years (1887–2019). In intense climate changes strongly manifested by the increased air temperature, these two climate indices were determined to significantly impact human health and the environment. This effect is especially evident in urban areas. The Zagreb Grič Observatory is located in the center of Zagreb and has not changed its location during the observed period. It has a long homogeneous series of climatological observations, enabling a detailed study of climate variation in the city, which is strongly influenced by various urbanization processes. In 133 years, both of the analyzed indicators showed a statistically insignificant downward trend. The Rescaled Adjusted Partial Sums (RAPS) method revealed statistically significant differences in DTR’s time series between three sub-periods: 1887–1953, 1954–1989, and 1990–2019. The time series of ATR during 133 years behaved statistically differently in four sub-periods: 1887–1905; 1906–1926; 1927–1964; and 1965–2019. The analysis of monthly values of DTR showed that the DTR values are the highest in the warm part of the year, from May to August, when they are twice as high as those during the cold period from November to December. With an increase in precipitation, the DTR values decrease, while they increase as the mean annual temperature increases.

This study explores the preferred behavior of self-management among chronic kidney disease (CKD) patients and offers suggestions for different patients from personalized medicine. According to some related references, a questionnaire was designed in 2020 to collect data from 131 patients with CKD in a general hospital. The Sampling patients showed no difference in their disease progress. The questionnaire covered two aspects of demographic and behavior with 29 items on six dimensions. Statistical methods such as a descriptive analysis of the F test in behavior dimensions on demographic characteristics and Principal component analysis from items have been applied to classify some kinds of self-management behavior into different groups. In the demographic insight, employment status closely relates to self-management behavior, and income is insignificant. In the behavior aspects, according to some key items, we found four types of self–management behavior preferred in the sorting: cognitive-knowledge, Diet-exercise-medical, emotion management, and exercise-medical, which were defined by behavior dimensions. Although patients had the same disease progress, their self-management behavior mainly existed in four types based on critical factors. According to their favorite behavior and personality group, healthcare stakeholders can offer lean support for improving patients' self-management of CKD in China.

In this paper, statistical analysis of the Acoustic Emission (AE) based b-value parameter has been carried out. In the initial phase, multi-linear regression was carried out on AE parameters released during the uniaxial compression of twelve cylindrical samples. The study exhibited that Counts, RMS values, and reciprocal of Absolute Energy are promising parameters to quantify the b-value. In a similar analysis, the AE behavior measured in terms of b-value was studied for standard cubes and cylinders. Firstly, the two-way ANOVA test was done on Counts, RMS values, and reciprocal of Absolute Energy, and a hypothesis was made that bvalue measured in the said parameters would be the same. The results support the hypothesis and prove statistically that the three variables are the same no matter whether they are taken from cubic specimens or from cylindrical specimens, even though the cracking behavior of cubes and cylinders is different. The results are also verified by a one-tailed F-test. [ABSTRACT FROM AUTHOR]

The inhibition actions of ethanolic extract of Enicostemma littorale (EL) leaves-Zn2+ for carbon steel (CS) surface in well water has been studied by mass-loss study, Tafel slopes, and Nyquist plots, Impacts of inhibitor concentration (100ppm-500ppm), temperatures (30°C-70°C), pH (3,8,12) and submersion period (1,3,7,12) were researched on the prevention capacity of the extract on CS submerged in well water. The adsorption of ethanolic extract of EL leaves onto the surface of CS pursued that as indicated by a Langmuir model. The Enicostemma littorale extract exposed a blended-type corrosion inhibitor in the considered environment as indicated by the electrochemical studies. Theoretical fittings for the corrosion kinetics and thermodynamic parameters have been determined to explain the method of adsorption of ethanolic extract of EL-Zn2+ on the CS surface.? Gads values are less than -20KJ/mol. From these values, the adsorption of an inhibitor system is physisorption on the CS surface. Energy-dispersive X-ray technique was utilized to identify the elemental composition of the corrosion substances or potentially adsorbed inhibitor film on the CS. Scanning electron microscopy, atomic force microscopy and Fourier transfer infrared assessments affirm the development of a defensive layer by the ethanolic extract of Enicostemma littorale leaves on the CS surface in well-water. [ABSTRACT FROM AUTHOR]

Due to the influence of uncontrollable factors such as the environment and instruments, noise is unavoidable in a spectral signal, which may affect the spectral resolution and analysis result. In the present work, a novel spectral denoising method is developed based on the Hilbert–Huang transform (HHT) and F-test. In this approach, the original spectral signal is first decomposed by empirical mode decomposition (EMD). A series of intrinsic mode functions (IMFs) and a residual (r) are obtained. Then, the Hilbert transform (HT) is performed on each IMF and r to calculate their instantaneous frequencies. The mean and standard deviation of instantaneous frequencies are calculated to further illustrate the IMF frequency information. Third, the F-test is used to determine the cut-off point between noise frequency components and non-noise ones. Finally, the denoising signal is reconstructed by adding the IMF components after the cut-off point. Artificially chemical noised signal, X-ray diffraction (XRD) spectrum, and X-ray photoelectron spectrum (XPS) are used to validate the performance of the method in terms of the signal-to-noise ratio (SNR). The results show that the method provides superior denoising capabilities compared with Savitzky–Golay (SG) smoothing.

Testing a global hypothesis for a set of variables is a fundamental problem in statistics with a wide range of applications. A few well-known classical tests include the Hotelling's T2 test, the F-test, and the empirical Bayes based score test. These classical tests, however, are not robust to the signal strength and could have a substantial loss of power when signals are weak or moderate, a situation we commonly encounter in contemporary applications. In this article, we propose a minimax optimal ridge-type set test (MORST), a simple and generic method for testing a global hypothesis. The power of MORST is robust and considerably higher than that of the classical tests when the strength of signals is weak or moderate. In the meantime, MORST only requires a slight increase in computation compared to these existing tests, making it applicable to the analysis of massive genome-wide data. We also provide the generalizations of MORST that are parallel to the traditional Wald test and Rao's score test in asymptotic settings. Extensive simulations demonstrated the robust power of MORST and that the Type I error of MORST was well controlled. We applied MORST to the analysis of the whole-genome sequencing data from the Atherosclerosis Risk in Communities study, where MORST detected 20%–250% more signal regions than the classical tests. for this article are available online. [ABSTRACT FROM AUTHOR]

In face of scarcity in the supply of non-traditional Brazilian woods properly treated for use in high quality musical instruments, pieces of Amazonian wood species muiracatiara (Astronium lecointei) and maçaranduba (Manilkara huberi) purchased in the common internal Brazilian timber market were examined. These species were pre-selected for use in fingerboards of acoustic and electric guitars due to similar properties with ebony (Diospyros crassiflora). Variabilities of elastic modulus parallel to grain and density were investigated inside wooden pieces. In addition, referred parameters were used in calculation of speed of sound. Statistical tests were performed in order to compare both species and revealed inequality for variances of dynamic elastic modulus (Ed) and speed of sound, but equality for density. Equality of means was also examined via unequal variance t-test. Despite color differences, lower variability of M. huberi led to the indication of this species as likely capable to substitute satisfactorily ebony in fingerboards manufacturing.

This study addresses concerns about amoxicillin (AMOX) in wastewater by enhancing its removal using Cu salt-modified lemon peel-based activated carbon (Cu-LPAC). The F-test is employed to assess variances between linear and non-linear methods for isotherm and kinetic models. The Polymath mass transfer (PMT) model is used to simulate kinetic data. AMOX adsorption onto Cu-LPAC increased by 38.93%, in comparison with pristine LPAC. The Cu-LPAC exhibiting BET surface area, pore volume, and pore diameter of 1018.52 m2/g, 0.5377 cm3/g, and 2.78 nm. The non-linear Langmuir and non-linear pseudo-second order (PSO) models fit the adsorption process. The Langmuir monolayer adsorption capacity (Q m) was 344.84 mg/g (linear) and 355.67 mg/g (non-linear). The F-test showed significance differences for Freundlich and PSO, while PMT model simulation accurately predicted the mesopores surface area to be 787.63 m2/g, yielding a small error of 4.31%. Cu-LPAC's AMOX adsorption was enhanced by ion dipole, hydrogen, and dipole-dipole bonds. [Display omitted] • The Cu-LPAC boosted AMOX adsorption by 38.93% compared to pristine LPAC. • The PMT predicted 787.63 m2/g of surface area, close to the actual value of 823.12 m2/g. • The Q m for Cu-LPAC was 344.84 mg/g (linear method) and 355.67 mg/g (non-linear method). • F-test revealed that the null hypothesis was accepted for Langmuir, Temkin and PFO. • AMOX adsorption onto Cu-LPAC was enhanced by the ion-dipole bond. [ABSTRACT FROM AUTHOR]

Noninvasive blood pressure estimation is crucial for cardiovascular and hypertension patients. Cuffless-based blood pressure estimation has received much attention recently for continuous blood pressure monitoring. This paper proposes a new methodology that combines the Gaussian process with hybrid optimal feature decision (HOFD) in cuffless blood pressure estimation. First, we can choose one of the feature selection methods: robust neighbor component analysis (RNCA), minimum redundancy, maximum relevance (MRMR), and F-test, based on the proposed hybrid optimal feature decision. After that, a filter-based RNCA algorithm uses the training dataset to obtain weighted functions by minimizing the loss function. Next, we combine the Gaussian process (GP) algorithm as the evaluation criteria, which is used to determine the best feature subset. Hence, combining GP with HOFD leads to an effective feature selection process. The proposed combining Gaussian process with the RNCA algorithm shows that the root mean square errors (RMSEs) for the SBP (10.75 mmHg) and DBP (8.02 mmHg) are lower than those of the conventional algorithms. The experimental results represent that the proposed algorithm is very effective.

Cuffless blood pressure (BP) monitoring is crucial for patients with cardiovascular disease and hypertension. However, conventional BP monitors provide only single-point estimates without confidence intervals. Therefore, the statistical variability in the estimates is indistinguishable from the intrinsic variability caused by physiological processes. This study introduced a novel method for improving the reliability of BP and confidence intervals (CIs) estimations using a hybrid feature selection and decision method based on a Gaussian process. F-test and robust neighbor component analysis were applied as feature selection methods for obtaining a set of highly weighted features to estimate accurate BP and CIs. Akaike’s information criterion algorithm was used to select the best feature subset. The performance of the proposed algorithm was confirmed through experiments. Comparisons with conventional algorithms indicated that the proposed algorithm provided the most accurate BP and CIs estimates. To the best of the authors’ knowledge, the proposed method is currently the only one that provides highly reliable BP and CIs estimates. Therefore, the proposed algorithm may be robust for concurrently estimating BP and CIs.

Speech frequency following responses (sFFRs) are increasingly used in translational auditory research. Statistically-based automated sFFR detection could aid response identification and provide a basis for stopping rules when recording responses in clinical and/or research applications. In this brief report, sFFRs were measured from 18 normal hearing adult listeners in quiet and speech-shaped noise. Two statistically-based automated response detection methods, the F-test and Hotelling's T2 (HT2) test, were compared based on detection accuracy and test time. Similar detection accuracy across statistical tests and conditions was observed, although the HT2 test time was less variable. These findings suggest that automated sFFR detection is robust for responses recorded in quiet and speech-shaped noise using either the F-test or HT2 test. Future studies evaluating test performance with different stimuli and maskers are warranted to determine if the interchangeability of test performance extends to these conditions. [ABSTRACT FROM AUTHOR]

In order to solve the problem that when test vector leakage assessment (TVLA) technology is used for side channel leakage detection, the mean difference between the two groups of power consumption samples (fixed plaintext and random plaintext) is small, and the t-test may miss detection and lead to false negative evaluation. The Bartlett and multi-classification F-test side channel leakage assessment method (Bartlett-F test) was proposed. In the Bartlett-F test, the Bartlett-test was used to the power samples with greater variance difference than mean difference to solve the problem of missing detection, and the multi-classification F-test was used to the power samples with greater mean difference than variance difference to solve the problem of false negative evaluation. In the test, there is leakage if the P-value is less than the threshold. The experimental results show that when the mean difference is less than the variance difference, the sample size required by Bartlett-test is 1.5x104 when the P-value of Bartlett-test is less than the threshold, while the sample size required by t-test is larger. When the variance difference is less than the mean difference, the sample size required by t-test is 2.0x10² when the P-value of t-test is less than the threshold, while the sample size required by F-test is only 1/10 of t-test. Therefore, Bartlett-F test can solve the problems of TVLA technology in leak detection. [ABSTRACT FROM AUTHOR]

Regression analysis is using the relationship between a known value and an unknown variable to estimate the unknown one. Here, an estimate of the dependent variable is made corresponding to given values of independent variables by placing the relationship between the variables in the form of a regression line. To determine how well the regression line obtained fits the given data points, F-test of overall significance is conducted. The issues involved in the F-test of overall significance are many and mathematics involved is rigorous, especially when more than two variables are involved. This study describes in details how the test can be conducted and finally gives the simplified approach of test using an online calculator.

Compatible stem taper and volume models were developed for Brutian pine (Pinus brutia Ten.), Cedar of Lebanon (Cedrus libani A. Rich.), and Cilicica fir (Abies cilicica Carr.) in Bucak forest region of Southern Turkey. The proposed models generally performed better for the whole tree for all tree species. Diameter and volume predictions at 10 points along the stem were made to verify the accuracy and precision of these models for the three species. More specifically, differences among different tree species in one region and regional differences of the taper equation were examined using the nonlinear extra sum of squares method. The results of the non-liner extra sum of squares method indicated that species-specific taper equations for estimating diameters along the stem and predicting merchantable or total volume for each region are required.

In 2014, the Ministry of Education of the Azerbaijan Republic launched an honours programme in several universities to introduce the advanced curriculum and interactive teaching methodology. The purpose of this research is to evaluate the success of the programme via the satisfaction level of the honours students with the delivery of the promised purposes of this programme, such as curriculum, teaching methodology and the programme administration in general. Preliminary results show that honours students are satisfied with each of those variables. Moreover, to understand whether the satisfaction of the students is purely due to the structure of the honours programmes, the satisfaction level of honours students was compared with that of non-honours students from the corresponding degree programmes. The results showed that honours students are more satisfied than the non-honours students in all the above-mentioned criteria. [ABSTRACT FROM AUTHOR]