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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.

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]

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]

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]

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.

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 1.5×104 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 2.0×102 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.

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.

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.

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]

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]

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.

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.

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]

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.

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.

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.

The growth of syariah’s banking customers under conventional banks is a result of the public's perception that the Islamic banking system as if the conventional system which only changed its name to syari'ah had an effect on people's interest in becoming customers. Therefore, it is necessary to know how many variables that affect the interest to become customers in Islamic banks. This research was quantitative by using multiple linear regression analysis methods to determine the influence of shared value, consumer knowledge, and communication partially and simultaneously to the interest of a customer. Simultaneously through the F-test variables X1, X2 and X3 had a significant influence on Y based on the F-count greater than the value of F-table of 2.7 (24.504>2.7) and the significance value F of 0.000 was smaller than the significant value of 0.05 (0,0000.05). The contribution value of the influence of X1, X2, and X3 on Y is 52.3% based on the test results of the coefficient of determination which had the value of R-Square (R2) of 0.523.

Background: Under-five mortality is a health indicator in population health and the health sciences. Force of death is a more accurate measure of mortality than others, which indicates the situation of mortality when time tends to zero. The purpose of this research is to construct a simulation for l x (the number of people living at exact age x) for under-five mortality in Bangladesh as a whole, and separately for rural and urban areas, and then estimating the mortality rate in regard to these matched models. Methods: Secondary data were collected from Bangladesh Demographic and Health Survey 2007. A polynomial model was selected to match the l x values. To prove the accuracy of the model, the rule of cross-validity was applied. Results: It has been shown that fourth degree polynomial models can be adjusted to l x values ​​with explanation of more than 94% variation. It was noted that the mortality rate shows a rapidly decreasing pattern for people aged 0-20 months, a monotonically increasing pattern for those aged 20-53.5 months and then it begins to decrease. It is found that the mortality rate in rural areas is higher than in urban areas of all ages. Conclusions: It has been shown that the mortality rate in rural areas is higher than for urban areas of all ages, except for those aged 53.5 months. The health situation should therefore be improved to reduce mortality in rural Bangladesh.

The quality of the scanning electron microscope (SEM) images can be estimated using the signal-to-noise ratio (SNR). SNR is defined as the ratio of the desired signal to background noise. The noise which appeared in the SEM image is called Gaussian noise. Thus, if the SNR value is high, the image will have better quality since there is more useful information (the signal) than unwanted data (the noise). However, existing SNR estimation methods such as Nearest Neighbourhood (NN), Linear Interpolation (LI), and combination of Nearest Neighbourhood and Linear Interpolation unable to provide satisfactory results in estimating the SNR value. So, to prevent the loss of important information of SEM images, the novel SNR estimation method named Cubic Spline Hermite Interpolation with Linear Least Square Regression (CSHILLSR) has been proposed and formulated. The proposed method is compared with existing methods in terms of absolute error of SNR values, F-test, and Student's t-test. The result shows that the proposed method having a lower absolute error as compared to other methods and there is no significant difference between the actual and estimated SNR value at a 95% confidence level. This indicates that the proposed CSHILLSR able to provide better accuracy in estimation of SNR value as compare to the existing methods. [ABSTRACT FROM AUTHOR]

Polypropylene (PP) is a semi-crystalline polymer that is brittle under severe conditions. To meet industry needs, and to increase the applications of polypropylene, its mechanical properties should be improved. In this research, the mechanical properties of polypropylene, such as tensile strength at break, tensile strength at yield, % elongation, and Young’s modulus, were improved using two types of additives. Additives used were calcium carbonate master batch filler composed of 80% calcium carbonate and 20% polyethylene, and a mixture of linear low-density polyethylene (LLDPE)/low density polyethylene (LDPE). Results showed that both tensile strength at break, and tensile strength at yield, decrease with increasing the amount of both additives. Percentage elongation of PP increased using both additives. The modulus of elasticity of PP increases by increasing the amount of both additives, until a value of 20 wt%. Analysis of variance (ANOVA test) or (F-test) shows significant differences between the effect of different weights of LLDPE/LDPE mixture and calcium carbonate filler on the four mechanical properties of polypropylene studied at a level of 0.05. T-tests are applied to compare between the effect of both calcium carbonate master batch filler and the mixture LLDPE/LDPE on the four mechanical properties of polypropylene studied. T-tests show no significant differences between the effect of both calcium carbonate master batch filler and the mixture LLDPE/LDPE on all mechanical properties of polypropylene studied at a level of 0.05.

The article is devoted to the study of the relationship between the lengths of orthologous proteins of four organisms, one of which is taken as the basic one ( more than 1200 proteins in total). The methods of multivariate statistical analysis are used, it is applied to pairs, triples and fours (strings) composed of lengths of orthologous proteins. The number of such lines is from 200 to 400. The analysis of pair correlations, orthogonal transformation and cluster analysis allowed us to distinguish two homogeneous clusters of four-lengths. At the same time, we studied the increments of the length of the orthologous protein relative to the basic organism. We showed that the lines form a non-uniform sample, and the increments form a homogeneous sample. Then the task was to expand the clusters with rows with incomplete data. It was shown that cluster analysis is not applicable for this task, so we used discriminant analysis with a training sample — clustering with complete data. A 100 percent separation of all incomplete rows by clusters was obtained; with the following description of the length dependences of clusters on the base. The adequacy of the resulting regression equations was tested. As a result of statistical analysis, the following conclusions were made. For a set of lengths of orthologous series, a generalizing factor was obtained, let's call it the size of an orthologic object from 4 lengths of orthologous proteins. For the given task such sizes of objects were obtained, and their average group values differ, they form two separate ranges of values, one for each group of the values obtained by other methods. For series of increments of the lengths of orthologous proteins from objects of four, an analysis performed by all methods showed homogeneity of the set. It was shown that the lengths of orthologous proteins have significant autocorrelation, as is the case with rows associated with the same basic series.

In large-scale multiple testing, the permutation test based on making a null statistic has been widely employed in the literature. Because it enables us to use the null permuted samples and estimate the p-value more accurately. Some test statistics, which can be modified to a null statistic, have been proposed for two independent groups. In this paper, we propose a new statistic and corresponding F-test, which can be applied for three or more groups. The simulation results demonstrate that the proposed method has a smaller interquartile range (IQR) than the usual permutation method and estimates p-values more accurately than its rival.

Historically, ANOVA has been the most prevalent statistical method used in educational and psychological research and today ANOVA continues to be widely used. A comprehensive review published in 1998 examined several APA journals and discovered persistent concerns in ANOVA reporting practices. The present authors examined all articles published in 2012 in three APA journals (Journal of Applied Psychology, Journal of Counseling Psychology, and Journal of Personality and Social Psychology) to review ANOVA reporting practices including p values and effect sizes. Results indicated that ANOVA continues to be prevalent in the reviewed journals as a test of the primary research question, as well as to test conditional assumptions prior to the primary analysis. Still, ANOVA reporting practices are essentially unchanged from what was previously reported. However, effect size reporting has improved.

Experimental densities and speeds of sound at temperatures 293.15, 298.15, 303.15, 308.15, and 318.15 K and viscosities at 298.15 K are reported in this paper for the binary liquid mixtures of ethanol + benzene and ethanol + pyridine over the entire composition range and atmospheric pressure. From these experimental data, the excess molar volumes, VE, deviations in viscosity, Δη, and isentropic compressibility ΔKs were calculated and fitted by the Redlich-Kister polynomial to determine the adjustable fitting parameters and the standard deviations. The optimal number of adjustable parameters to be included in a Redlich-Kister polynomial for fitting thermodynamic excess and deviation data has been determined through F-test. The variation of excess and deviation functions with composition and temperature has been interpreted in terms of molecular interactions between the components of the mixture and structural effects. [ABSTRACT FROM AUTHOR]

Herein, we establish the most appropriate model of exchange rate dynamics using computer modeling, mean-error indicators of approximation, and the average quadratic divergence with the Fourier series approach and timedependent behavior in a time series. This study was based on 360 daily observations of EUR/AZN currency exchanges covering the time period 03.02.2017-03.08.2018. Our main assumptions were: (1) the ability to describe the global dynamics of exchange rates by approximating the combinations of the linear trend and harmonic oscillations of various frequencies relative to this line; and (2) the possibility of developing a high-precision algorithm for short-term forecasting of changes in exchange rates. Harmonious oscillations were separated by using methods for the harmonic analysis of the table error in MS EXCEL. Eviews was used to calculate statistical estimates of the coefficients of factor variables with the types of sines and cosines that are suitable for all possible frequencies. Dynamic forecasting of exchange rates was enabled by setting up harmonious oscillatory models with straight-line trends. The necessary statistical procedures were implemented for authentication of the established models, evaluation of parameters, and verification of the algorithm's adequacy. [ABSTRACT FROM AUTHOR]

Background: Traditional formulations of salicylic acid in ointment bases have disadvantages of being greasy and irritant due to free crystals.

In industry, many phenomena and events that arise that cannot be predicted because they represent changes in production. Such random effects and events can significantly influence all aspects of the manufacturing process. The optimal design of any production system can be generated based on a complete, correct set of input information. However, such a requirement is unrealistic, as manufacturing systems are affected by several random factors. Current practice is based on determining the worst possible conditions in which a production system could run (the longest possible duration of outages in the supply chain, extreme weather conditions in agriculture, etc.). This article aimed to identify factors influencing the variability of the manufacturing process in the field of CNC (Computer Numerical Control) machining for food production. A secondary aim was to minimise the variability of the manufacturing process using a factorial design. The variability reduction was verified using statistical F-tests. The study on reducing variability in production was performed at the Czech Yuncheng Plate Making Co., Ltd., a professional rotogravure cylinder–making company. The cylinders are used for the food industry.