Standard Error Of Regression Research Articles

Experimental study of gas adsorption using high-performance activated carbon: Propane adsorption isotherm

Activated carbon is widely used for its diverse adsorptive abilities, with a vast range of current and emerging uses. This study developed a data set for high-performing activated carbon, its adsorption abilities with differing adsorbents, and an understanding of what deviations are present compared to the widely used adsorption models. This study included the construction of Tóth isotherms in varying conditions. Building a strong isotherm correlation is desired, with an understanding of the relationship between the pores of the activated carbon sample, operating parameters, and the adsorbent. The present data could complement efforts in designing adsorbed natural gas storage systems. Experimental data was collected using a Constant Volume Variable Pressure (CVVP) apparatus, consisting of a temperature-regulated vessel containing the activated carbon sample dosed with varying adsorbents through a controlled dosing vessel. Analysis of the derived data gave a well-fitted Tóth adsorption isotherm, giving the maximum specific adsorption capacity of the activated carbon to be 2.28 g of propane per gram of activated carbon with a standard error of regression

Cotton morphological traits tracking through spatiotemporal registration of terrestrial laser scanning time-series data.

Understanding the complex interactions between genotype-environment dynamics is fundamental for optimizing crop improvement. However, traditional phenotyping methods limit assessments to the end of the growing season, restricting continuous crop monitoring. To address this limitation, we developed a methodology for spatiotemporal registration of time-series 3D point cloud data, enabling field phenotyping over time for accurate crop growth tracking. Leveraging multi-scan terrestrial laser scanning (TLS), we captured high-resolution 3D LiDAR data in a cotton breeding field across various stages of the growing season to generate four-dimensional (4D) crop models, seamlessly integrating spatial and temporal dimensions. Our registration procedure involved an initial pairwise terrain-based matching for rough alignment, followed by a bird's-eye view adjustment for fine registration. Point clouds collected throughout nine sessions across the growing season were successfully registered both spatially and temporally, with average registration errors of approximately 3cm. We used the generated 4D models to monitor canopy height (CH) and volume (CV) for eleven cotton genotypes over two months. The consistent height reference established via our spatiotemporal registration process enabled precise estimations of CH (R 2=0.95, RMSE = 7.6cm). Additionally, we analyzed the relationship between CV and the interception of photosynthetically active radiation (IPAR f ), finding that it followed a curve with exponential saturation, consistent with theoretical models, with a standard error of regression (SER) of 11%. In addition, we compared mathematical models from the Richards family of sigmoid curves for crop growth modeling, finding that the logistic model effectively captured CH and CV evolution, aiding in identifying significant genotype differences. Our novel TLS-based digital phenotyping methodology enhances precision and efficiency in field phenotyping over time, advancing plant phenomics and empowering efficient decision-making for crop improvement efforts.

The usage of the autoregressive integrated moving average model for forecasting milk production in Egypt (2022-2025).

Milk is considered one of the most important capital goods and essential sources of animal protein in the diet of the Egyptian family, as well as an effective means to improve the economic condition of farmers, considering this important view, the policymakers need accurate and advance information regarding future supply for planning on the both short and long term. The study aims to forecast the production of milk in Egypt during the period from 2022 to 2025 using the Autoregressive Integrated Moving Average (ARIMA) model using time series data of milk production (MP) (1970-2021) obtained from the Central Agency for public mobilization and statistics (CAPMS). Augmented Dickey-Fullar Unit Root test, Partial autocorrelation function (PACF), and Autocorrelation function (ACF) of the time series sequence were used to judge the stationarity of the data. After confirming the stationarity of the data, the appropriate ARIMA model was selected based on certain statistical parameters like significant coefficients, values of adjusted R-squared, Akaike information criteria (AIC), Schwarz criterion (SC), and Standard Error of Regression. After the selection of the model based on the previous parameters, the verification of the model was employed by checking the residuals of the Correlogram-Q-Statistics test. The most fitted model to predict the future levels of MP in Egypt was ARIMA (1, 1, and 3). Using the ARIMA (1, 1, 3) model, it could be forecasted that the production of milk in Egypt would show an increasing trend from 6,152.606 thousand tons in 2022 to 6,360.829 thousand tons in 2025.

A Model Study of Strategies for Optimizing the Educational Environment and Enhancing Student Engagement in Civics and Political Science Courses

Abstract This paper briefly outlines the direction of optimization of the civic education environment, proposes a civic education resource sharing model and an incentive model for educational resource sharing using the resource sharing approach, defines the main body of resource sharing, and establishes a model of the incentive system with students as the main body. Analyze the symbolic meaning of the model and propose the assumption of an educational resource incentive. Combine the revenue maximization function, participation constraints, and incentive constraints to form the incentive model. Using the decision tree algorithm of data mining technology for student participation behavior mining, we get the overall participation average score of undergraduate group cooperative learning, which is 3.576, the overall level of involvement is moderately high. The grade level trend of undergraduate group cooperative learning participation is freshman>fourth year>three years>sophomore years. Combined with the simulation results of the incentive model to analyze the role of the incentive model on the behavioral input of student engagement, the standard error of regression of the teaching incentive of the educational environment of the Civics class on student engagement behavior is 0.473, and the overall mean of the teaching incentive perceived by the students is 3.77, which indicates that the incentive strategy implemented in the educational environment of the Civics class is more satisfactory to the students.

Bayesian Network Design for Crude Palm Oil (CPO) Price Prediction Driven by Fluctuation Patterns and Trends

Crude Palm Oil (CPO) becomes the alternative to petroleum because it can be refined into different products, such as biodiesel. The price of crude palm oil can be considered the future direction of the world economy. CPO prices are based on market demand and the oil supply from the CPO producer, where they fluctuate and have a huge influence on economy. The price pattern of CPO requires a variety of factors to predict accurately. It needs a lot of data analytics to predict and respond in quick time to the highly variable CPO market. In this work, a dynamic probability model is proposed to predict CPO prices using the Bayesian rule. The rule is formed to be effective in responding to changeable market demands and supplies. It formulates the approximation factors for indicating the expected price of the CPO for 12 months. The dynamic training data has been modelled to fit the Bayesian rule. The process of Prediction and hypothesizing price fluctuations is identified according to several level of Bayesian rule. It is then used to generate a measure of deviation between the actual and probable prices. Our Bayesian-based prediction pricing model is able to predict the price pattern that are comparable to existing benchmark data and shows a lower standard error of regression. The prediction approach can help the traders have a better analysis of price fluctuations in CPO demand and supply. Using Bayesian probability not only improves prediction rules, but it can also forecast CPO trades in high fluctuation situations.

Dental age estimation using the pulp/tooth ratio in lower canines in population of Bosnia and Herzegovina

Background/Aim:Dental age estimation in adults is a challenging process due to the lack of sufficient information on the accuracy of dental methods applied in the identification of persons without information on identity. Previous studies on dental age estimation in adults suggested that each method should be tested on a population other than one which was used to develop the specific method. The aim of this study was to estimate dental age in adults from Bosnia and Herzegovina with the radiographic analysis of the pulp/tooth ratio of lower canines and to determine differences from chronological age. Material and Methods: The sample for the research comprised 50 periapical radiographs, with visible intact lower canine teeth, derived from an archive of the Department of Dental Morphology, Dental Anthropology and Forensics. After the selection process, all radiographs were digitalized. Additional information used is the chronological age and sex of the person to whom the images belonged. The measurements of the pulp space and the lower canine tooth were performed using the ImageJ computer methods. The results of measurements were entered in formulas for assessments of dental age, as per equations given by the authors. Results: The research has shown that there is a significant difference in examined population between the dental age estimated by the analysis of pulp/tooth ratio of lower canine teeth and the chronological age. The standard error of regression for Cameriere model was 14.12 years, and this model proved to be statistically significant (p= 0.0017). Kvaal-Solheim model from 1994 has shown a standard error of 15.07 years, and did not prove to be statistically significant (p= 0.054), while another model from 1995, by the same authors, shows a standard error of 14.64 years, and it proved to be statistically significant (p= 0.011). Conclusions: It is possible to estimate dental age by means of pulp/tooth ratio. However, it is necessary to conduct further research that will include a larger number of examinees and different age groups and also consider the specificity of teeth in Bosnia - Herzegovina's population to acquire more accurate data on accuracy and reliability of those methods in this population.

Roadmap for outlier detection in univariate linear calibration in analytical chemistry: Tutorial review

AbstractAssessment of the adequacy of a proposed linear calibration curve is necessarily subjective in chemical analysis. If the outlier points in calibration are not identified and discarded, the constructed model will not have much validity and does not warrant the accuracy and precision of prediction step. Recognizing of influential points, outlier data, and discarding them is one of the steps in data processing that has been considered in various sciences. The outlier points can arise from (I) bad design of calibration set and (II) gross error in doing experiments. Therefore, we aimed to extract a map that recognizes the following issues: (A) the existence of data with high regression coverage that is far from the rest and will strongly affect the accuracy of the calibration equation, high leverage points; (B) large error in the experimental process: The recorded signal does not match the desired concentration; and (C) points with a concentration lower than the limit of quantification, which is calculated by considering the standard error of regression instead of the standard deviation of blank.The efficiency of the proposed roadmap will be reviewed, and this will give a new perspective on the calibration equation to avoid common mistakes in analytical chemistry. To achieve the above goal, visual and statistically significant tests will be used and all tests will be performed in a simple Microsoft Excel environment.

Further study of stress-strain deformation of some structural reinforcement steel rods with different diameters from a mini mill in Nigeria using theoretical and regression analysis

Further study of stress-strain deformation of some structural reinforcement steel rods with different diameters from a mini mill in Nigeria using theoretical and regression analysis has been undertaken. The work utilized result test of mechanical testing carried out on different sizes of reinforcement steel bars for concrete reinforcement ranging from 10 mm to 28mm in diameter. The deformation pattern of the work was highlighted using stress-strain graph and subjected to theoretical analysis, where the result showed that it is a ductile material with all the deformation regions associated with a ductile material. It equally has an elongation % of 9.36 at the point of failure during the test. Values of % elongation, ultimate tensile strength generated with different sizes of reinforcement steel bar were subjected to different regression models to establish their relationship, and to also find out which model best fit the relationship between the diameter variation of the steel rod and the % elongation, and the diameter variation of the steel rod and the ultimate tensile strength. The result showed that the relationship was linear, and linear regression model was better than hyperbolic curve model, and exponential function model. Therefore linear regression model was used to develop prediction model equations to estimate the values of % elongation and ultimate tensile strength. These models were evaluated using coefficient of determination r2, standard error of regression, confidence limits, standard errors of the intercept (a) and the gradient (b), confidence interval for intercept and gradient, and finally significance test was carried out on the intercept and the gradient. The standard error of regression for model equation Y1 was very small; 0.37, and that of model equation Y2 was 60.18. The coefficient of determination r2 was 21% for model equation Y1 and 1.49% for model equation Y2. The results also show that the general confidence interval has a narrower range than the individual confidence interval. The rank correlation coefficient has indicated that the association of the diameter variation of the steel rod was in perfect negative to the % elongation at failure and ultimate tensile strength. In conclusion this work has further thrown light to the stress-strain deformation of different diameters of structural reinforcement steel rods.

How precisely European equity ETFs mirror their flagship benchmarks? Evidence from funds replicating performance of Euro Stoxx 50 Index

The main goal of the article is to examine the tracking efficiency of a homogenous sample of 14 ETFs listed on European exchanges, replicating the performance of Euro Stoxx 50 Index—a benchmark index for blue chips from the euro area. This study provides some insights into the tracking quality of European ETFs over the long time horizon (2012–2021 period) including data from entire business cycle: both economic prosperity and COVID-19 crisis. The study has been made applying different tracking error calculation techniques and return intervals—daily, weekly and monthly. Passive investing may be a highly desirable, cheap and accurate method for long or short term investments in the largest 50 cap companies in the euro zone. Hence, this unique research may help to succeed in ETF selection process. The study reveals that ETFs are very effectively managed by keeping the TEs below 0.3% (for ETFs with accumulating share classes) and below 1% (for ETFs with distributing share classes). This shows that the ETFs with accumulating share classes perform much better—the average TE for three different methods is 0.11% for accumulating share classes ETFs and 0.33% for distributing share classes ETFs. It proofs, that it is not important whether to use the standard deviation of the difference between the return of an ETF and that of its benchmark index, or the standard error of regression in TE assessment, both methods give very similar results. However, TE calculation method signifies, if the average of the absolute difference between the return of an ETF and that of the index is used. Additionally, it is found that time intervals used in TE calculations matter—the shift from monthly to daily intervals results in reduction of TE levels. Using shorter intervals brings lower TE values of European ETFs.

Mapping Annual Urban Evolution Process (2001–2018) at 250 m: A normalized multi-objective deep learning regression

Global urbanization changes land cover patterns and affects the living environment of humans. However, urbanization and its evolution process, i.e., conversions among diverse land covers, are hard to measure, as existing land cover maps usually have low temporal resolutions; conversely, long-term and temporally dense land cover maps, such as vegetation-impervious-soil decomposition maps base on MODIS, ignore the important land cover of cropland in urban evolution process (UEP). To resolve the issue, this study suggests a novel model named time-extended non-crop vegetation-impervious-cropland (Time V-I-C) to represent and quantify different stages of UEP; then, a normalized multi-objective T-ConvLSTM (NMT) method is proposed to unmix cropland, non-crop vegetation, and impervious based on the intra-annual remotely-sensed time series, and obtain their fractions in each pixel for generating UEP maps. Consequently, UEP maps from 2001 to 2018 are generated for two Chinese urban agglomerations, i.e., Beijing-Tianjin-Hebei and Yangtze River Delta urban agglomerations. The mapping results have high accuracies with a small standard error of regression (SER) of 13.1%, small root mean square error (RMSE) of 12.6%, and small mean absolute error (MAE) of 8.4%, and the maps reveal the different UEP in the two urban agglomerations. Therefore, this study provides a new idea for expressing UEP and contributes to a wide range of urbanization studies and sustainable city development.

Development of an Analytical Model to Predict Stress–Strain Curves of Short Fiber-Reinforced Polymers with Six Independent Parameters

Mechanical properties of fiber-reinforced polymers are sensitive to environmental influences due to the presence of the polymer matrix but inhomogeneous and anisotropic due to the presence of the fibers. Hence, structural analysis with mechanical properties as a function of loading, environment, design, and material condition produces more precise, reliable, and economic structures. In the present study, an analytical model is developed that can predict engineering values as well as non-linear stress–strain curves as a function of six independent parameters for short fiber-reinforced polymers manufactured by injection molding. These parameters are the strain, temperature, humidity, fiber content, fiber orientation, and thickness of the specimen. A three-point test matrix for each independent parameter is used to obtain experimental data. To insert the effect of in-homogenous and anisotropic distribution of fibers in the analytical model, microCT analysis is done. Similarly, dynamic mechanical thermal analysis (DMTA) is done to insert the viscoelastic effect of the material. The least mean square regression method is used to predict empirical formulas. The standard error of regression for the fitting of the model with experimental stress–strain curves is closely controlled below 2% of the stress range. This study provides user-specific material data for simulations with specific material, loading, and environmental conditions.

Comparison of the analytical performances of X-ray fluorescence devices

Abstract X-ray fluorescence (XRF) is a technique frequently used for the elemental analysis of cultural heritage materials. Depending on components selection (i.e. the primary source, the detector and the focusing optics, if present), the analytical performance of the spectrometer, and its consequent suitability for a given purpose, may vary considerably. In this paper, we compare the analytical performance of four different devices, two commercial and two expressly designed by the authors, and we consider two figures of merit: the limit of detection (LOD) and the standard error of regression (SER). The measurements are performed on a set of 19 copper alloy standards.

Variable-Rate Fertigation Control for Hydroponics Using an On-The-Go Transpiration Monitoring System

HighlightsAn on-the-go monitoring system was developed for assessing spatial and temporal variations in crop growth.The system was used to realize real-time transpiration estimation for lettuce growth.A variable-rate fertigation system was developed based on on-the-go transpiration monitoring.The variable-rate fertigation system was validated in controlled-environment cultivation of hydroponic lettuce.Abstract. Variable-rate fertigation, as a key component of hydroponic cultivation, ensures the sustainable use of water. However, this fertigation method is challenging to implement because of the difficulty associated with assessing the water needs of plants, which vary with plant growth stage and environmental conditions. In this study, an on-the-go crop growth monitoring system was developed to measure the canopy cover of lettuce plants on a daily basis. The canopy cover during the vegetative growth period could be estimated with an accuracy of 98.5% ±1.7% compared to manual segmentation. A modified Penman-Monteith equation was used to predict the transpiration rate of the lettuce plants. In the first cultivation test, the transpiration rate model exhibited a highly linear relationship with a slope of 0.91, coefficient of determination of >0.9, and standard error of regression of <0.51, compared to the directly measured transpiration rates. In the second cultivation test, an automated system that could variably control the fertigation volume was incorporated with the on-the-go system for monitoring the lettuce transpiration in real time. The proposed system could realize on-the-go mapping of the spatial distribution of the canopy cover of the growing lettuce plants, and the variable fertigation responded to the daily water consumption of the plants with an error of only 7.3%. Moreover, the stable canopy cover growth indicated the higher efficiency of variable fertigation compared to timer-based fertigation systems. The proposed variable-rate fertigation system is a promising and practical approach to implement hydroponics with reduced water and energy consumption. Keywords: Canopy cover, Hydroponic, On-the-go monitoring, Transpiration, Variable fertigation.

Optimizing Daylight Glare and Circadian Entrainment in a Daylight-Artificial Light Integrated Scheme

Human circadian rhythm varies with different combinations of light sources. Natural light and dark patterns regulate the sleeping and waking cycles of the human body. This study aimed to develop a simplified model for daylight glare and circadian entrainment in an existing controlled daylight-artificial light integrated system. In addition, the effects of window orientation, occupant position, artificial light, daylight, and a combination of both on Circadian Stimulus (CS) were investigated. In a daylit interior space, window orientation, shading control and occupant seating position play a significant role in the CS. Simplified models to predict Daylight Glare Probability (DGP) and CS were estimated from the dataset. The developed nonlinear CS estimation models have R-squared values of 0.983 and 0.974. The precision of the models was evaluated in terms of standard error of regression (S) and AIC score. Multi-Objective Genetic Algorithm based optimization technique was developed using the above models to minimize glare and maximize CS in the working space. This optimisation methodology aids in implementing a complete automation algorithm for the test workbench to achieve visual comfort and circadian entrainment.

An adapted Weibull function for agricultural applications

The Weibull function is applied extensively in the life sciences and engineering but underused in agriculture. The function was consequently adapted to include parameters and metrics that increase its utility for characterizing agricultural processes. The parameters included initial and final dependent variables (Y0 and YF, respectively), initial independent variable (x0), a scale constant (k), and a shape constant (c). The primary metrics included mode, integral average, domain, skewness, and kurtosis. Nested within the Weibull function are the Mitscherlich and Rayleigh functions where c is fixed at 1 and 2, respectively. At least one of the three models provided an excellent fit to six example agricultural datasets, as evidenced by large adjusted coefficient of determination (RA2 ≥ 0.9266), small normalized mean bias error (MBEN ≤ 1.49%), and small normalized standard error of regression (SERN ≤ 8.08%). The Mitscherlich function provided the most probable (PX) representation of corn (Zea mays L.) yield (PM = 87.2%); Rayleigh was most probable for soil organic carbon depth profile (PR = 96.4%); and Weibull was most probable for corn seedling emergence (PW = 100%), nitrous oxide emissions (PW = 100%), nitrogen mineralization (PW = 58.4%), and soil water desorption (PW = 100%). The Weibull fit to the desorption data was also equivalent to those of the well-established van Genuchten and Groenevelt–Grant desorption models. It was concluded that the adapted Weibull function has good potential for widespread and informative application to agricultural data and processes.

Kinetics Modelling of Uncatalyzed Esterification of Acetic Anhydride with Isoamyl Alcohol in a Microreactor System

Uncatalyzed esterification of acetic anhydride and isoamyl alcohol has been carried out in a miniaturized intensified reactor (MIR) to develop a kinetic equation using an ideal homogenous model. The effect of reaction temperature (65℃-85℃) and residence time (7.9–180.3 min) at constant molar ratio 2:1 of isoamyl alcohol to acetic anhydride were studied. The results showed that isoamyl acetate concentration production increased from 0.80 mol/L to 2.64 mol/L with residence time from 7.9 min to 180.3 min and also increased from 1.6 mol/L to 2.0 mol/L when reaction temperature increase from 65℃ to 85 ℃. High conversion (>97%) of acetic anhydride was obtained in the absence of a catalyst in a microreactor system in a short residence time of 90 min as compared to days in a traditional batch reactor. This is because of the high surface-to-volume ratio of a microreactor system which promotes excellent proximate contact between reactants to allow efficient heat and mass transfer for esterification and effective mixing. Three different kinetic equations which are the second-order model, autocatalytic and reversible reaction were used to fit the experimental data. The latter kinetic model represented well for the esterification reaction between acetic anhydride and isoamyl alcohol with a good fit of 0.38 average standard error of regression between predicted concentration compare to experimental result. From the simulation result, hydrolysis of isoamyl acetate was found to be more favorable compared to acetic anhydride. This can be seen from the rate constant of reverse reaction of isoamyl acetate, where k3=0.008530Lmol.s which is significantly higher compare to hydrolysis of acetic anhydride, where k4=3.983×10−8Lmol.s at initial water concentration of 0.2 M. Even at different simulated initial water concentration, rate constant of hydrolysis of isoamyl acetate, k3 was always greater than the hydrolysis of acetic anhydride, k4. The parameter of initial water concentration was important to be determined in which it allows to improve the fitness of the kinetic model.

Alternative diastolic function models of ventricular longitudinal filling velocity are mathematically identical.

The spatiotemporal features of normal in vivo cardiac motion are well established. Longitudinal velocity has become a focus of diastolic function (DF) characterization, particularly the tissue Doppler e'-wave, manifesting in early diastole when the left ventricle (LV) is a mechanical suction pump (dP/dV < 0). To characterize DF and elucidate mechanistic features, several models have been proposed and have been previously compared algebraically, numerically, and in their ability to fit physiological velocity data. We analyze two previously noncompared models of early rapid-filling lengthening velocity (Doppler e'-wave): parametrized diastolic filling (PDF) and force balance model (FBM). Our initial numerical experiments sampled FBM-generated e'(t) contours as input to determine PDF model predicted fit. The resulting exact numerical agreement [standard error of regression (SER) = 9.06 × 10-16] was not anticipated. Therefore, we analyzed all published FBM-generated e'(t) contours and observed identical agreement. We re-expressed FBM's algebraic expressions for e'(t) and observed for the first time that model-based predictions for lengthening velocity by the FBM and the PDF model are mathematically identical: e'(t) = γe-αtsinh(βt), thereby providing exact algebraic relations between the three PDF parameters and the six FBM parameters. Previous pioneering experiments have independently established the unique determinants of e'(t) to be LV relaxation, restoring forces (stiffness), and load. In light of the exact intermodel agreement, we conclude that the three PDF parameters, relaxation, stiffness (restoring forces), and load, are unique determinants of DF and e'(t). Thus, we show that only the PDF formalism can compute the three unique, independent, physiological determinants of long-axis LV myocardial velocity from e'(t).NEW & NOTEWORTHY We show that two separate, independently derived physiological (kinematic) models predict mathematically identical expressions for LV-lengthening velocity (Doppler e'-wave), indicating that damped harmonic oscillatory motion is a physiologically accurate model of diastolic function. Although both models predict the same "overdamped" velocity contour, only one model solves the "inverse problem" and generates unique, lumped parameters of relaxation, stiffness (restoring force), and load from the e'-wave.

Predicting the Stock Market Index Using Stochastic Time Series Arima Modelling: The Sample of BSE and NSE

Stock market is basically volatile and the prediction of its movement will be more useful to the stock traders to design their trading strategies. An intelligent forecasting will certainly abet to yield significant profits. Many important models have been proposed in the economics and finance literature for improving the prediction accuracy and this task has been carried out through the modelling based on time series analysis. The main aim of this paper is to check the stationarity in time series data and predicting the direction of change in stock market index using the stochastic time series ARIMA modelling. The best fit ARIMA (0,1,0) model was chosen for forecasting the values of time series, viz., BSE_CLOSE and NSE_CLOSE by considering the smallest values of AIC, BIC, RMSE, MAE, MAPE, Standard Error of Regression, and the relatively high Adjusted R2 values. Using this best fitted model, the predictions were made for the period ranging from 7th January, 2018 to 3rd June, 2018 (22 expected values) using the weekly data ranging from 6th January, 2014 to 31st December, 2017 (187 observed values). The results obtained from the study confirmed the prospective of ARIMA model to forecast the future time series in short-run and would assist the investing community in making the profitable investment decisions.

Predicting the Stock Market Index using Stochastic Time Series ARIMA Modelling: The Sample of BSE and NSE

The stock market is basically volatile, and the prediction of its movement will be more useful to the stock traders to design their trading strategies. An intelligent forecasting will certainly abet to yield significant profits. Many important models have been proposed in the economics and finance literature for improving the prediction accuracy, and this task has been carried out through modelling based on time-series analysis. The main aim of this paper was to check the stationarity in time series data and predicting the direction of change in stock market index using the stochastic time series ARIMA modelling. The best fit ARIMA (0,1,0) model was chosen for forecasting the values of time series, that is, BSE_CLOSE and NSE_CLOSE by considering the smallest values of AIC, BIC, RMSE, MAE, MAPE, standard error of regression, and the relatively high adjusted R 2 values. Using this best fitted model, the predictions were made for the period ranging from January 7, 2018 to June 3, 2018 (22 expected values) using the weekly data ranging from January 6, 2014 to December 31, 2017 (187 observed values). The results obtained from the study confirmed the prospectives of ARIMA model to forecast the future time series in short-run and would assist the investing community in making profitable investment decisions.

Fast 3D rosette spectroscopic imaging of neocortical abnormalities at 3 T: Assessment of spectral quality.

To use a fast 3D rosette spectroscopic imaging acquisition to quantitatively evaluate how spectral quality influences detection of the endogenous variation of gray and white matter metabolite differences in controls, and demonstrate how rosette spectroscopic imaging can detect metabolic dysfunction in patients with neocortical abnormalities. Data were acquired on a 3T MR scanner and 32-channel head coil, with rosette spectroscopic imaging covering a 4-cm slab of fronto-parietal-temporal lobes. The influence of acquisition parameters and filtering on spectral quality and sensitivity to tissue composition was assessed by LCModel analysis, the Cramer-Rao lower bound, and the standard errors from regression analyses. The optimized protocol was used to generate normative white and gray matter regressions and evaluate three patients with neocortical abnormalities. As a measure of the sensitivity to detect abnormalities, the standard errors of regression for Cr/NAA and Ch/NAA were significantly correlated with the Cramer-Rao lower bound values (R = 0.89 and 0.92, respectively, both with P < 0.001). The rosette acquisition with a duration of 9.6 min, produces a mean Cramer-Rao lower bound (%) over the entire slab of 4.6 ± 2.6 and 5.8 ± 2.3 for NAA and Cr, respectively. This enables a Cr/NAA standard error of 0.08 (i.e., detection sensitivity of 25% for a 50/50 mixed gray and white matter voxel). In healthy controls, the regression of Cr/NAA versus fraction gray matter in the cingulate differs from frontal and parietal regions. Fast rosette spectroscopic imaging acquisitions with regression analyses are able to identify metabolic differences across 4-cm slabs of the brain centrally and over the cortical periphery with high efficiency, generating results that are consistent with clinical findings. Magn Reson Med 79:2470-2480, 2018. © 2017 International Society for Magnetic Resonance in Medicine.