Set Of Statistical Parameters Research Articles

OLE! Dairy model

The process of intensification of the dairy sector has been characterized in recent decades by the increase in milk production per hectare, the increase in livestock density, the inclusion of more concentrates in the diet, and the improvement of the genetic merit of dairy cows. The use of models has productive, environmental, and economic advantages. The objectives of the study were to describe a new model, “OLE! Dairy model”, to (a) simulate the biophysical performance of a pasture-based dairy production system; (b) evaluate the predictive capacity of the model with a set of statistical parameters, comparing its results with the biophysical performance of experimental studies of dairy farm systems, and (c) calibrate by adjusting the technical coefficient. The experimental design combines two feeding strategies with a different proportion of pasture in the diet and two animal genotypes. We make a description of the biophysical component and the calculations proposed in the “OLE! Dairy model”. Then a variety of parameters was calculated for model testing, including the Mean Squared Error, the Relative Prediction Error, the square root of the MSE, the Concordance Correlation Coefficient, and the Model Efficiency. The model presented a good predictive capacity for stocking rate and concentrate, pasture, and reserve intake. The predictive capacity of the model for individual production and area production improves after performing a rapid calibration, which allows for avoiding overestimations or underestimations that generate erroneous measurements in the planning and management of milk production systems, and can be adjusted to different conditions of production of the region.

Assessing the statistical validity of momentum-deficit-based [formula omitted] measurements in turbulent configurations

An application-agnostic procedure is outlined for checking the validity of momentum-deficit-based drag measurements performed under different turbulent conditions in a wind tunnel. The approach defines a two-step methodology: the first stage characterizes the turbulent flowfield generated downstream a passive grid through a set of statistical parameters. Acceptable values for such parameters are determined by means of two criteria: compliance with the threshold value set by an analysis of the experimental uncertainties, and fulfilment of the isotropic condition for ensuring a well-established turbulent flowfield. Those two prerequisites define a set of turbulent configurations for which the momentum-deficit-based technique applies feasibly.The second stage of the procedure is configuration-specific, and undertakes drag measurements upon a NACA0021 airfoil subjected to a set of different turbulent configurations. It is shown that performing measurements under invalid turbulent conditions leads to inconsistent drag curves, which serves for defining a validity map based on the testable cases.

A novel approach for automatic Bengali question answering system using semantic similarity analysis

Finding the semantically accurate answer is one of the key challenges in advanced searching. In contrast to keyword-based searching, the meaning of a question or query is important here and answers are ranked according to relevance. It is very natural that there is almost no common word between the question sentence and the answer sentence. In this paper, an approach is described to find out the semantically relevant answers in the Bengali dataset. In the first part of the algorithm, a set of statistical parameters like frequency, index, part-of-speech (POS) is matched between a question and the probable answers. In the second phase, entropy and similarity are calculated in different modules. Finally, a sense score is generated to rank the answers. The algorithm is tested on a repository containing a total of 275,000 sentences. This Bengali repository is a product of Technology Development for Indian Languages (TDIL) project sponsored by Govt. of India and provided by the Language Research Unit of Indian Statistical Institute, Kolkata. The shallow parser, developed by the LTRC group of IIIT Hyderabad is used for POS tagging. The actual answer is ranked as 1st in 82.3% cases. The actual answer is ranked within 1st to 5th in 90.0% cases. The accuracy of the system is coming as 97.32% and precision of the system is coming as 98.14% using confusion matrix. The challenges and pitfalls of the work are reported at last in this paper.

Features extraction from non-destructive testing data in cyber-physical monitoring system of construction facilities

Current paper presents a features extraction method when analyzing the acoustic emission (AE) control data of the technical condition of construction facilities as a part of decision support in cyber physical monitoring system. A set of statistical parameters that describe local properties of AE time series in time and frequency domains has been proposed. Furthermore, a features calculation method based on utilizing sliding windows with overlays in two time scales was introduced. Presented method has been pilot-tested during the technical diagnostics of the oil tank containing defects of different hazard classes.

Evaluation of the differentiation ability of ecological points in generated environmental vector program "Ekada»

It is presented, that the quantitative characteristics of spring wheat are inherited in a complex process. In their determination, a significant role is played by genetic systems which are expressed depending on environmental factors. The task of ecological selection is to select forms with a wide reaction norm on changing the factors, which are limiting growth and development of cenoses. A new method for the dissociation of phenotypic dispersion in a three-factor dispersion analysis is proposed. The method is based on the average squares of variations and the expected structure of the average squares of variations. For the selection of genotypes with wide adaptation, one of the main conditions is a creation of a model ecological gradient. In the article, the example of the “Ekada” program demonstrates the formation of a similar gradient, the so-called "ecological vector". The ecological point, where the factors limiting the growth and development of plants are the most concentrated, is Bezenchuk (B). At the vector points of the Ulyanovsk (U) and Kazan (K), conditions are most favorable for the formation of a high crop. At the center of the vector is Karabalyk (F) with high parameters of the differentiating capacity of the environment, but atypical for the vector as a whole. The points Penza (P) and Chishmy (Ch) change position, approaching the left, then to the right points. Each point is characterized by a set of statistical parameters. An ecological vector B → (Ch) → F → (P) → U → K with a different pressure spectrum of the limiting factors of the environment in ontogeny along ecological points is formed. When selecting according to polygenic quantitative characteristics, the selectioner must take into account that individual genes and genotypic systems manifest differently in different combinations of environmental factors, i.e. to select less on a genotype, than on an epigenotype. It is demonstrated, that by the ratio of the share of the genotype-environment and the genotype component of the phenotype by the quantitative trait (χg/e / χg) the selection by quantitative characteristics along the ecological vector is several times more effective than at the local ecological point. In none of the points of the ecological vector, nor on one trait, direct selection is not advisable (in comparison with ecological selection).
 
 
 

Accuracy Estimation of the Modern Core Magnetic Field Models Using DMA-Methods for Recognition of the Decreased Geomagnetic Activity in Magnetic Observatory Data

A new approach to identify the signals of the Earth’s main magnetic field (core field) based on the magnetic observatory data processing is suggested. The algorithms implemented in the approach are based on the Discrete mathematical analysis (DMA). The developed method is used to analyze the data from 49 midand low-latitude observatories of the INTERMAGNET network collected in 2011–2015. The results are compared with the classical method for determining the periods of low magnetic activity of external origin which is adopted by the International Association for Geomagnetism and Aeronomy (IAGA). The advantages of the suggested new approach are demonstrated. Based on the data records for the selected time intervals, the time series of the core field components and their secular variations are obtained for each observatory. These data are compared to the values predicted by the most accurate core field models: SIFM, CHAOS-6, and EMM-2015. The accuracy of the models is estimated using a set of statistical parameters: Pearson’s coefficient of linear correlation, Spearman’s and Kendall’s coefficients of rank correlation, the mean and median values over the data sets, and the mean difference between the data obtained by the suggested method from observatory measurements and the model predictions.

ON INTERDISCIPLINARY COMPARATIVE STUDY OF ANALOGICAL FEEDBACK/ASSESSMENT MODELS APPLIED IN BLENDED LEARNING VERSUS COMPUTER AIDED LEARNING USING ARTIFICIAL NEURAL NETWORKS

This paper provides educationalists as well as researchers in computer science and engineering with a study of an interdisciplinary challenging pedagogical issue. More specifically, the presented study resulted in a set of interesting findings that originated from adopted realistic Artificial Neural Network (ANNs) modeling, which is associated with two educational analogical feedback/assessment processes. This piece of research considers comparative analysis and evaluation study of an educational phenomenon issue for two diverse teaching/learning methodologies namely :{ Blended Learning (BL) and Computer Aided Learning (CAL)}. More precisely, the introduced issue of this work addresses the two summative and formative assessment processes applied in educational field practice. Accordingly, this issue is concerned mainly with the modeling of two practical field case studies considering the two items of educational feedback/assessment. In other words, assessment is used in many ways in education, a great deal of attention is now given to its use in helping teaching and learning, described as the two performance assessment items (summative and formative). These are defined as: assessment for learning (A f L), or formative assessment, and assessment of learning (AO L), or summative assessment. It is noticed that (AO L), is focused on which is used to summarize what students know or can do at certain times in order to report achievement and progress. Herein, two parametric factor values of ANN (gain, and learning rate factors), have been considered for the two suggested instructional methodologies. That is considered in order to compare, analyze, and evaluate dynamically two items of academic performance namely: (Academic achievement outcome & Learning convergence time) for both methodologies. Interestingly, the running realistic ANN computer modeling for different numbers of neurons -that are contributing to the learning process- results in an investigative, comprehensive, and innovative systematic analysis of individual students’ differences. Finally, after performing a perceptive evaluation comparing two case studies of obtained experimental field results, two interesting findings have been concluded. Firstly, while comparing computed sets of statistical parameters, associated with the presented instructional methodologies (CAL & BL). That resulted in the observed analogy between both sets. Secondly, in the context of Feedback/Assessment performance, regarding both (BL& CAL); either Formative or Summative feedback /assessments, have been observed to be well analogous to each other.

A new deformable model based on fractional Wright energy function for tumor segmentation of volumetric brain MRI scans

Background and objectivesThe MRI brain tumors segmentation is challenging due to variations in terms of size, shape, location and features’ intensity of the tumor. Active contour has been applied in MRI scan image segmentation due to its ability to produce regions with boundaries. The main difficulty that encounters the active contour segmentation is the boundary tracking which is controlled by minimization of energy function for segmentation. Hence, this study proposes a novel fractional Wright function (FWF) as a minimization of energy technique to improve the performance of active contour without edge method. MethodIn this study, we implement FWF as an energy minimization function to replace the standard gradient-descent method as minimization function in Chan–Vese segmentation technique. The proposed FWF is used to find the boundaries of an object by controlling the inside and outside values of the contour. In this study, the objective evaluation is used to distinguish the differences between the processed segmented images and ground truth using a set of statistical parameters; true positive, true negative, false positive, and false negative. ResultsThe FWF as a minimization of energy was successfully implemented on BRATS 2013 image dataset. The achieved overall average sensitivity score of the brain tumors segmentation was 94.8 ± 4.7%. ConclusionsThe results demonstrate that the proposed FWF method minimized the energy function more than the gradient-decent method that was used in the original three-dimensional active contour without edge (3DACWE) method.

Effect of water absorption ratio on tensile strength of red sandstone and morphological analysis of fracture surfaces

Brazilian disc tests were undertaken on a number of red sandstone samples with different water absorption ratios. The tensile strength of the red sandstone decreases as the water absorption ratio increases. The fracture surfaces of failed red sandstone discs were scanned by Talysurf CLI 2000. With the aid of Talymap Gold software, based on ISO25178, a set of statistical parameters was obtained for the fracture surfaces. The maximum peak height (S p), maximum pit height (S v) and maximum height (S z) of the fracture surfaces exhibited the same decreasing trend with increasing water absorption. S a and S ku values for the fracture surfaces showed a downward trend as the water absorption ratio increased. The fractal dimensions of fracture surfaces were calculated and found to decrease as the water absorption ratio increased. Through analysis of PSD curves, the smallest dominant wavelength was observed to reflect the roughness of the fracture surfaces. Additionally, the results suggest that the roughness of fracture surfaces becomes small as the water absorption ratio increases.

EVALUATION OF DISPERSION MODEL PERFORMANCE IN PREDICTING SO2 CONCENTRATIONS FROM PETROLEUM REFINERY COMPLEX

The AERMOD and CALPUFF air dispersion models are tested for their performance in predicting ground level concentration of sulfur dioxide in Thailand. Emission data used in this study are obtained from petroleum refinery complex. Predicted results are compared with those measured data using the year 2012 as a reference year. A set of statistical parameters are employed to evaluate model performance. Overall results indicated that both AERMOD and CALPUFF can provide good results. However, AERMOD can perform better in predicting of extreme end of the concentration distribution at the receptor sites. The maximum ground level concentrations of sulfur dioxide within the modeling domain are about 359 and 456 µg/m 3 for AERMOD and CALPUFF simulations, respectively. This result indicates that CALPUFF provides more conservative of maximum result than predicted data from AERMOD. The decision to select an appropriate dispersion model in the study is accomplish by using the Multi-Criteria Attribute (MCA) analysis. Result from MCA supports that AERMOD is more appropriate to be applied for study of air dispersion in this area than CALPUFF system.

SHARE with CHARM

SHARE with CHARM program (SHAREv3) implements the statistical hadronization model description of particle production in relativistic heavy-ion collisions. Given a set of statistical parameters, SHAREv3 program evaluates yields and therefore also ratios, and furthermore, statistical particle abundance fluctuations. The physical bulk properties of the particle source is evaluated based on all hadrons produced, including the fitted yields. The bulk properties can be prescribed as a fit input complementing and/or replacing the statistical parameters. The modifications and improvements in the SHARE suite of programs are oriented towards recent and forthcoming LHC hadron production results including charm hadrons. This SHAREv3 release incorporates all features seen previously in SHAREv1.x and v2.x and, beyond, we include a complete treatment of charm hadrons and their decays, which further cascade and feed lighter hadron yields. This article is a complete and self-contained manual explaining and introducing both the conventional and the extended capabilities of SHARE with CHARM. We complement the particle list derived from the Particle Data Group tabulation composed of up, down, strange $u,d,s$ quarks (including resonances) with hadrons containing charm $c,\bar c$ quarks. We provide a table of the charm hadron decays including partial widths. The branching ratios of each charm hadron decays add to unity, which is achieved by including some charm hadron decay channels based on theoretical consideration in the absence of direct experimental information. A very successful interpretation of all available LHC results has been already obtained using this program.

Logistic Regression Realized with Artificial Neuron and Estimation Formulas

Abstract In the paper an experiment is described, that was designed and conducted to verify hypothesis that artificial neuron with sigmoidal activation function can efficiently solve the task of logistic regression in the case when the explaining variable is one-dimensional, and the explained variable is binomial. Computations were performed with 12 sets of statistical parameters, assumed for the generation of 65356 sets of data in each case. Comparative analysis of the obtained results with use of the reference values for the regression coefficients indicated that the investigated neuron can satisfactory perform the task, with efficiency similar to that obtained with classical logistic regression algorithm, when the teaching sets of input data, corresponding with output values 0 and 1, do not allow for simple separation. Moreover, it has been discovered that the simple formulas estimating the statistical distributions parameters from the samples, offer statistically superior assessment of the regression coefficient parameters.

PREDICTION OF STEM VOLUME OF ALSTONIA MACROPHYLLA GROWING AS EVEN-AGED MONOCULTURES USING DIAMETER AT BREAST HEIGHT AND TOTAL HEIGHT

A growth model to predict the stem volume of Alstonia macrophylla was constructed in this study using data collected from 23 plantations from Galle, Matara, Ratnapura and Kalutara districts. Trees own variables were mainly used as explanatory variables in this study and in addition, site quality indices were also tested. At the preliminary analysis, two different site quality types were identified for the selected plantations. Therefore, first, each site quality data set was separately fitted to the theoretical models developed in this study. The selected models indicated a high modelling efficiency and a negligible bias. Although the possibility of using a common model for both site types was tested, results proved that it was not possible. Therefore it was decided to recommend two models (same structure with different sets of statistical parameters) for two site types for the field use with the help of a given guideline to identify the plantations grown in two site types.

English

QSAR studies of thirty 2-arylbenzoxazole derivatives are carried out to probe their inhibitory activity against Cholesteryl Ester Transfer Protein (CETP). QSAR models have been obtained using multiple linear regression (MLR) analysis after manifestation of forward selection algorithm to cull significant descriptors out of descriptor’s pool. QSAR models are elected with adherent set of statistical parameters with R2=0.9431 and R2=0.9069. Validation of modeling includes method of Y-Scrambling and in addition to this, some other methods of validation. Moreover, QSAR approach of 2-arylbenzoxazoles are also attempted, supported and validated by flexible docking studies as well. The search strategies include evolutionary algorithm and edited form of Gehlhaar PLP scoring function. The same set of thirty candidates from 2-arylbenzoxazole derivatives is processed in molecular docking and their docking scores are found in agreement with QSAR studies reported. Remarkable CETP inhibitory activity is exhibited by a 2-arylbenzoxazole derivatives molecule 29C with most comprehensive set of interactions with rerank scores -102.167 and RMSD values 1.104. Hydrogen bond interactions along with hydrophobic and electrostatic interactions are mapped to confirm their potencies.

Conduction patterns in the coronary sinus during atrial fibrillation and their modification by pulmonary vein isolation

mathematical AV node model for which the parameters can be estimated from short-term analysis of the surface electrocardiogram. Methods: Atrial impulses are assumed to arrive to the AV node according to a Poisson process with a mean arrival rate. Each atrial impulse arriving at the AV node will result in a ventricular contraction, unless the atrial impulse is blocked. The atrial impulses are blocked at the AV node according to a timedependent probability, modeling AV nodal refractory period and concealed conduction. An atrial impulse arriving close in time to the previous ventricular contraction is more likely to be blocked; all atrial impulses arriving before the end of the AV nodal refractory period are blocked. Two different refractory periods (tau1 and tau2), corresponding to dual AV nodal paths, as well as the probability of an atrial impulse choosing either of these, are used in the model. The mean arrival rate is estimated by the AF frequency, obtained from the atrial activity of the surface electrocardiogram, and the shortest refractory period (tau1) is estimated from the lower envelope of the Poincare plot of the RR series. The other parameters characterizing the AV model are obtained from the series by means of maximum likelihood estimation. The output of the AV model is the probability density function (PDF) of the time for which the ventricular activations occur. The average absolute error between the normalized RR histogram and the estimated PDF is computed for bins of 20-millisecond size, spaced between 0 and 2 seconds. In addition, the absolute error between the mean and the variance of RR series and of the PDF is assessed. The model was tested on 115 recordings from patient with AF and congestive heart failure (NYHA II-III) enrolled in the MUerte Subita en Insuficiencia Cardiaca study. Results: The median error is 0.0021, and the 25th/75th percentile error is 0.0017/0.0026. We judged the maximum acceptable error to be 0.0025. Using this threshold, 81 (70%) of 115 recordings were well fitted by the model. The average error between the mean and the variance is 0.01 ± 0.02 and 0.01 ± 0.01, respectively. Conclusion: These preliminary results are encouraging because certain AV nodal properties can be noninvasively characterized by a set of statistical parameters with electrophysiological interpretation.

Flexural strength of infrared-transmitting window materials: bimodal Weibull statistical analysis

The results of flexural strength testing performed on brittle materials are usually interpreted in light of a "Weibull plot," i.e., by fitting the estimated cumulative failure probability (CFP) to a linearized semiempirical Weibull distribution. This procedure ignores the impact of the testing method on the measured stresses at fracture-specifically, the stressed area and the stress profile-thus resulting in inadequate characterization of the material under investigation. In a previous publication, the author reformulated Weibull's statistical theory of fracture in a manner that emphasizes how the stressed area and the stress profile control the failure probability distribution, which led to the concept of a characteristic strength, that is, the effective strength of a 1-cm2 uniformly stressed area. Fitting the CFP of IR-transmitting materials (AlON, fusion-cast CaF2, oxyfluoride glass, fused SiO2, CVD-ZnSe, and CVD-ZnS) was performed by means of nonlinear regressions but produced evidence of slight, systematic deviations. The purpose of this contribution is to demonstrate that upon extending the previously elaborated model to distributions involving two distinct types of defects-bimodal distributions-the fit agrees with estimated CFPs. Furthermore, the availability of two sets of statistical parameters (characteristic strength and shape parameter) can be taken advantage of to evaluate the failure-probability density, thus providing means of assessing the nature, the critical size, and the size distribution of surface/subsurface flaws.

A Real-Time Intelligent Wireless Mobile Station Location Estimator with Application to TETRA Network

Mobile location estimation has received considerable interest over the past few years due to its great potential in different applications such as logistics, patrol, and fleet management. Many mobile location estimation techniques had been proposed to improve the accuracy of location estimation. Location estimation based on artificial intelligence techniques is a recent alternative approach. In this paper, adaptive neuro-fuzzy inference system (ANFIS) is used as a robust location estimator to locate the mobile station (MS) using the MS geo-fencing area data within 9 km from a serving base station. Extensive evaluations and comparisons have been performed, and a set of statistical parameters has been obtained. From the comparison of the proposed ANFIS estimator with the neural-network-based estimators, it is found that ANFIS estimator is faster and more robust. Its average computation time (ACT) is 0.076 sec. While the ACT for multilayer perceptron (MLP) and radial-based function (RBF) neural networks is 0.88 and 1.7, respectively. Whereas on comparing ANFIS with other techniques, it is found that in ANFIS estimator, 67 percent of the estimated location errors do not exceed 149 m, while these for the statistical, multiple linear regression, and geometric are 170, 280, and 2,346 m, respectively. Thus, the results clearly reveal that the proposed ANFIS estimator outperforms all other techniques.

Sensitivity of probabilistic fracture mechanics analysis to variations in statistical parameters

Probability of failure ( p f) of a structure is usually calculated for a specified set of statistical parameters (mean, standard deviation, and probability distribution) that characterize random variables. This approach may not be efficient in cases where one would like to know the effect of variations in statistical parameters on the probability of failure. A method based on generating and analyzing randomly selected statistical parameters is proposed. The method consists of generating databases of mean and coefficient of variation (COV = mean/standard deviation) values of relevant fracture mechanics variables through a random process. The method was applied to surface cracks in a flat wide plate loaded under elastic conditions. Probability of failure was calculated for each database record using the first-order reliability method (FORM). Multiple linear regression analyses of the database records were performed with p f as dependent variable and statistical parameters as independent variables. The predicted p f values were in very good agreement with the directly calculated p f values for the specified variations of statistical parameters (±10%, ±15%, and ±20%), except those for fracture toughness and tensile stress, where variations should be limited to ±10% and ±15% ranges.

Data Compression of Measured Spun-yarn Irregularity Using a Wavelet–Stochastic Hybrid Method

Modern high-speed spinning machines are equipped with automated inspection and monitoring systems that generate large quantities of data, necessitating special methods for their analysis. A hybrid system, based on a stochastic model and wavelet analysis was developed and applied to a large data set (500 MB) of yarn diameters. Using a wavelet– stochastic hybrid approach, the data set could be reduced to a small fraction of its original size consisting of a set of statistical parameters for normal sub-blocks, a set of statistical parameters for abnormal sub-blocks for characterizing yarn and fabric defects and for providing their spatial information, and a set of wavelet coefficients for abnormal sub-blocks. The compression ratio was approximately 100 000:1 in the example given.

A Calibration Tutorial for Spectral Data. Part 1: Data Pretreatment and Principal Component Regression Using Matlab

Regression and calibration play an important role in analytical chemistry. All analytical instrumentation is dependent on a calibration that uses some regression model for a set of calibration samples. The ordinary least squares (OLS) method of building a multivariate linear regression (MLR) model has strict limitations. Therefore, biased or regularised regression models have been introduced. Some selected ones are ridge regression (RR), principal component regression (PCR) and partial least squares regression (PLS or PLSR). Also, artificial neural networks (ANN) based on back-propagation can be used as regression models. In order to understand regression models more is needed than just a set of statistical parameters. A deeper understanding of the underlying chemistry and physics is always equally important. For spectral data this means that a basic understanding of spectra and their errors is useful and that spectral representation should be included in judging the usefulness of the data treatment. A “constructed” spectrometric example is introduced. It consists of real spectrometric measurements in the range 408–1176 nm for 26 calibration samples and 10 test samples. The main response variable is litmus concentration, but other constituents such as bromocresolgreen and ZnO are added as interferents and also the pH is changed. The example is introduced as a tutorial. All calculations are shown in detail in Matlab. This makes it easy for the reader to follow and understand the calculations. It also makes the calculations completely traceable. The raw data are available as a file. In Part 1, the emphasis is on pretreatment of the data and on visualisation in different stages of the calculations. Part 1 ends with principal component regression calculations. Partial least squares calculations and some ANN results are presented in Part 2.