Will it be duststorm or thunderstorm today ?

ABSTRACT. Attempt to develop a distinct technique for the prediction of duststorm or duststorm followed by thundershower during pre-monsoon season over Gwalior, has been made, Two mean 0000 UTC tephigrams have been produced for the days when the duststorms and thunderstorms occurred. Difference is highlighted in the 0000 UTC surface and TEMP data on the days of duststorm/duststorm followed by thundershower with those on the days of thunderstorm. Statistical analysis of the duststorm data over Gwalior has also been carried out with respect to direction, time, fortnight and month of occurrence of the event. For prediction of peak gust speed (PGS) of squall due to duststorm a single regression equation has been developed.

Pengaruh Kemampuan Verbal dan Kemampuan Numerik Terhadap Kemampuan Menyelesaikan Soal Sistem Persamaan Linear Bentuk Cerita

This study aims to determine the effect of verbal ability and numerical ability, the effect of verbal ability, and the effect of numerical ability on the ability of solving linear equation system problems. This study is an ex post facto research. The population of this study were students of class XI SMAN 9 Mataram. The sample in this study were 28 students of class XI SMAN 9 Mataram represented by four students in each class. The data analysis used is multiple linear regression analysis and simple linear regression analysis. From the results of the data analysis, there is a significant influence between verbal ability and numerical ability on the ability to solve linear equation system problems with a value of Fhitung = 3,660 > Ftabel = 3,39. The magnitude of this influence is written in the form of a regression equation, namely Y = 24,602 + 0,140X1 + 0,224X2. This regression equation shows that if the verbal ability score increases by 1 with a fixed numerical ability score, then the ability to solve math problems will increase by 0,140; and if the numerical ability score increases by 1 with a fixed verbal ability score, the value of the ability to solve story questions will increase by 0,225 and plus 24,602 from other factors.

Predictive value of kinematic indicators for shot put result and selection of novice athletes

Purpose: to determine the most significant kinematic indicators in the sports selection of beginner shot putters. Material and Methods: This study was carried out on a sample of 9 students at the fourth stage of the competition in Division 1, which took place in the 2017/2018 academic year at the Faculty of Physical Education of Maysan University. The following kinematic (biomechanical) parameters were analyzed: the angle of release of the nucleus, the velocity of release, the height of the point of ejection of the nucleus and the speed of swing. The correlation coefficients were determined between the kinematic indicators and the result in the shot put, as well as the regression equation for the dependence of the result in the shot put on the knematic indicators. The data obtained in the study were presented in the form of the arithmetic mean, standard deviation, median, skewness coefficient, Pearson's correlation coefficient, analysis of variance and linear regression, which included the contribution coefficients of each analyzed indicator, standard error, reliability of the regression equation as a whole, and reliability of the coefficients contribution to the shot put result of each kinematic exponent. Results. It has been shown that the swing speed has the greatest influence on the result in the shot put among beginner athletes. The swing speed, shot angle, shot speed and shot height have significant relationships with the shot put result. The multiple regression equation for the dependence of the shot put result on the swing speed, shot angle, shot height and shot point turned out to be reliable in general. However, only the swing speed has a reliable coefficient of the regression equation. The shot angle tends to be the determining factor in the shot put result. The release rate and the height of the release point have significant correlations with the shot put result, although in the regression equation they have unreliable indicators of influence on the shot put result. Conclusions. When teaching beginner shot putters, the greatest attention should be paid to the pushing swing technique, namely the swing speed. The second most important indicator is the angle of the shot put, it is recommended to use the basic prediction equation, which determines the expected results in the selection of young athletes in shot put, with high reliability of the results obtained. These characteristics are recommended to be used for evaluating young athletes, as well as in the process of training and preparing athletes for competitions.

The Use of Econometric Analysis in Forecasting Macroeconomic Processes

The article discusses the issues related to the influence of various price categories of goods on the level of inflation. The possibility of using econometric analysis for predictive purposes is considered. An econometric model of multiple regression of the influence of consumer prices on the level of inflation is formed. A linear multiple regression equation is constructed. The selection of factors for the construction of an econometric model is made. The main groups of goods are identified, the prices of which form the level of inflation and can have a primary impact. The viability of the model and the possibility of forecasting macroeconomic indicators based on econometric analysis are proved.

New Construction of Functionalized CuO/Al2O3 Nanocomposite-Based Polymeric Sensor for Potentiometric Estimation of Naltrexone Hydrochloride in Commercial Formulations

Electrically conductive polymeric nanocomposites with nanoparticles are adaptable types of nanomaterials that are prospective for various applications. The extraordinary features of copper oxide (CuO) and aluminium oxide (Al2O3) nanostructures, encourages extensive studies to prospect these metal oxide nanocomposites as potential electroactive materials in sensing and biosensing applications. This study suggested a new CuO/Al2O3 nanocomposite-based polymeric coated wire membrane sensor for estimating naltrexone hydrochloride (NTX) in commercial formulations. Naltrexone hydrochloride and sodium tetraphenylborate (Na-TPB) were incorporated in the presence of polymeric polyvinyl chloride (PVC) and solvent mediator o-nitrophenyloctyl ether (o-NPOE) to form naltrexone tetraphenylborate (NTX-TPB) as an electroactive material. The modified sensor using NTX-TPB-CuO/Al2O3 nanocomposite displayed high selectivity and sensitivity for the discrimination and quantification of NTX with a linearity range 1.0 × 10−9–1.0 × 10−2 mol L−1 and a regression equation EmV = (58.25 ± 0.3) log [NTX] + 754.25. Contrarily, the unmodified coated wire sensor of NTX-TPB exhibited a Nernstian response at 1.0 × 10−5–1.0 × 10−2 mol L−1 and a regression equation EmV = (52.1 ± 0.2) log [NTX] + 406.6. The suggested modified potentiometric system was validated with respect to various criteria using the methodology recommended guidelines.

Origin and movement of cyclonic storms in the Bay of Bengal

Using 1891-1997 data of cyclonic storm positions an attempt is made to study probability of a storm located at about 500 kms in the Bay from the coast to strike or threaten a given station or a given state. It is found that probable location of formation of storms affecting Tamil Nadu coast is 9.3° N / 85.0° E in the month of October and November, for the storms affecting Andhra Pradesh is around 11.5° N/87. 0° E in May and October and for the storms affecting Orissa is 16. 0° N / 89. 5° E in May and October. The storms affecting West Bengal have their origin around 14.8° N / 88. 6° E in May and 17.0° N / 88. 8° E in October. It is also found that the track of a given storm during post monsoon months (October-December) depends on the track of the earlier storm formed in the same season over Bay. Mean direction of storm over the Bay of Bengal is estimated based on the mean direction of the storm formed earlier in the Bay by a regression equation.

CONSTRUCTION OF MATHEMATICAL MODELS USING THE METHODS OF CORRELATION AND REGRESSION ANALYSIS

The article discusses the construction of a mathematical model using the methods of correlation and regression analysis in determining the functional relationship between the quantities. When conducting an experiment, it is often necessary to establish the interdependence between two or more quantities in order to obtain an empirical formula. In some cases, this is a simple task, because these connections are almost obvious or known in advance. As a rule, to establish the relationship between different indicators, factors and characteristics is not a trivial task. There is a need to use some hypothesis in the form of functional dependence. In other words, it is necessary to replace this functional dependence with a fairly simple mathematical expression. Such a mathematical expression can be a linear equation or a polynomial. In order to use such experimental data to determine such a mathematical or functional relationship between variables, the methods of correlation and regression analysis are used. Correlation analysis provides an answer to the statistical hypothesis of the absence or presence of a relationship between variables with some predetermined confidence probability. Determination of the functional dependence between different values on their experimental values is carried out using regression analysis. It is based on the well-known method of least squares. Proposing one or another regression equation, the researcher determines both the very existence of the relationship between variables and its mathematical form. Regression analysis considers the relationship between the dependent quantity and non-dependent variables. This relationship is represented using a mathematical model, that is, an equation that connects the dependent and independent variables. Processing of experimental data using correlation and regression analysis allows us to build a statistical mathematical model in the form of a regression equation. Thus, the methods of correlation and regression analysis are closely related.

The importance of body composition assessment in the rehabilitation process

Introduction. This personal study provides several aspects of the importance of body composition assessment in rehabilitation process in order to manage fat mass (FM), fat-free mas imbalances (FFM), pre-sarcopenia status, sarcopenia and risks association and to improve global functionality. Health outcomes and risk estimations regarding fat mass and skeletal muscle mass (SMM) plays a major role and should be integrated into the rehabilitation process routine in order to avoid functional impairment and physical disability by applying specific kinetic programs. Material and method. A number of 14 subjects classified as outpatients who have received physical therapy at home- kinesiotherapy for post-fracture / dislocation status of the lower limbs in accordance with the medical recommendations and legislation in force. At the end of the rehabilitation phase, the body composition was measured using bio impedance in order to adjust the next step of the active rehabilitation. The measurements were obtained with a completely bioelectrical impedance analyzer (BIA). Single frequency BIA (SF-BIA) was used. For each subject major body compartments determined as FFM (including bone mineral tissue, total body water-TBW and visceral protein), SMM and FM were measured as a tissue-system by means of linear empirical equations stored in the system memory together with personal physical data. IBM SPSS software version 25 was used for statistical analysis. Results and discussions. Four age groups determined as follows: 21.43% for 18-39 years, 50-69 years, >70 years each and 35.71% for 40-49 years, based on the rate of muscle loss, because its integrity is essential for rehabilitation program. From the 14 subjects there are 57.14 % men and 42.86% women, from urban environment 78.57% and rural 21.43%. Mean Age is 48.79 years ± 18.792 Std. Deviation. Fat mass from BIA recorded 21.43% cases low and normal each, and high/very high 57.14% of total cases. Consequently, of BMI (body mass index) association, 57.14% are at normal weight, 35.71% overweight and with obesity and 7.14% underweight. One Sample Chi-Square test applied to BMI Type Associate with FM reveals the statistical significance, < .05(.014). Fat-free mass index (FFMI), fat mass index (FMI), skeletal mass index (SMI) were computed by adjusted with height square. FMI somatotype components results are 64.3% adipose cases, 21.4% intermediate and 14.3% lean. One Sample Chi-Square test applied to FMI Types reveals the statistical significance < .05(.046). Regression equation of standard BMI and FMI with scatter plots for 77.8% of cases was computed in the present study. FFMI somatotype components recorded 57.1% intermediate cases, 21.4% slender and solid each. Regression equation of standard BMI and FFMI with scatter plots for 57.4% of cases was computed. Three patients exceeded 15 seconds at the chair stand test so probable sarcopenia was identified. From BIA were extracted the value for the skeletal mass and SMI was calculated by height adjusted: 13 (92.86%) cases have normal values and one (7.14%) case have optimal value. Regression equation of standard BMI and SMI with scatter plots for 66.4% of cases was computed. Pearson correlation (CI =99%) denotes strong statistical relationship between BMI and FMI (r=0.882), FFMI (r=0.815), Age (r=0.659), Water (r=-0.693). FMI also correlates strongly with Age (r= 0.707), Water (r=-0.925) and Proteins values (r=-0.819). FFMI also correlates strongly with SMI (r=0.984). Water correlates with Protein (r=0.848, CI = 99%). Beta regression analysis strongly correlates SMI prediction with FFMI (ß=0.731), Water (ß=0.138) and Protein (ß=-0.370) for p<0.05. Anova significance of .000 (CI=99%) with applicability of 99.8% of the cases (R2 =0.998) proved that constant predictors: Water (%), FFMI, Proteins (%), FMI, BMI interact to influence SMM variability. 64.25% of subjects recorded an insufficient water level and 71.43% of subjects recorded an insufficient proteins level. Body composition evaluation should be integrated into routine clinical practice for the initial assessment and sequential follow-up and the strongest point of BIA is the possibility to replace invasive laboratory analysis with a quick, noninvasive test that can be carried out in a medical office. Body composition evaluation should be performed at the different stages of the disease, during the course of treatments and the rehabilitation phase. Conclusions. For each patient specific kinetic program will be developed. FMI increase (64.3% adipose cases) denotes the risk of metabolic syndrome and insulin resistance. Consequently, resistive and concentric exercises will be applied. For FFMI loss (57.1% intermediate cases, 21.4% slender) and SMI increasing (92.86% cases have normal values but not optimal ones, 21.43% pre-sarcopenia detected by positive chair test) resistance, eccentric/concentric exercises should be applied. All kinetic programs will be preceded by warm-up and followed by stretching taking into account cardiac reserve for each patient. Maximal/sub-maximal force exercises will be used age-related. Additional water (64.25% of subjects recorded an insufficient water level) and proteins levels (71.43% of subjects recorded an insufficient proteins level) must be balanced by nutritional support in accordance with rehabilitation consult and current physician approval in the interdisciplinary team. BIA may be an important supporting tool for health professionals in order to customize the rehabilitation programs for each patient. Keywords: body composition, rehabilitation, bioelectrical impedance, fat-free mass index, fat mass index, skeletal muscle index,

The Moisture Content and Absorption Levels of Carbon Dioxide in Binuang Bini (Octomeles sumatrana Miq) Trees For Climate Change Management

Binuang bini (Octomeles sumatrana Miq) is a fast-growing tree with numerous economic benefits, such as the provision of wood for carpentry purposes, building boards, water management, and absorption of carbon dioxide (CO2). Therefore, this tree species has great potential and needs to be included in Reducing Emission from Deforestation and Forest Degradation (REDD)+'s mitigation program to tackle climate change. In its development, REDD+ has made it possible to carry out carbon trading in the world. Therefore, countries capable of performing protective functions and carry out reforestation, afforestation, and restoration, have the opportunity to be involved in world carbon trading. This study aims to determine the moisture content and carbon absorption rate of Binuang bini trees as a first step to regulate the allometric equation using destructive and laboratory analysis. The results show that the water content in the roots, leaves, as well as the base, middle, and tip of the stem were: 73.69%, 68.39%, 65.59%, 61.22%, and 66.26%, respectively. Furthermore, the sample test results indicate a very close relationship between carbon concentration and absorbance in the O. sumatrana tree with a simple linear regression equation: Y = 0.002X + 0.0593 with R2 = 0.9896. Therefore, this regression equation can be used to calculate the carbon concentration sample for the O. sumatrana tree fraction. The carbon content in 3 tree samples with a breast height diameter of 9.24 cm, 10.08 cm, and 11.68 cm was 2,585 kg. 2,913 kg, and 4,654 kg, respectively. In addition, the carbon sequestration for each tree diameter per year is 1.581 kg year-1, 1,782 kg year-1and 2,847 kg year-1, respectively.