Social Integration of Second Generation Students in the Italian School System

Cultural divides and prejudice complicate the processes of integration and acculturation of migrant families living in a foreign country. Evaluating the impact of such phenomenon can be crucial for social stability and policy making. In this context, the education system has a leading role in fostering and attaining social integration, in particular when it comes to younger sections of the migrant population. In this work, we propose a method for the construction of a quantitative indicator capturing social integration of second generation students in the Italian school system according to areas defined by nationality of the students and administrative region in which they attend school. The indicator, based on survey data, is estimated by means of a 2-step methodology. In the first step, we choose an individual qualitative variable capturing social integration at the unit level, and we compute a first direct estimate of the indicator as the proportion of highly integrated students in each area. Such variable is isolated following alternatively a proxy variable approach or a latent variable model approach. In the second step, we make use of two alternative small area models to improve the estimates, dealing with missing values, low sample size and high variability in smaller domains. At the end, the 2-step methodology results in 4 alternative versions of a synthetic indicator of social integration, that can be used to rank nationalities and administrative regions.

Metabolic Abnormalities in Hypertensive patients and their Risk Factors

Background: Metabolic syndrome is a disorder which is categorized by the presence of various features like hypertension, obesity, insulin resistance and dyslipidemia. One of the basic features of this syndrome is hypertension that may lead to increased incidence of cardiovascular incidents. Aim: To determine the gender based comparison of metabolic syndrome among the hypertensive patients who reported in the outpatient department. Study Time: The present study was conducted from January 2019 to June 2019 over a period of six months. Methods: Known hypertensive patients aged between 20 to 50 years who fulfilled the selection criteria were included in this study. After informed written consent, the physical examination and required laboratory investigation were done. The data was entered and analysed by using SPSS version 23 Mean±standard deviation was recorded for the quantitative variables while frequency was utilized for the qualitative variable. The p-value of ≤0.05 was taken as significant. Results: A total of 85 known patients of hypertension were included in the study comprising (70.12%) males and (35.40%) females. They were evaluated for the metabolic syndrome using the Adult Treatment Panel III- A (ATP –III A) criteria. Their blood pressure was recorded and fasting blood sample were taken to determine the levels of serum glucose, HDL-cholesterol and triglyceride. Conclusion: Metabolic syndrome is more prevalent in the hypertensive patients Keywords: Metabolic syndrome, Hypertension, Dyslipidemia

Qualitative Data Clustering to Detect Outliers

Detecting outliers is a widely studied problem in many disciplines, including statistics, data mining, and machine learning. All anomaly detection activities are aimed at identifying cases of unusual behavior compared to most observations. There are many methods to deal with this issue, which are applicable depending on the size of the data set, the way it is stored, and the type of attributes and their values. Most of them focus on traditional datasets with a large number of quantitative attributes. The multitude of solutions related to detecting outliers in quantitative sets, a large and still has a small number of research solutions is a problem detecting outliers in data containing only qualitative variables. This article was designed to compare three different categorical data clustering algorithms: K-modes algorithm taken from MacQueen’s K-means algorithm and the STIRR and ROCK algorithms. The comparison concerned the method of dividing the set into clusters and, in particular, the outliers detected by algorithms. During the research, the authors analyzed the clusters detected by the indicated algorithms, using several datasets that differ in terms of the number of objects and variables. They have conducted experiments on the parameters of the algorithms. The presented study made it possible to check whether the algorithms similarly detect outliers in the data and how much they depend on individual parameters and parameters of the set, such as the number of variables, tuples, and categories of a qualitative variable.

ANALYSIS OF MIXED DATA TO SELECT BANANAS CLONES (Musa SPP.) TO BE INCLUDED IN A GERMPLASM BANK

In an asexually reproducing hybrid such as banana (Musa spp.), the assessment of clones in the short term is limited because replications are frequently unavailable in the proper number. The aim of this work is to propose the Multiple Factor Analysis of Mixed Data (MFAmix) as a tool for establishing objective criteria to identify banana clones that preserve variability for qualitative and quantitative variables. In the long term, the aim is the development of a banana germplasm bank. MFAmix was applied on a population composed of 124 banana clones collected from different farmers’ fields and four controls. Two groups of variables related to the agronomic aptitude of the clones were evaluated, one composed of nine quantitative variables, and the other, composed of three dichotomous qualitative variables. A Selection Index (SI) was built from the MFAmix coordinates in order to rank the clones and select a subset that allows to preserve the existing genetic variability. The first two axes of MFAmix explained a 49.47% of the total data variability. The set of the banana clones was successfully characterized based on quantitative and qualitative variables. In the long term, the creation of a banana germplasm bank should consider the height and diameter of the plant, the rachis bunch weight and the hands weight, and the qualitative variable plant leafiness. Key words: asexual hybrid, collection of germplasm, multivariate analysis, Musaceae.

ANALYSIS OF MIXED DATA TO SELECT BANANAS CLONES (Musa SPP.) TO BE INCLUDED IN A GERMPLASM BANK

In an asexually reproducing hybrid such as banana (Musa spp.), the assessment of clones in the short term is limited because replications are frequently unavailable in the proper number. The aim of this work is to propose the Multiple Factor Analysis of Mixed Data (MFAmix) as a tool for establishing objective criteria to identify banana clones that preserve variability for qualitative and quantitative variables. In the long term, the aim is the development of a banana germplasm bank. MFAmix was applied on a population composed of 124 banana clones collected from different farmers’ fields and four controls. Two groups of variables related to the agronomic aptitude of the clones were evaluated, one composed of nine quantitative variables, and the other, composed of three dichotomous qualitative variables. A Selection Index (SI) was built from the MFAmix coordinates in order to rank the clones and select a subset that allows to preserve the existing genetic variability. The first two axes of MFAmix explained a 49.47% of the total data variability. The set of the banana clones was successfully characterized based on quantitative and qualitative variables. In the long term, the creation of a banana germplasm bank should consider the height and diameter of the plant, the rachis bunch weight and the hands weight, and the qualitative variable plant leafiness. Key words: asexual hybrid, collection of germplasm, multivariate analysis, Musaceae.

Determine The Factors That Influence the Company's Failure by Using Logistic Regression

Predicting the failure of companies is one of the important and influencing matters, whether for the companies themselves, competing companies or investors. In addition, to the effect of this on the country's economies. In our research, the logistic regression technique was applied to know the most important variables that have a significant effect on the failure or success of companies. The research also includes the arrangement of these variables according to their importance in influencing the dependent variable that represents the failure or success of companies. The application was applied to a sample with size of (33) companies that included (16) successful companies and (17) failed companies. However, four quantitative variables were specified as well as the dependent variable (Y), which is a qualitative variable. And that the model which was built proved the tests significantly and its ability to classify correctly by 84.8%.

Limited Dependent Variables and Discrete Choice Modelling

Limited dependent variables considers regression models where the dependent variable takes limited values like zero and one for binary choice mowedels, or a multinomial model where there is a few choices like modes of transportation, for example, bus, train, or a car. Binary choice examples in economics include a woman’s decision to participate in the labor force, or a worker’s decision to join a union. Other examples include whether a consumer defaults on a loan or a credit card, or whether they purchase a house or a car. This qualitative variable is recoded as one if the female participates in the labor force (or the consumer defaults on a loan) and zero if she does not participate (or the consumer does not default on the loan). Least squares using a binary choice model is inferior to logit or probit regressions. When the dependent variable is a fraction or proportion, inverse logit regressions are appropriate as well as fractional logit quasi-maximum likelihood. An example of the inverse logit regression is the effect of beer tax on reducing motor vehicle fatality rates from drunken driving. The fractional logit quasi-maximum likelihood is illustrated using an equation explaining the proportion of participants in a pension plan using firm data. The probit regression is illustrated with a fertility empirical example, showing that parental preferences for a mixed sibling-sex composition in developed countries has a significant and positive effect on the probability of having an additional child. Multinomial choice models where the number of choices is more than 2, like, bond ratings in Finance, may have a natural ordering. Another example is the response to an opinion survey which could vary from strongly agree to strongly disagree. Alternatively, this choice may not have a natural ordering like the choice of occupation or modes of transportation. The Censored regression model is motivated with estimating the expenditures on cars or estimating the amount of mortgage lending. In this case, the observations are censored because we observe the expenditures on a car (or the mortgage amount) only if the car is bought or the mortgage approved. In studying poverty, we exclude the rich from our sample. In this case, the sample is not random. Applying least squares to the truncated sample leads to biased and inconsistent results. This differs from censoring. In the latter case, no data is excluded. In fact, we observe the characteristics of all mortgage applicants even those that do not actually get their mortgage approved. Selection bias occurs when the sample is not randomly drawn. This is illustrated with a labor participating equation (the selection equation) and an earnings equation, where earnings are observed only if the worker participates in the labor force, otherwise it is zero. Extensions to panel data limited dependent variable models are also discussed and empirical examples given.

Growth of forage palm cultivars irrigated with saline waters

Forage palm (Cactaceae) is considered an important resource in the semiarid region of Brazil. This study aimed to analyze the effect of different levels of salinity in irrigation water on the growth of three forage palm cultivars. The study was conducted at the Federal University of Campina Grande (7° 12’ 52.6’’ S; 35° 54’ 22.3’’ W) in 120 L pots open to the sky. The experiment was a complete randomized block, with four replications and a 4 × 3 factorial arrangement. The treatments consisted of four levels of salinity in irrigation water with electrical conductivity of 0.2, 2.0, 3.8, and 5.6 dS m–1, and three forage palm cultivars: Miúda or Doce (Nopalea cochenillifera Salm Dyck), Orelha de Elefante (Opuntia stricta), and Baiana or IPA–Sertânia (Nopalea cochenillifera Salm Dyck). At 150 days after treatments, it was evaluated: length, width and perimeter of primary and secondary cladodes and total number of cladodes. Variance analysis was performed using an F-test (p < 0.05); significant effects were subjected to quadratic regression analysis for the quantitative variable, and the Tukey test (p < 0.05) for the qualitative variable. Salinity levels in irrigation water did not affect the growth variables. There was a significant difference in the growth of forage palm as a function of its cultivar. The Baiana cultivar showed the highest vegetative growth of length and perimeter of primary and secondary cladodes, while Miúda and Orelha de Elefante presented the greatest cladode numbers for plant and cladode widths, respectively.

Utilizing the Logistic Regression Model in Analyzing the Categorical Data of Economic Effects

The categorical data has a significant role in representing statistical binary variables, and they are analyzed by means of grouping the response variable into ordered categories. Thereby, the dependent variable becomes of type binary qualitative variable. The data related to the financial position of world countries is classified within the categorical data. This work is to study the economic effects of an individual's different factors on determining the richness or poorness levels of a selected population of countries. Moreover, a logistic regression model is to be created to estimate these levels. As a sample of research, the categorical data relevant to the financial status of 20 Arabic countries were drawn from the website of the World Bank, WB. In addition, for comparison purpose, another similar set of categorical data was generated by MATLAB too. The paper has been based on two hypotheses, first is the well-known regression models, like the ordinary least squares or maximum likelihood, are not accurate in case of binary qualitative variables. Second, is utilizing the logistic regression model as an alternative model to achieve the paper goal. The paper results, for both WB data and MATLAB data, have successfully proved the ability of the logistic regression model in manipulating the categorical data and predicting the coefficients of the corresponding regression models.

Prevalence of Thumb Pain Among Physiotherapists Perform Manual Techniques During Clinical Practice

Th u m b p a i n a m o n g t h e physiotherapists is common occupational hazards. The physiotherapists working in their fields perform repetitively manual techniques, massage, mobilization and glides etc. This occupation needs tofind out the number of physiotherapists having thumb pain due to manual work they perform during clinicalpractice in different rehabilitation centers. Objective: To determine the frequency of thumb pain among physiotherapists perform manual techniques in clinical practice; a cross sectional study Methodology: A cross-sectional study design with convenient sampling technique was used, with sample size 190 to determine the frequency of thumbpain among physiotherapists in Lahore and data was analyzed by SPSS, mean and standard deviation wascalculated for the quantitative variable while qualitative variable was presented in the form of frequency and percentage.Results: In the present study a self-made questionnaire was distributed among 190 physiotherapists. Among 190 participants 58(30.5%)were male and 132(69.5%) were Females. 40(21.1%) felt pain while performing ischemic pressure release from thumb. 38(20.0%) felt pain while giving massage. 12(6.3%) felt pain while holding an objectbetween thumb and index finger. 2(1.1%) felt pain in circumduction. (5.3%) felt pain while making snuff box. 24(12.6%) felt pain in isometric exercises of thumb. 27(14.2%) felt pain in hyper flexion at DIP. 17(8.9%) felt pain in hyper flexion at PIP. 51(26.8%) rated 3 pain and 15(7.9%) rate 5 pain on VAS. Conclusions: Study concluded that frequency of thumb pain among physiotherapists working in different hospitals of Lahore was found to be 35.26%. This thumb pain was due to their professional techniques including, manual therapy, ischemic pressure release, massage, mobilization, and gliding etc.