Estratificação educacional no Brasil do século XXI

Kapitał społeczny ludzi starych na przykładzie mieszkańców miasta Białystok

In the current study, the variations of soil classes at the first and second levels of WRB (World Reference Base for soil resource) soil classification system were investigated by two machine learning including multinomial logistic regression (MLR) and random forest (RF) models in an arid floodplain which covers an area approximately 600 km2 located in Sistan region, Iran. The model’s performance was tested using 10-fold cross-validation by calculation of overall model accuracy and the kappa statistic. Three main Reference Soil Groups (RSGs) including Cambisols, Fluvisols, and Solonchaks at the first level, and 18 WRB soil groups at the second level were identified. Results showed that the overall accuracy at the first level of WRB was 53% and 49% with a kappa of 0.26 and 0.19 for MLR and RF models, respectively. At the second level of WRB, the overall accuracy was 11% and 21% with a kappa of 0 and 0.09 for MLR and RF models, respectively. Also, results showed that the MLR model had better performance (overall accuracy = 53%) at the first level of WRB, but the RF model showed better prediction (overall accuracy = 21%) at the second level of WRB. Multiresolution Valley Bottom Flatness Index (MrVBF), Normalized Difference Salinity Index (NDSI), Multiresolution of Ridge Top Flatness Index (MrRTF), convergence index, and channel network base level were among top covariates used for prediction at two levels of WRB. Results revealed the complexity of soil variations in this floodplain. Using other covariates such as soil texture and salinity maps can improve the prediction power. Increasing the size of sampling is recommended to improve the accuracy of the models in predicting the second level of WRB in this area.

Sanitary sewer pipes infrastructure system being in good condition is essential for providing safe conveyance of the wastewater from homes, businesses, and industries to the wastewater treatment plants. For sanitary sewer pipes to deliver the wastewater to the treatment plants, they must be in good condition. Most of the water utilities have aged sanitary sewer pipes. Water utilities inspect sewer pipes to decide which segments of the sanitary sewer pipes need rehabilitation or replacement. The process of inspecting the sewer pipes is described as condition assessment. This condition assessment process is costly and necessitates developing a model that predicts the condition rating of sanitary sewer pipes. The objective of this study is to develop Multinomial Logistic Regression (MLR) and Artificial Neural Network (ANN) models to predict sanitary sewer pipes condition rating using inspection and condition assessment data. MLR and ANN models are developed from the City of Dallas’s data. The MLR model is built using 80% of randomly selected data and validated using the remaining 20% of data. The ANN model is trained, validated, and tested. The significant physical factors influencing sanitary pipes condition rating include diameter, age, pipe material, and length. Soil type is the environmental factor that influences sanitary sewer pipes condition rating. The accuracy of the performance of the MLR and ANN is found to be 75% and 85%, respectively. This study contributes to the body of knowledge by developing models to predict sanitary sewer pipes condition rating that enables policymakers and sanitary sewer utilities managers to prioritize the sanitary sewer pipes to be rehabilitated and/or replaced.

Multimetric feature selection for analyzing multicategory outcomes of colorectal cancer: random forest and multinomial logistic regression models

This article aims to describe the reasons for sport participation in the Brazilian adult population according to gender, age and schooling level. This is a cross-sectional study with data from a National Household Sample Survey - 2015. The reasons to practice or not practice sports in the previous year were obtained by questionnaire with predetermined answer options. All analyses were performed in Stata 12.1 and stratified by gender. The prevalence of sports participation for men and women were 31.7% and 16.9%, respectively. The most frequent reasons for sports participation among men were: having fun, quality of life and performance. Among women, the most frequent reasons were: quality of life, performance and medical recommendation. Regarding reasons for not practicing sports, the most reported ones were: lack of time (38.8% for men and 37.8% for women), not enjoying (34.8% for men and 35.3% for women) and health problem (20.3% for men and 17.9% for women). The study findings showed the most frequent reasons for sport participation in Brazil. Understanding these reasons and how they affect different age ranges and educational levels may contribute to improve strategic planning to promote sports in middle-income countries.

Purpose – The aim of this paper is to perform an evaluation of the Brazilian informal labor market, following the lines suggested by Araujo and Souza. The paper focuses on the Brazilian labor market by using data from the National Household Sampling Survey – PNAD/IBGE, from 1995 to 2008.Design/methodology/approach – The paper uses four different methodologies: the OLS, pseudo‐panel with fixed effects, instrumental variables and the Heckman selection model. The data for this study were obtained at the National Household Sampling Survey – PNAD/IBGE, from 1995 to 2008, with an exception to the years of 1994 and 2000, when this survey did not occur. The cross‐section data were pooled and divided in two periods: 1995‐2002 and 2003‐2008. The sample ranges individuals from 22 to 57 years of age.Findings – The empirical findings show that although wages play an import role in the decision of workers and firms, all wage gaps between formal and informal markets reduced considerably, as has been pointed out by Matto...

To generate and evaluate a new logistic regression model for the prediction of successful expectant management of first trimester miscarriage. Data were collected prospectively from women diagnosed with 1st trimester miscarriage. Clinical and ultrasonographic variables were recorded for multivariate analysis. Clinically stable women who were managed expectantly were followed up for two weeks until the outcome was established: success or failure. A multinomial logistic regression (MLR) model was developed on 186 training cases for the prediction of successful expectant management and tested prospectively on a further 126 cases. The performance of the model was evaluated using receiver operating characteristic (ROC) curve as well as in terms of sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Two thousand and forty eight consecutive first trimester women underwent TVS. Complete data from 312 (15.2%) women with miscarriage managed expectantly were included in the final analysis. The most important independent prognostic variables for the MLR model were as follows: type of miscarriage at primary scan, vaginal bleeding and maternal age. When developed retrospectively on a training data set, MLR model gave an area under the ROC curve (AUC) of 0.796. Prospective validation of MLR model on a new test data set resulted in an AUC of 0.803. We have developed and validated a new mathematical model to predict successful management of first trimester miscarriage.

To predict 3-Level version of European Quality of Life-5 Dimensions (EQ-5D-3L) questionnaire utility from the chronic obstructive pulmonary disease (COPD) assessment test (CAT), the study attempts to collect EQ-5D-3L and CAT data from COPD patients. Response mapping under a backward elimination procedure was used for EQ-5D score predictions from CAT. A multinomial logistic regression (MLR) model was used to identify the association between the score and the covariates. Afterwards, the predicted scores were transformed into the utility. The developed formula was compared with ordinary least squares (OLS) regression models and models using Mean Rank Method (MRM). The MLR models performed as well as other models according to mean absolute error (MAE) and root mean squared error (RMSE) evaluations. Besides, the overestimation for low utility patients (utility ≤ 0.6) and underestimation for near health (utility > 0.9) in the OLS method was improved through the means of the MLR model based on bubble chart analysis. In conclusion, response mapping with the MLR model led to performance comparable to the OLS and MRM models for predicting EQ-5D utility from CAT data. Additionally, the bubble charts analysis revealed that the model constructed in this study and MRM could be a better predictive model.

The projected temporal evolution in the interannual variability of East Asian summer rainfall in the 21st century is investigated here, by analyzing the simulated results of 18 coupled models under the 20th century climate experiment and scenario A1B. The multi-model ensemble (MME) mean projects two prominent changes in the interannual variability of East Asian summer rainfall in the 21st century under scenario A1B. The first change occurs around the 2030s, with a small change before and a large increase afterward. The intensity of the interannual variability increases up to approximately 0.53 mm/d in the 2070s, representing an increase of approximately 30% relative to the early 21st century. The second change happens around the 2070s, with a decrease afterward. By the end of the 21st century, the increase is approximately 12% relative to the early 21st century. The interannual variability of two circulation factors, the western North Pacific subtropical high (WNPSH) and the East Asian upper-tropospheric jet (EAJ), are also projected to exhibit two prominent changes around the 2030s and 2070 under scenario A1B, with consistent increases and decreases afterward, respectively. The MME result also projects two prominent changes in the interannual variability of water vapor transported to East Asia at 850 hPa, which occurs separately around the 2040s and 2070s, with a persistent increase and decrease afterward. Meanwhile, the precipitable water interannual variability over East Asia and the western North Pacific is projected to exhibit two prominent enhancements around the 2030s and 2060s and an increase from 0.1 kg/m2 in the early 21st century to 0.5 kg/m2 at the end of the 21st century, implying a continuous intensification in the interannual variability of the potential precipitation. Otherwise, the intensities of the three factors’ (except EAJ) interannual variability are all projected to be stronger at the end of the 21st century than that in the early period. These studies indicate that the change of interannual variability of the East Asian summer rainfall is caused by the variability of both the dynamic and thermodynamic variables under scenario A1B. In the early and middle 21st century, both factors lead to an intensified interannual variability of rainfall, whereas the dynamic factors weaken the interannual variability, and the thermodynamic factor intensifies the interannual variability in the late period.

اهداف: سرمایۀ اجتماعی کلید استقرار جامعه مدنی و حیات شهروندی است و انباشت آن از اقتدارگرایی و تفکرات آمرانه، فرصت‌طلبی، بی‌اعتمادی، حس بدبینی و... جلوگیری می‌کند. در این راستا هدف پژوهش حاضر، ارزیابی و اولویت‌بندی میزان سرمایۀ اجتماعی در نواحی یازده‌گانه منطقۀ سه شهر اردبیل می‌باشد. روش: نوع تحقق کاربردی بوده، روش بررسی آن توصیفی‌تحلیلی است. براین اساس، جمع‌آوری داده‌ها از طریق پرسش‌نامه در حجم نمونۀ 382 نفر و تجزیه و تحلیل داده‌ها با استفاده از مدل‌های AHP و Topsis صورت گرفته است. یافته‌ها/نتایج: نتایج حاصل از گام‌های مختلف مدل تاپسیس نشان می‌دهد که به جهت سطح‌بندی سرمایۀ اجتماعی در نواحی منطقۀ سه تفاوت وجود دارد و نواحی با طبقات اجتماعی و اقتصادی و کالبدی بالا و رضایت بخش‌تر همچون 2، 1، 11 و 9 از رتبۀ به مراتب بهتری نسبت به سایر نواحی همچون 4، 6 و 7 دارا می‌باشند. نتیجه‌گیری: در این میان، وجود عوامل سرمایه‌ساز در سطح نواحی همچون مساجد و حسینیه‌ها و ... در جهت ارتقای شاخص‌های سرمایۀ اجتماعی در منطقۀ سه و در نهایت شهر، استفاده گردد. از سوی دیگر با توجه به اینکه نواحی 4 (با ضریب 1508/0) و 6 (با ضریب 4920/0) از کمترین میزان سطح سرمایۀ اجتماعی برخوردار است و در این نواحی شاخص‌های امنیت و اعتماد در حد پایین قرار دارد، لذا ارائۀ طرح‌های امنیتی و گسترش نظارت بر این منطقه، تبلیغات و فرهنگ‌سازی در جهت ارتقای سطح اعتماد و مشارکت عمومی می‌تواند از جمله این راهکارها باشد.

Trust or bust: Growth effects of knowledge, human and social capital revisited

This article describes, through a statistical analysis, the relationship between the Federal Government's Bolsa Família (BF) Program and the housing situation of those contemplated in the Program, based on the Continuous National Household Sample Survey - Continuous PNAD, 1st Visit for the year 2022. For this analysis, the microdata collected based on the questionnaire used in the research were consulted, as well as technical notes made available by the Brazilian Institute of Geography and Statistics (IBGE). Because it is a reliable source for obtaining statistical information on the demographic, social, economic and housing characteristics of the Brazilian population, and using quantitative methodology, the data were analyzed using the R software and the survey package. The results obtained through this statistical analysis showed that there is a significant number of low-income people who own their own homes; the value of the rent is extremely expensive, in most of the FUs, especially in the South, Southeast and Midwest regions of the country, which compromise more than 100% of the amount received by the BF. We can see that BF has a relationship with housing, considering that the amount received by the beneficiaries minimizes housing inequality, since this value helps families with essential costs for survival. In this sense, more studies are needed to help in the understanding of the conditions in which these dwellings are found, in order to promote and improve public policies aimed at housing in order to improve the living conditions of the low-income population.

The aim of this study is to investigate the practical application of the Multinomial (many explanatory variables and many categories) Logistic Regression (MLR) model, which is a fundamental tool for analyzing not only for scale data but also for categorical data with many explanatory variables. This method is primarily used when there is a single nominal or ordinal response variable with multiple categories or levels. MLR analysis has various applications across disciplines such as education, social sciences, healthcare, behavioural research, and some other fields. We utilized real data from the Azadi Heart Center at the Duhok Hospital in the Duhok Governorate to assess the practical applicability of the model. The main multinomial logistic regression model was used with five explanatory variables. Extensive statistical tests were performed to confirm the suitability of this model for the dataset. Furthermore, the model underwent a validation process wherein two observations were randomly selected from the dataset, and their categorization was predicted based on the values of the explanatory variables utilized. Our results suggest that the multinomial logistic regression model provides a useful method for distinguishing between the response variable and the set of explanatory factors that makes it easier to determine the exact influence of each variable and enables predictions about how a particular instance will be classified.

In 2015, the Federal Government launched the slogan Brasil Patria Educadora, placing education as a driving force behind the development of the nation. The contribution of this paper was to estimate educational rates of return for the Industry and Construction Sector from 2002 to 2013 and wage differentiation by gender, race, and geographic region. The Human Capital Theory and educational rates of return presented themselves as an appropriate theoretical framework. The proposed method was earnings equations with polygonal adjustment for education. Using the National Household Sample Survey – PNAD/IBGE, it was observed that educational returns in Construction Sector are higher than in Industry. Another significant result was the asymmetry of the educational return. In Construction, individuals with eight years of schooling or more receive five times more than workers with less education. In Industry, this proportion is three times higher for individuals with eleven years of study or more. This work confirms the understanding that education remained among the variables that contributed most to the increase in income, and educational inequality is translated into the income gap in the labor market.

In Vietnam in the last 10 years, there have been some issues of social life prominently discussed on the media, such as “the degradation of morality, personality and life style”, “exotic culture”, “emotionless disease”,... The cause of this condition is attributed to cultural changes in the negative direction. Cultural and social studies over the past two decades are almost consistent with the argument that cultural change is the root cause of social unrest, creating barriers to development. However, to say “social degradation”, what does it refer to as the comparative original status? And to say “cultural change”, which transition context does it fit in (before and after 1975, before and after 1986, or before and after 2000)? There is not yet sufficient answers for these questions. The question of context needs to be answered first in order to find out the causes. Besides , the impact of culture on society has been said as a default, but in fact, what is the working mechanism of this impact has not been fully explained. To contribute to the explanation of these issues, the paper explores culture as a form of capitals in a transformed society, from the perspective of the impact between cultural capital and social capital; it aims to search for the root causes and solutions .