Introduction: the presence of anti-CCP is an important prognostic tool for rheumatoid arthritis (RA), but its relationship with the activity of the disease and functional capacity is still being investigated. Objectives: to study the relationship between anti-CCP and the indices of disease activity, functional capacity and structural damage, by means of conventional radiography (CR) and magnetic resonance imaging (MRI), in stabilized RA. Methods: cross-sectional study of RA patients with one to 10 years of disease. The participants were subjected to clinical evaluation with anti-CCP screening. Disease activity was assessed by means of the Clinical Disease Activity Index (CDAI) and functional capacity by means of the Health Assessment Questionnaire (HAQ). CR was analyzed by the Sharp van der Heijde index (SmvH) and MRI by the Rheumatoid Arthritis Magnetic Resonance Image Scoring System (RAMRIS). Results: 56 patients were evaluated, with median (IIq) of 55 (47.5–60.0) years, 50 (89.3%) were female among whom 37 (66.1%) were positive for anti-CCP. The median (IIq) of CDAI, HAQ, SmvH and RAMRIS were 14.75 (5.42–24.97), 1.06 (0.28–1.75), 2 (0–8) and 15 (7–35), respectively. There was no association between anti-CCP and CDAI, HAQ, SmvH and RAMRIS. Conclusion: our results did not establish the association of anti-CCP with the severity of the disease. So far, we cannot corroborate the anti-CCP as a prognostic tool in RA established.

Fire hazard is often mapped as a static conditional probability of fire characteristics’ occurrence. We developed a dynamic product for operational risk management to forecast the probability of occurrence of fire radiative power in the locally possible near-maximum fire intensity range. We applied standard machine learning techniques to remotely sensed data. We used a block maxima approach to sample the most extreme fire radiative power (FRP) MODIS retrievals in free-burning fuels for each fire season between 2001 and 2020 and associated weather, fuel, and topography features in northwestern south America. We used the random forest algorithm for both classification and regression, implementing the backward stepwise repression procedure. We solved the classification problem predicting the probability of occurrence of near-maximum wildfire intensity with 75% recall out-of-sample in ten annual test sets running time series cross validation, and 77% recall and 85% ROC-AUC out-of-sample in a twenty-fold cross-validation to gauge a realistic expectation of model performance in production. We solved the regression problem predicting FRP with 86% r² in-sample, but out-of-sample performance was unsatisfactory. Our model predicts well fatal and near-fatal incidents reported in Peru and Colombia out-of-sample in mountainous areas and unimodal fire regimes, the signal decays in bimodal fire regimes.

During the COVID-19 pandemic, individuals and their families faced various risk factors, which in some cases resulted in divorce. Adolescents in such families had to grapple with COVID-19 across the world, the risk factors faced by adolescents have largely been under-risk factors associated with COVID-19 and divorce. Despite the rise of divorce during studied, especially among adolescents in South Africa. This study aimed to explore the risk factors experienced by adolescents from divorced households during the COVID-19 pandemic and make recommendations for policy and development. This study employed a phenomenological research design in alignment with qualitative research. Purposive sampling was used to recruit five female adolescents in Johannesburg. Data was collected using semi-structured interviews and focus groups. Data was analyzed thematically using Braun and Clarke’s six steps of data analysis. The findings revealed that conflict at home, mental illness, physical and social isolation, a lack of paternal support, and diminished educational performance emerged as risk factors faced by the participants. These findings underscore the need for psychological interventions to help address the risk factors faced by adolescents whose parents divorced during the pandemic and those who face similar circumstances during future crises.

This study aims to determine the extent of gender inequality in human resource development in Indonesia against Association of South East Asian Nations (ASEAN). This research using secondary data from various relevant sources. There are five dimensions that and are important for measuring gender equality, namely economic participation, economic opportunities, political empowerment, educational attainment, and health and welfare. The assessment was carried out on Indonesia and other countries in Southeast Asia. The results of the study show that Indonesia has the lowest gender development index (GDI) score compared to the average in ASEAN. Then, gender empowerment measure (GEM) Indonesia increased slowly. The most striking gap is in the income dimension, where men’s income far exceeds women’s income. This happens because women work less than men because women are more traditional in domestic roles in Indonesia, where women are prioritized in managing the household. However, for political indicators, there has been an increase in the number of women in parliament, but the target has not yet reached 30 percent of the total number of women in parliament. This situation shows that there is a reduction in the gender gap in the economy and politics. But the number is still too small, it is necessary to increase the equally distributed equivalent percentage (EDEP) for the Economic Participation Index, Parliamentary Representation Index and Income Index.

One significant importance of street vending in South Africa is its role in providing livelihoods and economic opportunities, especially for marginalized and vulnerable populations. However, Street vendors, particularly those selling agricultural commodities, face numerous challenges. Street vending in Moletjie Mmotong is a vital source of income and employment, offering affordable goods and services, including food, clothing, and household items. One potential solution is online selling, but there is limited knowledge about it in the informal sector. This study aims to analyze the factors affecting street vendors’ willingness to sell fruits and vegetables online in Moletjie Mmotong under Polokwane Municipality. Data was collected from 60 street vendors using a questionnaire and simple random sampling. Descriptive statistics identified and described the socio-economic characteristics of the vendors, while a binary logistic regression model analyzed the factors influencing their willingness to sell online. The study found that age, education level, gender, household size, and access to online selling information significantly influenced their willingness to sell online. The findings highlight the potential benefits of online selling for street vendors, such as increased sales and a broader customer base. The study recommends that governments provide training and workshops on online selling, develop educational programs, distribute educational materials, and create marketing strategies to support street vendors in transitioning to online platforms.