Background: The World Health Organization recommends that a child should be breastfed up to 2 years of age as it is essential for proper growth and development but population-based studies around the world have found conflicting results on the subject. Our study aims to analyze whether there is a relationship between the duration of breastfeeding and undernutrition among children aged from birth up to 3 years of age in Pakistan. Methods: A secondary analysis of the Pakistan Demographic and Health Survey 2013-2014 with 1072 children aged 3 years and under was conducted. The relationship between breastfeeding duration and undernutrition status was estimated through multiple logistic regression analysis. Results: The prevalence of stunting, wasting and underweight were 40.6%, 15.8% and 33.9% respectively, while prevalence of severe stunting is at 22.5%; severe wasting at 4.5% and severe underweight at 12.2% in children in our study. Odds of being stunted were significantly higher for children in their 3rd year of life [AOR: 4.35, CI 95% = (2.01, 9.33)] compared to children being breastfed in their 2nd year of life [AOR: 2.43, CI 95% = (1.55, 3.79) after being adjusted for maternal, child, demographic and healthcare access variables. Similarly, children being breastfed in their third year of life were more susceptible to developing severe stunting [AOR: 6.19, CI 95% = (3.31, 11.56)] in comparison to children in their second year [AOR: 2.84, CI 95% = (1.81, 4.46)]. There was no significant association between breastfeeding and wasting/severe wasting, or between breastfeeding and underweight/severe underweight. Conclusion: Breastfeeding in the 2nd and 3rd year of life was found to have significant relationship with stunting and severe stunting. Mothers need to be educated about the risks of prolonged breastfeeding to reduce the burden of undernutrition in the country.
This study's key purpose was to examine the impact of COVID-19 on SMEs' business norms and performance in China. The primary quantitative data have been collected through a survey questionnaire based on 330 participants. For analyzing the collected data, the use of the SEM technique has been made in this study. The researcher has adopted the data from the managers and employees belonging to the SMEs in China who are the research respondents. The data has been gathered from a sample size of 330. The number of distributed questionnaires was 340, whereas the responses gained were nearly 335, out of which 330 responses were selected. In the SEM technique, a CFA test was conducted to confirm the model's reliability and validity, whereas the results of path assessment were presented to examine the association between the variables. The findings of this study have confirmed the significant impact of COVID-19 on innovative operational procedures, profitability, remote work, and stakeholder satisfaction and safety.
According to statistics, there are 422 million speakers of the Arabic language. Islam is the second-largest religion in the world, and its followers constitute approximately 25% of the world’s population. Since the Holy Quran is in Arabic, nearly all Muslims understand the Arabic language per some analytical information. Many countries have Arabic as their native and official language as well. In recent years, the number of internet users speaking the Arabic language has been increased, but there is very little work on it due to some complications. It is challenging to build a robust recognition system (RS) for cursive nature languages such as Arabic. These challenges become more complex if there are variations in text size, fonts, colors, orientation, lighting conditions, noise within a dataset, etc. To deal with them, deep learning models show noticeable results on data modeling and can handle large datasets. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) can select good features and follow the sequential data learning technique. These two neural networks offer impressive results in many research areas such as text recognition, voice recognition, several tasks of Natural Language Processing (NLP), and others. This paper presents a CNN-RNN model with an attention mechanism for Arabic image text recognition. The model takes an input image and generates feature sequences through a CNN. These sequences are transferred to a bidirectional RNN to obtain feature sequences in order. The bidirectional RNN can miss some preprocessing of text segmentation. Therefore, a bidirectional RNN with an attention mechanism is used to generate output, enabling the model to select relevant information from the feature sequences. An attention mechanism implements end-to-end training through a standard backpropagation algorithm.
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