The awareness of renal stones amongst Syrian refugees in northern Jordan.

Kidney Stone Disease (KSD) is a globally prevalent condition that can be effectively addressed through proper education. This study investigated the awareness of kidney stones among refugees residing in northern Jordan. A questionnaire was administered to 487 refugees of diverse ages and socioeconomic backgrounds. Notably, 97.3% of the respondents had not attended university, and 30.8% held unskilled jobs. Upon familiarizing themselves with the signs and symptoms of kidney stones, 16.22% of men and 12.32% of women reported experiencing such symptoms. This revealed a significant association, suggesting that men might be more susceptible to developing kidney stones than women due to a lack of medical follow-up and examination for men in the camp. However, 38.77% of individuals were uncertain whether they had kidney stones. Furthermore, 38.96% of refugees were unsure about which healthcare professional to consult when experiencing kidney stone symptoms. This report highlights a serious issue with refugees' knowledge of the symptoms, causes, and treatments for kidney stones. The results indicate that Syrian refugees face challenges in acquiring adequate disease awareness, potentially related to issues of migration and war, including low levels of education, limited income, living in camps, and difficulties accessing treatments when needed. Implementing additional policies is necessary to address these challenges among Syrian refugees; however, further studies are needed to validate these findings.

A Hybrid Neural Network Model for Sales Forecasting Based on ARIMA and Search Popularity of Article Titles.

Enhancing sales and operations planning through forecasting analysis and business intelligence is demanded in many industries and enterprises. Publishing industries usually pick attractive titles and headlines for their stories to increase sales, since popular article titles and headlines can attract readers to buy magazines. In this paper, information retrieval techniques are adopted to extract words from article titles. The popularity measures of article titles are then analyzed by using the search indexes obtained from Google search engine. Backpropagation Neural Networks (BPNNs) have successfully been used to develop prediction models for sales forecasting. In this study, we propose a novel hybrid neural network model for sales forecasting based on the prediction result of time series forecasting and the popularity of article titles. The proposed model uses the historical sales data, popularity of article titles, and the prediction result of a time series, Autoregressive Integrated Moving Average (ARIMA) forecasting method to learn a BPNN-based forecasting model. Our proposed forecasting model is experimentally evaluated by comparing with conventional sales prediction techniques. The experimental result shows that our proposed forecasting method outperforms conventional techniques which do not consider the popularity of title words.