Background and purpose: Low back pain (LBP) is a common and costly problem during pregnancy and assumes to be a part of normal pregnancy caused by normal physiological changes. Pregnancy-related LBP is one of the main reasons for disability, absence from work and responsible for huge direct and indirect economical impact.  This article systematically reviews papers concerning the epidemiology and risk factors on LBP in pregnant women.  Materials and methods: MEDLINE, CINAHL, Rose-Net, OVID, and BIDS were searched for the period of 1985-2003, using the key words 'low back pain', 'epidemiology', 'pregnancy', 'risk factors'. In addition, references given in relevant publications were used. Results: Eighteen studies were identified. Eleven studies investigated epidemiology of LBP during pregnancy and the prevalence were reported >50% in 8 (73%) studies.  Ten studies were carried out on the epidemiology of LBP after pregnancy.  The prevalence of LBP were reported >40% in 6 (60%) studies.  Among different reported risk factors, previous LBP as well as younger age were the most important risk factors. Conclusion: Although there were some methodological flaws (e.g., small sample size of the study population, lack of clear classification and definition for LBP, different study design, different methods of study implementation, etc.) which precludes strong conclusion, it appears that LBP is one of the most common problem, during pregnancy and after delivery. It should also be noted that education and certain exercises can be beneficial to the pregnant women in the absence of medical complications.

Automatic topic detection seems unavoidable in social media analysis due to big text data which their users generate. Clustering-based methods are one of the most important and up-to-date categories in topic detection. The goal of this research is to have a wide study on this category. Therefore, this paper aims to study the main components of clustering-based-topic-detection, which are embedding methods, distance metrics, and clustering algorithms. Transfer learning and consequently pretrained language models and word embeddings have been considered in recent years. Regarding the importance of embedding methods, the efficiency of five new embedding methods, from earlier to recent ones, are compared in this paper. To conduct our study, two commonly used distance metrics, in addition to five important clustering algorithms in the field of topic detection, are implemented by the authors. As COVID-19 has turned into a hot trending topic on social networks in recent years, a dataset including one-month tweets collected with COVID-19-related hashtags is used for this study. More than 7500 experiments are performed to determine tunable parameters. Then all combinations of embedding methods, distance metrics and clustering algorithms (50 combinations) are evaluated using Silhouette metric. Results show that T5 strongly outperforms other embedding methods, cosine distance is weakly better than other distance metrics, and DBSCAN is superior to other clustering algorithms.

Evaluating the cutting rate (CR) of stones is important in the cost estimation and the planning of the stone processing plants. This research used regression models to estimate the stones’ CR based on their physico-mechanical characteristics. Stone processing factories in Mahallat City (Markazi province, Iran) were visited, and the CR of diamond circular saws was recorded on six different travertine stones. Next, the stone block samples were collected from the quarries for laboratory tests. Stones’ porosity (n), uniaxial compressive strength (UCS), and Schmidt hammer hardness (SH) were determined in the laboratory as their physico-mechanical characteristics. Correlation relationships of CR with physico-mechanical characteristics were evaluated using simple and multiple regression analyses, and estimator models were developed. Results showed that multiple regression models are more reliable than simple regression for estimating the stones’ CR. The validity of the developed multiple regression models was verified with the published data of one researcher. The findings indicated that these models are accurate enough for estimating the CR of stones. Consequently, the multiple regression models provide practical advantages for estimating the CR and save time and cost during the planning and design of the stone processing factories.

Background: Knee osteoarthritis (OA) is a prevalent joint disease. Clinical prediction models consider a wide range of risk factors for knee OA. This review aimed to evaluate published prediction models for knee OA and identify opportunities for future model development. Methods: We searched Scopus, PubMed, and Google Scholar using the terms knee osteoarthritis, prediction model, deep learning, and machine learning. All the identified articles were reviewed by one of the researchers and we recorded information on methodological characteristics and findings. We only included articles that were published after 2000 and reported a knee OA incidence or progression prediction model. Results: We identified 26 models of which 16 employed traditional regression-based models and 10 machine learning (ML) models. Four traditional and five ML models relied on data from the Osteoarthritis Initiative. There was significant variation in the number and type of risk factors. The median sample size for traditional and ML models was 780 and 295, respectively. The reported Area Under the Curve (AUC) ranged between 0. 6 and 1. 0. Regarding external validation, 6 of the 16 traditional models and only 1 of the 10 ML models validated their results in an external data set. Conclusion: Diverse use of knee OA risk factors, small, non-representative cohorts, and use of magnetic resonance imaging which is not a routine evaluation tool of knee OA in daily clinical practice are some of the main limitations of current knee OA prediction models.