A model to predict communications in dynamic social networks

Background: social networks are dynamic due to continuous increases in their members, communications, and links, while these links may be lost. This study was conducted with the aim of investigating the link and communication between social network users using the centrality criterion and decision tree. Methods: After checking the nodes in the network for each pair of unrelated nodes, some common nodes in the proximity list of these two groups were extracted as common neighbors. Analysis was performed based on common neighbors, association prediction process, and weighted common neighbors. Prediction accuracy improved. Centrality criteria were used to determine the weight of each group. New Big Data techniques were used to calculate centrality measures and store them as features of common neighbors. Personal characteristics of users were added to build complete data for training a data mining model. After modeling, the decision tree model was used to predict communication. Results: There was an increase in sensitivity, which indicated model power in identifying positive categories (i.e., communications) when users' characteristics were used. It means that the model could identify potential latent communications. It can be stated that users are more willing to make a relationship with users similar to them through common neighbors. Personal characteristics of users and centrality were effective in method efficiency, while removal of these properties in the learning process of the decision tree model caused a reduction in efficiency criteria. Conclusion: Prediction of latent communications through social networks was promising. Better results can be obtained from further studies

Seismic design of masonry-infilled frames: A review of codified approaches

This paper reviews the approach of eleven national codes on the analysis and design of masonry-infilled frames. It is shown that, in general, codes can be divided into two groups. The first group isolates the masonry and frame members by providing gaps to minimize the interaction between them. This method ensures that the complexities involved in analyzing the structure is avoided. However, the width of the gaps recommended is different for each of the codes. The second group takes advantage of the presence of high stiffness and strength masonry infill. In this technique, an equivalent-strut modeling strategy is mostly recommended. It is shown that the strut model suggested in each of the codes is different. An attempt to obtain a generic model for masonry-infilled frame failed largely due to the existence of many behavior-influencing parameters. Finally, it is suggested to have a paradigm shift in the modeling strategy where the masonry-infilled frames are classified into different categories and a model is suggested for each of them

Comparing Subcutaneous Tissue Responses to Freshly Mixed and Set Root Canal Sealers

INTRODUCTION: The purpose of this study was to compare the subcutaneous tissue responses of freshly mixed or set endodontic root canal sealers (i.e. RoekoSeal, AH26, AH Plus) in Wistar Albino rats. MATERIALS AND METHODS: Seventy-two male albino rats weighing 200-250g were used. The animals were randomly divided into six groups of 12 rats each. Root canal sealers were implanted in subcutaneous tissue in both freshly mixed and set conditions. The animals were sacrificed after 7, 14, and, 60 days. After histological preparation and Hematoxylin and Eosin (H&E) staining, the specimens were evaluated for capsule thickness, severity and extent of inflammation, and necrosis. Results were statistically analyzed using Multivariate ANOVA test. RESULTS: Differences between set and freshly mixed root canal sealers were significant (P=0.014), but not significant between test materials and controls, except for capsule thickness and extent of inflammation between control and AH26 (P=0.019 and P=0.006 respectively). The interaction between the type of material and setting condition was significant for capsule thickness and severity of inflammation in AH26 specimens at 14 and 60 days (P=0.001). CONCLUSION: Based on the results of this study assessing the biocompatibility, both set and freshly mixed states can be used. [Iranian Endodontic Journal 2009;4(4):152-7

Continuous Training and Deployment of Deep Learning Models

Deep Learning (DL) has consistently surpassed other Machine Learning methods and achieved state-of-the-art performance in multiple cases. Several modern applications like financial and recommender systems require models that are constantly updated with fresh data. The prominent approach for keeping a DL model fresh is to trigger full retraining from scratch when enough new data are available. However, retraining large and complex DL models is time-consuming and compute-intensive. This makes full retraining costly, wasteful, and slow. In this paper, we present an approach to continuously train and deploy DL models. First, we enable continuous training through proactive training that combines samples of historical data with new streaming data. Second, we enable continuous deployment through gradient sparsification that allows us to send a small percentage of the model updates per training iteration. Our experimental results with LeNet5 on MNIST and modern DL models on CIFAR-10 show that proactive training keeps models fresh with comparable—if not superior—performance to full retraining at a fraction of the time. Combined with gradient sparsification, sparse proactive training enables very fast updates of a deployed model with arbitrarily large sparsity, reducing communication per iteration up to four orders of magnitude, with minimal—if any—losses in model quality. Sparse training, however, comes at a price; it incurs overhead on the training that depends on the size of the model and increases the training time by factors ranging from 1.25 to 3 in our experiments. Arguably, a small price to pay for successfully enabling the continuous training and deployment of large DL models.BMBF, 01IS18037A, Verbundprojekt BIFOLD-BZML: Berlin Institute for the Foundations of Learning and DataBMBF, 01MD19002B, Verbundprojekt: "ExDRa" - Exploratory Data Science over Raw Data; Teilvorhaben: Systemunterstützung für Data Science PipelinesTU Berlin, Open-Access-Mittel – 202

High frequency of BRAF V600E mutation in Iranian population ameloblastomas

Ameloblastoma is a common locally invasive but slow-growing neoplasm of the jaws with an odontogenic origin. Association between BRAF V600E mutation and clinicopathologic features and behavior of ameloblastoma remains controversial. This study aimed to evaluate BRAF V600E gene mutation and expression of its related proteins with clinicopathologic parameters in conventional ameloblastoma. 50 Formalin-fixed paraffin-embedded blocks were included in this study. Immunohistochemistry was done using rabbit monoclonal BRAF V600E mutation-specific antibody VE1. Quantitative real-time polymerase chain reaction assay was used for evaluating of BRAF V600E mutation. Expression of BRAF V600E antibody was Positive in 42 out of 50 cases (84%). 46 (92%) out of 50 specimens showed BRAF V600E mutation. There were 13 cases of recurrence (26%). 3 out of 4 cases with negative mutations did not show recurrence. We report the highest frequency (92%) of BRAF V600E mutation in ameloblastomas in the Iranian population. Although there was not a significant association between BRAF V600E?positive immunoexpression and recurrence and clinicopathologic parameters, its high frequency could emphasize its role as a therapeutic marker in the future

Multimorbidity as an important issue among women: results of gender difference investigation in a large population-based cross-sectional study in West Asia

Objectives: To investigate the impact of gender on multimorbidity in northern Iran. Design: A cross-sectional analysis of the Golestan cohort data. Setting: Golestan Province, Iran. Study population: 49 946 residents (age 40–75 years) of Golestan Province, Iran. Main outcome measures: Researchers collected data related to multimorbidity, defined as co-existence of two or more chronic diseases in an individual, at the beginning of a representative cohort study which recruited its participants from 2004 to 2008. The researchers utilised simple and multiple Poisson regression models with robust variances to examine the simultaneous effects of multiple factors. Results: Women had a 25.0% prevalence of multimorbidity, whereas men had a 13.4% prevalence (p<0.001). Women of all age-groups had a higher prevalence of multimorbidity. Of note, multimorbidity began at a lower age (40–49 years) in women (17.3%) compared with men (8.6%) of the same age (p<0.001). This study identified significant interactions between gender as well as socioeconomic status, ethnicity, physical activity, marital status, education level and smoking (p<0.01). Conclusion: Prevention and control of multimorbidity requires health promotion programmes to increase public awareness about the modifiable risk factors, particularly among women

Identifying the Research Trends and Subfields of the social manufacturing paradigm

In recent years, studies on the paradigm of social Manufacturing and its applications have been developed as a new production paradigm and have led to the production of diverse and scattered knowledge in this field. Knowing the sub-fields, new topics and the research process of the social production paradigm can be of great help to researchers in this field. The current research has been carried out with the aim of identifying and categorizing research in the field of social Manufacturing, recognizing sub-fields and achieving a coherent view of its research process.This research has investigated the research field of social Manufacturing using bibliometric analysis. The data of this research was collected from 200 articles of the Scopus database and an analysis of the co-occurrence analysis of key words and bibliographic pairs was performed on them, and in this way the sub-fields and the research process of this field were identified.Based on the findings of this study, the research in the field of social Manufacturing has been categorized into 5 clusters and it has also been determined that in recent years, topics such as cloud computing, smart production, blockchain, Internet of Things, social physical cyber systems, innovation systems, society 5.0 and Digital twins have received more attention in research in this field. This research provides a framework of concepts and main topics of interest in the research field of social production, which provides a comprehensive perspective for researchers in this field that can help in choosing their research path

Timely referral to health centers for the prevention of cardiovascular diseases: IraPEN national program

IntroductionThe IraPEN program is an adapted version of the WHO-PEN program designed to prevent four major non-communicable diseases in Iran. This study aimed to determine the rate of compliance and related factors among individuals participating in the IraPEN program for the prevention of cardiovascular disease.MethodIn this study, compliance was defined as timely referral to the health center as scheduled, and the researchers approached four pilot sites of IraPEN from March 2016 to March 2018. Sex-stratified logistic regressions were applied to investigate factors related to compliance. However, it is important to note that in this study, compliance was defined as compliance to revisit, not compliance to taking prescribed medications or behavioral lifestyle changes.ResultsThe total compliance rate, including timely compliance and early and late compliance, was 16.5% in men and 23.3% in women. The study found that cardiovascular risk factors such as diabetes, hypertension, hypercholesterolemia, and being underweight were associated with lower compliance. The higher calculated risk of CVD was associated with higher compliance, but after adjusting for cardiovascular risk factors, high-risk individuals showed lower compliance. There was negligible interaction between sex and other factors for compliance.ConclusionThe compliance rate with scheduled programs for cardiovascular preventive strategies was very low, and high-risk individuals were less compliant, regardless of their high level of risk factors. The study recommends further training to increase awareness and knowledge regarding the IraPEN program and the prevention of non-communicable diseases among high-risk populations

Bridging performance of new eco-friendly lost circulation materials

Lost circulation is one of the most important concerns of the drilling industry, causing excessive expenditure and increasing the non-productive drilling time. In this study, various lost circulation materials (LCMs) were used to control the lost circulation of two types of drilling fluids, bentonite mud and a new eco-friendly mud, named RIA-X, which has a remarkable effect on decreasing the amount of lost circulation in fractured and highly permeable reservoirs. The Bridging Material Test (BMT) apparatus was used to investigate the effectiveness of various LCMs in fractures of various sizes and to select the LCM and combination with the best performance. The use of three-dimensional fractures is one of the most notable points of this work, which makes the experimental conditions similar to those of real wells. The lost control performance of the new eco-friendly LCMs in RIA-X mud was tested in field. The outcomes show that the designed LCMs are able to control severe lost circulation that regular processes such as cementing or drilling with foam cannot deal with