Distributed human computation framework for linked data co-reference resolution

Distributed Human Computation (DHC) is a technique used to solve computational problems by incorporating the collaborative effort of a large number of humans. It is also a solution to AI-complete problems such as natural language processing. The Semantic Web with its root in AI is envisioned to be a decentralised world-wide information space for sharing machine-readable data with minimal integration costs. There are many research problems in the Semantic Web that are considered as AI-complete problems. An example is co-reference resolution, which involves determining whether different URIs refer to the same entity. This is considered to be a significant hurdle to overcome in the realisation of large-scale Semantic Web applications. In this paper, we propose a framework for building a DHC system on top of the Linked Data Cloud to solve various computational problems. To demonstrate the concept, we are focusing on handling the co-reference resolution in the Semantic Web when integrating distributed datasets. The traditional way to solve this problem is to design machine-learning algorithms. However, they are often computationally expensive, error-prone and do not scale. We designed a DHC system named iamResearcher, which solves the scientific publication author identity co-reference problem when integrating distributed bibliographic datasets. In our system, we aggregated 6 million bibliographic data from various publication repositories. Users can sign up to the system to audit and align their own publications, thus solving the co-reference problem in a distributed manner. The aggregated results are published to the Linked Data Cloud

Machine Learning Approaches to predict Intra-Uterine Insemination Success Rate- Application of Artificial Intelligence in Infertility

Introduction: Assisted Reproductive Technology (ART) has been widely utilized for infertility management. Despite its low success rate, Intra-Uterine Insemination (IUI) is one of the first alternatives and most important approaches regarding many cases of infertility treatment. Given the numerous influencing factors and limitations associated with time and resources, the development of a reliable model to predict the success rate of ART methods can significantly contribute to decision-making processes. Materials and methods: We reviewed the demographic, clinical, and laboratory data regarding 157 IUI treatment cycles among 124 women using their partner’s sperm from May2017 to June2019. Primary outcome measures were clinical pregnancy and live birth. Some prediction models were constructed and compared to the logistic regression analysis. Results: Woman’s mean age was 30.1 ± 5.2 years and the infertility had a female cause in 24.3% of the cases, male cause in 32.6% of cases, and combined causes in 32.6% of the cases. Concerning the first IUI cycle, the clinical pregnancy rate per cycle was 16.9% (N= 21). Data were prepared according to cross-industry standard process for data mining (CRISP-DM) methodology, and the following models were fitted to the data: J48 Decision Tree, Perceptron Multilayer (MLP) Neural Network, Support Vector Machine (SVM) with radial basis function (RBF) kernel, K-Nearest Neighbors (KNN) with one neighborhood, and Bayesian Network. J48 Decision Tree, with a sensitivity of 95% and specificity of 98%, had the most optimal performance, and the KNN model was the weakest one. Conclusion: To predict the results of IUI as a simple and less invasive therapy for infertile couples, some models were applied based on artificial intelligence and J48 Decision Tree was recommended

Impact of Acute Phase Epigallocatechin-3-gallate Supplementation on Consciousness and S100B Serum Levels in TBI Patients: A Double Blind Randomized Clinical Trial

Background and Aim: Traumatic brain injury is one of the leading causes of mortality and disability in young adults. Epigallocatechin-3-gallate, the antioxidant compound of green tea, has been proposed to have antioxidant and anti-inflammatory properties. This study evaluates the potential effects of epigallocatechin-3-gallate on the early clinical outcome and serum S100B levels (biomarker for brain tissue damage severity) in patients with moderate to severe traumatic brain injury. Methods and Materials/Patients: Thirty patients with moderate to severe traumatic brain injury admitted to the intensive care unit were enrolled. The patients were randomly allocated to treatment with either a daily oral dose of 400 mg epigallocatechin-3-gallate or placebo (distilled water) for seven days. The main outcome measures were duration of mechanical ventilation and ICU stay, Glasgow Coma Scale, and S100B protein level. Results: The results revealed a significant improvement in consciousness level after seven days in the epigallocatechin-3-gallate group (2.93±3.9 unit improvement in GCS versus 0.14±3.05 reduction in GCS, p-value:0.033). There was also a significantly shorter duration of mechanical ventilation in the epigallocatechin-3-gallate compared to the control group (5.1 days versus 9.8 days, p-value:0.02). Reduction of the serum S100B level was slightly higher in the epigallocatechin-3-gallate group (23.96 versus 18.6 pg/ml) but the difference was not statistically significant. Conclusion: Epigallocatechin-3-gallate supplementation had beneficial effects on consciousness level of the patients with moderate to severe traumatic brain injury in the acute phase

Faculty Members’ Job Satisfaction in Selected Military Universities

Introduction: Motivation and job satisfaction of faculty can play a fundamental role in their performance. Inattention to staff’s job satisfaction causes cessation, relative stagnation and evanescence of them. Therefore this study aimed to determine job satisfaction of academic members in two selected Tehran’s military universities in year 2010. Methods: In this descriptive, cross-sectional study, 150 academic members were selected and received the survey questionnaire. The questionnaire, including 45 items with 5 point Likert scale, had been developed and validated in Isfahan University of Medical Sciences. Then data were analyzed using independent t, ANOVA and Pearson correlation tests. Results: Mean scores of job satisfaction domains ,in a descending order, belonged to mode of supervision and communication(46.51± 10.61), nature of the job (23.83± 4.33), job security (23.76 ± 5.60), physical conditions and job environment(20.43± 6.79), promotion opportunities (17.29 ± 5.93), and salary and benefits(15.12± 5.39). Results of Pearson correlation test showed that there was a positive correlation between satisfaction score and the following domains in a descending order: mode of supervision and communication, promotion opportunities, job security, physical conditions and job environment, salary and benefits, and nature of job. Conclusion: Academic members had a relatively good job satisfaction. Attention to supervision and promotion opportunities can improve the current situatio

Evaluation of effect of confinement on the collapse probability of reinforced concrete frames subjected to earthquakes

Confinement of reinforced concrete members has repeatedly proven crucial when structures are exposed to strong ground motions. Yet, unconfined concrete is still widely used in developing countries with significant seismic hazard. To quantify the effect of lack of confinement on collapse probability of reinforced concrete frames, incremental dynamic analyses were conducted on two 4-story reinforced concrete moment resisting frame buildings using 5 ground motions. The structures are identical and comply to Eurocode demands except for the fact that one is completely unconfined whereas the other is confined according to the code. The analyses were conducted in OpenSEES using Mander’s material model for confined and unconfined concrete. Based on the results of the analyses, collapse fragility curves were created for each structure. The results show statistically significant effect of confinement on the collapse probability of the reinforced concrete frame. The prabability of collapse is 1.2 and 12 %, respectively, for the code complying and the unconfined structure exposed to a design level earthquake.publishedVersio

Fear control and danger control amid COVID-19 dental crisis: Application of the Extended Parallel Process Model

OBJECTIVES: There is high risk of contamination with COVID-19 virus during routine dental procedures and infection control is crucial. The aim of this study was to determine the factors associated with Covid-19 preventive behaviors among oral health care providers using an extended parallel process model (EPPM). METHODS: In a cross-sectional study, short text message invite surveys were sent to 870 oral health care providers in west part of Iran. Data were collected through validated self-report EPPM questionnaires. Descriptive statistics, Chi-square and Fishers exact tests were used for data analysis. RESULTS: In total, 300 completed questionnaires were received and the mean age of respondents was 29.89 ± 11.17 years (range: 20–75 years). Among the study population, 284 (94.67%) perceived the threat of infection highly. Washing hands frequently with water and soap and use of hand sanitizer was reported by 93.33%, of participants. Age (P = 0.010), sex (P = 0.002) and occupation field (P = 0.010) were significantly associated with danger control responses. Data identified that those oral health care providers that were on the danger control response adopted preventive behaviors more strictly than those on fear control response. CONCLUSION: The results of this study showed how degrees of perceived threat and perceived efficacy influenced oral health providers’ willingness to perform recommended health behaviors. These findings can assist public health agencies in developing educational programs specifically designed for promoting preventive behaviors among oral health providers in pandemic situations