Brane worlds and dark matter

Two problems related to dark matter is considered in the context of a brane world model in which the confinement of gauge fields on the brane is achieved by invoking a confining potential. First, we show that the virial mass discrepancy can be addressed if the conserved geometrical term appearing in this model is considered as an energy momentum tensor of an unknown type of matter, the so-called X-matter whose equation of state is also obtained. Second, the galaxy rotation curves are explained by assuming an anisotropic energy momentum tensor for the X-matter.Comment: 13 pages, 1 figure, to appear in IJMP

Determining the effect of aloe Vera and aerobic exercise on lactate de-hydrogenase in male athletes

Aloe Vera is a medicinal plant as antioxidants reduce cell damage and used. The purpose of this study was to determine the effect of Aloe Vera on lactate de-hydrogenase after a period of aerobic exercise in male athletes. Methods This study applied the method according to the nature of the study, based on semi-empirical research and a review of the pre-test, post-test supplements and placebo groups, respectively. In this study,20 male students weight was 64.85 ± .51 and height was 172.05 ± 6.4 were randomly assigned to two groups of Imam Ali College of Physical Education Supplement (n = 10) and placebo (10 people). Then aerobic training was conducted for 4 weeks in the supplemented group were taking 3 capsules, each capsule contains 2 grams of dried Aloe Vera and placebo group were taking 3 capsules containing Dextrin daily after every meal. To determine the index of LDH were used and blood samples were collected 24 hours before and after each test Cooper with student in the lab. To describe data, analytical data, and for the mean and standard deviation of repeated measures ANOVA and independent T-test was used for comparison between groups Significance level was P ≤ 0.05. The use of Aloe Vera during aerobic exercise significantly, cautious reduced LDH (P=0.006) in the supplement group compared to placebo was 15 reduction. Conclusion: Overall the findings of this study showed that Aloe Vera reduces lactate de-hydrogenase. This result may reflect the role of Aloe Vera has anti-inflammatory and antioxidant

The effect of direct and indirect education on attitudes of parents of children with attention-deficit/hyperactivity disorder towards medication treatments

Background and aims: Attention-deficit/hyperactivity disorder (ADHD) is one of the most common childhood behavioral disorders causing hyperactivity, attention deficit and education decline among children. Regarding the importance of medication treatment, this study was performed to compare the effect of two methods (Direct and Indirect) of education of parents of children with ADHD on their attitudes about medication, treatment satisfaction and medication compliance. Methods: In this clinical trial study, eighty parents of children with ADHD, referred to child psychiatry clinic in Shahrekord were randomly assigned to direct and indirect education groups. The first group (direct education) was attended in groups four sessions. The second group was given education booklet with the same content. Participants were evaluated before and one month after education in terms of their attitudes to medication, treatment satisfaction and treatment compliance. Results: 61 parents of 81 participants continued the study. After intervention, the mean scores of attitudes about medication and satisfaction with treatment were significantly improved in the direct education group (P0.05). Both groups had significantly increased treatment compliance one month after education (P<0.001). Change of mean score attitudes to medication consumption at the end of the periods was significantly different in direct group compared to indirect education group (P<0.01). Conclusion: Direct education was more effective than indirect education on improvement of attitudes towards medication and increase in treatment satisfaction, and both methods resulted in increased medication compliance. Regarding the importance of medication treatment in this disorder, using results of this study can have significant influence on function of children with this disorder in family and school environment

Investigation of Inhibition Effect and Determination of Some Quantum Chemical Parameters of an Organic Compound on the Carbon Steel in Sulfuric Acid Medium

The inhibition ability of [3-(4-methyl-pyridin-2-y1)-4-oxo-2-phenylimino-thiazolidin-5-ylidene]-acetic acid ethyl ester (MOTAE) on the corrosion behavior of carbon steel in 0.5 M sulfuric acid solution was investigated using weight loss and potentiodynamic polarization techniques. The inhibition efficiencies increased as the concentration of the compound was increased. The calculated inhibition efficiencies from the investigated methods were in good agreement. Potentiodynamic polarization measurements indicate that MOTAE acts as a mixed type inhibitor. The adsorption of inhibitor on the steel surface obeys Langmuir adsorption isotherm. Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM)‎ were used to characterize the surface of the alloy. The structure of inhibitor was optimized using three quantum chemical levels. Some quantum chemical parameters as well as Mulliken charge densities for this molecule were computed and discussed

The Effect of Epidemiological Cohort Creation on the Machine Learning Prediction of Homelessness and Police Interaction Outcomes Using Administrative Health Care Data

Background: Mental illness can lead to adverse outcomes such as homelessness and police interaction and understanding of the events leading up to these adverse outcomes is important. Predictive models may help identify individuals at risk of such adverse outcomes. Using a fixed observation window cohort with logistic regression (LR) or machine learning (ML) models can result in lower performance when compared with adaptive and parcellated windows. Method: An administrative healthcare dataset was used, comprising of 240,219 individuals in Calgary, Alberta, Canada who were diagnosed with addiction or mental health (AMH) between April 1, 2013, and March 31, 2018. The cohort was followed for 2 years to identify factors associated with homelessness and police interactions. To understand the benefit of flexible windows to predictive models, an alternative cohort was created. Then LR and ML models, including random forests (RF), and extreme gradient boosting (XGBoost) were compared in the two cohorts. Results: Among 237,602 individuals, 0.8% (1,800) experienced first homelessness, while 0.32% (759) reported initial police interaction among 237,141 individuals. Male sex (AORs: H=1.51, P=2.52), substance disorder (AORs: H=3.70, P=2.83), psychiatrist visits (AORs: H=1.44, P=1.49), and drug abuse (AORs: H=2.67, P=1.83) were associated with initial homelessness (H) and police interaction (P). XGBoost showed superior performance using the flexible method (sensitivity =91%, AUC =90% for initial homelessness, and sensitivity =90%, AUC=89% for initial police interaction) Conclusion: This study identified key features associated with initial homelessness and police interaction and demonstrated that flexible windows can improve predictive modeling.Comment: to be published in Frontiers in Digital Health, Health Informatic

Stemming text-based web page classification using machine learning algorithms: a comparison

The research aim is to determine the effect of word-stemming in web pages classification using different machine learning classifiers, namely Naive Bayes (NB), k-Nearest Neighbour (k-NN), Support Vector Machine (SVM) and Multilayer Perceptron (MP). Each classifiers' performance is evaluated in term of accuracy and processing time. This research uses BBC dataset that has five predefined categories. The result demonstrates that classifiers' performance is better without word stemming, whereby all classifiers show higher classification accuracy, with the highest accuracy produced by NB and SVM at 97% for F1 score, while NB takes shorter training time than SVM. With word stemming, the effect on training and classification time is negligible, except on Multilayer Perceptron in which word stemming has effectively reduced the training time

Effect of malathion insecticide on liver tissue and enzymes of Rutilus rutilus caspicus of the Caspian Sea

Malathion is an organophosphate insecticide which uses to destroy insects and pests of fruit trees, ornamental plants and agricultural corps. In the present study, effect of Malathion on liver and selected enzymes (SGOT, SGPT and ALP) was studied in Caspian Roach (Rutilus rutilus caspicus). Four treatments with three replications were designed to carry out the survey. Four groups of experimental fish (containing 30 fish in each group) were exposed to different concentrations of Malathion. e. 0, 0.01, 0.05 and 0.1 ppm respectively for 23 days. Blood collection was done in 3rd, 13th and 23th after exposure to Malathion and also 30 days after recovery in clean water and enzymes were measured using standard kits. Also liver tissues were isolated to histological examination. Results showed that tissues of control group (0ppm) were normal and there were no damages, yet there were hepatocytes degeneration, picnotic in nuclear, hepatocytes vacuolization, vascular congestion and sinusoid congestion in liver of other groups. Tissue damages were increased in higher malathion concentration and over time. Results related to enzymes showed that there were no significant differences in SGOT of fish treated with low concentrations of malathion (0.01 and 0.05 ppm) and control group but it was increased in highest concentration (p<0.05). Yet, SGPT increased significantly after passing 23 days in all fish exposed to malathion But ALP changes trend was decreasing

Virial mass in DGP brane cosmology

We study the virial mass discrepancy in the context of a DPG brane-world scenario and show that such a framework can offer viable explanations to account for the mass discrepancy problem. This is done by defining a geometrical mass $\mathcal{N}$ that we prove to be proportional to the virial mass. Estimating $\mathcal{N}$ using observational data, we show that it behaves linearly with $r$ and has a value of the order of $M_{200}$, pointing to a possible resolution of the virial mass discrepancy. We also obtain the radial velocity dispersion of galaxy clusters and show that it is compatible with the radial velocity dispersion profile of such clusters. This velocity dispersion profile can be used to differentiate various models predicting the virial mass.Comment: 12 pages, 1 figure, to appear in CQ

Zero-shot learning and its applications from autonomous vehicles to COVID-19 diagnosis: A review

The challenge of learning a new concept, object, or a new medical disease recognition without receiving any examples beforehand is called Zero-Shot Learning (ZSL). One of the major issues in deep learning based methodologies such as in Medical Imaging and other real-world applications is the requirement of large annotated datasets prepared by clinicians or experts to train the model. ZSL is known for having minimal human intervention by relying only on previously known or trained concepts plus currently existing auxiliary information. This is ever-growing research for the cases where we have very limited or no annotated datasets available and the detection / recognition system has human-like characteristics in learning new concepts. This makes the ZSL applicable in many real-world scenarios, from unknown object detection in autonomous vehicles to medical imaging and unforeseen diseases such as COVID-19 Chest X-Ray (CXR) based diagnosis. In this review paper, we introduce a novel and broaden solution called Few / one-shot learning, and present the definition of the ZSL problem as an extreme case of the few-shot learning. We review over fundamentals and the challenging steps of Zero-Shot Learning, including state-of-the-art categories of solutions, as well as our recommended solution, motivations behind each approach, their advantages over each category to guide both clinicians and AI researchers to proceed with the best techniques and practices based on their applications. Inspired from different settings and extensions, we then review through different datasets inducing medical and non-medical images, the variety of splits, and the evaluation protocols proposed so far. Finally, we discuss the recent applications and future directions of ZSL. We aim to convey a useful intuition through this paper towards the goal of handling complex learning tasks more similar to the way humans learn. We mainly focus on two applications in the current modern yet challenging era: coping with an early and fast diagnosis of COVID-19 cases, and also encouraging the readers to develop other similar AI-based automated detection / recognition systems using ZSL