Study Habits and Their Relationship with Academic Performance among Students of Abadan School of Nursing

Background & Objective : Improvement in learners’ academic performance is one of the main goals of educational centers. Academic performance is affected by a lot of factors study habit is one of them. Considering the importance of one’s study habits in academic performance, this study was conducted to identify the students’ study habits and their relationship with academic performance in Abadan School of Nursing. Methods : This cross sectional study was conducted on 150 students of Abadan School of Nursing in 2007. Data was collected using Palsane & Sharma Study Habit Inventory questionnaire which was completed in a self-directed way at the time of holding final exams. Results : According to the results of this study, the mean score ( ± SD) of the students’ study habits was 48.26 ( ± 11.6) out of 90. 11.33 percent of the students had unsatisfactory study habits while 80.7% and 8% had relatively satisfactory and satisfactory study habits, respectively. There was a significant, week and positive relationship between the students’ study habits and their academic performance (P=0.001, r= 0.27). There wasn’t any significant relationship between study habits and semester, age and marital status. Conclusion : Generally speaking, the students’ study habits are considered to be moderate that is to say that their study method is not of good quality. Considering the importance of study habits in academic performance and achievement, planning to improve students’ study habits and methods and interventions in this regard can be effective. Keywords: Study habits, Study skills, Academic performance, Nursing student

Ultraviolet aging study on bitumen modified by a composite of clay and fumed silica nanoparticles

In this study, surface morphology, rheological and chemical properties were investigated of bitumen, which was modified by a composite of clay and fumed silica nanoparticles, and exposed to ultraviolet (UV) aging in laboratory. The volume fraction of the nanoparticles within the binder ranged from 1 to 3%, the temperature range considered was 30 to 70 °C. Surface morphology, rheological and chemical binder properties were analyzed using field emission scanning electron microscopy (FESEM), dynamic shear rheometer (DSR), and Fourier transform infrared (FT-IR) spectroscopy. It was found, that the bitumen modification through clay and fumed silica nanoparticles changed resulting binder properties significantly. The index of carbonyl and oxidation degree decreased, and the clay and fumed silica nanoparticles improved aging resistance to ultraviolet (UV) radiation considerably. The results indicate that the mechanical stability of the modified bitumen is very much driven by the specific concentration of clay and fumed silica nanoparticles

Effect of Fumed Silica Nanoparticles on Ultraviolet Aging Resistance of Bitumen

In this study, bitumen modified by fumed silica nanoparticles was characterized through dynamic shear rheometer tests, scanning electron microscopy, and Fourier transform infrared spectroscopy. The fumed silica nanoparticles were used in three different ratios, i.e., 0.1, 0.2 and 0.3 wt.-% of bitumen. Specifically, the modified bitumen characteristics were studied after laboratory aging by analyzing the chemical composition and rheological properties. From the determination of oxidation degree and carbonyl index it was found that the resistance of the modified bitumen to ultraviolet aging was improved with the increasing nanoparticle content. In bitumen modified by fumed silica nanoparticles, the nanoparticles were well dispersed. Moreover, the results illustrated that the bitumen properties were improved, and the improvement effect of 0.1 wt.-% fumed silica nanoparticles was more distinct than the higher concentrations

Effect of exercise on Mesencephalic astrocyte derived neurotrophic factor levelsin the striatum of rats suffering from Parkinsons

Background and aims: The aim of this study, regarding the results of the previous researches and the effects of voluntary exercise on neurotrophic factors in treating PD, was to evaluate changes MANF level of rats` stratum exposed to neurotoxin injected by the stereotaxic surgery following the four weeks of treadmill running. Methods: Twenty four rats were divided into four groups: Sham, Parkinson control, Parkinson exercise, and healthy control. Exercise group exercised for 4 weeks, 5 days per week and 2 15-minute sessions having at least 1 h interval. The purpose of injecting 6-OHDA into the brain striatum was to create an experimental model of PD. Three weeks after the injection of 6-hydroxydopamine (6-OHDA), Apo morphine rotational test was carried out in order to verify the rats with Parkinson latest. MANF levels in the striatum were measured by ELISA. Data was analyzed by one-way analysis of variance (ANOVA) and Tukey post-hoc test. Results: The findings showed that there is a significant difference in the striatum MANF level of Parkinson control group (26.91±9 pg/mg) compared to the healthy control group (45.22±2 pg/mg) (P≤0.05). Furthermore, the striatum MANF level in Parkinson exercise group (29.35±2 pg/mg) had an increase in comparison with the Parkinson control group (26.91±9 pg/mg), but the difference was not significant (P=0.997). Conclusion: This research has shown that performing treadmill running program cannot increase the MANF level of striatum. Therefore, we cannot decisively consider a neural protective role for this training protocol and it necessitates further studies

Exploring 3D Data and Beyond in a Low Data Regime

3D object classification of point clouds is an essential task as laser scanners, or other depth sensors, producing point clouds are now a commodity on, e.g., autonomous vehicles, surveying vehicles, service robots, and drones. There have been fewer advances using deep learning methods in the area of point clouds compared to 2D images and videos, partially because the data in a point cloud are typically unordered as opposed to the pixels in a 2D image, which implies standard deep learning architectures are not suitable. Additionally, we identify there is a shortcoming of labelled 3D data in many computer vision tasks, as collecting 3D data is significantly more costly and difficult. This implies using zero- or few-shot learning approaches, where some classes have not been observed often or at all during training. As our first objective, we study the problem of 3D object classification of point clouds in a supervised setting where there are labelled samples for each class in the dataset. To this end, we introduce the {3DCapsule}, which is a 3D extension of the recently introduced Capsule concept by Hinton et al. that makes it applicable to unordered point sets. The 3DCapsule is a drop-in replacement of the commonly used fully connected classifier. It is demonstrated that when the 3DCapsule is applied to contemporary 3D point set classification architectures, it consistently shows an improvement, in particular when subjected to noisy data. We then turn our attention to the problem of 3D object classification of point clouds in a Zero-shot Learning (ZSL) setting, where there are no labelled data for some classes. Several recent 3D point cloud recognition algorithms are adapted to the ZSL setting with some necessary changes to their respective architectures. To the best of our knowledge, at the time, this was the first attempt to classify unseen 3D point cloud objects in a ZSL setting. A standard protocol (which includes the choice of datasets and determines the seen/unseen split) to evaluate such systems is also proposed. In the next contribution, we address the hubness problem on 3D point cloud data, which is when a model is biased to predict only a few particular labels for most of the test instances. To this end, we propose a loss function which is useful for both Zero-Shot and Generalized Zero-Shot Learning. Besides, we tackle 3D object classification of point clouds in a different setting, called the transductive setting, wherein the test samples are allowed to be observed during the training stage but then as unlabelled data. We extend, for the first time, transductive Zero-Shot Learning (ZSL) and Generalized Zero-Shot Learning (GZSL) approaches to the domain of 3D point cloud classification by developing a novel triplet loss that takes advantage of the unlabeled test data. While designed for the task of 3D point cloud classification, the method is also shown to be applicable to the more common use-case of 2D image classification. Lastly, we study the Generalized Zero-Shot Learning (GZSL) problem in the 2D image domain. However, we also demonstrate that our proposed method is applicable to 3D point cloud data. We propose using a mixture of subspaces which represents input features and semantic information in a way that reduces the imbalance between seen and unseen prediction scores. Subspaces define the cluster structure of the visual domain and help describe the visual and semantic domain considering the overall distribution of the data

RELATION OF RELIGIOUS BELIEFS WITH ANXIETY AND DEPRESSION IN FAMILIES WITH MENTAL PATIENTS

Introduction: Religious beliefs as an important factor in mental health to be considered. The aim of this study was to determine the relationship between religious beliefs and anxiety and depression in family caregivers in families with patients with mental disorders. Method: This study is causal - comparative. The study population consisted of families with mentally ill patients were hospitalized in psychiatric wards. The sample consisted of 152 family. Research Environment was psychiatric wards in hospitals of Ahvaz Jundishapur University of Medical Sciences. Data Collection tools were demographic Characteristics, Hospital Anxiety and Depression Scale [HADS] and religious beliefs questionnaire. Descriptive statistics were used for to describe data and for determine the relationship between qualitative variables was used of X2, SPSS 22 software was used. Results: 63.8 percent of family caregivers were male and mostly aged between 30 and 50 years.48 percent of family caregivers has symptoms of anxiety and 67.3 percent have symptoms of depression. Significant relationship was observed between anxiety and religious beliefs [P- value = 0.002]. No significant relationship was observed between depression and religious beliefs [P- value = 0.417]. Conclusion: A religious belief is effective factor in the mental health of family caregivers and to help them be better able to take care of the patient. Whatever religious beliefs of family caregivers are stronger, their anxiety are less and the level of their mental health are more. Keywords: Religious beliefs, anxiety, depression, Family caregivers, patients with mental disorder