Study of the Impacts of Educational Technology on Academic Progress of Students in Bamian Boys’ Center High School

The aim of this research was to study the impact of educational technology on the academic progress of Bamyan boys’ high school students in 1401 (2022). This research was applied in terms of purpose and a descriptive survey in terms of nature. Societal statistics of this research were the whole of 83 teachers at Bamian Boys’ High School. So, from the societal statistics, we used simple random sampling with confidence levels of 95% and 5% error for 70 people selected according to Morgan’s table and 64 of them answered the questionnaires. For data collection, a questionnaire measuring the attitude towards the use of technology in education (including 50 questions) was specially prepared for teachers and fixed to check their academic progress. SPSS 24 statistical software was used in data analysis. The research outcome indicates factors like the teacher’s understanding and the benefits of implementing educational technology and equipment’s effects on the academic progress of school students. The result means that a spiritual relationship exists between educational technology and the academic progress of students

A Review on the Cerebrovascular Segmentation Methods

© 2018 IEEE. This paper explores various methods that have been proposed for the segmentation of the cerebrovascular structure. All of the methods listed are a combination old, new, automatic and semiautomatic models that produce promising results. Each method will be explained along with its advantages and disadvantages. Each of the methods explained are further explored in this paper with variety algorithms produced by using certain models to target certain areas in the cerebrovascular structure. These algorithms were developed to segment cerebrovascular structures from scans obtained from various image modalities e.g., time of flight magnetic-resonance angiography (TOF-MRA), and computed tomography angiography (CTA)

How Terrorism Affect Economic Growth in Pakistan: By Using Co-Integration Approach

The aim of this study is to explore the long-term relationship between economic growth and terrorism. Johansen Co-integration analysis is used to gauge the effect of terrorism on economic growth.  Annual time series data used from the period 1981-2015. Data of terrorism is obtained from GTD (Global Terrorism Database) while the data of economic growth is obtained from WDI World Bank Indicator. The analysis suggests the finding that there is a long-term relationship among the key macroeconomic indicators and terrorism. The present paper fills the gap by using the literature and econometric sophisticated techniques that delivered proof for the relationship among many macroeconomic indicators and terrorism. Keywords: Co-integration, Economic Growth, Terrorism JEL Classification: Z 00

Studying the Prevalence of Organizational Indifference in Bamyan University

This research was conducted with the aim of investigating the prevalence of organizational indifference in Bamyan University. The research method was a descriptive method. The statistical population included all lecturers and employees of Bamyan University, whose total number reached (269). In this research, full sampling method was used and all members of the statistical community were selected as statistical samples. To collect data, organizational indifference questionnaire of Danaeefard, Hassanzadeh and Salarieh (2010) was used. The validity of the questionnaire was calculated using the item analysis method and its reliability was calculated using Cronbach's alpha scale, and its alpha coefficient was obtained (0.914). This questionnaire was sent to 269 people, of whom (214) responded. The data obtained at two levels (descriptive and inferential) were analyzed using the statistical methods of Pearson's correlation coefficient, one-sample t-test and IBM SPSS23 statistical software. The results of data analysis showed that the prevalence of organizational indifference and its dimensions are less than average level (Q2=3) and more than low level (Q1=2)

Medical image analysis for the early prediction of hypertension

Recently, medical image analysis has become a vital evolving technology that is used in the early diagnosis of various diseases. Medical imaging techniques enable physicians to capture noninvasive images of structures inside the human body (such as bones, tissues, or blood vessels) as well as their functions (such as brain activity). In this study, magnetic resonance angiography (MRA) images have been analyzed to help physicians in the early prediction of hypertension. Hypertension is a progressive disease that may take several years before being fully understood. In the United States, hypertension afflicts one in every three adults and is a leading cause of mortality in more than half a million patients every year. Specific alterations in human brains’ cerebrovasculature have been observed to precede the onset of hypertension. This study presents a computer-aided diagnosis system (CAD) that can predict hypertension prior to the systemic onset of the disease. This MRA-based CAD system is able to detect, track, and quantify the hypertension-related cerebrovascular alterations, then it makes a decision based on the analyzed data about whether each subject is at a high risk of developing hypertension or not. Such kind of prediction can help clinicians in taking proactive and preventative steps to stop the progress of the disease and mitigate adverse events

Incremental composition process for the construction of component-based management

Cyber-physical systems (CPS) are composed of software and hardware components. Many such systems (e.g., IoT based systems) are created by composing existing systems together. Some of these systems are of critical nature, e.g., emergency or disaster management systems. In general, component-based development (CBD) is a useful approach for constructing systems by composing pre-built and tested components. However, for critical systems, a development method must provide ways to verify the partial system at different stages of the construction process. In this paper, for system architectures, we propose two styles: rigid architecture and flexible architecture. A system architecture composed of independent components by coordinating exogenous connectors is in flexible architecture style category. For CBD of critical systems, we select EX-MAN from flexible architecture style category. Moreover, we define incremental composition mechanism for this model to construct critical systems from a set of system requirements. Incremental composition is defined to offer preservation of system behaviour and correctness of partial architecture at each incremental step. To evaluate our proposed approach, a case study of weather monitoring system (part of a disaster management) system was built using our EX-MAN tool

Using 3-D CNNs and Local Blood Flow Information to Segment Cerebral Vasculature

© 2018 IEEE. The variability of the strength (increase or decrease) of the blood flow signals throughout the range of slices of the MRA volume is a big challenge for any segmentation approach because it introduces more inhomogenities to the MRA data and hence less accuracy. In this paper, a novel cerebral blood vessel segmentation framework using Time-Of-Flight Magnetic Resonance Angiography (TOF-MRA) is proposed to handle this challenge. The segmentation framework is based on using three dimensional convolutional neural networks (3D-CNN) to segment the cerebral blood vessels taking into account the variability of blood flow signals throughout the MRA scans. It consists of the following two steps: i) bias field correction to handle intensity inhomogeneity which are caused by magnetic settings, ii)instead of constructing one CNN model for the whole TOF-MRA brain, the TOF-MRA volume is divided into two compartments, above Circle of Willis (CoW) and at and below CoW to account for blood flow signals variability across the MRA volume\u27s slices, then feed these two volumes into the three dimensional convolutional neural networks (3D-CNN). The final segmentation result is the combination of the output of each model. The proposed framework is tested on in-vivo data (30 TOF-MRA data sets). Both qualitative and quantitative validation with respect to ground truth (delineated by an MRA expert) are provided. The proposed approach achieved a high segmentation accuracy with 84.37% Dice similarity coefficient, sensitivity of 86.14%, and specificity of 99.00%

NEURORESTORATIVE MULBERRIES POTENTIAL OF ALZHEIMER'S DISEASE IN ANIMAL MODEL

Objective: Alzheimer's disease (AD) is one of the most common neurodegenerative disorders. The present study aimed to identify the potential neurorestorative of Morus alba and Morus rubra fruits extract on aluminum chloride (AlCl3)-induced AD rats.Methods: Each Morus species extract M. alba and M. rubra at a dose 300 mg/kg body weight (b.wt.) was orally administered, daily for 6 weeks post AlCl3 induction (100 mg/kg b.wt./day/6 weeks, orally). The polyphenols content was determined according to the colorimetric method of Folin–Ciocalteu using gallic acid as a standard. In addition, brain neurotransmitters, total protein content (TPC), and antioxidant biomarkers were determined. Histopathological assessment was performed on the brains of rats.Results: Administration of AlCl3 neurotoxicity induced a significant increment of brain acetylcholinesterase, and nitric oxide, while significant decrease in four monoamines (norepinephrine, epinephrine, 5-hydroxytryptamine serotonin, and dopamin), along with brain tissue ATPase, and TPC were determined. AlCl3-induced changes in peroxidative products, such as protein carbonyl, catalase, and superoxide dismutase activity. AlCl3 induction resulted in significant decrease in antioxidant enzymes, serum level of total antioxidant capacity, and caused histological alteration for brain tissue. Treatment of AD-rats with M. alba and M. rubra (300 mg/kg b.wt./day) causes modulation of most biochemical markers brain function and confirmed by brain histopathological study.Conclusion: Collectively, the present results indicated that M. rubra and M. alba exerts neuroprotection against AlCl3-induced brain pathology. To the best of our knowledge, none of the literature studies have demonstrated the effect of these Morus species against Al-neurotoxicity at biochemical and electron microscopic levels

Left ventricle segmentation and quantification using deep learning

Cardiac MRI is a widely used noninvasive tool that can provide us with an evaluation of cardiac anatomy and function. It can also be used for heart diagnosis. Heart diagnosis through the estimation of physiological heart parameters requires careful segmentation of the left ventricle (LV) from the images of cardiac MRI. Therefore we aim at building a new deep learning method for the automated delineation and quantification of the LV from cine cardiac MRI. Our goal is to reach lower errors for the calculated heart parameters than the previous works by introducing a new deep learning cardiac segmentation method. Our pipeline starts with an accurate LV localization by finding LV cavity center point using a fully convolutional neural network (FCN) model called FCN1. Then, from all heart sections, we extract a region of interest (ROI) that encompasses the LV. A segmentation for the LV cavity and myocardium is performed from the extracted ROIs using FCN called FCN2. The FCN2 model is associated with multiple bottleneck layers and uses less memory footprint than traditional models such as U-net. Furthermore, we introduced a novel loss function called radial loss that works on minimizing the distance between the ground truth LV contours and the predicted contours. After myocardial segmentation, we estimate the functional and mass parameters of the LV. We used the Automated Cardiac Diagnosis Challenge (ACDC-2017) dataset to validate our pipeline, which provided better segmentation, accurate calculation of heart parameters, and produced fewer errors compared to other approaches applied on the same dataset. Additionally, our segmentation approach showed that it can generalize well across different datasets by validating its performance on a locally collected cardiac dataset. To sum up, we propose a novel deep learning framework that we can translate it into a clinical tool for cardiac diagnosis

Accurate Segmentation of Cerebrovasculature from TOF-MRA Images Using Appearance Descriptors

© 2013 IEEE. Analyzing cerebrovascular changes can significantly lead to not only detecting the presence of serious diseases e.g., hypertension and dementia, but also tracking their progress. Such analysis could be better performed using Time-of-Flight Magnetic Resonance Angiography (ToF-MRA) images, but this requires accurate segmentation of the cerebral vasculature from the surroundings. To achieve this goal, we propose a fully automated cerebral vasculature segmentation approach based on extracting both prior and current appearance features that have the ability to capture the appearance of macro and micro-vessels in ToF-MRA. The appearance prior is modeled with a novel translation and rotation invariant Markov-Gibbs Random Field (MGRF) of voxel intensities with pairwise interaction analytically identified from a set of training data sets. The appearance of the cerebral vasculature is also represented with a marginal probability distribution of voxel intensities by using a Linear Combination of Discrete Gaussians (LCDG) that its parameters are estimated by using a modified Expectation-Maximization (EM) algorithm. The extracted appearance features are separable and can be classified by any classifier, as demonstrated by our segmentation results. To validate the accuracy of our algorithm, we tested the proposed approach on in-vivo data using 270 data sets, which were qualitatively validated by a neuroradiology expert. The results were quantitatively validated using the three commonly used metrics for segmentation evaluation: the Dice coefficient, the modified Hausdorff distance, and the absolute volume difference. The proposed approach showed a higher accuracy compared to two of the existing segmentation approaches