Analysis of Information Networks of Freshman Engineering Students

The main purpose of this study was to determine if social interaction within the University of Tennessee, Knoxville college freshman engineering classrooms correlates with academic performance. Also of interest was whether the interactions between genders had a significant affect on academic performance. Better academic performance is cited in the literature as improving retention and graduation rates; therefore, if factors that affect academic performance can be understood measures can be taken to help students perform better. Five UT freshman engineering classes were surveyed to determine their level of involvement with the rest of the members in their class. Academic performance of the class as a whole and of each gender was retrieved from the class’s instructor at the end of the semester. The demographic information revealed that there are significantly fewer females in engineering than males, however, the percent enrolled is consistent with that of the national average. Social networking analysis of the interactions within the class revealed that the women have a higher percent of interaction within the class than males do. However, classes overall do not have that much interaction. The relationships did show that social interaction within a class could have an affect on student’s academic performance. While there was no significant relationship between the overall class grades and overall class density, strong relationships were revealed between overall class grades and gender-to-gender interaction and gender grades with respect to gender-to-gender interaction. A significant positive relationship was made between receiving A’s and an increase in male-to-female interaction (p = 0.038). As the A’s within a class increase, other grades will decrease; and thus, the class’s academic success rate increases. A weak positive relationship was made between the percent of males receiving A’s and the amount of male-to-female interaction; however, given a larger dataset, there may have been statistical significance

Family Food Environment and Child Eating Behavior in a Private School of Abu Dhabi

Aim: Dietary habits developed during childhood and continued  through adulthood.    Children’s eating behaviours should be  monitorining to avoid possible  nutritional deficiencies which have been found to be strongly related to the development of future disease such as  obesity, diabetes type 2 and others. The main aim of this study is to explore the relationship between family food environment and the eating behavior during  dinnertime among children aged 4 to 6 years old in Abu Dhabi. A cross-sectional  study was carried out  that examined the relationship between family food environment and child’s  eating behaviour around dinnertime.Methods: 61 families participated in the study with their children aged 4-6 years old from a private school. They completed a questionnaire that included questions about demographics, eating behaviour and food environment.Results: 82% of the mothers were reported to be responsible for feeding the children. Most of the families had dinner together three or more times a week. Half of the children got a high score in the child’s eating behaviour scale, indicating that they had positive eating behaviour. The results also showed that children of highly educated mothers were more likely to have positive eating behaviour, compared to children of mothers with lower education (p < .05). There was a significant positive correlation between modelling of eating and child eating behaviour ( Pearson’s r = .56, p < .01), and a significant negative correlation between pressure to eat and child eating behaviour (Pearson’s r = -.35, p < .01).Conclusion: This study is in line with other studies showing that aspects in the family food environment have an influence on eating behaviour of children. Educating parents on food environment and its impact on child behaviour is crucial in order to make them able to develop feeding strategies most likely to benefit children's’  health

Représentations sociales de l'identité linguistique de l'enseignant et comportements interactionnels : étude de cas expérimentale dans une classe de F.L.E. au Bahreïn

As well as the identity categorization – that we refer to by the concept of linguistic identity – of French foreign language teachers has always represented a problematic issue in the field of language appropriation, the notion of social representation is currently becoming a major theoretical challenge in this field. This research presents an experimental study that focuses on social representation regarding the linguistic identity of the teacher and its effect(s) on learners’ interactional behavior in a French foreign language class located in Bahrain. The purpose of this research is first to study the interactional behavior of learners via classroom observation and by adopting conversational analysis methods, and secondly to study the role of social representations in language practice of French foreign language learners. For this purpose, we have carried out a thematic content analysis of epilinguistic discourses that have been collected via a semi-structured interview to determine if linguistic identity differences led to interactional behavior changes. This thesis aims at proposing an empirical approach that does not rely only on the analysis of epilinguistic discourse but also takes into account language practice and thus compares what’s said and what’s doneOutre la catégorisation socio-identitaire de l’enseignant de FLE – résumée dans la notion d’identité linguistique – qui représente depuis toujours une problématique très répandue dans le domaine de l’appropriation des langues, la notion de représentation sociale commence aujourd’hui à constituer un enjeu théorique majeur en ce domaine. Ce travail de recherche présente une étude expérimentale portant sur les représentations sociales de l’identité linguistique de l’enseignant et ses conséquences sur les comportements interactionnels des élèves dans une classe de FLE située au royaume du Bahreïn. Il a pour objet deux axes d’étude : a) les comportements interactionnels des apprenantes, que nous recueillerons par le biais d’observations de classes et que nous étudierons en adoptant une analyse conversationnelle fondée sur l’interprétation des phénomènes langagiers ; b) une réflexion sur la notion de représentations sociales orientée vers son rôle déterminant dans les pratiques langagières, étayée par une analyse de contenu thématique des discours épilinguistiques recueillis à l’aide d’un entretien semi-directif afin de déterminer si la perception de la différence d’identité linguistique est à l’origine de la dynamique des comportements interactionnels. L’objectif de ce travail est également de proposer une approche empirique ne reposant pas seulement sur l’analyse des discours sur les représentations sociales mais qui prend aussi en compte les pratiques langagières et confronte ainsi le dire et le fair

Is Aberrant Reno-Renal Reflex Control of Blood Pressure a Contributor to Chronic Intermittent Hypoxia-Induced Hypertension?

Renal sensory nerves are important in the regulation of body fluid and electrolyte homeostasis, and blood pressure. Activation of renal mechanoreceptor afferents triggers a negative feedback reno-renal reflex that leads to the inhibition of sympathetic nervous outflow. Conversely, activation of renal chemoreceptor afferents elicits reflex sympathoexcitation. Dysregulation of reno-renal reflexes by suppression of the inhibitory reflex and/or activation of the excitatory reflex impairs blood pressure control, predisposing to hypertension. Obstructive sleep apnoea syndrome (OSAS) is causally related to hypertension. Renal denervation in patients with OSAS or in experimental models of chronic intermittent hypoxia (CIH), a cardinal feature of OSAS due to recurrent apnoeas (pauses in breathing), results in a decrease in circulating norepinephrine levels and attenuation of hypertension. The mechanism of the beneficial effect of renal denervation on blood pressure control in models of CIH and OSAS is not fully understood, since renal denervation interrupts renal afferent signaling to the brain and sympathetic efferent signals to the kidneys. Herein, we consider the currently proposed mechanisms involved in the development of hypertension in CIH disease models with a focus on oxidative and inflammatory mediators in the kidneys and their potential influence on renal afferent control of blood pressure, with wider consideration of the evidence available from a variety of hypertension models. We draw focus to the potential contribution of aberrant renal afferent signaling in the development, maintenance and progression of high blood pressure, which may have relevance to CIH-induced hypertension

Audio-Based Drone Detection and Identification Using Deep Learning Techniques with Dataset Enhancement through Generative Adversarial Networks

Drones are becoming increasingly popular not only for recreational purposes but in day-to-day applications in engineering, medicine, logistics, security and others. In addition to their useful applications, an alarming concern in regard to the physical infrastructure security, safety and privacy has arisen due to the potential of their use in malicious activities. To address this problem, we propose a novel solution that automates the drone detection and identification processes using a drone’s acoustic features with different deep learning algorithms. However, the lack of acoustic drone datasets hinders the ability to implement an effective solution. In this paper, we aim to fill this gap by introducing a hybrid drone acoustic dataset composed of recorded drone audio clips and artificially generated drone audio samples using a state-of-the-art deep learning technique known as the Generative Adversarial Network. Furthermore, we examine the effectiveness of using drone audio with different deep learning algorithms, namely, the Convolutional Neural Network, the Recurrent Neural Network and the Convolutional Recurrent Neural Network in drone detection and identification. Moreover, we investigate the impact of our proposed hybrid dataset in drone detection. Our findings prove the advantage of using deep learning techniques for drone detection and identification while confirming our hypothesis on the benefits of using the Generative Adversarial Networks to generate real-like drone audio clips with an aim of enhancing the detection of new and unfamiliar drones

Promoting Walkability in Streets: Analytical Study of Salem Street, Sulaimani, Iraq

Walking represents a vital transport system for people to maintain balanced and healthy lifestyles and to improve the environmental conditions. Within a contemporary metropolitan society, other means of transport are often preferred. The presented paper aims to examine the methods of how to encourage people to walk. By organizing it into three main sections to begin with, the work of in order to investigate the nature of the metropolitan individual and the contemporary society. The second section builds on the outcomes of the first section. Hence it provides a clear rationale for the adoption of strategies that would encourage people to walk. Salim Street which is one of the important and vibrant streets inside Sulaimani city is being chosen for this study, its possibility to be a walk able street is analyzed. The third section provides practical solutions and steeps on how to promote walking among the contemporary society. then, a brief conclusion summarizes the key arguments encompassed in the presented paper and draws wider implications and recommendations for city planners to build more pedestrian friendly streets

Chronic intermittent hypoxia impairs diuretic and natriuretic responses to volume expansion in rats with preserved low-pressure baroreflex control of the kidney

We examined the effects of exposure to chronic intermittent hypoxia (CIH) on baroreflex control of renal sympathetic nerve activity (RSNA) and renal excretory responses to volume expansion (VE) before and after intra-renal TRPV1 blockade by capsaizepine (CPZ). Male Wistar rats were exposed to 96 cycles of hypoxia per day for 14 days (CIH), or normoxia. Urine flow and absolute Na+ excretion during VE were less in CIH-exposed rats, but the progressive decrease in RSNA during VE was preserved. Assessment of the high-pressure baroreflex revealed an increase in the operating and response range of RSNA and decreased slope in CIH-exposed rats with substantial hypertension (+19mmHg basal mean arterial pressure, MAP), but not in a second cohort with modest hypertension (+12mmHg). Intra-renal CPZ caused diuresis, natriuresis and a reduction in MAP in sham and CIH-exposed rats. Following intra-renal CPZ, diuretic and natriuretic responses to VE in CIH-exposed rats were equivalent to sham. TPRV1 expression in the renal pelvic wall was similar in both experimental groups. Exposure to CIH did not elicit glomerular hypertrophy, renal inflammation or oxidative stress. We conclude that exposure to CIH: 1) does not impair the low-pressure baroreflex control of RSNA; 2) has modest effects on the high-pressure baroreflex control of RSNA, most likely indirectly due to hypertension; 3) can elicit hypertension in the absence of kidney injury; and 4) impairs diuretic and natriuretic responses to fluid overload. Our results suggest that exposure to CIH causes renal dysfunction, which may be relevant to obstructive sleep apnea

Automatic Autism Spectrum Disorder Detection Using Artificial Intelligence Methods with MRI Neuroimaging: A Review

Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, the process of diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist the specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We conclude by suggesting future approaches to detecting ASDs using AI techniques and MRI neuroimaging

Inhibition of alpha-synuclein seeded fibril formation and toxicity by herbal medicinal extracts.

Recent studies indicated that seeded fibril formation and toxicity of α-synuclein (α-syn) play a main role in the pathogenesis of certain diseases including Parkinson's disease (PD), multiple system atrophy, and dementia with Lewy bodies. Therefore, examination of compounds that abolish the process of seeding is considered a key step towards therapy of several synucleinopathies. Using biophysical, biochemical and cell-culture-based assays, assessment of eleven compounds, extracted from Chinese medicinal herbs, was performed in this study for their effect on α-syn fibril formation and toxicity caused by the seeding process. Salvianolic acid B and dihydromyricetin were the two compounds that strongly inhibited the fibril growth and neurotoxicity of α-syn. In an in-vitro cell model, these compounds decreased the insoluble phosphorylated α-syn and aggregation. Also, in primary neuronal cells, these compounds showed a reduction in α-syn aggregates. Both compounds inhibited the seeded fibril growth with dihydromyricetin having the ability to disaggregate preformed α-syn fibrils. In order to investigate the inhibitory mechanisms of these two compounds towards fibril formation, we demonstrated that salvianolic acid B binds predominantly to monomers, while dihydromyricetin binds to oligomeric species and to a lower extent to monomers. Remarkably, these two compounds stabilized the soluble non-toxic oligomers lacking β-sheet content after subjecting them to proteinase K digestion. Eleven compounds were tested but only two showed inhibition of α-syn aggregation, seeded fibril formation and toxicity in vitro. These findings highlight an essential beginning for development of new molecules in the field of synucleinopathies treatment.The work conducted by Dr. El-Agnaf laboratory was supported by Qatar Biomedical Research Institute under the Start-up Fund SF 2017–007. Funding for this work was provided in part by NIH/NIA grant R37AG019391 to D.E. This study was made possible by NPRP grant 4–1371–1-223 from the Qatar National Research Fund (a member of Qatar Foundation). The funding bodies provided financial support for this study; they had no role in the study design, performance, data collection and analysis, decision to publish and preparation/writing of the manuscript