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Background Stunting affects more than 161 million children worldwide and can compromise cognitive development beginning early in childhood. There is a paucity of research using neuroimaging tools in conjunction with sensitive behavioral assays in low-income settings, which has hindered researchers’ ability to explain how stunting impacts brain and behavioral development. We employed high-density EEG to examine associations among children’s physical growth, brain functional connectivity (FC), and cognitive development. Methods We recruited participants from an urban impoverished neighborhood in Dhaka, Bangladesh. One infant cohort consisted of 92 infants whose height (length) was measured at 3, 4.5, and 6 months; EEG data were collected at 6 months; and cognitive outcomes were assessed using the Mullen Scales of Early Learning at 27 months. A second, older cohort consisted of 118 children whose height was measured at 24, 30, and 36 months; EEG data were collected at 36 months; and Intelligence Quotient (IQ) scores were assessed at 48 months. Height-for-age (HAZ) z -scores were calculated based on the World Health Organization standard. EEG FC in different frequency bands was calculated in the cortical source space. Linear regression and longitudinal path analysis were conducted to test the associations between variables, as well as the indirect effect of child growth on cognitive outcomes via brain FC. Results In the older cohort, we found that HAZ was negatively related to brain FC in the theta and beta frequency bands, which in turn was negatively related to children’s IQ score at 48 months. Longitudinal path analysis showed an indirect effect of HAZ on children’s IQ via brain FC in both the theta and beta bands. There were no associations between HAZ and brain FC or cognitive outcomes in the infant cohort. Conclusions The association observed between child growth and brain FC may reflect a broad deleterious effect of malnutrition on children’s brain development. The mediation effect of FC on the relation between child growth and later IQ provides the first evidence suggesting that brain FC may serve as a neural pathway by which biological adversity impacts cognitive development.

In the past several years there have been considerable interest developed towards study on distributed networks. The key underlying application under such technology is mobile ad hoc networks (MANETs), which have been exploiting the range of research opportunity. In MANET due to infrastructure less network and dynamic topology changes, security becomes one of the important issues. The defense strategies such as intrusion detection system (IDS) impose a method to build efficient detection of malicious nodes. Game theory is mainly used to study security problems identification in MANET. The Bayesian Hybrid Detection (BHD) is applied to detect the malicious nodes. A BHD allows the defender to adjust based on opponent observation. The simulation is carried out using the MATLAB for malicious nodes detection. The security degree is measured by the payoff index and system stability index (SSI). Also the processing vs. accuracy index level is measured to identify reliability of detection. The proposed system enables for enhancing security in MANET's by modeling the interactions among a malicious node with number of legitimate nodes. This is suitable for future works on multilayer security problem in MANET.

This book provides successful, evidence-based approaches and practices for quality assurance related to various aspects of online programs that can be adopted or adapted by faculty, leaders, and institutions looking to create, improve, and evaluate online programs in higher education.

The interest in and demand for online terminal degress across disciplines by professionals wishing to conduct research and fulfill doctoral degree requirements at a distance is only increasing. But what these programs look like, how they are implemented, and how they might be evaluated are the questions that challenge administrators and pedagogues alike. This book presents a model for a doctoral program that bridges theory, research, and practice and is offered completely or largely online. In their described program model, Kumar and Dawson enable researching professionals to build an online communtiy of inquiry, engage in critical discourse within and across disciplines, learn from and with experts and peers, and generate new knowledge. Their program design is grounded in the theoretical and research foundations of online, adult, and doctoral education, curriculum design and community-building, implementation and evaluation. The authors, who draw on their experience of implementing a similar program at the University of Florida, not only share data collected from students and faculty members but also reflect on lessons learned working on the program in diverse educational contexts. An important guide for program leaders who wish to develop and sustain an online professional doctorate, An Online Doctorate for Researching Professionals will also be a valuable resource for higher education professionals seeking to include e-learning components in existing on-campus doctoral programs.

Social distancing is one of the simple and effective shields for every individual to control spreading of virus in present scenario of pandemic coronavirus disease (COVID-19). However, existing application of social distancing is a basic model and it is also characterized by various pitfalls in case of dynamic monitoring of infected individual accurately. Review of existing literature shows that there has been various dedicated research attempt towards social distancing using available technologies, however, there are further scope of improvement too. This paper has introduced a novel framework which is capable of computing the level of threat with much higher degree of accuracy using distance and duration of stay as elementary parameters. Finally, the model can successfully classify the level of threats using deep learning. The study outcome shows that proposed system offers better predictive performance in contrast to other approaches.

The proliferation of online graduate programs, and more recently, higher education institutions’ moves to online interactions due to the COVID-19 crisis, have led to graduate student mentoring increasingly occurring online. Challenges, strategies, and outcomes associated with online mentoring of graduate students are of primary importance for the individuals within a mentoring dyad and for universities offering online or blended graduate education. The nature of mentoring interactions within an online format presents unique challenges and thus requires strategies specifically adapted to such interactions. There is a need to examine how mentoring relationships have been, and can best be, conducted when little to no face-to-face interaction occurs. This paper undertook a literature review of empirical studies from the last two decades on online master’s and doctoral student mentoring. The main themes were challenges, strategies and best practices, and factors that influence the online mentoring relationship. The findings emphasized the importance of fostering interpersonal aspects of the mentoring relationship, ensuring clarity of expectations and communications as well as competence with technologies, providing access to peer mentor groups or cohorts, and institutional support for online faculty mentors. Within these online mentoring relationships, the faculty member becomes the link to an otherwise absent yet critical experience of academia for the online student, making it imperative to create and foster an effective relationship based on identified strategies and best practices for online mentoring.

Anthropometric indicators, including stunting, underweight, and wasting, have previously been associated with poor neurocognitive outcomes. This link may exist because malnutrition and infection, which are known to affect height and weight, also impact brain structure according to animal models. However, a relationship between anthropometric indicators and brain structural measures has not been tested yet, perhaps because stunting, underweight, and wasting are uncommon in higher-resource settings. Further, with diminished anthropometric growth prevalent in low-resource settings, where biological and psychosocial hazards are most severe, one might expect additional links between measures of poverty, anthropometry, and brain structure. To begin to examine these relationships, we conducted an MRI study in 2-3-month-old infants growing up in the extremely impoverished urban setting of Dhaka, Bangladesh. The sample size was relatively small because the challenges of investigating infant brain structure in a low-resource setting needed to be realized and resolved before introducing a larger cohort. Initially, fifty-four infants underwent T1 sequences using 3T MRI, and resulting structural images were segmented into gray and white matter maps, which were carefully evaluated for accurate tissue labeling by a pediatric neuroradiologist. Gray and white matter volumes from 29 infants (79 ​± ​10 days-of-age; F/M ​= ​12/17), whose segmentations were of relatively high quality, were submitted to semi-partial correlation analyses with stunting, underweight, and wasting, which were measured using height-for-age (HAZ), weight-for-age (WAZ), and weight-for-height (WHZ) scores. Positive semi-partial correlations (after adjusting for chronological age and sex and correcting for multiple comparisons) were observed between white matter volume and HAZ and WAZ; however, WHZ was not correlated with any measure of brain volume. No associations were observed between income-to-needs or maternal education and brain volumetric measures, suggesting that measures of poverty were not associated with total brain tissue volume in this sample. Overall, these results provide the first link between diminished anthropometric growth and white matter volume in infancy. Challenges of conducting a developmental neuroimaging study in a low-resource country are also described. •This is the first structural MRI study of infants growing up in an extreme poverty.•Anthropomorphic growth is linked to white matter volume in 2-month-old infants.•Challenges of conducting infant MRI in Bangladesh are discussed.

Background Event-related potentials (ERP) data are widely used in brain studies that measure brain responses to specific stimuli using electroencephalogram (EEG) with multiple electrodes. Previous ERP data analyses haven’t accounted for the structured correlation among observations in ERP data from multiple electrodes, and therefore ignored the electrode-specific information and variation among the electrodes on the scalp. Our objective was to evaluate the impact of early adversity on brain connectivity by identifying risk factors and early-stage biomarkers associated with the ERP responses while properly accounting for structured correlation. Methods In this study, we extend a penalized generalized estimating equation (PGEE) method to accommodate structured correlation of ERPs that accounts for electrode-specific data and to enable group selection, such that grouped covariates can be evaluated together for their association with brain development in a birth cohort of urban-dwelling Bangladeshi children. The primary ERP responses of interest in our study are N290 amplitude and the difference in N290 amplitude. Results The selected early-stage biomarkers associated with the N290 responses are representatives of enteric inflammation (days of diarrhea, MIP1b, retinol binding protein (RBP), Zinc, myeloperoxidase (MPO), calprotectin, and neopterin), systemic inflammation (IL-5, IL-10, ferritin, C Reactive Protein (CRP)), socioeconomic status (household expenditure), maternal health (mother height) and sanitation (water treatment). Conclusions Our proposed group penalized GEE estimator with structured correlation matrix can properly model the complex ERP data and simultaneously identify informative biomarkers associated with such brain connectivity. The selected early-stage biomarkers offer a potential explanation for the adversity of neurocognitive development in low-income countries and facilitate early identification of infants at risk, as well as potential pathways for intervention. Trial registration The related clinical study was retrospectively registered with https://doi.org/ClinicalTrials.gov , identifier NCT01375647, on June 3, 2011.

Doctor of Education (EdD) programs have experienced much development and attention in the last two decades. Simultaneously, the steady growth of online education has resulted in several universities offering online EdD programs, including in Educational Technology and related fields. This article provides an overview of the structure and goals of online and hybrid EdD programs in Educational Technology and related fields. Data collected from the websites of 13 highly ranked online and hybrid EdD programs in Educational Technology and a follow-up survey with program coordinators was analyzed for the program foci, length, delivery format, dissertation formats and processes, and professional outcomes. The structure of these programs, curriculum (core courses, specialized courses, research courses, dissertation credits) and professional outcomes are presented, and areas of consideration for others embarking on creating online or hybrid EdD programs and those engaged in improving their existing programs are provided.