Narrative, Ideology, and Power in an Online Support Group for Alzheimer Disease: A Discourse Analytic Study

Despite the rapid proliferation of online support groups, only recently has research begun to support the idea that these groups can be a beneficial source of social support for participants. However, this research has not explored in detail how these groups function or what it is about online talk in these groups that allows supportive experiences to happen. A model for online support group interaction indicates that narrative structure and ideological content of messages influence how participants evaluate and respond to messages. These evaluations contribute to the social process that enables the enactment of social support. The online support group is a venue where participants produce and maintain alternative ideologies that validate participant experiences. Practical strategies for nurses, social worker and health professionals are proposed

Begin with Worship Pastor Longevity in Mind: A Biblical Guide for the Evangelical Worship Pastor Search Committee

Within contemporary Western Protestant evangelicalism, congregations rely heavily on search committees for onboarding pastoral staff. Though this measure for pastoral acquisition is commonly practiced throughout contemporary Western Protestant evangelicalism, the ambiguity and/ or unexpressed expectations of the committee inhibit the securing of a candidate who offers greater longevity and effectiveness, thus provoking subsequent and frequent pastoral turnover. Provided the innate need for mitigating the unintentional oversight and understudied approach by the pastoral search committee, this research investigates and assesses the governable factors on the committee’s behalf that calibrate the search towards securing compatibility by way of effective leadership and longevity. This qualitative study seeks to examine and itemize the biblical qualifiers for pastoral leadership, and the effects of acquisition oversight by pastoral search committees, in addition to, the results of deliberate and decisive supervision by the pastoral search committees. The study is significant in that a lack of literature with respect to the correlation between the qualifications of worship pastors and the professional-pastoral demands necessitated for vocational ministry is insufficient. Inasmuch, many congregations succumb to acquiring unfit practitioners who are ineffective and/ or short-term leaders. The research is likely to uncover an alarming insufficiency in the professional-pastoral instates provided by the seeking church, in addition to a gross inconsistency of linear practice across the spectrum of the contemporary evangelical church

Knowledge, Attitudes, and Practices on Green Technology of a Higher Education Institution in Southern Mindanao, Philippines

Higher education institutions (HEIs) are considered catalysts in environmental sustainability efforts on a global scale. This study determines the knowledge, attitudes, and practices on green technology, particularly on waste minimization, reduction of energy and resource use, and reduction of the carbon footprint of respondents from an HEI in Southern Mindanao, Philippines. A survey was conducted among 141 respondents composed of administrators, faculty, staff, and students of the HEI. Mean, variance, Pearson r, Kruskal-Wallis, and Mann-Whitney tests were the statistical tools used in this study. There was an excellent overall level of knowledge as well as a positive attitude towards green technology. In terms of practices, respondents observed green technology about 50% of the time. There was a significant difference in the level of knowledge between students and staff and between faculty and staff. Likewise, the extent of the practice varied among the respondents. Furthermore, results showed a low relationship between the level of knowledge and extent of practice and also a low relationship between the level of attitude and extent of the practice. The HEI should embark on developing transformative strategies geared towards becoming a green university that embodies sustainable development goals and principles. Keywords: sustainable development · waste minimization · energy use · resources use · carbon footprint · greening universitie

A Narrative Summary of High Flow Nasal Cannula Therapy in the Adult Population

Purpose: The aim of this narrative review is to outline the mechanism of action of HFNC therapy, the clinical benefits of its use, cautions of its clinical application and limitations of previous research. Methods: A literature review was conducted using the following databases as sources: Medline, PubMed, and Google Scholar. Only publications written in English were used in this clinical review. Keywords used in the search included the following: high-flow nasal cannula, heated humidified oxygen, oxygen therapy, non-invasive ventilation, and respiratory failure. Results: The literature reveals HFNC therapy significantly decreased the use of mechanical ventilation (invasive or non-invasive) in patients experiencing respiratory failure. HFNC therapy was better tolerated by patients and decreased the patient’s work of breathing when compared to a conventional oxygen therapy (i.e., non-rebreather oxygen mask). Other clinical benefits of using HFNC when changing a patient from conventional facemask oxygen therapy to a HFNC device are significant improvements in PaO2, respiratory rate and overall comfort. Conclusions: High flow nasal cannula (HFNC) therapy serves as an alternative to conventional oxygen therapy to deliver elevated concentrations of oxygen to patients experiencing acute respiratory failure. Information detailed in this article suggests HFNC therapy is an effective therapy for improving a patient’s oxygenation status when experiencing acute respiratory failure in adults. The literature reveals, it is reasonable to initiate HFNC in adults with acute hypoxemic respiratory failure without hypercapnia, as an alternative to standard oxygen therapy or noninvasive positive pressure ventilation

High-Energy Astrophysics in the 2020s and Beyond

With each passing decade, we gain new appreciation for the dynamic, connected, and often violent nature of the Universe. This reality necessarily places the study of high-energy processes at the very heart of modern astrophysics. This White Paper illustrates the central role of high-energy astrophysics to some of the most pressing astrophysical problems of our time, the formation/evolution of galaxies, the origin of the heavy elements, star and planet formation, the emergence of life on exoplanets, and the search for new physics. We also highlight the new connections that are growing between astrophysicists and plasma physicists. We end with a discussion of the challenges that must be addressed to realize the potential of these connections, including the need for integrated planning across physics and astronomy programs in multiple agencies, and the need to foster the creativity and career aspirations of individual scientists in this era of large projects.Comment: Astro2020 White Paper submissio

The Integral Equation Method for a Steady Kinematic Dynamo Problem

With only a few exceptions, the numerical simulation of cosmic and laboratory hydromagnetic dynamos has been carried out in the framework of the differential equation method. However, the integral equation method is known to provide robust and accurate tools for the numerical solution of many problems in other fields of physics. The paper is intended to facilitate the use of integral equation solvers in dynamo theory. In concrete, the integral equation method is employed to solve the eigenvalue problem for a hydromagnetic dynamo model with a spherically symmetric, isotropic helical turbulence parameter alpha. Three examples of the function alpha(r) with steady and oscillatory solutions are considered. A convergence rate proportional to the inverse squared of the number of grid points is achieved. Based on this method, a convergence accelerating strategy is developed and the convergence rate is improved remarkably. Typically, quite accurate results can be obtained with a few tens of grid points. In order to demonstrate its suitability for the treatment of dynamos in other than spherical domains, the method is also applied to alpha^2 dynamos in rectangular boxes. The magnetic fields and the electric potentials for the first eigenvalues are visualized.Comment: 22 pages, 18 figures, to appear in J. Comp. Phy

Automated Computational Processing of 3-D MR Images of Mouse Brain for Phenotyping of Living Animals

Magnetic resonance (MR) imaging provides a method to obtain anatomical information from the brain in vivo that is not typically available by optical imaging because of this organ's opacity. MR is nondestructive and obtains deep tissue contrast with 100-µm^3 voxel resolution or better. Manganese-enhanced MRI (MEMRI) may be used to observe axonal transport and localized neural activity in the living rodent and avian brain. Such enhancement enables researchers to investigate differences in functional circuitry or neuronal activity in images of brains of different animals. Moreover, once MR images of a number of animals are aligned into a single matrix, statistical analysis can be done comparing MR intensities between different multi-animal cohorts comprising individuals from different mouse strains or different transgenic animals, or at different time points after an experimental manipulation. Although preprocessing steps for such comparisons (including skull stripping and alignment) are automated for human imaging, no such automated processing has previously been readily available for mouse or other widely used experimental animals, and most investigators use in-house custom processing. This protocol describes a stepwise method to perform such preprocessing for mouse

Are Out-of-Distribution Detection Methods Effective on Large-Scale Datasets?

Supervised classification methods often assume the train and test data distributions are the same and that all classes in the test set are present in the training set. However, deployed classifiers often require the ability to recognize inputs from outside the training set as unknowns. This problem has been studied under multiple paradigms including out-of-distribution detection and open set recognition. For convolutional neural networks, there have been two major approaches: 1) inference methods to separate knowns from unknowns and 2) feature space regularization strategies to improve model robustness to outlier inputs. There has been little effort to explore the relationship between the two approaches and directly compare performance on anything other than small-scale datasets that have at most 100 categories. Using ImageNet-1K and Places-434, we identify novel combinations of regularization and specialized inference methods that perform best across multiple outlier detection problems of increasing difficulty level. We found that input perturbation and temperature scaling yield the best performance on large scale datasets regardless of the feature space regularization strategy. Improving the feature space by regularizing against a background class can be helpful if an appropriate background class can be found, but this is impractical for large scale image classification datasets