Strengthening Experiential Learning: Innovative Virtual Engagement Strategies of Community Education Organizations

This study focused on the impact of COVID-19 on K-12 access to community education organizations such as museums, theaters, and art studios. Participants from five community education organizations were interviewed to explore and understand their experiences of developing and promoting virtual resources. While each organization responded differently, three approaches for adaptation and innovation were critical: existing virtual presence, collaboration, and responding to e-learning fatigue. Organizations found that the leveraging of technology in the short term may enhance K-12 access to their resources in the future.

Graphic Communities: Comics as Visual and Virtual Resources for Self- and Collective Care

Note: this commentary is intended for the special issue, "Comics in and of The Moment." Abstract: This essay discusses the ways in which print and web comics are used to represent the lived experiences of mental illness. Beginning with a brief overview of mental health-focused comic strips and graphic memoirs and turning to a discussion of the mental illness comics of Instagram, the article outlines how comics are being used as platforms for self- and collective care. Instead of prioritizing a visual/discourse analysis of each web comic, this piece focuses on the comment threads of each Instagram post and examines the conversations which develop amongst users. By doing so, this essay begins a critical discussion of the ways in which comics may be used as mental health resources. While grounded in a discussion of Covid-19-related increases to mental illness symptoms, this piece is also interested in how comics may be used as therapeutic supports in a post-pandemic world.

A HYBRID RESOURCE ALLOCATION APPROACH FOR 5G IOT APPLICATIONS

5G cellular network expects to sustain various QoS (Quality of Service) requirements and provide customers with multiple services based on their requirements. Implementing 5G networks in an IoT (Internet of Things) infrastructure can help serving the requirements of IoT devices in a 100x faster and more efficient manner. This objective can be accomplished by applying the network slicing approach, where it partitions a single physical infrastructure into multiple virtual resources that can be distributed among different devices independently. This paper merges the benefits of both the static allocation and the network slicing approach to propose a mechanism that can allocate resources efficiently among multiple customers. The allocation mechanism based on a pre-defined policy between the slice provider and the customer is to specify the attributes that will be computed before any allocation process. Network slicing is the idiosyncratic latest 5G technology which produces diverse requirements to sustain the traditional network infrastructure's adequate granularity level. The main objective of this paper is to present a simulation suite for a network consists of base stations, including clients whose probable scenarios of 5G can attain high standards of network operation plus perform a better and easier analysis of various concepts. Network slicing methodology is enhanced at blocking. Further, it was obvious that the block ratio correspondingly increased the usage of the bandwidth. Based on the results, network slicing methodology enhanced at blocking and the block ratio correspondingly increased the usage of the bandwidth.

I contain multitudes: A qualitative investigation of impacts of COVID-19 on alcohol use disorder recovery

Objective: In March 2020, restrictions on in-person gatherings were introduced due to the COVID-19 pandemic. As a result, integral alcohol use disorder (AUD) recovery resources migrated to virtual platforms. Given the importance of these resources for maintaining recovery, this study investigated how these restrictions impacted recovery attempts and explored participant experiences with virtual resources. Methods: Participants attempting recovery from an AUD (N=62; M age = 48.2; %F=53.2; 71% White) were recruited from two larger research projects. Participants completed virtual interviews, responding to questions about their experience during the COVID-19 lockdown, impacts on recovery, and experiences with online resources. Interviews were recorded, transcribed, and analyzed using a thematic coding process. Results: The three overarching themes identified were: Effect on Recovery, Virtual Recovery Resources, and Effect on General Life. Within each overarching theme, lower-order parent themes and subthemes reflected varied participant experiences. Specifically, one group of participants cited negative impacts due to COVID-19, a second group reported positive impacts, and a third group where each participant reported experiencing both positive and negative impacts. Additionally, while participants had positive experiences with virtual resources, they also cited negative experiences and identified suggestions for improvement and other requested resources. Conclusions: Findings suggest that while many participants experienced significant hardships, a proportion of participants experienced unexpected positive impacts as well. Additionally, findings highlight that virtual resources may not work for everyone, and a longterm replacement of in-person with virtual resources may leave some individuals without the support they need to maintain recovery.

Load balancing in virtualized environments using virtual machine migration: A comprehensive survey

Virtualization is a core and requisite technology in Cloud Computing that provisions scalable virtual resources for execution of varied applications. It enables the cloud datacenter resources to be multiplexed within numerous virtual computing environments recognized as virtual machines. These virtual machines consolidates varied applications with diversified resource requirements. It prompts to increase in load imbalance level leading to reduced performance and SLA violations. In order to achieve load balancing across virtual machines varied approaches are presented in literature and virtual machine migration based load balancing is a popular move in this direction. In this work, recent literature on different migration based load balancing schemes are reviewed. The objective of the work is highlight the features, advantages and shortcomings of the considered literature. Alongside that, the effort is conferred to provide an analytical view over different perspectives which will motivate the research in this area.

Developing and piloting a simulated placement experience for students

COVID-19 and rising student numbers are affecting healthcare education, particularly access to clinical placements. As healthcare education is increasingly supported by technology and non-traditional teaching methods, educational experiences gained through clinical placement also require new approaches. This article explores and discusses the use of a simulated clinical placement for a dietetic student cohort. During this virtual placement, students were able to explore and experience a virtual clinical setting and immerse themselves in a placement experience. A vast range of virtual resources were linked to the online placement portal, including statutory and mandatory training, dietetic resources, patient journeys and interprofessional communication. Advantages of this approach include that all students experience a given situation, unlike in traditional placements where workloads, variety and engagement vary; there is also no risk to patient safety. The aim is to enhance the learning experience to create effective, efficient clinicians. This virtual placement for dietetics is part of a bigger project to develop and evaluate the use of a virtual placement framework in a range of professions. The concept of virtual placement may have been brought forward by the COVID-19 crisis but was inevitable with the move to more technology-enhanced learning tools.

Comparison of Machine Learning algorithms for the Burnout projection

The present study aims to carry out a projection of student burnout risk detection in young university students using Machine Learning technics (Neuronal Networks, KNN, SVM, Random Forest). A descriptive method was proposed, with a cross-sectional and stratified design in which a sample of 791 students from 4 different universities. This study opens up an innovative field of research by integrating resources from psychological evaluation and virtual resources, in addition, it would allow the generation of preventive actions to treat various implications of Burnout in school dropout and low academic performance through the analysis of information and the generation of algorithms that allow the projection of burnout risk. Due to the combination of experience of professionals in psychology, education and engineering, as well as the contribution to the projection of a syndrome that affects students, makes this article an innovative proposal.

Increasing Confidence and Mental Health in Caregivers

The purpose of our critically appraised topic is to synthesize the highest-level evidence available regarding interventions for increasing confidence and mental health outcomes in caregivers taking loved ones home from inpatient rehabilitation. The final portfolio contains six research articles from peer-reviewed journals. Study designs include randomized control trials, a systematic review, and a pretest-posttest without a control group. All studies relate directly to the components of the PICO question. Four of the articles discussed both caregiver confidence and mental health while two articles discussed only mental health. There is strong evidence to support that in-person hands on training, in person discussion-based training, and/or virtual resources helped increase confidence in caregivers of patients. There is mixed evidence and only limited improvement to support mental health. The findings from this critically appraised topic will be used to draft new ideas for practice guidelines for addressing caregiver education and caregiver mental health in an inpatient rehabilitation facility.

On the issue of searching information on the Internet

Information is the cornerstone of the development of a person, social group, and society as a whole. In modern conditions, information plays a key role in all spheres of the national economy, and its timely search and analysis is fundamentally important. The mass of information on the Internet includes data that meet various (sometimes paradoxical) user requests and, at the same time, when accessing virtual resources, the key problem is often the redundancy of links and the difficulty of finding information that is strictly relevant to the user’s request. The article provides an overview of approaches to organizing the search for necessary information in the continuum of the information space; it is emphasized that success in finding the necessary information is determined by two main factors (conditions): the correct, clear formulation of the search query and knowledge of search engines and search tools.

Machine-Learning Based Memory Prediction Model for Data Parallel Workloads in Apache Spark

A lack of memory can lead to job failures or increase processing times for garbage collection. However, if too much memory is provided, the processing time is only marginally reduced, and most of the memory is wasted. Many big data processing tasks are executed in cloud environments. When renting virtual resources in a cloud environment, it is necessary to pay the cost according to the specifications of resources (i.e., the number of virtual cores and the size of memory), as well as rental time. In this paper, given the type of workload and volume of the input data, we analyze the memory usage pattern and derive the efficient memory size of data-parallel workloads in Apache Spark. Then, we propose a machine-learning-based prediction model that determines the efficient memory for a given workload and data. To determine the validity of the proposed model, we applied it to data-parallel workloads which include a deep learning model. The predicted memory values were in close agreement with the actual amount of required memory. Additionally, the whole building time for the proposed model requires a maximum of 44% of the total execution time of a data-parallel workload. The proposed model can improve memory efficiency up to 1.89 times compared with the vanilla Spark setting.