Usage Metrics Research Articles

Analysis of 3.5 million SARS-CoV-2 sequences reveals unique mutational trends with consistent nucleotide and codon frequencies

BackgroundSince the onset of the SARS-CoV-2 pandemic, bioinformatic analyses have been performed to understand the nucleotide and synonymous codon usage features and mutational patterns of the virus. However, comparatively few have attempted to perform such analyses on a considerably large cohort of viral genomes while organizing the plethora of available sequence data for a month-by-month analysis to observe changes over time. Here, we aimed to perform sequence composition and mutation analysis of SARS-CoV-2, separating sequences by gene, clade, and timepoints, and contrast the mutational profile of SARS-CoV-2 to other comparable RNA viruses.MethodsUsing a cleaned, filtered, and pre-aligned dataset of over 3.5 million sequences downloaded from the GISAID database, we computed nucleotide and codon usage statistics, including calculation of relative synonymous codon usage values. We then calculated codon adaptation index (CAI) changes and a nonsynonymous/synonymous mutation ratio (dN/dS) over time for our dataset. Finally, we compiled information on the types of mutations occurring for SARS-CoV-2 and other comparable RNA viruses, and generated heatmaps showing codon and nucleotide composition at high entropy positions along the Spike sequence.ResultsWe show that nucleotide and codon usage metrics remain relatively consistent over the 32-month span, though there are significant differences between clades within each gene at various timepoints. CAI and dN/dS values vary substantially between different timepoints and different genes, with Spike gene on average showing both the highest CAI and dN/dS values. Mutational analysis showed that SARS-CoV-2 Spike has a higher proportion of nonsynonymous mutations than analogous genes in other RNA viruses, with nonsynonymous mutations outnumbering synonymous ones by up to 20:1. However, at several specific positions, synonymous mutations were overwhelmingly predominant.ConclusionsOur multifaceted analysis covering both the composition and mutation signature of SARS-CoV-2 gives valuable insight into the nucleotide frequency and codon usage heterogeneity of SARS-CoV-2 over time, and its unique mutational profile compared to other RNA viruses.

Evaluation of a Psychoeducational Group to Expand Mobile Application Knowledge and Use in a Veteran Residential Treatment Program.

Despite the availability of free, evidence-informed mental health mobile applications (apps) to support Veterans and Service Members, interventions are needed to ensure patients are aware of the developed resources. A psychoeducational group was developed and evaluated by a quality improvement project in the context of a Department of Veterans Affairs residential treatment program. Four weekly group sessions introduced 82 Veterans to two similarly themed apps at each group and supported Veteran installation, introduction to the clinical subject matter, app orientation and demonstration, and device/app troubleshooting. Although 94% owned a smartphone, prior to the group, seven of eight apps introduced during the group had been used by fewer than 10% of participants. Following group participation, the proportion of participants agreeing that they were comfortable using mental health apps increased from 33% at baseline to 75%. Similarly, relative to 54% of participants at baseline who agreed that they were aware of available mental health mobile applications, the proportion rose to 89% after the group. Most participants rated three apps as "helpful" or "very helpful" (Breath2Relax, Mindfulness Coach, and PTSD Coach) and most participants reported they were likely to use these apps in the future. Results and qualitative feedback identified needed improvements to the group, including the replacement of some featured apps and the inclusion of automated app usage metrics. Well-designed implementation studies of dissemination strategies are needed to inform best practices for the adoption of these promising interventions.

Факторы доходности Ethereum как платформы для создания децентрализованных приложений

By now, cryptocurrencies have almost become a part of the modern financial asset space, but the cryptocurrency market itself is not homogeneous, and individual cryptocurrencies can differ significantly in their properties and functions. For example, the cryptocurrency Ether is second in capitalization after Bitcoin, but the Ethereum and Bitcoin blockchains differ significantly in their properties and functions. In particular, Ethereum is the most popular digital platform for creating decentralized applications (dApps). The purpose of this work is to try to answer the question "Does the market take into account the features of the Ethereum blockchain in the price dynamics of the Ether cryptocurrency?" This question is also directly related to the search for potential fundamental factors that can explain the price dynamics of Ether. The main econometric method used in the study is generalized autoregressive conditional heteroskedasticity (GARCH) models. Having evaluated about 15 thousand different specifications of GARCH models, where various Ethereum blockchain usage metrics were used as explanatory variables, we obtained the results that Ethereum network usage metrics do not significantly correlate with Ether cryptocurrency returns. Moreover, these metrics are also unable to explain the relative strengthening/weakening of Ether relative to Bitcoin. Thus, we conclude that despite the presence of a number of special functional properties of the Ethereum blockchain, the price dynamics of the Ether cryptocurrency does not reflect them.

Internet of Things (IoT) based automated sanitizer dispenser and COVID-19 statistics reporter in a post-pandemic world.

Coronavirus is among the deadliest viruses of the 21st century. There is still a Coronavirus epidemic that affects most countries worldwide today. To prevent future outbreaks and protect public health, it is essential to invest in research and innovation on vaccines, treatments, diagnostic tests, public health infrastructure, and emergency response planning. Additionally, we need to work on mitigation strategies and take a comprehensive and multidisciplinary approach to prevent and fight against the virus. For the purpose of preventing the spread of microbial organisms, it is essential to take advantage of automatic sanitizer dispensers by deploying them in public places. This is one of the most feasible and effective ways to ensure that people have easy access to hand sanitizer and can reduce the spread of germs. The proposed solution is a contactless sanitizer dispenser with an integrated temperature monitoring system, as well as an alert system for users who exhibit the symptom of infection. Moreover, the proposed solution has added advantage of interfacing with an electronic door so that we can easily implement it at the entrance of a public building/public transportation. This dispenser will also collect data that can be used to identify a symptomatic user and alert the appropriate authorities for safe quarantine. In addition, it is also used to monitor usage metrics, record user entries, and conduct statistical surveys using the ThinkSpeak platform. The proposed model could be a feasible solution to prevent the entry of infected persons and asymptomatic carriers indoors. This can be achieved by implementing automated temperature screening before allowing entry into the building. This can help identify individuals who are potentially infected with the virus and prevent them from entering the premises and potentially spreading the disease to others. Overall, the proposed model is a comprehensive and practical solution that can help to prevent the entry of infected persons and asymptomatic carriers indoors and help to keep the public safe.

Machine learning approach for detecting and combating bring your own device (BYOD) security threats and attacks: a systematic mapping review

Bring your own device (BYOD) paradigm that permits employees to come with their own mobile devices to join the organizational network is rapidly changing the organizational operation method by enhancing flexibility, productivity, and efficiency. Despite these benefits, security issues remain a concern in organizational settings. A considerable number of studies have been conducted and published in this domain without a detailed review of the security solution mechanisms. Moreover, some reviews conducted focused more on conventional approaches such as mobile content management, and application content management. Hence, the implementation of security in BYOD using the conventional method is ineffective. Thus, machine learning approaches seem to be the promising approach, which provides a solution to the security problem in the BYOD environment. This study presents a comprehensive systematic mapping review that focused on the application of the machine learning approach for the mitigation of security threats and attacks in the BYOD environment by highlighting the current trends in the existing studies. Five academic databases were searched and a total of 753 of the primary studies published between 2012 and 2021 were initially retrieved. These studies were screened based on their title, abstract and full text to check their eligibility and relevance for the study. However, forty primary studies were included and analyzed in the systematic mapping review (SMR). Based on the analysis and bubble plot mapping, significant research trends were identified on security threats and attacks, machine learning approaches, datasets usage, and evaluation metrics. The SMR result demonstrates the rise in the number of investigations regarding malware and unauthorized access to existing security threats and attacks. The SMR study indicates that supervised learning approaches such as SVM, DT, and RF are the most employed learning model by the previous research. Thus, there is an open research issue in the application of unsupervised learning approaches such as clustering and deep learning approaches. Therefore, the SMR has set the pace for creating new ground research in the machine learning implementation in the BYOD environment, which will offer invaluable insight into the study field, and researchers can employ it to find a research gap in the research domain.

Association of ResearchGate research influence score with other metrics of top cited sports biomechanics scholars

Abstract Study aim: Scientific networking sites are increasingly popular in connecting researchers and providing influence metrics, however the research on measures like the ResearchGate Research Influence Score (RG RIS) are limited. This study documented the associations between RG RIS, usage, and prestige metrics from Google Scholar (GS), RG, and Scopus for top cited scientists in sports biomechanics. Material and methods: Research usage (total citations: C), prestige (Hirsch index: h), and RG RIS metrics were extracted from GS, RG, and Scopus for the top sixty cited scholars that used either the label “’73ports biomechanics” or “sport biomechanics” in their GS Profile. Results: RG RIS was strongly correlated (r = 0.796–0.895) with all usage and prestige metrics. There were very strong correlations among the three citation metrics (r = 0.929–0.967) and among the three h indexes (r = 0.960–0.974). Conclusions: The recent RG RIS does not provide unique scientific influence information about sport(s) biomechanics researchers beyond common citation metrics. The RG RIS was strongly associated with total citation and h-index values from GS, RG, and Scopus for top 60 cited sport(s) biomechanics researchers based on GS Profiles. The scientific usage and prestige factors previously reported in bibliometric research was supported by very strong associations among the three C and among the three h index values from these two scientific networking and one database service.

Research Article A new Informatics Framework for Evaluating the Codon Usage Metrics, Evolutionary Models and Phylogeographic reconstruction of Tomato yellow leaf curl virus (TYLCV) in different regions of Asian countries

Tomato yellow leaf curl virus (TYLCV) is a major devastating viral disease, majorly affecting the tomato production globally. The disease is majorly transmitted by the Whitefly. The Begomovirus (TYLCV) having a six major protein coding genes, among them the C1/AC1 is evidently associated with viral replication. Owing to immense role of C1/AC1 gene, the present study is an initial effort to elucidate the factors shaping the codon usage bias and evolutionary pattern of TYLCV-C1/AC1 gene in five major Asian countries. Based on publically available nucleotide sequence data the Codon usage pattern, Evolutionary and Phylogeographic reconstruction was carried out. The study revealed the presence of significant variation between the codon bias indices in all the selected regions. Implying that the codon usage pattern indices (eNC, CAI, RCDI, GRAVY, Aromo) are seriously affected by selection and mutational pressure, taking a supremacy in shaping the codon usage bias of viral gene. Further, the tMRCA age was 1853, 1939, 1855, 1944, 1828 for China, India, Iran, Oman and South Korea, respectively for TYLCV-C1/AC1 gene. The integrated analysis of Codon usage bias, Evolutionary rate and Phylogeography analysis in viruses signifies the positive role of selection and mutational pressure among the selected regions for TYLCV (C1/AC1) gene.

Intelligent proactive fault tolerance at the edge through resource usage prediction

The proliferation of demanding applications and edge computing establishes the need for efficient management of the underlying computing infrastructures, urging the providers to rethink their operational methods. In this paper, we propose an Intelligent Proactive Fault Tolerance (IPFT) method that leverages the edge resource usage predictions through Recurrent Neural Networks (RNNs). More specifically, we focus on the process faults, which are related with the inability of the infrastructure to provide Quality of Service (QoS) in acceptable ranges due to the lack of processing power. In order to tackle this challenge we propose a composite deep learning architecture that predicts the resource usage metrics of the edge nodes and triggers proactive node replications and task migration. Taking also into consideration that the edge computing infrastructure is also highly dynamic and heterogeneous, we propose an innovative Hybrid Bayesian Evolution Strategy (HBES) algorithm for automated adaptation of the resource usage models. The proposed resource usage prediction mechanism has been experimentally evaluated and compared with other state of the art methods with significant improvements in terms of Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Additionally, the IPFT mechanism that leverages the resource usage predictions has been evaluated in an extensive simulation in CloudSim Plus and the results show significant improvement compared to the reactive fault tolerance method in terms of reliability and maintainability.

Collaborating neuroscience online: The case of the Human Brain Project forum.

This paper analyzes user interactions on the public-access online forum of the Human Brain Project (HBP), a major European Union-funded neuroscience research initiative, to understand the utility of the Forum for collaborative problem solving. We construct novel data using discussion forum posts and detailed user profiles on the HBP Forum. We find that HBP Forum utilization is comparable to that of a leading general-interest coding platform, and that online usage metrics quickly recovered after an initial Covid-19-related dip. Regression results show that user interactions on the Forum are more active for questions on programming and in HBP core areas. Further, Cox proportional hazard analyses show that such problems are solved faster. Forum posts with users from different countries tend to be discussed more actively but solved slower. Higher shares of administrator support tend to solve problems faster. There are no clear patterns regarding gender and seniority. Our results suggest that building novel collaborative forums can support researchers working on complex topics in challenging times.

Usage pattern analysis of academic articles from Chinese journals

Usage metrics have become increasingly popular in scientometric with the advent of electronic resources. While most current researches relied on the usage data retrieved from English publishers. In this study, the usage data was extended to Chinese publishers. We collected and analyzed the usage data of six well-known Chinese OA (Open Access) journals in different fields from journals’ official websites and Chinese Academic Journals Full-text Database (CJFD) in China National Knowledge Infrastructure (CNKI). It was found that websites’ information architecture affected usage pattern. “Progressive rule”-based site guided users to view full-text articles more than download them and “parallel rule”-based site guided users to download full-text articles more than view them. CNKI proved its popularity in China. Although CNKI is a non-OA platform and has a time delay in indexing full-text articles, but they still attracted a large number of users. PDF tended to be the preferred format than HTML in most journals in the study. Also, CNKI citation were more skewedly distributed than usage data in most journals and it showed strong or moderate correlation between CNKI citation and CNKI full-text downloads in most journals. Finally, it depicted that usage data of most articles was at their peak around pagination month and at the bottom at very earlier or later months.

Advanced Metering Infrastructure for Distribution Planning and Operation: Closing the loop on grid-edge visibility

In the recent history of electric utilities, the potential to have visibility of the grid edge is gaining significance for the reliable planning and operation of a clean energy smart grid. Before the recent large-scale customer adoption of distributed energy resources (DERs), distribution networks were planned with a simpler fit-and-forget philosophy. The importance of customer-sited DERs to achieve climate change goals marks a major shift for utility operations. Changes to traditional fit-and-forget planning paradigms require better availability of grid-edge data. Smart metering is an enterprise-wide tool that is enabling visibility when and where it has been most needed. As of 2020, the rollout of smart metering, or advanced metering infrastructure (AMI), had reached more than 100 million meters in the United States, and nearly half of all electricity customers are now equipped with a smart meter ( <xref ref-type="fig" rid="fig1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Figure 1</xref> ). Smart meters are quickly becoming a ubiquitous data capture feature of smart grids. AMI enables utilities to record and measure electricity usage and power-flow metrics at a minimum of hourly intervals and at least once a day. At a minimum, AMI enables interval metering, automatic meter reading enabling accurate and time-interval billing, and the ability to provide feedback on customer energy consumption.

Supporting equity, economies of scale, and interoperability by applying the International Data Space model to open science usage and impact reporting

The OA Book Usage Data Trust effort (www.oabookusage.org) is exploring how an International Data Space (IDS) could address issues of trust, data quality, and resource-burdens for OA usage data reporting and analytics. Currently individual libraries, publishers, and scholarly publishing platforms and services must normalize, curate, and aggregate their usage and impact metrics as provided from numerous platforms. While well-resourced organizations may have the time and expertise to curate and aggregate open and privileged usage data across provided COUNTER-compliant and non-COUNTER compliant reports; smaller presses, publishers, and innovative projects often lack the resources necessary to process incoming usage data and benefit from robust OA impact analytics. To address this disparity and improve the quality and interoperability of OA book usage data, OA book publishing stakeholders are exploring how to adopt the international data space (IDS) models and standards. Awarded 1.2Million USD by the Mellon Foundation, a team led by OPERAS, OpenAIRE, the University of North Texas, and Johns Hopkins University are hosting community consultations to determine what data stewardship and security practices are needed to achieve trusted direct usage data exchange at scale.

Association Between Twitter Mention and Open-Access Status on Article Citation Metrics in the Field of Ophthalmology.

It is possible that social media use can boost not just articles' social impact but the number of citations and academic influence as well. If a positive correlation between Twitter usage and citation metrics exists in the ophthalmology literature, it is important to broadcast this information to the ophthalmology community so they can use Twitter to increase academic engagement with their research. There has also been an increase in the number of articles available as open access. Therefore, it is important to evaluate the presence of an open-access citation advantage in the field of ophthalmology. This study aims to evaluate the relationship between Twitter mention and open access status on citation metrics in the ophthalmology literature. We conducted a retrospective cross-sectional study comparing article citation metrics to Twitter mentions and open access status. We gathered data on ophthalmology research articles from the six highest-ranked ophthalmology journals published as part of a January 2019 issue. Data were collected in April 2022, 38 months after online publication. Data on citations for each article was based on Google Scholar and Scopus websites. The Altmetric Bookmarklet extension was used to determine the amount of social engagement each article received. The open-access status of each article was based on the status listed in its corresponding journal. Two-tailed t-tests were used to compare social media engagement and open access status with the number of Google Scholar and Scopus citations. A total of 102 original research articles were analyzed. 89 (87.3%) articles received a Twitter mention. Articles tweeted at least once had a significantly higher Google Scholar score (27.2 +/- 4) compared to articles not tweeted (16.4 +/- 1.7; 1.7-fold increase, p<0.05). Likewise, the average Scopus score was significantly higher for tweeted articles (18.6 +/- 2.6) compared to articles not tweeted (11.8 +/- 1.6; 1.6-fold increase, p<0.05). Articles listed as open access had a significantly higher number of Twitter mentions (11.8 +/- 1.8) compared to articles that were not open access (5.6 +/- 0.7; 2.1-fold increase, p<0.05). Open-access articles also had higher citation scores compared to articles that are not open access, but this relationship was not statistically significant. This is the first study to evaluate the relationship between article Twitter mention and citation score in the field of ophthalmology. It demonstrates a significant positive correlation between the article Twitter mention and citation score and provides further evidence that social media engagement can be beneficial to the dissemination of academic information. Further studies on the relationship between social media engagement and article dissemination are warranted in the field of ophthalmology.

Unsub Extender: A Python-based web application for visualizing Unsub data

Abstract This article introduces Unsub Extender, a free tool to help libraries analyze their Unsub data export files. Unsub is a collection development dashboard that gathers and forecasts journal-level usage metrics to provide academic libraries with deeper measurements than traditional cost per use. Unsub gives libraries richer and more nuanced data to analyze their subscriptions, but it does not include a way to easily visualize the complex and interrelated data points it provides. Unsub Extender (https://unsubextender.lib.iastate.edu) is a free Python-based web application that takes an Unsub export file and automates the creation of interactive plots and visualizations. The tool loads with example data to explore, and users upload their specific Unsub file to quickly populate the premade plots with actual data. Graphs are interactive and live updating and support zoom, click-and-drag, and hover. Filters are specified through sliders to model scenarios and focus on areas of interest. A drop-down menu allows users to change a journal’s decision status, and graphs update automatically. After evaluating journals, users can export the modified data set to save their decisions. Unsub Extender proposes best practice in analyzing the increasingly common Unsub export file. It simplifies the analysis, eliminates duplication of effort, and enables libraries worldwide to make better, more data-driven decisions.

A Systematic Review of Methodologies and Outcome Measures of Mobile Integrated Health - Community Paramedicine Programs

Introduction: Mobile integrated health – community paramedicine (MIH-CP) uses patient-centered, mobile resources in the out-of-hospital environment to increase access to care and reduce unnecessary emergency department (ED) usage. The objective of this systematic review is to characterize the outcomes and methodologies used by MIH-CP programs around the world and assess the validity of the ways programs evaluate their effectiveness. Methods: The PubMed, Embase, CINAHL, and Scopus databases were searched for peer-reviewed literature related to MIH-CP programs. We included all full-length studies whose programs met the National Association of Emergency Medical Technicians definition, had MIH-CP–related interventions, and measured outcomes. We excluded all non-English papers, abstract-only, and incomplete studies. Our initial literature review identified 6434 titles. We screened 178 full-text studies to assess for eligibility and identified 33 studies to include in this review. These 33 include four randomized controlled trials, 17 cohort studies, eight 8 case series, and four 4 cross-sectional studies. Of the 29 non-randomized trials, five used matched controls, 13 used pre-post, and 11 used no controls. Outcomes measured were hospital usage (24 studies), ED visits (15), EMS usage (23), patient satisfaction (8), health-related outcomes (8), and cost (9). Studies that evaluated hospital usage reported one of several outcome measures: hospital admissions (11), ED length of stay (3), and hospital readmission rate (2). EMS usage was measured by ambulance transports (12) and EMS calls (10). Cost outcomes observed were ambulance transport savings (7), ED visit savings (4), hospital admission savings (3), and cost per quality-adjusted life year (2). Most studies assessing MIH-CP programs reported success of their interventions. However, significant heterogeneity of outcome measures and varying quality of study methodologies exist among studies. Future studies designed with adequately matched controls and applying uniform core metrics for cost savings and health care usage are needed to better evaluate the effectiveness of MIH-CP programs.

Exploring Retention, Usage, and Efficacy of Web-Based Delivery of Positive Emotion Regulation Skills During the COVID-19 Pandemic.

COVID-19 prompted distress and increased reliance on digital mental health interventions, which previously demonstrated low rates of retention and adherence. This single-arm trial evaluated whether self-guided, web-based, positive affect regulation skills (PARK) were engaging and associated with changes in well-being during the pandemic. Over 6 weeks, PARK delivers brief lessons and practices in skills designed to increase positive emotions: noticing positive events, savoring, gratitude, mindfulness, positive reappraisal, personal strengths, and self-compassion. Patient-Reported Outcome Measurement Information System (PROMIS) computer adaptive tests of anxiety, depression, social isolation, positive affect, and meaning and purpose were administered at baseline, post-intervention, and 6 months after baseline. Retention and usage of PARK were measured by the web-based assessment and intervention platforms. The sample (n = 616) was predominantly female, non-Hispanic, white, and well-educated. Of those who completed baseline, only 42% completed a follow-up assessment; 30% never logged into PARK. Among those who did, 86% used at least one skill, but only 14% completed PARK. Across retention and usage metrics, older age predicted more engagement. In multivariable models, people of color and people with greater baseline anxiety were more likely to complete PARK. All well-being indicators improved over time, with greater improvements in anxiety and social isolation among participants who accessed at least one PARK skill compared to those who did not. Retention and usage rates mirrored pre-pandemic trends, but within this select sample, predictors of engagement differed from prior research. Findings underscore the need for additional efforts to ensure equitable access to digital mental health interventions and research. Trials registration: NCT04367922.

The Use of Data Science in Digital Marketing Techniques: Work Programs, Performance Sequences and Methods.

Over the past decade, there have been tremendous advances in the use of data science to facilitate decision making and extract and thus act upon insights from large data sets in digital business environments. However, despite these advances, there is still a lack of relevant evidence on actions to improve data science management in digital businesses. To fill this gap in the literature. The purpose of this study is to review (i) analytical methods, (ii) usage, and performance metrics based on (iii). Data science used in digital business techniques and strategies. To this end, a comprehensive literature search was carried out on the important scientific contributions made so far in this area of ​​research. The results provide an overview of the most important applications of data science in digital business. Generate insights related to the creation of innovative data mining and knowledge discovery techniques. Important theoretical implications are discussed and a list of topics for further research in this area is provided. This report aims to develop recommendations for enhancing digital business strategies for enterprises, marketing, and non-technical researchers, and identify directions for further research on innovative data mining and discovery applications in science.

The Use of Data Science in Digital Marketing Techniques: Work Programs, Performance Sequences and Methods.

Over the past decade, there have been tremendous advances in the use of data science to facilitate decision making and extract and thus act upon insights from large data sets in digital business environments. However, despite these advances, there is still a lack of relevant evidence on actions to improve data science management in digital businesses. To fill this gap in the literature. The purpose of this study is to review (i) analytical methods, (ii) usage, and performance metrics based on (iii). Data science used in digital business techniques and strategies. To this end, a comprehensive literature search was carried out on the important scientific contributions made so far in this area of ​​research. The results provide an overview of the most important applications of data science in digital business. Generate insights related to the creation of innovative data mining and knowledge discovery techniques. Important theoretical implications are discussed and a list of topics for further research in this area is provided. This report aims to develop recommendations for enhancing digital business strategies for enterprises, marketing, and non-technical researchers, and identify directions for further research on innovative data mining and discovery applications in science.

Addressing Therapeutic Inertia: Development and Implementation of an Electronic Health Record-Based Diabetes Intensification Tool.

To assess whether an electronic health record (EHR)-based diabetes intensification tool can improve the rate of A1C goal attainment among patients with type 2 diabetes and an A1C ≥8%. An EHR-based tool was developed and sequentially implemented in a large, integrated health system using a four-phase, stepped-wedge design (single pilot site [phase 1] and then three practice site clusters [phases 2-4]; 3 months/phase), with full implementation during phase 4. A1C outcomes, tool usage, and treatment intensification metrics were compared retrospectively at implementation (IMP) sites versus nonimplementation (non-IMP) sites with sites matched on patient population characteristics using overlap propensity score weighting. Overall, tool utilization was low among patient encounters at IMP sites (1,122 of 11,549 [9.7%]). During phases 1-3, the proportions of patients achieving the A1C goal (<8%) were not significantly improved between IMP and non-IMP sites at 6 months (range 42.9-46.5%) or 12 months (range 46.5-53.1%). In phase 3, fewer patients at IMP sites versus non-IMP sites achieved the goal at 12 months (46.7 vs. 52.3%, P = 0.02). In phases 1-3, mean changes in A1C from baseline to 6 and 12 months (range -0.88 to -1.08%) were not significantly different between IMP and non-IMP sites. Times to intensification were similar between IMP and non-IMP sites. Utilization of a diabetes intensification tool was low and did not influence rates of A1C goal attainment or time to treatment intensification. The low level of tool adoption is itself an important finding highlighting the problem of therapeutic inertia in clinical practice. Testing additional strategies to better incorporate, increase acceptance of, and improve proficiency with EHR-based intensification tools is warranted.

Intelligent Horizontal Autoscaling in Edge Computing using a Double Tower Neural Network

Edge computing is characterized by varying workload intensities that have a strong effect on applications’ performance and requirements in terms of resources. Thus, in order to maintain a sustainable performance a resource autoscaling mechanism that will automatically add or remove computational nodes is needed. This autoscaling mechanism must ensure that the user’s requirements in terms of Quality of Service (QoS) are satisfied, while respecting the Service Level Agreements (SLAs) and delivering always-on services using affordable on-demand computing solutions. The autoscaling could take place proactively or reactively and the adjustability of the resources could be static or dynamic. To this end, the research goal of this paper is to design a novel autoscaling mechanism that minimizes the task execution times and maximizes the corresponding throughput while using the minimum number of resources. In this direction, we propose an Intelligent Horizontal Proactive Autoscaling (IHPA) mechanism that leverages resource usage metrics of processing edge nodes such as CPU, RAM and Bandwidth in order to provide timely scale up and scale down decisions. The IHPA is based on a double tower Deep Learning (DL) architecture. In order to find a close to optimal DL architecture and guarantee the generality of our approach we also propose the innovative hybrid Bayesian Evolution Strategy method. We conduct an extensive simulation of the IHPA with three baseline task offloading mechanisms keeping the same statistical task generation distribution. We also compare the performance of our proposed IHPA against a reactive autoscaling approach and the Kubernetes Horizontal autoscaling mechanism. The results show that our framework shows significant improvements in terms of latency and throughput, while making an optimal use of the available resources.