bluuebunny/crossref_metadata_2025 huggingface dataset:
Data-Driven Nonlinear Constitutive Relations for Rarefied Flow Computations
Authors: Zhao Wenwen , Jiang Lijian , Yao Shaobo , Chen Weifang
Published: July 2021

To overcome the defects of traditional rarefied numerical methods such as the Direct Simulation Monte Carlo (DSMC) method and unified Boltzmann equation schemes and extend the covering range of macroscopic equations in high Knudsen number flows, data-driven nonlinear constitutive relations (DNCR) are proposed firstly through machine learning method. Based on the training data from both Navier-Stokes (NS) solver and unified gas kinetic scheme (UGKS) solver, the map between discrepancies of stress tensors and heat flux and feature vectors is established after training phase. Through the obtained off-line training model, new test case excluded from training data set could be predicated rapidly and accurately by solving conventional equations with modified stress tensor and heat flux. Finally, conventional one-dimensional shock wave cases and two-dimensional hypersonic flows around a blunt circular cylinder are presented to assess the capability of the developed method through a various comparisons between DNCR, NS, UGKS, DSMC and experimental results. The improvement of the predictive capability of the coarse-graining model could make DNCR method to be an effective tool in rarefied gas community, especially for hypersonic engineering applications.
Gonad development in farmed male and female South African abalone, Haliotis midae, fed artificial and natural diets under a range of husbandry conditions
Authors: Meusel Esther , Menanteau-Ledouble Simon , Naylor Matthew , Kaiser Horst , El-Matbouli Mansour
Published: July 2021

Background
Growth rate is considered one of the most important factors in the farming of Haliotis midae and somatic growth rates decline after abalone reach sexual maturity. Artificial diets are suspected to accelerate maturation, in particular when soya meal is used as a protein source, because of this plant’s high concentration of phytoestrogens.
Results
We fed two artificial diets and a natural diet, kelp. The first artificial diet had fishmeal as its main source of protein while the other, Abfeed® S34, replaced some of the fish proteins with soya meal. The effect of diet on the gonad development of 27-month-old farmed Haliotis midae, raised at two stocking densities, was analysed. For each gonad sample the development phase was determined based on both histological criteria and the gonad bulk index (GBIn). The hypothesized link between dietary protein source and gonad development could not be established by either morphological criteria or GBIn. Similarly, stocking density did not have a significant effect on GBIn-values. GBIn values were significantly correlated to the results from the histological analyses in males and females.
Conclusions
The results from this study suggest that artificial diets including those using soya meal are a valid alternative in the feeding of abalone. Moreover, it provides a data base for future research on factors influencing gonad development in this species.
Synthesis and Photoluminescence Properties of Eu3+-Doped Na2YMg2V3O12: A Novel Red-Emitting Phosphor for White-Light-Emitting Diodes
Authors: Zhang Weiyi , Shao Yanqiu , Zhu Ying , Shao Yingying , Huang Zhaohui , Xin Min , Liu Yangai , He Can , Zhang Xiaoguang
Published: July 2021

The fabrication and luminescent properties of novel Na2YMg2V3O12:Eu3+ phosphors produced by conventional solid-state reactions were investigated. Self-activated emission of the Na2YMg2V3O12 host produces a broad emission band ranging from 400 to 700 nm with a maximum peak at 530 nm, ascribed to the charge transfer in the (VO4)3− groups. Excitation with near-UV (365 nm) light causes the Na2YMg2V3O12:Eu3+ phosphors to emit bright red light, including both the broad emission band of the (VO4)3− groups and the sharp emission peaks of Eu3+ ions. At a quenching concentration of 0.03 mol, the Eu3+ ion emission peaks were located at 597, 613, 654 and 710 nm. As-prepared Na2YMg2V3O12:Eu3+ phosphors also exhibited stable emission at high temperatures. Furthermore, a designed and packaged white-light-emitting diode (WLED) lamp, including the obtained phosphors, commercial (Ba,Sr)2SiO4:Eu2+ green phosphors, BaMgAl10O17:Eu2+ blue phosphors and a near-ultraviolet (n-UV) chip, emitted bright white light with a good chromaticity coordinate of (0.3068, 0.3491), a satisfactory colour rendering index of 88.20 and a properly correlated colour temperature of 4460.52 K. These results indicate the potential of this Na2YMg2V3O12:Eu3+ phosphor as a red-emitting phosphor for solid-state illumination.
Gelcast Zirconia Ceramics With High Strength and Simultaneously High Translucency for Dental Applications
Authors: Kastyl Jaroslav , Chlup Zdenek , Pouchly Vaclav , Song Lu , Scasnovic Erik , Trunec Martin
Published: July 2021

Translucent zirconia represents a favourite material for monolithic ceramic dental restorations. However, materials approaches employed so far to improve the translucency of zirconia ceramics are accompanied by a significant decline in strength. Thus, we aimed to develop dental 3Y-TZP ceramics that can provide excellent strength and, simultaneously, enhanced translucency. In this investigation, machinable tetragonal zirconia ceramics based on fine mesostructured zirconia particles stabilized with 3 mol% of yttria and prepared by the gelcasting processing method were developed. Properties of sintered samples were characterised, namely: shrinkage, density, structure, surface roughness, hardness, biaxial strength, and total forward transmittance. Zirconia ceramics with an average biaxial strength of 1184 MPa and a total forward transmittance of 46.7% for a 0.5 mm thick sample at a wavelength of 600 nm were obtained. These ceramics exhibited homogeneous structure with grains sizes up to 620 nm and purely tetragonal phase composition. The developed ceramics provided a favourable combination of high translucency comparable even with the mixed cubic/tetragonal structure of a common 4Y-TZP, and very high strength that is achievable only in the pure tetragonal 3Y-TZP.
Enhanced photocatalytic and biological observations of green synthesized AC, AC/Ag and AC/Ag/TiO2 nanocomposites
Authors: AMALANATHAN M , Parvathiraja C. , Alothman Asma A. , Wabaidur Saikh M. , Islam Mohammad Ataul
Published: July 2021

In this study, AC/Ag/TiO2 nanocomposite was successfully synthesized by hydrothermal method using jasmine flower extract. The reactions of reduction, stabilization and capping was executed from the biomolecules of jasmine flower extract. The decoration of activated carbon and noble metal to the metal oxide enhanced the properties in all ways. As the modified structural, optical and morphological properties of as prepared nanocomposite was characterized using various techniques such as, X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), UV-Visible spectroscopy, Scanning electron microscope (SEM) with Energy dispersive X-ray spectroscopy (EDX), and Transmission electron microscope (TEM). The photocatalytic activities under sunlight were evaluated by the degradation of methylene blue (MB). Antibacterial activity was tested against E-coli and S. aureus. The characterization results show that AC/Ag/TiO2 nanocomposite is crystalline, needle like morphology and highly optically active catalyst. The investigated AC/Ag/TiO2 nanocomposite shows 96% maximum degradation efficiency at the end of 120 minutes undervisible light irradiation. The degradation efficiency and antibacterial activity is readily higher than that of commercial TiO2. The plasmonic support to the activated carbon and titanium nanoparticles creates large surface area, active sites and accelerated the free radical generation. These characteristics demonstrated that the prepared AC/Ag/TiO2 nanocomposite material is highly suitable for the decomposition of methylene blue and waste water treatment.
Effect of Health Comorbidities on Death from COVID-19 in Immigrants in Mexico
Authors: Martínez-Martínez Oscar A. , Valenzuela-Moreno Karla A. , Coutiño Brenda
Published: July 2021

BackgroundComorbidities increase the risk of death for patients with COVID-19, however, little is known about how comorbidities affect immigrants, as well as their prognosis in the case of contracting the virus. Therefore, this article aims to determine which comorbidities are associated with the probability of death among immigrants in Mexico. Methods We use a sample of migrants (N = 3,567) registered in the public database published in the National Epidemiological Surveillance System of the Mexican Ministry of Health; the technique used was a Probit regression. Results The results show that comorbidities commonly associated with death from COVID-19, are not significant when present in immigrants, also, migrants have fewer comorbidities than the native born. These findings could be explained by the Healthy Immigrant Effect, which states that migration is a self-selection process, in which those who migrate are the healthiest. However, the cases of migrants who have died from COVID-19 are related to the time they have taken to go to hospitals and to problems of access to health care.ConclusionsThe immigrant populations (especially those in transit and refugees) do pose certain challenges to public policies in the countries of destination, so specific measures need to be taken in order to protect immigrant communities from the spread of the virus. It is important to improve the conditions during the migratory journey, avoiding overcrowding, as well as testing in various places (shelters, immigration controls, among others) in order to determine the levels of positivity in this group. ​However, the detection of more cases of COVID-19 among immigrants, should not equal to the denial for entry. Formal mechanisms should be put into place to guarantee the right to asylum and non-refoulement, even for migrants who test positive for the virus.
Should the United States Rejoin the Trans-Pacific Trade Deal?
Authors: Itakura Ken , Lee Hiro
Published: July 2021

Before the Trans-Pacific Partnership (TPP) entered into force, the United States withdrew from the trade accord. Eleven other TPP signatories decided to revive the agreement, which led to the implementation of the Comprehensive and Progressive Agreement for TPP (CPTPP). The objectives of this paper are to estimate economic welfare effects under alternative scenarios of TPP/CPTPP, to evaluate the extent of losses to the US from its withdrawal from TPP and expected gains from rejoining the Trans-Pacific trade accord, and to examine whether the US economy would have to undergo extensive sectoral adjustments from its participation. We employ a dynamic computable general equilibrium (CGE) model to examine these issues. The results suggest that the US loses an opportunity to gain 0.4 percent in its economic welfare by withdrawing from TPP, but it would be able to recover most of its projected welfare gains by reengaging with CPTPP. Since sectoral output adjustments in the US are small, its adjustment costs from participation in CPTPP would be limited. In addition, there exist political incentives for the US to become a member of this trade accord.
The phylodynamics of SARS-CoV-2 during 2020 in Finland — Disappearance and re-emergence of introduced strains.
Authors: Nguyen Phuoc Truong , Kant Ravi , Broeck Frederik Van den , Suvanto Maija T. , Alburkat Hussein , Virtanen Jenni , Ahvenainen Ella , Castren Robert , Hong Samuel L. , Baele Guy , Ahava Maarit J. , Jarva Hanna , Jokiranta Suvi Tuulia , Kallio-Kokko Hannimari , Kekäläinen Eliisa , Kirjavainen Vesa , Kortela Elisa , Kurkela Satu , Lappalainen Maija , Liimatainen Hanna , Suchard Marc A. , Hannula Sari , Ellonen Pekka , Sironen Tarja , Lemey Philippe , Vapalahti Olli , Smura Teemu
Published: July 2021

Finland has had a low incidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) infections as compared to most European countries. Here we report the origins and turnover of SARS-CoV-2 lineages circulating in Finland in 2020. SARS-CoV-2 introduced to Finland in January 2020 and spread rapidly across southern Finland during spring. We observed rapid turnover among Finnish lineages during this period. Clade 20C became the most prevalent among sequenced cases and was replaced by other strains in fall 2020. Bayesian phylogeographic reconstructions suggested 42 independent introductions into Finland during spring 2020, mainly from Italy, Austria, and Spain, which might have been the source for a third of cases. The investigations of the original introductions of SARS-CoV-2 to Finland during the early stages of the pandemic and of the subsequent lineage dynamics could be utilized to assess the role of transboundary movements and effects of early intervention and public health measures.
Increasing the efficiency of multy-variant calculations of electromagnetic field distribution in matrix of a polygradient separator
Authors: Jasim Jasim Mohmed Jasim , Shvedchykova Iryna , Panasiuk Igor , Romanchenko Julia , Melkonova Inna
Published: July 2021

An approach is proposed to carry out multivariate calculations of the magnetic field distribution in the working gaps of a plate polygradient matrix of an electromagnetic separator, based on a combination of the advantages of two- and three-dimensional computer modeling. Two-dimensional geometric models of computational domains are developed, which differ in the geometric dimensions of the plate matrix elements and working air gaps. To determine the vector magnetic potential at the boundaries of two-dimensional computational domains, a computational 3D experiment is carried out. For this, three variants of the electromagnetic separator are selected, which differ in the size of the working air gaps of the polygradient matrices. For them, three-dimensional computer models are built, the spatial distribution of the magnetic field in the working intervals of the electromagnetic separator matrix and the obtained numerical values of the vector magnetic potential at the boundaries of the computational domains are investigated. The determination of the values of the vector magnetic potential for all other models is carried out by interpolation. The obtained values of the vector magnetic potential are used to set the boundary conditions in a computational 2D experiment. An approach to the choice of a rational version of a lamellar matrix is substantiated, which provides a solution to the problem according to the criterion of the effective area of the working area. Using the method of simple enumeration, a variant of the structure of a polygradient matrix with rational geometric parameters is selected. The productivity of the electromagnetic separator with rational geometric parameters of the matrix increased by 3–5 % with the same efficiency of extraction of ferromagnetic inclusions in comparison with the basic version of the device
Comparison between some meteorological elements measured by ECMWF and Earth stations in different regions in Iraq
Authors: A. Basheer Rajaa , AL-Rijabo Dr. Waleed I.
Published: July 2021

The aim of this paper is to validate the data of three meteorological elements Air Temperature (Ta) , Relative Humidity (RH), Wind Speed (WS) from the European Center For-Medium Range Weather Forecasting (ECMWF) , against ground stations data using several Models at six stations well distributed in Iraq (Mosul, Kirkuk, Baghdad, Kut, Nasiriya, and Basra).

Due to the difficulties which experienced by the ground climate stations in Iraq from a shortage of devices and equipment for measuring the various climatic elements, which led to a huge shortage of data throughout time for political, economic and natural disasters. It is found that researchers can adopt the data of satellite stations to monitor the climate because let’s found that there is a highly significance Correlations between the data of these stations and the data of the ground stations for climate monitoring

Five Mathematical Models were used for that [Linear Models, Quadratic Models, Exponential Models, Logarithmic Models, and Power Models]. The performance of these models were evaluated by comparing the calculated (Ta, RH, WS) from earth stations.

Those mathematical correlations help to be able to calculate the ground data in state of there is no ground climate stations data.

Several statistical tests Correlation Coefficient (R), Coefficient of Determination (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) were used to control the validation and goodness of these Models.

The R2 obtained from these Models were very high in all stations. This means that, there is a highly significance Correlations between (Ta, RH, WS) estimated and [Ta, RH, WS] measured in all station
Duration on ART, Alcohol Use and HIV Stage May Predict Risky Sexual Behavior in a Resource-limited Environment: A Cross-sectional Study
Authors: New-Aaron Moses , Kingi Happiness , Meza Jane L. , Goedert Martha H. , Kibusi Stephen M , Mkhoi Mkhoi L. , Mayengo Caroline Damian , Charles James , Shabani Siraji , New-Aaron Temitope O. , Sumba Samwel , Cheney Anlan
Published: July 2021

Background:

The intention of antiretroviral therapy (ART) and regular clinic visits are
to engender safe sex attitudes among HIV-infected individuals. However, this may not be the case
due to the perceived therapeutic benefits of ART and may result in exposure to drug-resistant HIV
strains.

Objective:

We aimed to determine the prevalence and predict the factors associated with risky sexual
behaviors among ART users in a resource-limited environment.

Methods:

Two hundred and ninety-one sexually active ART-users aged 18-50 years and seeking
care at the HIV clinic in Dodoma, Tanzania, participated in this study. The outcome variables modeled
in a logistic regression were condom use, multiple sex partners, casual sex partners, and payment
for sex. The predictors included in the models were the patients' socio-demographic characteristics.
In addition, a new variable, sexual risk scores, was generated by culminating all the outcome
variables. Finally, a multiple Poisson regression with the socio-demographic variables of the
participants was used to model the sexual risk scores.

Results:

Patients reported inconsistent/no condom use (44%), payment for sex (4%), casual sex encounters
(13%), multiple sex partners (21%), and STD symptoms (15%). While having a casual
sexual partner was significantly associated with age group in a Pearson Chi-square (p=0.0147), participants
≤35 years old were less likely to have single-sex partners than older participants (AOR:
0.188, 95 C.I: 0.042-0.849). Meanwhile, the likelihood of condom use was higher among participants
with no HIV-infected family members (AOR= 2.409, 95% C.I:1.236,4.697) and among participants
who had single-sex partners (AOR= 2.721, 95% C.I.: 1.115,6.640); these participants were
less likely to report STD symptoms (AOR=0.265, 95% C.I.: 0.081-0.865). Adjusted analysis
showed that estimated mean sexual risk scores significantly increased (mean, λ=1.61, 95%
C.I:1.0817-2.4063) for recent ART recipients (within 1-3 years vs. ≥8 years). However, sexual risk
scores of participants with HIV stage 3 were 38.8% lower than participants at stage 4 (95% C.I.:
0.3910-0.9558), and non-alcohol drinkers had an adjusted mean sexual risk score of 29% lower
than participants who were alcohol drinkers (95% C.I.: 0.5102-0.9879).

Conclusion:

Researchers should prioritize patients at HIV CTC for education concerning safe sexual
practices for those characterized by alcohol consumption, younger age (less than 35 years old),
HIV stage 4, or commencement of ART within three years.
THE CROSSFIRE OF CORPORATE REAL ESTATE SUSTAINABLE MANAGEMENT WITH CORPORATE SUSTAINABLE OBJECTIVES IN MALAYSIA
Authors: Fauzi Nurul Sahida , Johari Noraini , Chuweni Nor Nazihah , Mohd Ali Siti Nadiah , Arshad Huraizah , @Mohamed NurulanisAhmad
Published: July 2021

The emergence of corporations dabbling in sustainable development has caused a shift from the conventional way of managing office building to a more systematic approach involving high technology. Various discussions espouse the positive impact of sustainable office buildings on business. However, previous studies only discussed the elements involved, but most of these discussions did not specify which element that significantly contributed to business objectives. Thus, this research attempts to discover the relationship between corporate real estate sustainable management (CRESM) and corporate sustainability (CS) objectives to determine the most significant element of CRESM influencing overall CS objectives. A questionnaire survey was carried out involving 117 combinations of corporate real estate managers who are directly involved in managing sustainable offices in Malaysia. Data was then analyzed using IBM SPSS Smart PLS. Results indicate six elements of CRESM significantly affect CS objectives that hope to assist corporate real estate managers to well manage their sustainable office buildings as well as contributing to achieve their business objectives.
Data Empowerment of Decision-Makers in an Era of a Pandemic: Intersection of “Classic” and Artificial Intelligence in the Service of Medicine
Authors: Geva Gil A , Ketko Itay , Nitecki Maya , Simon Shoham , Inbar Barr , Toledo Itay , Shapiro Michael , Vaturi Barak , Votta Yoni , Filler Daniel , Yosef Roey , Shpitzer Sagi A , Hir Nabil , Peri Markovich Michal , Shapira Shachar , Fink Noam , Glasberg Elon , Furer Ariel
Published: July 2021

Background

The COVID-19 outbreak required prompt action by health authorities around the world in response to a novel threat. With enormous amounts of information originating in sources with uncertain degree of validation and accuracy, it is essential to provide executive-level decision-makers with the most actionable, pertinent, and updated data analysis to enable them to adapt their strategy swiftly and competently.

Objective

We report here the origination of a COVID-19 dedicated response in the Israel Defense Forces with the assembly of an operational Data Center for the Campaign against Coronavirus.

Methods

Spearheaded by directors with clinical, operational, and data analytics orientation, a multidisciplinary team utilized existing and newly developed platforms to collect and analyze large amounts of information on an individual level in the context of SARS-CoV-2 contraction and infection.

Results

Nearly 300,000 responses to daily questionnaires were recorded and were merged with other data sets to form a unified data lake. By using basic as well as advanced analytic tools ranging from simple aggregation and display of trends to data science application, we provided commanders and clinicians with access to trusted, accurate, and personalized information and tools that were designed to foster operational changes and mitigate the propagation of the pandemic. The developed tools aided in the in the identification of high-risk individuals for severe disease and resulted in a 30% decline in their attendance to their units. Moreover, the queue for laboratory examination for COVID-19 was optimized using a predictive model and resulted in a high true-positive rate of 20%, which is more than twice as high as the baseline rate (2.28%, 95% CI 1.63%-3.19%).

Conclusions

In times of ambiguity and uncertainty, along with an unprecedented flux of information, health organizations may find multidisciplinary teams working to provide intelligence from diverse and rich data a key factor in providing executives relevant and actionable support for decision-making.
Integrated Smoking Cessation for Smokers With Serious Mental Illness: Protocol for a Convergent Mixed Methods Implementation Evaluation Study
Authors: Schnitzer Kristina , Maravić Melissa Culhane , Arntz Diana , Phillips Nathaniel L , Pachas Gladys , Evins A Eden , Fetters Michael
Published: July 2021

Background

Tobacco smoking is associated with significant morbidity and premature mortality in individuals with serious mental illness. A 2-year pragmatic clinical trial (PCORI PCS-1504-30472) that enrolled 1100 individuals with serious mental illness in the greater Boston area was conducted to test 2 interventions for tobacco cessation for individuals with serious mental illness: (1) academic detailing, which delivers education to primary care providers and highlights first-line pharmacotherapy for smoking cessation, and (2) provision of community health worker support to smoker participants. Implementing and scaling this intervention in other settings will require the systematic identification of barriers and facilitators, as well as the identification of relevant subgroups, effective and unique components, and setting-specific factors.

Objective

This protocol outlines the proposed mixed methods evaluation of the pragmatic clinical trial to (1) identify barriers and facilitators to effective implementation of the interventions, (2) examine group differences among primary care physicians, and (3) identify barriers that stakeholders such as clinical, payor, and policy leaders would anticipate to impact the implementation of effective components of the intervention.

Methods

Qualitative interviews will be conducted with all study community health workers and selected smoker participants, primary care providers, and other stakeholders. Measures of performance and engagement will guide purposive sampling. The Consolidated Framework for Implementation Research will guide qualitative data collection and analysis in accordance with the following framework approach: (1) familiarization, (2) identifying a thematic framework, (3) indexing, (4) charting, and (5) mapping and interpretation. Joint display analyses will be constructed to analyze and draw conclusions across the quantitative and qualitative data.

Results

The 3-year cluster-randomized trial has concluded, and the analysis of primary outcomes is underway. Results from the pragmatic trial and this mixed methods implementation evaluation will be used to help disseminate, scale, and expand a systems intervention.

Conclusions

The results of this mixed methods implementation evaluation will inform strategies for dissemination and solutions to potential barriers to the implementation of interventions from a smoking cessation trial for individuals with serious mental illness.

International Registered Report Identifier (IRRID)

DERR1-10.2196/25390
Radiation Oncologists’ Perceptions of Adopting an Artificial Intelligence–Assisted Contouring Technology: Model Development and Questionnaire Study
Authors: Zhai Huiwen , Yang Xin , Xue Jiaolong , Lavender Christopher , Ye Tiantian , Li Ji-Bin , Xu Lanyang , Lin Li , Cao Weiwei , Sun Ying
Published: July 2021

Background

An artificial intelligence (AI)–assisted contouring system benefits radiation oncologists by saving time and improving treatment accuracy. Yet, there is much hope and fear surrounding such technologies, and this fear can manifest as resistance from health care professionals, which can lead to the failure of AI projects.

Objective

The objective of this study was to develop and test a model for investigating the factors that drive radiation oncologists’ acceptance of AI contouring technology in a Chinese context.

Methods

A model of AI-assisted contouring technology acceptance was developed based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model by adding the variables of perceived risk and resistance that were proposed in this study. The model included 8 constructs with 29 questionnaire items. A total of 307 respondents completed the questionnaires. Structural equation modeling was conducted to evaluate the model’s path effects, significance, and fitness.

Results

The overall fitness indices for the model were evaluated and showed that the model was a good fit to the data. Behavioral intention was significantly affected by performance expectancy (β=.155; P=.01), social influence (β=.365; P<.001), and facilitating conditions (β=.459; P<.001). Effort expectancy (β=.055; P=.45), perceived risk (β=−.048; P=.35), and resistance bias (β=−.020; P=.63) did not significantly affect behavioral intention.

Conclusions

The physicians’ overall perceptions of an AI-assisted technology for radiation contouring were high. Technology resistance among Chinese radiation oncologists was low and not related to behavioral intention. Not all of the factors in the Venkatesh UTAUT model applied to AI technology adoption among physicians in a Chinese context.
Predicting Patient Deterioration: A Review of Tools in the Digital Hospital Setting
Authors: Mann Kay D , Good Norm M , Fatehi Farhad , Khanna Sankalp , Campbell Victoria , Conway Roger , Sullivan Clair , Staib Andrew , Joyce Christopher , Cook David
Published: July 2021

Background

Early warning tools identify patients at risk of deterioration in hospitals. Electronic medical records in hospitals offer real-time data and the opportunity to automate early warning tools and provide real-time, dynamic risk estimates.

Objective

This review describes published studies on the development, validation, and implementation of tools for predicting patient deterioration in general wards in hospitals.

Methods

An electronic database search of peer reviewed journal papers from 2008-2020 identified studies reporting the use of tools and algorithms for predicting patient deterioration, defined by unplanned transfer to the intensive care unit, cardiac arrest, or death. Studies conducted solely in intensive care units, emergency departments, or single diagnosis patient groups were excluded.

Results

A total of 46 publications were eligible for inclusion. These publications were heterogeneous in design, setting, and outcome measures. Most studies were retrospective studies using cohort data to develop, validate, or statistically evaluate prediction tools. The tools consisted of early warning, screening, or scoring systems based on physiologic data, as well as more complex algorithms developed to better represent real-time data, deal with complexities of longitudinal data, and warn of deterioration risk earlier. Only a few studies detailed the results of the implementation of deterioration warning tools.

Conclusions

Despite relative progress in the development of algorithms to predict patient deterioration, the literature has not shown that the deployment or implementation of such algorithms is reproducibly associated with improvements in patient outcomes. Further work is needed to realize the potential of automated predictions and update dynamic risk estimates as part of an operational early warning system for inpatient deterioration.
An Augmented Reality Device for Remote Supervision of Ultrasound Examinations in International Exercise Science Projects: Usability Study
Authors: Rigamonti Lia , Secchi Matteo , Lawrence Jimmy B , Labianca Luca , Wolfarth Bernd , Peters Harm , Bonaventura Klaus , Back David Alexander
Published: July 2021

Background

Support for long-distance research and clinical collaborations is in high demand and has increased owing to COVID-19–related restrictions on travel and social contact. New digital approaches are required for remote scientific exchange.

Objective

This study aims to analyze the options of using an augmented reality device for remote supervision of exercise science examinations.

Methods

A mobile ultrasound examination of the diameter and intima-media thickness of the femoral and carotid arteries was remotely supervised using a head-mounted augmented reality device. All participants were provided with a link to a YouTube video of the technique in advance. In part 1, 8 international experts from the fields of engineering and sports science were remotely connected to the study setting. Internet connection speed was noted, and a structured interview was conducted. In part 2, 2 remote supervisors evaluated 8 physicians performing an examination on a healthy human subject. The results were recorded, and an evaluation was conducted using a 25-item questionnaire.

Results

In part 1, the remote experts were connected over a mean distance of 1587 km to the examination site. Overall transmission quality was good (mean upload speed: 28.7 Mbps, mean download speed: 97.3 Mbps, mean ping: 21.6 milliseconds). In the interview, participants indicated that the main potential benefits would be to the fields of education, movement analysis, and supervision. Challenges regarding internet connection stability and previous training with the devices used were reported. In part 2, physicians’ examinations showed good interrater correlation (interclass correlation coefficient: 0.84). Participants valued the experienced setting as highly positive.

Conclusions

The study showed the good feasibility of the chosen design and a highly positive attitude of all participants toward this digital approach. Head-mounted augmented reality devices are generally recommended for collaborative research projects with physical examination–based research questions.
Patient Portals to Support Palliative and End-of-Life Care: Scoping Review
Authors: Ingle M Pilar , Valdovinos Cristina , Ford Kelsey L , Zhou Shou , Bull Sheana , Gornail Starlynne , Zhang Xuhong , Moore Susan , Portz Jennifer
Published: July 2021

Background

Although patient portals are widely used for health promotion, little is known about the use of palliative care and end-of-life (PCEOL) portal tools available for patients and caregivers.

Objective

This study aims to identify and assess the user perspectives of PCEOL portal tools available to patients and caregivers described and evaluated in the literature.

Methods

We performed a scoping review of the academic literature directed by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) extension for Scoping Review and searched three databases. Sources were included if they reported the development or testing of a feature, resource, tool, or intervention; focused on at least one PCEOL domain defined by the National Coalition for Hospice and Palliative Care; targeted adults with serious illness or caregivers; and were offered via a patient portal tethered to an electronic medical record. We independently screened the titles and abstracts (n=796) for eligibility. Full-text (84/796, 10.6%) sources were reviewed. We abstracted descriptions of the portal tool name, content, targeted population, and reported user acceptability for each tool from included sources (n=19).

Results

In total, 19 articles describing 12 tools were included, addressing the following PCEOL domains: ethical or legal (n=5), physical (n=5), and psychological or psychiatric (n=2). No tools for bereavement or hospice care were identified. Studies have reported high acceptability of tools among users; however, few sources commented on usability among older adults.

Conclusions

PCEOL patient portal tools are understudied. As medical care increasingly moves toward virtual platforms, future research should investigate the usability and acceptability of PCEOL patient portal resources and evaluate their impact on health outcomes.
Prediction of Critical Care Outcome for Adult Patients Presenting to Emergency Department Using Initial Triage Information: An XGBoost Algorithm Analysis
Authors: Yun Hyoungju , Choi Jinwook , Park Jeong Ho
Published: July 2021

Background

The emergency department (ED) triage system to classify and prioritize patients from high risk to less urgent continues to be a challenge.

Objective

This study, comprising 80,433 patients, aims to develop a machine learning algorithm prediction model of critical care outcomes for adult patients using information collected during ED triage and compare the performance with that of the baseline model using the Korean Triage and Acuity Scale (KTAS).

Methods

To predict the need for critical care, we used 13 predictors from triage information: age, gender, mode of ED arrival, the time interval between onset and ED arrival, reason of ED visit, chief complaints, systolic blood pressure, diastolic blood pressure, pulse rate, respiratory rate, body temperature, oxygen saturation, and level of consciousness. The baseline model with KTAS was developed using logistic regression, and the machine learning model with 13 variables was generated using extreme gradient boosting (XGB) and deep neural network (DNN) algorithms. The discrimination was measured by the area under the receiver operating characteristic (AUROC) curve. The ability of calibration with Hosmer–Lemeshow test and reclassification with net reclassification index were evaluated. The calibration plot and partial dependence plot were used in the analysis.

Results

The AUROC of the model with the full set of variables (0.833-0.861) was better than that of the baseline model (0.796). The XGB model of AUROC 0.861 (95% CI 0.848-0.874) showed a higher discriminative performance than the DNN model of 0.833 (95% CI 0.819-0.848). The XGB and DNN models proved better reclassification than the baseline model with a positive net reclassification index. The XGB models were well-calibrated (Hosmer-Lemeshow test; P>.05); however, the DNN showed poor calibration power (Hosmer-Lemeshow test; P<.001). We further interpreted the nonlinear association between variables and critical care prediction.

Conclusions

Our study demonstrated that the performance of the XGB model using initial information at ED triage for predicting patients in need of critical care outperformed the conventional model with KTAS.
Rehabilitation Using Mobile Health for Older Adults With Ischemic Heart Disease in the Home Setting (RESILIENT): Protocol for a Randomized Controlled Trial (Preprint)
Authors: Dodson John A , Schoenthaler Antoinette , Sweeney Greg , Fonceva Ana , Pierre Alicia , Whiteson Jonathan , George Barbara , Marzo Kevin , Drewes Wendy , Rerisi Elizabeth , Mathew Reena , Aljayyousi Haneen , Chaudhry Sarwat I , Hajduk Alexandra M , Gill Thomas M , Estrin Deborah , Kovell Lara , Jennings Lee A , Adhikari Samrachana
Published: July 2021

BACKGROUND

Participation in ambulatory cardiac rehabilitation remains low, especially among older adults. Although mobile health cardiac rehabilitation (mHealth-CR) provides a novel opportunity to deliver care, age-specific impairments may limit older adults’ uptake, and efficacy data are currently lacking.

OBJECTIVE

This study aims to describe the design of the rehabilitation using mobile health for older adults with ischemic heart disease in the home setting (RESILIENT) trial.

METHODS

RESILIENT is a multicenter randomized clinical trial that is enrolling patients aged ≥65 years with ischemic heart disease in a 3:1 ratio to either an intervention (mHealth-CR) or control (usual care) arm, with a target sample size of 400 participants. mHealth-CR consists of a commercially available mobile health software platform coupled with weekly exercise therapist sessions to review progress and set new activity goals. The primary outcome is a change in functional mobility (6-minute walk distance), which is measured at baseline and 3 months. Secondary outcomes are health status, goal attainment, hospital readmission, and mortality. Among intervention participants, engagement with the mHealth-CR platform will be analyzed to understand the characteristics that determine different patterns of use (eg, persistent high engagement and declining engagement).

RESULTS

As of December 2021, the RESILIENT trial had enrolled 116 participants. Enrollment is projected to continue until October 2023. The trial results are expected to be reported in 2024.

CONCLUSIONS

The RESILIENT trial will generate important evidence about the efficacy of mHealth-CR among older adults in multiple domains and characteristics that determine the sustained use of mHealth-CR. These findings will help design future precision medicine approaches to mobile health implementation in older adults. This knowledge is especially important in light of the COVID-19 pandemic that has shifted much of health care to a remote, internet-based setting.

CLINICALTRIAL

ClinicalTrials.gov NCT03978130; https://clinicaltrials.gov/ct2/show/NCT03978130

INTERNATIONAL REGISTERED REPORT

DERR1-10.2196/32163
Deep Learning in mHealth for Cardiovascular Disease, Diabetes, and Cancer: Systematic Review (Preprint)
Authors: Triantafyllidis Andreas , Kondylakis Haridimos , Katehakis Dimitrios , Kouroubali Angelina , Koumakis Lefteris , Marias Kostas , Alexiadis Anastasios , Votis Konstantinos , Tzovaras Dimitrios
Published: July 2021

BACKGROUND

Major chronic diseases such as cardiovascular disease (CVD), diabetes, and cancer impose a significant burden on people and health care systems around the globe. Recently, deep learning (DL) has shown great potential for the development of intelligent mobile health (mHealth) interventions for chronic diseases that could revolutionize the delivery of health care anytime, anywhere.

OBJECTIVE

The aim of this study is to present a systematic review of studies that have used DL based on mHealth data for the diagnosis, prognosis, management, and treatment of major chronic diseases and advance our understanding of the progress made in this rapidly developing field.

METHODS

A search was conducted on the bibliographic databases Scopus and PubMed to identify papers with a focus on the deployment of DL algorithms that used data captured from mobile devices (eg, smartphones, smartwatches, and other wearable devices) targeting CVD, diabetes, or cancer. The identified studies were synthesized according to the target disease, the number of enrolled participants and their age, and the study period as well as the DL algorithm used, the main DL outcome, the data set used, the features selected, and the achieved performance.

RESULTS

In total, 20 studies were included in the review. A total of 35% (7/20) of DL studies targeted CVD, 45% (9/20) of studies targeted diabetes, and 20% (4/20) of studies targeted cancer. The most common DL outcome was the diagnosis of the patient’s condition for the CVD studies, prediction of blood glucose levels for the studies in diabetes, and early detection of cancer. Most of the DL algorithms used were convolutional neural networks in studies on CVD and cancer and recurrent neural networks in studies on diabetes. The performance of DL was found overall to be satisfactory, reaching >84% accuracy in most studies. In comparison with classic machine learning approaches, DL was found to achieve better performance in almost all studies that reported such comparison outcomes. Most of the studies did not provide details on the explainability of DL outcomes.

CONCLUSIONS

The use of DL can facilitate the diagnosis, management, and treatment of major chronic diseases by harnessing mHealth data. Prospective studies are now required to demonstrate the value of applied DL in real-life mHealth tools and interventions.
Partisan Differences in Legislators’ Discussion of Vaccination on Twitter During the COVID-19 Era: Natural Language Processing Analysis (Preprint)
Authors: Engel-Rebitzer Eden , Stokes Daniel C , Meisel Zachary F , Purtle Jonathan , Doyle Rebecca , Buttenheim Alison M
Published: July 2021

BACKGROUND

The COVID-19 era has been characterized by the politicization of health-related topics. This is especially concerning given evidence that politicized discussion of vaccination may contribute to vaccine hesitancy. No research, however, has examined the content and politicization of legislator communication with the public about vaccination during the COVID-19 era.

OBJECTIVE

The aim of this study was to examine vaccine-related tweets produced by state and federal legislators during the COVID-19 era to (1) describe the content of vaccine-related tweets; (2) examine the differences in vaccine-related tweet content between Democrats and Republicans; and (3) quantify (and describe trends over time in) partisan differences in vaccine-related communication.

METHODS

We abstracted all vaccine-related tweets produced by state and federal legislators between February 01, 2020, and December 11, 2020. We used latent Dirichlet allocation to define the tweet topics and used descriptive statistics to describe differences by party in the use of topics and changes in political polarization over time.

RESULTS

We included 14,519 tweets generated by 1463 state legislators and 521 federal legislators. Republicans were more likely to use words (eg, “record time,” “launched,” and “innovation”) and topics (eg, Operation Warp Speed success) that were focused on the successful development of a SARS-CoV-2 vaccine. Democrats used a broader range of words (eg, “anti-vaxxers,” “flu,” and “free”) and topics (eg, vaccine prioritization, influenza, and antivaxxers) that were more aligned with public health messaging related to the vaccine. Polarization increased over most of the study period.

CONCLUSIONS

Republican and Democratic legislators used different language in their Twitter conversations about vaccination during the COVID-19 era, leading to increased political polarization of vaccine-related tweets. These communication patterns have the potential to contribute to vaccine hesitancy.
Empirical Research on the Downturn in Church Attendance in Australia: The youth without Religion
Authors: Awuku-Gyampoh Ransford Kwabena. , Sarpong Akoto Justina , Ocran Catherine , Formijang Bah
Published: July 2021

The church has played a significant role in the lives of Australia’s people since the European settlement. It used to provide several welfare services such as educational, health, and orphanages, even more than the government. Australian churches played a significant role in shaping the culture of Australians. Australia was the only country with no newspaper on Sunday as they kept Sunday as a regular holiday and kept everything closed. Indeed, for Australia’s farmers, religion was so important that they decided to remain clear of their religion and, in 1901, to lead up the Federation. As the years passed, church attendance reduced, and others chose no religion. Few considered religion as least important, resulting in an overall decline in Australia’s churches. The paper reiterated the downturn in church attendance in Australia, found reasons for the downturn, and how the youth can be driven to attend the church. Innovation, discipleship, evangelism, oneness, care, hospitality, service to the community, and social media presence were discovered to be strategies for motivating the younger generation, first-time worshippers and new converts to the church.
Towards an Intelligent Personalized Persuasive Conversational System for Human Interaction on Divining the Future Event and Assisting by Using Artificial Intelligence
Authors: Sharma Sweta
Published: July 2021

In the next wave of insurgence, humans may endeavour self-reflection which can lead to an effortless talk and to find out if an event will fructify. Training the system on how to make accurate prognostication with the help of machine learning and statistical models can lead to an intelligent personalized conversational system. The Chatbot industry is ever-growing and after the COVID-19 pandemic and rigorous lockdowns all around the world, people have realized the importance of human interaction in their lives. We are developing this model to create a more intimate relationship between the system and humans. For this purpose, many open-source platforms are available. Artificial Intelligence Markup Language (AIML) is derived from Extensible Markup Language (XML) which is used to build up a conversational agent artificially. The success of this project will help the model in observing and understanding human emotions which will ultimately help it to form a more personalized relationship to delineate the future course of events.