bluuebunny/crossref_metadata_2025_split huggingface dataset:
Studying Institutional Situational Awareness Through Anonymous Incident Reporting
Authors: Kurapati Srikanth , Boyd Carter J. , King Timothy W. , Awad Samir S. 
Published: June 2020

Background and Objectives:
The efficacy of anonymous incident reporting (AIR) is critical to creating a culture of safety. Prior studies have sought to establish AIR in a similar manner as aviation, nuclear power, and other industries. However, health care presents unique challenges that differ greatly from these industries. We present a straightforward method using statistical process control to study the progression and efficacy of AIR.

Methods:
This study represents a retrospective review of all anonymous incident reports and surgical critical events from 2012 to 2017 at a single-institution, 500-bed, university-based, metropolitan Veterans Affairs Administration Medical Center located in Texas. This work was approved by the Veterans Administration Quality Board and deemed to be an appropriate quality improvement project. This project did not require institutional review board approval.

Results:
There was an exponential increase in AIRs in the first 15 months from 1 report per month to 168 reports in the ninth month (1425% increase). The results then plateaued over time (first year: 1017, second year: 1634, and third year: 1938—common-cause variation). A logarithmic regression was performed for progression of AIRs per month yielding the equation y = −7E-13ln(x) + 142.92, Pearson Correlation Coefficient = 0.55, where y represents number of reports and x time by month. The highest number of Critical Incident Tracking Notification System (CITNS) reports was observed early in the self-reporting process and decreased over time (first year: 5, second year: 2, third year: 1, fourth year: 1, and fifth year: 0). The numbers of AIR and CITNS reports were found to be inversely related with a Pearson correlation coefficient of −0.4.

Conclusions:
Statistical process control can be applied to an institution's AIR program to study progression and situational awareness.
Impact of collision models on the physical properties and the stability of lattice Boltzmann methods
Authors: Coreixas C. , Wissocq G. , Chopard B. , Latt J. 
Published: June 2020

The lattice Boltzmann method (LBM) is known to suffer from stability issues when the collision model relies on the BGK approximation, especially in the zero viscosity limit and for non-vanishing Mach numbers. To tackle this problem, two kinds of solutions were proposed in the literature. They consist in changing either the numerical discretization (finite-volume, finite-difference, spectral-element, etc.) of the discrete velocity Boltzmann equation (DVBE), or the collision model. In this work, the latter solution is investigated in detail. More precisely, we propose a comprehensive comparison of (static relaxation time based) collision models, in terms of stability, and with preliminary results on their accuracy, for the simulation of isothermal high-Reynolds number flows in the (weakly) compressible regime. It starts by investigating the possible impact of collision models on the macroscopic behaviour of stream-and-collide based D2Q9-LBMs, which clarifies the exact physical properties of collision models on LBMs. It is followed by extensive linear and numerical stability analyses, supplemented with an accuracy study based on the transport of vortical structures over long distances. In order to draw conclusions as generally as possible, the most common moment spaces (raw, central, Hermite, central Hermite and cumulant), as well as regularized approaches, are considered for the comparative studies. LBMs based on dynamic collision mechanisms (entropic collision, subgrid-scale models, explicit filtering, etc.) are also briefly discussed.This article is part of the theme issue ‘Fluid dynamics, soft matter and complex systems: recent results and new methods’.
Compressibility in lattice Boltzmann on standard stencils: effects of deviation from reference temperature
Authors: Hosseini S. A. , Darabiha N. , Thévenin D. 
Published: June 2020

With growing interest in the simulation of compressible flows using the lattice Boltzmann (LB) method, a number of different approaches have been developed. These methods can be classified as pertaining to one of two major categories: (i) solvers relying on high-order stencils recovering the Navier–Stokes–Fourier equations, and (ii) approaches relying on classical first-neighbour stencils for the compressible Navier–Stokes equations coupled to an additional (LB-based or classical) solver for the energy balance equation. In most cases, the latter relies on a thermal Hermite expansion of the continuous equilibrium distribution function (EDF) to allow for compressibility. Even though recovering the correct equation of state at the Euler level, it has been observed that deviations of local flow temperature from the reference can result in instabilities and/or over-dissipation. The aim of the present study is to evaluate the stability domain of different EDFs, different collision models, with and without the correction terms for the third-order moments. The study is first based on a linear von Neumann analysis. The correction term for the space- and time-discretized equations is derived via a Chapman–Enskog analysis and further corroborated through spectral dispersion–dissipation curves. Finally, a number of numerical simulations are performed to illustrate the proposed theoretical study.This article is part of the theme issue ‘Fluid dynamics, soft matter and complex systems: recent results and new methods’.
On relaxation systems and their relation to discrete velocity Boltzmann models for scalar advection–diffusion equations
Authors: Simonis Stephan , Frank Martin , Krause Mathias J. 
Published: June 2020

The connection of relaxation systems and discrete velocity models is essential to the progress of stability as well as convergence results for lattice Boltzmann methods. In the present study we propose a formal perturbation ansatz starting from a scalar one-dimensional target equation, which yields a relaxation system specifically constructed for its equivalence to a discrete velocity Boltzmann model as commonly found in lattice Boltzmann methods. Further, the investigation of stability structures for the discrete velocity Boltzmann equation allows for algebraic characterizations of the equilibrium and collision operator. The methods introduced and summarized here are tailored for scalar, linear advection–diffusion equations, which can be used as a foundation for the constructive design of discrete velocity Boltzmann models and lattice Boltzmann methods to approximate different types of partial differential equations.This article is part of the theme issue ‘Fluid dynamics, soft matter and complex systems: recent results and new methods’.
The lattice Fokker–Planck equation for models of wealth distribution
Authors: Kaushal Shaurya , Ansumali Santosh , Boghosian Bruce , Johnson Merek 
Published: June 2020

Recent work on agent-based models of wealth distribution has yielded nonlinear, non-local Fokker–Planck equations whose steady-state solutions describe empirical wealth distributions with remarkable accuracy using only a few free parameters. Because these equations are often used to solve the ‘inverse problem’ of determining the free parameters given empirical wealth data, there is much impetus to find fast and accurate methods of solving the ‘forward problem’ of finding the steady state corresponding to given parameters. In this work, we derive and calibrate a lattice Boltzmann equation for this purpose.This article is part of the theme issue ‘Fluid dynamics, soft matter and complex systems: recent results and new methods’.
A lattice-Boltzmann free surface model for injection moulding of a non-Newtonian fluid
Authors: Chiappini Daniele 
Published: June 2020

The aim of this work is to present a lattice Boltzmann (LB) model devoted to dealing with non-Newtonian free surface flow. The combination of LB solver with a free-surface model allows dealing with multiphase flows where the density ratio in between the two considered phases is so high that the lighter phase can be neglected. For this particular set of multiphase models, the interface between the two phases is numerically reconstructed and transported via a diffusion equation. Moreover, the application of a Carreau approach for viscosity modelling allows the introduction of effects related to shear stress on fluid flow evolution. Two different non-Newtonian silicon-like materials have been considered here, namely the polystyrene and acrylonitrile butadiene styrene. Here, the author, after the mandatory model validation with a reference configuration, presents some applications of injection moulding for two different test-cases: the former is the injection in a labyrinth-like gasket, whereas the latter is the injection in a porous media.
This article is part of the theme issue ‘Fluid dynamics, soft matter and complex systems: recent results and new methods’.
Boltzmann equation and hydrodynamic equations: their equilibrium and non-equilibrium behaviour
Authors: Verma Mahendra K. 
Published: June 2020

This short article summarizes the key features of equilibrium and non-equilibrium aspects of Boltzmann and hydrodynamic equations. Under equilibrium, the Boltzmann equation generates uncorrelated random velocity that corresponds tok2energy spectrum for the Euler equation. The latter spectrum is produced using initial configuration with many Fourier modes of equal amplitudes but with random phases. However, for a large-scale vortex as an initial condition, earlier simulations exhibit a combination ofk−5/3(in the inertial range) andk2(for large wavenumbers) spectra, with the range ofk2spectrum increasing with time. These simulations demonstrate an approach to equilibrium or thermalization of Euler turbulence. In addition, they also show how initial velocity field plays an important role in determining the behaviour of the Euler equation. In non-equilibrium scenario, both Boltzmann and Navier–Stokes equations produce similar flow behaviour, for example, Kolmogorov’sk−5/3spectrum in the inertial range.This article is part of the theme issue ‘Fluid dynamics, soft matter and complex systems: recent results and new methods’.
The Citation Cloud of a Biomedical Article: Enabling Citation Analysis
Authors: Smalheiser Neil R. , Schneider Jodi , Torvik Vetle I. , Fragnito Dean P. , Tirk Eric E. 
Published: June 2020

AbstractUsing open citations provided by iCite and other sources, we have built an extension to PubMed that allows any user to visualize and analyze the “citation cloud” around any target article A: the set of articles cited by A; those which cite A; those which are co-cited with A; and those which are bibliographically coupled to A. This greatly enables the study of citations by the scientific community. The Citation Cloud can be accessed by running any query on the Anne O’Tate value-added PubMed search interface http://arrowsmith.psych.uic.edu/cgi-bin/arrowsmith_uic/AnneOTate.cgi and clicking on the Citations button next to any retrieved article.
A downscaling approach to compare COVID-19 count data from databases aggregated at different spatial scales
Authors: Python Andre , Bender Andreas , Blangiardo Marta , Illian Janine B. , Lin Ying , Liu Baoli , Lucas Tim , Tan Siwei , Wen Yingying , Svanidze Davit , Yin Jianwei 
Published: June 2020

AbstractAs the COVID-19 pandemic continues to threaten various regions around the world, obtaining accurate and reliable COVID-19 data is crucial for governments and local communities aiming at rigorously assessing the extent and magnitude of the virus spread and deploying efficient interventions. Using data reported between January and February 2020 in China, we compared counts of COVID-19 from near-real time spatially disaggregated data (city-level) with fine-spatial scale predictions from a Bayesian downscaling regression model applied to a reference province-level dataset. The results highlight discrepancies in the counts of coronavirus-infected cases at district level and identify districts that may require further investigation.
Assessment of clinical characteristics and mortality-associated factors in COVID-19 Critical cases in Kuwait
Authors: Ayed Mariam , Borahmah Abdulwahab A , Yazdani Anwar , Sultan Ahmad , Mossad Ahmad , Rawdhan Hanouf 
Published: June 2020

AbstractPurposeTo assess the clinical characteristics and identify mortality predicting factors in intensive care unit (ICU)-admitted COVID-19 confirmed patients.MethodsWe recruited and analyzed COVID-19 infected adult patients (age≥ 18 years) who were admitted to the ICU at Jaber AlAhmad Al Sabah Hospital,Kuwait, between 1stMarch, 2020and 30thApril 2020. The patients were followed up to 20th May, 2020. The risk factors associated with in-hospital mortality were assessed using multiple regression analysis.ResultsWe recruited a total of 103 ICU patients in this retrospective cohort. The median age of the patients was 53 years (Interquartile Range (IQR): 44-63 years). The fatality rate was 43.7%. Among the patients, majority were males (85.5%) and 38% patients had more than two comorbidities. Pre-existing hypertension (OR:3.2,95%CI: 1.2-8.9), moderate/severe ARDS (OR: 3.4, 95%CI: 1.1-10.8),lymphocyte counts <0.5 (OR: 6.1, 95%CI: 1.2-29.8)albumin < 22 (OR: 7.5, 95%CI: 2.1-26.2), procalcitonin >0.2 (OR: 3.8, 95%CI: 1.3-7.8), D-Dimer >1200 (OR: 5.1, 95%CI: 1.2-21.6), and the need forcontinuous renal replacement therapy (OR: 9.3, 95%CI: 2.4-36.2) weresignificantly associated with mortality.ConclusionThis study describes the clinical characteristics and predictors ofmortality among ICU patients. Early identification of risk factors to mortality might help in their better outcome.
Predicting Antibody Developability from Sequence using Machine Learning
Authors: Chen Xingyao , Dougherty Thomas , Hong Chan , Schibler Rachel , Zhao Yi Cong , Sadeghi Reza , Matasci Naim , Wu Yi-Chieh , Kerman Ian 
Published: June 2020

AbstractAntibodies are prominent therapeutic agents but costly to develop. Existing approaches to predict developability depend on structure, which requires expensive laboratory or computational work to obtain. To address this issue, we present a machine learning pipeline to predict developability from sequence alone using physicochemical and learned embedding features. Our approach achieves high sensitivity and specificity on a dataset of 2400 antibodies. These results suggest that sequence is predictive of developability, enabling more efficient development of antibodies.
Questionnaire based Prediction of Hypertension using Machine Learning
Authors: Chaturvedi Abhijat , Srivastava Siddharth , Rai Astha , Cheema A S , Chelimela Desham , Aravindakshan Rajeev 
Published: June 2020

AbstractMachine Learning has proven its ability in healthcare as an assisting technology for health care providers either by saving precious time or timely alerts or vitals monitoring. However, their application in real world is limited by availability of data. In this paper, we show that simple machine learning algorithms especially neural networks, if designed carefully, are extremely effective even with limited amount of data. Specifically with exhaustive experiments on standard Modified National Institute of Standards and Technology database (MNIST) dataset we analyse the impact of various parameters for effective performance. Further, on a custom dataset collected at a tertiary care hospital for hypertension analysis, we apply these design considerations to achieve better performance as compared to competitive baselines. On a real world dataset of only a few hundred patients, we show the effectiveness of these design choices and report an accuracy of 75% in determining whether a patient suffers from hypertension.
Clinical Outcomes of Patients with COVID-19 and Chronic Inflammatory and Autoimmune Rheumatic Diseases: A Multicentric Matched-Cohort Study
Authors: Pablos José L. , Galindo María , Carmona Loreto , Retuerto Miriam , Lledó Ana , Blanco Ricardo , González-Gay Miguel A. , Martinez-Lopez David , Castrejón Isabel , Álvaro-Gracia José M. , Fernández-Fernández David , Mera-Varela Antonio , Manrique-Arija Sara , Mena-Vázquez Natalia , Fernández-Nebro Antonio ,   RIER investigators group
Published: June 2020

ABSTRACTBackgroundThe impact of inflammatory rheumatic diseases on COVID-19 severity is poorly known. Here we compare the outcomes of a cohort of rheumatic patients with a matched control cohort to identify potential risk factors for severe illness.MethodsIn this comparative cohort study, we identified hospital PCR+ COVID-19 rheumatic patients with chronic inflammatory arthritis (IA) or autoimmune/immunomediated diseases (AI/IMID). Non-rheumatic controls were randomly sampled 1:1, and matched by age, sex, and PCR date. The main outcome was severe COVID-19, defined as death, invasive ventilation, ICU admission, or serious complications. We assessed the association between the outcome and potential prognostic variables, adjusted by COVID treatment, using logistic regression.ResultsThe cohorts were composed of 456 rheumatic and non-rheumatic patients, in equal numbers. Mean age was 63 [IQR 53-78] and male sex 41% in both cohorts. Rheumatic diseases were IA (60%) and AI/IMID (40%). Most patients (74%) had been hospitalised, and the risk of severe COVID was 31.6% in the rheumatic and 28.1% in the non-rheumatic cohort. Ageing, male sex and previous comorbidity (obesity, diabetes, hypertension, cardiovascular, or lung disease) increased the risk in the rheumatic cohort by bivariate analysis. In logistic regression analysis, independent factors associated with severe COVID were increased age (OR 5.31; CI 3.14-8.95), male sex (2.13; CI 1.35-3.36) and having an AI/IMID (OR 1.98; CI 1.15-3.41).ConclusionIn patients with chronic inflammatory rheumatic diseases aging, sex and having an AI/IMID but not IA nor previous immunosuppressive therapies were associated with severe COVID-19.
The influence of comorbidity on the severity of COVID-19 disease: A systematic review and analysis
Authors: Zaki Nazar , Mohamed Elfadil A. , Ibrahim Sahar , Khan Gulfaraz 
Published: June 2020

AbstractBackgroundA novel form of coronavirus disease (SARS-CoV-2) has spread rapidly across the world. What risk factors influence the severity of the disease is of considerable importance.AimThis research offers a systematic review and meta-analysis of the correlation between common clinical conditions and comorbidities and the severity of COVID-19.MethodologyTwo independent researchers searched Europe PMC, Google Scholar, and PubMed databases for articles related to influence comorbidities have on the progress of the disease. A search engine was also created to screen a further 59,000 articles in COVID-19 Open Research Dataset (CORD-19). Random-effects modeling was used to pool 95% confidence intervals (CIs) and odds ratios (ORs). The significance of all comorbidities and clinical conditions to the severity of the disease was evaluated by employing machine-learning techniques. Publication bias was assessed by using funnel-plots and Egger's test. Heterogeneity was tested using I2.ResultsThe meta-analysis incorporated 12 studies spanning 4,101 confirmed COVID-19 patients who were admitted to Chinese hospitals. The prevalence of the most commonly associated co-morbidities and their corresponding odds ratio for disease severity were as follows: coronary heart disease (OR 2.97 [CI: 1.99-4.45], p < 0.0001), cancer (OR 2.65 [CI: 1.12-6.29], p < 0.03), cardiovascular disease (OR 2.89 [CI: 1.90-4.40], p < 0.0001), COPD (OR 3.24 [CI: 1.66-6.32], p = 0.0), and kidney disease (OR 2.2.4 [CI: 1.01-4.99], p = 0.05) with low or moderate level of heterogeneity. The most frequently exhibited clinical symptoms were fever (OR 1.37 [CI: 1.01-1.86], p = 0.04), myalgia/fatigue (OR 1.31 [CI: 1.11-1.55], p = 0.0018), and dyspnea (OR 3.61, [CI: 2.57-5.06], p = <0.0001). No significant associations between disease severity and liver disease, smoking habits, and other clinical conditions, such as a cough, respiratory/ARDS, diarrhea or chest tightness/pain were found. The meta-analysis also revealed that the incubation period was positively associated with disease severity.ConclusionExisting comorbidities, including COPD, cardiovascular disease, and coronary heart disease, increase the severity of COVID-19. Some studies found a statistically significant association between comorbidities such as diabetes and hypertension and disease severity. However, these studies may be biased due to substantial heterogeneity.
Covid-19 Pandemic- Pits and falls of major states of India
Authors: Singh Abhinesh , Gupte Samriddhi S 
Published: June 2020

AbstractCovid-19, just like SARS and MERS before it, is a disease caused by corona virus and can lead to severe respiratory diseases in humans. With the outbreak of novel corona virus, WHO on 30thJanuary 2020 declared it a Public Health Emergency and further on 11thMarch 2020, Covid-19 disease was declared a pandemic. India in the initial stages of the pandemic dealt with it in a very effective manner. With timely implementation of lockdown, India was able to contain the spread of Covid-19 to some extent. However with the recently announced Unlock 1.0, the SARS CoV-2 is expected to spread. This study aims to track and analyze the Covid-19 situation in major states that constitute of 70 percent of the total cases. Thus the states selected for the study are: Maharashtra, Delhi, Tamil Nadu, Gujarat, Uttar Pradesh and Rajasthan. These are the states which had more than ten thousand Covid-19 patients as/on June 10th2020. The analysis period is from March 25thto June 10thand the data source is India’s Covid-19 tracker. To assess the previous and current Covid-19 situations in these states indicators such as Active rates, Recovery rate, Case fatality rate, Test positivity rate, tests per million, cases per million, test per confirmed case has been used. The study finds that although the absolute number of active cases may be rising, however it is showing a decreasing trend with an increase in recovery rates. With increasing number of Covid-19 cases, testing also has increased however not in the similar proportion and thus by developed nation standard we are lagging. With increasing TPR and cases per million, Delhi is well on its way to surpass even Mumbai which till now has proven to be worst hit in this pandemic. An interesting finding is that of test per confirmed case which shows that every 6thperson in Maharashtra and every 8thin Delhi is showing positive result of Covid-19 test. Given such an increase and unlocked India, Delhi might soon enter into the third stage of community transmission where source of 50 percent or more cases would be unknown. There has been an increase in the Covid-19 related health infrastructure with the public-private partnership which involved both private hospitals and lab joining hands to battle Covid-19, however, affordability still remains an issue. If experts are to be believed, pandemic isn’t over because we’ve unlocked. The worst is yet to come as Covid-19 is predicted to peak in mid-July to August in India. Thus, it’d be advisable to not venture out unnecessarily just because restrictions have been lifted. Also, following the guidelines-hand-washing, avoiding public gathering, social distancing and covering nose and mouth has now become imperative.