Time-varying Analysis Research Articles

Time-varying seismic resilience analysis of coastal bridges by considering multiple durability damage factors

PurposeFor coastal bridges, the ability to recover traffic functions after the earthquake has crucial implications for post-disaster reconstruction, which makes resilience become a significant index to evaluate the seismic behavior. However, the deterioration of the material is particularly prominent in coastal bridge, which causes the degradation of the seismic behavior. As far, the research studies on resilience of coastal bridges considering multiple degradation factors and different disaster prevention capability are scarce. For further evaluating the seismic behavior of coastal bridge in the long-term context, the seismic resilience is conducted in this paper with considering multiple durability damage.Design/methodology/approachThe fuzzy theory and time-varying fragility analysis are combined in this paper to obtain the life-cycle resilience of coastal bridges.FindingsThe results show that durability damage has a remarkable impact on the resilience. After 100 years of service, the seismic resilience of bridge with poor disaster-prevention capability has greatest reduction, about 18%. In addition, the improvement of the disaster prevention capability can stabilize the resilience of the bridge at a higher level.Originality/valueIn this paper, the time-varying fragility analysis of case bridge are evaluated with considering chloride ion erosion and concrete carbonization, firstly. Then, combining fuzzy theory and fragility analysis, the triangular fuzzy values of resilience parameters under different service period are obtained. Finally, the life-cycle resilience of bridge in different disaster prevention capability is analyzed.

Vaccination, human mobility, and COVID-19 health outcomes: Empirical comparison before and during the outbreak of SARS‐Cov-2 B.1.1.529 (Omicron) variant

The B.1.1.529 (Omicron) variant surge has raised concerns about the effectiveness of vaccines and the impact of imprudent reopening. Leveraging over two years of county-level COVID-19 data in the US, this study aims to investigate relationships among vaccination, human mobility, and COVID-19 health outcomes (assessed via case rate and case-fatality rate), controlling for socioeconomic, demographic, racial/ethnic, and partisan factors. A set of cross-sectional models was first fitted to empirically compare disparities in COVID-19 health outcomes before and during the Omicron surge. Then, time-varying mediation analyses were employed to delineate how the effects of vaccine and mobility on COVID-19 health outcomes vary over time. Results showed that vaccine effectiveness against case rate lost significance during the Omicron surge, while its effectiveness against case-fatality rate remained significant throughout the pandemic. We also documented salient structural inequalities in COVID-19-related outcomes, with disadvantaged populations consistently bearing a larger brunt of case and death tolls, regardless of high vaccination rates. Last, findings revealed that mobility presented a significantly positive relationship with case rates during each wave of variant outbreak. Mobility substantially mediated the direct effect from vaccination to case rate, leading to a 10.276 % (95 % CI: 6.257, 14.294) decrease in vaccine effectiveness on average. Altogether, our study implies that sole reliance on vaccination to halt COVID-19 needs to be re-examined. Well-resourced and coordinated efforts to enhance vaccine effectiveness, mitigate health disparity and selectively loosen non-pharmaceutical interventions are essential to bringing the pandemic to an end.

Dynamic linkages between shipping and commodity markets: Evidence from a novel asymmetric time-frequency method

This study extends the existing literature in this area by examining the connectedness and shock spillover between commodity and shipping markets using a new novel time-varying frequency and quantile connectedness method developed by Chatziantoniou et al (2022) based on B&K (2018) and Ando et al (2018). Connectedness and shock transmission between the markets were analysed with daily data covering July 4, 2012 to July 20, 2022. A major value added of this study to the existing literature is the examination of the asymmetric effect of commodity price changes (or return) on the connectedness of the markets. Mean-frequency total connectedness analysis indicates that, the overall shipping market (BDI) is both the transmitter (to) and receiver of the highest shock from the entire market connectedness system. In the short-term, the agricultural markets dominate as both the transmitters and receivers of the major shocks to and from the entire market system, while in the medium-term, the shipping markets dominate as both the transmitters and receivers of the largest shocks to the entire market system. However, in the long-term, connectedness and shock propagation were very low. The time-varying quantile analysis reveals that, connectedness was very strong before, during and after COVID-19 at the bearish and bullish market conditions. Further, the time-varying frequency connectedness analysis shows that, although total connectedness is relatively high overtime, it was propelled by short-term dynamics. Metal markets are connected among themselves, and with both agricultural and shipping markets. Agricultural markets are connected among themselves, and with shipping markets, which are only connected among themselves. There is evidence of the asymmetric effect of commodity return dynamics on the connectedness of the markets. Some important policy recommendations were drawn from the findings.

Unraveling the Fate and Transport of DNAPLs in Heterogeneous Aquifer Systems—A Critical Review and Bibliometric Analysis

Dense nonaqueous phase liquid (DNAPL) in the subsurface environment beyond the permissible limit poses a threat to human health and a suite of ecological services. An accurate prediction of the concentration and mass fluxes of DNAPL at environmentally sensitive locations and their temporal variations can be obtained using robust and efficient fate and transport mathematical models. Thus, this study evaluated 412 articles published from 1990 to 2022 utilizing the Scopus® database to provide a quantitative overview of the present trends and future perspectives of the DNAPL transport research field, especially fate and transport models via bibliometric analysis. The major findings of the published literature based on the scale of the study and type of modeling framework, relationships of governing parameters with a scale of study, and recent developments in the mathematical models were discussed. The country-citation analysis revealed the USA and Canada as leading countries in DNAPL fate and transport research field. The findings of this study uncovered a need for studies considering low-permeability and stagnant regions, as well as the variable nature of the flow, transport, and reaction parameters to understand the complex plume evolution dynamics of DNAPLs under field-scale conditions. The nonlinear interactions between various flow and transport phenomena should be quantified during a model’s development by applying spatial- and time-varying global sensitivity analyses. The outcomes of the bibliometric survey, visual analysis, and concise review presented in this study can provide a wide range of references, emerging topics, and prospects to emphasize less focused on topics of DNAPL transport research.

Long-term Visit-to-Visit Variability in Hemoglobin A1c and Kidney-Related Outcomes in Persons With Diabetes

To characterize associations between long-term visit-to-visit variability of hemoglobin A1c (HbA1c) and risk of adverse kidney outcomes in patients with diabetes. Observational study. 93,598 adults with diabetes undergoing routine care in Stockholm, Sweden. Categories of baseline and time-varying HbA1c variability score (HVS, the percentage of total HbA1c measures that vary by>0.5% [5.5mmol/mol] during a 3-year window): 0-20%, 21%-40%, 41%-60%, 61%-80%, and 81%-100%, with 0-20% as the reference group. Chronic kidney disease (CKD) progression (composite of>50% estimated glomerular filtration rate [eGFR] decline and kidney failure), acute kidney disease (AKI by clinical diagnosis or transient creatinine elevations according to KDIGO criteria), and worsening of albuminuria. Multivariable Cox proportional hazards regression. Compared with persons showing low HbA1c variability (HVS 0-20%), any increase in variability was associated with a higher risk of adverse kidney outcomes beyond mean HbA1c. For example, for patients with a baseline HbA1c variability of 81%-100%, the adjusted HR was 1.6 (95% CI, 1.47-1.74) for CKD progression, 1.23 [1.16-1.3] for AKI, and 1.28 [1.21-1.36] for worsening of albuminuria. The results were consistent across subgroups (diabetes subtypes, baseline eGFR, or albuminuria categories), in time-varying analyses and in sensitivity analyses including time-weighted average HbA1c or alternative metrics of variability. Observational study, limitations of claims data, lack of information on diet, body mass index, medication changes, and diabetes duration. Higher long-term visit-to-visit HbA1c variability is consistently associated with the risks of CKD progression, AKI, and worsening of albuminuria. The evidence for current guideline recommendations derives from clinical trials that focus on a single HbA1c as the definitive measure of efficacy of an intervention. However, long-term visit-to-visit fluctuations of HbA1c may provide additional value in the prediction of future kidney complications. We evaluated the long-term fluctuations in glycemic control in almost 100,000 persons with diabetes undergoing routine care in Stockholm, Sweden. We observed that higher long-term HbA1c fluctuation is consistently associated with the risks of chronic kidney disease progression, worsening of albuminuria and acute kidney injury. This finding supports a role for long-term glycemic variability in the development of kidney complications and illustrates the potential usefulness of this metric for risk stratification at the bedside beyond a single HbA1c test.

The association of allopurinol with persistent physical disability and frailty in a large community based older cohort.

The protective effects of allopurinol on physical function in older adults are not well understood, despite its potential to improve functional gains and reduce sarcopenia. This study aims to determine the association between allopurinol, persistent physical disability, and frailty in older gout patients. This analysis used data from a randomized trial in an older cohort, ASPirin in Reducing Events in the Elderly (ASPREE). ASPREE recruited 19,114 participants aged ≥65 years without prior cardiovascular events, dementia, or independence-limiting physical disability at trial enrolment. This analysis examined the association of baseline and time-varying allopurinol use with persistent physical disability and new-onset frailty in participants with gout at baseline (self-report or use of any anti-gout medications). Frailty was measured using the Fried frailty phenotype (score ≥3/5) and a deficit accumulation frailty index (FI) (score >0.21/1.0). Multivariable Cox proportional-hazards models were used for main analyses. This analysis included 1155 gout participants, with 630 taking allopurinol at baseline and 525 not. During a median follow-up of 5.7 years, 113 new allopurinol users were identified. Compared with nonusers, baseline allopurinol use was associated with a significant risk reduction of persistent physical disability (Adjusted HR 0.46, 95% CI 0.23-0.92, p = 0.03). The strength of the association was modestly attenuated in the time-varying analysis (Adjusted HR 0.56, 0.29-1.08, p = 0.08). No significant associations with frailty measures were observed for either baseline allopurinol use (Fried frailty: Adjusted HR 0.83, 0.62-1.12; FI: Adjusted HR 0.96, 0.74-1.24) or time-varying allopurinol use (Fried frailty: Adjusted HR 0.92, 0.69-1.24; FI: Adjusted HR 1.02, 0.78-1.33). Allopurinol use in older adults with gout is associated with a reduced risk of persistent physical disability but not associated with risk of frailty.

Inflammation is associated with incident hypertension in patients with axial spondyloarthritis: A longitudinal cohort study

ABSTRACT Objectives To elucidate the risk factors for the development of incident hypertension (IHT) in patients with axial spondyloarthritis (axSpA). Methods We conducted a retrospective cohort study in axSpA patients who were recruited from 2001 to 2019 from a university clinic in Hong Kong. Patients with HT and/or anti-hypertensive drug use at baseline were excluded. They were followed until the end of 2020. The outcome was IHT, defined by a diagnosis and a prescription for an antihypertensive drug. Baseline and time-varying Cox regression analyses adjusting for age, sex, and body mass index (BMI), were used to assess the relationship between drug use, inflammatory burden, and IHT. Results Four hundred and thirteen patients [age: 34(25–43) years, male: 319 (77.2%)] were recruited. After a median follow-up of 12 (6–17) years, 58 patients (14%) developed IHT (IHT+group). Among all the baseline variables, disease duration and delay in diagnosis were the independent predictors for IHT based on the Cox regression model. In the multivariate Cox regression analysis, baseline disease duration, delay in diagnosis and time-varying ESR levels were independent predictors associated with an increased risk of IHT. IHT risk was significantly increased in patients with disease duration >5 years. The use of anti-inflammatory drugs was not associated with the development of IHT. Conclusion Higher inflammatory burden as reflected by a longer disease duration, delay diagnosis and higher ESR levels, were predictors associated with IHT after adjusting for traditional CV risk factors. These data support routine screening for hypertension in axSpA patients, especially those with longer disease duration.

PO-05-186 LEVERAGING A NATURAL EXPERIMENT TO DETERMINE THE EFFECT OF AIR POLLUTION ON VENTRICULAR ARRHYTHMIAS AND PHYSICAL ACTIVITY: THE 2020 U.S. WILDFIRES

Particulate matter (PM) air pollution is associated with atherosclerotic cardiovascular disease. While the biological processes that link PM exposure to atherosclerotic disease may also trigger arrhythmias, there are limited data on the association of PM with ventricular arrhythmias. The high levels of PM pollution as a consequence of the 2020 U.S. wildfires provide a unique opportunity to explore the effects of air pollution on cardiac arrhythmias, physical activity, and heart failure markers using device remote monitoring data. To evaluate the association of PM exposure to ventricular arrhythmias, physical activity, and markers of heart failure. We performed a retrospective cohort study using the CERTITUDE database of patients with Biotronik CIEDs. We included patients with at least one remote data transmission on or after 1/1/2020. The primary predictors were Air Quality Index (AQI), PM < 10 μm in diameter (PM10), and PM < 2.5 μm in diameter (PM2.5). We evaluated the predictors as continuous and binary variables for values ≧ the 90th percentile for the 2020 calendar year. We determined day-level air pollutant levels using US EPA data cross-linked to patients’ Zip codes as a proxy for patient location, and we examined the outcomes of interest listed in the Table. We evaluated the association of air pollution to the outcomes of interest with (1) a case-crossover analysis using a conditional logistic regression model followed by (2) a time-varying exposure analysis using an Andersen-Gill model and day level air-pollutant levels as a time-varying exposure. The study cohort included 28349 patients (32% female, age 72.8±11.9), of whom 1822 (6.4%) had loop recorders, 17448 (61.6%) had pacemakers, and 9079 (32%) had defibrillators. In the case-crossover analysis, AQI and PM2.5 demonstrated non-significant trends toward higher ATP and shocks (Table). AQI and PM2.5 had significant but non-meaningful associations with change in heart rate, and PM2.5 with physical activity and thoracic impedance. The time-varying exposure analysis demonstrated significant but non-meaningful associations (ORs 0.99-1). In contrast to prior literature on ambient air pollution, these natural experiment analyses did not demonstrate acute or sub-acute association between AQI, PM2.5, or PM10 with ventricular arrhythmias, physical activity, or heart failure markers in CIED patients. Mediators of these findings merit further investigation to clarify causal and non-causal mechanisms.

Clinical outcomes and predictors of a gap in direct-acting oral anticoagulant therapy in the elderly: A time-varying analysis of a nationwide cohort study

IntroductionAs direct-acting oral anticoagulants (DOACs) have short half-lives of around 12 h, even a short gap in DOAC therapy may diminish anticoagulation effects, increasing risks of adverse clinical outcomes. We aimed to evaluate clinical consequences of a gap in DOAC therapy with atrial fibrillation (AF) and to identify its potential predictors. Materials and methodsIn this retrospective cohort study, we included DOAC users aged over 65 years with AF from the 2018 Korean nationwide claims database. We defined a gap in DOAC therapy as no claim for a DOAC one or more days after the due date of a refill prescription. We used a time-varying-analysis method. The primary outcome was a composite of death and thrombotic events including ischemic stroke/transient ischemic attack or systemic embolism. Potential predictors of a gap included sociodemographic and clinical factors. Results and conclusionsAmong 11,042 DOAC users, 4857 (44.0 %) patients had at least one gap. Standard national health insurance, non-metropolitan locations of medical institutions, history of liver disease, chronic obstructive pulmonary disease, cancer, or dementia, and use of diuretics or non-oral agents were associated with increased risks of a gap. In contrast, history of hypertension, ischemic heart disease, or dyslipidemia were associated with a decreased risk of a gap. A short gap in DOAC therapy was significantly associated with a higher risk of the primary outcome compared to no gap (hazard ratio 4.04, 95 % confidence interval 2.95–5.52). The predictors could be utilized to identify at-risk patients to provide additional support to prevent a gap.

Analysis of EBIC time-variation using 2D simulation including charge states in V Se–V Cu divacancy complex

Electron beam induced current (EBIC) measurements have been widely used to investigate charge carrier collection in Cu(In,Ga)Se2 solar cells. However, we found that this electron beam irradiation could significantly change the EBIC signal intensity during the measurement. In this study, the charge state variation of the V Se–V Cu divacancy proposed by Lany et al. was introduced into the device simulator to explain the phenomenon. In the simulation, the defects take on three different charged states, i.e. positive, neutral, and negative states, where their transitions are affected by the quasi-Fermi level position in the bandgap. The transient response of the EBIC signal was successfully explained by incorporating these complex state defects.

GMT: A deep learning approach to generalized multivariate translation for scientific data analysis and visualization

In scientific visualization, despite the significant advances of deep learning for data generation, researchers have not thoroughly investigated the issue of data translation. We present a new deep learning approach called generalized multivariate translation (GMT) for multivariate time-varying data analysis and visualization. Like V2V, GMT assumes a preprocessing step that selects suitable variables for translation. However, unlike V2V, which only handles one-to-one variable translation during training and inference, GMT enables one-to-many and many-to-many variable translation in the same framework. We leverage the recent StarGAN design from multi-domain image-to-image translation to achieve this generalization capability. We experiment with different loss functions and injection strategies to explore the best choices and leverage pre-training for performance improvement. We compare GMT with other state-of-the-art methods (i.e., Pix2Pix, V2V, StarGAN). The results demonstrate the overall advantage of GMT in translation quality and generalization ability.

A piezoelectric energy harvester with inner beam adapting to low and high wind speeds: modeling, simulation and experiment

Traditional vortex-induced vibration energy harvesters could transform wind or water energy into electricity at low flowing speeds. However, it has the disadvantage of narrow working speed band, which limits wide application in velocity-changing environments. A piezoelectric harvester with an inner beam for harvesting wind energy at both low and high wind speed regions is presented. A comprehensive nonlinear distributed fluid–solid–electric governing equations for vortex-induced vibration piezoelectric energy harvesting are derived and the theoretical results show that dimensions of outer beam and diameter of attached cylinder can affect optimal wind speed and maximum power output at both low and high wind speeds. In contrast, the dimensions of the inner beam and mass block only have impacts at high wind speeds. The equivalent circuit modeling method is utilized to analyze energy harvesting output characteristics. Analogies between mechanical and electrical domains are built, and the governing equations are converted to circuit equations. Then the circuit equations are settled in electrical software for time-varying analysis. The electrical circuit simulation results show that the optimal load resistance is 400 kΩ at low wind speed and 500 kΩ at low wind speed, which is consistent with theoretical results. The prototypes were fabricated and experiments were carried out in a wind tunnel. Experimental results indicate that energy harvester could generate power at both low and high speeds. Mass block has great impact on optical speed and working wind speed band. The energy harvester with 7.06 g mass block could output 127.36 μW at 2.65 m s−1 and 63.63 μW at 4.4 m s−1. Numerical and circuit simulation results are consistent with experimental results on optical load resistances and optical wind speeds. This design provides a feasible method for broadening wind speed region for energy harvesting.

Dynamics of return and volatility spill-over between developed, emerging and African equity markets during the Covid-19 pandemic and Russia–Ukraine war

We contribute to the growing literature examining spill-over effects between international equity markets in the “new normal” disposition and extend upon previous studies to include more recent periods covering the Russia–Ukraine war. Using the Diebold and Yilmaz network method, we estimate the returns and volatility connectedness between developed, emerging and African markets over the period 11 March 2020 to 30 June 2022. Our findings can be summarised in three points. Firstly, the static connectedness analysis informs us that emerging and African (developed) markets are the main net receivers (transmitters) of systemic shocks over the sample period. Secondly, the time-varying connectedness analysis further informs us that network connectedness is higher during the Russia–Ukraine war compared to the announcement of Covid-19 variants. Thirdly, the time-varying market specific analysis distinguishes which individual equities are most or least vulnerable to systemic shocks during the Covid-19 pandemic and Russia–Ukraine war. These findings are relevant for investors in their search for better hedging opportunities in equity markets. Moreover, market regulators should take heed of our findings as the observed build-up of systemic risk following the Russia–Ukraine conflict is an indicative of contagion effects experienced.

A single-loop time-variant reliability evaluation via a decoupling strategy and probability distribution reconstruction

In this paper, a single-loop approach for time-variant reliability evaluation is proposed based on a decoupling strategy and probability distribution reconstruction. The most attractive feature of the proposed method is that the reliability at a specified time instant can be captured by performing time-invariant reliability analysis only once. In this method, the expansion optimal linear estimation is first employed to discretize the loading stochastic process. Then, a decoupling strategy that decouples the loading stochastic process and degradation processes is developed to formulate a single-loop method for time-variant reliability analysis, where an equivalent extreme value limit state function (EEV-LSF) is obtained. To improve the accuracy and robustness, the Box–Cox transformation is applied to get a transformed EEV-LSF. The maximum entropy method with fractional exponential moments is employed to robustly derive the probability distribution of transformed EEV-LSF. Once the probability distribution is captured, the time-variant failure probability can be readily computed. To handle a large number of random variables, a weighted sampling method is applied for moment assessment to ensure an efficient solution. Numerical examples including a complex real-world case are studied to validate the proposed method, where pertinent Monte Carlo simulations and PHI2 method are conducted for comparisons.

Text and Dynamic Network Analysis for Measuring Technological Convergence: A Case Study on Defense Patent Data

Identifying technology convergence is getting harder, especially in fast-evolving and cross-technological fields. On the other side, natural language processing and network analysis researchers is to provide a novel method for mapping technologies and their relations over time, in order to identify dynamic patterns of convergence and to test it on the C4ISTAR field, a defense-related cross-technological field. The methodology automatically extracts technologies from a corpus of scientific papers, policy documents, websites, and company reports using Named Entity Recognition approaches. These technologies are then retrieved from the second corpus of more than 300 thousand patents related to the C4ISTAR domain, measuring their cooccurrences over time. Finally, using the time-varying network analysis, we were able to identify and measure the pattern of technological convergence.

Time-varying seismic fragility analysis of durability damage of high-tensile reinforcement ECC bridge pier

Time-varying seismic fragility analysis of durability damage of high-tensile reinforcement ECC bridge pier

278 Eight Pharmacokinetic-related Genetic Variants Were Not Associated with Bleeding from Direct Oral Anticoagulants in Non-valvular Atrial Fibrillation Patients

OBJECTIVES/GOALS: Assess the association of PK-related single nucleotide variants (SNVs) with the risk of bleeding from DOACs in non-valvular AF patients. METHODS/STUDY POPULATION: A retrospective cohort study was carried out with 2,364 Caucasians with non-valvular AF and treated with rivaroxaban or apixaban. Patients were genotyped as part of the genomic biobank at the University of Michigan Health System. The primary endpoint was a composite of major and clinically relevant non-major (CRNM) bleeding. Cox proportional hazards regression with time-varying analysis assessed the association of 8 SNVs in 5 PK genes (ABCB1, ABCG2, CYP3A4, CYP3A5, CYP2J2) with the risk of bleeding from DOACs in unadjusted and covariate-adjusted models. Six tests were performed as 3 of the SNVs are in the same haplotype. P-values below the Bonferroni-corrected level of 8.33e-3 were considered statistically significant. RESULTS/ANTICIPATED RESULTS: A total of 412 (17.4%) major and CRNM bleeding events occurred over 2.27 ± 2.03 years of follow-up. None of the PK SNVs were significantly associated with bleeding risk on DOACs in both unadjusted and covariate-adjusted Cox regression models. DISCUSSION/SIGNIFICANCE: The effects of these eight genetic variants on exposure to DOACs may not be strong enough to translate into differences in clinical outcomes. Especially if the genetic inheritance underlying the risk of bleeding from DOACs is polygenic, reinforcing the need for further genomic studies on this subject.

Numerical solution hybridized by machine-leaning-based algorithm to provide an efficient method for analyzing thermomechanical shock behavior of the circumferentially-graded graphene-plates reinforced composite sandwich panel

The present research bridges the gap between a numerical solution with efficient machine-learning-based algorithms to provide a strong platform for predicting thermomechanical shock behavior of the sandwich cylindrical panels with polymer-made core and circumferentially-graded graphene-plates reinforced (CG-GPLR) nanocomposite face-sheets. The comprehensive declaration of the elasticity theory is considered to establish the governing equations of the system in three orthogonal directions. The well-known Lord-Shulman heat transfer theorem is implemented to present the time-variant nature of the system’s heat. The differential quadrature method (DQM) is used as a numerical approach to solve the spatial dependency of the governing differential equations. While the Laplace transform solves the time dependency of the governing differential equations. In order to decline the computational cost of predicting the time-variant analysis of the thermomechanical shock response of the system, an efficient machine-learning-based algorithm is employed in a way that it only requires the thermomechanical shock information of the selective set of points known as the training set. After completion of the training procedure, this machine-learning predictor can easily predict the thermoelastic response of all points included either as the train set or a new point. Validation of the applied solution is performed through a comparative process between the present results with those determined in the published literature. The results of this study are in excellent compatibility with those obtained in a solo numerical solution.

Time-varying evolutionary mechanism analysis of the multiphase flow during high-velocity oxygen-fuel (HVOF) thermal spraying WC-12Co particle

HVOF thermal spraying with the fuel of aviation kerosene has unique advantages for spraying WC-12Co ceramic particles. The time-varying evolution mechanism of the multiphase flow formed by the particles wrapping with the combustion flame flow is quantitatively revealed, which is essential for preparing of high-quality coatings. In this study, the breakup and gasification process of kerosene droplet was considered. The fuel droplet, flame flow and spraying particles were directly coupled. Based on the computational fluid dynamics (CFD) method, a transient evolution model of HVOF thermal spraying multiphase flow with JP5000 spray gun was established. The Euler-Lagrange method of bidirectional coupling was used to track the continuous and discrete phases. The spraying combustion flame characteristics were expressed by the realizable k-ε turbulence model and eddy dissipation model (EDM). The results show that the multiphase coupling transient evolution model quantitatively captures the nonlinear changes of the breaking and gasification for the kerosene droplet, combustion reaction, particle flight behavior and others during spraying. The kerosene droplets in the combustion chamber aggregate with each other to form large droplets, which are rapidly broken and gasified in the peripheral areas. Due to the violent combustion, a gas-phase circulation was formed by the heat and cold air. As the combustion progresses, the flame flow becomes stable and orderly. When particles with a size of 20 to 30 μm impact the substrate, the particle temperature is close to the solid phase temperature, and the velocity is fast. The particle temperature and velocity decrease with the increase of the particle size.

Nurse Staffing Level, Length of Work Experience, and Risk of Health Care-Associated Infections Among Hospital Patients: A Prospective Record Linkage Study.

Nurse understaffing may have several adverse consequences for patients in hospitals, such as health care-associated infections (HAIs), but there is little longitudinal evidence available on staffing levels and HAIs with consideration of incubation times to confirm this. Using daily longitudinal data, we analyzed temporal associations between nurse understaffing and limited work experience, and the risk of HAIs. The study was based on administrative data of 40 units and 261,067 inpatient periods for a hospital district in Finland in 2013-2019. Survival analyses with moving time windows were used to examine the association of nurse understaffing and limited work experience with the risk of an HAI 2 days after exposure, adjusting for individual risk factors. We reported hazard ratios (HRs) with 95% CIs. Neither nurse understaffing nor limited work experience were associated with the overall risk of HAIs. The results were inconsistent across staffing measures and types of HAIs, and many of the associations were weak. Regarding specific HAI types, 1-day exposure to low proportion of nurses with >3 years of in-hospital experience and low proportion of nurses more than 25 years old were associated with increased risk of bloodstream infections (HR=1.30; 95% CI: 1.04-1.62 and HR=1.40; 95% CI: 1.07-1.83). Two-day exposure to low nursing hours relative to target hours was associated with an increased risk of surgical-site infections (HR=2.64, 95% CI: 1.66-4.20). Data from time-varying analyses suggest that nursing staff shortages and limited work experience do not always increase the risk of HAI among patients.