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Abstract Background The purpose of this study was to explore the job burnout of primary healthcare workers in Guangzhou during the prevention and control of COVID-19 epidemic and its influencing factors from the perspective of institutional operation and management in 2021–2022. Methods A cross-sectional study involved 866 primary healthcare workers from different districts of Guangzhou, China. The Chinese version of the Maslach Burnout Inventory-General Survey (MBI-GS) was utilized to assess job burnout. From the perspective of organizational operation and management, the possible causes of job burnout among primary healthcare workers during COVID-19 have been categorized into 7 major aspects. Univariate and multivariate logistic regression analyses were conducted to identify influencing factors for job burnout in primary healthcare workers. Results The detection rate of job burnout among primary healthcare workers was 78.29%. Men (OR = 2.39) and whose institution was located in urban–rural fringe (OR = 1.56) were more likely to detect job burnout. Conversely, institution heads showed a lower risk of job burnout. From the perspective of institutional operation and management, workers who were not satisfied with personnel management (OR = 2.41), materials and vehicles (OR = 2.89), subsidies and compensation (OR = 2.18), humanistic care (OR = 2.11), superior management (OR = 8.32) were found to have a higher risk of job burnout. Conclusion The detection rate of job burnout among primary healthcare workers in Guangzhou was relatively high during the period of COVID-19. When there is another sudden major epidemic, the managers of institutions can focus on and deal with the problems related to the operation and management of institutions such as personnel management, materials and vehicles, subsidies and compensation, humanistic care, and superior management, so as to provide logistical support for the workers and alleviate their job burnout.

BackgroundPrimary healthcare workers in China were vital in controlling COVID-19, facing unprecedented challenges due to the pandemic’s suddenness and transmissibility. This paper analyzed their work in Guangzhou, highlighting key difficulties in past epidemic control efforts.MethodsSemi-structured qualitative interviews were conducted with selected primary healthcare workers in Guangzhou City and audio-recorded until saturation of themes was reached. The data were analyzed independently for themes using NVivo V.12.ResultsThematic analysis of qualitative interview data revealed that challenges and difficulties encountered in grassroots epidemic prevention and control work during the COVID-19 pandemic were primarily categorized into seven dimensions: human resource allocation, material and vehicle support, epidemic prevention subsidies and compensation, humanistic care, upper-level management, horizontal collaboration, and resident cooperation.ConclusionBased on the findings, the research team recommends that administrative departments and institutions related to primary healthcare should appropriately schedule personnel work and rest time, adjust work content flexibly, optimize the allocation of medical and logistical resources, and increase investment in primary healthcare work as well as staff protection.

This paper studies the autoregressive integrated moving average (ARIMA) state space model combined with Kalman smoothing to impute missing values in a univariate time series before detecting change points. We estimate a scale-dependent time-average variance constant that depends on the length of the data section and is robust to mean shifts under serial dependence. The consistency of the proposed estimator is shown under the assumption allowing heavy tailedness. Integrating the proposed estimator with the moving sum and wild binary segmentation procedures to determine the number and locations of change points is discussed. Furthermore, the performance of the proposed methods is evaluated through extensive simulation studies and applied to the Beijing multi-site air quality dataset to impute missing values and detect mean changes in the data.

In periods of dramatic stock price volatility, the identification of change points in stock price time series is important for analyzing the structural changes in financial market data, as well as for risk prevention and control in the financial market. As their residuals follow a generalized error distribution, the problem of estimating the change point parameters of the β-ARCH model is solved by combining the Kalman filtering method and the Bayes method innovatively, and we give a method for parameter estimation of the Bayes factors for the occurrences of change points, the expected values of the change point positions, and the variance of the change points. By detecting the change points of the price of eight stocks with a high number of limit up and limit down changes occurring in the observation period, the following conclusions are obtained: (1) Change point detection using the β-ARCH model based on the Bayes method is effective. (2) For different values of β, this research study finds that based on the classical ARCH model (i.e., β=1) of the change point parameter, the results are relatively optimal. (3) The accuracy of change point detection can be improved by correcting stock short-term effects by using the Kalman filtering method.

BackgroundThe first 1000 days of life, encompassing pregnancy and the first 2 years after birth, represent a critical period for human health development. Despite this significance, there has been limited research into the associations between mixed exposure to air pollutants during this period and the development of asthma/wheezing in children. Furthermore, the finer sensitivity window of exposure during this crucial developmental phase remains unclear. ObjectiveThis study aims to assess the relationships between prenatal and postnatal exposures to various ambient air pollutants (particulate matter 2.5 [PM2.5], carbon monoxide [CO], sulfur dioxide [SO2], nitrogen dioxide [NO2], and ozone [O3]) and the incidence of childhood asthma/wheezing. In addition, we aimed to pinpoint the potential sensitivity window during which air pollution exerts its effects. MethodsWe conducted a prospective birth cohort study wherein pregnant women were recruited during early pregnancy and followed up along with their children. Information regarding maternal and child characteristics was collected through questionnaires during each round of investigation. Diagnosis of asthma/wheezing was obtained from children’s medical records. In addition, maternal and child exposures to air pollutants (PM2.5 CO, SO2, NO2, and O3) were evaluated using a spatiotemporal land use regression model. To estimate the mutual associations of exposure to mixed air pollutants with the risk of asthma/wheezing in children, we used the quantile g-computation model. ResultsIn our study cohort of 3725 children, 392 (10.52%) were diagnosed with asthma/wheezing. After the follow-up period, the mean age of the children was 3.2 (SD 0.8) years, and a total of 14,982 person-years were successfully followed up for all study participants. We found that each quartile increase in exposure to mixed air pollutants (PM2.5, CO, SO2, NO2, and O3) during the second trimester of pregnancy was associated with an adjusted hazard ratio (HR) of 1.24 (95% CI 1.04-1.47). Notably, CO made the largest positive contribution (64.28%) to the mutual effect. After categorizing the exposure according to the embryonic respiratory development stages, we observed that each additional quartile of mixed exposure to air pollutants during the pseudoglandular and canalicular stages was associated with HRs of 1.24 (95% CI 1.03-1.51) and 1.23 (95% CI 1.01-1.51), respectively. Moreover, for the first year and first 2 years after birth, each quartile increment of exposure to mixed air pollutants was associated with HRs of 1.65 (95% CI 1.30-2.10) and 2.53 (95% CI 2.16-2.97), respectively. Notably, SO2 made the largest positive contribution in both phases, accounting for 50.30% and 74.70% of the association, respectively. ConclusionsExposure to elevated levels of mixed air pollutants during the first 1000 days of life appears to elevate the risk of childhood asthma/wheezing. Specifically, the second trimester, especially during the pseudoglandular and canalicular stages, and the initial 2 years after birth emerge as crucial susceptibility windows. Trial RegistrationChinese Clinical Trial Registry ChiCTR-ROC-17013496; https://tinyurl.com/2ctufw8n

BackgroundMost Chlamydia trachomatis (CT) infections are asymptomatic. The infection can persist and lead to severe sequelae. Therefore, screening for CT can primarily prevent serious sequelae.AimTo systematically evaluate CT screening from the perspective of health economics, summarize previous findings from different target populations, and make practical recommendations for developing local CT screening strategies.MethodsPubMed, Web of Science, Embase, Cochran Library, and National Health Service Economic Evaluation Database (Ovid) were searched from January 1, 2000, to March 4, 2023. Studies reporting the cost-effectiveness, cost-benefit, or cost-utility of CT screening were eligible to be included. A narrative synthesis was used to analyze and report the results following the PRISMA guidelines. The Consensus on Health Economic Criteria (CHEC) list was used to assess the methodological quality of included studies.ResultsOur review finally comprised 39 studies addressing four populations: general sexually active people (n = 25), pregnant women (n = 4), women attending STD and abortion clinics (n = 4), and other high-risk individuals (n = 6). The total number of participants was ~7,991,198. The majority of studies assessed the cost-effectiveness or cost-utility of the screening method. The results showed that the following screening strategies may be cost-effective or cost-saving under certain conditions: performing CT screening in young people aged 15–24 in the general population, military recruits, and high school students; incorporating CT screening into routine antenatal care for pregnant women aged 15–30; opportunistic CT screening for women attending STD and abortion clinics; home-obtained sampling for CT screening using urine specimens or vaginal swab; performing CT screening for 14–30-year-old people who enter correctional institutions (i.e., jail, detention) as soon as possible; providing CT screening for female sex workers (FSWs) based on local incidence and prevalence; adding routine CT screening to HIV treatment using rectal samples from men who have sex with men (MSM).ConclusionWe found that CT screening in general sexually active people aged 15–24, military recruits, high school students, pregnant women aged 15–30, women attending STD and abortion clinics, people entering jail, detention, FSWs, and MSM has health economic value. Due to the different prevalence of CT, diversities of economic conditions, and varying screening costs among different populations and different countries, regions, or settings, no uniform and standard screening strategies are currently available. Therefore, each country should consider its local condition and the results of health economic evaluations of CT screening programs in that country to develop appropriate CT screening strategies.

Ocean fronts are a response to the variabilities of marine hydrographic elements and are an important mesoscale ocean phenomenon, playing a significant role in fish farming and fishing, sea-air exchange, marine environmental protection, etc. The horizontal gradients of sea surface temperature (SST) are frequently applied to reveal ocean fronts. Up to now, existing spatiotemporal prediction approaches have suffered from low prediction precision and poor prediction quality for non-stationary data, particularly for long-term prediction. It is a challenging task for medium- and long-term fine-grained prediction for SST sequences and fronts in oceanographic research. In this study, SST sequences and fronts are predicted for future variation trends based on continuous mean daily remote sensing satellite of SST data. To enhance the precision of the predicted SST sequences and fronts, this paper proposes a novel memory-contextual spatiotemporal transfer network (MCSTNet) for SST sequence and front predictions. MCSTNet involves three components: the encoder-decoder structure, a time transfer module, and a memory-contextual module. The encoder-decoder structure is used to extract the rich contextual and semantic information in SST sequences and frontal structures from the SST data. The time transfer module is applied to transfer temporal information and fuse low-level, fine-grained temporal information with high-level semantic information to improve medium- and long-term prediction precision. And the memory-contextual module is employed to fuse low-level, spatiotemporal information with high-level semantic information to enhance short-term prediction precision. In the training process, mean squared error (MSE) loss and contextual loss are combined to jointly guide the training of MCSTNet. Extensive experiments demonstrate that MCSTNet predicts more authentic and reasonable SST sequences and fronts than the state-of-the-art (SOTA) models on the SST data.

In this paper, we present a novel ocean visualization framework, which focuses on analyzing multidimensional and spatiotemporal ocean data. GPU-based visualization methods are explored to effectively visualize ocean data. An improved ray casting algorithm for heterogeneous multisection ocean volume data is presented. A two-layer spherical shell is taken as the ocean data proxy geometry, which enables oceanographers to obtain a real geographic background based on global terrain. An efficient ray sampling technique including an adaptive sampling technique and a preintegrated transfer function is proposed to achieve high-effectiveness and high-efficiency rendering. Moreover, an interactive transfer function is also designed to analyze the 3D structure of ocean temperature and salinity anomaly phenomena. Based on the framework, an integrated visualization system called i4Ocean is created. The visualization of ocean temperature and salinity anomalies extracted interactively by the transfer function is demonstrated.

The symmetrical circular shape of mesoscale eddies has been widely used in their scientific researches. Recently, an elliptical average eddy shape has been confirmed for eddies in the global ocean using multi-satellite altimeter data. As a regional extension of a previous study on the geometry of global eddies, a mean eddy shape in the South China Sea (SCS) has been derived by averaging a large number of orientational eddy boundaries. The mean shape is approximately a mathematic ellipse with a semimajor axis of 101.3 km and a semiminor axis of 61.3 km. Its size is larger than the global one. The principal eddy orientation in the SCS is 74°/254° (nearly northeast-southwest), different from that of eddies in the global ocean (171°/351°, nearly east–west). Composite analyses of chlorophyll (CHL) concentrations and sea surface temperature anomalies (SSTA) indicate a dipole structure for circular eddies in the non-rotated coordinate system. While a monopole structure for elliptical eddies in the eddy-centric coordinate system is obtained. The results demonstrate that the elliptical shape of eddies affects oceanographical variables. The findings provide a new approach for exploring the role of air–sea interactions on oceanic eddies.

In this paper, we present a transfer function-based approach to developing an interactive visualization algorithm for ocean mesoscale eddies. Spatiotemporal coherence and viewport coherence are achieved. Evenly spaced streamlines are only integrated for visible portions of the datasets that maintain a relatively stable visual pattern resolution. The interactive transfer function is introduced to extract 2D and 3D eddy features, such as the Okubo-Weiss parameter, from background ocean currents, and a highly efficient GPU-based framework with an output-sensitive performance is utilized. Using the high-resolution 2D/3D ocean current datasets Maps of Sea Level Anomalies (MSLA) and Hybrid Coordinate Ocean Model (HYCOM), the feasibility and efficiency of our framework are demonstrated.

This paper proposes an algorithm named EddyGraph for tracking mesoscale eddy splitting and merging events. Twenty-seven years (January 1993–December 2019) of sea level anomaly (SLA) data are analyzed in the Northwest Pacific Ocean (105°E–165°W, 0°N–60°N). First, we propose a multilevel eddy identification method based on SLA to obtain an eddytree data set, representing a spatial topological tree structure of closed SLA contours with mononuclear eddies, multicore eddies and eddy seeds as the leaf nodes and eddygroups (reflecting the spatial topological relationship among eddies) as the intermediate nodes. The EddyGraph tracking algorithm is applied to the eddytree data set, which results in eddy-directed acyclic graphs (Eddy-DAGs). Only eddies contained within a common eddygroup are tracked as sources in merging events or sinks in splitting events. Furthermore, we extract typical splitting and merging events and composite the sea surface temperature anomalies (SSTAs) inside the eddygroups and eddies during these events. The results confirm that merging eddies in the same eddygroup degenerate into a single eddy and that a splitting eddy evolves into eddies within the same parent eddygroup. Moreover, we match a merging event of cyclonic eddies with in situ data of both drifters and loopers in Lagrangian trajectories. Finally, we present EddyGraph, a data set of mesoscale eddy tracking in the Northwest Pacific Ocean (105°E–165°W, 0°N–60°N).

In this paper, we propose a new hybrid mesoscale eddy tracking method to enhance the eddy tracking accuracy from global satellite altimeter data. This method integrates both physical and geometric eddy properties (including the distance between eddies, the area and amplitude of eddy, and the shape of the eddy edge) via the output of detection and the calculation of Hausdorff distance, which could describe the similarity between eddy boundaries. We applied the proposed hybrid method to several previously reported eddies and compared the results with those from two traditional tracking methods. A quantitative comparison indicates that the hybrid algorithm can better reveal eddy signals in terms of their spatial scale, amplitude, lifespan, and splitting. The hybrid method was used for global mesoscale eddies tracking from 1993 to 2015. Global distributions of net eddy numbers revealed that the sources of eddies are located along the eastern boundaries of the world oceans, while the sinks of eddies are located along the western boundaries. The lifespan distribution of eddies exhibited steep growth from high and low latitudes to middle latitudes. A clear divergent pathway demonstrates that cyclonic/anticyclonic eddies tend to travel poleward/equatorward in the world oceans.

In this paper we present a research of propagation characteristics of global Rossby wave and mesoscale eddies, and we preliminarily discussing the relationship between them from multiple datasets analysis. By filtering the MSLA-H data and by means of optimized SSH method we have extracted signals of the Rossby wave, and estimated the propagation speed (zonal phase speed) of the Rossby wave and eddies. Validation for the identification of the Rossby wave also has been completed with the Argo temperature and salinity data. The prime focus covers: propagation speed comparison between the Rossby wave and the eddies, propagation characteristics in different regions. Overlaying the signals of the Rossby wave with the signatures of the eddies indicates that the Rossby wave and the eddies propagates together (westward only) in the mid-latitude, but differences appear with increasing of latitude, especially in some areas affected by ocean current, for instance, the West Wind Drift (WWD) and the North Atlantic Drift (NAD). Actually we have found that the currents led the eddies, and the Rossby wave might play an accelerative or moderative role in the eddies propagation, as a result of the velocities of the eddies and the currents were matched well, but comparison between the Rossby wave and the eddies revealed disparity. The findings are useful for understanding the relationship between the Rossby wave and mesoscale eddies. [ABSTRACT FROM AUTHOR]

Considering interaction forces (gravity and buoyancy force, drag force, interaction potential force, and Brownian force) between nanoparticles and a base fluid, a two-phase Lattice Boltzmann model for natural convection of nanofluid is developed in this work. It is applied to investigate the natural convection in a square enclosure (the left wall is kept at a high constant temperature (TH), and the top wall is kept at a low constant temperature (TC)) filled with Al2O3/H2O nanofluid. This model is validated by comparing numerical results with published results, and a satisfactory agreement is shown between them. The effects of different nanoparticle fractions and Rayleigh numbers on natural convection heat transfer of nanofluid are investigated. It is found that the average Nusselt number of the enclosure increases with increasing nanoparticle volume fraction and increases more rapidly at a high Rayleigh number. Also, the effects of forces on nanoparticle volume fraction distribution in the square enclosure are studied in this paper. It is found that the driving force of the temperature difference has the biggest effect on nanoparticle volume fraction distribution. In addition, the effects of interaction forces on flow and heat transfer are investigated. It is found that Brownian force, interaction potential force, and gravity-buoyancy force have positive effects on the enhancement of natural convective heat transfer, while drag force has a negative effect. [ABSTRACT FROM AUTHOR]