Center Of Data Research Articles

Global and national insights into the decline of hypertrophic cardiomyopathy as a cause of sudden death in the young

Abstract Background & Objective Hypertrophic cardiomyopathy (HCM) has been reported as a leading cause of sudden cardiac death (SCD) in the young but appears less common in contemporary cohorts of young SCD. This research aimed to determine if HCM is a common cause of SCD in the young and whether this has changed over time. Methods We analysed three national databases, the National Echo Database Australia (NEDA), Centre for Disease Control Wide-ranging Online Data for Epidemiologic Research (CDC-WONDER) and the National Coronial Information System of Australia (NCIS) to determine incidence of SCD due to HCM in ≤ 35 years old from 1999-2017. Sub-analyses assessed for differences in sex and obstructive and non-obstructive HCM phenotypes. Results NEDA: Of 40,738 individual echocardiograms 2005-2017, 868 (2.13%) exhibited the HCM phenotype. 982 patients died ≤ 35 years, of which 4 deaths were attributable to HCM (0.4%). CDC-WONDER: 41% reduction in HCM related mortality/million people across 1999-2016 (p<.0001). Similar reduction was seen when stratified for age, sex and non-obstructive/obstructive HCM phenotype. NCIS: 36% reduction in HCM-related SCD was observed between 2001-2010 and 2011-2016 periods (p=.049). Conclusions HCM rarely causes SCD and accounts for <5% of SCD in the young. Rates of HCM-related SCD have been decreasing over the last two decades. Improved survival likely relates to improved case detection and medical management.Abstract Figure

Sex, Racial/Ethnic, and Regional Disparities in Pulmonary Embolism Mortality Trends in the USA, 1999-2020.

While the National Institutes of Health emphasize integrating sex as a biological variable into research, specific considerations of sex-related differences in pulmonary embolism (PE) mortality trends remain scarce. This study examines sex-based PE mortality trends across regional and demographic groups in the USA from 1999 to 2020. A retrospective analysis of National Center for Health Statistics mortality data from 1999 to 2020 was conducted. Using ICD-10 code I26, PE decedents were identified. Piecewise linear regression assessed sex-based temporal trends in PE mortality by age, race/ethnicity, and census region. Annual percentage changes and average annual percentage changes were derived using Weighted Bayesian Information Criteria. The 95% confidence intervals were estimated using the empirical quantile method. From 1999 to 2020, a total of 179,273 individuals died in the USA due to PE, resulting in an age-adjusted mortality rate of 2.5 per 100,000 persons (95% CI, 2.5-2.5). While men and women exhibited comparable rates in recent time segments and across most subcategories, a higher mortality trend among males compared to females was observed among non-Hispanic White and Hispanic individuals and residents of the Western US census region. These results remained robust even after excluding data from 2020, accounting for the potential impact of the COVID-19 pandemic. Our study highlights sex-based disparities in PE mortality trends in the USA from 1999 to 2020. Despite overall stable mortality rates, higher trends among males were evident in specific demographic groups and regions. These findings emphasize the importance of targeted interventions to mitigate PE-related mortality discrepancies across diverse populations.

Ice Sheet Mass Changes over Antarctica Based on GRACE Data

Assessing changes of the mass balance in the Antarctic ice sheet in the context of global warming is a key focus in polar study. This study analyzed the spatiotemporal variation in the Antarctic ice sheet’s mass balance, both as a whole and by individual basins, from 2003 to 2016 and from 2018 to 2022 using GRACE RL06 data published by the Center for Space Research (CSR) and ERA-5 meteorological data. It explored the lagged relationships between mass balance and precipitation, net surface solar radiation, and temperature, and applied the random forest method to examine the relative contributions of these factors to the ice sheet’s mass balance within a nonlinear framework. The results showed that the mass loss rates of the Antarctic ice sheet during the study periods were −123.3 ± 6.2 Gt/a and −24.8 ± 52.1 Gt/a. The region with the greatest mass loss was the Amundsen Sea in West Antarctica (−488.8 ± 5.3 Gt/a and −447.9 ± 14.7 Gt/a), while Queen Maud Land experienced the most significant mass accumulation (44.9 ± 1.0 Gt/a and 30.0 ± 3.2 Gt/a). The main factors contributing to surface ablation of the Antarctic ice sheet are rising temperatures and increased surface net solar radiation, each showing a lag effect of 1 month and 2 months, respectively. Precipitation also affects the loss of the ice sheet to some extent. Over time, the contribution of precipitation to the changes in the ice sheet’s mass balance increases.

Responding to COVID-19: an exploration of EU country responses and directions for further research.

During COVID-19, scientists advising policymakers were forced to deal with high uncertainty and risks in an environment of unknowns. Evidence on which policies and measures were effective in responding to the pandemic remains underdeveloped to answer the key question 'what worked and why?'. This study aims to provide a basis for studies to go further to answer this critical question, by starting to look efficacy or how countries ensured that health services remained available and what measures were enacted to protect and treat their populations and workers. We applied a three-phase sequential mixed methods design. In phase one, we started with a qualitative content analysis of the EU Country Profile reports to retrieve and analyse data on COVID-19 responses taken by 29 countries in the European region. Phase two is the step of data transformation, converting qualitative data into numerical codes that can be statistically analysed, which are then used in a quantitative cross-national comparative analysis that comprises phase three. The quantifying process resulted in a numerical indicator to measure the 'response efficacy' of the 29 countries, which is used in phase three's association of the response measure with country performance indicators that were derived from European Centre for Disease Control (ECDC) COVID-19 case and death rate data. Through comparing the frequency of COVID-19 measures taken, we found that many countries in the European region undertook similar actions but with differing effects. The cross-national analysis revealed an expected relationship: a lower COVID-19 response efficacy appeared to be related to a higher case and death rates. Still, marked variation for countries with similar response efficacy indicators was found, signalling that the combination and sequence of implementation of COVID-19 responses is possibly just as important as their efficacy in terms of which response measures were implemented. Many European countries employed similar COVID-19 measures but still had a wide variation in their case and death rates. To unravel the question 'what worked and why?', we suggest directions from which more refined research can be designed that will eventually contribute to mitigate the impact of future pandemics and to be better prepared for their economic and human burden.

Building a Greener Future : The Role of Data Centers in Eco-Friendly Computing

This comprehensive article explores the critical issue of environmental sustainability in the rapidly evolving landscape of information technology, focusing on the substantial environmental impact of data centers. It delves into innovative strategies and technologies to create more sustainable and eco-friendly computing environments. The article covers key areas such as energy efficiency, renewable energy integration, green cloud computing, edge computing, circular economy practices, and AI-powered optimization. By examining these aspects, the article demonstrates how the IT industry is working to minimize its environmental footprint while meeting the growing demands of our digital world. It highlights not only the environmental benefits of these initiatives but also their economic advantages, showcasing how sustainable practices in computing can lead to significant cost savings and competitive advantages in the market.

Effects of shuttle balance exercises on gait speed, postural control, and quality of life in older males: A randomized controlled trial.

Enhancing physical function and quality of life in older adults at higher risk of falls is challenging because of the lack of established interventions. This study examines the impact of a 6-week balance training program using the shuttle balance device on gait speed, postural control, and quality of life in older men. This single-blinded randomized controlled trial was conducted in a research laboratory. Thirty-two participants aged ≥60years were randomly assigned to either an experimental group (EG; n=16) or a control group (CG; n=14). The EG participated in a 6-week shuttle balance exercise program, while the CG maintained regular physical activity routines. The main outcome measures included gait speed (assessed via the timed up and go test), postural control (assessed via center of pressure data on a force plate), and quality of life (evaluated using the SF-36 questionnaire). Post-intervention, the EG showed significant improvements compared with the CG (p<0.05). The 95% confidence intervals for the differences between groups were as follows: closed eyes (CE) mean velocity (-39.07, -0.13), CE sway area (-48.86, -0.18), SF-36 total score (9.01, 16.81), SF-36 physical functioning (7.00, 24.81), SF-36 physical role functioning (1.80, 27.57), SF-36 pain (15.01, 36.82), SF-36 general health state (7.48, 26.08), SF-36 vitality (5.60, 28.35), and SF-36 mental health (0.21, 21.12). A 6-week shuttle balance training program significantly improves postural control and quality of life in older males. These findings suggest the potential effectiveness of shuttle balance exercises in enhancing physical function and well-being in this population. Further research is needed to validate these findings and explore the long-term effects with larger sample sizes.

A novel deep learning-based method for generating floor response spectra of building structures

Accurately and efficiently generating floor response spectra is crucial for the seismic design and analysis of nonstructural components, and seismic analysis of equipment placed on floors. In this article, a direct spectra-to-spectra method for generating floor response spectra is designed based on deep learning algorithms. The dataset was developed using the records of 102 building observation arrays in the Center for Engineering Strong Motion Data, which included 48 concrete structures, 44 steel structures and 10 masonry structures. The procedure involves training a deep learning model (FRSNet) with the ground response spectra and easily obtained structural parameters as inputs and the floor response spectra as outputs. Analysis results show that the prediction performance of the model is good on concrete structures, steel structures and masonry structures. The proposed model has sufficient accuracy, and the prediction performance is better than that of other models compared in this article. Finally, the proposed model is validated through two numerical models, a shaking table model structure and an actual structure. Convincing results confirm the accurate prediction capabilities of the proposed method.

The role of diurnal variation in development of musculoskeletal pain during prolonged standing

BackgroundResearch suggests mechanical changes occur to the body over the course of the day including shrinking of the intervertebral disc height which can increase rotational stiffness and rigidity of the spinal column. Further, pain sensitivity has been observed to fluctuate throughout the day, suggesting variations in pain sensitivity. Previous work has identified biomechanical and neuromuscular features related to the development of musculoskeletal pain during prolonged standing; however, many protocols have not controlled for time of day. Research questionDoes the time of day (i.e., diurnal variation) have an effect on the common neuromuscular and biomechanical variables characterizing the development of standing musculoskeletal pain in a two-hour prolonged standing protocol? MethodsA convenience sample of 20 healthy young adults (10 female and 10 male) completed two 2-hour prolonged standing bouts (8:00 a.m. and 8:00 p.m.) on separate days. Visual analogue scales were used to measure pain perception of the low back, legs, and feet throughout each protocol. Neuromuscular control was measured using surface EMG on muscles of the trunk and lower extremities. Postural sway was captured using measures of joint range of motion, and the analysis of center of pressure trajectory data using a force plate. ResultsDiurnal variation was observed to influence the development of musculoskeletal pain. Specifically, pain perception was found to be increased in the evenings as more participants were classified as pain developers in the PM sessions, and mean leg and foot pain perceptions were higher in the evening. Reduced postural sway patterns, and elevated co-contraction indices were found to be associated with the development of pain. SignificanceThese results suggest that future research assessing standing-pain protocols should control for time of day. Additionally, further research is warranted to explore the mechanistic causes of the development of standing pain.

Analysis of depth spatial variations of accelerograms recorded from the Center for Engineering Strong Motion Data: Numerical Simulation for Multipoint Ground Motions

Analysis of depth spatial variations of accelerograms recorded from the Center for Engineering Strong Motion Data: Numerical Simulation for Multipoint Ground Motions

Stroke Measures Analysis of pRognostic Testing-Mortality nomogram predicts long-term mortality after ischemic stroke.

Predicting long-term mortality is essential for understanding prognosis and guiding treatment decisions in patients with ischemic stroke. Therefore, this study aimed to develop and validate the method for predicting 1- and 5-year mortality after ischemic stroke. We used data from the linked dataset comprising the administrative claims database of the Health Insurance Review and Assessment Service and the Clinical Research Center for Stroke registry data for patients with acute stroke within 7 days of onset. The outcome was all-cause mortality following ischemic stroke. Clinical variables linked to long-term mortality following ischemic stroke were determined. A nomogram was constructed based on the Cox's regression analysis. The performance of the risk prediction model was evaluated using the Harrell's C-index. This study included 42,207 ischemic stroke patients, with a mean age of 66.6 years and 59.2% being male. The patients were randomly divided into training (n = 29,916) and validation (n = 12,291) groups. Variables correlated with long-term mortality in patients with ischemic stroke, including age, sex, body mass index, stroke severity, stroke mechanisms, onset-to-door time, pre-stroke dependency, history of stroke, diabetes mellitus, hypertension, coronary artery disease, chronic kidney disease, cancer, smoking, fasting glucose level, previous statin therapy, thrombolytic therapy, such as intravenous thrombolysis and endovascular recanalization therapy, medications, and discharge modified Rankin Scale were identified as predictors. We developed a predictive system named Stroke Measures Analysis of pRognostic Testing-Mortality (SMART-M) by constructing a nomogram using the identified features. The C-statistics of the nomogram in the developing and validation groups were 0.806 (95% confidence interval (CI), 0.802-0.812) and 0.803 (95% CI, 0.795-0.811), respectively. The SMART-M method demonstrated good performance in predicting long-term mortality in ischemic stroke patients. This method may help physicians and family members understand the long-term outcomes and guide the appropriate decision-making process.

Emerging trends and demographic disparities in anal cancer mortality across the United States census regions: An analysis of National Center for Health Statistics mortality data.

Anal cancer, despite its rarity, is a matter of serious concern in the United States, with an uptrend in recent years and marked racial disparities in mortality rates. The aim of this work was to investigate anal cancer mortality trends and sex race disparities in the United States from 1999 to 2020. This is a retrospective study using data from the CDC WONDER database (1999-2020). We investigated deaths attributed to anal cancer, identified by the ICD-10 code C21.1, and excluded individuals aged 14 years and under. The Mann-Kendall trend test was used to investigate temporal trends and a t-test was used to compare continuous variables. Both male and female age-adjusted mortality attributed to anal cancer increased significantly during the study period across all subgroups, including race (Black and White), US Census region (Northeast, Midwest, South and West) and age (15-64 and ≥65 years) (p < 0.001 for all comparisons). For each subgroup, women demonstrated significantly higher rates of mortality than men, except in the Black population, where Black men had higher rates than Black women (0.40 vs. 0.29, p < 0.001). Additionally, Black men had significantly higher mean mortality rates than White men (0.40 vs. 0.27, p < 0.001). The highest rates of anal cancer mortality were among geriatric individuals, especially women aged ≥65 years, at 1.18 per 100 000. The rise in anal cancer mortality and racial and sex disparities present a significant challenge for healthcare providers and policy makers. Further studies are required to devise evidence-based strategies to effectively tackle this challenge.

Portable technology for postural control measurement: Comparing head position with center of pressure data.

Standing is a basic human function that healthy adults take for granted, yet it is a complex perceptual-motor process that requires sensation of position and motion from the sensory systems. We assessed agreement between center of pressure data from a laboratory force-platform and head position data from an HTC Vive head-mounted display (HMD) for the evaluation of standing postural control. We investigated the impact of different statistical choices when assessing the relationship between two measurements. Specifically: 1) How does correlation and agreement statistics relate before and after logarithmic transformation? 2) Is there systemic or proportional bias between the force-platform and HMD measurements? We tested 37 adults (26 controls, 11 with unilateral vestibular hypofunction) standing on foam, observing a static or dynamic visual scene projected from the HMD. We quantified anterior-posterior and medio-lateral sway via Directional Path, Root Mean Square Velocity, Variance, and Power Spectral Density (PSD) from a force-platform and the HMD. Intra-class correlations (ICCs) were moderate-to-good for the non-transformed data and good-to-excellent after logarithmic transformation for all outcomes except for PSD above 1 Hz. Correlations were higher than ICCs. Bland-Altman plots indicated proportional bias but not after logarithmic transformation. Both devices correlated linearly, and measure people's postural responses but cannot be used interchangeably, mostly because they appear to diverge with larger sway as evident on Bland-Altman plots of non-transformed data. Agreement between devices was excellent for low frequency movement but poor for high frequency small corrective movements.

Distributed virtual selective-forwarding units and SDN-assisted edge computing for optimization of multi-party WebRTC videoconferencing

Network service providers (NSP) have growing interest in placing network intelligence and services at network edges by deploying software-defined network (SDN) and network function virtualization infrastructure. In multi-party WebRTC videoconferencing using scalable video coding, a selective forwarding unit (SFU) provides connectivity between peers with heterogeneous bandwidth and terminals. An important question is where in the network to place the SFU service in order to minimize end-to-end delay between all pairs of peers. Clearly, there is no single optimal place for a cloud SFU for all possible peer locations. We propose placing virtual SFUs at network edges leveraging NSP edge datacenters to optimize end-to-end delay and usage of overall network resources. The main advantage of the distributed edge-SFU framework is that each peer video stream travels the shortest path to reach other peers similar to mesh connection model, whereas each peer uploads a single stream to its edge-SFU avoiding the upload bottleneck. While the proposed distributed edge-SFU framework applies to both best-effort and managed service models, this paper proposes a premium managed, edge-integrated multi-party WebRTC service architecture with bandwidth and delay guarantees within access networks by SDN-assisted slicing of edge networks. The performance of the proposed distributed edge-SFU service architecture is demonstrated by means of experimental results.

Social determinants of health and somatic growth in preterm infants and children with chronic lung disease.

Bronchopulmonary dysplasia (BPD), a common complication of prematurity, is associated with impaired lung function and increased morbidity in childhood. These children display heterogeneous somatic growth patterns which may ultimately impact their risk for later respiratory disease. We aim to understand the relationship between socioenvironmental exposures and growth in this population. A retrospective longitudinal cohort study was conducted using participants (n = 616) enrolled in the Johns Hopkins Outpatient BPD Registry. Growth measurements between 0 and 36 months of age were obtained. Somatic growth measurements, both corrected and uncorrected for gestational age, were converted to z-scores using Center for Disease Control and Prevention normative data. Using a participant's residential zip code, we utilized the 2019 Area Deprivation Index (ADI), the Childhood Opportunity Index (COI) 2.0, and neighborhood food insecurity data from the Maryland Food Bank. Linear regression analysis was performed employing individual socioenvironmental measures as independent variables and growth z-scores as dependent variables in univariate analysis. Univariate analysis was repeated adjusting for gestational age, BPD severity, and the presence of a gastrostomy tube (GT). Of the three measures analyzed, national COI (nCOI) showed a significant association with mean weight z-scores, mean ADI showed some association with mean weight z-scores, and the percentage of a community experiencing food insecurity showed no association with mean weight z-scores. After adjusting for gestational age, BPD severity, and presence of a GT, children living in areas of greater opportunity (higher nCOI values), had significantly lower weight z-scores at 12 months corrected and at 24 and 36 months uncorrected CONCLUSIONS: Our findings suggest that the COI 2.0, a multidimensional measure, captures more facets of an individual's social environment, as compared to the singular nature of a measure of food insecurity. There are several potential explanations for the phenomenon seen, and further understanding of this dynamic is crucial for designing effective interventions and policies to better address inequities in outcomes.

Contextual Acceptance of COVID-19 Mitigation Mobile Apps in the United States: Mixed Methods Survey Study on Postpandemic Data Privacy.

The COVID-19 pandemic gave rise to countless user-facing mobile apps to help fight the pandemic ("COVID-19 mitigation apps"). These apps have been at the center of data privacy discussions because they collect, use, and even retain sensitive personal data from their users (eg, medical records and location data). The US government ended its COVID-19 emergency declaration in May 2023, marking a unique time to comprehensively investigate how data privacy impacted people's acceptance of various COVID-19 mitigation apps deployed throughout the pandemic. This research aims to provide insights into health data privacy regarding COVID-19 mitigation apps and policy recommendations for future deployment of public health mobile apps through the lens of data privacy. This research explores people's contextual acceptance of different types of COVID-19 mitigation apps by applying the privacy framework of contextual integrity. Specifically, this research seeks to identify the factors that impact people's acceptance of data sharing and data retention practices in various social contexts. A mixed methods web-based survey study was conducted by recruiting a simple US representative sample (N=674) on Prolific in February 2023. The survey includes a total of 60 vignette scenarios representing realistic social contexts that COVID-19 mitigation apps could be used. Each survey respondent answered questions about their acceptance of 10 randomly selected scenarios. Three contextual integrity parameters (attribute, recipient, and transmission principle) and respondents' basic demographics are controlled as independent variables. Regression analysis was performed to determine the factors impacting people's acceptance of initial data sharing and data retention practices via these apps. Qualitative data from the survey were analyzed to support the statistical results. Many contextual integrity parameter values, pairwise combinations of contextual integrity parameter values, and some demographic features of respondents have a significant impact on their acceptance of using COVID-19 mitigation apps in various social contexts. Respondents' acceptance of data retention practices diverged from their acceptance of initial data sharing practices in some scenarios. This study showed that people's acceptance of using various COVID-19 mitigation apps depends on specific social contexts, including the type of data (attribute), the recipients of the data (recipient), and the purpose of data use (transmission principle). Such acceptance may differ between the initial data sharing and data retention practices, even in the same context. Study findings generated rich implications for future pandemic mitigation apps and the broader public health mobile apps regarding data privacy and deployment considerations.

AI Services-Oriented Dynamic Computing Resource Scheduling Algorithm Based on Distributed Data Parallelism in Edge Computing Network of Smart Grid

Massive computational resources are required by a booming number of artificial intelligence (AI) services in the communication network of the smart grid. To alleviate the computational pressure on data centers, edge computing first network (ECFN) can serve as an effective solution to realize distributed model training based on data parallelism for AI services in smart grid. Due to AI services with diversified types, an edge data center has a changing workload in different time periods. Selfish edge data centers from different edge suppliers are reluctant to share their computing resources without a rule for fair competition. AI services-oriented dynamic computational resource scheduling of edge data centers affects both the economic profit of AI service providers and computational resource utilization. This letter mainly discusses the partition and distribution of AI data based on distributed model training and dynamic computational resource scheduling problems among multiple edge data centers for AI services. To this end, a mixed integer linear programming (MILP) model and a Deep Reinforcement Learning (DRL)-based algorithm are proposed. Simulation results show that the proposed DRL-based algorithm outperforms the benchmark in terms of profit of AI service provider, backlog of distributed model training tasks, running time and multi-objective optimization.

Predictive Analytics Supporting Labor Market Success: A Career Explorer for Job Seekers and Workforce Professionals in Michigan

Career Explorer provides customized career exploration tools for workforce development staff and job seekers in Michigan. There are two separate Career Explorer modules: a staff-mediated service and a self-service for job seekers. The system was developed by the Michigan Center for Data and Analytics in collaboration with the W.E. Upjohn Institute for Employment Research and Michigan Works! Southwest. It was funded by the U.S. Department of Labor's Office of Workforce Investment and the Schmidt Futures’ Data for the American Dream (D4AD) project. In this paper, the authors describe the machine learning models behind the predictive analytics of the frontline staff-mediated version of Career Explorer. These models were trained on program administrative data. Additionally, the authors describe the self-service version of Career Explorer, which provides clients with customized labor market information based on published U.S. Bureau of Labor Statistics data. Career Explorer became an active feature of Michigan's online reemployment services system in June 2021.

The Impact of Delayed Transition From Noninvasive to Invasive Mechanical Ventilation on Hospital Mortality in Immunocompromised Patients With Sepsis.

To determine whether mortality differed between initial invasive mechanical ventilation (IMV) or noninvasive ventilation (NIV) followed by delayed IMV in immunocompromised patients with sepsis. Retrospective analysis using the National Data Center for Medical Service claims data in China from 2017 to 2019. A total of 3530 hospitals across China. A total of 36,187 adult immunocompromised patients with sepsis requiring ventilation. None. The primary outcome was hospital mortality. Patients were categorized into NIV initiation or IMV initiation groups based on first ventilation. NIV patients were further divided by time to IMV transition: no transition, immediate (≤ 1 d), early (2-3 d), delayed (4-7 d), or late (≥ 8 d). Mortality was compared between groups using weighted Cox models. Over the median 9-day follow-up, mortality was similar for initial NIV versus IMV (adjusted hazard ratio [HR] 1.006; 95% CI, 0.959-1.055). However, among NIV patients, a longer time to IMV transition is associated with stepwise increases in mortality, from immediate transition (HR 1.65) to late transition (HR 2.51), compared with initial IMV. This dose-response relationship persisted across subgroups and sensitivity analyses. Prolonged NIV trial before delayed IMV transition is associated with higher mortality in immunocompromised sepsis patients ultimately intubated.

Does digital finance development affect corporate cash holdings? Evidence from China

ABSTRACT This paper examines how the level of development of digital finance affects corporate cash holdings. By utilising the Digital Finance Inclusive Finance Index compiled by the Internet Finance Research Center of Peking University and data from China's listed companies, this paper finds that the development level of digital finance where a company is located is negatively correlated with corporate cash holdings. Furthermore, the negative effect of digital finance is more pronounced for non-state-owned enterprises and companies in the early stages of their life cycles. Additional tests show that digital finance can reduce a corporation's financial constraints and debt costs, and the coverage breadth and usage depth of digital finance have also significantly reduced the level of cash holdings for enterprises. Our findings provide insights for understanding the role of digital finance in firm behaviour in emerging economies.

Solutions for Tropical Vertical Motion under Convective Quasi-Equilibrium Constraints

Abstract Solutions for tropical convection (vertical motion), including both the first (deep) and the second baroclinic (shallow) modes, subject to convective quasi-equilibrium (CQE) constraints are formulated. Under CQE assumption, tropical convection ω(p, x, y) can be decomposed into a product of height-dependent variable Ωi(p) and space-dependent variable ∇·vi(x, y) with the former constrained by conservation of moist static energy (MSE) or dry static energy (DSE) perturbations, depending on whether the atmospheric column is dominated by ascending or descending motions. We then evaluate the roles of deep and shallow modes of convection in transporting moisture and static energy against observations using the European Centre for Medium-Range Weather Forecasts reanalysis data. The moisture transport by deep mode produces a spatial pattern similar to observations, except for an obvious underestimate of the magnitude over the eastern Pacific convergence zone (EPCZ) and cold tongue areas, where the contribution of shallow mode may account for up to 25% of the total moisture transport. In contrast, the MSE transport by deep mode exhibits a very poor performance, especially over the EPCZ where the observational MSE transport is negative, but a positive value is predicted by deep mode. Including the contribution of shallow mode immediately remedies this deficiency, due to a better representation of the bottom-heavy structure of ascending motions over the EPCZ. These improvements apply to almost the entire tropics, although the correlation tends to decrease away from the convergence zones. Since simple atmospheric models often assume a single heating (forcing) profile to represent the effect of cumulus convection, the present study highlights the importance and feasibility of including both deep and shallow modes in a simple atmospheric model, while at the same time maintaining the simple model framework, to more accurately represent the moisture and MSE transports by convection in the tropics.