Browse Articles

Adaptive neural observer-based output feedback anti-actuator fault control of a nonlinear electro-hydraulic system with full state constraints

Assessing cardiovascular disease risk and social determinants of health: A comparative analysis of five risk estimation instruments using data from the Eastern Caribbean Health Outcomes Research Network

Background Accurate assessment of cardiovascular disease (CVD) risk is crucial for effective prevention and resource allocation. However, few CVD risk estimation tools consider social determinants of health (SDoH), despite their known impact on CVD risk. We aimed to estimate 10-year CVD risk in the Eastern Caribbean Health Outcomes Research Network Cohort Study (ECS) across multiple risk estimation instruments and assess the association between SDoH and CVD risk. Methods Five widely used CVD risk estimation tools (Framingham and WHO laboratory, both laboratory and non-laboratory-based, and ASCVD) were applied using data from ECS participants aged 40–74 without a history of CVD. SDoH variables included educational attainment, occupational status, household food security, and perceived social status. Multivariable logistic regression models were used to compare differences in the association between selected SDoH and high CVD risk according to the five instruments. Findings Among 1,777 adult participants, estimated 10-year CVD risk varied substantially across tools. Framingham non-lab and ASCVD demonstrated strong agreement in categorizing participants as high risk. Framingham non-lab categorized the greatest percentage as high risk, followed by Framingham lab, ASCVD, WHO lab, and WHO non-lab. Fifteen times more people were classified as high risk by Framingham non-lab compared with WHO non-lab (31% vs 2%). Mean estimated 10-year risk in the sample was over 2.5 times higher using Framingham non-lab vs WHO non-lab (17.3% vs 6.6%). We found associations between food insecurity, those with the lowest level compared to the highest level of education, and non-professional occupation and increased estimated CVD risk. Interpretation Our findings highlight significant discrepancies in CVD risk estimation across tools and underscore the potential impact of incorporating SDoH into risk assessment. Further research is needed to validate and refine existing risk tools, particularly in ethnically diverse populations and resource-constrained settings, and to develop race- and ethnicity-free risk estimation models that consider SDoH.

Atherogenic index of plasma and cardiovascular outcomes in female patients undergoing percutaneous coronary intervention: insights from a retrospective cohort study

Prevalence of accessory navicular in Japanese children: A cross-sectional study using ultrasound–Katsuragi Integrated Defense for Locomotive Syndrome in children study

The accessory navicular (AN) is an accessory bone located on the posteromedial aspect of the navicular tuberosity that can cause pain following overuse or trauma, particularly during childhood. However, the detailed epidemiological characteristics of AN in children have not been well studied. This study aimed to clarify the prevalence of AN and painful AN among Japanese children by examining the characteristics according to sex and age. This cross-sectional study used data from the Katsuragi Integrated Defense for Locomotive Syndrome in Children Study, focusing on musculoskeletal disorders in 875 children aged 6–15 years, with 1750 feet being assessed. Children were divided into five age groups: 6–7, 8–9, 10–11, 12–13, and 14–15. AN was detected using ultrasound to avoid radiation exposure. The sex- and age-group-dependent prevalence of AN and painful AN were calculated, and statistical analyses examined sex differences in prevalence by age group. The overall prevalence of AN was 15.1%, higher in females (17.9%) than in males (12.3%). The prevalence of AN increased with age in both sexes. Among cases diagnosed with AN, 20.8% were symptomatic, with a unimodal peak observed at ages 12–13 in males and 10–11 in females. No statistically significant differences were observed in the proportion of painful AN between sexes. This is the first large-scale epidemiological study on AN in children. The overall prevalence of AN was 15.1%, higher in females than in males. Additionally, 20.8% of patients with AN experienced pain. The results of this study provide important epidemiological data to support clinical management strategies for pediatric patients with AN.

SOFCs integrated with SMES under dynamic power control using Chernobyl disaster optimizer

Abstract The current study uses the Chernobyl disaster optimizer (CDO), a new metaheuristic optimizer, to identify the seven unknown parameters of solid oxide fuel cells (SOFCs). The procedures of the CDO is based on physical behavior of the elaborated radiations from the well-known Chernobyl disaster according to their mass, speed, frequency, and degree of ionization. The sum of square errors (SMSE) among the estimated and the real measured output voltage datasets of SOFCs is minimized employing the CDO. Set of boundaries of the SOFC’s process is taken into consideration with the problem formulation. SOFCs stack’s model is examined at 800οC and 900οC and its performance is confirmed. The CDO extracts more precise SOFCs’ parameters compared to other competitors. The CDO’s convergence patterns and the SOFCs unit’s performance are studied and proved at steady-state by comparing its results to a number of recognized algorithms under varied operating scenarios. A significant SMSE’s values of 3.46 µV2 and 7.38 µV2 are attained at 800οC and 900οC, respectively by the CDO. As a result, the polarization principal curves of the measured and estimated voltage datasets are checked and verified with very close matching. The dynamic behavior of the SOFCs stack is examined in relation to direct load, electric networks, and superconducting magnetic energy storage devices (SMES) for additional validation and illustration. The role of the SOFCs stack in controlling the active and reactive power delivered to the network and direct load is investigated using two controllers: one to control the inverter, which converts the SOFC’s dc output to the main network, and the other to control the SMES. The Simulink/MATLAB environment is used to indicate the validity of the proposed framework under both steady-state and dynamical conditions. The comprehensive assessments show that the CDO capabilities are very effective when used with microgrids.

Transitions in intensive care: Investigating critical slowing down post extubation

Complex biological systems undergo sudden transitions in their state, which are often preceded by a critical slowing down of dynamics. This results in longer recovery times as systems approach transitions, quantified as an increase in measures such as the autocorrelation and variance. In this study, we analysed paediatric patients in intensive care for whom mechanical ventilation was discontinued through removal of the endotracheal tube (extubation). Some patients failed extubation, and required a re-intubation within 48 hours. We investigated whether critical slowing down could be observed post failed extubations, prior to re-intubation. We tested for significant increases (p <.05) between extubation and re-intubation, in the variance and autocorrelation, over the time series data of heart rate, respiratory rate and mean blood pressure. The autocorrelation of the heart rate showed a significantly higher proportion of increases in the group that failed extubation, compared who those who did not. It also showed a significantly higher magnitude of increase for the failed extubation group in a t-test. Moreover, incorporating these magnitudes significantly improved the fit of a logistic regression model when compared to a model that solely used the mean and standard deviation of the vital signs. While immediate clinical utility is limited, the work marks an important first step towards using dynamical systems theory to understand the dynamics of signals measured at the bedside during intensive care.

Reinforcement learning based route optimization model to enhance energy efficiency in internet of vehicles

The status of China’s international division of labour from Marx’s theory of international value

Based on Marx’s theory of international value, this paper analyzes the carrier of unequal exchange and the path of international transfer of surplus value, constructs an international division of labour status index by comprehensively observing the organic composition of capital in the export and import sectors, and tries to accurately grasp the characteristics of the current international division of labour pattern coexisting between horizontal products and vertical products. At the same time, based on the data of all available HS92 six-digit code categories in various countries in the world from 1993 to 2016, the level of international division of labour in China is empirically analysed. The results show that: (1) there is no breakthrough in the situation of China’s low status of international division of labour; (2) the high capital-organic composition of the import sector is the main reason for the low status of international division of labour; (3) the continuous decline in the absolute value of captured surplus is a global phenomenon; (4) WTO accession has not significantly improved the status of international division of labour. Finally, the idea of improving the status of international division of labour through the development of domestic regional cooperation is put forward.

Symbolic regression for strength prediction of eccentrically loaded concrete-filled steel tubular columns

Abstract Concrete-filled steel tube (CFST) columns are widely employed in high-rise buildings, long-span bridges, and seismic-resistant structures due to their superior load-bearing capacity, structural efficiency, and resilience under extreme loading conditions. This study uses symbolic regression with structural design code provisions to predict the eccentric strength of concrete filled-steel tubular columns with circular shape (CCFST) and rectangular shape (RCFST). Previous studies have used two distinct approaches for estimating eccentric strength: explainable models based on theoretical derivations and black-box models derived from machine learning (ML) methods. This study proposes a hybrid model derived from the design code standards, with performance enhanced by the symbolic regression technique. This model is based on a comprehensive experimental database of 464 tests for CCFST columns and 313 tests for RCFST columns under eccentric loading from various research papers. The developed code-based symbolic regression (C-SR) displays both robust and interpretable, demonstrating high prediction accuracy with mean values of the prediction-to-actual ratios of 1.006 and 0.997 and coefficient of variation (CoV) values of 0.117 and 0.098 for CCFSTs and RCFSTs, respectively, while providing explainable mathematical expressions that align with the mechanical principles of code provisions. The developed C-SR model is benchmarked against EC4 and AISC360 standards and evaluated against the various ML techniques, demonstrating acceptable performance. The results highlight the C-SR model’s effectiveness in providing reliable predictions and valuable insights for practical engineering applications.

First report of MDR virulent Pseudomonas aeruginosa in apparently healthy Japanese quail (Coturnix japonica) in Bangladesh

Pseudomonas aeruginosa (P. aeruginosa) is a major pathogen associated conditions like septicaemia, respiratory disorders, and diarrhoea in poultry, particularly in Japanese quail (Coturnix japonica). The infection causes huge economical losses due to its high transmissibility, mortality and zoonotic potential. This study aimed to isolate, identify, detect virulence genes, and profile multidrug resistance (MDR) of P. aeruginosa from Japanese quail. Oral and rectal swabs were collected from 110 apparently healthy quail birds across various districts in Bangladesh. Bacterial isolation and identification were performed using cultural, morphological, biochemical, and polymerase chain reaction (PCR) methods. Antibiotic susceptibility was assessed using the disc diffusion method, and virulence genes were detected through PCR. Multivariable logistic regression was used to identify risk factors for P. aeruginosa infection. Both conventional and PCR methods revealed that 25 (22.73%) of the quail birds were positive for P. aeruginosa. The results showed that quail birds in Narsingdi were five times more likely to harbor Pseudomonas species (OR: 5.02; 95% CI: 1.34–18.84) compared to those in Mymensingh Sadar. Additionally, quail birds younger than eight weeks had nearly six times higher odds (OR: 5.93; 95% CI: 1.96–17.91) of carrying Pseudomonas compared to older birds. Female quail birds had almost four times higher odds (OR: 3.77; 95% CI: 1.30–10.93) of harboring Pseudomonas species than males. All 25 P. aeruginosa isolates exhibited multi drug-resistance (MDR) patterns. Virulence gene analysis revealed the consistent presence of exoA and rhlR in all isolates, while exoS, exoY, rhlI, and rhlAB showed variable distribution. The high prevalence of MDR and virulent P. aeruginosa in apparently healthy quail birds particularly in Mymensingh and Dhaka divisions, highlights the urgent need for a comprehensive ‘One Health’ approach to proactively address and mitigate the potential risk this organism poses to both quail and human populations.

The association between body composition and overall survival in patients with advanced non-small cell lung cancer

Comparative analysis of data-driven models for spatially resolved thermometry using emission spectroscopy

A methodology is proposed, which addresses the caveat that line-of-sight emission spectroscopy presents in that it cannot provide spatially resolved temperature measurements in non-homogeneous temperature fields. The aim of this research is to explore the use of data-driven models in measuring temperature distributions in a spatially resolved manner using emission spectroscopy data. Two categories of data-driven methods are analyzed: (i) Feature engineering and classical machine learning algorithms, and (ii) end-to-end convolutional neural networks (CNN). In total, combinations of fifteen feature groups and fifteen classical machine learning models, and eleven CNN models are considered and their performances explored. The results indicate that the combination of feature engineering and machine learning provides better performance than the direct use of CNN. Notably, feature engineering, which is comprised of physics-guided transformation, signal representation-based feature extraction and Principal Component Analysis is found to be the most effective. Moreover, it is shown that when using the extracted features, the ensemble-based, light blender learning model offers the best performance with RMSE, RE, RRMSE and R values of 64.3, 0.017, 0.025 and 0.994, respectively. The proposed method, based on feature engineering and the light blender model, is capable of measuring nonuniform temperature distributions from low-resolution spectra, even when the species concentration distribution in the gas mixtures is unknown.

A comprehensive DFT/TDDFT investigation into the influence of electron acceptors on the photophysical properties of ullazine-based D-π-A-π-A photosensitizers

Impact of COVID-19 vaccination on hospitalization, hospital utilization and expenditure for COVID-19: A retrospective cohort analysis of a South African private health insured population

This study quantifies the impact of COVID-19 vaccination on hospitalization for COVID-19 infection in a South African private health insurance population. This retrospective cohort study is based on the analysis of demographic and claims records for 550,332 individuals belonging to two health insurance funds between 1 March 2020 and 31 December 2022. A Cox Proportional Hazards model was used to estimate the impact of vaccination (non-vaccinated, partly vaccinated, fully vaccinated) on COVID-19 hospitalization risk; and zero-inflated negative binomial models were used to estimate the impact of vaccination on hospital utilization and hospital expenditure for COVID-19 infection, with adjustments for age, sex, comorbidities and province of residence. In comparison to the non-vaccinated, the hospitalization rate for COVID-19 was 94.51% (aHR 0.06, 95%CI 0.06, 0.07) and 93.49% (aHR 0.07, 95%CI 0.06, 0.07) lower for the partly and fully vaccinated respectively; hospital utilization was 17.70% (95% CI 24.78%, 9.95%) and 20.04% (95% CI 28.26%, 10.88%) lower; the relative risk of zero hospital days was 4.34 (95% CI 4.02, 4.68) and 18.55 (95% CI 17.12, 20.11) higher; hospital expenditure was 32.83% (95% CI 41.06%, 23.44%) and 55.29% (95% CI 61.13%, 48.57%) lower; and the relative risk of zero hospital expenditure was 4.38 (95% CI 4.06, 4.73) and 18.61 (95% CI 17.18, 20.16) higher for the partly and fully vaccinated respectively. Taken together, findings indicate that all measures of hospitalization for COVID-19 infection were significantly lower in the partly or fully vaccinated in comparison to the non-vaccinated. The use of real-world data and an aggregated level of analysis resulted in the study having several limitations. While the overall results may not be generalizable to other populations, the findings add to the evidence based on the impact of COVID-19 vaccination during the period of the pandemic.

Comparative proteomic analysis of astrocytoma tissues from patients with and without seizures

Interventions to improve health care provider implementation and patient adherence of patients to recommendations on geriatric assessment and management in older adults: A scoping review protocol

The world population is aging. Comprehensive Geriatric assessment (CGA) has been proven to improve the well-being of older adults. However, evidence suggests not all clinicians implement these recommendations in their practice; nor do all patients adhere to them. Currently, there is no up-to-date review of interventions that can improve older adults’ adherence to CGA recommendations and health care provider/clinician implementation of those recommendations. The objective of this scoping review protocol is to describe the methodology of the scoping review with the aim to identify interventions that have been tested to improve adherence to CGA recommendations. We will use the Arksey and O’Malley framework and subsequent extension by Levac and colleagues to complete the scoping review. We searched OVID MEDLINE, OVID Embase, EBSCO CINAHL, APA PsychInfo, and Cochrane CENTRAL databases from inception to November 14, 2024, and will include a review of reference lists of included studies. Studies eligible for inclusion are studies of any design that examined one or more interventions aiming to improve clinician implementation of and patient adherence to CGA in any clinical setting. We will use standard methods for study selection, data abstraction, assessment of methodological quality of individual studies, and data synthesis. Results will be analyzed and reported using descriptive numerical summaries and narrative analysis. Findings from the scoping review will be published in a manuscript and presented at scientific conferences.

Assessment of environmental impacts of heavy metal pollution in rice in Nanning, China

Heart rate variability is more sensitive to stress than heart rate in specialist police undergoing selection

Police tactical group (PTG) officers respond to the most demanding and high-risk police situations. As such, PTG personnel require exceptional physical fitness, and selection for employment often evaluates fitness both directly and indirectly. While heart rate (HR) is often used to measure physical effort, heart rate variability (HRV) may be a valuable tool for measuring stress holistically. The primary aim of this research was to investigate whether HRV was more sensitive than HR at monitoring workload during key PTG selection activities. As aerobic fitness is associated with workload during these tasks, a secondary aim was to investigate relationships between HRV, HR and aerobic fitness during the same tasks. The relationships between HRV (percentage of adjacent R-R intervals varying by 50% or more; pRR50%) and HR, as measured by ambulatory electrocardiograms obtained during a specialist police selection course, as well as aerobic fitness, as determined via total shuttles completed on the 20-meter multistage fitness test (MSFT; ‘beep test’), were investigated. This study included a cohort of six male PTG candidates (n = 6) undergoing selection. As illustrated by a time-series plot, HR values were generally unremarkable, but HRV values were potentially depressed, and tentatively indicated overstress when count data from consecutive short-term analyses were derived. The MSFT was significantly, positively, correlated with pRR50% (ρ (6) = 0.812, p = 0.050, Fisher’s z = 1.132). The MSFT and nonlinear HRV, frequency domain HRV, and HR were not significantly correlated. When assessed by linear regression, neither HRV nor HR were predicted by MSFT score. These findings indicate that HR alone is likely not sufficiently sensitive to provide detail on the stress response of candidates undertaking essential tactical tasks that combine physical stressors with cognitive load in adverse conditions. HRV analysis may provide additional insights regarding candidate suitability, particularly during dynamic and multifaceted assessments, though the causal direction of the relationship between HRV and aerobic fitness remains unclear.

Effect of a virtual walking and exercise-based intervention on muscle strength and activation in people with incomplete spinal cord injury

Myelin ensheathment and drug responses of oligodendrocytes are modulated by stiffness of artificial axons

Myelination is a key biological process wherein glial cells such as oligodendrocytes wrap myelin around neuronal axons, forming an insulative sheath that accelerates signal propagation down the axon. A major obstacle to understanding myelination is the challenge of visualizing and reproducibly quantifying this inherently three-dimensional process in vitro . To this end, we previously developed artificial axons (AAs), a biocompatible platform consisting of 3D-printed hydrogel-based axon mimics designed to more closely recapitulate the micrometer-scale diameter and sub-kilopascal mechanical stiffness of biological axons. First, we present our platform for fabricating AAs with tunable axon diameter, stiffness, and inter-axonal spacing. Second, we demonstrate that increasing the Young’s modulus E or stiffness of polymer comprising the AAs increases the extent of myelin ensheathment by rat oligodendrocytes. Third, we demonstrate that the responses of oligodendrocytes to pro-myelinating compounds are also dependent on axon stiffness, which can affect compounds efficacy and the relative ranking. These results reinforce the importance of studying myelination in mechanically representative environments, and highlight the importance of considering biophysical cues when conducting drug screening studies.