Assessment of left ventricular ejection fraction in artificial intelligence based on left ventricular opacification.

Background: Left ventricular opacification (LVO) improves the accuracy of left ventricular ejection fraction (LVEF) by enhancing the visualization of the endocardium. Manual delineation of the endocardium by sonographers has observer variability. Artificial intelligence (AI) has the potential to improve the reproducibility of LVO to assess LVEF. Objectives: The aim was to develop an AI model and evaluate the feasibility and reproducibility of LVO in the assessment of LVEF. Methods: This retrospective study included 1305 echocardiography of 797 patients who had LVO at the Department of Ultrasound Medicine, Union Hospital, Huazhong University of Science and Technology from 2013 to 2021. The AI model was developed by 5-fold cross validation. The validation datasets included 50 patients prospectively collected in our center and 42 patients retrospectively collected in the external institution. To evaluate the differences between LV function determined by AI and sonographers, the median absolute error (MAE), spearman correlation coefficient, and intraclass correlation coefficient (ICC) were calculated. Results: In LVO, the MAE of LVEF between AI and manual measurements was 2.6% in the development cohort, 2.5% in the internal validation cohort, and 2.7% in the external validation cohort. Compared with two-dimensional echocardiography (2DE), the left ventricular (LV) volumes and LVEF of LVO measured by AI correlated significantly with manual measurements. AI model provided excellent reliability for the LV parameters of LVO (ICC > 0.95). Conclusions: AI-assisted LVO enables more accurate identification of the LV endocardium and reduces observer variability, providing a more reliable way for assessing LV function.

Integral sliding mode control and stability for Markov jump systems with structured perturbations and time-varying delay driven by fractional Brownian motion.

The issues of stability and sliding mode control (SMC) for time-varying delay Markov jump systems (MJSs) with structured perturbations constrained by fractional Brownian motion (fBm) are explored. First, constructing a novel Lyapunov-Krasovskii functional (LKF) with exponential terms that contain the double-integral term, the pth moment exponential stability conditions are derived by utilizing the generalized fractional Itoˆ formula and conditional mathematical expectation. Subsequently, by designing the innovative integral sliding mode surface (SMS) associated with time-varying delay and the SMC law, the state trajectories of the dynamic systems can reach the designed SMS within a finite time. Ultimately, the numerical experiment is executed to confirm and ensure the accuracy and reliability of the obtained results.

Sliding Mode Control for Markov Jump Singularly Perturbed Systems Under Piecewise Homogeneous Stochastic Communication Protocol.

This work involves the sliding mode control (SMC) issue for a class of Markov jump singularly perturbed systems (MJSPSs) under consideration of unmatched external disturbances and communication constraints. For the first time, the piecewise homogeneous Markov chain (MC) which depends on the system mode and the controller mode is applied to control the scheduling of stochastic communication protocol (SCP), so that the MCs in the system models, the controller and the SCP constitute a three-layer nonstationary Markov model (NMM). This model perfectly describes the different objects of the three MCs and reflects the mutual regulation among them. The critical issue is to devise an adaptive controller and a sliding surface (SS) simultaneously under SCP scheduling. By applying a standard singular sliding mode surface, an appropriate nonstationary SMC law is established to promise the accessibility of the SS and the stability of the closed-loop system (CLS), and meet the expected performance indicator. Further, using the mode-dependent Lyapunov function and piecewise homogeneous Markov model method, sufficient criteria are obtained. The specific expression of the control gain is obtained by matrix decoupling technology. Finally, a numerical simulation is furnished to testify the correctness of the conclusion.

Trends by age and sex and projections of disease prevalence from studying hypertensive heart disease in China over the past three decades.

BACKGROUND: Hypertensive heart disease (HHD) is a common cause of cardiovascular disease and mortality worldwide, and its burden is increasing with aging populations. OBJECTIVES: This study aimed to estimate the prevalence and mortality rates of HHD in mainland China and Taiwan Province using data from the Global Burden of Disease Study 2019 (GBD 2019), and forecast the development trend of HHD from 2020 to 2024. METHODS: We obtained data on number of cases, deaths, crude prevalence rate, crude death rate, age-standardized prevalence rate (ASPR), and age-standardized death rate (ASDR) for mainland China and Taiwan Province from 1990 to 2019 from the GBD 2019. Joinpoint software was used to estimate average annual percentage change (AAPC) with 95% confidence intervals, and the number of HHD cases in China from 2022 to 2024 was predicted by the exponential smoothing method. RESULTS: Between 1990 and 2019, HHD cases and deaths increased in mainland China, but the ASPR and ASDR decreased by 5.96% and 48.72%, respectively. In Taiwan Province, ASPR and ASDR decreased by 7.66% and 52.14%, respectively. The number of HHD cases and death rates varied by region, age, and sex, with a higher number of cases in mainland China than in Taiwan Province. By 2024, the number of HHD cases in mainland China was projected to be over 9.6 million cases, and in Taiwan Province, it was projected to surpass 120,000 cases. CONCLUSION: The differences in HHD cases between mainland China and Taiwan Province in terms of age and sex indicated the need for effective prevention and control measures, especially targeting the elderly population. These findings can inform policymakers and health professionals in the development of targeted prevention and treatment strategies and resource allocation for HHD in China.

Implementing a continuous quality-improvement framework for tuberculosis infection prevention and control in healthcare facilities in China, 2017-2019.

BACKGROUND: Tuberculosis (TB) infection prevention and control (IPC) in healthcare facilities is key to reducing transmission risk. A framework for systematically improving TB IPC through training and mentorship was implemented in 9 healthcare facilities in China from 2017 to 2019. METHODS: Facilities conducted standardized TB IPC assessments at baseline and quarterly thereafter for 18 months. Facility-based performance was assessed using quantifiable indicators for IPC core components and administrative, environmental, and respiratory protection controls, and as a composite of all control types We calculated the percentage changes in scores over time and differences by IPC control type and facility characteristics. RESULTS: Scores for IPC core components increased by 72% during follow-up when averaged across facilities. The percentage changes for administrative, environmental, and respiratory protection controls were 39%, 46%, and 30%, respectively. Composite scores were 45% higher after the intervention. Overall, scores increased most during the first 6 months. There was no association between IPC implementation and provincial economic development or volume of TB services. CONCLUSIONS: TB IPC policies and practices showed most improvement early during implementation and did not differ consistently by facility characteristics. The training component of the project helped increase the capacity of healthcare professionals to manage TB transmission risks. Lessons learned here will inform national TB IPC guidance.

Preassigned-time synchronization for complex-valued memristive neural networks with reaction-diffusion terms and Markov parameters.

This study addresses the preassigned-time synchronization for complex-valued memristive neural networks with reaction-diffusion terms and Markov parameters. Employing a preassigned-time stable control strategy, two distinct controllers with varying power exponent parameters are designed to ensure that synchronization can be achieved within a predefined time frame. Unlike existing finite/fixed-time results, a priori specification of the settling time is addressed. Furthermore, Green's formula and boundary conditions are efficiently applied to overcome potential symmetry loss. Additionally, the activation function's constraint range is more lenient compared to existing constraints. Finally, the effectiveness of the presented methods are demonstrated through two examples.

Global burden of inflammatory bowel disease 1990-2019: A systematic examination of the disease burden and twenty-year forecast.

BACKGROUND: Inflammatory bowel disease (IBD) is an idiopathic intestinal disease with various levels and trends in different countries and regions. Understanding the current burden and trends of IBD in various geographical locations is essential to establish effective strategies for prevention and treatment. We report the average annual percentage change (AAPC) and estimated annual percentage change (EAPC) in age-standardized rates (ASR) of IBD in different regions based on the Global Burden of Disease (GBD) study from 1990-2019, and the relationships between IBD and the human development index (HDI) and socio-demographic index (SDI). The prevalence trends of IBD were predicted by gender from 2019-2039. AIM: To comprehensively investigate IBD data, providing further insights into the management of this chronic disease. METHODS: We collected the information on the incidence of IBD from the GBD study from 1990-2019 to calculate the AAPC and EAPC in ASR of IBD in different regions. The relationships between IBD, HDI, and SDI were analyzed. The Nordpred and Bayesian age-period-cohort models were used to predict the prevalence trends of IBD by gender from 2019-2039, and the reliability of the results was validated. Statistics of all the data in this study were performed using R software (version 4.2.1). RESULTS: North America consistently had the highest IBD ASR, while Oceania consistently had the lowest. East Asia had the fastest average annual growth in ASR (2.54%), whereas Central Europe had the fastest decline (1.38%). Countries with a low age-standardized incidence rates in 1990 showed faster growth in IBD while there was no significant correlation in 2019. Additionally, IBD increased faster in countries with a low age-standardized death rates in 1990, whereas the opposite was true in 2019. Analysis of SDI and IBD ASR showed that countries with a high SDI generally had a higher IBD ASR. Finally, the projections showed a declining trend in the incidence of IBD from 2019-2039, but a gradual increase in the number of cases. CONCLUSION: As the global population increases and ages, early monitoring and prevention of IBD is important to reduce the disease burden, especially in countries with a high incidence of IBD.

Adaptive neural network control for Markov jumping systems against deception attacks.

This paper proposes an innovative approach for mitigating the effects of deception attacks in Markov jumping systems by developing an adaptive neural network control strategy. To address the challenge of dual-mode monitoring mechanisms, two independent Markov chains are used to describe the state changes of the system and the intermittent actuator. By employing a mapping technique, these individual chains are amalgamated into a unified joint Markov chain. Additionally, to effectively approximate the unbounded false signals injected by deception attacks, an adaptive neural network technique is skillfully built. A mode monitoring scheme is implemented to design an asynchronous control law that links the mode information between the joint Markov chain and controller with fewer modes. The paper derives sufficient criteria for the mean-square bounded stability of the resulting system based on Lyapunov theories. Finally, a numerical experiment is conducted to demonstrate the effectiveness of the proposed method.

Asynchronous control for semi-Markov switching systems with singular perturbation and an improved protocol.

This study addresses the asynchronous control problem for a semi-Markov switching system in the presence of singular perturbation and an improved triggering protocol. To decrease the occupation of network resources, an improved protocol is skillfully established by adopting two auxiliary offset variables. Unlike the existing protocols, the established improved protocol is capable of arranging information transmission with more degrees of freedom, thereby reducing the communication frequency and maintaining control performance. Apart from the reported hidden Markov model, a nonhomogeneous hidden semi-Markov model is used to handle the mode mismatch between the systems and controllers. Benefiting from Lyapunov techniques, parameter-dependent sufficient conditions are devised to ensure a stochastically stable subject to a predetermined performance. Finally, the validity and practicability of the theoretical results are verified via a numerical example and a tunnel diode circuit model.

A Dynamic-Memory Event-Triggered Protocol to Multiarea Power Systems With Semi-Markov Jumping Parameter.

This work deals with the dynamic-memory event-triggered-based load frequency control issue for interconnected multiarea power systems (IMAPSs) associated with random abrupt variations and deception attacks. To facilitate the transient faults, a semi-Markov process is addressed to model the dynamic behavior of IMAPSs. In order to modulate transmission frequency, a novel area-dependent dynamic-memory event-triggered protocol (DMETP) is scheduled by resorting to a set of the historically released packets (HRPs), which ensures better dynamic performance. From the viewpoint of the defender, the randomly occurring deception attack is taken into account, which is regulated by a Bernoulli-distributed scalar. Benefitting from the DMETP scheduling, a novel framework of the memory-based asynchronous control strategy is formulated, in which the hidden semi-Markov model is adopted to reveal the mode mismatches. Based on the Lyapunov theory, sufficient conditions are established to ensure the stochastic stability of the resulting systems. In the end, the simulation result is presented to reveal the efficiency of the proposed dynamic-memory event-triggered-based approach.

Proportional-Integral Observer-Based State Estimation for Markov Memristive Neural Networks With Sensor Saturations.

This article investigates the resilient proportional-integral observer (PIO) problem for Markov switching memristive neural networks (MSMNNs) with randomly occurring sensor saturation within a finite-time interval. The Markov switching of memristive neural networks is regulated by a higher level deterministic switching signal, whose transition probabilities are piecewise time-varying and can be depicted by the average dwell-time strategy. Meanwhile, a Bernoulli stochastic process associated with an uncertain packet arriving rate is adopted to describe the randomly occurring sensor saturation. The aim is to design a resilient PIO such that the augmented dynamic has the property of stochastic finite-time boundedness while meeting the desired performance index. By applying the Lyapunov method and the average dwell-time scheme, sufficient criteria are established for MSMNNs, and a unified design method is presented for the existence of the PIO. Lastly, the attained theoretical results are validated via a numerical simulation.

Asynchronous H∞ control of Markov jump discrete-time systems with incomplete transition probability and unreliable links.

In this study, an asynchronous H∞ state feedback controller is devised for Markov jump discrete-time systems (MJDTSs) with time-varying delay. "Asynchronous" means that the system switching mode θk, the controller mode ϑk and the quantizer mode λk are different from each other. The first one is homogeneous and the last two are non-homogeneous. In particular, as a promotion of existing work, we firstly attempt to propose the transition probabilities (TPs) of the three Markov chains (MCs) are not completely known. In addition, the discrete time-varying delay and its infinitely distributed ones are considered. Moreover, according to the Lyapunov stability theory and stochastic process, it is established for the sufficient criterion to ensure the stochastic stability of resulting closed-loop MJDTSs with an H∞ attenuation performance index. The feasibility and effectiveness of the proposed method are validated by three examples.

A Hierarchical Structure Approach to Finite-Time Filter Design for Fuzzy Markov Switching Systems With Deception Attacks.

This work is concerned with the issue of finite-time filter design for a type of Takagi-Sugeno (T-S) fuzzy Markov switching system (MSSs) with deception attacks (DAs). In view of communication network security, the randomly occurring DAs are considered in the measurement output (MO), in which the malicious unknown but bounded signals are launched by the adversary. Notably, to characterize the fallibility of the communication links between the MO and the filter, the packet dropouts, DAs, and quantization effects are taken into account simultaneously, which signifies that the resulting system is much more applicable than the existing results. Meanwhile, to deal with the phenomenon of asynchronous switching, a hierarchical structure approach is adopted, which involves the existing nonsynchronous/synchronous strategy as special cases. By means of a fuzzy-basis-dependent Lyapunov strategy, sufficient criteria are formulated such that the resulting system is stochastic finite-time boundedness under randomly occurring DAs. Finally, a double-inverted pendulum model and a numerical example are provided to validate the feasibility of the attained method.

Evaluation and Image Analysis of Enterprise Human Resource Management Based on the Simulated Annealing-Optimized BP Neural Network.

With the continuous development of social economy and the intensification of social competition, human resource management plays a more and more important role in the whole resource system. How to give full play to the advantages of human resources has become the key issue of human resource management evaluation. However, the current human resource management evaluation system has some problems, such as poor timeliness, one-sidedness, and subjectivity. Therefore, this paper proposes a BP image neural network optimized based on the simulated annealing algorithm to realize enterprise human resource management evaluation and image analysis. Through the learning of different time series samples, the average weight distribution scheme of main indicators is obtained, in which the average weight proportions of c 1, c 2, c 3, and c 4 are 25.5%, 24.8%, 17.9%, and 31.9%, respectively. In the comprehensive evaluation of enterprise employees, the error between the actual output and expected output is less than 4.5%. The results show that the BP image neural network based on simulated annealing algorithm has high accuracy in the image analysis and evaluation of enterprise human resource management. The output analysis results meet the actual needs of the enterprise and the personal development of employees and provide a decision-making scheme for the evaluation of enterprise human resource management.

Comparison of yield and relative costs of different screening algorithms for tuberculosis in active case-finding: a cross-section study.

BACKGROUND: Part of tuberculosis (TB) patients were missed if symptomatic screening was based on the main TB likely symptoms. This study conducted to compare the yield and relative costs of different TB screening algorithms in active case-finding in the whole population in China. METHODS: The study population was screened based on the TB likely symptoms through a face-to-face interview in selected 27 communities from 10 counties of 10 provinces in China. If the individuals had any of the enhanced TB likely symptoms, both chest X-ray and sputum tests were carried out for them furtherly. We used the McNemar test to analyze the difference in TB detection among four algorithms in active case-finding. Of four algorithms, two were from WHO recommendations including 1a/1c, one from China National Tuberculosis Program, and one from this study with the enhanced TB likely symptoms. Furthermore, a two-way ANOVA analysis was performed to analyze the cost difference in the performance of active case-finding adjusted by different demographic and health characteristics among different algorithms. RESULTS: Algorithm with the enhanced TB likely symptoms defined in this study could increase the yield of TB detection in active case-finding, compared with algorithms recommended by WHO (p < 0.01, Kappa 95% CI: 0. 93-0.99) and China NTP (p = 0.03, Kappa 95% CI: 0.96-1.00). There was a significant difference in the total costs among different three algorithms WHO 1c/2/3 (F = 59.13, p < 0.01). No significant difference in the average costs for one active TB case screened and diagnosed through the process among Algorithms 1c/2/3 was evident (F = 2.78, p = 0.07). The average costs for one bacteriological positive case through algorithm WHO 1a was about two times as much as the costs for one active TB case through algorithms WHO 1c/2/3. CONCLUSIONS: Active case-finding based on the enhanced symptom screening is meaningful for TB case-finding and it could identify more active TB cases in time. The findings indicated that this enhanced screening approach cost more compared to algorithms recommend by WHO and China NTP, but the increased yield resulted in comparative costs per patient. And it cost much more that only smear/bacteriological-positive TB cases are screened in active case-finding.

Assessment of basic reproductive number for COVID-19 at global level: A meta-analysis.

BACKGROUND: There are large knowledge gaps regarding how transmission of 2019 novel coronavirus disease (COVID-19) occurred in different settings across the world. This study aims to summarize basic reproduction number (R0) data and provide clues for designing prevention and control measures. METHODS: Several databases and preprint platforms were retrieved for literature reporting R0 values of COVID-19. The analysis was stratified by the prespecified modeling method to make the R0 values comparable, and by country/region to explore whether R0 estimates differed across the world. The average R0 values were pooled using a random-effects model. RESULTS: We identified 185 unique articles, yielding 43 articles for analysis. The selected studies covered 5 countries from Asia, 5 countries from Europe, 12 countries from Africa, and 1 from North America, South America, and Australia each. Exponential growth rate model was most favored by researchers. The pooled global R0 was 4.08 (95% CI, 3.09-5.39). The R0 estimates for new and shifting epicenters were comparable or even higher than that for the original epicenter Wuhan, China. CONCLUSIONS: The high R0 values suggest that an extraordinary combination of control measures is needed for halting COVID-19.

Multi-source financing for tuberculosis treatment in China: key issues and challenges.

BACKGROUND: The End Tuberculosis (TB) Strategy of the World Health Organization highlights the need for patient-centered care and social protection measures that alleviate the financial hardships faced by many TB patients. In China, TB treatments are paid for by earmarked government funds, social health insurance, medical assistance for the poor, and out-of-pocket payments from patients. As part of Phase III of the China-Gates TB project, this paper introduces multi-source financing of TB treatment in the three provinces of China and analyzes the challenges of moving towards universal coverage and its implications of multi-sectoral engagement for TB care. MAIN TEXT: The new financing policies for TB treatment in the three provinces include increased reimbursement for TB outpatient care, linkage of TB treatment with local poverty alleviation programs, and use of local government funds to cover some costs to reduce out-of-pocket expenses. However, there are several challenges in reducing the financial burdens faced by TB patients. First, medical costs must be contained by reducing the profit-maximizing behaviors of hospitals. Second, treatment for TB and multi-drug resistant TB (MDR-TB) is only available at county hospitals and city or provincial hospitals, respectively, and these hospitals have low reimbursement rates and high co-payments. Third, many patients with TB and MDR-TB are at the edge of poverty, and therefore ineligible for medical assistance, which targets extremely poor individuals. In addition, the local governments of less developed provinces often face fiscal difficulties, making it challenging to use of local government funds to provide financial support for TB patients. We suggest that stakeholders at multiple sectors should engage in transparent and responsive communications, coordinate policy developments, and integrate resources to improve the integration of social protection schemes. CONCLUSIONS: The Chinese government is examining the establishment of multi-source financing for TB treatment by mobilization of funds from the government and social protection schemes. These efforts require strengthening the cooperation of multiple sectors and improving the accountability of different government agencies. All key stakeholders must take concrete actions in the near future to assure significant progress toward the goal of alleviating the financial burden faced by TB and MDR-TB patients.

Synchronization for Quantized Semi-Markov Switching Neural Networks in a Finite Time.

Finite-time synchronization (FTS) is discussed for delayed semi-Markov switching neural networks (S-MSNNs) with quantized measurement, in which a logarithmic quantizer is employed. The stochastic phenomena of structural and parametrical changes are modeled by a semi-Markov process whose transition rates are time-varying to depend on the sojourn time. Practical systems subject to unpredictable structural changes, such as quadruple-tank process systems, are described by delayed S-MSNNs. A key issue under the consideration is how to design a feedback controller to guarantee the FTS between the master system and the slave system. For this purpose, by using the weak infinitesimal operator, sufficient conditions are constructed to realize FTS of the resulting error system over a finite-time interval. Then, the solvability conditions for the desired finite-time controller can be determined under a linear matrix inequality framework. Finally, the theoretical findings are illustrated by the quadruple-tank process model.

Resilient asynchronous state estimation of Markov switching neural networks: A hierarchical structure approach.

This paper deals with the issue of resilient asynchronous state estimation of discrete-time Markov switching neural networks. Randomly occurring signal quantization and packet dropout are involved in the imperfect measured output. The asynchronous switching phenomena appear among Markov switching neural networks, quantizer modes and filter modes, which are modeled by a hierarchical structure approach. By resorting to the hierarchical structure approach and Lyapunov functional technique, sufficient conditions are achieved, and asynchronous resilient filters are derived such that filtering error dynamic is stochastically stable. Finally, two examples are included to verify the validity of the proposed method.

Novel Inequalities to Global Mittag-Leffler Synchronization and Stability Analysis of Fractional-Order Quaternion-Valued Neural Networks.

This article is concerned with the problem of the global Mittag-Leffler synchronization and stability for fractional-order quaternion-valued neural networks (FOQVNNs). The systems of FOQVNNs, which contain either general activation functions or linear threshold ones, are successfully established. Meanwhile, two distinct methods, such as separation and nonseparation, have been employed to solve the transformation of the studied systems of FOQVNNs, which dissatisfy the commutativity of quaternion multiplication. Moreover, two novel inequalities are deduced based on the general parameters. Compared with the existing inequalities, the new inequalities have their unique superiorities because they can make full use of the additional parameters. Due to the Lyapunov theory, two novel Lyapunov-Krasovskii functionals (LKFs) can be easily constructed. The novelty of LKFs comes from a wider range of parameters, which can be involved in the construction of LKFs. Furthermore, mainly based on the new inequalities and LKFs, more multiple and more flexible criteria are efficiently obtained for the discussed problem. Finally, four numerical examples are given to demonstrate the related effectiveness and availability of the derived criteria.