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Background and purposeWe investigated the prevalence, awareness, and control of vascular risk factors (VRFs) and the use of antithrombotic and statin agents in HCHS (Hispanic Community Health Study)/SOL (Study of Latinos) participants with self-reported history of stroke or transient ischemic attack.MethodsSociodemographic characteristics, medications, and prevalence of different VRFs were recorded. VRF diagnoses and goals were based on the recommendations of professional organizations. Factors associated with optimal VRF control and use of antithrombotic and statin agents were investigated using multivariate logistic regression.ResultsThe analysis included 404 participants (39% men). The prevalences of hypertension, dyslipidemia, and diabetes were 59%, 65%, and 39%, respectively. Among those who met the diagnostic criteria for these diagnoses, the frequencies of awareness were 90%, 75%, and 83%, respectively. In participants who were aware of their VRFs, the prevalences of controlled hypertension, dyslipidemia, and diabetes were 46%, 32%, and 54%. Approximately 46% of the participants were on antithrombotics, 39% on statins, and 26% on both. Only 38% of those with atrial fibrillation received anticoagulation. In multivariate analyses adjusted for baseline sociodemographic characteristics, older age was associated with uncontrolled hypertension and diabetes. Residing in the United States for ≥10 years and born in the United States were associated with uncontrolled diabetes, female sex with uncontrolled dyslipidemia, and lack of health insurance with decreased use of statins and hyperlipidemia.ConclusionsHispanic/Latino adults in the United States have high prevalence and awareness of VRFs but low adherence to secondary stroke prevention strategies. Older adults, women, and uninsured people are vulnerable groups that may benefit from targeted interventions. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02060344.

Given the dominant failure mode of steel bridge deck pavement layers, which is flexural–tensile damage, the dynamic modulus parameters conventionally determined through uniaxial compression testing are found to be inadequate for the design or performance analysis of these layers. In order to simulate the actual stress of a pavement structure under wheel load, the four-point bending fatigue test method and uniaxial compression test method are used to measure the dynamic modulus of an epoxy asphalt mixture, and the differences between the two test methods are analyzed. Furthermore, the four-point bending fatigue test is employed to investigate the dynamic modulus and phase angle properties across varying temperatures and frequencies, facilitating the creation of master curves for these properties and utilizing Sigmoidal models to correlate dynamic modulus data at diverse temperature conditions. This study delves into the influence of epoxy resin content, mixture composition, and aging on the dynamic modulus. The experimental results show that the dynamic modulus measured by uniaxial compression exceeds that obtained from bending fatigue tests, with the difference initially increasing and then decreasing as temperature rises. This discrepancy significantly impacts the mechanical calculations of pavement layers, underscoring the importance of selecting the appropriate testing method. Temperature, frequency, and epoxy resin content have pronounced effects on the viscoelastic properties of the mixtures. Specifically, as temperature increases, the dynamic modulus undergoes a decrease, whereas the phase angle exhibits an increase. Additionally, the dynamic modulus augments with an increase in loading frequency, while the phase angle exhibits varied trends with frequency shifts across different temperatures. Both the WLF and Sigmoidal models are effective in constructing master curve representations for the dynamic flexural modulus and phase angle. The incorporation of epoxy resin transforms asphalt from a primarily viscous to a more elastic material, significantly enhancing the viscoelastic properties of the mixture. Notably, mixtures with 50% and 60% epoxy resin content exhibit comparable dynamic moduli and phase angles, while displaying notably superior performance compared to those with 40% epoxy resin content. For large-scale steel bridge deck pavement, 50% epoxy resin content is recommended. Moreover, epoxy asphalt mixtures demonstrate robust aging resistance, with minimal variations in the dynamic modulus and phase angle before and after aging. The research results can enable the acquisition of dynamic modulus and phase angle data in the whole temperature domain and the whole frequency domain, and provide reliable mixed performance parameters for the study of different application environmental performance of steel bridge deck pavement. [ABSTRACT FROM AUTHOR]

Temperature stability is a critical factor affecting the performance of the most subsystems in the lithography system, due to the high precision and sensitivity of system components to temperature variations. The temperature control system of the lithography machine is characterized by its large inertial constant, time delay characteristics, as well as susceptibility to multiple disturbances. The temperature control system of the lithography machine chiefly requires response speed, high accuracy, and stable and constant temperature control. The contribution of this study is not only avoiding complex precision modeling processes based on real-time parameter estimation and neural network self-tuning but also improving the performance of temperature control in real time under external disturbances. A novel adaptive algorithm with a cascade structure based on generalized predictive control (GPC) and backpropagation (BP) neural network proportional-integral (PI) control is successfully proposed for high accuracy temperature control of lithography machine with a large inertial constant, time delay, and multiple disturbances. In this study, firstly, the liquid circulating temperature control system is developed based on heat exchanger and heater. Secondly, an adaptive controller composed of GPC and BP neural network PI control is successfully proposed. A BP neural network is employed to enable the parameters of the PI controller to adjust in real time, and the mathematical model parameters of the control system are identified in real time by the least square method. Also, the performance of the proposed controller is evaluated comparing with conventional PI controller and GPC controller in terms of robustness and quantitative study of error analysis. Finally, the temperature stability and robustness of the temperature control system controlled with the proposed adaptive GPC-PI algorithm has been investigated by the simulation results carried out in different working scenarios. The simulation results show that the steady-state error from the proposed algorithm is less than 0.01°C under the action of disturbance input. It can effectively counteract the influence of environmental interference and time-varying system parameters. The results of the simulation experiment indicate that the proposed adaptive GPC and PI control algorithm exhibits significant advantages in terms of control accuracy, anti-interference ability, and robustness compared to the conventional control method. [ABSTRACT FROM AUTHOR]

Reasonable stope structural parameters are very important to ensure the safety and efficiency of mining. In this paper, based on the elastic-plastic constitutive model of Mohr-Coulomb strength criterion, the reasonable span and critical span are calculated by using simply supported beam theory and mine room width calculation formula. PLAXIS2D finite element analysis software was used to conduct numerical simulation research on 7 m × 9 m, 5 m × 12 m stope structure parameter schemes and 7 m × 9m, 5 m × 12 m waste rock filling schemes. The optimal structure parameters of the stope were determined based on the calculation and analysis of displacement variation rule, surrounding rock stress distribution and plastic zone. The analysis and simulation results show that Case 1 one-time mining 7 m × 9 m and Case 3 one-time mining 5 m × 12 m, the plastic zone is connected, the simulation calculation is not convergent, and the stope is unstable. Case 2 waste rock filling 7 m × 9 m and Case 4 waste rock filling 5 m × 12 m, the distribution and change of stress, displacement and plastic zone in the goaf under the two cases are compared, and finally the waste rock filling 7 m × 9 m is obtained can improve the economy and safety of mining in the mining area, and verify the feasibility of implementing stope structural parameters and waste rock filling mining system is verified.

Contrastive learning, as an unsupervised technique, is widely employed in image segmentation to enhance segmentation performance even when working with small labeled datasets. However, generating positive and negative data pairs for medical image segmentation poses a challenge due to the presence of similar tissues and organs across different slices in datasets. To tackle this issue, we propose a novel contrastive learning strategy that leverages the relative position differences between image slices. Additionally, we combine global and local features to address this problem effectively. In order to enhance segmentation accuracy and reduce isolated mis‐segmented regions, we employ a two‐dimensional fully connected conditional random field for iterative optimization of the segmentation results. With only 10 labeled samples, our proposed method is able to achieve average dice scores of 0.876 and 0.899 on the public and private dataset heart segmentation tasks, surpassing the PCL method's 0.801 and 0.852. Experimental results on both public and private MRI datasets demonstrate that our proposed method yields significant improvements in medical segmentation tasks with limited annotated samples, outperforming existing semi‐supervised and self‐supervised techniques. [ABSTRACT FROM AUTHOR]

Aims: HNF1B syndrome is caused by defects in the hepatocyte nuclear factor 1B (HNF1B) gene, which leads to maturity‐onset diabetes of the young type 5 and congenital organ malformations. This study aimed to identify a gene defect in a patient presenting with diabetes and severe diarrhea, while also analyzing the prevalence of hypomagnesemia and its correlation with the HNF1B genotype. Materials and Methods: Whole exome sequencing was used to identify responsible point mutations and small indels in the proband and their family members. Multiplex ligation‐dependent probe amplification was carried out to identify HNF1B deletions. Furthermore, an analysis of published data on 539 cumulative HNF1B cases, from 29 literature sources, was carried out to determine the correlation between the HNF1B genotype and the phenotype of serum magnesium status. Results: Using multiplex ligation‐dependent probe amplification, we identified a de novo heterozygous HNF1B deletion in the patient, who showed dorsal pancreas agenesis and multiple kidney cysts, as detected by magnetic resonance imaging. Magnesium supplementation effectively alleviated the symptoms of diarrhea. Hypomagnesemia was highly prevalent in 192 out of 354 (54.2%) patients with HNF1B syndrome. Compared with patients with intragenic mutations, those with HNF1B deletions were more likely to suffer from hypomagnesemia, with an odds ratio of 3.1 (95% confidence interval 1.8–5.4). Conclusions: Hypomagnesemia is highly prevalent in individuals with HNF1B syndrome, and those with HNF1B deletion are more susceptible to developing hypomagnesemia compared with those with intragenic mutations. The genotype–phenotype associations in HNF1B syndrome have significant implications for endocrinologists in terms of genotype detection, treatment decisions and prognosis assessment. [ABSTRACT FROM AUTHOR]