Larger Thyroid Volume and Adequate Iodine Nutrition in Chinese Schoolchildren: Local Normative Reference Values Compared with WHO/IGN

Objective. Thyroid volume measured by ultrasound to define goiter needs reliable local thyroid volume reference from iodine-sufficient populations. The aim of this study is to explore the reference interval for normal thyroid volume in schoolchildren aged 8–10 years from Zhejiang Province, China. Methods. A probability-proportionate-to-size sampling method was applied to select a representative sample of 1213 children aged 8–10 years in Zhejiang Province to detect the thyroid volume, salt iodine, and urine iodine. Results. Median urinary iodine concentration in involved schoolchildren was 178.30 (125.00) μg l−1, with the percentage of samples less than 100 μg l−1 as 12.69% and more than 300 μg l−1 as 15.25%. Thyroid volume was significantly correlated with age and anthropometric measurements independently of each other. The 97th percentile of thyroid volume in our study was larger generally than the new international reference. Conclusions. The iodine nutritional status in Zhejiang Province was at an adequate level. Despite some limitations in this study, we initially established the reference values for thyroid volume in 8–10-year-old schoolchildren in Zhejiang Province, China, as a local reference to be used for monitoring iodine deficiency disorders

Parametric Analysis of the Toothed Electromagnetic Spring Based on the Finite Element Model

Active vibration control shows excellent performance in vibration isolation. In this work, the finite element model of a toothed electromagnetic spring (TES) is established using ANSYS Maxwell software. Subsequently, a static characteristic experiment of the TES is carried out, and the validity of the model is verified. Based on the established finite element model, the influence of key structural parameters on the static characteristics of the electromagnetic spring is analyzed. The results show that the parameters of the magnetic teeth have a significant impact on the performance of the electromagnetic spring. As the number of teeth increases, the electromagnetic force first increases and then decreases. With the increase in the tooth height or width, the maximum electromagnetic force gradually increases to the maximum value and then stabilizes. It should be noted that the tooth width simultaneously affects the maximum electromagnetic force, stiffness characteristics, and effective working range of the TES. This work provides a basis for further exploring the application of electromagnetic springs within the field of active vibration control

Dynamic Characteristic Analysis of a Toothed Electromagnetic Spring Based on the Improved Bouc—Wen Model

Electromagnetic spring active isolators have attracted extensive attention in recent years. The standard Bouc–Wen model is widely used to describe hysteretic behavior but cannot accurately describe asymmetric behavior. The standard Bouc–Wen model is improved to better describe the dynamic characteristic of a toothed electromagnetic spring. The hysteresis model of toothed electromagnetic spring is established by adding mass, damping, and asymmetric correction terms with direction. Subsequently, the particle swarm optimization algorithm is used to identify the parameters of the established model, and the results are compared with those obtained from the experiment. The results show that the current has a significant impact on the dynamic curve. When the current increases from 0.5 A to 2.0 A, the electromagnetic force sharply increases from 49 N to 534 N. Under different excitations and currents, the residual points predicted by the model proposed in this work fall basically in the horizontal band region of −20–20 N (for an applied current of 1.0 A) and −40–80 N (for an application of 4.5 mm/s). Furthermore, the maximum relative error of the model is 12.75%. The R2 of the model is higher than 0.98 and the highest value is 0.9993, proving the accuracy of the established model

Prevalence, Risk Factors, and Causes of Visual Impairment in an Elderly Chinese Uygur Population in Southern Xinjiang

Purpose. To investigate the prevalence, risk factors, and major causes of visual impairment (VI) in an elderly Chinese Uygur population in southern Xinjiang. Methods. This was a population-based cross-sectional study. Participants aged 50 years and older from Haohan Country, Xinjiang Uygur Autonomous Region, were enrolled from August 2018 to December 2018 using cluster sampling. Participants underwent examinations including presenting visual acuity (PVA), pinhole vision, slit-lamp, intraocular pressure, and direct ophthalmoscopy. Participants’ education and demographic information was collected by a questionnaire. The prevalence, risk factors, and major causes of vision loss were evaluated. Results. A total of 1465 participants (85.4% response rate) were enrolled. The mean age of the subjects was 59.1 ± 9.7 years. The prevalence of mild VI, moderate VI, severe VI, and blindness in the better eye was 13.3%, 12.8%, 2.9%, and 3.4%, respectively. The prevalence of low vision and blindness in this study was higher than that in Altay & Tacheng and Changji in northern Xinjiang, lower than that in Luxi, and similar to that in Tibet. The multiple logistic regression analysis showed that age, education level, and body mass index (BMI) were significantly associated with low vision and blindness (P≤0.001,<0.05,0.002, respectively). The major causes of low vision were cataract (42.6%), refractive error (19.6%), and glaucoma (12.6%), whereas the primary causes of blindness were cataract (34%), glaucoma (34%), and retinitis pigmentosa (10%). Conclusions. VI is an important public health issue among elderly Uygur individuals in the area, especially for those with low education levels. Cataract is the leading cause of low vision and blindness

Underwater Acoustic Source Localization via Kernel Extreme Learning Machine

Fiber-optic hydrophones have received extensive research interests due to their advantage in ocean underwater target detection. Here, kernel extreme learning machine (K-ELM) is introduced to source localization in underwater ocean waveguide. As a data-driven machine learning method, K-ELM does not need a priori environment information compared to the conventional method of match field processing. The acoustic source localization is considered as a supervised classification problem, and the normalized sample covariance matrix formed over a number of snapshots is utilized as an input. The K-ELM is trained to classify sample covariance matrices (SCMs) into different depth and range classes with simulation. The source position can be estimated directly from the normalized SCMs with K-ELM. The results show that the K-ELM method achieves satisfactory high accuracy on both range and depth localization. The proposed K-ELM method provides an alternative approach for ocean underwater source localization, especially in the case with less a priori environment information.</jats:p