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Automatic grading method of pomelo fruit according to the shape and size is urgently needed in the industry since the work mainly depends on artificial judgment currently. In this research, a method, which detected the vertical and horizontal size of pomelo by using contour coordinate transformation fitting, fruit shape feature extraction and direction angle compensation algorithm, while it determined the shape defects based on fruit shape index, was proposed. The image acquisition system was self-designed and built up with a CMOS camera, a dot matrix LED light source, a plane mirror, the computer, a box and brackets. The image data containing whole surface information of Shatian pomelo samples with different sizes and shapes were collected by this system. The G-B component grayscale image was chosen for denoising and segmentation. The Laplacian edge detection algorithm was implemented to extract the edge pixels of the fruit. The polynomial fitting method was applied to converse the rectangular coordinates to polar coordinates so that the fruit shape description was simplified. The characteristic point polar angle value was used to compensate the random direction of the vertical and horizontal diameters of the sample. Then the vertical and horizontal diameters of fruit were calculated after classifying the sample shapes into the spherical and the pear-like categories. For the involved 168 pomelo samples, the average error, maximum absolute error and average relative error of the vertical diameters were 2.23 mm, 7.39 mm and 1.6% respectively, while these parameters of the horizontal diameters were 2.21 mm, 7.66 mm and 1.4% respectively. The fruit shape discriminant model was established by using BP neural network algorithm based on the seven features extracted from the fitting function and verified by independent validation set including 3 peak heights, 3 peak widths and 1 trough value difference. The total recognition rate of shape identification was 83.7%. The results illustrated that the method had the potential to measuring the pomelo size and shape for grading fast and non-destructively.

The prevalence of isolated systolic hypertension (ISH) has doubled between 2002−2005 and 2014 among the oldest‐old population in China. However, the prevalence and characteristics of ISH among the oldest‐old population in southwestern China remain less known. This study aimed to investigate the prevalence of ISH among the oldest‐old population in Chengdu and identify associated factors to provide valuable information for disease etiology and prevention. We recruited 1,312 participants aged over 80 years by using a stratified cluster sampling method between September 2015 and June 2016, from three districts (Jinjiang, Qingyang, and Longquanyi) of Chengdu, the largest city of southwest China. A structured questionnaire, anthropometric data, and blood pressure were collected according to the standard method. Blood pressure was measured three times by using a standardized mercury sphygmomanometer after a 10‐minute seated rest. Of 1312 participants, 53.0% (n = 695) had ISH. The prevalence of ISH in men and women was 54.7% and 51.3%, respectively, with no significant sex difference (P =.222). The prevalence of ISH increased with advanced age in men (P for trend = 0.029), 52.5% for the 80−84 years group, 55.2% for the 85−89 years group, and 70.4% for the 90–98 years group, respectively. Multivariable logistic regression analyses found that drinking (OR = 1.85, 95%CI = 1.26−2.71), being overweight (OR = 1.88, 95%CI = 1.19−2.96), and having a higher heart rate (OR = 0.66, 95%CI = 0.51−0.86) were associated with ISH. Stratified by sex, these three factors remained significant in men. Our work highlights that the burden of ISH is substantial among the oldest‐old population in southwestern China. [ABSTRACT FROM AUTHOR]

Vegetation plays a crucial role in nature, with intricate interactions between it and the geographical environment. The Yangtze River Basin (YRB) refers to the third largest river basin globally and an essential ecological security barrier in China. Monitoring vegetation dynamics in the basin is of profound significance for addressing climate change, soil erosion, and biodiversity loss in the basin's ecosystems. Here, we investigate the spatiotemporal variations of vegetation at both the basin and land cover scales in the YRB from 2000 to 2020. We elucidate the determinants driving the changes and explore future normalized difference vegetation index (NDVI) trends. The results indicate that NDVI in the YRB increased at a rate of 0.0032 year−1 (p < 0.01) over the past 21 years, and it is anticipated to maintain an upward trend in the future. Regions in the upper and middle reaches of the YRB demonstrated higher NDVI, whereas regions in the headwater area and the lower reaches showed lower NDVI. Significant vegetation improvement was primarily concentrated in the central part of the basin, while noticeable vegetation degradation was observed in the eastern region. Temperature and wind speed were identified as the primary controlling factors affecting vegetation greenness. Global‐scale climate oscillations played a significant role in driving periodic variations in NDVI, with La Niña events tending to increase NDVI, while El Niño events hindered its rise. Land cover types were influenced by long‐term interactions between natural factors and human activities, although short‐term vegetation variations might be more affected by the latter. Our findings provide valuable insights into the mechanisms behind vegetation variability driven by multiple variables, and the strong vegetation carbon sink capacity advances the conservation and development of ecosystems. [ABSTRACT FROM AUTHOR]

Recovering copper foil and crushed aluminum from end-of-life vehicles (ELVs) is a significant issue in the recycling industry. As a key technology for sorting aluminum, copper, and other non-ferrous metals, eddy current separation (ECS) is efficient in isolating the non-ferrous metals according to their different electrical conductivity and density. However, further research is still needed in the separation of large-size copper foil and crushed aluminum from scrapped vehicles. In this study, support vector regression (SVR) and the sparrow search algorithm (SSA) are exploited for the first time to be used in optimizing the Halbach magnetic roller. Firstly, the numerical simulation results are based on the response surface methodology (RSM). Then, the accuracy of four kernel functions employing SVR is compared to select a kernel function. The sparrow search algorithm (SSA) is proposed to optimize the structural parameters of the Halbach magnetic roller, concentrating on the above-selected kernel function. Meanwhile, the parameters are confirmed. Numerical simulation results indicate that machine learning for magnetic roller optimization is feasible. [ABSTRACT FROM AUTHOR]

Fracture initiation in plastic metals is attributed to the development of voids. Analyzing the nucleation and growth processes of voids facilitates the study of plastic deformation and fracture mechanisms in metal materials. Uniaxial tensile tests were conducted on two high-quality carbon structural steels, and the microfracture surface morphology of the tensile specimens was observed by using a scanning electron microscope (SEM). From the perspective of vacancy condensation, the nucleation mechanism of voids in the absence of inclusions or particles was analyzed. Based on the continuum damage mechanics theory and the Rice–Tracy (R-T) model, a damage parameter considering the void volume fraction was derived, and a plastic potential function, hardening curve, and constitutive model for the plastic deformation process of the plastic metal material were established. Based on the uniaxial tensile test data of the two sheets of high-quality carbon steel, the strain range data in the hardening stage were converted into true stress–plastic strain data, and the established hardening curve was used to fit the true stress–plastic strain data. The results showed good agreement between the established hardening curve and the experimental results, which effectively reflected the deformation process of ductile fractures in plastic metal materials. [ABSTRACT FROM AUTHOR]

Background. Tuberculosis (TB), a multisystemic disease with protean presentation, remains a major global health problem. Although concurrent pulmonary tuberculosis (PTB) and extrapulmonary tuberculosis (EPTB) cases are commonly observed clinically, knowledge regarding concurrent PTB-EPTB is limited. Here, a large-scale multicenter observational study conducted in China aimed to study the epidemiology of concurrent PTB-EPTB cases by diagnostically defining TB types and then implementing association rules analysis. Methods. The retrospective study was conducted at 21 hospitals in 15 provinces in China and included all inpatients with confirmed TB diagnoses admitted from Jan 2011 to Dec 2017. Association rules analysis was conducted for cases with concurrent PTB and various types of EPTB using the Apriori algorithm. Results. Evaluation of 438,979TB inpatients indicated PTB was the most commonly diagnosed (82.05%) followed by tuberculous pleurisy (23.62%). Concurrent PTB-EPTB was found in 129,422 cases (29.48%) of which tuberculous pleurisy was the most common concurrent EPTB type observed. The multivariable logistic regression models demonstrated that odds ratios of concurrent PTB-EPTB cases varied by gender and age group. For PTB cases with concurrent EPTB, the strongest association was found between PTB and concurrent bronchial tuberculosis (lift = 1.09). For EPTB cases with concurrent PTB, the strongest association was found between pharyngeal/laryngeal tuberculosis and concurrent PTB (lift = 1.11). Confidence and lift values of concurrent PTB-EPTB cases varied with gender and age. Conclusions. Numerous concurrent PTB-EPTB case types were observed, with confidence and lift values varying with gender and age. Clinicians should screen for concurrent PTB-EPTB in order to improve treatment outcomes. [ABSTRACT FROM AUTHOR]