Trends and predictors of early initiation, exclusive, and continued breastfeeding in Bangladesh (2004-2018): a multilevel analysis of Demographic and Health Survey data

The early initiation of breastfeeding (EIBF) within one hour of birth, exclusive breastfeeding (EBF) to 6 months, and continued breastfeeding (CBF) to 2 years are key infant and young child feeding guidelines promoted globally for optimal child health and development. Using publicly available national survey data from the five most recent, consecutive Bangladesh Demographic and Health Surveys (2004, 2007, 2011, 2014, 2017-18), we assessed the trends in these key breastfeeding indicators. Multiple multilevel logistic regression models were built to assess sociodemographic predictors of breastfeeding using the latest 2017-18 dataset. Both EIBF and EBF have increased significantly between 2004 and 2017-18, from 26% to 60% and 36% to 68%, respectively and CBF decreased from 94% to 85%. Caesarean section delivery conferred lower EIBF practice (OR= 0.34, 95% CI: 0.27 to 0.42) compared to vaginal delivery. Women who were currently working had 32% lower odds of EBF (OR= 0.68, 95% CI: 0.48 to 0.95). Compared to delivery at home, women who delivered in a health facility had 81% higher odds of EBF (OR= 1.81, 95% CI: 1.25 to 2.34). Larger family size (≥5) also predicted EBF (OR= 1.70, 95% CI: 1.21 to 2.40). Rural residency was associated with 2.39 (95% CI 1.32 to 4.31) times of higher odds of CBF. Administrative region (division) was also predictive of the various breastfeeding indicators. Although Bangladesh currently exceeds the 2019 global prevalence rates for these three breastfeeding indicators, efforts should be made to continue improving EIBF and EBF, and to prevent future decreases in CBF.

Bas-relief Modeling from RGB Monocular Images with Regional Division Characteristics

Bas-relief, a form of sculpture art representation, has the general characteristics of sculpture art and satisfies people’s visual and tactile feelings by fully leveraging the advantages of painting art in composition, subject matter, and spatial processing. Bas-relief modeling using images is generally classified into the method based on the three-dimensional (3D) model, that based on the image depth restoration, and that based on multi-images. The 3D model method requires the 3D model of the object in advance. Bas-relief modeling based on the image depth restoration method usually either uses a depth camera to obtain object depth information or restores the depth information of pixels through the image. Bas-relief modeling based on the multi-image requires a short running time and has high efficiency in processing high resolution level images. Our method can automatically obtain the pixel height of each area in the image and can adjust the concave–convex relationship of each image area to obtain a bas-relief model based on the RGB monocular image. First, the edge contour of an RGB monocular image is extracted and refined by the Gauss difference algorithm based on tangential flow. Subsequently, the complete image contour information is extracted and the region-based image segmentation is used to calibrate the region. This method has improved running speed and stability compared with the traditional algorithm. Second, the regions of the RGB monocular image are divided by the improved connected-component labeling algorithm. In the traditional region calibration algorithm, the contour search strategy and the inner and outer contour definition rules of the image considered result in a low region division efficiency. This study uses an improved contour-based calibration algorithm. Then, the 3D pixel point cloud of each region is calculated by the shape-from-shading algorithm. The concave–convex relationships among these regions can be adjusted by human–computer interaction to form a reasonable bas-relief model. Lastly, the bas-relief model is obtained through triangular reconstruction using the Delaunay triangulation algorithm. The final bas-relief modeling effect is displayed by OpenGL. In this study, six groups of images are selected for conducting regional division tests, and the results obtained by the proposed method and other existing methods are compared. The proposed algorithm shows improved image processing running time for different complexity levels compared with the traditional two-pass scanning method and seed filling method (by approximately 2 s) and with the contour tracking method (by approximately 4 s). Next, image depth recovery experiments are conducted on four sets of images, namely the “ treasure seal,” “Wen Emperor seal,” “lily pattern,” and “peacock pattern,” and the results are compared. The depth of the image obtained by the traditional algorithm is generally lower than the actual plane, and the relative height of each region is not consistent with the actual situation. The proposed algorithm provides height values consistent with the height value information judged in the original image and adjusts the accurate concave–convex relationships. Moreover, the noise in the image is reduced and relatively smooth surfaces are obtained, with accurate concave–convex relationships. The proposed bas-relief model based on RGB monocular images can automatically determine the pixel height of each image area in the image and adjust the concave–convex relationship of each image area. In addition, it can recover the 3D model of the object from the image, enrich the object of bas-relief modeling, and expand the creation space of bas-relief, thereby improving the production efficiency of the bas-relief model based on RGB monocular images. The method has certain shortcomings, which require further exploration. For example, during the process of image contour extraction for region division, small differences exist between the obtained result and the actual situation, which can in turn affect the image depth recovery in the later stage. In addition, partial distortion may occur in the process of 3D reconstruction, which requires further research on point cloud data processing to reconstruct a high-quality three-dimensional surface.

Research on Credit Risk Identification of Internet Financial Enterprises Based on Big Data

The advent of the era of big data has provided a new way of development for Internet financial credit collection. The traditional methods of credit risk identification of Internet financial enterprises cannot get the characteristics of credit risk zoning, leading to large errors in the results of credit risk identification. Therefore, this paper proposes a new method of credit risk identification based on big data for Internet financial enterprises. According to the big data perspective, the credit risk assessment steps of Internet financial enterprises are analyzed and the weight of assessment indicators is calculated using the improved analytic hierarchy process (AHP), and the linear weighted synthesis method is applied to comprehensively assess the credit of clients. Using the unique characteristics of big data credit risk region division, the big data credit risk is determined by rule-based matching method. The eXtreme Gradient Boosting (XGBoost) machine learning algorithm is used to establish a credit risk identification model of Internet financial enterprises. The kappa coefficient and ROC curve are used to evaluate the performance of the proposed method. Experimental results show that the proposed method can accurately assess the credit risk of Internet financial enterprises.

Research on Region Division of Large Workpiece Based on Dexterous Workspace of Robot

In order to complete the processing of large-scale workpieces, a region division of large-scale workpieces based on robot dexterous workspace is studied. The linkage coordinate system of the robot is established by D-H method, and the forward kinematics equation of the robot is obtained; Monte Carlo method is used to analyze the workspace of the robot, and MATLAB is used to program to draw the workspace and task space of the robot; Taking an example part as the object, the feature of the part is studied, and the process of determining the task space area of the workpiece in the dexterous workspace of the robot is given, and the region of the workpiece is divided based on the size of the task space and the geometric features of the workpiece.

Study on Fracture Distribution and Local Brittleness Characteristics Based on Stepwise Regression Method

The brittleness of rock is an important parameter that influences and controls the evolution mechanism of the fracture and formation of a fracture net. The existing methods of brittle characterization are describing the brittleness of rock mass as a whole. They lack reliability descriptions to guide the fracture strike and improve the volume of the reservoir. It is considered that the macroscopic brittle fracture of a rock is the process of continuous initiation and propagation of local fractures in the rock mass under the action of external loads. The macroscopic fracture is the appearance caused by a local rupture to a certain extent, and the local rupture is the root cause of macroscopic fracture. The study of the local brittleness of a rock can reveal the intrinsic nature of its fracture behavior and can reflect the evolution mechanism of fracture more directly and accurately. In this paper, coring sampling in field outcrop is first carried out, and the break evolution law of core is described by a CT scanner. The mineral compositions in the core are determined by a mineral analysis diffractometer. The regulation of the rock local brittleness with different mineral contents is analyzed. And a new method for local brittle region division and characterization of rock has been developed. This method gives the connotation relation of the rock brittle fracture as a whole induced by a local brittle fracture. And it provides a new approach to study the law of a rock fracture.

Improved KNN Algorithm Based on Preprocessing of Center in Smart Cities

The KNN algorithm is one of the most famous algorithms in machine learning and data mining. It does not preprocess the data before classification, which leads to longer time and more errors. To solve the problems, this paper first proposes a PK-means++ algorithm, which can better ensure the stability of a random experiment. Then, based on it and spherical region division, an improved KNNPK+ is proposed. The algorithm can select the center of the spherical region appropriately and then construct an initial classifier for the training set to improve the accuracy and time of classification.

Novel KNN Algorithms for Spherical Regions Based on Clustering and Region Division

The KNN classification algorithm is one of the most commonly used algorithm in the AI field. But classical KNN classification algorithm does not preprocess data before classification calculation, which results in a long time required for classification and a decrease in classification accuracy. To solve the above problems, this paper proposes two improved algorithms, namely KNNTS, and KNNTS-PK+. The two improved algorithms are based on KNNPK+ algorithm, which uses PK-Means + + algorithm to select the center of the spherical region, and sets the radius of the region to form a sphere to divide the data set in the space. The KNNPK+ algorithm improves the classification accuracy on the premise of stabilizing the classification efficiency of KNN classification algorithm. In order to improve the classification efficiency of KNN algorithm on the premise that the accuracy of KNN classification algorithm remains unchanged, KNNTS algorithm is proposed. It uses tabu search algorithm to select the radius of spherical region, and uses spherical region division method with equal radius to divide the data set in space. On the basis of the first two improved algorithms, KNNTS-PK+ algorithm combines them to divide the data sets in space. After preprocessing the data by two methods, experiments are carried out on the new data set and the classification results were obtained. Results revealed show that the two improved algorithms can effectively improve the classification accuracy and efficiency after the data samples are cut reasonably.

Size and shape regional differentiation during the development of the spine in the nine-banded armadillo (Dasypus novemcinctus)

Xenarthrans (armadillos, anteaters, sloths and their extinct relatives) are unique among mammals in displaying a distinctive specialization of the posterior trunk vertebrae - supernumerary vertebral xenarthrous articulations. This study seeks to understand how xenarthry develops through ontogeny and if its development impacts regionalisation patterns (thoracic vs lumbar). Using 3D geometric morphometrics on the neural arches of vertebrae, we explore phenotypic, allometric, and disparity patterns of the different axial morphotypes during ontogeny of nine-banded armadillos. Shape-based regionalisation analyses showed that adult thoracolumbar column is divided into three regions according to the presence or absence of ribs and the presence or absence of xenarthrous articulations. A three-region-division was retrieved in almost all specimens through development, although younger stages (e.g. embryos, neonates) have more region boundary variability. In size-based regionalisation analyses, thoracolumbar vertebrae are separated into two regions according to the presence or absence of xenarthry. We show that xenarthrous thoracic vertebrae grow at a slower rate, while anterior thoracics and lumbar grow at a faster rate relatively, with rates decreasing anteroposterioly in the former and increasing anteroposterioly in the latter. We propose that different proportions between vertebrae and vertebral regions might result from differences in growth pattern and timing of ossification, which might in turn correlate with expression patterns of Hox genes.