Search

This study presents a classification model that incorporates significant enhancements based on the Bayesian method and genetic algorithm (BGA). Firstly, the prior probabilities in each iteration are determined using the ratio of the number of elements in each group, obtained through clustering techniques, to the total number of elements in the training set. Secondly, an automatic selection process optimizes the training set to minimize classification errors. Finally, the traditional genetic algorithm operators are improved by utilizing the Bayes error as the objective function. These improvements combine to create an effective classification model. Additionally, the BGA demonstrates effective performance on real data using the established MATLAB procedure. A numerical example illustrates the superiority of the proposed algorithm compared to existing methods. The study also applies the BGA for image classification using the Gabor filter, which extracts essential image features. The proposed model outperforms popular methods in classifying various numerical and image datasets. These applications highlight the potential of this study in real-world scenarios.

Abstract In this study, we construct a 3-D shear wave velocity structure of the crust and upper mantle in South China Sea and its surrounding regions by surface wave dispersion analysis. We use the multiple filter technique to calculate the group velocity dispersion curves of fundamental mode Rayleigh and Love waves with periods from 14 s to 120 s for earthquakes occurred around the Southeast Asia. We divide the study region (80° E–140° E, 16° S–32° N) into 3° × 3° blocks and use the constrained block inversion method to get the regionalized dispersion curve for each block. At some chosen periods, we put together laterally the regionalized group velocities from different blocks at the same period to get group velocity image maps. These maps show that there is significant heterogeneity in the group velocity of the study region. The dispersion curve of each block was then processed by surface wave inversion method to obtain the shear wave velocity structure. Finally, we put the shear wave velocity structures of all the blocks together to obtain the three-dimensional shear wave velocity structure of crust and upper mantle. The three-dimensional shear wave velocity structure shows that the shear wave velocity distribution in the crust and upper mantle of the South China Sea and its surrounding regions displays significant heterogeneity. There are significant differences among the crustal thickness, the lithospheric thickness and the shear wave velocity of the lid in upper mantle of different structure units. This study shows that the South China Sea Basin, southeast Sulu Sea Basin and Celebes Sea Basin have thinner crust. The thickness of crust in South China Sea Basin is 5–10 km; in Indochina is 25–40 km; in Peninsular Malaysia is 30–35 km; in Borneo is 30–35 km; in Palawan is 35 km; in the Philippine Islands is 30–35 km, in Sunda Shelf is 30–35 km, in Southeast China is 30–40 km, in West Philippine Basin is 5–10 km. The South China Sea Basin has a lithosphere with thickness of about 45–50 km, and the shear wave velocity of its lid is about 4.3–4.7 km/s; Indochina has a lithosphere with thickness of about 55–70 km, and the shear wave velocity of its lid is about 4.3–4.5 km/s; Borneo has a lithosphere with thickness of about 55–60 km, and the shear wave velocity of its lid is about 4.1–4.3 km/s; the Philippine Islands has a lithosphere with thickness of about 55–60 km, and the shear wave velocity of its lid is about 4.2–4.3 km/s, West Philippine Basin has a lithosphere with thickness of about 50–55 km, and the shear wave velocity of its lid is about 4.7–4.8 km/s, Sunda Self has a lithosphere with thickness of about 55–65 km, and the shear wave velocity of its lid is about 4.3 km/s. The Red-River Fault Zone probably penetrates to a depth of at least 200 km and is plausibly the boundary between the South China Block and the Indosinia Block.

A 26-year-old man had a massive intraabdominal hemorrhage from a hepatocellular adenoma (HCA). The tumor arose within a liver that demonstrated generalized nodular regenerative hyperplasia. The patient had no factors predisposing to either HCA or nodular regenerative hyperplasia (NRH) of the liver. Although rare, HCA should be included in the differential diagnosis of spontaneous intraperitoneal hemorrhage even in young men. The coexistence of HCA and NRH of the liver in this patient may indicate a common pathogenesis.

ABSTRACTThe rapid spread of strains of malaria parasites that are resistant to several drugs has threatened global malaria control. Hence, the aim of this study was to predict the antimalarial activity of chemical compounds that possess anti-hemozoin-formation activity as a new means of antimalarial drug discovery. After the initial in vitroanti-hemozoin-formation high-throughput screening (HTS) of 9,600 compounds, a total of 224 hit compounds were identified as hemozoin inhibitors. These 224 compounds were tested for in vitroerythrocytic antimalarial activity at 10 μM by using chloroquine-mefloquine-sensitive Plasmodium falciparumstrain 3D7A. Two independent experiments were conducted. The physicochemical properties of the active compounds were extracted from the ChemSpider and SciFinder databases. We analyzed the extracted data by using Bayesian model averaging (BMA). Our findings revealed that lower numbers of S atoms; lower distribution coefficient (log D) values at pH 3, 4, and 5; and higher predicted distribution coefficient (ACD log D) values at pH 7.4 had significant associations with antimalarial activity among compounds that possess anti-hemozoin-formation activity. The BMA model revealed an accuracy of 91.23%. We report new prediction models containing physicochemical properties that shed light on effective chemical groups for synthetic antimalarial compounds and help with in silicoscreening for novel antimalarial drugs.