Regarding growth pattern and cytological characteristics, borderline ovarian tumors fall between benign and malignant, but they tend to develop malignancy. Currently, it is difficult to accurately diagnose ovarian cancer using common medical imaging methods, and histopathological examination is routinely used to obtain a definitive diagnosis. However, such examination requires experienced pathologists, being labor-intensive, time-consuming, and possibly leading to interobserver bias. By using second-harmonic generation imaging and k-nearest neighbors classifier in conjunction with automated machine learning tree-based pipeline optimization tool, we developed a computer-aided diagnosis method to classify ovarian tissues as being malignant, benign, borderline, and normal, obtaining areas under the receiver operating characteristic curve of 1.00, 0.99, 0.98, and 0.97, respectively. These results suggest that diagnosis based on second-harmonic generation images and machine learning can support the rapid and accurate detection of ovarian cancer in clinical practice.

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In multiple-input-multiple-output (MIMO) systems, the selection of receive and transmit antennas is not just effective in increasing system capacity, but also in reducing RF link costs and system complexity. The exhaustive algorithm, i.e. the joint transmit and receive antenna selection (JTRAS) with the best accuracy, can search all the subsets of both transmit and receive antennas in order to find the optimal solution. However, with the increase of the number of antennas, the computational complexity is too large and its applicability is limited. In this paper, the antennas are coded by fractional coding with the maximization of channel capacity as the basic criterion, and three intelligent algorithms, namely genetic algorithm, cat swarm algorithm and particle swarm algorithm, are applied for antenna selection. The simulation results demonstrate that all three algorithms can efficiently accomplish the antenna selection. In the end, we compare them in terms of speed, accuracy and complexity of the search in MIMO systems.

Because of the impact of the COVID-19 pandemic, more and more people are choosing to buy food online, including eggs. Although this mode of shopping is very modern, many Chinese consumers lack scientific knowledge when selecting eggs. In this study, we used the multivariate statistical analysis and sensory analysis to evaluate and compare the qualities of online sale of free-range and cage eggs. How feeding conditions influence the quality of eggs and how physical characteristics influence the price of eggs were also studied in this work. Our research showed that there is a lack of scientific support for distinguish freerange eggs from cage eggs on the appearance, the color of the yolk does not represent the amount of protein in yolk which may actually be affected by fodders. Moreover, the nutritional quality of free-range eggs is no better than that of cage eggs. Sensory analysis showed that free-range eggs taste better, which is likely because of the higher yolk ratio. Multivariate linear regression analysis showed that Rearing systems, fodder type and yolk color have significant impact on the price (Price = 0.428 × Rearing system −0.235 × Fodder type + 0.191 × Yolk color).

Nowadays, parking problem in urban cities has getting more and more urgent. Most of the problems are mainly due to the information share and management. In the work, we develop a cloud platform based on Huawei soft Ker cloud. On the cloud platform, we build a new smart parking platform, and implement methods, programs, and simulation test system for design and debugging. The functions of license plate recognition, car search and navigation, payment, parking lot search, intelligent parking are implemented in the platform through code debugging.