No abstract

Three-dimensional (3D) graphene has attracted increasing attention in electrochemical devices. However, the existing preparation technologies usually involve a solvent process, which introduces defects and functional groups into the 3D network. Here, we find the defects and functional groups influence the electrochemical stability of graphene. After an electrochemical process, the current decreases by more than 1 order of magnitude, indicating remarkable etching of graphene. To improve the electrochemical stability, we develop a solvent-free preparation process to produce 3D graphene for the first time. After growth on a 3D microporous copper by chemical vapor deposition (CVD), the copper template is removed by a high temperature evaporation process, resulting in 3D graphene network without any solvent process involved. The samples exhibit remarkably improved stability with durable time 2 times, compared with normal CVD samples, and 55 times, compared with reduced graphite oxide, and no obvious etching is observed at 1.6 V versus saturated calomel electrode, showing great potential for application in future 3D graphene-based high stable electrochemical devices.

BackgroundDevelopmental dysplasia of the hip (DDH) is a common orthopedic disease in children. In clinical surgery, it is essential to quickly and accurately locate the exact position of the lesion, and there are still some controversies relating to DDH status. We adopt artificial intelligence (AI) to solve the above problems.MethodsIn this paper, automatic DDH measurements and classifications were achieved using a three-stage pipeline. In the first stage, we used Mask-RCNN to detect the local features of the image and segment the bony pelvis, including the ilium, pubis, ischium, and femoral heads. For the second stage, local image patches focused on semantically related areas for DDH landmarks were extracted by high-resolution network (HRNet). In the third stage, some radiographic results are obtained. In the above process, we used 1,265 patient x-ray samples as the training set and 133 samples from two other medical institutions as the verification set. The results of AI were compared with three orthopedic surgeons for reliability and time consumption.ResultsAI-aided diagnostic system's Tönnis and International Hip Dysplasia Institute (IHDI) classification accuracies for both hips ranged from 0.86 to 0.95. The measurements of numerical indices showed that there was no statistically significant difference between surgeons and AI. Tönnis and IHDI indicators were similar across the AI system, intermediate surgeon, and junior surgeon. Among some objective interpretation indicators, such as acetabular index and CE angle, there were good stability and consistency among the four observers. Intraclass consistency of acetabular index and CE angle among surgeons was 0.79–0.98, while AI was 1.00. The measurement time required by AI was significantly less than that of the doctors.ConclusionThe AI-aided diagnosis system can quickly and automatically measure important parameters and improve the quality of clinical diagnosis and screening referral process with a convenient and efficient way.

No abstract

With the continuous development and improvement of artificial intelligence technology, machine learning technology has also been extensively developed, which has promoted the development of computer vision, image processing, natural language processing, and other fields. Purpose. This article aims to apply the image processing technology based on machine learning in the detection of childhood diseases and propose the application of image processing technology to the detection of childhood diseases. This article introduces machine learning, image recognition technology, and related algorithms in detail and experiments on image recognition technology based on machine learning. The experimental results show that image recognition technology based on machine learning can well identify white blood cells that are difficult to distinguish with the naked eye, with a recognition rate of up to 90%. Applying image recognition technology based on machine learning in disease diagnosis has greatly improved the level of medical diagnosis.

Neonatal necrotizing enterocolitis is a severe neonatal intestinal disease. Timely identification of surgical indications is essential for newborns in order to seek the best time for treatment and improve prognosis. This paper attempts to establish an algorithm model based on multimodal clinical data to determine the features of surgical indications and construct an auxiliary diagnosis model. The proposed algorithm adds hypergraph constraints on the two modal data based on Joint Nonnegative Matrix Factorization (JNMF), aiming to mine the higher-order correlations of the two data features. In addition, the adjacency matrix of the two kinds of data is used as a network regularization constraint to prevent overfitting. Orthogonal and L1-norm regulations were introduced to avoid feature redundancy and perform feature selection, respectively, and confirmed 14 clinical features. Finally, we used three classifiers, random forest, support vector machine, and logistic regression, to perform binary classification of patients requiring surgery. The results show that when the features selected by the proposed algorithm model are classified by random forest, the area under the ROC curve is 0.8, which has high prediction accuracy.

No abstract

In-Ga-Zn-O (IGZO) nanometer thin-film transistors (TFTs) are promising candidates for liquid crystal display (LCD) drivers and human body sensors. It is critically important to study the temperature dependence of IGZO TFTs on electrical properties. However, the mechanism of the enhanced IGZO TFT function at different temperatures has not been fully determined. Here, a single transistor was used to act as a temperature sensor to save the space, and transfer curves shifting positively were found for the first time, different from conventional temperature-dependent behaviors. This behavior suggests at least two mechanisms that dominate and are responsible for the different shifts. According to the Arrhenius law, the formula between temperature (T) and threshold voltage (V TH ) was modified. Besides, two different values of activation energy (E a ) on different temperature ranges indicate that there are two main mechanisms. For further verification, different experimental approaches were conducted to study the temperature effects, including subgap density of states (DOS), X-ray photoelectron spectroscopy (XPS), and simulation experiments. This mechanism, shown here for the first time, might better the understanding of TFTs and, thus, further their applications in medicine and beyond.