A Photocatalytic Organic Wastewater Disposing System Based on Solar Energy

AbstractThe paper presents a photocatalytic organic wastewater disposing system, with functions of automatic examination and drainage, for disposing organic wastewater. It is based on photocatalysis and oxidization of complex TiO2 nonmaterial, the system is made up of complex TiO2 nonmaterial carrier units, examining and controlling unit, solar energy unit and auxiliary magnetic field unit. Light absorption diagram shows the vanishing of impurity after adequate time

Electronic Information Major Practice Teaching Reform Concentrated Research and Innovation

AbstractBringing forward the concentration of practical teaching system reform ideas are based on product-oriented, hierarchical progressive. The idea associates all parts of practice, establishing a large framework system. This approach reflects the intrinsic link between knowledge, reached through the practice of teaching will be the focus of a comprehensive knowledge of the purpose. Its implementation will help to change the traditional teaching mode, and to further promote the theory of teaching, thereby raising the overall quality of training

Positional multi-length and mutual-attention network for epileptic seizure classification

The automatic classification of epilepsy electroencephalogram (EEG) signals plays a crucial role in diagnosing neurological diseases. Although promising results have been achieved by deep learning methods in this task, capturing the minute abnormal characteristics, contextual information, and long dependencies of EEG signals remains a challenge. To address this challenge, a positional multi-length and mutual-attention (PMM) network is proposed for the automatic classification of epilepsy EEG signals. The PMM network incorporates a positional feature encoding process that extracts minute abnormal characteristics from the EEG signal and utilizes a multi-length feature learning process with a hierarchy residual dilated LSTM (RDLSTM) to capture long contextual dependencies. Furthermore, a mutual-attention feature reinforcement process is employed to learn the global and relative feature dependencies and enhance the discriminative abilities of the network. To validate the effectiveness PMM network, we conduct extensive experiments on the public dataset and the experimental results demonstrate the superior performance of the PMM network compared to state-of-the-art methods

An IoT-Based Framework of Webvr Visualization for Medical Big Data in Connected Health

Recently, telemedicine has been widely applied in remote diagnosis, treatment and counseling, where the Internet of Things (IoT) technology plays an important role. In the process of telemedicine, data are collected from remote medical equipment, such as CT machine and MRI machine, and then transmitted and reconstructed locally in three-dimensions. Due to the large amount of data to be transmitted in the reconstructed model and the small storage capacity, data need to be compressed progressively before transmission. On this basis, we proposed a lightweight progressive transmission algorithm based on large data visualization in telemedicine to improve transmission efficiency and achieve lossless transmission of original data. Moreover, a novel four-layer system architecture based on IoT has been introduced, including the sensing layer, analysis layer, network layer and application layer. In this way, the three-dimensional reconstructed data at the local end is compressed and transmitted to the remote end, and then visualized at the remote end to show reconstructed 3D models. Thus, it is conducive to doctors in remote real-time diagnosis and treatment, and then realize the data processing and transmission between doctors, patients and medical equipment

Integrin α6 targeted cancer imaging and therapy

Integrins represent ideal targets for molecular imaging and targeted therapy of cancer and their role in cancer has been reviewed extensively elsewhere. Except for αVβ3 and αVβ5, the remaining integrins were not systematically considered and tested as potential therapeutic targets. In recent years, the studies on integrin α6 as a cancer imaging and therapeutic target are increasing, due to their highly expressed in several cancers, and their expression has been associated with poor survival. Integrin α6 appears to be a particularly attractive target for cancer imaging and therapy, and therefore we have developed a wide array of integrin α6-target molecular probes for molecular imaging and targeted therapy of different cancers. Despite the studies on integrin α6 as a cancer imaging and therapeutic target increasing in recent years, most of them were derived from preclinical mouse models, revealing that much more can be done in the future. The development of integrin α6 drugs may now be at an important point, with opportunities to learn from previous research, to explore new approaches. In this review, we will briefly introduce integrin α6 and highlighted the recent advances in integrin α6 targeted imaging and therapeutics in cancer

Patient-Specific Coronary Artery 3D Printing Based on Intravascular Optical Coherence Tomography and Coronary Angiography

Despite the new ideas were inspired in medical treatment by the rapid advancement of three-dimensional (3D) printing technology, there is still rare research work reported on 3D printing of coronary arteries being documented in the literature. In this work, the application value of 3D printing technology in the treatment of cardiovascular diseases has been explored via comparison study between the 3D printed vascular solid model and the computer aided design (CAD) model. In this paper, a new framework is proposed to achieve a 3D printing vascular model with high simulation. The patient-specific 3D reconstruction of the coronary arteries is performed by the detailed morphological information abstracted from the contour of the vessel lumen. In the process of reconstruction which has 5 steps, the morphological details of the contour view of the vessel lumen are merged along with the curvature and length information provided by the coronary angiography. After comparing with the diameter of the narrow section and the diameter of the normal section in CAD models and 3D printing model, it can be concluded that there is a high correlation between the diameter of vascular stenosis measured in 3D printing models and computer aided design models. The 3D printing model has high-modeling ability and high precision, which can represent the original coronary artery appearance accurately. It can be adapted for prevascularization planning to support doctors in determining the surgical procedures

3D Spatial Pyramid Dilated Network for Pulmonary Nodule Classification

Lung cancer mortality is currently the highest among all kinds of fatal cancers. With the help of computer-aided detection systems, a timely detection of malignant pulmonary nodule at early stage could improve the patient survival rate efficiently. However, the sizes of the pulmonary nodules are usually various, and it is more difficult to detect small diameter nodules. The traditional convolution neural network uses pooling layers to reduce the resolution progressively, but it hampers the network’s ability to capture the tiny but vital features of the pulmonary nodules. To tackle this problem, we propose a novel 3D spatial pyramid dilated convolution network to classify the malignancy of the pulmonary nodules. Instead of using the pooling layers, we use 3D dilated convolution to learn the detailed characteristic information of the pulmonary nodules. Furthermore, we show that the fusion of multiple receptive fields from different dilated convolutions could further improve the classification performance of the model. Extensive experimental results demonstrate that our model achieves a better result with an accuracy of 88.6 % , which outperforms other state-of-the-art methods

In Situ Formation of Z-Scheme Bi₂WO₆/WO₃ Heterojunctions for Gas-Phase CO₂ Photoreduction with H₂O by Photohydrothermal Treatment

We report a new photohydrothermal method to prepare a Bi2WO6/WO3 catalytic material for CO2 photoreduction by solar concentrators. The photohydrothermal treatment improves the physico-chemical properties of the Bi2WO6/WO3 material and forms well contact Bi2WO6/WO3 heterojunctions, which increase the maximum reaction rate of CO2 photoreduction to 8.2 times under the simulated light, and the hydrocarbon yield under the real concentrating solar light achieves thousands of μmol·gcata−1. The reason for the high activity is attributed to the direct Z-scheme effect of Bi2WO6/WO3 heterojunctions and the photothermal effect during the course. These findings highlight the utilization of solar energy in CO2 photoreduction and open avenues for the rational design of highly efficient photocatalysts