Crop disease diagnosis is an essential step in crop disease treatment and is a hot issue in agricultural research. However, in agricultural production, identifying only coarse-grained diseases of crops is insufficient because treatment methods are different in different grades of even the same disease. Inappropriate treatments are not only ineffective in treating diseases but also affect crop yield and food safety. We combine IoT technology with deep learning to build an IoT system for crop fine-grained disease identification. This system can automatically detect crop diseases and send diagnostic results to farmers. We propose a multidimensional feature compensation residual neural network (MDFC–ResNet) model for fine-grained disease identification in the system. MDFC–ResNet identifies from three dimensions, namely, species, coarse-grained disease, and fine-grained disease and sets up a compensation layer that uses a compensation algorithm to fuse multidimensional recognition results. Experiments show that the MDFC–ResNet neural network has better recognition effect and is more instructive in actual agricultural production activities than other popular deep learning models.

The application of deep learning methods to diagnose diseases has become a new research topic in the medical field. In the field of medicine, skin disease is one of the most common diseases, and its visual representation is more prominent compared with the other types of diseases. Accordingly, the use of deep learning methods for skin disease image recognition is of great significance and has attracted the attention of researchers. In this study, we review 45 research efforts on the identification of skin disease by using deep learning technology since 2016. We analyze these studies from the aspects of disease type, data set, data processing technology, data augmentation technology, model for skin disease image recognition, deep learning framework, evaluation indicators, and model performance. Moreover, we summarize the traditional and machine learning-based skin disease diagnosis and treatment methods. We also analyze the current progress in this field and predict four directions that may become the research topic in the future. Our results show that the skin disease image recognition method based on deep learning is better than those of dermatologists and other computer-aided treatment methods in skin disease diagnosis, especially the multi deep learning model fusion method has the best recognition effect.

In this paper, an array antenna for generating multi-mode orbital angular momentum (OAM) vortex waves and having electrically controlled mode reconfigurable function is proposed. The proposed antenna is based on a reconfigurable feed network (RFN) consisting of multiple PIN diodes as reconfigurable switches, a direct current (DC) control board and a 2×4 radial uniform circular array. By controlling the coding sequence of the corresponding PIN diode DC bias voltage on the RFN using a programmable microcontroller, the OAM modes generated by the proposed antenna can be dynamically reconfigured between l=0, ±1, and ±2. Moreover, the OAM beams can be generated in a wide frequency band of 5.5GHz to 6.1 GHz and 5.65GHz to 6.1GHz for l=±1, ±2 modes and l=0 mode, respectively. The prototype of the proposed antenna is simulated and fabricated to validate the proposed design. Both far-field and near-field experiments were performed to verify the feasibility of the proposed design. The results of the numerical simulation and measurement have a good agreement with each other.

We design multifunctional CDDP-VPA@ZrMOF-Cys-PEG nanoparticles (CVZP NPs) based on the properties of valproic acid (VPA) that can downregulate the expression of vascular endothelial growth factor (VEGF) to reduce the drug resistance of tumor cells. In vivo experiments confirm that chemotherapy combined with microwave thermal therapy (MWTT) can significantly improve the therapeutic effect of cisplatin-resistant lung cancer.

This paper proposes an electronically reconfigurable multimode orbital angular momentum (OAM) patch array antenna using single pole double throw radio frequency switch that can complete dynamic switching of three modes (-1,0,1) at 2.4 GHz. As a new type of multiplexing technology, the application value of OAM technology in radar imaging and wireless communication has been continuously verified. Considering that many theoretical algorithms are proposed, a multimode vortex antenna that meets the experimental requirements should be developed. The proposed antenna consists of RF switch, programmable microcontroller, phase-shifted feed network, and $2\times2$ rectangular array. Different feeder networks were selected by controlling the RF switch to generate vortex beams with different modes. The proposed antenna was numerically simulated and physically produced. The feasibility of the design was verified through experiments. The simulation and design results are basically the same. The proposed scheme has simple structure, easy to design and deploy, and has important significance in the design of many mode vortex antenna applications in communication and radar imaging.