Crop Disease Identification by Fusing Multiscale Convolution and Vision Transformer.

With the development of smart agriculture, deep learning is playing an increasingly important role in crop disease recognition. The existing crop disease recognition models are mainly based on convolutional neural networks (CNN). Although traditional CNN models have excellent performance in modeling local relationships, it is difficult to extract global features. This study combines the advantages of CNN in extracting local disease information and vision transformer in obtaining global receptive fields to design a hybrid model called MSCVT. The model incorporates the multiscale self-attention module, which combines multiscale convolution and self-attention mechanisms and enables the fusion of local and global features at both the shallow and deep levels of the model. In addition, the model uses the inverted residual block to replace normal convolution to maintain a low number of parameters. To verify the validity and adaptability of MSCVT in the crop disease dataset, experiments were conducted in the PlantVillage dataset and the Apple Leaf Pathology dataset, and obtained results with recognition accuracies of 99.86% and 97.50%, respectively. In comparison with other CNN models, the proposed model achieved advanced performance in both cases. The experimental results show that MSCVT can obtain high recognition accuracy in crop disease recognition and shows excellent adaptability in multidisease recognition and small-scale disease recognition.

Exploring Research on the Construction and Application of Knowledge Graphs for Aircraft Fault Diagnosis.

Fault diagnosis is crucial for repairing aircraft and ensuring their proper functioning. However, with the higher complexity of aircraft, some traditional diagnosis methods that rely on experience are becoming less effective. Therefore, this paper explores the construction and application of an aircraft fault knowledge graph to improve the efficiency of fault diagnosis for maintenance engineers. Firstly, this paper analyzes the knowledge elements required for aircraft fault diagnosis, and defines a schema layer of a fault knowledge graph. Secondly, with deep learning as the main method and heuristic rules as the auxiliary method, fault knowledge is extracted from structured and unstructured fault data, and a fault knowledge graph for a certain type of craft is constructed. Finally, a fault question-answering system based on a fault knowledge graph was developed, which can accurately answer questions from maintenance engineers. The practical implementation of our proposed methodology highlights how knowledge graphs provide an effective means of managing aircraft fault knowledge, ultimately assisting engineers in identifying fault roots accurately and quickly.

Green winner determination method based on environmental performance and minimum adjustment consensus in 4PL transportation service procurement.

Due to the intensified environmental protection consciousness of enterprises and consumers, the green winner determination (GWD) considering environmental performance becomes very important for the 4PL transportation service procurement. In this paper, a new GWD method is studied, which considers different types of attributes including those related to environmental performance and the consensus reaching process (CRP). To characterize multiple types of attributes, linguistic terms, interval numbers, and crisp numbers are combined. To achieve an acceptable consensus level among linguistic evaluations given by different experts, a minimum adjustment consensus model is constructed. And on this basis, an interactive CRP is proposed. Integrating the heterogeneous information addressing process and the CRP, a HC-VIKOR method is developed to promote the 4PL's operational efficiency and service quality. Further, a numerical example is designed to demonstrate the effectiveness of the proposed method. Sensitivity analysis reveals that both the acceptable consensus threshold and the weight of group utility have a significant influence on the winner determination result. Comparison analysis shows that the proposed method outperforms the existing methods. Our study not only extends the traditional winner determination but also provides decision support for the 4PL to provide transportation services efficiently.

Application of U-Net with Global Convolution Network Module in Computer-Aided Tongue Diagnosis.

The rapid development of intelligent manufacturing provides strong support for the intelligent medical service ecosystem. Researchers are committed to building Wise Information Technology of 120 (WIT 120) for residents and medical personnel with the concept of simple smart medical care and through core technologies such as Internet of Things, Big Data Analytics, Artificial Intelligence, and microservice framework, to improve patient safety, medical quality, clinical efficiency, and operational benefits. Among them, how to use computers and deep learning technology to assist in the diagnosis of tongue images and realize intelligent tongue diagnosis has become a major trend. Tongue crack is an important feature of tongue states. Not only does change of tongue crack states reflect objectively and accurately changed circumstances of some typical diseases and TCM syndrome but also semantic segmentation of fissured tongue can combine the other features of tongue states to further improve tongue diagnosis systems' identification accuracy. Although computer tongue diagnosis technology has made great progress, there are few studies on the fissured tongue, and most of them focus on the analysis of tongue coating and body. In this paper, we do systematic and in-depth researches and propose an improved U-Net network for image semantic segmentation of fissured tongue. By introducing the Global Convolution Network module into the encoder part of U-Net, it solves the problem that the encoder part is relatively simple and cannot extract relatively abstract high-level semantic features. Finally, the method is verified by experiments. The improved U-Net network has a better segmentation effect and higher segmentation accuracy for fissured tongue image dataset. It can be used to design a computer-aided tongue diagnosis system.

RF-Medisys: a radio frequency identification-based electronic medical record system for improving medical information accessibility and services at point of care.

This paper presents an innovative electronic medical records (EMR) system, RF-MediSys, which can perform medical information sharing and retrieval effectively and which is accessible via a 'smart' medical card. With such a system, medical diagnoses and treatment decisions can be significantly improved when compared with the conventional practice of using paper medical records systems. Furthermore, the entire healthcare delivery process, from registration to the dispensing or administration of medicines, can be visualised holistically to facilitate performance review. To examine the feasibility of implementing RF-MediSys and to determine its usefulness to users of the system, a survey was conducted within a multi-disciplinary medical service organisation that operates a network of medical clinics and paramedical service centres throughout Hong Kong Island, the Kowloon Peninsula and the New Territories. Questionnaires were distributed to 300 system users, including nurses, physicians and patients, to collect feedback on the operation and performance of RF-MediSys in comparison with conventional paper-based medical record systems. The response rate to the survey was 67%. Results showed a medium to high level of user satisfaction with the radiofrequency identification (RFID)-based EMR system. In particular, respondents provided high ratings on both 'user-friendliness' and 'system performance'. Findings of the survey highlight the potential of RF-MediSys as a tool to enhance quality of medical services and patient safety.

Professional Practice and Innovation: RF-MediSys: A Radio Frequency Identification-Based Electronic Medical Record System for Improving Medical Information Accessibility and Services at Point of Care.

This paper presents an innovative electronic medical records (EMR) system, RF-MediSys, which can perform medical information sharing and retrieval effectively and which is accessible via a 'smart' medical card. With such a system, medical diagnoses and treatment decisions can be significantly improved when compared with the conventional practice of using paper medical records systems. Furthermore, the entire healthcare delivery process, from registration to the dispensing or administration of medicines, can be visualised holistically to facilitate performance review. To examine the feasibility of implementing RF-MediSys and to determine its usefulness to users of the system, a survey was conducted within a multi-disciplinary medical service organisation that operates a network of medical clinics and paramedical service centres throughout Hong Kong Island, the Kowloon Peninsula and the New Territories. Questionnaires were distributed to 300 system users, including nurses, physicians and patients, to collect feedback on the operation and performance of RF-MediSys in comparison with conventional paper-based medical record systems. The response rate to the survey was 67%. Results showed a medium to high level of user satisfaction with the radiofrequency identification (RFID)-based EMR system. In particular, respondents provided high ratings on both 'user-friendliness' and 'system performance'. Findings of the survey highlight the potential of RF-MediSys as a tool to enhance quality of medical services and patient safety.