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The purpose of this paper is to model and analyze energy transfer through near‐field resonant coupling for high power light‐emitting diode (HPLED) illumination, with the intention to increase the appreciation and use of the coupled mode theory (CMT) other than the usual equivalent circuit method.

The CMT is extensively used to analyze the wireless energy transfer system because of its generality, simplicity, accuracy and intuitive understanding of near‐field resonant energy coupling mechanism.

The CMT forms a general way to model and analyze the non‐radiative magnetic resonant coupling systems. It is suitable not only for low frequency coupling but also for high frequency (of million‐Hertz) in which the circuit parameters are not easily obtained. Optimal coupling condition corresponding to the maximum power transfer is identified based on the CMT, and the multiple limit cycle phenomenon caused by the nonlinear nature of the HPLED is also described on the CMT model.

This paper takes advantages of CMT, i.e. generality, simplicity, accuracy and intuitive understanding to analyze the near‐field resonant energy coupling system. Key characteristics of the systems are explored based on the CMT, not the usual equivalent circuit method. The influence of nonlinear nature of the high power LED on energy transfer is also investigated. This work seeks a more general way than conventional equivalent circuit method to model and analyze the resonant magnetic system and the results obtained could facilitate better understanding of the resonant magnetic coupling mechanism and optimal design of the near‐field energy transfer system.

The purpose of this paper is to provide the historical background of genealogical records and analyze the value of Chinese genealogical research through the study of names and genealogical resources.

The paper examines the historical evolution and value of Chinese genealogical records, with the focus on researching the Islamic Chinese names used by the people living in Guilin. The highlight of this paper includes the analysis and evolution of the Islamic Chinese names commonly adopted by the local people in Guilin. It concludes with the recommendations on emphasizing and making the best use of genealogical records to enhance the research value of Chinese overseas studies.

The paper covers the history of Islam and describes how the religion was introduced into China, as well as Muslims' ethnicity and identity. It also places focus on the importance of building a research collection in Asian history and Chinese genealogy.

This research study has a strong subject focus on Chinese genealogy, Asian history, and Islamic Chinese surnames. It is a narrow field that few researchers have delved into.

The results of this study will assist students, researchers, and the general public in tracing the origin of their surnames and developing their interest in the social and historical value of Chinese local history and genealogies.

The study of Chinese surnames is, by itself, a particular field for researching the social and political implications of contemporary Chinese society during the time the family members lived.

Very little research has been done in the area of Chinese local history and genealogy. The paper would be of value to researchers such as historians, sociologists, ethnologists and archaeologists, as well as students and anyone interested in researching a surname origin, its history and evolution.

Plant cultivation holds a pivotal role in agriculture, necessitating precise disease identification for the overall health of plants. This research conducts a comprehensive comparative analysis between two prominent deep learning algorithms, convolutional neural network (CNN) and DenseNet121, with the goal of enhancing disease identification in tomato plant leaves.

The dataset employed in this investigation is a fusion of primary data and publicly available data, covering 13 distinct disease labels and a total of 18,815 images for model training. The data pre-processing workflow prioritized activities such as normalizing pixel dimensions, implementing data augmentation and achieving dataset balance, which were subsequently followed by the modeling and testing phases.

Experimental findings elucidated the superior performance of the DenseNet121 model over the CNN model in disease classification on tomato leaves. The DenseNet121 model attained a training accuracy of 98.27%, a validation accuracy of 87.47% and average recall, precision and F1-score metrics of 87, 88 and 87%, respectively. The ultimate aim was to implement the optimal classifier for a mobile application, namely Tanamin.id, and, therefore, DenseNet121 was the preferred choice.

The integration of private and public data significantly contributes to determining the optimal method. The CNN method achieves a training accuracy of 90.41% and a validation accuracy of 83.33%, whereas the DenseNet121 method excels with a training accuracy of 98.27% and a validation accuracy of 87.47%. The DenseNet121 architecture, comprising 121 layers, a global average pooling (GAP) layer and a dropout layer, showcases its effectiveness. Leveraging categorical_crossentropy as the loss function and utilizing the stochastic gradien descent (SGD) Optimizer with a learning rate of 0.001 guides the course of the training process. The experimental results unequivocally demonstrate the superior performance of DenseNet121 over CNN.