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Introduction: Durian is one of the tropical fruits that requires soil nutrients in its cultivation. It is important to understand the relationship between the content of critical nutrients, such as nitrogen (N), phosphorus (P), and potassium (K) in the soil and durian yield. How to optimize the fertilization plan is also important to the durian planting. Methods: Thus, this study proposes an Improved Radial Basis Neural Network Algorithm (IM-RBNNA) in the durian precision fertilization. It uses the gray wolf algorithm to optimize the weights and thresholds of the RBNNA algorithm, which can improve the prediction accuracy of the RBNNA algorithm for the soil nutrient content and its relationship with the durian yield. It also collects the soil nutrients and historical yield data to build the IM-RBNNA model and compare with other similar algorithms. Results: The results show that the IM-RBNNA algorithm is better than the other three algorithms in the average relative error, average absolute error, and coefficient of determination between the predicted and true values of soil N, K, and P fertilizer contents. It also predicts the relationship between soil nutrients and yield, which is closer to the true value. Discussion: It shows that the IM-RBNNA algorithm can accurately predict the durian soil nutrient content and yield, which is benefited for farmers to make agronomic plans and management strategies. It uses soil nutrient resources efficiently, which reduces the environmental negative impacts. It also ensures that the durian tree can obtain the appropriate amount of nutrients, maximize its growth potential, reduce production costs, and increase yields.

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With the popularization of big data technology, agricultural data processing systems have become more intelligent. In this study, a data processing method for farmland environmental monitoring based on improved Spark components is designed. It introduces the FAST-Join (Join critical filtering sampling partition optimization) algorithm in the Spark component for equivalence association query optimization to improve the operating efficiency of the Spark component and cluster. The experimental results show that the amount of data written and read in Shuffle by Spark optimized by the FAST-join algorithm only accounts for 0.958 and 1.384% of the original data volume on average, and the calculation speed is 202.11% faster than the original. The average data processing time and occupied memory size of the Spark cluster are reduced by 128.22 and 76.75% compared with the originals. It also compared the cluster performance of the FAST-join and Equi-join algorithms. The Spark cluster optimized by the FAST-join algorithm reduced the processing time and occupied memory size by an average of 68.74 and 37.80% compared with the Equi-join algorithm, which shows that the FAST-join algorithm can effectively improve the efficiency of inter-data table querying and cluster computing.

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In recent years, the development of healthcare monitoring devices requires high performance and compact in-body sensor antennas. A normal-mode helical antenna (NMHA) is one of the most suitable candidates that meets the criteria, especially with the ability to achieve high efficiency when the antenna structure is in self-resonant mode. It was reported that when the antenna was placed in a human body, the antenna efficiency was decreased due to the increase of its input resistance (Rin). However, the reason for Rin increase was not clarified. In this paper, the increase of Rin is ensured through experiments and the physical reasons are validated through electromagnetic simulations. In the simulation, the Rin is calculated by placing the NMHA inside a human's stomach, skin and fat. The dependency of Rin to conductivity (σ) is significant. Through current distribution calculation, it is verified that the reason of the increase in Rin is due to the decrease of antenna current. The effects of Rin to bandwidth (BW) and electrical field are also numerically clarified. Furthermore, by using the fabricated human body phantom, the measured Rin and bandwidth are also obtained. From the good agreement between the measured and simulated results, the condition of Rin increment is clarified.

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Researchers are increasingly showing interest in the application of a Butler matrix for fifth-generation (5G) base station antennas. However, the design of the Butler matrix is challenging at millimeter wave because of the very small wavelength. The literature has reported issues of high insertion losses and incorrect output phases at the output ports of the Butler matrix, which affects the radiation characteristics. To overcome these issues, the circuit elements of the Butler matrix such as the crossover, the quadrature hybrid and the phase shifter must be designed using highly accurate dimensions. This paper presents a low-loss and compact single-layer 8 × 8 Butler matrix operating at 28 GHz. The optimum design of each circuit element is also demonstrated in detail. The designed Butler matrix was fabricated to validate the simulated results. The measured results showed return losses of less than -10 dB at 28 GHz. The proposed Butler matrix achieved a low insertion loss and a low phase error of ± 2 dB and ± 10°, respectively. In sum, this work obtained a good agreement between the simulated and measured results.

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