Abstract With the rapid development and popularization of Internet technology, the management of network data configuration in universities faces new challenges and opportunities. The application of university network data configuration management based on Internet platforms has emerged, aiming to improve the efficiency and quality of university network data configuration management. This article will analyze this application, and explore its advantages, challenges,... With the rapid development and popularization of Internet technology, the management of network data configuration in universities faces new challenges and opportunities. The application of university network data configuration management based on Internet platforms has emerged, aiming to improve the efficiency and quality of university network data configuration management. This article will analyze this application, and explore its advantages, challenges, and solutions. University network data configuration management based on internet platforms can quickly process and store a large amount of data, improving the efficiency of data configuration management. This application can be accessed anytime and anywhere, making it convenient for managers and users to access the required data anytime and anywhere. Data configuration management through internet platforms can reduce manpower and material investment, as well as management costs. Read More Read Less

Abstract This article adopts the fuzzy comprehensive evaluation model and grey correlation degree method to conduct risk assessment on energy enterprises. Firstly, a risk assessment index system for energy enterprises was constructed, including financial, market, production, and environmental indicators. Then, a fuzzy comprehensive evaluation model is used to evaluate each indicator quantitatively, and the interrelationships between different indicators are comprehensively considered... This article adopts the fuzzy comprehensive evaluation model and grey correlation degree method to conduct risk assessment on energy enterprises. Firstly, a risk assessment index system for energy enterprises was constructed, including financial, market, production, and environmental indicators. Then, a fuzzy comprehensive evaluation model is used to evaluate each indicator quantitatively, and the interrelationships between different indicators are comprehensively considered to obtain a comprehensive evaluation result. Next, the grey correlation method is used to analyze the comprehensive evaluation results and obtain the correlation degree between each indicator and the ideal state, to identify the risk points and weak links of energy enterprises. Through example verification, the fuzzy comprehensive evaluation model and grey correlation degree method used in this article can effectively assess the risk of energy enterprises. The evaluation results can clearly reflect the overall situation and existing risk points of energy enterprises, providing a scientific basis for risk management. Read More Read Less

Abstract The performance of an optical receiver in a digital optical communication link is studied. In the design of an optical receiver, it is vital that the module is capable of converting and shaping the optical signal while meeting or surpassing the maximum BER. Ultimately, the noise influence on the signal will determine the system sensitivity. The challenge is to find... The performance of an optical receiver in a digital optical communication link is studied. In the design of an optical receiver, it is vital that the module is capable of converting and shaping the optical signal while meeting or surpassing the maximum BER. Ultimately, the noise influence on the signal will determine the system sensitivity. The challenge is to find a way to determine the QoS of an optical transmission channel independent of data format and bit rate within a short time frame. The Q-factor itself significantly reduces measurement time and is thus more cost-effective. The analysis is based, assuming an input signal with impairment from factors like inter-symbol interference, jitter, and transmitter relative intensity noise. Read More Read Less