The application of improved densenet algorithm in accurate image recognition.

Image recognition technology belongs to an important research field of artificial intelligence. In order to enhance the application value of image recognition technology in the field of computer vision and improve the technical dilemma of image recognition, the research improves the feature reuse method of dense convolutional network. Based on gradient quantization, traditional parallel algorithms have been improved. This improvement allows for independent parameter updates layer by layer, reducing communication time and data volume. The introduction of quantization error reduces the impact of gradient loss on model convergence. The test results show that the improvement strategy designed by the research improves the model parameter efficiency while ensuring the recognition effect. Narrowing the learning rate is conducive to refining the updating granularity of model parameters, and deepening the number of network layers can effectively improve the final recognition accuracy and convergence effect of the model. It is better than the existing state-of-the-art image recognition models, visual geometry group and EfficientNet. The parallel acceleration algorithm, which is improved by the gradient quantization, performs better than the traditional synchronous data parallel algorithm, and the improvement of the acceleration ratio is obvious. Compared with the traditional synchronous data parallel algorithm and stale synchronous parallel algorithm, the optimized parallel acceleration algorithm of the study ensures the image data training speed and solves the bottleneck problem of communication data. The model designed by the research improves the accuracy and training speed of image recognition technology and expands the use of image recognition technology in the field of computer vision.Please confirm the affiliation details of [1] is correct.The relevant detailed information in reference [1] has been confirmed to be correct.

Immunomodulatory Effects of Vitamin D and Zinc on Viral Infection.

Several nutrients are crucial in enhancing the immune system and preserving the structural integrity of bodily tissue barriers. Vitamin D (VD) and zinc (Zn) have received considerable interest due to their immunomodulatory properties and ability to enhance the body's immune defenses. Due to their antiviral, anti-inflammatory, antioxidative, and immunomodulatory properties, the two nutritional powerhouses VD and Zn are crucial for innate and adaptive immunity. As observed with COVID-19, deficiencies in these micronutrients impair immune responses, increasing susceptibility to viral infections and severe disease. Ensuring an adequate intake of VD and Zn emerges as a promising strategy for fortifying the immune system. Ongoing clinical trials are actively investigating their potential therapeutic advantages. Beyond the immediate context of the pandemic, these micronutrients offer valuable tools for enhancing immunity and overall well-being, especially in the face of future viral threats. This analysis emphasizes the enduring significance of VD and Zn as both treatment and preventive measures against potential viral challenges beyond the current health crisis. The overview delves into the immunomodulatory potential of VD and Zn in combating viral infections, with particular attention to their effects on animals. It provides a comprehensive summary of current research findings regarding their individual and synergistic impacts on immune function, underlining their potential in treating and preventing viral infections. Overall, this overview underscores the need for further research to understand how VD and Zn can modulate the immune response in combatting viral diseases in animals.

Prediction Method of Soft Fault and Service Life of DC-DC-Converter Circuit Based on Improved Support Vector Machine.

A data-driven prediction method is proposed to predict the soft fault and estimate the service life of a DC-DC-converter circuit. First, based on adaptive online non-bias least-square support-vector machine (AONBLSSVM) and the double-population particle-swarm optimization (DP-PSO), the prediction model of the soft fault is established. After analyzing the degradation-failure mechanisms of multiple key components and considering the influence of the co-degradation of these components over time on the performance of the circuit, the output ripple voltage is chosen as the fault-characteristic parameter. Finally, relying on historical output ripple voltages, the prediction model is utilized to gradually deduce the predicted values of the fault-characteristic parameter; further, in conjunction with the circuit-failure threshold, the soft fault and the service life of the circuit can be predicted. In the simulation experiment, (1) a time-series prediction is made for the output ripple voltage using the model proposed herein and the online least-square support-vector machine (OLS-SVM). Comparative analyses of fitting-assessment indicators of the predicted and experimental curves confirm that our model is superior to OLS-SVM in both modeling efficiency and prediction accuracy. (2) The effectiveness of the service life prediction method of the circuit is verified.