Improved EfficientNet for corn disease identification.

Introduction: Corn is one of the world's essential crops, and the presence of corn diseases significantly affects both the yield and quality of corn. Accurate identification of corn diseases in real time is crucial to increasing crop yield and improving farmers' income. However, in real-world environments, the complexity of the background, irregularity of the disease region, large intraclass variation, and small interclass variation make it difficult for most convolutional neural network models to achieve disease recognition under such conditions. Additionally, the low accuracy of existing lightweight models forces farmers to compromise between accuracy and real-time. Methods: To address these challenges, we propose FCA-EfficientNet. Building upon EfficientNet, the fully-convolution-based coordinate attention module allows the network to acquire spatial information through convolutional structures. This enhances the network's ability to focus on disease regions while mitigating interference from complex backgrounds. Furthermore, the adaptive fusion module is employed to fuse image information from different scales, reducing interference from the background in disease recognition. Finally, through multiple experiments, we have determined the network structure that achieves optimal performance. Results: Compared to other widely used deep learning models, this proposed model exhibits outstanding performance in terms of accuracy, precision, recall, and F1 score. Furthermore, the model has a parameter count of 3.44M and Flops of 339.74M, which is lower than most lightweight network models. We designed and implemented a corn disease recognition application and deployed the model on an Android device with an average recognition speed of 92.88ms, which meets the user's needs. Discussion: Overall, our model can accurately identify corn diseases in realistic environments, contributing to timely and effective disease prevention and control.

Improved YOLOX-Tiny network for detection of tobacco brown spot disease.

Introduction: Tobacco brown spot disease caused by Alternaria fungal species is a major threat to tobacco growth and yield. Thus, accurate and rapid detection of tobacco brown spot disease is vital for disease prevention and chemical pesticide inputs. Methods: Here, we propose an improved YOLOX-Tiny network, named YOLO-Tobacco, for the detection of tobacco brown spot disease under open-field scenarios. Aiming to excavate valuable disease features and enhance the integration of different levels of features, thereby improving the ability to detect dense disease spots at different scales, we introduced hierarchical mixed-scale units (HMUs) in the neck network for information interaction and feature refinement between channels. Furthermore, in order to enhance the detection of small disease spots and the robustness of the network, we also introduced convolutional block attention modules (CBAMs) into the neck network. Results: As a result, the YOLO-Tobacco network achieved an average precision (AP) of 80.56% on the test set. The AP was 3.22%, 8.99%, and 12.03% higher than that obtained by the classic lightweight detection networks YOLOX-Tiny network, YOLOv5-S network, and YOLOv4-Tiny network, respectively. In addition, the YOLO-Tobacco network also had a fast detection speed of 69 frames per second (FPS). Discussion: Therefore, the YOLO-Tobacco network satisfies both the advantages of high detection accuracy and fast detection speed. It will likely have a positive impact on early monitoring, disease control, and quality assessment in diseased tobacco plants.

Simulation of a Hemispherical Chamber for Thermal Inkjet Printing.

It is crucial to improve printing frequency and ink droplet quality in thermal inkjet printing. This paper proposed a hemispherical chamber, and we used the CFD (computational fluid dynamics model) to simulate the inkjet process. During the whole simulation process, we first researched the hemispherical chamber's inkjet state equipped with straight, conical shrinkage, and conical diffusion nozzles. Based on the broken time and volume of the liquid column, the nozzle geometry of the hemispherical chamber was determined to be a conical shrinkage nozzle with a specific size of 15 µm in height and 15 µm in diameter at the top, and 20 µm in diameter at the bottom. Next, we researched the inkjet performance of the square chamber, the round chamber, and the trapezoidal chamber. The round chamber showed the best inkjet performance using 1.8 µs as the driving time and 10 MPa as the maximum bubble pressure. After that, we compared the existing thermal inkjet printing heads. The results showed that the hemispherical chamber inkjet head had the best performance, achieving 30 KHz high-frequency printing and having the most significant volume ratio of droplet to the chamber, reaching 14.9%. As opposed to the current 15 KHz printing frequency of the thermal inkjet heads, the hemispherical chamber inkjet head has higher inkjet performance, and the volume ratio between the droplet and the chamber meets the range standard of 10-15%. The hemispherical chamber structure can be applied to thermal inkjet printing, office printing, 3D printing, and bio-printing.

The comparison of economic impacts of COVID-19 between China and the United States.

The outbreak of the COVID-19 in 2020 has a severe impact on all countries. This paper first studies the impact of the epidemic on the macro-economy in China, which is one of the countries with early epidemic situation, in the rapid development stage of the epidemic situation, and the specific performance in macro-economy aspects. On this basis, study the development trend and macroeconomic performance of the United States as one of the most serious countries, and the relationship between the epidemic development and macro-economy in the United States. As for the differences in epidemic control measures between China and the US, China's indicators are significant in terms of sentiment factors, but the impact of COVID-19 on the sentiment factors in the US is different. The Chinese government has not adopted unlimited easing and maintained consistent policies. At present, the economic impact of COVID-19 on the US is far greater than that of China.