Jiaji Pan

Industrial defect detection faces severe challenges in data-scarce scenarios. Machine vision methods are commonly used for detecting defects on textile surfaces. Velcro (a common type of fiber fastener) poses high demands on detection algorithms due to its complex structure and diverse defects. Existing detection methods are often limited by insufficient training samples, resulting in poor model generalization capabilities. This paper addresses the challenge of Velcro defect detection in small-sample scenarios by proposing an improved model based on YOLOv8n. By incorporating an attention mechanism and the Wise-IoUv3 loss function, the model’s ability to process small-sample data is significantly enhanced. Experiments show that when the GAM attention module is combined with WIoUv3, detection performance is improved without loss (mAP improved by 1.2%), while other attention modules (such as CBAM) may experience performance degradation. On the self-built hook-and-loop fastener defect dataset, the proposed method achieves a mAP value of 0.826, significantly outperforming the baseline model. This study provides a plug-and-play, highly compatible optimization solution for resource-constrained industrial vision detection systems.

To elucidate the effectiveness of the containment strategies against the pandemic, a Multi-Criteria Decision Making (MCDM) model is established to evaluate the government’s performance against COVID-19. In this study, the Analytic Hierarchy Process (AHP), Entropy, and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method are used in determining the performance of the public health system. We adopt both subjective and objective weighting methods for a more accurate evaluation. In addition, the evaluation of performance against COVID-19 is conducted in various aspects and divided into different periods. Data Envelopment Analysis (DEA) is applied to evaluate the sustainability of the public health system. Composite scores of the public health system are determined based on the performance and sustainability assessment. The five countries, South Korea, Japan, Germany, Australia, and China are rated with higher composite scores. On the country, the US, Indonesia, Egypt, South Africa, and Brazil receive lower rating scores among the countries for evaluation. This modeling study can provide a practical quantitative justification for developing containment policies and suggestions for improving the public health system in more countries or areas.

This study aims to improve control schemes for COVID-19 by a numerical model with estimation of parameters. We established a multi-level and multi-objective nonlinear SEIDR model to simulate the virus transmission. The early spread in Japan was adopted as a case study. The first 96 days since the infection were divided into five stages with parameters estimated. Then, we analyzed the trend of the parameter value, age structure ratio, and the defined PCR test index (standardization of the scale of PCR tests). It was discovered that the self-healing rate and confirmed rate were linear with the age structure ratio and the PCR test index using the stepwise regression method. The transmission rates were related to the age structure ratio, PCR test index, and isolation efficiency. Both isolation measures and PCR test medical screening can effectively reduce the number of infected cases based on the simulation results. However, the strategy of increasing PCR test medical screening would encountered a bottleneck effect on the virus control when the index reached 0.3. The effectiveness of the policy would decrease and the basic reproduction number reached the extreme value at 0.6. This study gave a feasible combination for isolation and PCR test by simulation. The isolation intensity could be adjusted to compensate the insufficiency of PCR test to control the pandemic.