Under the background of engineering education certification, the traditional personnel training model can’t meet the requirements of high-quality personnel training under the new engineering background. Taking Surveying and mapping engineering major of Liaoning Institute of Science and Technology as an example, this paper explores the continuous improvement of the output-oriented talent training model through collaborative education of talents training objectives, curriculum system, practical teaching system, teacher team construction, enterprises and graduates. Over the years, the surveying and mapping engineering major of our school has achieved good results in personnel training. The major actively ADAPTS to the regional development of the local economy, closely connects with the needs of regional talents, and highlights its characteristics in serving the local economy.

The purpose of this paper is to explore the performance of ridge regression and the random forest model improved by genetic algorithm in predicting the Boston house price data set and conduct a comparative analysis. To achieve it, the data is divided into training set and test set according to the ratio of 70-30. The RidgeCV library is used to select the best regularization parameter for the Ridge regression model, and for the random forest model, the genetic algorithm is used to optimize the model's hyperparameters. The result shows that compared with ridge regression, the random forest model improved by genetic algorithm can perform better in the regression problem of Boston house prices.

With the rapid economic growth, the concept of digital economy and sustainable development has gradually become the main task facing our country. This paper constructs the evaluation system of the development level of digital economy and the comprehensive index of regional sustainable development by the entropy weight method, uses the two-way fixed effect model to explore the influence mechanism of digital economy on the sustainable development of the Yangtze River Delta region.

This study explores project-based learning in science teaching models. Firstly, the theoretical basis of project-based learning is analyzed, the existing science teaching mode is evaluated, and the construction and implementation strategy of the science teaching mode based on project-based learning is proposed. Then, through empirical research, this study found that this model can effectively improve students' academic performance, enhance students' interest in learning, and improve students' hands-on ability. However, the implementation of this model requires teachers to have a high level of professionalism and adequate teaching resources. Finally, this study concludes that the project-based learning science teaching model is a potential teaching model that deserves further exploration and practice.

This study aims to explore the design and application of a learning achievement evaluation model, in order to improve the quality of teaching in the field of education and promote student development. This article starts with the importance of constructing a learning effectiveness evaluation model, and then clarifies the basic concepts and related theories of learning effectiveness evaluation, providing theoretical support for subsequent model design. In the model design section of learning effectiveness evaluation, propose the model design principles, indicator selection, and construction process to ensure the accuracy and comparability of the evaluation model construction. In the application and evaluation section of the learning effectiveness evaluation model, the application and evaluation methods of the main models in practical teaching were explored. Finally, the issues that need to be noted in the design process of the evaluation model were proposed in order to design a more high-quality evaluation system and promote the improvement of education quality.

To save patients’ lives, it is important to go for an early diagnosis of intracranial hemorrhage (ICH). For diagnosing ICH, the widely used method is non-contrast computed tomography (NCCT). It has fast acquisition and availability in medical emergency facilities. To predict hematoma progression and mortality, it is important to estimate the volume of intracranial hemorrhage. Radiologists can manually delineate the ICH region to estimate the hematoma volume. This process takes time and undergoes inter-rater variability. In this research paper, we develop and discuss a fine segmentation model and a coarse model for intracranial hemorrhage segmentations. Basically, two different models are discussed for intracranial hemorrhage segmentation. We trained a 2DDensNet in the first model for coarse segmentation and cascaded the coarse segmentation mask output in the fine segmentation model along with input training samples. A nnUNet model is trained in the second fine stage and will use the segmentation labels of the coarse model with true labels for intracranial hemorrhage segmentation. An optimal performance for intracranial hemorrhage segmentation solution is obtained.