Modern education attaches great importance to the innovation of teaching concepts, and teachers should be guided by it to provide students with high-quality educational resources and learning environment. Teachers should conduct in-depth research on auditing course materials, set certain training goals for students, and optimize their teaching ideas to conduct diversified evaluations of students. Teachers should create an environment for students to learn auditing and choose corresponding teaching methods based on their learning situation. Teachers should also guide students to master the courses and basic theories of auditing, so that they have certain operational skills and can apply relevant theories to analyze and develop problems encountered in the management profession.

Fire hazard is often mapped as a static conditional probability of fire characteristics’ occurrence. We developed a dynamic product for operational risk management to forecast the probability of occurrence of fire radiative power in the locally possible near-maximum fire intensity range. We applied standard machine learning techniques to remotely sensed data. We used a block maxima approach to sample the most extreme fire radiative power (FRP) MODIS retrievals in free-burning fuels for each fire season between 2001 and 2020 and associated weather, fuel, and topography features in northwestern south America. We used the random forest algorithm for both classification and regression, implementing the backward stepwise repression procedure. We solved the classification problem predicting the probability of occurrence of near-maximum wildfire intensity with 75% recall out-of-sample in ten annual test sets running time series cross validation, and 77% recall and 85% ROC-AUC out-of-sample in a twenty-fold cross-validation to gauge a realistic expectation of model performance in production. We solved the regression problem predicting FRP with 86% r² in-sample, but out-of-sample performance was unsatisfactory. Our model predicts well fatal and near-fatal incidents reported in Peru and Colombia out-of-sample in mountainous areas and unimodal fire regimes, the signal decays in bimodal fire regimes.

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.

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.

Climate change has affected the coasts of the world due to numerous factors, including the change in the intensity and frequencies of the storms and the increase in the mean sea level, among others. Argentina has extensive coastal areas, and research and monitoring tasks are expensive and require a significant number of personnel to cover large geographical areas. Given this, citizen science has become a tool to increase scientific research's spatial and temporal extension. Therefore, the paper aims to analyze the methodology and development of the citizen science project in Villa Gesell and its lessons for applying them in future coastal environmental monitoring projects. The methodology was based on an experience of the project co-created between activists and researchers. This project included four phases for social and physical aspects: training for the citizens, theoretic and practical aspects of coastal dynamics, and how to measure its geomorphological and oceanographic variations; data collection: the activists who received the training performed the measurements to monitor the beach; data analysis by scientists; and dissemination of results; the report data were disseminated by citizens in their community. The analysis of case studies in citizen science projects generates a fundamental learning arena to apply in future projects. Among the positive aspects were the phases established for their development and the methodology used to collect beach monitoring data.