Wanwu Li

With the development of tourism, tourists’ travel behaviour presents a multi-dimensional and complicated trend (Mandal et al., 2026). Tourism behaviour research and feature mining can provide techn...

The dynamic map is applied instead of traditional static maps in the teaching of geographic information science (GIS)‐related majors so as to meet the need for teaching animations and teaching interaction in new teaching methods such as mobile personalized learning, massive open online course, and Flipped Classroom. The research introduces the concept and the making tools of dynamic maps, studies the making principles and key technologies of dynamic maps, and constructs the relevant theories such as the “frame” model based on time granularity, the dynamic interpolation algorithm based on spatial characteristics, dynamic symbolic logic model, and algorithm. Based on the above principles and theories, taking a dynamic map of the change in the population of each city in Shandong province as an example, the research demonstrates the making method and process of the dynamic maps for teaching with Flash MX. At last, it analyzes the role of dynamic map in GIS‐related courses teaching and evaluates the teaching effect of dynamic maps.

Salmonella, a Gram-negative bacterium that infects humans and animals, causes diseases ranging from gastroenteritis to severe systemic infections. Here, we discuss various strategies used by Salmonella against host cell defenses. Epithelial cell invasion largely depends on a Salmonella pathogenicity island (SPI)-1-encoded type 3 secretion system, a molecular syringe for injecting effector proteins directly into host cells. The internalization of Salmonella into macrophages is primarily driven by phagocytosis. After entering the host cell cytoplasm, Salmonella releases many effectors to achieve intracellular survival and replication using several secretion systems, primarily an SPI-2-encoded type 3 secretion system. Salmonella-containing vacuoles protect Salmonella from contacting bactericidal substances in epithelial cells and macrophages. Salmonella modulates the immunity, metabolism, cell cycle, and viability of host cells to expand its survival in the host, and the intracellular environment of Salmonella-infected cells promotes its virulence. This review provides insights into how Salmonella subverts host cell defenses for survival.

It is crucial to advance network education exam informatization and mobile learning. One of them is the creation of a database system for exams, which contains crucial information. This study aims to analyze and solves key technologies including constraint check paper forming algorithms. This study using fuzzy query methods and spatial queries design a single exam table structure and exam papers for the exam database. The process of this study including collect and sort the complete set of GIS exams at major universities across the country, design a variety of GIS single questions, and build an exam database system. The result of this research is a personalized random paper formation system and query and spatial analysis by college and area for exam papers for the first time. It describes rich functions and stable performance by testing. The built system becomes an indispensable technical support instead of paper-based exams for information exams and scientific exams of many colleges and universities.

This study constructs four deep-learning OceanTDLx series models and uses a WinR-AdaGrad gradient descent algorithm to train and optimize the constructed models. Through an analysis of the loss, accuracy, and time consumption of the four models (i.e., OceanTDL2, OceanTDL3, OceanTDL5 and OceanTDL8), we reveal that the models’ performance does not improve when the number of layers is increased and that OceanTDL5 provides the optimal performance. OceanTDL5 is compared with OceanTDA9 (a model that we previously constructed), and the curves for training loss_batch and training accuracy_batch indicate that OceanTDL5 is more suitable than OceanTDA9 for detecting distributed targets, particularly semi-melted sea ice, which is intertwined and easily confused with seawater. We process the SAR (Synthetic Aperture Radar) data of the research area and obtain a data set with a 10-m resolution, which is then used to verify the effectiveness of the constructed models for sea ice detection. The results reveal that OceanTDL5 has a detection capacity of approximately 55.6 km2 /s and a detection accuracy rate of 97.5%. Compared with traditional ocean target detection methods, OceanTDL5 has greater detection speed and accuracy.

To adapt to the needs of teaching modes such as network distance education and mobile online learning, and overcome the space‐time limitation of traditional offline education, the construction of online courses has become an important task of informatization in education. The research analyzes several key technologies for the construction of online courses and proposes solutions for making dynamic maps and demonstration animations, sharing relevant teaching resources, cloud classroom design, special experiment design, and online testing and question & answer (Q & A). On this basis, with the index system of “Modern Distance Education Standards” as a reference, the national standard and evaluation plan of online courses as guides, and computer technology and multimedia technology as means, the research develops an online course case of Marine Geographic Information System (MGIS). Based on the.NET platform, construct a teaching resource website supporting MGIS. The online course and its supporting teaching resources website have realized the characteristic function of personalized self‐learning, dynamic demonstration of knowledge, online testing according to different learning stages, interesting experiments to stimulate learning enthusiasm, intelligent feedback learning effect, and so on. Through application and practice in teaching, the website of online courses and resources has achieved a good teaching effect.

Constant False‐Alarm Rate (CFAR) algorithm is a mature method in marine target detection at present, the key of which is the choice of probability model. The most classical probability model is Gaussian distribution, which is suitable for modeling the calm ocean surface. Complex ocean conditions generally do not obey to Gaussian distribution, which limits the performance of CFAR algorithm. Therefore, we propose Adjoint Covariance Correction Model (ACCM) to improve the performance of CFAR marine target detection. Analyzing the distribution characteristic of ocean clutter under complex ocean conditions based on experiments, we find that it has long‐tail characteristic. Aiming at this characteristic, we have conducted fitting experiment on 23 kinds of probability density functions (PDFs) and find Loglogistic distribution has a better fitting effect on long‐tail characteristic, therefore, we use it to model ocean clutter under complex ocean conditions for the first time. In order to further improve the fitting goodness, we add a variance correction term to the Loglogistic model to construct ACCM for marine target extraction. The experiment result shows that ACCM effectively fits the long‐tail characteristic caused by Synthetic Aperture Radar backscatter under complex ocean conditions. The goodness of fit improves by 50% compared with Loglogistic model, and the amount of false alarms is 81.42% of that of Loglogistic model. The extraction accuracy of marine target characteristic parameter based on ACCM is 11.58% higher than that of Loglogistic model, and 12.18% higher than that of two‐parameter CFAR model based on standard error function (named SEF‐CFAR).

Deep learning adjusts parameters to optimize the model through model training. Training algorithm is the key to model optimization and implementation. Therefore, the improvement of model training algorithm is of great significance to deep learning. Based on the original gradient algorithm, the paper proposes a new gradient descent algorithm AdaGrad Restricted by Windows (AdaRW) for Deep learning model training optimization. Aiming at the defects of AdaGrad algorithm, the new algorithm uses the subset of window to limit historical accumulation, so as to slow down the attenuation of learning rate, and improve the speed of model training. The paper constructs OceanTDA9, a Deep learning model of marine target detection for Synthetic Aperture Radar (SAR) data, and adopts the proposed AdaRW algorithm to train the model based on SAR data with a resolution of 10 m in the Bohai Sea. Experiment shows that the accuracy and loss of the algorithm are better than those of AdaGrad and Stochastic Gradient Descent (SGD) algorithms, and the standard deviation is better than that of Adam algorithm.