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To improve the practical effect of an information system security course for students majoring in information security, a teaching method based on the theory of inventive problem solving (TIPS) that combines case-based teaching and project practice was introduced in the class. Adopting innovative thinking methods, we instructed students on the projects and goals of curriculum design. Using TIPS tools, the students completed complex projects. To test the teaching results, we examined 121 students who received case-based teaching using the TIPS method to design innovative projects. Quantitative data were collected using a statistically analysed anonymised questionnaire, while qualitative data collected from student reflective reports were thematically analysed. Through the in-depth development involved in the teaching of this course, their thinking and problem-solving ability significantly improved. The teaching effect evaluations indicated that this teaching method effectively improved students' practical abilities. This method was found to be conducive to planning students' future careers.

Understanding criminal motives is crucial for analyzing criminal psychology and predicting judicial outcomes. Traditional methods for crime motive analysis are heavily based on statistical techniques, requiring specialized knowledge and substantial human resources. With the increasing availability of judicial data, such as legal documents, machine learning approaches hold great potential in this domain. However, a significant challenge is the lack of comprehensive datasets to train these models, and the distribution of crime motive categories in publicly available legal texts often exhibits a long-tailed imbalance. This imbalance can lead to model bias, where the model tends to predict more common criminal motives. To address these challenges, we collected 11,589 legal documents from China Judgements Online (2019–2024) to create a crime motive text dataset. To mitigate the long-tailed issue, we propose a Category-Aware Balanced Contrastive Learning (CA-BCL) method, which effectively enhances the model's representation of long-tailed data. Specifically, CA-BCL first balances the sampling process to alleviate the class imbalance during prototype construction and then applies balanced contrastive learning to improve the model's ability to generalize to long-tailed categories, leading to better overall classification performance. Our experimental results demonstrate that CA-BCL significantly outperforms existing text classification models in crime motive classification, while also showing strong generalization capabilities on standard text classification benchmark. [ABSTRACT FROM AUTHOR]

This study used the Massachusetts Institute of Technology general circulation model (MITgcm) to simulate the seabed liquefaction process in the Chengdao area. The liquefied soil and the overlying water are considered as a two-layer fluid system in the model. By constructing various topographies using Gaussian functions, it is possible to achieve an agreement between simulation and measurement. The simulation results reveal the characteristics of pore pressure in different topographies. The main findings include the following: (1) The expansion of liquefaction zone leads to an accelerated attenuation of pore pressure amplitude. (2) A decrease in topographic slope diminishes the wave group characteristics of pore pressure due to the sensitivity of certain wave components with specific frequencies to topographic slope. [ABSTRACT FROM AUTHOR]

Based on the method for identifying the boundary of movable water bodies (MWBB), the spatial distribution of reclamation projects in the Guangxi Beibu Gulf were identified over the past 40 years and the impact of these engineering facilities on hydrodynamics was also evaluated. The results showed that 163.8 km2 of natural sea areas in the Guangxi Beibu Gulf were occupied through reclamation in the last 40 years. The effects of land reclamation on tidal amplitude were more pronounced in the second period (2001–2018) than in the first period (1987–2001), particularly in the tidal channels of Qinzhou Bay and Fangcheng Bay, where the amplitude difference ranged from 8 to 15 cm, representing a 40–55% increase. The reduction in the sea area because of land reclamation has changed the hydrodynamics in the Guangxi Beibu Gulf, including reducing the tidal volume, altering the amplitude variations, and increasing the seaward residual currents, all of which could cause significant problems for the coastal environment. [ABSTRACT FROM AUTHOR]

Criminal cases often exhibit imbalance and cannot be extended by data augmentation when classified into attribution types. To solve the problem of unbalance data in offenders' attribution classification, this paper proposes a criminal psychological attribution assessment model by an improved Balanced TF-Distinguishing IDF method (B-TF-dIDF) and constructed a hybrid network with attention method to fuse numerical and text features for improving the accuracy. First, as a statistical method, B-TF-dIDF is presented to reduce the impact of class-imbalance for extraction of numerical features, in which a balanced element is added to reduce the effects of incorrect type keywords on classification, and a distinguishing element is added to discriminate the types of keywords. Then, an improved hybrid network model composed of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) is constructed to balance the influence of different lengths of text samples for extracting the semantic features of criminal texts. For evaluating different feature weights by their importance, Spatial Attention is used to improve CNN in the feature maps. Moreover, the self-attention is also performed to re-evaluate the mixed features. Finally, the softmax classifier provides a scientific basis for developing a hierarchical treatment mechanism further. Additionally, we build a criminal data set with labels from real cases for testing. The experiment proved that the proposed model is better than other related methods in various evaluation indicators, including the micro and macro scopes. Moreover, the F1 of minority samples has increased by 6%–8%, indicating that the proposed method can reduce the impact of class-imbalance. • A hybrid model composed of LSTM and CNN is constructed to classify criminal psychology attribution into three types. • The novel statistical method B-TF-dIDF has been constructed to process unbalanced data and extract numerical features. • Text and numerical features are fused and re-evaluated by Self-attention. [ABSTRACT FROM AUTHOR]

In 3 consecutive years from 2016 to 2018, extreme ocean warming events, or marine heatwaves (MHWs), occurred during boreal summers in the East China Sea (ECS) and South Yellow Sea (SYS), which is unprecedented in the past four decades based on the satellite record. In this study, we used a high‐resolution hydrodynamic model based on Finite Volume Community Ocean Model (FVCOM) to simulate the evolution of these warming events. An upper ocean temperature budget (0–20 m) analysis based on the model results shows that the shortwave radiation and the ocean advection anomalies jointly contributed to the anomalous warming in the three successive summers (June–August) in the SYS and the north part of the ECS. In addition, the reduction of surface wind speeds during the 2016 and 2017 summers further weakened the vertical mixing, thereby enhancing the anomalous warming in the north part of the ECS adjacent to the SYS. During the three summers, the increases of shortwave radiation were closely related to the East Asian Summer Monsoon (EASM) variability, which reduced the cloud cover in the ECS and SYS, whereas the advection anomalies were mostly associated with regional wind anomalies. In summer 2018, upper ocean heat was transported into the central trough of the SYS, accumulated in an anticyclonic eddy generated by the anomalous wind stress curls. Therefore, despite the primary driver of the MHWs is the EASM variation, regional processes are critical to driving the spatial pattern of the MHW intensity in the ECS and SYS. Plain Language Summary: Marine heatwaves, known as periods of extreme warming at the sea surface, can last for days to months and cause damages to the marine environment and marine life. In the East China Sea and the South Yellow Sea, the frequent occurrences of harmful algae blooms are often associated with marine heatwaves. Satellite data reveal that marine heatwaves occurred in the East China Sea and the South Yellow Sea during the three boreal summers from 2016 to 2018, which is unprecedented in the past four decades. Using a numerical model of the ocean, we examined the marine heatwaves during these three successive summers. We show that the increased solar radiation, ocean current anomalies, and reduced vertical mixing were three critical factors for the warming events in the three summers. This study helps the fisheries and aquaculture industries in the East China Sea and the South Yellow Sea to better manage the environmental risks under a warming climate by predictions of marine heatwaves. Key Points: Marine heatwaves in the ECS and SYS during 2016–2018 summers were caused by shortwave radiation, current, and vertical mixing anomaliesShortwave radiation increases in summers were due to reductions of the cloud cover, closely related to the East Asian Summer Monsoon variabilityAn anticyclonic eddy in SYS in summer 2018, driven by regional wind anomalies, played a significant role in trapping anomalous heat [ABSTRACT FROM AUTHOR]