Teachers’ Classroom Discourse Pattern for Postgraduates Majoring in Foreign Linguistics: A Perspective of Legitimation Code Theory

“Introduction to English Linguistics (IEL)” is a professional knowledge course for postgraduates majoring in Foreign Linguistics. In traditional classrooms, teachers usually use knowledge code in their discourse, emphasizing the inculcation of theoretical knowledge in Foreign Linguistics. However, faced with large numbers of concepts, terminology, and academic opinions, students generate lots of confusion. This study quantitatively analyzes the teaching situation of IEL course under the framework of Legitimation Code Theory, and explores the influence of different teachers’ classroom discourse patterns on students’ learning efficiency. It is found that the use of knower code in teachers’ classroom discourse is an important factor in students’ learning efficiency while not being affirmatively proved to be effective in a pronounced manner, suggesting that a more advanced model of teachers’ classroom discourse pattern combining both knowledge code and knower code for postgraduates majoring in Foreign Linguistics is urgently needed

Enhancing Maritime Navigational Safety: Ship Trajectory Prediction Using ACoAtt–LSTM and AIS Data

Predicting ship trajectories plays a vital role in ensuring navigational safety, preventing collision incidents, and enhancing vessel management efficiency. The integration of advanced machine learning technology for precise trajectory prediction is emerging as a new trend in sophisticated geospatial applications. However, the complexity of the marine environment and data quality issues pose significant challenges to accurate ship trajectory forecasting. This study introduces an innovative trajectory prediction method, combining data encoding representation, attribute correlation attention module, and long short-term memory network. Initially, we process AIS data using data encoding conversion technology to improve representation efficiency and reduce complexity. This encoding not only preserves key information from the original data but also provides a more efficient input format for deep learning models. Subsequently, we incorporate the attribute correlation attention module, utilizing a multi-head attention mechanism to capture complex relationships between dynamic ship attributes, such as speed and direction, thereby enhancing the model’s understanding of implicit time series patterns in the data. Finally, leveraging the long short-term memory network’s capability for processing time series data, our approach effectively predicts future ship trajectories. In our experiments, we trained and tested our model using a historical AIS dataset. The results demonstrate that our model surpasses other classic intelligent models and advanced models with attention mechanisms in terms of trajectory prediction accuracy and stability

Optimized APF-ACO Algorithm for Ship Collision Avoidance and Path Planning

The primary objective of this study is to investigate maritime collision avoidance and trajectory planning in the presence of dynamic and static obstacles during navigation. Adhering to safety regulations is crucial when executing ship collision avoidance tasks. To address this issue, we propose an optimized APF-ACO algorithm for collision avoidance and path planning. First, a ship collision avoidance constraint model is constructed based on COLREGs to enhance the safety and applicability of the algorithm. Then, by introducing factors such as velocity, position, and shape parameters, the traditional APF method is optimized, creating a dynamic APF gradient for collision avoidance decision making in the face of dynamic obstacles. Furthermore, the optimized APF method is integrated with the ant colony optimization algorithm, the latter modified to overcome the inherent local optimality issues in the APF method. Ultimately, validations are conducted in three areas: static avoidance and planning in restricted sea areas, avoidance under conditions of mixed static and dynamic obstacles, and avoidance in situations of multiple ship encounters. These serve to illustrate the feasibility and efficacy of the proposed algorithm in achieving dynamic ship collision avoidance while simultaneously completing path-planning tasks

ThunderSVM: A fast SVM library on GPUs and CPUs

The Journal of Machine Learning Research797-80

A case of primary cutaneous CD4+ small/medium T-cell lymphoproliferative disorder

Primary cutaneous CD4+ small/medium T-cell lymphoproliferative disorder is indolent clinical behaviour and uncertain malignant potential. Histologically, these lesions show a predominance of small to medium-size CD4+ pleomorphic T-cell expressing follicular helper T-cell markers. We report the case of a 59-year-old female who presented with nodules on the left chest for 3 years. Dermatological examination showed four red nodules localized on the left chest with angiotelectasis without tenderness. The histopathological manifestation was consistent with the diagnosis of primary cutaneous CD4+ small/medium T-cell lymphoproliferative disorder. We focus on the clinical appearance, histopathological features, diagnosis, and differential diagnosis of primary cutaneous CD4+ small/medium T-cell lymphoproliferative disorder

ThunderGBM: Fast GBDTs and random forests on GPUs

The Journal of Machine Learning Research1-

Numerical calculation and experimental analysis of thermal environment in industrialized aquaculture facilities.

With the increasing market demand for high-quality aquatic products, the application of industrialized aquaculture facilities may get more attention. In order to improve the poor performance of thermal insulation, the accuracy of the numerical model was verified in this study through actual measured data. The model verification results shown that the average relative errors of the measured and calculated values of indoor air temperature, water temperature and roof inner surface temperature in the industrialized aquaculture workshop is within 2.5%, it suggested that the numerical calculation results are accurate. Furthermore, the thermal environment and thermal insulation performance of industrialized aquaculture facilities in winter were conducted based on the numerical calculations. After optimized the thermophysical parameters of the workshop enclosure structure, we found that the water body temperature could reach 21°C (which was close to the breeding temperature of grouper (Epinephelinae). Therefore, the numerical calculation method was further used to analyze the energy consumption of aquaculture water in January of a typical year in this area by heating to three constant temperatures (22, 25, and 28°C). When the aquaculture water was heated to the three constant temperature states, it needed to consume 8.56×105, 1.02×106 and 1.22×106 MJ of energy respectively, which were equal to the amount of energy released by the complete combustion of 29.3, 35.1 and 41.8 t standard coal. Moreover, it is concluded that the artificial temperature increase in winter maintains the temperature in the range of 22~25°C to provide the highest heating efficiency. This conclusion can provide theoretical basis and application reference for industrialized aquaculture in winter

Grid information table.

With the increasing market demand for high-quality aquatic products, the application of industrialized aquaculture facilities may get more attention. In order to improve the poor performance of thermal insulation, the accuracy of the numerical model was verified in this study through actual measured data. The model verification results shown that the average relative errors of the measured and calculated values of indoor air temperature, water temperature and roof inner surface temperature in the industrialized aquaculture workshop is within 2.5%, it suggested that the numerical calculation results are accurate. Furthermore, the thermal environment and thermal insulation performance of industrialized aquaculture facilities in winter were conducted based on the numerical calculations. After optimized the thermophysical parameters of the workshop enclosure structure, we found that the water body temperature could reach 21°C (which was close to the breeding temperature of grouper (Epinephelinae). Therefore, the numerical calculation method was further used to analyze the energy consumption of aquaculture water in January of a typical year in this area by heating to three constant temperatures (22, 25, and 28°C). When the aquaculture water was heated to the three constant temperature states, it needed to consume 8.56×105, 1.02×106 and 1.22×106 MJ of energy respectively, which were equal to the amount of energy released by the complete combustion of 29.3, 35.1 and 41.8 t standard coal. Moreover, it is concluded that the artificial temperature increase in winter maintains the temperature in the range of 22~25°C to provide the highest heating efficiency. This conclusion can provide theoretical basis and application reference for industrialized aquaculture in winter.</div

Heat loss in constant temperature aquaculture workshop (28°C).

Heat loss in constant temperature aquaculture workshop (28°C).</p

Optimization scheme table.

With the increasing market demand for high-quality aquatic products, the application of industrialized aquaculture facilities may get more attention. In order to improve the poor performance of thermal insulation, the accuracy of the numerical model was verified in this study through actual measured data. The model verification results shown that the average relative errors of the measured and calculated values of indoor air temperature, water temperature and roof inner surface temperature in the industrialized aquaculture workshop is within 2.5%, it suggested that the numerical calculation results are accurate. Furthermore, the thermal environment and thermal insulation performance of industrialized aquaculture facilities in winter were conducted based on the numerical calculations. After optimized the thermophysical parameters of the workshop enclosure structure, we found that the water body temperature could reach 21°C (which was close to the breeding temperature of grouper (Epinephelinae). Therefore, the numerical calculation method was further used to analyze the energy consumption of aquaculture water in January of a typical year in this area by heating to three constant temperatures (22, 25, and 28°C). When the aquaculture water was heated to the three constant temperature states, it needed to consume 8.56×105, 1.02×106 and 1.22×106 MJ of energy respectively, which were equal to the amount of energy released by the complete combustion of 29.3, 35.1 and 41.8 t standard coal. Moreover, it is concluded that the artificial temperature increase in winter maintains the temperature in the range of 22~25°C to provide the highest heating efficiency. This conclusion can provide theoretical basis and application reference for industrialized aquaculture in winter.</div