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Compared with the application of Digital Twin (DT) in the industrial field, the application of DT in the field of education is still in its infancy. In this paper, a Digital Twin Campus (DTC) for teaching and learning is proposed. It is argued that DTC possesses two characteristics. First, DTC has a wide variety of employment orientations for students or teachers. Second, teaching-learning resources in DTC system is huge but divisible. These characteristics result in the optional difficulty for teaching-learning objects, i.e., curriculum resources, book information resources, and electronic-article resources. To solve this problem, this paper proposes a Double-layer Collaborative Filtering Algorithm Framework (DCFAF) to recommend teaching-learning objects for digital twin teachers or students in DTC. The recommended objects will be further optimized by simulation and prediction in the virtual space of DTC. DCFAF is designed based on the principle that similar teachers (or students) may prefer similar teaching-learning resources, and for the first time, it is given double-layer property by applying the divisible characteristics of teaching-learning resources. The double-layer property can effectively solve the problem of data sparsity in collaborative filtering algorithms. Finally, the superiority of DCFAF is verified on benchmark data sets including MovieLens100k and MovieLens1M which possess the above two characteristics of DTC. DTC constructed in this way can be expected to carry out simulation, prediction, optimization and feedback continuously with the help of DCFAF, so as to realize deep integration of the teaching-learning process between real campus and virtual campus to some extent.

Land surface temperature (LST) is important in a variety of applications, such as urban thermal environment monitoring and water resource management. In this paper, eleven candidate split-window (SW) algorithms were adapted to Thermal Infrared Sensor-2 (TIRS-2) data of the Landsat 9 satellite for estimating the LST. The simulated dataset produced by extensive radiative transfer modeling and five global atmospheric profile databases was used to determine the SW algorithm coefficients. Ground measurements gathered at Surface Radiation Budget Network sites were used to confirm the efficiency of the SW algorithms after their performance was initially examined using the independent simulation dataset. Five atmospheric profile databases perform similarly in training accuracy under various subranges of total water vapor. The candidate SW algorithms demonstrate superior performance compared to the radiative transfer equation algorithm, exhibiting a reduction in overall bias and RMSE by 1.30 K and 1.0 K, respectively. It is expected to provide guidance for the generation of the Landsat 9 LST using the SW algorithms. [ABSTRACT FROM AUTHOR]

There has always been high interest in predicting the solder joint fatigue life in advanced packaging with high accuracy and efficiency. Artificial Intelligence Plus (AI+) is becoming increasingly popular as computational facilities continue to develop. This study will introduce machine learning (a core component of AI). With machine learning, metamodels that approximate the attributes of systems or functions are created to predict the fatigue life of advanced packaging. However, the prediction ability is highly dependent on the size and distribution of the training data. Increasing the amount of training data is the most intuitive approach to improve prediction performance, but this implies a higher computational cost. In this research, the adaptive sampling methods are applied to build the machine learning model with a small dataset sampled from an existing database. The performance of the model will be visualized using predefined criteria. Moreover, ensemble learning can be used to improve the performance of AI models after they have been fully trained. [ABSTRACT FROM AUTHOR]

Traditional methods, including direct solution methods based on Newton's method and indirect solution methods based on thermodynamic principles, are the mainstream methods used to solve the volume-temperature flash calculation (called NVT-flash), even though they suffer from drawbacks such as sensitivity to initial value and complexity of derivative calculations. A constrained backtracking search algorithm (CBSA), proposed in 2024, was the first and only metaheuristic algorithm to successfully tackle the NVT-flash problem, which overcomes shortcomings of traditional methods. Considering the advantages of metaheuristic algorithms, a constrained gray prediction evolutionary algorithm with a surrogate model based on quadratic interpolation (CGPE-QI) is proposed in this paper to deal with the NVT-flash problem. CGPE-QI considers total Helmholtz free energy as the objective function, moles vector, and volume of a single phase as variables. Constraints to solve the NVT-flash problem are addressed by using a direct search method and an exterior point method. Numerical experiments on two-phase equilibrium of pure substance and mixtures are carried out employing CGPE-QI. Experimental results are the same as those obtained by traditional methods, which confirms that CGPE-QI can effectively tackle the NVT-flash problem and possesses energy decay property. In particular, the results demonstrate that CGPE-QI is more competitive than CBSA in terms of convergence speed, stability, and calculation cost. CGPE-QI proposed in this paper is the second metaheuristic algorithm to successfully solve the NVT-flash problem, illustrating that metaheuristic algorithms have great potential in solving phase equilibrium calculation problems. [ABSTRACT FROM AUTHOR]

The field of object detection has widespread applicability in many areas. Despite the multitude of object detection methods that are already established, complex scenes with occlusions still prove challenging due to the loss of information and dynamic changes that reduce the distinguishable features between the target and its background, resulting in lower detection accuracy. Addressing the shortcomings in detecting obscured objects in complex scenes with existing models, a novel approach has been proposed on the YOLOv8n architecture. First, the enhancement begins with the addition of a small object detection head atop the YOLOv8n architecture to keenly detect and pinpoint small objects. Then, a blended mixed local channel attention mechanism is integrated within YOLOv8n, which leverages the visible segment features of the target to refine the feature extraction hampered by occlusion impacts. Subsequently, Soft-NMS is introduced to optimize the candidate bounding boxes, solving the issue of missed detection under overlapping similar targets. Lastly, using universal object detection evaluation metrics, a series of ablation experiments on public datasets (CityPersons) were conducted alongside comparison trials with other models, followed by testing on various datasets. The results showed an average precision (map@0.5) reaching 0.676, marking a 6.7% improvement over the official YOLOv8 under identical experimental conditions, a 7.9% increase compared to Gold-YOLO, and a 7.1% rise over RTDETR, also demonstrating commendable performance across other datasets. Although the computational load increased with the addition of detection layers, the frames per second (FPS) still reached 192, which meets the real-time requirements for the vast majority of scenarios. Such findings indicate that the refined method not only significantly enhances performance on occluded datasets but can also be transferred to other models to boost their performance capabilities. [ABSTRACT FROM AUTHOR]