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Cloud model similarity algorithm is an important part of cloud modelling theory. Most of the existing cloud model similarity algorithms suffer from poor discriminability, poor classification, unstable results, and low time efficiency. In this paper, a new similarity algorithm is proposed that considers the triangular cloud model distance and shape. First, according to the D T distance formula, a new exponential closeness measure is defined, with which the distance similarity of cloud models is characterized. Then, the shape similarity is calculated according to the variance of the cloud model cloud drops. Finally, the two similarities are synthesized to define a similarity algorithm for determining the distance from the D T distance formula and shape based on the triangular cloud model (DDTSTCM). In this paper, discriminability, stability, efficiency and theoretical interpretability are taken as the evaluation indices. Equipment security system capability evaluation experiment, cloud model differentiation simulation experiment and time series classification accuracy experiment are set up to verify the effectiveness of the algorithm in terms of the four above aspects. The experimental results show that DDTSTCM has good differentiation and excellent classification effects. In the classification experiment for the time series, the average classification accuracy of DDTSTCM reaches 91.78%, which is at least 2.78% higher than those of the other seven commonly used algorithms. The CPU running efficiency of DDTSTCM is also extremely high, and the average CPU running time of group training is always on the order of milliseconds, which effectively reduces the time cost. Finally, a case study is conducted to analyse a risk assessment problem for China's island microgrid industry, and the evaluation results based on DDTSTCM are in line with human cognition and have good value for engineering applications. [ABSTRACT FROM AUTHOR]

Clouds are crucial in regulating the Earth's energy budget. Global cloud top heights have been easily retrieved from satellite measurements, but there are few methods for determining cloud base height (CBH) from satellite measurements. The Cloud Base Altitude Spatial Extrapolator (CBASE) algorithm was proposed to derive the height of the lower-troposphere liquid cloud base by using the Cloud-Aerosol Lidar with Orthogonal polarization cloud aerosol LiDAR (CALIOP) profiles and weather observations at airports from aviation routine and special weather report (METARs and SPECIs, called METAR) observation data in the United States. A modification to the CBASE algorithm over China (CNMETAR-CBASE) is presented in this paper. In this paper, the ability of the CBASE algorithm to calculate CBH in China is evaluated, and METAR observations over China (CNMETAR) were then used to modify the CBASE algorithm. The results including CNMETAR observation data in China can better retrieve CBH over China compared with the results using the original CBASE algorithm, and the accuracy of the global CBH results has been improved. Overestimations of CBH with the original algorithm range from 500 to 800 m in China, which have been reduced to about 300 m with an improved algorithm. The deviations calculated by the algorithm also have a significant reduction, from 480 m (CBASE) to 420 m (CNMETAR-CBASE). In conclusion, the modified CBASE algorithm not only calculates the CBH more accurately in China but also improves the results of the global CBH retrieved from satellites. [ABSTRACT FROM AUTHOR]

In the field of risk assessment, the traditional econometric models are generally used to assess credit risk. And with the introduction of the "dual-carbon" goals to promote the development of a low-carbon economy, the scale of green credit in China has rapidly expanded. But with the advent of the big data era, due to the poor interpretability of a traditional single machine learning model, it is difficult to capture nonlinear relationships, and there are shortcomings in prediction accuracy and robustness. This paper selects the adjusted ensemble learning model based on the homogeneous and heterogeneous factors for user default prediction, which can efficiently process large quantities of high-dimensional data. This article adjusts each model to adapt to the task and innovatively compares various models. In this paper, the missing value filling method, feature selection, and ensemble model are studied and discussed, and the optimal ensemble model is obtained. When comparing the predictions of single models and ensemble models, the accuracy, sensitivity, specificity, F1-Score, Kappa, and MCC of Categorical Features Gradient Boosting (CatBoost) and Random undersampling Boosting (RUSBoost) all reach 100%. The experimental results prove that the algorithm based on adjusted homogeneous and heterogeneous ensemble learning can predict the user default efficiently and accurately. This paper also provides some references for establishing a risk assessment index system. [ABSTRACT FROM AUTHOR]

In response to the intensification of urbanization, characterized by increasingly dense land use, the proliferation of high-rise residences has emerged as a predominant housing solution, particularly evident in developed cities like those in China. With a primary aim of enhancing the residential living environment within high-rise structures, this study advocates for a comprehensive whole life cycle perspective. By employing digital technology and conducting experiments centered on a representative case study, this paper focuses on optimizing the functional layout of public activity spaces within high-rise residential projects. Specifically, this article aims to address challenges such as the inadequate configuration and articulation of various public spaces, along with the underutilization of peripheral activity areas. Central to the optimization efforts is the utilization of the Kruskal algorithm, which enables the analysis of flow lines and functions, ultimately leading to the derivation of optimal solutions. Through a detailed analysis of our case study, this paper provides actionable insights into enhancing the feasibility and effectiveness of public spaces within high-rise residential projects. Recognizing the pivotal role of public space layout in shaping residents' living experiences, the importance of addressing this issue early in the design phase was emphasized. By integrating scientifically driven digital technology solutions, smart, inclusive, and convenient communities that cater to the diverse needs of their inhabitants are aspired to be created. [ABSTRACT FROM AUTHOR]

In stock trading, a common phenomenon is that the trends of stocks in the same industry are very similar. In contrast, the movements of stocks in different industries are often different. Therefore, applying the same model to all stock trading is inappropriate without distinguishing the industries in which the stocks belong. However, recommending an optimal industry stock trading model is very challenging based on performance evaluation indicators. First, the indicators of the trading model are diverse. Second, the ranking of multiple indicators is often inconsistent. In the paper, we model the problem to be solved as a multi-criteria decision-making process. Therefore, we first divide stock dataset into nine industries according to their main business. Then, we apply several machine learning algorithms as candidate models to generate trading signals. Second, we conduct daily trading backtesting based on the trading signals to obtain multiple performance evaluation indicators. Third, we propose an optimal recommendation algorithm for the industry stock trading model with TODIM. The experimental results in the US stock market and China's A-share market show that the proposed algorithm can get a better trading model out-of-sample industry stock. Moreover, we effectively evaluate the generalization ability of the algorithm based on the proposed metrics. Finally, the proposed long–short portfolios based on the algorithm have achieved returns exceeding the benchmark on most out-of-sample datasets. [ABSTRACT FROM AUTHOR]

Background: Investigating emotion sequence patterns in the posts of discussion forums in massive open online courses (MOOCs) holds a vital role in shaping online interactions and impacting learning achievement. While the majority of research focuses on the relationship between emotions and interactions in MOOC forum discussions, research on identifying the crucial difference in emotion sequence patterns among different interaction groups remains in its infancy. Objectives: This research utilizes deep learning and sequential pattern mining to investigate whether there are differences in emotion sequence patterns across different groups of learners who exhibit various types of interactions in online discussion forums. Methods: Data from a comprehensive array of sources, including log files, discussion texts and scores from 498 learners in online discussion forums, were collected for this study. The agglomerative hierarchical algorithm is used to classify learners into groups with different levels of interactions. Additionally, we implement and evaluate multiple deep learning models for detecting different emotions from online discussions. Relevant emotion sequence patterns were identified using sequence pattern analysis and the identified emotion sequence patterns were compared across different groups with different levels of interactions. Results and Conclusions: Using an agglomerative hierarchical algorithm, we classified learners into three distinct groups characterized by different levels of interactions: high, average and low level. Leveraging the bi‐directional long short‐term memory model for emotion detection yielded the highest predictive performance, with an impressive F‐measure of 94.01%, a recall rate of 93.83% and an accuracy score of 95.01%. The results also revealed that learners in the low‐level interaction group experienced more emotion transition from boredom to frustration than the other two groups. Therefore, the aggregation of students into groups and the utilization of their MOOC log data offer educators the capability to provide adaptive emotional feedback, customize assessments and offer more personalized attention as needed. Lay Description: What is currently known about the subject matter: Emotions are dynamic over time when learners experience cognitive disequilibrium/equilibrium.Online interactions are critical components, which influence learners' emotional state, cognitive processes and learning achievement.It is not clear what are differences in emotion sequence patterns across the groups with different interaction types. What the paper adds: An agglomerative hierarchical algorithm was implemented to cluster learners into three groups by analysing behavioural data.We explore possibilities for automated classification of emotions using deep learning approaches.Learners in the low‐level interaction group experienced more emotion transition from boredom to frustration. Implications for practitioners: A considerable amount of effort should be expended to identify and respond to learners who experience boredom and frustration emotions.Designing interventions or scaffolding to facilitate learners' interaction and promote favourable emotions.Educators could provide more personalized support based on learners' online interaction cluster. [ABSTRACT FROM AUTHOR]

ith the rapid development of technology, processing the explosive growth of meteorological data on traditional standalone computing has become increasingly time-consuming, which cannot meet the demands of scientific research and business. Therefore, this paper proposes the implementation of the parallel Clustering Large Application based upon RANdomized Search (CLARANS) clustering algorithm on the Spark cloud computing platform to cluster China’s climate regions using meteorological data from 1988 to 2018. The aim is to address the challenge of applying clustering algorithms to large datasets. In this paper, the morphological similarity distance is adopted as the similarity measurement standard instead of Euclidean distance, which improves clustering accuracy. Furthermore, the issue of local optima caused by an improper selection of initial clustering centers is addressed by utilizing the max-distance criterion. Compared to the k-means clustering algorithm already implemented in the Spark platform, the proposed algorithm has strong robustness, can reduce the interference of outliers in the dataset on clustering results, and has higher parallel performance than the frequently used serial algorithms, thus improving the efficiency of big data analysis. This experiment compares the clustered centroid data with the annual average meteorological data of representative cities in the five typical meteorological regions that exist in China, and the results show that the clustering results are in good agreement with the meteorological data obtained from the National Meteorological Science Data Center. This algorithm has a positive effect on the clustering analysis of massive meteorological data and deserves attention in scientific research activities. [ABSTRACT FROM AUTHOR]

With the rapid development of technology, processing the explosive growth of meteorological data on traditional standalone computing has become increasingly time-consuming, which cannot meet the demands of scientific research and business. Therefore, this paper proposes the implementation of the parallel Clustering Large Application based upon RANdomized Search (CLARANS) clustering algorithm on the Spark cloud computing platform to cluster China’s climate regions using meteorological data from 1988 to 2018. The aim is to address the challenge of applying clustering algorithms to large datasets. In this paper, the morphological similarity distance is adopted as the similarity measurement standard instead of Euclidean distance, which improves clustering accuracy. Furthermore, the issue of local optima caused by an improper selection of initial clustering centers is addressed by utilizing the max-distance criterion. Compared to the k-means clustering algorithm already implemented in the Spark platform, the proposed algorithm has strong robustness, can reduce the interference of outliers in the dataset on clustering results, and has higher parallel performance than the frequently used serial algorithms, thus improving the efficiency of big data analysis. This experiment compares the clustered centroid data with the annual average meteorological data of representative cities in the five typical meteorological regions that exist in China, and the results show that the clustering results are in good agreement with the meteorological data obtained from the National Meteorological Science Data Center. This algorithm has a positive effect on the clustering analysis of massive meteorological data and deserves attention in scientific research activities. [ABSTRACT FROM AUTHOR]

A trend in the development of spaceborne Synthetic Aperture Radar (SAR) technology is the shift from a single-satellite repeated observation mode to a multi-satellite collaborative observation mode. However, current multi-satellite collaborative geolocation algorithms face challenges, such as geometric model mismatch and poor baseline estimation accuracy, arising from highly dynamic changes among multi-satellites. This paper introduces a high-precision and efficient geolocation algorithm for a spaceborne bistatic interferometric SAR (BiInSAR) system based on an improved range–Doppler (IRD) model. The proposed algorithm encompasses three key contributions. Firstly, a comprehensive description of the spatial baseline geometric model unique to the bistatic configuration is provided, with a specific focus on deriving the perpendicular baseline expression. Secondly, IRD geolocation functions are established to meet the specific requirements of the bistatic configuration. Then, a novel BiInSAR geolocation algorithm based on the IRD's functions is proposed, which can significantly improve the target geolocation accuracy by modifying the range–Doppler equation to suit the bistatic configuration. Meanwhile, a low-coupling parallel calculation method is proposed, which can improve the calculation speed by two to three times. Finally, the accuracy and efficiency of the algorithm are demonstrated using experimental data acquired by the TH-2 satellite, which is China's first spaceborne BiInSAR system. The experimental results prove that the IRD algorithm exhibits geolocation accuracy with an average error of less than 1 m and a standard deviation of less than 2.5 m while maintaining computational efficiency at a calculation speed of 1,429,678 pixels per second. [ABSTRACT FROM AUTHOR]

Solving Rank Constrained Least Squares via Recursive Importance Sketching In statistics and machine learning, we sometimes run into the rank-constrained least squares problems, for which we need to find the best low-rank fit between sets of data, such as trying to figure out what factors are affecting the data, filling in missing information, or finding connections between different sets of data. This paper introduces a new method for solving this problem called the recursive importance sketching algorithm (RISRO), in which the central idea is to break the problem down into smaller, easier parts using a unique technique called "recursive importance sketching." This new method is not only easy to use, but it is also very efficient and gives accurate results. We prove that RISRO converges in a local quadratic-linear and quadratic rate under some mild conditions. Simulation studies also demonstrate the superior performance of RISRO. In this paper, we propose a recursive importance sketching algorithm for rank-constrained least squares optimization (RISRO). The key step of RISRO is recursive importance sketching, a new sketching framework based on deterministically designed recursive projections, and it significantly differs from the randomized sketching in the literature. Several existing algorithms in the literature can be reinterpreted under this new sketching framework, and RISRO offers clear advantages over them. RISRO is easy to implement and computationally efficient, and the core procedure in each iteration is to solve a dimension-reduced least squares problem. We establish the local quadratic-linear and quadratic rate of convergence for RISRO under some mild conditions. We also discover a deep connection of RISRO to the Riemannian Gauss–Newton algorithm on fixed rank matrices. The effectiveness of RISRO is demonstrated in two applications in machine learning and statistics: low-rank matrix trace regression and phase retrieval. Simulation studies demonstrate the superior numerical performance of RISRO. Funding: Y. Luo and A. Zhang were partially supported by the National Science Foundation [Grant CAREER-2203741]. W. Huang was partially supported by the Fundamental Research Funds for the Central Universities [Grant 20720190060] and the National Natural Science Foundation of China [Grant 12001455]. X. Li was partially supported by the National Natural Science Foundation of China [Grants 62141407 and 12271107], the Chenguang Program by the Shanghai Education Development Foundation and Shanghai Municipal Education Commission [Grant 19CG02], and the Shanghai Science and Technology Program [Grant 21JC1400600]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2023.2445. [ABSTRACT FROM AUTHOR]

The multi-objective optimisation of high-speed rail profile with small radius curve is studied in the paper. A multi-objective mathematical model for rail profile optimisation of high-speed railway is established. The CN60 rail profile is parameterised into a series of generalised functions of design variables. In order to guarantee the smoothness of the rail profile and meet the maximum grinding depth of rail in China, the constraints are employed. The wheel-rail vertical clearance and equivalent conicity of wheelset are taken as objective functions, and a rail wear prediction programme is compiled. Contact line method is employed to complete the detection algorithm of wheel-rail contact points. Finally, NSGA-II Algorithm is adopted to solve the Pareto-optimal front of the optimisation model. A set of solutions are retrieved from the Pareto optimal front solution as the optimised profile. The optimised rail profile and the original rail profile are matched with the LMA wheel profile (a certain worn type of wheel profiles for EMU in China) respectively. It is testified that the rail profile could effectively reduce the rail wear and improve curving performance. The new method proposed in this paper can provide some reference for the optimisation design of high-speed rail profile with small radius curve. [ABSTRACT FROM AUTHOR]

Landslides, widespread and highly dangerous geological disasters, pose significant risks to humankind and the ecological environment. Consequently, predicting landslides is vital for disaster prevention and mitigation strategies. At present, the predominant methods for predicting landslide susceptibility are evolving from conventional machine learning techniques to deep learning approaches. At present, the predominant methods for predicting landslide susceptibility are evolving from conventional machine learning techniques to deep learning approaches. Prior studies have shown that in the context of landslide susceptibility, these models frequently underperform relative to tree-based machine learning algorithms. This shortcoming has restricted the application of deep learning in this domain. To overcome this challenge, this study presents the TabNet algorithm, which combines the interpretability and selective feature extraction of tree models with the representation learning and comprehensive training capabilities of neural network models. This paper explores the potential of employing the TabNet algorithm for landslide susceptibility analysis in China's WanZhou region and evaluates its performance against traditional machine learning techniques. The experimental data indicate that the TabNet algorithm achieves a recall score of 0.898 and an AUC of 0.915, demonstrating a generalization capability that is comparable to that of classical machine learning algorithms. [ABSTRACT FROM AUTHOR]

The sparse triangular solver (SpTRSV) is mostly used for scientific and engineering applications. The structured-grid triangular solver of regular dependencies (STRSV) is a special kind of SpTRSV. Some general SpTRSVs that disregards the regularity of the matrix are unsuitable for solving this problem. This paper proposes an efficient parallel algorithm for STRSV on the SW26010 (a kind of China independently designed many-core processors), namely swStructTRSV. The algorithm makes full use of the fine-grained and low latency communication characteristics of the SW26010 to reduce the waiting time for synchronization, maximizes the regularity of access to improve memory access bandwidth, and achieves overlap between memory access and computation simultaneously. Moreover, the idea of the algorithm can be extended to incomplete LU factorization (ILU factorization) because of consistent dependencies. The experimental results on a core group(8 * 8 network composed of 64 cores) of SW26010 show that swStructTRSV can achieve an average speedup of over 30 in the sequential version. swStructTRSV on SW26010 achieves solving speedups of 2.2 and 6.3 over the fast STRSV (fSpTRSV) previously implemented on SW26010 and MKL on Intel Xeon Gold 6132, respectively. swStructTRSV significantly outperforms cuSparse on NVIDIA TITAN RTX in terms of overall execution time. [ABSTRACT FROM AUTHOR]

This paper presents a massively parallel approach of the multilevel fast multipole algorithm (PMLFMA) on homegrown many-core SW26010 cluster of China, noted as (SW-PMLFMA), for 3-D electromagnetic scattering problems. In this approach, the multilevel fast multipole algorithm (MLFMA) octree is first partitioned among management processing elements (MPEs) of SW26010 processors following the ternary partitioning scheme using the message passing interface (MPI). Then, the computationally intensive parts of the PMLFMA on each MPI process, matrix filling, aggregation and disaggregation are accelerated by using all the 64 computing processing elements (CPEs) in the same core group of the MPE via the Athread parallel programming model. Different parallelization strategies are designed for many-core accelerators to ensure a high computational throughput. In coincidence with the special characteristic of local Lagrange interpolation, the compressed sparse row (CSR) and the compressed sparse column (CSC) sparse matrix storage format is used for storing interpolation and anterpolation matrices, respectively, together with a specially designed cache mechanism of hybrid dynamic and static buffers using the scratchpad memory (SPM) to improve data access efficiency. Numerical results are included to demonstrate the efficiency and versatility of the proposed method. The proposed parallel scheme is shown to have excellent speedup. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is the most common cause of dementia. Accurate prediction and diagnosis of AD and its prodromal stage, i.e., mild cognitive impairment (MCI), is essential for the possible delay and early treatment for the disease. In this paper, we adopt the data from the China Longitudinal Aging Study (CLAS), which was launched in 2011, and includes a joint effort of 15 institutions all over the country. Four thousand four hundred and eleven people who are at least 60 years old participated in the project, where 3,514 people completed the baseline survey. The survey collected data including demographic information, daily lifestyle, medical history, and routine physical examination. In particular, we employ ensemble learning and feature selection methods to develop an explainable prediction model for AD and MCI. Five feature selection methods and nine machine learning classifiers are applied for comparison to find the most dominant features on AD/MCI prediction. The resulting model achieves accuracy of 89.2%, sensitivity of 87.7%, and specificity of 90.7% for MCI prediction, and accuracy of 99.2%, sensitivity of 99.7%, and specificity of 98.7% for AD prediction. We further utilize the SHapley Additive exPlanations (SHAP) algorithm to visualize the specific contribution of each feature to AD/MCI prediction at both global and individual levels. Consequently, our model not only provides the prediction outcome, but also helps to understand the relationship between lifestyle/physical disease history and cognitive function, and enables clinicians to make appropriate recommendations for the elderly. Therefore, our approach provides a new perspective for the design of a computer-aided diagnosis system for AD and MCI, and has potential high clinical application value. [ABSTRACT FROM AUTHOR]

Recommender systems on online platforms are often accused of polarizing user attention and consumption. The authors examine this phenomenon using a quasi-experiment conducted by Zhihu, the largest online knowledge-sharing platform (or Q&A community) in China. Zhihu originally used a content-based filtering algorithm, which recommends content to users on the basis of the topics to which each user has subscribed. After more than a year, Zhihu moved to a social filtering algorithm, which recommends content with which users' social connections are already engaged. The authors find that this algorithm change increased the creation of social ties by approximately 15% but decreased question subscriptions by 20% and answer contributions by 23%. The authors show that users' increased social interests mainly involved following popular users, leading to a greater concentration of social interests on the platform. However, users' topical interests became less concentrated, as popular topics received significantly fewer subscribers than unpopular topics. The authors explain these findings by exploring the underlying mechanism. They show that compared with content-based filtering algorithms, social filtering algorithms are more likely to expose general users to content consumed by their followees, who are more interested in niche topics than general users are. [ABSTRACT FROM AUTHOR]

Visually inspecting integrated circuit (IC) wire bonding defects is important to ensuring the product quality after the packaging process. The availability of IC X-ray images offers an unprecedented opportunity of studying the image-based automatic IC wire inspection. In this paper, a data-driven method consists of data pre-processing, feature engineering, and classification is developed to address such problem. The data pre-processing is composed of a chip identification algorithm for locating and separating IC chip image patches from the raw images as well as a wire segmentation algorithm for obtaining the wire region. Next, geometric features extracted from the segmented wires are fed into classification models for identifying defects. Five data mining methods are utilised to develop classification models. The vision detection system (VDS) and convolutional neural networks (CNN) are considered as benchmarks. In computational studies, the effectiveness of the developed method is validated by using X-ray images collected from a semiconductor back-end factory in Mainland China. A comparative analysis is conducted to determine the most suitable classifier for the developed method in the chip classification and the SVM model is finally selected. Advantages of the developed method are verified by benchmarking against the VDS and CNN. [ABSTRACT FROM AUTHOR]

In recent years, the convergence of the Internet of Things (IoT) and machine learning technologies has instigated advancements in IoT, and machine learning has revolutionized SMEs, reshaping their role in regional economic growth. These technologies provide SMEs with innovative tools, fostering connectivity within regional ecosystems. This transformation is crucial for China's economic realignment in its 'new normal' phase, with enterprises driving social and economic progress. This study proposed the XGBoost algorithm and IoT data enhance collaboration among SMEs in regional economies, fostering innovation and driving economic development. The role of IoT and machine learning in promoting sustainable regional economic prosperity. Technological innovation is essential for enterprise survival and growth in a competitive landscape. This paper examines the influence of environmental factors and geography on economic development across China's regions. It focuses on SME coupling and coordination, contributing to regional economic progress through performance evaluation. IoT integration provides SMEs with abundant real-time data, enabling deep insights into customer behavior, supply chains, and production. Simultaneously, the XGBoost algorithm efficiently processes and extracts actionable insights from this data. Data from 11 provinces in the Yangtze River economic belt highlights Jiangsu Province's leadership in regional innovation performance for 2015 and 2020. The empirical results, grounded in datasets amalgamating information from these provinces, attest to the potential of this IoT and XGBoost-driven approach. Boasting an impressive accuracy rate of 91.7%, surpassing alternative machine learning techniques like SVM, KNN, DT, RF, and LR, this research underscores the effectiveness of this integrated strategy in optimizing SME operations. Additionally, it computes the coupling coordination degree, innovation environment ranking, mean value, and numerical changes from 2010 to 2020, with an average innovation coupling coordination degree of 0.1624 across 30 Chinese provinces. [ABSTRACT FROM AUTHOR]

The vulnerable children refer to the special group of children with deviation in the process of children's psychological development and personality formation due to growth dilemmas. This may incur a series of serious social and family problems. The vulnerable children mainly cover the children suffering from children's psychological problems, such as childhood autism, autism, social anxiety and hypersensitivity, fear, depression, and PTSD arising from other factors. At present, the research results at home and abroad mainly focus on the psychological dynamic correlation investigation and solution discussion of a certain kind of difficult factor in the children's psychological development based on statistical data by the experimental methods, such as scale and model, and there is a blind spot in the humanistic orientation theory construction of psychological treatment for vulnerable children, causing the social reflection on children's psychological predicament from the humanistic perspective cannot be performed in related researches and going against searching for universal and integral theoretical paradigm for solving related problems. Sophisticated technologies for the observations have emerged increasingly for enabling the psychological features of vulnerable children through developmental cognitive neuroscience experiments. This paper introduces humanistic art therapy theory, focuses on the construction of a theoretical paradigm, and verifies its effectiveness based on the experimental results on the psychological development of vulnerable children, with an efficient performance. This study mainly refers to children with difficulty in social inclusion and psychological development. The results showed that two kinds of art therapy can obviously improve the psychological disorders of vulnerable children. The goal was to enhance self-cognition, strengthen emotional interaction, and implement positive motivation. [ABSTRACT FROM AUTHOR]

The aim of this work is to investigate dynamic division of highway control subareas based on improved Label Propagation Algorithm (LPA) for implementing different signal control strategies in the subareas to improve operation efficiency. A highway network is used as the studied object, where each road segment is defined as a node and the network is divided into three types of nodes: only mainline segment, an on-off-ramp and its connecting mainline, and an overpass. The system is abstracted as a topological set of nodes and their relationships. The concept of multiroad segment correlation degree with predictive function of congestion and dissipation ranges is proposed. The correlation degree model of multiroad segments based on Pearson Correlation Coefficient (PCC) and the importance degree model of road segment based on technique for order preference by similarity to ideal solution (TOPSIS) are established, respectively. Then a dynamic division method of control subareas for highway network is proposed based on improved LPA, where the label updating model based on the importance degree of road segment is established by semisupervised classification algorithm. Multiple scenarios were discussed using the highway network of Baoding City in Hebei Province of China as an example. The results show that the number of subareas is moderate, the internal state is uniform, and the calculation is simple and efficient. The control effect is remarkable especially when the vehicle distribution is uneven. Total delay time is reduced by 10.47%, 4.38%, 4.24%, 9.73%, and 2.33%, respectively, and total travel time is reduced by 12.20%, 7.15%, 7.02%, 11.85%, and 3.78%, respectively, compared with no control method, global control method, the static division method by clustering algorithm, the method by normal LPA algorithm, and the method by Newman algorithm. [ABSTRACT FROM AUTHOR]

Objective: Depth camera-based measurement has demonstrated efficacy in automated assessment of upper limb Fugl-Meyer Assessment for paralysis rehabilitation. However, there is a lack of adequately sized studies to provide clinical support. Thus, we developed an automated system utilizing depth camera and machine learning, and assessed its feasibility and validity in a clinical setting. Design: Validation and feasibility study of a measurement instrument based on single cross-sectional data. Setting: Rehabilitation unit in a general hospital Participants: Ninety-five patients with hemiparesis admitted for inpatient rehabilitation unit (2021–2023). Main measures: Scores for each item, excluding those related to reflexes, were computed utilizing machine learning models trained on participant videos and readouts from force test devices, while the remaining reflex scores were derived through regression algorithms. Concurrent criterion validity was evaluated using sensitivity, specificity, percent agreement and Cohen's Kappa coefficient for ordinal scores of individual items, as well as correlations and intraclass correlation coefficients for total scores. Video-based manual assessment was also conducted and compared to the automated tools. Result: The majority of patients completed the assessment without therapist intervention. The automated scoring models demonstrated superior validity compared to video-based manual assessment across most items. The total scores derived from the automated assessment exhibited a high coefficient of 0.960. However, the validity of force test items utilizing force sensing resistors was relatively low. Conclusion: The integration of depth camera technology and machine learning models for automated Fugl-Meyer Assessment demonstrated acceptable validity and feasibility, suggesting its potential as a valuable tool in rehabilitation assessment. [ABSTRACT FROM AUTHOR]

As China becomes more and more international, the number of people traveling abroad is also increasing. The demand for English recognition is becoming more and more vigorous, and traditional translation software is time-consuming, laborious, and less accurate. This article optimizes the target detection model YOLOV3. Firstly, the image is divided into multiple model structures, and the K-means++ clustering algorithm is used to determine the target detection prior frame value and the high frame of the corresponding frame according to the characteristics of the English image. Then, by using K-means++ clustering algorithm to optimize the anchor parameters, the model structure is better adapted to the English identification dataset scene; finally, the feature information extracted by the DarkNet-53 model is spliced to improve the structure of the YOLOV3 convolutional layer, using 3090 graphics card GPU to perform multiscale training and testing. Experimental results show that the improved YOLOV3 algorithm in this paper has a mAP of 0.95 on the English identification dataset and a detection speed of 50fps, which is 0.11 higher than the mAP before optimization. Therefore, optimizing the YOLOV3 algorithm in this article has a good effect. In the future, English translation will become a necessary software program for Chinese people to go abroad. [ABSTRACT FROM AUTHOR]

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Fractional-order derivative is attracting more and more attention of researchers in image processing because of its better property in restoring more texture than the total variation. To improve the performance of fractional-order variation model in image restoration, a truncated fractional-order variation model was proposed in Chan and Liang [Truncated fractional-order variation model for image restoration, J. Oper. Res. Soc. China]. In this paper, we propose a dual approach to solve this truncated fractional-order variation model on noise removal. The proposed algorithm is based on the dual approach proposed by Chambolle [An algorithm for total variation minimisation and applications, J. Math Imaging Vis. 20 (2004), pp. 89–97]. Conversely, the Chambolle's dual approach can be treated as a special case of the proposed algorithm with fractional order α = 1. The work of this paper modifies the result in Zhang et al. [Adaptive fractional-order multi-scale method for image denoising, J. Math. Imaging Vis. 43(1) (2012), pp. 39–49. Springer Netherlands 0924–9907, Computer Science, pp. 1–11, 2011], where the convergence is not analysed. Based on the truncation, the convergence of the proposed dual method can be analysed and the convergence criteria can be provided. In addition, the accuracy of the reconstruction is improved after the truncation is taken. [ABSTRACT FROM AUTHOR]

In landslide disaster warning, a variety of monitoring and warning methods are commonly adopted. However, most monitoring and warning methods cannot provide information in advance, and serious losses are often caused when landslides occur. To advance the warning time before a landslide, an innovative advance landslide prediction and warning model based on a stacking fusion algorithm using Baishuihe landslide data is proposed in this paper. The Baishuihe landslide area is characterized by unique soil and is in the Three Gorges region of China, with a subtropical monsoon climate. Based on Baishuihe historical data and real-time monitoring of the landslide state, four warning level thresholds and trigger conditions for each warning level are established. The model effectively integrates the results of multiple prediction and warning submodels to provide predictions and advance warnings through the fusion of two stacking learning layers. The possibility that a risk priority strategy can be used as a substitute for the stacking model is also discussed. Finally, an experimental simulation verifies that the proposed improved model can not only provide advance landslide warning but also effectively reduce the frequency of false warnings and mitigate the issues of traditional single models. The stacking model can effectively support disaster prevention and reduction and provide a scientific basis for land use management. [ABSTRACT FROM AUTHOR]

The integrated energy systems, usually including electric energy, natural gas and thermal energy, play a pivotal role in the energy Internet project, which could improve the accommodation of renewable energy through multienergy complementary ways. Focusing on the regional integrated energy system composed of electrical microgrid and natural gas network, a fault risk warning method based on the improved RelieF-softmax method is proposed in this paper. The raw data-set was first clustered by the K-maxmin method to improve the preference of the random sampling process in the RelieF algorithm, and thereby achieved a hierarchical and non-repeated sampling. Then, the improved RelieF algorithmis used to identify the feature vectors, calculate the feature weights, and select the preferred feature subset according to the initially set threshold. In addition, a correlation coefficient method is applied to reduce the feature subset, and further eliminate the redundant feature vectors to obtain the optimal feature subset. Finally, the softmax classifier is used to obtain the early warnings of the integrated energy system. Case studies are conducted on an integrated energy system in the south of China to demonstrate the accuracy of fault risk warning method proposed in this paper. [ABSTRACT FROM AUTHOR]

The construction and operation of China's rail transit system have entered a high-speed development stage, and the rapid increase of train speed and mileage has brought greater challenges to the safety and reliability of the rail transit system. Network planning evaluation is the key to the early decision-making of urban rail transit project, which directly determines the success or failure of the whole project. How to scientifically and reasonably evaluate the urban rail transit information resource network planning has become a difficult problem for many urban planners to solve. Therefore, this paper studies the optimization of the communication resource allocation algorithm and the comprehensive evaluation of its application for urban rail transit planning. In this paper, based on CVNN structure, the network prototype is an extension of RVNN structure. In the abstract, its processing unit is composed of a pair of real-number processors that can realize certain operations. HNN is a fully connected recurrent neural network based on the idea of the energy function, which is helpful to understand the calculation mode of HNN, and the research shows that HNN can solve many combinatorial optimization problems. In addition, the combination of neural network and genetic algorithm with simulated annealing mechanism can also bring new directions for research. On the basis of experimental analysis, it can be concluded that in general, the error reduction rate of the optimization scheme designed in this paper can reach 58.6% on average. In practical application, the accuracy of the optimal bit error rate is 52.4%. [ABSTRACT FROM AUTHOR]

Global warming has constituted a major global problem. Carbon dioxide emissions from the burning of fossil fuels are the main cause of global warming. Therefore, carbon dioxide emission forecasting has attracted widespread attention. Aiming at the problem of carbon dioxide emissions forecasting, this paper proposes a new hybrid forecasting model of carbon dioxide emissions, which combines the marine predator algorithm (MPA) and multi-kernel support vector regression. For further strengthening the prediction accuracy, a novel variant of MPA is proposed, called EGMPA, which introduces the elite opposition-based learning strategy and the golden sine algorithm into MPA. Algorithm test results show that EGMPA can effectively improve the convergence speed and optimization accuracy. The carbon dioxide emission data of China from 1965 to 2020 are taken as the research objects. Root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to evaluate the performance of the proposed model. The proposed multi-kernel support vector regression model is used to forecast China's carbon dioxide emissions during the "14th Five-Year Plan" period. The results show that the proposed model has RMSE of 37.43 Mt, MAE of 30.63 Mt, and MAPE of 0.32%, which significantly improves the prediction accuracy and can accurately and effectively predict China's carbon dioxide emissions. During the "14th Five-Year Plan" period, China's carbon dioxide emissions will continue to show an increasing trend, but the growth rate will slow down significantly. [ABSTRACT FROM AUTHOR]

With the economic globalization continuous growth of China's socioeconomic level tends to be internationalized, China's attention to English has been significantly improved. However, the domestic English teaching level is limited, so it is impossible to correct students' English pronunciation and make a reasonable evaluation at all times so that oral training has certain disadvantages. However, the computer-aided language learning system at home and abroad focuses on the practice of words and grammar, and the evaluation indicators are less and not comprehensive. In view of the complexity of English pronunciation changes, traditional speech recognition is difficult to recognize speech speed and improve its accuracy. Furthermore, to strengthen the English pronunciation of domestic students, a nonlinear network structure is studied in depth to simulate the human brain to analyze a model of speech recognition is established Mel frequency cepstrum characteristic parameters of human ear model and deep belief network. In this paper, the traditional computer pronunciation evaluation method is improved in an all-round way, and a set of high-quality speech recognition system of speech recognition method is constructed. Aiming at the above problems, it takes the students as the research, which proves that the method adopted in this paper can give the learners accurate pronunciation quality analysis report and guidance and correct their intonation and improve the learning effect, and the experimental data verify that the improved speech recognition system model recognition ability is higher than the traditional model. [ABSTRACT FROM AUTHOR]

Precise segmentation of the hippocampus is essential for various human brain activity and neurological disorder studies. To overcome the small size of the hippocampus and the low contrast of MR images, a dual multilevel constrained attention GAN for MRI-based hippocampus segmentation is proposed in this paper, which is used to provide a relatively effective balance between suppressing noise interference and enhancing feature learning. First, we design the dual-GAN backbone to effectively compensate for the spatial information damage caused by multiple pooling operations in the feature generation stage. Specifically, dual-GAN performs joint adversarial learning on the multiscale feature maps at the end of the generator, which yields an average Dice coefficient (DSC) gain of 5.95% over the baseline. Next, to suppress MRI high-frequency noise interference, a multilayer information constraint unit is introduced before feature decoding, which improves the sensitivity of the decoder to forecast features by 5.39% and effectively alleviates the network overfitting problem. Then, to refine the boundary segmentation effects, we construct a multiscale feature attention restraint mechanism, which forces the network to concentrate more on effective multiscale details, thus improving the robustness. Furthermore, the dual discriminators D1 and D2 also effectively prevent the negative migration phenomenon. The proposed DMCA-GAN obtained a DSC of 90.53% on the Medical Segmentation Decathlon (MSD) dataset with tenfold cross-validation, which is superior to the backbone by 3.78%. [ABSTRACT FROM AUTHOR]

Loess landslides are one of the geological hazards prevalent in mountainous areas of Loess Plateau, seriously threatening people's lives and property safety. Accurate identification of landslides is a prerequisite for reducing the risk of landslide hazards. Traditional landslide interpretation methods often have the disadvantage of being laborious and difficult to use on a large scale compared with the recently developed deep learning-based landslide detection methods. In this study, we propose an improved deep learning model, landslide detection- you only look once (LD-YOLO), based on the existing you only look once (YOLO) model for the intelligent identification of old and new landslides in loess areas. Specifically, remote sensing images of landslides in Baoji City, Shaanxi Province, China are acquired from the Google Earth Engine platform. The landslide images of Baoji City (excluding Qianyang County) are used to establish a loess landslide dataset for training the model. The landslide data of Qianyang County is used to verify the detection performance of the model. The focal and efficient IoU (Focal-EIoU) loss function and efficient channel attention (ECA) mechanism are incorporated into the 7th version of YOLO (YOLOv7) model to construct the LD-YOLO model, which makes it more suitable for the landslide detection task. The experiments yielded an improved LD-YOLO model with average precision of 92.05%, precision of 92.31%, recall of 90.28%, and F1-score of 91.28% for loess landslide detection. The landslides in Qianyang County were divided into two test sets, new landslides and old landslides, which were used to test the detection performance of LD-YOLO for both types of landslides. The results show that LD-YOLO detects old landslides with a detection precision of 82.75% and a recall of 80%. When detecting new landslides, the detection precision is 94.29% and the recall is 91.67%. It indicates that our proposed LD-YOLO model has strong detection performance for both new and old landslides in loess areas. Through a proposed solution that can realize the accurate detection of landslides in loess areas, this paper provides a valuable reference for the application of deep learning methods in landslide identification. [ABSTRACT FROM AUTHOR]

Exploring the improvement of classroom teaching effect under the background of sports education integration in China has important practical significance and theoretical value. Integration of yard sports education and classroom course education in the school is a novel concept put forward by China's sports and education circles to deepen the combination of sports and education. Although it is only a word different from the combination of sports and education to integrating sports and course education in the school, it is a groundbreaking theoretical and practical innovation for China in terms of changing the competitive sports development model and cultivating exceptional athletes. It is a new way to promote the sustainable development of sports and education according to the scientific outlook on development. Its fundamental significance is to change the closed state of sports and education departments, put sports and education in the background of economic and social development in a certain region, and fundamentally reform the content and mode of education. Therefore, this paper proposes a comprehensive rating model of classroom teaching effects based on deep learning (DL) and machine learning (ML) techniques. In this paper, the Jaffe expression dataset is used to train and test the utilized ML and DL models such as ResNet50, random forest (RF), logistic regression (LR), K-nearest neighbor (k-NN), and decision tree (DT). Further, with the help of artificial intelligence (AI) techniques, the algorithms can objectively evaluate the classroom teaching effect after the integration of physical education with classroom education and provide important guidance for the modernization and intellectualization of China's educational methods in the future. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid economic development, the development speed of all walks of life has entered a new level, and the power industry has also developed rapidly. Driven by market demand, China's power transmission range and power transmission capacity will enter a new level. At the same time, the problems brought about by the development of the power system are equally severe. Due to the large load density in individual areas, the detection of short-circuit current must be improved as an important issue. The purpose of this paper is to study how to improve the practical model of short-circuit current calculation of photovoltaic power plants, so that it can be well applied to the current high-density current detection in China. Therefore, this paper improves the recursive least squares (RLS) algorithm and applies it to the practical model of short-circuit current calculation of photovoltaic power plants and describes the improvement process of the algorithm in detail. At the same time, this paper designs relevant experiments and analysis to count the data of the improved RLS algorithm in the short-circuit current calculation of the actual photovoltaic power station and combines the data of this part to test and analyze the ability of the algorithm. The experimental results in this paper show that the improved RLS algorithm has a very good improvement in the calculation accuracy of the short-circuit current calculation of photovoltaic power plants in the actual model calculation. At the same time, the calculation efficiency is also improved, and the current tracking effect is also improved by 7%. [ABSTRACT FROM AUTHOR]

Lack of trust, lack of standards, and low efficiency are the three biggest problems in China's trade financing at present. With the development and application of new generation technologies such as big data, cloud computing, artificial intelligence, and blockchain technology, China is in the stage of financial technology 3.0 under the deep integration of finance and technology. In the field of financial technology, the most concerned is the application of blockchain technology in trade finance business. With the successive construction of various blockchain platforms and the acceleration of the internationalization process, the international trade credit risk behind it is also increasing. Among many financial services, trade finance is the most closely integrated field with blockchain technology. In this context, preventing the risks in the business process of international trade enterprises, so as to reduce the cost of financial transactions, improve the effectiveness of financial services, and better serve the real economy is not only the internal development needs of enterprises, but also the national financial strategy needs. In view of the above problems, this paper analyzes the risk factors faced by multinational trading enterprises in the transaction process through the transaction data of some multinational enterprises on mobile phones, and constructs a credit evaluation system of international trading enterprises based on blockchain, in order to enhance the trade risk resistance ability of international trading companies. [ABSTRACT FROM AUTHOR]

The main core of software engineering key technologies is the development of software services, ensuring the scientificity, security, and stability of the application software engineering system. At present, China's economic development urgently needs the support of software engineering technology. Based on the T-ACO algorithm, the scientificity of software engineering and the accuracy of data have been significantly improved compared with traditional software engineering technology. It plays an important role in promoting the follow-up software engineering technology. In order to effectively analyze the key technology of engineering software, an improved ant colony algorithm based on T distribution is proposed in this paper. Because the basic ant colony algorithm is easy to fall into the local optimum and the optimization accuracy is low, in the optimization process, at the beginning of the pheromone update, the introduction of the T distribution is helpful for the basic ant colony algorithm to make up for its shortcomings. Adding pheromone variables to the basic ant colony algorithm improves the diversity of the ant colony, thereby eliminating the limitations of local optimal solutions. At the same time, the T-ACO algorithm also improves the search accuracy and convergence speed of automatic data generation in software engineering. In this paper, the performance of the T-ACO algorithm is simulated by experiments. Experimental analysis shows that when the population size is small, the T-ACO algorithm may sometimes not converge to the optimal solution, but when the population size is large (≥50), the T-ACO algorithm may converge to the optimal solution. It can realize the coverage of the total path by the output test case set. While the other two algorithms can achieve full path coverage, they are not stable, resulting in an average coverage between 90% and 100%. The T-ACO algorithm not only has good accuracy in creating test case sets, but also has good algorithm performance, and it is suitable as a multipath test case creation algorithm. [ABSTRACT FROM AUTHOR]

The proper application of machine learning and genetic algorithms in the early stage of library design can obtain better all-around building performance. The all-around performance of the library, such as indoor temperature, solar radiation, indoor lighting, etc., must be fully considered in the initial design stage. Aiming at building performance optimization and based on the method of "generative design", this paper constructs the library's comprehensive performance evaluation workflow and rapid prediction combined with the LightGBM algorithm. A library in a cold region of China is taken as the research object to verify its application. In this study, 5000 scheme samples generated in the iterative genetic optimization process were taken as data sets. The LightGBM algorithm was used to classify and predict design schemes, with a precision of 0.78, recall rate of 0.93, and F1-Score of 0.851. This method can help architects to fully exploit the optimization potential of the building's all-around performance in the initial stage of library design and ensure the timely interaction and feedback between design decisions and performance evaluation. [ABSTRACT FROM AUTHOR]

A large number of insect pests in the forest will seriously affect the construction of forest resources and agriculture in China. In this regard, in order to deeply understand and analyze the existing forest pest detection technology, it is found that it cannot meet practical needs. In order to prevent the harm caused by forest pests, it is necessary to correctly identify the types of pests and take targeted control measures. Therefore, this paper proposes a forest pest detection algorithm based on improved YOLOX. Firstly, aiming at the problem that there are few image data of real deep forest pests in the wild, we use Mosaic, Mixup, and random erasure data enhancement to preprocess the images. Secondly, in order to extract fine-grained features, shallow information is introduced into the existing network architecture, and a two-way cross-scale feature fusion mechanism is adopted. Finally, the improved YOLOX algorithm proposed in this paper has achieved the best results on the public forest pest dataset IP102. [ABSTRACT FROM AUTHOR]

In recent years, for China, animation industry is a relatively new and mature emerging national sunrise industry after animation industry, which appears on the world stage more and more frequently and is widely concerned and valued by people from all over the world. Therefore, this paper innovatively uses the convolutional neural network algorithm to innovate the color matching generation of animation characters and improve the traditional technology of color matching for animation characters. In this paper, we mainly use Generative Adversarial Network (GAN), Deep Convolutional Generative Adversarial Network and VGG model, and multiscale discriminator theory and use ACGAN research method. And we study this paper's innovative LMV-ACGAN research method, and we have come to the conclusion that other models have higher collapse rate than this model; this model has higher color matching of anime characters. Color matching improves with the increase of convolutional neural network utilization, etc. Moreover, superior and minor reviews of this study are provided to make later researchers understand this study more rationally and objectively. [ABSTRACT FROM AUTHOR]

Knowledge map is a new method of knowledge management with the information revolution. This paper is aimed at forming a systematic and standardized huge redundant knowledge structure, which can be used to mine the knowledge structure and the relationship between knowledge and visualize it in a graphical way, in order to obtain more representative information and improve the classification accuracy of text classification model. In this paper, a knowledge map construction method based on the Text CNN algorithm is proposed for the subject of Nautical English. It is of practical significance and academic value to make use of knowledge map to study Chinese Maritime English, which is helpful to the development of Chinese Maritime English and provides guidance. In order to maintain the diversity of particle swarm optimization, the Text CNN algorithm is combined with the construction of Maritime English subject knowledge map, and the network parameters and structure are optimized. Using knowledge map to study China Maritime English has important practical significance and academic value and has certain guiding significance for the development of China Maritime English. [ABSTRACT FROM AUTHOR]

With the rapid development of China's economy, people are eager for an effective way to relieve work pressure and strengthen their health at the same time. Outdoor sport is one of the best choices for people. However, the amount of recommended data on the network is very large. As a result, when people understand outdoor sports through the network, they cannot effectively obtain the information they want. This is the problem of "information overload," and personalized recommendation system can effectively alleviate this problem. In order to effectively recommend outdoor sports to users, a useful attempt was made in the personalized recommendation system for outdoor sports in this paper. The specific work of this paper is as follows: firstly, the current situation of outdoor sports in China was summarized, and the related technologies of the recommendation system were studied, including user modeling technology, recommendation target modeling technology, and recommendation algorithm. In order to obtain better recommendation effect, this paper proposes to mix user-based collaborative filtering recommendation algorithm, project-based collaborative filtering recommendation algorithm, and content-based recommendation algorithm. The hybrid algorithm adopts the way of feature expansion and weighted combination. Firstly, the hybrid model (model 1) of user-based collaborative filtering recommendation and content-based recommendation is obtained. Secondly, the hybrid model (model 2) based on project collaborative filtering recommendation and content-based recommendation was obtained. Finally, model 1 and model 2 were combined together to get a hybrid model with better final recommendation effect. For the common cold start problem in the recommendation system, the system adopts content-based recommendation algorithm to solve it. [ABSTRACT FROM AUTHOR]

Exploring the improvement of classroom teaching effect under the background of sports education integration in China has important practical significance and theoretical value. Integration of yard sports education and classroom course education in the school is a novel concept put forward by China's sports and education circles to deepen the combination of sports and education. Although it is only a word different from the combination of sports and education to integrating sports and course education in the school, it is a groundbreaking theoretical and practical innovation for China in terms of changing the competitive sports development model and cultivating exceptional athletes. It is a new way to promote the sustainable development of sports and education according to the scientific outlook on development. Its fundamental significance is to change the closed state of sports and education departments, put sports and education in the background of economic and social development in a certain region, and fundamentally reform the content and mode of education. Therefore, this paper proposes a comprehensive rating model of classroom teaching effects based on deep learning (DL) and machine learning (ML) techniques. In this paper, the Jaffe expression dataset is used to train and test the utilized ML and DL models such as ResNet50, random forest (RF), logistic regression (LR), K-nearest neighbor (k-NN), and decision tree (DT). Further, with the help of artificial intelligence (AI) techniques, the algorithms can objectively evaluate the classroom teaching effect after the integration of physical education with classroom education and provide important guidance for the modernization and intellectualization of China's educational methods in the future. [ABSTRACT FROM AUTHOR]

A new algorithm is proposed to estimate the cooling amplitude over North China induced by each Ural blocking event. Taking full account of potential transient temperature rises during the cooling process and the lag time of cooling relative to the blocking, this algorithm provides more detailed analysis which should not be possible by using former methods. The amplitude of the Ural blocking-related cooling events is found to have a broad distribution. Further, although most Ural blocking events lead to severe cold surges over North China, the number of Ural blocking events that do not induce significant cooling over North China cannot be ignored. The possible reasons for the wide range in cooling amplitude are explored in terms of the lifetimes and geographical centers of the blocking highs, the circulation patterns preceding the onset of the cooling events, and the snowfall associated with cooling events. Larger amplitude cooling events occur in Ural blocking highs that have longer lifetimes and northwestward displacements of their geographical centers. The northward displacement of a Ural blocking center favors the advection of extremely cold air from the Arctic regions, which accumulates in West Siberia and subsequently gives rise to the most severe cold surges over North China. The lack of activities of cold surges before the blocking-related cooling events not only amplifies the cooling amplitude directly, but also increases the occurrence probabilities of snowfalls through its modulation on the local specific humidity. The increased albedo and subsequent snow-melt induced cooling further amplify the cooling amplitude. [ABSTRACT FROM AUTHOR]

The parameters' inversion of saturated–unsaturated is important in ensuring the safety of earth dams; many scholars have conducted some research regarding the inversion of hydraulic conductivity based on seepage pressure monitoring data. The van Genuchten model is widely used in saturated–unsaturated seepage analysis, which considers the permeability connected to the water content of the soil and the soil's shape parameters. A BP neural artificial network is a mature prediction technique based on enough data, and the marine predator algorithm is a new nature-inspired metaheuristic inspired by the movement of animals in the ocean. The BP neural artificial network and marine predator algorithm are applied in the permeability coefficient inversion of a core-rock dam in China; the results show that in the normal operation status, the BP network shows better accuracy, and the average of the absolute error and variance of the absolute error are both minimum values, which are 2.21 m and 1.43 m, respectively. While the water storage speed changes, the marine predator algorithm shows better accuracy; the objective function is calculated to be 0.253. So, the marine predator algorithm is able to accurately reverse the desired results in some situations. According to the actual condition, employing suitable methods for the inverse permeability coefficient of a dam can effectively ensure the safe operation of dams. [ABSTRACT FROM AUTHOR]

With the rapid development of social media, people can access a large amount of data in a short period of time, and big data technology has emerged. With the vigorous development of cloud computing and big data, the method of mining audience interests through a large amount of data to guide film and television creation has attracted more and more attention from experts and scholars. In order to understand the current situation of film and television drama creation in China and provide suggestions for the shortcomings in the industry, this article mainly analyzes the application of intelligent algorithms for traffic prediction models and big data analysis in film and television creation. This intelligent algorithm can predict the potential audience of movies or TV dramas, helping producers and investors make decisions. This system utilizes artificial intelligence technology to select suitable actors for characters based on their matching degree and past work performance. This article applies intelligent algorithms to big data processing to improve the accuracy of data processing. This article explores the application of intelligent algorithms and big data analysis in film and television creation. Using machine learning algorithms to predict the potential audience of a movie or TV series based on historical data, providing decision-making basis for investors and producers. This system utilizes AI technology to select suitable actors for characters based on their matching degree with characters and past work performance, improving the scientific and accurate selection of roles. The application of these technologies helps to improve production efficiency and quality, reduce costs and risks, and inject new impetus into the sustainable development of the film and television industry. [ABSTRACT FROM AUTHOR]

Background: Assignments that involve writing based on several texts are challenging to many learners. Formative feedback supporting learners in these tasks should be informed by the characteristics of evolving written product and by the characteristics of learning processes learners enacted while developing the product. However, formative feedback in writing tasks based on multiple texts has almost exclusively focused on essay product and rarely included SRL processes. Objectives: We explored the viability of using product and process features to develop machine learning classifiers that identify low‐ and high‐performing essays in a multi‐text writing task. Methods: We examined learning processes and essay submissions of 163 graduate students working on an authentic multi‐text writing assignment. We utilised learners' trace data to obtain process features and state‐of‐the‐art natural language processing methods to obtain product features for our classifiers. Results and Conclusions: Of four popular classifiers examined in this study, Random Forest achieved the best performance (accuracy = 0.80 and recall = 0.77). The analysis of important features identified in the Random Forest classification model revealed one product (coverage of reading topics) and three process (elaboration/organisation, re‐reading and planning) features as important predictors of writing quality. Major Takeaways: The classifier can be used as a part of a future automated writing evaluation system that will support at scale formative assessment in writing tasks based on multiple texts in different courses. Based on important predictors of essay performance, a guidance can be tailored to learners at the outset of a multi‐text writing task to help them do well in the task. Lay Description: What is already known about this topic?: Both product and process features should be used to inform formative feedback on writing.Providing product‐ and process‐oriented feedback to learners is challenging.Automatic writing evaluation systems have mainly relied upon product features.Automated analysis of learners' trace data and their essay drafts is a promising venue. What this paper adds?: An accurate machine learning classifier that identifies low‐ and high‐scoring essays.The classifier utilized both product and process features.We obtained process features from learners' trace data in digital learning environment.We computed product features using state‐of‐the‐art text analytical methods. Implications for practice and/or policy: The classifier can be used as a part of a future automated writing evaluation system.We revealed learning processes and essay characteristics that influence performance.Based on important predictors of performance, formative feedback can be given to learners. [ABSTRACT FROM AUTHOR]

Regarding the problem of epidemic outbreak prevention and control, infectious disease dynamics models cannot support urban managers in reducing urban-scale healthcare costs through community-scale control measures, as they usually have difficulty meeting the requirements for simulation at different scales. In this paper, we propose combining contact networks at different spatial scales to study the COVID-19 outbreak in Shanghai from March to July 2022, calculate the initial R t through the number of cases at the beginning of the outbreak, and evaluate the effectiveness of dynamic non-pharmaceutical interventions (NPIs) adopted at different time periods in Shanghai using our proposed approach. In particular, our proposed contact network is a three-layer multi-scale network that is used to distinguish social interactions occurring in areas of different sizes, as well as to distinguish between intensive and non-intensive population contacts. This susceptible–exposure–infection–quarantine–recovery (SEIQR) epidemic model constructed based on a multi-scale network can more effectively assess the feasibility of small-scale control measures, such as assessing community quarantine measures and mobility restrictions at different moments and phases of an epidemic. Our experimental results show that this model can meet the simulation needs at different scales, and our further discussion and analysis show that the spread of the epidemic in Shanghai from March to July 2022 can be successfully controlled by implementing a strict long-term dynamic NPI strategy. [ABSTRACT FROM AUTHOR]

Intelligent compaction (IC) technology have been used for quality control and quality assurance (QC/QA) in subgrade construction. The effective regression correlations between Intelligent Compaction Measurement Values (ICMV) and In-situ Measurement Values (ISMV, including compaction degree and subgrade modulus ELWD) are the essential prerequisite of using IC technology for QC/QA. This paper presents the results from an experimental research study that was conducted from a practical subgrade project of China to explore the regression relationships between ICMV and ISMV. Three types of ICMV, including CMV, CCV and Evib, were collected along with the corresponding positions of the rollers. Two types of ISMV, containing compaction degree and ELWD, were measured by ring sampler method and light weight deflectometer (LWD) at specified test points, respectively. Based on these data, the influences of roller parameters and subgrade properties on the regression relationships of ICMV and ISMV were investigated. In addition, linear regression and 5 nonlinear regression algorithms were compared. The results suggest that ICMV reflect the stiffness of subgrade more than reflecting the density. In the regression of ICMV and ISMV, subgrade properties are more important than roller parameters while both of them should not be neglected. The influences of underlying stiffness and roller amplitude are linear while that of roller speed and moisture content are nonlinear. Nonlinear algorithms, especially the random forest, have the better performance compared to linear algorithm. Moreover, the combination of random forest and linear algorithm can further improve the accuracy of ISMV prediction. [ABSTRACT FROM AUTHOR]

The AES is one of the most widely used block ciphers in the world, the 3D block cipher is a 3-dimensional AES-like cipher proposed at CANS 2008, and the TANGRAM block cipher is an awardwinning algorithm of the recent National Cryptographic Algorithm Design Competition in China. The yoyo tricks is a powerful cryptanalysis technique based on zero difference, and its main idea is based on whether a plaintext pair and a pair generated by this plaintext pair share the same property after several rounds of encryption. This paper uses yoyo tricks to make an attack on 6-round AES with restricted ciphertexts, reducing the best previously published time complexity by 25:6 in the context of yoyo tricks. Second, the yoyo tricks are extended to n-tuple (n ≥ 3) and relations between the yoyo tricks and related differentials are established, more specifically, the related differentials can be represented by yoyo tricks, and 3-tuple yoyo tricks are used to launch a key recovery attack on 11-round 3D. Finally, the early abort technique is used to the exchange attack, where 2126 chosen plaintexts are used to make a distinguishing attack on 25-round TANGRAM with a 128-bit block size (denoted as TANGRAM-128), which is the first 25-round distinguisher for TANGRAM-128. [ABSTRACT FROM AUTHOR]

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Whether it is a traditional industry or a Frontier field, it has unveiled the trend of industrial IoT construction and application, which plays a vital role in building a strong manufacturing country and promoting high-quality economic development in China. HHL algorithm has become one of the important quantum algorithms, but there are few researches on the construction of quantum circuits and the application of quantum sequencing. In this paper, a model based on the quantum circuit corresponding to the HHL algorithm to deal with the quantum application problem is proposed. A quantum circuit based on HHL algorithm is used to solve the linear system, and the numerical solution of the target partial differential equation is obtained. Finally, the experimental analysis shows that, in the process of processing quantum computer application problems based on quantum circuit, it can reduce the computation amount of quantum circuit corresponding to HHL algorithm, improve the simulation efficiency of quantum circuit, and reduce the occupation of hardware resources, which has a certain effectiveness and superiority. This discussion brings new ideas for intelligent IoT technology and provides implications for the study of simplified methods of HHL algorithms corresponding to quantum circuits to deal with computer application problems. [ABSTRACT FROM AUTHOR]