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After the completion of the Three Gorges Dam, it increases the risk of inducing an earthquake. We use the GRACE Gravity Field Model to analyze the relationship between the operation of the Three Gorges Reservoir and the change of gravity field in western Sichuan. The research results indicate that the reservoir water level and the western Sichuan gravitational field are positively correlated. In the early stage of rising water level, the change of gravity field is not apparent, and the change of gravity field gradually increases with time. Therefore, the change of reservoir water level affects the gravity field in western Sichuan. The dynamic changes of the gravity field can reflect the Earth’s material change and deformation process and are closely related to earthquakes. Consequently, the Three Gorges Dam will indirectly affect the seismicity in western Sichuan by affecting the gravity field. The research provides valuable information for studying regional reservoir earthquake disasters and supports related policy decisions.

In this article, the integrity of the seismic catalog obtained (1970–2014, M > 2.8) was verified according to the Gutenberg–Richter relation, the appropriate minimum magnitude was determined, and the whole region was divided into five areas according to the geological structure background of the whole research object and the trend of the historical seismic zone. We applied multifractal analysis in each partition. The results showed that although in different geological backgrounds, before major earthquakes, the earthquake time series information dimension had different degrees of growth, and the parameter after the flame had different degrees of decline. To a certain extent, this reflected the seismic energy accumulation and release process. In addition, the variation of fractal parameters in scale-free regions of time series and spatial distribution series also indicated that these two kinds of sequences in different regions show the characteristics of a multifractal structure rather than a single and uniform fractal structure.

In this study, earthquakes with magnitudes above 3.0 on the Richter scale in southwest China from 1900 to 2013 are taken as research objects. The spatio-temporal migration characteristics of seismic activity in the area of concern are studied by using geostatistics method to explore the rules of seismic activity. From the perspective of geostatistics and based on z-value, eight major seismic zones in southwest China were studied zone-wise by using the geostatistics tool ArcGIS and the seismic analysis tool ZMAP to explore the spatial and temporal migration characteristics of earthquakes. It was found that each seismic zone had its own quiet period with relatively inactive seismic activity. Simultaneously, through the study of the time-varying characteristics of the seismic belt, it was found that the time-varying regularity of the seismic frequency of the fault belt with the same or related geological structure background is roughly consistent. In conclusion, the seismic activity in southwest China will still be active in the future.

Recently, all kinds of geological disasters happen frequently on the earth. In China, there are countless earthquakes every year, which greatly affect the country’s economic level and development as well as the people’s life and health. The analysis of seismic activity is becoming more and more significant. In this article, the spatial distribution of China’s seismic activities was analyzed by using the provincial seismic data from 1970 to 2013. On the basis of spatial autocorrelation analysis theory, Global Moran’s I, Local Moran’s I, and the Local Indicators of Spatial Association are used to measure the geospatial distribution characteristics of China’s seismic activities. The research results show that earthquakes in mainland China have significant global autocorrelation characteristics as a whole, and the global autocorrelation coefficients are all positive. And the Z-value test (P < 0.05) shows that earthquakes in mainland China present a spatial agglomeration pattern. Furthermore, we observed a reduction trend in disparities of seismic activity among regions in China.

At present, super-resolution algorithms are employed to tackle the challenge of low image resolution, but it is difficult to extract differentiated feature details based on various inputs, resulting in poor generalization ability. Given this situation, this study first analyzes the features of some feature extraction modules of the current super-resolution algorithm and then proposes an adaptive feature fusion block (AFB) for feature extraction. This module mainly comprises dynamic convolution, attention mechanism, and pixel-based gating mechanism. Combined with dynamic convolution with scale information, the network can extract more differentiated feature information. The introduction of a channel spatial attention mechanism combined with multi-feature fusion further enables the network to retain more important feature information. Dynamic convolution and pixel-based gating mechanisms enhance the module's adaptability. Finally, a comparative experiment of a super-resolution algorithm based on the AFB module is designed to substantiate the efficiency of the AFB module. The results revealed that the network combined with the AFB module has stronger generalization ability and expression ability. [ABSTRACT FROM AUTHOR]

Recent advancements in natural language processing have given rise to numerous pre-training language models in question-answering systems. However, with the constant evolution of algorithms, data, and computing power, the increasing size and complexity of these models have led to increased training costs and reduced efficiency. This study aims to minimize the inference time of such models while maintaining computational performance. It also proposes a novel Distillation model for PAL-BERT (DPAL-BERT), specifically, employs knowledge distillation, using the PAL-BERT model as the teacher model to train two student models: DPAL-BERT-Bi and DPAL-BERT-C. This research enhances the dataset through techniques such as masking, replacement, and n-gram sampling to optimize knowledge transfer. The experimental results showed that the distilled models greatly outperform models trained from scratch. In addition, although the distilled models exhibit a slight decrease in performance compared to PAL-BERT, they significantly reduce inference time to just 0.25% of the original. This demonstrates the effectiveness of the proposed approach in balancing model performance and efficiency. [ABSTRACT FROM AUTHOR]

This study addresses the limitations of Transformer models in image feature extraction, particularly their lack of inductive bias for visual structures. Compared to Convolutional Neural Networks (CNNs), the Transformers are more sensitive to different hyperparameters of optimizers, which leads to a lack of stability and slow convergence. To tackle these challenges, we propose the Convolution-based Efficient Transformer Image Feature Extraction Network (CEFormer) as an enhancement of the Transformer architecture. Our model incorporates E-Attention, depthwise separable convolution, and dilated convolution to introduce crucial inductive biases, such as translation invariance, locality, and scale invariance, into the Transformer framework. Additionally, we implement a lightweight convolution module to process the input images, resulting in faster convergence and improved stability. This results in an efficient convolution combined Transformer image feature extraction network. Experimental results on the ImageNet1k Top-1 dataset demonstrate that the proposed network achieves better accuracy while maintaining high computational speed. It achieves up to 85.0% accuracy across various model sizes on image classification, outperforming various baseline models. When integrated into the Mask Region-Convolutional Neural Network (R-CNN) framework as a backbone network, CEFormer outperforms other models and achieves the highest mean Average Precision (mAP) scores. This research presents a significant advancement in Transformer-based image feature extraction, balancing performance and computational efficiency. [ABSTRACT FROM AUTHOR]

Typically, the current and speed loop closure of servo motor of the parallel platform is accomplished with incremental PI regulation. The control method has strong robustness, but the parameter tuning process is cumbersome, and it is difficult to achieve the optimal control state. In order to further optimize the performance, this paper proposes a double-loop control structure based on fuzzy integral and neuron proportional integral (FI-NPI). The structure makes full use of the control advantages of the fuzzy controller and integrator to improve the performance of speed closed-loop control. And through the feedforward branch, the speed error is used as the teacher signal for neuron supervised learning, which improves the effect of current closed-loop control. Through comparative simulation experiments, this paper verifies that the FI-NPI controller has a faster dynamic response speed than the traditional PI controller. Finally, in this paper, the FI-NPI controller is implemented in C language in the servo-driven lower computer, and the speed closed-loop test of the BLDC motor is carried out. The experimental results show that the FI-NPI double-loop controller is better than the traditional double-PI controller in performance indicators such as convergence rate and RMSE, which confirms that the FI-NPI double-loop controller is more suitable for BLDC servo control. [ABSTRACT FROM AUTHOR]

With the development of recommendation technology, it is of great significance to analyze users' digital footprints on social networking sites, extract user behavior rules, and make a relatively accurate assessment of each user's needs, to provide personalized services for users. It has been found that the users' behavior on social networking sites has a great correlation with the user's personalities. The OCEAN model's five characteristics can cover all aspects of user personality. There are some shortcomings in the current micro-directional propagation method. This paper has improved the traditional collaborative filtering method and proposed a collaborative filtering method based on user clustering. The model first clusters the users according to their OCEAN model, and then it filters the users collaboratively in the cluster to which the user belongs with the collaborative filtering method based on an optimized singular value decomposition (SVD) recommendation algorithm, called the BiasSVD recommendation algorithm -- to reduce the dimensionality of the data. Then it generates recommendations. Experiments show that clustering users' OCEAN models can improve the accuracy of recommendations. Compared with the entire user space, it greatly reduces the recommendation time, and effectively solves the cold start problem in micro directional propagation. [ABSTRACT FROM AUTHOR]

How to eliminate useless information in the vast network information and retain effective information is a problem that needs to be continuously explored in the field of deep learning. This paper conducts text classification on the network evaluation frequently encountered in daily life, mainly to screen out the meaningless comments published by Internet users, to have access to more useful information. In this paper, a text filtering model was constructed based on word vector and Long Short-Term Memory (LSTM) and improved by adding Deep Averaging Networks (DAN) and convolutional neural network (CNN). The major improvement of the LSTM&DAN model was to retain the original word vector information and to improve the accuracy of the text classification model without increasing hyperparameter and model structure complexity. The LSTM&CNN model mainly combines the advantages of convolutional neural network in exploring the deep information of text, which was an improvement over the original LSTM. It was proved by experiments that this improvement is meaningful. Compared with the shallow neural network, the accuracy has been greatly improved. [ABSTRACT FROM AUTHOR]

In recent years, with economic development, urbanization has been accelerating. In the past 100 years, the global average temperature has increased by 0.5°C, and according to the predictions of most global climate models, the temperature will continue to increase by 1.5°C to 3.0°C in the next 100 years. Under the influence of global warming, the urban heat island effect problem is becoming more and more serious, bringing much harm. Studying the thermal effect and its influencing factors is of great significance for sustainable urban development. In this study, the seasonal and interannual surface temperature changes of the study area are inverted based on the SW-TES algorithm, and the LST inversion of the algorithm is simply verified. At the same time, a cell dichotomous model was established to explore the influencing factors of the urban heat island effect and the influence of different land use types and normalized vegetation indices in the Hangzhou metropolitan area was analyzed. The results showed that the overall heating trend of the study area showed a heating trend, the expansion rate of regional construction land was relatively fast, and the scale of urban land was increasing. Based on the results of surface temperature changes of areas with different land types, we obtained the temperature trends of each land type [from 2005 to 2018] and found that there was a negative correlation between surface temperature and normalized vegetation index. This study provides a theoretical basis to evaluate the urban heat island effect and analyze factors that impact it and would promote sustainable urban development. [ABSTRACT FROM AUTHOR]

With the rapid economic development, the serious air pollution in Beijing attracts increasing attention in the last decade. Seen as one whole complex and grey system, the causal relationship between the social development and the air pollution in Beijing has been quantitatively analyzed in this paper. By using the grey relational model, the aim of this study is to explore how the socio-economic and human activities affect on the air pollution in the city of Beijing, China. Four air pollutants, as the particulate matter with size 2.5 micrometers or less (PM2.5), particulate matter with size 10 micrometers or less (PM10), sulfur dioxide (SO2) and nitrogen dioxide (NOx), are selected as the indicators of air pollution. Additionally, fifteen socio-economic indicators are selected to account for the regional socio-economic characteristics (economy variables, energy consumption variables, pollution emissions variables, environment and construction activity variables). The results highlight that all variables are associated with the concentrations of the four selected air pollutants, but with notable differences between the air pollutants. Most of the socio-economic indicators, such as industrial output, total energy consumption are highly correlated with PM2.5, while PM10, SO2, and NOx present in general moderate correlations with most of the socio-economic variables. Contrary to other studies and reports this study reveals that vehicles and life energy do not have the strongest effect on air pollution in Beijing. This study provides useful information to reduce air pollution and support decision-making for sustainable development.