在高速发展的信息社会，大数据已然成为国家之间争夺的重要资源，谁掌握了数据谁就掌控了话语权。大数据的意识形态性日趋明显，其主要表现为话语霸权、文化霸权、数据迷信和数据霸权。大数据作为一种技术手段，通过对数据的一系列处理，即数据摄取、数据挖掘、数据分析、数据监控、数据预测，完整体现了大数据意识形态性的技术生成过程。对大数据意识形态性的实际应用，有助于我国主流意识形态的构建，有助于利用大数据精准“灌输”主流意识形态、促进主流意识形态的话语体系和理论创新、维护主流意识形态的安全，牢牢把控住马克思主义意识形态的主流地位。
Smart transportation is a crucial element in the development of smart cities construction. Accurate forecasting of traffic dynamics is one of the key contents of smart transportation. In this paper, a fractional grey model based on fractional order accumulation is used to predict the short-term traffic flow in cities. The advantage of this model is that it can embody the principle of new information priority. The prediction effect is compared with that of some traditional integer order grey prediction models and some artificial intelligence models. The fractional order grey model's advantage in predicting short-term traffic flow is confirmed.
Due to the complex underground conditions, the logging data are often distorted or missing which brings great challenges to the work of well logging. Precise prediction of logging curve is critical for the exploration and development of the petroleum engineering. In this paper different prediction models using deep learning methods was presented based on the missing or incomplete logging data of oil wells in China. The performance of three deep learning methods, namely Back Propagation Neural Network(BPNN), Long Short-Term Memory (LSTM) model, enhanced Long Short-Term Memory (FC_LSTM) model was experimentally investigated with the training data collected from the wells in adjacent areas. The training model could predict and complete the missing logging data due to the complex underground conditions. The evaluation criteria are based on mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). Without additional measurement cost, the correlation between distorted or missing data and previous and subsequent data is fully considered and the whole missing data block is reconstructed through iterative strategy. When predicting the distorted or missing part of the logging data, the performance ranking of the three models from high to low is FC_LSTM,LSTM and BPNN. The methods proposed in this paper could accurately improves the work in the fields of well logging.
The tensor nuclear norm has achieved good results in the restoration of hyperspectral images (HSI), but it has the problem of direction sensitivity and cannot well characterize the correlation between different dimensions of hyperspectral images. In order to overcome the directional sensitivity issue of tensor nuclear norm, a method for hyperspectral images restoration using rotational three-order unfolding is proposed. Firstly, in order to describe the correlation between each dimension of hyperspectral images, the rotational three-order unfolding operator is defined; then, based on this operator, a restoration model for hyperspectral images is established; finally, an efficient algorithm for solving the restoration model of hyperspectral images is designed based on the alternating direction method of multipliers (ADMM). The experimental findings regarding hyperspectral image restoration indicate that the proposed method demonstrates superior performance compared to the other methods evaluated, particularly in terms of peak signal to noise ratio (PSNR).
Captive breeding of lake sheep is an important project in animal husbandry and ecological protection. Optimizing the space utilization rate of lake sheep is of great significance for improving breeding efficiency and protecting the ecological environment. This study addresses the space utilization problem of captive lake sheep, taking the annualized number of sheep as the research object, we use the grouping principle and the time synchronization dislocation principle to establish a grouping synchronization dislocation of sheep production chain model, and use the diminishing enumeration algorithm to model and solve the number of sheep under ideal and non-ideal state respectively, to get the maximum number of sheep in the annualized pen, which will maximize the space utilization of captive lake sheep. utilization rate can be maximized.
The application of big data technology in the development of smart tourism is increasingly being promoted and impacting the development trend of the tourism industry in our lives. This paper deeply explores the practical application of big data in smart tourism and its significant impact on the tourism industry. By exploring the promotion and use of big data in the smart tourism industry, we can foresee the development trend of segmentation, personalization and precision in the future tourism industry. It deeply analyzes the nature of big data technology and how it influences and promotes the progress of the smart tourism industry. In its impact on the tourism industry, the application of big data not only changes the planning method of the tourism industry, improves industry efficiency, but also opens up an advanced, passenger-centered service model. However, there are many challenges in the application of big data in smart tourism. How to protect tourist data security, how to ensure the accuracy of data, etc., all require us to further study and explore. We have explored The application of machine learning algorithms in tourism big data. In addition, what the big data will bring the future landscape of the tourism industry. In-depth understanding and correct use of big data technology will promote us to realize high-efficiency, high-quality, and personalized services in the smart tourism industry, and will also bring greater convenience and added value to our social life.
20 listed companies with lithium batteries as their main business were selected for the study. Input and output indicators are established from the perspective of technological innovation. The DEA method is applied to measure the technological innovation efficiency of the target listed companies. The analysis results show that from 2019 to 2021, the overall innovation efficiency of China's listed lithium battery companies is in a mildly ineffective state. only one company reaches technical efficiency effectiveness in both 2019 and 2020, increasing to two in 2021, while other companies still have certain gaps. The main problem lies in the lack of scale and low pure technical efficiency of some enterprises. The above technically inefficient enterprises should strengthen their own management techniques and resource allocation efficiency. Optimise the input-output structure and promote technological innovation efficiency.
This study aims to explore the impact of Digital Learning Environments in vocational colleges on students' Employment Skills and analyze the mediating effect of AI Applications. By surveying 500 students and 100 teachers from five vocational colleges in China and using structural equation modeling (SEM) for data analysis, the results indicate a significant positive effect of Digital Learning Environments on students' Employment Skills, with AI Applications playing a partial mediating role. The study finds that optimizing Digital Learning Environments and reasonably applying AI Applications can effectively enhance students' Employment Skills. The findings provide empirical support and theoretical guidance for the digital transformation of vocational education and the application of AI Applications.
Complex probabilistic models often require computationally intractable high-dimensional integrals in machine learning and Bayesian inference. There are many models that provide approximations to Bayesian probabilistic models, one of which is the Markov Chain Monte Carlo (MCMC). Based on the traditional MCMC model, a Neural Networks Langevin Monte Carlo (NNLMC) model for Langevin dynamics and neural network optimization is proposed. The model reconstructs and optimizes the calculation method and loss function in the traditional algorithm, and improves the convergence speed of the model. In order to verify the convergence speed and efficiency of the sampler, this paper compares the proposed model with the existing Hamiltonian Monte Carlo (HMC), Metropolis-Adjusted Langevin Algorithm (MALA), and Magnetic Hamiltonian Monte Carlo (MHMC) model in terms of autocorrelation, maximum mean difference, the effective length of the sample and the consumption time. Experimental results show that the NNLMC model can efficiently sample from the target distribution.
In recent years, cross-border e-commerce has emerged as a primary choice for international consumers, with China's export cross-border e-commerce industry experiencing significant growth. This paper focuses on SHEIN, a prominent Chinese cross-border e-commerce enterprise, using it as a case study to analyze its global digital marketing strategy through the SICAS model. By examining SHEIN's approach, this study offered actionable insights and strategic recommendations for other Chinese cross-border e-commerce enterprises aiming to expand globally. These findings aim to contribute to the advancement of China's cross-border e-commerce industry.
This study introduces a novel methodology for evaluating tennis player performance through the application of Hierarchical Markov Models (HMM). By dissecting the game's flow and modeling the transitions between states, our proposed model captures the intricate interplay of short-term and long-term dependencies that characterize match dynamics. Utilizing the HMM framework, we model the scoring sequences and transitions across various levels of the game, ranging from individual points to entire matches. Our analysis of match data unveils a nuanced perspective on player performance, pinpointing critical junctures within matches and offering a visual representation of match progression through directed graphs. The findings demonstrate that HMMs are adept at encapsulating the complexities of tennis matches, thereby providing fresh insights into player strategies and performance metrics.
Emerging technologies have given rise to the utilization of the Unlicensed band by the fifth Generation Mobile Communication Technology (5G-U). LTE-U enables efficient Device-to-Device communication by utilizing unlicensed spectrum alongside Wireless Local Area Network (WLAN). The issues related to the capabilities and obstacles of this innovative approach are tackled. This paper focuses on examining the security requirements and the concept of idle sub-frames. A new approach is introduced in this study, focusing on the coexistence of WLAN with Device-to-Device Communication in the context of 5G-U. A model based on Markov Chains has been introduced to predict the availability of idle subframes and validate them in collaboration with the data provided by the spectrum agent. A specialized entity, known as the spectrum observer, actively contributes to the precise identification of idle subframes. An approach involving auction mechanisms is suggested to manage channel occupancy in D2D communication scenarios. Estimation of bidding values involves analysis of interference and collision probabilities. Assessment of bidding values is done through the utilization of an innovative algorithm called Firefly based Particle Swarm Optimization to analyze Signal Interference Noise Ratio (SINR) and spectral efficiency in individual devices. Incorporating hybrid Elliptic Curve Cryptography with RSA algorithm facilitates D2D communication during idle sub-frames allocation. Subsequently, the Ns-3 simulator is utilized for conducting experimental evaluation, followed by the assessment of results regarding average throughput, delay, dropping system secrecy rate, and rate.
To address the problems of incomplete extraction of rolling bearing fault features and low recognition accuracy, a fault diagnosis model based on local mean decomposition, improved multi-scale dispersion entropy and whale optimization algorithm to optimize the deep extreme learning machine is proposed. Firstly, the model decomposes the original vibration signal into multiple product function components using local mean decomposition; secondly, the spurious PF signal components are removed using mutual information and kurtosis screening criteria; thirdly, the multi-scale fault features of the signal components are extracted by improved multi-scale dispersion entropy; finally, the obtained fault features are input into the deep extreme learning machine optimized by the whale algorithm for training and testing. After conducting experiments with rolling bearings and comparing with the control group, the experimental analysis shows that the model can effectively extract the fault features and improve the identification.accuracy of rolling bearing fault.
In the e-commerce environment, inventory management is the key factor to ensure enterprise operational efficiency and customer satisfaction. Traditional inventory management methods can no longer meet the increasingly complex market needs, and it is urgent to adopt advanced data analysis technology to optimize the inventory strategy. This paper will explore how to use advanced data analysis technologies, such as machine learning and big data analysis, to optimize the electronic commerce inventory. Through case analysis and experimental research, the effectiveness of these technologies in improving inventory turnover rate, reducing inventory cost, and improving customer service is verified.
In digitalization, information technology drives financial transformation in enterprises, and the economic function of financial management in enterprise management has changed. This study constructs a satisfaction evaluation model for undergraduate students majoring in financial management based on the digital background and analyzes student satisfaction through evaluation. Collect data through questionnaire surveys and interviews, and extract critical factors through factor analysis. The evaluation results show that students are generally satisfied with the course, but teaching resources and teacher-student interaction must be optimized. Suggest strengthening the construction of digital teaching platforms, promoting personalized learning and differentiated teaching, and strengthening communication between teachers and students. This model provides a scientific basis for improving teaching quality and student satisfaction.
