Python Technology Research Articles

Impact of artificial intelligence technology applications on corporate energy consumption intensity

Artificial intelligence (AI), as a new technology, not only revolutionizes economic development, but also provides an opportunity for environment governance. Extant studies primarily explore the environmental performance of AI from a macro perspective, while evidence on how AI technology applications affect firms’ energy-saving behavior is scarce. Employing Python technology to recognize AI-related keywords in the annual reports of listed enterprises and adopting data on corporate energy consumption from 2011 to 2020, we explore the impact of AI on corporate energy consumption intensity (CECI) and its mechanisms. We observe that AI technology applications reduce CECI. After a range of robustness tests, the conclusions are still solid. The mechanism analysis reveals that AI cuts CECI through spurring firm green innovation, stimulating firms to introduce new equipment, and reducing firms’ internal management costs. Heterogeneity analysis reveals that this negative impact is more prominent for SOEs and private enterprises’ energy intensity; we also find that this effect is more pronounced for high-tech industry enterprises and high-polluting enterprises. Our findings provide micro evidence for policymakers to reduce corporate energy intensity and realize energy conservation and emission abatement targets.

QML Powered Interface for Diffusion Imaging

In the field of medical imaging, Diffusion Imaging (DI) has emerged as a powerful technique for investigating the microstructural properties of biological tissues. However, the complexity of DI analysis software often poses a significant barrier to its widespread adoption, as it typically requires proficiency in Python programming and command-line interactions. This technical barrier can limit the accessibility of DI technology to individuals without extensive technical expertise, hindering its potential impact in various medical and research applications To address this challenge, we propose a novel solution that leverages the capabilities of Query Markup Language (QML) to develop a user-friendly interface for Diffusion Imaging. By combining the power of Python technology, which forms the core of DI analysis, with the intuitive interface design capabilities of QML, our project aims to democratize DI analysis and make it accessible to a broader audience, including medical professionals, researchers, and students. Our research focuses on bridging the gap between the technical complexities of DI analysis and user accessibility. The proposed QML-powered interface will feature modern UI elements with fluid animations, ensuring a seamless and engaging user experience. Crucially, it will abstract away the intricacies of Python programming and command-line interactions, allowing users to concentrate on the analysis and interpretation of DI data without the burden of technical hurdles.

Agriyug: A Python-Based Approach to ECommerce in Agriculture

This research explores the evolving landscape of e-commerce, particularly in the context of agricultural product transactions. With a growing trend in online shopping, users increasingly seek a seamless and enjoyable experience, specifically in the domain of purchasing agricultural goods. This study delves into the transformative role of Python in shaping the infrastructure of e-commerce for agricultural products. The research initiative, labelled as "AgriYug," aims to provide valuable insights into the changing dynamics and prospects of agricultural e-commerce. The focus is on enhancing the online shopping experience for farm products by leveraging the capabilities of Python technology. This research outlines the development of an e-commerce platform specifically designed to empower Indian farmers by enabling direct sales to consumers without intermediary intervention. Aligned with the national sentiment recognizing farmers as the backbone of the country, the platform addresses the economic challenges faced by farmers due to middlemen in the supply chain. The objective is to enhance farmers' economic autonomy and provide consumers with affordable, fresh produce. This initiative contributes to the broader goal of optimizing the agricultural market by eliminating excessive intermediary fees, ensuring fair compensation for farmers, and creating a more efficient supply chain.

Typhoon disaster emergency forecasting method based on big data.

Typhoons are natural disasters characterized by their high frequency of occurrence and significant impact, often leading to secondary disasters. In this study, we propose a prediction model for the trend of typhoon disasters. Utilizing neural networks, we calculate the forgetting gate, update gate, and output gate to forecast typhoon intensity, position, and disaster trends. By employing the concept of big data, we collected typhoon data using Python technology and verified the model's performance. Overall, the model exhibited a good fit, particularly for strong tropical storms. However, improvements are needed to enhance the forecasting accuracy for tropical depressions, typhoons, and strong typhoons. The model demonstrated a small average error in predicting the latitude and longitude of the typhoon's center position, and the predicted path closely aligned with the actual trajectory.

Haze Pollution, Climate Risk Perception and Demand for Commercial Health Insurance

Haze pollution not only affects the quality of economic growth and the image of the government, but also seriously affects public health. With the improvement of climate risk perception, the public is actively seeking all kinds of risk management measures to combat the hazards of haze pollution. The study of the relationship between haze pollution, climate risk perception and the development of commercial health insurance is of great significance to the formulation of haze control policies and the improvement of social security system. This paper attempts to explore the relationship between haze pollution, climate risk perception and commercial health insurance demand by establishing static and dynamic models based on panel data from 31 provinces in China from 2005 to 2022. Also, the spatial spillover effects of haze pollution in different regions on the development of commercial health insurance in other regions are investigated by establishing a spatial Durbin model. It is found that (1) haze pollution has a lagged positive effect on the demand for commercial health insurance; (2) haze pollution has a significant spatial spillover effect on the development of commercial health insurance; (3) this study used Python technology to construct a climate risk perception index, and found that the moderating effect of the residents’ perception of climate risk existed significantly, and that the residents might take other measures to manage the risk in the short term, but in the long term, the climate risk perception showed a positive correlation with the level of education. This paper provides implications for government departments and social media to strengthen education and publicity, improve residents’ awareness of risk diversification, and deepen the concept of commercial health protection.

The energy saving effects of digital infrastructure construction: Empirical evidence from Chinese industry

Digital infrastructure serves as a crucial foundation for development in the era of the digital economy. In light of this, this paper takes digital infrastructure construction as its focal point. Utilizing Python technology and the Meta-frontier non-radial directional distance function to calculate the level of digital infrastructure construction and industrial energy efficiency. It then employs a dynamic panel data model to analyze the impact effects and mechanisms of digital infrastructure construction on industrial energy efficiency. Additionally, threshold models and modified gravity models are employed to explore the nonlinear effects and spatial impacts of digital infrastructure construction on industrial energy efficiency. The research findings reveal that the enhancement of digital infrastructure significantly improves industrial energy efficiency. Moreover, indicate a shift in the impact of digital infrastructure construction on industrial energy efficiency from inhibitory to promotional as the level of digital infrastructure construction increases. The mechanism of this effect suggests that digital infrastructure construction can promote the improvement of industrial energy efficiency through green technological innovation and the advancement of industrial structure. Further analysis shows that the spatial network characteristics of digital infrastructure construction notably elevate the level of industrial energy utilization efficiency. This study provides practical policy implications for sustainable development.

Digital measuring the ocular morphological parameters of guinea pig eye in vivo with Python.

To quantitatively measure ocular morphological parameters of guinea pig with Python technology. Thirty-six eyeballs of eighteen 3-week-old guinea pigs were measured with keratometer and photographed to obtain the horizontal, coronal, and sagittal planes respectively. The corresponding photo pixels-actual length ratio was acquired by a proportional scale. The edge coordinates were identified artificially by ginput function. Circle and conic curve fitting were applied to fit the contour of the eyeball in the sagittal, coronal and horizontal view. The curvature, curvature radius, eccentricity, tilt angle, corneal diameter, and binocular separation angle were calculated according to the geometric principles. Next, the eyeballs were removed, canny edge detection was applied to identify the contour of eyeball in vitro. The results were compared between in vivo and in vitro. Regarding the corneal curvature and curvature radius on the horizontal and sagittal planes, no significant differences were observed among results in vivo, in vitro, and the keratometer. The horizontal and vertical binocular separation angles were 130.6°±6.39° and 129.8°±9.58° respectively. For the corneal curvature radius and eccentricity in vivo, significant differences were observed between horizontal and vertical planes. The Graphical interface window of Python makes up the deficiency of edge detection, which requires too much definition in Matlab. There are significant differences between guinea pig and human beings, such as exotropic eye position, oblique oval eyeball, and obvious discrepancy of binoculus. This study helps evaluate objectively the ocular morphological parameters of small experimental animals in emmetropization research.

A Comparative Analysis of Japanese Learners’ Translation Bias Using Neurosemantic Analysis

Abstract In today’s increasingly frequent cultural exchanges between China and Japan, accurate and error-free Japanese translation has become an inevitable choice for cross-cultural communication. In this paper, based on twin neural network and attention mechanism, BiLSTM model is combined with sentence semantic similarity matching algorithm to construct a Japanese translation bias sentence semantic similarity model. The Japanese corpus data were collected and preprocessed by Python technology, and the Japanese translation corpus database was searched and counted using Wordsmith and AntConc tools. For the Japanese learners’ translation bias in the Japanese translation process, a comparative analysis was carried out in several aspects, such as end-of-sentence modal expressions, consecutive translations, and word frequency effects. The study results show that the difference in the frequency distribution of Japanese learners’ modal expressions is only 4.66% compared with that of native speakers of Japanese. Still, the difference between the two is significant at the 1% level, and the difference in the frequency of Japanese learners’ use of the modal expression “yes” is 56 sentences per 10,000 sentences. The frequency of Japanese learners’ use of successive expressions was 30.1 percentage points higher than that of native speakers. The neural semantic analysis method combined with the Japanese translation corpus can clarify the translation bias of Japanese learners in the process of Japanese translation, which can provide a reference for enhancing the translation quality of Japanese learning.

An analysis of the value and linguistic features of contemporary literary texts based on knowledge graph

Abstract In this paper, we crawled and analyzed the dataset of contemporary literature works through Python technology, and after preprocessing the data with the use of the word splitting algorithm, we applied the CasRel entity-relationship extraction model based on the Transformer and the BERT model to efficiently extract the entity knowledge in contemporary literature works. Then, a knowledge map of modern literature was established by combining the Neo4j graph database. Several novels by Mo Yan, Jia Pingwa and Chen Yan were selected as cases to analyze the textual values and linguistic features of contemporary literary works. The Analysis reveals significant differences in the linguistic features and connotative values of works by different authors. For example, the average frequency of periods per thousand words in Mo Yan’s works is 0.2187, while the corresponding frequency in Jia Pingwa’s works is 36.06% lower than that of Mo Yan. The cumulative application frequency of monosyllabic and disyllabic words in Chen Yan’s works exceeds 80%, and the lexical density ranges between 67.5% and 71.5%. By clarifying the linguistic features of different contemporary literary works, it can help academic creators better find the resonance point with readers and provide diversified innovative paths for literary creation.

Knowledge Graph-based Diversity Analysis of Supplier Holographic Portraits

Abstract Fully understand the development of suppliers in order to make better supplier selection. This paper is based on the knowledge graph, through the knowledge updating of the knowledge graph combined with the Transformer model for knowledge extraction of supplier entity relationship, forming the ternary semantic information of supplier entity relationship. Then, based on the big data platform for the construction of supplier holographic portrait and knowledge storage, through information integration, analysis and other links to identify the supplier attributes for label definition. Taking cell phone product suppliers as an example, we use Python technology to obtain relevant data and validate the specific role of supplier holographic portrait in terms of the supplier’s comprehensive strength, behavioral prediction, transaction closeness, and comprehensive evaluation. The results show that: the correlation between the comprehensive strength of suppliers and the amount of winning bids is strong, and its R2 test result is 0.5924, and it can realize the behavioral prediction of suppliers in the supply chain. Supplier H offers a range of cell phone products in 2022, which is 17.62%<unk>21.17% higher than the benchmark market price. The holographic portrait of suppliers based on a knowledge graph combined with a big data platform can meet the need to carry out an all-around analysis of suppliers and provide more accurate support for diversified decision-making on the demand side.

A Study of Differences in Sentential Characteristics of Japanese Language Teaching in Colleges and Universities Based on Semantic Contrastive Analysis

Abstract Colleges and universities actively carry out Japanese language teaching courses to promote the friendly cooperation and communication between China and Japan. However, improving students’ Japanese intercultural communication ability still faces specific difficulties and practical obstacles due to cultural differences. In this paper, firstly, a Japanese language teaching corpus is collected based on Python technology to establish a Japanese language teaching corpus, based on which a Japanese language flipped classroom teaching model is constructed in colleges and universities. Secondly, to understand the semantic distribution of Japanese vocabulary in the Japanese teaching corpus, the Hidden Markov Model is used to semantically segment Japanese language, and the attention mechanism based on categories is used to extract and classify the textual features of the Japanese corpus. Finally, the model’s teaching effect and sentence features are analyzed using University of N as an example. The results show that after the teaching experiment, the average score of Japanese application proficiency of the experimental class is 86.57, which is 14.495% higher than that of the control class. 22.52% of the sentence length frequency ratios in spoken corpora were distributed in the [10,20] range, and the highest values of sentence length frequency ratios in written corpora, academic corpora, and policy corpora fell in the range of 20-30. Teaching Japanese language in higher education needs to target the lexical meanings of different sentence lengths to help students improve their Japanese language application skills.

The effects of enterprises' attention to digital economy on innovation and cost control: Evidence from A-stock market of China

China's digital economy has made amazing achievements, which brings deep impacts on enterprise innovation. Based on unbalance panel dataset covering more than two thousand manufacturing listed companies in A-stock market of China during the 2011 to 2018 period, this paper employs two-way fixed effects (TWFE) model to examine the effects of attention to digital economy on enterprise innovation. The primary explanatory variable in this research is attention degree that enterprises pay to the digital economy measured by Python technology and text analysis. Additionally, the intermediate effect model is adopted to check the underlying mechanisms of cost control in enterprises, which is also impacted by the digital economy. Several novel findings emerge. First, the number of patent applications increase as enterprises pay more attention to the digital economy. Digital economy has positive impacts on different innovation processes, not only promotes invention, but also promotes appearance design. Second, digital technology and business model as two aspects of digital economy have different effects on innovation. The attention to digital technology has positive impacts on invention patents and design patents, while business model only has a positive impact on design patents. Third, enterprises that pay attention to the digital economy are more likely to increase their R&D expenditure and decrease their sales and finance expenses, which encourages the innovation output. This paper explains these findings in the context of China and makes some specific suggestions for enterprises to promote digital transformation and innovation.

Developing a sustainable career for designers by using the salary-skills relationship for Chinese designers

The value of designers is determined by market-based compensation which reflects the status of the design industry, the career paths of designers, reform directions in design education and the deconstruction of design skills. This article aims to elucidate designers' requirements, responsibilities and compensation by scrutinizing online job advertisements. Previous studies have focused on macro design aspects such as propaganda and procedural methods, neglecting individual designers' compensation and treatment. This study used Python technology to search for online job and analyzed 1,108 good job postings using content analysis and descriptive statistics. Additionally, interviews were conducted to investigate the skill requirements of company designers. The findings revealed vital skill points that contribute to increased salaries for designers. High-paid designers possess core skills in managing design strategies and business processes. Middle-paid designers demonstrate abilities in executing design projects and working in teams. Low-paid designers perform better and have better software capabilities. The research’s findings guide students in choosing their majors and research options and identify core skills for designers already in the workforce. Furthermore, this study offers objective data for enterprise talent acquisition and development strategies.

Car Parking Space Identifying System Using Python

Nowadays, in India the concept of smart cites are gaining great popularity. The smart cities are incomplete without smart car parking system. The traffic congestion caused by vehicles is an alarming problem at global scale and it has been growing exponentially, also it increases pollution and wastage of time. A good solution this problem could use of python based smart car parking system to decrease pollution as well as parking time. In this project we propose a system to find the parking area. It also avoids congestions. The objective of this project is to smart car parking system using python. This project is used to Smart Parking system consists of an on-site deployment of a python technology that is used to monitor and signalize the state of availability of each single parking space using IR Sensor Key Word: LCD, Python, Raspberry pi, Fire sensor, Gas sensor.

Analysis of configuration distribution methods in service application environments

This work is an analysis of methods and solutions enabling centralization of the storage of configuration values in ser-vice application environments. It presents a comparison focused on the user's interaction with the available solutions and performance tests during the operation. Consul, ZooKeeper tools and the implementation of the configuration serv-er with the use of the Spring Cloud Config library were tested. Created test environment with the use of Java Spring, Kafka and Python technologies was used for this purpose. The aim of the research is to determine the best tool for working in large microservice networks and the most optimal in the context of working with the user. The presented results confirm the advantage of the Consul tool in terms of efficiency and interface quality over other solutions.

A Study of Public Attitudes toward Shanghai's Image under the Influence of COVID-19: Evidence from Comments on Sina Weibo.

With the advent of the Internet era, Chinese users tend to choose to express their opinions on social media platforms represented by Sina Weibo. The changes in people's emotions toward cities from the microblogging texts can reflect the image of cities presented on mainstream social media, and thus target a good image of cities. In this paper, we collected microblog data containing "Shanghai" from 1 January 2019 to 1 September 2022 by Python technology, and we used three methods: Term Frequency-Inverse Document Frequency keyword statistics, Latent Dirichlet Allocation theme model construction, and sentiment analysis by Zhiwang Sentiment Dictionary. We also explore the impact of the COVID-19 epidemic on Shanghai's urban image in the context of the "Shanghai Territorial Static Management", an important public opinion topic during the COVID-19 epidemic. The results of the study show that the "Shanghai-wide static management" of COVID-19 epidemic has significantly reduced the public's perception of Shanghai and negatively affected the city's image. By analyzing the data results, we summarize the basic characteristics of Shanghai's city image and provide strategies for communicating Shanghai's city image in the post-epidemic era.

Design Andimplementation of the Stock Data Analysis System based on Django

In order to facilitate stock market browsing and data analysis for shareholders, a stock data analysis system is designed and implemented. This system can obtain daily stock data information and enable shareholders to conduct preliminary screening according to the trend of visual data. It can also browse shareholders' consultation for exchange comments, and users can add their own stocks to observe and wait for the best trading opportunity. The system is divided into two roles, administrator user and general user. The system includes four modules: the information module of individual accounts of shareholders, the real-time stock market module, the management module of self selected shares and the advisory forum module. The development of this system adopts Python technology, B/S framework, and Django framework. Because the stock market contains massive stock data, it uses Doris to store stock market data to improve efficient and highly available queries. For the visualization part of stock history data, ECharts is used for chart display.

Malicious attacks on the web and crawling of information data by Python technology

AbstractOn the Internet full of big data, crawlers can greatly improve the efficiency of information search. This paper briefly introduced Python and the hacker attack technology and crawler program based on Python. After that, the web hacker attack program was embedded into the crawler program to crawl the deeper hidden information restricted by permission. Then, the conventional crawler program, C language crawler program, and Python‐based crawler program were tested. The results showed that the normal crawler program failed to crawl the complete information data when facing the web page with permission restriction; the C language crawler program has a low efficiency though it could crawl the hidden information; the Python‐based crawler program obtained the complete information data after bypassing the permission by Structured Query Language (SQL) injection of the web hacker attack module. To sum up, applying Python can write crawler programs in a relatively simple way and embed a hacker attack program to crawl the hidden information on the web.

Research on the Influence of Heterogeneous Beliefs and Media Reports on Investor Behavior from the Perspective of Informationization

Based on the background of informatization, the development and progress of science and technology has brought an immeasurable impact on the securities market. Therefore, this article uses python technology to crawl the number of individual stock news and study the influence of media attention on investor behavior under the constraint of heterogeneous beliefs. It is found that there is a significant positive correlation between the degree of media attention and the degree of investors’ heterogeneous beliefs, and investors are more likely to choose stocks that are frequently reported by the media; further research has found that media reports intensify the degree of investor’s heterogeneous beliefs, leading to stock transactions. The amount has risen abnormally. Therefore, while enjoying informatization, it is necessary to guard against the risks it bring.

The Spatial Structure and Evolution Educational Anxiety—Research on Provincial Panel Based on Big Data Search

This research uses Python technology to obtain the daily Baidu index data of 175 groups of relevant vocabulary, such as “education and training” in 31 provinces and cities from 2011 to 2020. Using Baidu search index to construct a data model of “education anxiety” total index and macro variables, it explores the temporal and spatial distribution and regional characteristics of education anxiety in various provinces in China. Research shows that at the regional level, the level of educational anxiety in East China, Central South, and North China is relatively high, and it has further increased over time, and the level of educational anxiety in Southeast, Northeast and Northwest regions is also gradually increasing. At the provincial level: by 2020, 15 provinces will enter the high level of education anxiety, and the level of education anxiety in other provinces will also increase significantly. The fixed effect model found that factors such as provincial GDP, urbanization rate, and education expenditure have statistically significant effects on “education anxiety”. Based on this, policy recommendations for alleviating education anxiety are put forward.