This paper seeks to discuss focused prototype development of self-driving, autonomous, driverless, electric cars with emphasis on subsystem advancement constituting the progress of the technology. The introduction lays special emphasis on the increased role of autonomous technology in transforming transportation by underlining its potential to enhance safety, effectiveness, and sustainability. Some technical background is provided with the definition of what an autonomous car is and its evolution timeline. Electrical vehicle current advancement is also described in detail. At last, comparative analysis of further prototype developments and subsystems with respect to their usefulness and prospects is given. This assessment serves to contribute to the present discourse on self-driving vehicle technology, and the role that these vehicles will play in on-going transport modal shift.

Purpose – This study investigates the impact of career planning education on university students’ entrepreneurial intentions by examining the mediating roles of self-efficacy and perceived behavioral control, as well as the moderating effects of digital competency and risk propensity. Design/methodology/approach – Data were collected from 450 university students through a structured questionnaire. The research model was tested using structural equation modeling with bootstrapping procedures for mediation analysis and hierarchical regression for moderation effects. Findings – The results reveal that career planning education positively influences entrepreneurial intentions both directly ( =0.312, p<0.01) and indirectly through self-efficacy ( =0.178, p<0.01) and perceived behavioral control ( =0.133, p<0.01). Digital competency ( =0.156, p<0.01) and risk propensity ( =0.143, p<0.01) positively moderate these relationships. Practical implications – The findings suggest that higher education institutions should integrate digital skills development into career planning curricula and tailor educational approaches to students' individual characteristics to enhance entrepreneurial intentions effectively. Originality/value – This study extends the theory of planned behavior by incorporating digital competency as a crucial moderating factor and demonstrating the specific mechanisms through which career planning education influences entrepreneurial intentions in the digital era.

As a result of continuous economic development and accelerated urbanization, the agriculture development has had to change from the traditional mode of agricultural production to the modern mode of agricultural production. What kind of method can better help the development of modern agricultural production mode has become one of the current research topics that has attracted much attention. In response to this problem, the field of modern agricultural production models becomes highly relevant for research. With the in-depth study of modern agricultural production, the research on Internet of Things (IoT) technology in rural characteristic ecological agriculture (ECO) is gradually carried out, and its functional advantages are of great significance to promote the development of modern agriculture. This paper aimed to study the application of IoT technology in the development of rural characteristic ECO. The analysis and research of IoT and ECO enables it to be applied to the construction of an ecological farmland information monitoring system to address the problem of enhancing the ECO development with rural characteristics. In this paper, IoT technology, information detection and ECO were analyzed; the performance of the method was experimentally analyzed; the relevant theoretical formulas were utilized for interpretation. The outcomes demonstrated that the incidence of pests and diseases in field A using the IoT-assisted information monitoring system was 31.11% lower than that in field B, and the use of pesticides was reduced by 15.69%. It can be learned that IoT technology can meet the needs of enhancing the development level of rural characteristic ECO, and the level of agricultural development and work efficiency have been greatly improved.

With the rapid development of society, the emergence of society and people’s daily life have put forward higher quality requirements for power supply. The original distribution system cannot monitor and control the circuit condition in real time. The power grid operation efficiency is low, and the loss of electric energy in the transmission process is large, resulting in the unstable power supply to users. With the development of smart grid, distribution automation has become the goal of Power System (PS) development. There are many noise data in the process of medium voltage distribution communication. In this paper, the medium voltage high-speed analog Communication Technology (CT) was applied to distribution automation. By modulating the signal and other operations, automatic power distribution can be realized, which can effectively shorten the maintenance time of fault circuits and quickly share power data resources. This paper compared the traditional medium-voltage distribution with the distribution automation based on the medium-voltage high-speed analog CT. The experimental results showed that the average power supply reliability of the traditional medium-voltage distribution and distribution automation was 88.90% and 95.56% respectively in the 10 kV voltage. In the 20 kV voltage, the average power supply reliability of traditional medium-voltage distribution and distribution automation was 90.24% and 97.04% respectively. Therefore, the application of medium-voltage high-speed analog CT in distribution network to distribution automation can effectively improve the reliability of power supply.

Financial digital management is a new type of financial management method. Through information technology, the financial management process has been digitized, and with the help of technical means such as data analysis and artificial intelligence, financial management automation has been achieved. Traditional financial management methods often require a large amount of manual intervention and processing, which is prone to problems such as cumbersome data processing, time-consuming and labor-intensive, and prone to errors. With the development of computer technology and network technology, digital management has become a new trend in financial management. This article analyzed the application of blockchain and cloud computing technology in financial digital management, and selected 12 enterprises as the research objects. The traditional financial management model and the financial digital management model of blockchain and cloud computing technology were respectively adopted to compare the differences in financial process efficiency, data accuracy, labor cost savings, digital management, and financial risk management between the two models. The experimental results of this article indicated that under the financial digital management mode using blockchain and cloud computing technology, the processing time of the revenue and expenditure process was 4.45 hours in terms of financial process efficiency. In terms of data accuracy, the accuracy rate of accounting was 99.7%. In terms of labor cost savings, the labor cost was 1.505 million yuan/year. In digital management, the data processing efficiency score was 92. In financial risk management, the accuracy score of risk assessment and prediction was 93, which was better than traditional financial management models. The adoption of blockchain and cloud computing technology in financial digital management can significantly improve multiple key indicators such as financial management efficiency, data accuracy, and security. This model has important value and significance for enterprises.

The popularity of the Internet and mobile smart terminals has changed many forms of learning, and the mobile learning model was born in this environment. As a new learning mode, mobile learning has brought certain development opportunities for college English writing teaching. In the current educational environment, many students hold various mobile devices, which also motivates them to have a strong willingness to learn on mobile. It can be said that the application of mobile learning to English writing is quite suitable. At present, the application of mobile learning in college English writing is not mature enough, and there are often a series of problems such as shortage of resources and network freezes, which also reduces students’ enthusiasm for learning. In order to further improve the fluency and maturity of the mobile learning mode, this paper has combined the wireless network to study the new mobile learning mode of college English writing. By building a mobile learning framework based on wireless network, innovating mobile learning writing content and computing learning resource categories, a new mobile learning mode of college English writing has been finally formed. The experimental results have shown that the new model has mobilized students’ enthusiasm for learning and further improved the writing efficiency. Compared with the old model, the efficiency has increased by 6.73%.

Traditional power load forecasting (PLF) usually uses statistical models or time series analysis methods, but they often only consider historical load data and ignore the impact of meteorological, temperature, humidity and other factors on load, resulting in inaccurate load forecasting. Moreover, traditional methods have limited real-time performance in power load data transmission and cannot respond to changing load demands in a timely manner, which limits the real-time and accuracy of PLF. Wireless networks (WN) and intelligent sensing technology (IST) were used to obtain real-time charge data, and these data were intelligently analyzed to improve prediction performance. WN and IST were used to improve the transmission efficiency and prediction accuracy of PLF. This article studied the transmission delay and integration delay of power load data in WN, and conducted experimental tests on the root mean square error (RMSE) of CER Electricity Data, REFIT Power Data, and Umass Smart Data Set datasets using an intelligent sensing algorithm based on sensors to study their predictive effect on power load. As the number of users continues to increase, the transmission delay and integration delay of power load data were also increasing. During the process of increasing the number of users from 0 to 500, the transmission delay increased from 389ms to 735ms; the integration delay increased from 568ms to 1086ms. The power load prediction algorithm based on intelligent perception technology had average prediction RMSEs of 0.2885, 0.2716, and 0.2618 for CER Electricity Data, REFIT Power Data, and Umass Smart Data Set datasets, respectively. In WN, the transmission delay and integration delay of power load data are relatively small, and with the increase of the number of users, the impact of this delay is relatively small, which can have the effect of supporting the transmission and integration of power data for a large number of users. The power load prediction algorithm based on intelligent perception technology has good prediction results for different datasets and can accurately predict power loads.

This research presents an innovative machine learning framework for predicting library space utilization patterns through the integration of multi-modal deep learning architectures and ensemble methodologies. The proposed system combines Long Short-Term Memory (LSTM) networks with attention mechanisms and sophisticated feature engineering techniques to achieve superior prediction accuracy while maintaining computational efficiency. The methodology encompasses three primary contributions: (1) development of a comprehensive feature extraction pipeline incorporating spatial, temporal, and environmental data streams; (2) implementation of a novel LSTM-Attention hybrid architecture with adaptive learning rate optimization; and (3) integration of ensemble learning techniques for robust prediction performance. The framework demonstrates significant improvements over existing approaches, achieving 96.8% prediction accuracy across diverse operational scenarios. Experimental validation, conducted using an extensive dataset comprising 2.1M samples collected over 33 months from multiple library facilities, demonstrates the framework’s effectiveness. The proposed model achieves a Mean Absolute Error (MAE) of 0.142 and Root Mean Square Error (RMSE) of 0.186, representing a 39.8% reduction in prediction error compared to baseline approaches. The system’s computational efficiency is evidenced by an average processing time of 45.3ms per prediction, with a memory footprint of 512MB. The research contributes to the field of intelligent library management systems by establishing a theoretically grounded and practically implementable solution for space utilization prediction. The framework’s superior performance in capturing complex spatial-temporal patterns, combined with its computational efficiency, makes it suitable for real-time applications in resource-constrained environments. These advances provide a foundation for enhanced space management strategies in modern library systems.

Amidst the digital economy and ESG policy frameworks, digital transformation emerges as the prime strategy for high-tech companies to enhance their corporate performance. The research investigates the impact of high-tech organizations’ digital transformation on their performance, utilizing data from A-share listed tech firms in Shanghai and Shenzhen spanning 2018 to 2022.The research indicates that digital transformation enhances the performance of high-tech firms in the context of ESG. The modulating mechanism shows that executive compensation will weaken the impact of digital transformation on enterprise performance. The intermediary mechanism demonstrates that internal control and cost effect contribute to the mediating influence on the relationship between enterprise performance and digital transformation. Each of them has successfully cleared multiple tests for robustness. At the same time, there is a certain heterogeneity in the influence of high-tech enterprises on firm performance, and the improvement effect on firm performance is significant in the east and the growth and maturity period. The research presents new empirical evidence and acts as a benchmark for understanding how digital transformation affects high-tech companies’ performance.

Purpose – This study aimed to explore the internal structure of sustainable employability of liberal arts college students in China and develop a comprehensive scale to facilitate research on this topic and establish a theoretical framework for cultivating sustainable employability of liberal arts college students in China. Design/methodology/approach – Through theoretical derivation and open questionnaire and the Delphi method, the main dimensions of sustainable employability of liberal arts college students are explored. The components elments of each dimension are explored through a text analysis of 189 job advertisements. Through 392 questionnaires and statistical analysis techniques, a scale is developed for measuring the sustainable employability of liberal arts college students. Findings – This study found three dimensions characterizing the sustainable employability of liberal arts students in China: attribute characteristics, general ability of employment, and innovation-driven ability. Additionally, the attribute characteristics encompassed five attribute elements, the general employment ability included six, and the innovation-driven ability included four. This study also developed a 34-item scale for measuring the sustainable employability of liberal arts students that demonstrated good reliability and validity. Originality/value – This study was among the first to investigate the internal structure of sustainable employability of liberal arts students in China.