Entrepreneurship education-infiltrated computer-aided instruction system for college Music Majors using convolutional neural network

Intermodal travel planning and decision support integrated with transportation and energy systems

The fast urbanization in China makes it all the more important to find sustainable solutions that are both comprehensive and energy-efficient. Because of its important role in lowering logistical expenses and pollutant emissions, intermodal transport is generally seen as an effective method of coordinating transportation operations, helping to address the growing economic and environmental issues. Considering the characteristics of a growing city, this article lays out a multi-criteria method for selecting which new initiatives for China's public transportation system should be prioritized. "Electric municipality bus," "light rail system," and "modernization to the current fleet and optimization" are the three enhancement initiatives that are outlined. Using transportation-related economic, social, and environmental sub-criteria, this research applies TOPSIS, an analytic hierarchy method and fuzzy approach for order preference by resemblance to ideal circumstance application, to prioritize transportation projects. The study aims to improve city life in Chongqing, China, by identifying the most environmentally friendly development projects. Applying the analytical hierarchy method (AHP), the relative importance of several sustainability criteria was established for use in making strategic decisions. The alternative projects for the given city have been ranked using the fuzzy TOPSIS approach. However, the investigated results show the supportive response of hybrid to environmental sustainability and vice versa for non-hybrid vehicles. However, the energy consumption in public transport remains a leading hurdle in sustainability across the three modes of transport: taxis, rail transit and buses. Finally, computer trains in public transport also surprisingly deal with environmental sustainability to keep the current & forthcoming generation from ecological harm. However, theoretical and empirical policy suggestions have been proposed to become clean & green shortly.

Supply chain optimization for environmental sustainability and economic growth

As the globe strives to solve severe environmental challenges, the concept of a low-carbon economy that prioritizes low energy use, little pollution, and sustainable development is gaining support. The supply chain management industry is not safe from the possibilities and threats posed by this new development. In light of the emerging norm, it is imperative that all supply chain links be economically and ecologically sustainable. For conventional businesses, ensuring environmental advantages and practicing the issue of equitably dividing supply network node profits is exacerbated by green supply chain management. This paper was prompted by the increasing need for information on green supply chain management (GSCM). GSCM is based on the idea of incorporating ecological considerations into traditional SCM practices. Therefore, GSCM is vital in shaping the cumulative environmental effect of businesses engaged in supply chain operations. To assess environmental sustainability requirements, we provide a best-worst method (BWM), a subset of China-based sectors in order to fill this void. The BWM was used to evaluate and quantify the impact of a variety of industrial operations and criteria on environmental quality. To make sure this approach is effective and reliable, we polled 34 experts for their input on which indications from our preliminary literature analysis would be most useful. This study's findings, supported by a sensitivity analysis, indicated stated "waste management" was the single most important indication for China-area businesses to achieve environmental sustainability. The results of this study provide industry managers, decision-makers, and practitioners with the information they need to choose areas of focus during the implementation phase that will have the most impact on promoting social sustainability in their organizational supply chain and moving toward sustainable growth.

Which qualities should built environment possess to ensure satisfaction of higher-education students with remote education during pandemics?

The built environment may be considered a complex system comprising several parts (including people, organizations, and physical structures). Since the emergence of the COVID-19 virus in recent years, there has been an explosion in the usage of mobile learning applications for classroom instruction. Research on the diffusion of mobile learning is lacking. This study examines the perceptions and experiences of many aspects of online education from the viewpoint of Chinese HEIs and university students who shifted to online modes of instruction during the COVID-19 outbreak. This research aimed to determine whether or not college students were happy with the resources available to them, how their teachers communicated with them, how they were evaluated, and the conditions in which they studied at home. It also looked at the pros and cons of online learning from the students' points of view. For this study, researchers opted for a cross-sectional survey using an online questionnaire. Information was gathered from 450 students from different Chinese tertiary institutions. To this end, we reflect on the experiences and perspectives of postgraduate students in urban design and discuss the results of an online survey conducted during the 2021-2022 academic year's subject delivery period, which included activities such as assessment, feedback, field study visits, workshops, and the use of digital platforms. PLS-SEM, which stands for partial least square structural equation modeling, is used in order to evaluate the proposed model. Therefore, the findings show that the research model offered for this investigation could explain the diversity in the actual use of mobile learning systems. This would provide helpful insight into the effect of educational, environmental, and quality factors on the actual use of remote education systems. It was shown that quality concerns acted as a mediator between institutional policy, change management, and the implementation of mobile learning systems in actual classrooms. The findings also indicate that the level of pleasure students feel acts as a moderating factor between the degree to which the mobile learning system is used and its overall level of quality. According to the findings of these studies, academic institutions may enhance their use of mobile learning applications and get more benefits from these systems if they adhere to the suggestions stated in these studies.

Does higher education promote sustainable development? Role of green technology and financial performance

How do digitalizing businesses help them achieve sustainable growth? This research examines the mediating function of green technology innovation in answering this question by defining sustainable development performance in terms of corporations' financial and environmental success. The educational system in China is examined to see how much of an impact it has on eco-innovation, as well as the relationship between green technology and innovation. The IFE test was utilized to determine whether or not the associations between variables such as GDP per capita, urbanization, green technology, higher education, and carbon dioxide emissions will continue to exist between 2004 and 2020 in China. The data for this analysis came from 30 of China's provinces. The findings of both the short-term and long-term CS-ARDL estimations demonstrated a positive link between eco-innovation and GDP per capita, green technology, higher education, and CO2 emissions. On the other hand, a negative correlation was found between urbanization and eco-innovation. The next topic covered in the research was how the effects of green technology might be seen in areas such as GDP per capita, higher education, and carbon dioxide emissions. The findings might provide valuable knowledge that developing economies can use to construct a feasible, sustainable path.

Does the digital economy matter for higher education? The role of ICT and green innovations in top polluted economies

The upsurge in information and communication technology is considered a key determinant for higher education. Moreover, green innovation is also the main catalyst of higher education. This research explores the role of information and communication technology and green innovation on higher education for the top 25 polluted economies from 2003 to 2020. This study considers the determinants of higher education (HE): economic growth, information and communication technology, green innovation, government expenditure, and carbon emission. The study employs FMOLS, DOLS, and MMQR tests to check the results of variables in this study. Economic growth, information and communication technology, green innovation, and government expenditure significantly impact higher education in this model. However, carbon emission has a negative impact on higher education. Moreover, the moderate effect of green innovation on information and communication technology, government expenditure, and carbon emission positively relates to higher education. In terms of outcomes, this study suggests how a country can maximize higher education.

Integrating multi-criteria decision-making with hybrid deep learning for sentiment analysis in recommender systems

Expert assessments with pre-defined numerical or language terms can limit the scope of decision-making models. We propose that decision-making models can incorporate expert judgments expressed in natural language through sentiment analysis. To help make more informed choices, we present the Sentiment Analysis in Recommender Systems with Multi-person, Multi-criteria Decision Making (SAR-MCMD) method. This method compiles the opinions of several experts by analyzing their written reviews and, if applicable, their star ratings. The growth of online applications and the sheer amount of available information have made it difficult for users to decide which information or products to select from the Internet. Intelligent decision-support technologies, known as recommender systems, leverage users' preferences to suggest what they might find interesting. Recommender systems are one of the many approaches to dealing with information overload issues. These systems have traditionally relied on single-grading algorithms to predict and communicate users' opinions for observed items. To boost their predictive and recommendation abilities, multi-criteria recommender systems assign numerous ratings to various qualities of products. We created, manually annotated, and released the technique in a case study of restaurant selection using 'TripAdvisor reviews', 'TMDB 5000 movies', and an 'Amazon dataset'. In various areas, cutting-edge deep learning approaches have led to breakthrough progress. Recently, researchers have begun to focus on applying these methods to recommendation systems, and different deep learning-based recommendation models have been suggested. Due to its proficiency with sparse data in large data systems and its ability to construct complex models that characterize user performance for the recommended procedure, deep learning is a formidable tool. In this article, we introduce a model for a multi-criteria recommender system that combines the best of both deep learning and multi-criteria decision-making. According to our findings, the suggested system may give customers very accurate suggestions with a sentiment analysis accuracy of 98%. Additionally, the metrics, accuracy, precision, recall, and F1 score are where the system truly shines, much above what has been achieved in the past.

Optimizing decision trees for English Teaching Quality Evaluation (ETQE) using Artificial Bee Colony (ABC) optimization

Changes in educational systems and English teaching strategies have increased the need for automatic methods for English Teaching Quality Evaluation (ETQE). A practical model for ETQE applies in different fields, determines the most relevant factors in teaching quality (TQ), and has optimal performance in different conditions. This paper presents a new method based on Artificial Intelligence (AI) and meta-heuristic algorithms to solve the ETQE problem. The proposed method performs the prediction process in two phases: "determination of related indicators" and "quality prediction". During the first phase, after introducing a set of 24 candidate indicators, an optimal subset of them having maximum correlation with ETQE and minimum redundancy are selected using Artificial Bee Colony (ABC) algorithm. In the second phase of the proposed method, a Classification and Regression Tree (CART) model optimized by ABC are applied to predict ETQ based on the indicators determined in the first phase. In this learning model, split points of decision nodes are determined by ABC in a way that the prediction accuracy would be maximized. The performance of the proposed method has been evaluated in two different teaching environments. The performance of the proposed method has been evaluated in two different teaching environments. The studied teaching environments are face-to-face (FF) and online classes that were held for middle school and university students, respectively. Based on the obtained results, the proposed method can predict the ETQ with an accuracy of more than 98.99% in both tested scenarios, which results in an increase of at least 1.11% compared to the previous methods. The efficiency of the proposed model in both studied scenarios prove the generality of this method to be used in real-world applications.

The role of deep learning in the innovation of smart classroom teaching mode under the background of internet of things and fuzzy control

Electronic components are rapidly updated in the context of expanding application requirements, and communication protocols used in combination with various electronic devices are also emerging. On this basis, IoT technology has developed a variety of sensor devices and gateways, which are widely used in cities. In the field of wisdom, applying IoT technology to classrooms can effectively improve the deficiencies of traditional teaching models. Fuzzy control theory is usually based on fuzzy sets in mathematics, and is combined with neural network, genetic and probability algorithms to form a calculation method. Fuzzy calculation has the ability to simplify the system input of a variety of complex variables, and its applications in the field of education are mainly: provide evaluation of teachers' teaching effectiveness. The advancement of science and technology has promoted the change and updating of the teaching mode. With the continuous advancement of basic education curriculum reform and the continuous deepening of classroom teaching reform, classroom teaching is also in urgent need of reform, from traditional classrooms to smart classrooms. Smart classrooms combine advanced technology with teachers' teaching. Through the dynamic data, the analysis instantly understands the student's learning situation, and then integrates it into education and teaching in a targeted manner. This paper conducts a questionnaire survey on the current situation of smart classroom teaching, and summarizes the current teaching problems. Then, combining the Internet of Things, fuzzy control and deep learning technology, from the two aspects of school teachers and students, it is proposed for smart classroom to promote students' learning effect. With its novel and new-style teaching advantages, smart classroom has gradually entered the public's vision and gained the attention and support of the majority of educators. Taking Grand Wisdom Classroom as an example, it uses the "Internet +" way of thinking and the new generation of information technology such as big data and cloud computing to create intelligent and efficient classrooms, realizing the whole process of application before, during and after class, and promoting the development of students' wisdom. Under the mobile Internet model, students and teachers can communicate anytime and anywhere. Combined with the analysis and application of our big data technology, data-based precision teaching becomes possible. In a real sense, learning before teaching can be realized and teaching can be determined by learning.

New-type urbanization efficiency measurement in Shanghai under the background of industry city integration

With the development of urbanization, the problem of the disintegrated between industry and city became more prominent, exploring the reasons. The efficiency of new-type industry has been the crucial factor in city-industry integration. This paper constructs the measurement index system of new-type urbanization via DEA-BCC methodology, starting from the quality of urbanization to analyze the efficiency of urbanization. This paper chooses the total energy consumption, general public budget expenditure, and the proportion of employment personnel in the tertiary industry in all urban units as input variables. The total retail sales of consumer goods, urbanization rate, average annual concentration of pm2.5 (popW), and built-up area as output variables. This paper uses DEA method to measure the comprehensive efficiency value, technical efficiency value, and scale efficiency value of new urbanization in Shanghai, and analyzes the influencing factors of urbanization efficiency. The results show the following: (1) The overall level of comprehensive efficiency value, technical efficiency value, and scale efficiency of Shanghai's new-type urbanization are relatively high, especially the technical efficiency basically stays at a high level. The overall trend of scale efficiency and comprehensive efficiency is consistent, and the comprehensive efficiency is greatly influenced by scale efficiency. (2) The technical efficiency of urbanization in Shanghai is close to the optimal, and there is little space for further increasing technological input to improve the comprehensive efficiency of new-type urbanization. The scale efficiency is slightly lower than the technical efficiency, and there is still some space for optimization. (3) In terms of urbanization input indicators, Shanghai's total energy consumption and general public budget input were too much in the early years, which led to the reduction of urbanization efficiency, and the situation has been improved in recent years. In terms of the output index of urbanization, increasing the total retail sales of social consumer goods and the output of built-up area can make the urbanization efficiency of Shanghai reach the optimal efficiency.

How Green Mindfulness and Green Shared Vision Interact to Influence Green Creative Behavior

Aim

The study is based on the self-determination theory and aims to investigate the mediating role of green intrinsic motivation and the moderating role of green shared vision in the association between frontline managers' green mindfulness and green creative behavior to leverage their capacity to think creatively and act sustainably.

Methods

The study employs a time-lagged, multi-source research methodology to collect data from frontline managers of service businesses in the tourism and hospitality industry. Data are analyzed using SmartPLS Structural Equation Model to evaluate the structural and measurement models. The authors evaluated the measurement model by employing the criteria of internal consistency: reliability and Cronbach's alpha, validity: convergent and discriminant validity; and the structural model using the path coefficient, coefficient of determination, predictive relevance, and goodness-of-fit metrics.

Results

Our findings indicate that green mindfulness significantly improves frontline managers' green creative behavior. Additionally, green intrinsic motivation mediates the connection between green mindfulness and green creative behavior. In addition, the direct effect of green mindfulness on green intrinsic motivation as well as the indirect effect of green mindfulness on green creative behavior through green intrinsic motivation, are both significantly moderated by green shared vision.

Discussion

To the best of the authors' knowledge, this is one of the few efforts that outstretch the boundary conditions of green mindfulness and green creative behavior through the mediating role of green intrinsic motivation and the moderating role of green shared vision.

Entrepreneurship education of college students and entrepreneurial psychology of new entrepreneurs under causal attribution theory

With the rapid development of information technology, the society’s demand for innovative talents has become increasingly prominent. The purpose of this study is to optimize the teaching strategies of entrepreneurship education for college students, further cultivate college students’ entrepreneurial ideas, and promote the formation of entrepreneurial values. The problems existing in entrepreneurship education in colleges and universities are studied based on entrepreneurial psychology and attribution theory. A questionnaire survey is conducted on the problems with a high probability of entrepreneurial failure of college students. The heads of new ventures in Xi’an are selected. Then, 300 questionnaires are distributed, and 209 are returned. The survey results are analyzed using failure attribution and failure learning. Suggestions are provided for management strategies of new ventures. The results show that the Corrected Item-Total Correlation (CITC) value of R⁻¹ is 0.65, and the CITC value of R⁻² is 0.35. In addition, the Kaiser-Meyer-Olkin (KMO) values of entrepreneurial failure attribution and entrepreneurial failure mode are both greater than 0.7, which indicates that the scale of entrepreneurial failure attribution has good validity and can be used for factor analysis. However, the KMO values of entrepreneurial failure attribution and entrepreneurial failure learning model are both greater than 0.7, and the significance of Bartlett sphericity test is 0.00, which indicates that the survey has good validity. The research has practical application and reference value for the cultivation of college students’ innovative and entrepreneurial ability.

Psychological healing function of poetry appreciation based on educational psychology and aesthetic analysis

With the development of society, the rapidly developing social environment has played a significant role in the particular group of college students. College students will inevitably suffer setbacks and psychological obstacles in their studies and daily life. This work aims to ameliorate college students’ various mental illnesses caused by anxiety and confusion during the critical period of status transformation. Educational psychology theory, aesthetic theory, and poetry appreciation are applied to the mental health education of college students to obtain a satisfying psychological healing effect. First, this work summarizes the connotation and characteristics of college student’s mental health and defines educational psychology. Secondly, the long tradition of Chinese poetry teaching is introduced. Besides, the theoretical basis of poetry therapy and aesthetic psychology is expounded, and foreign poetry is discussed. In addition, poetry appreciation is used to promote personality shaping and psychological healing of college students based on the theory of educational psychology and poetry appreciation psychotherapy. In addition, mental health education for college students is studied from the perspectives of psychological health, mental health education, college students’ mental health education, and appreciation of ancient poetry. In addition, the principle and significance of college students’ mental health education are discussed from the perspective of poetry appreciation. Finally, an experimental study is conducted on college students and patients in a specific hospital department by issuing questionnaires to verify the practical application effect of this method in psychotherapy. The survey results indicate that the scores of college students who have completed a one-semester poetry appreciation course in different dimensions of mental disorders are lower than those of those who have not completed the course. At the same time, in the scores of 16 personality traits, the positive trait scores of the experimental group are higher than those of the control group. Comparing scores before and after class also reflects the positive effect of poetry appreciation intervention on college students’ personality shaping. It can be concluded that poetry appreciation has a strong effect on promoting college students’ mental health and personality shaping and improving college students’ psychological problems.