At present, against the backdrop of deepening reform in vocational education and industry integration in China, the cooperation between schools and enterprises in energy technology research and development - characterised by rapid technological iteration, long investment cycles, and strong policy dependence - has become a vital pathway for talent cultivation. While short-term incentives can enhance cooperation efficiency, they often induce opportunistic behaviour. Long-term collaboration, though conducive to strengthening the stability of cooperation between profit and non-profit organisations, faces a series of challenges such as high initial costs and lengthy implementation cycles. This study expands the application of evolutionary game theory to the field of vocational education, providing a theoretical foundation for advancing school-enterprise cooperation and deepening the integration of industry and education in practice.
This paper focuses on the challenging problem of cable tunnel cost prediction, aiming to achieve accurate estimation with the help of advanced technology so as to improve the level of project cost management. Facing the multi-source heterogeneous data generated by cable tunnel construction - characterised by diverse engineering design parameters, construction techniques, and external environmental factors - traditional prediction methods are often ineffective. Therefore, a cost forecasting model based on graph neural network (GNN) is constructed. In this paper, various optimisation strategies are employed, including the use of weighted mean squared error (WMSE) as the loss function and the stochastic gradient descent (SGD) optimisation algorithm. The results indicate that this model is efficient and reliable for cable tunnel cost prediction and can provide strong support for engineering cost management in practical applications.
The purpose of this paper is to investigate how digital finance might support the superior growth of China's green energy economy and its regional diversity. In order to systematically uncover the mechanism of digital finance in optimising resource allocation, supporting green technological innovation, and promoting green consumption behaviour, this paper uses theoretical analysis and a deep learning model as its research objects. The findings demonstrate the considerable regional variation in the promotion of digital finance. As a result, this paper examines and validates the mechanism of digital finance in a number of areas, including resource optimisation, technical innovation, and consumer guidance. It also demonstrates the policy support and the regulatory impact of regional economic growth level. This paper contributes to the research in the areas of digital finance and the green energy economy and serves as a reference for maximising regional development policies, reducing regional disparities, and fostering sustainable development.
Geographical indication agricultural products in Heilongjiang Province play a crucial role in advancing the sustainable development of the agricultural energy-economic system, with multidimensional impacts on the economy, energy, and ecology. Accordingly, this paper proposes that Heilongjiang Province should optimise and adjust the spatial layout of GI agricultural products, cultivate industrial clusters, promote industrial structure upgrading and the integrated development of the three industries, adhere to the marketisation of agricultural product brands, and strengthen brand marketing and promotion, thereby further leveraging the synergistic driving effect of GI agricultural products on the coordinated development of the agricultural energy-economic-ecological system.
This paper combines sparse representation with deep autoencoder (DAE) to propose a method for power transformer sound modelling and fault diagnosis. When a power transformer malfunctions, it is usually accompanied by abnormal sounds. During the operation of transformers, the sound signals generated by structural vibrations such as iron cores and windings contain rich equipment status information. Accurate detection and analysis of sound signals can achieve mechanical fault diagnosis of transformers. Traditional detection methods are no longer able to meet the constantly changing needs of the electrical power system (EPS). This paper constructs a fault sound recognition model that combines sparse representation and DAE. This model optimises the encoding process by introducing sparse constraints, thereby enhancing the ability to extract key fault signal features. The results indicate that the model has advantages in effective feature extraction, diagnostic speed, and fault diagnosis accuracy.
Responsible innovation has become an increasingly relevant yet conceptually fragmented theme in product development. This paper employs scientometric analysis of 3438 publications (1951-2023) to map the articulation of responsibility, sustainability, and green/eco-innovation in the product development literature. We observe a shift from technically focused product development and engineering design toward socially and environmentally oriented themes, such as social responsibility, environmental sustainability, and green innovation, with an increasing convergence among key constructs in the most recent decade. Building on periodised cluster maps, we develop a Responsibility in Innovation Matrix that locates constructs along two dimensions: triple-bottom-line emphasis (economic, environmental, social) and level of analysis (project-level 'zooming in' vs. organisational-level 'zooming out'). The matrix clarifies overlap zones and persistent boundary issues and provides a structured lens for scholars and managers seeking to locate, compare and evaluate constructs of responsible product development.
This study explores the specific system of values that effectively fosters a sustainability-oriented culture (SOC) within organisations. It investigates whether cultivating such a culture can lead to competitive advantages through sustainability-oriented innovation (SOI). Drawing on the competing values framework, we develop a conceptual model that illustrates how particular value systems give rise to an SOC, which in turn influences innovation focused on sustainability. Our findings confirm a positive relationship between SOC and SOI, highlighting the importance of fostering appropriate value systems as a foundational step in establishing both. In particular, the pronounced flexibility associated with the human resources (HR) and open systems (OS) dimensions of values emerges as a strong indicator for cultivating an SOC. This study contributes to the literature on organisational culture and sustainability by identifying the value systems that effectively support the development of a sustainability-oriented culture.
In order to study the impact of government funding on the R&D strategy of enterprises, and explore whether the innovative development of enterprises is driven away by government policies. In this study, an evaluation method and analytical model were developed to investigate the perspective of companies that are highly receptive to funding. We focused on the top 30 companies which received substantial government funding. Specifically, this study analysed the first cooperative patent classification (CPC) with government-interest (GI) patents constituting more than 1% of all GI patents. To investigate the aforementioned characteristics and classify each company accordingly, the number and citation status of GI and non-GI patents were also compared within each company's technological domain. Assessment of patent concentration, number, and influence revealed that individual enterprises employ diverse implementation practices and R&D strategies when obtaining patents through government funding. In conclusion, this study presents a well-defined classification model for enterprise types. The proposed four-quadrant framework not only aids the government in identifying suitable partners and determining optimal funding allocation but also assists enterprises in assessing their current status and strategic positioning.
The innovation strategy of enterprises has different directions, and the impact of innovation consciousness on the selection of dual innovation has research value. Enterprise managers, as important creators of awareness, also play a crucial role in innovation activities. Based on this, this study takes the perspective of managerial characteristics and examines the impact of enterprise consciousness on dual innovation, as well as the role played by managers, using Chinese listed companies from 2012 to 2022 as samples. The findings of this study are as follows. First, enterprise innovation awareness significantly promotes dual innovation activities, with a stronger effect on incremental innovation. Second, the impact of enterprise innovation awareness on dual innovation varies depending on the size and ownership structure of the enterprise. Third, managers play an important role in enterprise innovation, with younger, higher-educated, and more adventurous managers displaying stronger innovation intentions. Finally, the study confirms that female managers have a motivating effect on enterprise innovation. Our research provides positive insights and valuable references for Chinese enterprises to enhance their innovation quality.
The escalating demand for rare metals, driven by their pivotal role in IoT and the energy transition, presents complex challenges for supply chains. This study explores rare metal management, focusing on the key drivers of consumer concern regarding ethical and environmental issues. Based on 18 in-depth interviews, the research examines how consumer perspectives influence the sustainability of rare metal supply chains. The central research question addresses: "Drawing on consumer perspectives, how can the key drivers and solutions identified be leveraged to enhance sustainability in the supply chain processes of rare metals?". Findings highlight solutions such as improved product design, substitution strategies, and enhanced recycling initiatives. An analysis chart links these consumer-driven solutions to sustainable supply chain practices. Using a Grounded Theory approach, this article contributes to the sustainable supply chain literature by offering actionable insights for firms seeking to align their practices with ethical and environmental expectations.
Aiming at the quality of innovative practice education, the current evaluation method has the problems of poor evaluation effect and low evaluation efficiency. This paper proposes a new evaluation method by combining entropy weight method and grey correlation method. First, build an evaluation index system to obtain indicators, and then calculate the weight of each indicator through entropy weight method. Finally, use the grey correlation method to obtain the correlation degree of each indicator, sort the indicators according to the size of the correlation degree, build an evaluation model, input the evaluation index data into the evaluation model, and get the score. The simulation results show that the proposed method has high evaluation accuracy and short evaluation time.
