Narrow Results

Faced with the growing demand for personalized services and the challenge of high service quality standards in the market, the traditional service industry model is no longer able to meet current needs, and transformation and upgrading are urgently needed. Therefore, it is particularly crucial to deeply analyze how digital network computing can effectively empower and accelerate the high-quality development process of the service industry, while identifying and analyzing the core influencing factors in this process. Digital network computing relies on vast digital knowledge and information resources, with highly interconnected network infrastructure as the cornerstone, integrating advanced information and communication technologies to achieve real-time transmission, processing, and analysis of data, thereby driving the continuous emergence of intelligent decision-making and innovative applications. Its distinctive characteristics include high network interconnectivity, deep data mining and utilization, intelligent decision support, and continuous technological innovation drive. The purpose of this study is to deepen the understanding of the internal mechanism of the integration and development of digital network computing and the service industry, and to reveal the multiple paths and key factors that promote high-quality development of the service industry. Through system analysis, we hope to provide scientific decision-making basis for policy makers, valuable strategic suggestions for service enterprises on the road of digital transformation, and jointly promote the service industry to move toward a more prosperous, efficient, and sustainable new stage of development.

The estimation of the concrete unit cost in construction engineering plays a vital role in controlling construction costs and ensuring timely project completion. To achieve this, a fast estimation algorithm for concrete unit costs is proposed. To ensure accurate estimation and reduce the computational burden on the convolutional neural network (CNN) for rapid estimation, the factors influencing concrete costs are identified and arranged using the interpretive structural modeling (ISM) method. The factors affecting the concrete unit cost of high-rise construction are selected as the input data sequence for the CNN model. The feature map of the input data is extracted through the convolution layer. After applying the pooling operation to the feature map, the processed data is passed into the fully connected layer through the final pooling layer, where the final calculation is performed to obtain the estimated concrete unit cost. Experimental results indicate that to ensure fast convergence and minimize estimation errors in CNN estimation, optimal network parameters are determined through multiple experiments. The best configuration includes a $3\times 3$ convolution kernel and 11 convolutional cores. This algorithm, which uses the ISM method to select influential factors, achieves high accuracy in estimating the concrete unit cost. In actual engineering applications, the error between the estimated and actual results is minimal, with a maximum difference of only 1 yuan.

Agricultural water use is a crucial component of the water supply and demand balance in the Yellow River Basin, accounting for an average of 75% of the total water usage annually. Understanding the mechanisms influencing the evolution of agricultural water use in various cities within the Yellow River Basin is of significant practical importance for maintaining a stable water balance and promoting high-quality agricultural development in the region. The factors affecting the evolution of agricultural water use are categorized into seven major groups: water resource endowment, water use conditions, hydraulic construction, among others. These encompass 21 quantitative indicators such as total water resources, grain sowing area and agricultural production policy indicators (APPI), which can either drive or inhibit agricultural water use. By comparing and analyzing the influencing factors selected in different studies, and considering the differences between various methods, the comprehensive contribution rates of these factors are calculated to identify the driving and inhibiting factors affecting the trend of agricultural water use in the Yellow River Basin. The study reveals that different time periods and cities have distinct driving and inhibiting indicators for agricultural water use. Overall, time series analysis shows that the GDP of the primary agricultural economy (${\mathrm{\text{GDP}}}_1)$ is often a driving indicator of agricultural water use, while annual precipitation (AP), water-saving irrigation machinery (WSIM) in agricultural modernization, and APPI are often inhibiting indicators. The findings provide a basis and guidance for formulating effective water resource management strategies and agricultural development policies.

This paper explores the performance and obtains a reasonable cleaning effect of the cleaning system of combine harvester and studies the relationship between the cleaning effect of the combine harvester cleaning system and its influencing factors. We established a neural network model between the cleaning loss rate and the clean system parameters. First, we tested the results of the cleaning performance of each group under different combinations of conditions, and analyzed the direct or indirect relationship between the cleaning loss rate and the parameters in the experiment under each working condition. Then, according to the experimental data obtained in the experiment, we predict the clearance loss rate for several sets of conditions by this model. The experimental results show that the prediction results of the model can meet the experimental requirements under the condition that the accuracy is not very high.

With the purpose of clarifying the energy-saving influencing factors of the participants in the construction project, qualitative analysis and scientific measurement analysis were combined in this paper to define and identify the energy-saving influencing factors of the construction project lifecycle. First, a qualitative analysis of the research literature of the natural science index (SCI) and social science citation index (SSCI) databases was conducted through a combination of literature analysis and expert interviews, constructing a list of the energy-saving influencing factors in the lifecycle of construction projects. Second, the knowledge map of the energy-saving research literature of construction projects in the past 20 years from the Web of Science (WOS) database was drawn through Citespace. Then the hotspots of energy-saving research in international construction projects were identified through keyword co-occurrence map as energy saving, energy efficiency, and energy consumption. The main research subjects were divided into four categories: energy-saving management and incentive policies, project users’ energy-saving behaviors and awareness, various energy-saving new materials and technologies, and energy-saving management and control at the operation and maintenance stage. Next, nine main clusters on energy-saving research were identified through cluster analysis, mainly distributed in various energy-saving technology research, energy-saving behavior and power research of owners and residents, policy and regulation research, and energy-saving management and control research in the operation and maintenance stage. Finally, the knowledge mapping analysis results were classified and defined according to the action scope of each stage of the construction project, and the identification of the energy-saving influencing factors in the construction project lifecycle was completed.

For the accuracy of network security management, an ISM-based analysis method on the influencing factors of network security situation (NSS) is proposed. The 17 factors of NSS were constructed first, the ISM of NSS influencing factors was established, and Matlab2019 was used for simulation. The results showed that the offensive and defensive game factors, such as attack method, attack tool, attack path, and defense strategy, are the direct influencing factors of NSS; the internal environmental factors such as operating system, application service, network bandwidth, network security device, user security awareness, and network topology are the necessary influencing factors; the external environmental factors such as legal environment, institutional environment, technical environment and business importance (economic environment) are the indirect influencing factors; the vulnerability factors such as asset value, vulnerability and open port are the fundamental influencing factors. The corresponding advice for management was put forward at the end of the paper.

In order to explore new approaches of training postgraduates of taught postgraduate programs, this paper proposed an analysis method to discuss the supply-side influencing factors of “vocational master of education” using the interpretative structural model (ISM). The research results showed that the quality of postgraduates, recruitment plan and the number of graduated students are the direct influencing factors of the supply side of “vocational master of education”. Teaching with practices, dissertation, postgraduate management, employment policy and the actual recruitment are the necessary influencing factors. Training program and courses, faculty, basic environment, entrance examination, program promotion, professional direction and planned enrolment are the indirect influencing factors. The strategic environment, the profession of secondary vocational schools and the demand of teachers in these schools are the fundamental influencing factors. Therefore, when studying the supply side of “vocational master of education”, we analysed top-down with specific emphasis on its hierarchy to strengthen the synergy effect of the subject.

Prediction is an important way to analyse stroke risk management. This study explored the critical influencing factors of stroke, used the classical multilayer perception (MLP) and radial basis function (RBF) machine learning (ML) algorithms to develop the model for stroke prediction. The two models were trained with Bagging and Boosting ensemble learning algorithms. The performances of the prediction models were also compared with other classical ML algorithms. The result showed that (1) total cholesterol (TC) and other nine factors were selected as principal factors for the stroke prediction; (2) the MLP model outperformed RBF model in terms of accuracy, generalization and inter-rater reliability; (3) ensemble algorithm was superior to single algorithms for high-dimension dataset in this study. It may come to the conclusion that this study improved the stroke prediction methods and contributed much to the prevention of stroke.

Creativity has been identified as a critical activity in distributed product development. Various methods and tools support distributed product development and distributed teams in general, but none with a focus on creative problem solving. To tackle this challenge, a systematic literature review has been conducted to gather the influencing factors on distributed creativity to be used as a starting point for supporting creativity in distributed product development teams. Within this submission, an impact model is developed, focusing on the success factors of creativity. Furthermore, the interconnection between the factors is modelled. Barriers to creative problem solving are included as well but will be the focus of a following publication.

The seismic performance of a steel pier of box section was studied through low-cycle cyclic testing. The damaged specimens were repaired by filling with concrete and welding steel plates. The low-cycle cyclic test was then repeated. The effects of repairs were investigated by comparison of failure mode, energy dissipation performance, and ductility before and after repair. To supplement the data, the influence of different factors on the seismic bearing capacity and ductility of steel piers were analyzed by finite element method. The repair effects were compared by threshold of the displacement from the experiment. Based on the displacement angle response of the nonlinear dynamic time history analysis, the seismic performance is checked. The results show that repair had favourable effects on the damaged specimens. The horizontal bearing capacity and ductility of the specimens filled with concrete are significantly enhanced. Reinforcement by steel plates can increase the ductility and cumulative energy dissipation of the steel pier. An axial compression ratio of 0.2 and a concrete filling ratio of 30% are suggested. The horizontal bearing capacity can be improved by increasing the steel strength while the concrete strength shows little effect. The angular displacement from nonlinear dynamic time-history analysis was less than the test threshold, so the existing methods used for seismic performance verification are safe.

In order to find out the sensitivity of the thermophysical and structural parameters to the thermodynamic characteristics of twin-tube hydraulic shock absorbers, based on the bench test, a method for calculating the time-varying rate of the external work on the shock absorber oil is proposed. And then, a thermodynamic model of the twin-tube hydraulic shock absorber is established by using the basic thermodynamic principles. By analyzing the influence of each parameter on the thermodynamic characteristics of the shock absorber, it can be seen that, the radius of the working cylinder outer wall has the greatest influence on the temperature rise of the shock absorber, followed by the thermal conductivity of the oil, the height of the oil, the heat transfer length of the cylinder barrel, the radius of the oil storage cylinder outer wall, the emissivity of the oil storage cylinder outer wall, the height of the nitrogen, the thermal conductivity of the nitrogen, the specific heat capacity of the oil, the density of the oil, the thermal conductivity of the cylinder, and the mass of the working oil. The kinematic viscosity of the oil has the least influence on the temperature rise of the shock absorber. The research can provide an effective theoretical guidance and reference for the design of the twin-tube hydraulic shock absorber.

As an emissions abatement mechanism focusing on property rights theory and market trading methods, carbon emissions rights trading plays an important role in achieving low-carbon economic development, which has already garnered broad worldwide recognition. In the aftermath of the implementation of an initial, seven-province/city, carbon emissions rights trading pilot project, in 2017 China launched a carbon emissions trading rights market on a national basis. The authors of this paper provide a theoretical basis for research into this trading market’s impacts on energy conservation, reduction of emissions, and on economically sustaining, healthy development, from the following four perspectives: the history of the market’s development, a comparison of carbon reduction mechanisms associated with differing carbon tax levies, the effects of carbon emissions rights trading’s implementation, and the influencing factors on such trading. They systematically summarize, sort out, and evaluate the most recent, related carbon emissions rights trading literature, and then based on this analysis offer up insights regarding possible developments and refinements of future carbon emissions rights trading research.

Based on the panel data of 202 prefecture-level cities within 14 national-level city clusters in China from 2007 to 2016, we established a dynamic panel model to measure the economic growth effects of city clusters and analyzed the main influencing factors. The results show that: (i) Technology has a significant impact on the economic growth of city clusters; the narrowing development gap between regions can help city clusters produce good economic growth effects; the city clusters, if more agglomerated, can help better utilize factors, and thus promote coordinated regional development. (ii) City clusters with multiple central cities boast a stronger engine of economic growth, and the impacts of factors such as technology and clustering degree on their economic growth are more noticeable. (iii) Geographical factors will also affect city clusters’ economic growth. The economic growth of city clusters in Southern China has been more strongly powered by the factors such as technology, clustering degree and human capital than those in Northern China. From the spatial perspective and by using the threshold panel method, we further explored the mechanisms with which the central cities within a city cluster can influence economic growth depending on their accessibility. The results manifest that the more accessible the central cities within a city cluster are, the stronger role they can play in leading and driving the economic growth of surrounding areas. In the future, it is important to promote the transformation of single-core and dual-core city clusters into multi-core city clusters, and give full play to the role of central cities in leading the development of surrounding areas. It is also necessary to vigorously develop technology and transportation to further facilitate the high-quality growth of city clusters.

Since the 1990s, the global climate governance pattern has kept evolving from the initial two camps of developed and developing countries to the current pattern of multi-polarity, featuring the withdrawal and return of Paris Agreement by the United States, the declining leadership of the EU, the coalition of BASIC countries, and the rise of the least developed countries and small island developing states as newly emerging forces. This evolution mainly results from the combined effects of three factors: (i) The changes in the carbon emission pattern driven by population, economic growth, and technological progress; (ii) the stronger influences and power of discourse of the least developed countries and small island developing states as derived from the impacts of and vulnerability to climate change; and (iii) the impacts brought about by uncertain factors such as the uncertainties in terms of science, politics, and technological progress. These factors will still affect the trend of global climate governance in the future. The carbon emissions of developed countries will continue to take a less share in the world’s total, while the proportion of India and the least developed countries in this respect will rise rapidly, which will make global climate governance face a dilemma. Technological progress and the positive actions of non-state entities indicate that the international climate system needs reform and innovation. The rapid development of China over the past three decades has been synchronized with the evolution of the global governance structure, and has naturally become one of the internal factors driving the evolution of climate governance pattern. In the face of various pressure and challenges, China has been pushed to the forefront of global climate governance. China should observe the general trends within and outside the country, and respond to them rationally: (i) Set the proper role of China in the new pattern of global climate governance, i.e. a cooperation leader who should make positive contributions and avoid premature advance; (ii) innovate the concept and institutional system of global climate governance, and study and put forward the Chinese approach that is positive, pragmatic, and operable; (iii) help low-income countries cope with climate change by virtue of renewable energy technology and industrial cooperation, and achieve a win–win situation by encouraging Chinese enterprises to “go out” and helping low-income countries effectively control carbon emissions; and (iv) strengthen the climate cooperation with non-state actors, give play to their special role, and promote China’s comprehensive reform and opening-up.

This study explores female professors’ willingness to have a second child in Chengdu. Based on a qualitative study, this article focuses on those who have had at least one child and examine their fertility intentions, childbirth behavior and influencing factors of second-child fertility according to their occupational characteristics and academic achievements. In-depth interviews with 24 female professors in Chengdu were conducted in 2018–2020. It finds that their reproductive choices are the cross-effect result of state policy, external support and personal condition. Their educational experience also plays an important role in deciding their choice of having two children. This research aims to shed light on Chinese women’s reproductive intention and fertility behavior and tries to offer policy suggestions under the two-child policy in China.

Implementing digital technologies has made organizations more collaborative. Knowledge-based collaborative activities management has become the main organizational model of work. Identifying the factors that influence organizational collaboration is crucial to organizational design and ensuring effective digital transformation. Currently, collaboration has not received enough attention. Enterprises lack an understanding of its importance. This chapter quantitatively studies collaborative activities and their influencing factors. The regression analysis results show that the invisible independent variable such as knowledge-based innovation has a greater effect on organizational collaboration. We further conduct predictive analysis for organizational collaboration, and the predictive analysis results help the two case study companies to clearly understand their current status of organizational collaboration and the level of their respective influencing factors.

This paper constructs the cultural tourism industry development index. Based on the principal component analysis, by collecting the relevant data on China’s inter-provincial cultural tourism industry from 2015 to 2019, one can measure the development level of the cultural tourism industry. Based on the conclusion, one can use the fixed effect model to analyze the influencing factors of the development of the cultural and tourism industry. According to the results, one can put forward the relevant countermeasures.

The influence mechanism, path of independent innovation, and open innovation of Liaoning’s equipment manufacturing industry on its international competitiveness is not only an important issue to be studied by the theoretical circles but also a realistic issue that the government, relevant departments, and enterprises need to pay attention to. This paper first analyzes the current situation of Liaoning’s equipment manufacturing industry from the aspects of industrial production capacity, current market situation, and industrial scale. Then, it evaluates the international competitiveness of Liaoning’s equipment manufacturing industry by using the RCA index and TC index. It constructs the evaluation index system of international competitiveness of the equipment manufacturing industry and uses SPSS software to do the factor analysis of the international competitiveness indicators of Liaoning’s equipment manufacturing industry and gets the result of Liaoning’s equipment manufacturing industry’s international competitiveness. Lastly, it analyzes the factors influencing the international competitiveness of Liaoning’s equipment manufacturing industry and puts forward countermeasures to improve the international competitiveness of Liaoning’s equipment manufacturing industry from multiple perspectives.