Index System Of Factors Research Articles

Predicting Cd accumulation in crops and identifying nonlinear effects of multiple environmental factors based on machine learning models

The traditional prediction of the Cd content in grains (Cdg) of crops primarily relies on the multiple linear regression models based on soil Cd content (Cds) and pH, neglecting inter-factorial interactions and nonlinear causal links between external environmental factors and Cdg. In this study, a comprehensive index system of multi-type environmental factors including soil properties, geology, climate, and anthropogenic activity was constructed. The machine learning models of the tree-based ensemble, support vector regression, artificial neural network for predicting Cdg of rice and wheat based on the environmental factor indexes significantly improved the accuracy than the traditional models of linear regression based on soil properties. Among them, the tree-based ensemble models of XGboost and random forest exhibited highest accuracies for predicting Cdg of rice and wheat, with R2 in the test dataset of 0.349 and 0.546, respectively. This study found that soil properties, including Cds, pH, and clay, have greater impacts on Cdg of rice and wheat, with combined contribution rates accounting for 65.2 % and 29.7 % respectively. Since wheat sampling areas are located in central and northern China, they are more constrained by precipitation and temperature than rice sampling areas in the south. Geologic and climate factors have a greater impact on Cdg of wheat, with a combined contribution rate of 49.9 %, which is higher than the corresponding rate of 20.9 % in rice. Furthermore, the Cdg of rice and wheat did not exhibit an absolute linear relationship with Cds, and excessively high Cds can reduce the bioconcentration factor of Cd accumulation in crops. Meanwhile, other environmental factors such as temperature, precipitation, elevation have marginal effects on the increase of Cdg of crops. This study provides a novel framework to optimize traditional soil plant transfer models, as well as offer a step towards realizing high precision prediction of Cd content in crops.

Research on fire hazard assessment of metro baggage based on improved hierarchical analysis

Underground station passenger flow is large, the number of parcels carried by passengers is large and varied, and the parcels carried have an impact on the fire hazard and evacuation of the station. In order to determine the weights of the passenger luggage risk and environmental factor index system in the fire risk evaluation of underground stations in a more realistic way, an optimized and improved hierarchical analysis method for determining the judgement matrix is proposed, which improves the traditional nine-scaled method and adopts the three-scaled method for the four major categories of luggage, namely, handbags, rucksacks, portable power tools and trolley cases. The advantage of this method is that there is no need for consistency judgement in determining packages with a wide range of types and uncertain contents, thus simplifying the calculation. Meanwhile, the reasonableness and reliability of the method is verified by combining it with an actual metro station fire risk assessment system.

The Multi-level Hierarchical Structure Analysis of Influencing Factors of Coal Mine Managers' Blame Avoidance Behavior

BackgroundIn the Chinese coal industry, widespread blame avoidance behavior (BAB) greatly impacts coal mine accidents. Therefore, it is necessary to stop the BAB of coal mine managers and raise the management level of coal mine enterprises for the safe development of Chinese coal industry. MethodsBased on the semi-structured interviews and questionnaire surveys (20 middle-level managers in coal mines), this paper used the Grounded Theory and Nvivo Software qualitative research methodology to open, spindle, and selectively encode the interview data. Then, an index system of factors influencing BAB of coal mine managers was constructed. The influence degree, affected degree, centrality degree, cause degree of each influencing factor were calculated and the hierarchical model of influencing factors of BAB of coal mine managers was established by the DEMATEL-ISM method which is a decision support tool used to evaluate and analyze the interdependencies between influencing factors. ResultsIndex system of factors influencing BAB of coal mine managers included four levels and 12 influencing factors: individual, organizational, institutional environmental, and situational factors. The hierarchical model identified eight causal factors and four consequential factors, of which safety management (15.355), work attitude (14.380), and work group performance (14.281) in the top three of the centrality rankings are the key factors affecting the avoidance behavior of coal mine managers; A 3-level multilevel structure was constructed to reflect the interactions among the factors influencing the BAB of coal mine managers in terms of direct, indirect, and root causes, and corresponding improvement measures were proposed. ConclusionThis study offers a theoretical complement and practical guidance for stopping BAB of coal mine managers.

Research on Coupling Effect Measurement and Coupling Risk Simulation of Green Building Construction Safety Risk Factors

The construction of green buildings is an important direction for the transformation and development of the construction industry, but it is beset with problems such as a lack of construction experience, immature new technologies, and unstable material properties; these issues bring risks to the construction stage of green buildings, and the coupling of uncertain risk factors in the construction process of green buildings may lead to unfavorable results. The purpose of this study is to explore the coupling degree of green building construction safety risk factors and the changing trend in their coupling combinations at the system risk level. First, the risk factor index system was defined by reading the literature and gathering expert opinions, and the coupling degree between risk factors was measured using an improved coupling degree model. Then, a system dynamics model was established to simulate and analyze the coupling effects among the risk factors and determine the combinations with the greatest influence. The results show that the risk probability is proportional to the risk coupling value, the human–environment coupling value is the largest, and the material equipment–management coupling value is the smallest. The human–environment system simulation shows that reducing the coupling value of system factors will promote a decrease in the total level of system risk. According to the research conclusions, measures to prevent risk coupling are proposed, which offer theoretical references for green building practitioners carrying out risk management; these measures hold a certain guiding significance for the risk control and future development of green buildings.

Agglomeration Characteristics and Influencing Factors of Urban Innovation Spaces Based on the Distribution Data of High-Tech Enterprises in Harbin

In the knowledge economy, innovation is playing an increasingly important role in urban sustainable development. Thus, how to strengthen the construction of innovation spaces has become a significant issue. Based on data from high-tech enterprises in the central urban area of Harbin, this study analyzes the distribution, agglomeration characteristics, and influencing factors of urban innovation spaces. The influencing factor index system was constructed based on four aspects: innovation support, service facilities, the social environment, and the natural environment. After analyzing these influencing factors through comparing the results obtained with an ordinary least squares (OLS) model and a geographical weighted regression (GWR) model, the GWR model was determined to have a better fitting effect and was thus used for further analysis. The findings are as follows: (1) Five closely related clusters of innovation spaces are identified. Innovation space has a significant agglomerating distribution pattern. (2) Overall, factors supporting innovation and transportation station factors have a positive effect on the agglomeration of innovation spaces, while natural environment factors have a negative effect on small innovation areas. (3) By region, the aggregation of innovation spaces in downtown areas is slightly negatively affected by service facilities, whereas those in the Harbin New District are mainly supported by policies and those in the Pingfang District are positively affected by the quantity of the labor force. Finally, based on the research results, some suggestions are put forward to promote the development of urban innovation spaces. This paper has certain guiding significance for optimizing and improving the construction and development of urban innovation spaces.

Influencing factors and countermeasures on intelligent transformation and upgrading of logistics firms: A case study in China.

This study explores the influencing factors on intelligent transformation and upgrading of China's logistics firms under smart logistics, and designs the corresponding framework to guide the practice of firms. By analyzing the characteristics of smart logistics and the transformation and upgrading needs of traditional logistics, from the micro perspective of logistics firms, this paper constructs influencing factor index system of smart transformation and development from four dimensions: logistics technology innovation, logistics big data sharing, logistics management upgrading and logistics decision-making transformation. Logistics firms are divided into firms with medium scale and above and small and medium-sized firms according to their scale. Then EWIF-AHP model is proposed to measure the weight of index system and score the decision-making, so as to evaluate the impact of various influencing factors on transformation and development of logistics firms. The results show that, for logistics firms above medium scale, logistics technology innovation and logistics big data sharing have the most significant impact on transformation and development, followed by logistics management upgrading and logistics decision-making transformation. For small and medium-sized logistics firms, the biggest factor is the upgrading of logistics management, followed by the upgrading of logistics technology, which is almost as important as the influencing factors of the upgrading of logistics management, and followed by the sharing of logistics big data and the transformation of logistics decision-making. Therefore, corresponding countermeasures and suggestions for intelligent transformation of logistics firms have been put forward.

Analysis of Factors Affecting Road Transport Accidents of Hazardous Materials Based on PG-BN

To analyze the factors affecting road accidents involving hazardous materials, the Bayesian network (BN) model was used to fit the accident data. However, considering the possible overfitting phenomenon of the BN model, the model was optimised by combining Pearson’s chi-squared test and Granger causality test (PG) methods. First, the data of hazardous materials accidents were preprocessed, and the index system of factors affecting hazardous materials road transport was constructed from five dimensions of “people, vehicles, hazmat, roads, and environment”; second, Pearson’s chi-squared test and the Granger causality test were used to screen the factors affecting hazardous materials road transport accidents and to determine the causal relationship between the factors; finally, the BN model was constructed with accident severity and accident processing time as target nodes, and the results were analyzed and validated. The results show that the overall relative error rate of the model is less than 10% and can be used to explore the risk factors of hazardous materials transport accidents; weather, visibility, lighting, intersection type, road condition, road type, driver condition, vehicle type, etc. are all important factors affecting the severity of hazardous materials transport accidents. The study can serve as a reference for the safety supervision and management of hazardous materials transport enterprises and industrial management departments.

Analysis of Influential Factors of Ideological and Political Education Reform in Colleges and Universities--Based on Multiple Linear Regression Perspective

Abstract The reform of ideological and political education in higher education encompasses a broad spectrum of challenges, many of which continue to impede its effectiveness. This paper delves into these reforms by constructing an index system of factors that influence the effectiveness of ideological and political education, and it posits several research hypotheses. Within this framework, factors identified from the index system serve as independent variables, while the efficacy of ideological and political education reforms serves as the dependent variable. A multiple linear regression model is employed to analyze these relationships, with evaluations conducted through fit, residual, and variance tests. Correlation analysis is utilized to explore the interactions among the influencing factors, and hypothesis testing is performed on each variable in relation to the impact of Civic and Political Education reform. The analysis reveals that teacher-level factors provide a substantial explanatory power of 79.6%, positively impacting the reform efforts. Conversely, the regression correlation coefficient at the student level stands at 0.165, indicating a negligible linear relationship with the reform outcomes. All hypotheses were validated, with the exception of H5. This study not only provides a foundational reference for enhancing current reforms in Civic and Political Education but also lays the groundwork for future strategic planning in this crucial educational domain.

What and how influence the safety knowledge sharing of new generation of construction workers in China: a study based on DEMATEL and ISM

PurposeNew generation of construction workers (NGCWs) who were born in the 1980s and later have gradually become the main workforce of Chinese construction industry. They may behave differently when dealing with knowledge-related activities due to divergent characteristics caused by generational discrepancy. To provide a theoretical foundation for construction companies and safety managers to improve safety management, this research explores the factors and paths impacting the NGCWs' ability to share their safety knowledge.Design/methodology/approachBased on literature review, main factors that influence the safety knowledge sharing of the NGCWs were identified. Decision-Making Trial and Evaluation Laboratory and Interpretive Structural Modeling were applied to identify the hierarchical and contextual relations among the factors influencing the safety knowledge sharing of the NGCWs.FindingsThe results showed that sharing atmosphere ranked first in centrality and had a high degree of influence and being influenced, indicating itself an extremely important influencing factor of safety knowledge sharing of NGCWs. Six root influencing factors were identified, including individual characteristics, work pressure, sharing platform, incentive mechanism, leadership support and safety management system.Research limitations/implicationsThe number of influencing factors of safety knowledge sharing of the NGCWs identified in this study is limited, and the data obtained by the expert scoring method is subjective. In future studies, the model should be further developed and validated by incorporating experts from different fields to improve its integrity and applicability.Practical implicationsThe influencing factors identified in this paper can provide a basis for construction companies and safety managers to improve productivity and safety management by taking relevant measures to promote safety knowledge sharing. The research contributes to the understanding knowledge management in the context of the emerging market. It helps to answer the question of how the market can maintain the economic growth success through effective knowledge management.Originality/valueThis paper investigates the influencing factors of NGCWs' safety knowledge sharing from the perspective of intergenerational differences, and the 13 influencing factor index system established expands the scope of research on factors influencing safety knowledge sharing among construction workers and fills the gap in safety knowledge sharing research on young construction workers. Furthermore, this paper establishes a multi-layer recursive structure model to clarify the influence path of the influencing factors and contributes to the understanding of safety knowledge sharing mechanism.

Research on Financial Risk Factors under the Perspective of Cross-border Mergers and Acquisitions of Chinese Enterprises

Chinese enterprises are increasingly participating in cross-border mergers and acquisitions (M&A). However, due to the complexity of the M&A process, poor decision-making often leads to financial risks after the completion of the M&A. Therefore, this study aims to explore the influencing factors of financial risks in cross-border M&A. The research selected 67 listed companies that engaged in cross-border M&A transactions from 2018 to 2021 as the research sample. The Z-score model was utilized to measure financial risks, and a financial risk influencing factor index system was constructed based on literature research and factor analysis to assess the financial risk status and influencing factors before and after cross-border M&A transactions. The findings of this study reveal that cross-border M&A significantly increases financial risks for enterprises, and the payment and financing stages are key factors influencing the financial risks of cross-border M&A transactions.

Augmented reality and indoor positioning based mobile production monitoring system to support workers with human-in-the-loop

With the increasing demand for product customization, the exponential increase in large-scale and small-batch production orders has yielded new challenges for the capacity of traditional production lines. Due to the information redundancy and complex production process on the production site of the factory, the traditional monitoring system is incapable of meeting the information interaction requirements between the factory management level and the executive level. Meanwhile, the traditional augmented reality (AR) based on image recognition is not suitable in the complex industrial environments. To address the gap, we propose a novel mobile production monitoring system (AIMPMs) with human-in-the-loop control by leveraging the cutting-edge AR and indoor positioning technique. In the proposed system, Ultra-wideband (UWB) and Inertial Measurement Unit (IMU) fusion indoor positioning technology is proposed, which provides accurate indoor positioning information for the production factors in the factory. Subsequently, we build the lightweight indoor map for positioning that can serve as the location reference and path planning, and a nearest-neighbor decision algorithm with double rejection decision (NN-DRD) is proposed to match the positioning features to trigger virtual monitoring information. Finally, the AIMPMs is applied in a hydraulic cylinder factory to verify its enforceability and effectiveness, and the human factors evaluation model and index system are constructed to evaluate two systems, (1) the mobile monitoring system based on positioning information, and (2) the mobile monitoring system based on image recognition, respectively. The experimental results indicate that after the application of AIMPMs, the physiological and mental fatigue of the production personnel is immensely decreased. Therefore, the system realizes a smarter and highly humanized human-machine interaction mode with human-in-the-loop control.

Research on classification and navigational risk factors of intelligent ship

Based on combing the concept and development of intelligent ship, this paper brings forward the summary and classification of intelligent ships proposed by different institutions, and the main classification schemes are compared accordingly. Then one of these classification schemes is selected to study what are the key navigational risks under each grading level, with a detailed analysis of these risk factors. Finally, the index system of navigational risk factors for intelligent ships under different classification standards is constructed to lay a foundation for a further study of intelligent ship safe navigation, and at the same time avoid some risk factors in advance for the maritime management department, ship management companies, and ship design and research institutes.

Efficiency of risk management for tunnel security of megaprojects construction in China based on system dynamics

ABSTRACT There have been repeated security incidents in megaproject tunnels, construction security issues are particularly serious, and the management of security risks is receiving increasing attention. This study was conducted to improve the efficiency of tunnel security risk management, the security risk management process was adopted as an entry point, based on the Observe-Orient-Decision-Action (OODA) model, the influencing factors index system for the efficiency of security risk management in megaproject tunnels from four dimensions: observe, orient, decision and action. The AHP-EWM method is applied to assign index weights to model the system dynamics of security risk management efficiency. The Vensim software was used to simulate and analyse the effect of different indices on the efficiency of security risk management under different conditions, which provides a reference for improving the efficiency of security risk management. The results of the study show that building a comprehensive and uninterrupted risk information awareness network, real-time adaptation of risk control strategies based on the complexity of risks, and shortening risk management delays can effectively increase the efficiency of dynamic security risk management. This provides a theoretical and practical reference for effectively improving the efficiency of risk management for tunnel security in megaproject construction.

Individual and Combined Effects of 3D Buildings and Green Spaces on the Urban Thermal Environment: A Case Study in Jinan, China

This study aimed to accurately grasp the impact mechanism and change rule of buildings and green spaces on land surface temperature (LST), which is of great significance for alleviating urban heat islands (UHIs) and formulating adaptation measures. Taking Jinan, China, as the study area, combined multisource remote sensing data were used in this study to construct an index system of the influencing factors. We used a spatial regression model to explore the relative contribution of the influencing indicators on LST. We also drew a marginal utility curve to quantify the heating/cooling effect of the leading indicators. The results showed that, firstly, among the 3D building indicators, the leading indicators affecting LST were the degree of spatial convergence (SCD) and the building surface area (BSA). Among the green space indicators, the largest patch index (LPI), green coverage rate (GCR), and edge density (ED) were significantly negatively correlated with LST. Secondly, when we considered the 15 indicators comprehensively, SCD was the most influential indicator, with a contribution of 24.7%, and the contribution of the green space indicators to LST was significantly reduced. Thirdly, among the leading indicators, SCD was positively correlated with LST. When SCD was less than 60%, LST increased by about 0.38 °C for every 10% increase. When GCR > 44%, LST was significantly reduced, and when GCR > 62%, a cooling effect of 1.1 °C was observed. Beyond this threshold, the cooling effect will not improve significantly. This study shows that when 3D buildings are densely distributed and crowded, the cooling effect of green space will be limited to some extent by 3D buildings. The key to mitigating UHIs is to rationally configure and optimize the spatial structure of 3D buildings.

A study on predicting crisis information dissemination in epidemic-level public health events

Crisis information dissemination plays a key role in the development of emergency responses to epidemic-level public health events. Therefore, clarifying the causes of crisis information dissemination and making accurate predictions to effectively control such situations have attracted extensive attention. Based on media richness theory and persuasion theory, this study constructs an index system of crisis information dissemination impact factors from two aspects: the crisis information publisher and the published crisis information content. A multi-layer perceptron is used to analyze the weight of the index system, and the prediction is transformed into a pattern classification problem to test crisis information dissemination. In this study, COVID-19 is considered a representative event. An experiment is conducted to predict the crisis information dissemination of COVID-19 in two megacities. Data related to COVID-19 from these two megacities are acquired from the well-known Chinese social media platform Weibo. The experimental results show that not only the identity but also the social influence of the information publisher has a significant impact on crisis information dissemination in epidemic-level public health events. Furthermore, the proposed model achieves more than 95% test accuracy, precision rate, recall value and f1-score in the prediction task. The study provides decision-making support for government departments and a guide for correctly disseminating crisis information and public opinion during future epidemic-level public health events.

Multi Agent County Energy Internet Project Contribution Evaluation Model

Energy Internet is an advanced form of modern power grid, representing the future energy development trend and direction. Judging the collaborative efficiency level of county energy internet participants and putting forward countermeasures to improve the efficiency of participants with low collaborative efficiency can strengthen the level of cooperation among participants and speed up the promotion speed of county energy internet. Firstly, the affecting factors of the planning are identified through interview, and the factor index system of county energy Internet is established. Secondly, the entropy weight method is used to determine the weight of each factor. Finally, taking the county energy internet as an example, this paper analyzes the contribution degree of collaborative efficiency of different participants.

English

How to develop a low-carbon and environmentally friendly agricultural economy under the conditions of financial diversity is a realistic problem that appears in the context of the current era. In this manuscript, taking cities in western, central and eastern China as examples, the entropy method was used to systematically study the impact of financial diversity on the development of low-carbon and environmentally friendly agricultural economy. Through this study, a low-carbon environmental protection agricultural development factor index system based on financial diversification was constructed, and the entropy method was used to determine the weight of agricultural financial indicators. The results showed that the indicators of agricultural economic and financial ecological diversification ranged from 2.315 to 4.872, with an average value of 3.214. The financial diversity of eastern urban areas was higher than that of central and western cities. The development level of China's low-carbon environmental protection agricultural finance varies greatly from region to region, and there is a large room for balanced development. The regression analysis of low-carbon environmental protection agricultural economic development factors showed that the financial ecological diversity in the eastern region has a significant inhibitory effect on the development of low-carbon finance; the financial ecological diversity in the central region is conducive to the development of low-carbon agricultural finance; the financial ecological diversity in the western region is negatively related to the development of lowcarbon agricultural finance. Financial diversity has a good positive correlation with urban carbon emissions. Although complex financial diversity optimizes the structure of the agricultural economy, it also emits excessive agricultural carbon emissions, which is detrimental to the sustainable development of cities. The output value of the coal industry also has a good positive correlation with urban carbon emissions. The reduction of carbon emissions should speed up the transformation of coal-based energy companies and the development of new energy sources. Environmental protection investment has a weak positive correlation with urban carbon emissions, and environmental protection investment is far from being able to match the increase in carbon emissions. This research can be used for the construction and optimization of agricultural economic indicators

Research on Influence Factors of Low-carbon Economy Based on Principal Component Analysis (Taking Beijing as an Example)

It is of great significance to identify the influence factors of low-carbon economy in Beijing. In view of this, this paper proposes an analysis model of influence factors of low-carbon economy in Beijing based on principal component analysis method. Firstly, from the perspective of influencing the development of low-carbon economy, the index of influencing factors of Beijing's low-carbon economy is initially selected, and then Pearson correlation coefficient is used to screen the indicators. Finally, the index system of influence factors of Beijing's low-carbon economy is constructed by the principal component analysis method. Coal consumption and per capita GDP are the key factors affecting the low-carbon economy of Beijing. The daily sewage treatment capacity and the total sown area of crops are the major factors affecting the low-carbon economy of Beijing.

Analysis of the Factors Affecting China’s Manufacturing Servitization from the Perspective of the Ecological Environment

Manufacturing servitization (MS) can effectively alleviate the contradiction between economic growth and ecological carrying capacity and can promote energy conservation and emission reduction in the manufacturing industry (MI). However, China’s MI is in the primary stage of servitization and lacks sufficient ability to provide services; thus, the environmental benefits of MS are not obvious. Therefore, in the context of current pressure to normalize environmental protection, how servitization can drive low-carbon development in MI while taking into account economic development has become an important topic at present. Thus, this study constructs an evaluation index system of factors based on a driver–pressure–state–impact–response (DPSIR) model, and uses the decision-making trial and evaluation laboratory (DEMATEL) and interpretive structural modeling (ISM) model to analyze the causal relationship and key elements among the influencing factors. The results show that from the perspective of the ecological environment, many factors affect MS, although to varying degrees. Among them, the proportion of R&D personnel, input intensity, and the proportion of clean energy are the main factors. Based on the transmission mechanism among these factors, we propose two paths to realizing the service-oriented, low-carbon development of China’s MI.

Sustainable Coupling Coordination and Influencing Factors of Sports Facilities Construction and Social Economy Development in China

Sports facilities are a crucial physical safeguard and means of sustaining sporting activities. The steady and healthy development of sports facilities can promote the sustainable development of the social economy. In order to explore the coordination status and influencing factors between sports facilities construction and the social economy, this paper built a coupling coordination evaluation index system and dynamic factor index system for the sports facilities construction and social economy development by using the entropy method, coupling coordination model and random effect model. This paper assessed the comprehensive level, coupling coordination relationship, and influencing factors of the sports facilities and social economy development of nine cities in the Fujian Province across China from 2016 to 2020. The results show the overall level of sports facilities construction in Fujian is better than the social economy development, and the two systems are interacting with each other. The level of coupling and coordination gradually changes from good coordination to quality sustainable coordination. The coupling coordinated relationship between the two systems is developing in a positive way. In addition, industrial development, the level of urban development, and environmental development as influencing factors have a clear positive effect on the degree of coupling coordination. This study provides several recommendations for cities to achieve sustainable coordinated development. This paper also may provide a direction and path for future research.