scholarly journals Prediction of Vegetable Supply in Henan Province Based on PSO-GM (1, N) Model

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Xueqiang Guo ◽  
Bingjun Li
Keyword(s):  
Influencing Factors ◽  
Prediction Models ◽  
Empirical Test ◽  
Henan Province ◽  
Grey Prediction ◽  
Key Factors ◽  
Growth Trend ◽  
Factors Affecting ◽  
Steady Growth ◽  
Lasso Method

GM (1, N) model is one of the grey prediction models considering the influence of many factors. This paper improves GM (1, N) model and constructs PSO-GM (1, N) model. Firstly, Lasso method is used to select the influencing factors, then the priority of influencing factors and the value of parameter N in GM (1, N) model are determined, and finally PSO method is used to optimize GM (1, N) model. Taking the vegetable supply in Henan Province as the research object, this paper makes an empirical test by using PSO-GM (1, N) model. The results show that the key factors affecting the vegetable supply in Henan Province are the number of rural employees, highway mileage, and application of pesticide. The vegetable supply in Henan Province will continue to show a steady growth trend in the next three years.

scholarly journals Key Factors Influencing Low-Carbon Behaviors of Staff in Star-Rated Hotels—An Empirical Study of Eastern China

2020 ◽  
Vol 17 (21) ◽  
pp. 8222
Author(s):  
Jie Li ◽  
Peng Mao ◽  
Hui Liu ◽  
Jiawei Wei ◽  
Hongyang Li ◽  
...  
Keyword(s):  
Influencing Factors ◽  
Structural Equation ◽  
Eastern China ◽  
Consumer Attitude ◽  
Low Carbon ◽  
Key Factors ◽  
Factors Affecting ◽  
Individual Level ◽  
Multiple Dimensions ◽  
Perceived Behavior Control

To guide sustainable development in the hospitality industry requires hotel staff engagement, so what causes and how to facilitate the implementation of low-carbon behaviors should be high priorities. However, most prior studies focused on hotel guest behavior or discussed, on an individual level, the psychological aspects of the factors of the low-carbon behavior of either managers or employees. Therefore, this research aims to examine the effect of influencing factors inside and outside of the hotel context on hotel staff’s low-carbon behaviors in star-rated hotels. A set of influencing factors were identified by using literature retrieval, ground theory and in-depth interviews. Structural equation modelling was then applied with 440 valid questionnaires collected from representative star-rated hotels in Eastern China. The results revealed that low-carbon managerial activities, strategic orientation, social norms, and perceived behavior control were four key factors affecting the low-carbon behavior adoption of staff from star-rated hotels. Among them, low-carbon managerial activities were found to be the strongest factor affecting hotel staff’s low-carbon behaviors. Consumer attitude, however, exerted no significant impact. Targeted strategies were finally proposed for the improvement of hotel staff’s low-carbon behavior from the perspectives of hoteliers and governments. This study contributes to the generation mechanism of low-carbon behavior among staff and, in practice, towards behavioral improvement by providing comprehensive insights about the attribution of factors belonging to multiple dimensions related to the low-carbon behavior of staff in the hotel industry.

scholarly journals Urban Ecological Efficiency and Its Influencing Factors—A Case Study in Henan Province, China

2019 ◽  
Vol 11 (18) ◽  
pp. 5048 ◽  
Author(s):  
Tao Liu ◽  
Jixia Li ◽  
Juan Chen ◽  
Shaolei Yang
Keyword(s):  
Economic Development ◽  
Influencing Factors ◽  
Efficiency Analysis ◽  
Henan Province ◽  
Measure Data ◽  
Ecological Efficiency ◽  
Ecological Civilization ◽  
Growth Trend ◽  
Static And Dynamic Analysis ◽  
Significant Difference

The urban ecological civilization construction relates to welfare of the people and the national future. It is an important field of the high-quality economic development to improve the urban ecological efficiency level. The purpose of this research is to provide a new perspective and method for the quantitative study of the urban sustainable development, and also to provide some decision-making references for the improvement of the urban ecological efficiency in Henan province. This paper uses the slacks-based measure-data envelopment analysis (SBM-DEA) model containing the undesirable output and the Malmquist index model to fully evaluate the urban ecological efficiency level in Henan province during the period of 2005–2016, via both the static and dynamic analysis. Based on this, the bootstrap regression model is applied in analyzing the influencing factors of the urban ecological efficiency. The research shows three findings. First, according to the static efficiency analysis, the urban ecological efficiency in Henan province is low as a whole and has a big promotion space. Moreover, there is a significant difference in the urban ecological efficiency level among the five regions because of the different geographical locations and social and economic development situations of the cities. Second, according to the dynamic efficiency analysis, in the last 12 years, the urban ecological efficiency in Henan province has shown an overall growth trend, and the technological progress has played a major role in promoting the urban ecological efficiency in Henan province. Third, according to the influencing factor analysis, the governmental financial support hinders the improvement of the urban ecological efficiency in Henan province, while the level of opening to the outside world, the urban population density, and the urban greening level promote it.

Significance analysis of influencing factors of highway freight transportation in China and multi-variable grey prediction for its development

2021 ◽  
pp. 1-10
Author(s):  
Lu Wang
Keyword(s):  
Development Strategy ◽  
Influencing Factors ◽  
Average Distance ◽  
Freight Transportation ◽  
Development Trend ◽  
Development Prospect ◽  
Grey Prediction ◽  
Transportation Industry ◽  
Main Mode ◽  
Factors Affecting

With the prosperity of national economy and the development of highway construction, highway freight transportation plays an increasingly important role in the market economy. Due to its great flexible characteristic, highway freight transportation has been the main mode of transportation in China. On the macro level, there are many factors affecting the development of highway freight transportation especially under the background of the new era. Based on the historical data of the development of highway freight transportation, grey entropy analysis method is applied to analyze the significance of influencing factors for the development of highway freight transportation whose key indicator is highway freight turnover. Then GM (1, N) model is established to predict the development trend of highway freight turnover and its significant influencing factors. Finally, main problems existing in highway freight transportation and development prospect was discussed and analyzed. The research results show that the three most significant factors affecting the development of road freight turnover in China are the total state revenue, GDP and average distance of highway freight. The established GM (1, N) model can conduct high precision prediction for the development of highway freight transportation. Opportunities and challenges of highway freight transportation industry coexist and its development prospect is promising. It is expected to provide beneficial references for the development strategy and decision-making of highway freight transportation in China.

Power Consumption Prediction Modeling of Cement Manufacturing Based on the Improved Multiple Non-Linear Regression Algorithm

2014 ◽  
Vol 687-691 ◽  
pp. 5185-5189 ◽  
Author(s):  
Hui Zhao ◽  
Ning Zhang ◽  
Hong Jun Wang
Keyword(s):  
Linear Regression ◽  
Power Consumption ◽  
Prediction Models ◽  
Principal Component ◽  
Key Factors ◽  
Factors Affecting ◽  
Non Linear ◽  
Cement Manufacturing ◽  
Power Consumption Prediction ◽  
Consumption Prediction

The principal component analysis (PCA) is applied in this paper, since the existing power consumption prediction models of cement manufacturing influenced by many factors are quite complex and have low accuracy. In this way, four new key factors affecting the power consumption of cement manufacturing are obtained instead of the eleven original ones, with the complexity of the computing model simplified. Built upon this is the power consumption prediction model of cement manufacturing based on an improved multiple non-linear regression algorithm. Then the efficiency of the model, obviously improved the forecasting precision, is verified in Pingyi Zhonglian Cement Plant. In other words, a theoretical basis for cement plants power consumption forecasting management is provided in this paper.

scholarly journals Key factors affecting the spatial variation of summer maize yield in Henan Province, China

资源科学 ◽  
2019 ◽  
Vol 41 (10) ◽  
pp. 1935-1948
Author(s):  
Zhenfu WU ◽  
Yanfeng ZHAO ◽  
Daoquan CHENG ◽  
Jie CHEN ◽  
Keyword(s):  
Spatial Variation ◽  
Maize Yield ◽  
Henan Province ◽  
Key Factors ◽  
Summer Maize ◽  
Factors Affecting

scholarly journals Key factors affecting Rayleigh instability of ultrathin 4H hexagonal gold nanoribbons

2020 ◽  
Vol 2 (7) ◽  
pp. 3027-3032
Author(s):  
Peifeng Li ◽  
Weibing Liao ◽  
Lijie Yue ◽  
Zhanxi Fan ◽  
Feng Rao
Keyword(s):  
Influencing Factors ◽  
Rayleigh Instability ◽  
Key Factors ◽  
Factors Affecting

The evolution processes and influencing factors of Rayleigh instability in ultrathin 4H Au NRBs were investigated by in situ TEM.

scholarly journals Research on the Influencing Factors of the Construction of Tourism and Leisure Characteristic Towns in Sichuan Province under the Background of New Urbanization

2021 ◽  
Author(s):  
Yi-ping WANG ◽  
Xian-li ZHANG
Keyword(s):  
Influencing Factors ◽  
Field Research ◽  
Economic Transformation ◽  
Sichuan Province ◽  
Tourism Industry ◽  
Scientific Development ◽  
Key Factors ◽  
Factors Affecting ◽  
Questionnaire Surveys ◽  
New Urbanization

Promoting the construction of characteristic towns under the background of new urbanization is an important way for my country to break the bottleneck of economic development and realize economic transformation and upgrading. In recent years, although the construction of characteristic towns in Sichuan Province has achieved remarkable results and a large number, especially tourist and leisure characteristic towns accounted for the largest proportion, they still face urgent problems such as avoiding redundant construction, achieving scientific development, and overall planning. This study takes 20 cultural tourism characteristic towns selected by the first batch of Sichuan Province as the research object, combined with field research and tourist questionnaire surveys, and screened out relevant influencing factors of characteristic towns from different aspects such as transportation, economy, industry, ecology, historical and cultural heritage. Analyze the correlation with the development level of characteristic towns in order to find out the key factors affecting the development of characteristic towns of this type, provide a policy basis for the scientific development and overall planning of reserve characteristic towns in our province, and contribute to the construction of new urbanization And provide advice and suggestions on the development of tourism industry in our province.

IDENTIFICATION OF THE KEY FACTORS FOR ACCURATE LIFE-CYCLE COST ESTIMATION FOR CONSTRUCTION

2014 ◽  
Vol 1 (1) ◽  
Author(s):  
Ayedh Alqahtani ◽  
Andrew Whyte
Keyword(s):  
Life Cycle ◽  
Cost Estimation ◽  
Life Cycle Cost ◽  
Prediction Models ◽  
Key Factors ◽  
Plant Resources ◽  
Factors Affecting ◽  
Low Visibility ◽  
The Cost ◽  
Cost Factors

A major limitation of Life-Cycle Cost (LCC) estimation/prediction modelling is the current typical reliance only on those factors that can be readily quantified and come easily to hand. While estimation of the cost of the most common labor, material and plant resources receive consideration because of their high visibility factor, there are several non-cost factors (low visibility factors) affecting the estimate that are often overlooked and, it is argued here, require equal consideration in estimation processes that seek optimum accuracy. Unfortunately, such (low-visibility) factors are neglected or ignored by current prediction models. Identification of these non-cost factors (low visibility factors) affects LCC estimate accuracy and can improve estimation process confidence. This paper critically reviews secondary research on identification of these important non-cost factors and subsequently determines their influence on the accuracy level(s) of construction cost estimation.

Development prediction of logistics industry in Henan province and its dynamic analysis

2014 ◽  
Vol 4 (2) ◽  
pp. 186-194 ◽  
Author(s):  
Yimin Huang
Keyword(s):  
Future Development ◽  
Regional Economy ◽  
Prediction Models ◽  
Development Trend ◽  
Henan Province ◽  
Grey Prediction ◽  
Logistics Industry ◽  
Content Type ◽  
The Future ◽  
Freight Volume

Purpose – The purpose of this paper is to establish a group of grey prediction models and relative degree model to study the characteristics and trend of the logistics industry development in Henan province scientifically. The study results can provide references for the development policy of the logistics industry in Henan province. Design/methodology/approach – The paper constructs grey prediction models and grey buffer operator models which are related to the distribution of logistics industry in Henan province, and selects prediction models by comparing model accuracy, and use them to forecast the development trend of logistics industry in future ten years of Henan province. Using the grey relative models, the paper analyses development dynamic and prospect which support the development of logistics industry, and provide some references for transferring the pattern of economic growth of Henan province, forming new economic growth point and formulating relevant policies. High prediction accuracy models are selected to forecast the future development trend of logistics industry in the next ten years. Findings – Results show that the modern logistics industry in Henan province has been a steady growth in overall, the main growth points of the logistics industry development in Henan province are roadway miles (km), roadway (100 million tonnes/km), freight turnover (100 million tonnes/km) and waterway (100 million tonnes), the growth points for the future development of logistics industry in Henan province are the roadway freight volume, roadway passenger volume and waterway freight volume. Practical implications – Regional economic competition has become an important index for measuring a country's economic development level. Logistics industry plays an important role in the regional economic development, such as promoting coordinated development of regional economy and upgrading industrial optimization, and playing a major role in industrial transfer. Hence, logistics industry, which is urgently needed to solve by the government, has become important forces for promoting the growth of economy and a basic pillar industries of regional economy. Originality/value – The paper presents the systematic results of development prediction of modern logistics industry in Henan province and its dynamic analysis by using grey systems theory, not only to predict the trend of the development of the logistics industry, also to analyse the future development of logistics industry in the leading power factors.

The Research of Key Factors Affecting the Implementation of Lean Construction

2013 ◽  
Vol 838-841 ◽  
pp. 3183-3189
Author(s):  
Shu Quan Li ◽  
Shao Pei Hu ◽  
De Hui Sun ◽  
Jing Li
Keyword(s):  
Construction Industry ◽  
Influencing Factors ◽  
Construction Projects ◽  
Rough Set Theory ◽  
Field Research ◽  
Lean Construction ◽  
Key Factors ◽  
Factors Affecting ◽  
Survey Questionnaires ◽  
Management Expert

The application of the ideas and methods of lean construction is still in its infancy in China's construction industry, there are still many factors that restrict the implementation of lean construction, and how to find the key influencing factors is worthy study. We summarized the 28 influencing factors of Implementation of lean construction through project management expert interviews and field research in the construction site, and conducted a survey of 300 construction projects, distributed 710 questionnaires. After analysis of the survey questionnaires by using rough set theory and stepwise discriminate analysis finalized the 14 key factors affecting the implementation of lean construction and construction project performance.

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