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.

Application of a novel grey forecasting model with time power term to predict China's GDP

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Chong Liu ◽  
Wanli Xie ◽  
Tongfei Lao ◽  
Yu-ting Yao ◽  
Jun Zhang
Keyword(s):  
Economic Development ◽  
Prediction Model ◽  
Future Development ◽  
Development Trend ◽  
Forecasting Model ◽  
Grey Prediction ◽  
Content Type ◽  
Grey Prediction Model ◽  
Grey Forecasting ◽  
Grey Forecasting Model

PurposeGross domestic product (GDP) is an important indicator to measure a country's economic development. If the future development trend of a country's GDP can be accurately predicted, it will have a positive effect on the formulation and implementation of the country's future economic development policies. In order to explore the future development trend of China's GDP, the purpose of this paper is to establish a new grey forecasting model with time power term to forecast GDP.Design/methodology/approachFirstly, the shortcomings of the traditional grey prediction model with time power term are found out through analysis, and then the generalized grey prediction model with time power term is established (abbreviated as PTGM (1,1, α) model). Secondly, the PTGM (1,1, α) model is improved by linear interpolation method, and the optimized PTGM (1,1, α) model is established (abbreviated as OPTGM (1,1, α) model), and the parameters of the OPTGM (1,1, α) model are solved by the quantum genetic algorithm. Thirdly, the advantage of the OPTGM (1,1, α) model over the traditional grey models is illustrated by two real cases. Finally the OPTGM (1,1, α) model is used to predict China's GDP from 2020 to 2029.FindingsThe OPTGM (1,1, α) model is more suitable for predicting China's GDP than other grey prediction models.Originality/valueA new grey prediction model with time power term is proposed.

Grey prediction models for the standard limit of vehicle noise

2017 ◽  
Vol 232 (7) ◽  
pp. 973-979 ◽  
Author(s):  
Zhendong Zhao ◽  
Changzheng Hu
Keyword(s):  
Prediction Models ◽  
System Development ◽  
Original Data ◽  
Grey Theory ◽  
Grey Prediction ◽  
Vehicle Noise ◽  
Direct Model ◽  
The Future ◽  
Quadratic Fitting ◽  
Verhulst Model

With an increasing number of vehicles and increasing environmental protection requirements, countries have accelerated the rate of revision of automobile noise standards and legislation. Scientific prediction of the limiting values in future noise standards is helpful to promote the development of automobile noise reduction technology and measurement analysis technology. The development of noise standard limits has its own objective laws and is restricted to the current and future developments in automotive technology. The amplitude of noise will be reduced increasingly less in the future. Grey prediction theory can explore the variation rules by processing a few effective data. In this paper, grey theory is used to deal with the limited original data in the vehicle noise standard. Non-equal-interval quadratic fitting of the grey Verhulst direct model to predict the future noise standard limits is selected on the basis of calculation and comparison of different models. The Verhulst model is employed to describe the system development by using the characteristics of saturation. By means of quadratic fitting, the accuracy of the Verhulst model can be further improved. The simulation results show the validity and the accuracy of the model. The prediction result is useful for standards and regulations makers and for car manufacturers.

Multi-phase information aggregation and dynamic synthetic evaluation based on grey inspiriting control lines

2014 ◽  
Vol 4 (2) ◽  
pp. 154-163 ◽  
Author(s):  
San-dang Guo ◽  
Sifeng Liu ◽  
Zhigeng Fang ◽  
Lingling Wang
Keyword(s):  
Future Development ◽  
Information Aggregation ◽  
Development Trend ◽  
Future Application ◽  
Phase Information ◽  
Content Type ◽  
Multi Stage ◽  
Comprehensive Evaluations ◽  
Set Up ◽  
Multi Phase

Purpose – The purpose of this paper is to put forward a multi-stage information aggregation method based on grey inspiriting control lines to evaluate the objects dynamically and comprehensively. Design/methodology/approach – According to the evaluation value of the objects, the positive and negative incentive lines were set up and the predicted values were solved based on the grey GM(1, 1) model, so the value with expected information could be evaluated. In the evaluation, the part above the positive incentive line should be “rewarded” and that below the negative incentive line should be “punished” appropriately. Thereby the double incentive effects of “the current development situation and future development trend” to objects could be implemented on the basis of control. Findings – This method can primarily describe the decision maker's expectancy of the development of evaluation objects and make the evaluation results have better practical application value. Research limitations/implications – Many comprehensive evaluations were always based on the past information. However, the future development trend of the evaluated object is also very important. This study can be used in the evaluation for future application and development. Originality/value – The paper succeeds in providing not only a method of multi-phase information aggregation with expectancy information, but also a simple and convenient method solving nonlinear inspiring lines objectively.

Bankruptcy prediction using multivariate grey prediction models

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yi-Chung Hu ◽  
Peng Jiang ◽  
Hang Jiang ◽  
Jung-Fa Tsai
Keyword(s):  
Time Series ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Financial Data ◽  
Grey Prediction ◽  
Grey System ◽  
Content Type ◽  
Business Environments ◽  
The Face ◽  
Financial Decision

PurposeIn the face of complex and challenging economic and business environments, developing and implementing approaches to predict bankruptcy has become important for firms. Bankruptcy prediction can be regarded as a grey system problem because while factors such as the liquidity, solvency and profitability of a firm influence whether it goes bankrupt, the precise manner in which these factors influence the discrimination between failed and non-failed firms is uncertain. In view of the applicability of multivariate grey prediction models (MGPMs), this paper aimed to develop a grey bankruptcy prediction model (GBPM) based on the GM (1, N) (BP-GM (1, N)).Design/methodology/approachAs the traditional GM (1, N) is designed for time series forecasting, it is better to find an appropriate permutation of firms in the financial data as if the resulting sequences are time series. To solve this challenging problem, this paper proposes GBPMs by integrating genetic algorithms (GAs) into the GM (1, N).FindingsExperimental results obtained for the financial data of Taiwanese firms in the information technology industries demonstrated that the proposed BP-GM (1, N) performs well.Practical implicationsAmong artificial intelligence (AI)-based techniques, GBPMs are capable of explaining which of the financial ratios has a stronger impact on bankruptcy prediction by driving coefficients.Originality/valueApplying MGPMs to a problem without relation to time series is challenging. This paper focused on bankruptcy prediction, a crucial issue in financial decision-making for businesses, and proposed several GBPMs.

ICOs, the next generation of IPOs

2019 ◽  
Vol 46 (6) ◽  
pp. 761-783 ◽  
Author(s):  
Mohammad Hashemi Joo ◽  
Yuka Nishikawa ◽  
Krishnan Dandapani
Keyword(s):  
Initial Public Offering ◽  
Future Development ◽  
Design Methodology ◽  
Detailed Comparison ◽  
Content Type ◽  
Blockchain Technology ◽  
Capital Funding ◽  
The Future ◽  
Public Offering ◽  
Further Development

Purpose The purpose of this paper is to recognize the benefits of the initial coin offering (ICO) as a way of raising funds and to present a detailed comparison between the ICO and the initial public offering to realize the future possibilities that this new funding method holds. Design/methodology/approach It is an exhaustive review of the ICO, the mechanism of crowdfunding, the blockchain technology behind it, benefits and current shortcomings of the ICO, and the potential future development of the ICO as a convenient and efficient way of raising capital. Findings ICOs have brought billions of dollars of funding to startups and projects worldwide in less than two years. Concurrently, many successful ICOs yielded extremely high returns to investors and believers of this new way of funding businesses. Research limitations/implications While the ICO is a revolutionary vehicle for business funding, it has raised concerns among users as well as potential investors about its risk and lack of regulation. The future of this innovative funding method highly depends on further development and placement of appropriate regulatory supervision, better understanding of risk and benefits and attaining the confidence of users. Originality/value This is a review of the advantages and drawbacks of the ICO. If the current fraud, market and cybersecurity risks can be mitigated and standardized regulations are developed, the ICO has a future to become an established way of capital funding or even replace the existing options, regardless of the size and age of companies.

The Current Situation and Treatment and Disposal Techniques of Antibiotic Bacterial Residues in China

2014 ◽  
Vol 587-589 ◽  
pp. 820-823 ◽  
Author(s):  
Hong Jun Han ◽  
Shi Yi Zheng ◽  
Wen Cheng Ma ◽  
Ji Hua Huang ◽  
Ling Yue Chen ◽  
...  
Keyword(s):  
Future Development ◽  
Development Trend ◽  
Current Situation ◽  
Energy Regeneration ◽  
The Future ◽  
Treatment And Disposal ◽  
Pharmaceutical Enterprises

China is the largest antibiotic producer, producing a large amount of antibiotic bacterial residues classified to hazard wastes in 2008. It is urgent to find suitable techniques to dispose antibiotic bacterial residues for pharmaceutical enterprises. In this paper, the characteristics of antibiotic bacterial residues are discussed and the current situation and treatment and disposal techniques are summarized, including the technology of composting, incineration, feed stuff, landfill and energy regeneration and so on. In the last, the future development trend of dealing with antibiotic bacterial residues is prospected.

A survey of liveness detection methods for face biometric systems

Sensor Review ◽  
2017 ◽  
Vol 37 (3) ◽  
pp. 346-356 ◽  
Author(s):  
Yang Xin ◽  
Yi Liu ◽  
Zhi Liu ◽  
Xuemei Zhu ◽  
Lingshuang Kong ◽  
...  
Keyword(s):  
Face Recognition ◽  
Future Development ◽  
Recognition System ◽  
Fingerprint Recognition ◽  
Detection Methods ◽  
Content Type ◽  
Liveness Detection ◽  
Biometric Systems ◽  
The Future ◽  
Face Biometric

Purpose Biometric systems are widely used for face recognition. They have rapidly developed in recent years. Compared with other approaches, such as fingerprint recognition, handwriting verification and retinal and iris scanning, face recognition is more straightforward, user friendly and extensively used. The aforementioned approaches, including face recognition, are vulnerable to malicious attacks by impostors; in such cases, face liveness detection comes in handy to ensure both accuracy and robustness. Liveness is an important feature that reflects physiological signs and differentiates artificial from real biometric traits. This paper aims to provide a simple path for the future development of more robust and accurate liveness detection approaches. Design/methodology/approach This paper discusses about introduction to the face biometric system, liveness detection in face recognition system and comparisons between the different discussed works of existing measures. Originality/value This paper presents an overview, comparison and discussion of proposed face liveness detection methods to provide a reference for the future development of more robust and accurate liveness detection approaches.

Grey system based novel approach for stock market forecasting

2015 ◽  
Vol 5 (2) ◽  
pp. 178-193 ◽  
Author(s):  
R.M. Kapila Tharanga Rathnayaka ◽  
D.M.K.N Seneviratna ◽  
Wei Jianguo
Keyword(s):  
Time Series ◽  
Prediction Models ◽  
Stock Exchange ◽  
Moving Average ◽  
Grey Prediction ◽  
Grey System ◽  
Short Term ◽  
Content Type ◽  
Stock Market Forecasting ◽  
Novel Approach

Purpose – Making decisions in finance have been regarded as one of the biggest challenges in the modern economy today; especially, analysing and forecasting unstable data patterns with limited sample observations under the numerous economic policies and reforms. The purpose of this paper is to propose suitable forecasting approach based on grey methods in short-term predictions. Design/methodology/approach – High volatile fluctuations with instability patterns are the common phenomenon in the Colombo Stock Exchange (CSE), Sri Lanka. As a subset of the literature, very few studies have been focused to find the short-term forecastings in CSE. So, the current study mainly attempted to understand the trends and suitable forecasting model in order to predict the future behaviours in CSE during the period from October 2014 to March 2015. As a result of non-stationary behavioural patterns over the period of time, the grey operational models namely GM(1,1), GM(2,1), grey Verhulst and non-linear grey Bernoulli model were used as a comparison purpose. Findings – The results disclosed that, grey prediction models generate smaller forecasting errors than traditional time series approach for limited data forecastings. Practical implications – Finally, the authors strongly believed that, it could be better to use the improved grey hybrid methodology algorithms in real world model approaches. Originality/value – However, for the large sample of data forecasting under the normality assumptions, the traditional time series methodologies are more suitable than grey methodologies; especially GM(1,1) give some dramatically unsuccessful results than auto regressive intergrated moving average in model pre-post stage.

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