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.

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.

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.

Mathematical modelling of salt purification by recrystallization. A cross-current flow model and its comparison with the countercurrent arrangement

1986 ◽  
Vol 51 (11) ◽  
pp. 2489-2501
Author(s):  
Benitto Mayrhofer ◽  
Jana Mayrhoferová ◽  
Lubomír Neužil ◽  
Jaroslav Nývlt
Keyword(s):  
Flow Model ◽  
Current Flow ◽  
Countercurrent Flow ◽  
Flow Models ◽  
Multi Stage ◽  
Stage Crystallization ◽  
The Cross ◽  
Current Flows ◽  
Set Up ◽  
Cross Current

A model is derived for a multi-stage crystallization with cross-current flows of the solution and the crystals being purified. The purity of the product is compared with that achieved in the countercurrent arrangement. A suitable function has been set up which allows the cross-current and countercurrent flow models to be compared and reduces substantially the labour of computation for the countercurrent arrangement. Using the recrystallization of KAl(SO4)2.12 H2O as an example, it is shown that, when the cross-current and countercurrent processes are operated at the same output, the countercurrent arrangement is more advantageous because its solvent consumption is lower.

Sampling Theory

2019 ◽  
Author(s):  
David Hankin ◽  
Michael S. Mohr ◽  
Kenneth B. Newman
Keyword(s):  
Natural Resource ◽  
Adaptive Sampling ◽  
Sampling Theory ◽  
Special Importance ◽  
Unequal Probability ◽  
Multi Stage ◽  
R Programming ◽  
Multi Phase ◽  
Systematic Cluster ◽  
Parameter Values

We present a rigorous but understandable introduction to the field of sampling theory for ecologists and natural resource scientists. Sampling theory concerns itself with development of procedures for random selection of a subset of units, a sample, from a larger finite population, and with how to best use sample data to make scientifically and statistically sound inferences about the population as a whole. The inferences fall into two broad categories: (a) estimation of simple descriptive population parameters, such as means, totals, or proportions, for variables of interest, and (b) estimation of uncertainty associated with estimated parameter values. Although the targets of estimation are few and simple, estimates of means, totals, or proportions see important and often controversial uses in management of natural resources and in fundamental ecological research, but few ecologists or natural resource scientists have formal training in sampling theory. We emphasize the classical design-based approach to sampling in which variable values associated with units are regarded as fixed and uncertainty of estimation arises via various randomization strategies that may be used to select samples. In addition to covering standard topics such as simple random, systematic, cluster, unequal probability (stressing the generality of Horvitz–Thompson estimation), multi-stage, and multi-phase sampling, we also consider adaptive sampling, spatially balanced sampling, and sampling through time, three areas of special importance for ecologists and natural resource scientists. The text is directed to undergraduate seniors, graduate students, and practicing professionals. Problems emphasize application of the theory and R programming in ecological and natural resource settings.

Review of short-text classification

2019 ◽  
Vol 15 (2) ◽  
pp. 155-182 ◽  
Author(s):  
Issa Alsmadi ◽  
Keng Hoon Gan
Keyword(s):  
Social Networks ◽  
Text Classification ◽  
Development Trend ◽  
Practical Reasons ◽  
Significant Implication ◽  
Classification Problems ◽  
Content Type ◽  
Short Text ◽  
Promising Area ◽  
Low Performance

PurposeRapid developments in social networks and their usage in everyday life have caused an explosion in the amount of short electronic documents. Thus, the need to classify this type of document based on their content has a significant implication in many applications. The need to classify these documents in relevant classes according to their text contents should be interested in many practical reasons. Short-text classification is an essential step in many applications, such as spam filtering, sentiment analysis, Twitter personalization, customer review and many other applications related to social networks. Reviews on short text and its application are limited. Thus, this paper aims to discuss the characteristics of short text, its challenges and difficulties in classification. The paper attempt to introduce all stages in principle classification, the technique used in each stage and the possible development trend in each stage.Design/methodology/approachThe paper as a review of the main aspect of short-text classification. The paper is structured based on the classification task stage.FindingsThis paper discusses related issues and approaches to these problems. Further research could be conducted to address the challenges in short texts and avoid poor accuracy in classification. Problems in low performance can be solved by using optimized solutions, such as genetic algorithms that are powerful in enhancing the quality of selected features. Soft computing solution has a fuzzy logic that makes short-text problems a promising area of research.Originality/valueUsing a powerful short-text classification method significantly affects many applications in terms of efficiency enhancement. Current solutions still have low performance, implying the need for improvement. This paper discusses related issues and approaches to these problems.

How to turn managers into data-driven decision makers

2019 ◽  
Vol 25 (3) ◽  
pp. 553-578 ◽  
Author(s):  
Kevin Daniel André Carillo ◽  
Nadine Galy ◽  
Cameron Guthrie ◽  
Anne Vanhems
Keyword(s):  
Measurement Instrument ◽  
Data Driven ◽  
Business Analytics ◽  
Business World ◽  
Content Type ◽  
Multi Stage ◽  
Attitudes Toward Statistics ◽  
Attitude Toward Business ◽  
Professional Contexts ◽  
The Way

Purpose The purpose of this paper is to emphasize the need to engender a positive attitude toward business analytics in order for firms to more effectively transform into data-driven businesses, and for business schools to better prepare future managers. Design/methodology/approach This paper develops and validates a measurement instrument that captures the attitude toward business statistics, the foundation of business analytics. A multi-stage approach is implemented and the validation is conducted with a sample of 311 students from a business school. Findings The instrument has strong psychometric properties. It is designed so that it can be easily extrapolated to professional contexts and extended to the entire domain of business analytics. Research limitations/implications As the advent of a data-driven business world will impact the way organizations function and the way individuals think, work, communicate and interact, it is crucial to engage a transdisciplinary dialogue among domains that have the expertise to help train and transform current and future professionals. Practical implications The contribution provides educators and organizations with a means to measure and monitor attitudes toward statistics, the most anxiogenic component of business analytics. This is a first step in monitoring and developing an analytics mindset in both managers and students. Originality/value By demonstrating how the advent of the data-driven business era is transforming the DNA and functioning of organizations, this paper highlights the key importance of changing managers’ and all employees’ (to a lesser extent) mindset and way of thinking.

Washington will engage more with South-east Asia

2021 ◽  
Keyword(s):  
United States ◽  
East Asia ◽  
The United States ◽  
South East Asia ◽  
National Unity ◽  
Content Type ◽  
Key Issues ◽  
Set Up

Significance She addressed two key issues during her trip: tensions in post-coup Myanmar and China’s growing regional footprint. Shortly after she left the region, the United States announced that it would donate unused COVID-19 vaccines abroad, including to South-east Asia. Impacts Washington will tighten its sanctions on the Myanmar military while supporting ASEAN’s five-point plan to ease the country’s crisis. The National Unity Government, a parallel administration to Myanmar’s junta set up by its opponents, will try to attract greater US backing. Manila and Washington may extend negotiations over renewing their Visiting Forces Agreement to prevent the pact expiring in August.

Using transfer learning for diabetic retinopathy stage classification

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Enas M.F. El Houby
Keyword(s):  
Diabetic Retinopathy ◽  
Transfer Learning ◽  
Grade Level ◽  
Design Methodology ◽  
Content Type ◽  
Stage Classification ◽  
Set Up ◽  
Retinal Fundus ◽  
Automated Methods

PurposeDiabetic retinopathy (DR) is one of the dangerous complications of diabetes. Its grade level must be tracked to manage its progress and to start the appropriate decision for treatment in time. Effective automated methods for the detection of DR and the classification of its severity stage are necessary to reduce the burden on ophthalmologists and diagnostic contradictions among manual readers.Design/methodology/approachIn this research, convolutional neural network (CNN) was used based on colored retinal fundus images for the detection of DR and classification of its stages. CNN can recognize sophisticated features on the retina and provides an automatic diagnosis. The pre-trained VGG-16 CNN model was applied using a transfer learning (TL) approach to utilize the already learned parameters in the detection.FindingsBy conducting different experiments set up with different severity groupings, the achieved results are promising. The best-achieved accuracies for 2-class, 3-class, 4-class and 5-class classifications are 86.5, 80.5, 63.5 and 73.7, respectively.Originality/valueIn this research, VGG-16 was used to detect and classify DR stages using the TL approach. Different combinations of classes were used in the classification of DR severity stages to illustrate the ability of the model to differentiate between the classes and verify the effect of these changes on the performance of the model.

Research from China finds no significant difference in impact of public service motivation (PSM) in public and private sectors

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Keyword(s):  
Public Sector ◽  
Private Sector ◽  
Employee Performance ◽  
Intention To Leave ◽  
Public And Private ◽  
Content Type ◽  
The Public ◽  
Significant Difference ◽  
Set Up ◽  
The Impact

Purpose The authors assumed PSM would be higher in the public sector, but they set up a trial to find out if this was the case. Design/methodology/approach To test their theories, the authors conducted two independent surveys. The first consisted of 220 usable responses from public sector employees in Changsha, China. The second survey involved 260 usable responses from private sector employees taking an MBA course at a university in the Changsha district. A questionnaire was used to assess attitudes. Findings The results found no significant difference between the impact of public sector motivation (PSM) on employee performance across the public and private sectors. The data showed that PSM had a significant impact on self-reported employee performance, but the relationship did not differ much between sectors. Meanwhile, it was in the private sector that PSM had the greatest impact on intention to leave. Originality/value The authors said the research project was one of the first to test if the concept of PSM operated in the same way across sectors. It also contributed, they said, to the ongoing debate about PSM in China.

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