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Article
Publication date: 1 August 2016

Bingyou Jiang, Zegong Liu, Shulei Shi, Feng Cai, Jian Liu, Mingyun Tang and Baiquan Lin

The purpose of this paper is to understand a flameproof distance necessary to avoid the flame harms to underground personnel which may have great significance to the safety of…

319

Abstract

Purpose

The purpose of this paper is to understand a flameproof distance necessary to avoid the flame harms to underground personnel which may have great significance to the safety of underground personnel and the disaster relief of gas explosions in coal mines.

Design/methodology/approach

Through a roadway with a length of 100 m and a cross-section area of 80 mm×80 mm, the flame propagation of premixed methane-air deflagrations were simulated by using AutoReaGas software for various fuel concentrations (7, 8, 9.5, 11, and 14 percent), fuel volumes (0.0128, 0.0384, 0.064, and 0.0896 m3), initial temperatures (248, 268, 288, 308, and 328 K), and initial pressures (20, 60, 101.3, 150, and 200 kPa).

Findings

The maximum combustion rate for each point follows a changing trend of increasing and decreasing with the distance increasing from the ignition source, and it increases with the fuel volume increasing or the initial pressure increasing, and decreases with the initial temperature increasing. However, increasing the initial temperature increases the flame arrival time for each point. The flameproof distance follows a changing trend of increasing and decreasing with the fuel concentration increasing, and it linearly increases with the fuel volume increasing or the initial temperature increasing. However, the flameproof distances are all 17 m for various initial pressures.

Originality/value

Increasing initial temperature increases flame arrival time for each test point. Flameproof distance increases and then decreases with fuel concentration increasing. Increasing fuel volume or initial temperature linearly increases flameproof distance. Initial pressure has little impact on the flameproof distance.

Details

International Journal of Numerical Methods for Heat & Fluid Flow, vol. 26 no. 6
Type: Research Article
ISSN: 0961-5539

Keywords

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Article
Publication date: 20 January 2025

Linglai Zeng, Mingyun Gao and Haoze Cang

The interval number prediction of power generation can provide a reference for the rational planning of the power system. For the nonlinearity, uncertainty and complex trends of…

5

Abstract

Purpose

The interval number prediction of power generation can provide a reference for the rational planning of the power system. For the nonlinearity, uncertainty and complex trends of power generation in East China, a matrixed nonlinear grey Bernoulli model combined with the weighted conformable fractional accumulation generating operator (MWCFNGBM(1,1,tα)) is proposed.

Design/methodology/approach

First, the original sequence fluctuations are smoothed with the weighted conformable fractional accumulation generating operator. The time power term is introduced into the nonlinear grey Bernoulli model to enhance the flexibility and adaptability of predicting nonlinear and complex sequences. The model parameters are further matrixed so that the interval number sequences can be modeled directly. The improved MPA is chosen to optimize the nonlinear parameters through the algorithm comparison. Finally, the Cramer rule is used to derive the time recursive formula.

Findings

The validity and superiority of the MWCFNGBM(1,1,tα) is verified by the model comparison experiment. The total power generation in East China is predicted and analyzed from 2024 to 2027. The prediction shows that it will grow steadily over the next four years.

Originality/value

The trend of power generation in East China is complex in the short term. It is of research significance to use the grey model for short-term interval prediction of power generation. For the data characteristics of power generation, a grey interval number prediction model for power generation prediction is proposed.

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Article
Publication date: 8 January 2020

Zheng Li, Xizhen Zhou, Samuel Jung and Jun Li

The purpose of this paper is to review the evolution of policies and practices of innovation in China for the past 40 years.

729

Abstract

Purpose

The purpose of this paper is to review the evolution of policies and practices of innovation in China for the past 40 years.

Design/methodology/approach

This is a review paper. It adopts a different multi-dimensional, qualitative methodology to examine China’s trajectory of innovation from the economic reform since 1978, highlighting “China” experiences in the developing innovation-driven economy, also pointing the challenges that China faces in this transition process and future prospects. The analysis of China’s innovation performance was based mostly on secondary data from sources and institutions that use statistical data to build country rankings, such as the global innovation index and global competitiveness index.

Findings

It is found that the institutional foundations of the national innovation system in China are already being laid, and so far, China has made extraordinary progress regarding innovation performance from country to region and from business to individual. However, some critical challenges in its innovation-driven development still need urgent attention and effective efforts to reinforce them.

Originality/value

This paper aims to fill the gap in the literature by providing an overview of the evolution of the policies and practices of innovation development in China since the 1978 economic reforms and explores the Chinese experiences in transforming into an innovation-driven economy.

Details

Chinese Management Studies, vol. 14 no. 2
Type: Research Article
ISSN: 1750-614X

Keywords

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Article
Publication date: 10 July 2023

Surabhi Singh, Shiwangi Singh, Alex Koohang, Anuj Sharma and Sanjay Dhir

The primary aim of this study is to detail the use of soft computing techniques in business and management research. Its objectives are as follows: to conduct a comprehensive…

402

Abstract

Purpose

The primary aim of this study is to detail the use of soft computing techniques in business and management research. Its objectives are as follows: to conduct a comprehensive scientometric analysis of publications in the field of soft computing, to explore the evolution of keywords, to identify key research themes and latent topics and to map the intellectual structure of soft computing in the business literature.

Design/methodology/approach

This research offers a comprehensive overview of the field by synthesising 43 years (1980–2022) of soft computing research from the Scopus database. It employs descriptive analysis, topic modelling (TM) and scientometric analysis.

Findings

This study's co-citation analysis identifies three primary categories of research in the field: the components, the techniques and the benefits of soft computing. Additionally, this study identifies 16 key study themes in the soft computing literature using TM, including decision-making under uncertainty, multi-criteria decision-making (MCDM), the application of deep learning in object detection and fault diagnosis, circular economy and sustainable development and a few others.

Practical implications

This analysis offers a valuable understanding of soft computing for researchers and industry experts and highlights potential areas for future research.

Originality/value

This study uses scientific mapping and performance indicators to analyse a large corpus of 4,512 articles in the field of soft computing. It makes significant contributions to the intellectual and conceptual framework of soft computing research by providing a comprehensive overview of the literature on soft computing literature covering a period of four decades and identifying significant trends and topics to direct future research.

Details

Industrial Management & Data Systems, vol. 123 no. 8
Type: Research Article
ISSN: 0263-5577

Keywords

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Article
Publication date: 28 September 2023

Moh. Riskiyadi

This study aims to compare machine learning models, datasets and splitting training-testing using data mining methods to detect financial statement fraud.

4264

Abstract

Purpose

This study aims to compare machine learning models, datasets and splitting training-testing using data mining methods to detect financial statement fraud.

Design/methodology/approach

This study uses a quantitative approach from secondary data on the financial reports of companies listed on the Indonesia Stock Exchange in the last ten years, from 2010 to 2019. Research variables use financial and non-financial variables. Indicators of financial statement fraud are determined based on notes or sanctions from regulators and financial statement restatements with special supervision.

Findings

The findings show that the Extremely Randomized Trees (ERT) model performs better than other machine learning models. The best original-sampling dataset compared to other dataset treatments. Training testing splitting 80:10 is the best compared to other training-testing splitting treatments. So the ERT model with an original-sampling dataset and 80:10 training-testing splitting are the most appropriate for detecting future financial statement fraud.

Practical implications

This study can be used by regulators, investors, stakeholders and financial crime experts to add insight into better methods of detecting financial statement fraud.

Originality/value

This study proposes a machine learning model that has not been discussed in previous studies and performs comparisons to obtain the best financial statement fraud detection results. Practitioners and academics can use findings for further research development.

Details

Asian Review of Accounting, vol. 32 no. 3
Type: Research Article
ISSN: 1321-7348

Keywords

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Article
Publication date: 30 July 2024

Ananthajit Ajaya Kumar and Ashwani Assam

Deep-learning techniques are recently gaining a lot of importance in the field of turbulence. This study focuses on addressing the problem of data imbalance to improve the…

52

Abstract

Purpose

Deep-learning techniques are recently gaining a lot of importance in the field of turbulence. This study focuses on addressing the problem of data imbalance to improve the performance of an existing deep learning neural network to infer the Reynolds-averaged Navier–Stokes solution, proposed by Thuerey et al. (2020), in the cases of airfoils with high wake formation behind them. The model is based on a U-Net architecture, which calculates pressure and velocity solutions for fluid flow around an airfoil.

Design/methodology/approach

In this work, we propose various methods for training the model on selectively generated data with different distributions, which would be representative of the under-performing test samples. The property we chose for selectively generating data was the fraction of negative x-velocity in the domain. We have used Grad-CAM to compare the layer activations of different models trained using the proposed methods.

Findings

We observed that using our methods, the average performance on the samples with high wake formation (i.e. flow over airfoils at high angle of attack) has improved. Using one of the proposed methods, an average performance improvement of 15.65% was observed for samples of unknown airfoils compared to a similar model trained using the original method.

Originality/value

This work demonstrates the use of imbalanced learning in the field of fluid mechanics. The performance of the model is improved by giving significance to the distribution of the training data without changes to the model architecture.

Details

Engineering Computations, vol. 41 no. 7
Type: Research Article
ISSN: 0264-4401

Keywords

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Article
Publication date: 4 May 2021

Nor Hamizah Miswan, Chee Seng Chan and Chong Guan Ng

This paper develops a robust hospital readmission prediction framework by combining the feature selection algorithm and machine learning (ML) classifiers. The improved feature…

340

Abstract

Purpose

This paper develops a robust hospital readmission prediction framework by combining the feature selection algorithm and machine learning (ML) classifiers. The improved feature selection is proposed by considering the uncertainty in patient's attributes that leads to the output variable.

Design/methodology/approach

First, data preprocessing is conducted which includes how raw data is managed. Second, the impactful features are selected through feature selection process. It started with calculating the relational grade of each patient towards readmission using grey relational analysis (GRA) and the grade is used as the target values for feature selection. Then, the influenced features are selected using the Least Absolute Shrinkage and Selection Operator (LASSO) method. This proposed method is termed as Grey-LASSO feature selection. The final task is the readmission prediction using ML classifiers.

Findings

The proposed method offered good performances with a minimum feature subset up to 54–65% discarded features. Multi-Layer Perceptron with Grey-LASSO gave the best performance.

Research limitations/implications

The performance of Grey-LASSO is justified in two readmission datasets. Further research is required to examine the generalisability to other datasets.

Originality/value

In designing the feature selection algorithm, the selection on influenced input variables was based on the integration of GRA and LASSO. Specifically, GRA is a part of the grey system theory, which was employed to analyse the relation between systems under uncertain conditions. The LASSO approach was adopted due to its ability for sparse data representation.

Details

Grey Systems: Theory and Application, vol. 11 no. 4
Type: Research Article
ISSN: 2043-9377

Keywords

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