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Ikram M, Sayagh Y. The Consequences of COVID-19 Disruption on Sustainable Economy in the Top 30 High-Tech Innovative Countries. GLOBAL JOURNAL OF FLEXIBLE SYSTEMS MANAGEMENT 2023; 24:247-269. [PMID: 37101930 PMCID: PMC10068236 DOI: 10.1007/s40171-023-00338-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 03/06/2023] [Indexed: 04/05/2023]
Abstract
This study aims to investigate the effects of the COVID-19 pandemic on the innovation index, Gross Domestic Product (GDP), high technology exports, and human development (HDI) in the world's leading 30 high-tech innovative countries. Using grey relational analysis models, the association between COVID-19 and other economic development indices was investigated. The model selects the country least affected by the pandemic from the top 30 innovative countries through a conservative (maximin) method based on grey association values. Data was collected from World Bank databases and analyzed to compare pre- and post-COVID-19 periods (2019, 2020). The outcomes of this study provide essential recommendations for industries and decision-makers with suitable action plans to preserve economic systems from further harm caused by the global COVID-19 outbreak. The ultimate goal is to boost the innovation index, GDP, high-tech exports, and HDI of high-tech economies and pave the way for a sustainable economy. To the author's knowledge, this is the first study to develop a multidimensional framework to assess COVID-19's impact on the sustainable economy of top 30 high-tech innovative countries, and to conduct a comparative analysis to identify the strong and weak effects of COVID-19 on sustainable economic growth.
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Affiliation(s)
- Muhammad Ikram
- grid.442822.a0000 0004 1789 8654School of Business Administration, Al Akhawayn University in Ifrane, Avenue Hassan II, P.O. Box 104, 53000 Ifrane, Morocco
| | - Youssef Sayagh
- grid.442822.a0000 0004 1789 8654School of Business Administration, Al Akhawayn University in Ifrane, Avenue Hassan II, P.O. Box 104, 53000 Ifrane, Morocco
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Chen S, Xu K, Yao X, Ge J, Li L, Zhu S, Li Z. Information fusion and multi-classifier system for miner fatigue recognition in plateau environments based on electrocardiography and electromyography signals. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 211:106451. [PMID: 34644668 DOI: 10.1016/j.cmpb.2021.106451] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 09/28/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Human factors are important contributors to accidents, especially human error induced by fatigue. In this study, field tests and analyses were conducted on physiological indexes extracted from electrocardiography (ECG) and electromyography (EMG) signals in miners working under the extreme conditions of a plateau environment. To provide insights into models for fatigue classification and recognition based on machine learning, multi-modal feature information fusion and miner fatigue identification based on ECG and EMG signals as physiological indicators were studied. METHODS Fifty-five miners were randomly selected as field test subjects, and characteristic signals were extracted from 110 groups of ECG and EMG signals as the basic signals for fatigue analysis. We conducted principal component analysis (PCA) and grey relational analysis (GRA) on the measurement indicators. Support vector machine (SVM), random forest (RF) and extreme gradient boosting (XG-Boost) machine learning models were used for fatigue classification based on multi-modal information fusion. The area under the receiver operating characteristic (ROC) curve and the confusion matrix were used to evaluate the performance of the recognition models. RESULTS The ECG and EMG signals showed obvious changes with fatigue. The results of fatigue model identification showed that PCA feature fusion was superior to GRA feature fusion for all three machine learning approaches, and XG-Boost achieved the best performance, with a recognition accuracy of 89.47%, a sensitivity and specificity of 100%, and an AUC of 0.90. The SVM model also showed good recognition performance (89.47% accuracy, AUC=0.89). The worst performance was that of the RF model, with a recognition accuracy of only 78.95%. CONCLUSIONS This study shows that the physiological indexes of ECG and EMG exhibit obvious, regular changes with fatigue and that it is feasible to use SVM, RF and XG-Boost models for miner fatigue identification. The PCA fusion technique can improve the identification accuracy more than the GRA method. XG-Boost classification yields the best accuracy and robustness. This study can serve as a reference for clinical research on the identification of human fatigue at high altitudes and for the clinical study of acute mountain sickness and human acclimatization to high altitudes.
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Affiliation(s)
- Shoukun Chen
- Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China
| | - Kaili Xu
- Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China.
| | - Xiwen Yao
- Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China.
| | - Ji Ge
- Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China; School of Resources and Environmental Engineering, Jilin Institute of Chemical Technology, Jilin 132022, China.
| | - Li Li
- Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China
| | - Siyi Zhu
- Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China
| | - Zhengrong Li
- Yunnan Diqing Non-ferrous Metals Co., Ltd, Yunnan 674400, China
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Safety Assessment of Casting Workshop by Cloud Model and Cause and Effect-LOPA to Protect Employee Health. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17072555. [PMID: 32276454 PMCID: PMC7178204 DOI: 10.3390/ijerph17072555] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Revised: 03/26/2020] [Accepted: 04/06/2020] [Indexed: 11/24/2022]
Abstract
Safety assessment of a casting workshop will provide a clearer understanding of the important safety level required for a foundry. The main purpose of this study was to construct a composite safety assessment method to protect employee health using the cloud model and cause and effect–Layer of Protection Analysis (LOPA). In this study, the weights of evaluation indicators were determined using the subjective analytic hierarchy process and objective entropy weight method respectively. Then, to obtain the preference coefficient of the integrated weight more precisely, a new algorithm was proposed based on the least square method. Next, the safety level of the casting workshop was presented based on the qualitative and quantitative analysis of the cloud model, which realized the uncertainty conversion between qualitative concepts and their corresponding quantitative values, as well as taking the fuzziness and randomness into account; the validity of cloud model evaluation was validated by grey relational analysis. In addition, cause and effect was used to proactively identify factors that may lead to accidents. LOPA was used to correlate corresponding safety measures to the identified risk factors. 6 causes and 19 sub-causes that may contribute to accidents were identified, and 18 potential remedies, or independent protection layers (IPLs), were described as ways to protect employee health in foundry operations. A mechanical manufacturing business in Hunan, China was considered as a case study to demonstrate the applicability and benefits of the proposed safety assessment approach.
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Xu Q, Xu K, Li L, Yao X. Optimization of sand casting performance parameters and missing data prediction. ROYAL SOCIETY OPEN SCIENCE 2019; 6:181860. [PMID: 31598220 PMCID: PMC6731703 DOI: 10.1098/rsos.181860] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Accepted: 07/15/2019] [Indexed: 06/10/2023]
Abstract
Due to a wide range of applications, sand casting occupies an important position in modern casting practice. The main purpose of this study was to optimize the performance parameters of sand casting based on grey relational analysis and predict the missing data using back propagation (BP) neural network. First, the influence of human factors was eliminated by adopting the objective entropy weight method, which also saved manpower. The larger variation degree in the evaluation indicators, indicating that the evaluated projects had good discrimination in this regard, the larger weight should be given to these evaluation indicators. Second, the performance parameters of sand casting were optimized based on grey relational analysis, providing a reference for sand milling. The larger the grey relational degree, the closer the evaluated project was to the ideal project. Third, this paper provided a new method for determining the number of hidden neurons in a network according to the mean square error of training samples, and venting quality was predicted based on BP neural network. The relevant theory was deduced before predicting missing data, such that there will be a general understanding regarding the prediction principle of BP neural network. Fourth, to demonstrate the validity of BP neural network adopted in the process of missing data prediction, grey system theory was applied to compare the result of missing data prediction.
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Affiliation(s)
| | - Kaili Xu
- Authors for correspondence: Kaili Xu e-mail:
| | | | - Xiwen Yao
- Authors for correspondence: Xiwen Yao e-mail:
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Zhang J, Xu K, You G, Wang B, Zhao L. Causation Analysis of Risk Coupling of Gas Explosion Accident in Chinese Underground Coal Mines. RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2019; 39:1634-1646. [PMID: 30970163 DOI: 10.1111/risa.13311] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2016] [Revised: 10/07/2018] [Accepted: 01/02/2019] [Indexed: 06/09/2023]
Abstract
The coal mine production industry is a complex sociotechnical system with interactive relationships among several risk factors. Currently, causation analysis of gas explosion accidents is mainly focused on the aspects of human error and equipment fault, while neglecting the interactive relationships among risk factors. A new method is proposed through risk coupling. First, the meaning of risk coupling of a gas explosion is defined, and types of risk coupling are classified. Next, the coupled relationship and coupled effects among risk factors are explored through combining the interpretative structural modeling (ISM) and the NK model. Twenty-eight representative risk factors and 16 coupled types of risk factors are obtained through analysis of 332 gas explosion accidents in coal mines in China. Through the application of the combined ISM-NK model, an eight-level hierarchical model of risk coupling of a gas explosion accident is established, and the coupled degrees of different types of risk coupling are assessed. The hierarchical model reveals that two of the 28 risk factors, such as state policies, laws, and regulations, are the root risk factors for gas explosions; nine of the 28 risk factors, such as flame from blasting, electric spark, and local gas accumulation, are direct causes of gas explosions; whereas 17 of the risk factors, such as three-violation actions, ventilation system, and safety management, are indirect ones. A quantitative analysis of the NK model shows that the probability of gas explosion increases with the increasing number of risk factors. Compared with subjective risk factors, objective risk factors have a higher probability of causing gas explosion because of risk coupling.
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Affiliation(s)
- Jinjia Zhang
- Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, Northeastern University, Shenyang, China
| | - Kaili Xu
- Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, Northeastern University, Shenyang, China
| | - Greg You
- School of Science, Engineering and Information Technology, Federation University Australia, Ballarat, Australia
| | - Beibei Wang
- Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, Northeastern University, Shenyang, China
| | - Lei Zhao
- School of Science, Engineering and Information Technology, Federation University Australia, Ballarat, Australia
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Xu Q, Xu K. Quality evaluation of Chinese red wine based on cloud model. J Food Biochem 2019; 43:e12787. [PMID: 31608470 DOI: 10.1111/jfbc.12787] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Revised: 12/23/2018] [Accepted: 01/10/2019] [Indexed: 12/18/2022]
Abstract
Determining the quality of red wine is based on many qualitative and quantitative factors. Compared with other evaluation methods, the cloud model has an uncertainty transformation between a qualitative concept and its corresponding quantitative value, and the uncertainty transformation included fuzziness and randomness, which is suitable for solving the complexity of red wine evaluation. This study introduced the cloud model into quality evaluation of red wine for the first time, and a novel algorithm of comprehensive cloud model was proposed based on an addition algorithm of two cloud models. Furthermore, to validate the cloud model adopted in our red wine evaluation system, we used the gray relational analysis and fuzzy evaluation method. The evaluation result for the red wine sample was Good, and the result confirmed that our cloud model can be used to evaluate the quality of red wine. PRACTICAL APPLICATIONS: In 2013, China surpassed France to become the largest country of red wine consumption in the world. Red wine is made by a natural fermentation process. There are several components that make up red wine, but the most abundant is grape juice. Ethyl alcohol is the second most abundant element and it is made naturally by the fermentation of the sugar in grape. There are more than 1,000 remaining components in the recipe for red wine, where 300 are comparatively important. Although the proportion of these components is not high, they are important factors in determining the quality of red wine. Sensory evaluation is the most common method used to determine the quality of red wine. This work has identified a cloud model that can be used, based on sensory evaluation, to determine the quality of red wine.
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Affiliation(s)
- Qingwei Xu
- School of Resources and Civil Engineering, Northeastern University, Shenyang, China
| | - Kaili Xu
- School of Resources and Civil Engineering, Northeastern University, Shenyang, China
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Xu Q, Xu K, Yao X, Li J, Li L. Thermal decomposition characteristics of foundry sand for cast iron in nitrogen atmosphere. ROYAL SOCIETY OPEN SCIENCE 2018; 5:181091. [PMID: 30662727 PMCID: PMC6304113 DOI: 10.1098/rsos.181091] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Accepted: 11/13/2018] [Indexed: 06/09/2023]
Abstract
Sand casting, currently the most popular approach to the casting production, has wide adaptability and low cost. The thermal decomposition characteristics of foundry sand for cast iron were determined for the first time in this study. Thermogravimetry was monitored by simultaneous thermal analyser to find that there was no obvious oxidation or combustion reaction in the foundry sand; the thermal decomposition degree increased as the heating rate increased. There was an obvious endothermic peak at about 846 K due to the transition of quartz from β to α phase. A novel technique was established to calculate the starting temperature of volatile emission in determining the volatile release parameter of foundry sand for cast iron. Foundry sand does not readily evaporate because its volatile content is only about 2.68 wt% and its main components have high-temperature stability. The thermal decomposition kinetics parameters of foundry sand, namely activation energy and pre-exponential factor, were obtained under kinetics theory. The activation energy of foundry sand for cast iron was small, mainly due to the wide temperature range of thermal decomposition in the foundry sand.
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Affiliation(s)
- Qingwei Xu
- Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, People's Republic of China
| | - Kaili Xu
- Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, People's Republic of China
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Xu Q, Xu K, Yao X, Zhang J, Wang B. Sand casting safety assessment for foundry enterprises: fault tree analysis, Heinrich accident triangle, HAZOP-LOPA, bow tie model. ROYAL SOCIETY OPEN SCIENCE 2018; 5:180915. [PMID: 30473838 PMCID: PMC6227990 DOI: 10.1098/rsos.180915] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2018] [Accepted: 09/27/2018] [Indexed: 06/09/2023]
Abstract
Sand casting operations, though commonplace, pose a significant threat of explosion accidents. This paper presents a novel sand casting safety assessment technique based on fault tree analysis, Heinrich accident triangle, hazard and operability-layer of protection analysis (HAZOP-LOPA) and bow tie model components. Minimal cut sets and minimal path sets are first determined based on fault tree analysis, then the frequency of sand casting explosion accidents is calculated based on the Heinrich accident triangle. Third, the risk level of venting quality can be reduced by adopting HAZOP-LOPA; the residual risk level of venting quality remains excessive even after adopting two independent protective layers. The bow tie model is then adopted to determine the causes and consequences of venting quality. Five preventative measures are imposed to enhance the venting quality of foundry sand accompanied by 16 mitigative safety measures. Our results indicate that the risk attributable to low foundry sand venting quality can be minimized via bow tie analysis.
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Affiliation(s)
| | - Kaili Xu
- Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, People's Republic of China
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Xu Q, Xu K. Assessment of air quality using a cloud model method. ROYAL SOCIETY OPEN SCIENCE 2018; 5:171580. [PMID: 30839727 PMCID: PMC6170556 DOI: 10.1098/rsos.171580] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Accepted: 09/04/2018] [Indexed: 06/09/2023]
Abstract
To effectively control air pollution, it is necessary to obtain a preliminary assessment of air quality. The purpose of this study was to introduce a cloud model method in air pollution assessment. First, the standard cloud models of air pollution indicators were obtained, and the calculating process of numerical characteristics employed by the standard cloud model was explained. Second, the levels of air pollution indicators were presented based on the qualitative and quantitative analysis of cloud models, which realized the uncertainty conversion between qualitative concepts and their corresponding quantitative values, as well as taking the fuzziness and randomness into account. Air quality assessment results including SO2, NO2, CO, O3, PM10 and PM2.5 were analysed. Third, the cloud model adopted in the assessment process of air quality was validated by grey relational analysis, and the results confirmed the validity of cloud model assessment. Fourth, the air pollution level of the air quality index (AQI) was determined, and the fuzziness and randomness of the assessment results were thoroughly analysed by taking entropy and hyper entropy into consideration. Fifth, seasonal variations in different air pollution indicators were analysed to proffer a series of recommendations for government policy decision-makers and travellers. The cloud model provided a new method for air quality assessment.
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Affiliation(s)
| | - Kaili Xu
- Author for correspondence: Kaili Xu e-mail:
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