Entropy-Weight-Method-Based Integrated Models for Short-Term Intersection Traffic Flow Prediction

Qu, Wenrui; Li, Jinhong; Song, Weihua; Li, Xiaoran; Zhao, Yue; Dong, Hanlin; Wang, Yanfei; Zhao, Qun; Qi, Yi

doi:10.3390/e24070849

Cited by 29 publications

(13 citation statements)

References 46 publications

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“…The use of the entropy weight method in this study for calculating index weights is supported by existing literature, which suggests its effectiveness in providing a comprehensive and objective evaluation for decision-making studies [40]. The findings, therefore, gain credibility through the application of this widely accepted methodology.…”

Section: Discussionmentioning

confidence: 52%

RETRACTED: Heatwave Induced Health Vulnerability Assessment in Bangladesh

Mehrin,

Zuhra,

Rahman

2024

Preprint

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Heatwaves emerge as a substantial environmental hazard, posing a severe threat to public health, especially in the tropical countries of South Asia. Despite Bangladesh's susceptibility to natural disasters, including an unprecedented temperature surge, heat stress lacks official recognition as a disaster. This study aims to assess the spatial distribution of health vulnerability to heat waves at the district level, in Bangladesh. Utilizing a framework incorporating exposure, sensitivity, and adaptive capacity, the vulnerability index score for each district was computed. Ten indicators, weighted through the entropy method, revealed health professional presence as the most influential, while factors like air temperature and illiteracy held lower weights in the assessment. The research reveals that Kurigram exhibits high sensitivity to heat, attributed to increased agricultural engagement, higher illiteracy rates, and a larger proportion of the population living below the poverty line. Conversely, Sherpur is identified as having the lowest adaptive capacity, primarily due to a scarcity of health professionals and limited green spaces within the district. Furthermore, Sherpur and Khagrachhari are identified as the most vulnerable districts to heat waves from health perspective. This research facilitates strategic resource allocation by pinpointing vulnerable districts and provides insights into the underlying reasons, informing policy formulation and encouraging international collaboration.

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Section: Discussionmentioning

confidence: 52%

RETRACTED: Heatwave Induced Health Vulnerability Assessment in Bangladesh

Mehrin,

Zuhra,

Rahman

2024

Preprint

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“…Qu et al (2022) established an energy consumption model based on the Elman algorithm in studying energy consumption in cloud computing environments and energy-saving scheduling tasks. They found that the accuracy of the energy consumption model based on the Elman algorithm is higher than that of the multiple linear regression model [37]. They proved the correctness of this paper to study the comparison method between the CNN algorithm and the Elman algorithm in CDC energy consumption.…”

Section: Discussionmentioning

confidence: 58%

Energy saving strategy of cloud data computing based on convolutional neural network and policy gradient algorithm

Yang

et al. 2022

PLoS ONE

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Cloud Data Computing (CDC) is conducive to precise energy-saving management of user data centers based on the real-time energy consumption monitoring of Information Technology equipment. This work aims to obtain the most suitable energy-saving strategies to achieve safe, intelligent, and visualized energy management. First, the theory of Convolutional Neural Network (CNN) is discussed. Besides, an intelligent energy-saving model based on CNN is designed to ameliorate the variable energy consumption, load, and power consumption of the CDC data center. Then, the core idea of the policy gradient (PG) algorithm is introduced. In addition, a CDC task scheduling model is designed based on the PG algorithm, aiming at the uncertainty and volatility of the CDC scheduling tasks. Finally, the performance of different neural network models in the training process is analyzed from the perspective of total energy consumption and load optimization of the CDC center. At the same time, simulation is performed on the CDC task scheduling model based on the PG algorithm to analyze the task scheduling demand. The results demonstrate that the energy consumption of the CNN algorithm in the CDC energy-saving model is better than that of the Elman algorithm and the ecoCloud algorithm. Besides, the CNN algorithm reduces the number of virtual machine migrations in the CDC energy-saving model by 9.30% compared with the Elman algorithm. The Deep Deterministic Policy Gradient (DDPG) algorithm performs the best in task scheduling of the cloud data center, and the average response time of the DDPG algorithm is 141. In contrast, the Deep Q Network algorithm performs poorly. This paper proves that Deep Reinforcement Learning (DRL) and neural networks can reduce the energy consumption of CDC and improve the completion time of CDC tasks, offering a research reference for CDC resource scheduling.

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“…This measure is employed to mitigate potential estimation biases, and we employ the entropy weight method for this re-evaluation. The entropy weight method has a well-established presence in comprehensive evaluation contexts dealing with multidimensional evaluation indices (Qu et al, 2022). This method assigns greater weight to indices with a pronounced influence in a comprehensive evaluation system.…”

Section: Alternative Measure Of Digital Affordancementioning

confidence: 99%

The nonlinear relationship between digital affordances and firm‐level export performance: The moderating role of organizational ambidexterity

Erdey,

Liu,

Nagy

2024

Manage Decis Econ

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A diminished trade cost has been ascribed to the advancement of digital transformation, particularly digital technologies and platforms. Nevertheless, whether this decline in trade costs manifests in the extensive or intensive margin of trade remains an unresolved query. This study conducts a comprehensive analysis of the influence of digital affordances on firm‐level export performance. Specifically, we posit that digital affordances will manifest a convex influence on firms' export decisions and intensive margin of export. Furthermore, we propose that the relationship between digital affordances and firm‐level export decisions will be contingent upon the degree of firms' organizational ambidexterity. To empirically test our hypotheses, we leverage a dataset comprising 13,251 firm‐level observations gathered by the World Bank Enterprise Surveys (WBES) unit across 19 Organization for Economic Cooperation and Development (OECD) countries. Our study substantiates a U‐shaped nexus between digital affordances and firm‐level export performance. Importantly, our analysis supports that this U‐shaped nexus is accentuated when firms present a high level of organizational ambidexterity.

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Entropy-Weight-Method-Based Integrated Models for Short-Term Intersection Traffic Flow Prediction

Cited by 29 publications

References 46 publications

RETRACTED: Heatwave Induced Health Vulnerability Assessment in Bangladesh

RETRACTED: Heatwave Induced Health Vulnerability Assessment in Bangladesh

Energy saving strategy of cloud data computing based on convolutional neural network and policy gradient algorithm

The nonlinear relationship between digital affordances and firm‐level export performance: The moderating role of organizational ambidexterity

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