The Impact of the Biomass Crop Assistance Program on the United States Forest Products Market: An Application of the Global Forest Products Model

Jiang, Wei; Carter, Douglas R.; Fu, Hanliang; Jacobson, Michael G.; Zipp, Katherine Y.; Jin, Jiang; Yang, Long

doi:10.3390/f10030215

Cited by 29 publications

(18 citation statements)

References 22 publications

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“…The experiments over the benchmark data sets show its advantage over the state-of-the-art deep learning-based multi-task models. In the future, we will apply the proposed method to other fields, such as global forest product prediction (Jiang, Carter, Fu, Jacobson, Zipp, Jin and Yang, 2019;Liu, Gao, Chen, Fu, Jiang, Wang and Kou, 2019;Liu, Wang and Fu, 2018), biomaterials science (Zhang, Kramer, Smith, Allen, Leeper, Li, Morton, Gallazzi and Ulery,…”

Section: Resultsmentioning

confidence: 99%

Low-Rank Deep Convolutional Neural Network for Multitask Learning

Shang

Wang³

2019

Computational Intelligence and Neuroscience

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In this paper, we propose a novel multitask learning method based on the deep convolutional network. The proposed deep network has four convolutional layers, three max-pooling layers, and two parallel fully connected layers. To adjust the deep network to multitask learning problem, we propose to learn a low-rank deep network so that the relation among different tasks can be explored. We proposed to minimize the number of independent parameter rows of one fully connected layer to explore the relations among different tasks, which is measured by the nuclear norm of the parameter of one fully connected layer, and seek a low-rank parameter matrix. Meanwhile, we also propose to regularize another fully connected layer by sparsity penalty so that the useful features learned by the lower layers can be selected. The learning problem is solved by an iterative algorithm based on gradient descent and back-propagation algorithms. The proposed algorithm is evaluated over benchmark datasets of multiple face attribute prediction, multitask natural language processing, and joint economics index predictions. The evaluation results show the advantage of the low-rank deep CNN model over multitask problems.

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Section: Resultsmentioning

confidence: 99%

Low-Rank Deep Convolutional Neural Network for Multitask Learning

Shang

Wang³

2019

Computational Intelligence and Neuroscience

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“…For i � 1 to N do Calculate the value of the control parameter Aaccording to equation (7). Update the values of parameters A, C according to equation (2). Update the position of the remaining ω wolves according to equations (3) and (4).…”

Section: Elm Optimization Modelmentioning

confidence: 99%

“…In recent years, with the release of chemical gases, environmental pollution problems have become increasingly serious [1,2]. In order to effectively control environmental pollution, it is necessary to monitor the degree of environmental pollution in a timely manner and analyze the composition of environmental pollutants in order to better solve environmental pollution problems.…”

Section: Introductionmentioning

confidence: 99%

“…Among the chemical emissions emitted from the ecological environment, circulating fluidized bed (CFB) combustion is one of the main coal combustion methods in China. It has the advantages of wide fuel application range, good load regulation performance, low pollutant discharge, and easy utilization of ash [2][3][4]. e combination of CFB combustion mode and ultrasupercritical parameter technology will be the inevitable development direction of CFB boilers in the future.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Chemical Gas Emissions Based on Ecological Environment

Chen

2020

Journal of Chemistry

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With the serious pollution of the ecological environment, there are a large number of harmful gases in the chemical gases emitted by the industry. Relevant intelligent chemical algorithms control the emission of chemical gases, which can effectively reduce emissions and predict emissions more accurately. This paper proposes a gray wolf optimization algorithm based on chaotic search strategy combined with extreme learning machine to predict chemical emission gases, taking a 330 MW pulverized coal-fired boiler as a test object and establishing chemical emissions of CNGWO-ELM. The prediction model, by using the relevant data collected by DCS as training samples and test samples, trains and tests the model. Simulation experiments show that the chemical emission prediction model of CNGWO-ELM has better accuracy and stronger generalization ability, with higher practical value.

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“…So far, many scholars have analyzed the influencing factors of urban recycled water reuse behavior from different perspectives, including environmental education, social trust, risk factors, geographic space etc. [34][35][36][37]. Many scholars have studied the recycled water reuse behavior with Internet and big data.…”

Section: Research Model and Hypothesesmentioning

confidence: 99%

Effect of Cognitive Variables and Emotional Variables on Urban Residents’ Recycled Water Reuse Behavior

Gao

Khan

2019

Sustainability

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Urban residents’ perception of recycled water reuse is the foundation for the realization of recycled water reuse behavior. However, even though the perception factor is equipped, it does not mean that urban residents will use recycled water continuously for sure. Therefore, in this research, the authors try to put cognitive factors and emotional factors into a unified behavioral process. Based on this theory framework, the paper will interpret the initiation, formation and continuing process of recycled water reuse behavior of urban residents. On the basis of previous studies, this study established a theoretical model to study the influence of cognitive factors and emotional factors on recycled water reuse behavior of the residents. Based on the data of 325 samples, the direct and indirect relationship between the variables in the model is verified through path analysis and mediation analysis. The empirical results show that: firstly, the urban residents’ perception of recycled water reuse can activate their emotion for recycled water, and the emotion includes both positive emotion and negative emotion; secondly, although the recognition of recycled water can stimulate both positive and negative emotional factors, there are great differences between positive and negative emotions on the initiation, formation and sustainability of recycled water behavior. Negative emotion has a certain effect on the initiation of recycled water reuse behavior, but it has no significant effect on the formation and sustainability of recycled water reuse behavior. By contrast, positive emotion has no significant effect on the initiation of recycled water reuse behavior, but it has a significant effect on the formation and sustainability of recycled water reuse behavior. That is to say, at different stages, the recycled water reuse behaviors are affected differently by positive emotions and negative emotions. Thirdly, compared with negative emotional variables, positive emotions have a greater impact on individual recycled water reuse behavior. Positive emotional variables can significantly mediate the impact of cognitive variables on recycled water reuse behavior habits. In other words, positive emotions play a vital role in the sustainability of recycled water reuse.

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The Impact of the Biomass Crop Assistance Program on the United States Forest Products Market: An Application of the Global Forest Products Model

Cited by 29 publications

References 22 publications

Low-Rank Deep Convolutional Neural Network for Multitask Learning

Low-Rank Deep Convolutional Neural Network for Multitask Learning

Prediction of Chemical Gas Emissions Based on Ecological Environment

Effect of Cognitive Variables and Emotional Variables on Urban Residents’ Recycled Water Reuse Behavior

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