Research on Big Data Digging of Hot Topics about Recycled Water Use on Micro-Blog Based on Particle Swarm Optimization

Fu, Hanliang; Li, Zhaoxing; Liu, Zhijian; Wang, Zelin

doi:10.3390/su10072488

Cited by 96 publications

(39 citation statements)

References 34 publications

(35 reference statements)

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“…Age does not significantly influence the Miscanthus supply decision but does negatively influence switchgrass and willow supply decisions, which is consistent with the result of Joshi and Mehmood (), implying that older farmers may have less time to learn (and reap) the benefits of a new crop; hence, they may also be less willing to adopt a new crop (Tyndall, Berg, & Colletti, ). Education has a strong impact on the landowners’ willingness—people who have a college degree are more likely to supply compared to landowners who have a high school degree or less, which is consistent with the literature on the adoption of new agricultural technology (Fu & Liu, ; Fu et al., ; Jensen et al., ; Mooney et al., ). We also noticed that an increase in the distance to the nearest pellet facility negatively influences landowners’ decisions to grow bioenergy crops.…”

Section: Resultssupporting

confidence: 83%

Modeling spatial dependence and economic hotspots in landowners’ willingness to supply bioenergy crops in the northeastern United States

Jiang¹,

Zipp

Langholtz

et al. 2019

GCB Bioenergy

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This paper investigates the spatial heterogeneity of landowners’ willingness to supply three bioenergy crops: switchgrass, Miscanthus, and willow, in the northeastern United States. Spatial heterogeneity might arise for several reasons. For example, landowners closer to bioenergy processing plants might be more likely to be willing to supply bioenergy crops, and landowners who are more willing to supply bioenergy crops may be spatially clustered because they share similar land attributes, demographics, experiences, and/or values. Using high‐resolution GIS data related to the location of pellet plants utilizing bioenergy crops and survey data related to landowners’ characteristics including spatial location, we estimate a spatial probit model to explain the variation in individual‐specific reservation prices (RPs)—the feedstock price at which landowners become willing to supply a bioenergy crop. We find that respondents’ RP is lower the closer they live to their nearest pellet plant and spatial dependency is only present for switchgrass supply. We also identify three economic hotspots (areas with high potential supply and low RPs) for each bioenergy crop. We believe that bioenergy supply chains could be developed around these hotspots.

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Section: Resultssupporting

confidence: 83%

Modeling spatial dependence and economic hotspots in landowners’ willingness to supply bioenergy crops in the northeastern United States

Jiang¹,

Zipp

Langholtz

et al. 2019

GCB Bioenergy

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“…Nevertheless, the relationship among all three independent endogenous components was not appointed and regulated by local contexts [6]. The creation of the Livelihood Vulnerability Index (LVI) in this study used the following mathematical [27][28][29]:…”

Section: The Livelihood Vulnerability Evaluation Modelmentioning

confidence: 99%

The Influence of Poverty Alleviation Resettlement on Rural Household Livelihood Vulnerability in the Western Mountainous Areas, China

Liu

2018

Sustainability

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Poverty alleviation resettlement (PAR) is China's largest-ever resettlement program and one of China's flagship poverty alleviation initiatives. Resorting to this state-led conversation and development program, the central and provincial governments aim to lift the poor out of the poverty trap and into sustainable livelihoods, by delivering improvements in housing conditions, infrastructure services, public amenities, and living standards. Taking Ankang as an example, this study examines the PAR from the perspective of vulnerability through a household survey conducted in Ankang prefecture of Shaanxi province, China. A total of six townships in Ankang are covered, with 657 valid questionnaires collected. This study shows that there is a difference in exposure, sensitivity, and the adaptive capacity of rural households with different relocation characteristics, hence generating different livelihood vulnerabilities. The PAR generally achieves the target of livelihood vulnerability reduction. Specifically, the project-induced relocation has a significant positive effect on vulnerability, but there is a significant negative correlation between livelihood vulnerability and relocation region, relocation time, and relocation subsidy. Challenges and problems remain to be addressed for the next phases of the PAR, including diminishing the financial burden on those relocated and providing free public transportation services, carrying out community-building programs and updating the household registration institution, balancing the redistribution and sharing of farmland, furnishing assistance measures for employment searches and training in specific skills, and creating an impartial project to safeguard the non-movers from the significant negative impacts on their physical and spiritual dimensions.

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“…III. THE PROPOSED METHOD As we all know, the original data contains much useful information, such as the given label information and the underlying geometric information residing in the data [35][36][37][38]. If we can appropriately utilize these information to guide the projection learning, then a better classification performance will be obtained.…”

Section: Discriminatively Regularized Least-squaresmentioning

confidence: 99%

Adaptive Locality Preserving Regression

Wen

Zhong

Zhang

et al. 2020

IEEE Trans. Circuits Syst. Video Technol.

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This paper proposes a novel discriminative regression method, called adaptive locality preserving regression (ALPR) for classification. In particular, ALPR aims to learn a more flexible and discriminative projection that not only preserves the intrinsic structure of data, but also possesses the properties of feature selection and interpretability. To this end, we introduce a target learning technique to adaptively learn a more discriminative and flexible target matrix rather than the pre-defined strict zero-one label matrix for regression. Then a locality preserving constraint regularized by the adaptive learned weights is further introduced to guide the projection learning, which is beneficial to learn a more discriminative projection and avoid overfitting. Moreover, we replace the conventional 'Frobenius norm' with the special l2,1 norm to constrain the projection, which enables the method to adaptively select the most important features from the original high-dimensional data for feature extraction. In this way, the negative influence of the redundant features and noises residing in the original data can be greatly eliminated. Besides, the proposed method has good interpretability for features owning to the row-sparsity property of the l2,1 norm. Extensive experiments conducted on the synthetic database with manifold structure and many realworld databases prove the effectiveness of the proposed method.

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Research on Big Data Digging of Hot Topics about Recycled Water Use on Micro-Blog Based on Particle Swarm Optimization

Cited by 96 publications

References 34 publications

Modeling spatial dependence and economic hotspots in landowners’ willingness to supply bioenergy crops in the northeastern United States

Modeling spatial dependence and economic hotspots in landowners’ willingness to supply bioenergy crops in the northeastern United States

The Influence of Poverty Alleviation Resettlement on Rural Household Livelihood Vulnerability in the Western Mountainous Areas, China

Adaptive Locality Preserving Regression

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