A suitable relational model for temporal databases (abstract only)

Optical remote sensing data are freely available on a global scale. However, the satellite image processing and analysis for quick, accurate, and precise forest above ground biomass (AGB) evaluation are still challenging and difficult. This paper is aimed to develop a novel method for precise, accurate, and quick evaluation of the forest AGB from optical remote sensing data. Typically, the ground forest AGB was calculated using an empirical model from ground data for biophysical parameters such as tree density, height, and diameter at breast height (DBH) collected from the field at different elevation strata. The ground fraction of vegetation cover (FVC) in each ground sample location was calculated. Then, the fraction of vegetation cover (FVC) from optical remote sensing imagery was calculated. In the first stage of method implementation, the relation model between the ground FVC and ground forest AGB was developed. In the second stage, the relational model was established between image FVC and ground FVC. Finally, both models were fused to derive the relational model between image FVC and forest AGB. The validation of the developed method was demonstrated utilizing Sentinel-2 imagery as test data and the Tundi reserved forest area located in the Dhanbad district of Jharkhand state in eastern India was used as the test site. The result from the developed model was ground validated and also compared with the result from a previously developed crown projected area (CPA)-based forest AGB estimation approach. The results from the developed approach demonstrated superior capabilities in precision compared to the CPA-based method. The average forest AGB estimation of the test site obtained by this approach revealed 463 tons per hectare, which matches the previous estimate from this test site.

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How Can Agricultural Corporate Build Sustainable Competitive Advantage through Green Intellectual Capital? A New Environmental Management Approach to Green Agriculture

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18157900 ◽

2021 ◽

Vol 18 (15) ◽

pp. 7900

Author(s):

Chulin Pan ◽

Yufeng Jiang ◽

Mingliang Wang ◽

Shuang Xu ◽

Ming Xu ◽

...

Keyword(s):

Environmental Management ◽

Competitive Advantage ◽

Intellectual Capital ◽

Moderated Mediation ◽

Mediation Effect ◽

Relational Model ◽

Management Approach ◽

Sustainable Competitive Advantage ◽

Green Innovation ◽

Mediation Effects

Based on natural resource-based theory, this study constructed a relational model between green intellectual capital, green innovation, and an agricultural corporate sustainable competitive advantage. The samples included a total of 341 agricultural companies in China, and multiple regression methods are used for the analysis. The results showed that green product innovation and green process innovation had a mediation effect between green human capital, green structural capital, green relational capital, and the sustainable competitive advantage of agricultural corporate. Beyond the simple moderation effect, a new integrated moderated-mediation effect model was established. It was shown that environmental leadership, green organizational identification, and green dynamic capability had different moderated-mediation effects under different conditions. The study is expected to close the previous research gaps and insufficiency in agricultural corporate environmental management and green agricultural. The empirical results and conclusions bring enlightenment and meaningful theoretical guidance to managers, researchers, practitioners, and policy makers in the green and sustainable development of agricultural corporates. The new environmental management path can help agricultural corporates conduct green innovation effectively, adapt to the green agricultural products market, and achieve sustainable competitive advantage. Ultimately, this will help to accelerate the development of green agriculture.

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WBS-free scheduling method based on database relational model

International Journal of Systems Assurance Engineering and Management ◽

10.1007/s13198-021-01106-x ◽

2021 ◽

Author(s):

Xiaokang Han

Keyword(s):

Relational Model ◽

Scheduling Method

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Cache-efficient sweeping-based interval joins for extended Allen relation predicates

The VLDB Journal ◽

10.1007/s00778-020-00650-5 ◽

2021 ◽

Author(s):

Danila Piatov ◽

Sven Helmer ◽

Anton Dignös ◽

Fabio Persia

Keyword(s):

Data Structure ◽

Experimental Evaluation ◽

State Of The Art ◽

Temporal Databases ◽

Access Method ◽

Wide Range ◽

Interval Relation ◽

Cache Efficient ◽

Join Algorithms ◽

Better Than

AbstractWe develop a family of efficient plane-sweeping interval join algorithms for evaluating a wide range of interval predicates such as Allen’s relationships and parameterized relationships. Our technique is based on a framework, components of which can be flexibly combined in different manners to support the required interval relation. In temporal databases, our algorithms can exploit a well-known and flexible access method, the Timeline Index, thus expanding the set of operations it supports even further. Additionally, employing a compact data structure, the gapless hash map, we utilize the CPU cache efficiently. In an experimental evaluation, we show that our approach is several times faster and scales better than state-of-the-art techniques, while being much better suited for real-time event processing.

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The Group Engagement Model: Procedural Justice, Social Identity, and Cooperative Behavior

Personality and Social Psychology Review ◽

10.1207/s15327957pspr0704_07 ◽

2003 ◽

Vol 7 (4) ◽

pp. 349-361 ◽

Cited By ~ 958

Author(s):

Tom R. Tyler ◽

Steven L. Blader

Keyword(s):

Social Identity ◽

Procedural Justice ◽

Cooperative Behavior ◽

Relevant Information ◽

Psychological Research ◽

Relational Model ◽

Mediation Hypothesis ◽

Value Model ◽

Group Engagement ◽

Group Engagement Model

The group engagement model expands the insights of the group-value model of procedural justice and the relational model of authority into an explanation for why procedural justice shapes cooperation in groups, organizations, and societies. It hypothesizes that procedures are important because they shape people's social identity within groups, and social identity in turn influences attitudes, values, and behaviors. The model further hypothesizes that resource judgments exercise their influence indirectly by shaping social identity. This social identity mediation hypothesis explains why people focus on procedural justice, and in particular on procedural elements related to the quality of their interpersonal treatment, because those elements carry the most social identity-relevant information. In this article, we review several key insights of the group engagement model, relate these insights to important trends in psychological research on justice, and discuss implications of the model for the future of procedural justice research.

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