scholarly journals Minimum/maximum autocorrelation factors applied to grade estimation

2014 ◽  
Vol 67 (2) ◽  
pp. 209-214
Author(s):  
Camilla Zacché da Silva ◽  
João Felipe Coimbra Leite Costa
Keyword(s):  
Original Data ◽  
Mineral Deposits ◽  
Correlated Variables ◽  
Grade Estimation ◽  
Data Space ◽  
Geostatistical Approach ◽  
Covariance Modeling ◽  
Estimation Problems ◽  
Direct Covariance

It is frequent to face estimation problems when dealing with mineral deposits involving multiple correlated variables. The resulting model is expected to reproduce data correlation. However, is not guaranteed that the correlation observed among data will be reproduced by the model, if the variables are estimated independently, and this correlation is not explicitly taken into account. The adequate geostatistical approach to address this estimation problem is co-kriging which requires cross and direct covariance modeling of all variables, satisfying the LMC. An alternative is to decorrelate the variables and estimate each independently, using for instance, the minimum/maximum autocorrelation factors (MAF) approach, which uses a linear transformation on the correlated variables, transforming them to a new uncorrelated set. The transformed data can be estimated through kriging. Afterwards, the estimates are back-transformed to the original data space. The methodology is illustrated in a case study where three correlated variables are estimated using the MAF method combined with kriging and through co-kriging, used as a benchmark. The results show less than a 2% deviation between both methodologies.

A geostatistical approach to winter road surface condition estimation using mobile RWIS data

2019 ◽  
Vol 46 (6) ◽  
pp. 511-521
Author(s):  
Lian Gu ◽  
Tae J. Kwon ◽  
Tony Z. Qiu
Keyword(s):  
Robust Regression ◽  
Surface Condition ◽  
Road Surface ◽  
Road Maintenance ◽  
Surface Conditions ◽  
Modelling Framework ◽  
Geostatistical Approach ◽  
Winter Road ◽  
Systematic Framework

In winter, it is critical for cold regions to have a full understanding of the spatial variation of road surface conditions such that hot spots (e.g., black ice) can be identified for an effective mobilization of winter road maintenance operations. Acknowledging the limitations in present study, this paper proposes a systematic framework to estimate road surface temperature (RST) via the geographic information system (GIS). The proposed method uses a robust regression kriging method to take account for various geographical factors that may affect the variation of RST. A case study of highway segments in Alberta, Canada is used to demonstrate the feasibility and applicability of the method proposed herein. The findings of this study suggest that the geostatistical modelling framework proposed in this paper can accurately estimate RST with help of various covariates included in the model and further promote the possibility of continuous monitoring and visualization of road surface conditions.

Testing Growth-Center Hypotheses

1974 ◽  
Vol 6 (2) ◽  
pp. 185-189 ◽  
Author(s):  
G L Gaile
Keyword(s):  
Temporal Resolution ◽  
Original Data ◽  
Growth Center

A technique developed by Casetti, King, and Odland (Casetti et al., 1971) for testing growth-center hypotheses is analyzed and found to be sound in principle, but requiring modification in certain specifics. Refinements and modifications relative to problems of scale, ‘center-specificity’, directionality, and spatial-temporal resolution are explained and demonstrated through empirical reanalysis of Casetti et al.'s original data, and also a case study of intraurban Milwaukee as a growth-center analog.

scholarly journals A Geostatistical Investigation into the Effective Spatiotemporal Coverage of Road Weather Information Systems in Alberta, Canada

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Xu Wang ◽  
Lian Gu ◽  
Tae J. Kwon ◽  
Tony Z. Qiu
Keyword(s):  
Information Systems ◽  
Weather Conditions ◽  
Road Maintenance ◽  
The Road ◽  
Weather Information ◽  
Geostatistical Approach ◽  
Adverse Weather ◽  
Winter Road ◽  
Winter Road Maintenance

Inclement weather acutely affects road surface and driving conditions and can negatively impact traffic mobility and safety. Highway authorities have long been using road weather information systems (RWISs) to mitigate the risk of adverse weather on traffic. The data gathered, processed, and disseminated by such systems can improve both the safety of the traveling public as well as the effectiveness of winter road maintenance operations. As the road authorities continue to invest in expanding their existing RWIS networks, there is a growing need to determine the optimal deployment strategies for RWISs. To meet such demand, this study presents an innovative geostatistical approach to quantitatively analyze the spatiotemporal variations of the road weather and surface conditions. With help of constructed semivariograms, this study quantifies and examines both the spatial and temporal coverage of RWIS data. A case study of Alberta, which is one of the leaders in Canada in the use of RWISs, was conducted to indicate the reliability and applicability of the method proposed herein. The findings of this research offer insight for constructing a detailed spatiotemporal RWIS database to manage and deploy different types of RWISs, optimize winter road maintenance resources, and provide timely information on inclement road weather conditions for the traveling public.

scholarly journals The exercise pressor response to indoor rock climbing

2020 ◽  
Vol 129 (2) ◽  
pp. 404-409
Author(s):  
Nigel A. Callender ◽  
Peter W. Hart ◽  
Girish M. Ramchandani ◽  
Parminder S. Chaggar ◽  
Andrew J. Porter ◽  
...  
Keyword(s):  
Resistance Exercise ◽  
Upper Limb ◽  
Pressor Response ◽  
Original Data ◽  
Rock Climbing ◽  
Arterial Catheter ◽  
Cardiovascular Stress ◽  
Blood Pressures ◽  
Training Exercises

This case study provides original data on the exercise pressor response to indoor rock climbing and associated training exercises through the use of an indwelling femoral arterial catheter. Our subjects exhibited systolic/diastolic blood pressures that exceeded values often reported during upper-limb resistance exercise. Our data extend the understanding of the cardiovascular stress associated with indoor rock climbing.

scholarly journals Revolutionary Homophobia: Explaining State Repression against Sexual Minorities

2019 ◽  
Vol 50 (4) ◽  
pp. 1459-1480 ◽  
Author(s):  
Joshua Tschantret
Keyword(s):  
Sexual Minorities ◽  
Social Groups ◽  
Original Data ◽  
State Repression ◽  
Bisexual And Transgender ◽  
Strategic Incentives ◽  
Lgbt People ◽  
Domestic Instability ◽  
The Relationship

AbstractWhy do unthreatening social groups become targets of state repression? Repression of lesbian, gay, bisexual and transgender (LGBT) people is especially puzzling since sexual minorities, unlike many ethnic minorities, pose no credible violent challenge to the state. This article contends that revolutionary governments are disproportionately oppressive toward sexual minorities for strategic and ideological reasons. Since revolutions create domestic instability, revolutionaries face unique strategic incentives to target ‘unreliable’ groups and to demonstrate an ability to selectively punish potential dissidents by identifying and punishing ‘invisible’ groups. Moreover, revolutionary governments are frequently helmed by elites with exclusionary ideologies – such as communism, fascism and Islamism – which represent collectivities rather than individuals. Elites adhering to these views are thus likely to perceive sexual minorities as liberal, individualistic threats to their collectivist projects. Statistical analysis using original data on homophobic repression demonstrates that revolutionary governments are more likely to target LGBT individuals, and that this effect is driven by exclusionary ideologues. Case study evidence from Cuba further indicates that the posited strategic and ideological mechanisms mediate the relationship between revolutionary government and homophobic repression.

scholarly journals Graph Regularized Nonnegative Matrix Factorization with Sparse Coding

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Chuang Lin ◽  
Meng Pang
Keyword(s):  
Sparse Coding ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Geometrical Structure ◽  
State Of The Art ◽  
Nonnegative Matrix ◽  
Target Function ◽  
Original Data ◽  
Data Space ◽  
Detailed Derivation

In this paper, we propose a sparseness constraint NMF method, named graph regularized matrix factorization with sparse coding (GRNMF_SC). By combining manifold learning and sparse coding techniques together, GRNMF_SC can efficiently extract the basic vectors from the data space, which preserves the intrinsic manifold structure and also the local features of original data. The target function of our method is easy to propose, while the solving procedures are really nontrivial; in the paper we gave the detailed derivation of solving the target function and also a strict proof of its convergence, which is a key contribution of the paper. Compared with sparseness constrained NMF and GNMF algorithms, GRNMF_SC can learn much sparser representation of the data and can also preserve the geometrical structure of the data, which endow it with powerful discriminating ability. Furthermore, the GRNMF_SC is generalized as supervised and unsupervised models to meet different demands. Experimental results demonstrate encouraging results of GRNMF_SC on image recognition and clustering when comparing with the other state-of-the-art NMF methods.

Sign in / Sign up

Export Citation Format

Share Document