535 research outputs found
RORS: Enhanced Rule-based OWL Reasoning on Spark
The rule-based OWL reasoning is to compute the deductive closure of an
ontology by applying RDF/RDFS and OWL entailment rules. The performance of the
rule-based OWL reasoning is often sensitive to the rule execution order. In
this paper, we present an approach to enhancing the performance of the
rule-based OWL reasoning on Spark based on a locally optimal executable
strategy. Firstly, we divide all rules (27 in total) into four main classes,
namely, SPO rules (5 rules), type rules (7 rules), sameAs rules (7 rules), and
schema rules (8 rules) since, as we investigated, those triples corresponding
to the first three classes of rules are overwhelming (e.g., over 99% in the
LUBM dataset) in our practical world. Secondly, based on the interdependence
among those entailment rules in each class, we pick out an optimal rule
executable order of each class and then combine them into a new rule execution
order of all rules. Finally, we implement the new rule execution order on Spark
in a prototype called RORS. The experimental results show that the running time
of RORS is improved by about 30% as compared to Kim & Park's algorithm (2015)
using the LUBM200 (27.6 million triples).Comment: 12 page
Revisiting energy efficiency and energy related CO2 emissions: Evidence from RCEP economies
Since the last four decades, energy demand has been reached to
the utmost level, which also leads to emissions and causes environmental
degradation, global warming and climate change all
over the world. In this sense, policy makers have suggested various
measures including renewable adoption and energy efficiency.
Current study aims to investigate the influence of
economic growth, energy consumption, renewable electricity output,
and energy efficiency on the energy related emissions. A
panel of 12 RCEP economies are examined covering the period
1990-2020. Since the data follows irregular path, therefore a novel
method of moment panel quantile regression is employed along
with the Granger causality test. The empirical results indicate that
economic growth and energy consumption significantly enhances
energy related emissions, where the magnitude and significance
level is found strengthening from lower to upper quantiles (Q0.25,
Q0.50, Q0.75 and Q0.90). Conversely, renewable electricity and energy
efficiency are the significant tools for lowering energy related
emissions in the region. Additionally, a unidirectional causality is
found from energy consumption and renewable electricity output
to energy related emissions. However, a feedback effect is validated
between economic growth, energy efficiency, and energy
related emissions. Based on the empirical findings, this study suggests
enhancement of renewable electricity output and adoption
of energy efficient technologies to reduce environmental degradation
and emission level
Where to park an autonomous vehicle?:Results of a stated choice experiment
The future innovation and growing popularity of autonomous vehicles have the potential to significantly impact the spatiotemporal distribution of parking demand. However, little knowledge is gained on how people will choose to park their autonomous cars. In principle, an autonomous vehicle is not necessarily parked close by like traditional vehicles leveraging the automated driving and parking capability, still, the decision made by people is important for policymakers in urban and transportation planning. This study attempts to gain useful insights to understand people's parking location choices for autonomous vehicles. A stated choice experiment was designed, allowing people to choose a parking location for autonomous vehicles in varied contexts, including time windows, picking-up times, and the requirement for on-time arrival at the next activity. We found that similar to conventional cars people generally prefer cheaper and/or closer parking lots for autonomous vehicles. However, the distance between a parking lot and the activity location is relatively longer in the case of autonomous vehicles. The amount of time an autonomous vehicle spends in congestion while picking up the users influences the choice of parking locations. Moreover, substantial preference heterogeneity between individual people was found in the parking choice behavior. The maximum value of access time for autonomous cars is 34 $/h which is higher than the empirical value of walking time for conventional cars. Results of elasticity indicate that the influence of parking fees is larger than that of access time and congestion time.</p
Scenario Analyses of Land Use Conversion in the North China Plain: An Econometric Approach
Scenario analysis and dynamic prediction of land use structure which involve many driving factors are helpful to investigate the mechanism of land use changes and even to optimize land use allocation for sustainable development. In this study, land use structure changes during 1988–2010 in North China Plain were discerned and the effects of various natural and socioeconomic driving factors on land use structure changes were quantitatively analyzed based on an econometric model. The key drivers of land use structure changes in the model are county-level net returns of land resource. In this research, we modified the net returns of each land use type for three scenarios, including business as usual (BAU) scenario, rapid economic growth (REG) scenario, and coordinated environmental sustainability (CES) scenario. The simulation results showed that, under different scenarios, future land use structures were different due to the competition among various land use types. The land use structure changes in North China Plain in the 40-year future will experience a transfer from cultivated land to built-up area, an increase of forestry, and decrease of grassland. The research will provide some significant references for land use management and planning in the study area
Recent advances in theory and technology of oil and gas geophysics
Oil and gas are important energy resources and industry materials. They are stored in pores and fractures of subsurface rocks over thousands of meters in depth, making the finding and distinguishing them to be a significant challenge. The geophysical methods, especially the seismic and well-logging methods, are the effective ways to identify the oil and gas reservoirs and are widely used in industry. Due to the complexity of near surface and subsurface structures of new exploration targets, the geophysical methods based on advanced computation methods and physical principles are continuously proposed to cope with the emerging challenges. Thus, some new advances in theory and technology of oil and gas geophysics are summarized in this editorial material, especially focusing on the geophysical data processing, numerical simulation technology, rock physics modeling, and reservoir characterization.Document Type: EditorialCited as: Wang, Y., Liu, Y., Zou, Z., Bao, Q., Zhang, F., Zong, Z. Recent advances in theory and technology of oil and gas geophysics. Advances in Geo-Energy Research, 2023, 9(1): 1-4. https://doi.org/10.46690/ager.2023.07.0
Combined First- and Second-Order Variational Model for Image Compressive Sensing
A hybrid variational model combined first- and second-order total variation for image reconstruction from its finite number of noisy compressive samples is proposed in this paper. Inspired by majorization-minimization scheme, we develop an efficient algorithm to seek the optimal solution of the proposed model by successively minimizing a sequence of quadratic surrogate penalties. Both the nature and magnetic resonance (MR) images are used to compare its numerical performance with four state-of-the-art algorithms. Experimental results demonstrate that the proposed algorithm obtained a significant improvement over related state-of-the-art algorithms in terms of the reconstruction relative error (RE) and peak signal to noise ratio (PSNR)
Viewpoint-Aware Loss with Angular Regularization for Person Re-Identification
Although great progress in supervised person re-identification (Re-ID) has
been made recently, due to the viewpoint variation of a person, Re-ID remains a
massive visual challenge. Most existing viewpoint-based person Re-ID methods
project images from each viewpoint into separated and unrelated sub-feature
spaces. They only model the identity-level distribution inside an individual
viewpoint but ignore the underlying relationship between different viewpoints.
To address this problem, we propose a novel approach, called
\textit{Viewpoint-Aware Loss with Angular Regularization }(\textbf{VA-reID}).
Instead of one subspace for each viewpoint, our method projects the feature
from different viewpoints into a unified hypersphere and effectively models the
feature distribution on both the identity-level and the viewpoint-level. In
addition, rather than modeling different viewpoints as hard labels used for
conventional viewpoint classification, we introduce viewpoint-aware adaptive
label smoothing regularization (VALSR) that assigns the adaptive soft label to
feature representation. VALSR can effectively solve the ambiguity of the
viewpoint cluster label assignment. Extensive experiments on the Market1501 and
DukeMTMC-reID datasets demonstrated that our method outperforms the
state-of-the-art supervised Re-ID methods
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