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