RUNES II : A Distributed Rule Engine Based on Rete Network in Cloud Computing

Rui Zhou,Guowei Wang,Jinghan Wang,Jing Li 보안공학연구지원센터 2014 International Journal of Grid and Distributed Comp Vol.7 No.6

In recent years, cloud computing has drawn more and more attention. Increasingly amount of systems and applications have been constructed in cloud environments, yet few researches of rule engine has been done. Rule engine technologies have been widely used in the development of enterprise information systems. These rule-based systems may encounter the problem of low performance, when a large amount of fact data are matched with these rules. Deploying rule engines in cloud environments can increase the capability and efficiency of these systems. In this paper, we propose an approach to implement rule engine based on a message-passing concurrency model in cloud computing. The approach can be extended conveniently and it can deal with extensive rules and facts efficiently. To improve the performance of the rule engine, an algorithm of allocation is proposed. A resource cost model is explored to make high efficient use of resources in cloud. In addition, we implement the rule engine system RUNES II in cloud platform and conduct experiments to show its performance.

MOF-derived NiFe2O4 nanoparticles on molybdenum disulfide: Magnetically reusable nanocatalyst for the reduction of nitroaromatics in aqueous media

Jinghan Wang,김재현,Jeewon Bu,Dokyoon Kim,Soo Young Kim,남기태,Rajender S. Varma,Rajender S. Varma,Ho Won Jang,Rafael Luque,쇼코히메르모하메드레자 한국공업화학회 2022 Journal of Industrial and Engineering Chemistry Vol.107 No.-

Metal organic framework (MOF)-derived nanocatalysts on various nanostructured supports are generallyefficient heterogeneous catalysts employable for organic transformations. Herein, nickel Prussian blue(PB) nanoparticles were deployed to form NiFe2O4 nanocatalysts supported on 2D molybdenum disulfide(MoS2) through a facile heat treatment. NiFe2O4 nanocatalysts could be uniformly dispersed on the highspecific-area MoS2 surface, representing a highly efficient, inexpensive, and magnetically recoverablenanocomposite catalyst. The semiconductor property of MoS2 ensures a high electric conductivity, thusenhancing electron transfer between the reductant and the reactant. Furthermore, the strong magneticcharacteristics enable its convenient separation from the reaction mixture. NiFe2O4 nanoparticles onMoS2-supported produces multiple electron transfer pathways and overcomes known drawbacks of catalyticmethods that use metals alone, endowing long-term cycle stability. Overall, MoS2/NiFe2O4 exhibitedan excellent catalytic activity and high yields in the reduction of nitrobenzene in water, maintainedeven after five cycles.

Inflow Conditions for Modelling the Neutral Equilibrium ABL Based on Standard k-ε Model

Jinghan Wang,Chao Li,Yiqing Xiao,Jinping ou Council on Tall Building and Urban Habitat Korea 2022 International journal of high-rise buildings Vol.11 No.4

Reproducing the horizontally homogeneous atmospheric boundary layer in computational wind engineering is essential for predicting the wind loads on structures. One of the important issues is to use fully developed inflow conditions, which will lead to the consistence problem between inflow condition and internal roughness. Thus, by analyzing the previous results of computational fluid dynamic modeling turbulent horizontally homogeneous atmospheric boundary layer, we modify the past hypotheses, detailly derive a new type of inflow condition for standard k-ε turbulence model. A group of remedial approaches including formulation for wall shear stress and fixing the values of turbulent kinetic energy and turbulent dissipation rate in first wall adjacent layer cells, are also derived to realize the consistence of inflow condition and internal roughness. By combing the approaches with four different sets of inflow conditions, the well-maintained atmospheric boundary layer flow verifies the feasibility and capability of the proposed inflow conditions and remedial approaches.

β-Carotene Production from Dunaliella salina Cultivated with Bicarbonate as Carbon Source

Yimei Xi,Jinghan Wang,Song Xue,Zhanyou Chi 한국미생물·생명공학회 2020 Journal of microbiology and biotechnology Vol.30 No.6

Bicarbonate has been considered as a better approach for supplying CO2 to microalgae cells microenvironments than gas bubbling owing to cost-effectiveness and easy operation. However, the β-carotene production was too low in Dunaliella salina cultivated with bicarbonate in previous studies. Also, the difference in photosynthetic efficiency between these two carbon sources (bicarbonate and CO2) has seldom been discussed. In this study, the culture conditions, including NaHCO3, Ca2+, Mg2+ and microelement concentrations, were optimized when bicarbonate was used as carbon source. Under optimized condition, a maximum biomass concentration of 0.71 g/l and corresponding β-carotene content of 4.76% were obtained, with β-carotene yield of 32.0 mg/l, much higher than previous studies with NaHCO3. Finally, these optimized conditions with bicarbonate were compared with CO2 bubbling by online monitoring. There was a notable difference in Fv/Fm value between cultivations with bicarbonate and CO2, but there was no difference in the Fv/Fm periodic changing patterns. This indicates that the high concentration of NaHCO3 used in this study served as a stress factor for β-carotene accumulation, although high productivity of biomass was still obtained.

Reinforcement of Power System Performance Through Optimal Allotment of Distributed Generators Using Metaheuristic Optimization Algorithms

Mirsaeidi Sohrab,Li Shangru,Devkota Subash,He Jinghan,Li Meng,Wang Xiaojun,Konstantinou Charalambos,Said Dalila Mat,Muttaqi Kashem M. 대한전기학회 2022 Journal of Electrical Engineering & Technology Vol.17 No.5

Owing to the acute shortage of electric power in the majority of countries, short-term measures such as installation of Distributed Generators (DGs) have attracted much attention in recent decades. Employment of DGs can provide numerous advantages for the power systems through reduction of losses, escalation of the voltage profi le, as well as mitigation of pollutant emissions. However, in case they are not optimally allotted, they may even lead to aggravation of the network operation from diff erent aspects. The aim of this paper is to explore the optimal size and location of DGs using metaheuristic optimization algorithms so that the network performance is enhanced. The salient feature of the proposed strategy compared to the previous works is that it contemplates optimal allotment of DGs under various objectives, i.e. minimization of total network active and reactive power losses, and Cumulative Voltage Deviation (CVD), with diff erent weight values. Furthermore, the impact of enhancement in the number of DGs on diff erent aspects of power system performance is investigated. Finally, to increase the accuracy of the results, three diff erent nature-inspired optimization algorithms, i.e. Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), and Particle Swarm Optimization (PSO) are deployed, and their speed in approaching the global optimum is compared with each other. The simulation results on IEEE 14-bus system indicate that the proposed strategy not only can reinforce the overall network performance through reduction of active and reactive power losses, and voltage deviation but also lead to the improvement of network voltage profi le.

QTL Mapping of Kernel Oil Content of Chromosome 6 in a High Oil Maize Mutant (Zea mays L.)

Jing Han,Hong Wu Wang,Shao Jiang Chen 한국유전학회 2008 Genes & Genomics Vol.30 No.4

An infrequent high oil maize mutant CE03005 (white seed color and high oil content) from ethyl methane sulphonate pollen mutagenesis was investigated in this research. A F2 population derived from CE03005×B73 was used to analysis the genetic effects influencing kernel oil content and to map kernel oil content QTL. Ears from three generations were harvested and the individual kernel oil content of each generation was measured with nuclear magnetic resonance. Genetic analysis showed that the average oil content of white kernels was significantly higher than that of yellow kernels. Genotypic and phenotypic analyses were compared using composite interval mapping. A QTL (oilc6-m1)associated with high kernel oil content was detected in the three generations at bin 6.01 of chromosome 6 near the phenotypic marker SC (seed color). The mapping results were congruent with the genetic analysis. The results explained the reason why the oil content of the white colored kernels was higher than that of the yellow ones because the oil content QTL linked tightly with the seed color gene in chromosome 6 in the mutant. The way of QTL mapping was credible. Molecular and phenotypic markers identified could be used for breeding purpose and would lend convenience in cloning QTL.

Functional Characterization of aroA from Rhizobium Leguminosarum with Significant Glyphosate Tolerance in Transgenic Arabidopsis

( Jing Han ),( Yong Sheng Tian ),( Jing Xu ),( Li Juan Wang ),( Bo Wang ),( Ri He Peng ),( Quan Hong Yao ) 한국미생물 · 생명공학회 2014 Journal of microbiology and biotechnology Vol.24 No.9

Glyphosate is the active component of the top-selling herbicide, the phytotoxicity of which is due to its inhibition of the shikimic acid pathway. 5-Enolpyruvylshikimate-3-phosphate synthase (EPSPS) is a key enzyme in the shikimic acid pathway. Glyphosate tolerance in plants can be achieved by the expression of a glyphosate-insensitive aroA gene (EPSPS). In this study, we used a PCR-based two-step DNA synthesis method to synthesize a new aroA gene (aroAR. leguminosarum) from Rhizobium leguminosarum. In vitro glyphosate sensitivity assays showed that aroAR. leguminosarum is glyphosate tolerant. The new gene was then expressed in E. coli and key kinetic values of the purified enzyme were determined. Furthermore, we transformed the aroA gene into Arabidopsis thaliana by the floral dip method. Transgenic Arabidopsis with the aroAR. leguminosarum gene was obtained to prove its potential use in developing glyphosate-resistant crops.

Cytisine, a Partial Agonist of α4β2 Nicotinic Acetylcholine Receptors, Reduced Unpredictable Chronic Mild Stress-Induced Depression-Like Behaviors

( Jing Han ),( Dong Sheng Wang ),( Shui Bing Liu ),( Ming Gao Zhao ) 한국응용약물학회 2016 Biomolecules & Therapeutics(구 응용약물학회지) Vol.24 No.3

Cytisine (CYT), a partial agonist of α4β2-nicotinic receptors, has been used for antidepressant efficacy in several tests. Nicotinic receptors have been shown to be closely associated with depression. However, little is known about the effects of CYT on the depression. In the present study, a mouse model of depression, the unpredictable chronic mild stress (UCMS), was used to evaluate the activities of CYT. UCMS caused significant depression-like behaviors, as shown by the decrease of total distances in open field test, and the prolonged duration of immobility in tail suspension test and forced swimming test. Treatment with CYT for two weeks notably relieved the depression-like behaviors in the UCMS mice. Next, proteins related to depressive disorder in the brain region of hippocampus and amygdala were analyzed to elucidate the underlying mechanisms of CYT. CYT significantly reversed the decreases of 5-HT1A, BDNF, and mTOR levels in the hippocampus and amygdala. These results imply that CYT may act as a potential anti-depressant in the animals under chronic stress.

MRU-Net: A remote sensing image segmentation network for enhanced edge contour Detection

Jing Han,Weiyu Wang,Yuqi Lin,Xueqiang LYU 한국인터넷정보학회 2023 KSII Transactions on Internet and Information Syst Vol.17 No.12

Remote sensing image segmentation plays an important role in realizing intelligent city construction. The current mainstream segmentation networks effectively improve the segmentation effect of remote sensing images by deeply mining the rich texture and semantic features of images. But there are still some problems such as rough results of small target region segmentation and poor edge contour segmentation. To overcome these three challenges, we propose an improved semantic segmentation model, referred to as MRU-Net, which adopts the U-Net architecture as its backbone. Firstly, the convolutional layer is replaced by BasicBlock structure in U-Net network to extract features, then the activation function is replaced to reduce the computational load of model in the network. Secondly, a hybrid multi-scale recognition module is added in the encoder to improve the accuracy of image segmentation of small targets and edge parts. Finally, test on Massachusetts Buildings Dataset and WHU Dataset the experimental results show that compared with the original network the ACC、mIoU and F1 value are improved, and the imposed network shows good robustness and portability in different datasets.