Physical Model Investigation of a Compact Waste Water Pumping Station

Kirst, Kilian,Hellmann, D.H.,Kothe, Bernd,Springer, Peer Korean Society for Fluid machinery 2010 International journal of fluid machinery and syste Vol.3 No.4

To provide required flow rates of cooling or circulating water properly, approach flow conditions of vertical pump systems should be in compliance with state of the art acceptance criteria. The direct inflow should be vortex free, with low pre-rotation and symmetric velocity distribution. Physical model investigations are common practice and the best tool of prediction to evaluate, to optimize and to document flow conditions inside intake structures for vertical pumping systems. Optimization steps should be accomplished with respect to installation costs and complexity on site. The report shows evaluation of various approach flow conditions inside a compact waste water pumping station. The focus is on the occurrence of free surface vortices and the evaluation of air entrainment for various water level and flow rates. The presentation of the results includes the description of the investigated intake structure, occurring flow problems and final recommendations.

Envelope-Stress Sensing Mechanism of Rcs and Cpx Signaling Pathways in Gram-Negative Bacteria

Cho Seung-Hyun,Dekoninck Kilian,Collet Jean-Francois 한국미생물학회 2023 The journal of microbiology Vol.61 No.3

The global public health burden of bacterial antimicrobial resistance (AMR) is intensified by Gram-negative bacteria, which have an additional membrane, the outer membrane (OM), outside of the peptidoglycan (PG) cell wall. Bacterial twocomponent systems (TCSs) aid in maintaining envelope integrity through a phosphorylation cascade by controlling gene expression through sensor kinases and response regulators. In Escherichia coli, the major TCSs defending cells from envelope stress and adaptation are Rcs and Cpx, which are aided by OM lipoproteins RcsF and NlpE as sensors, respectively. In this review, we focus on these two OM sensors. β-Barrel assembly machinery (BAM) inserts transmembrane OM proteins (OMPs) into the OM. BAM co-assembles RcsF, the Rcs sensor, with OMPs, forming the RcsF-OMP complex. Researchers have presented two models for stress sensing in the Rcs pathway. The first model suggests that LPS perturbation stress disassembles the RcsF-OMP complex, freeing RcsF to activate Rcs. The second model proposes that BAM cannot assemble RcsF into OMPs when the OM or PG is under specific stresses, and thus, the unassembled RcsF activates Rcs. These two models may not be mutually exclusive. Here, we evaluate these two models critically in order to elucidate the stress sensing mechanism. NlpE, the Cpx sensor, has an N-terminal (NTD) and a C-terminal domain (CTD). A defect in lipoprotein trafficking results in NlpE retention in the inner membrane, provoking the Cpx response. Signaling requires the NlpE NTD, but not the NlpE CTD; however, OM-anchored NlpE senses adherence to a hydrophobic surface, with the NlpE CTD playing a key role in this function.

An Improved Non-parametric Bayesian Independence Test for Probabilistic Learning of the Dependence Structure Among Continuous Random Variables

변지은,Junho Song,Kilian Zwirglmaier,Daniel Straub 대한토목학회 2018 KSCE JOURNAL OF CIVIL ENGINEERING Vol.22 No.3

Probabilistic analysis of real-world complex systems such as civil infrastructures requires an effective identification of dependence among the input random variables. The correct modelling of such dependence is crucial for the accuracy and efficiency of a probabilistic assessment and decision-support. In particular, deciding if a pair of random variables is independent is an important step, and several methodologies have been developed for this task. The non-parametric Bayesian independence test is noteworthy among these, since it can deal with data sets whose distributions are unknown and it provides posterior probabilities of independence, which can be helpful in decision making. This paper first summarizes the general procedure of the nonparametric Bayesian independence test, and then examines the application of various types of non-informative priors – uniform, Jeffreys’ and reference priors – from both the theoretical and numerical viewpoint. In the end, the reference prior is recommended as the most suitable prior distribution for the purpose of Bayesian independence test. Furthermore, efficient and accurate discretization algorithms are proposed to facilitate a non-parametric Bayesian independence test of continuous random variables. Five numerical examples are studied to test the validity of the priors, and demonstrate the accuracy and efficiency of the proposed test algorithms. The supporting source codes and data used in the numerical examples are available for download at https:// github.com/jieunbyun/GitHub-BIT-code.

Matrix-based Bayesian Network for efficient memory storage and flexible inference

Byun, Ji-Eun,Zwirglmaier, Kilian,Straub, Daniel,Song, Junho Elsevier 2019 Reliability engineering & system safety Vol.185 No.-

<P><B>Abstract</B></P> <P>For real-world civil infrastructure systems that consist of a large number of functionally and statistically dependent components, such as transportation systems or water distribution networks, the Bayesian Network (BN) can be a powerful tool for probabilistic inference. In a BN, the statistical relationship between multiple random variables (r.v.’s) is modeled through a directed acyclic graph. The complexity of inference in the BN depends not only on the number of r.v.’s, but also the graphical structure. As a consequence, the application of standard BN techniques may become infeasible even with a moderate number of r.v.’s as the size of an event set exponentially increases with the number of r.v.’s. Moreover, when the exhaustive set that is required for full quantification of a discrete BN node becomes intractably large, only approximate inference algorithms are feasible, which do not require the full (explicit) description of all BN nodes. We address both issues in discrete BNs by proposing a matrix-based Bayesian Network (MBN) that facilitates efficient modeling of joint probability mass functions and flexible inference. The MBN is developed for exact as well as approximate BN inference. The efficiency and applicability of the MBN are demonstrated by numerical examples. The supporting source code and data are available for download at https://github.com/jieunbyun/GitHub-MBN-code.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A new data structure for discrete Bayesian Network is proposed. </LI> <LI> Both exact and approximate algorithms are developed for BN inference. </LI> <LI> Existing BN inference methodologies are compatible with the proposed data structure. </LI> <LI> Exact and approximate inferences of BNs are unified and generalized. </LI> <LI> Numerical examples demonstrate the performance of the proposed methodology. </LI> </UL> </P>

Chemical profiling in surface waters using two types of polar organic chemical integrative sampler (POCIS)

Yoonah Jeong,Hyun-ah Kwon,Andreas Schaffer,Kilian E.C. Smith,Hyun Pyo Jeon 한국분석과학회 2017 학술대회논문집 Vol.2017 No.11

An Improved Non-parametric Bayesian Independence Test for Probabilistic Learning of the Dependence Structure Among Continuous Random Variables

Byun, Ji-Eun,Song, Junho,Zwirglmaier, Kilian,Straub, Daniel Springer-Verlag 2018 KSCE JOURNAL OF CIVIL ENGINEERING Vol.22 No.3

<P>Probabilistic analysis of real-world complex systems such as civil infrastructures requires an effective identification of dependence among the input random variables. The correct modelling of such dependence is crucial for the accuracy and efficiency of a probabilistic assessment and decision-support. In particular, deciding if a pair of random variables is independent is an important step, and several methodologies have been developed for this task. The non-parametric Bayesian independence test is noteworthy among these, since it can deal with data sets whose distributions are unknown and it provides posterior probabilities of independence, which can be helpful in decision making. This paper first summarizes the general procedure of the nonparametric Bayesian independence test, and then examines the application of various types of non-informative priors - uniform, Jeffreys' and reference priors - from both the theoretical and numerical viewpoint. In the end, the reference prior is recommended as the most suitable prior distribution for the purpose of Bayesian independence test. Furthermore, efficient and accurate discretization algorithms are proposed to facilitate a non-parametric Bayesian independence test of continuous random variables. Five numerical examples are studied to test the validity of the priors, and demonstrate the accuracy and efficiency of the proposed test algorithms. The supporting source codes and data used in the numerical examples are available for download at code.</P>

Novel Molecular Method for Identification of Streptococcus pneumoniae Applicable to Clinical Microbiology and 16S rRNA Sequence-Based Microbiome Studies

Scholz, C.F.P.,Poulsen, K.,Kilian, M. AMERICAN SOCIETY FOR MICROBIOLOGY 2012 Journal of clinical microbiology Vol.50 No.6

Cellular Adaptation to VEGF-Targeted Antiangiogenic Therapy Induces Evasive Resistance by Overproduction of Alternative Endothelial Cell Growth Factors in Renal Cell Carcinoma ¹ ²

Han, Kyung Seok,Raven, Peter A.,Frees, Sebastian,Gust, Kilian,Fazli, Ladan,Ettinger, Susan,Hong, Sung Joon,Kollmannsberger, Cristian,Gleave, Martin E.,So, Alan I. Neoplasia Press 2015 Neoplasia Vol.17 No.11

<P>Vascular endothelial growth factor (VEGF)–targeted antiangiogenic therapy significantly inhibits the growth of clear cell renal cell carcinoma (RCC). Eventually, therapy resistance develops in even the most responsive cases, but the mechanisms of resistance remain unclear. Herein, we developed two tumor models derived from an RCC cell line by conditioning the parental cells to two different stresses caused by VEGF-targeted therapy (sunitinib exposure and hypoxia) to investigate the mechanism of resistance to such therapy in RCC. Sunitinib-conditioned Caki-1 cells <I>in vitro</I> did not show resistance to sunitinib compared with parental cells, but when tested <I>in vivo</I>, these cells appeared to be highly resistant to sunitinib treatment. Hypoxia-conditioned Caki-1 cells are more resistant to hypoxia and have increased vascularity due to the upregulation of VEGF production; however, they did not develop sunitinib resistance either <I>in vitro</I> or <I>in vivo</I>. Human endothelial cells were more proliferative and showed increased tube formation in conditioned media from sunitinib-conditioned Caki-1 cells compared with parental cells. Gene expression profiling using RNA microarrays revealed that several genes related to tissue development and remodeling, including the development and migration of endothelial cells, were upregulated in sunitinib-conditioned Caki-1 cells compared with parental and hypoxia-conditioned cells. These findings suggest that evasive resistance to VEGF-targeted therapy is acquired by activation of VEGF-independent angiogenesis pathways induced through interactions with VEGF-targeted drugs, but not by hypoxia. These results emphasize that increased inhibition of tumor angiogenesis is required to delay the development of resistance to antiangiogenic therapy and maintain the therapeutic response in RCC.</P>

Development of Rice DNA markers with Diversity-Array Technology (DArT)

Yong Gu Cho,Susan McCouch,Kai Man Peng,Sujin Patarapuwado,Andrzej Kilian 한국육종학회 2002 한국육종학회 학술발표회 발표요지 Vol.34 No.1

Active Control of the Vortex Induced Pressure Fluctuations in a Hydro Turbine Model via Axial and Radial Jets at the Crown Tip

Ivan Litvinov,Daniil Suslov,Mikhail Tsoy,Evgeny Gorelikov,Sergey Shtork,Sergey Alekseenko,Kilian Oberleithner 한국유체기계학회 2023 International journal of fluid machinery and syste Vol.16 No.4

This paper presents an active method to control the pressure fluctuations induced by the rotating vortex rope (RVR) in a Francis hydro turbine model under part load conditions. The control method is based on the injection of axial or radial jets through a stagnant crown attached to the hydro turbine runner. A wide range of injection strategies are com-pared, and the effectiveness of suppressing pressure fluctuations is analyzed in terms of the spatial distribution of the jets and the flow rate required to suppress the oscillations. The experiments are performed on a fully automated aerody-namic test rig. The pressure fluctuations are quantified using data from the four acoustic sensors placed at a cross sec-tion in the cone of the hydro turbine draft tube. The best suppression of pressure fluctuations is achieved with a radial actuator. At a control flow rate of 2% of the main flow, the pressure fluctuations at the vortex rope frequency are re-duced by 80% in terms of PSD compared to the baseline case without control. The presented control method will be useful for extending the operating range of Francis hydro turbines.