Search

Performing high-fidelity computational fluid dynamics (HF-CFD) to predict the flow and heat transfer state of the coolant in the reactor core is expensive, especially in scenarios that require extensive parameter search, such as uncertainty analysis and design optimization. This work investigated the performance of utilizing a multi-fidelity reduced-order model (MF-ROM) in PWR rod bundles simulation. Firstly, basis vectors and basis vector coefficients of high-fidelity and low-fidelity CFD results are extracted separately by the proper orthogonal decomposition (POD) approach. Secondly, a surrogate model is trained to map the relationship between the extracted coefficients from different fidelity results. In the prediction stage, the coefficients of the low-fidelity data under the new operating conditions are extracted by using the obtained POD basis vectors. Then, the trained surrogate model uses the low-fidelity coefficients to regress the high-fidelity coefficients. The predicted high-fidelity data is reconstructed from the product of extracted basis vectors and the regression coefficients. The effectiveness of the MF-ROM is evaluated on a flow and heat transfer problem in PWR fuel rod bundles. Two data-driven algorithms, the Kriging and artificial neural network (ANN), are trained as surrogate models for the MF-ROM to reconstruct the complex flow and heat transfer field downstream of the mixing vanes. The results show good agreements between the data reconstructed with the trained MF-ROM and the high-fidelity CFD simulation result, while the former only requires to taken the computational burden of low-fidelity simulation. The results also show that the performance of the ANN model is slightly better than the Kriging model when using a high number of POD basis vectors for regression. Moreover, the result presented in this paper demonstrates the suitability of the proposed MF-ROM for high-fidelity fixed value initialization to accelerate complex simulation.

As a valid numerical method to obtain a high-resolution result of a flow field, computational fluid dynamics (CFD) have been widely used to study coolant flow and heat transfer characteristics in fuel rod bundles. However, the time-consuming, iterative calculation of Navier-Stokes equations makes CFD unsuitable for the scenarios that require efficient simulation such as sensitivity analysis and uncertainty quantification. To solve this problem, a reduced-order model (ROM) based on proper orthogonal decomposition (POD) and machine learning (ML) is proposed to simulate the flow field efficiently. Firstly, a validated CFD model to output the flow field data set of the rod bundle is established. Secondly, based on the POD method, the modes and corresponding coefficients of the flow field were extracted. Then, an deep feed-forward neural network, due to its efficiency in approximating arbitrary functions and its ability to handle high-dimensional and strong nonlinear problems, is selected to build a model that maps the non-linear relationship between the mode coefficients and the boundary conditions. A trained surrogate model for modes coefficients prediction is obtained after a certain number of training iterations. Finally, the flow field is reconstructed by combining the product of the POD basis and coefficients. Based on the test dataset, an evaluation of the ROM is carried out. The evaluation results show that the proposed POD-ROM accurately describe the flow status of the fluid field in rod bundles with high resolution in only a few milliseconds.

This research serves to advance the development of engineering computational fluid dynamics (CFD) computing efficiency for the analysis of pressurized water reactor (PWR) core using rod-type fuel assemblies with mixing vanes (one kind of typical PWR core). In this research, a CFD scheme based on the reconstruction of the initial fine flow field (RIF CFD scheme) is proposed and analyzed. The RIF scheme is based on the quantitative regulation of flow velocities in the rod-type PWR core and the principle that the CFD computing efficiency can be improved greatly by a perfect initialization. In this paper, it is discovered that the RIF scheme can significantly improve the computing efficiency of the CFD computation for the rod-type PWR core. Furthermore, the RIF scheme also can reduce the computing resources needed for effective data storage of the large fluid domain in a rod-type PWR core. Moreover, a flow-ranking RIF CFD scheme is also designed based on the ranking of the flow rate, which enhances the utilization of the flow field with a closed flow rate to reconstruct the fine flow field. The flow-ranking RIF CFD scheme also proved to be very effective in improving the CFD efficiency for the rod-type PWR core.

In this paper, the coupling of fine-mesh computational fluid dynamics (CFD) thermal-hydraulics (TH) code and neutronics code is achieved using the Ansys Fluent User Defined Function (UDF) for code development, including parallel meshing mapping, data computation, and data transfer. Also, some CFD schemes are designed for mesh mapping and data transfer to guarantee physical conservation in the coupling computation. Because there is no rigorous research that gives robust guidance on the various CFD schemes that must be obtained before the fine-mesh coupling computation, this work presents a quantitative analysis of the CFD meshing and mapping schemes to improve the accuracy of the value and location of key physical prediction. Furthermore, the effect of the sub-pin scale coupling computation is also studied. It is observed that even the pin-resolved coupling computation can also create a large deviation in the maximum value and spatial locations, which also proves the significance of the research on mesh mapping and data transfer for CFD code in a coupling computation.

Aim To investigate the impact of circumferential tumour location on neoadjuvant chemoradiotherapy (CRT) response and its prognostic value for locally advanced rectal cancer (LARC) patients after CRT and surgery. Methods A retrospective study was performed on 486 patients with LARC who received neoadjuvant CRT and surgical treatment. The rate of pathological complete response (pCR) and survival among patients with anteriorly, laterally, and posteriorly located tumours were compared. Logistic regression was performed to identify pCR predictors. Results The anterior tumours exhibited the highest pCR rate of 26.7%, which was slightly higher than the 20.0% and 12.3% for lateral and posterior tumours, respectively (P = 0.006). The 5-year Overall survival (OS) rates after CRT were similar among the anterior, lateral, and posterior groups (anterior vs lateral vs posterior: 81.1% vs 89.9% vs 84.1%, P = 0.6368). Multivariate analysis revealed that the circumferential tumour location, post-CRT serum CEA and post-CRT tumour thickness measured by MRI were independently correlated with achieving pCR. Conclusion This study is the first, to the best of our knowledge, to show that anterior LARC exhibited the highest pCR rate after neoadjuvant CRT. Patients with anterior rectal cancers do not have different prognoses from those with non-anterior cancers if they undergo neoadjuvant CRT.

Purpose: Preoperative sarcopenia has been proved to be associated with worse postoperative outcomes in cancer patients. This study aimed to evaluate whether preoperative sarcopenia affects the perioperative outcomes, adjuvant chemotherapy, and long-term outcomes of patients with stage Ⅲ colon cancer.Methods: Total 218 patients who underwent curative resection for stage Ⅲ colon cancer in our department from January, 2015 and December, 2018 were retrospectively analyzed. Sarcopenia was assessed by total psoas index, which measured the total area the level of L3 vertebral body and normalized according to patients’ height. Perioperative complications, postoperative adjuvant chemotherapy, and long-term prognosis were retrospectively analyzed.Results: Of 218 patients, 100(45.9%) patients were diagnosed with sarcopenia. Sarcopenia did not add the risk of perioperative complications (20.0% vs 15.3%, P=0.357), but it increased hospital stays (7.6±3.9 vs 6.7±2.2 days, P=0.042). Patients with sarcopenia had a lower rate of receiving adjuvant chemotherapy (70.0% vs 82.2%, p=0.033) and less likely to receive adequate adjuvant chemotherapy (58.6% vs 70.1, P=0.08). Patients with adequate adjuvant chemotherapy had significantly better 3-year OS (89.8% vs 79.5, P=0.005) and a tendency of better 3-year DFS (76.4% vs 63.6%, P=0.055) than those with inadequate adjuvant chemotherapy and Non-adjuvant chemotherapy. Compared with the patients without sarcopenia, patients with sarcopenia had significantly worse 3-year DFS (76.9% vs 62.8%, P=0.026).Conclusion: Preoperative sarcopenia was an important indicator to predict the compliance of AC in stage Ⅲ colon cancer patients, and it is also a significant prognostic factor of worse 3-year DFS.

Chen (2018) proposed a scalable spectral clustering algorithm for cosine similarity to handle the task of clustering large data sets. It runs extremely fast, with a linear complexity in the size of the data, and achieves state of the art accuracy. This paper conducts perturbation analysis of the algorithm to understand the effect of discarding a perturbation term in an eigendecomposition step. Our results show that the accuracy of the approximation by the scalable algorithm depends on the connectivity of the clusters, their separation and sizes, and is especially accurate for large data sets.

Increased arterial stiffness is highly prevalent in patients with hypertension and is associated with cardiovascular (CV) risk. Increased white blood cell (WBC) counts may also be an independent risk factor for arterial stiffness and CV events. The aim of the study was to investigate the relationship between differential WBC counts and brachial-ankle pulse wave velocity (baPWV) in hypertensive adults. A total of 14 390 participants were included in the final analysis. A multivariate linear regression model was applied for the correlation analysis of WBC count and baPWV. Higher WBC counts were associated with a greater baPWV: adjusted β = 10 (95% CI, 8-13, P < .001). The same significant association was also found when WBC count was assessed as categories or quartiles. In addition, the effect of differential WBC subtypes, including neutrophil count and lymphocyte count on baPWV, showed the similar results. These findings showed that baPWV has positive associations with differential WBC counts in hypertensive adults.

Background: Glioblastomas (GBMs) are grade IV central nervous system tumors characterized by a poor prognosis and a median overall survival of 15 months. The glioblastoma cell population is more genetically unstable, resistant to chemotherapy, more angiogenic, and more malignant. Because ubiquitin-specific proteases (USPs) remove ubiquitin from oncogenic protein substrates, effective inhibition of ubiquitin-specific protease 7 (USP7), which is highly expressed in GBMs, is a potentially critical therapeutic approach. Methods: Immunohistochemistry and Western blotting were used to detect high expression of USP7 in GBM. in vitro studies were performed by Western blotting, immunofluorescence, and flow cytometry to detect apoptosis following inhibition of USP7. Anti-apoptotic substrates of USP7 were defined by Co-IP and TMT proteomics. Nude mouse intracranial xenograft models were constructed to verify whether inhibition of USP7 inhibited the proliferation rate of tumours.Results: USP7 is significantly upregulated in glioblastoma samples. Interfering with USP7 in GBMs induced significant apoptosis, which also occurred after treatment with P5091, a novel small molecule inhibitor of USP7. Mechanistically, apoptosis of GBMs after interference with USP7 function is achieved by stabilizing a key anti-apoptotic protein, ADP-ribosylation factor 4 (ARF4).Furthermore, USP7 interacts directly with ARF4 and catalyzes the removal of the K48-linked polyubiquitinated chain that binds to ARF4, thereby stabilizing the protein. In in vivo experiments, P5091 significantly inhibited tumor growth and promoted the expression of apoptotic genes. Conclusion: Targeted inhibition of USP7 enhances the ubiquitination of ARF4 and ultimately mediates the apoptosis of GBM cells. In a clinical sense, P5091 as a specific inhibitor of USP7 may be an effective approach for the treatment of GBM.

In a microgrid with multiple types of power sources, the distribution and capacity configuration of power sources will have a greater impact on the economics of the microgrid. Aiming at the grid-connected hybrid AC/DC microgrid of wind, solar, and storage, the power flow model with the goal of reducing the economic cost of the microgrid operation was established. Taking the self-balancing rate and converter loss of the microgrid as the evaluation indicators of the microgrid, a suitable operation control strategy is proposed. At the same time, using the actual data of a certain place, the genetic algorithm is used to optimize the solution, and the optimal scheme of its capacity configuration is given, which verifies the effectiveness of the control strategy proposed in this paper. The results show that increasing the battery capacity and coordinated configuration of wind turbines and photovoltaics can make the microgrid have better operating economy.

In a subchannel analysis, the assumptions of the physical models may be invalid when three-dimensional (3-D) effects play an important role because a large-scale model cannot consider a small-scale physical process. However, in a pressurized water reactor (PWR), the flow process has a high 3-D effect due to the effect of complex structures, such as dimple, spring, and mixing vane. A computational fluid dynamics (CFD) analysis can give more detailed physical information. So, the modeling assumptions of the subchannel analysis codes were analyzed using data from CFD analysis, and some issues were found: The spatial acceleration of the cross-flow rate and the viscous force from fluid to fluid should not be neglected; the lateral pressure gradient not only is a driving force but also can be a resistance at some vertical range; the traditional “resistant force term” has the same direction with the cross flow at some vertical ranges. To improve the subchannel code, one physical term considering both the...

Without efficiency, the CFD simulation of a reactor will not be affordable. However, numerous narrow flow channels with thousands of complex spacer grid exist in a typical pressurized water reactor (PWR) core. The huge burden in both computational resources and time is a key challenge. To realize a reduction of the computational burden, research on flow characteristics affected by RANS models and typical structures of PWR have been done. Based on the flow characteristics, a CFD scheme was designed by a hybrid application of multiple RANS models (MRANS). Different from the traditional turbulence scheme which just use one single RANS model, MRANS keeps the isotropic RANS models used in the major domains, and the anisotropic RANS modes used in the small local domains. After some validations, it was found that MRANS can successfully keep the accuracy and improve the computing efficiency by nearly two times. This superiority in efficiency could play an important role in the CFD simulation for a whole PWR.

Single-scale Gaussian-kernel support vector machines (SVM) have achieved competitive accuracy in many practical tasks; however, a fundamental limitation of the underlying model is its use of a single bandwidth parameter which essentially assumes that the training and test data has a uniform scale everywhere. In cases of data with multiple scales, only one parameter may be unable to fully capture the heterogeneous scales present in the data. In this paper, we present two efficient approaches to constructing multiscale Gaussian kernels for SVM classification by following the multiple-kernel learning research by Gonen and Alpaydin (J Mach Learn Res 12: 2211–2268, 2011) and a self-tuning spectral clustering procedure introduced by Zelnik-Manor and Perona (Advances in Neural Information Processing Systems 17:1601–1608, 2004) in the unsupervised setting, respectively. The resulting kernels adapt to the different scales of the data and are directly computable from the training data, thus avoiding expensive hyperparameter tuning tasks. Numerical experiments demonstrate that our multiscale kernels lead to superior accuracy and fast speed.

Background This study aimed to test the feasibility and titration methods used to achieve specific blood pressure (BP) control targets in hypertensive patients of rural China. Methods A randomized, controlled, open-label trial was conducted in Rongcheng, China. We enrolled 105 hypertensive participants aged over 60 years, and who had no history of stroke or cardiovascular disease. The patients were randomly assigned to one of three systolic-BP target groups: standard: 140 to Discussion The optimal target for systolic blood pressure (SBP) lowering is still uncertain worldwide and such information is critically needed, especially in China. However, in China the rates of awareness, treatment and control are only 46.9%, 40.7%, and 15.3%, respectively. It is challenging to achieve BP control in the real world and it is very important to develop population-specific BP-control protocols that fully consider the population’s characteristics, such as age, sex, socio-economic status, compliance with medication, education level, and lifestyle. This randomized trial showed the feasibility and safety of the titration protocol to achieve desirable SBP targets ( Trial registration Feasibility Study of the Intensive Systolic Blood Pressure Control; ClinicalTrials.gov, ID: NCT02817503. Registered retrospectively on 29 June 2016.

In order to solve the problem of non-stationary vibration signals of rolling bearings and large sample size, a small sample fault diagnosis method based on wavelet packet theory and Random Forest algorithm was proposed in this paper. Firstly, the signal was preprocessed, then three-layer wavelet packet decomposition was carried out, and the decomposition coefficients are extracted as feature vectors, which were used to train BP Neural Network, Support Vector Machine (SVM) and Random Forest model. Through the diagnosis of multi-fault states of rolling bearings, the diagnostic accuracy rates of the three groups of classifiers are respectively 25%, 93.75% and 100%, which proves that Random Forest has better diagnosis effect and robustness for rolling bearings with small samples.

In the treatment of resistant hypertension, physiologically individualized therapy based on phenotyping with plasma renin activity (PRA) and plasma aldosterone significantly improves blood pressure control. Patients with a low-renin/low aldosterone (Liddle) phenotype respond best to amiloride, while those with low-renin/high aldosterone respond best to aldosterone antagonists, and those with high renin/high aldosterone (renal phenotype) respond best to angiotensin receptor blockers (ARB). However, it is important to measure PRA in a stimulated condition to distinguish between low levels due to high salt intake, licorice or nonsteroidal inflammatory drugs and low levels due to suppression by excess aldosterone secretion or renal tubular genetic variants causing retention of salt and water (Liddle phenotype). In the past, both diuretics and angiotensin converting inhibitors (ACEi) have been used for this purpose, and it has been assumed that these classes of drugs are equivalent. In this study of 2896 patients with hypertension, we evaluated that assumption. We found important differences among diuretics alone, ACEi/ARB alone, and ACEi/ARB + diuretics, which all stimulated PRA. However, ACEi/ARB lowers plasma aldosterone, and beta blockers lower PRA. Among patients with systolic pressure ≥ 180 mmHg ± diastolic pressure ≥ 100 mmHg stimulated only by diuretics, the phenotypes were 25% Liddle, 38% IA, 8.7% renal, and 28.3% mixed. In choosing physiologically individualized therapy based on PRA and aldosterone, it is important to consider the classes of stimulating drugs. Phenotypes are best distinguished by taking into account the aldosterone/PRA ratio in addition to the levels of PRA and aldosterone.

A fault diagnosis method based on wavelet packet (WP) reconstruction of energy entropy, particle swarm optimization (PSO) and least squares support vector machine (LSSVM) is proposed for non-stationary vibration signals of rolling bearings. Firstly, the vibration signal is preprocessed, followed by 3-layer wavelet packet decomposition, and the energy entropy percentage of the reconstruction coefficient is extracted as the feature vector. Then, the 8-dimensional fault feature vector is reduced to a 2-dimensional feature vector by principal component analysis (PCA). Finally, the 2-dimensional feature vector is taken as the input sample of PSO-LSSVM. In order to diagnose the three fault states of the inner ring, the ball and the outer ring of the rolling bearing, four LSSVM classifiers are established. After the simulation analysis of the bearing vibration data, the diagnostic accuracy rate of the LSSVM multi-classifier group was 100%, which proves the feasibility and effectivity of the method.

A fault diagnosis method based on principal component analysis (PCA) and support vector machine (SVM) model is proposed to solve the problem of high dimension and large sample size of wind turbine fault data. Firstly, The PCA is used to extract low-dimensional fault features from high-dimensional fault data to eliminate the correlation between features. Then, the grid search algorithm (GSA) is used to optimize the loss parameters and kernel function parameters of the SVM model. Secondly, low-dimensional fault features are used as input training classifiers for SVM. Finally, fault diagnosis is carried out through feature classification. Simulation results have shown that the diagnostic accuracy could reach 100% when Polynomial kernel function and two-dimensional principal component analysis were used, indicating that this method can quickly and effectively diagnose various faults.

We briefly review recent progress in techniques for modeling and analyzing hyperspectral images and movies, in particular for detecting plumes of both known and unknown chemicals. For detecting chemicals of known spectrum, we extend the technique of using a single subspace for modeling the background to a “mixture of subspaces” model to tackle more complicated background. Furthermore, we use partial least squares regression on a resampled training set to boost performance. For the detection of unknown chemicals, we view the problem as an anomaly detection problem and use novel estimators with low-sampled complexity for intrinsically low-dimensional data in high dimensions that enable us to model the “normal” spectra and detect anomalies. We apply these algorithms to benchmark datasets made available by the Automated Target Detection program cofunded by NSF, DTRA, and NGA, and compare, when applicable, to current state-of-the-art algorithms, with favorable results.

The Pressurized Water Reactor (PWR) has numerous complex and narrow structures, such as a spacer grid, mixing vane, and narrow channels. Experiments can be used to research the flow characteristics in a reactor. However, using some simplification and approximation cannot be avoided: for example only several fuel rods are used and some solid walls are imposed for modeling the flow in a reactor core. The influence of these simplifications needs to be considered and discussed clearly, since the influence certainly exists and it may result in errors. To obtain a good understanding, this research has studied the impact created by the experimental conditions. The regularities found in this research can be used as a guide for the optimal design and utilization of an experiment to study the physical processes in a PWR.

Prediction and analysis of coolant flow and mixing problems in the reactor pressure vessel (RPV) are some of the most critical studies to ensure system safety and balance of nuclear power plant. The flow mixing problem in the reactor primary system with complicated interior structure is a large domain three-dimensional problem. In this paper, we investigated the performance of utilizing multiple RANS models (MRANS) coupling method in the computational fluid dynamics (CFD) code ANSYS FLUENT to study flow mixing. Based on the domain decomposition technique, the 2-equation and 7-equation RANS models were coupled and applied to simulate the flow mixing phenomenon in the Rossendorf Coolant Mixing Model (ROCOM) test facility. First, a CFD model of ROCOM was developed, and the explicit domain decomposition coupling simulation was achieved with a user-defined Python control program. To ensure the best CFD model was utilized, three different turbulence models and varying resolution grids were used for turbulence model feasibility analysis and grid sensitivity testing. Then the applicability of MRANS coupling method was evaluated by comparing the results from the MRANS simulations with those obtained from flow mixing experiments. In addition, single and MRANS model simulation results were compared. Results show good agreements between the MRANS coupling simulations and the experimental data. Compared with overall simulations by single RANS models, the MRANS coupling method also have superior performance in accuracy and efficiency for the CFD simulation study of mixing phenomena in the reactor primary coolant system.

Through the investigation of the research status of commonly used system reliability modeling and analysis methods, this paper mainly gives a new definition and rules for more stringent system reliability common signals. At the same time, the precise algorithm for the common signals of the existing GO method is improved. Furthermore, a reliability modeling and analysis method for nuclear power systems with common signals based on GO method is established. Finally, the lifting mechanism inside the nuclear island was selected as a system analysis case with common signals, and the reliability modeling and analysis are performed using the method proposed in this paper. The results of the analysis were compared with the results of quantitative analysis of the Monte Carlo method, which verified the correctness and applicability of the proposed method.

We propose a unified scalable computing framework for three versions of spectral clustering - Normalized Cut (Shi and Malik, 2000), the Ng-Jordan-Weiss (NJW) algorithm (2001), and Diffusion Maps (Coifman and Lafon, 2006), in the setting of cosine similarity. We assume that the input data is either sparse (e.g., as a document-term frequency matrix) or of only a few hundred dimensions (e.g., for small images or data obtained through PCA). We show that in such cases, spectral clustering can be implemented solely based on efficient operations on the data matrix such as elementwise manipulation, matrix-vector multiplication and low-rank SVD, thus entirely avoiding the weight matrix. Our algorithm is simple to implement, fast to run, accurate and robust to outliers. We demonstrate its superior performance through extensive experiments which compare our scalable algorithm with the plain implementation on several benchmark data sets.

To obtain a better subchannel analysis tool for PWR core, an investigation on the physical modelling was done via CFD analysis in this paper. A detailed discussion on the TH status was carried out to consider the suitability of the physical modelling in the subchannel analysis. The counteracted effect on the balance of the mass, momentum, and energy was found. Its influence on the physical mechanism was analyzed for the TH status in PWR core. Then it was found that the subchannel analysis based on each flow channel is very necessary for the accuracy of the 3D engineering TH analysis for the whole PWR core.

Numerical simulation has been widely used in nuclear reactor safety analyses to gain insight into key phenomena. The Critical Heat Flux (CHF) is one of the limiting criteria in the design and operation of nuclear reactors. It is a two-phase flow phenomenon, which rapidly decreases the heat transfer performance at the rod surface. This paper presents a numerical simulation of a steady state flow in a vertical pipe to predict the CHF phenomena. The detailed Computational Fluid Dynamic (CFD) modeling methodology was developed using FLUENT. Eulerian two-phase flow model is used to model the flow and heat transfer phenomena. In order to gain the peak wall temperature accurately and stably, the effect of different turbulence models and wall functions are investigated based on different grids. Results show that O type grid should be used for the simulation of CHF phenomenon. Grids with Y+ larger than 70 are recommended for the CHF simulation because of the acceptable results of all the turbulence models while Grids with Y+ lower than 50 should be avoided. To predict the dry-out position accurately in a fine grid, Realizable k-ε model with standard wall function is recommended. These conclusions have some reference significance to better predict the CHF phenomena of vertical pipe. It can also be expanded to rod bundle of Boiling Water Reactor (BWR) by using same pressure condition.

In this paper, we present a large area nanosecond pulse gas-liquid diffuse discharge plasma with small flow rate of helium as working gas. The discharge images, the waveforms of pulse voltage & current, the emission spectra, and the gas temperature are measured or calculated. As a energy-efficient sterilization for sewage, the efficiency of sewage sterilization in both static liquid and flowed liquid are investigated. It is found that an optimal value of helium gas flow rate in the production of reactive oxygen species (OH, O) can be obtained when the He flow rate is 5mlmin � 1 , under this condition, high inactivation efficiencies of bacteria and fungus can be presented.

CFD simulation for a PWR is an important part for the development of Numerical Virtual Reactor (NVR) in Harbin Engineering University of China. CFD simulation can provide the detailed information of the flow and heat transfer process in a PWR. However, a large number of narrow flow channels with numerous complex structures (mixing vanes, dimples, springs, etc.) are located in a typical PWR. To obtain a better CFD simulation, the challenges created by these structural features were analyzed and some quantitative regularity and estimation were given in this paper. It was found that both computing resources and time are in great need for the CFD simulation of a whole reactor. These challenges have to be resolved, so two schemes were designed to assist/realize the reduction of the simulation burden on resources and time. One scheme is used to predict the combined efficiency of the simulation conditions (configuration of computing resources and application of simulation schemes), so it can assist the better choice/decision of the combination of the simulation conditions. The other scheme is based on the suitable simplification and modification, and it can directly reduce great computing burden.

Multi-physics coupling analysis is one of the most important fields among the analysis of nuclear power plant. The basis of multi-physics coupling is the coupling between neutronics and thermal-hydraulic because it plays a decisive role in the computation of reactor power, outlet temperature of the reactor core and pressure of vessel, which determines the economy and security of the nuclear power plant. This paper develops a coupling method which uses OPENFOAM and the REMARK code. OPENFOAM is a 3-dimension CFD open-source code for thermal-hydraulic, and the REMARK code (produced by GSE Systems) is a real-time simulation multi-group core model for neutronics while it solves diffusion equations. Additionally, a coupled computation using these two codes is new and has not been done. The method is tested and verified using data of the QINSHAN Phase II typical nuclear reactor which will have 16 × 121 elements. The coupled code has been modified to adapt unlimited CPUs after parallelization. With the further development and additional testing, this coupling method has the potential to extend to a more large-scale and accurate computation.

Presently noninvasive methods were employed to the diagnosis of nonalcoholic fatty liver disease (NAFLD), including fatty liver index (FLI), hepatic steatosis index (HSI), product of fasting triglyceride and glucose levels (TyG), and single nucleotide polymorphism (SNP), whereas the accuracy of those indexes need to be improved. Our study aimed to investigate the feasibility of a new index comprehensive index (CI), consisting of 6 serum biomarkers and anthropometric parameters through multivariate logistic regression analysis, to the earlier detection of NAFLD, and the diagnostic value of 5 SNPs (S1: rs2854116 of apolipoprotein C3 [APOC3], S2: rs4149267 of ATP-binding cassette transporter [ABCA1], S3: rs13702 of lipoprotein lipase [LPL], S4: rs738409 of protein 3 [patatin-like phospholipase domain containing protein 3 (PNPLA3)], S5: rs780094 of glucokinase regulatory protein gene [GCKR]) for NAFLD were also explored. Area under the receiver operating characteristic curves (AUROC) and Youden index (YI) were calculated to assess the diagnostic value. The AUROC of CI was higher than FLI, HSI, and TyG (CI: 0.897, FLI: 0.873, HSI: 0.855, TyG: 0.793). Therefore, CI might be a better index for the diagnosis of NAFLD. Although there had no statistical significance (P = .123), the AUROC and YI were increased when CI combined with rs2854116 (S1) (AUROC = 0.902, YI = 0.6844). The combination of CI with S1 showed even better diagnostic accuracy than CI, which suggests the potential value of rs2854116 for the diagnosis of NAFLD.

Adaptive dictionary learning has become a hot-topic research field during the past decade. Though several algorithms have been proposed and achieved impressive results, they are all computationally intensive due to the lack of structure in their output dictionaries. In this paper we build upon our previous work and take a geometric approach to develop better, more efficient algorithms that can learn adaptive structured dictionaries. While inheriting many of the advantages in the previous construction, the new algorithm better utilizes the geometry of data and effectively removes translational invariances from the data, thus able to produce smaller, more robust dictionaries. We demonstrate the performance of the new algorithm on two data sets, and conclude the paper by a discussion of future work.

We present a fast, versatile and adaptive-multiscale algorithm for analyzing a wide-variety of DNA microarray data. Its primary application is in normalization of array data as well as subsequent identification of ‘enriched targets’, e.g. differentially expressed genes in expression profiling arrays and enriched sites in ChIP-on-chip experimental data. We show how to accommodate the unique characteristics of ChIP-on-chip data, where the set of ‘enriched targets’ is large, asymmetric and whose proportion to the whole data varies locally. Contact: lerman@umn.edu Supplementary information: Supplementary figures, related preprint, free software as well as our raw DNA microarray data with PCR validations are available at as well as Bioinformatics online.

Data sets are often modeled as samples from some probability distribution lying in a very high dimensional space. In practice, they tend to exhibit low intrinsic dimensionality, which enables both fast construction of efficient data representations and solving statistical tasks such as regression of functions on the data, or even estimation of the probability distribution from which the data is generated. In this paper we introduce a novel multiscale density estimator for high dimensional data and apply it to the problem of detecting changes in the distribution of dynamic data, or in a time series of data sets. We also show that our data representations, which are not standard sparse linear expansions, are amenable to compressed measurements. Finally, we test our algorithms on both synthetic data and a real data set consisting of a times series of hyperspectral images, and demonstrate their high accuracy in the detection of anomalies.

Geometric Wavelets is a new multi-scale data representation technique which is useful for a variety of applications such as data compression, interpretation and anomaly detection. We have developed an interactive visualization with multiple linked views to help users quickly explore data sets and understand this novel construction. Currently the interface is being used by applied mathematicians to view results and gain new insights, speeding methods development.

Multi-manifold modeling is increasingly used in segmentation and data representation tasks in computer vision and related fields. While the general problem, modeling data by mixtures of manifolds, is very challenging, several approaches exist for modeling data by mixtures of affine subspaces (which is often referred to as hybrid linear modeling). We translate some important instances of multi-manifold modeling to hybrid linear modeling in embedded spaces, without explicitly performing the embedding but applying the kernel trick. The resulting algorithm, Kernel Spectral Curvature Clustering, uses kernels at two levels - both as an implicit embedding method to linearize nonflat manifolds and as a principled method to convert a multiway affinity problem into a spectral clustering one. We demonstrate the effectiveness of the method by comparing it with other state-of-the-art methods on both synthetic data and a real-world problem of segmenting multiple motions from two perspective camera views., accepted to 2009 ICCV Workshop on Dynamical Vision

OBJECTIVE To investigate the effects of amlodipine, a dihydropyridine calcium-channel blocker, alone or combined with terazosin, on urodynamics in rats with benign prostatic hyperplasia (BPH) and in female rats with detrusor instability (DI). MATERIALS AND METHODS The rat model of BPH was induced by subcutaneous injection of testosterone (0.5 mg per rat for 21 days). The female rat model of DI was induced by partial bladder outlet ligation for 6 weeks. The rats were intragastrically administered with assigned drugs (amlodipine, terazosin or both combined) for 14 days in three experiments. Continuous cystometry was assessed in rats under urethane anaesthesia. RESULTS Amlodipine at 0.5, 1 and 3 mg/kg significantly decreased the bladder index (BI), threshold pressure (TP), and micturition pressure (MP), and significantly increased intermicturition duration (IMD) in BPH rats. Moreover, amlodipine 0.5 mg/kg plus terazosin 0.4 mg/kg gave the greatest improvements in all urodynamic variables, with a greater decrease in BI, TP, MP and greater increase in IMD than with either of the drugs used alone in BPH and DI rats, and had a similar efficacy to terazosin 1 mg/kg. The combined treatment also gave a greater decrease in nonvoiding bladder contractions in DI rats. CONCLUSIONS Amlodipine alone or combined with terazosin might have the potential to alleviate lower urinary tract symptoms (LUTS). The combined therapy appears to be more suitable for LUTS with predominantly irritative symptoms.

We apply the Spectral Curvature Clustering (SCC) algorithm to a benchmark database of 155 motion sequences, and show that it outperforms all other state-of-the-art methods. The average misclassification rate by SCC is 1.41% for sequences having two motions and 4.85% for three motions., Comment: Accepted to 2009 ICCV Workshop on Dynamical Vision

To investigate the therapeutic efficacy and safety of Amlodipine alone or in combination with terazosin for the presence of lower urinary tract symptoms (LUTS) and hypertension. LUTS and hypertension often coexist in elderly men.A total of 355 patients with Stage 1 or 2 hypertension and LUTS (as defined by an International Prostate Symptom Score ofor =10) were randomly assigned to receive 2 mg of terazosin (n = 117), 5 mg of Amlodipine (n = 119), or 5 mg of Amlodipine plus 2 mg of terazosin (n = 119) once daily for a total of 28 days. The primary outcomes were a reduction in the total and subscores of the International Prostate Symptom Score and blood pressure. Analyses were performed by intention to treat. This trial is registered with ClinicalTrials.gov (No. NCT00693199).At day 28 of the trial, the Amlodipine plus terazosin group demonstrated comparable efficacy in lowering the total International Prostate Symptom Score and significant improvement in the presence of overactive bladder compared with the terazosin group (P.05) and significant improvement in quality of life compared with the Amlodipine group (P.05). The Amlodipine plus terazosin group also achieved the greatest blood pressure control compared with either the terazosin group (P.01) or Amlodipine group (P.05). All 3 treatment regimens were well tolerated by the study patients.The results of this 4-week, double-blind, randomized trial have demonstrated that in Chinese male hypertensive patients with LUTS, low-dose Amlodipine plus terazosin therapy appears to be a safe and effective combination therapy to control both conditions, especially for those with predominant overactive bladder symptoms.