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A stochastic prognostic framework for modeling the population dynamics of convective clouds and representing them in climate models is proposed. The framework follows the non-equilibrium statistical mechanical approach to constructing a master equation for representing the evolution of the number of convective cells of a specific size and their associated cloud-base mass flux, given a large-scale forcing. In this framework, referred to as STOchastic framework for Modeling Population dynamics of convective clouds (STOMP), the evolution of convective cell size is predicted from three key characteristics of convective cells: (i) the probability of growth, (ii) the probability of decay, and (iii) the cloud-base mass flux. STOMP models are constructed and evaluated against CPOL radar observations at Darwin and convection permitting model (CPM) simulations. Multiple models are constructed under various assumptions regarding these three key parameters and the realisms of these models are evaluated. It is shown that in a model where convective plumes prefer to aggregate spatially and the cloud-base mass flux is a non-linear function of convective cell area, then the mass flux manifests a recharge-discharge behavior under steady forcing. Such a model also produces observed behavior of convective cell populations and CPM simulated cloud-base mass flux variability under diurnally varying forcing. In addition to its use in developing understanding of convection processes and the controls on convective cell size distributions, this modeling framework is also designed to be capable of serving as a non-equilibrium closure formulations for spectral mass flux parameterizations.

Unfortunately, in the original publication of the article, the voucher number in the second paragraph was published erroneously as “YNUSM20160817008”. The corrected voucher number should read as “YNUST201406180002”.

Background/Aims: Epidermolytic palmoplantar keratoderma (EPPK) is an autosomal dominant genodermatosis. It is characterized by diffuse yellow keratoses on the palmoplantar epidermis, with an erythematous border. The keratin 9 gene (KRT9) and less frequently the keratin 1 gene (KRT1) are responsible for EPPK. This study aims to identify and analyse genetic defects responsible for EPPK in a Han Chinese pedigree. Methods: A four-generation Han Chinese pedigree containing five individuals affected with EPPK was recruited. Exome sequencing, Sanger sequencing, and bioinformatics tools were conducted to identify the mutation in this pedigree. HaCaT cells were transfected with either wild-type or mutated KRT9. Confocal laser immunofluorescence assay, imaging processing, and statistical analysis were performed to evaluate wild-type and mutant KRT9 groups. Results: A novel heterozygous c.1369C>T transition (p.Leu457Phe) in exon 6 of the KRT9 gene was identified in four patients. It co-segregated with the disorder in the family. Functional analysis showed that withdrawal of the filament network from the cell periphery and particle formation were present in about 10% of Leu457Phe-transfected HaCaT cells, while approximately 3% of cells transfected with wild-type KRT9 showed this phenotype. The particles in mutant group were larger than that in wild-type group (P-value < 0.05). Conclusion: The variant may be the disease-causing missense mutation and produce dominant negative effects by interrupting keratin network formation. This study indicates the pathogenic role of the KRT9 gene mutation in this pedigree with EPPK, and may be helpful in genetic counseling, prenatal diagnosis and gene-targeted therapies of EPPK.

Due to the complexity of the oral environment, understanding the relationship between the enamel microstructure and corrosion behavior of mammalian teeth is important for the development and bionic design of novel dental materials with corrosion resistance. In this paper, enamel microstructure and its influence on corrosion and tribocorrosion behavior of bovine and human molars are studied using focused ion beam & scanning electronic microscopy (FIB-SEM), transmission electron microscope (TEM), nanoindentation, and wear testing. It was found that the regions with transversal HAP nanocrystallites have better corrosion resistance than those with longitudinal crystallites. Apart from the chemical composition, the microstructure of enamel also involves in its surface corrosion. HAP nanocrystallite orientation changes the acid permeability into the enamel surface. Transversal nanocrystallites result in zigzagged acid permeation channels to increase the difficulty of acid penetration and then improve the corrosion resistance of enamel surface. The higher the fraction of transversal nanocrystallites on a surface, the better is the corrosion resistance. These findings extend our understanding of the corrosion/wear mechanisms of dental enamel with different microstructures.

The wreathed hornbill (Rhyticeros undulatus) has been listed in the IUCN Red List of Threatened Species, because of decreasing population trend. There is a lack of genomic information on this species, which can provide valuable resource for conservation biology and phylogenetic studies on hornbill birds. Here we first characterized the complete mitochondrial genome (mitogenome) sequence of R. undulatus by next-generation sequencing and conducted phylogenomic analysis of hornbill species with mitogenome sequences available. The complete mitogenome of R. undulatus was 17,842 bp in length and incorporated 13 protein-coding genes (PCGs), 2 ribosomal RNA (rRNA) genes, 22 transfer RNA genes, and 1 control region (D-loop). The overall nucleotide composition of this mitogenome was 30.3% A, 22.4% T, 32.6% C, and 14.7% G, with AT content of 52.7%. Phylogenomic analysis using mitogenomes of eight hornbill birds showed that R. undulatus was more closely related to Penelopides panini and two Aceros species.

Regional drought frequency analysis was carried out in the Poyang Lake basin (PLB) from 1960–2014 based on three standardized drought indices: the standardized precipitation index (SPI), the standardized precipitation evapotranspiration index (SPEI) and the standardized Palmer drought index (SPDI). Drought events and characteristics were extracted. A Gumbel–Hougaard (GH) copula was selected to construct the bivariate probability distribution of drought duration and severity, and the joint return periods (T a ) were calculated. Results showed that there were 50 (50 and 40) drought events in the past 55 years based on the SPI (SPEI and SPDI), and 9 (8 and 10) of them were severe with T a more than 10 years, occurred in the 1960s, the 1970s and the 2000s. Overall, the three drought indices could detect the onset of droughts and performed similarly with regard to drought identification. However, for the SPDI, moisture scarcity was less frequent, but it showed more severe droughts with substantially higher severity and longer duration droughts. The conditional return period (Ts|d) was calculated for the spring drought in 2011, and it was 66a and 54a, respectively, based on the SPI and SPDI, which was consistent with the record. Overall, the SPI, only considering the precipitation, can as effectively as the SPEI and SPDI identify the drought process over the PLB under the present changing climate. However, drought is affected by climate and land-cover changes; thus, it is necessary to integrate the results of drought frequency analysis based on different drought indices to improve the drought risk management.

With increasing computational capabilities, cumulus parameterizations that are adaptable to the smaller grid spacing and temporal interval for high-resolution climate model simulations are needed. In this study, we propose a method to improve the resolution adaptability of the Zhang-McFarlane (ZM) scheme, by implementing spatial and temporal averaging to the CAPE tendency. This method allows for a more consistent application of the quasi-equilibrium (QE) hypothesis at high spatial and temporal resolutions. The resolution adaptability of the original ZM scheme, the scheme with spatial averaging, and the scheme with spatiotemporal averaging at 4–32 km grid spacings are assessed using the Weather Research and Forecasting (WRF) model by comparing to cloud resolving model (CRM) simulation results coarse-grained to these same grid spacings. We show the original ZM scheme has poor resolution adaptability, with spatiotemporally averaged subgrid convective transport and convective precipitation increasing significantly as the resolution increases. The spatial averaging method improves the resolution adaptability of the ZM scheme and better conserves total transport and total precipitation. Temporal averaging further improves the resolution adaptability of the scheme. With better constrained (although smoothed) convective transport and precipitation, both the spatial distribution and time series of total precipitation at 4 and 8 km grid spacings are improved with the averaging methods. The results could help develop resolution adaptability for other cumulus parameterizations that are based on the QE assumption.

Innovative elastoplastic J 2 − flow models with nonlinear combined hardening are proposed for the purpose of simulating multi-axial thermo-coupled fatigue failure for metals and alloys, in a direct sense without involving any usual damage-like variables and any ad hoc failure criteria. As contrasted with usual models, the yield condition and the loading-unloading conditions need not be imposed as extrinsic coercive conditions but may be automatically incorporated into a free, smooth flow rule in a more realistic sense. Novel results in three respects are available directly from model predictions, namely, (i) complex features of thermo-coupled fatigue failure may be automatically represented by simple asymptotic properties of the hardening functions introduced, (ii) critical failure states may be derived with a criterion in unified form, and, in particular, (iii) direct procedures may be established for determining fatigue lives under either stress cycles or thermal cycles. Simulation results in the simplest cases of the proposed model are in good agreement with experimental data.

Anthracoceros coronatus (Bucerotiformes: Bucerotidae) has been listed in near threatened category in the IUCN Red List of Threatened Species, due to population decline. Mitochondrial genome (mitogenome) sequence is informative and very useful for systematics and conservation biology studies. In this study, we first determined and characterized the complete mitogenome of A. coronatus using next-generation sequencing platform. The complete mitogenome of A. coronatus was 17,214 bp in length, with an AT ratio of 52.8%. It contained the typical structure of 13 protein-coding genes, 2 ribosomal RNA genes, 22 transfer RNA genes and a non-coding control region or D-loop region, with an identical gene order to that of most other vertebrates. The complete mitogenome sequence of A. coronatus would not only provide additional molecular markers of choice for detecting illegal poaching and trafficking of this bird species, but also would contribute to phylogenomic study of hornbill birds.

Buceros bicornis (Bucerotiformes: Bucerotidae) has been listed as a near threatened species by the International Union for Conservation of Nature and Natural Resources (IUCN) due to over-hunting and habitat loss. Thus, it is a great challenge for us to efficiently protect the Buceros bicornis resource from extinct. Molecular biology research can provide the scientific basis for the conservation of key endangered species. In this work, we first reported the complete mitochondrial genome (mitogenome) sequence of Buceros bicornis by next-generation sequencing (NGS). The total length of the complete mitogenome was 17,117 bp and its overall base composition was estimated to be 30.2% for A, 23.5% for T, 32.3% for C and 14.0% for G, indicating an A + T (53.7%)-rich feature in the Buceros bicornis complete mitogenome. In addition, it contained the typical structure of 13 protein-coding genes (PCGs), 2 ribosomal RNA genes, 22 transfer RNA genes (tRNAs) and a non-coding control region (D-loop region). The newly obtained mitogenome sequence seems to be useful for conservation biology and evolutionary studies on hornbill bird species, which would contribute to enrich the Buceros bicornis mitogenome resource and promote its biological research.

It has been reported that long non-coding RNA PANDA was disregulated in varieties types of tumor, but its expression level and biological role in hepatocellular carcinoma (HCC) remains contradictory. We detected PANDA expression in two independent cohorts (48 HCC patients following liver transplantation and 84 HCC patients following liver resection), and found that PANDA was down-regulated in HCC. Thereafter we explored its function in cancer biology by inversing its low expression. Surprisingly, overexpression of PANDA promoted HCC proliferation and carcinogenesis in vitro and in vivo. Mechanistically, PANDA repressed transcriptional activity of senescence associated inflammatory factor IL8, which leaded to inhibition of cellular senescence. Therefore, our research help to better understand the complex role of PANDA in HCC, and suggest more thoughtful strategies should be applied before it can be treated as a potential therapeutic target.

To investigate the effect of acupuncture plus mild hypothermia on neurological function impairment score, cerebral infarct size and apoptosis-related factors in cerebral ischemia reperfusion injury (CIRI) rats. Sixty healthy male Sprague-Dawley (SD) rats were routinely reared for 1 week. Ten rats were randomly selected as the sham operation group and 10 rats as the blank control group, while the remaining 40 rats were subjected to preparing the middle cerebral artery occlusion (MCAO) model by modified filament occlusion method. The 40 MCAO rats were further randomly divided into a model group, an acupuncture group, a mild hypothermia group and an acupuncture plus mild hypothermia group, with 10 rats in each group. Rats in the sham operation group, the blank control group and the model group did not accept treatment except binding; rats in the acupuncture group received acupuncture treatment; rats in the mild hypothermia group received mild hypothermia treatment; rats in the acupuncture plus mild hypothermia group received acupuncture and mild hypothermia treatment. 72 h after the treatment, neurological function impairment score was performed; the infarct area ratio was determined by 2,3,5-tripheyl tetrazolium chloride (TTC) staining; apoptosis of brain cells was observed by TUNEL method; the expressions of Bcl-2, Bax and Caspase-3 were detected by immunohistochemistry. Compared with the blank control group and the sham operation group, the neurological function impairment score, cerebral infarct area ratio, apoptosis, and the expressions of Bax and Caspase-3 in the model group were significantly increased, while the expression of Bcl-2 was significantly decreased, and there were significant between-group differences (all P

Early cancer diagnosis in molecular level shows great promise in relevant prevention and clinical treatment. Herein, we developed a novel method to detect and evaluate biomarkers on cancer cell surface. Based on the excellent selectivity of AS1411 aptamer to targeted nucleolin-overexpressed cells, we used a fluorescent probe with a pH-sensitive function to label the AS1411 aptamer modified with azide by click-reaction. The spectral characteristics of fluorophore naphthalimide has a good pH dependence. By this way, nucleolin-overexpressed cell could be discriminated from normal cell. Further, this strategy could also discriminate the breast cancer tissue from the adjacent benign tissue in formalin-fixed and parrffin- embedded (FFPE) tissue specimens. It is considered that our method has the potential to be applied in medical detection.

// Rongliang Tong 1, 2, * , Beng Yang 1, 3, * , Heng Xiao 4, * , Chuanhui Peng 1, 2 , Wendi Hu 1, 2 , Xiaoyu Weng 1, 2 , Shaobing Cheng 1, 2 , Chengli Du 2 , Zhen Lv 1 , Chaofeng Ding 1 , Lin Zhou 2, 5 , Haiyang Xie 2, 5 , Jian Wu 1, 5 and Shusen Zheng 1, 2, 5 1 Department of Surgery, Division of Hepatobiliary and Pancreatic Surgery, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310000, China 2 Key Laboratory of Combined Multi-Organ Transplantation, Ministry of Public Health, Hangzhou 310000, China 3 Key Laboratory of Organ Transplantation, Zhejiang Province, Hangzhou 310000, China 4 Department of Hepatobiliary Surgery, First Affiliated Hospital, Chongqing Medical University, Chongqing 400016, China 5 The Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang University, Hangzhou 310000, China * These authors have contributed equally to this work Correspondence to: Jian Wu, email: drwujian@hotmail.com Shusen Zheng, email: shusenzheng@zju.edu.cn Keywords: KCTD11, hepatocellular carcinoma, Hippo pathway, p21, cell adhesion Received: June 14, 2016 Accepted: March 29, 2017 Published: April 17, 2017 ABSTRACT A lack of effective prognostic biomarkers and molecular targets is a serious problem in hepatocellular carcinoma. KCTD11, reported as a tumor suppressor, are still not well understood. In this study, KCTD11 was found low-expressed in HCC tissues and cell lines. The HCC patients with low expression of KCTD11 suggested shorter overall survival. We found KCTD11 inhibiting cell proliferation in vitro and tumor growth in vivo , by activating p21 and repressing cycle related proteins. KCTD11 also inhibited cell adhesion by decreasing CTGF and CLDN1. We found CTGF binding COL3A1 in HCCLM3, which might lead to reduction of COL3A1 expression. KCTD11 also inhibited cell migration and invasion in HCC, by repressing MMPs and EMT. We found the tumor suppression function of KCTD11 was at least partly through activating Hippo pathway in HCC. Base on the enhanced Hippo pathway, KCTD11 could activate p21 by stabilizing p53 or promoting the MST1/ GSK3β/p21 signaling in HCC. Overall, these results suggest that KCTD11 works as a tumor suppressor and owns prognostic and therapeutic potentials in HCC.

Although Reynolds-Averaged Navier-Stokes (RANS) equations are still the dominant tool for engineering design and analysis applications involving turbulent flows, standard RANS models are known to be unreliable in many flows of engineering relevance, including flows with separation, strong pressure gradients or mean flow curvature. With increasing amounts of 3-dimensional experimental data and high fidelity simulation data from Large Eddy Simulation (LES) and Direct Numerical Simulation (DNS), data-driven turbulence modeling has become a promising approach to increase the predictive capability of RANS simulations. Recently, a data-driven turbulence modeling approach via machine learning has been proposed to predict the Reynolds stress anisotropy of a given flow based on high fidelity data from closely related flows. In this work, the closeness of different flows is investigated to assess the prediction confidence a priori. Specifically, the Mahalanobis distance and the kernel density estimation (KDE) technique are used as metrics to quantify the distance between flow data sets in feature space. The flow over periodic hills at Re=10595 is used as the test set and seven flows with different configurations are individually used as training set. The results show that the prediction error of the Reynolds stress anisotropy is positively correlated with Mahalanobis distance and KDE distance, demonstrating that both extrapolation metrics can be used to estimate the prediction confidence a priori. A quantitative comparison using correlation coefficients shows that the Mahalanobis distance is less accurate in estimating the prediction confidence than KDE distance. The extrapolation metrics introduced in this work and the corresponding analysis provide an approach to aid in the choice of the data source and to assess the prediction confidence for data-driven turbulence modeling., 31 pages, 13 figures

A hyperelastic model is proposed to characterize large elastic deformation behaviors of soft solids. Novelties in four respects are incorporated in this model, namely, (i) general compressible deformations may be treated and, accordingly, the usually-assumed incompressibility constraint and associated issues may be bypassed; (ii) applicability for all kinds of material samples may be automatically ensured, in the sense that any given test data for three deformation modes may be exactly fitted, including uniaxial, equi-biaxial and plane-strain extension; (iii) model parameters of direct physical meanings may be provided to characterize both strain-stiffening and softening effects; and (iv) breaking behavior may be simulated. Numerical results for model validation are in good agreement with Treloar’s classic data for rubbers and with extensive data for various kinds of polymer gels.

Human tooth enamel, the most mineralized tissue in body, contains less than 2 wt% protein. Consequently, the importance of the protein to enamel mechanical response has always been overlooked. In this study, the role of minor protein in providing enamel microstructure and mechanical performance, especially tribological properties, were studied using deproteinization treatment and nano-indentation/scratch technique. Via the change from the original to the deproteinated conditions, a nanostructure degeneration from the assembly of hydroxyapatite (HA) crystals into nano-fibers to crystal aggregation has been found between the high-wear-resistance and low-wear-resistance on the enamel surface. Correspondingly, an energy dissipation to cause a unit volume of wear on enamel surface decreases by 50%, and wear volume increases by 80%. With the presence of protein, the occurrence of enamel wear requires to break the interfacial protein bonding between the HA crystals in nano-fibers and the break dissipates considerable energy, which benefits the enamel to resist wear. Thus, the protein in enamel, although of a very low content, is essential to resisting wear by mediating the assembly of rigid HA crystals via interfacial protein bonding. Replicating functions of the protein component will be critical to the successful development of bio-inspired materials that are designed for wear-resistance.

Supplemental material, Supplemental_Material_for_DiffusionMRIderivedVesselDensity_XiaoAndWang for Diffusion MRI Derived per Area Vessel Density as a Surrogate Biomarker for Detecting Viral Hepatitis B-Induced Liver Fibrosis: A Proof-of-Concept Study by Ben-Heng Xiao, Hua Huang, Li-Fei Wang, Shi-Wen Qiu, Sheng- Wen Guo and Yì Xiáng J. Wáng in SLAS Technology

Inverse problems in computational mechanics consist of inferring physical fields that are latent in the model describing some observable fields. For instance, an inverse problem of interest is inferring the Reynolds stress field in the Navier–Stokes equations describing mean fluid velocity and pressure. The physical nature of the latent fields means they have their own set of physical constraints, including boundary conditions. The inherent ill-posedness of inverse problems, however, means that there exist many possible latent fields that do not satisfy their physical constraints while still resulting in satisfactory agreement in the observation space. These physical constraints must therefore be enforced through the problem formulation. So far there has been no general approach to enforce boundary conditions on latent fields in inverse problems in computational mechanics, with these constraints often simply ignored. In this work we demonstrate how to enforce boundary conditions in Bayesian inversion problems by choice of the statistical model for the latent fields. Specifically, this is done by modifying the covariance kernel to guarantee that all realizations satisfy known values or derivatives at the boundary. As a test case the problem of inferring the eddy viscosity in the Reynolds-averaged Navier–Stokes equations is considered. The results show that enforcing these constraints results in similar improvements in the output fields but with latent fields that behave as expected at the boundaries.

Computational fluid dynamics models based on Reynolds-averaged Navier–Stokes equations with turbulence closures still play important roles in engineering design and analysis. However, the development of turbulence models has been stagnant for decades. With recent advances in machine learning, data-driven turbulence models have become attractive alternatives worth further explorations. However, a major obstacle in the development of data-driven turbulence models is the lack of training data. In this work, we survey currently available public turbulent flow databases and conclude that they are inadequate for developing and validating data-driven models. Rather, we need more benchmark data from systematically and continuously varied flow conditions (e.g., Reynolds number and geometry) with maximum coverage in the parameter space for this purpose. To this end, we perform direct numerical simulations of flows over periodic hills with varying slopes, resulting in a family of flows over periodic hills which ranges from incipient to mild and massive separations. We further demonstrate the use of such a dataset by training a machine learning model that predicts Reynolds stress anisotropy based on a set of mean flow features. We expect the generated dataset, along with its design methodology and the example application presented herein, will facilitate development and comparison of future data-driven turbulence models.

Evolutionary multitasking is currently under focus as a new application of evolutionary computation where a search population operates independent problems in parallel. An evolutionary multifactorial optimization has proposed in recent years as an instance of evolutionary multitasking, it is inspired by human cognition ability processing several tasks. An actual evolutionary multifactorial optimization uses a relationship among different problems and assigns resources to each problem. The parallelism of population-based search is expected to be a good solver for the multitask optimization where different tasks should be optimized. In this paper, we propose a hybrid swarm intelligence based multifactorial optimization. A hybrid swarm is combined with particle swarm model and firefly model, and multi-factorization by using the skill factor that is used in the multifactorial evolutionary algorithm. Then we apply the proposed algorithm to the multiple car structure design benchmark where three different cars are treated to have a light but a satisfying stiffness and safety. In other words, the simultaneous three cars design problem is treated as a multitask optimization. The experimental results show that the hybridization gives more efficient search performance than the base swarm intelligence based multifactorial optimization.

Big data of mobile network privacy is vulnerable to clear text attack in the process of storage and mixed network information sharing, which leads to information leakage. Through the mixed encryption of data of mobile network privacy big data to improve the confidentiality and security of mobile network privacy big data, a mobile network privacy big data hybrid encryption algorithm based on hyperchaos theory is proposed. The hybrid encryption key of mobile network privacy big data is constructed by using hyperchaotic nonlinear mapping hybrid encryption technology. Combined with the feature distribution of mobile network privacy big data, the mixed encrypted public key is designed by using Logistic hyperchaotic arrangement method, and a hyperchaotic analytic cipher and block cipher are constructed by using Rossle chaotic mapping. The random piecewise linear combination method is used to design the coding and key of mobile network privacy big data. According to the two-dimensional coding characteristics of mobile network privacy big data in the key authorization protocol, the hybrid encryption and decryption key of mobile network privacy big data is designed, and the mixed encryption and decryption key of mobile network privacy big data is constructed, Realize the privacy of mobile network big data mixed encryption output and key design. The simulation results show that this method has good confidentiality and strong steganography performance, which improves the anti-attack ability of big data, which is used to encrypt the privacy of mobile network.

Multifactorial optimization (MFO) has attracted attention in the field of evolutionary computation as the third trial where is utilizing multiple search points to optimize multitask optimization. MFO is a method that will optimize multiple individual tasks simultaneously with using some kinds of relation among the target tasks. To make the search efficient, in previous research, we developed a novel MFO algorithm named Task Selective Artificial Bee Colony (TSABC), an improvement of TSABC by introducing a procedure like Firefly Algorithm (FA) is proposed. Then, by applying the proposed method to the real-world car structure design optimization problem, the effectiveness of the proposed method is presented.

AimLiver vessel density can be evaluated by an imaging biomarker DDVD (diffusion derived vessel density): DDVD/area(b0b2) = Sb0/ROIarea0 – Sb2/ROIarea2, where Sb0 and Sb2 refer to the liver signal whenbis 0 or 2 (s/mm2); ROIarea0 and ROIarea2 refer to the region-of-interest onb= 0 or 2 images; and Sb2 may be replaced by Sb15 (b=15). This concept was validated in this study.Materials and MethodsLiver diffusion images were acquired at 1.5T. For a scan-rescan repeatability study of 6 subjects,b-values of 0 and 2 were used. The validation study composed of 26 healthy volunteers and 19 consecutive suspected chronic viral hepatitis-b patients, and diffusion images with 16b-values of 0, 2, 4, 7, 10, 15, 20, 30, 46, 60, 72, 100, 150, 200, 400, 600 were acquired. Four patients did not have liver fibrosis, and the rest were four stage-1, three stage-2, four stage 3, and one stage-4 patients respectively.ResultsIntraclass correlation coefficient for repeatability was 0.994 for DDVD/area(Sb0Sb2), and 0.978 for DDVD/area(Sb0Sb15). In the validation study, DDVD/area(Sb0Sb2) and area(Sb0Sb15) were 14.80±3.06 and 26.58±3.97 for healthy volunteers, 10.51±1.51 and 20.15±2.21 for stage 1-2 fibrosis patients, and 9.42±0.87 and 19.42±1.89 for stage 3-4 fibrosis patients. For 16 patients where IVIM analysis was performed, a combination of DDVD/area, PF, and Dfast achieved the best differentiation for non-fibrotic livers and fibrotic livers. DDVD/area were weakly correlated with PF or Dfast.ConclusionBoth DDVD/area(Sb0Sb2) and area(Sb0Sb15) are useful imaging biomarker to separate fibrotic and non-fibrotic livers, with fibrotic livers had lower measurements.

Reynolds-averaged Navier–Stokes (RANS) simulations with turbulence closure models continue to play important roles in industrial flow simulations. However, the commonly used linear eddy-viscosity models are intrinsically unable to handle flows with non-equilibrium turbulence (e.g. flows with massive separation). Reynolds stress models, on the other hand, are plagued by their lack of robustness. Recent studies in plane channel flows found that even substituting Reynolds stresses with errors below 0.5 % from direct numerical simulation databases into RANS equations leads to velocities with large errors (up to 35 %). While such an observation may have only marginal relevance to traditional Reynolds stress models, it is disturbing for the recently emerging data-driven models that treat the Reynolds stress as an explicit source term in the RANS equations, as it suggests that the RANS equations with such models can be ill-conditioned. So far, a rigorous analysis of the condition of such models is still lacking. As such, in this work we propose a metric based on local condition number function for a priori evaluation of the conditioning of the RANS equations. We further show that the ill-conditioning cannot be explained by the global matrix condition number of the discretized RANS equations. Comprehensive numerical tests are performed on turbulent channel flows at various Reynolds numbers and additionally on two complex flows, i.e. flow over periodic hills, and flow in a square duct. Results suggest that the proposed metric can adequately explain observations in previous studies, i.e. deteriorated model conditioning with increasing Reynolds number and better conditioning of the implicit treatment of the Reynolds stress compared to the explicit treatment. This metric can play critical roles in the future development of data-driven turbulence models by enforcing the conditioning as a requirement on these models.

Background: Recently a small cohort study demonstrated that intravoxel incoherent motion (IVIM) diffusion MRI can detect early stage liver fibrosis. Using modified IVIM data acquisition parameters, the current study aims to confirm this finding. Methods: Twenty-six healthy volunteers, three patients of chronic viral hepatitis-b but without fibrosis and one mild liver steatosis subject, and 12 viral hepatitis-b patients with fibrosis (stage 1–2=7, stage 3–4=5) were included in this study. With a 1.5-T MR scanner and respiration-gating, IVIM diffusion imaging was acquired using a single-shot echo-planar sequence with a b -value series of 2, 0, 1, 15, 20, 30, 45, 50, 60, 80, 100, 200, 300, 600, 800 s/mm 2 . Signal measurement was performed on right liver parenchyma. The first three very low b -values were excluded to improve the curve fitting stability, and bi-exponential segmented fitting was performed using the 12 b -values of 15~800 s/mm 2 . Both threshold b -values of 60 s/mm 2 and 200 s/mm 2 were tested. With a 3-dimensional tool, Dslow ( D ), PF ( f ) and Dfast ( D* ) values were placed along the x-axis, y-axis, and z-axis, and a plane was defined to separate healthy volunteers from liver fibrosis patients. Results: Threshold b -value of 60 s/mm 2 was preferred over 200 s/mm 2 for separating healthy volunteers and liver fibrosis patients. The IVIM measures of the four patients without fibrosis resembled those of healthy volunteers. When threshold b -value =60 s/mm 2 was applied, PF (PF

A study shows that when trained using channel flow data at just one Reynolds number and informed with known flow physics, the neural network works robustly as a wall model in large-eddy simulation (LES) of channel flow at any Reynolds number. It also outperforms the equilibrium wall model in LES of a 3D boundary layer flow.

Pixel value ordering and prediction error expansion (PVO+PEE) is a very successful reversible data hiding (RDH) scheme. A series of studies were proposed to improve the performance of the PVO-based scheme. However, the embedding capacity of those schemes is quite limited. We propose a two-step prediction-error-triplet expansion RDH scheme based on PVO. A three-dimensional state transition map for the prediction-error triplet is also proposed to guide the embedding of the two-step scheme. By properly designing the state transitions, the proposed scheme can embed secret data or expand without embedding by modifying just a single entry of the triplet. The experimental results show that the proposed scheme significantly enlarges the embedding capacity of the PVO-based scheme and further reduces the distortion due to embedding.

Reconstruction of turbulent flow based on data assimilation methods is of significant importance for improving the estimation of flow characteristics by incorporating limited observations. Existing works mainly focus on using only one observation data source, e.g., velocity, wall pressure, lift or drag force, to reconstruct the flow. In practical applications observations are disparate data sources that often vary in dimension and quality. Simultaneously incorporating these disparate data is worth investigation to improve the flow reconstruction. In this work, we investigate the disparate data assimilation with ensemble methods to enhance the reconstruction of turbulent mean flows. Specifically, a regularized ensemble Kalman method is employed to incorporate the observation of velocity and different sources of wall quantities (e.g., wall shear stress, wall pressure distribution, lift and drag force). Three numerical examples are used to demonstrate the capability of the proposed framework for assimilating disparate observation data. The first two cases, i.e., a one-dimensional planar channel flow and a two-dimensional transitional flow over plate, are used to incorporate both the sparse velocity and wall friction. In the third case of the flow over periodic hills, the wall pressure distribution and the lift and drag force are regarded as observation in addition to velocity, to recover the flow fields. The results demonstrate the merits of incorporating various disparate data sources to improve the accuracy of the flow-field estimation. The ensemble-based method can assimilate disparate data non-intrusively and robustly without requiring significant changes to the model simulation codes. The method demonstrated here opens up possibilities for assimilating realistic experimental data, which are often disparate.

The emerging push of the differentiable programming paradigm in scientific computing is conducive to training deep learning turbulence models using indirect observations. This paper demonstrates the viability of this approach and presents an end-to-end differentiable framework for training deep neural networks to learn eddy viscosity models from indirect observations derived from the velocity and pressure fields. The framework consists of a Reynolds-averaged Navier–Stokes (RANS) solver and a neural-network-represented turbulence model, each accompanied by its derivative computations. For computing the sensitivities of the indirect observations to the Reynolds stress field, we use the continuous adjoint equations for the RANS equations, while the gradient of the neural network is obtained via its built-in automatic differentiation capability. We demonstrate the ability of this approach to learn the true underlying turbulence closure when one exists by training models using synthetic velocity data from linear and nonlinear closures. We also train a linear eddy viscosity model using synthetic velocity measurements from direct numerical simulations of the Navier–Stokes equations for which no true underlying linear closure exists. The trained deep-neural-network turbulence model showed predictive capability on similar flows.

The current research status of seismic lateral bearings and blocks of small and medium span beam bridges are briefly reviewed. The current research status of the block and bearing is analyzed and discussed, and the future research trend is discussed. The research results show that the research on the bearing is focused on the discussion of the mechanism and influence of the friction coefficient, friction force and slip performance of the plate rubber bearing; in terms of the stop, various new stop structure forms emerge in an endless stream; with the in-depth research of the two, Researchers began to pay attention to the mutual interaction of the support and the block when the actual earthquake occurred, and began to test the conceptual design, but how to fully consider the overall seismic performance of the bridge under the interaction of the two in the small and medium-span girder bridge The impact of this requires more in-depth research.

A consistent dual-mesh hybrid LES/RANS framework for turbulence modeling has been proposed recently (H. Xiao, P. Jenny, A consistent dual-mesh framework for hybrid LES/RANS modeling, J. Comput. Phys. 231 (4) (2012)). To better enforce componentwise Reynolds stress consistency between the LES and the RANS simulations, in the present work the original hybrid framework is modified to better exploit the advantage of more advanced RANS turbulence models. In the new formulation, the turbulent stresses in the filtered equations in the under-resolved regions are directly corrected based on the Reynolds stresses provided by the RANS simulation. More precisely, the new strategy leads to implicit LES/RANS consistency, where the velocity consistency is achieved indirectly via imposing consistency on the Reynolds stresses. This is in contrast to the explicit consistency enforcement in the original formulation, where forcing terms are added to the filtered momentum equations to achieve directly the desired average velocity and velocity fluctuations. The new formulation keeps the averaging procedure for the filtered quantities and at the same time preserves the ability of the original formulation to conform with the physical differences between LES and RANS quantities. The modified formulation is presented, analyzed, and then evaluated for plane channel flow and flow over periodic hills. Improved predictions are obtained compared with the results obtained using the original formulation.

The objective of this study is two-fold. Firstly, new finite strain elastoplasticity models are proposed from a fresh standpoint to achieve a comprehensive representation of thermomechanical behavior of metals and alloys over the whole deformation range up to failure. As contrasted with the usual elastoplasticity models, such new models of much simpler structure are totally free, in the sense that both the yield condition and the loading–unloading conditions need not be introduced as extrinsic coercive conditions but are automatically incorporated as inherent constitutive features into the models. Furthermore, the new models are shown to be thermodynamically consistent, in a further sense that both the specific entropy function and the Helmholtz free energy function may be presented in explicit forms, such that the thermodynamic restriction stipulated by Clausius–Duhem inequality for the intrinsic dissipation may be identically satisfied. Secondly, it is then demonstrated that the thermo-coupled fatigue failure behavior under combined cyclic changes of stress and temperature may be derived as direct consequences from the new models. This novel result implies that the new model can directly characterize the thermo-coupled fatigue failure behavior of metals and alloys, without involving any usual damage-like variables as well as any ad hoc additional criteria for failure. In particular, numerical examples show that, under cyclic changes of temperature, the fatigue characteristic curve of fatigue life versus temperature amplitude may be obtained for the first time from model prediction both in the absence and in the presence of stress. Results are in agreement with the salient features of metal fatigue failure.

It is reported that there exist deformable media which display quantum effects just as quantum entities do. As such, each quantum entity usually treated as a point particle may be represented by a deformable medium, the dynamic behavior of which is prescribed by four dynamic state variables, including mass density, velocity, internal pressure, and intrinsic angular momentum. In conjunction with the finding of the characteristic equation characterizing the physical nature of such media, it is found that a complex field quantity may be introduced to uncover a perhaps unexpected correlation, i.e., the governing dynamic equations for such media may be exactly reduced to the Schrodinger equation, from which the closed-form solutions for all the four dynamic state variables can be obtained. It turns out that this complex field quantity is just the wavefunction in the Schrodinger equation. Moreover, the dynamic effects peculiar to spin are derivable as direct consequences. It appears that these results provide a missing link in quantum theory, in the sense of disclosing the physical origin and nature of both the wavefunction and the wave equation. Now, the inherent indeterminacy in quantum theory may be rendered irrelevant. The consequences are explained for certain long-standing fundamental issues.