Uncertainty quantification of inverse analysis for geomaterials using probabilistic programming

Uncertainty is an essentially challenging for safe construction and long-term stability of geotechnical engineering.The inverse analysis is commonly utilized to determine the physico-mechanical parameters.However,conventional inverse analysis cannot deal with uncertainty in geotechnical and geological systems.In this study,a framework was developed to evaluate and quantify uncertainty in inverse analysis based on the reduced-order model(ROM)and probabilistic programming.The ROM was utilized to capture the mechanical and deformation properties of surrounding rock mass in geomechanical problems.Probabilistic programming was employed to evaluate uncertainty during construction in geotechnical engineering.A circular tunnel was then used to illustrate the proposed framework using analytical and numerical solution.The results show that the geomechanical parameters and associated uncertainty can be properly obtained and the proposed framework can capture the mechanical behaviors under uncertainty.Then,a slope case was employed to demonstrate the performance of the developed framework.The results prove that the proposed framework provides a scientific,feasible,and effective tool to characterize the properties and physical mechanism of geomaterials under uncertainty in geotechnical engineering problems.

Uncertainty quantification of mechanism motion based on coupled mechanism—motor dynamic model for ammunition delivery system

In this paper,a dynamic modeling method of motor driven electromechanical system is presented,and the uncertainty quantification of mechanism motion is investigated based on this method.The main contribution is to propose a novel mechanism-motor coupling dynamic modeling method,in which the relationship between mechanism motion and motor rotation is established according to the geometric coordination of the system.The advantages of this include establishing intuitive coupling between the mechanism and motor,facilitating the discussion for the influence of both mechanical and electrical parameters on the mechanism,and enabling dynamic simulation with controller to take the randomness of the electric load into account.Dynamic simulation considering feedback control of ammunition delivery system is carried out,and the feasibility of the model is verified experimentally.Based on probability density evolution theory,we comprehensively discuss the effects of system parameters on mechanism motion from the perspective of uncertainty quantization.Our work can not only provide guidance for engineering design of ammunition delivery mechanism,but also provide theoretical support for modeling and uncertainty quantification research of mechatronics system.

Multi-Compartment SCFA Quantification in Human

Short-chain fatty acids (SCFA) play an important role in human biochemistry. They originate primarily from the digestive system through carbohydrates microbial fermentation. Most SCFA produced in the colon are absorbed by the intestinal wall and enter the bloodstream to be distributed throughout the body for multiple purposes. At the intestinal level, SCFA play a role in controlling fat storage and fatty acid metabolism. The effects of these beneficial compounds therefore concern overall health. They facilitate energy expenditure and are valuable allies in the fight against obesity and diabetes. SCFA are also involved in the regulation of the levels of several neurotransmitters such as GABA (γ-aminobutyric acid), glutamate, serotonin, dopamine, and norepinephrine. Their role is also highlighted in many inflammatory and neurodegenerative diseases such as Alzheimer’s disease (AD) or Parkinson’s disease (PD). To have a realistic picture of the distribution of SCFA in different biological compartments of the human body, we propose to study SCFA simultaneously in five human biological samples: feces, saliva, serum, cerebrospinal fluid (CSF), and urine, as well as in Dried Blood Spot (DBS). To evaluate their concentration and repeatability, we used 10 aliquots from pooled samples, analyzed by 3-nitrophenylhydrazine (3-NPH) derivation and liquid chromatography coupled with high sensitivity mass spectrometry (LC-QqQ-MS). We also evaluated the SCFA assay on Dried Blood Spot (DBS). In this work, we adapted the pre-analytical parts for each sample to be able to use a common calibration curve, thus facilitating multi-assay quantification studies and so being less time-consuming. Moreover, we proposed new daughter ions from the same neutral loss (43 Da) to quantify SCFAs, thus improving the sensitivity. In conclusion, our methodology, based on a unique calibration curve for all samples for each SCFA, is well-suited to quantified them in a clinical context.

Investigation on mechanical properties regulation of rock-like specimens based on 3D printing and similarity quantification

3D printing is widely adopted to quickly produce rock mass models with complex structures in batches,improving the consistency and repeatability of physical modeling.It is necessary to regulate the mechanical properties of 3D-printed specimens to make them proportionally similar to natural rocks.This study investigates mechanical properties of 3D-printed rock analogues prepared by furan resin-bonded silica sand particles.The mechanical property regulation of 3D-printed specimens is realized through quantifying its similarity to sandstone,so that analogous deformation characteristics and failure mode are acquired.Considering similarity conversion,uniaxial compressive strength,cohesion and stress–strain relationship curve of 3D-printed specimen are similar to those of sandstone.In the study ranges,the strength of 3D-printed specimen is positively correlated with the additive content,negatively correlated with the sand particle size,and first increases then decreases with the increase of curing temperature.The regulation scheme with optimal similarity quantification index,that is the sand type of 70/140,additive content of 2.5‰and curing temperature of 81.6℃,is determined for preparing 3D-printed sandstone analogues and models.The effectiveness of mechanical property regulation is proved through uniaxial compression contrast tests.This study provides a reference for preparing rock-like specimens and engineering models using 3D printing technology.

High-dimensional uncertainty quantification of projectile motion in the barrel of a truck-mounted howitzer based on probability density evolution method

This paper proposed an efficient research method for high-dimensional uncertainty quantification of projectile motion in the barrel of a truck-mounted howitzer.Firstly,the dynamic model of projectile motion is established considering the flexible deformation of the barrel and the interaction between the projectile and the barrel.Subsequently,the accuracy of the dynamic model is verified based on the external ballistic projectile attitude test platform.Furthermore,the probability density evolution method(PDEM)is developed to high-dimensional uncertainty quantification of projectile motion.The engineering example highlights the results of the proposed method are consistent with the results obtained by the Monte Carlo Simulation(MCS).Finally,the influence of parameter uncertainty on the projectile disturbance at muzzle under different working conditions is analyzed.The results show that the disturbance of the pitch angular,pitch angular velocity and pitch angular of velocity decreases with the increase of launching angle,and the random parameter ranges of both the projectile and coupling model have similar influence on the disturbance of projectile angular motion at muzzle.

Quantification of the concrete freeze–thaw environment across the Qinghai–Tibet Plateau based on machine learning algorithms

The reasonable quantification of the concrete freezing environment on the Qinghai–Tibet Plateau(QTP) is the primary issue in frost resistant concrete design, which is one of the challenges that the QTP engineering managers should take into account. In this paper, we propose a more realistic method to calculate the number of concrete freeze–thaw cycles(NFTCs) on the QTP. The calculated results show that the NFTCs increase as the altitude of the meteorological station increases with the average NFTCs being 208.7. Four machine learning methods, i.e., the random forest(RF) model, generalized boosting method(GBM), generalized linear model(GLM), and generalized additive model(GAM), are used to fit the NFTCs. The root mean square error(RMSE) values of the RF, GBM, GLM, and GAM are 32.3, 4.3, 247.9, and 161.3, respectively. The R^(2) values of the RF, GBM, GLM, and GAM are 0.93, 0.99, 0.48, and 0.66, respectively. The GBM method performs the best compared to the other three methods, which was shown by the results of RMSE and R^(2) values. The quantitative results from the GBM method indicate that the lowest, medium, and highest NFTC values are distributed in the northern, central, and southern parts of the QTP, respectively. The annual NFTCs in the QTP region are mainly concentrated at 160 and above, and the average NFTCs is 200 across the QTP. Our results can provide scientific guidance and a theoretical basis for the freezing resistance design of concrete in various projects on the QTP.

Revolutionizing disease diagnosis and management:Open-access magnetic resonance imaging datasets a challenge for artificial intelligence driven liver iron quantification

Artificial intelligence(AI),particularly machine learning(ML)and deep learning(DL)techniques,such as convolutional neural networks(CNNs),have emerged as transformative technologies with vast potential in healthcare.Body iron load is usually assessed using slightly invasive blood tests(serum ferritin,serum iron,and serum transferrin).Serum ferritin is widely used to assess body iron and drive medical management;however,it is an acute phase reactant protein offering wrong interpretation in the setting of inflammation and distressed patients.Magnetic resonance imaging is a non-invasive technique that can be used to assess liver iron.The ML and DL algorithms can be used to enhance the detection of minor changes.However,a lack of open-access datasets may delay the advancement of medical research in this field.In this letter,we highlight the importance of standardized datasets for advancing AI and CNNs in medical imaging.Despite the current limitations,embracing AI and CNNs holds promise in revolutionizing disease diagnosis and treatment.

运用Real-time quantification PCR方法建立副溶血性弧菌在即食虾中的生长预测模型

运用Real-time quantification PCR(real-time qPCR)方法建立副溶血性弧菌在即食虾中生长预测模型。首先构建质粒标准品,梯度稀释后建立标准曲线,然后用Real-time qPCR方法检测虾中副溶血性弧菌的数量,最后建立37℃下即食虾中副溶血性弧菌生长预测模型,并与传统涂布计数方法进行比较。结果表明,Real-time qPCR方法和传统计数方法均可建立Gmopertz模型、Logistic模型和Richards模型,模型拟合的相关系数R2均在0.9以上。基于Real-timeqPCR方法省时省力、特异性好等优点,用Real-time qPCR方法建立微生物预测模型是未来预测微生物学领域的一种发展趋势。

Real-Time PCR Technique and Its Application in Quantification of Plant Nucleic Acid Molecules

Real-time PCR is a closed DNA amplification system that skillfully integrates biochemical, photoelectric and computer techniques. Fluorescence data acquired once per cycle provides rapid absolute quantification of initial template copy numbers as PCR products are generated. This technique significantly simplifies and accelerates the process of producing reproducible quantification of nucleic acid molecules. It not only is a sensitive, accurate and rapid quantitative method, but it also provides an easier way to calculate the absolute starting copy number of nucleic acid molecules to be tested. Together with molecular bio-techniques, like microarray, real-time PCR will play a very important role in many aspects of molecular life science such as functional gene analysis and disease molecular diagnostics. This review introduces the detailed principles and application of the real-time PCR technique, describes a recently developed system for exact quantification of AUX/IAA genes In Arabidopsis, and discusses the problems with the real-time PCR process.

规划设计学中的调查分析法14—数量化Ⅰ类(Quantification Theory Type Ⅰ)

回归及重回归分析和预测,被广泛地应用于各个行业中。但是,有些相当于回归分析中的说明变数,不是数量数据,而是非数量数据,例如:影响入园人数的因素有星期(平日、休日)、天气状况(晴天、阴天、雨雪天等)、季节(春、夏、秋、冬)等。这些因素因为不是“量”的数据,所以不能用回归或重回归等方法进行分析和预测,但这种情况在规划设计的调查中经常出现,所以数量化Ⅰ类,可以帮助人们进行与回归分析和预测相同的分析,这是十分有效的一种分析方法。

Precore/basal core promoter mutants quantification throughout phases of hepatitis B virus infection by Simpleprobe

AIM:To investigate precore/basal core promoter(PC/BCP) mutants throughout hepatitis B virus(HBV) infection and to determine their relationship to hepatitis B early antigen(HBeA g) titers.METHODS:We enrolled 191 patients in various stages of HBV infection at the Huashan Hospital and the Taizhou Municipal Hospital from 2010 to 2012.None of the patients received antiviral therapy.HBV DNA from serum,was quantified by real-time PCR.The HBV genotype was determined by direct sequencing of the S gene.We used the Simpleprobe ultrasensitivequantitative method to detect PC/BCP mutants in each patient.We compared the strain number,percentage,and the changes in PC/BCP mutants in different phases,and analyzed the relationship between PC/BCP mutants and HBe Ag by multiple linear regression and logistic regression.RESULTS:Patients with HBV infection(n = 191) were assigned to groups by phase:Immune tolerance(IT) = 55,Immune clearance(IC) = 67,Low-replicative(LR) = 49,and HBeA g-negative hepatitis(ENH) = 20.Of the patients(male,112; female,79) enrolled,122 were HBe Ag-positive and 69 were HBe Ag-negative.The median age was 33 years(range:18-78 years).PC and BCP mutation detection rates were 84.82%(162/191) and 96.86%(185/191),respectively.In five HBe Ag-negative cases,we detected double mutation G1896A/G1899 A.The logarithm value of PC mutant quantities(log10 PC) significantly differed in IT,IC,and LR phases,as well as in the ENH phase(F = 49.350,P < 0.001).The logarithm value of BCP mutant quantities(log10 BCP) also differed during the four phases(F = 25.530,P < 0.001).Log10 PC and log10 BCP values were high in the IT and IC phases,decreased in the LR phase,and increased in the ENH phase,although the absolute value at this point remained lower than that in the IT and IC phases.PC mutant quantity per total viral load(PC%) and BCP mutant quantity per total viral load(BCP%) differed between phases(F = 20.040,P < 0.001; F = 10.830,P < 0.001),with PC% and BCP% gradually increasing in successive phases.HBeA g titers negatively correlated with PC%(Spearman's rho =-0.354,P < 0.001) and BCP%(Spearman's rho =-0.395,P < 0.001).The negative correlation between PC% and HBeA g status was significant(B =-5.281,P = 0.001),but there was no such correlation between BCP% and HBeA g status(B =-0.523,P = 0.552).CONCLUSION:PC/BCP mutants become predominant in a dynamic and continuous process.Log10 PC,log10 BCP,PC% and BCP% might be combined to evaluate disease progression.PC% determines HBeA g status.

Quantificational indexes for design and evaluation of copper staves for blast furnaces

The quantificational and normative design is the precondition of improving the design of copper staves for blast furnaces. Based on a 3-dimensional temperature field calculation model, from the view point of heat transfer and long campaigns note with the core of forming accretion, the forming-accretion-ability （FAA） and the rib hot surface maximum temperature difference （ATmax） as quantificational indexes to direct and evaluate the design of copper staves for blast furnaces were presented. The application of the two indexes in design essentially embodies the new long campaigns in the stage of design. With the application of the two indexes, good results can be obtained. Firstly, it was suggested that the rib height of a copper stave can be reduced to 15 mm, which is a new method and theory for the reduction of copper staves. Secondly, the influence of insert on FAA and ATmax, is decided by the volume of insert. According to this, the principle of design for the hot surface geometry of copper staves was put forward that the ratio of the rib hot surface to the copper stave hot surface （abbreviated as the ratio of rib to stave） must be maintained in the range of 45% to 55%; for the present copper stave with a 35-40 mm thick rib, the ratio of rib to stave in the range of 50% to 55% can optimize the design of copper staves; for the copper stave with a smaller rib thickness, for example 15 ram, the ratio of rib to stave in the range of 45% to 50% can optimize the design of copper staves. It can be summarized that the thicker the rib thickness, the larger is the ratio of rib to stave. 2008 University of Science and Technology Beijing. All rights reserved.

Value of elastography point quantification in improving the diagnostic accuracy of early diabetic kidney disease

BACKGROUND Diabetic kidney disease(DKD)is a common complication of diabetes.The patient’s prognosis is poor once DKD progresses to advanced stage.Accurate diagnosis and timely treatment of early DKD are important for improving patient’s prognosis and reducing mortality.AIM To explore the value of elastography point quantification(ElastPQ)in improving the accuracy of early DKD diagnosis.METHODS A total of 69 patients with type 2 diabetes were recruited from Naval Military Medical University Affiliated Gongli Hospital.Patients were divided into early DKD group and medium DKD group according to pathological results and urinary albumin excretion rate(UAER).Another 40 patients with simple diabetes were included as the diabetes group.The baseline data,laboratory diagnostic indicators,and ultrasound indicators for each patient were recorded.The differences of the indicators in the three groups were compared.Multivariate logistic regression was used to analyze the influencing factors of the development from simple diabetes into early DKD and from early DKD into medium DKD.Receiver operating characteristic analyses of potential indicators in identifying early DKD and medium DKD,and early DKD and simple diabetes were established.RESULTS Multivariate logistic regression analysis showed that UAER(P<0.001),renocortical Young's Modulus(YM)(P<0.001),and renal parenchymal thickness(P=0.013)were the independent influencing factors of the development from early DKD into medium DKD.Diabetes duration(P=0.041),UAER(P=0.034),and renocortical YM(P=0.017)were the independent influencing factors of the development from simple diabetes into early DKD.Receiver operating characteristic analysis indicated that UAER,renocortical YM,and renal parenchymal thickness were accurate in identifying early DKD and medium DKD[all area under curve(AUC)>0.9].The accuracy of UAER(AUC=0.744),diabetes duration(AUC=0.757),and renocortical YM(AUC=0.782)for the diagnosis of early DKD and simple diabetes were limited.However,the combined diagnosis of UAER,diabetes duration,and renocortical YM was accurate in identifying early DKD and simple diabetes(AUC=0.906),which was significantly higher than any of the three indicators(all P<0.05).CONCLUSION ElastPQ is of great value in the diagnosis of early DKD.When combined with the diabetes duration and UAER,it is expected to diagnose accurately early DKD.

Computational intelligence approach for uncertainty quantification using evidence theory

As an alternative or complementary approach to the classical probability theory,the ability of the evidence theory in uncertainty quantification（UQ） analyses is subject of intense research in recent years.Two state-of-the-art numerical methods,the vertex method and the sampling method,are commonly used to calculate the resulting uncertainty based on the evidence theory.The vertex method is very effective for the monotonous system,but not for the non-monotonous one due to its high computational errors.The sampling method is applicable for both systems.But it always requires a high computational cost in UQ analyses,which makes it inefficient in most complex engineering systems.In this work,a computational intelligence approach is developed to reduce the computational cost and improve the practical utility of the evidence theory in UQ analyses.The method is demonstrated on two challenging problems proposed by Sandia National Laboratory.Simulation results show that the computational efficiency of the proposed method outperforms both the vertex method and the sampling method without decreasing the degree of accuracy.Especially,when the numbers of uncertain parameters and focal elements are large,and the system model is non-monotonic,the computational cost is five times less than that of the sampling method.

Development of a fast and precise method for simultaneous quantification of the PLGA monomers lactic and glycolic acid by HPLC

Poly(lactide-co-glycolide acid)(PLGA) is an extraordinary well-described polymer and has excellent pharmaceutical properties like high biocompatibility and good biodegradability. Hence, it is one of the most used materials for drug delivery and biomedical systems, also being present in several US Food and Drug Administration-approved carrier systems and therapeutic devices. For both applications, the quantification of the polymer is inalienable. During the development of a production process, parameters like yield or loading efficacy are essential to be determined. Although PLGA is a well-defined biomaterial,it still lacks a sensitive and convenient quantification approach for PLGA-based systems. Thus, we present a novel method for the fast and precise quantification of PLGA by RP-HPLC. The polymer is hydrolyzed into its monomers, glycolic acid and lactic acid. Afterwards, the monomers are derivatized with the absorption-enhancing molecule 2,4′-dibromoacetophenone. Furthermore, the wavelength of the derivatized monomers is shifted to higher wavelengths, where the used solvents show a lower absorption,increasing the sensitivity and detectability. The developed method has a detection limit of 0.1 mg/mL,enabling the quantification of low amounts of PLGA. By quantifying both monomers separately, information about the PLGA monomer ratio can be also directly obtained, being relevant for degradation behavior. Compared to existing approaches, like gravimetric or nuclear magnetic resonance measurements, which are tedious or expensive, the developed method is fast, ideal for routine screening, and it is selective since no stabilizer or excipient is interfering. Due to the high sensitivity and rapidity of the method, it is suitable for both laboratory and industrial uses.

Quantification of epicardial fat:Which method can predict significant coronary artery disease?

AIM:To compare the predictive value of three methods of epicardial fat(EF) assessment for presence of significant coronary artery disease(CAD) [i.e.,epicardial fat volume(EFV),EFV indexed with body surface area(EFV/BSA) and EFV indexed with body mass index(EFV/BMI)].METHODS:The study was performed on 170 patients(85 women and 85 men) with clinical suspicion of CAD.They aged 26-89 years with a median age of 54 years.The patients were classified into three groups:Group 1:58 patients with normal coronary arteries; group 2:48 patients with non-significant CAD and group 3:64 patients with significant CAD.The three methods for assessment of epicardial fat were retrospectively studied to determine the best method to predict the presence of significant CAD.RESULTS:The three methods for epicardial fat quantification and measurements,i.e.,EFV,EFV/BSA and EFV/BMI with post- hoc analysis showed a significant difference between patients with significant coronary artery disease compared to the normal group.Receiver operating characteristic curve analysis showed no significant difference between the three methods of epicardial fat measurements,the area under curve ranging between 0.6 and 0.62.The optimal cut-off was 80.3 cm3 for EFV,2.4 cm3/m2 for EFV indexed with BMI and 41.7 cm3/(kg/m2) for EFV indexed with BSA.For this cut-off the sensitivity ranged between 0.92 and 0.94,while specificity varied from 0.31 to 0.35.CONCLUSION:Any one of the three methods for assessment of epicardial fat can be used to predict significant CAD since all have the same equivalent predictive value.

Quantification of choline concentration following liver cell apoptosis using ~1H magnetic resonance spectroscopy

AIM: To evaluate the feasibility of quantifying liver choline concentrations in both normal and apoptotic rabbit livers in vivo, using 1H magnetic resonance spectroscopy (1H-MRS). METHODS: 1H-MRS was performed in 18 rabbits using a 1.5T GE MR system with an eight-channel head/neck receiving coil. Fifteen rabbits were injected with sodium selenite at a dose of 10 μmol/kg to induce the liver cell apoptosis. Point-resolved spectroscopy sequencelocalized spectra were obtained from 10 livers once before and once 24 h after sodium selenite injection in vivo. T1 and T2 relaxation time of water and choline was measured separately in the livers of three healthy rabbits and three selenite-treated rabbits. Hematoxylin and eosin and dUTP-biotin nick end labeling (TUNEL) staining was used to detect and confirm apoptosis. Choline peak areas were measured relative to unsuppressed water using LCModel. Relaxation attenuation was corrected using the average of T1 and T2 relaxation time. The choline concentration was quantified using a formula, which was tested by a phantom with a known concentration. RESULTS: Apoptosis of hepatic cells was confirmed by TUNEL assay. In phantom experiment, the choline concentration (3.01 mmol/L), measured by 1H-MRS, was in good agreement with the actual concentration (3 mmol/L). The average T1 and T2 relaxation time of choline was 612 ± 15 ms and 74 ± 4 ms in the control group and 670 ± 27 ms and 78 ± 5 ms in apoptotic livers in vivo, respectively. Choline was quantified in 10 rabbits, once before and once after the injection with sodium selenite. The choline concentration decreased from 14.5 ± 7.57 mmol/L before sodium selenite injection to 10.8 ± 6.58 mmol/L (mean ± SD, n = 10) after treatment (Z = -2.395, P < 0.05, two-sample paired Wilcoxon test). CONCLUSION: 1H-MRS can be used to quantify liver choline in vivo using unsuppressed water as an internal reference. Decreased liver choline concentrations are found in sodium selenite-treated rabbits undergoing liver cell apoptosis.

Accurate quantification of astaxanthin from Haematococcus crude extract spectrophotometrically

The influence of alkali on astaxanthin and the optimal working wave length for measurement of astaxanthin from Haematocoecus crude extract were investigated, and a spectrophotometric method for precise quantification of the astaxanthin based on the method of Boussiba et al. was established. According to Boussiba＇s method, alkali treatment destroys chlorophyll. However, we found that： 1） carotenoid content declined for about 25% in Haematococcus fresh cysts and up to 30% in dry powder of Haematococcus broken cysts after alkali treatment; and 2） dimethyl sulfoxide （DMSO）-extracted chlorophyll of green Haematococeus bares little absorption at 520-550 nm. Interestingly, a good linear relationship existed between absorbance at 530 nm and astaxanthin content, while an unknown interference at 540-550 nm was detected in our study. Therefore, with 530 nm as working wavelength, the alkali treatment to destroy chlorophyll was not necessary and the influence of chlorophyll, other carotenoids, and the unknown interference could be avoided. The astaxanthin contents of two samples were measured at 492 nm and 530 nm; the measured values at 530 nm were 2.617 g/100 g and 1.811 g/100 g. When compared with the measured values at 492 nm, the measured values at 530 nm decreased by 6.93% and 11.96%, respectively. The measured values at 530 nm are closer to the true astaxanthin contents in the samples. The data show that 530 nm is the most suitable wave length for spectrophotometric determination to the astaxanthin in Haematococcus crude extract.

Analysis of actuator delay and its effect on uncertainty quantification for real-time hybrid simulation

Uncertainties in structure properties can result in different responses in hybrid simulations. Quantification of the effect of these tmcertainties would enable researchers to estimate the variances of structural responses observed from experiments. This poses challenges for real-time hybrid simulation （RTHS） due to the existence of actuator delay. Polynomial chaos expansion （PCE） projects the model outputs on a basis of orthogonal stochastic polynomials to account for influences of model uncertainties. In this paper, PCE is utilized to evaluate effect of actuator delay on the maximum displacement from real-time hybrid simulation of a single degree of freedom （SDOF） structure when accounting for uncertainties in structural properties. The PCE is first applied for RTHS without delay to determine the order of PCE, the number of sample points as well as the method for coefficients calculation. The PCE is then applied to RTHS with actuator delay. The mean, variance and Sobol indices are compared and discussed to evaluate the effects of actuator delay on uncertainty quantification for RTHS. Results show that the mean and the variance of the maximum displacement increase linearly and exponentially with respect to actuator delay, respectively. Sensitivity analysis through Sobol indices also indicates the influence of the single random variable decreases while the coupling effect increases with the increase of actuator delay.

Evaluation of HBsAg Quantification as Surrogate to HBV DNA Viral Load in Hepatitis B Infected Patients in Anambra State, Nigeria

Hepatitis B is an infectious disease of great public health importance. Nigeria is one of the countries with the highest incidence of Hepatitis B Virus (HBV) infection worldwide. However, the accessibility and affordability of HBV DNA quantification (viral load) assay is the key laboratory test for therapy initiation, and monitoring is a challenge to HBV management. This study aimed at determining the relationship between HBV DNA quantification and routine haemato-serological parameters in order to develop a more cost-effective diagnostic algorithm for Hepatitis B management. Cross sectional study design was used with a total of 264 subjects comprising of 88 HBsAg seropositive treatment naïve subjects, 88 HBsAg seropositive subjects on antiviral therapy as case subjects and 88 age-matched apparently healthy HBsAg seronegative individuals were recruited as control subjects. Hepatitis B Virus DNA assay was performed using real time PCR technique while ELISA technique was used for Hepatitis B surface antigen quantification. HBsAg quantification showed strong positive correlation with HBV DNA viral load both in treatment and non-treatment groups (r = 0.673;p < 0.001). However, the Receiver Operation Characteristics curve indicated a very poor performance characteristics (AUC = 0.537, p = 0.002). The non-treatment group has higher viral load (M = 805.50 IU/ml) compared with treatment group (M = 65.50 IU/ml) (p < 0.001). There was a significant difference in HBV DNA levels among the four serological patterns observed in the study (p < 0.001). This study has revealed that HBsAg quantification has strong correlation with HBV viral load but might not be efficient in clinical practice as a predictor of serum HBV viral load due to its poor performance characteristics in identifying high positive viral load.