Improvement of the Algorithm for Computing Adjusted Structure Number for Determination of Backlog Maintenance

The pavement strength is very important for the evaluation of backlog maintenance. The current trend in many developing countries used pavement conditions index-PCI in estimating maintenance costs. The PCI can only justify periodic and routine recurrent maintenance. The condition strength is rarely determined in a flexible pavement creating an opportunity for back long maintenance. This paper reports the study conducted to develop and improve the algorithm for estimating the adjusted structure number to estimate the remaining thickness of the flexible pavement. The analysis of eight ways of computing structure numbers from FWD data ware analyzed and found that the improvement of the HDM 3 - 4 models can influence the usefulness of data collected from road asset management in Tanzania. The algorithm for estimating structural numbers from CBR was improved to compute adjusted structural numbers finally used to estimate the remaining life of the flexible pavement. The analysis of the network of about 6900 km including ST and AM was found that 64.72% were very good, 12% were Good, 10% were fair and 7.84% were poor and 5.4% were very poor.

Structured condition numbers and statistical condition estimation for the LDU factorization

In this article, we consider the structured condition numbers for LDU, factorization by using the modified matrix-vector approach and the differential calculus, which can be represented by sets of parameters. By setting the specific norms and weight parameters, we present the expressions of the structured normwise, mixed, componentwise condition numbers and the corresponding results for unstructured ones. In addition, we investigate the statistical estimation of condition numbers of LDU factorization using the probabilistic spectral norm estimator and the small-sample statistical condition estimation method, and devise three algorithms. Finally, we compare the structured condition numbers with the corresponding unstructured ones in numerical experiments.

Wind tunnel simulation of wind loading on a solid structure of revolution

The wind tunnel simulations of wind loading on a solid structure of revolution with one smooth and five rough surfaces were conducted using wind tunnel tests. Timemean and fluctuating pressure distributions on the surface were obtained, and the relationships between the roughness Reynolds number and pressure distributions were analyzed and discussed. The results show that increasing the surface roughness can significantly affect the pressure distribution, and the roughness Reynolds numbers play an important role in the change of flow patterns. The three flow patterns of subcritical, critical and supercritical flows can be classified based on the changing patterns of both the mean and the fluctuating pressure distributions. The present study suggests that the wind tunnel results obtained in the supercritical pattern reflect more closely those of full-scale solid structure of revolution at the designed wind speed.

Preparation , Structure and DC Conductivity of Organic Semiconductor PA NI-C_4H_6O_6

A new organic semiconductor tartaric acid doped salt of emeraldine polyaniline (PANI-C 4H 6O 6) has been obtained by the method of oxidative polymerization of monomeric aniline with ammonium persulfate in acidic solution. The structure was characterized by Fourier Transform Infrared technique (FTIR) and X-ray diffraction (XRD). The temperature dependence dc conductivity δ dc ( T ) shows a semiconductor behavior and follows the quasi one dimensional variable range hopping (Q1D-VRH) model. Data on δ dc ( T ) are also discussed.

STRUCTURED CONDITION NUMBERS FOR THE TIKHONOV REGULARIZATION OF DISCRETE ILL-POSED PROBLEMS

The possibly most popular regularization method for solving the least squares problem rain ‖Ax - b‖2 with a highly ill-conditioned or rank deficient coefficient matrix A is the x Tikhonov regularization method. In this paper we present the explicit expressions of the normwise, mixed and componentwise condition numbers for the Tikhonov regularization when A has linear structures. The structured condition numbers in the special cases of nonlinear structure i.e. Vandermonde and Cauchy matrices are also considered. Some comparisons between structured condition numbers and unstructured condition numbers are made by numerical experiments. In addition, we also derive the normwise, mixed and componentwise condition numbers for the Tikhonov regularization when the coefficient matrix, regularization matrix and right-hand side vector are all perturbed, which generalize the results obtained by Chu et al. [Numer. Linear Algebra Appl., 18 （2011）, 87-103].

Concept Paper： Developing Pavement Structural Deterioration Curves

A SN （structural number） can be calculated for a road pavement from the properties and thicknesses of the surface, basecourse, sub-base and subgrade. Historically, the cost of collecting structural data has been very high. Data was initially collected using Benkelman Beams and now by FWD （falling weight deflectometer）. The structural strength of pavements weakens over time due to environmental and traffic loading factors but due to a lack of data, no structural deterioration curve for pavements has been implemented in a PMS （pavement management system）. IRI （international roughness index） is a measure of the road longitudinal profile and has been used as a proxy for a pavement’s structural integrity. This paper offers two conceptual methods to develop PSDC （pavement structural deterioration curves）. Firstly, structural data are grouped in sets by design ESA （equivalent standard axles）. An ISN （“initial” SN）, SNI （intermediate SN） and a TSN （terminal SN）, are used to develop the curves. Using FWD data, the ISN is the SN after the pavement is rehabilitated （Financial Accounting “Modern Equivalent”）. Intermediate SNIs, are SNs other than the ISN and TSN. The TSN was defined as the SN of the pavement when it was approved for pavement rehabilitation. The second method is to use TSD （traffic speed deflectometer） data. The road network already divided into road blocks, is grouped by traffic loading. For each traffic loading group, road blocks that have had a recent pavement rehabilitation, are used to calculate the ISN and those planned for pavement rehabilitation to calculate the TSN. The remaining SNs are used to complete the age-based or if available, historical traffic loading-based SNIs.

ASYMPTOTIC BEHAVIOR FOR RANDOM WALK IN RANDOM ENVIRONMENT WITH HOLDING TIMES

In this article, we mainly discuss the asymptotic behavior for multi-dimensional continuous-time random walk in random environment with holding times. By constructing a renewal structure and using the point ＂environment viewed from the particle＂, under General Kalikow＇s Condition, we show the law of large numbers （LLN） and central limit theorem （CLT） for the escape speed of random walk.

The Effect of Biological and Cytotoxic Therapy for Rheumatoid Arthritis on Human Chromosomes

Objectives： An open-label prospective, combined basic and clinical controlled study was done to investigate the effects of biological therapy using rituximab, and cytotoxic drug treatment with methotrexate on morphology and quantifitiation of chromosomes in rheumatoid arthritis patients. Methods： This study is follow-up of a prior publication, with new observations in comparison with control subjects. A total of 16 subjects were divided into two groups. Group I comprised 8 seropositive rheumatoid arthritis patients who were analysed for the primary end point of possible cytotoxic effects of rituximab and methotrexate. Group II included 8 healthy individuals who served as controls. Assessment was done before treatment with rituximab, and 4 weeks after initiation of therapy. Patients were randomly assigned to receive infusion of rituximab in a full dose of 2.0 g divided into two doses of 1.0 g on days I and 15. The lymphocytes from periphereal blood was cultured by the Moorhead method. Results： Normal male and female Karyograms were observed after full courses of therapy with rituximab. In one female patient who had been receiving longstanding cytotoxic therapy with methotrexate, 2% of chromosomal mitosis showed structural abnormalities. Following the discontinuation of methotrexate and the administration of rituximab, her karyogram became normal. Conclusion： The results from this study indicated that rituximab therapy was safe for the number and structure of human chromosomes, while methotrexate showed chromosomal aberration in one female RA patient. After discontinuation of this longstanding treatment, the karyogram of the same patient returned to normal.

Estimation of flexible pavement structural capacity using machine learning techniques

The most common index for representing structural condition of the pavement is the structural number.The current procedure for determining structural numbers involves utilizing falling weight deflectometer and ground-penetrating radar tests,recording pavement surface deflections,and analyzing recorded deflections by back-calculation manners.This procedure has two drawbacks:falling weight deflectometer and ground-penetrating radar are expensive tests;back-calculation ways has some inherent shortcomings compared to exact methods as they adopt a trial and error approach.In this study,three machine learning methods entitled Gaussian process regression,M5P model tree,and random forest used for the prediction of structural numbers in flexible pavements.Dataset of this paper is related to 759 flexible pavement sections at Semnan and Khuzestan provinces in Iran and includes“structural number”as output and“surface deflections and surface temperature”as inputs.The accuracy of results was examined based on three criteria of R,MAE,and RMSE.Among the methods employed in this paper,random forest is the most accurate as it yields the best values for above criteria(R=0.841,MAE=0.592,and RMSE=0.760).The proposed method does not require to use ground penetrating radar test,which in turn reduce costs and work difficulty.Using machine learning methods instead of back-calculation improves the calculation process quality and accuracy.