On the Rule of Mixtures for Predicting Stress-Softening and Residual Strain Effects in Biological Tissues and Biocompatible Materials

In this work, we use the rule of mixtures to develop an equivalent material model in which the total strain energy density is split into the isotropic part related to the matrix component and the anisotropic energy contribution related to the fiber effects. For the isotropic energy part, we select the amended non-Gaussian strain energy density model, while the energy fiber effects are added by considering the equivalent anisotropic volumetric fraction contribution, as well as the isotropized representation form of the eight-chain energy model that accounts for the material anisotropic effects. Furthermore, our proposed material model uses a phenomenological non-monotonous softening function that predicts stress softening effects and has an energy term, derived from the pseudo-elasticity theory, that accounts for residual strain deformations. The model’s theoretical predictions are compared with experimental data collected from human vaginal tissues, mice skin, poly(glycolide-co-caprolactone) (PGC25 3-0) and polypropylene suture materials and tracheal and brain human tissues. In all cases examined here, our equivalent material model closely follows stress-softening and residual strain effects exhibited by experimental data

PREDICTING THE BOILING POINT OF PCDD/Fs BY THE QSPR METHOD BASED ON THE MOLECULAR DISTANCE-EDGE VECTOR INDEX

The quantitative structure property relationship (QSPR) for the boiling point (Tb) of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) was investigated. The molecular distance-edge vector (MDEV) index was used as the structural descriptor. The quantitative relationship between the MDEV index and Tb was modeled by using multivariate linear regression (MLR) and artificial neural network (ANN), respectively. Leave-one-out cross validation and external validation were carried out to assess the prediction performance of the models developed. For the MLR method, the prediction root mean square relative error (RMSRE) of leave-one-out cross validation and external validation was 1.77 and 1.23, respectively. For the ANN method, the prediction RMSRE of leave-one-out cross validation and external validation was 1.65 and 1.16, respectively. A quantitative relationship between the MDEV index and Tb of PCDD/Fs was demonstrated. Both MLR and ANN are practicable for modeling this relationship. The MLR model and ANN model developed can be used to predict the Tb of PCDD/Fs. Thus, the Tb of each PCDD/F was predicted by the developed models.

Predicting Market Crashes: An Examination of the Performance of Macroeconomic Indicators and Technical Analysis

The purpose of this study is to examine macroeconomic indicators‟ and technical analysis‟ ability to signal market crashes. Indicators examined were Yield Spread, The Purchasing Managers Index and the Consumer Confidence Index. Technical Analysis indicators were moving average, Moving Average Convergence-Divergence and Relative Strength Index. We studied if commonly used macroeconomic indicators can be used as a warning system for a stock market crashes as well. The hypothesis is that the signals of recession can be used as signals of stock market crash and that way a basis for a hedging strategy. The data is collected from the U.S. markets from the years 1983-2010. Empirical studies show that macroeconomic indicators have been able to explain the future GDP development in the U.S. in research period and they were statistically significant. A hedging strategy that combined the signals of yield spread and Consumer Confidence Index gave most useful results as a basis of a hedging strategy in selected time period. It was able to outperform buy-and-hold strategy as well as all of the technical indicator based hedging strategies.

The role of P16ink4a and P53 immunostaining in predicting recurrence of HG-CIN after conization treatment

Objective: Io evaluate the expression of p16INK4a and p53 biomarkers in conization specimens from patients with high grade cervical intraepithelial neoplasia (HG-CIN), correlating them with the ability to predict the recurrence. Methods : we conducted a retrospective study of patients with HG-CIN in cervical biopsy treated with conization between January 1999 and January 2006 who had a minimum follow-up of 18 months. The expression of the p16 and p53 was assessed by tissue microarrays and correlated with disease recurrence. For analysis, we used the test of proportions (chi-square), considering value p<0.05, 95% CI and calculations of sensitivity, specificity and accuracy of these immunomarkers in predicting recurrence. Results : the series comprised 83 patients aged between 16 and 86 years (35±11.7), divided into two groups: 30 with HG-CIN recurrence (study group) and 53 without recurrence (control group). Mean age, parity, smoking and conization technique were similar in both groups. The p53 expression was present in 43% of the study group and 57% of the control group, and the p16 was present in 43% of the study group and in 57% of the control group (p>0.05). p53 had a positive predictive value (PPV) of 42% and negative predictive value (NPV) of 73%, sensitivity 70%, specificity of 47% and accuracy of 59%. The p16, PPV 42%, NPV 72%, sensitivity 66%, specificity of 49% and accuracy of 56%. Conclusion : immunohistochemistry expression of p53 and p16 showed low sensitivity and low specificity as predictors of HG-CIN recurrence after conization treatment.

Factors predicting mortality in type 2 diabetes. With special reference to physical exercise, blood pressure, proteinuria, inflammation and P wave duration

Background: Type 2 diabetes patients have a 2-4 fold risk of cardiovascular disease (CVD) compared to the general population. In type 2 diabetes, several CVD risk factors have been identified, including obesity, hypertension, hyperglycemia, proteinuria, sedentary lifestyle and dyslipidemia. Although much of the excess CVD risk can be attributed to these risk factors, a significant proportion is still unknown. Aims: To assess in middle-aged type 2 diabetic subjects the joint relations of several conventional and non-conventional CVD risk factors with respect to cardiovascular and total mortality. Subjects and methods: This thesis is part of a large prospective, population based East-West type 2 diabetes study that was launched in 1982-1984. It includes 1,059 middle-aged (45-64 years old) participants. At baseline, a thorough clinical examination and laboratory measurements were performed and an ECG was recorded. The latest follow-up study was performed 18 years later in January 2001 (when the subjects were 63-81 years old). The study endpoints were total mortality and mortality due to CVD, coronary heart disease (CHD) and stroke. Results: Physically more active patients had significantly reduced total, CVD and CHD mortality independent of high-sensitivity C-reactive protein (hs-CRP) levels unless proteinuria was present. Among physically active patients with a hs-CRP level >3 mg/L, the prognosis of CVD mortality was similar to patients with hs-CRP levels ≤3 mg/L. The worst prognosis was among physically inactive patients with hs-CRP levels >3 mg/L. Physically active patients with proteinuria had significantly increased total and CVD mortality by multivariate analyses. After adjustment for confounding factors, patients with proteinuria and a systolic BP <130 mmHg had a significant increase in total and CVD mortality compared to those with a systolic BP between 130 and 160 mmHg. The prognosis was similar in patients with a systolic BP <130 mmHg and ≥160 mmHg. Among patients without proteinuria, a systolic BP <130 mmHg was associated with a non-significant reduction in mortality. A P wave duration ≥114 ms was associated with a 2.5-fold increase in stroke mortality among patients with prevalent CHD or claudication. This finding persisted in multivariable analyses. Among patients with no comorbidities, there was no relationship between P wave duration and stroke mortality. Conclusions: Physical activity reduces total and CVD mortality in patients with type 2 diabetes without proteinuria or with elevated levels of hs-CRP, suggesting that the anti-inflammatory effect of physical activity can counteract increased CVD morbidity and mortality associated with a high CRP level. In patients with proteinuria the protective effect was not, however, present. Among patients with proteinuria, systolic BP <130 mmHg may increase mortality due to CVD. These results demonstrate the importance of early intervention to prevent CVD and to control all-cause mortality among patients with type 2 diabetes. The presence of proteinuria should be taken into account when defining the target systolic BP level for prevention of CVD deaths. A prolongation of the duration of the P wave was associated with increased stroke mortality among high-risk patients with type 2 diabetes. P wave duration is easy to measure and merits further examination to evaluate its importance for estimation of the risk of stroke among patients with type 2 diabetes.

A temperature predicting model for manufacturing processes requiring coiling

A model for predicting temperature evolution for automatic controling systems in manufacturing processes requiring the coiling of bars in the transfer table is presented. Although the method is of a general nature, the presentation in this work refers to the manufacturing of steel plates in hot rolling mills. The predicting strategy is based on a mathematical model of the evolution of temperature in a coiling and uncoiling bar and is presented in the form of a parabolic partial differential equation for a shape changing domain. The mathematical model is solved numerically by a space discretization via geometrically adaptive finite elements which accomodate the change in shape of the domain, using a computationally novel treatment of the resulting thermal contact problem due to coiling. Time is discretized according to a Crank-Nicolson scheme. Since the actual physical process takes less time than the time required by the process controlling computer to solve the full mathematical model, a special predictive device was developed, in the form of a set of least squares polynomials, based on the off-line numerical solution of the mathematical model.

Predicting the range of inbreeding depression of inbred lines in cross-pollinated populations

The objectives of this paper were to derive the genetic variance of inbreeding depression ( ) and to predict the range of inbreeding depression (RID) in cross-pollinated populations. The variance of inbreeding depression is a function of the genetic variances related to dominance effects (, D2, and ), and of the inbreeding coefficients of the two generations in which inbreeding depression is measured (Ft and Fg). The results showed that the higher the level of dominance of a trait, the higher the variance of inbreeding depression. The magnitudes of were expected to be lower in improved (mean gene frequencies = > 0.6) and in unimproved ( < 0.4) populations, than in composite populations ( » 0.5). Data from a maize population used to illustrate the study showed that the range of inbreeding depression in the S¥ generation of selfing was from 48.7% to 85.3% for grain yield, and from 13.9% to 24.5% for plant height. A mating design outlined to estimate the genetic variance of inbreeding depression, the range of inbreeding depression, and of the range of inbred lines is presented.

Predicting variety composite means without diallel crossing

Prediction of variety composite means was shown to be feasible without diallel crossing the parental varieties. Thus, the predicted mean for a quantitative trait of a composite is given by: Yk = a1 sigmaVj + a2sigmaTj + a3 - a4, with coefficients a1 = (n - 2k)/k²(n - 2); a2 = 2n(k - 1)/k²(n - 2); a3 = n(k - 1)/k(n - 1)(n - 2); and a4 = n²(k - 1)/k(n - 1)(n - 2); summation is for j = 1 to k, where k is the size of the composite (number of parental varieties of a particular composite) and n is the total number of parent varieties. Vj is the mean of varieties and Tj is the mean of topcrosses (pool of varieties as tester), and and are the respective average values in the whole set. Yield data from a 7 x 7 variety diallel cross were used for the variety means and for the "simulated" topcross means to illustrate the proposed procedure. The proposed prediction procedure was as effective as the prediction based on Yk = - ( -)/k, where and refer to the mean of hybrids (F1) and parental varieties, respectively, in a variety diallel cross. It was also shown in the analysis of variance that the total sum of squares due to treatments (varieties and topcrosses) can be orthogonally partitioned following the reduced model Yjj’ = mu + ½(v j + v j’) + + h j+ h j’, thus making possible an F test for varieties, average heterosis and variety heterosis. Least square estimates of these effects are also given

Characteristic Approach to Predicting Analysts’ Forecast Errors in the Finnish Market

Extensive literature shows that analysts’ forecasts and recommendations are often biased. Thus, it is important for the financial market to be able to recognize this bias to be able to correctly valuate public companies. This thesis uses characteristic approach, which was introduced by So (2013, pp. 615-640), to forecast analysts’ forecast errors and tests if predictable forecast error is fully incorporated into share prices. Data is collected of listed Finnish companies. Thesis’ timeframe spans over ten years from 2004 to 2013 consisting of 788 firm-years. Although there is earlier evidence that the characteristic approach is able to predict analysts’ forecast errors, no support for this is found in the Finnish market. This thesis contributes to the current knowledge by showing that the characteristic approach does not work universally as such but requires development to work especially in the smaller markets.

Predicting bankruptcy of Finnish limited liability companies from historical financial statements

This master’s thesis studies the probability of bankruptcy of Finnish limited liability companies as a part of credit risk assessment. The main idea of this thesis is to build and test bankruptcy prediction models for Finnish limited liability companies that can be utilized in credit decision making. The data used in this thesis consists of historical financial statements from 2112 Finnish limited liability companies, half of which have filed for bankruptcy. A total of four models are developed, two with logistic regression and two with multivariate discriminant analysis (MDA). The time horizon of the models varies from 1 to 2 years prior to the bankruptcy, and 14 different financial variables are used in the model formation. The results show that the prediction accuracy of the models ranges between 81.7% and 88.9%, and the best prediction accuracy is achieved with the one year prior the bankruptcy logistic regression model. However the difference between the best logistic model and the best MDA model is minimal. Overall based on the results of this thesis it can be concluded that predicting bankruptcy is possible to some extent, but naturally the results are not perfect.

Artificial neural network models for predicting 1-year mortality in elderly patients with intertrochanteric fractures in China

The mortality rate of older patients with intertrochanteric fractures has been increasing with the aging of populations in China. The purpose of this study was: 1) to develop an artificial neural network (ANN) using clinical information to predict the 1-year mortality of elderly patients with intertrochanteric fractures, and 2) to compare the ANN's predictive ability with that of logistic regression models. The ANN model was tested against actual outcomes of an intertrochanteric femoral fracture database in China. The ANN model was generated with eight clinical inputs and a single output. ANN's performance was compared with a logistic regression model created with the same inputs in terms of accuracy, sensitivity, specificity, and discriminability. The study population was composed of 2150 patients (679 males and 1471 females): 1432 in the training group and 718 new patients in the testing group. The ANN model that had eight neurons in the hidden layer had the highest accuracies among the four ANN models: 92.46 and 85.79% in both training and testing datasets, respectively. The areas under the receiver operating characteristic curves of the automatically selected ANN model for both datasets were 0.901 (95%CI=0.814-0.988) and 0.869 (95%CI=0.748-0.990), higher than the 0.745 (95%CI=0.612-0.879) and 0.728 (95%CI=0.595-0.862) of the logistic regression model. The ANN model can be used for predicting 1-year mortality in elderly patients with intertrochanteric fractures. It outperformed a logistic regression on multiple performance measures when given the same variables.

PREDICTING DIFFUSIVITIES IN DENSE FLUIDS

In this work the Weeks-Chandler-Andersen (WCA) perturbation theory coupled with the Enskog’s solution of the Boltzmann equation for dense hard-sphere fluids is employed for estimating diffusion coefficients in compressed pure liquids and fluids and dense fluid mixtures. The effect of density correction on the estimation of diffusivities is analyzed using the Carnahan-Starling pair correlation function and the correlation of Speedy and Harris which have been proposed as models of self-diffusion coefficient of hard-sphere fluids. The approach presented here is based on the smooth hard-sphere theory without any binary adjustable parameters and can be readily used for estimating diffusivities in multicomponent fluid mixtures. It is shown that the correlated and the predicted diffusivities are in good agreement with the experimental data and much better than estimates of Wilke-Chang equation.

Securin, Cdc20 and Cdc27 in predicting the outcome of human breast cancer

Deregulated proliferation has been recognized among the most important factors promoting breast cancer development and progression. The aim of the project is to gain understanding of the role of specific cell cycle regulators of metaphase-anaphase transition and evaluate their potential in breast cancer prognostication and treatment decisions. Metaphase-anaphase transition is triggered by activation of anaphase promoting complex (APC) which is activated by a cascade of regulatory proteins, among them securin, Cdc20 and Cdc27. These proteins promote the metaphase–anaphase transition and participate in the timely separation of the chromatids. This study is based on a patient material of approximately 600 breast cancer patients and up to 22 years of follow-up. As the main observation, based on DNA cytometric and immunohistochemical methods, securin, Cdc20 and Cdc27 protein expressions were associated with abnormal DNA content and outcome of breast cancer. In the studied patient material, high securin expression alone and in combination with Cdc20 and Cdc27 predicted up to 9.8-fold odds for aneuploid DNA content in human breast cancer. In Kaplan–Meier analyses, high expression of securin systematically indicated decrease in breast cancer survival as compared to low expression cases. The adverse effect of high securin expression was further strengthened by combining it with Cdc20 or Cdc27 expressions, resulting in up to 6.8-fold risk of breast cancer death. High securin and Cdc20 expression was also associated with triple-negative breast cancer type with high statistical significance. Securin, Cdc20 or Cdc27 have not previously been investigated in a clinically relevant large breast cancer patient material or in association with DNA ploidy. The present findings suggest that the studied proteins may serve as potential biomarkers for identification of aggressive course of disease and unfavourable outcome of human breast cancer, and that they may provide a future research aim for understanding abnormal proliferation in malignant disease.

Metrics for Predicting User Behavior and Experience in an Interactive Storytelling System

In much of the previous research into the field of interactive storytelling, the focus has been on the creation of complete systems, then evaluating the performance of those systems based on user experience. Less focus has been placed on finding general solutions to problems that manifest in many different types of interactive storytelling systems. The goal of this thesis was to identify potential candidates for metrics that a system could use to predict player behavior or how players experience the story they are presented with, and to put these metrics to an empirical test. The three metrics that were used were morality, relationships and conflict. The game used for user testing of the metrics, Regicide is an interactive storytelling experience that was created in conjunction with Eero Itkonen. Data, in the forms of internal system data and survey answers, collected through user testing, was used to evaluate hypotheses for each metric. Out of the three chosen metrics, morality performed the best in this study. Though further research and refinement may be required, the results were promising, and point to the conclusion that user responses to questions of morality are a strong predictor for their choices in similar situations later on in the course of an interactive story. A similar examination for user relationships with other characters in the story did not produce promising results, but several problems were recognized in terms of methodology and further research with a better optimized system may yield different results. On the subject of conflict, several aspects, proposed by Ware et al. (2012), were evaluated separately. Results were inconclusive, with the aspect of directness showing the most promise.