Deformation and stability regression models for soil nail walls

Lateral displacement and global stability are the two main stability criteria for soil nail walls. Conventional design methods do not adequately address the deformation behaviour of soil nail walls, owing to the complexity involved in handling a large number of influencing factors. Consequently, limited methods of deformation estimates based on empirical relationships and in situ performance monitoring are available in the literature. It is therefore desirable that numerical techniques and statistical methods are used in order to gain a better insight into the deformation behaviour of soil nail walls. In the present study numerical experiments are conducted using a 2 4 factorial design method. Based on analysis of the maximum lateral deformation and factor-of-safety observations from the numerical experiments, regression models for maximum lateral deformation and factor-of-safety prediction are developed and checked for adequacy. Selection of suitable design factors for the 2 4 factorial design of numerical experiments enabled the use of the proposed regression models over a practical range of soil nail wall heights and in situ soil variability. It is evident from the model adequacy analyses and illustrative example that the proposed regression models provided a reasonably good estimate of the lateral deformation and global factor of safety of the soil nail walls.

Land use regression model (LUR) for ultrafine particles in Brisbane

Traffic-related air pollution has been associated with a wide range of adverse health effects. One component of traffic emissions that has been receiving increasing attention is ultrafine particles(UFP, < 100 nm), which are of concern to human health due to their small diameters. Vehicles are the dominant source of UFP in urban environments. Small-scale variation in ultrafine particle number concentration (PNC) can be attributed to local changes in land use and road abundance. UFPs are also formed as a result of particle formation events. Modelling the spatial patterns in PNC is integral to understanding human UFP exposure and also provides insight into particle formation mechanisms that contribute to air pollution in urban environments. Land-use regression (LUR) is a technique that can use to improve the prediction of air pollution.

Placental abruption : Studies on incidence, risk factors and potential predictive biomarkers

Placental abruption, one of the most significant causes of perinatal mortality and maternal morbidity, occurs in 0.5-1% of pregnancies. Its etiology is unknown, but defective trophoblastic invasion of the spiral arteries and consequent poor vascularization may play a role. The aim of this study was to define the prepregnancy risk factors of placental abruption, to define the risk factors during the index pregnancy, and to describe the clinical presentation of placental abruption. We also wanted to find a biochemical marker for predicting placental abruption early in pregnancy. Among women delivering at the University Hospital of Helsinki in 1997-2001 (n=46,742), 198 women with placental abruption and 396 control women were identified. The overall incidence of placental abruption was 0.42%. The prepregnancy risk factors were smoking (OR 1.7; 95% CI 1.1, 2.7), uterine malformation (OR 8.1; 1.7, 40), previous cesarean section (OR 1.7; 1.1, 2.8), and history of placental abruption (OR 4.5; 1.1, 18). The risk factors during the index pregnancy were maternal (adjusted OR 1.8; 95% CI 1.1, 2.9) and paternal smoking (2.2; 1.3, 3.6), use of alcohol (2.2; 1.1, 4.4), placenta previa (5.7; 1.4, 23.1), preeclampsia (2.7; 1.3, 5.6) and chorioamnionitis (3.3; 1.0, 10.0). Vaginal bleeding (70%), abdominal pain (51%), bloody amniotic fluid (50%) and fetal heart rate abnormalities (69%) were the most common clinical manifestations of placental abruption. Retroplacental blood clot was seen by ultrasound in 15% of the cases. Neither bleeding nor pain was present in 19% of the cases. Overall, 59% went into preterm labor (OR 12.9; 95% CI 8.3, 19.8), and 91% were delivered by cesarean section (34.7; 20.0, 60.1). Of the newborns, 25% were growth restricted. The perinatal mortality rate was 9.2% (OR 10.1; 95% CI 3.4, 30.1). We then tested selected biochemical markers for prediction of placental abruption. The median of the maternal serum alpha-fetoprotein (MSAFP) multiples of median (MoM) (1.21) was significantly higher in the abruption group (n=57) than in the control group (n=108) (1.07) (p=0.004) at 15-16 gestational weeks. In multivariate analysis, elevated MSAFP remained as an independent risk factor for placental abruption, adjusting for parity ≥ 3, smoking, previous placental abruption, preeclampsia, bleeding in II or III trimester, and placenta previa. MSAFP ≥ 1.5 MoM had a sensitivity of 29% and a false positive rate of 10%. The levels of the maternal serum free beta human chorionic gonadotrophin MoM did not differ between the cases and the controls. None of the angiogenic factors (soluble endoglin, soluble fms-like tyrosine kinase 1, or placental growth factor) showed any difference between the cases (n=42) and the controls (n=50) in the second trimester. The levels of C-reactive protein (CRP) showed no difference between the cases (n=181) and the controls (n=261) (median 2.35 mg/l [interquartile range {IQR} 1.09-5.93] versus 2.28 mg/l [IQR 0.92-5.01], not significant) when tested in the first trimester (mean 10.4 gestational weeks). Chlamydia pneumoniae specific immunoglobulin G (IgG) and immunoglobulin A (IgA) as well as C. trachomatis specific IgG, IgA and chlamydial heat-shock protein 60 antibody rates were similar between the groups. In conclusion, although univariate analysis identified many prepregnancy risk factors for placental abruption, only smoking, uterine malformation, previous cesarean section and history of placental abruption remained significant by multivariate analysis. During the index pregnancy maternal alcohol consumption and smoking and smoking by the partner turned out to be the major independent risk factors for placental abruption. Smoking by both partners multiplied the risk. The liberal use of ultrasound examination contributed little to the management of women with placental abruption. Although second-trimester MSAFP levels were higher in women with subsequent placental abruption, clinical usefulness of this test is limited due to low sensitivity and high false positive rate. Similarly, angiogenic factors in early second trimester, or CRP levels, or chlamydial antibodies in the first trimester failed to predict placental abruption.

A Nonlinear Approach for Re-entry Guidance of Reusable Launch Vehicles Using Model Predictive Static Programming

A nonlinear suboptimal guidance scheme is developed for the reentry phase of the reusable launch vehicles. A recently developed methodology, named as model predictive static programming (MPSP), is implemented which combines the philosophies of nonlinear model predictive control theory and approximate dynamic programming. This technique provides a finite time nonlinear suboptimal guidance law which leads to a rapid solution of the guidance history update. It does not have to suffer from computational difficulties and can be implemented online. The system dynamics is propagated through the flight corridor to the end of the reentry phase considering energy as independent variable and angle of attack as the active control variable. All the terminal constraints are satisfied. Among the path constraints, the normal load is found to be very constrictive. Hence, an extra effort has been made to keep the normal load within a specified limit and monitoring its sensitivity to the perturbation.

The usefulness of fair values in improving the predictive ability of earnings: Evidence from international banks

One of the objectives of general-purpose financial reporting is to provide information about the financial position, financial performance and cash flows of an entity that is useful to a wide range of users in making economic decisions. The current focus on potentially increased relevance of fair value accounting weighed against issues of reliability has failed to consider the potential impact on the predictive ability of accounting. Based on a sample of international (non-U.S.) banks from 24 countries during 2009-2012, we test the usefulness of fair values in improving the predictive ability of earnings. First, we find that the increasing use of fair values on balance-sheet financial instruments enhances the ability of current earnings to predict future earnings and cash flows. Second, we provide evidence that the fair value hierarchy classification choices affect the ability of earnings to predict future cash flows and future earnings. More precisely, we find that the non-discretionary fair value component (Level 1 assets) improves the predictability of current earnings whereas the discretionary fair value components (Level 2 and Level 3 assets) weaken the predictive power of earnings. Third, we find a consistent and strong association between factors reflecting country-wide institutional structures and predictive power of fair values based on discretionary measurement inputs (Level 2 and Level 3 assets and liabilities). Our study is timely and relevant. The findings have important implications for standard setters and contribute to the debate on the use of fair value accounting.

A fast dual algorithm for kernel logistic regression

This paper gives a new iterative algorithm for kernel logistic regression. It is based on the solution of a dual problem using ideas similar to those of the Sequential Minimal Optimization algorithm for Support Vector Machines. Asymptotic convergence of the algorithm is proved. Computational experiments show that the algorithm is robust and fast. The algorithmic ideas can also be used to give a fast dual algorithm for solving the optimization problem arising in the inner loop of Gaussian Process classifiers.

Generalised regression estimation for domain class frequencies

This study examines the properties of Generalised Regression (GREG) estimators for domain class frequencies and proportions. The family of GREG estimators forms the class of design-based model-assisted estimators. All GREG estimators utilise auxiliary information via modelling. The classic GREG estimator with a linear fixed effects assisting model (GREG-lin) is one example. But when estimating class frequencies, the study variable is binary or polytomous. Therefore logistic-type assisting models (e.g. logistic or probit model) should be preferred over the linear one. However, other GREG estimators than GREG-lin are rarely used, and knowledge about their properties is limited. This study examines the properties of L-GREG estimators, which are GREG estimators with fixed-effects logistic-type models. Three research questions are addressed. First, I study whether and when L-GREG estimators are more accurate than GREG-lin. Theoretical results and Monte Carlo experiments which cover both equal and unequal probability sampling designs and a wide variety of model formulations show that in standard situations, the difference between L-GREG and GREG-lin is small. But in the case of a strong assisting model, two interesting situations arise: if the domain sample size is reasonably large, L-GREG is more accurate than GREG-lin, and if the domain sample size is very small, estimation of assisting model parameters may be inaccurate, resulting in bias for L-GREG. Second, I study variance estimation for the L-GREG estimators. The standard variance estimator (S) for all GREG estimators resembles the Sen-Yates-Grundy variance estimator, but it is a double sum of prediction errors, not of the observed values of the study variable. Monte Carlo experiments show that S underestimates the variance of L-GREG especially if the domain sample size is minor, or if the assisting model is strong. Third, since the standard variance estimator S often fails for the L-GREG estimators, I propose a new augmented variance estimator (A). The difference between S and the new estimator A is that the latter takes into account the difference between the sample fit model and the census fit model. In Monte Carlo experiments, the new estimator A outperformed the standard estimator S in terms of bias, root mean square error and coverage rate. Thus the new estimator provides a good alternative to the standard estimator.

Ship collision avoidance and COLREGS compliance using simulation-based control behavior selection with predictive hazard assessment

This paper describes a concept for a collision avoidance system for ships, which is based on model predictive control. A finite set of alternative control behaviors are generated by varying two parameters: offsets to the guidance course angle commanded to the autopilot and changes to the propulsion command ranging from nominal speed to full reverse. Using simulated predictions of the trajectories of the obstacles and ship, compliance with the Convention on the International Regulations for Preventing Collisions at Sea and collision hazards associated with each of the alternative control behaviors are evaluated on a finite prediction horizon, and the optimal control behavior is selected. Robustness to sensing error, predicted obstacle behavior, and environmental conditions can be ensured by evaluating multiple scenarios for each control behavior. The method is conceptually and computationally simple and yet quite versatile as it can account for the dynamics of the ship, the dynamics of the steering and propulsion system, forces due to wind and ocean current, and any number of obstacles. Simulations show that the method is effective and can manage complex scenarios with multiple dynamic obstacles and uncertainty associated with sensors and predictions.

Applicability of Statistical Learning Algorithms for Spatial Variability of Rock Depth

Two algorithms are outlined, each of which has interesting features for modeling of spatial variability of rock depth. In this paper, reduced level of rock at Bangalore, India, is arrived from the 652 boreholes data in the area covering 220 sqa <.km. Support vector machine (SVM) and relevance vector machine (RVM) have been utilized to predict the reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth. The support vector machine (SVM) that is firmly based on the theory of statistical learning theory uses regression technique by introducing epsilon-insensitive loss function has been adopted. RVM is a probabilistic model similar to the widespread SVM, but where the training takes place in a Bayesian framework. Prediction results show the ability of learning machine to build accurate models for spatial variability of rock depth with strong predictive capabilities. The paper also highlights the capability ofRVM over the SVM model.

Social Media as a Form of Organizational Knowledge Sharing

This study investigates the role of social media as a form of organizational knowledge sharing. Social media is investigated in terms of the Web 2.0 technologies that organizations provide their employees as tools of internal communication. This study is anchored in the theoretical understanding of social media as technologies which enable both knowledge collection and knowledge donation. This study investigates the factors influencing employees’ use of social media in their working environment. The study presents the multidisciplinary research tradition concerning knowledge sharing. Social media is analyzed especially in relation to internal communication and knowledge sharing. Based on previous studies, it is assumed that personal, organizational, and technological factors influence employees’ use of social media in their working environment. The research represents a case study focusing on the employees of the Finnish company Wärtsilä. Wärtsilä represents an eligible case organization for this study given that it puts in use several Web 2.0 tools in its intranet. The research is based on quantitative methods. In total 343 answers were obtained with the aid of an online survey which was available in Wärtsilä’s intranet. The associations between the variables are analyzed with the aid of correlations. Finally, with the aid of multiple linear regression analysis the causality between the assumed factors and the use of social media is tested. The analysis demonstrates that personal, organizational and technological factors influence the respondents’ use of social media. As strong predictive variables emerge the benefits that respondents expect to receive from using social media and respondents’ experience in using Web 2.0 in their private lives. Also organizational factors such as managers’ and colleagues’ activeness and organizational guidelines for using social media form a causal relationship with the use of social media. In addition, respondents’ understanding of their responsibilities affects their use of social media. The more social media is considered as a part of individual responsibilities, the more frequently social media is used. Finally, technological factors must be recognized. The more user-friendly social media tools are considered and the better technical skills respondents have, the more frequently social media is used in the working environment. The central references in relation to knowledge sharing include Chun Wei Choo’s (2006) work Knowing Organization, Ikujiro Nonaka and Hirotaka Takeuchi’s (1995) work The Knowledge Creating Company and Linda Argote’s (1999) work Organizational Learning.

Minimum enclosing spheres formulations for support vector ordinal regression

We present two new support vector approaches for ordinal regression. These approaches find the concentric spheres with minimum volume that contain most of the training samples. Both approaches guarantee that the radii of the spheres are properly ordered at the optimal solution. The size of the optimization problem is linear in the number of training samples. The popular SMO algorithm is adapted to solve the resulting optimization problem. Numerical experiments on some real-world data sets verify the usefulness of our approaches for data mining.

Construction and use of linear regression models for processor performance analysis

Processor architects have a challenging task of evaluating a large design space consisting of several interacting parameters and optimizations. In order to assist architects in making crucial design decisions, we build linear regression models that relate Processor performance to micro-architecture parameters, using simulation based experiments. We obtain good approximate models using an iterative process in which Akaike's information criteria is used to extract a good linear model from a small set of simulations, and limited further simulation is guided by the model using D-optimal experimental designs. The iterative process is repeated until desired error bounds are achieved. We used this procedure to establish the relationship of the CPI performance response to 26 key micro-architectural parameters using a detailed cycle-by-cycle superscalar processor simulator The resulting models provide a significance ordering on all micro-architectural parameters and their interactions, and explain the performance variations of micro-architectural techniques.

Predictive testing for familial adenomatous polyposis in a rural South Indian community

During the course of genome studies in a rural community in the South Indian state of Karnataka, DNA-based investigations and counselling for familial adenomatous polyposis (FAP) were requested via the community physician. The proposita died in 1940 and FAP had been clinically diagnosed in 2 of her 5 children, both deceased. DNA samples from 2 affected individuals in the third generation were screened for mutations in the APC gene, and a frame-shift mutation was identified in exon 15 with a common deletion at codon 1061. Predictive testing for the mutation was then organized on a voluntary basis. There were 11 positive tests, including confirmatory positives on 2 persons diagnosed by colonoscopy, and to date surgery has been successfully undertaken on 3 previously undiagnosed adults. The ongoing success of the study indicates that, with appropriate access to the facilities offered by collaborating centres, predictive testing is feasible for diseases such as FAP and could be of significant benefit to communities in economically less developed countries.

Rannansiirtyminen ja ihmisen toimintaympäristön muutos Mätäjokilaakson alueella esihistoriallisella ajalla

In this thesis, I study the changing ladscape and human environment of the Mätäjoki Valley, West-Helsinki, using reconstructions and predictive modelling. The study is a part of a larger project funded by the city of Helsinki aming to map the past of the Mätäjoki Valley. The changes in landscape from an archipelago in the Ancylus Lake to a river valley are studied from 10000 to 2000 years ago. Alongside shore displacement, we look at the changing environment from human perspective and predict the location of dwelling sitesat various times. As a result, two map series were produced that show how the landscape changed and where inhabitance is predicted. To back them up, we have also looked at what previous research says about the history of the waterways, climate, vegetation and archaeology. The changing landscape of the river valley is reconstructed using GIS methods. For this purpose, new laser point data set was used and at the same time tested in the context landscape modelling. Dwelling sites were modeled with logistic regression analysis. The spatial predictive model combines data on the locations of the known dwelling sites, environmental factors and shore displacement data. The predictions were visualised into raster maps that show the predictions for inhabitance 3000 and 5000 years ago. The aim of these maps was to help archaeologists map potential spots for human activity. The produced landscape reconstructions clarified previous shore displacement studies of the Mätäjoki region and provided new information on the location of shoreline. From the shore displacement history of the Mätäjoki Valley arise the following stages: 1. The northernmost hills of the Mätäjoki Valley rose from Ancylus Lake approximately 10000 years ago. Shore displacement was fast during the following thousand years. 2. The area was an archipelago with a relatively steady shoreline 9000 7000 years ago. 8000 years ago the shoreline drew back in the middle and southern parts of the river valley because of the transgression of the Litorina Sea. 3. Mätäjoki was a sheltered bay of the Litorina Sea 6000 5000 years ago. The Vantaanjoki River started to flow into the Mätäjoki Valley approximately 5000 years ago. 4. The sediment plains in the southern part of the river valley rose from the sea rather quickly 5000 3000 years ago. Salt water still pushed its way into the southermost part of the valley 4000 years ago. 5. The shoreline proceeded to Pitäjänmäki rapids where it stayed at least a thousand years 3000 2000 years ago. The predictive models managed to predict the locations of dwelling sites moderately well. The most accurate predictions were found on the eastern shore and Malminkartano area. Of the environment variables sand and aspect of slope were found to have the best predictive power. From the results of this study we can conclude that the Mätäjoki Valley has been a favorable location to live especially 6000 5000 years ago when the climate was mild and vegetation lush. The laser point data set used here works best in shore displacement studies located in rural areas or if further specific palaeogeographic or hydrologic analysis in the research area is not needed.