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.

Taiwanese migrants in Australia: An investigation of their accultration and well-being

Taiwanese migrants settled in Brisbane, Australia (N=271) completed a questionnaire battery available in both Mandarin and English. A series of multiple and hierarchical regression analyses were used to investigate the factors associated with these migrants’ acculturation and indicators of psychological well-being. Results indicated that various personal factors (age, English language proficiency and duration of stay) were associated with acculturation and indicators of psychological wellbeing. Acculturation was not associated with wellbeing. Social support was associated with the indicators of the participants’ wellbeing. The outcome indicated that although associated with similar personal and environmental factors, acculturation and psychological wellbeing occurred separately. The study highlights the significance of certain personal resources and social support.

Psychological distress may affect nutrition indicators in Australian adults

- Objective The purpose of this research was to explore which demographic and health status variables moderated the relationship between psychological distress and three nutrition indicators: the consumption of fruits, vegetables and takeaway. - Method We analysed data from the 2009 Self-Reported Health Status Survey Report collected in the state of Queensland, Australia. Adults (N = 6881) reported several demographic and health status variables. Moderated logistic regression models were estimated separately for the three nutrition indicators, testing as moderators demographic (age, gender, educational attainment, household income, remoteness, and area-level socioeconomic status) and health status indicators (body mass index, high cholesterol, high blood pressure, and diabetes status). - Results Several significant interactions emerged between psychological distress, demographic (age, area-level socioeconomic status, and income level), and health status variables (body mass index, diabetes status) in predicting the nutrition indicators. Relationships between distress and the nutrition indicators were not significantly different by gender, remoteness, educational attainment, high cholesterol status, and high blood pressure status. - Conclusions The associations between psychological distress and several nutrition indicators differ amongst population subgroups. These findings suggest that in distressed adults, age, area-level socio-economic status, income level, body mass index, and diabetes status may serve as protective or risk factors through increasing or decreasing the likelihood of meeting nutritional guidelines. Public health interventions for improving dietary behaviours and nutrition may be more effective if they take into account the moderators identified in this study rather than using global interventions.

Changes in cannabis use among psychotic clients without specialised substance use treatment

The need to address substance use among people with psychosis has been well established. However, treatment studies targeting substance use in this population have reported mixed results. Substance users with psychosis in no or minimal treatment control groups achieve similar reductions in substance use compared to those in more active substance use treatment, suggesting a role for natural recovery from substance use. This meta-analysis aims to quantify the amount of natural recovery from substance use within control groups of treatment studies containing samples of psychotic substance users, with a particular focus on changes in cannabis use. A systematic search was conducted to identify substance use treatment studies. Meta-analyses were performed to quantify reductions in the frequency of substance use in the past 30 days. Significant but modest reductions (mean reduction of 0.3–0.4 SD across the time points) in the frequency of substance use were found at 6 to 24 months follow up. The current study is the first to quantify changes in substance use in samples enrolled in no treatment or minimal treatment control conditions. These findings highlight the potential role of natural recovery from substance use among individuals with psychosis, although they do not rule out effects of regression to the mean. Additionally, the results provide a baseline from which to estimate likely changes or needed effects sizes in intervention studies. Future research is required to identify the processes underpinning these changes, in order to identify strategies that may better support self-management of substance use in people with psychosis.

A “business researcher” view on opportunities for psychology in entrepreneurship research

In this short essay I offer some “business researcher” advice on how to leverage a strong background in psychology when attempting to contribute to the maturing field of “entrepreneurship research”. Psychologists can benefit from within-discipline research, e.g. on emergence, small groups, fit, and expertise as well as method strengths in, e.g. experimentation, operationalisation of constructs, and multi-level modelling. However, achieving full leverage of these strengths requires a clear conceptualisation of “entrepreneurship” as well as insights into the challenges posed by the nature of this class of phenomena.

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.

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.

Kiinnostusta vai ahdistusta? : Opettajaksi opiskelevien akateemiset tunteet, motivaatio ja opiskelun ongelmat suhteessa hyvinvointiin ja menestykseen aktivoivalla luentokurssilla

Previous studies indicate that positive learning experiences are related to academic achievement as well as to well-being. On the other hand, emotional and motivational problems in studying may pose a risk for both academic achievement and well-being. Thus, emotions and motivation have an increasing role in explaining university students learning and studying. The relations between emotions, motivation, study success and well-being have been less frequently studied. The aim of this study was to investigate what kind of academic emotions, motivational factors and problems in studying students experienced five days before an exam of an activating lecture course, and the relations among these factors as well as their relation to self-study time and study success. Furthermore, the effect of all these factors on well-being, flow experience and academic achievement was examined. The term academic emotion was defined as emotion experienced in academic settings and related to studying. In the present study the theoretical background to motivational factors was based on thinking strategies and attributions, flow experience and task value. Problems in studying were measured in terms of exhaustion, anxiety, stress, lack of interest, lack of self-regulation and procrastination. The data were collected in December 2009 in an activating educational psychology lecture course by using a questionnaire. The participants (n=107) were class and kindergarten teacher students from the University of Helsinki. Most of them were first year students. The course grades were also gathered. Correlations and stepwise regression analysis were carried out to find out the factors that were related to or explained study success. The clusters that presented students´ problems in studying as well as thinking strategies and attributions, were found through hierarchical cluster analysis. K-means cluster analysis was used to form the final groups. One-way analysis of variance, Kruskal-Wallis test and crosstabs were conducted to see whether the students in different clusters varied in terms of study success, academic emotions, task value, flow, and background variables. The results indicated that academic emotions measured five days before the exam explained about 30 % of the variance of the course grade; exhaustion and interest positively, and anxiety negatively. In addition, interest as well as the self-study time best explained study success on the course. The participants were classified into three clusters according to their problems in studying as well as their thinking strategies and attributions: 1) ill-being, 2) carefree, and 3) committed and optimistic students. Ill-being students reported most negative emotions, achieved the worst grades, experienced anxiety rather than flow and were also the youngest. Carefree students, on the other hand, expressed the least negative emotions and spent the least time on self-studying, and like committed students, experienced flow. In addition, committed students reported positive emotions the most often and achieved the best grades on the course. In the future, more in-depth understanding how and why especially young first year students experience their studying hard is needed, because early state of the studies is shown to predict later study success.

Semi-Supervised Classification Using Sparse Gaussian Process Regression

Gaussian Processes (GPs) are promising Bayesian methods for classification and regression problems. They have also been used for semi-supervised learning tasks. In this paper, we propose a new algorithm for solving semi-supervised binary classification problem using sparse GP regression (GPR) models. It is closely related to semi-supervised learning based on support vector regression (SVR) and maximum margin clustering. The proposed algorithm is simple and easy to implement. It gives a sparse solution directly unlike the SVR based algorithm. Also, the hyperparameters are estimated easily without resorting to expensive cross-validation technique. Use of sparse GPR model helps in making the proposed algorithm scalable. Preliminary results on synthetic and real-world data sets demonstrate the efficacy of the new algorithm.

Optimization of an ammonia reactor using regression analysis

This paper presents an optimization algorithm for an ammonia reactor based on a regression model relating the yield to several parameters, control inputs and disturbances. This model is derived from the data generated by hybrid simulation of the steady-state equations describing the reactor behaviour. The simplicity of the optimization program along with its ability to take into account constraints on flow variables make it best suited in supervisory control applications.

Notions of Nation : A Critical Discursive Psychology perspective on the Isle of Man

At the the heart of this study can be seen the dual concern of how the nation is represented as a categorical entity and how this is put to use in everyday social interactions.This can be seen as a reaction to the general approach to categorisation and identity functions that tend to be reified and essentialized within the social sciences. The empirical focus of this study is the Isle of Man, a crown dependency situated geographically central within the British Isles while remaining political outside the United Kingdom. The choice of this site was chosen explicitly as ‘notions of nation’ expressed on the island can be seen as being contested and ephemerally unstable. To get at these ‘notions of nation’ is was necessary to choose specific theoretical tools that were able to capture the wider cultural and representational domain while being capable of addressing the nuanced and functional aspects of interaction. As such, the main theoretical perspective used within this study was that of critical discursive psychology which incorporates the specific theoretical tools interpretative repertoires, ideological dilemmas and subject positions. To supplement these tools, a discursive approach to place was taken in tandem to address the form and function of place attached to nationhood. Two methods of data collection were utilized, that of computer mediated communication and acquaintance interviews. From the data a number of interpretative repertoires were proposed, namely being, essential rights, economic worth, heritage claims, conflict orientation, people-as-nation and place-as-nation. Attached to such interpretative repertoires were the ideological dilemmas region vs. country, people vs. place and individualism vs. collectivism. The subject positions found are much more difficult to condense, but the most significant ones were gender, age and parentage. The final focus of the study, that of place, was shown to be more than just an unreflected on ‘container’ of people but was significant in terms of the rhetorical construction of such places for how people saw themselves and the discursive function of the particular interaction. As such, certain forms of place construction included size, community, temporal, economic, safety, political and recognition. A number of conclusions were drawn from the above which included, that when looking at nation categories we should take into account the specific meanings that people attach to such concepts and to be aware of the particular uses they are put to in interaction. Also, that it is impossible to separate concepts neatly, but it is necessary to be aware of the intersection where concepts cross, and clash, when looking at nationhood.