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This thesis concerns with a computer‐controlled assembly system using a robot. The system performs simple assemblies, determines the three dimensional position and orientation of parts which are supplied one by one, grasps the part from its respective appropriate direction, and inserts it into the other part.

Traffic accident on inter-city expressways might cause large-scale traffic congestion. It might increase travel times of many drivers and it produces a large social loss. This study aims to estimate the social loss of travel time of drivers caused by traffic accidents on inter-city expressway using traffic simulation model, and to evaluate the effects of outflow recommendations when an accident occurs on the expressway. The traffic simulation model on Tomei Expressway is constructed to estimate the dynamic traffic congestion. Travel time losses of drivers are estimated by the simulation results with hypothetical traffic accidents. It is understood that the total losses of travel times are depending on the positions of accident spots and the occurrence times of accidents, because it might influence to congestion at the bottlenecks of flow capacity. Moreover, the effect of traffic control in emergency situation is discussed. The influences of outflow recommendations for drivers are estimated using the traffic simulation model.

This study aims to examine the effects of industrial production (IP), inflation and investment on suicide mortality in Turkey as a developing country over the 1988–2018 period.

Fourier cointegration test and dynamic ordinary least square regression were used in this study.

IP and investment have a statistically significant and negative impact on suicide mortality, whereas inflation has a statistically significant and positive effect on suicide mortality.

The results of this study have important implications for policymakers and potentially the creation and implementation of suicide prevention policies. Not only do investment promotion, IP and disinflation policies in developing countries have a significant effect on economic growth but they also have a substantial impact on mental health.

Although previous studies have investigated the impact of economic growth and unemployment on suicide deaths in Turkey, no research has probed the effect of economic factors, except for unemployment and gross domestic product, on suicide. Thus, given the hidden unemployment and informal sector in developing economies, it is vital to examine the impact of IP, inflation and investment on mental health.

In this paper, we investigate the information content of non-current assets (NCA) among firms listed on the main board of Bursa Malaysia. Specifically, we investigate the information content of tangible and intangible NCA during the economic crisis period of 1997–1998. Our empirical analysis uses time-varying and fixed effects models for the period 1995–1999. We measure information content based on the association of analysts’ earnings forecasts errors (AFE) with both capitalized tangible and intangible NCA. We find evidence of higher information content in tangible NCA compared to intangible NCA during the Asian economic crisis period of 1997–1998. Our evidence is consistent with the assumption that tangible assets are more reliable compared to intangible assets for prediction of expected cash flows during economic crisis periods.

The purpose of this paper, is to predict the various phases and crystal structure from multi-component alloys. Nowadays, the concept and strategies of the development of multi-principal element alloys (MPEAs) significantly increase the count of the potential candidate of alloy systems, which demand proper screening of large number of alloy systems based on the nature of their phase and structure. Experimentally obtained data linking elemental properties and their resulting phases for MPEAs is profused; hence, there is a strong scope for categorization/classification of MPEAs based on structural features of the resultant phase along with distinctive connections between elemental properties and phases.

In this paper, several machine-learning algorithms have been used to recognize the underlying data pattern using data sets to design MPEAs and classify them based on structural features of their resultant phase such as single-phase solid solution, amorphous and intermetallic compounds. Further classification of MPEAs having single-phase solid solution is performed based on crystal structure using an ensemble-based machine-learning algorithm known as random-forest algorithm.

The model developed by implementing random-forest algorithm has resulted in an accuracy of 91 per cent for phase prediction and 93 per cent for crystal structure prediction for single-phase solid solution class of MPEAs. Five input parameters are used in the prediction model namely, valence electron concentration, difference in the pauling negativeness, atomic size difference, mixing enthalpy and mixing entropy. It has been found that the valence electron concentration is the most important feature with respect to prediction of phases. To avoid overfitting problem, fivefold cross-validation has been performed. To understand the comparative performance, different algorithms such as K-nearest Neighbor, support vector machine, logistic regression, naïve-based approach, decision tree and neural network have been used in the data set.

In this paper, the authors described the phase selection and crystal structure prediction mechanism in MPEA data set and have achieved better accuracy using machine learning.

The production of steel pipes with guaranteed external collapse pressure (e.g. high collapse casings for oil wells) requires the implementation of an accurate process control. To develop that process control it is necessary to investigate how different parameters affect the external collapse pressure of the pipes. Experimental/numerical techniques implemented to investigate the collapse behavior of steel pipes are presented. The discussion of the experimental techniques includes the description of the facilities for performing external pressure collapse tests and the description of an imperfections measuring system. The numerical techniques include 2D and 3D finite element models. The effects on the value of the pipes’ external collapse pressure of their shape, residual stresses and material properties are discussed.

This secondary data analysis explores the unmet needs of adolescents experiencing maternal cancer in Ireland. Research has shown that one of the challenges adolescents deal with at the time of maternal cancer is having unmet needs that can impact negatively on their experience and their ability to cope through this difficult challenge.

Fifteen adolescents completed qualitative interviews as part of a larger study that explored the experience of adolescent adjustment to maternal cancer. The transcripts of these original interviews were analysed using a secondary content analysis underpinned by the categories included in the Offspring Cancer Needs Instrument (Patterson et al., 2013).

The findings of this study suggest a necessity to individually explore the unmet needs of adolescents as these were not uniform even within a small sample of 15 adolescents. Unmet needs change and evolve over time as does maternal illness. Adolescents themselves identified the need for more education in the general public and in clinical practitioners on how to respond appropriately to their needs. It is crucial that adolescent's needs and emotions are validated at the time as part of the support provided for them.

This study provides important recommendations for practice and policy on how to provide tailored supports for adolescent who experience cancer in their families as currently there is a lack of effective and evidence-based targeted supports for this specific age group.

The purpose of this paper is to address the problem of profiling human behaviour patterns captured in surveillance videos for the application of online normal behaviour recognition and anomaly detection.

A novel framework is developed for automatic behaviour profiling and online anomaly detection without any manual labeling of the training dataset.

Experimental results demonstrate the effectiveness and robustness of the authors' approach using noisy and sparse datasets collected from one real surveillance scenario.

To discover the topics, co‐clustering topic model not only captures the correlation between words, but also models the correlations between topics. The major difference between the conventional co‐clustering algorithms and the proposed CCMT is that CCMT shows a major improvement in terms of recall, i.e. interpretability.

Considering the unmeasurable states of the systems and the previewed reference signal, a novel fuzzy observer-based preview controller, which is a mixed controller of the fuzzy observer-based controller, fuzzy integrator and preview controller, is considered to address the tracking control problem.

The authors employ an augmentation technique to construct an augmented error system for uncertain T-S fuzzy discrete-time systems with time-varying uncertainties. Additionally, the authors obtain the corresponding linear matrix inequality (LMI) conditions for designing the preview controller.

This paper discusses the preview tracking problem for nonlinear systems. First, considering the unmeasurable states of the systems and the previewed reference signal, a novel fuzzy observer-based preview controller, which is a mixed controller of the fuzzy observer-based controller, fuzzy integrator, and preview controller, is considered to address the tracking control problem. Then, using the fuzzy Lyapunov functional with the linear matrix inequality (LMI) technique, new sufficient conditions for the asymptotic stability of the augmented system are derived by applying the LMI technique. The preview controller and fuzzy observer can be designed in one step. Finally, a numerical example is used to illustrate the effectiveness of the results.

An augmented error system is successfully constructed by the state augmentation approach. A novel preview controller is designed to address the tracking control problem. The preview controller and fuzzy observer can be designed in one step.

The purpose of this paper is to investigate whether European banks use commission and fee income (CF) to smooth reported earnings or to persistently increase reported earnings as an income-increasing earnings management strategy.

The author tests the income-smoothing hypothesis following the approach of Stubben (2010) and Ahmed et al. (1999).

The author finds that European banks use CF to smooth reported earnings and this behaviour is pronounced among non-too-big-to-fail (NTBTF) European banks compared to too-big-to-fail (TBTF) European banks. The author also finds a positive and significant correlation between interest income and non-interest income (CF) indicating increased systematic risk due to reduced diversification benefits. The author also finds that the CF of NTBTF banks is procyclical with fluctuating economic conditions but not for TBTF banks. Also, the author finds evidence for income-increasing earnings management in the post-crisis period, for larger European banks and when banks have higher ex post interest income, implying that the propensity to engage in income-increasing earnings management significantly depends on bank size and ex post interest margin considerations. The findings have policy implications.

The author examines alternative financial numbers that banks use to manage earnings. The author focusses on income smoothing via CF among European banks, a context that has not been explored in the literature.