Bayesian Networks for the Age Classification of Living Individuals: a Study on Transition Analysis

Over the past few decades, age estimation of living persons has represented a challenging task for many forensic services worldwide. In general, the process for age estimation includes the observation of the degree of maturity reached by some physical attributes, such as dentition or several ossification centers. The estimated chronological age or the probability that an individual belongs to a meaningful class of ages is then obtained from the observed degree of maturity by means of various statistical methods. Among these methods, those developed in a Bayesian framework offer to users the possibility of coherently dealing with the uncertainty associated with age estimation and of assessing in a transparent and logical way the probability that an examined individual is younger or older than a given age threshold. Recently, a Bayesian network for age estimation has been presented in scientific literature; this kind of probabilistic graphical tool may facilitate the use of the probabilistic approach. Probabilities of interest in the network are assigned by means of transition analysis, a statistical parametric model, which links the chronological age and the degree of maturity by means of specific regression models, such as logit or probit models. Since different regression models can be employed in transition analysis, the aim of this paper is to study the influence of the model in the classification of individuals. The analysis was performed using a dataset related to the ossifications status of the medial clavicular epiphysis and results support that the classification of individuals is not dependent on the choice of the regression model.

Analysis of translational errors in frame-based and frameless cranial radiosurgery using an anthropomorphic phantom

Abstract Objective: To evaluate three-dimensional translational setup errors and residual errors in image-guided radiosurgery, comparing frameless and frame-based techniques, using an anthropomorphic phantom. Materials and Methods: We initially used specific phantoms for the calibration and quality control of the image-guided system. For the hidden target test, we used an Alderson Radiation Therapy (ART)-210 anthropomorphic head phantom, into which we inserted four 5mm metal balls to simulate target treatment volumes. Computed tomography images were the taken with the head phantom properly positioned for frameless and frame-based radiosurgery. Results: For the frameless technique, the mean error magnitude was 0.22 ± 0.04 mm for setup errors and 0.14 ± 0.02 mm for residual errors, the combined uncertainty being 0.28 mm and 0.16 mm, respectively. For the frame-based technique, the mean error magnitude was 0.73 ± 0.14 mm for setup errors and 0.31 ± 0.04 mm for residual errors, the combined uncertainty being 1.15 mm and 0.63 mm, respectively. Conclusion: The mean values, standard deviations, and combined uncertainties showed no evidence of a significant differences between the two techniques when the head phantom ART-210 was used.

Quantitative market and sales forecast accuracy analysis

The main objective of this master’s thesis was to quantitatively study the reliability of market and sales forecasts of a certain company by measuring bias, precision and accuracy of these forecasts by comparing forecasts against actual values. Secondly, the differences of bias, precision and accuracy between markets were explained by various macroeconomic variables and market characteristics. Accuracy and precision of the forecasts seems to vary significantly depending on the market that is being forecasted, the variable that is being forecasted, the estimation period, the length of the estimated period, the forecast horizon and the granularity of the data. High inflation, low income level and high year-on-year market volatility seems to be related with higher annual market forecast uncertainty and high year-on-year sales volatility with higher sales forecast uncertainty. When quarterly market size is forecasted, correlation between macroeconomic variables and forecast errors reduces. Uncertainty of the sales forecasts cannot be explained with macroeconomic variables. Longer forecasts are more uncertain, shorter estimated period leads to higher uncertainty, and usually more recent market forecasts are less uncertain. Sales forecasts seem to be more uncertain than market forecasts, because they incorporate both market size and market share risks. When lead time is more than one year, forecast risk seems to grow as a function of root forecast horizon. When lead time is less than year, sequential error terms are typically correlated, and therefore forecast errors are trending or mean-reverting. The bias of forecasts seems to change in cycles, and therefore the future forecasts cannot be systematically adjusted with it. The MASE cannot be used to measure whether the forecast can anticipate year-on-year volatility. Instead, we constructed a new relative accuracy measure to cope with this particular situation.

The single principle of compositional data analysis, continuing fallacies, confusionsand misunderstandings and some suggested remedies

In any discipline, where uncertainty and variability are present, it is important to haveprinciples which are accepted as inviolate and which should therefore drive statisticalmodelling, statistical analysis of data and any inferences from such an analysis.Despite the fact that two such principles have existed over the last two decades andfrom these a sensible, meaningful methodology has been developed for the statisticalanalysis of compositional data, the application of inappropriate and/or meaninglessmethods persists in many areas of application. This paper identifies at least tencommon fallacies and confusions in compositional data analysis with illustrativeexamples and provides readers with necessary, and hopefully sufficient, arguments topersuade the culprits why and how they should amend their ways

Sampling in chemical analysis

The uncertainty of any analytical determination depends on analysis and sampling. Uncertainty arising from sampling is usually not controlled and methods for its evaluation are still little known. Pierre Gy’s sampling theory is currently the most complete theory about samplingwhich also takes the design of the sampling equipment into account. Guides dealing with the practical issues of sampling also exist, published by international organizations such as EURACHEM, IUPAC (International Union of Pure and Applied Chemistry) and ISO (International Organization for Standardization). In this work Gy’s sampling theory was applied to several cases, including the analysis of chromite concentration estimated on SEM (Scanning Electron Microscope) images and estimation of the total uncertainty of a drug dissolution procedure. The results clearly show that Gy’s sampling theory can be utilized in both of the above-mentioned cases and that the uncertainties achieved are reliable. Variographic experiments introduced in Gy’s sampling theory are beneficially applied in analyzing the uncertainty of auto-correlated data sets such as industrial process data and environmental discharges. The periodic behaviour of these kinds of processes can be observed by variographic analysis as well as with fast Fourier transformation and auto-correlation functions. With variographic analysis, the uncertainties are estimated as a function of the sampling interval. This is advantageous when environmental data or process data are analyzed as it can be easily estimated how the sampling interval is affecting the overall uncertainty. If the sampling frequency is too high, unnecessary resources will be used. On the other hand, if a frequency is too low, the uncertainty of the determination may be unacceptably high. Variographic methods can also be utilized to estimate the uncertainty of spectral data produced by modern instruments. Since spectral data are multivariate, methods such as Principal Component Analysis (PCA) are needed when the data are analyzed. Optimization of a sampling plan increases the reliability of the analytical process which might at the end have beneficial effects on the economics of chemical analysis,

Centralized Motion Control of a Linear Tooth Belt Drive: Analysis of the Performance and Limitations

A centralized robust position control for an electrical driven tooth belt drive is designed in this doctoral thesis. Both a cascaded control structure and a PID based position controller are discussed. The performance and the limitations of the system are analyzed and design principles for the mechanical structure and the control design are given. These design principles are also suitable for most of the motion control applications, where mechanical resonance frequencies and control loop delays are present. One of the major challenges in the design of a controller for machinery applications is that the values of the parameters in the system model (parameter uncertainty) or the system model it self (non-parametric uncertainty) are seldom known accurately in advance. In this thesis a systematic analysis of the parameter uncertainty of the linear tooth beltdrive model is presented and the effect of the variation of a single parameter on the performance of the total system is shown. The total variation of the model parameters is taken into account in the control design phase using a Quantitative Feedback Theory (QFT). The thesis also introduces a new method to analyze reference feedforward controllers applying the QFT. The performance of the designed controllers is verified by experimentalmeasurements. The measurements confirm the control design principles that are given in this thesis.

Analysis on firm innovation boundaries

Innovation has been widely recognized as an important driver of firm competitiveness, and the firm’s internal research and development (R&D) activities are often considered to have a critical role in innovation activities. Internal R&D is, however, not the source of innovation as firms may tap into knowledge necessary for innovation also through various types of sourcing agreements or by collaborating with other organizations. The objective of this study is to analyze the way firms go about organizing efficiently their innovation boundaries. Within this context, the analysis is focused, firstly, on the relation between innovation boundaries and firm innovation performance and, secondly, on the factors explaining innovation boundary organization. The innovation literature recognizes that the sources of innovation depend on the nature of technology but does not offer a sufficient tool for analyzing innovation boundary options and their efficiency. Thus, this study suggests incorporating insights from transaction cost economics (TCE) complemented with dynamic governance costs and benefits into the analysis. The thesis consists of two parts. The first part introduces the background of the study, research objectives, an overview of the empirical studies, and the general conclusions of the study. The second part is formed of five publications. The overall results firstly indicate that although the relation between firm innovation boundary options is partly industry sector-specific, the firm level search strategies and knowledge transfer capabilities are important for innovation performance independently of the sector. Secondly, the results show that the attributes suggested by TCE alone do not offer a sufficient explanation of innovation boundary selection, especially under conditions of high levels of (radical) uncertainty. Based on the results, the dynamic governance cost and benefit framework complements the static TCE when firm innovation boundaries are scrutinized.

MCMC Analysis for Optimization of Stochastic Models

In any decision making under uncertainties, the goal is mostly to minimize the expected cost. The minimization of cost under uncertainties is usually done by optimization. For simple models, the optimization can easily be done using deterministic methods.However, many models practically contain some complex and varying parameters that can not easily be taken into account using usual deterministic methods of optimization. Thus, it is very important to look for other methods that can be used to get insight into such models. MCMC method is one of the practical methods that can be used for optimization of stochastic models under uncertainty. This method is based on simulation that provides a general methodology which can be applied in nonlinear and non-Gaussian state models. MCMC method is very important for practical applications because it is a uni ed estimation procedure which simultaneously estimates both parameters and state variables. MCMC computes the distribution of the state variables and parameters of the given data measurements. MCMC method is faster in terms of computing time when compared to other optimization methods. This thesis discusses the use of Markov chain Monte Carlo (MCMC) methods for optimization of Stochastic models under uncertainties .The thesis begins with a short discussion about Bayesian Inference, MCMC and Stochastic optimization methods. Then an example is given of how MCMC can be applied for maximizing production at a minimum cost in a chemical reaction process. It is observed that this method performs better in optimizing the given cost function with a very high certainty.

Stability Analysis in Multicriteria Discrete Portfolio Optimization.

Almost every problem of design, planning and management in the technical and organizational systems has several conflicting goals or interests. Nowadays, multicriteria decision models represent a rapidly developing area of operation research. While solving practical optimization problems, it is necessary to take into account various kinds of uncertainty due to lack of data, inadequacy of mathematical models to real-time processes, calculation errors, etc. In practice, this uncertainty usually leads to undesirable outcomes where the solutions are very sensitive to any changes in the input parameters. An example is the investment managing. Stability analysis of multicriteria discrete optimization problems investigates how the found solutions behave in response to changes in the initial data (input parameters). This thesis is devoted to the stability analysis in the problem of selecting investment project portfolios, which are optimized by considering different types of risk and efficiency of the investment projects. The stability analysis is carried out in two approaches: qualitative and quantitative. The qualitative approach describes the behavior of solutions in conditions with small perturbations in the initial data. The stability of solutions is defined in terms of existence a neighborhood in the initial data space. Any perturbed problem from this neighborhood has stability with respect to the set of efficient solutions of the initial problem. The other approach in the stability analysis studies quantitative measures such as stability radius. This approach gives information about the limits of perturbations in the input parameters, which do not lead to changes in the set of efficient solutions. In present thesis several results were obtained including attainable bounds for the stability radii of Pareto optimal and lexicographically optimal portfolios of the investment problem with Savage's, Wald's criteria and criteria of extreme optimism. In addition, special classes of the problem when the stability radii are expressed by the formulae were indicated. Investigations were completed using different combinations of Chebyshev's, Manhattan and Hölder's metrics, which allowed monitoring input parameters perturbations differently.

Fuzzy real option analysis in patent related decision making and patent valuation

The shift towards a knowledge-based economy has inevitably prompted the evolution of patent exploitation. Nowadays, patent is more than just a prevention tool for a company to block its competitors from developing rival technologies, but lies at the very heart of its strategy for value creation and is therefore strategically exploited for economic pro t and competitive advantage. Along with the evolution of patent exploitation, the demand for reliable and systematic patent valuation has also reached an unprecedented level. However, most of the quantitative approaches in use to assess patent could arguably fall into four categories and they are based solely on the conventional discounted cash flow analysis, whose usability and reliability in the context of patent valuation are greatly limited by five practical issues: the market illiquidity, the poor data availability, discriminatory cash-flow estimations, and its incapability to account for changing risk and managerial flexibility. This dissertation attempts to overcome these impeding barriers by rationalizing the use of two techniques, namely fuzzy set theory (aiming at the first three issues) and real option analysis (aiming at the last two). It commences with an investigation into the nature of the uncertainties inherent in patent cash flow estimation and claims that two levels of uncertainties must be properly accounted for. Further investigation reveals that both levels of uncertainties fall under the categorization of subjective uncertainty, which differs from objective uncertainty originating from inherent randomness in that uncertainties labelled as subjective are highly related to the behavioural aspects of decision making and are usually witnessed whenever human judgement, evaluation or reasoning is crucial to the system under consideration and there exists a lack of complete knowledge on its variables. Having clarified their nature, the application of fuzzy set theory in modelling patent-related uncertain quantities is effortlessly justified. The application of real option analysis to patent valuation is prompted by the fact that both patent application process and the subsequent patent exploitation (or commercialization) are subject to a wide range of decisions at multiple successive stages. In other words, both patent applicants and patentees are faced with a large variety of courses of action as to how their patent applications and granted patents can be managed. Since they have the right to run their projects actively, this flexibility has value and thus must be properly accounted for. Accordingly, an explicit identification of the types of managerial flexibility inherent in patent-related decision making problems and in patent valuation, and a discussion on how they could be interpreted in terms of real options are provided in this dissertation. Additionally, the use of the proposed techniques in practical applications is demonstrated by three fuzzy real option analysis based models. In particular, the pay-of method and the extended fuzzy Black-Scholes model are employed to investigate the profitability of a patent application project for a new process for the preparation of a gypsum-fibre composite and to justify the subsequent patent commercialization decision, respectively; a fuzzy binomial model is designed to reveal the economic potential of a patent licensing opportunity.

Price movement in the Brazilian land market (1994-2010): an analysis in the light of post-Keynesian theory

ABSTRACT The present study aims to evaluate crop, pasture and forest land prices in Brazil, between 1994 and 2010, in the light of Post-Keynesian theory. The results provide evidence that land, more than just a simple factor of production, must be conceived of as an economic asset. In fact, the price of rural land is determined not only by the expected profitability deriving from agricultural activities but also by the agents' expectations about its future appreciation and liquidity in an economic environment permeated with uncertainty. In this context, as an object of speculation, land has been particularly important as a store of value.

Opportunity meets uncertainty. Towards sustainable business in the Arctic region

The Arctic region is experiencing a significant change in terms of climate change and a growing economic interest towards its natural resources and emerging business opportunities. The purpose of this study is to explore how can Finnish companies create sustainable business in the Arctic. This is done by examining the arctic business environment, identifying sectors with growth potential, addressing challenges related to operating in the Arctic and suggesting how to ensure sustainability and succeed in the globally competed arctic market. The theoretical framework is based on theories of sustainable development, corporate social responsibility and the role of strategy in creating sustainable business. Empirical data was collected by using qualitative research methods: first, background knowledge was formed based on written documents and, secondly, six expert interviews were conducted in early 2014. The interviewees represented the viewpoints of companies, political decision makers and NGO’s. The analysis of the data was conducted using thematic categorization. The empirical findings of the study suggest that in order to create sustainable business in the Arctic companies should adopt a long-term perspective, embrace a holistic approach to sustainability, understand interdependencies between the dimensions of sustainability and aim at high-level engagement in responsible behavior. To succeed in the arctic market core competencies, customer needs, multivendor cooperation and long-term presence need to be invested in on a company level. In addition, to promote and advance arctic development on a national level support is needed in terms of investments in infrastructure, funding research and design, creating a regulative framework and removing barriers of trade.

The Ontario Municipal Freedom of Information and Protection of Individual Privacy Act: an analysis of its application to school boards

In Canada freedom of information must be viewed in the context of governing -- how do you deal with an abundance of information while balancing a diversity of competing interests? How can you ensure people are informed enough to participate in crucial decision-making, yet willing enough to let some administrative matters be dealt with in camera without their involvement in every detail. In an age when taxpayers' coalition groups are on the rise, and the government is encouraging the establishment of Parent Council groups for schools, the issues and challenges presented by access to information and protection of privacy legislation are real ones. The province of Ontario's decision to extend freedom of information legislation to local governments does not ensure, or equate to, full public disclosure of all facts or necessarily guarantee complete public comprehension of an issue. The mere fact that local governments, like school boards, decide to collect, assemble or record some information and not to collect other information implies that a prior decision was made by "someone" on what was important to record or keep. That in itself means that not all the facts are going to be disclosed, regardless of the presence of legislation. The resulting lack of information can lead to public mistrust and lack of confidence in those who govern. This is completely contrary to the spirit of the legislation which was to provide interested members of the community with facts so that values like political accountability and trust could be ensured and meaningful criticism and input obtained on matters affecting the whole community. This thesis first reviews the historical reasons for adopting freedom of information legislation, reasons which are rooted in our parliamentary system of government. However, the same reasoning for enacting such legislation cannot be applied carte blanche to the municipal level of government in Ontario, or - ii - more specifially to the programs, policies or operations of a school board. The purpose of this thesis is to examine whether the Municipal Freedom of Information and Protection of Privacy Act, 1989 (MFIPPA) was a neccessary step to ensure greater openness from school boards. Based on a review of the Orders made by the Office of the Information and Privacy Commissioner/Ontario, it also assesses how successfully freedom of information legislation has been implemented at the municipal level of government. The Orders provide an opportunity to review what problems school boards have encountered, and what guidance the Commissioner has offered. Reference is made to a value framework as an administrative tool in critically analyzing the suitability of MFIPPA to school boards. The conclusion is drawn that MFIPPA appears to have inhibited rather than facilitated openness in local government. This may be attributed to several factors inclusive of the general uncertainty, confusion and discretion in interpreting various provisions and exemptions in the Act. Some of the uncertainty is due to the fact that an insufficient number of school board staff are familiar with the Act. The complexity of the Act and its legalistic procedures have over-formalized the processes of exchanging information. In addition there appears to be a concern among municipal officials that granting any access to information may be violating personal privacy rights of others. These concerns translate into indecision and extreme caution in responding to inquiries. The result is delay in responding to information requests and lack of uniformity in the responses given. However, the mandatory review of the legislation does afford an opportunity to address some of these problems and to make this complex Act more suitable for application to school boards. In order for the Act to function more efficiently and effectively legislative changes must be made to MFIPPA. It is important that the recommendations for improving the Act be adopted before the government extends this legislation to any other public entities.

Scales, networks and uncertainty : an examination of environmental policy-making in Ontario

Through a case-study analysis of Ontario's ethanol policy, this thesis addresses a number of themes that are consequential to policy and policy-making: spatiality, democracy and uncertainty. First, I address the 'spatial debate' in Geography pertaining to the relevance and affordances of a 'scalar' versus a 'flat' ontoepistemology. I argue that policy is guided by prior arrangements, but is by no means inevitable or predetermined. As such, scale and network are pragmatic geographical concepts that can effectively address the issue of the spatiality of policy and policy-making. Second, I discuss the democratic nature of policy-making in Ontario through an examination of the spaces of engagement that facilitate deliberative democracy. I analyze to what extent these spaces fit into Ontario's environmental policy-making process, and to what extent they were used by various stakeholders. Last, I take seriously the fact that uncertainty and unavoidable injustice are central to policy, and examine the ways in which this uncertainty shaped the specifics of Ontario's ethanol policy. Ultimately, this thesis is an exercise in understanding sub-national environmental policy-making in Canada, with an emphasis on how policy-makers tackle the issues they are faced with in the context of environmental change, political-economic integration, local priorities, individual goals, and irreducible uncertainty.

How health organizations can apply scenario based planning and analytic technologies to make strategic consequential decisions in a time of uncertainty

The magnitude of the cervical cancer problem, coupled with the potential for prevention with recent technological advances, made it imperative to step back and reassess strategic options for dealing with cervical cancer screening in Kenya. The purpose of this qualitative study was: 1) to explore the extent to which the Participatory Action Research (PAR) methodology and the Scenario Based Planning (SBP) method, with the application of analytics, could enable strategic, consequential, informed decision making, and 2) to determine how influential Kenyan decision makers could apply SBP with analytic tools and techniques to make strategic, consequential decisions regarding the implementation of a Cervical Self Sampling Program (CSSP) in both urban and rural settings. The theoretical paradigm for this study was action research; it was experiential, practical, and action oriented, and resulted in co-created knowledge that influenced study participants’ decision making. Action Africa Help International (AAHI) and Brock University collaborated with Local Decision Influencing Participants (LDIP’s) to develop innovative strategies on how to implement the CSSP. SBP tools, along with traditional approaches to data collection and analysis, were applied to collect, visualize and analyze predominately qualitative data. Outputs from the study included: a) a generic implementation scenario for a CSSP (along with scenarios unique to urban and rural settings), and b) 10 strategic directions and 22 supporting implementation strategies that address the variables of: 1) technical viability, 2) political support, 3) affordability, 4) logistical feasibility, 5) social acceptability, and 6) transformation/sustainability. In addition, study participants’ capacity to effectively engage in predictive/prescriptive strategic decision making was strengthened.