Development of Measurement Systems in Scientific Research: Case Study

In recent years, technological advancements in microelectronics and sensor technologies have revolutionized the field of electrical engineering. New manufacturing techniques have enabled a higher level of integration that has combined sensors and electronics into compact and inexpensive systems. Previously, the challenge in measurements was to understand the operation of the electronics and sensors, but this has now changed. Nowadays, the challenge in measurement instrumentation lies in mastering the whole system, not just the electronics. To address this issue, this doctoral dissertation studies whether it would be beneficial to consider a measurement system as a whole from the physical phenomena to the digital recording device, where each piece of the measurement system affects the system performance, rather than as a system consisting of small independent parts such as a sensor or an amplifier that could be designed separately. The objective of this doctoral dissertation is to describe in depth the development of the measurement system taking into account the challenges caused by the electrical and mechanical requirements and the measurement environment. The work is done as an empirical case study in two example applications that are both intended for scientific studies. The cases are a light sensitive biological sensor used in imaging and a gas electron multiplier detector for particle physics. The study showed that in these two cases there were a number of different parts of the measurement system that interacted with each other. Without considering these interactions, the reliability of the measurement may be compromised, which may lead to wrong conclusions about the measurement. For this reason it is beneficial to conceptualize the measurement system as a whole from the physical phenomena to the digital recording device where each piece of the measurement system affects the system performance. The results work as examples of how a measurement system can be successfully constructed to support a study of sensors and electronics.

Genetic, prenatal and postnatal determinants of weight gain and obesity in young children – The STEPS Study

Genetic, Prenatal and Postnatal Determinants of Weight Gain and Obesity in Young Children – The STEPS Study University of Turku, Faculty of Medicine, Department of Paediatrics, University of Turku Doctoral Program of Clinical Investigation (CLIPD), Turku Institute for Child and Youth Research. Conditions of being overweight and obese in childhood are common health problems with longlasting effects into adulthood. Currently 22% of Finnish boys and 12% of Finnish girls are overweight and 4% of Finnish boys and 2% of Finnish girls are obese. The foundation for later health is formed early, even before birth, and the importance of prenatal growth on later health outcomes is widely acknowledged. When the mother is overweight, had high gestational weight gain and disturbances in glucose metabolism during pregnancy, an increased risk of obesity in children is present. On the other hand, breastfeeding and later introduction of complementary foods are associated with a decreased obesity risk. In addition to these, many genetic and environmental factors have an effect on obesity risk, but the clustering of these factors is not extensively studied. The main objective of this thesis was to provide comprehensive information on prenatal and early postnatal factors associated with weight gain and obesity in infancy up to two years of age. The study was part of the STEPS Study (Steps to Healthy Development), which is a follow-up study consisting of 1797 families. This thesis focused on children up to 24 months of age. Altogether 26% of boys and 17% of girls were overweight and 5% of boys and 4% of girls were obese at 24 months of age according to New Finnish Growth references for Children BMI-for-age criteria. Compared to children who remained normal weight, the children who became overweight or obese showed different growth trajectories already at 13 months of age. The mother being overweight had an impact on children’s birth weight and early growth from birth to 24 months of age. The mean duration of breastfeeding was almost 2 months shorter in overweight women in comparison to normal weight women. A longer duration of breastfeeding was protective against excessive weight gain, high BMI, high body weight and high weight-for-length SDS during the first 24 months of life. Breast milk fatty acid composition differed between overweight and normal weight mothers, and overweight women had more saturated fatty acids and less n-3 fatty acids in breast milk. Overweight women also introduced complementary foods to their infants earlier than normal weight mothers. Genetic risk score calculated from 83 obesogenic- and adiposity-related single nucleotide polymorphisms (SNPs) showed that infants with a high genetic risk for being overweight and obese were heavier at 13 months and 24 months of age than infants with a low genetic risk, thus possibly predisposing to later obesity in obesogenic environment. Obesity Risk Score showed that children with highest number of risk factors had almost 6-fold risk of being overweight and obese at 24 months compared to children with lowest number of risk factors. The accuracy of the Obesity Risk Score in predicting overweight and obesity at 24 months was 82%. This study showed that many of the obesogenic risk factors tend to cluster within children and families and that children who later became overweight or obese show different growth trajectories already at a young age. These results highlight the importance of early detection of children with higher obesity risk as well as the importance of prevention measures focused on parents. Keywords: Breastfeeding, Child, Complementary Feeding, Genes, Glucose metabolism, Growth, Infant Nutrition Physiology, Nutrition, Obesity, Overweight, Programming

Customer Experience Management – How to Improve the Customer Experience Management and its Measurement

The main goal of this master’s thesis was to find out, how to improve customer experience management and measurement. This study is a qualitative case study, in which the data collection method has been interviews. In addition, some of the company’s customer experience measurement methods have been analyzed. The theoretical background is applied in practice by interviewing 5 representatives from the case company. In the case company, the management has launched a customer experience focused program, and given guidelines for customer experience improvement. In the case company, customer experience is measured with different methods, one example is asking the recommendation readiness from a customer. In order to improve the customer experience management, the case company should define, what the company means with customer experience and what kind of customer experience the company is aiming to create. After the encounter, the customer should be left with feelings of satisfaction, positivity and trust. The company should focus on easiness in its processes, on top of which the processes should work fluently. The customer experience management should be improved through systematic planning, and by combining and standardizing different measures. In addition, some channel-based measures should be used. The measurement conducted should be more customer focused, and the case company should form an understanding, which touch points are the most relevant to measure.

Fusion of Data from Quadcopter’s Inertial Measurement Unit Using Complementary Filter

A quadcopter is a helicopter with four rotors, which is mechanically simple device, but requires complex electrical control for each motor. Control system needs accurate information about quadcopter’s attitude in order to achieve stable flight. The goal of this bachelor’s thesis was to research how this information could be obtained. Literature review revealed that most of the quadcopters, whose source-code is available, use a complementary filter or some derivative of it to fuse data from a gyroscope, an accelerometer and often also a magnetometer. These sensors combined are called an Inertial Measurement Unit. This thesis focuses on calculating angles from each sensor’s data and fusing these with a complementary filter. On the basis of literature review and measurements using a quadcopter, the proposed filter provides sufficiently accurate attitude data for flight control system. However, a simple complementary filter has one significant drawback – it works reliably only when the quadcopter is hovering or moving at a constant speed. The reason is that an accelerometer can’t be used to measure angles accurately if linear acceleration is present. This problem can be fixed using some derivative of a complementary filter like an adaptive complementary filter or a Kalman filter, which are not covered in this thesis.

Measurement of social value creation with non-financial measures

This research examines the concept of social entrepreneurship which is a fairly new business model. In the field of business it has become increasingly popular in recent years. The growing awareness of the environment and concrete examples of impact created by social entrepreneurship have encouraged entrepreneurs to address social problems. Society’s failures are tried to redress as a result of business activities. The purpose of doing business is necessarily no longer generating just profits but business is run in order to make a social change with the profit gained from the operations. Successful social entrepreneurship requires a specific nature, constant creativity and strong desire to make a social change. It requires constant balancing between two major objectives: both financial and non-financial issues need to be considered, but not at the expense of another. While aiming at the social purpose, the business needs to be run in highly competitive markets. Therefore, both factors need equally be integrated into an organization as they are complementary, not exclusionary. Business does not exist without society and society cannot go forward without business. Social entrepreneurship, its value creation, measurement tools and reporting practices are under discussion in this research. An extensive theoretical basis is covered and used to support the findings coming out of the researched case enterprises. The most attention is focused on the concept of Social Return on Investment. The case enterprises are analyzed through the SROI process. Social enterprises are mostly small or medium sized. Naturally this sets some limitations in implementing measurement tools. The question of resources requires the most attention and therefore sets the biggest constraints. However, the size of the company does not determine all – the nature of business and the type of social purpose need to be considered always. The mission may be so concrete and transparent that in all cases any kind of measurement would be useless. Implementing measurement tools may be of great benefit – or a huge financial burden. Thus, the very first thing to carefully consider is the possible need of measuring value creation.

Performance measurement model for projects from a strategic management perspective

This research studied the project performance measurement from the perspective of strategic management. The objective was to find a generic model for project performance measurement that emphasizes strategy and decision making. Research followed the guidelines of a constructive research methodology. As a result, the study suggests a model that measures projects with multiple meters during and after projects. Measurement after the project is suggested to be linked to the strategic performance measures of a company. The measurement should be conducted with centralized project portfolio management e.g. using the project management office in the organization. Metrics, after the project, measure the project’s actual benefit realization. During the project, the metrics are universal and they measure the accomplished objectives relation to costs, schedule and internal resource usage. Outcomes of these measures should be forecasted by using qualitative or stochastic methods. Solid theoretical background for the model was found from the literature that covers the subjects of performance measurement, projects and uncertainty. The study states that the model can be implemented in companies. This statement is supported by empirical evidence from a single case study. The gathering of empiric evidence about the actual usefulness of the model in companies is left to be done by the evaluative research in the future.

Ionsonde Measurement Trend

Time series analysis has gone through different developmental stages before the current modern approaches. These can broadly categorized as the classical time series analysis and modern time series analysis approach. In the classical one, the basic target of the analysis is to describe the major behaviour of the series without necessarily dealing with the underlying structures. On the contrary, the modern approaches strives to summarize the behaviour of the series going through its underlying structure so that the series can be represented explicitly. In other words, such approach of time series analysis tries to study the series structurally. The components of the series that make up the observation such as the trend, seasonality, regression and disturbance terms are modelled explicitly before putting everything together in to a single state space model which give the natural interpretation of the series. The target of this diploma work is to practically apply the modern approach of time series analysis known as the state space approach, more specifically, the dynamic linear model, to make trend analysis over Ionosonde measurement data. The data is time series of the peak height of F2 layer symbolized by hmF2 which is the height of high electron density. In addition, the work also targets to investigate the connection between solar activity and the peak height of F2 layer. Based on the result found, the peak height of the F2 layer has shown a decrease during the observation period and also shows a nonlinear positive correlation with solar activity.

Sustainable development: conceptualizations and measurement

The paper builds up from a review of some expected, but other quite surprising results regarding country estimates for the year 2000 of genuine saving, a sustainability indicator developed by a World Bank research team. We examine this indicator, founded on neoclassical welfare theory, and discuss one of its major problems. Theoretical developments from ecological economics are then considered, together with insights from Georgescu-Roegen's approach to the production process, in search for an alternative approach. A model with potentially fruitful contributions in this direction is reviewed; it points the course efforts could take enable sustainability evaluations based on a more realistic set of interrelated monetary and biophysical indicators.

Utilization of Statistical Methods for Management in the Forest Industry

The strongest wish of the customer concerning chemical pulp features is consistent, uniform quality. Variation may be controlled and reduced by using statistical methods. However, studies addressing the application and benefits of statistical methods in forest product sector are scarce. Thus, the customer wish is the root cause of the motivation behind this dissertation. The research problem addressed by this dissertation is that companies in the chemical forest product sector require new knowledge for improving their utilization of statistical methods. To gain this new knowledge, the research problem is studied from five complementary viewpoints – challenges and success factors, organizational learning, problem solving, economic benefit, and statistical methods as management tools. The five research questions generated on the basis of these viewpoints are answered in four research papers, which are case studies based on empirical data collection. This research as a whole complements the literature dealing with the use of statistical methods in the forest products industry. Practical examples of the application of statistical process control, case-based reasoning, the cross-industry standard process for data mining, and performance measurement methods in the context of chemical forest products manufacturing are brought to the public knowledge of the scientific community. The benefit of the application of these methods is estimated or demonstrated. The purpose of this dissertation is to find pragmatic ideas for companies in the chemical forest product sector in order for them to improve their utilization of statistical methods. The main practical implications of this doctoral dissertation can be summarized in four points: 1. It is beneficial to reduce variation in chemical forest product manufacturing processes 2. Statistical tools can be used to reduce this variation 3. Problem-solving in chemical forest product manufacturing processes can be intensified through the use of statistical methods 4. There are certain success factors and challenges that need to be addressed when implementing statistical methods