Ono: an open platform for social robotics

In recent times, the focal point of research in robotics has shifted from industrial ro- bots toward robots that interact with humans in an intuitive and safe manner. This evolution has resulted in the subfield of social robotics, which pertains to robots that function in a human environment and that can communicate with humans in an int- uitive way, e.g. with facial expressions. Social robots have the potential to impact many different aspects of our lives, but one particularly promising application is the use of robots in therapy, such as the treatment of children with autism. Unfortunately, many of the existing social robots are neither suited for practical use in therapy nor for large scale studies, mainly because they are expensive, one-of-a-kind robots that are hard to modify to suit a specific need. We created Ono, a social robotics platform, to tackle these issues. Ono is composed entirely from off-the-shelf components and cheap materials, and can be built at a local FabLab at the fraction of the cost of other robots. Ono is also entirely open source and the modular design further encourages modification and reuse of parts of the platform

Salmonella in broiler production in Finland – a Quantitative Risk Assessment

The study assessed the risk salmonella incurred for the Finnish consumers through broiler meat and broiler-derived products available in Finland. The effects of the interventions due to the national salmonella control programme were examined. The assessment covered the production chain from primary production (alive animals) to consumer. The Risk Assessment were focused on 1999, when the prevalence of salmonella in broilers sent to slaughter was the highest it had been since the control program was initiated (so-called “worst case scenario”)

A Bayesian network decision model for supporting the diagnosis of dementia, Alzheimer׳s disease and mild cognitive impairment

AbstractPopulation aging has been occurring as a global phenomenon with heterogeneous consequences in both developed and developing countries. Neurodegenerative diseases, such as Alzheimer׳s Disease (AD), have high prevalence in the elderly population. Early diagnosis of this type of disease allows early treatment and improves patient quality of life. This paper proposes a Bayesian network decision model for supporting diagnosis of dementia, AD and Mild Cognitive Impairment (MCI). Bayesian networks are well-suited for representing uncertainty and causality, which are both present in clinical domains. The proposed Bayesian network was modeled using a combination of expert knowledge and data-oriented modeling. The network structure was built based on current diagnostic criteria and input from physicians who are experts in this domain. The network parameters were estimated using a supervised learning algorithm from a dataset of real clinical cases. The dataset contains data from patients and normal controls from the Duke University Medical Center (Washington, USA) and the Center for Alzheimer׳s Disease and Related Disorders (at the Institute of Psychiatry of the Federal University of Rio de Janeiro, Brazil). The dataset attributes consist of predisposal factors, neuropsychological test results, patient demographic data, symptoms and signs. The decision model was evaluated using quantitative methods and a sensitivity analysis. In conclusion, the proposed Bayesian network showed better results for diagnosis of dementia, AD and MCI when compared to most of the other well-known classifiers. Moreover, it provides additional useful information to physicians, such as the contribution of certain factors to diagnosis

Firm and Industrial Dynamics Over the Business Cycles

This dissertation consists of three essays. In Chapter 1, we proposes a dynamic multi-sector production network model in which firms receive news on the future product-specific demand of a representative household. Since production takes time and firms in the production sectors are connected via input-output links, news on the future final demand of an individual product changes firms\u27 forecasts of their future sales, creating economy-wide effects named as forecast shocks. Forecast shocks are transferred upwards through the supplier-customer connections in the network, from the buyer of an input good to the producer. The model explains the asymmetry in the transmission of individual shocks in the network and how shocks to the expectations generate real, persistent effects. The equilibrium is analytically solved and calibrated to the U.S. economy. Quantitative analysis then follows to examine the model performance. In Chapter 2, we incorporate a firm\u27s project choice decision into a firm dynamics model with business cycle features to explain this empirical finding both qualitatively and quantitatively. In particular, all projects available have the same expected flow return and differ from one another only in the riskiness level. The endogenous option of exiting the market and limited funding for new investment jointly play an important role in motivating firms\u27 risk-taking behavior. The model predicts that relatively small firms are more likely to take risk and that the cross-sectional productivity dispersion, measured as the variance/standard deviation of firm-level profitability, is larger in recessions. In Chapter 3, we consider the impact of job rotation in a directed search model in which firm sizes are endogenously determined, and match quality is initially unknown. In a large firm, job rotation allows the firm to at least partially ameliorate losses from mismatches of workers to jobs. As a result, in the unique equilibrium, large firms have higher labor productivity and lower separation rate. In contrast to the standard directed search model with multi-vacancy firms, this model can generate a positive correlation between firm size and wage without introducing exogenous productivity shocks or a non-concave production function

INTRODUCTION TO NEUTROSOPHIC MEASURE, NEUTROSOPHIC INTEGRAL, AND NEUTROSOPHIC PROBABILITY

Neutrosophic Science means development and applications of neutrosophic logic/set/measure/integral/probability etc. and their applications in any field