Autonomous navigation accuracy using simulated horizon sensor and sun sensor observations

A relatively simple autonomous system which would use horizon crossing indicators, a sun sensor, a quartz oscillator, and a microprogrammed computer is discussed. The sensor combination is required only to effectively measure the angle between the centers of the Earth and the Sun. Simulations for a particular orbit indicate that 2 km r.m.s. orbit determination uncertainties may be expected from a system with 0.06 deg measurement uncertainty. A key finding is that knowledge of the satellite orbit plane orientation can be maintained to this level because of the annual motion of the Sun and the predictable effects of Earth oblateness. The basic system described can be updated periodically by transits of the Moon through the IR horizon crossing indicator fields of view

A social psychological study of ethnonyms: Cognitive representation of the ingroup and intergroup hostility

Ethnonyms (M. G. Levin & L. P. Potapov, 1964; from the Greek roots meaning "a national group" and "name") are the names an in-group uses to distinguish itself from out-groups. There has been no social psychological research to date exploring the effects of ethnonyms. The authors report the results of 3 studies examining the potential effects of various features of ethnonyms on intergroup behavior. Analyses of archival data indicate that among indigenous African cultures (Study 1), indigenous Native American cultures (Study 2), and African Americans (Study 3), intergroup hostility was greater among in-groups characterized by less complex ethnonyms. Discussion considers the implications of these results and suggests new directions for research in the social psychological study of ethnonyms

Sustainability, self-sufficiency and management simplicity

School of Managemen

Insurance, credit, and technology adoption : field experimental evidence from Malawi

The adoption of new agricultural technologies may be discouraged because of their inherent riskiness. This study implemented a randomized field experiment to ask whether the provision of insurance against a major source of production risk induces farmers to take out loans to invest in a new crop variety. The study sample was composed of roughly 800 maize and groundnut farmers in Malawi, where by far the dominant source of production risk is the level of rainfall. We randomly selected half of the farmers to be offered credit to purchase high-yielding hybrid maize and improved groundnut seeds for planting in the November 2006 crop season. The other half of the farmers were offered a similar credit package but were also required to purchase (at actuarially fair rates) a weather insurance policy that partially or fully forgave the loan in the event of poor rainfall. Surprisingly, take up was lower by 13 percentage points among farmers offered insurance with the loan. Take-up was 33.0 percent for farmers who were offered the uninsured loan. There is suggestive evidence that the reduced take-up of the insured loan was due to the high cognitive cost of evaluating the insurance: insured loan take-up was positively correlated with farmer education levels. By contrast, the take-up of the uninsured loan was uncorrelated with farmer education.,Access to Finance,Debt Markets,Hazard Risk Management,Crops&Crop Management Systems

Using abm in managing territorial health services: the “home-care”.

In recent years in Italy, as in other European countries, profound changes have been introduced in health care at both the central and the regional levels. Most of them were oriented towards a shift from “hospital-centred” healthcare to healthcare based more on territorial services. This transition pursues two objectives: giving more effective responses to citizens’ needs and reducing public health expenditure. Changes that involve organizational structure must also be carried out with the introduction of measurement tools that can help in planning and can control the changes. Starting from the experiences of the healthcare system of the Tuscan Region of Italy, the paper aims to provide an experience of the ABM approach to measure both output and efficiency of territorial health services. Activity Based Management provides an appropriate method to examine territorial activities and to meet the fact-finding needs of national and regional policy, by considering the issues indicated by the territorial managers of the Tuscan healthcare system and the regional and national experiences in recent years. ABM focuses on managing activities as the route to improving value for users and for the local healthcare unit; this is accomplished by the measurement of activities and resources that determine the costs and performance of territorial services. This approach requires organization and integration of sets of data belonging to different systems such as financial and operational systems. The ABM model is complex but it can be used by policy makers for strategic perspective and for continuous improvement. Moreover, ABM meets managers’ demands, as the Tuscan territorial managers have confirmed in interviews. On the basis of experience obtained in territorial long term homecare , the paper underscores the principal issues arising from the process of conducting an ABM project in territorial health services through strong involvement of healthcare workers. The paper also presents the main outputs achieved.Home care, performance measurement, ABM.

A cell outage management framework for dense heterogeneous networks

In this paper, we present a novel cell outage management (COM) framework for heterogeneous networks with split control and data planes-a candidate architecture for meeting future capacity, quality-of-service, and energy efficiency demands. In such an architecture, the control and data functionalities are not necessarily handled by the same node. The control base stations (BSs) manage the transmission of control information and user equipment (UE) mobility, whereas the data BSs handle UE data. An implication of this split architecture is that an outage to a BS in one plane has to be compensated by other BSs in the same plane. Our COM framework addresses this challenge by incorporating two distinct cell outage detection (COD) algorithms to cope with the idiosyncrasies of both data and control planes. The COD algorithm for control cells leverages the relatively larger number of UEs in the control cell to gather large-scale minimization-of-drive-test report data and detects an outage by applying machine learning and anomaly detection techniques. To improve outage detection accuracy, we also investigate and compare the performance of two anomaly-detecting algorithms, i.e., k-nearest-neighbor- and local-outlier-factor-based anomaly detectors, within the control COD. On the other hand, for data cell COD, we propose a heuristic Grey-prediction-based approach, which can work with the small number of UE in the data cell, by exploiting the fact that the control BS manages UE-data BS connectivity and by receiving a periodic update of the received signal reference power statistic between the UEs and data BSs in its coverage. The detection accuracy of the heuristic data COD algorithm is further improved by exploiting the Fourier series of the residual error that is inherent to a Grey prediction model. Our COM framework integrates these two COD algorithms with a cell outage compensation (COC) algorithm that can be applied to both planes. Our COC solution utilizes an actor-critic-based reinforcement learning algorithm, which optimizes the capacity and coverage of the identified outage zone in a plane, by adjusting the antenna gain and transmission power of the surrounding BSs in that plane. The simulation results show that the proposed framework can detect both data and control cell outage and compensate for the detected outage in a reliable manner

Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work