Auditory brainstem responses in normal and abnormal preterm and fullterm infants

Imperial Users onl

Towards Identification of Relevant Variables in the observed Aerosol Optical Depth Bias between MODIS and AERONET observations

Measurements made by satellite remote sensing, Moderate Resolution Imaging Spectroradiometer (MODIS), and globally distributed Aerosol Robotic Network (AERONET) are compared. Comparison of the two datasets measurements for aerosol optical depth values show that there are biases between the two data products. In this paper, we present a general framework towards identifying relevant set of variables responsible for the observed bias. We present a general framework to identify the possible factors influencing the bias, which might be associated with the measurement conditions such as the solar and sensor zenith angles, the solar and sensor azimuth, scattering angles, and surface reflectivity at the various measured wavelengths, etc. Specifically, we performed analysis for remote sensing Aqua-Land data set, and used machine learning technique, neural network in this case, to perform multivariate regression between the ground-truth and the training data sets. Finally, we used mutual information between the observed and the predicted values as the measure of similarity to identify the most relevant set of variables. The search is brute force method as we have to consider all possible combinations. The computations involves a huge number crunching exercise, and we implemented it by writing a job-parallel program

A governance Frankenstein integrating the agency and institutional perspectives: focusing on audit committees

This thesis focuses on the relationships between corporate governance structures and the interactions between corporate governance actors. It places the audit committee at its central focus point, but within a holistic corporate eco-system. To develop a more rounded understanding of corporate governance phenomena, the relationships between governance structures and the interactions between governance actors are framed through the lenses of both agency theory and institutional theory. In particular, the different sets of assumptions underlying these two grand theories are assessed against the evidence of audit committees’ structures, policies and actor behaviours within their wider corporate eco-systems. Case-based methods are used to gather the evidence for this thesis and qualitative analysis is applied. Corporate governance phenomena associated with a large corporation and four local government entities in Australia is obtained. A three stage coding scheme and a two-step analysis approach is adopted. To reiterate, this thesis addresses the way in which governance actors and their structures interact as part of an organisation’s holistic corporate eco-system. A pluralistic approach to perceiving and interpreting this phenomenon is taken by combining both agency and institutional theoretical perspectives. It is the assumptions about organisational structures and key players’ behaviours on which these two theories are founded that are especially assessed in this thesis. Therefore, while agency theory and institutional theory have spawned their own sub-branches and sub-theories, these two theories are only addressed in this thesis at their broad level where their original assumptions have been formulated. The findings of this thesis suggest that the classical agency perspective of western-derived corporate governance can be better explained adopting a mixed theoretical framework which draws on both agency theory and institutional theory. In particular, agency theory better explains the purpose and function of corporate governance and its mechanisms, while institutional theory better explains its homogeneity and isomorphic change. Further, the thesis proposes an expanded governance model which incorporates both agency theory and institutional theory. This expanded governance model can be viewed as a theoretical Frankenstein which fits together an amalgam of assumed behaviours to better illustrate and more holistically capture the governance phenomena. Nonetheless, the model may also be disaggregated to explain the governance phenomenon applying either agency or institutional theory. Apart from this theory testing, the thesis provides insights into the descriptive governance workings of a large Australian listed company and also of Victorian local governments (i.e., city councils). The expanded governance model proposed in this study has implications for corporate and public sector regulators, as well as an organisation’s independent directors. They should re-think any myopic agency theory perspective that uses solely agency-based policies for developing governance mechanisms. The evidence in this study infers that relevant regulators and directors need to design mechanisms that are structured to accommodate multilayers of homogeneous institutional groups. They also should orient policies towards achieving the desired effect on the legitimacy standards of the superordinate social system within which institutional groups interact under the expanded governance model

Estimation and Bias Correction of Aerosol Abundance using Data-driven Machine Learning and Remote Sensing

Air quality information is increasingly becoming a public health concern, since some of the aerosol particles pose harmful effects to peoples health. One widely available metric of aerosol abundance is the aerosol optical depth (AOD). The AOD is the integrated light extinction coefficient over a vertical atmospheric column of unit cross section, which represents the extent to which the aerosols in that vertical profile prevent the transmission of light by absorption or scattering. The comparison between the AOD measured from the ground-based Aerosol Robotic Network (AERONET) system and the satellite MODIS instruments at 550 nm shows that there is a bias between the two data products. We performed a comprehensive analysis exploring possible factors which may be contributing to the inter-instrumental bias between MODIS and AERONET. The analysis used several measured variables, including the MODIS AOD, as input in order to train a neural network in regression mode to predict the AERONET AOD values. This not only allowed us to obtain an estimate, but also allowed us to infer the optimal sets of variables that played an important role in the prediction. In addition, we applied machine learning to infer the global abundance of ground level PM2.5 from the AOD data and other ancillary satellite and meteorology products. This research is part of our goal to provide air quality information, which can also be useful for global epidemiology studies

Update on a Continuing Saga: Eelgrass and Green Crabs in Casco Bay, Maine (Poster)

https://digitalcommons.usm.maine.edu/cbep-graphics-maps-posters/1035/thumbnail.jp