Biophysical modeling of a cochlear implant system: progress on closed-loop design using a novel patient-specific evaluation platform

The modern cochlear implant is one of the most successful neural stimulation devices, which partially mimics the workings of the auditory periphery. In the last few decades it has created a paradigm shift in hearing restoration of the deaf population, which has led to more than 324,000 cochlear implant users today. Despite its great success there is great disparity in patient outcomes without clear understanding of the aetiology of this variance in implant performance. Furthermore speech recognition in adverse conditions or music appreciation is still not attainable with today's commercial technology. This motivates the research for the next generation of cochlear implants that takes advantage of recent developments in electronics, neuroscience, nanotechnology, micro-mechanics, polymer chemistry and molecular biology to deliver high fidelity sound. The main difficulties in determining the root of the problem in the cases where the cochlear implant does not perform well are two fold: first there is not a clear paradigm on how the electrical stimulation is perceived as sound by the brain, and second there is limited understanding on the plasticity effects, or learning, of the brain in response to electrical stimulation. These significant knowledge limitations impede the design of novel cochlear implant technologies, as the technical specifications that can lead to better performing implants remain undefined. The motivation of the work presented in this thesis is to compare and contrast the cochlear implant neural stimulation with the operation of the physiological healthy auditory periphery up to the level of the auditory nerve. As such design of novel cochlear implant systems can become feasible by gaining insight on the question `how well does a specific cochlear implant system approximate the healthy auditory periphery?' circumventing the necessity of complete understanding of the brain's comprehension of patterned electrical stimulation delivered from a generic cochlear implant device. A computational model, termed Digital Cochlea Stimulation and Evaluation Tool (‘DiCoStET’) has been developed to provide an objective estimate of cochlear implant performance based on neuronal activation measures, such as vector strength and average activation. A patient-specific cochlea 3D geometry is generated using a model derived by a single anatomical measurement from a patient, using non-invasive high resolution computed tomography (HRCT), and anatomically invariant human metrics and relations. Human measurements of the neuron route within the inner ear enable an innervation pattern to be modelled which joins the space from the organ of Corti to the spiral ganglion subsequently descending into the auditory nerve bundle. An electrode is inserted in the cochlea at a depth that is determined by the user of the tool. The geometric relation between the stimulation sites on the electrode and the spiral ganglion are used to estimate an activating function that will be unique for the specific patient's cochlear shape and electrode placement. This `transfer function', so to speak, between electrode and spiral ganglion serves as a `digital patient' for validating novel cochlear implant systems. The novel computational tool is intended for use by bioengineers, surgeons, audiologists and neuroscientists alike. In addition to ‘DiCoStET’ a second computational model is presented in this thesis aiming at enhancing the understanding of the physiological mechanisms of hearing, specifically the workings of the auditory synapse. The purpose of this model is to provide insight on the sound encoding mechanisms of the synapse. A hypothetical mechanism is suggested in the release of neurotransmitter vesicles that permits the auditory synapse to encode temporal patterns of sound separately from sound intensity. DiCoStET was used to examine the performance of two different types of filters used for spectral analysis in the cochlear implant system, the Gammatone type filter and the Butterworth type filter. The model outputs suggest that the Gammatone type filter performs better than the Butterworth type filter. Furthermore two stimulation strategies, the Continuous Interleaved Stimulation (CIS) and Asynchronous Interleaved Stimulation (AIS) have been compared. The estimated neuronal stimulation spatiotemporal patterns for each strategy suggest that the overall stimulation pattern is not greatly affected by the temporal sequence change. However the finer detail of neuronal activation is different between the two strategies, and when compared to healthy neuronal activation patterns the conjecture is made that the sequential stimulation of CIS hinders the transmission of sound fine structure information to the brain. The effect of the two models developed is the feasibility of collaborative work emanating from various disciplines; especially electrical engineering, auditory physiology and neuroscience for the development of novel cochlear implant systems. This is achieved by using the concept of a `digital patient' whose artificial neuronal activation is compared to a healthy scenario in a computationally efficient manner to allow practical simulation times.Open Acces

CEO Shareholdings and Earnings Manipulation: A Behavioral Explanation

Empirical findings on the relationship between CEO shareholdings and earnings manipulation are inconclusive. In response, this study attempts to shed more light by suggesting that this relationship is influenced by situational contingencies that affect CEO perceptions of the costs and benefits associated with earnings manipulation. To support this perspective we draw on the prospect theory and the approach/inhibition theory of power to examine the relationship between CEO shareholdings and earnings manipulation in light of CEO power. We test this relationship on a sample of 16,873 observations from 2,257 US public firms. Findings show that increasing CEO shareholdings has a negative effect on earnings management, and on re-statements due to irregularities, and that duality positively moderates these relationships. The findings contribute to the corporate governance practice since they have implications for the design of CEO remuneration packages

Active Management of PV-Rich Low Voltage Networks

This thesis was submitted to The University of Manchester for the degree of Doctor of Philosophy in the Faculty of Science and Engineering.The increased penetration of residential-scale photovoltaic (PV) systems in European-style low voltage (LV) networks (i.e., long feeders with high number of connected customers) is leading to technical issues such as voltage rise and thermal overload of the most expensive network assets (i.e., transformer, cables). As these issues significantly limit the ability of LV networks to accommodate higher PV penetrations, Distribution Network Operators (DNOs) are required to proceed with expensive and time-consuming investments in order to reinforce or replace these assets. In contrast to this traditional approach of network reinforcement, which potentially leads to massive capital expenditure, the transition towards active LV networks where controllable elements, existing (i.e., PV systems) and likely to be adopted (i.e., battery energy storage systems, LV on-load tap changer transformers), can be managed in real-time, poses an attractive alternative. Although several active network management schemes have been recently proposed to increase the hosting capacity of PV-rich LV networks, they are mostly based on managing voltage issues only; and, in general, aim to solve technical issues separately. Integrated solutions aiming at managing simultaneously voltage and thermal issues are required, as recent studies demonstrate that both issues can coexist in PV-rich LV networks. More importantly the majority of studies, which commonly neglect the characteristics of real LV networks (e.g., unbalanced, three-phase, radial, multiple feeders with several branches, different types of customers), use complex optimisation techniques that require expensive communication infrastructure and extensive or full network observability (currently not available in LV networks). However, considering the extensiveness of LV networks around the world, practical, cost-effective and scalable solutions that use limited and already available information are more likely to be adopted by the industry. Considering the above gaps in the literature, this Thesis contributes by proposing innovative and scalable active network management schemes that use limited network monitoring and communication infrastructure to actively manage (1) Residential-scale PV systems, (2) Residential-scale Battery Energy Storage (BES) systems and (3) LV on-load tap changer (OLTC)-fitted transformers. The adoption of the proposed active network management schemes, which makes use of already available devices, information and requires limited monitoring (i.e., secondary distribution substation), allows making the transition towards active LV networks more practical and cost-effective. In addition, to tackle the challenges related to this research (i.e., lack of realistic LV network modelling with high resolution time-series analyses), this Thesis, being part of the industrial project Active Management of LV Networks (funded by EDF R&D) and having access to French data, contributes by considering a fully modelled typical real residential French LV network (three-phase four-wire) with different characteristics and number of customers. Moreover, realistic (1-min resolution) daily time-series household (from real smart meter data) and PV generation profiles are considered while a stochastic approach (i.e., Monte Carlo) is adopted to cater for the uncertainties related to household demand as well as PV generation and location

When Does the Board Blame the CEO for Poor Firm Performance? Extreme Resource Reallocation and the Board's Industry and CEO Experience

This study sheds light on our understanding of when boards dismiss the CEO by considering the inherent conflict created by the board's advisory role when the firm underperforms. Using a sample of US firms listed in Standard & Poor's ExecuComp for the period 2000–2012 we find that, when a firm underperforms, extreme resource reallocation increases the likelihood of CEO dismissal. This relationship is positively moderated by the board's industry and CEO experience. The study contributes to the literature on corporate governance by identifying the conditions that trigger dismissal of the CEO in light of boards’ motive to protect their reputation

Gender Salience and Recategorization of New Directors: The Role of Political Ideology

Pressure from stakeholders has resulted in increased board gender diversity. Such diversity, however, goes against the well-accepted concept of demographic homophily. In addition, other studies find that increased board gender diversity may not unequivocally lead to better firm decisions, which does not sit well with the assumption that demographic minorities bring diverse information/ideas to the board. This study advances an explanation for these inconsistencies in the literature by integrating symbolic management and recategorization theories to assert that boards outwardly conform to greater gender diversity, whilst choosing to reinforce value homophily by recategorizing female new directors based on shared political ideology. We test our hypotheses on a sample of 13,483 new director appointments in 2,473 US firms using fractional regression analysis. The findings show that the appointment of a new female director strengthens the association between the board's and the new director's political ideology. In addition, this relationship is strengthened when there is a female CEO, or when the new female director has a less similar demographic background. Moreover, supplemental analysis considering ethnic minority new director appointments shows similar results. The study makes important contributions toward the literatures on female new director selection, recategorization and political ideology. We shed light on why research is ambivalent regarding the benefits of gender diversity since findings show that boards compensate for gender diversity by becoming more homogeneous on political ideology, a value dimension that influences board decisions

A perspective on the influence of national corporate governance institutions and government’s political ideology on the speed to lockdown as a means of protection against Covid-19

This first wave study of the Covid-19 pandemic investigates why the governments of different countries proceeded to lockdown at different speeds. We draw upon the literature on Corporate Governance Institutions (CGIs) to theorize that governments’ decision-making is undertaken in the light of prevailing beliefs, norms, and rules of the collectivity, as portrayed by the focal country’s CGIs, in their effort to maintain legitimacy. In addition, drawing on motivated cognition we posit that the government’s political ideology moderates this relationship because decision-makers are biased when assessing the impact of lockdown on commerce. Running negative binomial regressions on a sample of 125 countries, we find that the more shareholder-oriented the CGIs, the slower the governmental response in shutting down the economy to protect from the pandemic. Moreover, the main relationship is stronger the more right-leaning the government’s ideology. Our study contributes to the research on corporate governance institutions and political ideology and illustrates how societal and ideological biases affect government decision-making, especially when important decisions about public welfare are taken with little information on hand