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The first essay researches the impact of the board characteristics on earnings management. Earnings management is a dependent variable and a proxy for discretionary accruals, which is predicted by applying three discretionary accruals models that include Jones model, modified Jones model and Kothari model. Independent variables comprise of board characteristics. The sample comprised of all listed companies on FTSE-350 from 2007 to 2018. Based on the results and findings of the Jones model, modified Jones model Kothari model and earnings management model, this research study provides empirical evidence proving the impact of the board size, board independence and gender diversity on the earnings management which is shown to be negative, implying larger boards with higher number of independent directors and female directors are in a better position to oversee the management activities and ensure higher quality of financial information. However, the impact of the CEO duality and board activity on earnings management is positive, illustrating that boards in which both positions CEO and board chairman are holding by one person and the board meets more frequently loses its control over necessary practices such as controlling earnings management. The prime objective of second research study is to study and understand the impact of the audit committee characteristics on the discretionary accruals in the companies listed on FTSE 350 from 2007 to 2018. The chosen period important because, the chosen period provides a window to test the Smith Report's recommendations for audit committees following the issue of the UK Code and to understand to what extent; the recommended characteristics of the audit committee can help the companies to improve the quality of financial reporting. In this research study, earnings management is a dependent variable, whereas audit committee characteristics comprise of independent variables that include audit committee size (ACS), audit committee independence (ACI), members on the audit committee having a background in accounting or finance or both (ACFEX) and the frequency of the audit committee meetings during a year (NACM). The empirical findings of the relationship between earnings management and audit committee characteristics show that ACS is negatively associated to earnings management, ACI is positive related to earnings management, ACFXP and earnings management are negative related, and the number of audit committee meetings (NACM) is negatively related to earnings management. Third essay empirically tests the impact of the CEO's and executive's compensation on the discretionary accruals. To better understand the relationship of the CEO's and executive's compensation with discretionary accruals, the entire compensation is decomposed, and the impact of each component of the total compensation is tested on the discretionary accruals. Earnings management is the dependent variable, which is the proxy for the discretionary accruals. The components of the CEO's and executive's compensation are independent variables, which include the CEO's total compensation, executive total compensation, CEO's equity-based compensation, executive's equity-based compensation, CEO's salary, executive's salary, CEO's bonus, executive's bonus, CEO equity to total compensation ratio and executive's equity to total compensation ratio. For this, a sample comprising of all listed companies on FTSE 350 is used. The sample period is from 2007 to 2018 and employs the annual data because most of the companies prepare and publish their financial statements annually. The empirical results and findings of this research study show that the CEO equitybased compensation, CEO bonus, CEO equity-based compensation to total compensation ratio, executive's equity-based compensation, executive's salary and executive's equity-based compensation to total compensation ratio are positively related to earrings management, whereas, CEO total compensation, CEO salary, executive total compensation and executive's bonus are negatively related with the earnings management.

A wireless sensor network comprises of tiny sensor nodes which communicate with each other through radio frequency communication links. In many applications of wireless sensor networks, the sensor nodes collect data or detect an event and report it to a central node for further processing. Location information of the node is sent with the data else it may not be useful. Therefore, a sensor node should know its geographic position coordinates. Localization solutions, such as GPS are not feasible due to their energy cost and size. Hence, sensor nodes estimate their positions using an algorithm. This thesis focuses on the localization of sensor nodes and related key issues, such as performance evaluation of localization algorithms, development of analytical model and analysis of localization error. Firstly, this thesis proposes three new novel metrics which can be used for the performance evaluation of three dierent aspects of localization algorithms. Alongside, we also present a comprehensive review of metrics which are used in literature for the measurement and characterization of localization errors. Secondly, we present an intelligent algorithm which we call ripple localization algorithm. The algorithm is distributed, energy ecient and does not require additional hardware for range estimation. The algorithm also provides control over localization granularity which makes it suitable for wide range of wireless sensor network and localization applications. Simulation results show that the algorithm gives good performance and localization accuracy even under irregular radio conditions. Thirdly, we give a new technique for solving multilateration equations and show that the overdetermined system of equations resulting from multilateration can be reduced to a pair of simultaneous equations which can then be solved using conventional techniques, such as Cramer's rule. Fourthly, we develop and present an analytical model of localization error resulting from trilateration. The multilateration solution technique and analytical model are veried using simulation. Finally, we analyze trilateration errors in short range wireless networks, such as wireless sensor networks and internet of things where the distance estimation errors are sometimes comparable to the actual distances. We investigate the minimum and maximum values of localization errors in these networks. We also derive a number of other useful results. For example, we show that the localization error due to positive range estimation errors equal to the actual distances is 3 times the localization error resulting from the same magnitude of negative range estimation errors. All the results are tested and verified using a comprehensive set of simulation experiments.

A fundamental study in to the correlation of nucleation mechanism with deposit morphology using silver salts as a well behaved system has been carried out in deep eutectic solvents i.e. mixtures of choline chloride (ChCl) with firstly ethylene glycol and secondly urea in a 1:2 molar ratio. The nucleation and growth kinetics of silver deposition were measured as a function of liquid type, deposition potential and silver salt type. The effect of different additives including surfactants (SDS and CTAB) and aqueous brightener (cresyl fast violet-CFV and crystal violet-CV) on nucleation was determined using electrochemical techniques such as cyclic voltammetery and chronoamperometery. Electrogravimetric studies were carried out using electrochemical quartz crystal microbalance (EQCM) to correlate the deposited mass of silver with findings from the electrochemical studies in both solvents. A qualitative evaluation of the nucleation and growth mechanism and quantitative estimation of the kinetic parameters of silver electrocrystallization process was carried out using the existing theoretical formalisms. A computer simulation was used to extract the nuclear number density, rate of nucleation, diffusion co-efficient and influence of the double layer charging by fitting the whole potentiostatic current transients using a ‘non-linear best fitting’ method. The analysis of the development of the surface by nucleation and growth of the silver deposition in real time was studied using the ex-situ AFM and in-situ DHM. The latter of these was the first demonstration of this technique for the study of nucleation and growth mechanism of metal deposition and the results showed good agreement with the ex-situ AFM findings.