An empirical investigation on firm-level performance of cross border mergers and acquisitions

This empirical study investigates the performance of cross border M&A. The first stage is to identify the determinants of making cross border M&A complete. One focus here is to extend the existing empirical evidence in the field of cross border M&A and exploit the likelihood of M&A from a different perspective. Given the determinants of cross border M&A completions, the second stage is to investigate the effects of cross border M&A on post-acquisition firm performance for both targets and acquirers. The thesis exploits a hitherto unused data base, which consists of those firms that are rumoured to be undertaking M&A, and then follow the deal to completion or abandonment. This approach highlights a number of limitations to the previous literature, which relies on statistical methodology to identify potential but non-existent mergers. This thesis changes some conventional understanding for M&A activity. Cross border M&A activity is underpinned by various motives such as synergy, management discipline, and acquisition of complementary resources. Traditionally, it is believed that these motives will boost the international M&A activity and improve firm performance after takeovers. However, this thesis shows that such factors based on these motives as acquirer’s profitability and liquidity and target’s intangible resource actually deter the completion of cross border M&A in the period of 2002-2011. The overall finding suggests that the cross border M&A is the efficiency-seeking activity rather than the resource-seeking activity. Furthermore, compared with firms in takeover rumours, the completion of M&A lowers firm performance. More specifically, the difficulties in transfer of competitive advantages and integration of strategic assets lead to low firm performance in terms of productivity. Besides, firms cannot realise the synergistic effect and managerial disciplinary effect once a cross border M&A is completed, which suggests a low post-acquisition profitability level.

Polarisation phenomena in mode locked fibre lasers and fibre Raman amplifiers griffon tutorial

For a fibre Raman amplifier with randomly varying birefringence, we provide insight on the validity of previously explored multi-scale techniques leading to polarisation pulling of the signal state of polarisation to the pump state of polarisation. Unlike previous study, we demonstrate that in addition to polarisation pulling a new random birefringence-mediated phenomenon that goes beyond existing multi-scale techniques can boost resonance-like gain fluctuations similar to the Stochastic Anti-Resonance. For mode locked fibre lasers we report on fast and slow polarisation dynamics of fundamental, bound state, and multipulsing vector solitons along with stretched pulses. We demonstrate that tuning cavity anisotropy and birefringence along with parameters of an injected signal with randomly varying state of polarisation provides access to the variety of vector waveforms previously unexplored.

Control of cascaded DC-DC converter based hybrid battery energy storage systems - Part II:Lyapunov approach

A cascaded DC-DC boost converter is one of the ways to integrate hybrid battery types within a grid-tie inverter. Due to the presence of different battery parameters within the system such as, state-of-charge and/or capacity, a module based distributed power sharing strategy may be used. To implement this sharing strategy, the desired control reference for each module voltage/current control loop needs to be dynamically varied according to these battery parameters. This can cause stability problem within the cascaded converters due to relative battery parameter variations when using the conventional PI control approach. This paper proposes a new control method based on Lyapunov Functions to eliminate this issue. The proposed solution provides a global asymptotic stability at a module level avoiding any instability issue due to parameter variations. A detailed analysis and design of the nonlinear control structure are presented under the distributed sharing control. At last thorough experimental investigations are shown to prove the effectiveness of the proposed control under grid-tie conditions.

Control of cascaded DC-DC converter-based hybrid battery energy storage systems - Part I:stability Issue

There is an emerging application which uses a mixture of batteries within an energy storage system. These hybrid battery solutions may contain different battery types. A DC-side cascaded boost converters along with a module based distributed power sharing strategy has been proposed to cope with variations in battery parameters such as, state-of-charge and/or capacity. This power sharing strategy distributes the total power among the different battery modules according to these battery parameters. Each module controller consists of an outer voltage loop with an inner current loop where the desired control reference for each control loop needs to be dynamically varied according to battery parameters to undertake this sharing. As a result, the designed control bandwidth or stability margin of each module control loop may vary in a wide range which can cause a stability problem within the cascaded converter. This paper reports such a unique issue and thoroughly investigates the stability of the modular converter under the distributed sharing scheme. The paper shows that a cascaded PI control loop approach cannot guarantee the system stability throughout the operating conditions. A detailed analysis of the stability issue and the limitations of the conventional approach are highlighted. Finally in-depth experimental results are presented to prove the stability issue using a modular hybrid battery energy storage system prototype under various operating conditions.

New SR drive with integrated charging capacity for plug-in hybrid electric vehicles (PHEVs)

Plug-in hybrid electric vehicles (PHEVs) provide much promise in reducing greenhouse gas emissions and, thus, are a focal point of research and development. Existing on-board charging capacity is effective but requires the use of several power conversion devices and power converters, which reduce reliability and cost efficiency. This paper presents a novel three-phase switched reluctance (SR) motor drive with integrated charging functions (including internal combustion engine and grid charging). The electrical energy flow within the drivetrain is controlled by a power electronic converter with less power switching devices and magnetic devices. It allows the desired energy conversion between the engine generator, the battery, and the SR motor under different operation modes. Battery-charging techniques are developed to operate under both motor-driving mode and standstill-charging mode. During the magnetization mode, the machine's phase windings are energized by the dc-link voltage. The power converter and the machine phase windings are controlled with a three-phase relay to enable the use of the ac-dc rectifier. The power converter can work as a buck-boost-type or a buck-type dc-dc converter for charging the battery. Simulation results in MATLAB/Simulink and experiments on a 3-kW SR motor validate the effectiveness of the proposed technologies, which may have significant economic implications and improve the PHEVs' market acceptance.

Direct sequence spread spectrum based PWM strategy for harmonic reduction and communication

Switched mode power supplies (SMPSs) are essential components in many applications, and electromagnetic interference is an important consideration in the SMPS design. Spread spectrum based PWM strategies have been used in SMPS designs to reduce the switching harmonics. This paper proposes a novel method to integrate a communication function into spread spectrum based PWM strategy without extra hardware costs. Direct sequence spread spectrum (DSSS) and phase shift keying (PSK) data modulation are employed to the PWM of the SMPS, so that it has reduced switching harmonics and the input and output power line voltage ripples contain data. A data demodulation algorithm has been developed for receivers, and code division multiple access (CDMA) concept is employed as communication method for a system with multiple SMPSs. The proposed method has been implemented in both Buck and Boost converters. The experimental results validated the proposed DSSS based PWM strategy for both harmonic reduction and communication.

In silico adjuvant design and validation

Adjuvants are substances that boost the protective immune response to vaccine antigens. The majority of known adjuvants have been identified through the use of empirical approaches. Our aim was to identify novel adjuvants with well-defined cellular and molecular mechanisms by combining a knowledge of immunoregulatory mechanisms with an in silico approach. CD4 + CD25 + FoxP3 + regulatory T cells (Tregs) inhibit the protective immune responses to vaccines by suppressing the activation of antigen presenting cells such as dendritic cells (DCs). In this chapter, we describe the identification and functional validation of small molecule antagonists to CCR4, a chemokine receptor expressed on Tregs. The CCR4 binds the chemokines CCL22 and CCL17 that are produced in large amounts by activated innate cells including DCs. In silico identified small molecule CCR4 antagonists inhibited the migration of Tregs both in vitro and in vivo and when combined with vaccine antigens, significantly enhanced protective immune responses in experimental models.

Ontology forecasting in scientific literature:semantic concepts prediction based on innovation-adoption priors

The ontology engineering research community has focused for many years on supporting the creation, development and evolution of ontologies. Ontology forecasting, which aims at predicting semantic changes in an ontology, represents instead a new challenge. In this paper, we want to give a contribution to this novel endeavour by focusing on the task of forecasting semantic concepts in the research domain. Indeed, ontologies representing scientific disciplines contain only research topics that are already popular enough to be selected by human experts or automatic algorithms. They are thus unfit to support tasks which require the ability of describing and exploring the forefront of research, such as trend detection and horizon scanning. We address this issue by introducing the Semantic Innovation Forecast (SIF) model, which predicts new concepts of an ontology at time t + 1, using only data available at time t. Our approach relies on lexical innovation and adoption information extracted from historical data. We evaluated the SIF model on a very large dataset consisting of over one million scientific papers belonging to the Computer Science domain: the outcomes show that the proposed approach offers a competitive boost in mean average precision-at-ten compared to the baselines when forecasting over 5 years.