An enhanced model for small-signal analysis of the phase-shifted full-bridge converter

This paper presents an in-depth critical discussion and derivation of a detailed small-signal analysis of the Phase-Shifted Full-Bridge (PSFB) converter. Circuit parasitics, resonant inductance and transformer turns ratio have all been taken into account in the evaluation of this topology’s open-loop control-to-output, line-to-output and load-to-output transfer functions. Accordingly, the significant impact of losses and resonant inductance on the converter’s transfer functions is highlighted. The enhanced dynamic model proposed in this paper enables the correct design of the converter compensator, including the effect of parasitics on the dynamic behavior of the PSFB converter. Detailed experimental results for a real-life 36V-to-14V/10A PSFB industrial application show excellent agreement with the predictions from the model proposed herein.1

Evaluating business value of IT towards optimisation of the application portfolio

Information technology has become heavily embedded in business operations. As business needs change over time, IT applications are expected to continue providing required support. Whether the existing IT applications are still fit for the business purpose they were intended or new IT applications should be introduced, is a strategic decision for business, IT and business-aligned IT. In this paper, we present a method which aims to analyse business functions and IT roles, and to evaluate business-aligned IT from both social and technical perspectives. The method introduces a set of techniques that systematically supports the evaluation of the existing IT applications in relation to their technical capabilities for maximising business value. Furthermore, we discuss the evaluation process and results which are illustrated and validated through a real-life case study of a UK borough council, and followed by discussion on implications for researchers and practitioners.

Integrating science into humanitarian and development planning and practice to enhance community resilience

There has been an increased emphasis upon the application of science for humanitarian and development planning, decision-making and practice; particularly in the context of understanding, assessing and anticipating risk (e.g. HERR, 2011). However, there remains very little guidance for practitioners on how to integrate sciences they may have had little contact with in the past (e.g. climate). This has led to confusion as to which ‘science’ might be of use and how it would be best utilised. Furthermore, since this integration has stemmed from a need to be more predictive, agencies are struggling with the problems associated with uncertainty and probability. Whilst a range of expertise is required to build resilience, these guidelines focus solely upon the relevant data, information, knowledge, methods, principles and perspective which scientists can provide, that typically lie outside of current humanitarian and development approaches. Using checklists, real-life case studies and scenarios the full guidelines take practitioners through a five step approach to finding, understanding and applying science. This document provides a short summary of the five steps and some key lessons for integrating science.

Weighted alpha-rate dominating sets in social networks

We are looking into variants of a domination set problem in social networks. While randomised algorithms for solving the minimum weighted domination set problem and the minimum alpha and alpha-rate domination problem on simple graphs are already present in the literature, we propose here a randomised algorithm for the minimum weighted alpha-rate domination set problem which is, to the best of our knowledge, the first such algorithm. A theoretical approximation bound based on a simple randomised rounding technique is given. The algorithm is implemented in Python and applied to a UK Twitter mentions networks using a measure of individuals’ influence (klout) as weights. We argue that the weights of vertices could be interpreted as the costs of getting those individuals on board for a campaign or a behaviour change intervention. The minimum weighted alpha-rate dominating set problem can therefore be seen as finding a set that minimises the total cost and each individual in a network has at least alpha percentage of its neighbours in the chosen set. We also test our algorithm on generated graphs with several thousand vertices and edges. Our results on this real-life Twitter networks and generated graphs show that the implementation is reasonably efficient and thus can be used for real-life applications when creating social network based interventions, designing social media campaigns and potentially improving users’ social media experience.

Long term individual load forecast under different electrical vehicles uptake scenarios

More and more households are purchasing electric vehicles (EVs), and this will continue as we move towards a low carbon future. There are various projections as to the rate of EV uptake, but all predict an increase over the next ten years. Charging these EVs will produce one of the biggest loads on the low voltage network. To manage the network, we must not only take into account the number of EVs taken up, but where on the network they are charging, and at what time. To simulate the impact on the network from high, medium and low EV uptake (as outlined by the UK government), we present an agent-based model. We initialise the model to assign an EV to a household based on either random distribution or social influences - that is, a neighbour of an EV owner is more likely to also purchase an EV. Additionally, we examine the effect of peak behaviour on the network when charging is at day-time, night-time, or a mix of both. The model is implemented on a neighbourhood in south-east England using smart meter data (half hourly electricity readings) and real life charging patterns from an EV trial. Our results indicate that social influence can increase the peak demand on a local level (street or feeder), meaning that medium EV uptake can create higher peak demand than currently expected.

Distributed parallelization of greedy Mobile Network Optimization algorithms

The Mobile Network Optimization (MNO) technologies have advanced at a tremendous pace in recent years. And the Dynamic Network Optimization (DNO) concept emerged years ago, aimed to continuously optimize the network in response to variations in network traffic and conditions. Yet, DNO development is still at its infancy, mainly hindered by a significant bottleneck of the lengthy optimization runtime. This paper identifies parallelism in greedy MNO algorithms and presents an advanced distributed parallel solution. The solution is designed, implemented and applied to real-life projects whose results yield a significant, highly scalable and nearly linear speedup up to 6.9 and 14.5 on distributed 8-core and 16-core systems respectively. Meanwhile, optimization outputs exhibit self-consistency and high precision compared to their sequential counterpart. This is a milestone in realizing the DNO. Further, the techniques may be applied to similar greedy optimization algorithm based applications.

Asynchronous distributed parallelization of mobile network optimization algorithms

It has been years since the introduction of the Dynamic Network Optimization (DNO) concept, yet the DNO development is still at its infant stage, largely due to a lack of breakthrough in minimizing the lengthy optimization runtime. Our previous work, a distributed parallel solution, has achieved a significant speed gain. To cater for the increased optimization complexity pressed by the uptake of smartphones and tablets, however, this paper examines the potential areas for further improvement and presents a novel asynchronous distributed parallel design that minimizes the inter-process communications. The new approach is implemented and applied to real-life projects whose results demonstrate an augmented acceleration of 7.5 times on a 16-core distributed system compared to 6.1 of our previous solution. Moreover, there is no degradation in the optimization outcome. This is a solid sprint towards the realization of DNO.

Massive Open Online Courses and sustainability

Massive Open Online Courses (MOOCs) have become very popular among learners millions of users from around the world registered with leading platforms. There are hundreds of universities (and other organizations) offering MOOCs. However, sustainability of MOOCs is a pressing concern as MOOCs incur up front creation costs, maintenance costs to keep content relevant and on-going support costs to provide facilitation while a course is being run. At present, charging a fee for certification (for example Coursera Signature Track and FutureLearn Statement of Completion) seems a popular business model. In this paper, the authors discuss other possible business models and their pros and cons. Some business models discussed here are: Freemium model – providing content freely but charging for premium services such as course support, tutoring and proctored exams. Sponsorships – courses can be created in collaboration with industry where industry sponsorships are used to cover the costs of course production and offering. For example Teaching Computing course was offered by the University of East Anglia on the FutureLearn platform with the sponsorship from British Telecom while the UK Government sponsored the course Introduction to Cyber Security offered by the Open University on FutureLearn. Initiatives and Grants – The government, EU commission or corporations could commission the creation of courses through grants and initiatives according to the skills gap identified for the economy. For example, the UK Government’s National Cyber Security Programme has supported a course on Cyber Security. Similar initiatives could also provide funding to support relevant course development and offering. Donations – Free software, Wikipedia and early OER initiatives such as the MIT OpenCourseware accept donations from the public and this could well be used as a business model where learners could contribute (if they wish) to the maintenance and facilitation of a course. Merchandise – selling merchandise could also bring revenue to MOOCs. As many participants do not seek formal recognition (European Commission, 2014) for their completion of a MOOC, merchandise that presents their achievement in a playful way could well be attractive for them. Sale of supplementary material –supplementary course material in the form of an online or physical book or similar could be sold with the revenue being reinvested in the course delivery. Selective advertising – courses could have advertisements relevant to learners Data sharing – though a controversial topic, sharing learner data with relevant employers or similar could be another revenue model for MOOCs. Follow on events – the courses could lead to follow on summer schools, courses or other real-life or online events that are paid-for in which case a percentage of the revenue could be passed on to the MOOC for its upkeep. Though these models are all possible ways of generating revenue for MOOCs, some are more controversial and sensitive than others. Nevertheless unless appropriate business models are identified the sustainability of MOOCs would be problematic.

Which factors drive rental depreciation rates for office and industrial properties?

As new buildings are constructed in response to changes in technology or user requirements, the value of the existing stock will decline in relative terms. This is termed economic depreciation and it may be influenced by the age and quality of buildings, amount and timing of expenditure, and wider market and economic conditions. This study tests why individual assets experience different depreciation rates, applying panel regression techniques to 375 UK office and industrial assets. Results suggest that rental value depreciation rates reduce as buildings get older, while a composite measure of age and quality provides more explanation of depreciation than age alone. Furthermore, economic and local real estate market conditions are significant in explaining how depreciation rates change over time.

Extraction of unexpected rules from Twitter hashtags and its application to sport events

Twitter has become a dependable microblogging tool for real time information dissemination and newsworthy events broadcast. Its users sometimes break news on the network faster than traditional newsagents due to their presence at ongoing real life events at most times. Different topic detection methods are currently used to match Twitter posts to real life news of mainstream media. In this paper, we analyse tweets relating to the English FA Cup finals 2012 by applying our novel method named TRCM to extract association rules present in hash tag keywords of tweets in different time-slots. Our system identify evolving hash tag keywords with strong association rules in each time-slot. We then map the identified hash tag keywords to event highlights of the game as reported in the ground truth of the main stream media. The performance effectiveness measure of our experiments show that our method perform well as a Topic Detection and Tracking approach.

Improving the estimation of probability of bidder participation in procurement auctions

Anticipating the number and identity of bidders has significant influence in many theoretical results of the auction itself and bidders' bidding behaviour. This is because when a bidder knows in advance which specific bidders are likely competitors, this knowledge gives a company a head start when setting the bid price. However, despite these competitive implications, most previous studies have focused almost entirely on forecasting the number of bidders and only a few authors have dealt with the identity dimension qualitatively. Using a case study with immediate real-life applications, this paper develops a method for estimating every potential bidder's probability of participating in a future auction as a function of the tender economic size removing the bias caused by the contract size opportunities distribution. This way, a bidder or auctioner will be able to estimate the likelihood of a specific group of key, previously identified bidders in a future tender.

Providing extrinsic reward for test performance undermines long-term memory acquisition

Based on numerous studies showing that testing studied material can improve long-term retention more than restudying the same material, it is often suggested that the number of tests in education should be increased to enhance knowledge acquisition. However, testing in real-life educational settings often entails a high degree of extrinsic motivation of learners due to the common practice of placing important consequences on the outcome of a test. Such an effect on the motivation of learners may undermine the beneficial effects of testing on long-term memory because it has been shown that extrinsic motivation can reduce the quality of learning. To examine this issue, participants learned foreign language vocabulary words, followed by an immediate test in which one-third of the words were tested and one-third restudied. To manipulate extrinsic motivation during immediate testing, participants received either monetary reward contingent on test performance or no reward. After 1 week, memory for all words was tested. In the immediate test, reward reduced correct recall and increased commission errors, indicating that reward reduced the number of items that can benefit from successful retrieval. The results in the delayed test revealed that reward additionally reduced the gain received from successful retrieval because memory for initially successfully retrieved words was lower in the reward condition. However, testing was still more effective than restudying under reward conditions because reward undermined long-term memory for concurrently restudied material as well. These findings indicate that providing performance–contingent reward in a test can undermine long-term knowledge acquisition.

The housing market channel of monetary policy transmission in the Euro area

Purpose – The purpose of this paper is to explore the role of the housing market in the monetary policy transmission to consumption among euro area member states. It has been argued that the housing market in one country is then important when its mortgage market is well developed. The countries in the euro area follow unitary monetary policy, however, their housing and mortgage markets show some heterogeneity, which may lead to different policy effects on aggregate consumption through the housing market. Design/methodology/approach – The housing market can act as a channel of monetary policy shocks to household consumption through changes in house prices and residential investment – the housing market channel. We estimate vector autoregressive models for each country and conduct a counterfactual analysis in order to disentangle the housing market channel and assess its importance across the euro area member states. Findings – We find little evidence for heterogeneity of the monetary policy transmission through house prices across the euro area countries. Housing market variations in the euro area seem to be better captured by changes in residential investment rather than by changes in house prices. As a result we do not find significantly large house price channels. For some of the countries however, we observe a monetary policy channel through residential investment. The existence of a housing channel may depend on institutional features of both the labour market or with institutional factors capturing the degree of household debt as is the LTV ratio. Originality/value – The study contributes to the existing literature by assessing whether a unitary monetary policy has a different impact on consumption across the euro area countries through their housing and mortgage markets. We disentangle monetary-policy-induced effects on consumption associated with variations on the housing markets due to either house price variations or residential investment changes. We show that the housing market can play a role in the monetary transmission mechanism even in countries with less developed mortgage markets through variations in residential investment.

Mobile sink based fault diagnosis scheme for wireless sensor networks

Network diagnosis in Wireless Sensor Networks (WSNs) is a difficult task due to their improvisational nature, invisibility of internal running status, and particularly since the network structure can frequently change due to link failure. To solve this problem, we propose a Mobile Sink (MS) based distributed fault diagnosis algorithm for WSNs. An MS, or mobile fault detector is usually a mobile robot or vehicle equipped with a wireless transceiver that performs the task of a mobile base station while also diagnosing the hardware and software status of deployed network sensors. Our MS mobile fault detector moves through the network area polling each static sensor node to diagnose the hardware and software status of nearby sensor nodes using only single hop communication. Therefore, the fault detection accuracy and functionality of the network is significantly increased. In order to maintain an excellent Quality of Service (QoS), we employ an optimal fault diagnosis tour planning algorithm. In addition to saving energy and time, the tour planning algorithm excludes faulty sensor nodes from the next diagnosis tour. We demonstrate the effectiveness of the proposed algorithms through simulation and real life experimental results.

Racial Conflict in the United States of America : A Deconstructive Perspective on Native Speaker by Changrae Lee

Written about the time of the Golden Venture incident, Chang-rae Lee’s Native Speaker makes a particular reference to that incident, whereby implying that particular immigrants, on the grounds of their racial identities, are mistreated and considered as aliens by some Americas. While some whites discriminate against immigrants, there is widespread ethnic tension between Korean Americans and African Americans. Significantly, racial conflict between Koreans and blacks and the racist attitude of some whites toward immigrants are mirrored in the relationship between the Korean-American protagonist Henry and his American wife Lelia. That is, due to their different racial identities they do not understand each other and they always argue. However, toward the end of the novel, Henry and Lelia come to understand each other. While ethnic conflict between Koreans and blacks and certain whites’ discriminatory attitudes toward immigrants is serious one, the novel suggests the unimportance of racial identity. In other words, the novel concludes that there is no discriminatory treatment of immigrants and, in fact, every one is a native Speaker in America. In the novel there is no message of how racial conflict could be resolved. However, this essay suggests that by investigating how the tension between Henry and Lelia is resolved, one could suggest a solution for the ethnicity problem in America and in real life.