Human error risk management methodology for rail crack incidents

The paper presents an innovative approach to modelling the causal relationships of human errors in rail crack incidents (RCI) from a managerial perspective. A Bayesian belief network is developed to model RCI by considering the human errors of designers, manufactures, operators and maintainers (DMOM) and the causal relationships involved. A set of dependent variables whose combinations express the relevant functions performed by each DMOM participant is used to model the causal relationships. A total of 14 RCI on Hong Kong’s mass transit railway (MTR) from 2008 to 2011 are used to illustrate the application of the model. Bayesian inference is used to conduct an importance analysis to assess the impact of the participants’ errors. Sensitivity analysis is then employed to gauge the effect the increased probability of occurrence of human errors on RCI. Finally, strategies for human error identification and mitigation of RCI are proposed. The identification of ability of maintainer in the case study as the most important factor influencing the probability of RCI implies the priority need to strengthen the maintenance management of the MTR system and that improving the inspection ability of the maintainer is likely to be an effective strategy for RCI risk mitigation.

Measuring the expressed emotion in Chinese family caregivers of persons with dementia: Validation of a Chinese version of the Family Attitude Scale

- Background Expressed emotion (EE) captures the affective quality of the relationship between family caregivers and their care recipients and is known to increase the risk of poor health outcomes for caregiving dyads. Little is known about expressed emotion in the context of caregiving for persons with dementia, especially in non-Western cultures. The Family Attitude Scale (FAS) is a psychometrically sound self-reporting measure for EE. Its use in the examination of caregiving for patients with dementia has not yet been explored. - Objectives This study was performed to examine the psychometric properties of the Chinese version of the FAS (FAS-C) in Chinese caregivers of relatives with dementia, and its validity in predicting severe depressive symptoms among the caregivers. - Methods The FAS was translated into Chinese using Brislin's model. Two expert panels evaluated the semantic equivalence and content validity of this Chinese version (FAS-C), respectively. A total of 123 Chinese primary caregivers of relatives with dementia were recruited from three elderly community care centers in Hong Kong. The FAS-C was administered with the Chinese versions of the 5-item Mental Health Inventory (MHI-5), the Zarit Burden Interview (ZBI) and the Revised Memory and Behavioral Problem Checklist (RMBPC). - Results The FAS-C had excellent semantic equivalence with the original version and a content validity index of 0.92. Exploratory factor analysis identified a three-factor structure for the FAS-C (hostile acts, criticism and distancing). Cronbach's alpha of the FAS-C was 0.92. Pearson's correlation indicated that there were significant associations between a higher score on the FAS-C and greater caregiver burden (r = 0.66, p < 0.001), poorer mental health of the caregivers (r = −0.65, p < 0.001) and a higher level of dementia-related symptoms (frequency of symptoms: r = 0.45, p < 0.001; symptom disturbance: r = 0.51, p < 0.001), which serves to suggest its construct validity. For detecting severe depressive symptoms of the family caregivers, the receiving operating characteristics (ROC) curve had an area under curve of 0.78 (95% confidence interval (CI) = 0.69–0.87, p < 0.0001). The optimal cut-off score was >47 with a sensitivity of 0.720 (95% CI = 0.506–0.879) and specificity of 0.742 (95% CI = 0.643–0.826). - Conclusions The FAS-C is a reliable and valid measure to assess the affective quality of the relationship between Chinese caregivers and their relatives with dementia. It also has acceptable predictability in identifying family caregivers with severe depressive symptoms.

A non-orthogonal cooperative multiple access (NCMA) protocol and low ML decoding complexity codes

A half-duplex constrained non-orthogonal cooperative multiple access (NCMA) protocol suitable for transmission of information from N users to a single destination in a wireless fading channel is proposed. Transmission in this protocol comprises of a broadcast phase and a cooperation phase. In the broadcast phase, each user takes turn broadcasting its data to all other users and the destination in an orthogonal fashion in time. In the cooperation phase, each user transmits a linear function of what it received from all other users as well as its own data. In contrast to the orthogonal extension of cooperative relay protocols to the cooperative multiple access channels wherein at any point of time, only one user is considered as a source and all the other users behave as relays and do not transmit their own data, the NCMA protocol relaxes the orthogonality built into the protocols and hence allows for a more spectrally efficient usage of resources. Code design criteria for achieving full diversity of N in the NCMA protocol is derived using pair wise error probability (PEP) analysis and it is shown that this can be achieved with a minimum total time duration of 2N - 1 channel uses. Explicit construction of full diversity codes is then provided for arbitrary number of users. Since the Maximum Likelihood decoding complexity grows exponentially with the number of users, the notion of g-group decodable codes is introduced for our setup and a set of necesary and sufficient conditions is also obtained.

Distributed space-time codes for cooperative networks with partial CSI

Design criteria and full-diversity Distributed Space Time Codes (DSTCs) for the two phase transmission based cooperative diversity protocol of Jing-Hassibi and the Generalized Nonorthogonal Amplify and Forward (GNAF) protocol are reported, when the relay nodes are assumed to have knowledge of the phase component of the source to relay channel gains. It is shown that this under this partial channel state information (CSI), several well known space time codes for the colocated MIMO (Multiple Input Multiple Output) channel become amenable for use as DSTCs. In particular, the well known complex orthogonal designs, generalized coordinate interleaved orthogonal designs (GCIODs) and unitary weight single symbol decodable (UW-SSD) codes are shown to satisfy the required design constraints for DSTCs. Exploiting the relaxed code design constraints, we propose DSTCs obtained from Clifford Algebras which have low ML decoding complexity.

Detection of SFBC-OFDM signals in frequency- and time-selective MIMO channels

Use of space-frequency block coded (SFBC) OFDM signals is advantageous in high-mobility broadband wireless access, where the channel is highly time- as well as frequency-selective because of which the receiver experiences both inter-symbol interference (ISI) as well as inter-carrier interference (10). ISI occurs due to the violation of the 'quasi-static' fading assumption caused due to frequency- and/or time-selectivity of the channel. In addition, ICI occurs due to time-selectivity of the channel which results in loss of orthogonality among the subcarriers. In this paper, we are concerned with the detection of SFBC-OFDM signals on time- and frequency-selective MIMO channels. Specifically, we propose and evaluate the performance of an interference cancelling receiver for SFBC-OFDM which alleviates the effects of ISI and ICI in highly time- and frequency-selective channels.

Testing against changing correlation

A test for time-varying correlation is developed within the framework of a dynamic conditional score (DCS) model for both Gaussian and Student t-distributions. The test may be interpreted as a Lagrange multiplier test and modified to allow for the estimation of models for time-varying volatility in the individual series. Unlike standard moment-based tests, the score-based test statistic includes information on the level of correlation under the null hypothesis and local power arguments indicate the benefits of doing so. A simulation study shows that the performance of the score-based test is strong relative to existing tests across a range of data generating processes. An application to the Hong Kong and South Korean equity markets shows that the new test reveals changes in correlation that are not detected by the standard moment-based test.

The determinants of fair value measurements of banks: International evidence

The purpose of this study is to investigate the accounting choice decisions of banks to employ Level 3 inputs in estimating the value of their financial assets and liabilities. Using a sample of 146 bank-year observations from 18 countries over 2009-2012, this study finds banks’ incentives to use Level 3 valuation inputs are associated with both firm-level and country-level determinants. At the firm-level, leverage, profitability (in term of net income), Tier 1 capital ratio, size and audit committee independence are associated with the percentage of Level 3 valuation inputs. At the country-level, economy development, legal region, legal enforcement and investor rights are also associated with the Level 3 classification choice. Lastly, ‘secrecy’, the proxy for culture dimensions and values, is found to be positively associated with the use of Level 3 valuation inputs. Altogether, these findings suggest that banks use the discretion available under Level 3 inputs opportunistically to avoid violating debt covenants limits, to increase earnings and manage their capital ratios. Results of this study also highlight that corporate governance quality at the firm-level (e.g. audit committee independence) and institutional features can constrain banks’ opportunistic behaviors in using the discretion available under Level 3 inputs. The results of this study have important implications for standard setters and contribute to the debate on the use of fair value accounting in an international context.

Strategies for improving safety and health of ethnic minority construction workers

The construction industries of developed countries are faced with an aging workforce and a shortage of recruits. It is common for migrant workers/ethnic minorities (EMs) who are already part of the society to join the construction industry. With increasing involvement of EMs in the construction industry, effective strategies for improving their safety and health are urgently needed. The existing body of knowledge is mainly derived from research conducted in English-speaking countries with Western cultures. Research on safety of migrant/EM construction workers in multidialect Asian countries with Eastern cultures has been lacking. This study aimed to identify various strategies for improving the safety and health of EM construction workers from the Asian perspective. Twenty-two face-to-face semistructured interviews were performed with safety professionals in Hong Kong followed by two rounds of Delphi survey with 18 safety experts to verify the interview findings and rank the relative importance of the strategies. The study unveiled 14 strategies for improving the safety performance of EM workers. The three most important ones identified were: (1) to provide safety training in EM native languages; (2) that government and industry associations should play an active role in promoting health and safety awareness of EM workers, and; (3) to encourage EM workers to learn the local language. This study contributes to filling the research gap by evaluating the strategies for improving safety of migrant/EM construction workers in Asian countries with Eastern cultures in which English is not the first language. Research findings would assist occupational health and safety experts and relevant stakeholders in designing strategies for improving the safety and health of EM workers, which will ultimately improve overall safety performance of the construction industry.

Safety of Repair, Maintenance, Minor Alteration, and Addition (RMAA) Works: A New Focus of Construction Safety

"Safety of RMAA works is an almost uncharted topic of rising importance internationally. Small construction contractors are particularly dependant on RMAA work, especially during times of recession, and they undertake more risks on these jobs than large companies do. This book is based on unique international research and consultancy projects which detail, investigate, and suggest solutions to the specific challenges of safety in RMAA works, based on case studies. Starting with an overview of safety in the wider construction industries of developed countries, the first half of this book also provides a comprehensive summary of relevant rules, regulations, and the resulting safety performances. The systems in the UK, US and Hong Kong are described and contrasted, giving the reader an understanding of how different regulatory approaches have yielded a variety of results. From this solid introduction, specific problems observed in RMAA work are examined through case studies, with reference to the underlying cultural and demographic factors, and a variety of practical engineering and management solutions are explored. This important and practical international work is essential reading for postgraduate students of health and safety in construction, construction project management, or construction in developing countries, as well as policy-makers and construction project managers."--Publisher website

Participation in Urban Interaction Design for Civic Engagement

The future of civic engagement is characterised by both technological innovation as well as new technological user practices that are fuelled by trends towards mobile, personal devices; broadband connectivity; open data; urban interfaces; and cloud computing. These technology trends are progressing at a rapid pace, and have led global technology vendors to package and sell the “Smart City” as a centralised service delivery platform predicted to optimise and enhance cities’ key performance indicators – and generate a profitable market. The top-down deployment of these large and proprietary technology platforms have helped sectors such as energy, transport, and healthcare to increase efficiencies. However, an increasing number of scholars and commentators warn of another “IT bubble” emerging. Along with some city leaders, they argue that the top-down approach does not fit the governance dynamics and values of a liberal democracy when applied across sectors. A thorough understanding is required, of the socio-cultural nuances of how people work, live, play across different environments, and how they employ social media and mobile devices to interact with, engage in, and constitute public realms. Although the term “slacktivism” is sometimes used to denote a watered down version of civic engagement and activism that is reduced to clicking a “Like” button and signing online petitions, we believe that we are far from witnessing another Biedermeier period that saw people focus on the domestic and the non-political. There is plenty of evidence to the contrary, such as post-election violence in Kenya in 2008, the Occupy movements in New York, Hong Kong and elsewhere, the Arab Spring, Stuttgart 21, Fukushima, the Taksim Gezi Park in Istanbul, and the Vinegar Movement in Brazil in 2013. These examples of civic action shape the dynamics of governments, and in turn, call for new processes to be incorporated into governance structures. Participatory research into these new processes across the triad of people, place and technology is a significant and timely investment to foster productive, sustainable, and liveable human habitats. With this article, we want to reframe the current debates in academia and priorities in industry and government to allow citizens and civic actors to take their rightful centrepiece place in civic movements. This calls for new participatory approaches for co-inquiry and co-design. It is an evolving process with an explicit agenda to facilitate change, and we propose participatory action research (PAR) as an indispensable component in the journey to develop new governance infrastructures and practices for civic engagement. We do not limit our definition of civic technologies to tools specifically designed to simply enhance government and governance, such as renewing your car registration online or casting your vote electronically on election day. Rather, we are interested in civic media and technologies that foster citizen engagement in the widest sense, and particularly the participatory design of such civic technologies that strive to involve citizens in political debate and action as well as question conventional approaches to political issues. The rationale for this approach is an alternative to smart cities in a “perpetual tomorrow,” based on many weak and strong signals of civic actions revolving around technology seen today. It seeks to emphasise and direct attention to active citizenry over passive consumerism, human actors over human factors, culture over infrastructure, and prosperity over efficiency. First, we will have a look at some fundamental issues arising from applying simplistic smart city visions to the kind of a problem a city poses. We focus on the touch points between “the city” and its civic body, the citizens. In order to provide for meaningful civic engagement, the city must provide appropriate interfaces.

Cluster based core vector machine

Core Vector Machine(CVM) is suitable for efficient large-scale pattern classification. In this paper, a method for improving the performance of CVM with Gaussian kernel function irrespective of the orderings of patterns belonging to different classes within the data set is proposed. This method employs a selective sampling based training of CVM using a novel kernel based scalable hierarchical clustering algorithm. Empirical studies made on synthetic and real world data sets show that the proposed strategy performs well on large data sets.

Minimum enclosing spheres formulations for support vector ordinal regression

We present two new support vector approaches for ordinal regression. These approaches find the concentric spheres with minimum volume that contain most of the training samples. Both approaches guarantee that the radii of the spheres are properly ordered at the optimal solution. The size of the optimization problem is linear in the number of training samples. The popular SMO algorithm is adapted to solve the resulting optimization problem. Numerical experiments on some real-world data sets verify the usefulness of our approaches for data mining.

Transient signal detection using wigner-ville distribution and wavelet denoising

The problem of detecting an unknown transient signal in noise is considered. The SNR of the observed data is first enhanced using wavelet domain filter The output of the wavelet domain filter is then transformed using a Wigner-Ville transform,which separates the spectrum of the observed signal into narrow frequency bands. Each subband signal at the output of the Wigner-ville block is subjected kto wavelet based level dependent denoising (WBLDD)to supress colored noise A weighted sum of the absolute value of outputs of WBLDD is passed through an energy detector, whose output is used as test statistic to take the final decision. By assigning weights proportional to the energy of the corresponding subband signals, the proposed detector approximates a frequency domain matched filter Simulation results are presented to show that the performance of the proposed detector is better than that of the wavelet packet transform based detector.

A suprathreshold stochastic resonance based pre-processor for direction-of-arrival estimation

In this paper we propose a nonlinear preprocessor for enhancing the performance of processors used for direction-of-arrival (DOA) estimation in heavy-tailed non-Gaussian noise. The preprocessor based on the phenomenon of suprathreshold stochastic resonance (SSR), provides SNR gain. The preprocessed data is used for DOA estimation by the MUSIC algorithm. Simulation results are presented to show that the SSR preprocessor provides a significant improvement in the performance of MUSIC in heavy-tailed noise at low SNR.

Scalable non-linear Support Vector Machine using hierarchical clustering

This paper discusses a method for scaling SVM with Gaussian kernel function to handle large data sets by using a selective sampling strategy for the training set. It employs a scalable hierarchical clustering algorithm to construct cluster indexing structures of the training data in the kernel induced feature space. These are then used for selective sampling of the training data for SVM to impart scalability to the training process. Empirical studies made on real world data sets show that the proposed strategy performs well on large data sets.