Long memory or shifting means in geophysical time series?

In the literature many papers state that long-memory time series models such as Fractional Gaussian Noises (FGN) or Fractionally Integrated series (FI(d)) are empirically indistinguishable from models with a non-stationary mean, but which are mean reverting. We present an analysis of the statistical cost of model mis-specification when simulated long memory series are analysed by Atheoretical Regression Trees (ART), a structural break location method. We also analysed three real data sets, one of which is regarded as a standard example of the long memory type. We find that FGN and FI(d) processes do not account for many features of the real data. In particular, we find that the data sets are not H-self-similar. We believe the data sets are better characterized by non-stationary mean models.     

Learning the structure of dynamic Bayesian networks from time series and steady state measurements

Dynamic Bayesian networks (DBN) are a class of graphical models that has become a standard tool for modeling various stochastic time-varying phenomena. In many applications, the primary goal is to infer the network structure from measurement data. Several efficient learning methods have been introduced for the inference of DBNs from time series measurements. Sometimes, however, it is either impossible or impractical to collect time series data, in which case, a common practice is to model the non-time series observations using static Bayesian networks (BN). Such an approach is obviously sub-optimal if the goal is to gain insight into the underlying dynamical model. Here, we introduce Bayesian methods for the inference of DBNs from steady state measurements. We also consider learning the structure of DBNs from a combination of time series and steady state measurements. We introduce two different methods: one that is based on an approximation and another one that provides exact computation. Simulation results demonstrate that dynamic network structures can be learned to an extent from steady state measurements alone and that inference from a combination of steady state and time series data has the potential to improve learning performance relative to the inference from time series data alone.     

Prospective risk analysis of health care processes: A systematic evaluation of the use of HFMEA™ in Dutch health care

The aim of this study was to evaluate the use of Healthcare Failure Mode and Effect Analysis (HFMEA?) in Dutch health care by means of user feedback. Thirteen HFMEA? analyses of various health care processes were successfully concluded and on average took 69 person-hours (excluding reporting). These results show that HFMEA? can successfully be applied in Dutch health care. However, the user feedback also uncovered several perceived drawbacks, such as the fact that HFMEA? is very time-consuming and that, particularly, the risk assessment part of HFMEA? is difficult to carry out. Moreover, a lack of guidance with regard to the identification of failure mode causes and effective actions might influence the quality of the outcomes of an HFMEA? analysis. Several suggestions are put forward to improve the perceived utility and acceptance of HFMEA?. Nevertheless, future research is necessary to evaluate the actual effects of these recommendations. Error modelling and risk analysis, and their contribution to explaining human performance in socio-technical systems, traditionally belong to the field of ergonomics. The user feedback on HFMEA? and the suggestions that are put forward may also be useful for (H)FMEA and hazard analysis and critical control point applications in sectors other than health care.     

Rider motion identification during normal bicycling by means of principal component analysis

Recent observations of a bicyclist riding through town and on a treadmill show that the rider uses the upper body very little when performing normal maneuvers and that the bicyclist may, in fact, primarily use steering input for control. The observations also revealed that other motions such as lateral movement of the knees were used in low speed stabilization. In order to validate the hypothesis that there is little upper body motion during casual cycling, an in-depth motion capture analysis was performed on the bicycle and rider system.     

Macro-grammatical evolution for nonlinear time series modeling��a case study of reservoir inflow forecasting

Streamflow forecasting is significantly important for planning and operating water resource systems. However, streamflow formation is a highly nonlinear, time varying, spatially distributed process and difficult to forecast. This paper proposes a nonlinear model which incorporates improved real-coded grammatical evolution (GE) with a genetic algorithm (GA) to predict the ten-day inflow of the De-Chi Reservoir in central Taiwan. The GE is a recently developed evolutionary-programming algorithm used to express complex relationships among long-term nonlinear time series. The algorithm discovers significant input variables and combines them to form mathematical equations automatically. Utilizing GA with GE optimizes an appropriate type of function and its associated coefficients. To enhance searching efficiency and genetic diversity during GA optimization, the macro-evolutionary algorithm (MA) is processed as a selection operator. The results using an example of theoretical nonlinear time series problems indicate that the proposed GEMA yields an efficient optimal solution. GEMA has the advantages of its ability to learn relationships hidden in data and express them automatically in a mathematical manner. When applied to a real world case study, the fittest equation generated through GEMA used only a single input variable in a reasonable nonlinear form. The predicting accuracies of GEMA were better than those of the traditional linear regression (LR) model and as good as those of the back-propagation neural network (BPNN). In addition, the predicting of ten-day reservoir inflows reveals the effectives of GEMA, and standardization is beneficial to model for seasonal time series.     

Breaking the walls of social exclusion of women rural by means of ICTs: The case of ‘digital divides’ in Galician

This paper examines the digital divides for women in rural areas within the Information Society, referring particularly to the Galician experience (Autonomous Region in the North West of Spain). In order to explore the barriers to social and labor exclusion it was elaborated a survey relating with the use and impact of ITC in social and labor experiences of rural women. The results showed that rural women are aware of the key role played for ITC on their way to achieve the participation on decision making process as well as in social life and labor. The main barriers to the Internet penetration in women social life were employment, education and income. From an individual point of view, the existence of non-users was explained by a combination of access problems, lack of ICT skills or rather negative attitudes towards ITC. Finally, a bunch of policy recommendations is proposed focus on providing better Internet access in rural areas and to sensitize the population, particularly the oldest, about the importance of gender equality in access to technology in order to avoid digital barriers for rural women and reduce social exclusion of this group.     

Encryption of speech signal with multiple secret keys in time and transform domains

This paper introduces a new speech cryptosystem, which is based on permutation and masking of speech segments using multiple secret keys in both time and transform domains. The main key is generated, randomly, using a Pseudo Noise (PN) sequence generator, and two other keys are generated from the main key to be used in the subsequent rounds of encryption. Either the Discrete Cosine Transform (DCT) or the Discrete Sine Transform (DST) can be used in the proposed cryptosystem to remove the residual intelligibility resulting from permutation and masking in the time domain. In the proposed cryptosystem, the permutation process is performed with circular shifts calculated from the key bits. The utilized mask is also generated from the secret key by circular shifts. The proposed cryptosystem has a low complexity, small delay, and high degree of security. Simulation results prove that the proposed cryptosystem is robust to the presence of noise.     

Factors influencing health care professionals’ adoption of mobile platform of medical and senior care in China

A mobile platform of medical and senior care (MPMSC) is a platform that can provide one-stop medical and senior care services through mobile devices. This study developed a research model based on the value-based adoption model to understand the factors influencing health care professionals’ adoption of MPMSC. Empirical analysis has shown that perceived value and legal concerns can predict health care professionals’ intention to adopt MPMSC and that outcome expectations, perceived mobility, perceived effort, and privacy concerns can predict perceived value. Research findings can enhance the understanding of the factors influencing the adoption of MPMSC by health care professionals.     

Prevalence of work-related musculoskeletal and non-musculoskeletal injuries in health care workers: the implications for work disability management

To compare the prevalence of musculoskeletal and non-musculoskeletal injuries in a cohort of workers in Manitoba health care sector, who made injury claims between 2002 and 2012. A retrospective study, using secondary data from WCB Manitoba. Logistic regression was used to determine odds ratios for nature of work-related injuries among different groups of workers. Idiopathic work-related musculoskeletal disorders (WMSD) constituted 74.6% of all the injury claims. Injury risks varied between worker groups, and a significant difference was found within the female group, χ² = 13.4, p = 0.0094, but not among the males. Compared to the married, single workers were more likely to experience traumatic than idiopathic injuries (OR = 1.72, CI, 1.28, 2.29), and much higher risk of traumatic injuries than non-WMSD (OR = 1.93, CI, 1.31, 2.84). Work-related injury risks vary significantly across occupations and worker groups, with idiopathic injuries being higher in high physical tasks.

Practitioner Summary: To compare musculoskeletal and non-musculoskeletal injuries among workers in health care sector, and explore the implications for work disability management. Retrospective study, using workers’ compensation claims data. The risk of idiopathic work-related musculoskeletal disorders continue to be high compared to traumatic and non-musculoskeletal disorders, particularly in tasks that involved high physical activities.     

Evolution of cooperation in reputation system by group-based scheme

Reputation systems are very useful in large online communities in which users may frequently have the opportunity to interact with users with whom they have no prior experience. Recently, how to enhance the cooperative behaviors in the reputation system has become to one of the key open issues. Emerging schemes focused on developing efficient reward and punishment mechanisms or capturing the social or economic properties of participants. However, whether this kind of method can work widely or not has been hard to prove until now. Research in evolutionary game theory shows that group selection (or multilevel selection) can favor the cooperative behavior in the finite population. Furthermore, some recent works give fundamental conditions for the evolution of cooperation by group selection. In the paper, we extend the original group selection theory and propose a group-based scheme to enhance cooperation for online reputation systems. Related concepts are defined to capture the social structure and ties among participants in reputation system, e.g., group, assortativity, etc. Also, we use a Fermi distribution function to reflect the bounded rationality of participants and the existence of stochastic factors in evolutionary process. Extended simulations show that our scheme can enhance cooperation and improve the average performance of participants (e.g. payoff) in reputation system.     

Development of Box–Jenkins type time series models by combining conventional and orthonormal basis filter approaches

A unified scheme for developing Box–Jenkins (BJ) type models from input–output plant data by combining orthonormal basis filter (OBF) model and conventional time series models, and the procedure for the corresponding multi-step-ahead prediction are presented. The models have a deterministic part that has an OBF structure and an explicit stochastic part which has either an AR or an ARMA structure. The proposed models combine all the advantages of an OBF model over conventional linear models together with an explicit noise model. The parameters of the OBF–AR model are easily estimated by linear least square method. The OBF–ARMA model structure leads to a pseudo-linear regression where the parameters can be easily estimated using either a two-step linear least square method or an extended least square method. Models for MIMO systems are easily developed using multiple MISO models. The advantages of the proposed models over BJ models are: parameters can be easily and accurately determined without involving nonlinear optimization; a prior knowledge of time delays is not required; and the identification and prediction schemes can be easily extended to MIMO systems. The proposed methods are illustrated with two SISO simulation case studies and one MIMO, real plant pilot-scale distillation column.     

Mobile IT in health – the case of short messaging service in an HIV awareness program

ABSTRACT

This study aims to augment our understanding of user intention to use mobile IT in health. Experiential dispositions and technology perceptions around a mobile service that is currently in use to access other value-seeking services are integrated to present an enriched characterization of intention to use m-health. Primary data from a pressing health context in a developing economy are collected to validate the model. The results demonstrate that previous experience from value services received on a mobile service enhances user attention, which in turn positively impacts the perceived usefulness of an incoming m-health program, which then influences user intention to adopt m-health services delivered on that mobile service. Overall, the findings provide a comprehensive understanding of user intention to accept m-health. Additionally, our results provide insights toward the choice of mobile technology and indicate aspects of message framing that may ensure practicable deployment and successful implementation of m-health programs.     

A reflexive analysis of ‘context’ in privacy research: Two case studies in HIV care

Privacy is a much discussed and politically charged topic in contemporary healthcare. Yet, studying the actual privacy practices of healthcare professionals and patients remains extremely challenging. In this paper we reflect on our experiences using qualitative methods in two projects on HIV care, the first researching internet use by a particular group of patients, and the second looking at issues of information technology integration in hospitals. Our aim in doing so is to highlight some of the opportunities and challenges involved in including an explicit focus on ‘context’ in qualitative privacy research in healthcare. We suggest that adopting a more reflexive approach to the way methods are used in relation to ‘context’ in privacy-related HCI research provides opportunities for understanding how different ‘privacy contexts’ are enacted in and through our research practices in different environments.     

Incorporating resting state dynamics in the analysis of encephalographic responses by means of the Mahalanobis–Taguchi strategy

The analysis of encephalographic responses has mostly been attempted via signal analytic techniques aiming at revealing the useful information from recordings which are considered as contaminated by the ubiquitous ongoing (or background) brain activity. There is continuously accumulating evidence for the existence of well-defined resting-state-networks (RSNs) in the brain, which play a crucial role in the generation of spontaneous activity and the associated neural responses. Hence, the signal plus noise is no longer a valid model and the ongoing fluctuations may influence the response.We introduce here the use of a multivariate statistical methodology, known as Mahalanobis–Taguchi (MT) strategy, which can be tailored to the spontaneous fluctuations so as to optimize the subsequent response detection. A subject-specific version of the MT strategy that combines the original methodology with a clustering algorithm for refining the training set is presented. The proposed methodology serves as an explorative tool for the detailed study of temporal patterning in brain responses.We demonstrate the potential of approach by applying it to experimental magneto-encephalographic (MEG) data. The results indicate vividly the effectiveness of the MT-strategy in analyzing and enhancing auditory responses.     

Inadequacy of the A 2/σ2 criterion in the mode of detection of signals by means of statistical methods

The inadequacy of the A ₂/σ₂ criterion in the detection of stroboscopically transformed noisy signals by means of super-wide-band radar is illustrated by means of simulation and analytic calculations. Statistical methods from [1, 2] are used for discrete stroboscopic transformation.     

Toward the regulation of ubiquitous mobile government: a case study on location-based emergency services in Australia

Mobile alerts, notifications and location-based emergency warning systems are now an established part of mobile government strategies in an increasing number of countries worldwide. In Australia the national emergency warning system (NEWS) was instituted after the tragic Black Saturday Victorian Bushfires of February 2009. NEWS has enabled the provision of public information from the government to the citizen during emergencies anywhere and any time. Moving on from traditional short message service (SMS) notifications and cell broadcasting to more advanced location-based services, this paper explores the major issues faced by government, business and society at large, toward the realization of a fully fledged emergency system for personal mobile devices. This qualitative study contains two phases: phase 1 gathered issues from the general public via an open-ended survey question, and phase 2 gathered issues from key informant interviews. The data was analyzed using qualitative content analysis techniques. The results are presented in a narrative form granting detailed insight into the main challenges faced in the deployment of a mobile government application. The complex interplay between government agencies, telecommunications carriers and the Australian public is presented, ultimately leading down a path of regulation. By using a qualitative approach it is hoped that the intimate lessons learnt in the Australian landscape can be applied to other nations considering mobile government applications. The outcome of the paper is predominantly practical providing a series of recommendations toward the successful deployment of mobile government applications.     

How do thermal conditions in intensive care units affect the health and well-being of intensivists?

This study presents situational risk scenarios to predict the potential implications of predicted and perceived thermal configurations on the health and well-being of health care professionals in intensive care units (ICUs). Nine ICUs were selected, and thermal variables were collected; simultaneously, 128 health care professionals were interviewed to assess their perceptions, satisfaction, and health conditions related to their workplace environment. In scenarios with thermal comfort (either predicted or perceived), the risk of exposure to physical and psychological symptoms was reduced. This effect also extends to predictions and perceptions tending toward slightly cooler temperatures. In situations with a predicted mean vote below −1.5 and above 0.5, symptom complaints increased, even when the health care professionals perceived their environment as thermally comfortable, with the most extreme cases generating an increase of up to 27% in the baseline probability. Adjusting the workplace environment to be thermally comfortable can reduce symptom complaints.     

Two-stage classifications for improving time-to-failure estimates: a case study in prognostic of train wheels

In order to meet the need for higher equipment availability and lower maintenance cost, much attention is being paid to the development of prognostic systems. Such systems support a proactive maintenance strategy by continuously monitoring the components of interest and predicting their failures sufficiently in advance to avoid disruptions during operation. Recent research demonstrated the potential of a comprehensive data mining methodology for building prognostic models from readily available operational and maintenance data. This approach builds a binary classifier that can determine the likelihood of a failure within a broad target window but cannot provide precise time to failure (TTF) estimations. This paper introduces a two-stage classification approach that helps improve the precision of TTF estimations. The new approach uses the initial methodology to learn a variety of base classifiers and then relies on meta-learning to integrate them. The paper details the model building process and demonstrates the usefulness of the proposed approach through a real-world prognostic application.     

Understanding precipitation patterns and land use interaction in Tibet using harmonic analysis of SPOT VGT‐S10 NDVI time series

Time series analysis of Normalized Difference Vegetation Index (NDVI) imagery is a powerful tool in studying land use and precipitation interaction in data‐scarce and inaccessible areas. The Fast Fourier Transform (FFT) was applied to the annual time series of 36 average dekadal NDVI images. The dekadal annual average pattern was calculated from 189 NDVI images from April 1998 to June 2003 acquired with the VEGETATION instruments of the SPOT‐4 and SPOT‐5 satellites in Tibet. It is shown that the first two harmonic terms of a Fourier series suffice to distinguish between land use classes. The results indicate that the highest biomass production occurs before the monsoon peak. Regression analysis with 15 meteorological stations has shown that the total amount of precipitation during the growing season shows the strongest relation with the sum of the amplitudes of the first two harmonic terms (R ²?=?0.72). Inter‐annual NDVI variation based on Fourier‐transformed time series was studied and it was shown that, early in the season, the expected NDVI behaviour of the up‐coming season could be forecast; if linked to food production this might provide a robust early warning system. The most important conclusion from this work is that harmonic time series analysis yields more reliable results than ordinary time series analysis.     

Robust design of (s,S) inventory policy parameters in supply chains with demand and lead time uncertainties

Demand and lead time uncertainties have significant effects on supply chain behaviour. In this paper, we present a single-product three-level multi-period supply chain with uncertain demands and lead times by using robust techniques to study the managerial insights of the supply chain inventory system under uncertainty. We formulate this problem as a robust mixed-integer linear program with minimised expected cost and total cost variation to determine the optimal (s, S) values of the inventory parameters. Several numerical studies are performed to investigate the supply chain behaviour. Useful guidelines for the design of a robust supply chain are also provided. Results show that the order variance and the expected cost in a supply chain significantly increase when the manufacturer’s review period is an integer ratio of the distributor’s and the retailer’s review periods.