Behavioral biometrics for persistent single sign-on

Driven by the rapid development of ubiquitous and pervasive computing, personalized services and applications are deployed to support our lives. Accordingly, the number of interfaces and devices (smartphone, tablet computer, etc.) provided to access and consume these services is growing continuously. To simplify the complexity of managing many accounts with different credentials, Single Sign-On (SSO) solutions have been introduced. However, a single password for many accounts represents a single-point-of-failure. Furthermore, once initiated SSO session is a high potential risk when the working station is left unlocked and unattended. In this paper, we present a conception of a Persistent Single Sign-On (PSSO) for ubiquitous home environments by involving the capabilities of Behavioral Biometrics to check the identity of the user continuously in an unobtrusive manner.

Linking scientific evidence and decision making : A case study of hand hygiene interventions

We consider how data from scientific research should be used for decision making in health services. Whether a hand hygiene intervention to reduce risk of nosocomial infection should be widely adopted is the case study. Improving hand hygiene has been described as the most important measure to prevent nosocomial infection. 1 Transmission of microorganisms is reduced, and fewer infections arise, which leads to a reduction in mortality2 and cost savings.3 Implementing a hand hygiene program is itself costly, so the extra investment should be tested for cost-effectiveness.4,5 The first part of our commentary is about cost-effectiveness models and how they inform decision making for health services. The second part is about how data on the effectiveness of hand hygiene programs arising from scientific studies are used, and 2 points are made: the threshold for statistical inference of .05 used to judge effectiveness studies is not important for decision making,6,7 and potentially valuable evidence about effectiveness might be excluded by decision makers because it is deemed low quality.8 The ideas put forward will help researchers and health services decision makers to appraise scientific evidence in a more powerful way.

The effects of cold air (-110��C) and water (8��C) cryotherapy on knee skin temperature

Exposure to cold air, whole body cryotherapy (WBC), is a novel treatment employed by athletes. In WBC individuals dressed in minimal clothing are exposed to a temperature below -100��C for 2-4 min. The use of WBC has been advocated as a treatment for various knee injuries. PURPOSE: To compare the effects of two modalities of cryotherapy, -110��C WBC and 8��C cold water immersion (CWI) on knee skin temperature (Tsk). METHODS: With ethical approval and written informed consent 10 healthy active male participants (26.5��4.9 yr, 183.5��6.0 cm, 90.7��19.9 kg, 26.8��5.0 kg/m2, 23.0��9.3% body fat (measured by DXA), 7.6 �� 2.0 mm patellar skin fold; mean��SD) were exposed to 4 min of CWI and WBC. The treatment order was randomised in a controlled crossover design, with a minimum of 7 days between treatments. During WBC participants stood in a chamber (-60��3��C) for 20 s before entering the main chamber (-110��C��3��C) where they remained for 3 min and 40 s. For CWI participants were seated in a tank filled with cold water (8��0.3��C) and immersed to the level of the sternum for 4 min. Right knee Tsk was assessed via non-contact, infrared thermal imaging. A quadrilateral region of interest was created using inert markers placed 5 cm above and below the most superior and inferior aspect of the patella. Tsk within this quadrilateral was recorded pre, immediately post and every 10 min thereafter for 60 min. Tsk changes were examined using a two-way (treatment x time) repeated measures analyses of variance. In addition, a paired sample t-test was used to compare baseline Tsk before both treatments. RESULTS: Knee Tsk was similar before treatment (WBC: 29.9��0.7��C, CWI: 29.6��0.9��C, p>0.05). There was a significant main effect for treatment (p<0.05) and time (p<0.001). Compared to baseline, Tsk was significantly reduced (p<0.05) immediately post and at 10, 20, 30, 40, 50 and 60 min after both cooling modalities. Knee Tsk was lower (p<0.05) immediately after WBC (19.0��0.9��C) compared to CWI (20.5��0.6��C). However, from 10 to 60 min post, knee Tsk was lower (p<0.05) following the CWI treatment. CONCLUSION: WBC elicited a greater decrease in knee Tsk compared to CWI immediately after treatment. However, both modalities display different recovery patterns and Tsk after CWI was significantly lower than WBC at 10, 20, 30, 40, 50 and 60 min after treatment.

Book review: "Ideologies of Breast Cancer : Feminist Perspectives" Edited by Laura K. Potts. New York: St. Martin���s Press

Laura K. Potts���s edited collection of research on the meanings of breast cancer includes authors from the United Kingdom, the United States, and Canada whose perspectives draw on literary criticism, sociology, psychology, and cultural studies among others. The research employs various methodological approaches���for example, media analysis (Saywell et al.), autobiographical narratives (Potts), and analysis of social activism (Fishman)���to elucidate the multiple dimensions and diversity of breast cancer experiences. The first of two parts, ���Meanings of Breast Cancer,��� presents the problematical relationship between biomedicine and women���s constructions of breast cancer knowledge, the sexualized and maternalized breast in the print media about breast cancer, environmental risks to women���s health in the Bay Area of San Francisco, and women���s narratives of breast cancer and situating the self. In part 2, ���Discourses of Risk and Breast Cancer,��� examination of the discourses of prevention and risks to health are taken up in relation to breast cancer screening, the problem of prophylactic mastectomy for hereditary breast cancer, and environmental activism...

Predicting fitness for chemotherapy in older cancer patients

Background: The Vulnerable Elders Survey-13 (VES-13) is increasingly used to screen for older patients who can proceed to intensive chemotherapy without further comprehensive assessment. This study compared the VES-13 determination of fitness for treatment with the oncologist's assessments of fitness. Method: Sample: Consecutive series of solid tumour patients ���65 years (n=175; M=72; range=65-86) from an Australian cancer centre. Patients were screened with the VES-13 before proceeding to usual treatment. Blinded to screening, oncologists concurrently predicted patient fitness for chemotherapy. A sample of 175 can detect, with 90% power, kappa coefficients of agreement between VES-13 and oncologists��� assessments >0.90 ("almost perfect agreement"). Separate backward stepwise logistic regression analyses assessed potential predictors of VES-13 and oncologists��� ratings of fitness. Results: Kappa coefficient for agreement between VES-13 and oncologists��� ratings of fitness was 0.41 (p<0.001). VES-13 and oncologists��� assessments agreed in 71% of ratings. VES-13 sensitivity = 83.3%; specificity = 57%; positive predictive value = 69%; negative predictive value = 75%. Logistic regression modelling indicated that the odds of being vulnerable to chemotherapy (VES-13) increased with increasing depression (OR=1.42; 95% CI: 1.18, 1.71) and decreased with increased functional independence assessed on the Bartel Index (OR=0.82; CI: 0.74, 0.92) and Lawton instrumental activities of daily living (OR=0.44; CI: 0.30, 0.65); RSquare=.65. Similarly, the odds of a patient being vulnerable to chemotherapy, when assessed by physicians, increased with increasing age (OR=1.15; CI: 1.07, 1.23) and depression (OR=1.23; CI: 1.06, 1.43), and decreased with increasing functional independence (OR=0.91; CI: 0.85, 0.98); RSquare=.32. Conclusions: Our data indicate moderate agreement between VES-13 and clinician assessments of patients��� fitness for chemotherapy. Current ���one-step��� screening processes to determine fitness have limits. Nonetheless, screening tools do have the potential for modification and enhanced predictive properties in cancer care by adding relevant items, thus enabling fit patients to be immediately referred for chemotherapy.

Exploring subscription renewal intention of operational cloud enterprise systems - a stakeholder perspective

Retaining customers is a relevant topic throughout all service industries. However, only limited attention has been directed towards studying the antecedents of subscription renewal in the context of operational cloud enterprise systems. Cloud services have historically been offered as subscription-based services with the (theoretical) possibility of seamless service cancellation, in contrast to classical IT-Outsourcing contracts or license-based software installations of on-premise enterprise systems. In this work, we investigate the central concept of subscription renewal by focusing on different facets of IS success and their relevance for distinct employee cohorts. Analyzing inter-cohort differences has strong practical implications, as it helps IT vendors to focus on specific IT-related factors when trying to retain customers. Therefore an empirical study was undertaken. The hypotheses were developed on an individual level and tested using survey responses of IT decision makers within companies which adopted cloud enterprise systems. Gathered data was then analyzed using PLS. The results show that subscription renewal intention of the strategic cohort is mainly based on perceived system quality, whereas information quality explains most of the variance of subscription renewal in the management cohort. Beneath the cloud enterprise systems specific contributions, the work adds to the theoretical body of research related to IS success and IS continuation, as well as stakeholder perspectives.

A data mining driven risk profiling method for road asset management

Road surface skid resistance has been shown to have a strong relationship to road crash risk, however, applying the current method of using investigatory levels to identify crash prone roads is problematic as they may fail in identifying risky roads outside of the norm. The proposed method analyses a complex and formerly impenetrable volume of data from roads and crashes using data mining. This method rapidly identifies roads with elevated crash-rate, potentially due to skid resistance deficit, for investigation. A hypothetical skid resistance/crash risk curve is developed for each road segment, driven by the model deployed in a novel regression tree extrapolation method. The method potentially solves the problem of missing skid resistance values which occurs during network-wide crash analysis, and allows risk assessment of the major proportion of roads without skid resistance values.

Faster than the speed of print : reconciling ���big data��� social media analysis and academic scholarship

The promise of ���big data��� has generated a significant deal of interest in the development of new approaches to research in the humanities and social sciences, as well as a range of important critical interventions which warn of an unquestioned rush to ���big data���. Drawing on the experiences made in developing innovative ���big data��� approaches to social media research, this paper examines some of the repercussions for the scholarly research and publication practices of those researchers who do pursue the path of ���big data������centric investigation in their work. As researchers import the tools and methods of highly quantitative, statistical analysis from the ���hard��� sciences into computational, digital humanities research, must they also subscribe to the language and assumptions underlying such ���scientificity���? If so, how does this affect the choices made in gathering, processing, analysing, and disseminating the outcomes of digital humanities research? In particular, is there a need to rethink the forms and formats of publishing scholarly work in order to enable the rigorous scrutiny and replicability of research outcomes?

On structural considerations for governing the cloud

In this research, we suggest appropriate information technology (IT) governance structures to manage the cloud computing resources. The interest in acquiring IT resources a utility is gaining momentum. Cloud computing resources present organizations with opportunities to manage their IT expenditure on an ongoing basis, and are providing organizations access to modern IT resources to innovate and manage their continuity. However, cloud computing resources are no silver bullet. Organizations would need to have appropriate governance structures and policies in place to ensure its effective management and fit into existing business processes to leverage the promised opportunities. Using a mixed method design, we identified four possible governance structures for managing the cloud computing resources. These structures are a chief cloud officer, a cloud management committee, a cloud service facilitation centre, and a cloud relationship centre. These governance structures ensure appropriate direction of cloud computing resources from its acquisition to fit into the organizations business processes.

Elementary teachers��� knowledge of legislative and policy duties for reporting child sexual abuse

This study examined elementary school teachers��� knowledge of their legislative and policy-based reporting duties with respect to child sexual abuse. Data were collected from 470 elementary school teachers from urban and rural government and nongovernment schools in 3 Australian states, which at the time of the study had 3 different legislative reporting duties for teachers. Teachers completed the 8-part Teacher Reporting Questionnaire (TRQ). Multinomial logistic regression analysis was used to determine factors associated with (a) teachers��� legislation knowledge and (b) teachers��� policy knowledge. Teachers with higher levels of knowledge had a combination of pre- and in-service training about child sexual abuse and more positive attitudes toward reporting, held administration positions in their school, and had reported child sexual abuse at least once during their teaching career. They were also more likely to work in the state with the strongest legislative reporting duty, which had been in place the longest.

Dynamic effects of electrical pitting in steam-turbine tilting-pad thrust-bearings

This paper merges the analysis of a case history and the simplified theoretical model related to a rather singular phenomenon that may happen in rotating machinery. Starting from the first, a small industrial steam turbine experienced a very strange behavior during megawatt load. When the unit was approaching the maximum allowed power, the temperature of the babbitt metal of the pads of the thrust bearing showed constant increase with an unrecoverable drift. Bearing inspection showed that pad trailing edge had the typical aspect of electrical pitting. This kind of damage was not reparable and bearing pads had to replaced. This problem occurred several times in sequence and was solved only by adding further ground brushes to the shaft-line. Failure analysis indicated electrodischarge machining as the root fault. A specific model, able to take into consideration the effect of electrical pitting and loading capacity decreasing as a consequence of the damage of the babbitt metal, is proposed in the paper and shows that the phenomenon causes the irretrievable failure of the thrust bearing.

Fault symptoms of rolling element bearings under variable operating conditions - a multi domain analysis

Diagnostics of rolling element bearings have been traditionally developed for constant operating conditions, and sophisticated techniques, like Spectral Kurtosis or Envelope Analysis, have proven their effectiveness by means of experimental tests, mainly conducted in small-scale laboratory test-rigs. Algorithms have been developed for the digital signal processing of data collected at constant speed and bearing load, with a few exceptions, allowing only small fluctuations of these quantities. Owing to the spreading of condition based maintenance in many industrial fields, in the last years a need for more flexible algorithms emerged, asking for compatibility with highly variable operating conditions, such as acceleration/deceleration transients. This paper analyzes the problems related with significant speed and load variability, discussing in detail the effect that they have on bearing damage symptoms, and propose solutions to adapt existing algorithms to cope with this new challenge. In particular, the paper will i) discuss the implication of variable speed on the applicability of diagnostic techniques, ii) address quantitatively the effects of load on the characteristic frequencies of damaged bearings and iii) finally present a new approach for bearing diagnostics in variable conditions, based on envelope analysis. The research is based on experimental data obtained by using artificially damaged bearings installed on a full scale test-rig, equipped with actual train traction system and reproducing the operation on a real track, including all the environmental noise, owing to track irregularity and electrical disturbances of such a harsh application.

The combination of empirical mode decomposition and minimum entropy deconvolution for roller bearing diagnostics in non-stationary operation

Diagnostics is based on the characterization of mechanical system condition and allows early detection of a possible fault. Signal processing is an approach widely used in diagnostics, since it allows directly characterizing the state of the system. Several types of advanced signal processing techniques have been proposed in the last decades and added to more conventional ones. Seldom, these techniques are able to consider non-stationary operations. Diagnostics of roller bearings is not an exception of this framework. In this paper, a new vibration signal processing tool, able to perform roller bearing diagnostics in whatever working condition and noise level, is developed on the basis of two data-adaptive techniques as Empirical Mode Decomposition (EMD), Minimum Entropy Deconvolution (MED), coupled by means of the mathematics related to the Hilbert transform. The effectiveness of the new signal processing tool is proven by means of experimental data measured in a test-rig that employs high power industrial size components.

Hydraulic instability onset detection in Kaplan turbines by monitoring shaft vibrations

Design of hydraulic turbines has often to deal with hydraulic instability. It is well-known that Francis and Kaplan types present hydraulic instability in their design power range. Even if modern CFD tools may help to define these dangerous operating conditions and optimize runner design, hydraulic instabilities may fortuitously arise during the turbine life and should be timely detected in order to assure a long-lasting operating life. In a previous paper, the authors have considered the phenomenon of helical vortex rope, which happens at low flow rates when a swirling flow, in the draft tube conical inlet, occupies a large portion of the inlet. In this condition, a strong helical vortex rope appears. The vortex rope causes mechanical effects on the runner, on the whole turbine and on the draft tube, which may eventually produce severe damages on the turbine unit and whose most evident symptoms are vibrations. The authors have already shown that vibration analysis is suitable for detecting vortex rope onset, thanks to an experimental test campaign performed during the commissioning of a 23 MW Kaplan hydraulic turbine unit. In this paper, the authors propose a sophisticated data driven approach to detect vortex rope onset at different power load, based on the analysis of the vibration signals in the order domain and introducing the so-called "residual order spectrogram", i.e. an order-rotation representation of the vibration signal. Some experimental test runs are presented and the possibility to detect instability onset, especially in real-time, is discussed.

A cognitive processing framework for learning analytics

Incorporating a learner���s level of cognitive processing into Learning Analytics presents opportunities for obtaining rich data on the learning process. We propose a framework called COPA that provides a basis for mapping levels of cognitive operation into a learning analytics system. We utilise Bloom���s taxonomy, a theoretically respected conceptualisation of cognitive processing, and apply it in a flexible structure that can be implemented incrementally and with varying degree of complexity within an educational organisation. We outline how the framework is applied, and its key benefits and limitations. Finally, we apply COPA to a University undergraduate unit, and demonstrate its utility in identifying key missing elements in the structure of the course.