Bias of Maximum-Likelihood estimates in logistic and Cox regression models: A comparative simulation study

Parameter estimates of logistic and Cox regression models are biased for finite samples. In a simulation study we investigated for both models the behaviour of the bias in relation to sample size and further parameters. In the case of a dichotomous explanatory variable x the magnitude of the bias is strongly influenced by the baseline risk defined by the constants of the models and the risk resulting for the high risk group. To conduct a direct comparison of the bias of the two models analyses were based on the same simulated data. Overall, the bias of the two models appear to be similar, however, the Cox model has less bias in situations where the baseline risk is high

'I have to live with the decisions I make': laying a foundation for decision making for children with life-limiting conditions and life-threatening illnesses.

The relationship between parents and clinician is critical to the care and treatment of children with life-limiting conditions (LLCs) and life-threatening illnesses (LTIs). This relationship is built and maintained largely in consultations. In this article we lay out factors that bear on the success of clinical consultations and the maintenance of the essential clinician-parent relationship at progression or deterioration of LLCs or LTIs. We suggest an approach to engaging parents in conversations about care and treatment that recognises and appreciates the dilemmas which clinicians and parents face and in so doing provides a way for everyone to live with the decisions that are made. A close analysis of a consultation at progression and excerpts of encounters among parents, clinician and researcher are used to illustrate our approach to research, analysis and development of recommendations for clinical practice

Abrupt transition in quasiparticle dynamics at optimal doping in a cuprate superconductor system

We report time-resolved measurements of the photoinduced change in reflectivity, Delta R, in the Bi2Sr2Ca(1-y)Dy(y)Cu2O(8+delta) (BSCCO) system of cuprate superconductors as a function of hole concentration. We find that the kinetics of quasiparticle decay and the sign of Delta R both change abruptly where the superconducting transition temperature Tc is maximal. These coincident changes suggest that a sharp transition in quasiparticle dynamics takes place precisely at optimal doping in the BSCCO system.Comment: 10 pages, 4 figure

Current understanding of decision-making in adolescents with cancer: A narrative systematic review

BACKGROUND: Policy guidance and bioethical literature urge the involvement of adolescents in decisions about their healthcare. It is uncertain how roles and expectations of adolescents, parents and healthcare professionals influence decision-making and to what extent this is considered in guidance. AIMS: To identify recent empirical research on decision-making regarding care and treatment in adolescent cancer: (1) to synthesise evidence to define the role of adolescents, parents and healthcare professionals in the decision-making process and (2) to identify gaps in research. DESIGN: A narrative systematic review of qualitative, quantitative and mixed-methods research. We adopted a textual approach to synthesis, using a theoretical framework of interactionism to interpret findings. DATA SOURCES: The databases MEDLINE, PsycINFO, SCOPUS, EMBASE and CINHAL were searched from 2001 through May 2015 for publications on decision-making for adolescents (13-19 years) with cancer. RESULTS: Twenty-eight articles were identified. Adolescents and parents initially find it difficult to participate in decision-making due to a lack of options in the face of protocol-driven care. Parent and adolescent preferences for information and response to loss of control vary between individuals and over time. No studies indicate parental or adolescent preference for a high degree of independence in decision-making. CONCLUSION: Striving to make parents and adolescents fully informed or urge them towards more independence than they prefer may add to distress and confusion. This may interfere with their ability to participate in their preferred way in decisions about care and treatment. Future research should include analysis of on-ground interactions among parents, adolescents and clinicians across the trajectory

Determination of the spin-flip time in ferromagnetic SrRuO3 from time-resolved Kerr measurements

We report time-resolved Kerr effect measurements of magnetization dynamics in ferromagnetic SrRuO3. We observe that the demagnetization time slows substantially at temperatures within 15K of the Curie temperature, which is ~ 150K. We analyze the data with a phenomenological model that relates the demagnetization time to the spin flip time. In agreement with our observations the model yields a demagnetization time that is inversely proportional to T-Tc. We also make a direct comparison of the spin flip rate and the Gilbert damping coefficient showing that their ratio very close to kBTc, indicating a common origin for these phenomena

Observation of ferromagnetic resonance in strontium ruthenate (SrRuO3)

We report the observation of ferromagnetic resonance (FMR) in SrRuO3 using the time-resolved magneto-optical Kerr effect. The FMR oscillations in the time-domain appear in response to a sudden, optically induced change in the direction of easy-axis anistropy. The high FMR frequency, 250 GHz, and large Gilbert damping parameter, alpha ~ 1, are consistent with strong spin-orbit coupling. We find that the parameters associated with the magnetization dynamics, including alpha, have a non-monotonic temperature dependence, suggestive of a link to the anomalous Hall effect.Comment: submitted to Phys. Rev. Let

Improving SIEM for critical SCADA water infrastructures using machine learning

Network Control Systems (NAC) have been used in many industrial processes. They aim to reduce the human factor burden and efficiently handle the complex process and communication of those systems. Supervisory control and data acquisition (SCADA) systems are used in industrial, infrastructure and facility processes (e.g. manufacturing, fabrication, oil and water pipelines, building ventilation, etc.) Like other Internet of Things (IoT) implementations, SCADA systems are vulnerable to cyber-attacks, therefore, a robust anomaly detection is a major requirement. However, having an accurate anomaly detection system is not an easy task, due to the difficulty to differentiate between cyber-attacks and system internal failures (e.g. hardware failures). In this paper, we present a model that detects anomaly events in a water system controlled by SCADA. Six Machine Learning techniques have been used in building and evaluating the model. The model classifies different anomaly events including hardware failures (e.g. sensor failures), sabotage and cyber-attacks (e.g. DoS and Spoofing). Unlike other detection systems, our proposed work helps in accelerating the mitigation process by notifying the operator with additional information when an anomaly occurs. This additional information includes the probability and confidence level of event(s) occurring. The model is trained and tested using a real-world dataset