Effectiveness of nurse home-visiting for disadvantaged families: results of a natural experiment

Extent: 9p.Objective: To evaluate the effects of a postnatal home-visiting programme delivered by community health nurses to socially disadvantaged mothers in South Australia. Design: The intervention group of 428 mothers lived in metropolitan Adelaide and the comparison group of 239 mothers lived in regional towns where the programme was not yet available. All participating mothers met health service eligibility criteria for enrolment in the home-visiting programme. Participants in both groups were assessed at baseline (mean child age=14.4 weeks SD=2.3), prior to programme enrolment, and again when the children were aged 9, 18 and 24 months. Setting: State-wide community child health service. Participants: 667 socially disadvantaged mothers enrolled consecutively. 487 mothers (73%) completed the 24-month assessment. Intervention: Two-year postnatal home-visiting programme based on the Family Partnership Model. Primary outcome measures: Parent Stress Index (PSI), Kessler Psychological Distress Scale and the Ages and Stages Questionnaire. Results: Mixed models adjusting for baseline differences were used to compare outcomes in the two groups. The mothers in the home-visiting group reported greater improvement on the PSI subscales assessing a mother's perceptions on the quality of their relationship with their child (1.10, 95% CI 0.06 to 2.14) and satisfaction with their role as parents (0.46, 95% CI −0.15 to 1.07) than mothers in the comparison group. With the exception of childhood sleeping problems, there were no other significant differences in the outcomes across the two groups. Conclusions: The findings suggest that home-visiting programmes delivered by community health nurses as part of routine clinical practice have the potential to improve maternal–child relationships and help mothers adjust to their role as parents.Michael Gifford Sawyer, Linda Frost, Kerrie Bowering, John Lync

Evolving Systems: Adaptive Key Component Control and Inheritance of Passivity and Dissipativity

We propose a new framework called Evolving Systems to describe the self-assembly, or autonomous assembly, of actively controlled dynamical subsystems into an Evolved System with a higher purpose. Autonomous assembly of large, complex flexible structures in space is a target application for Evolving Systems. A critical requirement for autonomous assembling structures is that they remain stable during and after assembly. The fundamental topic of inheritance of stability, dissipativity, and passivity in Evolving Systems is the primary focus of this research. In this paper, we develop an adaptive key component controller to restore stability in Nonlinear Evolving Systems that would otherwise fail to inherit the stability traits of their components. We provide sufficient conditions for the use of this novel control method and demonstrate its use on an illustrative example

X-ray observations of AM Herculis from OSO-8

The white dwarf binary system AM Herculis (2A1815+500) was observed in X-rays at both low energies (E less 10 keV) and higher energies. The exact shape of the spectrum, particularly at the higher energies, has yet to be determined. Results from the high energy scintillation spectrometer on OSO-8 are presented. These are combined with results published elsewhere obtained concurrently with the proportional counter on the same satellite, thereby giving for the first time coincident observations of AM Her over the range 2 to 250 keV

A network-based communication platform for a cognitive computer

Street is a reconfigurable parallel computer architecture. It executes a production language directly in hardware with the aim of realising advanced cognitive agents in a more energy efficient manner than conventional computers. Street requires frequent communication between many processing elements and to make this communication more energy efficient, a network-based communication platform, StreetNet, is proposed in this paper. It maps the processing elements onto a 2D mesh architecture optimized according to the data dependencies between them. A deadlock-free deterministic routing function is considered for this platform along with the concept of sleep period, analogous to human sleeping, to reorganize the placements of processing elements based on runtime traffic statistics. These mechanisms serve to reduce total network traffic and hence minimise energy consumption.Mostafa W. Numan, Jesse Frost, Braden J. Phillips, and Michael Liebel

Non-adjacent dependency learning in infancy, and its link to language development

To acquire language, infants must learn how to identify words and linguistic structure in speech. Statistical learning has been suggested to assist both of these tasks. However, infants’ capacity to use statistics to discover words and structure together remains unclear. Further, it is not yet known how infants’ statistical learning ability relates to their language development. We trained 17-month-old infants on an artificial language comprising non-adjacent dependencies, and examined their looking times on tasks assessing sensitivity to words and structure using an eye-tracked head-turn-preference paradigm. We measured infants’ vocabulary size using a Communicative Development Inventory (CDI) concurrently and at 19, 21, 24, 25, 27, and 30 months to relate performance to language development. Infants could segment the words from speech, demonstrated by a significant difference in looking times to words versus part-words. Infants’ segmentation performance was significantly related to their vocabulary size (receptive and expressive) both currently, and over time (receptive until 24 months, expressive until 30 months), but was not related to the rate of vocabulary growth. The data also suggest infants may have developed sensitivity to generalised structure, indicating similar statistical learning mechanisms may contribute to the discovery of words and structure in speech, but this was not related to vocabulary size

Integrating Systems Health Management with Adaptive Controls for a Utility-Scale Wind Turbine

Increasing turbine up-time and reducing maintenance costs are key technology drivers for wind turbine operators. Components within wind turbines are subject to considerable stresses due to unpredictable environmental conditions resulting from rapidly changing local dynamics. Systems health management has the aim to assess the state-of-health of components within a wind turbine, to estimate remaining life, and to aid in autonomous decision-making to minimize damage. Advanced adaptive controls can provide the mechanism to enable optimized operations that also provide the enabling technology for Systems Health Management goals. The work reported herein explores the integration of condition monitoring of wind turbine blades with contingency management and adaptive controls. Results are demonstrated using a high fidelity simulator of a utility-scale wind turbine

The 78.4 day period of Cygnus XR-1

A search for a 78.4 day modulation in the high energy X-ray flux observed with OSO-8 and in the U-band optical polarization is reported. It is suggested that if such a modulation does exist, it is more likely to be related to the rotation of the free modes of oscillation of the primary than to the existence of a third body in the system