Effects of pediatric obesity on joint kinematics and kinetics during 2 walking cadences

Objective: To determine whether differences existed in lower-extremity joint biomechanics during self-selected walking cadence (SW) and fast walking cadence (FW) in overweight- and normal-weight children.---------- Design: Survey.---------- Setting: Institutional gait study center.---------- Participants: Participants (N=20; mean age ± SD, 10.4±1.6y) from referred and volunteer samples were classified based on body mass index percentiles and stratified by age and sex. Exclusion criteria were a history of diabetes, neuromuscular disorder, or recent lower-extremity injury.---------- Main Outcome Measures: Sagittal, frontal, and transverse plane angular displacements (degrees) and peak moments (newton meters) at the hip, knee, and ankle joints.---------- Results: The level of significance was set at P less than .008. Compared with normal-weight children, overweight children had greater absolute peak joint moments at the hip (flexor, extensor, abductor, external rotator), the knee (flexor, extensor, abductor, adductor, internal rotator), and the ankle (plantarflexor, inverter, external/internal rotators). After including body weight as a covariate, overweight children had greater peak ankle dorsiflexor moments than normal-weight children. No kinematic differences existed between groups. Greater peak hip extensor moments and less peak ankle inverter moments occurred during FW than SW. There was greater angular displacement during hip flexion as well as less angular displacement at the hip (extension, abduction), knee (flexion, extension), and ankle (plantarflexion, inversion) during FW than SW.---------- Conclusions: Overweight children experienced increased joint moments, which can have long-term orthopedic implications and suggest a need for more nonweight-bearing activities within exercise prescription. The percent of increase in joint moments from SW to FW was not different for overweight and normal-weight children. These findings can be used in developing an exercise prescription that must involve weight-bearing activity.

A biomechanical investigation of dual growing rods used for fusionless scoliosis correction

Background The use of dual growing rods is a fusionless surgical approach to the treatment of early onset scoliosis (EOS), which aims of harness potential growth in order to correct spinal deformity. The purpose of this study was to compare the in-vitro biomechanical response of two different dual rod designs under axial rotation loading. Methods Six porcine spines were dissected into seven level thoracolumbar multi-segmental units. Each specimen was mounted and tested in a biaxial Instron machine, undergoing nondestructive left/right axial rotation to peak moments of 4Nm at a constant rotation rate of 8deg.s-1. A motion tracking system (Optotrak) measured 3D displacements of individual vertebrae. Each spine was tested in an un-instrumented state first and then with appropriately sized semi-constrained growing rods and ‘rigid’ rods in alternating sequence. Range of motion, neutral zone size and stiffness were calculated from the moment-rotation curves and intervertebral ranges of motion were calculated from Optotrak data. Findings Irrespective of test sequence, rigid rods showed significantly reduction of total rotation across all instrumented levels (with increased stiffness) whilst semi-constrained rods exhibited similar rotation behavior to the un-instrumented (P<0.05). An 11% and 8% increase in stiffness for left and right axial rotation respectively and 15% reduction in total range of motion was recorded with dual rigid rods compared with semi-constrained rods. Interpretation Based on these findings, the semi-constrained growing rods do not increase axial rotation stiffness compared with un-instrumented spines. This is thought to provide a more physiological environment for the growing spine compared to dual rigid rod constructs.

Spectral–texture feature extraction using statistical moments with application to object-based vegetation species classification

The use of appropriate features to characterise an output class or object is critical for all classification problems. In order to find optimal feature descriptors for vegetation species classification in a power line corridor monitoring application, this article evaluates the capability of several spectral and texture features. A new idea of spectral–texture feature descriptor is proposed by incorporating spectral vegetation indices in statistical moment features. The proposed method is evaluated against several classic texture feature descriptors. Object-based classification method is used and a support vector machine is employed as the benchmark classifier. Individual tree crowns are first detected and segmented from aerial images and different feature vectors are extracted to represent each tree crown. The experimental results showed that the proposed spectral moment features outperform or can at least compare with the state-of-the-art texture descriptors in terms of classification accuracy. A comprehensive quantitative evaluation using receiver operating characteristic space analysis further demonstrates the strength of the proposed feature descriptors.

The 2013 creative industries statistical analysis for Western Australia

The Department of Culture and the Arts undertook the first mapping of Perth’s creative industries in 2007 in partnership with the City of Perth and the Departments of Industry and Resources and the Premier and Cabinet. The 2013 Creative Industries Statistical Analysis for Western Australia report has updated the mapping with the 2011 Census employment data to provide invaluable information for the State’s creative industries, their peak associations and potential investors. The report maps sector employment numbers and growth between the 2006 and 2011 Census in the areas of music, visual and performing arts, film, TV and radio, advertising and marketing, software and digital content, publishing, and architecture and design, which includes designer fashion.

A framework for understanding and generating integrated solutions for residential peak energy demand

Supplying peak energy demand in a cost effective, reliable manner is a critical focus for utilities internationally. Successfully addressing peak energy concerns requires understanding of all the factors that affect electricity demand especially at peak times. This paper is based on past attempts of proposing models designed to aid our understanding of the influences on residential peak energy demand in a systematic and comprehensive way. Our model has been developed through a group model building process as a systems framework of the problem situation to model the complexity within and between systems and indicate how changes in one element might flow on to others. It is comprised of themes (social, technical and change management options) networked together in a way that captures their influence and association with each other and also their influence, association and impact on appliance usage and residential peak energy demand. The real value of the model is in creating awareness, understanding and insight into the complexity of residential peak energy demand and in working with this complexity to identify and integrate the social, technical and change management option themes and their impact on appliance usage and residential energy demand at peak times.

Systems modelling of the socio-technical aspects of residential electricity use and network peak demand

Provision of network infrastructure to meet rising network peak demand is increasing the cost of electricity. Addressing this demand is a major imperative for Australian electricity agencies. The network peak demand model reported in this paper provides a quantified decision support tool and a means of understanding the key influences and impacts on network peak demand. An investigation of the system factors impacting residential consumers’ peak demand for electricity was undertaken in Queensland, Australia. Technical factors, such as the customers’ location, housing construction and appliances, were combined with social factors, such as household demographics, culture, trust and knowledge, and Change Management Options (CMOs) such as tariffs, price,managed supply, etc., in a conceptual ‘map’ of the system. A Bayesian network was used to quantify the model and provide insights into the major influential factors and their interactions. The model was also used to examine the reduction in network peak demand with different market-based and government interventions in various customer locations of interest and investigate the relative importance of instituting programs that build trust and knowledge through well designed customer-industry engagement activities. The Bayesian network was implemented via a spreadsheet with a tick box interface. The model combined available data from industry-specific and public sources with relevant expert opinion. The results revealed that the most effective intervention strategies involve combining particular CMOs with associated education and engagement activities. The model demonstrated the importance of designing interventions that take into account the interactions of the various elements of the socio-technical system. The options that provided the greatest impact on peak demand were Off-Peak Tariffs and Managed Supply and increases in the price of electricity. The impact in peak demand reduction differed for each of the locations and highlighted that household numbers, demographics as well as the different climates were significant factors. It presented possible network peak demand reductions which would delay any upgrade of networks, resulting in savings for Queensland utilities and ultimately for households. The use of this systems approach using Bayesian networks to assist the management of peak demand in different modelled locations in Queensland provided insights about the most important elements in the system and the intervention strategies that could be tailored to the targeted customer segments.

Some Remarks on the Approximation of Transitional Density in Stochastic Differential Equations

Aijt-Sahalia (2002) introduced a method to estimate transitional probability densities of di®usion processes by means of Hermite expansions with coe±cients determined by means of Taylor series. This note describes a numerical procedure to ¯nd these coe±cients based on the calculation of moments. One advantage of this procedure is that it can be used e®ectively when the mathematical operations required to ¯nd closed-form expressions for these coe±cients are otherwise infeasible.