Inpatient facial expression recognition with SMS alert

Facial expression recognition (FER) has been dramatically developed in recent years, thanks to the advancements in related fields, especially machine learning, image processing and human recognition. Accordingly, the impact and potential usage of automatic FER have been growing in a wide range of applications, including human-computer interaction, robot control and driver state surveillance. However, to date, robust recognition of facial expressions from images and videos is still a challenging task due to the difficulty in accurately extracting the useful emotional features. These features are often represented in different forms, such as static, dynamic, point-based geometric or region-based appearance. Facial movement features, which include feature position and shape changes, are generally caused by the movements of facial elements and muscles during the course of emotional expression. The facial elements, especially key elements, will constantly change their positions when subjects are expressing emotions. As a consequence, the same feature in different images usually has different positions. In some cases, the shape of the feature may also be distorted due to the subtle facial muscle movements. Therefore, for any feature representing a certain emotion, the geometric-based position and appearance-based shape normally changes from one image to another image in image databases, as well as in videos. This kind of movement features represents a rich pool of both static and dynamic characteristics of expressions, which playa critical role for FER. The vast majority of the past work on FER does not take the dynamics of facial expressions into account. Some efforts have been made on capturing and utilizing facial movement features, and almost all of them are static based. These efforts try to adopt either geometric features of the tracked facial points, or appearance difference between holistic facial regions in consequent frames or texture and motion changes in loca- facial regions. Although achieved promising results, these approaches often require accurate location and tracking of facial points, which remains problematic.

All-environment visual place recognition with SMART

This paper presents Sequence Matching Across Route Traversals (SMART); a generally applicable sequence-based place recognition algorithm. SMART provides invariance to changes in illumination and vehicle speed while also providing moderate pose invariance and robustness to environmental aliasing. We evaluate SMART on vehicles travelling at highly variable speeds in two challenging environments; firstly, on an all-terrain vehicle in an off-road, forest track and secondly, using a passenger car traversing an urban environment across day and night. We provide comparative results to the current state-of-the-art SeqSLAM algorithm and investigate the effects of altering SMART’s image matching parameters. Additionally, we conduct an extensive study of the relationship between image sequence length and SMART’s matching performance. Our results show viable place recognition performance in both environments with short 10-metre sequences, and up to 96% recall at 100% precision across extreme day-night cycles when longer image sequences are used.

Self assembly of plasmonic core-satellite nano-assemblies mediated by hyperbranched polymer linkers

The morphology of plasmonic nano-assemblies has a direct influence on optical properties, such as localised surface plasmon resonance (LSPR) and surface enhanced Raman scattering (SERS) intensity. Assemblies with core-satellite morphologies are of particular interest, because this morphology has a high density of hot-spots, while constraining the overall size. Herein, a simple method is reported for the self-assembly of gold NPs nano-assemblies with a core-satellite morphology, which was mediated by hyperbranched polymer (HBP) linkers. The HBP linkers have repeat units that do not interact strongly with gold NPs, but have multiple end-groups that specifically interact with the gold NPs and act as anchoring points resulting in nano-assemblies with a large (~48 nm) core surrounded by smaller (~15 nm) satellites. It was possible to control the number of satellites in an assembly which allowed optical parameters such as SPR maxima and the SERS intensity to be tuned. These results were found to be consistent with finite-difference time domain (FDTD) simulations. Furthermore, the multiplexing of the nano-assemblies with a series of Raman tag molecules was demonstrated, without an observable signal arising from the HBP linker after tagging. Such plasmonic nano-assemblies could potentially serve as efficient SERS based diagnostics or biomedical imaging agents in nanomedicine.

COPD self-management education : Vietnamese nurses' intention to educate, influencing factors and knowledge base

This thesis used a cross-sectional survey to examine how Vietnamese nurses educate COPD patients about the self-management of their disease. Further, the study also describes the characteristics of Vietnamese nurses working in COPD specialty areas as well as examining their COPD knowledge and the factors which influence their delivery of COPD self-management education when caring for COPD patients. The study found that while Vietnamese nurses are likely to deliver self-management education to the COPD patients they care for, there are areas for improvement in nurses' knowledge in all areas of COPD management and self management education.

Exploring the factor structure of a new self- and other-oriented alcohol expectancy scale relating to sexual coercion and vulnerability

Introduction and Aim: Sexual assaults commonly involve alcohol use by the perpetrator, victim, or both. Beliefs about alcohol’s effects may impact on people’s perceptions of and responses to men and women who have had such experiences while intoxicated from alcohol. This study aimed to develop an alcohol expectancy scale that captures young adults’ beliefs about alcohol’s role in sexual aggression and victimisation. Design and Methods: Based on pilot focus groups, an initial pool of 135 alcohol expectancy items was developed, checked for readability and face validity, and administered via a cross-sectional survey to 201 male and female university students (18-25 years). Items were specified in terms of three target drinkers: self, men, and women. In addition, a social desirability measure was included. Results: Principal Axis Factoring revealed a 4-factor solution for the targets men and women and a 5-factor solution for the target self with 72 items retained. Factors related to sexual coercion, sexual vulnerability, confidence, self-centredness, and negative cognitive and behavioural effects. Social desirability issues were evident for the target self, but not for the targets men and women. Discussion and Conclusions: Young adults link alcohol’s effects with sexual vulnerabilities via perceived risky cognitions and behaviours. Due to social desirability, these expectancies may be difficult to explicate for the self but may be accessible instead via other-oriented assessment. The Sexual Coercion and Vulnerability Alcohol Expectancy Scale has potential as a tool to elucidate the established tendency for observers to excuse intoxicated sexual perpetrators while blaming intoxicated victims.

An examination of microfinance self-help groups and the poorest of the poor women in Tamil Nadu, India

This study examined the formation and operation of women's microfinance self-help groups in southern India and investigated whether or not the poorest of the poor women were accepted as members of those groups. The study found that caste was used as a selection criterion. Many eligible women excluded themselves from joining the self-help group due to their own lack of education, age, poor health, poverty and lack of trust in the system. The research revealed that self-help groups enhanced women's income and education, improved village infrastructure, and reduced household conflict. Factors that might prevent inclusion of the poorest of the poor in future microfinance programs were identified.

Maternal feeding self-efficacy and fruit and vegetable intakes in infants. Results from the SAIDI study

Adequate consumption of fruits and vegetables (FV) is a characteristic of a healthy diet but remains a challenge in nutrition interventions. This cross-sectional study explored the multi-directional relationships between maternal feeding self-efficacy, parenting confidence, child feeding behaviour, exposure to new food and FV intake in a cohort of 277 infants. Mothers with healthy infants weighing ≥2500 g and ≥37 weeks gestation were recruited post-natally from 11 South Australian hospitals. Socio-demographic datawere collected at recruitment. At 6 months postnatal, infantswereweighed and measured, andmothers completed a questionnaire exploring their perceptions of child feeding behaviour and child exposure to newfoods. The questionnaire also included the Short Temperament Scale for Infants, Kessler 10 to measure maternal psychological distress and 5 items measuring maternal feeding self-efficacy. The number of occasions and variety of FV (number of subgroups within food groups) consumed by infants were estimated from a 24-hour dietary recall and 2 days food record. Structural equation modellingwas performed using Mplus version 6.11. Median (IQR) variety scores were 2 (1–3) for fruit and 3 (2–5) for vegetable intake. The most popular FV consumed were apple (n = 108, 45.0%) and pumpkin (n = 143, 56.3%). None of the variables studied predicted the variety of child fruit intake. Parenting confidence, exposure to new foods and child feeding behaviourwere indirectly related to child vegetable intake through maternal feeding self-efficacy while total number of children negatively predicted child vegetable variety (p < 0.05). This highlights the need for addressing antecedents of maternal feeding self-efficacy and the family eating environment as key strategies towards development of healthy eating in children.

Measuring the biases in self-reported disability status : Evidence from aggregate data

Self-reported health status measures are generally used to analyse Social Security Disability Insurance's (SSDI) application and award decisions as well as the relationship between its generosity and labour force participation. Due to endogeneity and measurement error, the use of self-reported health and disability indicators as explanatory variables in economic models is problematic. We employ county-level aggregate data, instrumental variables and spatial econometric techniques to analyse the determinants of variation in SSDI rates and explicitly account for the endogeneity and measurement error of the self-reported disability measure. Two surprising results are found. First, it is shown that measurement error is the dominating source of the bias and that the main source of measurement error is sampling error. Second, results suggest that there may be synergies for applying for SSDI when the disabled population is larger. © 2011 Taylor & Francis.

Multi-scale bio-inspired place recognition

This paper presents a novel place recognition algorithm inspired by the recent discovery of overlapping and multi-scale spatial maps in the rodent brain. We mimic this hierarchical framework by training arrays of Support Vector Machines to recognize places at multiple spatial scales. Place match hypotheses are then cross-validated across all spatial scales, a process which combines the spatial specificity of the finest spatial map with the consensus provided by broader mapping scales. Experiments on three real-world datasets including a large robotics benchmark demonstrate that mapping over multiple scales uniformly improves place recognition performance over a single scale approach without sacrificing localization accuracy. We present analysis that illustrates how matching over multiple scales leads to better place recognition performance and discuss several promising areas for future investigation.

Nanoherding : plasma-chemical synthesis and electric-charge-driven self organization of SiO2 nanodots

We report on the chemical synthesis of the arrays of silicon oxide nanodots and their self-organization on the surface via physical processes triggered by surface charges. The method based on chemically active oxygen plasma leads to the rearrangement of nanostructures and eventually to the formation of groups of nanodots. This behavior is explained in terms of the effect of electric field on the kinetics of surface processes. The direct measurements of the electric charges on the surface demonstrate that the charge correlates with the density and arrangement of nanodots within the array. Extensive numerical simulations support the proposed mechanism and prove a critical role of the electric charges in the self-organization. This simple and environment-friendly self-guided process could be used in the chemical synthesis of large arrays of nanodots on semiconducting surfaces for a variety of applications in catalysis, energy conversion and storage, photochemistry, environmental and biosensing, and several others.

Independent learning skills, self-determination theory and psychological well-being : strategies for supporting the first year university experience

The purpose of this article is to explain why the first year in higher education experience of Australian tertiary students can be improved through the explicit teaching of independent learning skills. Becoming an independent learner has many benefits, but the focus of this piece is upon the connection between independent learning and the improvement of student psychological well-being. High psychological distress levels appear to start in the first year of university education. We argue that explicitly teaching students independent learning skills is an important curriculum-based strategy that will contribute to the significant task of addressing this issue.

Self-organized Au nanoarrays on vertical graphenes : an advanced three-dimensional sensing platform

A three-dimensional surface enhanced Raman scattering (SERS)/plasmonic sensing platform based on plasma-enabled, catalyst-free, few-layer vertical graphenes decorated with self-organized Au nanoparticle arrays is demonstrated. This platform is viable for multiple species detection and overcomes several limitations of two-dimensional sensors.

Control of density of self-organized carbon nanotube arrays by catalyst pretreatment through plasma immersion ion implantation

A simple and effective method of controlling the growth of vertically aligned carbon nanotube arrays in a lowerature plasma is presented. Ni catalyst was pretreated by plasma immersion ion implantation prior to the nanotube growth by plasma-enhanced chemical vapor deposition. Both the size distribution and the areal density of the catalyst nanoparticles decrease due to the ion-surface interactions. Consequently, the resulting size distribution of the vertically aligned carbon nanotubes is reduced to 50 ∼ 100 nm and the areal density is lowered (by a factor of ten) to 10 8 cm -2, which is significantly different from the very-high-density carbon nanotube forests commonly produced by thermal chemical vapor deposition. The efficiency of this pretreatment is compared with the existing techniques such as neutral gas annealing and plasma etching. These results are highly relevant to the development of the next-generation nanoelectronic and optoelectronic devices that require effective control of the density of nanotube arrays.

Self-organization in arrays of surface-grown nanoparticles : characterization, control, driving forces

Some important issues related to the self-organization in the arrays of nanoparticles on solid surfaces exposed to the low-temperature plasma are analysed and discussed. The available tools for the characterization of the size and position uniformity in nanoarrays are examined. The technique capable of revealing the realistic adsorbed atom and adsorbed radical capture zone pattern based on the surface physics is indicated as the most promising characterization tool. The processes responsible for the self-organization are analysed, the main driving forces of the self-organization are discussed, and possible ways to control the self-organization by controlling the plasma parameters are introduced. A view on the possible ways to further improve the methods of nanoarray characterization and self-organization is presented as well.