Violence risk assessment in psychiatric patients in China: a systematic review

Objectives: The aim of this study was to undertake a systematic review on violence risk assessment instruments used for psychiatric patients in China.

Methods: A systematic search was conducted from 1980 until 2014 to identify studies that used psychometric tools or structured instruments to assess aggression and violence risk. Information from primary studies was extracted, including demographic characteristics of the samples used, study design characteristics, and reliability and validity estimates.

Results: A total of 30 primary studies were identified that investigated aggression or violence; 6 reported on tools assessing aggression while an additional 24 studies reported on structured instruments designed to predict violence. Although measures of reliability were typically good, estimates of predictive validity were mostly in the range of poor to moderate, with only 1 study finding good validity. These estimates were typically lower than that found in previous work for Western samples.

Conclusion: There is currently little evidence to support the use of current violence risk assessment instruments in psychiatric patients in China. Developing more accurate and scalable approaches are research priorities.

Synthesis of an indium oxide nanoparticle embedded graphene three-dimensional architecture for enhanced lithium-ion storage

Indium oxide nanoparticles were synthesised by using a facile and scalable strategy. The as-prepared nanoparticles (20-40 nm) were in situ and homogeneously distributed in a three-dimensional (3D) graphene architecture subsequently during the fabrication process. The obtained nanocomposite acts as a high capacity anode material for lithium-ion batteries and demonstrates good cycle stability. A drastically enhanced capacity of 750 mA h g^-1 in comparison with that of bare In2O3 nanoparticles can be maintained after 100 cycles, along with an improved high rate performance (210 mA h g^-1 at 1 A g^-1 and 120 mA h g^-1 at 2 A g^-1). The excellent performance is linked with the indium oxide nanoparticles and the unique 3D interconnected porous graphene structure. The highly conductive and porous 3D graphene structure greatly enhances the performance of lithium-ion batteries by protecting the nanoparticles from the electrolyte, stabilizing the nanoparticles during cycles and buffering the volume expansion upon lithium insertion.

The extended Infant Feeding, Activity and Nutrition Trial (InFANT Extend) Program : a cluster-randomized controlled trial of an early intervention to prevent childhood obesity

BACKGROUND: Understanding how we can prevent childhood obesity in scalable and sustainable ways is imperative. Early RCT interventions focused on the first two years of life have shown promise however, differences in Body Mass Index between intervention and control groups diminish once the interventions cease. Innovative and cost-effective strategies seeking to continue to support parents to engender appropriate energy balance behaviours in young children need to be explored. METHODS/DESIGN: The Infant Feeding Activity and Nutrition Trial (InFANT) Extend Program builds on the early outcomes of the Melbourne InFANT Program. This cluster randomized controlled trial will test the efficacy of an extended (33 versus 15 month) and enhanced (use of web-based materials, and Facebook® engagement), version of the original Melbourne InFANT Program intervention in a new cohort. Outcomes at 36 months of age will be compared against the control group. DISCUSSION: This trial will provide important information regarding capacity and opportunities to maximize early childhood intervention effectiveness over the first three years of life. This study continues to build the evidence base regarding the design of cost-effective, scalable interventions to promote protective energy balance behaviors in early childhood, and in turn, promote improved child weight and health across the life course. TRIAL REGISTRATION: ACTRN12611000386932 . Registered 13 April 2011.

Novel approach for information discovery in autonomous wireless sensor networks

Autonomous Wireless sensor networks(WSNs) have sensors that are usually deployed randomly to monitor one or more phenomena. They are attractive for information discovery in large-scale data rich environments and can add value to mission–critical applications such as battlefield surveillance and emergency response systems. However, in order to fully exploit these networks for such applications, energy efficient, load balanced and scalable solutions for information discovery are essential. Multi-dimensional autonomous WSNs are deployed in complex environments to sense and collect data relating to multiple attributes (multi-dimensional data). Such networks present unique challenges to data dissemination, data storage of in-network information discovery. In this paper, we propose a novel method for information discovery for multi-dimensional autonomous WSNs which sensors are deployed randomly that can significantly increase network lifetime and minimize query processing latency, resulting in quality of service (QoS) improvements that are of immense benefit to mission–critical applications. We present simulation results to show that the proposed approach to information discovery offers significant improvements on query resolution latency compared with current approaches.

Cost-effective authentic and anonymous data sharing with forward security

Data sharing has never been easier with the advances of cloud computing, and an accurate analysis on the shared data provides an array of benefits to both the society and individuals. Data sharing with a large number of participants must take into account several issues, including efficiency, data integrity and privacy of data owner. Ring signature is a promising candidate to construct an anonymous and authentic data sharing system. It allows a data owner to anonymously authenticate his data which can be put into the cloud for storage or analysis purpose. Yet the costly certificate verification in the traditional public key infrastructure (PKI) setting becomes a bottleneck for this solution to be scalable. Identity-based (ID-based) ring signature, which eliminates the process of certificate verification, can be used instead. In this paper, we further enhance the security of ID-based ring signature by providing forward security: If a secret key of any user has been compromised, all previous generated signatures that include this user still remain valid. This property is especially important to any large scale data sharing system, as it is impossible to ask all data owners to re-authenticate their data even if a secret key of one single user has been compromised. We provide a concrete and efficient instantiation of our scheme, prove its security and provide an implementation to show its practicality.

Prediciton of emergency events: a multi-task multi-label learning approach

Prediction of patient outcomes is critical to plan resources in an hospital emergency department. We present a method to exploit longitudinal data from Electronic Medical Records (EMR), whilst exploiting multiple patient outcomes. We divide the EMR data into segments where each segment is a task, and all tasks are associated with multiple patient outcomes over a 3, 6 and 12 month period. We propose a model that learns a prediction function for each task-label pair, interacting through two subspaces: the first subspace is used to impose sharing across all tasks for a given label. The second subspace captures the task-specific variations and is shared across all the labels for a given task. The proposed model is formulated as an iterative optimization problems and solved using a scalable and efficient Block co-ordinate descent (BCD) method. We apply the proposed model on two hospital cohorts - Cancer and Acute Myocardial Infarction (AMI) patients collected over a two year period from a large hospital emergency department. We show that the predictive performance of our proposed models is significantly better than those of several state-of-the-art multi-task and multi-label learning methods.

Small-variance asymptotics for bayesian nonparametric models with constraints

The users often have additional knowledge when Bayesian nonparametric models (BNP) are employed, e.g. for clustering there may be prior knowledge that some of the data instances should be in the same cluster (must-link constraint) or in different clusters (cannot-link constraint), and similarly for topic modeling some words should be grouped together or separately because of an underlying semantic. This can be achieved by imposing appropriate sampling probabilities based on such constraints. However, the traditional inference technique of BNP models via Gibbs sampling is time consuming and is not scalable for large data. Variational approximations are faster but many times they do not offer good solutions. Addressing this we present a small-variance asymptotic analysis of the MAP estimates of BNP models with constraints. We derive the objective function for Dirichlet process mixture model with constraints and devise a simple and efficient K-means type algorithm. We further extend the small-variance analysis to hierarchical BNP models with constraints and devise a similar simple objective function. Experiments on synthetic and real data sets demonstrate the efficiency and effectiveness of our algorithms.

Design and fabrication of a capacitance based wearable pressure sensor using e-textiles

This paper addresses the methods used for the design and fabrication of a capacitance based wearable pressure sensor fabricated using neoprene and (SAC) plated Nylon Fabric. The experimental set up for the pressure sensor is comprised of a shielded grid of sensing modules, a 555 timer based transduction circuitry, and an Arduino board measuring the frequency of signal to a corresponding pressure. The fundamental design parameters addressed during the development of the pressure sensor presented in this paper are based on size, simplicity, cost, adaptability, and scalability. The design approach adopted in this paper results in a sensor module that is less obtrusive, has a thinner and flexible profile, and its sensitivity is easily scalable for ‘smart’ product applications across industries associated to sports performance, ergonomics, rehabilitation, etc.

Big data for networking

Big data analytics has shown great potential in optimizing operations, making decisions, spotting business trends, preventing threats, and capitalizing on new sources of revenues in various fields such as manufacturing, healthcare, finance, insurance, and retail. The management of various networks has become inefficient and difficult because of their high complexities and interdependencies. Big data, in forms of device logs, software logs, media content, and sensed data, provide rich information and facilitate a fundamentally different and novel approach to explore, design, and develop reliable and scalable networks. This Special Issue covers the most recent research results that address challenges of big data for networking. We received 45 submissions, and ultimately nine high quality papers, organized into two groups, have been selected for inclusion in this Special Issue.