Electrically conductive graphene-filled polymer composites with well organized three-dimensional microstructure

Electrically conductive graphene-filled polystyrene nanocomposites with well-organized three dimensional (3D) microstructures were simply prepared by electrostatic assembly integrated latex technology. First, positively charged polystyrene was synthesized via disperse polymerization in ethanol/water medium by using a cationic co-monomer, and then directly co-assembled with graphene oxide. Eventually, a honeycomb-like graphene 3D framework was embedded in polystyrene matrix after in situ chemical reduction and hot compression molding. Due to the 3D conductive pathway derived from graphene based network evidenced by morphology studies, the fabricated nanocomposites show excellent electrical properties, i.e. extremely low percolation threshold of 0.09 vol% and high saturated conductivity of 25.2 S/m at GNs content of 1.22 vol%. © 2014 Elsevier B.V.

Dynamic cloud service selection using an adaptive learning mechanism in multi-cloud computing

Cloud service selection in a multi-cloud computing environment is receiving more and more attentions. There is an abundance of emerging cloud service resources that makes it hard for users to select the better services for their applications in a changing multi-cloud environment, especially for online real time applications. To assist users to efficiently select their preferred cloud services, a cloud service selection model adopting the cloud service brokers is given, and based on this model, a dynamic cloud service selection strategy named DCS is put forward. In the process of selecting services, each cloud service broker manages some clustered cloud services, and performs the DCS strategy whose core is an adaptive learning mechanism that comprises the incentive, forgetting and degenerate functions. The mechanism is devised to dynamically optimize the cloud service selection and to return the best service result to the user. Correspondingly, a set of dynamic cloud service selection algorithms are presented in this paper to implement our mechanism. The results of the simulation experiments show that our strategy has better overall performance and efficiency in acquiring high quality service solutions at a lower computing cost than existing relevant approaches.

Popularity of reinforcement-based and belief-based learning models: an evolutionary approach

In an evolutionary model, players from a given population meet randomly in pairs each instant to play a coordination game. At each instant, the learning model used is determined via some replicator dynamics that respects payoff fitness. We allow for two such models: a belief-based best-response model that uses a costly predictor, and a costless reinforcement-based one. This generates dynamics over the choice of learning models and the consequent choices of endogenous variables. We report conditions under which the long run outcomes are efficient (or inefficient) and they support the exclusive use of either of the models (or their co-existence).

Primary voltage control of a single-phase inverter using linear quadratic regulator with integrator

This paper proposes a linear quadratic regulator with integral action, ensuring fast dynamic response and resisting capability of voltage deviation from instantaneous reference grid voltage, to control the inverter voltage that can also be used in a microgrid. The proposed control strategy is based on a linear quadratic regulator, minimizing the cost function of the system, with integral action used to impede voltage degradation from a reference voltage for outside disturbances of the system, such as abrupt load change. The combined integral term assists to recover the voltage difference between grid and reference grid voltage. The validity of the proposed controller has been tested with linear and non-linear loads under various conditions. In both cases, the effectiveness of the controller has been proved. The result of the proposed controller is good to track the instantaneous reference grid voltage.

A new evidence-based approach to weight management in primary care: the Counterweight Programme

Background/Aims Obesity has become a global epidemic, and a major preventable cause of morbidity and mortality. Management strategies and treatment protocols are however poorly developed and evaluated. The aim of the Counterweight Programme is to develop an evidence-based model for the management of obesity in primary care.

Methods The Counterweight Programme is based on the theoretical model of Evidence-Based Quality Assessment aimed at improving the management of obese adults (18–75 years) in primary care. The model consists of four phases: (1) practice audit and needs assessment, (2) practice support and training, (3) practice nurse-led patient intervention, and (4) evaluation. Patient intervention consisted of screening and treatment pathways incorporating evidence-based approaches, including patient-centred goal setting, prescribed eating plans, a group programme, physical activity and behavioural approaches, anti-obesity medication and weight maintenance strategies. Weight Management Advisers who are specialist obesity dietitians facilitated programme implementation. Eighty practices were recruited of which 18 practices were randomized to act as controls and receive deferred intervention 2 years after the initial audit.

Results By February 2004, 58 of the 62 (93.5%) intervention practices had been trained to run the intervention programme, 47 (75.8%) practices were active in implementing the model and 1256 patients had been recruited (74% female, 26% male, mean age 50.6 years, SD 14). At baseline, 75% of patients had at one or more co-morbidity, and the mean body mass index (BMI) was 36.9 kg/m2 (SD 5.4). Of the 1256 patients recruited, 91% received one of the core lifestyle interventions in the first 12 months. For all patients followed up at 12 months, 34% achieved a clinical meaningful weight loss of 5% or more. A total of 51% of patients were classed as compliant in that they attended the required level of appointments in 3, 6, and 12 months. For fully compliant patients, weight loss improved with 43% achieving a weight loss of 5% or more at 12 months.

Conclusion The Counterweight Programme is an evidence-based weight management model which is feasible to implement in primary care.