Synthesis of Epitaxial Metal Oxide Nanocrystals via a Phase Separation Approach

Perovskite phase instability of BiMnO3 has been exploited to synthesize epitaxial metal oxide magnetic nanocrystals. Thin film processing conditions are tuned to promote the breakdown of the perovskite precursor into Bi2O3 matrix and magnetic manganese oxide islands. Subsequent cooling in vacuum ensures complete volatization of the Bi2O3, thus leaving behind an array of self-assembled magnetic Mn3O4 nanostructures. Both shape and size can be systematically controlled by the ambient oxygen environments and deposition time.As such, this approach can be extended to any other Bi-based complex ternary oxide system as it primarily hinges on the breakdown of parent Bi-based precursor and subsequent Bi2O3 volatization.

High pressure CO2 absorption studies on imidazolium-based ionic liquids:Experimental and simulation approaches

A combined experimental-computational study on the CO absorption on 1-butyl-3-methylimidazolium hexafluophosphate, 1-ethyl-3-methylimidazolium bis[trifluoromethylsulfonyl]imide, and 1-butyl-3-methylimidazolium bis[trifluoromethylsulfonyl]imide ionic liquids is reported. The reported results allowed to infer a detailed nanoscopic vision of the absorption phenomena as a function of pressure and temperature. Absorption isotherms were measured at 318 and 338K for pressures up to 20MPa for ultrapure samples using a state-of-the-art magnetic suspension densimeter, for which measurement procedures are developed. A remarkable swelling effect upon CO absorption was observed for pressures higher than 10MPa, which was corrected using a method based on experimental volumetric data. The experimental data reported in this work are in good agreement with available literature isotherms. Soave-Redlich-Kwong and Peng-Robinson equations of state coupled with bi-parametric van der Waals mixing rule were used for successful correlations of experimental high pressure absorption data. Molecular dynamics results allowed to infer structural, energetic and dynamic properties of the studied CO+ionic liquids mixed fluids, showing the relevant role of the strength of anion-cation interactions on fluid volumetric properties and CO absorption. © 2012 Elsevier B.V.

Activity Enhancement of Tetrahexahedral Pd Nanocrystals by Bi Decoration towards Ethanol Electrooxidation in Alkaline Media

Tetrahexahedral Pd nanocrystals (THH Pd NCs) were prepared on a glassy carbon electrode using a programmed square-wave potential electrodeposition method, and modified by Bi adatoms with a range of coverages via the cyclic voltammetry method. The reactivity of the catalysts prepared towards ethanol electrooxidation reaction (EOR) was studied in alkaline medium at various temperatures and under other conditions that practical fuel cells operate. Significant activity enhancements were observed for the Bi-modified THH Pd NCs with an optimum Bi coverage (θBi) of around 0.68 being obtained. Furthermore, it was found that increasing temperature from 25 ºC to 60 ºC enhances the reactivity significantly. The general kinetics data of EOR on Bi-decorated and bare THH Pd NCs have also been obtained, from the activation energy calculated based on Arrhenius plots, and compared. At the optimum Bi coverage, an enhancement in the activity of almost 3 times was achieved, and the corresponding activation energy was found to be reduced significantly.

Improved lexicon-based sentiment analysis for social media analytics

Social media channels, such as Facebook or Twitter, allow for people to express their views and opinions about any public topics. Public sentiment related to future events, such as demonstrations or parades, indicate public attitude and therefore may be applied while trying to estimate the level of disruption and disorder during such events. Consequently, sentiment analysis of social media content may be of interest for different organisations, especially in security and law enforcement sectors. This paper presents a new lexicon-based sentiment analysis algorithm that has been designed with the main focus on real time Twitter content analysis. The algorithm consists of two key components, namely sentiment normalisation and evidence-based combination function, which have been used in order to estimate the intensity of the sentiment rather than positive/negative label and to support the mixed sentiment classification process. Finally, we illustrate a case study examining the relation between negative sentiment of twitter posts related to English Defence League and the level of disorder during the organisation’s related events.

Clustering-Based Ensemble Learning for Activity Recognition in Smart Homes

Application of sensor-based technology within activity monitoring systems is becoming a popular technique within the smart environment paradigm. Nevertheless, the use of such an approach generates complex constructs of data, which subsequently requires the use of intricate activity recognition techniques to automatically infer the underlying activity. This paper explores a cluster-based ensemble method as a new solution for the purposes of activity recognition within smart environments. With this approach activities are modelled as collections of clusters built on different subsets of features. A classification process is performed by assigning a new instance to its closest cluster from each collection. Two different sensor data representations have been investigated, namely numeric and binary. Following the evaluation of the proposed methodology it has been demonstrated that the cluster-based ensemble method can be successfully applied as a viable option for activity recognition. Results following exposure to data collected from a range of activities indicated that the ensemble method had the ability to perform with accuracies of 94.2% and 97.5% for numeric and binary data, respectively. These results outperformed a range of single classifiers considered as benchmarks.

Clustering-Based Ensemble Method as an Alternative to Stacking

One of the most popular techniques of generating classifier ensembles is known as stacking which is based on a meta-learning approach. In this paper, we introduce an alternative method to stacking which is based on cluster analysis. Similar to stacking, instances from a validation set are initially classified by all base classifiers. The output of each classifier is subsequently considered as a new attribute of the instance. Following this, a validation set is divided into clusters according to the new attributes and a small subset of the original attributes of the instances. For each cluster, we find its centroid and calculate its class label. The collection of centroids is considered as a meta-classifier. Experimental results show that the new method outperformed all benchmark methods, namely Majority Voting, Stacking J48, Stacking LR, AdaBoost J48, and Random Forest, in 12 out of 22 data sets. The proposed method has two advantageous properties: it is very robust to relatively small training sets and it can be applied in semi-supervised learning problems. We provide a theoretical investigation regarding the proposed method. This demonstrates that for the method to be successful, the base classifiers applied in the ensemble should have greater than 50% accuracy levels.

Predictors of caregiver burden across the home-based palliative care trajectory in Ontario, Canada

Family caregivers of patients enrolled in home-based palliative care programmes provide unpaid care and assistance with daily activities to terminally ill family members. Caregivers often experience caregiver burden, which is an important predictor of anxiety and depression that can extend into bereavement. We conducted a longitudinal, prospective cohort study to comprehensively assess modifiable and non-modifiable patient and caregiver factors that account for caregiver burden over the palliative care trajectory. Caregivers (n = 327) of patients with malignant neoplasm were recruited from two dedicated home-based palliative care programmes in Southern Ontario, Canada from 1 July 2010 to 31 August 2012. Data were obtained from bi-weekly telephone interviews with caregivers from study admission until death, and from palliative care programme and home-care agency databases. Information collected comprised patient and caregiver demographics, utilisation of privately and publicly financed resources, patient clinical status and caregiver burden. The average age of the caregivers was 59.0 years (SD: 13.2), and almost 70% were female. Caregiver burden increased over time in a non-linear fashion from study admission to patient death. Increased monthly unpaid care-giving time costs, monthly public personal support worker costs, emergency department visits and low patient functional status were associated with higher caregiver burden. Greater use of hospice care was associated with lower burden. Female caregivers tended to report more burden compared to men as death approached, and burden was higher when patients were male. Low patient functional status was the strongest predictor of burden. Understanding the influence of modifiable and non-modifiable factors on the experience of burden over the palliative trajectory is essential for the development and targeting of programmes and policies to support family caregivers and reduce burden. Supporting caregivers can have benefits such as improved caregiver health outcomes, and enhancing their ability to meet care-giving demands, thereby potentially allowing for longer patient care in the home setting.