Key performance indicators – A new approach for specifying concrete and assessing state of health of concrete structure

The focus of this paper is to outline a method for consolidating and implementing the work on performance-based specification and testing. First part of the paper will review the mathematical significance of the variables used in common service life models. The aim is to identify a set of significant variables that influence the ingress of chloride ions into concrete. These variables are termed as Key Performance Indicators (KPI’s). This will also help to reduce the complexity of some of the service life models and make them more appealing for practicing engineers. The second part of the paper presents a plan for developing a database based on these KPI’s so that relationships can then be drawn between common concrete mix parameters and KPI’s. This will assist designers in specifying a concrete with adequate performance for a particular environment. This, collectively, is referred to as the KPI based approach and the concluding remarks will outline how the authors envisage the KPI theory to relate to performance assessment and monitoring.

Views of pharmacy graduates and pharmacist tutors on evidence-based practice in relation to over-the-counter consultations: a qualitative study

RATIONALE, AIMS AND OBJECTIVES: Health care services offered to the public should be based on the best available evidence. We aimed to explore pharmacy tutors' and trainees' views on the importance of evidence when making decisions about over-the-counter (OTC) medicines and also to investigate whether the tutor influenced the trainee in practice.

METHODS: Following ethical approval and piloting, semi-structured interviews were conducted with pharmacy graduates (trainees) and pharmacist tutors. Transcribed interview data were entered into the NVivo software package (version 10), coded and analysed via thematic analysis.

RESULTS: Twelve trainees (five males, seven females) and 11 tutors (five males, six females) participated. Main themes that emerged were (in)consistency and contradiction, confidence, acculturation, and continuation and perpetuation. Despite having an awareness of the importance and potential benefits, an evidence-based approach did not seem to be routinely or consistently implemented in practice. Confidence in products was largely derived from personal use and patient feedback. A lack of discussion about evidence was justified on the basis of not wanting to lessen patient confidence in requested product(s) or possibly negating the placebo effect. Trainees became acculturated to 'real-life' practice; university teaching and evidence was deemed less relevant than meeting customer expectations. The tutor's actions were mirrored by their trainee resulting in continuation and perpetuation of the same professional attitudes and behaviours.

CONCLUSIONS: Evidence appeared to have limited influence on OTC decision making. The tutor played a key role in the trainee's professional development. More work could be performed to investigate how evidence can be regarded as relevant and something that is consistently implemented in practice.

Region-based Mixture of Gaussians Modelling for Foreground Detection in Dynamic Scenes

One of the most widely used techniques in computer vision for foreground detection is to model each background pixel as a Mixture of Gaussians (MoG). While this is effective for a static camera with a fixed or a slowly varying background, it fails to handle any fast, dynamic movement in the background. In this paper, we propose a generalised framework, called region-based MoG (RMoG), that takes into consideration neighbouring pixels while generating the model of the observed scene. The model equations are derived from Expectation Maximisation theory for batch mode, and stochastic approximation is used for online mode updates. We evaluate our region-based approach against ten sequences containing dynamic backgrounds, and show that the region-based approach provides a performance improvement over the traditional single pixel MoG. For feature and region sizes that are equal, the effect of increasing the learning rate is to reduce both true and false positives. Comparison with four state-of-the art approaches shows that RMoG outperforms the others in reducing false positives whilst still maintaining reasonable foreground definition. Lastly, using the ChangeDetection (CDNet 2014) benchmark, we evaluated RMoG against numerous surveillance scenes and found it to amongst the leading performers for dynamic background scenes, whilst providing comparable performance for other commonly occurring surveillance scenes.

A Reliability Assessment Approach for the Urban Energy System and Its Application in Energy Hub Planning

With the increasing utilization of combined heat and power plants (CHP), electrical, gas, and thermal systems are becoming tightly integrated in the urban energy system (UES). However, the three systems are usually planned and operated separately, ignoring their interactions and coordination. To address this issue, the coupling point of different systems in the UES is described by the energy hub model. With this model, an integrated load curtailment method is proposed for the UES. Then a Monte Carlo simulation based approach is developed to assess the reliability of coordinated energy supply systems. Based on this approach, a reliability-optimal energy hub planning method is proposed to accommodate higher renewable energy penetration. Numerical studies indicate that the proposed approach is able to quantify the UES reliability with different structures. Also, optimal energy hub planning scheme can be determined to ensure the reliability of the UES with high renewable penetration.

Functional identity and diversity of animals predict ecosystem functioning better than species-based indices

Drastic biodiversity declines have raised concerns about the deterioration of ecosystem functions and have motivated much recent research on the relationship between species diversity and ecosystem functioning. A functional trait framework has been proposed to improve the mechanistic understanding of this relationship, but this has rarely been tested for organisms other than plants. We analysed eight datasets, including five animal groups, to examine how well a trait-based approach, compared with a more traditional taxonomic approach, predicts seven ecosystem functions below- and above-ground. Trait-based indices consistently provided greater explanatory power than species richness or abundance. The frequency distributions of single or multiple traits in the community were the best predictors of ecosystem functioning. This implies that the ecosystem functions we investigated were underpinned by the combination of trait identities (i.e. single-trait indices) and trait complementarity (i.e. multi-trait indices) in the communities. Our study provides new insights into the general mechanisms that link biodiversity to ecosystem functioning in natural animal communities and suggests that the observed responses were due to the identity and dominance patterns of the trait composition rather than the number or abundance of species per se.

Android Malware Detection: an Eigenspace Analysis Approach

The battle to mitigate Android malware has become more critical with the emergence of new strains incorporating increasingly sophisticated evasion techniques, in turn necessitating more advanced detection capabilities. Hence, in this paper we propose and evaluate a machine learning based approach based on eigenspace analysis for Android malware detection using features derived from static analysis characterization of Android applications. Empirical evaluation with a dataset of real malware and benign samples show that detection rate of over 96% with a very low false positive rate is achievable using the proposed method.

Heller's myotomy and pneumatic dilatation in the treatment of achalasia: a population-based case–control study assessing long-term quality of life

Long-term health-related quality-of-life (HRQL) outcomes have not been widely reported in the
treatment of achalasia. The aims of this study were to examine long-term disease-specific and general HRQL in
achalasia patients using a population-based case–control method, and to assess HRQL between treatment interventions.
Manometrically diagnosed achalasia cases (n = 120) were identified and matched with controls (n = 115)
using a population-based approach. Participants completed general (SF-12) and disease-specific (Achalasia Severity
Questionnaire [ASQ]) HRQL questionnaires, as appropriate, in a structured interview. Mean composite scores
for SF-12 (Mental Component Summary score [MCS-12] and Physical Component Summary score [PCS-12]) and
ASQ were compared between cases and controls, or between intervention groups, using an independent t-test.
Adjusted mean differences in HRQL scores were evaluated using a linear regression model. Achalasia cases were
treated with a Heller’s myotomy (n = 43), pneumatic dilatation (n = 44), or both modalities (n = 33). The median
time from last treatment to HRQL assessment was 5.7 years (interquartile range 2.4–11.5). Comparing achalasia
patients with controls, PCS-12 was significantly worse (40.9 vs. 44.2, P = 0.01), but MCS-12 was similar. However,
both PCS-12 (39.9 vs. 44.2, P = 0.03) and MCS-12 (46.7 vs. 53.5, P = 0.004) were significantly impaired in those
requiring dual treatment compared with controls. Overall however, there was no difference in adjusted HRQL
between patients treated with Heller’s myotomy, pneumatic dilatation or both treatment modalities. In summary,
despite treatment achalasia patients have significantly worse long-term physical HRQL compared with population
controls. No HRQL differences were observed between the treatment modalities to suggest a benefit of one
treatment over another.

A wavelet based drive-by bridge inspection system

This paper investigates a low-cost wavelet-based approach for the preliminary monitoring of bridge structures, consisting of the use of a vehicle fitted with accelerometers on its axles. The approach aims to reduce the need for direct instrumentation of the bridge. A time-frequency analysis is carried out in order to identify the existence and location of damage from vehicle accelerations. Firstly, in theoretical simulations, a simplified vehicle-bridge interaction model is used to investigate the effectiveness of the approach. A number of damage indicators are evaluated and compared. A range of parameters such as the bridge span, vehicle speed, damage level and location, signal noise and road roughness are varied in simulations. Secondly, a scaled laboratory experiment is carried out to validate the results of the theoretical analysis and assess the ability of the selected damage indicators to detect changes in the bridge response from vehicle accelerations. 

Dynamic Distance-Based Shape Features for Gait Recognition

We propose a novel skeleton-based approach to gait recognition using our Skeleton Variance Image. The core of our approach consists of employing the screened Poisson equation to construct a family of smooth distance functions associated with a given shape. The screened Poisson distance function approximation nicely absorbs and is relatively stable to shape boundary perturbations which allows us to define a rough shape skeleton. We demonstrate how our Skeleton Variance Image is a powerful gait cycle descriptor leading to a significant improvement over the existing state of the art gait recognition rate.

Modular approach to select bacteriophages targeting Pseudomonas aeruginosa for their application to children suffering with cystic fibrosis

This review discusses the potential application of bacterial viruses (phage therapy) towards the eradication of antibiotic resistant Pseudomonas aeruginosa in children with cystic fibrosis (CF). In this regard, several potential relationships between bacteria and their bacteriophages are considered. The most important aspect that must be addressed with respect to phage therapy of bacterial infections in the lungs of CF patients is in ensuring the continuity of treatment in light of the continual occurrence of resistant bacteria. This depends on the ability to rapidly select phages exhibiting an enhanced spectrum of lytic activity among several well-studied phage groups of proven safety. We propose a modular based approach, utilizing both mono-species and hetero-species phage mixtures. With an approach involving the visual recognition of characteristics exhibited by phages of well studied phage groups on lawns of the standard P. aeruginosa PAO1 strain, the simple and rapid enhancement of the lytic spectrum of cocktails is permitted, allowing the development of tailored preparations for patients capable of circumventing problems associated with phage resistant bacterial mutants.