Directional Modulation Far-field Pattern Separation Synthesis Approach

In this study, a far-field power pattern separation approach is proposed for the synthesis of directional modulation (DM) transmitter arrays. Separation into information patterns and interference patterns is enabled by far-field pattern null steering. Compared with other DM synthesis methods, for example, bit error rate-driven DM optimisation and orthogonal vector injection, the approach developed in this study facilitates manipulation of artificial interference spatial distributions. With such capability more interference power can be projected into those spatial directions most vulnerable to eavesdropping, that is, the information side lobes. In such a fashion, information leaked through radiation side lobes can be effectively mitigated in a transmitter power efficient manner. Furthermore, for the first time, the authors demonstrate how multi-beam DM transmitters can be synthesised via this approach.

Pseudomonas aeruginosa exhibits frequent recombination, but only a limited association between genotype and ecological setting

Pseudomonas aeruginosa is an opportunistic pathogen and an important cause of infection, particularly amongst cystic fibrosis (CF) patients. While specific strains capable of patient-to-patient transmission are known, many infections appear to be caused by unique and unrelated strains. There is a need to understand the relationship between strains capable of colonising the CF lung and the broader set of P. aeruginosa isolates found in natural environments. Here we report the results of a multilocus sequence typing (MLST)-based study designed to understand the genetic diversity and population structure of an extensive regional sample of P. aeruginosa isolates from South East Queensland, Australia. The analysis is based on 501 P. aeruginosa isolates obtained from environmental, animal and human (CF and non-CF) sources with particular emphasis on isolates from the Lower Brisbane River and isolates from CF patients obtained from the same geographical region. Overall, MLST identified 274 different sequence types, of which 53 were shared between one or more ecological settings. Our analysis revealed a limited association between genotype and environment and evidence of frequent recombination. We also found that genetic diversity of P. aeruginosa in Queensland, Australia was indistinguishable from that of the global P. aeruginosa population. Several CF strains were encountered frequently in multiple ecological settings; however, the most frequently encountered CF strains were confined to CF patients. Overall, our data confirm a non-clonal epidemic structure and indicate that most CF strains are a random sample of the broader P. aeruginosa population. The increased abundance of some CF strains in different geographical regions is a likely product of chance colonisation events followed by adaptation to the CF lung and horizontal transmission among patients.

Pattern Recognition Approach for Road Collision Hotspots Analysis: Case Study of Northern Ireland

In order to address road safety effectively, it is essential to understand all the factors, which
attribute to the occurrence of a road collision. This is achieved through road safety
assessment measures, which are primarily based on historical crash data. Recent advances
in uncertain reasoning technology have led to the development of robust machine learning
techniques, which are suitable for investigating road traffic collision data. These techniques
include supervised learning (e.g. SVM) and unsupervised learning (e.g. Cluster Analysis).
This study extends upon previous research work, carried out in Coll et al. [3], which
proposed a non-linear aggregation framework for identifying temporal and spatial hotspots.
The results from Coll et al. [3] identified Lisburn area as the hotspot, in terms of road safety,
in Northern Ireland. This study aims to use Cluster Analysis, to investigate and highlight any
hidden patterns associated with collisions that occurred in Lisburn area, which in turn, will
provide more clarity in the causation factors so that appropriate countermeasures can be put
in place.

Characterisation of the frequent exacerbator phenotype in COPD patients in a large UK primary care population

BACKGROUND: The 'frequent exacerbator' is recognised as an important phenotype in COPD. Current understanding about this phenotype comes from prospective longitudinal clinical trials in secondary/tertiary care with little information reported in primary care populations.

AIMS: To characterize the frequent-exacerbator phenotype and identify associated risk factors in a large UK primary care COPD population.

METHODS: Using a large database of primary care patients from 80 UK general practices, patients were categorised using GOLD 2014 criteria into high and low risk groups based on exacerbation history. A multivariate logistic regression model was used to investigate covariates associated with the frequent-exacerbator phenotype and risk of experiencing a severe exacerbation (leading to hospitalisation).

RESULTS: Of the total study population (n = 9219), 2612 (28%) fulfilled the criteria for high risk frequent-exacerbators. Independent risk factors (adjusted odds ratio [95% CI]) for ≥2 exacerbations were: most severely impaired modified Medical Research Council (mMRC) dyspnoea score (mMRC grade 4: 4.37 [2.64-7.23]), lower FEV1 percent predicted (FEV1 <30%: 2.42 [1.61-3.65]), co-morbid cardiovascular disease (1.42 [1.19-1.68]), depression (1.56 [1.22-1.99]) or osteoporosis (1.54 [1.19-2.01]), and female gender (1.20 [1.01-1.43]). Older patients (≥75 years), those with most severe lung impairment (FEV1 <30%), those with highest mMRC score and those with co-morbid osteoporosis were identified as most at risk of experiencing exacerbations requiring hospitalisation.

CONCLUSIONS: Although COPD exacerbations occur across all grades of disease severity, female patients with high dyspnoea scores, more severely impaired lung function and co-morbidities are at greatest risk. Elderly patients, with severely impaired lung function, high mMRC scores and osteoporosis are associated with experience of severe exacerbations requiring hospitalisation.

Pool Evolution: A Parallel Pattern for Evolutionary and Symbolic Computing

We introduce a new parallel pattern derived from a specific application domain and show how it turns out to have application beyond its domain of origin. The pool evolution pattern models the parallel evolution of a population subject to mutations and evolving in such a way that a given fitness function is optimized. The pattern has been demonstrated to be suitable for capturing and modeling the parallel patterns underpinning various evolutionary algorithms, as well as other parallel patterns typical of symbolic computation. In this paper we introduce the pattern, we discuss its implementation on modern multi/many core architectures and finally present experimental results obtained with FastFlow and Erlang implementations to assess its feasibility and scalability.

Ecological dynamics of extinct species in empty habitat networks. 1. The role of habitat pattern and quantity, stochasticity and dispersal

We examined a remnant host plant (Primula veris L.) habitat network that was last inhabited by the rare butterfly Hamearis lucina L. in north Wales in 1943, to assess the relative contribution of several spatial parameters to its regional extinction. We first examined relationships between P. veris characteristics and H. lucina eggs in surviving H. lucina populations, and used these to predict the suitability and potential carrying capacity of the habitat network in north Wales. This resulted in an estimate of roughly 4500 eggs (ca 227 adults). We developed a discrete space, discrete time metapopulation model to evaluate the relative contribution of dispersal distance, habitat and environmental stochasticity as possible causes of extinction. We simulated the potential persistence of the butterfly in the current network as well as in three artificial (historical and present) habitat networks that differed in the quantity (current and X3) and fragmentation of the habitat (current and aggregated). We identified that reduced habitat quantity and increased isolation would have increased the probability of regional extinction, in conjunction with environmental stochasticity and H. lucina's dispersal distance. This general trend did not change in a qualitative manner when we modified the ability of dispersing females to stay in, and find suitable habitats (by changing the size of the grid cells used in the model). Contrary to most metapopulation model predictions, system persistence declined with increasing migration rate, suggesting that the mortality of migrating individuals in fragmented landscapes may pose significant risks to system-wide persistence. Based on model predictions for the present landscape we argue that a major programme of habitat restoration would be required for a re-established metapopulation to persist for > 100 years.

Conical Horn Antenna with Spiral Phase Plate for Difference Pattern Generation

This paper describes the performance characteristics and experimental validation of a compact conical horn antenna with a dielectric cylinder spiral phase plate attached at its aperture. This performs the function of a spatial phase imprinting device creating a helical wave-front which results in a null in the far field radiation pattern of the antenna assembly.

A Far-field Pattern Separation Approach for the Synthesis of Directional Modulation Transmitter Arrays (Invited)

In this paper a far-field power pattern separation approach is proposed for the synthesis of directional modulation (DM) transmitter arrays. Separation into information pattern and interference patterns is enabled by far-field pattern null steering. Compared with other DM synthesis methods, e.g., BER-driven DM optimization and orthogonal vector injection, this approach facilitates manipulation of artificial interference spatial distributions. With such capability more interference power can be projected into those most vulnerable to eavesdropping spatial directions in free space, i.e., the information sidelobes. In such a fashion information leaked through radiation sidelobes can be effectively mitigated in a transmitter power efficient manner. The proposed synthesis approach is further validated via bit error rate (BER) simulations.

Handling Sequential Observations in Intelligent Surveillance

Demand for intelligent surveillance in public transport systems is growing due to the increased threats of terrorist attack, vandalism and litigation. The aim of intelligent surveillance is in-time reaction to information received from various monitoring devices, especially CCTV systems. However, video analytic algorithms can only provide static assertions, whilst in reality, many related events happen in sequence and hence should be modeled sequentially. Moreover, analytic algorithms are error-prone, hence how to correct the sequential analytic results based on new evidence (external information or later sensing discovery) becomes an interesting issue. In this paper, we introduce a high-level sequential observation modeling framework which can support revision and update on new evidence. This framework adapts the situation calculus to deal with uncertainty from analytic results. The output of the framework can serve as a foundation for event composition. We demonstrate the significance and usefulness of our framework with a case study of a bus surveillance project.

Phenotypic characterization of an international Pseudomonas aeruginosa reference panel: strains of cystic fibrosis (CF) origin show less in vivo virulence than non-CF strains

Pseudomonas aeruginosa causes chronic lung infections in people with cystic fibrosis (CF) and acute opportunistic infections in people without CF. Forty two P. aeruginosa strains from a range of clinical and environmental sources were collated into a single reference strain panel to harmonise research on this diverse opportunistic pathogen. To facilitate further harmonized and comparable research on P. aeruginosa, we characterised the panel strains for growth rates, motility, virulence in the Galleria mellonella infection model, pyocyanin and alginate production, mucoid phenotype, lipopolysaccharide (LPS) pattern, biofilm formation, urease activity, antimicrobial and phage susceptibilities. Phenotypic diversity across the P. aeruginosa panel was apparent for all phenotypes examined agreeing with the marked variability seen in this species. However, except for growth rate, the phenotypic diversity among strains from CF versus non-CF sources was comparable. CF strains were less virulent in the G. mellonella model than non-CF strains (p=0.037). Transmissible CF strains generally lacked O antigen, produced less pyocyanin, and had low virulence in G. mellonella. Further, in the three sets of sequential CF strains, virulence, O-antigen expression and pyocyanin production were higher in the earlier isolate compared to the isolate obtained later in infection. Overall, full phenotypic characterization of the defined panel of P. aeruginosa strains increases our understanding of the virulence and pathogenesis of P. aeruginosa and may provide a valuable resource for the testing of novel therapies against this problematic pathogen.

A Continuous Time Markov Chain Based Sequential Analytical Approach for Composite Power System Reliability Assessment

This paper proposes a continuous time Markov chain (CTMC) based sequential analytical approach for composite generation and transmission systems reliability assessment. The basic idea is to construct a CTMC model for the composite system. Based on this model, sequential analyses are performed. Various kinds of reliability indices can be obtained, including expectation, variance, frequency, duration and probability distribution. In order to reduce the dimension of the state space, traditional CTMC modeling approach is modified by merging all high order contingencies into a single state, which can be calculated by Monte Carlo simulation (MCS). Then a state mergence technique is developed to integrate all normal states to further reduce the dimension of the CTMC model. Moreover, a time discretization method is presented for the CTMC model calculation. Case studies are performed on the RBTS and a modified IEEE 300-bus test system. The results indicate that sequential reliability assessment can be performed by the proposed approach. Comparing with the traditional sequential Monte Carlo simulation method, the proposed method is more efficient, especially in small scale or very reliable power systems.