A multi-genome analysis approach enables tracking of the invasion of a single Russian wheat aphid (Diuraphis noxia) clone throughout the New World

This study investigated the population genetics, demographic history and pathway of invasion of the Russian wheat aphid (RWA) from its native range in Central Asia, the Middle East and Europe to South Africa and the Americas. We screened microsatellite markers, mitochondrial DNA and endosymbiont genes in 504 RWA clones from nineteen populations worldwide. Following pathway analyses of microsatellite and endosymbiont data, we postulate that Turkey and Syria were the most likely sources of invasion to Kenya and South Africa, respectively. Furthermore, we found that one clone transferred between South Africa and the Americas was most likely responsible for the New World invasion. Finally, endosymbiont DNA was found to be a high resolution population genetic marker, extremely useful for studies of invasion over a relatively short evolutionary history time frame. This study has provided valuable insights into the factors that may have facilitated the recent global invasion by this damaging pest.

A multi-hierarchical motorway ramp signalling strategy

Ramp signalling is an access control for motorways, in which a traffic signal is placed at on-ramps to regulate the rate of vehicles entering the motorway and thus to preserve the motorway capacity. In general, ramp signalling algorithms fall into two categories: local control and coordinated control by their effective scope. Coordinated ramp signalling strategies make use of measurements from the entire motorway network to operate individual ramp signals for the optimal performances at the network level. This study proposes a multi-hierarchical strategy for coordinated ramp signalling. The strategy is structured in two layers. At the higher layer with a longer update interval, coordination group is assembled and disassembled based on the location of high-risk breakdown flow. At the lower layer with a shorter update interval, individual ramps are hired to serve the coordination and are also released based on the prevailing congestion level on the ramp. This strategy is modelled and applied to the northbound Pacific Motorway micro-simulation platform (AIMSUN). The simulation results show an effective congestion mitigation of the proposed strategy.

Proton-transfer and non-transfer compounds of the multi-purpose drug dapsone [4-(4-Aminophenylsulfonyl)aniline] with 3,5-dinitrosalicylic acid and 5-nitroisophthalic acid

The structures of the compounds from the reaction of the drug dapsone [4-(4-aminophenylsulfonyl)aniline] with 3,5-dinitrosalicylic acid, the salt hydrate [4-(4-aminohenylsulfonyl)anilinium 2-carboxy-4,6-dinitrophenolate monohydrate] (1) and the 1:1 adduct with 5-nitroisophthalic acid [4-(4-aminophenylsulfonyl)aniline 5-nitrobenzene-1,3-dicarboxylic acid] (2) have been determined. Crystals of 1 are triclinic, space group P-1, with unit cell dimensions a = 8.2043(3), b = 11.4000(6), c = 11.8261(6)Å, α = 110.891(5), β = 91.927(3), γ = 98.590(4)deg. and Z = 4. Compound 2 is orthorhombic, space group Pbcn, with unit cell dimensions a = 20.2662(6), b = 12.7161(4), c = 15.9423(5)Å and Z = 8. In 1, intermolecular analinium N-H…O and water O-H…O and O-H…N hydrogen-bonding interactions with sulfone, carboxyl, phenolate and nitro O-atom and aniline N-atom acceptors give a two-dimensional layered structure. With 2, the intermolecular interactions involve both aniline N-H…O and carboxylic acid O-H…O and O-H…N hydrogen bonds to sulfone, carboxyl, nitro and aniline acceptors, giving a three-dimensional network structure. In both structures π--π aromatic ring associations are present.

The IKBALS project: multi-modal reasoning in legal knowledge based systems

In attempting to build intelligent litigation support tools, we have moved beyond first generation, production rule legal expert systems. Our work integrates rule based and case based reasoning with intelligent information retrieval. When using the case based reasoning methodology, or in our case the specialisation of case based retrieval, we need to be aware of how to retrieve relevant experience. Our research, in the legal domain, specifies an approach to the retrieval problem which relies heavily on an extended object oriented/rule based system architecture that is supplemented with causal background information. We use a distributed agent architecture to help support the reasoning process of lawyers. Our approach to integrating rule based reasoning, case based reasoning and case based retrieval is contrasted to the CABARET and PROLEXS architectures which rely on a centralised blackboard architecture. We discuss in detail how our various cooperating agents interact, and provide examples of the system at work. The IKBALS system uses a specialised induction algorithm to induce rules from cases. These rules are then used as indices during the case based retrieval process. Because we aim to build legal support tools which can be modified to suit various domains rather than single purpose legal expert systems, we focus on principles behind developing legal knowledge based systems. The original domain chosen was theAccident Compensation Act 1989 (Victoria, Australia), which relates to the provision of benefits for employees injured at work. For various reasons, which are indicated in the paper, we changed our domain to that ofCredit Act 1984 (Victoria, Australia). This Act regulates the provision of loans by financial institutions. The rule based part of our system which provides advice on the Credit Act has been commercially developed in conjunction with a legal firm. We indicate how this work has lead to the development of a methodology for constructing rule based legal knowledge based systems. We explain the process of integrating this existing commercial rule based system with the case base reasoning and retrieval architecture.

Built heritage management systems : the framework of a digital tool for the conservation of Brisbane City Hall

Brisbane City Hall (BCH) is arguably one of Brisbane’s most notable and iconic buildings. Serving as the public’s central civic and municipal building since 1930, the importance of this heritage listed building to cultural significance and identity is unquestionable. This attribute is reflected within the local government, with a simplified image of the halls main portico entrance supplying Brisbane City Council with its insignia and trademark signifier. Regardless of these qualities, this building has been neglected in a number of ways, primarily in the physical sense with built materials, but also, and just as importantly, through inaccurate and undocumented works. Numerous restoration and renovation works have been undertaken throughout BCH’s lifetime, however the records of these amendments are far and few between. Between 2010 and 2013, BCH underwent major restoration works, the largest production project undertaken on the building since its initial construction. Just prior to this conservation process, the full extent of the buildings deterioration was identified, much of which there was little to no original documentation of. This has led to a number of issues pertaining to what investigators expected to find within the building, versus what was uncovered (the unexpected), which have resulted directly from this lack of data. This absence of record keeping is the key factor that has contributed to the decay and unknown deficiencies that had amassed within BCH. Accordingly, this raises a debate about the methods of record keeping, and the need for a more advanced process that is able to be integrated within architectural and engineering programs, whilst still maintaining the ability to act as a standalone database. The immediate objective of this research is to investigate the restoration process of BCH, with focus on the auditorium, to evaluate possible strategies to record and manage data connected to building pathology so that a framework can be developed for a digital heritage management system. The framework produced for this digital tool will enable dynamic uses of a centralised database and aims to reduce the significant data loss. Following an in-depth analysis of this framework, it can be concluded that the implementation of the suggested digital tool would directly benefit BCH, and could ultimately be incorporated into a number of heritage related built form.

Interpretation of association rules with multi-tier granule mining

This study was a step forward to improve the performance for discovering useful knowledge – especially, association rules in this study – in databases. The thesis proposed an approach to use granules instead of patterns to represent knowledge implicitly contained in relational databases; and multi-tier structure to interpret association rules in terms of granules. Association mappings were proposed for the construction of multi-tier structure. With these tools, association rules can be quickly assessed and meaningless association rules can be justified according to the association mappings. The experimental results indicated that the proposed approach is promising.

The relevance of shared experiences : a multi-level study of collective efficacy as a moderator of job control in the stressor-strain relationship

Karasek's Job Demand-Control model proposes that control mitigates the positive effects of work stressors on employee strain. Evidence to date remains mixed and, although a number of individual-level moderators have been examined, the role of broader, contextual, group factors has been largely overlooked. In this study, the extent to which control buffered or exacerbated the effects of demands on strain at the individual level was hypothesized to be influenced by perceptions of collective efficacy at the group level. Data from 544 employees in Australian organizations, nested within 23 workgroups, revealed significant three-way cross-level interactions among demands, control and collective efficacy on anxiety and job satisfaction. When the group perceived high levels of collective efficacy, high control buffered the negative consequences of high demands on anxiety and satisfaction. Conversely, when the group perceived low levels of collective efficacy, high control exacerbated the negative consequences of high demands on anxiety, but not satisfaction. In addition, a stress-exacerbating effect for high demands on anxiety and satisfaction was found when there was a mismatch between collective efficacy and control (i.e. combined high collective efficacy and low control). These results provide support for the notion that the stressor-strain relationship is moderated by both individual- and group-level factors.

Matching image sets via adaptive multi convex hull

Traditional nearest points methods use all the samples in an image set to construct a single convex or affine hull model for classification. However, strong artificial features and noisy data may be generated from combinations of training samples when significant intra-class variations and/or noise occur in the image set. Existing multi-model approaches extract local models by clustering each image set individually only once, with fixed clusters used for matching with various image sets. This may not be optimal for discrimination, as undesirable environmental conditions (eg. illumination and pose variations) may result in the two closest clusters representing different characteristics of an object (eg. frontal face being compared to non-frontal face). To address the above problem, we propose a novel approach to enhance nearest points based methods by integrating affine/convex hull classification with an adapted multi-model approach. We first extract multiple local convex hulls from a query image set via maximum margin clustering to diminish the artificial variations and constrain the noise in local convex hulls. We then propose adaptive reference clustering (ARC) to constrain the clustering of each gallery image set by forcing the clusters to have resemblance to the clusters in the query image set. By applying ARC, noisy clusters in the query set can be discarded. Experiments on Honda, MoBo and ETH-80 datasets show that the proposed method outperforms single model approaches and other recent techniques, such as Sparse Approximated Nearest Points, Mutual Subspace Method and Manifold Discriminant Analysis.

Physical activity recognition from accelerometer data using a multi-scale ensemble method

Accurate and detailed measurement of an individual's physical activity is a key requirement for helping researchers understand the relationship between physical activity and health. Accelerometers have become the method of choice for measuring physical activity due to their small size, low cost, convenience and their ability to provide objective information about physical activity. However, interpreting accelerometer data once it has been collected can be challenging. In this work, we applied machine learning algorithms to the task of physical activity recognition from triaxial accelerometer data. We employed a simple but effective approach of dividing the accelerometer data into short non-overlapping windows, converting each window into a feature vector, and treating each feature vector as an i.i.d training instance for a supervised learning algorithm. In addition, we improved on this simple approach with a multi-scale ensemble method that did not need to commit to a single window size and was able to leverage the fact that physical activities produced time series with repetitive patterns and discriminative features for physical activity occurred at different temporal scales.

Physical activity patterns of inner-city elementary schoolchildren

Purpose This study aimed to objectively measure the physical activity (PA) characteristics of a racially and ethnically diverse sample of inner-city elementary schoolchildren and to examine the influence of sex, race/ethnicity, grade level, and weight status on PA. Methods A total of 470 students in grades 4-6 from six inner-city schools in Philadelphia wore an ActiGraph GT3X+ accelerometer (Actigraph, Pensacola, FL) for up to 7 d. The resultant data were uploaded to a customized Visual Basic EXCEL macro to determine the time spent in sedentary (SED), light-intensity PA (LPA), and moderate- to vigorous-intensity PA (MVPA). Results On average, students accumulated 48 min of MVPA daily. Expressed as a percentage of monitoring time, students were sedentary for 63% of the time, in LPA 31% of the time, and in MVPA 6% of the time. Across all race/ethnicity and grade level groups, boys exhibited significantly higher levels of MVPA than girls did; fifth-grade boys exhibited significantly lower MVPA levels than fourth-and sixth-grade boys did, and sixth-grade girls exhibited significantly lower MVPA levels than fourth-and fifth-grade girls did. Hispanic children exhibited lower levels of MVPA than children from other racial/ethnic groups did, and overweight and obese children exhibited significantly lower MVPA levels than children in the healthy weight range did. Across the entire sample, only 24.3% met the current public health guidelines for PA. Physical inactivity was significantly greater among females, Hispanics, and overweight and obese students. Conclusions Fewer than one in four inner-city schoolchildren accumulated the recommended 60 min of MVPA daily. These findings highlight the need for effective and sustainable programs to promote PA in inner-city youth.

Multi-shot person re-identification via relational stein divergence

Person re-identification is particularly challenging due to significant appearance changes across separate camera views. In order to re-identify people, a representative human signature should effectively handle differences in illumination, pose and camera parameters. While general appearance-based methods are modelled in Euclidean spaces, it has been argued that some applications in image and video analysis are better modelled via non-Euclidean manifold geometry. To this end, recent approaches represent images as covariance matrices, and interpret such matrices as points on Riemannian manifolds. As direct classification on such manifolds can be difficult, in this paper we propose to represent each manifold point as a vector of similarities to class representers, via a recently introduced form of Bregman matrix divergence known as the Stein divergence. This is followed by using a discriminative mapping of similarity vectors for final classification. The use of similarity vectors is in contrast to the traditional approach of embedding manifolds into tangent spaces, which can suffer from representing the manifold structure inaccurately. Comparative evaluations on benchmark ETHZ and iLIDS datasets for the person re-identification task show that the proposed approach obtains better performance than recent techniques such as Histogram Plus Epitome, Partial Least Squares, and Symmetry-Driven Accumulation of Local Features.

Analysis of a city-region from the knowledge perspective : Tampere, Finland

Purpose The paper aims to evaluate the knowledge-based urban development (KBUD) dynamics of a rapidly emerging knowledge city-region, Tampere region, Finland. Design/methodology/approach The paper empirically investigates Tampere region’s development achievements and progress from the knowledge perspective. Findings The research, through qualitative and quantitative analyses, reveals the regional development strengths, weaknesses, opportunities and threats of Tampere region. Originality/value The paper provides useful suggestions based on the lessons learned from the Tampere case investigation that could shed light on the KBUD journey of city-regions.

Interpretation of association rules in multi-tier structures

Dealing with the large amount of data resulting from association rule mining is a big challenge. The essential issue is how to provide efficient methods for summarizing and representing meaningful discovered knowledge from databases. This paper presents a new approach called multi-tier granule mining to improve the performance of association rule mining. Rather than using patterns, it uses granules to represent knowledge that is implicitly contained in relational databases. This approach also uses multi-tier structures and association mappings to interpret association rules in terms of granules. Consequently, association rules can be quickly assessed and meaningless association rules can be justified according to these association mappings. The experimental results indicate that the proposed approach is promising

Enabling safe autonomous driving in real-world city traffic using multiple criteria decision making

This paper addresses the topic of real-time decision making for autonomous city vehicles, i.e., the autonomous vehicles' ability to make appropriate driving decisions in city road traffic situations. The paper explains the overall controls system architecture, the decision making task decomposition, and focuses on how Multiple Criteria Decision Making (MCDM) is used in the process of selecting the most appropriate driving maneuver from the set of feasible ones. Experimental tests show that MCDM is suitable for this new application area.