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In this chapter, an overview is provided of the types of fibre and matrix in common use and of how they are assembled into composites. Many types of reinforcement, mostly fibres, are available commercially. Their properties are related to atomic structure and the presence of defects, which must be controlled during manufacture. Matrices may be based on polymers, metals or ceramics. Choice of matrix is usually related to required properties, component geometry and method of manufacture. Certain composite properties may be sensitive to the nature of the reinforcement/matrix interface; this topic is covered in Chapter 7. Properties are also dependent on the arrangement and distribution of fibres, i.e. the fibre architecture, an expression that encompasses intrinsic features of the fibres, such as their diameter and length, as well as their volume fraction, alignment and spatial distribution. Fibre arrangements include laminae (sheets containing aligned long fibres) and laminates that are built up from these. Other continuous fibre systems, such as woven configurations, are also covered. Short fibre systems can be more complex and methods of characterising them are also briefly described.
To adjust future care policies for an ageing population, policy makers need to understand when and why older adults rely on different sources of care (e.g. informal support versus formal services). However, previous scholars have proposed competing conceptualisations of the link between formal and informal care, and empirical examinations have often lacked a dynamic approach. In this study, we applied an analytical method (sequence analysis), allowing for an exploratory and dynamic description of care utilisation. Based on 15 years of data from 473 community-dwelling older individuals in Denmark, we identified four distinct clusters of care trajectories. The probability of belonging to each cluster varied with predisposing factors (such as age and gender), needs factors (such as dependence in activities of daily living and medical conditions) and enabling factors (such as co-habitation and contact with adult children). A key finding was that trajectories characterised by sporadic use of informal care were associated with low needs and strong social relations, whereas trajectories characterised by reliance on formal care were associated with high needs and limited contact with children. Taken together, our findings provide new evidence on the associations between care use and multiple determining factors. The dynamic approach to studying care use reveals that sources of individual care utilisation change over time as the individual and societal determinants change.
Complexity of products and systems is increasing through digitalization, interdisciplinarity as well as high technology maturity and new business models. In consequence, new product development (NPD) projects need to manage and satisfy a large number of requirements from a broad range of stakeholders. Yet, NPD projects are often delayed due to requirement changes. In this paper, a new method for analyzing requirement change propagation is presented. The method is based on the assessment of requirement interrelations structured in a requirements structure matrix by a modified page-rank algorithm. By the method, a high number of strongly interrelated requirements can be analyzed in an efficient manner. Additionally, higher-level interrelations as well as the relative weights of requirements are also incorporated in the analysis. Hereby, an efficient holistic approach towards the analysis of requirement change propagation is proposed.
Recently there has been significant work in the social sciences involving ensembles of social networks, that is, multiple, independent, social networks such as students within schools or employees within organizations. There remains, however, very little methodological work on exploring these types of data structures. We present methods for clustering social networks with observed nodal class labels, based on statistics of walk counts between the nodal classes. We extend this method to consider only non-backtracking walks, and introduce a method for normalizing the counts of long walk sequences using those of shorter ones. We then present a method for clustering networks based on these statistics to explore similarities among networks. We demonstrate the utility of this method on simulated network data, as well as on advice-seeking networks in education.
Correlations are important to see connections between different facets of people and their experiences with systems. However, consideration of correlation coefficients alone can be misleading and, in particular, this chapter discusses how outliers and clustering can distort an interpretation of correlation. It also raises a note of caution for the many other methods that implicitly use correlation.
The study of finite approximations of probability measures has a long history. In Xu and Berger (2017), the authors focused on constrained finite approximations and, in particular, uniform ones in dimension d=1. In the present paper we give an elementary construction of a uniform decomposition of probability measures in dimension d≥1. We then use this decomposition to obtain upper bounds on the rate of convergence of the optimal uniform approximation error. These bounds appear to be the generalization of the ones obtained by Xu and Berger (2017) and to be sharp for generic probability measures.
With the advancement of high-throughput sequencing technologies, the amount of available sequencing data is growing at a pace that has now begun to greatly challenge the data processing and storage capacities of modern computer systems. Removing redundancy from such data by clustering could be crucial for reducing memory, disk space and running time consumption. In addition, it also has good performance on reducing dataset noise in some analysis applications. In this study, we propose a high-performance short sequence classification algorithm (HSC) for next generation sequencing (NGS) data based on efficient hash function and text similarity. First, HSC converts all reads into k-mers, then it forms a unique k-mer set by merging the duplicated and reverse complementary elements. Second, all unique k-mers are stored in a hash table, where the k-mer string is stored in the key field, and the ID of the reads containing the k-mer are stored in the value field. Third, each hash unit is transformed into a short text consisting of reads. Fourth, texts that satisfy the similarity threshold are combined into a long text, the merge operation is executed iteratively until there is no text that satisfies the merge condition. Finally, the long text is transformed into a cluster consisting of reads. We tested HSC using five real datasets. The experimental results showed that HSC cluster 100 million short reads within 2 hours, and it has excellent performance in reducing memory consumption. Compared to existing methods, HSC is much faster than other tools, it can easily handle tens of millions of sequences. In addition, when HSC is used as a preprocessing tool to produce assembly data, the memory and time consumption of the assembler is greatly reduced. It can help the assembler to achieve better assemblies in terms of N50, NA50 and genome fraction.
Funding for mental health services in England faces many challenges, including operating under financial constraints where it is not easy to demonstrate the link between activity and funding. Mental health services need to operate alongside and collaborate with acute physical hospital services, where there is a well-established system for paying for activity. The funding landscape is shifting at a rapid pace and we outline the distinctions between the three main options – block contracts, episodic payment and capitation. Classification of treatment episodes via clustering presents an opportunity to demonstrate activity and reward it within these payment approaches. We discuss the results of our research into how well the clustering system is performing against a number of fundamental criteria. We find that, according to these criteria, clusters are falling short of providing a sound basis for measuring and financing services. Nevertheless, we argue that clustering is the best available option and is essential for a more transparent funding approach for mental healthcare to demonstrate its claim on resources, and that clusters should therefore be a starting point for evolving a better funding system.
•Understand the different payment models currently being used and proposed in mental health services in England
•Understand the role of clustering in measuring mental health activity and providing a basis for funding
•Understand how a robust model of clustering can benefit the provision of mental health services
Assuming a society of conditional cooperators (or moody conditional cooperators), this computational study proposes a new perspective on the structural advantage of social network clustering. Previous work focused on how clustered structure might encourage initial outbreaks of cooperation or defend against invasion by a few defectors. Instead, we explore the ability of a societal structure to retain cooperative norms in the face of widespread disturbances. Such disturbances may abstractly describe hardships like famine and economic recession, or the random spatial placement of a substantial numbers of pure defectors (or round-1 defectors) among a spatially structured population of players in a laboratory game, etc.
As links in tightly clustered societies are reallocated to distant contacts, we observe that a society becomes increasingly susceptible to catastrophic cascades of defection: mutually-beneficial cooperative norms can be destroyed completely by modest shocks of defection. In contrast, networks with higher clustering coefficients can withstand larger shocks of defection before being forced to catastrophically low levels of cooperation. We observe a remarkably linear protective effect of clustering coefficient that becomes active above a critical level of clustering. Notably, both the critical level and the slope of this dependence is higher for decision-rule parameterizations that correspond to higher costs of cooperation. Our modeling framework provides a simple way to reinterpret the counter-intuitive and widely cited human experiments of Suri and Watts (2011) while also affirming the classical intuition that network clustering and higher levels of cooperation should be positively associated.
To track multiple ships and estimate the feature parameters of multiple emitters on board using electronic intelligence satellites under clutter interference, a long and random revisit time, and other complex conditions, a novel tracking algorithm using both kinematic (position and velocity) and feature information based on an improved Multiple Hypothesis Tracking (MHT) approach is proposed in this paper. Firstly, the characteristics of multi-ship tracking with multiple emitters using satellite electronic information are analysed, and a new model of an emitter is built as an extended target in geographical coordinates. Secondly, a pre-processing of measurements is utilised via hierarchical clustering using the location and feature information of emitters. Thirdly, feature information is incorporated into the MHT framework using Jensen-Shannon divergence distance and fuzzy C-means clustering to calculate track scores. Finally, we present the prediction and update of target states, especially the update of feature parameters, to realise joint kinematic and feature tracking of ships. The results of the simulation show that the proposed method has much better tracking performance than the standard MHT algorithm.
We present a statistical approach to data mining and quantitatively evaluating detrital age spectra for sedimentary provenance analyses and palaeogeographic reconstructions. Multidimensional scaling coupled with density-based clustering allows the objective identification of provenance end-member populations and sedimentary mixing processes for a composite crust. We compiled 58 601 detrital zircon U–Pb ages from 770 Precambrian to Lower Palaeozoic shelf sedimentary rocks from 160 publications and applied statistical provenance analysis for the Peri-Gondwanan crust north of Africa and the adjacent areas. We have filtered the dataset to reduce the age spectra to the provenance signal, and compared the signal with age patterns of potential source regions. In terms of provenance, our results reveal three distinct areas, namely the Avalonian, West African and East African–Arabian zircon provinces. Except for the Rheic Ocean separating the Avalonian Zircon Province from Gondwana, the statistical analysis provides no evidence for the existence of additional oceanic lithosphere. This implies a vast and contiguous Peri-Gondwanan shelf south of the Rheic Ocean that is supplied by two contrasting super-fan systems, reflected in the zircon provinces of West Africa and East Africa–Arabia.
Energy balance-related behaviours (EBRB) are established in childhood and seem to persist through to adulthood. A lower parental educational level was associated with unhealthy behavioural patterns. The aim of the study is to identify clusters of EBRB and examine their association with preschool children’s BMI and maternal, paternal and parental education. A subsample of the ToyBox study (n 5387) conducted in six European countries was used. Six behavioural clusters (‘healthy diet and low activity’, ‘active’, ‘healthy lifestyle’, ‘high water and screen time; low fruits and vegetables (F&V) and physical activity (PA)’, ‘unhealthy lifestyle’ and ‘high F&V consumers’) emerged. The healthiest group characterised by high water and F&V consumption and high PA z scores (‘healthy lifestyle’) was more prevalent among preschool children with at least one medium- or higher-educated parent and showed markedly healthier trends for all the included EBRB. In the opposite, the ‘unhealthy lifestyle’ cluster (characterised by high soft drinks and screen time z scores, and low water, F&V and PA z scores) was more prevalent among children with lower parental, paternal and maternal education levels. OR identified that children with lower maternal, paternal and parental education levels were less likely to be allocated in the ‘healthy lifestyle’ cluster and more likely to be allocated in the ‘unhealthy lifestyle’ cluster. The ‘unhealthy lifestyle’ cluster was more prevalent among children with parents in lower parental educational levels and children who were obese. Therefore, parental educational level is one of the key factors that should be considered when developing childhood obesity prevention interventions.
A detailed knowledge of the atomic structure of magnetic semiconductors is crucial to understanding their electronic and magnetic properties, which could enable spintronic applications. Energy-dispersive X-ray spectrometry (EDX) in the scanning transmission electron microscope and atom probe tomography (APT) experiments reveal the formation of Cr-rich regions in Cd1−xCrxTe layers grown by molecular beam epitaxy. These Cr-rich regions occur on a length scale of 6–10 nm at a nominal Cr composition of x=0.034 and evolve toward an ellipsoidal shape oriented along <111> directions at a composition of x=0.083. Statistical analysis of the APT reconstructed volume reveals that the Cr aggregation increases with the average Cr composition. The correlation with the magnetic properties of such (Cd,Cr)Te layers is discussed within the framework of strongly inhomogeneous materials. Finally, difficulties in accurately quantifying the Cr distribution in the CdTe matrix on an atomic scale by EDX and APT are discussed.
We study the evolution of cooperation in an interacting particle system with two types. The model we investigate is an extension of a two-type biased voter model. One type (called defector) has a (positive) bias α with respect to the other type (called cooperator). However, a cooperator helps a neighbor (either defector or cooperator) to reproduce at rate γ. We prove that the one-dimensional nearest-neighbor interacting dynamical system exhibits a phase transition at α = γ. A special choice of interaction kernels yield that for α > γ cooperators always die out, but if γ > α, cooperation is the winning strategy.
In non-life insurance, territory-based risk classification is useful for various insurance operations including marketing, underwriting, ratemaking, etc. This paper proposes a spatially dependent frequency-severity modeling framework to produce territorial risk scores. The framework applies to the aggregated insurance claims where the frequency and severity components examine the occurrence rate and average size of insurance claims in each geographic unit, respectively. We employ the bivariate conditional autoregressive models to accommodate the spatial dependency in the frequency and severity components, as well as the cross-sectional association between the two components. Using a town-level claims data of automobile insurance in Massachusetts, we demonstrate applications of the model output–territorial risk scores–in ratemaking and market segmentation.
To investigate spatial heterogeneity of stunting prevalence among children in Côte d’Ivoire and examine changes in stunting between 1994 and 2011, to assess the impact of the 2002–2011 civil war that led to temporary partitioning of the country.
Data from 1994, 1998 and 2011 Côte d’Ivoire Demographic and Health Surveys (DHS) were analysed using a geostatistical approach taking into account spatial autocorrelation. Stunting data were interpolated using ordinary kriging; spatial clusters with high and low stunting prevalence were identified using Kulldorff spatial scan statistics. Multilevel multivariable logistic regression was then carried out, with year of survey as the main independent variable and an interaction term for time by geographic zone (Abidjan, South, North).
Côte d’Ivoire, West Africa.
Children aged 0–35 months included in three DHS (n 6709).
Overall stunting prevalence was 30·7, 28·7 and 27·8 % in 1994, 1998 and 2011, respectively (P=0·32). Clusters with high prevalence were found in 1994 (in the West region, P<0·001) and 1998 (in the West and North-West regions, P<0·01 and P=0·01, respectively), but not in 2011. Abidjan was included in a cluster with low prevalence in all surveys (P<0·05). Risk of stunting did not change between 1994 and 2011 at national level (adjusted OR; 95 % CI: 1·39; 0·72, 2·64), but decreased in the South (0·74; 0·58, 0·94) and increased from 1998 to 2011 in Abidjan (1·96; 1·06, 3·64).
In Côte d’Ivoire, significant changes in stunting prevalence were observed at the sub-national level between 1994 and 2011.
The local electrode atom probe (LEAP) has become the primary instrument used for atom probe tomography measurements. Recent advances in detector and laser design, together with updated hit detection algorithms, have been incorporated into the latest LEAP 5000 instrument, but the implications of these changes on measurements, particularly the size and chemistry of small clusters and elemental segregations, have not been explored. In this study, we compare data sets from a variety of materials with small-scale chemical heterogeneity using both a LEAP 3000 instrument with 37% detector efficiency and a 532-nm green laser and a new LEAP 5000 instrument with a manufacturer estimated increase to 52% detector efficiency, and a 355-nm ultraviolet laser. In general, it was found that the number of atoms within small clusters or surface segregation increased in the LEAP 5000, as would be expected by the reported increase in detector efficiency from the LEAP 3000 architecture, but subtle differences in chemistry were observed which are attributed to changes in the way multiple hit detection is calculated using the LEAP 5000.
Radiation induced clustering affects the mechanical properties, that is the ductile to brittle transition temperature (DBTT), of reactor pressure vessel (RPV) steel of nuclear power plants. The combination of low Cu and high Ni used in some RPV welds is known to further enhance the DBTT shift during long time operation. In this study, RPV weld samples containing 0.04 at% Cu and 1.6 at% Ni were irradiated to 2.0 and 6.4×1023 n/m2 in the Halden test reactor. Atom probe tomography (APT) was applied to study clustering of Ni, Mn, Si, and Cu. As the clusters are in the nanometer-range, APT is a very suitable technique for this type of study. From APT analyses information about size distribution, number density, and composition of the clusters can be obtained. However, the quantification of these attributes is not trivial. The maximum separation method (MSM) has been used to characterize the clusters and a detailed study about the influence of the choice of MSM cluster parameters, primarily on the cluster number density, has been undertaken.
Many studies have attempted to determine whether an observed network exhibits a so-called “small-world structure.” Such determinations have often relied on a conceptual definition of small worldliness proposed by Watts and Strogatz in their seminal 1998 paper, but recently several quantitative indices of network small worldliness have emerged. This paper reviews and compares three such indices—the small-world quotient (Q), a small-world metric (ω), and the small-world index(SWI)—in the canonical Watts–Strogatz re-wiring model and in four real-world networks. These analyses suggest that researchers should avoid Q, and identify considerations that should guide the choice between ω and SWI.
Current group-average analysis suggests quantitative but not qualitative cognitive differences between schizophrenia (SZ) and bipolar disorder (BD). There is increasing recognition that cognitive within-group heterogeneity exists in both disorders, but it remains unclear as to whether between-group comparisons of performance in cognitive subgroups emerging from within each of these nosological categories uphold group-average findings. We addressed this by identifying cognitive subgroups in large samples of SZ and BD patients independently, and comparing their cognitive profiles. The utility of a cross-diagnostic clustering approach to understanding cognitive heterogeneity in these patients was also explored.
Hierarchical clustering analyses were conducted using cognitive data from 1541 participants (SZ n = 564, BD n = 402, healthy control n = 575).
Three qualitatively and quantitatively similar clusters emerged within each clinical group: a severely impaired cluster, a mild-moderately impaired cluster and a relatively intact cognitive cluster. A cross-diagnostic clustering solution also resulted in three subgroups and was superior in reducing cognitive heterogeneity compared with disorder clustering independently.
Quantitative SZ–BD cognitive differences commonly seen using group averages did not hold when cognitive heterogeneity was factored into our sample. Members of each corresponding subgroup, irrespective of diagnosis, might be manifesting the outcome of differences in shared cognitive risk factors.