Managing the complexity of information flow for construction small and medium-sized enterprises (CSMEs) using system dynamics and collaborative technologies

With the increase in e-commerce and the digitisation of design data and information,the construction sector has become reliant upon IT infrastructure and systems. The design and production process is more complex, more interconnected, and reliant upon greater information mobility, with seamless exchange of data and information in real time. Construction small and medium-sized enterprises (CSMEs), in particular,the speciality contractors, can effectively utilise cost-effective collaboration-enabling technologies, such as cloud computing, to help in the effective transfer of information and data to improve productivity. The system dynamics (SD) approach offers a perspective and tools to enable a better understanding of the dynamics of complex systems. This research focuses upon system dynamics methodology as a modelling and analysis tool in order to understand and identify the key drivers in the absorption of cloud computing for CSMEs. The aim of this paper is to determine how the use of system dynamics (SD) can improve the management of information flow through collaborative technologies leading to improved productivity. The data supporting the use of system dynamics was obtained through a pilot study consisting of questionnaires and interviews from five CSMEs in the UK house-building sector.

On the cardinality and complexity of the set of codings for self-similar sets with positive Lebesgue measure

Let λ1,…,λn be real numbers in (0,1) and p1,…,pn be points in Rd. Consider the collection of maps fj:Rd→Rd given by fj(x)=λjx+(1−λj)pj. It is a well known result that there exists a unique nonempty compact set Λ⊂Rd satisfying Λ=∪nj=1fj(Λ). Each x∈Λ has at least one coding, that is a sequence (ϵi)∞i=1 ∈{1,…,n}N that satisfies limN→∞fϵ1…fϵN(0)=x. We study the size and complexity of the set of codings of a generic x∈Λ when Λ has positive Lebesgue measure. In particular, we show that under certain natural conditions almost every x∈Λ has a continuum of codings. We also show that almost every x∈Λ has a universal coding. Our work makes no assumptions on the existence of holes in Λ and improves upon existing results when it is assumed Λ contains no holes.

Causation, complexity, and the concert: the pragmatics of causal explanation in international relations

A causal explanation provides information about the causal history of whatever is being explained. However, most causal histories extend back almost infinitely and can be described in almost infinite detail. Causal explanations therefore involve choices about which elements of causal histories to pick out. These choices are pragmatic: they reflect our explanatory interests. When adjudicating between competing causal explanations, we must therefore consider not only questions of epistemic adequacy (whether we have good grounds for identifying certain factors as causes) but also questions of pragmatic adequacy (whether the aspects of the causal history picked out are salient to our explanatory interests). Recognizing that causal explanations differ pragmatically as well as epistemically is crucial for identifying what is at stake in competing explanations of the relative peacefulness of the nineteenth-century Concert system. It is also crucial for understanding how explanations of past events can inform policy prescription.

Reverse correlation reveals how observers sample visual information when estimating three-dimensional shape

Human observers exhibit large systematic distance-dependent biases when estimating the three-dimensional (3D) shape of objects defined by binocular image disparities. This has led some to question the utility of disparity as a cue to 3D shape and whether accurate estimation of 3D shape is at all possible. Others have argued that accurate perception is possible, but only with large continuous perspective transformations of an object. Using a stimulus that is known to elicit large distance-dependent perceptual bias (random dot stereograms of elliptical cylinders) we show that contrary to these findings the simple adoption of a more naturalistic viewing angle completely eliminates this bias. Using behavioural psychophysics, coupled with a novel surface-based reverse correlation methodology, we show that it is binocular edge and contour information that allows for accurate and precise perception and that observers actively exploit and sample this information when it is available.

Preface

The ‘soft’ ionization technique matrix-assisted laser desorption/ionization (MALDI) is without doubt one of the great success stories of modern mass spectrometry (MS). In particular, the further development of MALDI and in general ‘soft’ laser ionization, focusing on their unique characteristics and advantages in areas such as speed, spatial resolution, sample preparation and low spectral complexity, have led to great advances in mass spectral profiling and imaging with an extremely auspicious future in (bio)medical analyses.

Syntactic complexity in the comprehension of wh-questions and relative clauses in typical language development and autism

This study investigates effects of syntactic complexity operationalised in terms of movement, intervention and (NP) feature similarity in the development of A’ dependencies in 4-, 6-, and 8-year old typically developing (TD) French children and children with Autism Spectrum Disorders (ASD). Children completed an off-line comprehension task testing eight syntactic structures classified in four levels of complexity: Level 0: No Movement; Level 1: Movement without (configurational) Intervention; Level 2: Movement with Intervention from an element which is maximally different or featurally ‘disjoint’ (mismatched in both lexical NP restriction and number); Level 3: Movement with Intervention from an element similar in one feature or featurally ‘intersecting’ (matched in lexical NP restriction, mismatched in number). The results show that syntactic complexity affects TD children across the three age groups, but also indicate developmental differences between these groups. Movement affected all three groups in a similar way, but intervention effects in intersection cases were stronger in younger than older children, with NP feature similarity affecting only 4-year olds. Complexity effects created by the similarity in lexical restriction of an intervener thus appear to be overcome early in development, arguably thanks to other differences of this intervener (which was mismatched in number). Children with ASD performed less well than the TD children although they were matched on non-verbal reasoning. Overall, syntactic complexity affected their performance in a similar way as in their TD controls, but their performance correlated with non-verbal abilities rather than age, suggesting that their grammatical development does not follow the smooth relation to age that is found in TD children.

Brief behavioural activation for adolescent depression: working with complexity and risk

Given the long-term negative outcomes associated with depression in adolescence, there is a pressing need to develop brief, evidence based treatments that are accessible to more young people experiencing low mood. Behavioural Activation (BA) is an effective treatment for adult depression, however little research has focused on the use of BA with depressed adolescents, particularly with briefer forms of BA. In this article we outline an adaptation of brief Behavioral Activation Treatment of Depression (BATD) designed for adolescents and delivered in eight sessions (Brief BA). This case example illustrates how a structured, brief intervention was useful for a depressed young person with a number of complicating and risk factors.

Tensor LRR and sparse coding-based subspace clustering

Subspace clustering groups a set of samples from a union of several linear subspaces into clusters, so that the samples in the same cluster are drawn from the same linear subspace. In the majority of the existing work on subspace clustering, clusters are built based on feature information, while sample correlations in their original spatial structure are simply ignored. Besides, original high-dimensional feature vector contains noisy/redundant information, and the time complexity grows exponentially with the number of dimensions. To address these issues, we propose a tensor low-rank representation (TLRR) and sparse coding-based (TLRRSC) subspace clustering method by simultaneously considering feature information and spatial structures. TLRR seeks the lowest rank representation over original spatial structures along all spatial directions. Sparse coding learns a dictionary along feature spaces, so that each sample can be represented by a few atoms of the learned dictionary. The affinity matrix used for spectral clustering is built from the joint similarities in both spatial and feature spaces. TLRRSC can well capture the global structure and inherent feature information of data, and provide a robust subspace segmentation from corrupted data. Experimental results on both synthetic and real-world data sets show that TLRRSC outperforms several established state-of-the-art methods.