Implementing innovation in construction: Contexts, relative boundedness and actor-network theory

Theoretical understanding of the implementation and use of innovations within construction contexts is discussed and developed. It is argued that both the rhetoric of the 'improvement agenda' within construction and theories of innovation fail to account for the complex contexts and disparate perspectives which characterize construction work. To address this, the concept of relative boundedness is offered. Relatively unbounded innovation is characterized by a lack of a coherent central driving force or mediator with the ability to reconcile potential conflicts and overcome resistance to implementation. This is a situation not exclusive to, but certainly indicative of, much construction project work. Drawing on empirical material from the implementation of new design and coordination technologies on a large construction project, the concept is developed, concentrating on the negotiations and translations implementation mobilized. An actor-network theory (ANT) approach is adopted, which emphasizes the roles that both human actors and non-human agents play in the performance and outcomes of these interactions. Three aspects of how relative boundedness is constituted and affected are described; through the robustness of existing practices and expectations, through the delegation of interests on to technological artefacts and through the mobilization of actors and artefacts to constrain and limit the scope of negotiations over new technology implementation.

Neural and wavelet network models for financial distress classification

This work analyzes the use of linear discriminant models, multi-layer perceptron neural networks and wavelet networks for corporate financial distress prediction. Although simple and easy to interpret, linear models require statistical assumptions that may be unrealistic. Neural networks are able to discriminate patterns that are not linearly separable, but the large number of parameters involved in a neural model often causes generalization problems. Wavelet networks are classification models that implement nonlinear discriminant surfaces as the superposition of dilated and translated versions of a single "mother wavelet" function. In this paper, an algorithm is proposed to select dilation and translation parameters that yield a wavelet network classifier with good parsimony characteristics. The models are compared in a case study involving failed and continuing British firms in the period 1997-2000. Problems associated with over-parameterized neural networks are illustrated and the Optimal Brain Damage pruning technique is employed to obtain a parsimonious neural model. The results, supported by a re-sampling study, show that both neural and wavelet networks may be a valid alternative to classical linear discriminant models.

Smart-phone based electrocardiogram wavelet decomposition and neural network classification

This paper discusses ECG classification after parametrizing the ECG waveforms in the wavelet domain. The aim of the work is to develop an accurate classification algorithm that can be used to diagnose cardiac beat abnormalities detected using a mobile platform such as smart-phones. Continuous time recurrent neural network classifiers are considered for this task. Records from the European ST-T Database are decomposed in the wavelet domain using discrete wavelet transform (DWT) filter banks and the resulting DWT coefficients are filtered and used as inputs for training the neural network classifier. Advantages of the proposed methodology are the reduced memory requirement for the signals which is of relevance to mobile applications as well as an improvement in the ability of the neural network in its generalization ability due to the more parsimonious representation of the signal to its inputs.

Imbalanced functional link between executive control network and reward network explain the online-game seeking behaviors in Internet gaming disorder

Literatures have shown that Internet gaming disorder (IGD) subjects show impaired executive control and enhanced reward sensitivities than healthy controls. However, how these two networks jointly affect the valuation process and drive IGD subjects' online-game-seeking behaviors remains unknown. Thirty-five IGD and 36 healthy controls underwent a resting-states scan in the MRI scanner. Functional connectivity (FC) was examined within control and reward network seeds regions, respectively. Nucleus accumbens (NAcc) was selected as the node to find the interactions between these two networks. IGD subjects show decreased FC in the executive control network and increased FC in the reward network when comparing with the healthy controls. When examining the correlations between the NAcc and the executive control/reward networks, the link between the NAcc - executive control network is negatively related with the link between NAcc - reward network. The changes (decrease/increase) in IGD subjects' brain synchrony in control/reward networks suggest the inefficient/overly processing within neural circuitry underlying these processes. The inverse proportion between control network and reward network in IGD suggest that impairments in executive control lead to inefficient inhibition of enhanced cravings to excessive online game playing. This might shed light on the mechanistic understanding of IGD.

EDITOR'S CHOICE: REVIEW: Trait matching of flower visitors and crops predicts fruit set better than trait diversity

Understanding the relationships between trait diversity, species diversity and ecosystem functioning is essential for sustainable management. For functions comprising two trophic levels, trait matching between interacting partners should also drive functioning. However, the predictive ability of trait diversity and matching is unclear for most functions, particularly for crop pollination, where interacting partners did not necessarily co-evolve. World-wide, we collected data on traits of flower visitors and crops, visitation rates to crop flowers per insect species and fruit set in 469 fields of 33 crop systems. Through hierarchical mixed-effects models, we tested whether flower visitor trait diversity and/or trait matching between flower visitors and crops improve the prediction of crop fruit set (functioning) beyond flower visitor species diversity and abundance. Flower visitor trait diversity was positively related to fruit set, but surprisingly did not explain more variation than flower visitor species diversity. The best prediction of fruit set was obtained by matching traits of flower visitors (body size and mouthpart length) and crops (nectar accessibility of flowers) in addition to flower visitor abundance, species richness and species evenness. Fruit set increased with species richness, and more so in assemblages with high evenness, indicating that additional species of flower visitors contribute more to crop pollination when species abundances are similar. Synthesis and applications. Despite contrasting floral traits for crops world-wide, only the abundance of a few pollinator species is commonly managed for greater yield. Our results suggest that the identification and enhancement of pollinator species with traits matching those of the focal crop, as well as the enhancement of pollinator richness and evenness, will increase crop yield beyond current practices. Furthermore, we show that field practitioners can predict and manage agroecosystems for pollination services based on knowledge of just a few traits that are known for a wide range of flower visitor species.

Molecular dynamics simulation of interactions between a sodium dodecyl sulfate micelle and a poly(ethylene oxide) polymer

We have performed atomistic molecular dynamics simulations of an anionic sodium dodecyl sulfate (SDS) micelle and a nonionic poly(ethylene oxide) (PEO) polymer in aqueous solution. The micelle consisted of 60 surfactant molecules, and the polymer chain lengths varied from 20 to 40 monomers. The force field parameters for PEO were adjusted by using 1,2-dimethoxymethane (DME) as a model compound and matching its hydration enthalpy and conformational behavior to experiment. Excellent agreement with previous experimental and simulation work was obtained through these modifications. The simulated scaling behavior of the PEO radius of gyration was also in close agreement with experimental results. The SDS-PEO simulations show that the polymer resides on the micelle surface and at the hydrocarbon-water interface, leading to a selective reduction in the hydrophobic contribution to the solvent-accessible surface area of the micelle. The association is mainly driven by hydrophobic interactions between the polymer and surfactant tails, while the interaction between the polymer and sulfate headgroups on the micelle surface is weak. The 40-monomer chain is mostly wrapped around the micelle, and nearly 90% of the monomers are adsorbed at low PEO concentration. Simulations were also performed with multiple 20-monomer chains, and gradual addition of polymer indicates that about 120 monomers are required to saturate the micelle surface. The stoichiometry of the resulting complex is in close agreement with experimental results, and the commonly accepted "beaded necklace" structure of the SDS-PEO complex is recovered by our simulations.

On the utility of input selection and pruning for financial distress prediction models

Analyzes the use of linear and neural network models for financial distress classification, with emphasis on the issues of input variable selection and model pruning. A data-driven method for selecting input variables (financial ratios, in this case) is proposed. A case study involving 60 British firms in the period 1997-2000 is used for illustration. It is shown that the use of the Optimal Brain Damage pruning technique can considerably improve the generalization ability of a neural model. Moreover, the set of financial ratios obtained with the proposed selection procedure is shown to be an appropriate alternative to the ratios usually employed by practitioners.

Action observation and robotic agents: learning and anthropomorphism

The ‘action observation network’ (AON), which is thought to translate observed actions into motor codes required for their execution, is biologically tuned: it responds more to observation of human, than non-human, movement. This biological specificity has been taken to support the hypothesis that the AON underlies various social functions, such as theory of mind and action understanding, and that, when it is active during observation of non-human agents like humanoid robots, it is a sign of ascription of human mental states to these agents. This review will outline evidence for biological tuning in the AON, examining the features which generate it, and concluding that there is evidence for tuning to both the form and kinematic profile of observed movements, and little evidence for tuning to belief about stimulus identity. It will propose that a likely reason for biological tuning is that human actions, relative to non-biological movements, have been observed more frequently while executing corresponding actions. If the associative hypothesis of the AON is correct, and the network indeed supports social functioning, sensorimotor experience with non-human agents may help us to predict, and therefore interpret, their movements.

Does regional infrastructure investment stimulate extra housing and commercial building supply? Long-run evidence across the English regions

This paper uses long-term regional construction data to investigate whether increases infrastructure investment in the English regions leads to subsequent rises in housebuilding and new commercial property, using time series modeling. Both physical (roads and harbours) and social infrastructure (education and health) impacts are investigated across nine regions in England. Significant effects for physical infrastructure are found across most regions and, also, some evidence of a social infrastructure effect. The results are not consistent across regions, which may be due to geographical differences and to network and diversionary effects. However, the results do suggest that infrastructure does have some impact but follows differential lag structures. These results provide a test of the hypothesis of the economic benefits of infrastructure investment in an approach that has not been used before.

Understanding the early stages of the innovation diffusion process: awareness, influence and communication networks

The themes of awareness and influence within the innovation diffusion process are addressed. The innovation diffusion process is typically represented as stages, yet awareness and influence are somewhat under-represented in the literature. Awareness and influence are situated within the contextual setting of individual actors but also within the broader institutional forces. Understanding how actors become aware of an innovation and then how their opinion is influenced is important for creating a more innovation-active UK construction sector. Social network analysis is proposed as one technique for mapping how awareness and influence occur and what they look like as a network. Empirical data are gathered using two modes of enquiry. This is done through a pilot study consisting of chartered professionals and then through a case study organization as it attempted to diffuse an innovation. The analysis demonstrates significant variations across actors’ awareness and influence networks. It is argued that social network analysis can complement other research methods in order to present a richer picture of how actors become aware of innovations and where they draw their influences regarding adopting innovations. In summarizing the findings, a framework for understanding awareness and influence associated with innovation within the UK construction sector is presented. Finally, with the UK construction sector continually being encouraged to be innovative, understanding and managing an actor’s awareness and influence network will be beneficial. The overarching conclusion thus describes the need not only to build research capacity in this area but also to push the boundaries related to the research methods employed.

Narratives of grassroots innovations: a comparison of voluntary simplicity and the transition movement in Italy

Grassroots innovations (GI) are promising examples of deliberate transformation of socio-technical systems towards resilience and sustainability. However, evidence is needed on the factors that limit or enable their success. This paper set out to study how GI use narratives to empower innovation in the face of incumbent socio-technical regimes. Institutional documents were comparatively analyzed to assess how the narratives influence the structure, form of action and external interactions of two Italian grassroots networks, Bilanci di Giustizia and Transition Network Italy. The paper finds an internal consistency between narratives and strategies for each of the two networks. The paper also highlights core similarities, but also significant differences in the ethical basis of the two narratives, and in the organizations and strategies. Such differences determine different forms of innovation empowerment and expose the niche to different potentials to transform incumbent regimes, or to the risk of being co-opted by them.

Performing knowledge requirements analysis for public organisations in a Virtual Learning Environment: a social network analysis approach

This paper describes an application of Social Network Analysis methods for identification of knowledge demands in public organisations. Affiliation networks established in a postgraduate programme were analysed. The course was executed in a distance education mode and its students worked on public agencies. Relations established among course participants were mediated through a virtual learning environment using Moodle. Data available in Moodle may be extracted using knowledge discovery in databases techniques. Potential degrees of closeness existing among different organisations and among researched subjects were assessed. This suggests how organisations could cooperate for knowledge management and also how to identify their common interests. The study points out that closeness among organisations and research topics may be assessed through affiliation networks. This opens up opportunities for applying knowledge management between organisations and creating communities of practice. Concepts of knowledge management and social network analysis provide the theoretical and methodological basis.

Norepinephrine ignites local hot spots of neuronal excitation: how arousal amplifies selectivity in perception and memory

Arousal sometimes enhances and sometimes impairs perception and memory. In our Glutamate Amplifies Noradrenergic Effects (GANE) model, glutamate at active synapses interacts with norepinephrine released by the locus coeruleus to create local ‘hot spots’ of activity that enable the selective effects of arousal. This hot spot mechanism allows local cortical regions to self-regulate norepinephrine release based on current activation levels. In turn, hot spots bias global energetic delivery and functional network connectivity to enhance processing of high priority representations and impair processing of lower priority representations.

Factors affecting dairy farmers' attitudes towards antimicrobial medicine usage in cattle in England and Wales

There has been growing concern about bacterial resistance to antimicrobials in the farmed livestock sector. Attention has turned to sub-optimal use of antimicrobials as a driver of resistance. Recent reviews have identified a lack of data on the pattern of antimicrobial use as an impediment to the design of measures to tackle this growing problem. This paper reports on a study that explored use of antibiotics by dairy farmers and factors influencing their decision-making around this usage. We found that respondents had either recently reduced their use of antibiotics, or planned to do so. Advice from their veterinarian was instrumental in this. Over 70% thought reducing antibiotic usage would be a good thing to do. The most influential source of information used was their own veterinarian. Some 50% were unaware of the available guidelines on use in cattle production. However, 97% thought it important to keep treatment records. The Theory of Planned Behaviour was used to identify dairy farmers’ drivers and barriers to reduce use of antibiotics. Intention to reduce usage was weakly correlated with current and past practice of antibiotic use, whilst the strongest driver was respondents’ belief that their social and advisory network would approve of them doing this. The higher the proportion of income from milk production and the greater the chance of remaining in milk production, the significantly higher the likelihood of farmers exhibiting positive intention to reduce antibiotic usage. Such farmers may be more commercially minded than others and thus more cost-conscious or, perhaps, more aware of possible future restrictions. Strong correlation was found between farmers’ perception of their social referents’ beliefs and farmers’ intent to reduce antibiotic use. Policy makers should target these social referents, especially veterinarians, with information on the benefits from, and the means to, achieving reductions in antibiotic usage. Information on sub-optimal use of antibiotics as a driver of resistance in dairy herds and in humans along with advice on best farm practice to minimise risk of disease and ensure animal welfare, complemented with data on potential cost savings from reduced antibiotic use would help improve poor practice.