The state-of-the-art on Intellectual Property Analytics (IPA): A literature review on artificial intelligence, machine learning and deep learning methods for analysing intellectual property (IP) data

Big data is increasingly available in all areas of manufacturing and operations, which presents an opportunity for better decision making and discovery of the next generation of innovative technologies. Recently, there have been substantial developments in the field of patent analytics, which describes the science of analysing large amounts of patent information to discover trends. We define Intellectual Property Analytics (IPA) as the data science of analysing large amount of IP information, to discover relationships, trends and patterns for decision making. In this paper, we contribute to the ongoing discussion on the use of intellectual property analytics methods, i.e artificial intelligence methods, machine learning and deep learning approaches, to analyse intellectual property data. This literature review follows a narrative approach with search strategy, where we present the state-of-the-art in intellectual property analytics by reviewing 57 recent articles. The bibliographic information of the articles are analysed, followed by a discussion of the articles divided in four main categories: knowledge management, technology management, economic value, and extraction and effective management of information. We hope research scholars and industrial users, may find this review helpful when searching for the latest research efforts pertaining to intellectual property analytics.The authors would like to acknowledge support of the Engineering and Physical Sciences Research Council (EPSRC)

Turbulent Flows and Pollution Dispersion around Tall Buildings Using Adaptive Large Eddy Simulation (LES)

The motivation for this work stems from the increased number of high-rise buildings/skyscrapers all over the world, and in London, UK, and hence the necessity to see their effect on the local environment. We concentrate on the mean velocities, Reynolds stresses, turbulent kinetic energies (TKEs) and tracer concentrations. We look at their variations with height at two main locations within the building area, and downstream the buildings. The pollution source is placed at the top of the central building, representing an emission from a Combined Heat and Power (CHP) plant. We see how a tall building may have a positive effect at the lower levels, but a negative one at the higher levels in terms of pollution levels. Mean velocities at the higher levels (over 60 m in real life) are reduced at both locations (within the building area and downstream it), whilst Reynolds stresses and TKEs increase. However, despite the observed enhanced turbulence at the higher levels, mean concentrations increase, indicating that the mean flow has a greater influence on the dispersion. At the lower levels (Z < 60 m), the presence of a tall building enhanced dispersion (hence lower concentrations) for many of the configurations

Stage Gate Decision Making: A Scoping Review of Technology Strategic Selection Criteria for Early-Stage Projects

Innovation is a hybrid concept that has evolved over time and adapts itself to changing condition. It plays a major role in the growth and economic competitiveness of firms, and consequently, firms invest in innovation to build knowledge and thus increase competitive advantage. Front End of Innovation (FEI) projects, and in particular technology development projects, are therefore a fundamental component of innovation and a crucial factor in developing new competitive advantage. To effectively and objectively manage and evaluate these early stage technology projects, which are by nature uncertain, a number of firms deploy some form of stage gate processes, utilizing strategic decision criteria. Via a descriptive scoping literature review, we identify 46 articles, which contain 473 uniquely identified strategic selection criteria that can fit in 9 categories. We find that technical feasibility is the most frequently occurring unique selection criteria, whereas the market category is the largest category by size of unique individual criteria with 23.3% of the share total. In this research, we contribute to the on-going discussion about early stage technology projects and their effective evaluation using strategic technology selection criteria. The summary of criteria provided with definitions may serve as guidelines for technology and innovation managers.EPSRC DTP Studentship for Ph

Enhancing CFD-LES air pollution prediction accuracy using data assimilation

It is recognised worldwide that air pollution is the cause of premature deaths daily, thus necessitating the development of more reliable and accurate numerical tools. The present study implements a three dimensional Variational (3DVar) data assimilation (DA) approach to reduce the discrepancy between predicted pollution concentrations based on Computational Fluid Dynamics (CFD) with the ones measured in a wind tunnel experiment. The methodology is implemented on a wind tunnel test case which represents a localised neighbourhood environment. The improved accuracy of the CFD simulation using DA is discussed in terms of absolute error, mean squared error and scatter plots for the pollution concentration. It is shown that the difference between CFD results and wind tunnel data, computed by the mean squared error, can be reduced by up to three order of magnitudes when using DA. This reduction in error is preserved in the CFD results and its benefit can be seen through several time steps after re-running the CFD simulation. Subsequently an optimal sensors positioning is proposed. There is a trade-off between the accuracy and the number of sensors. It was found that the accuracy was improved when placing/considering the sensors which were near the pollution source or in regions where pollution concentrations were high. This demonstrated that only 14% of the wind tunnel data was needed, reducing the mean squared error by one order of magnitude

Common Cause Versus Dynamic Mutualism: An Empirical Comparison of Two Theories of Psychopathology in Two Large Longitudinal Cohorts

Mental disorders are among the leading causes of global disease burden. To respond effectively, a strong understanding of the structure of psychopathology is critical. We empirically compared two competing frameworks, dynamic-mutualism theory and common-cause theory, that vie to explain the development of psychopathology. We formalized these theories in statistical models and applied them to explain change in the general factor of psychopathology (p factor) from early to late adolescence ( N = 1,482) and major depression in middle adulthood and old age ( N = 6,443). Change in the p factor was better explained by mutualism according to model-fit indices. However, a core prediction of mutualism was not supported (i.e., predominantly positive causal interactions among distinct domains). The evidence for change in depression was more ambiguous. Our results support a multicausal approach to understanding psychopathology and showcase the value of translating theories into testable statistical models for understanding developmental processes in clinical sciences

A domain decomposition non-intrusive reduced order model for turbulent flows

In this paper, a new Domain Decomposition Non-Intrusive Reduced Order Model (DDNIROM) is developed for turbulent flows. The method works by partitioning the computational domain into a number of subdomains in such a way that the summation of weights associated with the finite element nodes within each subdomain is approximately equal, and the communication between subdomains is minimised. With suitably chosen weights, it is expected that there will be approximately equal accuracy associated with each subdomain. This accuracy is maximised by allowing the partitioning to occur through areas of the domain that have relatively little flow activity, which, in this case, is characterised by the pointwise maximum Reynolds stresses.A Gaussian Process Regression (GPR) machine learning method is used to construct a set of local approximation functions (hypersurfaces) for each subdomain. Each local hypersurface represents not only the fluid dynamics over the subdomain it belongs to, but also the interactions of the flow dynamics with the surrounding subdomains. Thus, in this way, the surrounding subdomains may be viewed as providing boundary conditions for the current subdomain.We consider a specific example of turbulent air flow within an urban neighbourhood at a test site in London and demonstrate the effectiveness of the proposed DDNIROM

Common cause versus dynamic mutualism:An empirical comparison of two theories of psychopathology in two large longitudinal cohorts

Stress and Psychopatholog

Socio-legal studies and the humanities – law, interdisciplinarity and integrity

publication-status: Publishedtypes: ArticleInaugural lecture delivered at the SLSA Socio-Legal Studies and the Humanities Conference, at the Institute of Advanced Legal Studies, London, 5 November 2008.Published version; Published online by Cambridge University Press. Copyright © Cambridge University Press, 2009. Available online at http://journals.cambridge.org/This paper was delivered as a plenary lecture, designed to respond to the one-day special conference focus upon links between socio-legal studies and the humanities. The paper focuses in particular upon the relationship between law and the humanities. It may be argued that the role of empirically sourced socio-legal research is well accepted, given its tangible utility in terms of producing hard data which can inform and transform policy perspectives. However, scholarly speculation about the relationship between law and the humanities ranges from the indulgent to the hostile. In particular, legal scholars aligning themselves as ‘black letter’ commentators express strong opinions about such links, suggesting that scholarship purporting to establish links between the two fields is essentially spurious, bearing in mind the purposive role of law as a problem-solving mechanism. The paper sets out to challenge such assertions, indicating the natural connections between the two fields and the philosophical necessity of continued interaction, given the fact that certain aspects of human experience and nature cannot be plumbed by doctrine or empiricism or even by combinations of the two. Law must be understood to stand at the nexus of human experience, in a relationship of integrity, where the word is understood to mean both morally principled and culturally integrated. In particular, the development of human qualities, of character and moral sensibility informing normative values – and, ultimately, engagement with the world of law – is a process of subtle cultural as well as psychological significance, and may benefit from interrogation deriving from the wider fields of human discourse

How tall buildings affect turbulent air flows and dispersion of pollution within a neighbourhood

The city of London, UK, has seen in recent years an increase in the number of high-rise/multi-storey buildings (“skyscrapers”) with roof heights reaching 150 m and beyond, with the Shard being a prime example with a height of ∼310 m. This changing cityscape together with recent plans of local authorities of introducing Combined Heat and Power Plant (CHP) led to a detailed study in which CFD and wind tunnel studies were carried out to assess the effect of such high-rise buildings on the dispersion of air pollution in their vicinity. A new, open-source simulator, FLUIDITY, which incorporates the Large Eddy Simulation (LES) method, was implemented; the simulated results were subsequently validated against experimental measurements from the EnFlo wind tunnel. The novelty of the LES methodology within FLUIDITY is based on the combination of an adaptive, unstructured, mesh with an eddy-viscosity tensor (for the sub-grid scales) that is anisotropic. The simulated normalised mean concentrations results were compared to the corresponding wind tunnel measurements, showing for most detector locations good correlations, with differences ranging from 3% to 37%. The validation procedure was followed by the simulation of two further hypothetical scenarios, in which the heights of buildings surrounding the source building were increased. The results showed clearly how the high-rise buildings affected the surrounding air flows and dispersion patterns, with the generation of “dead-zones” and high-concentration “hotspots” in areas where these did not previously exist. The work clearly showed that complex CFD modelling can provide useful information to urban planners when changes to cityscapes are considered, so that design options can be tested against environmental quality criteria. This study shows how the presence of tall buildings affects the dispersion of air pollutants within a small neighbourhood, and how concentration hotspots can be generated in areas which were previously pollution- free

MIRTO: an open-source robotic platform for education

This paper introduces the MIddlesex RoboTic platfOrm (MIRTO), an open-source platform that has been used for teaching First Year Computer Science students since the academic year 2013/2014, with the aim of providing a physical manifestation of Software Engineering concepts that are often delivered using only abstract or synthetic case studies. In this paper we provide a detailed description of the platform, whose hardware specifications and software libraries are all released open source; we describe a number of teaching usages of the platform, report students’ projects, and evaluate some of its aspects in terms of effectiveness, usability, and maintenance