A constant craving for fresh brains and a taste for decaffeinated neighbours

This article is a radical rethinking of public international law through the use of Lacanian psychoanalysis. Its central thesis is that while contemporary scholarship addresses what Lacan calls the symbolic and imaginary registers including law, politics, and ideology, it continues to ignore and repress the dimension of the real. The article illustrates this with a clinical example examined by Kris and discussed by Lacan. Imagining public international law as an indefatigable neurotic in search of ‘fresh brains’, the article shows why meeting her in the domains of law and politics is not enough to satiate her appetite. What continues to resist is the ‘extimate’, the inhuman element within the human that the subject hides so well from herself that it is excluded in the interior. A major instance of the extimate is the ‘caffeinated neighbour’, that is, the neighbour who is not in our image because her disturbing core has not been subtracted. The article argues that unless international law comes to terms with this inevitably ugly and obscene core, in oneself as well as in the neighbour, it cannot hope to achieve any meaningful changes. That the need to recognize the extimate is the ethical demand facing international law now; unless we address it, our symptoms will continue to grow and we will continue to crave fresh brains

To be or not to be a (dead) father

This article argues that Hamlet’s tragedy shows the idiosyncratic intervention of the law in the creation of human subjectivity, an intervention that relies on the agency of a (dead) father to regulate the subject’s desire. Hamlet’s tragedy, it suggests, is characteristically modern not because Hamlet unconsciously desired to do what Oedipus consciously did, but because of the added ingredient of Hamlet’s, and of the father’s, knowledge: Hamlet not only knows of the father’s death, he also knows that the father knows. In Hamlet the prior father, the father of Totem and Taboo, is reincarnated in the person of Claudius so the ‘progress’ from Oedipus to Hamlet, is from tragedy to obscenity. The Crown as phallus is called upon to conceal the obscenity but Hamlet, like any good analyst, plays and displays language to reveal the rotten crime at the heart of the State of Denmark and of the Law

Semiparametric identification in panel data discrete response models

This paper studies semiparametric identification in linear index discrete response panel data models with fixed effects. Departing from the classic binary response static panel data model, this paper examines identification in the binary response dynamic panel data model and the ordered response static panel data model. It is shown that under mild distributional assumptions on the fixed effect and the time-varying unobservables point-identification fails, but informative bounds on the regression coefficients can still be derived. Partial identification is achieved by eliminating the fixed effect and discovering features of the distribution of the unobservable time-varying components that do not depend on the unobserved heterogeneity. Numerical analyses illustrate how the identification bounds change as the support of the explanatory variables varies.</p

New regression methods for measures of central tendency

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityMeasures of central tendency have been widely used for summarising statistical data, with the mean being the most popular summary statistic. However, in reallife applications it is not always the most representative measure of central location, especially when dealing with data which is skewed or contains outliers. Alternative statistics with less bias are the median and the mode. Median and quantile regression has been used in different fields to examine the effect of factors at different points of the distribution. Mode estimation, on the other hand, has found many applications in cases where the analysis focuses on obtaining information about the most typical value or pattern. This thesis demonstrates that mode also plays an important role in the analysis of big data, which is becoming increasingly important in many sectors of the global economy. However, mode regression has not been widely applied, even though there is a clear conceptual benefit, due to the computational and theoretical limitations of the existing estimators. Similarly, despite the popularity of the binary quantile regression model, computational straight forward estimation techniques do not exist. Driven by the demand for simple, well-found and easy to implement inference tools, this thesis develops a series of new regression methods for mode and binary quantile regression. Chapter 2 deals with mode regression methods from the Bayesian perspective and presents one parametric and two non-parametric methods of inference. Chapter 3 demonstrates a mode-based, fast pattern-identification method for big data and proposes the first fully parametric mode regression method, which effectively uncovers the dependency of typical patterns on a number of covariates. The proposed approach is demonstrated through the analysis of a decade-long dataset on the Body Mass Index and associated factors, taken from the Health Survey for England. Finally, Chapter 4 presents an alternative binary quantile regression approach, based on the nonlinear least asymmetric weighted squares, which can be implemented using standard statistical packages and guarantees a unique solution

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)

Assessing the Horizontal Homogeneity of the Atmospheric Boundary Layer (HHABL) Profile Using Different CFD Software

One of the main factors affecting the reliability of computational fluid dynamics (CFD) simulations for the urban environment is the Horizontal Homogeneity of the Atmospheric Boundary Layer (HHABL) profile—meaning the vertical profiles of the mean streamwise velocity, the turbulent kinetic energy, and dissipation rate are maintained throughout the streamwise direction of the computational domain. This paper investigates the preservation of the HHABL profile using three different commercial CFD codes—the ANSYS Fluent, the ANSYS CFD, and the Siemens STAR-CCM+ software. Three different cases were considered, identified by their different inlet conditions for the inlet velocity, turbulent kinetic energy, and dissipation rate profiles. Simulations were carried out using the RANS k-ε turbulence model. Slight variations in the eddy viscosity models, as well as in the wall boundary conditions, were identified in the different software, with the standard wall function with roughness being implemented in the Fluent applications, the scalable wall function with roughness in the CFX applications, and the blended wall function option in the STAR-CCM+ simulations. There was a slight difference in the meshing approach in the three different software, with a prism-layer option in the STAR-CCM+ software, which allowed a finer mesh near the wall/ground boundary. The results show all three software are able to preserve the horizontal homogeneity of the ABL—less than 0.5% difference between the software—indicating very similar degrees of accuracy

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

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

Modelling of Packed Bed and Coated Wall Microreactors for 6 Methanol Steam Reforming for Hydrogen Production

A Computational Fluid Dynamics (CFD) study has been conducted to assess the performance of packed bed and coated wall 9 microreactors for the steam reforming of methanol with a CuO/ZnO/Al2O3 based catalyst (BASF F3-01). The results obtained 10 were compared to experimental data from literature to assess the validity and robustness of the models, and a good 11 validation has been obtained. The performance of the packed bed and coated wall microreactors are similar at a constant 12 reforming temperature. It was found that methanol conversion is enhanced with increasing temperature, residence time, 13 steam to methanol ratio, and catalyst coating thickness. Furthermore, internal and external mass transfer phenomena were 14 investigated using the models, and it was found that there were no internal and external mass transfer resistances for this 15 reactor configurations. Further studies demonstrated that larger catalyst pellet sizes led to the presence of internal mass 16 transfer resistances, which in turn causes lower methanol conversions. The CFD models have exhibited a sound agreement 17 with the experimental data, hence they can be used to predict the steam reforming of methanol in microreactors