The paradox of nationalism: the common denominator of radical right and radical left euroscepticism

What can explain the strong euroscepticism of radical parties of both the right and the left? This article argues that the answer lies in the paradoxical role of nationalism as a central element in both party families, motivating opposition towards European integration. Conventionally, the link between nationalism and euroscepticism is understood solely as a prerogative of radical right-wing parties, whereas radical left-wing euroscepticism is associated with opposition to the neoliberal character of the European Union.This article contests this view. It argues that nationalism cuts across party lines and constitutes the common denominator of both radical right-wing and radical left-wing euroscepticism. It adopts a mixed-methods approach, combining intensive case study analysis with quantitative analysis of party manifestos. First, it traces the link between nationalism and euroscepticism in Greece and France in order to demonstrate the internal validity of the argument. It then undertakes a cross-country statistical estimation to assess the external validity of the argument and its generalisability across Europe.

Drug control, human rights, and the right to the highest attainable standard of health: a reply to Saul Takahashi

A recent article in this journal challenged claims that a human rights framework should be applied to drug control. This article questions the author’s assertions and reframes them in the context of socio-legal drug scholarship, aiming to build on the discourse concerning human rights and drug use. It is submitted that a rights-based approach is a necessary, indeed obligatory, ethical and legal framework through which to address drug use and that international human rights law provides the proper scope for determining where interferences with individual human rights might be justified on certain, limited grounds.

P-Prism: a computationally efficient approach to scaling up classification rule induction

Top Down Induction of Decision Trees (TDIDT) is the most commonly used method of constructing a model from a dataset in the form of classification rules to classify previously unseen data. Alternative algorithms have been developed such as the Prism algorithm. Prism constructs modular rules which produce qualitatively better rules than rules induced by TDIDT. However, along with the increasing size of databases, many existing rule learning algorithms have proved to be computational expensive on large datasets. To tackle the problem of scalability, parallel classification rule induction algorithms have been introduced. As TDIDT is the most popular classifier, even though there are strongly competitive alternative algorithms, most parallel approaches to inducing classification rules are based on TDIDT. In this paper we describe work on a distributed classifier that induces classification rules in a parallel manner based on Prism.

Random prism: an alternative to random forests

Ensemble learning techniques generate multiple classifiers, so called base classifiers, whose combined classification results are used in order to increase the overall classification accuracy. In most ensemble classifiers the base classifiers are based on the Top Down Induction of Decision Trees (TDIDT) approach. However, an alternative approach for the induction of rule based classifiers is the Prism family of algorithms. Prism algorithms produce modular classification rules that do not necessarily fit into a decision tree structure. Prism classification rulesets achieve a comparable and sometimes higher classification accuracy compared with decision tree classifiers, if the data is noisy and large. Yet Prism still suffers from overfitting on noisy and large datasets. In practice ensemble techniques tend to reduce the overfitting, however there exists no ensemble learner for modular classification rule inducers such as the Prism family of algorithms. This article describes the first development of an ensemble learner based on the Prism family of algorithms in order to enhance Prism’s classification accuracy by reducing overfitting.

Parallel random prism: a computationally efficient ensemble learner for classification

Generally classifiers tend to overfit if there is noise in the training data or there are missing values. Ensemble learning methods are often used to improve a classifier's classification accuracy. Most ensemble learning approaches aim to improve the classification accuracy of decision trees. However, alternative classifiers to decision trees exist. The recently developed Random Prism ensemble learner for classification aims to improve an alternative classification rule induction approach, the Prism family of algorithms, which addresses some of the limitations of decision trees. However, Random Prism suffers like any ensemble learner from a high computational overhead due to replication of the data and the induction of multiple base classifiers. Hence even modest sized datasets may impose a computational challenge to ensemble learners such as Random Prism. Parallelism is often used to scale up algorithms to deal with large datasets. This paper investigates parallelisation for Random Prism, implements a prototype and evaluates it empirically using a Hadoop computing cluster.

Synthesis and crystal structures of μ-oxido- and μ-hydroxido-bridged dinuclear iron(III) complexes with an N2O donor ligand - a theoretical study on the influence of weak forces on the Fe-O-Fe bridging angle

The synthesis and crystal structures of three nonheme di-iron(III) complexes with a tridentate N,N,O Schiff-base ligand, 2-({[2-(dimethylamino) ethyl] imino} methyl) phenol (HL), are reported. Complexes [Fe2OL2(NCO)(2)] (1a) and [Fe2OL2(SAL)(2)]center dot H2O [SAL = o-(CHO)C6H4O-] (1b) are unsupported mu-oxido-bridged dimers, and [Fe-2(OH)L-2(HCOO)(2)-(Cl)] (2) is a mu-hydroxido-bridged dimer supported by a formato bridging ligand. All complexes have been characterized by X-ray crystallography and spectroscopic analysis. Complex 1b has been reported previously; however, it has been reinvestigated to confirm the presence of a crucial water molecule in the solid state. Structural analyses show that in 1a the iron atoms are pentacoordinate with a bent Fe-O-Fe angle [142.7(2)degrees], whereas in 2 the metal centers are hexacoordinate with a normal Fe-OH-Fe bridging angle [137.9(2)degrees]. The Fe-O-Fe angles in complexes 1a and 1b differ significantly to those usually shown by (mu-oxido) Fe-III complexes. A theoretical study has been performed in order to rationalize this deviation. Moreover, the influence of the water molecule observed in the solid-state structure of 1b on the Fe-O-Fe angle is also analyzed theoretically.

Valuation 'accuracy' within the Right to Buy process

Based on surveys undertaken with local authorities and valuers who provide the valuations on which purchase prices for local authority houses under the Right to Buy are based, this paper reports on research which aims to establish the reasons for the differences between the initial valuations provided by the local authority valuers and those provided by the District Valuer on appeal. The paper reports on the reasons why tenants appeal the initial valuation and discusses issues of valuation accuracy, uncertainty and the different and imperfect data available to valuers employed by the organisations involved, as well as the factors within the valuation process, including the absence of any requirement to agree a value, which contribute to the different outcomes.

Random Prism: a noise-tolerant alternative to Random Forests

Ensemble learning can be used to increase the overall classification accuracy of a classifier by generating multiple base classifiers and combining their classification results. A frequently used family of base classifiers for ensemble learning are decision trees. However, alternative approaches can potentially be used, such as the Prism family of algorithms that also induces classification rules. Compared with decision trees, Prism algorithms generate modular classification rules that cannot necessarily be represented in the form of a decision tree. Prism algorithms produce a similar classification accuracy compared with decision trees. However, in some cases, for example, if there is noise in the training and test data, Prism algorithms can outperform decision trees by achieving a higher classification accuracy. However, Prism still tends to overfit on noisy data; hence, ensemble learners have been adopted in this work to reduce the overfitting. This paper describes the development of an ensemble learner using a member of the Prism family as the base classifier to reduce the overfitting of Prism algorithms on noisy datasets. The developed ensemble classifier is compared with a stand-alone Prism classifier in terms of classification accuracy and resistance to noise.

Neural stem cells and cell replacement therapy: making the right cells

The past few years have seen major advances in the field of NSC (neural stem cell) research with increasing emphasis towards its application in cell-replacement therapy for neurological disorders. However, the clinical application of NSCs will remain largely unfeasible until a comprehensive understanding of the cellular and molecular mechanisms of NSC fate specification is achieved. With this understanding will come an increased possibility to exploit the potential of stem cells in order to manufacture transplantable NSCs able to provide a safe and effective therapy for previously untreatable neurological disorders. Since the pathology of each of these disorders is determined by the loss or damage of a specific neural cell population, it may be necessary to generate a range of NSCs able to replace specific neurons or glia rather than generating a generic NSC population. Currently, a diverse range of strategies is being investigated with this goal in mind. In this review, we focus on the relationship between NSC specification and differentiation and discuss how this information may be used to direct NSCs towards a particular fate.

Housing or property? The dynamics of housing policy and property principles in the right to buy

This paper examines the interplay and tension between housing law and policy and property law, in the specific context of the right to buy (RTB). It focuses on funding arrangements between the RTB tenant and another party. It first examines how courts determine the parties' respective entitlements in the home, highlighting the difficulty of categorising, under traditional property law principles, a contribution in the form of the statutory discount conferred on the RTB tenant. Secondly, it considers possible exploitation of the RTB scheme, both at the macro level of exploitation of the policy underpinning the legislation and, at the micro level, of exploitation of the tenant. The measures contained in the Housing Act 2004 intended to curb exploitation of the RTB are analysed to determine what can be considered to be legitimate and illegitimate uses of the scheme. It is argued that, despite the government's implicit approval, certain funding arrangements by non-resident relatives fail to give effect to the spirit of the scheme.

Control and analysis of oriented thin films of lipid inverse bicontinuous cubic phases using grazing incidence small-angle X‑ray scattering

Lipid cubic phases are complex nanostructures that form naturally in a variety of biological systems, with applications including drug delivery and nanotemplating. Most X-ray scattering studies on lipid cubic phases have used unoriented polydomain samples as either bulk gels or suspensions of micrometer-sized cubosomes. We present a method of investigating cubic phases in a new form, as supported thin films that can be analyzed using grazing incidence small-angle X-ray scattering (GISAXS). We present GISAXS data on three lipid systems: phytantriol and two grades of monoolein (research and industrial). The use of thin films brings a number of advantages. First, the samples exhibit a high degree of uniaxial orientation about the substrate normal. Second, the new morphology allows precise control of the substrate mesophase geometry and lattice parameter using a controlled temperature and humidity environment, and we demonstrate the controllable formation of oriented diamond and gyroid inverse bicontinuous cubic along with lamellar phases. Finally, the thin film morphology allows the induction of reversible phase transitions between these mesophase structures by changes in humidity on subminute time scales, and we present timeresolved GISAXS data monitoring these transformations.