Origin of the Parry arc

Laboratory experiments to determine the preferred orientation of free-falling hexagonal prisms were performed at Reynolds numbers appropriate to falling ice crystals in the atmosphere. Hexagonal plates orient with their c axis vertical for aspect ratios < 0.9, whilst hexagonal columns fall with their c axis horizontal. A secondary alignment is also observed: regular hexagonal columns fall preferentially with two prism facets aligned vertically and not horizontally – the latter scenario was previously assumed to be responsible for the rare Parry arc. However, if the column is made scalene in its cross-section, it can orient such that a pair of prism facets is horizontal. This finding indicates that the development of scalene crystals may be key to the production of certain ice-crystal optical phenomena

Use of in situ FT-Raman spectroscopy to study the kinetics of the transformation of carbamazepine polymorphs

The solid-state transformation of carbamazepine from form III to form I was examined by Fourier Transform Raman spectroscopy. Using a novel environmental chamber, the isothermal conversion was monitored in situ at 130◦C, 138◦C, 140◦C and 150◦C. The rate of transformation was monitored by taking the relative intensities of peaks arising from two C H bending modes; this approach minimised errors due to thermal artefacts and variations in power intensities or scattering efficiencies from the samples in which crystal habit changed from a characteristic prism morphology (form III) to whiskers (form I). The solid-state transformation at the different temperatures was fitted to various solid-state kinetic models of which four gave good fits, thus indicating the complexity of the process which is known to occur via a solid–gas–solid mechanism. Arrhenius plots from the kinetic models yielded activation energies from 344 kJ mol−1 to 368 kJ mol−1 for the transformation. The study demonstrates the value of a rapid in situ analysis of drug polymorphic type which can be of value for at-line in-process control.

The civic zeitgeist: nationalism and liberal values in the European radical right

This article examines the relationship between nationalism and liberal values, and more specifically the redefinition of boundaries between national communities and others in the rhetoric of radical right parties in Europe. The aim is to examine the tension between radical right party discourse and the increasing need to shape this discourse in liberal terms. We argue that the radical right parties that successfully operate within the democratic system tend to be those best able to tailor their discourse to the liberal and civic characteristics of national identity so as to present themselves and their ideologies as the true authentic defenders of the nation's unique reputation for democracy, diversity and tolerance. Comparing the success of a number of European radical right parties ranging from the most electorally successful SVP to the more mixed BNP, FN and NPD, we show that the parties that effectively deploy the symbolic resources of national identity through a predominantly voluntaristic prism tend to be the ones that fare better within their respective political systems. In doing so, we challenge the conventional view in the study of nationalism which expects civic values to shield countries from radicalism and extremism.

Non-display uses of digital works: Google Books and beyond

With the advent of mass digitization projects, such as the Google Book Search, a peculiar shift has occurred in the way that copyright works are dealt with. Contrary to what has so far been the case, works are turned into machine-readable data to be automatically processed for various purposes without the expression of works being displayed to the public. In the Google Book Settlement Agreement, this new kind of usage is referred to as ‘non-display uses’ of digital works. The legitimacy of these uses has not yet been tested by Courts and does not comfortably fit in the current copyright doctrine, plainly because the works are not used as works but as something else, namely as data. Since non-display uses may prove to be a very lucrative market in the near future, with the potential to affect the way people use copyright works, we examine non-display uses under the prism of copyright principles to determine the boundaries of their legitimacy. Through this examination, we provide a categorization of the activities carried out under the heading of ‘non-display uses’, we examine their lawfulness under the current copyright doctrine and approach the phenomenon from the spectrum of data protection law that could apply, by analogy, to the use of copyright works as processable data.

PMCRI: a parallel modular classification rule induction framework

In a world where massive amounts of data are recorded on a large scale we need data mining technologies to gain knowledge from the data in a reasonable time. The Top Down Induction of Decision Trees (TDIDT) algorithm is a very widely used technology to predict the classification of newly recorded data. However alternative technologies have been derived that often produce better rules but do not scale well on large datasets. Such an alternative to TDIDT is the PrismTCS algorithm. PrismTCS performs particularly well on noisy data but does not scale well on large datasets. In this paper we introduce Prism and investigate its scaling behaviour. We describe how we improved the scalability of the serial version of Prism and investigate its limitations. We then describe our work to overcome these limitations by developing a framework to parallelise algorithms of the Prism family and similar algorithms. We also present the scale up results of a first prototype implementation.

Parallel induction of modular classification rules

The Distributed Rule Induction (DRI) project at the University of Portsmouth is concerned with distributed data mining algorithms for automatically generating rules of all kinds. In this paper we present a system architecture and its implementation for inducing modular classification rules in parallel in a local area network using a distributed blackboard system. We present initial results of a prototype implementation based on the Prism algorithm.

Parallel rule induction with information theoretic pre-pruning

In a world where data is captured on a large scale the major challenge for data mining algorithms is to be able to scale up to large datasets. There are two main approaches to inducing classification rules, one is the divide and conquer approach, also known as the top down induction of decision trees; the other approach is called the separate and conquer approach. A considerable amount of work has been done on scaling up the divide and conquer approach. However, very little work has been conducted on scaling up the separate and conquer approach.In this work we describe a parallel framework that allows the parallelisation of a certain family of separate and conquer algorithms, the Prism family. Parallelisation helps the Prism family of algorithms to harvest additional computer resources in a network of computers in order to make the induction of classification rules scale better on large datasets. Our framework also incorporates a pre-pruning facility for parallel Prism algorithms.

Towards a computationally efficient approach to modular classification rule induction

Induction of classification rules is one of the most important technologies in data mining. Most of the work in this field has concentrated on the Top Down Induction of Decision Trees (TDIDT) approach. However, alternative approaches have been developed such as the Prism algorithm for inducing modular rules. Prism often produces qualitatively better rules than TDIDT but suffers from higher computational requirements. We investigate approaches that have been developed to minimize the computational requirements of TDIDT, in order to find analogous approaches that could reduce the computational requirements of Prism.

Induction of modular classification rules: using Jmax-pruning

The Prism family of algorithms induces modular classification rules which, in contrast to decision tree induction algorithms, do not necessarily fit together into a decision tree structure. Classifiers induced by Prism algorithms achieve a comparable accuracy compared with decision trees and in some cases even outperform decision trees. Both kinds of algorithms tend to overfit on large and noisy datasets and this has led to the development of pruning methods. Pruning methods use various metrics to truncate decision trees or to eliminate whole rules or single rule terms from a Prism rule set. For decision trees many pre-pruning and postpruning methods exist, however for Prism algorithms only one pre-pruning method has been developed, J-pruning. Recent work with Prism algorithms examined J-pruning in the context of very large datasets and found that the current method does not use its full potential. This paper revisits the J-pruning method for the Prism family of algorithms and develops a new pruning method Jmax-pruning, discusses it in theoretical terms and evaluates it empirically.

Jmax-pruning: a facility for the information theoretic pruning of modular classification rules

The Prism family of algorithms induces modular classification rules in contrast to the Top Down Induction of Decision Trees (TDIDT) approach which induces classification rules in the intermediate form of a tree structure. Both approaches achieve a comparable classification accuracy. However in some cases Prism outperforms TDIDT. For both approaches pre-pruning facilities have been developed in order to prevent the induced classifiers from overfitting on noisy datasets, by cutting rule terms or whole rules or by truncating decision trees according to certain metrics. There have been many pre-pruning mechanisms developed for the TDIDT approach, but for the Prism family the only existing pre-pruning facility is J-pruning. J-pruning not only works on Prism algorithms but also on TDIDT. Although it has been shown that J-pruning produces good results, this work points out that J-pruning does not use its full potential. The original J-pruning facility is examined and the use of a new pre-pruning facility, called Jmax-pruning, is proposed and evaluated empirically. A possible pre-pruning facility for TDIDT based on Jmax-pruning is also discussed.

Computationally efficient induction of classification rules with the PMCRI and J-PMCRI frameworks

In order to gain knowledge from large databases, scalable data mining technologies are needed. Data are captured on a large scale and thus databases are increasing at a fast pace. This leads to the utilisation of parallel computing technologies in order to cope with large amounts of data. In the area of classification rule induction, parallelisation of classification rules has focused on the divide and conquer approach, also known as the Top Down Induction of Decision Trees (TDIDT). An alternative approach to classification rule induction is separate and conquer which has only recently been in the focus of parallelisation. This work introduces and evaluates empirically a framework for the parallel induction of classification rules, generated by members of the Prism family of algorithms. All members of the Prism family of algorithms follow the separate and conquer approach.

Developing construction professional services in the international market: SWOT analysis of China

Construction professional services (CPSs), such as architecture, engineering, and consultancy, are not only high value-added profit centers in their own right but also have a knock-on effect on other businesses, such as construction and the export of materials and machinery. Arguably, competition in the international construction market has shifted to these knowledge-intensive CPS areas. Yet CPSs represent a research frontier that has received scant attention. This research aims to enrich the body of knowledge on CPSs by examining strengths, weaknesses, opportunities, and threats (SWOT) of Chinese CPSs (CCPSs) in the international context. It does so by triangulating theories with quantitative and qualitative data gleaned from yearbooks, annual reports, interviews, seminars, and interactions with managers in major CCPS companies. It is found that CCPSs present both strengths and weaknesses in talents, administration systems, and development strategies in dealing with the external opportunities and threats brought about by globalization and market evolution. Low price, which has helped the Chinese construction business to succeed in the international market, is also a major CCPS strength. An opportunity for CCPSs is the relatively strong delivery capability possessed by Chinese contractors; by partnering with them CCPSs can better establish themselves in the international arena. This is probably the first ever comprehensive study on the performance of CCPSs in the international marketplace. The research is conducted at an opportune time, particularly when the world is witnessing the burgeoning force of Chinese businesses in many areas including manufacturing, construction, and, potentially, professional services. It adds new insights to the knowledge body of CPSs and provides valuable references to other countries faced with the challenge of developing CPS business efficiently in the international market.

Developing construction professional services in the international market: a SWOT analysis of China

In contrast to their bustling construction counterparts, Chinese construction professional services (CPS) such as architecture, engineering, and consultancy, seem still to be stagnant in the international market. CPS are not only high value-added profit centers in their own right, but also have a knock-on effect on subsequent businesses such as construction, and the export of materials and machinery. Arguably, competition in the international construction market has shifted to knowledge-intensive CPS. Yet,CPS represent a research area that has been paid scant attention. This research aims to add to the body of knowledge of CPS by examining strengths, weaknesses, opportunities, and threats (SWOT) of Chinese CPS (CCPS) in the international context. It does so by triangulating theories with quantitative and qualitative data gleaned from yearbooks, annual reports, interviews, seminars, and interactions with managers in major CCPS companies. It is found that CCPS present both strengths and weaknesses in talents, administration systems, and development strategies in dealing with the external opportunities and threats brought about by globalization and market evolvement. Low price, which has helped the Chinese construction business to succeed in the international market, is also a CCPS major strength. An opportunity for CCPS is the relatively strong delivery capability possessed by Chinese contractors. By partnering with them CCPS can better edge into the international arena. This is probably the first ever comprehensive study in investigating the performance of CCPS in the international market. The research is also timely, particularly when the world is witnessing the burgeoning force of Chinese businesses in many areas including manufacturing, construction, and potentially, professional services.

J-measure based hybrid pruning for complexity reduction in classification rules

Prism is a modular classification rule generation method based on the ‘separate and conquer’ approach that is alternative to the rule induction approach using decision trees also known as ‘divide and conquer’. Prism often achieves a similar level of classification accuracy compared with decision trees, but tends to produce a more compact noise tolerant set of classification rules. As with other classification rule generation methods, a principle problem arising with Prism is that of overfitting due to over-specialised rules. In addition, over-specialised rules increase the associated computational complexity. These problems can be solved by pruning methods. For the Prism method, two pruning algorithms have been introduced recently for reducing overfitting of classification rules - J-pruning and Jmax-pruning. Both algorithms are based on the J-measure, an information theoretic means for quantifying the theoretical information content of a rule. Jmax-pruning attempts to exploit the J-measure to its full potential because J-pruning does not actually achieve this and may even lead to underfitting. A series of experiments have proved that Jmax-pruning may outperform J-pruning in reducing overfitting. However, Jmax-pruning is computationally relatively expensive and may also lead to underfitting. This paper reviews the Prism method and the two existing pruning algorithms above. It also proposes a novel pruning algorithm called Jmid-pruning. The latter is based on the J-measure and it reduces overfitting to a similar level as the other two algorithms but is better in avoiding underfitting and unnecessary computational effort. The authors conduct an experimental study on the performance of the Jmid-pruning algorithm in terms of classification accuracy and computational efficiency. The algorithm is also evaluated comparatively with the J-pruning and Jmax-pruning algorithms.