The monitoring of ecological quality and the classification of standing waters in temperate regions: a review and proposal based on a worked scheme for British waters

This paper reviews the ways that quality can be assessed in standing waters, a subject that has hitherto attracted little attention but which is now a legal requirement in Europe. It describes a scheme for the assessment and monitoring of water and ecological quality in standing waters greater than about I ha in area in England & Wales although it is generally relevant to North-west Europe. Thirteen hydrological, chemical and biological variables are used to characterise the standing water body in any current sampling. These are lake volume, maximum depth, onductivity, Secchi disc transparency, pH, total alkalinity, calcium ion concentration, total N concentration,winter total oxidised inorganic nitrogen (effectively nitrate) concentration, total P concentration, potential maximum chlorophyll a concentration, a score based on the nature of the submerged and emergent plant community, and the presence or absence of a fish community. Inter alia these variables are key indicators of the state of eutrophication, acidification, salinisation and infilling of a water body.

Photovoltaic and solar-assisted ground-source heat pump systems

This review investigates the performance of photovoltaic and solar-assisted ground-source heat pumps in which solar heat is transferred to the ground to improve the coefficient of performance. A number of studies indicate that, for systems with adequately sized ground heat exchangers, the effect on system efficiency is small: about 1% improvement if the heat source is photovoltaic, a 1–2% decline if the source is solar thermal. With possible exceptions for systems in which the ground heat exchanger is undersized, or natural recharge from ground water is insufficient, solar thermal energy is better used for domestic hot water than to recharge ground heat. This appears particularly true outside the heating season, as although much of the heat extracted from the ground can be replaced, it seems to have little effect on the coefficient of performance. Any savings in electrical consumption that do result from an improved coefficient can easily be outweighed by an inefficient control system for the circulation pumps.

Expression of the efflux pump genes cmeB, cmeF and the porin gene porA in multiple-antibiotic-resistant Campylobacter jejuni

Aims: In Escherichia coli, increased expression of efflux pumps and/or decreased expression of porins can confer multiple antibiotic resistance (MAR), causing resistance to at least three unrelated classes of antibiotics, detergents and dyes. It was hypothesized that in Campylobacter jejuni, the efflux systems CmeABC, CmeDEF and the major outer membrane porin protein, MOMP (encoded by porA) could confer MAR. Methods: The expression of cmeB, cmeF and porA in 32 MAR C. jejuni isolated from humans or poultry was determined by comparative (C)-reverse transcriptase (RT)-PCR and denaturing DHPLC. A further 13 ethidium bromide-resistant isolates and three control strains were also investigated. Accumulation of ciprofloxacin carbonyl cyanide-m-chlorophenyl hydrazone (CCCP) was also determined for all strains. Results: Although resistance to ethidium bromide has been associated with MAR, expression of all three genes was similar in the ethidium bromide-resistant isolates. These data indicate that CmeB, CmeF and MOMP play no role in resistance to this agent in C. jejuni. Six MAR isolates over-expressed cmeB, 3/32 over-expressed cmeB and cmeF. No isolates over-expressed cmeF alone. Expression of porA was similar in all isolates. All nine isolates that over-expressed cmeB contained a mutation in cmeR, substituting glycine 86 with alanine. All cmeB over-expressing isolates also accumulated low concentrations of ciprofloxacin, which were restored to wild-type levels in the presence of CCCP. Conclusions: These data indicate that over-expression of cmeB is associated with MAR in isolates of C. jejuni. However, as cmeB was over-expressed by only one-third (9/32) of MAR isolates, these data also indicate other mechanisms of MAR in C. jejuni.

Fluoroquinolone treatment of experimental Salmonella enterica serovar Typhimurium DT104 infections in chickens selects for both gyrA mutations and changes in efflux pump gene expression

Objectives: To determine the efficacy of enrofloxacin (Baytril) in chickens in eradicating three different resistance phenotypes of Salmonella enterica and to examine the resistance mechanisms of resulting mutants. Methods: In two separate replicate experiments (I and 11), three strains of Salmonella enterica serovar Typhimurium DT104 [strain A, fully antibiotic-sensitive strain; strain B, isogenic multiple antibiotic-resistant (MAR) derivative of A; strain C, veterinary penta-resistant phenotype strain containing GyrA Phe-83], were inoculated into day-old chicks at similar to 10(3) Cfu/bird. At day 10, groups of chicks (n =10) were given either enrofloxacin at 50 ppm in their drinking water for 5 days or water alone (control). Caecal contents were monitored for presence of Salmonella and colonies were replica plated to media containing antibiotics or overlaid with cyclohexane to determine the proportion of isolates with reduced susceptibility. The MICs of antibiotics and cyclohexane tolerance were determined for selected isolates from the chicks. Mutations in topoisomerase genes were examined by DHPLC and expression of marA, soxS, acrB, acrD and acrF by RT-PCR. Results: In experiment 1, but not 11, enrofloxacin significantly reduced the numbers of strain A compared with the untreated control group. In experiment 11, but not 1, enrofloxacin significantly reduced the numbers of strain B. Shedding of strain C was unaffected by enrofloxacin treatment. Birds infected with strains A and B gave rise to isolates with decreased fluoroquinolone susceptibility. Isolates derived from strain A or B requiring > 128 mg/L nalidixic acid for inhibition contained GyrA Asn-82 or Phe-83. Isolates inhibited by 16 mg/L nalidixic acid were also less susceptible to antibiotics of other chemical classes and became cyclohexane-tolerant (e.g. MAR). Conclusions: These studies demonstrate that recommended enrofloxacin treatment of chicks rapidly selects for strains with reduced fluoroquinolone susceptibility from fully sensitive and MAR strains. It can also select for MAR isolates.

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.

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.

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.

J-PMCRI: a methodology for inducing pre-pruned modular classification rules

Inducing rules from very large datasets is one of the most challenging areas in data mining. Several approaches exist to scaling up classification rule induction to large datasets, namely data reduction and the parallelisation of classification rule induction algorithms. In the area of parallelisation of classification rule induction algorithms most of the work has been concentrated on the Top Down Induction of Decision Trees (TDIDT), also known as the ‘divide and conquer’ approach. However powerful alternative algorithms exist that induce modular rules. Most of these alternative algorithms follow the ‘separate and conquer’ approach of inducing rules, but very little work has been done to make the ‘separate and conquer’ approach scale better on large training data. This paper examines the potential of the recently developed blackboard based J-PMCRI methodology for parallelising modular classification rule induction algorithms that follow the ‘separate and conquer’ approach. A concrete implementation of the methodology is evaluated empirically on very large datasets.

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.

Distributed classification for pocket data mining

Distributed and collaborative data stream mining in a mobile computing environment is referred to as Pocket Data Mining PDM. Large amounts of available data streams to which smart phones can subscribe to or sense, coupled with the increasing computational power of handheld devices motivates the development of PDM as a decision making system. This emerging area of study has shown to be feasible in an earlier study using technological enablers of mobile software agents and stream mining techniques [1]. A typical PDM process would start by having mobile agents roam the network to discover relevant data streams and resources. Then other (mobile) agents encapsulating stream mining techniques visit the relevant nodes in the network in order to build evolving data mining models. Finally, a third type of mobile agents roam the network consulting the mining agents for a final collaborative decision, when required by one or more users. In this paper, we propose the use of distributed Hoeffding trees and Naive Bayes classifers in the PDM framework over vertically partitioned data streams. Mobile policing, health monitoring and stock market analysis are among the possible applications of PDM. An extensive experimental study is reported showing the effectiveness of the collaborative data mining with the two classifers.

Scaling up classification rule induction through parallel processing

The fast increase in the size and number of databases demands data mining approaches that are scalable to large amounts of data. This has led to the exploration of parallel computing technologies in order to perform data mining tasks concurrently using several processors. Parallelization seems to be a natural and cost-effective way to scale up data mining technologies. One of the most important of these data mining technologies is the classification of newly recorded data. This paper surveys advances in parallelization in the field of classification rule induction.

Homogeneous and heterogeneous distributed classification for pocket data mining

Pocket Data Mining (PDM) describes the full process of analysing data streams in mobile ad hoc distributed environments. Advances in mobile devices like smart phones and tablet computers have made it possible for a wide range of applications to run in such an environment. In this paper, we propose the adoption of data stream classification techniques for PDM. Evident by a thorough experimental study, it has been proved that running heterogeneous/different, or homogeneous/similar data stream classification techniques over vertically partitioned data (data partitioned according to the feature space) results in comparable performance to batch and centralised learning techniques.

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.