Competition in tree row agroforestry systems. 3. Soil water distribution and dynamics

The purpose of this study was to test the hypothesis that soil water content would vary spatially with distance from a tree row and that the effect would differ according to tree species. A field study was conducted on a kaolinitic Oxisol in the sub-humid highlands of western Kenya to compare soil water distribution and dynamics in a maize monoculture with that under maize (Zea mays L.) intercropped with a 3-year-old tree row of Grevillea robusta A. Cunn. Ex R. Br. (grevillea) and hedgerow of Senna spectabilis DC. (senna). Soil water content was measured at weekly intervals during one cropping season using a neutron probe. Measurements were made from 20 cm to a depth of 225 cm at distances of 75, 150, 300 and 525 cm from the tree rows. The amount of water stored was greater under the sole maize crop than the agroforestry systems, especially the grevillea-maize system. Stored soil water in the grevillea-maize system increased with increasing distance from the tree row but in the senna-maize system, it decreased between 75 and 300 cm from the hedgerow. Soil water content increased least and more slowly early in the season in the grevillea-maize system, and drying was also evident as the frequency of rain declined. Soil water content at the end of the cropping season was similar to that at the start of the season in the grevillea-maize system, but about 50 and 80 mm greater in the senna-maize and sole maize systems, respectively. The seasonal water balance showed there was 140 mm, of drainage from the sole maize system. A similar amount was lost from the agroforestry systems (about 160 mm in the grevillea-maize system and 145 mm in the senna-maize system) through drainage or tree uptake. The possible benefits of reduced soil evaporation and crop transpiration close to a tree row were not evident in the grevillea-maize system, but appeared to greatly compensate for water uptake losses in the senna-maize system. Grevillea, managed as a tree row, reduced stored soil water to a greater extent than senna, managed as a hedgerow.

The role of rules-based compliance systems in the new EU regulatory landscape: perspectives of institutional change and agency

The financial crisis of 2007-2009 and the subsequent reaction of the G20 have created a new global regulatory landscape. Within the EU, change of regulatory institutions is ongoing. The research objective of this study is to understand how institutional changes to the EU regulatory landscape may affect corresponding institutionalized operational practices within financial organizations and to understand the role of agency within this process. Our motivation is to provide insight into these changes from an operational management perspective, as well as to test Thelen and Mahoney?s (2010) modes of institutional change. Consequently, the study researched implementations of an Investment Management System with a rules-based compliance module within financial organizations. The research consulted compliance and risk managers, as well as systems experts. The study suggests that prescriptive regulations are likely to create isomorphic configurations of rules-based compliance systems, which consequently will enable the institutionalization of associated compliance practices. The study reveals the ability of some agents within financial organizations to control the impact of regulatory institutions, not directly, but through the systems and processes they adopt to meet requirements. Furthermore, the research highlights the boundaries and relationships between each mode of change as future avenues of research.

Unified framework for construction of rule based classification systems

Automatic generation of classification rules has been an increasingly popular technique in commercial applications such as Big Data analytics, rule based expert systems and decision making systems. However, a principal problem that arises with most methods for generation of classification rules is the overfit-ting of training data. When Big Data is dealt with, this may result in the generation of a large number of complex rules. This may not only increase computational cost but also lower the accuracy in predicting further unseen instances. This has led to the necessity of developing pruning methods for the simplification of rules. In addition, classification rules are used further to make predictions after the completion of their generation. As efficiency is concerned, it is expected to find the first rule that fires as soon as possible by searching through a rule set. Thus a suit-able structure is required to represent the rule set effectively. In this chapter, the authors introduce a unified framework for construction of rule based classification systems consisting of three operations on Big Data: rule generation, rule simplification and rule representation. The authors also review some existing methods and techniques used for each of the three operations and highlight their limitations. They introduce some novel methods and techniques developed by them recently. These methods and techniques are also discussed in comparison to existing ones with respect to efficient processing of Big Data.

The role of rules-based compliance systems in the new EU regulatory landscape: perspectives of institutional change

Purpose The research objective of this study is to understand how institutional changes to the EU regulatory landscape may affect corresponding institutionalized operational practices within financial organizations. Design/methodology/approach The study adopts an Investment Management System as its case and investigates different implementations of this system within eight financial organizations, predominantly focused on investment banking and asset management activities within capital markets. At the systems vendor site, senior systems consultants and client relationship managers were interviewed. Within the financial organizations, compliance, risk and systems experts were interviewed. Findings The study empirically tests modes of institutional change. Displacement and Layering were found to be the most prevalent modes. However, the study highlights how the outcomes of Displacement and Drift may be similar in effect as both modes may cause compliance gaps. The research highlights how changes in regulations may create gaps in systems and processes which, in the short term, need to be plugged by manual processes. Practical implications Vendors abilities to manage institutional change caused by Drift, Displacement, Layering and Conversion and their ability to efficiently and quickly translate institutional variables into structured systems has the power to ease the pain and cost of compliance as well as reducing the risk of breeches by reducing the need for interim manual systems. Originality/value The study makes a contribution by applying recent theoretical concepts of institutional change to the topic of regulatory change uses this analysis to provide insight into the effects of this new environment

Dynamic load balancing in parallel KD-tree k-means

One among the most influential and popular data mining methods is the k-Means algorithm for cluster analysis. Techniques for improving the efficiency of k-Means have been largely explored in two main directions. The amount of computation can be significantly reduced by adopting geometrical constraints and an efficient data structure, notably a multidimensional binary search tree (KD-Tree). These techniques allow to reduce the number of distance computations the algorithm performs at each iteration. A second direction is parallel processing, where data and computation loads are distributed over many processing nodes. However, little work has been done to provide a parallel formulation of the efficient sequential techniques based on KD-Trees. Such approaches are expected to have an irregular distribution of computation load and can suffer from load imbalance. This issue has so far limited the adoption of these efficient k-Means variants in parallel computing environments. In this work, we provide a parallel formulation of the KD-Tree based k-Means algorithm for distributed memory systems and address its load balancing issue. Three solutions have been developed and tested. Two approaches are based on a static partitioning of the data set and a third solution incorporates a dynamic load balancing policy.

Potential of legume-based grassland-livestock systems in Europe

European grassland-based livestock production systems face the challenge of producing more meat and milk to meet increasing world demands and to achieve this using fewer resources. Legumes offer great potential for achieving these objectives. They have numerous features that can act together at different stages in the soil–plant–animal–atmosphere system, and these are most effective in mixed swards with a legume proportion of 30–50%. The resulting benefits include reduced dependence on fossil energy and industrial N-fertilizer, lower quantities of harmful emissions to the environment (greenhouse gases and nitrate), lower production costs, higher productivity and increased protein self-sufficiency. Some legume species offer opportunities for improving animal health with less medication, due to the presence of bioactive secondary metabolites. In addition, legumes may offer an adaptation option to rising atmospheric CO2 concentrations and climate change. Legumes generate these benefits at the level of the managed land-area unit and also at the level of the final product unit. However, legumes suffer from some limitations, and suggestions are made for future research to exploit more fully the opportunities that legumes can offer. In conclusion, the development of legume-based grassland–livestock systems undoubtedly constitutes one of the pillars for more sustainable and competitive ruminant production systems, and it can be expected that forage legumes will become more important in the future.

Acrylamide and pyrazine formation in model systems containing asparagine

The effect of different sugars and glyoxal on the formation of acrylamide in low-moisture starch-based model systems was studied, and kinetic data were obtained. Glucose was more effective than fructose, tagatose, or maltose in acrylamide formation, whereas the importance of glyoxal as a key sugar fragmentation intermediate was confirmed. Glyoxal formation was greater in model systems containing asparagine and glucose rather than fructose. A solid phase microextraction GC-MS method was employed to determine quantitatively the formation of pyrazines in model reaction systems. Substituted pyrazine formation was more evident in model systems containing fructose; however, the unsubstituted homologue, which was the only pyrazine identified in the headspace of glyoxal-asparagine systems, was formed at higher yields when aldoses were used as the reducing sugar. Highly significant correlations were obtained for the relationship between pyrazine and acrylamide formation. The importance of the tautomerization of the asparagine-carbonyl decarboxylated Schiff base in the relative yields of pyrazines and acrylamide is discussed.

A CAD-based computer vision system

A vision system for recognizing rigid and articulated three-dimensional objects in two-dimensional images is described. Geometrical models are extracted from a commercial computer aided design package. The models are then augmented with appearance and functional information which improves the system's hypothesis generation, hypothesis verification, and pose refinement. Significant advantages over existing CAD-based vision systems, which utilize only information available in the CAD system, are realized. Examples show the system recognizing, locating, and tracking a variety of objects in a robot work-cell and in natural scenes.

Surface based hypothesis verification in intensity images using geometric and appearance data

In this paper we discuss current work concerning Appearance-based and CAD-based vision; two opposing vision strategies. CAD-based vision is geometry based, reliant on having complete object centred models. Appearance-based vision builds view dependent models from training images. Existing CAD-based vision systems that work with intensity images have all used one and zero dimensional features, for example lines, arcs, points and corners. We describe a system we have developed for combining these two strategies. Geometric models are extracted from a commercial CAD library of industry standard parts. Surface appearance characteristics are then learnt automatically by observing actual object instances. This information is combined with geometric information and is used in hypothesis evaluation. This augmented description improves the systems robustness to texture, specularities and other artifacts which are hard to model with geometry alone, whilst maintaining the advantages of a geometric description.

eRules: a modular adaptive classification rule learning algorithm for data streams

Advances in hardware and software in the past decade allow to capture, record and process fast data streams at a large scale. The research area of data stream mining has emerged as a consequence from these advances in order to cope with the real time analysis of potentially large and changing data streams. Examples of data streams include Google searches, credit card transactions, telemetric data and data of continuous chemical production processes. In some cases the data can be processed in batches by traditional data mining approaches. However, in some applications it is required to analyse the data in real time as soon as it is being captured. Such cases are for example if the data stream is infinite, fast changing, or simply too large in size to be stored. One of the most important data mining techniques on data streams is classification. This involves training the classifier on the data stream in real time and adapting it to concept drifts. Most data stream classifiers are based on decision trees. However, it is well known in the data mining community that there is no single optimal algorithm. An algorithm may work well on one or several datasets but badly on others. This paper introduces eRules, a new rule based adaptive classifier for data streams, based on an evolving set of Rules. eRules induces a set of rules that is constantly evaluated and adapted to changes in the data stream by adding new and removing old rules. It is different from the more popular decision tree based classifiers as it tends to leave data instances rather unclassified than forcing a classification that could be wrong. The ongoing development of eRules aims to improve its accuracy further through dynamic parameter setting which will also address the problem of changing feature domain values.

Towards the design of a unified semantics model for OLTP and OLAP systems

The concepts of on-line transactional processing (OLTP) and on-line analytical processing (OLAP) are often confused with the technologies or models that are used to design transactional and analytics based information systems. This in some way has contributed to existence of gaps between the semantics in information captured during transactional processing and information stored for analytical use. In this paper, we propose the use of a unified semantics design model, as a solution to help bridge the semantic gaps between data captured by OLTP systems and the information provided by OLAP systems. The central focus of this design approach is on enabling business intelligence using not just data, but data with context.

Evaluation of two soil carbon models using two Kenyan long term experimental datasets

RothC and Century are two of the most widely used soil organic matter (SOM) models. However there are few examples of specific parameterisation of these models for environmental conditions in East Africa. The aim of this study was therefore, to evaluate the ability of RothC and the Century to estimate changes in soil organic carbon (SOC) resulting from varying land use/management practices for the climate and soil conditions found in Kenya. The study used climate, soils and crop data from a long term experiment (1976-2001) carried out at The Kabete site at The Kenya National Agricultural Research Laboratories (NARL, located in a semi-humid region) and data from a 13 year experiment carried out in Machang'a (Embu District, located in a semi-arid region). The NARL experiment included various fertiliser (0, 60 and 120 kg of N and P2O5 ha(-1)), farmyard manure (FYM - 5 and 10 t ha(-1)) and plant residue treatments, in a variety of combinations. The Machang'a experiment involved a fertiliser (51 kg N ha(-1)) and a FYM (0, 5 and 10 t ha(-1)) treatment with both monocropping and intercropping. At Kabete both models showed a fair to good fit to measured data, although Century simulations for treatments with high levels of FYM were better than those without. At the Machang'a site with monocrops, both models showed a fair to good fit to measured data for all treatments. However, the fit of both models (especially RothC) to measured data for intercropping treatments at Machang'a was much poorer. Further model development for intercrop systems is recommended. Both models can be useful tools in soil C Predictions, provided time series of measured soil C and crop production data are available for validating model performance against local or regional agricultural crops. (C) 2007 Elsevier B.V. All rights reserved.

Scalability of efficient parallel K-Means

Clustering is defined as the grouping of similar items in a set, and is an important process within the field of data mining. As the amount of data for various applications continues to increase, in terms of its size and dimensionality, it is necessary to have efficient clustering methods. A popular clustering algorithm is K-Means, which adopts a greedy approach to produce a set of K-clusters with associated centres of mass, and uses a squared error distortion measure to determine convergence. Methods for improving the efficiency of K-Means have been largely explored in two main directions. The amount of computation can be significantly reduced by adopting a more efficient data structure, notably a multi-dimensional binary search tree (KD-Tree) to store either centroids or data points. A second direction is parallel processing, where data and computation loads are distributed over many processing nodes. However, little work has been done to provide a parallel formulation of the efficient sequential techniques based on KD-Trees. Such approaches are expected to have an irregular distribution of computation load and can suffer from load imbalance. This issue has so far limited the adoption of these efficient K-Means techniques in parallel computational environments. In this work, we provide a parallel formulation for the KD-Tree based K-Means algorithm and address its load balancing issues.