Strategic management of non-point source pollution from sewage sludge

In the UK, the recycling of sewage sludge to land is expected to double by 2006 but the security of this route is threatened by environmental concerns and health scares. Strategic investment is needed to ensure sustainable and secure sludge recycling outlets. At present, the security of this landbank for sludge recycling is determined by legislation relating to nutrient rather than potentially toxic elements (PTEs) applications to land - especially the environmental risk linked to soil phosphorus (P) saturation. We believe that not all land has an equal risk of contributing nutrients derived from applications to land to receiving waters. We are currently investigating whether it is possible to minimise nutrient loss by applying sludge to land outside Critical Source Areas (CSAs) regardless of soil P Index status. Research is underway to develop a predictive and spatially-sensitive, semi-distributed model of critical thresholds for sludge application that goes beyond traditional 'end-of-pipe" or "edge-of-field" modelling, to include hydrological flow paths and delivery mechanisms to receiving waters from non-point sources at the catchment scale.

Dynamic load balancing for the distributed mining of molecular structures

In molecular biology, it is often desirable to find common properties in large numbers of drug candidates. One family of methods stems from the data mining community, where algorithms to find frequent graphs have received increasing attention over the past years. However, the computational complexity of the underlying problem and the large amount of data to be explored essentially render sequential algorithms useless. In this paper, we present a distributed approach to the frequent subgraph mining problem to discover interesting patterns in molecular compounds. This problem is characterized by a highly irregular search tree, whereby no reliable workload prediction is available. We describe the three main aspects of the proposed distributed algorithm, namely, a dynamic partitioning of the search space, a distribution process based on a peer-to-peer communication framework, and a novel receiverinitiated load balancing algorithm. The effectiveness of the distributed method has been evaluated on the well-known National Cancer Institute’s HIV-screening data set, where we were able to show close-to linear speedup in a network of workstations. The proposed approach also allows for dynamic resource aggregation in a non dedicated computational environment. These features make it suitable for large-scale, multi-domain, heterogeneous environments, such as computational grids.

A customizable multi-agent system for distributed data mining

We present a general Multi-Agent System framework for distributed data mining based on a Peer-to-Peer model. Agent protocols are implemented through message-based asynchronous communication. The framework adopts a dynamic load balancing policy that is particularly suitable for irregular search algorithms. A modular design allows a separation of the general-purpose system protocols and software components from the specific data mining algorithm. The experimental evaluation has been carried out on a parallel frequent subgraph mining algorithm, which has shown good scalability performances.

A hierarchical distributed approach for mining molecular fragments

Recently, two approaches have been introduced that distribute the molecular fragment mining problem. The first approach applies a master/worker topology, the second approach, a completely distributed peer-to-peer system, solves the scalability problem due to the bottleneck at the master node. However, in many real world scenarios the participating computing nodes cannot communicate directly due to administrative policies such as security restrictions. Thus, potential computing power is not accessible to accelerate the mining run. To solve this shortcoming, this work introduces a hierarchical topology of computing resources, which distributes the management over several levels and adapts to the natural structure of those multi-domain architectures. The most important aspect is the load balancing scheme, which has been designed and optimized for the hierarchical structure. The approach allows dynamic aggregation of heterogenous computing resources and is applied to wide area network scenarios.

Distributed intelligent management of active networks

This paper focuses on improving computer network management by the adoption of artificial intelligence techniques. A logical inference system has being devised to enable automated isolation, diagnosis, and even repair of network problems, thus enhancing the reliability, performance, and security of networks. We propose a distributed multi-agent architecture for network management, where a logical reasoner acts as an external managing entity capable of directing, coordinating, and stimulating actions in an active management architecture. The active networks technology represents the lower level layer which makes possible the deployment of code which implement teleo-reactive agents, distributed across the whole network. We adopt the Situation Calculus to define a network model and the Reactive Golog language to implement the logical reasoner. An active network management architecture is used by the reasoner to inject and execute operational tasks in the network. The integrated system collects the advantages coming from logical reasoning and network programmability, and provides a powerful system capable of performing high-level management tasks in order to deal with network fault.

Distributed mining of molecular fragments

In real world applications sequential algorithms of data mining and data exploration are often unsuitable for datasets with enormous size, high-dimensionality and complex data structure. Grid computing promises unprecedented opportunities for unlimited computing and storage resources. In this context there is the necessity to develop high performance distributed data mining algorithms. However, the computational complexity of the problem and the large amount of data to be explored often make the design of large scale applications particularly challenging. In this paper we present the first distributed formulation of a frequent subgraph mining algorithm for discriminative fragments of molecular compounds. Two distributed approaches have been developed and compared on the well known National Cancer Institute’s HIV-screening dataset. We present experimental results on a small-scale computing environment.

Nitrogen recycling through the gut and the nitrogen economy of ruminants: An asynchronous symbiosis

The extensive development of the ruminant forestomach sets apart their N economy from that of nonruminants in a number of respects. Extensive pregastric fermentation alters the profile of protein reaching the small intestine, largely through the transformation of nitrogenous compounds into microbial protein. This process is fueled primarily by carbohydrate fermentation and includes extensive recycling of N between the body and gut lumen pools. Nitrogen recycling occurs via blood and gut lumen exchanges of urea and NH3, as well as endogenous gut and secretory N entry into the gut lumen, and the subsequent digestion and absorption of microbial and endogenous protein. Factors controlling urea transfer to the gut from blood, including the contributions of urea transporters, remain equivocal. Ammonia produced by microbial degradation of urea and dietary and endogenous AA is utilized by microbial fermentation or absorbed and primarily converted to urea. Therefore, microbial growth and carbohydrate fermentation affect the extent of NH3 absorption and urea N recycling and excretion. The extensive recycling of N to the rumen represents an evolutionary advantage of the ruminant in terms of absorbable protein supply during periods of dietary protein deficiency, or asynchronous carbohydrate and protein supply, but incurs a cost of greater N intakes, especially in terms of excess N excretion. Efforts to improve the efficiency of N utilization in ruminants by synchronizing fermentable energy and N availability have generally met with limited success with regards to production responses. In contrast, imposing asynchrony through oscillating dietary protein concentration, or infrequent supplementation, surprisingly has not negatively affected production responses unless the frequency of supplementation is less than once every 3 d. In some cases, oscillation of dietary protein concentration has improved N retention compared with animals fed an equal amount of dietary protein on a daily basis. This may reflect benefits of Orn cycle adaptations and sustained recycling of urea to the gut. The microbial symbiosis of the ruminant is inherently adaptable to asynchronous N and energy supply. Recycling of urea to the gut buffers the effect of irregular dietary N supply such that intuitive benefits of rumen synchrony in terms of the efficiency of N utilization are typically not observed in practice.

Mechanisms of internalization and recycling of the chemokine receptor, CCR5

CCR5 is a G protein-coupled receptor that binds several natural chemokines but it is also a coreceptor for the entry of M tropic strains of HIV-1 into cells. Levels of CCR5 on the cell surface are important for the rate of HIV-1 infection and are determined by a number of factors including the rates of CCR5 internalization and recycling. Here we investigated the involvement of the actin cytoskeleton in the control of ligand-induced internalization and recycling of CCR5. Cytochalasin D, an actin depolymerizing agent, inhibited chemokine-induced internalization of CCR5 and recycling of the receptor in stably transfected CHO cells and in the monocytic cell line, THP-1. CCR5 internalization and recycling were inhibited by Toxin B and C-3 exoenzyme treatment in CHO and THP-1 cells, confirming activation of members of the RhoGTPase family by CCR5. The specific Rho kinase inhibitor Y27632, however, had no effect on CCR5 internalization or recycling. Ligand-induced activation of CCR5 leads to Rho kinase-dependent formation of focal adhesion complexes. These data indicate that CCR5 internalization and recycling are regulated by actin polymerization and activation of small G proteins in a Rho-dependent manner.

Lessons learnt about water use, harvesting, recycling and drainage

This Information Paper is the third in a four-part series that looks at the lessons learnt from the BRE Innovation Park concerning compliance with the Code for Sustainable Homes published in November 2006. It focuses on water use, harvesting, recycling and drainage. The other parts deal with: building fabric; energy sources, overheating and ventilation; architecture, construction and sourcing.

Economic models and algorithms for distributed systems

Distributed computing paradigms for sharing resources such as Clouds, Grids, Peer-to-Peer systems, or voluntary computing are becoming increasingly popular. While there are some success stories such as PlanetLab, OneLab, BOINC, BitTorrent, and SETI@home, a widespread use of these technologies for business applications has not yet been achieved. In a business environment, mechanisms are needed to provide incentives to potential users for participating in such networks. These mechanisms may range from simple non-monetary access rights, monetary payments to specific policies for sharing. Although a few models for a framework have been discussed (in the general area of a "Grid Economy"), none of these models has yet been realised in practice. This book attempts to fill this gap by discussing the reasons for such limited take-up and exploring incentive mechanisms for resource sharing in distributed systems. The purpose of this book is to identify research challenges in successfully using and deploying resource sharing strategies in open-source and commercial distributed systems.

Parallel and distributed computing with Java

The Java language first came to public attention in 1995. Within a year, it was being speculated that Java may be a good language for parallel and distributed computing. Its core features, including being objected oriented and platform independence, as well as having built-in network support and threads, has encouraged this view. Today, Java is being used in almost every type of computer-based system, ranging from sensor networks to high performance computing platforms, and from enterprise applications through to complex research-based.simulations. In this paper the key features that make Java a good language for parallel and distributed computing are first discussed. Two Java-based middleware systems, namely MPJ Express, an MPI-like Java messaging system, and Tycho, a wide-area asynchronous messaging framework with an integrated virtual registry are then discussed. The paper concludes by highlighting the advantages of using Java as middleware to support distributed applications.