Environmental Sensor Networks (ESNs) facilitate the study of fundamental processes and the development of hazard response systems. They have evolved from passive logging systems that require manual downloading, into 'intelligent' sensor networks that comprise a network of automatic sensor nodes and communications systems which actively communicate their data to a Sensor Network Server (SNS) where these data can be integrated with other environmental datasets. The sensor nodes can be fixed or mobile and range in scale appropriate to the environment being sensed. ESNs range in scale and function and we have reviewed over 50 representative examples. Large Scale Single Function Networks tend to use large single purpose nodes to cover a wide geographical area. Localised Multifunction Sensor Networks typically monitor a small area in more detail, often with wireless adhoc systems. Biosensor Networks use emerging biotechnologies to monitor environmental processes as well as developing proxies for immediate use. In the future, sensor networks will integrate these three elements (Heterogeneous Sensor Networks). The communications system and data storage and integration (cyberinfrastructure) aspects of ESNs are discussed, along with current challenges which need to be addressed. We argue that Environmental Sensor Networks will become a standard research tool for future Earth System and Environmental Science. Not only do they provide a 'virtual' connection with the environment, they allow new field and conceptual approaches to the study of environmental processes to be developed. We suggest that although technological advances have facilitated these changes, it is vital that Earth Systems and Environmental Scientists utilise them.

Efficiently utilizing off-chip DRAM bandwidth is a critical issue in designing cost-effective, high-performance chip multiprocessors (CMPs). Conventional memory controllers deliver relatively low performance in part because they often employ fixed, rigid access scheduling policies designed for average-case application behavior. As a result, they cannot learn and optimize the long-term performance impact of their scheduling decisions, and cannot adapt their scheduling policies to dynamic workload behavior.We propose a new, self-optimizing memory controller design that operates using the principles of reinforcement learning (RL) to overcome these limitations. Our RL-based memory controller observes the system state and estimates the long-term performance impact of each action it can take. In this way, the controller learns to optimize its scheduling policy on the fly to maximize long-term performance. Our results show that an RL-based memory controller improves the performance of a set of parallel applications run on a 4-core CMP by 19% on average (up to 33%), and it improves DRAM bandwidth utilization by 22% compared to a state-of-the-art controller.

Abstract-Sensor networks for the natural environment require an understanding of earth science, combined with sensor, communications and computer technology. We discuss the evolution from data logging to sensor networks, describe our research from a glacial environment and highlight future challenges in this field.

SummaryIn vitro experiments show that pseudowollastonite (a-CaSiO 3 ) is a highly bioactive material that forms a hydroxyapatite surface layer on exposure to simulated body fluid and also to human parotid saliva. This finding is very significant, as it indicates that the pseudowollastonite can be physically and chemically integrated into the structure of living bone tissue, and therefore could be suitable for repair or replacement of living bone.The physical and chemical nature of the remodelled interface between the pseudowollastonite implants and the surrounding bone has been studied after in vivo implantation of 20 pseudowollastonite cylinders into rat tibias. The interfaces formed after 3, 6, 8 and 12 weeks of implantation were examined histologically using an optical microscope and also by analytical scanning electron microscopy.SEM and X-ray elemental analysis showed that the new bone was growing in direct contact with the implants. Other examinations found that the bone was fully mineralized. The ionic exchange taking place at the implant interface with the body fluids was essential in the process of the implant integration through a dissolution-precipitationtransformation mechanism. The study found the interface biologically and chemically active over the 12-week implantation period. The rate of new bone formation decreased after the first 3 weeks and reached constant value over the following 9 weeks. The osteoblastic cells migrated towards the interface and colonized the surface at the contact areas with the cortical regions and also bone marrow.

No abstract