Application of Knowledge-based Tools in Environmental Decision Support Systems

Decision support system often requires the combined knowledge of multiple domains. A knowledge-based approach is proposed to include not only the process modelling knowledge but also the descriptive knowledge in the integration. Descriptive knowledge such as survey statistics and expert opinions forms the core of a study on the uncertainty of the combined knowledge. It was found that the use of expert systems, neural network and belief causal network assist greatly in the implementation of these concepts. Examples are drawn from the combination of scientific and economic knowledge to solve some acid rain problems.decision support system; knowledge-based system; expert system; causal network

Sintering behaviour of Al-Cu-Mg-Si blends

The increasing demand for automotive industries to reduce the weight of the vehicles has led to a growing usage of Al alloy powder metallurgy (P/M) parts such as camshaft bearing caps, shock absorber pistons and brake calipers [1,2]. In order to control the sintered microstructure and mechanical properties of the aluminium alloy powder metallurgical (P/M) parts, it is essential to establish a fundamental understanding of the microstructural development during the sintering process. Current research at Birmingham University is focussed on the investigation of the sintering behaviour of Al-Cu-Mg-Si powder blends using a combination of Scanning Electron Microscopy, Energy Dispersive Microanaylsis (SEM) and Differential Scanning Calorimetry (DSC). This paper presents a detailed study of the effect of temperature and initial starting materials on the evolution of microstructure during the sintering of Al-Cu-Mg-Si blends for PM

A study of debinding behaviour and microstructural development of sintered Al-Cu-Sn alloy

A new approach is explored to achieve sintered aluminium alloy from metallic powder mixtures without compression or adding Mg. In this approach, mixtures of micron-sized aluminium powder (average size of 2.5 μm) and nano-sized alloying elemental powder of Cu and Sn (less than of 70nm), at appropriate proportions to compositions of Al-6wt%Cu, Al-6wt%Cu-3wt%Sn with and without adhesive binder were prepared by magnetic stirring. Then, the powder mixture was poured into a crucible and heat treated at a temperature of 600°C for 11 hours in inert atmosphere of N2 or Ar. In this paper, we investigate the debinding behavior of loosely packed Al-based powder mixture and the microstructural development and mechanical property sintered parts using a combination of thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), X-ray diffractrometry (XRD) and hardness test

Making alumina microcomponents from Al powder

Alumina microcomponents have distinguishing advantages over Si counterparts. However, the shrinkage of alumina, as high as 20%, makes it difficult to produce precision components that require a high tolerance. A new fabrication process is presented to greatly reduce the shrinkage. The process consists of forming an Al powdered component through sintering and transforming the Al powdered component into an alumina part. In this way, the shrinkage occurring in sintering the Al powder component will be compensated by the expansion occurred when Al transforms into alumina. The process involves producing micro-moulds, preparing metallic paste, filling the micro-moulds with the metallic paste, demoulding, sintering the green Al patterns and finally oxidising the sintered Al-based components to achieve alumina components. The process was proven successful. Characterization of the sintered alumina microcomponents has been undertaken, including SEM image analysis, density and scale measurements

Application of Knowledge-based Tools in Environmental Decision Support Systems

Decision support system often requires the combined knowledge of multiple domains. A knowledge-based approach is proposed to include not only the process modelling knowledge but also the descriptive knowledge in the integration. Descriptive knowledge such as survey statistics and expert opinions forms the core of a study on the uncertainty of the combined knowledge. It was found that the use of expert systems, neural network and belief causal network assist greatly in the implementation of these concepts. Examples are drawn from the combination of scientific and economic knowledge to solve some acid rain problems

Application of Knowledge-based Tools in Environmental Decision Support Systems

Decision support system often requires the combined knowledge of multiple domains. A knowledge-based approach is proposed to include not only the process modelling knowledge but also the descriptive knowledge in the integration. Descriptive knowledge such as survey statistics and expert opinions forms the core of a study on the uncertainty of the combined knowledge. It was found that the use of expert systems, neural network and belief causal network assist greatly in the implementation of these concepts. Examples are drawn from the combination of scientific and economic knowledge to solve some acid rain problems

Realization of quantum process tomography in NMR

Quantum process tomography is a procedure by which the unknown dynamical evolution of an open quantum system can be fully experimentally characterized. We demonstrate explicitly how this procedure can be implemented with a nuclear magnetic resonance quantum computer. This allows us to measure the fidelity of a controlled-not logic gate and to experimentally investigate the error model for our computer. Based on the latter analysis, we test an important assumption underlying nearly all models of quantum error correction, the independence of errors on different qubits.Comment: 8 pages, 7 EPS figures, REVTe

Using the IUCN Red List to map threats to terrestrial vertebrates at global scale

The Anthropocene is characterized by unparalleled human impact on other species, potentially ushering in the sixth mass extinction. Yet mitigation efforts remain hampered by limited information on the spatial patterns and intensity of the threats driving global biodiversity loss. Here we use expert-derived information from the International Union for Conservation of Nature Red List on threats to 23,271 species, representing all terrestrial amphibians, birds and mammals, to generate global maps of the six major threats to these groups: agriculture, hunting and trapping, logging, pollution, invasive species, and climate change. Our results show that agriculture and logging are pervasive in the tropics and that hunting and trapping is the most geographically widespread threat to mammals and birds. Additionally, current representations of human pressure underestimate the overall pressure on biodiversity, due to the exclusion of threats such as hunting and climate change. Alarmingly, this is particularly the case in areas of the highest biodiversity importance

The EDGE2 protocol: Advancing the prioritisation of Evolutionarily Distinct and Globally Endangered species for practical conservation action

The conservation of evolutionary history has been linked to increased benefits for humanity and can be captured by phylogenetic diversity (PD). The Evolutionarily Distinct and Globally Endangered (EDGE) metric has, since 2007, been used to prioritise threatened species for practical conservation that embody large amounts of evolutionary history. While there have been important research advances since 2007, they have not been adopted in practice because of a lack of consensus in the conservation community. Here, building from an interdisciplinary workshop to update the existing EDGE approach, we present an “EDGE2” protocol that draws on a decade of research and innovation to develop an improved, consistent methodology for prioritising species conservation efforts. Key advances include methods for dealing with uncertainty and accounting for the extinction risk of closely related species. We describe EDGE2 in terms of distinct components to facilitate future revisions to its constituent parts without needing to reconsider the whole. We illustrate EDGE2 by applying it to the world’s mammals. As we approach a crossroads for global biodiversity policy, this Consensus View shows how collaboration between academic and applied conservation biologists can guide effective and practical priority-setting to conserve biodiversity

CSF1R inhibitor JNJ-40346527 attenuates microglial proliferation and neurodegeneration in P301S mice

Neuroinflammation and microglial activation are significant processes in Alzheimer's disease pathology. Recent genome-wide association studies have highlighted multiple immune-related genes in association with Alzheimer's disease, and experimental data have demonstrated microglial proliferation as a significant component of the neuropathology. In this study, we tested the efficacy of the selective CSF1R inhibitor JNJ-40346527 (JNJ-527) in the P301S mouse tauopathy model. We first demonstrated the anti-proliferative effects of JNJ-527 on microglia in the ME7 prion model, and its impact on the inflammatory profile, and provided potential CNS biomarkers for clinical investigation with the compound, including pharmacokinetic/pharmacodynamics and efficacy assessment by TSPO autoradiography and CSF proteomics. Then, we showed for the first time that blockade of microglial proliferation and modification of microglial phenotype leads to an attenuation of tau-induced neurodegeneration and results in functional improvement in P301S mice. Overall, this work strongly supports the potential for inhibition of CSF1R as a target for the treatment of Alzheimer's disease and other tau-mediated neurodegenerative diseases