Probing the electrode-polymer interface in conjugated polymer devices with surface-enhanced Raman scattering

Journal ArticleA crucial consideration in organic devices is the role of the interface between a metal electrode and the active polymer material. Here, we use the conjugated polymer poly[2-methoxy-5-(2-ethylhexyl-oxy)-1,4-phenylene-vinylene] (MEH-PPV) with model gold and silver electrodes to perform surface-enhanced Raman scattering (SERS) on the metal-MEH-PPV interface. We observe significant differences between the SERS spectra on the two metals, which we assign to conformational changes of the phenyl rings within the polymer. The difference between gold and silver interfaces can be removed upon thermal annealing, suggesting structural relaxation. Resonance Raman spectra of the two interfaces are identical, implying that the overall conformation of the polymer backbone which supports the pi-electron transition remains unaffected by the different metals. SERS is uniquely sensitive to the interfacial metal/organic layer and provides an important in situ tool to optimizing organic device structures

A knowledge based machine tool maintenance planning system Using case-based reasoning techniques

In advanced manufacturing systems, Computer Numerical Control (CNC) machine tools are important equipment to manufacture product components of high precision, whilst from equipment maintenance point of view, they are regarded as the ‘products’ provided by machine tool manufacturers. Therefore, the reliability of CNC machine tools affects not only the quality of the components they manufacture, but also the reputation and profits of equipment suppliers. This paper presents a novel knowledge-based maintenance planning system to facilitate information and knowledge sharing between all stakeholders including machine tool manufacturers, users (manufacturing systems), maintenance service providers and part suppliers (for machine tools), in the emerging ‘Product-Service’ business model. Case Based Reasoning principles have been implemented to improve the efficiency of maintenance planning. Ontologies were adopted to represent field knowledge using adaptation guided retrievals based on semantic similarity and correlation. The adaption algorithm has been developed based on the Casual Theory and the dependence relationship to generate the solution for required maintenance problems. The proposed system was implemented using Content Management technologies, which proved to have advantages over traditional database systems in managing engineering knowledge, and has been verified using an example CNC machine tool. The results were commented by industrial collaborators as very promising and further exploitation in industry was recommended

Construction of Sly-miR393 Over-Expression Vector and Verification of Its Target Genes in Tomato

To understand the function of Sly-miR393 in tomato, the precursor sequences and potential target genes of Sly-miR393 were identificated from tomato genome database by computational homology search method. The Sly-miR393 gene was amplified from the genomic DNA by PCR and cloned into plant expression vector pLP35s-100. Sly-miR393 guided-cleavage to putative target  transcripts was validated u sing 5RACE RT-PCR. In this study, our results indicated that the precursor sequence of Sly-miR393 contains the complete hairpin  structure. TIR1/AFB auxin receptor genes contain recognition sites with high complementarities to Sly-miR393 sequence. In tomato, Sly-miR393 directs the cleavage of SlTIR1，SlTIR1-like1 and SlAFB mRNA, then auxin receptor homologous was validated to be as target of Sly-miR393. The pLP35s-pre-SlymiR393 vector containing Sly-miR393 gene was successfully constructed, which would provide significant evidence for further study of Sly-miR393 function in auxin signaling pathway in tomato

Knowledge management for maintenance, repair and service of manufacturing

Manufacturing equipment, such as numerical controlled machines and assembly cranes, requires constant maintenance and service in their operating lifecycle. Equipment maintenance plays an important role in avoiding unexpected failures and ensuring production efficiency. During maintenance operations, much data is generated and stored in databases. It is essential for manufacturing companies to develop a system to integrate equipment condition monitoring, fault prediction and knowledge base to support maintenance decisions. A case study, carried out within a power generator manufacturing organisation, was conducted to understand what the maintenance process is and how maintenance knowledge is currently managed. It was concluded that maintenance process is less efficient, and maintenance records, stored within internal databases, are not consistent, which makes knowledge hard to share, learn from and reuse. This paper proposes a Knowledge Management System for Maintenance, Repair and Service in Manufacturing Systems to support better maintenance decision and improve maintenance efficiency

Improving consensus contact prediction via server correlation reduction

<p>Abstract</p> <p>Background</p> <p>Protein inter-residue contacts play a crucial role in the determination and prediction of protein structures. Previous studies on contact prediction indicate that although template-based consensus methods outperform sequence-based methods on targets with typical templates, such consensus methods perform poorly on new fold targets. However, we find out that even for new fold targets, the models generated by threading programs can contain many true contacts. The challenge is how to identify them.</p> <p>Results</p> <p>In this paper, we develop an integer linear programming model for consensus contact prediction. In contrast to the simple majority voting method assuming that all the individual servers are equally important and independent, the newly developed method evaluates their correlation by using maximum likelihood estimation and extracts independent latent servers from them by using principal component analysis. An integer linear programming method is then applied to assign a weight to each latent server to maximize the difference between true contacts and false ones. The proposed method is tested on the CASP7 data set. If the top <it>L</it>/5 predicted contacts are evaluated where <it>L </it>is the protein size, the average accuracy is 73%, which is much higher than that of any previously reported study. Moreover, if only the 15 new fold CASP7 targets are considered, our method achieves an average accuracy of 37%, which is much better than that of the majority voting method, SVM-LOMETS, SVM-SEQ, and SAM-T06. These methods demonstrate an average accuracy of 13.0%, 10.8%, 25.8% and 21.2%, respectively.</p> <p>Conclusion</p> <p>Reducing server correlation and optimally combining independent latent servers show a significant improvement over the traditional consensus methods. This approach can hopefully provide a powerful tool for protein structure refinement and prediction use.</p