Epoxidized perfluoropolyethers: A route to hydrophobic,negative‐tone photoresists

The synthesis, formulation, and wafer level processing conditions of a heavily fluorinated hydrophobic photoresist was demonstrated. The synthesis is based on terminal epoxy modification of commercially available perfluoropolyethers. Structural characterization shows that terminal epoxide can open during the synthetic process, but in a simple formulation has a negligible effect on photoresolution of the photoresist. Formulation into a traditional photoresist requires careful selection of appropriate cosolvents to ensure solubility of the hydrophobic epoxy and hydrophilic photoacid generator while attaining adequate coating quality. Formulation processing conditions are presented and the chemical resistance of the resist through aggressive processing steps is demonstrated. Wafer level patterning using traditional photolithographic tools illustrates the applicability of the formulation and process conditions for traditional resist or microfluidic applications. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2011     

Computational intelligence for microarray data and biomedical image analysis for the early diagnosis of breast cancer

The objective of this paper was to perform a comparative analysis of the computational intelligence algorithms to identify breast cancer in its early stages. Two types of data representations were considered: microarray based and medical imaging based. In contrast to previous researches, this research also considered the imbalanced nature of these data. It was observed that the SMO algorithm performed better for the majority of the test data, especially for microarray based data when accuracy was used as performance measure. Considering the imbalanced characteristic of the data, the Naive Bayes algorithm was seen to perform highly in terms of true positive rate (TPR). Regarding the influence of SMOTE, a well-known imbalanced data classification technique, it was observed that there was a notable performance improvement for J48, while the performance of SMO remained comparable for the majority of the datasets. Overall, the results indicated SMO as the most potential candidate for the microarray and image dataset considered in this research.     

An integrated multiple criteria preference ranking approach to the Canadian west coast port congestion conflict

An integrative conflict analysis approach, incorporating an Analytic Hierarchy Process (AHP) based preference ranking method into the Graph Model for Conflict Resolution (GMCR), is employed to investigate the Canadian west coast port congestion dispute. The Canadian west coast has historically been an important gateway connecting North America to Asia thanks to its specific geographical and strategic location. Despite successful operations and maintenance of the port facilities to handle international trade during the past few decades, the west coast is now facing increasing congestion problems, resulting in significant delays in transporting goods from the west coast to other parts of Canada and the USA. The strategic analyses carried out in this research suggest potential resolutions in which Canada would expand port facilities at various locations, encouraging traders to continue choosing the Canadian west coast as one of their trade gateways to North America.     

Parkinson’s Disease tremor classification – A comparison between Support Vector Machines and neural networks

Deep Brain Stimulation has been used in the study of and for treating Parkinson’s Disease (PD) tremor symptoms since the 1980s. In the research reported here we have carried out a comparative analysis to classify tremor onset based on intraoperative microelectrode recordings of a PD patient’s brain Local Field Potential (LFP) signals. In particular, we compared the performance of a Support Vector Machine (SVM) with two well known artificial neural network classifiers, namely a Multiple Layer Perceptron (MLP) and a Radial Basis Function Network (RBN). The results show that in this study, using specifically PD data, the SVM provided an overall better classification rate achieving an accuracy of 81% recognition.     

Design-Through-Production Formulations

Ever since the topic of design and digital fabrication in architecture surfaced ten or so years ago (encouraged by organizations of ACADIA, ECAADE, SIGraDi, and CAADRIA), it has thrived as a productive strategy for advancing the discipline. Clearly, new maps have been charted in architectural discourse that will steer us toward a promising future. Beyond just developing skills to serve new methods of design-through-production, we must now question what ends this methodology serves. This writing is an attempt to chart three potential trajectories inherent in a design-through-production methodology 1) outlining an ethic of production at regional levels in light of the ocean of global information access, 2) investigating the formulation of form inherent in digital design methods, and 3) finding humanist aims through a technological lens. Finally, several pedagocical cases are offered as incremental examples to a collective body of work which applies the design-throughproduction methodology.     

The engineering aspects of automated prepreg layup: History,present and future

Highly consistent quality and cost-effective manufacture of advanced composites can be achieved through automation. It may therefore open up new markets and applications for composite products in aerospace, automotive, renewable energy, and consumer goods. Automated Tape Laying (ATL) and Automated Fibre Placement (AFP) are the two main technologies used to automate the layup of prepreg. The historical development and past research of both technologies is reviewed; with an emphasis on past issues in application and capability as well as their solution, including both thermoset and thermoplastic material layup. It is shown that past developments have moved away from simply emulating manual layup into the now unique layup procedures for ATL, and into the current AFP technology base. The state of the art for both technologies is discussed and current gaps in the understanding of both processes highlighted. From this, future research needs and developments are derived and discussed.     

Direct stamping of silver nanoparticles toward residue-free thick electrode

Direct stamping of functional materials has been developed for cost-effective and process-effective manufacturing of nano/micro patterns. However, there remain several challenging issues like the perfect removal of the residual layer and realization of high aspect ratio. We have demonstrated facile fabrication of flexible strain sensors that have microscale thick interdigitated capacitors with no residual layer by a simple direct stamping with silver nanoparticles (AgNPs). Polyurethane (PU) prepolymer was utilized as an adhesive layer to transfer AgNPs more efficiently during the separation step of the flexible stamp from directly stamped AgNPs. Scanning electron microscopy images and energy dispersive x-ray spectroscopy analysis revealed residue-free transfer of microscale thick interdigitated electrodes onto two different flexible substrates (elastomeric and brittle) for the application to highly sensitive strain sensors.     

Quantitative biology of single neurons

The building blocks of complex biological systems are single cells. Fundamental insights gained from single-cell analysis promise to provide the framework for understanding normal biological systems development as well as the limits on systems/cellular ability to respond to disease. The interplay of cells to create functional systems is not well understood. Until recently, the study of single cells has concentrated primarily on morphological and physiological characterization. With the application of new highly sensitive molecular and genomic technologies, the quantitative biochemistry of single cells is now accessible.