Processing-structure-property relationships in solid-state shear pulverization: Parametric study of specific energy

Solid-state shear pulverization (SSSP) is a unique processing technique for mechanochemical modification of polymers, compatibilization of polymer blends, and exfoliation and dispersion of fillers in polymer nanocomposites. A systematic parametric study of the SSSP technique is conducted to elucidate the detailed mechanism of the process and establish the basis for a range of current and future operation scenarios. Using neat, single component polypropylene (PP) as the model material, we varied machine type, screw design, and feed rate to achieve a range of shear and compression applied to the material, which can be quantified through specific energy input (Ep). As a universal processing variable, Ep reflects the level of chain scission occurring in the material, which correlates well to the extent of the physical property changes of the processed PP. Additionally, we compared the operating cost estimates of SSSP and conventional twin screw extrusion to determine the practical viability of SSSP.

Engineering Design: the Information Component

The curriculum of the Bucknell University Chemical Engineering Department includes a required senior year capstone course titled Process Engineering, with an emphasis on process design. For the past ten years library research has been a significant component of the coursework, and students working in teams meet with the librarian throughout the semester to explore a wide variety of information resources required for their project. The assignment has been the same from 1989 to 1999. Teams of students are responsible for designing a safe, efficient, and profitable process for the dehydrogenation of ethylbenzene to styrene monomer. A series of written reports on their chosen process design is a significant course outcome. While the assignment and the specific chemical technology have not changed radically in the past decade, the process of research and discovery has evolved considerably. This paper describes the solutions offered in 1989 to meet the information needs of the chemical engineering students at Bucknell University, and the evolution in research brought about by online databases, electronic journals, and the Internet, making the process of discovery a completely different experience in 1999.

Mining for Class-Specific Motifs in Protein Sequence Classification

Background: In protein sequence classification, identification of the sequence motifs or n-grams that can precisely discriminate between classes is a more interesting scientific question than the classification itself. A number of classification methods aim at accurate classification but fail to explain which sequence features indeed contribute to the accuracy. We hypothesize that sequences in lower denominations (n-grams) can be used to explore the sequence landscape and to identify class-specific motifs that discriminate between classes during classification. Discriminative n-grams are short peptide sequences that are highly frequent in one class but are either minimally present or absent in other classes. In this study, we present a new substitution-based scoring function for identifying discriminative n-grams that are highly specific to a class. Results: We present a scoring function based on discriminative n-grams that can effectively discriminate between classes. The scoring function, initially, harvests the entire set of 4- to 8-grams from the protein sequences of different classes in the dataset. Similar n-grams of the same size are combined to form new n-grams, where the similarity is defined by positive amino acid substitution scores in the BLOSUM62 matrix. Substitution has resulted in a large increase in the number of discriminatory n-grams harvested. Due to the unbalanced nature of the dataset, the frequencies of the n-grams are normalized using a dampening factor, which gives more weightage to the n-grams that appear in fewer classes and vice-versa. After the n-grams are normalized, the scoring function identifies discriminative 4- to 8-grams for each class that are frequent enough to be above a selection threshold. By mapping these discriminative n-grams back to the protein sequences, we obtained contiguous n-grams that represent short class-specific motifs in protein sequences. Our method fared well compared to an existing motif finding method known as Wordspy. We have validated our enriched set of class-specific motifs against the functionally important motifs obtained from the NLSdb, Prosite and ELM databases. We demonstrate that this method is very generic; thus can be widely applied to detect class-specific motifs in many protein sequence classification tasks. Conclusion: The proposed scoring function and methodology is able to identify class-specific motifs using discriminative n-grams derived from the protein sequences. The implementation of amino acid substitution scores for similarity detection, and the dampening factor to normalize the unbalanced datasets have significant effect on the performance of the scoring function. Our multipronged validation tests demonstrate that this method can detect class-specific motifs from a wide variety of protein sequence classes with a potential application to detecting proteome-specific motifs of different organisms.

Influence of Engineering Instructors' Teaching and Learning Beliefs on Pedagogies in Engineering Science Courses

This study explored how academics' beliefs about teaching and learning influenced their teaching in engineering science courses typically taught in the second or third year of 4-year engineering undergraduate degrees. Data were collected via a national survey of 166 U. S. statics instructors and interviews at two different institutions with 17 instructors of engineering science courses such as thermodynamics, circuits and statics. The study identified a number of common beliefs about how to best support student learning of these topics; each is discussed in relation to the literature about student development and learning. Specific recommendations are given for educational developers to encourage use of research-based instructional strategies in these courses.