Fuzzy c-means density based clustering using data induced metric

We propose a new data induced metric to perform un supervised data classification (clustering). Our goal is to automatically recognize clusters of non-convex shape. We present a new version of fuzzy c-means al gorithm, based on the data induced metric, which is capable to identify non-convex d-dimensional clusters.

Dynamic nuclear polarisation enhanced (14)N overtone MAS NMR spectroscopy.

Dynamic nuclear polarisation (DNP) has been used to obtain magic angle spinning (14)N(OT) (nitrogen-14 overtone) solid-state NMR spectra from several model amino acids, with both direct and indirect observation of the (14)N(OT) signal. The crystalline solids were impregnated with biradical solutions of organic liquids that do not dissolve the crystalline phase. The bulk phase was then polarized via(1)H spin diffusion from the highly-polarized surface (1)H nuclei, resulting in (1)H DNP signal enhancements of around two orders of magnitude. Cross polarisation from (1)H nuclei directly to the (14)N overtone transition is demonstrated under magic angle spinning, using a standard pulse sequence with a relatively short contact time (on the order of 100 μs). This method can be used to acquire (14)N overtone MAS powder patterns that match closely with simulated line shapes, allowing isotropic chemical shifts and quadrupolar parameters to be measured. DNP enhancement also allows the rapid acquisition of 2D (14)N(OT) heteronuclear correlation spectra from natural abundance powder samples. (1)H-(14)N(OT) HETCOR and (13)C-(14)N(OT) HMQC pulse sequences were used to observe all single-bond H-N and C-N correlations in histidine hydrochloride monohydrate, with the spectra obtained in a matter of hours. Due to the high natural abundance of the (14)N isotope (99.6%) and the advantages of observing the overtone transition, these methods provide an attractive route to the observation of C-N correlations from samples at natural isotopic abundance and enable the high resolution measurement of (14)N chemical shifts and quadrupolar interaction parameters.

Crystal structure of (3S*,4S*)-4-hydroxy-2-(2`-methoxyethyl)-3-phenyl-3,4-dihydroisoquinol-1(2H)-one,C18H19NO3

C₁₈H₁₉N0₃, monoclinic, P12₁/n1 (No. 14), a =8.454(6) Å,
b =13.123(3) Å, c =14.698(3) Å, ß =104.85(3)^o, V =1576.2ų,
Z =4, R_gt(F) =0.062, wR_ref(F²) =0.184, T =293 K.

Enhancing and assessing group and team learning in architecture and related design contexts

EXECUTIVE SUMMARYTeamwork skills are essential in the design industry where practitioners negotiate often-conflicting design options in multi-disciplinary teams. Indeed, many of the bodies that accredit design courses explicitly list teamwork skills as essential attributes of design graduates e.g., the Australian Institute of Architects (AIA), Royal Institute of British Architects (RIBA), the National Council of Architectural Registration Boards (NCARB) of the United States and the Institution of Engineers, Australia (IEAust). In addition to the need to meet the demands of the accrediting bodies, there are many reasons for the ubiquitous use of teamwork assignments in design schools. For instance, teamwork learning is seen as being representative of work in practice where design is nearly always a collaborative activity. Learning and teaching in teamwork contexts in design education are not without particular challenges. In particular, two broad issues have been identified: first, many students leave academia without having been taught the knowledge and skills of how to design in teams; second, teaching, assessment and assignment design need to be better informed by a clear understanding of what leads to effective teamwork and the learning of teamwork skills. In recognition of the lack of a structured approach to integrating teamwork learning into the curricula of design programs, this project set out to answer three primary research questions: • How do we teach teamwork skills in the context of design? • How do we assess teamwork skills?• How do design students best learn teamwork skills?In addition, four more specific questions were investigated:1. Is there a common range of learning objectives for group-and-team-work in architecture and related design disciplines that will enable the teaching of consistent and measurable outcomes?2. Do group and team formation methods, learning styles and team-role preferences impact students’ academic and course satisfaction outcomes?3. What combinations of group-and-team formation methods, teaching and assessment models significantly improve learning outcomes?4. For design students across different disciplines with different learning styles and cultural origins, are there significant differences in performance, student satisfaction (as measured through questionnaires and unit evaluations), group-and-team working abilities and student participation?To elucidate these questions, a design-based research methodology was followed comprising an iterative series of enquiries: (a) A literature review was completed to investigate: what constitutes effective teamwork, what contributes to effectiveness in teams, what leads to positive design outcomes for teams, and what leads to effective learning in teams. The review encompassed a range of contexts: from work-teams in corporate settings, to professional design teams, to education outside of and within the design disciplines. The review informed a theoretical framework for understanding what factors impact the effectiveness of student design teams. (b) The validity of this multi-factorial Framework of Effectiveness in Student Design Teams was tested via surveys of educators’ teaching practices and attitudes, and of students’ learning experiences. 638 students and 68 teachers completed surveys: two pilot surveys for participants at the four partner institutions, which then informed two national surveys completed by participants from the majority of design schools across Australia. (c) The data collected provided evidence for 22 teamwork factors impacting team effectiveness in student design teams. Pedagogic responses and strategies to these 22 teamwork factors were devised, tested and refined via case studies, focus groups and workshops. (d) In addition, 35 educators from a wide range of design schools and disciplines across Australia attended two National Teaching Symposiums. The first symposium investigated the wider conceptualisation of teamwork within the design disciplines, and the second focused on curriculum level approaches to structuring the teaching of teamwork skills identified in the Framework.The Framework of Effectiveness in Student Design Teams identifies 22 factors impacting effective teamwork, along with teaching responses and strategies that design educators might use to better support student learning. The teamwork factors and teaching strategies are categorised according to three groups of input (Task Characteristics, Individual Level Factors and Team Level Factors), two groups of processes (Teaching Practice & Support Structures and Team Processes), and three categories of output (Task Performance, Teamwork Skills, and Attitudinal Outcomes). Eight of the 22 teamwork factors directly relate to the skills that need to be developed in students, one factor relates to design outputs, and the other thirteen factors inform pedagogies that can be designed for better learning outcomes. In Table 10 of Section 4, we outline which of the 22 teamwork factors pertain to each of five stakeholder groups (curriculum leaders, teachers, students, employers and the professional bodies); thus establishing who will make best use the information and recommendations we make. In the body of this report we summarise the 22 teamwork factors and teaching strategies informed by the Framework of Effectiveness in Student Design Teams, and give succinct recommendations arising from them. This material is covered in depth by the project outputs. For instance, the teaching and assessment strategies will be expanded upon in a projected book on Teaching Teamwork in Design. The strategies are also elucidated by examples of good practice presented in our case studies, and by Manuals on Teamwork for Teachers and Students. Moreover, the project website ( visited by representatives of stakeholder groups in Australia and Canada), is seeding a burgeoning community of practice that promises dissemination, critical evaluation and the subsequent refinement of our materials, tools, strategies and recommendations. The following three primary outputs have been produced by the project in answer to the primary research questions:1. A theoretical Framework of Effectiveness in Student Design Teams;2. Manuals on Teamwork for Teachers and Students (available from the website);3. Case studies of good/innovative practices in teaching and assessing teamwork in design;In addition, five secondary outputs/outcomes have been produced that provide more nuanced responses:4. Detailed recommendations for the professional accrediting bodies and curriculum leaders;5. Online survey data (from over 700 participants), plus Team Effectiveness Scale to determine the factors influencing effective learning and successful outputs for student design teams;6. A community of practice in policy, programs, practice and dialogue;7. A detailed book proposal (with sample chapter), submitted to prospective publishers, on Teaching Teamwork in Design; 8. An annotated bibliography (accessed via the project website) on learning, teaching and assessing teamwork.The project has already had an international impact. As well as papers presented in Canada and New Zealand, the surveys were participated in by six Canadian schools of architecture, whose teaching leaders also provided early feedback on the project aims and objectives during visits made to them by the project leader. In addition, design schools in Vancouver, Canada, and San Diego in the USA have already utilised the Teacher’s Manual, and in February 2014 the project findings were discussed at Tel Aviv University in a forum focusing on the challenges for sustainability in architectural education.

A unit root model for trending time-series energy variables

In this paper, we propose a GARCH-based unit root test that is flexible enough to account for; (a) trending variables, (b) two endogenous structural breaks, and (c) heteroskedastic data series. Our proposed model is applied to a range of time-series, trending, and heteroskedastic energy variables. Our two main findings are: first, the proposed trend-based GARCH unit root model outperforms a GARCH model without trend; and, second, allowing for a time trend and two endogenous structural breaks are important in practice, for doing so allows us to reject the unit root null hypothesis.