Disambiguation and co-authorship networks of the U.S. patent inventor database (1975–2010)

Research into invention, innovation policy, and technology strategy can greatly benefit from an accurate understanding of inventor careers. The United States Patent and Trademark Office does not provide unique inventor identifiers, however, making large-scale studies challenging. Many scholars of innovation have implemented ad-hoc disambiguation methods based on string similarity thresholds and string comparison matching; such methods have been shown to be vulnerable to a number of problems that can adversely affect research results. The authors address this issue contributing (1) an application of the Author-ity disambiguation approach (0170 and 0175) to the US utility patent database, (2) a new iterative blocking scheme that expands the match space of this algorithm while maintaining scalability, (3) a public posting of the algorithm and code, and (4) a public posting of the results of the algorithm in the form of a database of inventors and their associated patents. The paper provides an overview of the disambiguation method, assesses its accuracy, and calculates network measures based on co-authorship and collaboration variables. It illustrates the potential for large-scale innovation studies across time and space with visualizations of inventor mobility across the United States. The complete input and results data from the original disambiguation are available at (http://dvn.iq.harvard.edu/dvn/dv/patent); revised data described here are at (http://funglab.berkeley.edu/pub/disamb_no_postpolishing.csv); original and revised code is available at (https://github.com/funginstitute/disambiguator); visualizations of inventor mobility are at (http://funglab.berkeley.edu/mobility/).     

Robust self-triggered MPC with fast convergence for constrained linear systems

In this paper, a robust self-triggered model predictive control (MPC) scheme is proposed for linear discrete-time systems subject to additive disturbances, state and control constraints. To reduce the amount of computation on controller sides, MPC optimization problems are only solved at certain sampling instants which are determined by a novel self-triggering mechanism. The main idea of the self-triggering mechanism is to choose inter-sampling times by guaranteeing a fast decrease in optimal costs. It implies a fast convergence of system states to a compact set where it is ultimately bounded and a reduction of computation times to stabilize the system. Once the state enters a terminal region, the system can be stabilized to a robust invariant set by a state feedback controller. Robust constraint satisfaction is ensured by utilizing the worst-case set-valued predictions of future states in such a way that recursive feasibility is guaranteed for all possible realisations of disturbances. In the case where a priority is given to reducing communication costs rather than improvement in control performance in a neighborhood of the origin, a feedback control law based on nominal state predictions is designed in the terminal region to avoid frequent feedback. Performances of the closed-loop system are demonstrated by a simulation example.     

Interactions of Economics of Science and Science Education: Investigating the Implications for Science Teaching and Learning

In recent years, there has been upsurge of interest in the applications of interdisciplinary perspectives on science in science education. Within this framework, the implications of the so-called “economics of science” is virtually an uncharted territory. In this paper, we trace a set of arguments that provide a dialectic engagement with two conflicting agendas: (a) the broadening of science education to include the contextual positioning of science including economical dimensions of science, and (b) the guarding of the proliferation and reinforcement of those aspects of economics of science such as commodification of scientific knowledge that embraces inequity and restricted access to the products of the scientific enterprise. Our aim is broadly to engage, as science education researchers, in the debates in economics of science so as to investigate the reciprocal interactions that might exist with science education. In so doing, we draw out some recommendations whereby the goals of science education might provide as much input into the intellectual debates within philosophy of science on issues related to the commercialisation and commodification of scientific knowledge. We explore some implications of commodification of science in the context of modelling and argumentation in science education.     

The preparation experiences of elementary mathematics specialists: examining influences on beliefs,content knowledge,and teaching practices

Many in the field of mathematics education call for elementary schools to have elementary mathematics specialists (EMSs) who provide needed mathematical expertise and support for children and teachers. EMSs serve as a reasonable, immediate alternative to the challenges generated by elementary teachers needing improved mathematical knowledge for teaching in the classroom. However, limited inquiry has explored how to best prepare EMSs and how program features and learning activities influence their development. This mixed-method study identifies some of the interrelated benefits from a K-5 Mathematics Endorsement Program designed to prepare EMSs through examining changes in mathematical beliefs, specialized content knowledge (SCK), and classroom teaching practices during the program. Data (n = 32) were collected over the 2-semester program via belief surveys, a content knowledge assessment, observations of teaching practices, and individual interviews from elementary teachers participating in the program. The findings show some changes in beliefs can be made relatively quickly, other shifts in beliefs take more time and continued support, and changes in SCK and adoption of various aspects of standard-based pedagogy require considerably greater opportunities to learn. The described program features and learning experiences provided a context for these changes and offer considerations for EMS preparation programs.     

Prospective Elementary Mathematics Specialists’ developing instructional practices: support and mentorship during an authentic residency

Journal of Mathematics Teacher Education - This case study explored Prospective Elementary Mathematics Specialists’ (PEMSs) pedagogical practices during a K-5 Mathematics Endorsement (K-5 ME)...     

The effect of learning style on preference for web‐based courses and learning outcomes

As the number of courses offered online increases rapidly, it is important for teachers and institutions to identify specific learner characteristics of successful online students. This paper reports on a study that compared an online group of freshmen computer science majors with an equivalent on‐campus group to find if their individual learning styles play a role in the selection of course delivery mode (online or face to face) and in their academic achievement. No significant statistical differences were detected in learning styles and learning performance between the two groups. Implications for teaching practice and design of learning activities that resulted from this study are discussed.     

Comparing Numerical Methods to Estimate Vertical Jump Height Using a Force Platform

ABSTRACT

Males (n = 29) and females (n = 34) performed vertical jumps. Jump height was estimated from force platform data using five numerical methods and compared using intraclass correlation (ρ), and linear and rank regression standard error of estimate (SEE). Take-off velocity plus center of mass height at take-off and mechanical work methods were identical (ρ = 1.000) with small linear (SEE < 0.0001 cm) and rank order (SEE = 0) error. Integrating acceleration to displacement was nearly identical (ρ > 0.999), with small linear (SEE = 0.1 cm) and rank order (SEE < 1) errors compared to the above methods. Take-off velocity (ρ = 0.517) and flight time (ρ = 0.573) estimates were less than all other methods and had large linear (SEE ≥ 2.1 cm) and rank order (SEE > 4) errors. Take-off velocity and flight time methods should be avoided due to systematic and random error.