One weight $$\mathbb {Z}_2\mathbb {Z}_4$$ additive codes

We study one weight \(\mathbb {Z}_2\mathbb {Z}_4\) additive codes. It is shown that the image of an equidistant \(\mathbb {Z}_2\mathbb {Z}_4\) code is a binary equidistant code and that the image of a one weight \(\mathbb {Z}_2\mathbb {Z}_4\) additive code, with nontrivial binary part, is a linear binary one weight code. The structure and possible weights for all one weight \(\mathbb {Z}_2\mathbb {Z}_4\) additive codes are described. Additionally, a lower bound for the minimum distance of dual codes of one weight additive codes is obtained.     

Isolation and identification of (3-methoxyphenyl)acetonitrile as a phytotoxin from meadowfoam (Limnanthes alba) seedmeal

Ethyl ether, ethanol, and water extracts of meadowfoam (Limnanthes alba Hartweg ex. Benth.) seedmeal were prepared and bioassayed against velvetleaf (Abutilon theophrasti Medicus) and wheat (Triticum aestivum L. Cardinal). Both the ethyl ether and ethanol fractions, but not the water extract, inhibited velvetleaf and wheat radicle elongation. Fractionation of the extracts indicated that (3-methoxyphenyl)acetonitrile (3-MPAN) was the active compound from both extracts, comprising >97% of the active ethanol fraction. 3-Methoxybenzyl isothiocyanate, which had been previously shown to be the major breakdown product of glucolimnanthin, the majorL. alba glucosinolate, was not detected in either extract. Radicle elongation of velvetleaf and wheat were inhibited by 3-MPAN with I₅₀ (the concentration required to inhibit growth by 50%) values of approximately 4 × 10^–4 M (velvetleaf) and 7×10^–4 M (wheat).Mention of firms or products does not imply endorsement by the U.S. Department of Agriculture over other firms or products not mentioned.     

Feature sequencing in the rapid design system using a genetic algorithm

This paper addresses the feature sequencing problem in the Rapid Design System (RDS). The RDS is a feature-based design system that integrates product design and process planning. An important issue in feature-based process planning for machined parts is the order in which material is removed to form the resultant part. The order, or sequence, is partially dependent on the geometric relationships between features. The sequence affects the safety, the time it takes to machine the part, and the quality of the finished part. The sequence of material removal depends on two types of relations between features: (1) intersections and (2) interfeature associations. Both types of relations compound the search for an optimal sequence of material removal. Therefore, the research problem has been the discovery and development of a genetic algorithm (GA) that efficiently searches the solution space for all possible sequences and identifies the best sequences in terms of safety, time and quality.     

Linear Least-Squares Algorithms for Temporal Difference Learning

We introduce two new temporal difference (TD) algorithms based on the theory of linear least-squares function approximation. We define an algorithm we call Least-Squares TD (LS TD) for which we prove probability-one convergence when it is used with a function approximator linear in the adjustable parameters. We then define a recursive version of this algorithm, Recursive Least-Square TD (RLS TD). Although these new TD algorithms require more computation per time-step than do Suttons TD() algorithms, they are more efficient in a statistical sense because they extract more information from training experiences. We describe a simulation experiment showing the substantial improvement in learning rate achieved by RLS TD in an example Markov prediction problem. To quantify this improvement, we introduce the TD error variance of a Markov chain, TD, and experimentally conclude that the convergence rate of a TD algorithm depends linearly on TD. In addition to converging more rapidly, LS TD and RLS TD do not have control parameters, such as a learning rate parameter, thus eliminating the possibility of achieving poor performance by an unlucky choice of parameters.     

UPE: Utah prototyping environment for robot manipulators

Developing an environment that enables optimal and flexible design of robot manipulators using reconfigurable links, joints, actuators, and sensors is an essential step for efficient robot design and prototyping. Such an environment should have the right mix of software and hardware components for designing the physical parts and the controllers, and for the algorithmic control of the robot modules (kinematics, inverse kinematics, dynamics, trajectory planning, analog control and digital computer control). Specifying object-based communications and catalog mechanisms between the software modules, controllers, physical parts, CAD designs, and actuator and sensor components is a necessary step in the prototyping activities.In this paper, we propose a flexible prototyping environment for robot manipulators with the required subsystems and interfaces between the different components of this environment. This environment provides a close tie between the design parameters of the robot manipulator by the different subsystems involved in the design process. The design and implementation of this environment along with the implementation of some of the subsystems are presented, and some examples that demonstrate the functionality of the environment are discussed. This work was supported in part by DARPA grant N00014-91-J-4123, NSF grant CDA 9024721, and a University of Utah Research Committee grant. All opinions, findings, conclusions or recommendations expressed in this document are those of the author and do not necessarily reflect the views of the sponsoring agencies.     

Roadmap for the next generation of dynamic function allocation theories and strategies

Dynamic function allocation theories have matured, but not to the extent that they can be implemented in actual environments. The authors identify a roadmap to help to get to that stage, after a brief review of relevant function allocation theories and the gaps in knowledge in adaptive allocation theories. Past research has focused on short‐term implementations of dynamic function allocation, which last for minutes (but never more than a few hours). Stable and enduring implementations of dynamic function allocation, lasting over days or possibly years, require a better and scientific understanding of the interaction among trigger mechanisms, adaptation strategies, and interruption strategies. The possibility of an unintentional consequence of this adaptive allocation, that the automated agent becomes confined to limited levels of automation, has to be ruled out after experimental evidence. © 2010 Wiley Periodicals, Inc.     

Reasoning about fuzzy temporal information from the web: towards retrieval of historical events

When searching for information about historical events, queries are naturally formulated using temporal constraints. However, the structured temporal information needed to support such constraints is usually not available to information retrieval systems. Furthermore, the temporal boundaries of most historical events are inherently ill-defined, calling for suitable extensions of classical temporal reasoning frameworks. In this paper, we propose a framework based on a fuzzification of Allen’s Interval Algebra to cope with these issues. By using simple heuristic techniques to extract temporal information from web documents, we initially focus more on recall than on precision, relying on the subsequent application of a fuzzy temporal reasoner to improve the reliability of the extracted information, and to deal with conflicts that arise because of the vagueness of events. Experimental results indicate that a consistent and reliable knowledge base of fuzzy temporal relations can thus be obtained, which effectively allows us to target temporally constrained retrieval tasks.     

Directed searching optimization algorithm for constrained optimization problems

A directed searching optimization algorithm (DSO) is proposed to solve constrained optimization problems in this paper. The proposed algorithm includes two important operations — position updating and genetic mutation. Position updating enables the non-best solution vectors to mimic the best one, which is beneficial to the convergence of the DSO; genetic mutation can increase the diversity of individuals, which is beneficial to preventing the premature convergence of the DSO. In addition, we adopt the penalty function method to balance objective and constraint violations. We can obtain satisfactory solutions for constrained optimization problems by combining the DSO and the penalty function method. Experimental results indicate that the proposed algorithm can be an efficient alternative on solving constrained optimization problems.     

Saving time or innovating practice: Investigating perceptions and uses of Learning Management Systems

Learning Management Systems (LMS) are web-based systems that allow instructors and/or students to share materials, submit and return assignments, and communicate online. In this study, we explored the uses and perceived benefits of using a LMS to support traditional classroom teaching as reported by instructors and students at a large American Midwestern university. We examined two years of survey data focusing on specific uses of the LMS that emphasized either efficient communication or interactive teaching and learning practices. We matched aggregate user log data with corresponding survey items to see if system use was consistent with patterns seen in the survey results. Findings suggest that instructors and students value tools and activities for efficient communication more than interactive tools for innovating existing practices. However, survey item analysis reveals that instructors and students also highly value the teaching and learning tools within the LMS.