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.     

Learning the higher-order structure of a natural sound

Unsupervised learning algorithms paying attention only to second-order statistics ignore the phase structure (higher-order statistics) of signals, which contains all the informative temporal and spatial coincidences which we think of as 'features'. Here we discuss how an Independent Component Analysis (ICA) algorithm may be used to elucidate the higher-order structure of natural signals, yielding their independent basis functions. This is illustrated with the ICA transform of the sound of a fingernail tapping musically on a tooth. The resulting independent basis functions look like the sounds themselves, having similar temporal envelopes and the same musical pitches. Thus they reflect both the phase and frequency information inherent in the data.     

Validation of plant part measurements using a 3D reconstruction method suitable for high-throughput seedling phenotyping

In plant phenotyping, there is a demand for high-throughput, non-destructive systems that can accurately analyse various plant traits by measuring features such as plant volume, leaf area, and stem length. Existing vision-based systems either focus on speed using 2D imaging, which is consequently inaccurate, or on accuracy using time-consuming 3D methods. In this paper, we present a computer-vision system for seedling phenotyping that combines best of both approaches by utilizing a fast three-dimensional (3D) reconstruction method. We developed image processing methods for the identification and segmentation of plant organs (stem and leaf) from the 3D plant model. Various measurements of plant features such as plant volume, leaf area, and stem length are estimated based on these plant segments. We evaluate the accuracy of our system by comparing the measurements of our methods with ground truth measurements obtained destructively by hand. The results indicate that the proposed system is very promising.     

Analytical and numerical comparisons of biogeography-based optimization and genetic algorithms

We show that biogeography-based optimization (BBO) is a generalization of a genetic algorithm with global uniform recombination (GA/GUR). Based on the common features of BBO and GA/GUR, we use a previously-derived BBO Markov model to obtain a GA/GUR Markov model. One BBO characteristic which makes it distinctive from GA/GUR is its migration mechanism, which affects selection pressure (i.e., the probability of retaining certain features in the population from one generation to the next). We compare the BBO and GA/GUR algorithms using results from analytical Markov models and continuous optimization benchmark problems. We show that the unique selection pressure provided by BBO generally results in better optimization results for a set of standard benchmark problems. We also present comparisons between BBO and GA/GUR for combinatorial optimization problems, include the traveling salesman, the graph coloring, and the bin packing problems.     

Key activities for product derivation in software product lines

More and more organizations adopt software product lines to leverage extensive reuse and deliver a multitude of benefits such as increased quality and productivity and a decrease in cost and time-to-market of their software development. When compared to the vast amount of research on developing product lines, relatively little work has been dedicated to the actual use of product lines to derive individual products, i.e., the process of product derivation. Existing approaches to product derivation have been developed independently for different aims and purposes. While the definition of a general approach applicable to every domain may not be possible, it would be interesting for researchers and practitioners to know which activities are common in existing approaches, i.e., what are the key activities in product derivation. In this paper we report on how we compared two product derivation approaches developed by the authors in two different, independent research projects. Both approaches independently sought to identify product derivation activities, one through a process reference model and the other through a tool-supported derivation approach. Both approaches have been developed and validated in research industry collaborations with different companies. Through the comparison of the approaches we identify key product derivation activities. We illustrate the activities’ importance with examples from industry collaborations. To further validate the activities, we analyze three existing product derivation approaches for their support for these activities. The validation provides evidence that the identified activities are relevant to product derivation and we thus conclude that they should be considered (e.g., as a checklist) when developing or evaluating a product derivation approach.     

Adoption of Asynchronous Learning Tools by Traditional Full-Time Students: A Pilot Study

Comparing the performance of distance learning and on-campus students is a well-studied problem. Results have shown that distance learners can perform as well or better than on-campus students. Few studies, however, have explored how or why traditional on-campus students utilize the technology intended for distance learners. Many universities and corporations provide educational and training content simultaneously to both face-to-face and distance students. Thus, exploring how instructional technology typically designed for distance learning students could benefit traditional students is important to our understanding of present and future learning environments. To this end, a pilot study was undertaken that explored how graduate students – primarily business majors – adopted and used video-streaming technology for a one semester course on Knowledge Management Tools and Techniques. Results indicated that age, previous use of the technology, need for interaction and Internet connectivity all influenced the decisions of the subjects to adopt the technology. Two-thirds of the subjects used the technology, with a self-reported estimate of procuring 16% of the course content asynchronously. Most used video streaming for convenience reasons and to review class material.     

Reinforcement Learning Rules in a Repeated Game

This paper examines the performance of simple reinforcement learningalgorithms in a stationary environment and in a repeated game where theenvironment evolves endogenously based on the actions of other agents. Sometypes of reinforcement learning rules can be extremely sensitive to smallchanges in the initial conditions, consequently, events early in a simulationcan affect the performance of the rule over a relatively long time horizon.However, when multiple adaptive agents interact, algorithms that performedpoorly in a stationary environment often converge rapidly to a stableaggregate behaviors despite the slow and erratic behavior of individuallearners. Algorithms that are robust in stationary environments can exhibitslow convergence in an evolving environment.     

嵌入式操作系统管理汇聚应用

当今许多复杂的嵌入式系统要求信号处理和信号控制两种功能.过去这两种功能一直属于DSP和微控制器(MCU)两种独立的器件.然而,汇聚处理器的推出改变了这种现状,将DSP和MCU两种功能集成到一种灵活体系结构中,典型的代表有ADI公司的B1ackfin处理器系列.     

时间序列数据分析与预处理

时间序列分析中常常遇到的一个问题是如何有效地过滤噪音和约简数据。本文通过修改传统的离散的傅立叶变换来过滤噪音和进行数据的约简，并尽可能保留原始时间序列的全局变化趋势。为检验该方法的有效性，本文同时提出一个新颖的数据分类算法MCC，并用该算法对股票回报率的变化进行预测，实验结果显示，用MCC算法在预处理后的数据上进行预测，其预测的命中率达到63．68％，而在原始数据上进行预测，其预测的命中率只有48．98％。显然，通过对原始数据进行噪音过滤有效地改善了预测的精度。另外，数据的约简也提高了预测算法的效率。