美国校长对NCLB教育法案的反思

作为美国公立学校里的一位教育实践者,我担任了令人羡慕的小学校长职务.对于"不让一个孩子掉队"(NCLB)教育法案,我是坚定的拥护者,同时又是积极的反思者.     

Random set framework for multiple instance learning

Multiple instance learning (MIL) is a technique used for learning a target concept in the presence of noise or in a condition of uncertainty. While standard learning techniques present the learner with individual samples, MIL alternatively presents the learner with sets of samples. Although sets are the primary elements used for analysis in MIL, research in this area has focused on using standard analysis techniques. In the following, a random set framework for multiple instance learning (RSF-MIL) is proposed that can directly perform analysis on sets. The proposed method uses random sets and fuzzy measures to model the MIL problem, thus providing a more natural mathematical framework, a more general MIL solution, and a more versatile learning tool. Comparative experimental results using RSF-MIL are presented for benchmark data sets. RSF-MIL is further compared to the state-of-the-art in landmine detection using ground penetrating radar data.     

Software engineering education: A study on conducting collaborative senior project development

Project and teamwork training is recognized as an important aspect in software engineering (SE) education. Senior projects, which often feature industrial involvement, serve the function of a ‘capstone course’ in SE curricula, by offering comprehensive training in collaborative software development. Given the characteristics of student team projects and the social aspects of software development, instructional issues in such a course must include: how to encourage teamwork, how to formalize and streamline stakeholder participation, and how to monitor students’ work, as well as sustain their desired collaborative effort throughout the development. In this paper, we present an exploratory study which highlights a particular case and introduces the meetings-flow approach. In order to investigate how this approach could contribute to the project's results, we examined its quantitative benefits in relation to the development of the project. We also conducted focus group interviews to discuss the humanistic findings and educational effects pertaining to this approach.     

Solubilisation of dyes by surfactant micelles. Part 4: Influence of dye fixatives

Dye loss from unfixed dyed fabrics has been found to be insensitive to change in surfactant type or concentration. There was accompanying dye transfer to white fabric but this was reduced by Synperonic A7 in the case of fabrics dyed with CI Direct Green 26, due to solubilisation of the dye in nonionic micelles. The anionic surfactant, SDS, selectively displaced dye from fixed dyed fabrics, paralleling its behaviour with water soluble polymers. Similarly, dye loss was related to concentration of surfactant monomer, the effect increasing with SDS concentration up to its critical micelle concentration. Other anionic surfactants have been found to exhibit a similar trend, the effect increasing with their increasing surface activity. The commercial polymeric fixatives, Tinofix ECO and Indosol E50, were the most effective of those studied and the single-chain cationic surfactant, CTAB, was the least effective.     

美国艾默生公司压缩机应用技术讲座——第十八讲 R41OA空调制冷剂的特点

本文是有关介绍新型制冷剂专题的系列文章之二。第一篇文章回顾了R410A的发展史，介绍了R410A如何成为空调系统的新宠。本文将比较不同制冷剂的特性，并解释了全球越来越多的空调生产商倾向选择R410A的原因是在系统成本允许的条件下，他们不仅需要使效率更高，还要肩负环境保护的责任。上一篇文章阐述过，R22曾经是世界上使用最广泛的碳氟化合物制冷剂，而目前已出现了数种用以取代R22的候补产品，包括环保的含氢氟代烃（HFC）制冷剂-R134a，R410A和R407C。     

RELAX: a language to address uncertainty in self-adaptive systems requirement

Self-adaptive systems have the capability to autonomously modify their behavior at run-time in response to changes in their environment. Self-adaptation is particularly necessary for applications that must run continuously, even under adverse conditions and changing requirements; sample domains include automotive systems, telecommunications, and environmental monitoring systems. While a few techniques have been developed to support the monitoring and analysis of requirements for adaptive systems, limited attention has been paid to the actual creation and specification of requirements of self-adaptive systems. As a result, self-adaptivity is often constructed in an ad-hoc manner. In order to support the rigorous specification of adaptive systems requirements, this paper introduces RELAX, a new requirements language for self-adaptive systems that explicitly addresses uncertainty inherent in adaptive systems. We present the formal semantics for RELAX in terms of fuzzy logic, thus enabling a rigorous treatment of requirements that include uncertainty. RELAX enables developers to identify uncertainty in the requirements, thereby facilitating the design of systems that are, by definition, more flexible and amenable to adaptation in a systematic fashion. We illustrate the use of RELAX on smart home applications, including an adaptive assisted living system.     

Development of a national model of Pinus radiata stand volume from lidar metrics for New Zealand

Light detection and ranging (lidar) has been successfully used to describe a wide range of forest metrics at local scales. However, little research has tested the general applicability of this technology to describe commercially important stand dimensions, such as total stem volume (V), at national levels across broad environmental gradients.

Using an extensive national data set covering the spatial extent of Pinus radiata plantation forests in New Zealand, the key objectives of this study were to (1) develop regression models to best describe V for P. radiata from lidar metrics and (2) investigate whether these relationships could be improved using coincident environmental and stand-level information. Development of relationships between lidar metrics and forest volume are of particular importance for P. radiata, as this species constitutes approximately 90% of the 1.8 Mha plantation resource.

Using lidar mean height and the percentage of lidar ground returns, the initial model (model 1) accounted for 85% of the variance in V. Addition of stand stocking (number of stems ha^?1), measured within the plots, to the model (model 2) significantly (p < 0.001) improved predictions, with R ² increasing to 0.86 and the root mean square error declining from 80.1 m³ ha^?1 to 71.6 m³ ha^?1. For both models, partial responses show V to be most sensitive to lidar mean height, which was included in the model as a second-order polynomial.

Although environmental variables are established determinants for V, their inclusion did not significantly improve either model 1 or 2. Residual values for both models showed little apparent bias when plotted against stand-level information or a wide array of environmental variables, supporting the general applicability of these relationships.