A novel on-chip three-dimensional micromachined calorimeter with fully enclosed and suspended thin-film chamber for thermal characterization of liquid samples

A microfabricated calorimeter (μ-calorimeter) with an enclosed reaction chamber is presented. The 3D micromachined reaction chamber is capable of analyzing liquid samples with volume of 200 nl. The thin film low-stress silicon nitride membrane is used to reduce thermal mass of the calorimeter and increase the sensitivity of system. The μ-calorimeter has been designed to perform DC and AC calorimetry, thermal wave analysis, and differential scanning calorimetry. The μ-calorimeter fabricated with an integrated heater and a temperature sensor on opposite sides of the reaction chamber allows to perform thermal diffusivity and specific heat measurements on liquid samples with same device. Measurement results for diffusivity and heat capacitance using time delay method and thermal wave analysis are presented.     

Researchers Apply Lesson Study: A Cycle of Lesson Planning,Implementation, and Revision

Scripted lesson plans and/or professional development alone may not be sufficient to encourage teachers to reflect on the quality of their teaching and improve their teaching. One learning tool that teachers may use to improve their teaching is Lesson Study (LS). LS is a collaborative process involving educators, based on concepts of iteration and revision, to improve instruction. The initial use of an adapted version of LS referred to as a microteaching lesson study (MLS) is described in this article. Our purpose is to illustrate the process of MLS used by a group of researchers when developing lesson plans for teaching students with learning disabilities. We describe MLS, and then (1) share an application of MLS used by a research team in developing a writing intervention, and (2) discuss how the MLS format was mirrored with K‐12 teachers during professional development training.     

A local tree alignment approach to relation extraction of multiple arguments

In this paper, we address the problem of relation extraction of multiple arguments where the relation of entities is framed by multiple attributes. Such complex relations are successfully extracted using a syntactic tree-based pattern matching method. While induced subtree patterns are typically used to model the relations of multiple entities, we argue that hard pattern matching between a pattern database and instance trees cannot allow us to examine similar tree structures. Thus, we explore a tree alignment-based soft pattern matching approach to improve the coverage of induced patterns. Our pattern learning algorithm iteratively searches the most influential dependency tree patterns as well as a control parameter for each pattern. The resulting method outperforms two baselines, a pairwise approach with the tree-kernel support vector machine and a hard pattern matching method, on two standard datasets for a complex relation extraction task.     

Quantification of the physiological loading of one week of "pre-season" and one week of "in-season" training in professional soccer players

The aim of this study was to quantify the physiological loads of programmed "pre-season" and "in-season" training in professional soccer players. Data for players during each period were included for analysis (pre-season, n = 12; in-season, n = 10). We monitored physiological loading of training by measuring heart rate and rating of perceived exertion (RPE). Training loads were calculated by multiplying RPE score by the duration of training sessions. Each session was sub-categorized as physical, technical/tactical, physical and technical/tactical training. Average physiological loads in pre-season (heart rate 124 ± 7 beats · min(-1); training load 4343 ± 329 Borg scale · min) were higher compared with in-season (heart rate 112 ± 7 beats · min(-1); training load 1703 ± 173 Borg scale · min) (P < 0.05) and there was a greater proportion of time spent in 80-100% maximum heart rate zones (18 ± 2 vs. 5 ± 2%; P < 0.05). Such differences appear attributable to the higher intensities in technical/tactical sessions during pre-season (pre-season: heart rate 137 ± 8 beats · min(-1); training load 321 ± 23 Borg scale · min; in-season: heart rate 114 ± 9 beats · min(-1); training load 174 ± 27 Borg scale · min; P < 0.05). These findings demonstrate that pre-season training is more intense than in-season training. Such data indicate that these adjustments in load are a direct attempt to deliver training to promote specific training adaptations.     

Effects of rhizobacteria <Emphasis Type="Italic">Paenibacillus polymyxa</Emphasis> APEC136 and <Emphasis Type="Italic">Bacillus subtilis</Emphasis> APEC170 on biocontrol of postharvest pathogens of apple fruits

In this study, plant growth-promoting rhizobacteria (PGPR) were evaluated as potential biocontrol agents against postharvest pathogens of apple fruits. In vitro bioassays revealed that, out of 30 isolates screened, isolates APEC136 and APEC170 had the most significant inhibitory effects against the mycelial growth of several fungal pathogens. Analysis of 16S ribosomal RNA (rRNA) sequences identified the two effective isolates as Paenibacillus polymyxa and Bacillus subtilis, respectively. The two strains showed greater growth in brain-heart infusion broth than in other growth media. Treatment of harvested apples with suspensions of either strain reduced the symptoms of anthracnose disease caused by two fungal pathogens, Colletotrichum gloeosporioides and Colletotrichum acutatum, and white rot disease caused by Botryosphaeria dothidea. Increased productions of amylase and protease by APEC136, and increased productions of chitinase, amylase, and protease by APEC170 might have been responsible for inhibiting mycelial growth. The isolates caused a greater reduction in the growth of white rot than of anthracnose. These results indicate that the isolates APEC136 and APEC170 are promising agents for the biocontrol of anthracnose and white rot diseases in apples after harvest, and suggest that these isolates may be useful in controlling these diseases under field conditions.     

Seven Affordances of Computer-Supported Collaborative Learning: How to Support Collaborative Learning? How Can Technologies Help?

This article proposes 7 core affordances of technology for collaborative learning based on theories of collaborative learning and CSCL (Computer-Supported Collaborative Learning) practices. Technology affords learner opportunities to (1) engage in a joint task, (2) communicate, (3) share resources, (4) engage in productive collaborative learning processes, (5) engage in co-construction, (6) monitor and regulate collaborative learning, and (7) find and build groups and communities. We elaborate our framework using in-depth explorations of how technologies are actually used to support collaborative learning in CSCL research and identify representative design strategies and technology examples. While technology can play an important role in addressing the demands of collaborative learning, it needs to be considered in conjunction with pedagogical strategies and other social and cultural supports for collaborative learning. The supports also need to remain at an appropriate level so as not to interfere with long-term learning goals.     

Validation of a model of personalised learning

This article focuses on a Personalised Learning model which has 19 scales that were used to evaluate regional students’ perceptions of their readiness to learn, assessment processes, engagement, extent to which their learning is personalised and their associations with academic efficacy, academic achievement and student well-being. The data came from an average of 2700 students during each year of a 3-year study in six schools in provincial Victoria. A previously reported instrument was developed to measure students’ and teachers’ perceptions of the above factors affecting the implementation of Personalised Learning Plans (PLPs). It employed the latest scales to assess a range of PLP indicator variables, with all scales modified for use in an Australian context and with the total number of items kept to a minimum. Only scales that were more sensitive to PLPs were used in order to minimise the length of the instrument. There were three outcome variables: academic efficacy, academic achievement and student well-being. The emergent model demonstrates that addressing personalisation of learning and well-being depends on a combination of factors rather than “just getting one factor right”. This implies that there is a need for a coherent and collaborative approach for addressing the needs of students of low socioeconomic status.     

VQT: value cardinality and query pattern based R-schema to XML schema translation with implicit referential integrity

In this paper, we propose a new relational schema （R-schema） to XML schema translation algorithm, VQT, which analyzes the value cardinality and user query patterns and extracts the implicit referential integrities by using the cardinality property of foreign key constraints between columns and the equi-join characteristic in user queries. The VQT algorithm can apply the extracted implied referential integrity relation information to the R-schema and create an XML schema as the final result. Therefore, the VQT algorithm prevents the R-schema from being incorrectly converted into the XML schema, and it richly and powerfully represents all the information in the R-schema by creating an XML schema as the translation result on behalf of the XML DTD.     

The Effects of Conversational Language on Group Interaction and Group Performance in Computer-Supported Collaborative Argumentation

This study examined the effects of conversational language (e.g., asking questions, inviting replies, acknowledgments, referencing others by name, closing signatures, ‘I agree, but’, greetings, etc.) on the frequency and types of responses posted in reply to given types of messages (e.g., argument, evidence, critique, explanation), and how the resulting response patterns support and inhibit collaborative argumentation in asynchronous online discussions. Using event sequence analysis to analyze message-response exchanges in eight online group debates, this study found that (a) arguments elicited 41% more challenges when presented with more conversational language (effect size .32), (b) challenges with more conversational language elicited three to eight times more explanations (effect size .12 to .31), and (c) the number of supporting evidence elicited by challenges was not significantly different from challenges that used more versus less conversational language. Overall, these and other findings from exploratory post-hoc tests show that conversational language can help to produce patterns of interaction that foster high levels of critical discourse, and that some forms of conversational language are more effective in eliciting responses than others.