Context-dependent product line engineering with lightweight formal approaches

This paper proposes a new style of product line engineering methods. It focuses on constructing embedded systems that take into account the contexts such as the external physical environments. In current product line development projects, Feature Analysis is mainly conducted from the viewpoint of system configurations: how hardware and software components are configured to constitute a system. In most cases, contexts are not considered explicitly. As a result, unexpected and unfavorable behavior might emerge in a system if a developer does not recognize any possible conflicting combinations between the system and contexts. To deal with this problem, this paper provides the notion of a context-dependent product line, which is composed of the system and context lines. The former is obtained by analyzing a family of systems. The latter is obtained by analyzing features of contexts associated to the systems. The system and context lines contain reusable core assets. The configuration of selected system components and contexts can be formally checked at the specification level. In this paper, we show a development process that includes the creation of both product line assets as well as context assets.     

Gas analysis of the CVD process for high yield growth of carbon nanotubes over metal-supported catalysts

Changes in the gas composition during the methane chemical vapor deposition growth of single- and double-walled carbon nanotubes over metal-supported MgO catalysts were investigated in an attempt to increase the nanotube yield. Monitoring the gas composition by gas chromatography as a function of the reaction time provides information on the activity and lifetime of the catalyst. The degree of methane decomposition, i.e., the C-H bond dissociation, was closely related to the nanotube yield, and the Fe-Mo binary catalyst exhibited a high activity. The effects of water vapor on the catalytic nanotube growth were also studied by introducing water vapor in the inlet gas. An appropriate amount of water prolonged the lifetime of the catalyst and increased the nanotube yield by 35%.     

Estimation of irradiance and outdoor performance of photovoltaic modules by meteorological data

Three environmental factors of irradiance, solar spectral distribution and module temperature greatly affect the performances of photovoltaic (PV) modules. If the environmental factors can be estimated by basic meteorological data (BMD) announced by an official organization in various areas, the performances of PV modules can be estimated easily. In this study, a relationship between the environmental factors and the BMD was analyzed. The performances of Si-based (crystalline Si and thin-film Si) PV modules were estimated by the relationship. As a result, errors between the estimation and actual performances in the crystalline Si and thin-film PV modules were within 1.88% and 0.58% in Kusatsu city, Japan (34°58′N, 135°57′E). This methodology can be useful for rating the performance of PV modules at various areas.     

Fabrication and characterization of poly‐γ‐glutamic acid nanofiber

Poly‐γ‐glutamic acid (γ‐PGA) is a natural polymer that is widely recognized as a component in the viscous filaments of fermented soybean (natto). γ‐PGA is known for its superior biodegradability, biocompatibility and water retention characteristics. Crosslinked γ‐PGA is commonly used as a hydrogel, but it is not used in the fiber form because it is soluble in water. In this study, we demonstrate the use of γ‐PGA‐Na for production of water insoluble γ‐PGA nanofibers by electrospinning. This result was accomplished using an aqueous solvent containing 10 wt % of an oxazoline component polymer as the crosslinking agent and by heat treatment. The crosslinking reaction was evaluated by solid‐state NMR. The nanofiber webs showed a high level of moisture absorption capability while retaining their fibrous shape. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2011     

Effect of Long Chain Fatty Acids on Organocatalytic Aqueous Direct Aldol Reactions

In an organocatalyzed, aqueous direct aldol reaction, the addition of a long chain fatty acid (1 mol%) such as stearic acid or erucic acid improved the aldol product yield and the enantioselectivity with low catalyst loading (1 mol%). The small particle size of the emulsion (less than 1 μm) was a key to the enhanced reactivity as shown by dynamic light scattering (DLS) analyses.     

A 470 × 235‐ppi poly‐Si TFT‐LCD for high‐resolution 2‐D and 3‐D autostereoscopic displays

Abstract— We have developed a 470 × 235‐ppi poly‐Si TFT‐LCD with a novel pixel arrangement, called HDDP (horizontally double‐density pixels), for high‐resolution 2‐D and 3‐D autostereoscopic displays. 3‐D image quality is especially high in a lenticular‐lens‐equipped 3‐D mode because both the horizontal and vertical resolutions are high, and because these resolutions are equal. 3‐D and 2‐D images can be displayed simultaneously in the same picture. In addition, 3‐D images can be displayed anywhere and 2‐D characters can be made to appear at different depths with perfect legibility. No switching of 2‐D/3‐D modes is necessary, and the design's thin and uncomplicated structure makes it especially suitable for mobile terminals.     

Studying just-in-time defect prediction using cross-project models

Unlike traditional defect prediction models that identify defect-prone modules, Just-In-Time (JIT) defect prediction models identify defect-inducing changes. As such, JIT defect models can provide earlier feedback for developers, while design decisions are still fresh in their minds. Unfortunately, similar to traditional defect models, JIT models require a large amount of training data, which is not available when projects are in initial development phases. To address this limitation in traditional defect prediction, prior work has proposed cross-project models, i.e., models learned from other projects with sufficient history. However, cross-project models have not yet been explored in the context of JIT prediction. Therefore, in this study, we empirically evaluate the performance of JIT models in a cross-project context. Through an empirical study on 11 open source projects, we find that while JIT models rarely perform well in a cross-project context, their performance tends to improve when using approaches that: (1) select models trained using other projects that are similar to the testing project, (2) combine the data of several other projects to produce a larger pool of training data, and (3) combine the models of several other projects to produce an ensemble model. Our findings empirically confirm that JIT models learned using other projects are a viable solution for projects with limited historical data. However, JIT models tend to perform best in a cross-project context when the data used to learn them are carefully selected.