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Electron transfer through the mitochondrial electron transport chain (ETC) can be critically blocked by the dysfunction of protein complexes. Redox-active molecules have been used to mediate the electron transfer in place of the dysfunctional complexes; however, they are limited to replacing complex I and are known to be toxic. Here we report artificial mitochondrial electron transfer pathways that enhance ETC activity by exploiting inner-membrane-bound gold nanoparticles (GNPs) as efficient electron transfer mediators. The hybridization of mitochondria with GNPs, driven by electrostatic interaction, is successfully visualized in real time at the level of a single mitochondrion. By observing quantized quenching dips via plasmon resonance energy transfer, we reveal that the hybridized GNPs are bound to the inner membrane of mitochondria irrespective of the presence of the outer membrane. The ETC activity of mitochondria with GNPs such as membrane potential, oxygen consumption, and ATP production is remarkably increasediin vitro/i.

Dynamics of release and cellular uptake of aqueous CO from CO-releasing molecules (CORMs) significantly affect signaling and cell viability. So far, it has been mainly observed by IR, UV-visible, and fluorescence techniques, which suffer from poor sensitivity and slow response time. Here, we show how to directly probe the mass transfer of aqueous CO from CORMs to cells using a fluidic chamber integrated with live cells and Raman reporters of large-area Au@Pd core-shell nanoparticle assembly to emulate a physiologically relevant microenvironment. We sensitively and directly detect CO release from trace CORMs of as low as 100 nM by measuring the Raman transitions of CO via rapid chemisorption onto the surface of the Au@Pd nanoparticles. By using our method, we successfully observe the dynamics of CO release from CORM-2 despite its very short half-life. We also reveal that the initial rate of CO release from CORM-3 is dramatically decreased by tens to hundreds of times when exposed to physiologically relevant pH variations from 7.4 to 2.5, which can be attributed to the acid hydrolysis of the CO ligand. CORM-2 tends to quickly release CO regardless of pH, probably because of its rapid cleavage into two monomeric Ru complexes by the co-solvent. The decrease in the initial rate at lower temperatures is more significant for CORM-3 than for CORM-2. Finally, we observe that the cellular uptake of aqueous CO from CORM-3 by lung cancer cells is approximately 2 times higher than that of normal lung cells.

Forecasting global food production is of growing importance in the context of globalizing food supply chains and observed increases in the frequency of climate extremes. The National Agriculture and Food Research Organization–Asia-Pacific Economic Cooperation Climate Center (NARO-APCC) Crop Forecasting Service provides yield forecasts for global cropland on a monthly basis using seasonal temperature and precipitation forecasts as the main inputs, and 1 year of testing the operation of the service was recently completed. Here we evaluate the forecasts for the 2019 yields of major commodity crops by comparing with the reported yields and forecasts from the European Commission’s Joint Research Centre (JRC) and the U.S. Department of Agriculture (USDA). Forecasts for maize, wheat, soybean, and rice were evaluated for 20 countries located in the Northern Hemisphere, including 39 crop-producing states in the United States, for which 2019 reported yields were already publicly available. The NARO-APCC forecasts are available several months earlier than the JRC and USDA forecasts. The skill of the NARO-APCC forecasts was good in absolute terms, but the forecast errors in the NARO-APCC forecasts were almost always larger than those of the JRC and USDA forecasts. The forecast errors in the JRC and USDA forecasts decreased as the harvest approached, whereas those in the NARO-APCC forecasts were rather stable over the season, with some exceptions. Although this feature seems to be a disadvantage, it may turn into an advantage if skillful forecasts are achievable in the earlier stages of a season. We conclude by discussing relative advantages and disadvantages and potential ways to improve global yield forecasting.

Detection of trace carbon monoxide (CO) dissolved in an aqueous phase is key for monitoring and optimizing biological and chemical gas conversions. So far, irrespective of the nonequilibrium nature of these conversion processes, because of low water solubility of CO, such detection has been performed indirectly, under the assumption of thermodynamic equilibrium, by the combination of chromatographic measurement of relatively abundant CO in a gas phase and Henry's law. Direct and sensitive detection of dissolved CO under nonequilibrium has not been explored yet. Here, we report the direct, ultrasensitive, and real-time monitoring of nonequilibrium dynamics of CO in an aqueous phase during biochemical conversions by devising miniaturized fluidic reactors with built-in CO-specific optical probes via surface-enhanced Raman spectroscopy. As the sensitive and selective probes, we fabricate ligand-free Au@Pd core-shell nanoparticle monolayers to maximize the Raman signal of single CO in the aqueous phase. We confirm that under equilibrium conditions, aqueous and gaseous CO concentrations estimated by our method are in good agreement with those measured directly and indirectly by gas chromatography (GC). We show that our probe can detect the aqueous CO concentrations as low as ca. 0.01% with high signal reproducibility, which is 200-fold more sensitive than that achieved by infrared spectroscopy. Finally, we successfully observe the nonequilibrium dynamics of the aqueous CO during biochemical reactions, which cannot be sensed by other detection methods including even indirect measurement by GC. We anticipate that our method can be widely applied not only for monitoring of biochemical gas reactions on multiple scales from a large reactor to a single-molecule level but also for molecular imaging of biological systems.

Forecasting year-to-year variations in the yields of major crops globally is expected to have utility in strengthening the ability of societies to better respond to food production shocks and food price spikes triggered by climate extremes. However, substantial improvements to the methodology used in global crop forecasting are required to realize a reliable operational service. Here, we assess the reliability of global within-season and pre-season predictions of yield variability obtained by applying statistical yield models to seasonal temperature and precipitation hindcast data derived from a multi-model ensemble (MME). This analysis is performed for five individual atmosphere-ocean coupled general circulation models (GCMs) and the two MME datasets generated using the average method and the mosaic method. Four major crops, maize, rice, wheat and soybean are studied. The mosaic method reliably predicts the yield variability over a large portion (25–38%) of the global harvested area three months before harvesting. The areas where the mosaic method displays good prediction skill are two to three times larger than those achieved using the average method (9–15%). Using the mosaic method, reliable within-season predictions of national yield variability can be produced in 36%, 24%, 25% and 30% of the maize-, soybean, rice- and wheat-producing countries, respectively. The pre-season predictions are found to be reliable in 23–32% of the crop-producing countries. As yield variability at the national level is of interest to commodity and food security specialists, the mosaic method provides a basis for developing an operational global yield forecasting service. Keywords: Climate change adaptation, Major crops, Global yield forecasting, Seasonal climate forecasts, Yield variability

Here, we report the direct and precise determination of volumetric mass transfer coefficient (kLa) of Carbon monoxide (CO) in miniaturized gas-liquid reactors via sensitive probing of the Raman transitions of aqueous trace CO without sampling. We confirm that weak Raman signals of aqueous CO can be significantly amplified by using Au@Pd core-shell nanoparticle monolayer via surface-enhanced Raman scattering and the amplified Raman intensity is linearly proportional to the concentration of aqueous CO in the range of 20 to 200 μM that is indirectly estimated by gas chromatography. We successfully determine kLa of CO in a miniaturized column-type gas-liquid reactor with a working volume of 2 ml which is not determined by sampling and subsequent gas chromatography measurement for gaseous CO due to small headspace volume. We find that the kLa values in miniaturized column-type and plate-type reactors are up to two orders of magnitude lower than those in large-scale reactors, presumably due to the absence of mechanical stirring. The lower kLa revealed by our method is also validated by fluid dynamic simulation. In addition, our method allows to determine kLa in miniaturized reactors, either with internal baffles or at different gas flow rates and temperatures. The kLa values in the miniaturized reactors are found to be more sensitive to changes in the temperature and flow rate than in the large-scale reactors, which is likely attributable to both improved mixing due to higher flow rates and faster heating rate due to smaller working volume. We believe that our method has a huge impact on CO gas conversion ranging from design and scale-up of gas-liquid reactors, process monitoring and optimization to high-throughput screening of strains or catalysts.

Owing to its high sensitivity and high selectivity along with rapid response time, plasmonic detection has gained considerable interest in a wide variety of sensing applications. To improve the fieldwork applicability and reliability of plasmonic detection, the integration of plasmonic nanoparticles into optical devices is desirable. Herein, we propose an integrated label-free detection platform comprising a plasmonic cavity that allows sensitive molecular detection via either surface-enhanced Raman scattering (SERS) or plasmon resonance energy transfer (PRET). A small droplet of metal ion solution spontaneously produces a plasmonic cavity on the surface of uncured poly(dimethylsiloxane) (PDMS), and as PDMS is cured, the metal ions are reduced to form a plasmonic antennae array on the cavity surface. Unique spherical feature and the integrated metallic nanoparticles of the cavity provide excellent optical functions to focus the incident light in the cavity and to rescatter the light absorbed by the nanoparticles. The optical properties of the plasmonic cavity for SERS or PRET are optimized by controlling the composition, size, and density of the metal nanoparticles. By using the cavity, we accomplish both 1000-fold sensitive detection and real-time monitoring of reactive oxygen species secreted by live cells via PRET. In addition, we achieve sensitive detection of trace amounts of toxic environmental molecules such as 5-chloro-2-methyl-4-isothiazolin-3-one/2-methyl-4-isothiazol-3-one (CMIT/MIT) and bisphenol A, as well as several small biomolecules such as glucose, adenine, and tryptophan, via SERS.

We report the synthesis of colloidal plasmonic microspheres by taking advantage of emulsions of polydimethylsiloxane (PDMS) in metal precursor solution. Within the emulsion, both the spontaneous formation and threedimensional (3D) assembly of metal nanoparticles take place at room temperature. The number of the nanoparticles being assembled in the microsphere is controllable according to the concentration of a metal precursor. In addition, owing to the surface charge and porosity of PDMS, positively charged and neutral molecules can be more concentrated in the plasmonic microsphere. We use this plasmonic microsphere for the detection of environmentally and biologically important molecules via surface-enhanced Raman spectroscopy, since 3D assembly of metal nanoparticles in the microsphere is size-comparable to a probed volume of incident light.

Background: Coenzyme Q10 (CoQ10), also known as ubiquinone, is a fat-soluble antioxidant. Although CoQ10 has not been approved as medication by the Food and Drug Administration, it is widely used in dietary supplements. Some studies have shown that CoQ10 has anti-inflammatory effects on various autoimmune disorders.Methods: In this study, we investigated the anti-inflammatory effects of liposome/gold hybrid nanoparticles encoded with CoQ10 (LGNP-CoQ10). Both CoQ10 and LGNP-CoQ10 were administered orally to mice with collagen-induced arthritis (CIA) for 10 weeks. The inflammation pathology of joint tissues of CIA mice was then analyzed using hematoxylin and eosin and Safranin O staining, as well as immunohistochemistry analysis. We obtained immunofluorescence staining images of spleen tissues using confocal microscopy.Results: We found that pro-inflammatory cytokines were significantly decreased in LGNP-CoQ10 injected mice. Th17 cell and phosphorylated STAT3-expressed cell populations were also decreased in LGNP-CoQ10 injected mice. When human peripheral blood mononuclear cells (PBMCs) were treated with CoQ10 and LGNP-CoQ10, the IL-17 expression of PBMCs in the LGNP-CoQ10-treated group was significantly reduced.Conclusion: These results suggest that LGNP-CoQ10 has therapeutic potential for the treatment of rheumatoid arthritis.

Recently, seasonal climate forecasts (SCFs) and their advances have gained increasing attention in agricultural communities, specifically because of their potential to improve climate risk management by increasing preparedness and thus enhance agricultural and economic outcomes. Seasonal predictions of crop diseases and insect pests that provide timely and accurate forecasts are especially valuable not only to farmers and extension workers (i.e., to inform their crop management decisions) but also to governments (i.e., to increase national-level disaster preparedness). In this study, we used a case study in Bicol, Philippines, to introduce an array of implementation strategies to facilitate the use of SCFs in the agricultural sector. To demonstrate the full potential of SCFs in the Bicol region, we developed and applied seasonal disease predictions for rice, with sequential activities that included a baseline study on disease epidemics in the target area, the examination of available SCFs, the development of a decision support system for seasonal disease predictions, and an evaluation of this system using SCF hindcasts. Finally, we disseminated the resulting seasonal disease predictions in the target area using an agro-met bulletin. The present study demonstrated a successful example of a developmental framework for the application of SCFs to agricultural decision-making with the support of relevant SCF-linked agricultural models. These implementation strategies, in combination with the lessons learned, can help guide prospective efforts of establishing similar climate services that utilize SCFs in developing countries to improve the outcomes and thus lead to enhanced and sustainable food security.

Here we propose facile and scalable synthesis of two-dimensional (2D) dendritic platinum nanoparticle at room temperature by exploiting an oil-in-water emulsion. The interfacial synthesis selectively provides platinum nanoparticle with 2D structure in high yield by controlling key reactants such as the amount of oleic acid and the concentration of block copolymer. Electrocatalytic activity of 2D dendritic platinum nanoparticle for oxygen reduction and methanol oxidation reaction is also examined.

We assessed the impacts of climate change and agricultural autonomous adaptation measures (changes in crop variety and planting dates) on food consumption and risk of hunger considering uncertainties in socioeconomic and climate conditions by using a new scenario framework. We combined a global computable general equilibrium model and a crop model (M-GAEZ), and estimated the impacts through 2050 based on future assumptions of socioeconomic and climate conditions. We used three Shared Socioeconomic Pathways as future population and gross domestic products, four Representative Concentration Pathways as a greenhouse gas emissions constraint, and eight General Circulation Models to estimate climate conditions. We found that (i) the adaptation measures are expected to significantly lower the risk of hunger resulting from climate change under various socioeconomic and climate conditions. (ii) population and economic development had a greater impact than climate conditions for risk of hunger at least throughout 2050, but climate change was projected to have notable impacts, even in the strong emission mitigation scenarios. (iii) The impact on hunger risk varied across regions because levels of calorie intake, climate change impacts and land scarcity varied by region.

High-tech markets are unpredictable owing to rapid technology innovation, diverse customer needs, high competition, and other elements. Many scholars have attempted to explain the uncertainty in high-tech markets using their own various approaches. However, sufficiently clear ways to predict diverse changes and trends in high-tech markets have yet to be presented. Thus, this paper proposes a new approach model, that is, systematic market segmentation, to give more accurate information. Using an empirical dataset from the mobile handset market in the Republic of Korea, we conduct our research model consisting of three steps. First, we categorize nine basic segments. Second, we test the stability of these segments. Finally, we profile the characteristics of the customers and products. We conclude that the approach is able to offer more diagnostic information to both practitioners and scholars. It is expected to provide rich information for an appropriate marketing mix in practice.

Programmable liposomes are designed to selectively produce various liposome-nanoparticle hybrids., Hybrid liposome/metal nanoparticles are promising candidate materials for biomedical applications. However, the poor selectivity and low yield of the desired hybrid during synthesis pose a challenge. We designed a programmable liposome by selective encoding of a reducing agent, which allows self-crystallization of metal nanoparticles within the liposome to produce stable liposome/metal nanoparticles alone. We synthesized seven types of liposome/monometallic and more complex liposome/bimetallic hybrids. The resulting nanoparticles are tunable in size and metal composition, and their surface plasmon resonance bands are controllable in visible and near infrared. Owing to outer lipid bilayer, our liposome/Au nanoparticle shows better colloidal stability in biologically relevant solutions as well as higher endocytosis efficiency than gold nanoparticles without the liposome. We used this hybrid in intracellular imaging of living cells via surface-enhanced Raman spectroscopy, taking advantage of its improved physicochemical properties. We believe that our method greatly increases the utility of metal nanoparticles in in vivo applications.

Self-formation of colloidal oil droplets in water or water droplets in oil not only has been regarded as fascinating fundamental science but also has been utilized in an enormous number of applications in everyday life. However, the creation of three-dimensional (3D) architectures by a liquid droplet and an immiscible liquid interface has been less investigated than other applications. Here, we report interfacial energy-driven spontaneous self-formation of a 3D plasmonic optical structure at room temperature without an external force. Based on the densities and interfacial energies of two liquids, we simulated the spontaneous formation of a plasmonic optical structure when a water droplet containing metal ions meets an immiscible liquid polydimethylsiloxane (PDMS) interface. At the interface, the metal ions in the droplet are automatically reduced to form an interfacial plasmonic layer as the liquid PDMS cures. The self-formation of both an optical cavity and integrated plasmonic nanostructure significantly enhances the fluorescence by a magnitude of 1000. Our findings will have a huge impact on the development of various photonic and plasmonic materials as well as metamaterials and devices.

We suggest a value market segmentation method for high-tech markets. Our research scope is the mobile handset market, which has the technology characteristics of a high-tech industry such as short cycle innovations, short technology development periods, and quick changes in customer needs. Thus, researchers and company strategists would use value or benefit segmentation method for high-tech market segmenting, because the characteristics may react to frequent changes. A value based market segmenting items are stable items for a long time, and so precisely explain customer needs. Specially, values related with product’s attributes have been shown to be more powerful items. In this paper, we try to show the superiority of a value market segmentation method through a Korean mobile handset market empirical case study.

Finding security vulnerabilities requires a different mindset than finding general faults in software—thinking like an attacker. Therefore, security engineers looking to prioritize security inspection and testing efforts may be better served by a prediction model that indicates security vulnerabilities rather than faults. At the same time, faults and vulnerabilities have commonalities that may allow development teams to use traditional fault prediction models and metrics for vulnerability prediction. The goal of our study is to determine whether fault prediction models can be used for vulnerability prediction or if specialized vulnerability prediction models should be developed when both models are built with traditional metrics of complexity, code churn, and fault history. We have performed an empirical study on a widely-used, large open source project, the Mozilla Firefox web browser, where 21% of the source code files have faults and only 3% of the files have vulnerabilities. Both the fault prediction model and the vulnerability prediction model provide similar ability in vulnerability prediction across a wide range of classification thresholds. For example, the fault prediction model provided recall of 83% and precision of 11% at classification threshold 0.6 and the vulnerability prediction model provided recall of 83% and precision of 12% at classification threshold 0.5. Our results suggest that fault prediction models based upon traditional metrics can substitute for specialized vulnerability prediction models. However, both fault prediction and vulnerability prediction models require significant improvement to reduce false positives while providing high recall.

Convergence has recently become a key word in all industries including telecommunications. The most noted case is the media and IT convergence in Korea. The reason for this is that broadcasting is under the influence of IT technology development, such as broadband, digitalized, and interactive trajectories. Mobile Internet Protocol Television (IPTV) is one of the converging technologies, which also includes mobile handsets and IPTV. Mobile IPTV will offer TV content via a mobile IP network such as WiMax or 4G all-IP[1]. It is important to forecast and understand new technology developments, because precise forecasting ensures the successful market entrance of new technologies. In the case of Mobile IPTV, a market study is more important because technology development to realize service will be completed in a few years.In this paper, we study mobile IPTV adoption using the suggested model, namely CE-TAM that evaluates convergent technology. We expect to get some implications; new adoption model for convergent evolved technology will be verified and managerial strategies of mobile IPTV will be set up

Security inspection and testing require experts in security who think like an attacker. Security experts need to know code locations on which to focus their testing and inspection efforts. Since vulnerabilities are rare occurrences, locating vulnerable code locations can be a challenging task. We investigated whether software metrics obtained from source code and development history are discriminative and predictive of vulnerable code locations. If so, security experts can use this prediction to prioritize security inspection and testing efforts. The metrics we investigated fall into three categories: complexity, code churn, and developer activity metrics. We performed two empirical case studies on large, widely used open-source projects: the Mozilla Firefox web browser and the Red Hat Enterprise Linux kernel. The results indicate that 24 of the 28 metrics collected are discriminative of vulnerabilities for both projects. The models using all three types of metrics together predicted over 80 percent of the known vulnerable files with less than 25 percent false positives for both projects. Compared to a random selection of files for inspection and testing, these models would have reduced the number of files and the number of lines of code to inspect or test by over 71 and 28 percent, respectively, for both projects.

Previous studies have shown that software code attributes, such as lines of source code, and history information, such as the number of code changes and the number of faults in prior releases of software, are useful for predicting where faults will occur. In this study of two large industrial software systems, we investigate the effectiveness of adding information about calling structure to fault prediction models. Adding calling structure information to a model based solely on non-calling structure code attributes modestly improved prediction accuracy. However, the addition of calling structure information to a model that included both history and non-calling structure code attributes produced no improvement.

As information technology rapidly develops and the period required to enter new technology shortens, there emerges a wide variety of alternatives for consumers. When there are many alternatives in the market, users choose after making comparisons. This process of making comparisons is our research key. We established a research model to find a mixed effect that comes from new attitudes to technology adoption and alternative’s continuous usage intention. The effect is revealed through a relative attractiveness (RA) factor to explain a user’s process of comparison. We empirically test our research model in the new media services, such as mobile TV, web TV, and mobile IPTV. According to our research results, a continuous usage intention of an existing technology is an important factor to explain the adoption of a new technology. So, the contribution of our research is in finding a role for the RA factor in research in new technology adoption.

Leaf area index (LAI) is an important parameter to identify the water balance in forested watershed as a biological factor influencing directly on the evapotranspiration in the forest area. The purpose of this study was to estimate the LAI in a small forested watershed in summer and winter by applying the Terra/Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data to the LAI estimation method. In this study, the estimation was based on the absorption and scattering processes of the solar radiation in the vegetation canopy and the spectral reflectance characteristics of soil vegetation. First, we estimated LAI based on Price’s model by application of ASTER data on the forested watershed located in the Tenzan Mountains of Saga, Japan. To validate the results of LAI estimation, secondly, we compared them to the measured LAI obtained by a plant canopy analyzer (LAI-2000) on the observation area inside the target region. This study showed that the LAI estimation method was a feasible and accurate method as indicated by the high relationship (r = 0.97) between LAI derived from ASTER data and LAI measured by LAI-2000. This paper is the first report on LAI estimation using Terra/ASTER data based on Price’s model and field investigation. This LAI estimation method is a reliable and applicable method.

Climate change and mitigation measures have three major impacts on food consumption and the risk of hunger: (1) changes in crop yields caused by climate change; (2) competition for land between food crops and energy crops driven by the use of bioenergy; and (3) costs associated with mitigation measures taken to meet an emissions reduction target that keeps the global average temperature increase to 2 °C. In this study, we combined a global computable general equilibrium model and a crop model (M-GAEZ), and we quantified the three impacts on risk of hunger through 2050 based on the uncertainty range associated with 12 climate models and one economic and demographic scenario. The strong mitigation measures aimed at attaining the 2 °C target reduce the negative effects of climate change on yields but have large negative impacts on the risk of hunger due to mitigation costs in the low-income countries. We also found that in a strongly carbon-constrained world, the change in food consumption resulting from mitigation measures depends more strongly on the change in incomes than the change in food prices.

Two-dimensional (2D) gold nanoparticles can possess novel physical and chemical properties, which will greatly expand the utility of gold nanoparticles in a wide variety of applications ranging from catalysis to biomedicine. However, colloidal synthesis of such particles generally requires sophisticated synthetic techniques to carefully guide anisotropic growth. Here we report that 2D hyper-branched gold nanoparticles in the lateral size range of about 50 ~ 120 nm can be synthesized selectively on a 2D immiscible oil/water interface in a few minutes at room temperature without structure-directing agents. An oleic acid/water interface can provide diffusion-controlled growth conditions, leading to the structural evolution of a smaller gold nucleus to 2D nanodendrimer and nanourchin at the interface. Simulations based on the phase field crystal model match well with experimental observations on the 2D branching of the nucleus, which occurs at the early stage of growth. Branching results in higher surface area and stronger near-field enhancement of 2D gold nanoparticles. This interfacial synthesis can be scaled up by creating an emulsion and the recovery of oleic acid is also achievable by centrifugation.

In automated requirements traceability, significant improvements can be realized through incorporating user feedback into the trace retrieval process. However, existing feedback techniques are designed to improve results for individual queries. In this paper we present a novel technique designed to extend the benefits of user feedback across multiple trace queries. Our approach, named Trace Query Transformation (TQT), utilizes a novel form of Association Rule Mining to learn a set of query transformation rules which are used to improve the efficacy of future trace queries. We evaluate TQT using two different kinds of training sets. The first represents an initial set of queries directly modified by human analysts, while the second represents a set of queries generated by applying a query optimization process based on initial relevance feedback for trace links between a set of source and target documents. Both techniques are evaluated using requirements from theWorldVista Healthcare system, traced against certification requirements for the Commission for Healthcare Information Technology. Results show that the TQT technique returns significant improvements in the quality of generated trace links.

Numerous trace retrieval algorithms incorporate the standard tf-idf (term frequency, inverse document frequency) technique to weight various terms. In this paper we address Grand Challenge C-GC1 by comparing the effectiveness of computing idf based only on the local terms in the query, versus computing it based on general term usage as documented in the American National Corpus. We also address Grand Challenges L-GC1 and L-GC2 by setting ourselves the additional task of designing and conducting the experiments using the alpha-release of TraceLab. TraceLab is an experimental workbench which allows researchers to graphically model and execute a traceability experiment as a workflow of components. Results of the experiment show that the local idf approach exceeds or matches the global approach in all of the cases studied.

Previous studies have shown that software code attributes, such as lines of source code, and history information, such as the number of code changes and the number of faults in prior releases of software, are useful for predicting where faults will occur. In this study of an industrial software system, we investigate the effectiveness of adding information about calling structure to fault prediction models. The addition of calling structure information to a model based solely on non-calling structure code attributes provided noticeable improvement in prediction accuracy, but only marginally improved the best model based on history and non-calling structure code attributes. The best model based on history and non-calling structure code attributes outperformed the best model based on calling and non-calling structure code attributes.

Leaf area index (LAI) is an important factor to identify the water balance in the area of forest as a biological factor influencing directly on the evapotranspiration occurs in the forest area. This study is purposed to estimate the LAI on the forest area in summer and winter by applying the Terra-Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data to the LAI estimation method based on the absorption and scattering process of the vertical element of electromagnetic waves and the characteristics of reflection spectrum of soil-vegetation. Estimated LAI was derived from application of ASTER data on the northern forest area of Saga City as the target region in this study. LAI measured directly by means of LAI-2000 on the observation point inside the target region was compared to the estimated LAI derived from the ASTER data. This study proved that LAI estimation method was a feasible and accurate method as indicated by the high relationship (r=0.91) between LAI derived from ASTER data and LAI measured directly.

Software complexity is often hypothesized to be the enemy of software security. We performed statistical analysis on nine code complexity metrics from the JavaScript Engine in the Mozilla application framework to investigate if this hypothesis is true. Our initial results show that the nine complexity measures have weak correlation (ρ=0.30 at best) with security problems for Mozilla JavaScript Engine. The study should be replicated on more products with design and code-level metrics. It may be necessary to create new complexity metrics to embody the type of complexity that leads to security problems.

Complexity is often hypothesized to be the enemy of software security. If this hypothesis is true, complexity metrics may be used to predict the locale of security problems and can be used to prioritize inspection and testing efforts. We performed statistical analysis on nine complexity metrics from the JavaScript Engine in the Mozilla application framework to find differences in code metrics between vulnerable and nonvulnerable code and to predict vulnerabilities. Our initial results show that complexity metrics can predict vulnerabilities at a low false positive rate, but at a high false negative rate.

Convergence is a key word in all of industries as well as telecommunication industry recently. The most noted case is media (specially, broadcasting) and IT convergent one in Korea. The reason is that broadcasting is under the influence of IT technology development trajectory such as digitalized, broadband, interactive and so on. For example, there are mobile TV (in Korea, DMB), IPTV, and mobile IPTV. Especially, mobile IPTV will be offered TV contents via mobile IP network such as WiMax or 4G all-IP. The determinants influencing mobile IPTV adoption decisions are analyzed using extended TAM model. Furthermore, we verify the moderator effects of experience and similar services of mobile IPTV.

An increasing number of cyber attacks are occurring at the application layer when attackers use malicious input. These input validation vulnerabilities can be exploited by (among others) SQL injection, cross site scripting, and buffer overflow attacks. Statement coverage and similar test adequacy metrics have historically been used to assess the level of functional and unit testing which has been performed on an application. However, these currently-available metrics do not highlight how well the system protects itself through validation. In this paper, we propose two SQL injection input validation testing adequacy metrics: target statement coverage and input variable coverage. A test suite which satisfies both adequacy criteria can be leveraged as a solid foundation for input validation scanning with a blacklist. To determine whether it is feasible to calculate values for our two metrics, we perform a case study on a web healthcare application and discuss some issues in implementation we have encountered. We find that the web healthcare application scored 96.7% target statement coverage and 98.5% input variable coverage.

Nowadays, market environments can be expressed in terms of increased competition, the diverse demands of customers, and the rapid innovation of IT technologies and so forth. These environmental changes have left companies facing a highly competitive market, and company revenues are gradually decreasing. Virtual enterprise is considered as the one of the alternatives available to facilitate adjustment to the new digital economy and save companies from falling into a red ocean. The contents of this research include the definition of virtual enterprise, and the characteristics and technologies for embodying it in a company. The paper suggests a new flow model for a decision support system in a virtual enterprise set-up, assesses critical success factors and weights as per the factors from case studies. From the previous analysis, paper defines the needs of collaborative activities in virtual enterprise and suggests the ubiquitous virtual enterprise reference model.

University computer science and software engineering students must build reliability and security into their software applications from the start of development. A graduate-level Software Testing and Reliability course at North Carolina State University has a learning objective of using appropriate testing techniques for the development of a reliable and secure system. Beginning in Fall 2005, the semester project has involved the development and testing of the open source and freely-available iTrust Medical Records system prototype. Our vision is to build a community of educators, students, and medical professionals that can collaborate and use the iTrust project as a development platform and testbed for secure and reliable application development.

This study assessed the health burden attributable to childhood underweight through 2050 focusing on disability-adjusted life years (DALYs), by considering the latest scenarios for climate change studies (representative concentration pathways and shared socioeconomic pathways (SSPs)) and conducting sensitivity analysis. A regression model for estimating DALYs attributable to childhood underweight (DAtU) was developed using the relationship between DAtU and childhood stunting. We combined a global computable general equilibrium model, a crop model, and two regression models to assess the future health burden. We found that (i) world total DAtU decreases from 2005 by 28???63% in 2050 depending on the socioeconomic scenarios. Per capita DAtU also decreases in all regions under either scenario in 2050, but the decreases vary significantly by regions and scenarios. (ii) The impact of climate change is relatively small in the framework of this study but, on the other hand, socioeconomic conditions have a great impact on the future health burden. (iii) Parameter uncertainty of the regression models is the second largest factor on uncertainty of the result following the changes in socioeconomic condition, and uncertainty derived from the difference in global circulation models is the smallest in the framework of this study.