Static and dynamic contact angle measurement on rough surfaces using sessile drop profile analysis with application to water management in low temperature fuel cells

Fuel Cells are a promising alternative energy technology. One of the biggest problems that exists in fuel cell is that of water management. A better understanding of wettability characteristics in the fuel cells is needed to alleviate the problem of water management. Contact angle data on gas diffusion layers (GDL) of the fuel cells can be used to characterize the wettability of GDL in fuel cells. A contact angle measurement program has been developed to measure the contact angle of sessile drops from drop images. Digitization of drop images induces pixel errors in the contact angle measurement process. The resulting uncertainty in contact angle measurement has been analyzed. An experimental apparatus has been developed for contact angle measurements at different temperature, with the feature to measure advancing and receding contact angles on gas diffusion layers of fuel cells.

Taking an Object-Centric View on Dynamic Information with Object Flow Analysis

A large body of research analyzes the runtime execution of a system to extract abstract behavioral views. Those approaches primarily analyze control flow by tracing method execution events or they analyze object graphs of heap snapshots. However, they do not capture how objects are passed through the system at runtime. We refer to the exchange of objects as the object flow, and we claim that object flow is necessary to analyze if we are to understand the runtime of an object-oriented application. We propose and detail Object Flow Analysis, a novel dynamic analysis technique that takes this new information into account. To evaluate its usefulness, we present a visual approach that allows a developer to study classes and components in terms of how they exchange objects at runtime. We illustrate our approach on three case studies.

Using Dynamic Analysis for API Migration

When changing the API of a framework, we need to migrate its clients. This is best done automatically. In this paper, we focus on API migration where the mechanism for inversion of control changes. We propose to use dynamic analysis for such API migration since structural refactorings alone are often not sufficient. We consider JExample as a case-study. JExample extends JUnit with first-class dependencies and fixture injection. We investigate how dynamically collected information about test coverage and about instances under test can be used to detect dependency injection candidates.

Increasing the dynamic range for the analysis of boron in PGAA

Prompt gamma activation analysis (PGAA) is especially sensitive for elements with high neutron-capture cross sections, like boron, which can be detected down to a level of ng/g. However, if it is a major component, the high count rate from its signal will distort the spectra, making the evaluation difficult. A lead attenuator was introduced in front of the HPGe-detector to reduce low-energy gamma radiation and specifically the boron gamma rays reaching the detector, whose thickness was found to be optimal at 10 mm. Detection efficiencies with and without the lead attenuator were compared, and it was shown that the dynamic range of the PGAA technique was significantly increased. The method was verified with the analyses of stoichiometric compounds: TiB2, NiB, PVC, Alborex, and Alborite.

Characterization of microcirculation in multiple sclerosis lesions by dynamic texture parameter analysis (DTPA)

OBJECTIVE Texture analysis is an alternative method to quantitatively assess MR-images. In this study, we introduce dynamic texture parameter analysis (DTPA), a novel technique to investigate the temporal evolution of texture parameters using dynamic susceptibility contrast enhanced (DSCE) imaging. Here, we aim to introduce the method and its application on enhancing lesions (EL), non-enhancing lesions (NEL) and normal appearing white matter (NAWM) in multiple sclerosis (MS). METHODS We investigated 18 patients with MS and clinical isolated syndrome (CIS), according to the 2010 McDonald's criteria using DSCE imaging at different field strengths (1.5 and 3 Tesla). Tissues of interest (TOIs) were defined within 27 EL, 29 NEL and 37 NAWM areas after normalization and eight histogram-based texture parameter maps (TPMs) were computed. TPMs quantify the heterogeneity of the TOI. For every TOI, the average, variance, skewness, kurtosis and variance-of-the-variance statistical parameters were calculated. These TOI parameters were further analyzed using one-way ANOVA followed by multiple Wilcoxon sum rank testing corrected for multiple comparisons. RESULTS Tissue- and time-dependent differences were observed in the dynamics of computed texture parameters. Sixteen parameters discriminated between EL, NEL and NAWM (pAVG = 0.0005). Significant differences in the DTPA texture maps were found during inflow (52 parameters), outflow (40 parameters) and reperfusion (62 parameters). The strongest discriminators among the TPMs were observed in the variance-related parameters, while skewness and kurtosis TPMs were in general less sensitive to detect differences between the tissues. CONCLUSION DTPA of DSCE image time series revealed characteristic time responses for ELs, NELs and NAWM. This may be further used for a refined quantitative grading of MS lesions during their evolution from acute to chronic state. DTPA discriminates lesions beyond features of enhancement or T2-hypersignal, on a numeric scale allowing for a more subtle grading of MS-lesions.

Sequential updating via dynamic hierarchical models: A Bayesian approach to longitudinal data analysis

Most statistical analysis, theory and practice, is concerned with static models; models with a proposed set of parameters whose values are fixed across observational units. Static models implicitly assume that the quantified relationships remain the same across the design space of the data. While this is reasonable under many circumstances this can be a dangerous assumption when dealing with sequentially ordered data. The mere passage of time always brings fresh considerations and the interrelationships among parameters, or subsets of parameters, may need to be continually revised. ^ When data are gathered sequentially dynamic interim monitoring may be useful as new subject-specific parameters are introduced with each new observational unit. Sequential imputation via dynamic hierarchical models is an efficient strategy for handling missing data and analyzing longitudinal studies. Dynamic conditional independence models offers a flexible framework that exploits the Bayesian updating scheme for capturing the evolution of both the population and individual effects over time. While static models often describe aggregate information well they often do not reflect conflicts in the information at the individual level. Dynamic models prove advantageous over static models in capturing both individual and aggregate trends. Computations for such models can be carried out via the Gibbs sampler. An application using a small sample repeated measures normally distributed growth curve data is presented. ^

High-resolution dynamic computer simulation analysis of the behavior of sample components with pI values outside the pH gradient established by carrier ampholyte CIEF

The behavior of sample components whose pI values are outside the pH gradient established by 101 hypothetical biprotic carrier ampholytes covering a pH 6-8 range was investigated by computer simulation under constant current conditions with concomitant constant electroosmosis toward the cathode. Data obtained with the sample being applied between zones of carrier ampholytes and on the anodic side of the carrier ampholytes were studied and found to evolve into zone structures comprising three regions between anolyte and catholyte. The focusing region with the pH gradient is bracketed by two isotachopheretic zone structures comprising selected sample and carrier components as isotachophoretic zones. The isotachophoretic structures electrophoretically migrate in opposite direction and their lengths increase with time due to the gradual isotachophoretic decay at the pH gradient edges. Due to electroosmosis, however, the overall pattern is being transported toward the cathode. Sample components whose pI values are outside the established pH gradient are demonstrated to form isotachophoretic zones behind the leading cation of the catholyte (components with pI values larger than 8) and the leading anion of the anolyte (components with pI values smaller than 6). Amphoteric compounds with appropriate pI values or nonamphoteric components can act as isotachophoretic spacer compounds between sample compounds or between the leader and the sample with the highest mobility. The simulation data obtained provide for the first time insight into the dynamics of amphoteric sample components that do not focus within the established pH gradient.

Enhancing Food Sovereignty and Food Security through the Revitalization of Indigenous Knowledge: Experiences from the Bolivian Andes

Endogenous development is defined as development that values primarily locally available resources and the way people organized themselves for that purpose. It is a dynamic and evolving concept that also embraces innovations and complementation from other than endogenous sources of knowledge; however, only as far as they are based on mutual respect and the recognition of cultural and socioeconomic self-determination of each of the parties involved. Experiences that have been systematized in the context of the BioAndes Program are demonstrating that enhancing food security and food sovereignty on the basis of endogenous development can be best achieved by applying a ‘biocultural’ perspective: This means to promote and support actions that are simultaneously valuing biological (fauna, flora, soils, or agrobiodiversity) and sociocultural resources (forms of social organization, local knowledge and skills, norms, and the related worldviews). In Bolivia, that is one of the Latin-American countries with the highest levels of poverty (79% of the rural population) and undernourishment (22% of the total population), the Program BioAndes promotes food sovereignty and food security by revitalizing the knowledge of Andean indigenous people and strengthening their livelihood strategies. This starts by recognizing that Andean people have developed complex strategies to constantly adapt to highly diverse and changing socioenvironmental conditions. These strategies are characterized by organizing the communities, land use and livelihoods along a vertical gradient of the available eco-climatic zones; the resulting agricultural systems are evolving around the own sociocultural values of reciprocity and mutual cooperation, giving thus access to an extensive variety of food, fiber and energy sources. As the influences of markets, competition or individualization are increasingly affecting the life in the communities, people became aware of the need to find a new balance between endogenous and exogenous forms of knowledge. In this context, BioAndes starts by recognizing the wealth and potentials of local practices and aims to integrate its actions into the ongoing endogenous processes of innovation and adaptation. In order to avoid external impositions and biases, the program intervenes on the basis of a dialogue between exogenous, mainly scientific, and indigenous forms of knowledge. The paper presents an analysis of the strengths and weaknesses of enhancing endogenous development through a dialogue between scientific and indigenous knowledge by specifically focusing on its effects on food sovereignty and food security in three ‘biocultural’ rural areas of the Bolivian highlands. The paper shows how the dialogue between different forms of knowledge evolved alongside the following project activities: 1) recuperation and renovation of local seeds and crop varieties (potato – Solanum spp., quinoa – Chenopodium quinoa, cañahua – Chenopodium pallidicaule); 2) support for the elaboration of community-based norms and regulations for governing access and distribution of non-timber forest products, such as medicinal, fodder, and construction plants; 3) revitalization of ethnoveterinary knowledge for sheep and llama breeding; 4) improvement of local knowledge about the transformation of food products (sheep-cheese, lacayote – Cucurbita sp. - jam, dried llama meat, fours of cañahua and other Andean crops). The implementation of these activities fostered the community-based livelihoods of indigenous people by complementing them with carefully and jointly designed innovations based on internal and external sources of knowledge and resources. Through this process, the epistemological and ontological basis that underlies local practices was made visible. On this basis, local and external actors started to jointly define a renewed concept of food security and food sovereignty that, while oriented in the notions of well being according to a collectively re-crafted world view, was incorporating external contributions as well. Enabling and hindering factors, actors and conditions of these processes are discussed in the paper.

Improved Techniques for Acquisition and Analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Detecting Vascular Permeability in the Central Nervous System

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a noninvasive technique for quantitative assessment of the integrity of blood-brain barrier and blood-spinal cord barrier (BSCB) in the presence of central nervous system pathologies. However, the results of DCE-MRI show substantial variability. The high variability can be caused by a number of factors including inaccurate T1 estimation, insufficient temporal resolution and poor contrast-to-noise ratio. My thesis work is to develop improved methods to reduce the variability of DCE-MRI results. To obtain fast and accurate T1 map, the Look-Locker acquisition technique was implemented with a novel and truly centric k-space segmentation scheme. In addition, an original multi-step curve fitting procedure was developed to increase the accuracy of T1 estimation. A view sharing acquisition method was implemented to increase temporal resolution, and a novel normalization method was introduced to reduce image artifacts. Finally, a new clustering algorithm was developed to reduce apparent noise in the DCE-MRI data. The performance of these proposed methods was verified by simulations and phantom studies. As part of this work, the proposed techniques were applied to an in vivo DCE-MRI study of experimental spinal cord injury (SCI). These methods have shown robust results and allow quantitative assessment of regions with very low vascular permeability. In conclusion, applications of the improved DCE-MRI acquisition and analysis methods developed in this thesis work can improve the accuracy of the DCE-MRI results.

Can FDI promote export diversification and sophistication of host countries? : dynamic panel system GMM analysis

Recent trade literature highlights the importance of export diversification and upgrading in fostering faster and sustainable economic growth. This study investigates the impact of FDI inflow and stock on the level of export diversification and sophistication in host country's export baskets. By utilizing the dynamic panel data model, we find that the five-year lagged FDI inflow correlates positively with both export diversification and sophistication, and FDI stock makes the positive contribution to export sophistication. These findings provide support for the possibility of successful capabilities transfer to and building by local firms. We also find that these positive impacts of FDI exist only in developing countries.