Flux Partitioning of Species Approach in Binary Interdiffusion Process

Validation of the flux partitioning of species model has been illustrated. Various combinations of inequality expression for the fluxes of species A and B in two successively grown hypothetical intermetallic phases in the interdiffusion zone have been considered within the constraints of this concept. Furthermore, ratio of intrinsic diffusivities of the species A and B in those two phases has been correlated in four different cases. Moreover, complete and or partial validation or invalidation of this model with respect to both the species, has been proven theoretically and also discussed with the Co-Si system as an example.

Polymorphic phase transition among the titania crystal structures using a solution-based approach: from precursor chemistry to nucleation process

Nanocrystalline titania are a robust candidate for various functional applications owing to its non-toxicity, cheap availability, ease of preparation and exceptional photochemical as well as thermal stability. The uniqueness in each lattice structure of titania leads to multifaceted physico-chemical and opto-electronic properties, which yield different functionalities and thus influence their performances in various green energy applications. The high temperature treatment for crystallizing titania triggers inevitable particle growth and the destruction of delicate nanostructural features. Thus, the preparation of crystalline titania with tunable phase/particle size/morphology at low to moderate temperatures using a solution-based approach has paved the way for further exciting areas of research. In this focused review, titania synthesis from hydrothermal/solvothermal method, conventional sol-gel method and sol-gel-assisted method via ultrasonication, photoillumination and ILs, thermolysis and microemulsion routes are discussed. These wet chemical methods have broader visibility, since multiple reaction parameters, such as precursor chemistry, surfactants, chelating agents, solvents, mineralizer, pH of the solution, aging time, reaction temperature/time, inorganic electrolytes, can be easily manipulated to tune the final physical structure. This review sheds light on the stabilization/phase transformation pathways of titania polymorphs like anatase, rutile, brookite and TiO2(B) under a variety of reaction conditions. The driving force for crystallization arising from complex species in solution coupled with pH of the solution and ion species facilitating the orientation of octahedral resulting in a crystalline phase are reviewed in detail. In addition to titanium halide/alkoxide, the nucleation of titania from other precursors like peroxo and layered titanates are also discussed. The nonaqueous route and ball milling-induced titania transformation is briefly outlined; moreover, the lacunae in understanding the concepts and future prospects in this exciting field are suggested.

An Optical How Feature and McFIS Based Approach for 3-Dimensional Human Action Recognition

We propose to develop a 3-D optical flow features based human action recognition system. Optical flow based features are employed here since they can capture the apparent movement in object, by design. Moreover, they can represent information hierarchically from local pixel level to global object level. In this work, 3-D optical flow based features a re extracted by combining the 2-1) optical flow based features with the depth flow features obtained from depth camera. In order to develop an action recognition system, we employ a Meta-Cognitive Neuro-Fuzzy Inference System (McFIS). The m of McFIS is to find the decision boundary separating different classes based on their respective optical flow based features. McFIS consists of a neuro-fuzzy inference system (cognitive component) and a self-regulatory learning mechanism (meta-cognitive component). During the supervised learning, self-regulatory learning mechanism monitors the knowledge of the current sample with respect to the existing knowledge in the network and controls the learning by deciding on sample deletion, sample learning or sample reserve strategies. The performance of the proposed action recognition system was evaluated on a proprietary data set consisting of eight subjects. The performance evaluation with standard support vector machine classifier and extreme learning machine indicates improved performance of McFIS is recognizing actions based of 3-D optical flow based features.

A CAPABILITY APPROACH BASED STAKEHOLDER ANALYSIS FOR THE BASE OF THE PYRAMID: A CASE STUDY OF THE FIREWOOD BASED COOK-STOVES

People living under $2 income per day, referred as Base of the Pyramid (BoP), face undesired situations like lack of nutrition, health, education etc. Design as a process of changing current undesired situation to a desired situation has failed. A crucial reason behind these failures is lack of normative basis to identify and understand the absent or unsatisfied stakeholder. Currently stakeholder analysis in the design is heuristic. This paper uses a normative framework of Capability Approach (CA) for the stakeholder analysis. A brief discussion on stakeholder theory and analysis is used to identify gaps in the literature. The constructs of the CA are discussed for its suitability to the purpose. Along with methodological details, data generated from the stakeholder interviews, focus groups in a case study of dissemination of improved cook-stoves is used to interlink the theory with the practice. The scope of this work is in identifying and investigating the motives of the stakeholders in the involvement in the product. Though a lot of insights to discern and manage crucial stakeholders is inbuilt in the methodology, this work does not claim explicit coverage of these aspects.

Robust approach to dynamic statistical process control

Statistical Process Control (SPC) technique are well established across a wide range of industries. In particular, the plotting of key steady state variables with their statistical limit against time (Shewart charting) is a common approach for monitoring the normality of production. This paper aims with extending Shewart charting techniques to the quality monitoring of variables driven by uncertain dynamic processes, which has particular application in the process industries where it is desirable to monitor process variables on-line as well as final product. The robust approach to dynamic SPC is based on previous work on guaranteed cost filtering for linear systems and is intended to provide a basis for both a wide application of SPC monitoring and also motivate unstructured fault detection.

The marine protected area planning process in Washington state: Recommendations for increased effectiveness

In Washington State, the Department of Natural Resources (WA DNR) is responsible for managing state-owned aquatic lands. Aquatic reserves are one of many Marine Protected Area (MPA) designations in WA State that aim to protect sensitive aquatic and ecological habitat. We analyzed the designation and early planning processes of WA State aquatic reserves, identified gaps in the processes, and recommend action to improve the WA State aquatic reserve early planning approach. (PDF contains 4 pages)

Learning and management skills: the internationalization of Grupo Arcor. Analysis from a process-based approach

This paper analyzes the path of the international expansion of Grupo Arcor, an Argentine multinational company specializing in confectionery. The objective is to entify corporate strategies and business learning that led this Latin American firm to establish itself as one of the leading manufacturers in confectionery industry ,particularly in the 21st Century. The analysis is primarily qualitative in order to identify the economic dimension as a determinant in the internationalization process; a processbased approach from the Uppsala Model is used for this. However, the study is also complemented with a regression analysis to test if the firm was driven to expand internationally by the expectations on the degree of globalization of the industry and the accumulation of experience in foreign markets, and if the company was influenced by psychic distance in choosing the location of its investment; given the influence of these variables in Grupo Arcor business strategies. Our findings suggest that Grupo Arcor, was able to become global due to strategies such as vertical integration, diversification of products and geographical markets (based on psychic distance) and indeed some strategies were consequence of the globalization of the sector and the accumulation of experience in foreign markets.

A bayesian probabilistic approach to structural health monitoring

A Bayesian probabilistic methodology for on-line structural health monitoring which addresses the issue of parameter uncertainty inherent in problem is presented. The method uses modal parameters for a limited number of modes identified from measurements taken at a restricted number of degrees of freedom of a structure as the measured structural data. The application presented uses a linear structural model whose stiffness matrix is parameterized to develop a class of possible models. Within the Bayesian framework, a joint probability density function (PDF) for the model stiffness parameters given the measured modal data is determined. Using this PDF, the marginal PDF of the stiffness parameter for each substructure given the data can be calculated.

Monitoring the health of a structure using these marginal PDFs involves two steps. First, the marginal PDF for each model parameter given modal data from the undamaged structure is found. The structure is then periodically monitored and updated marginal PDFs are determined. A measure of the difference between the calibrated and current marginal PDFs is used as a means to characterize the health of the structure. A procedure for interpreting the measure for use by an expert system in on-line monitoring is also introduced.

The probabilistic framework is developed in order to address the model parameter uncertainty issue inherent in the health monitoring problem. To illustrate this issue, consider a very simplified deterministic structural health monitoring method. In such an approach, the model parameters which minimize an error measure between the measured and model modal values would be used as the "best" model of the structure. Changes between the model parameters identified using modal data from the undamaged structure and subsequent modal data would be used to find the existence, location and degree of damage. Due to measurement noise, limited modal information, and model error, the "best" model parameters might vary from one modal dataset to the next without any damage present in the structure. Thus, difficulties would arise in separating normal variations in the identified model parameters based on limitations of the identification method and variations due to true change in the structure. The Bayesian framework described in this work provides a means to handle this parametric uncertainty.

The probabilistic health monitoring method is applied to simulated data and laboratory data. The results of these tests are presented.

Future of earthquake early warning : quantifying uncertainty and making fast automated decisions for applications

Earthquake early warning (EEW) systems have been rapidly developing over the past decade. Japan Meteorological Agency (JMA) has an EEW system that was operating during the 2011 M9 Tohoku earthquake in Japan, and this increased the awareness of EEW systems around the world. While longer-time earthquake prediction still faces many challenges to be practical, the availability of shorter-time EEW opens up a new door for earthquake loss mitigation. After an earthquake fault begins rupturing, an EEW system utilizes the first few seconds of recorded seismic waveform data to quickly predict the hypocenter location, magnitude, origin time and the expected shaking intensity level around the region. This early warning information is broadcast to different sites before the strong shaking arrives. The warning lead time of such a system is short, typically a few seconds to a minute or so, and the information is uncertain. These factors limit human intervention to activate mitigation actions and this must be addressed for engineering applications of EEW. This study applies a Bayesian probabilistic approach along with machine learning techniques and decision theories from economics to improve different aspects of EEW operation, including extending it to engineering applications.

Existing EEW systems are often based on a deterministic approach. Often, they assume that only a single event occurs within a short period of time, which led to many false alarms after the Tohoku earthquake in Japan. This study develops a probability-based EEW algorithm based on an existing deterministic model to extend the EEW system to the case of concurrent events, which are often observed during the aftershock sequence after a large earthquake.

To overcome the challenge of uncertain information and short lead time of EEW, this study also develops an earthquake probability-based automated decision-making (ePAD) framework to make robust decision for EEW mitigation applications. A cost-benefit model that can capture the uncertainties in EEW information and the decision process is used. This approach is called the Performance-Based Earthquake Early Warning, which is based on the PEER Performance-Based Earthquake Engineering method. Use of surrogate models is suggested to improve computational efficiency. Also, new models are proposed to add the influence of lead time into the cost-benefit analysis. For example, a value of information model is used to quantify the potential value of delaying the activation of a mitigation action for a possible reduction of the uncertainty of EEW information in the next update. Two practical examples, evacuation alert and elevator control, are studied to illustrate the ePAD framework. Potential advanced EEW applications, such as the case of multiple-action decisions and the synergy of EEW and structural health monitoring systems, are also discussed.