Structuring product development processes

This paper proposes operational frameworks for structuring product development processes. The primary objective of this research is to develop procedures to minimize iterations during the development process which adversely affect development time and costs. Several procedures are introduced to restructure the development process. The computation of the corresponding product development times is facilitated by two Markov models addressing different types of learning. The methodologies are employed to identify a set of managerial concerns in restructuring the product development processes.The developed framework has become an integral part of a re-engineering project for the development of rocket engines at Rocketdyne Division of Rockwell International. Throughout the paper, the methodologies are illustrated with the help of this process.     

Statistical learning control of uncertain systems: theory and algorithms

It has recently become clear that many control problems are too difficult to admit analytic solutions. New results have also emerged to show that the computational complexity of some “solved” control problems is prohibitive. Many of these control problems can be reduced to decidability problems or to optimization questions. Even though such questions may be too difficult to answer analytically, or may not be answered exactly given a reasonable amount of computational resources, researchers have shown that we can “approximately” answer these questions “most of the time”, and have “high confidence” in the correctness of the answers.     

Study on nitridation processes of β-Ga2O3 single crystal

Nitridated /3-Ga2Os (100) substrate was investigated as the substrate for CaN epitaxial growth.The effects of nitridation temperature and surface roughness of -Ga2O3 wafers on the formation of CaN were studied.     

Dynamic Environments as a Tool to Preserve Desired Output in a Chemical Reaction Network

Current efforts to design functional molecular systems have overlooked the importance of coupling out-of-equilibrium behaviour with changes in the environment. Here, the authors use an oscillating reaction network and demonstrate that the application of environmental forcing, in the form of periodic changes in temperature and in the inflow of the concentration of one of the network components, removes the dependency of the periodicity of this network on temperature or flow rates and enforces a stable periodicity across a wide range of conditions. Coupling a system to a dynamic environment can thus be used as a simple tool to regulate the output of a network. In addition, the authors show that coupling can also induce an increase in behavioural complexity to include quasi-periodic oscillations.     

Estimation of the lowest limit of 1/f noise in semiconductor materials

A lowest limit of 1/f noise in semiconductor materials has not yet been reported; we do not even know if such a lowest limit exists. 1/f noise in semiconductors has recently been brought into relation with 1/f noise in quantum dots and other materials. These materials exhibit on-off states which are power-law distributed over a wide range of timescales. We transfer such findings to semiconductors, assuming that the g-r process is also controlled by such on-off states. As a result, we obtain 1/f noise which can be expressed as Hooge's relation. Based on the intermittent g-r process, we estimate the lowest limit of 1/f noise in semiconductor materials. We show that this limit is inversely proportional to the dopant concentration; to detect the lowest limit of 1/f noise, the number of centers should be as small as possible. We also find a smooth dependence of 1/f noise and g-r noise on time.     

Open multistate Majorana model

The Majorana model in the presence of dissipation and dephasing is considered. First, it is proven that increasing the Hilbert space dimension the system becomes more and more fragile to quantum noise, whether dephasing or dissipation are mainly present. Second, it is shown that, contrary to its ideal counterpart, the dynamics related to the open Majorana model cannot be considered as the combined dynamics of a set of independent spin-1/2 models.     

PFAS Degradation in Ultrapure and Groundwater Using Non-Thermal Plasma

Perfluoroalkyl substances (PFAS) represent one of the most recalcitrant class of compounds of emerging concern and their removal from water is a challenging goal. In this study, we investigated the removal efficiency of three selected PFAS from water, namely, perfluorooctanoic acid (PFOA), perfluorohexanoic acid (PFHxA) and pefluorooctanesulfonic acid (PFOS) using a custom-built non-thermal plasma generator. A modified full factorial design (with 2 levels, 3 variables and the central point in which both quadratic terms and interactions between couple of variables were considered) was used to investigate the effect of plasma discharge frequency, distance between the electrodes and water conductivity on treatment efficiency. Then, the plasma treatment running on optimized conditions was used to degrade PFAS at ppb level both individually and in mixture, in ultrapure and groundwater matrices. PFOS 1 ppb exhibited the best degradation reaching complete removal after 30 min of treatment in both water matrices (first order rate constant 0.107 min⁻¹ in ultrapure water and 0.0633 min⁻¹ in groundwater), while the degradation rate of PFOA and PFHxA was slower of around 65% and 83%, respectively. During plasma treatment, the production of reactive species in the liquid phase (hydroxyl radical, hydrogen peroxide) and in the gas phase (ozone, NO_x) was investigated. Particular attention was dedicated to the nitrogen balance in solution where, following to NO_x hydrolysis, total nitrogen (TN) was accumulated at the rate of up to 40 mg^N L⁻¹ h⁻¹.     

Characterization of Pathogen Airborne Inoculum Density by Information Theoretic Analysis of Spore Trap Time Series Data

In a previous study, air sampling using vortex air samplers combined with species-specific amplification of pathogen DNA was carried out over two years in four or five locations in the Salinas Valley of California. The resulting time series data for the abundance of pathogen DNA trapped per day displayed complex dynamics with features of both deterministic (chaotic) and stochastic uncertainty. Methods of nonlinear time series analysis developed for the reconstruction of low dimensional attractors provided new insights into the complexity of pathogen abundance data. In particular, the analyses suggested that the length of time series data that it is practical or cost-effective to collect may limit the ability to definitively classify the uncertainty in the data. Over the two years of the study, five location/year combinations were classified as having stochastic linear dynamics and four were not. Calculation of entropy values for either the number of pathogen DNA copies or for a binary string indicating whether the pathogen abundance data were increasing revealed (1) some robust differences in the dynamics between seasons that were not obvious in the time series data themselves and (2) that the series were almost all at their theoretical maximum entropy value when considered from the simple perspective of whether instantaneous change along the sequence was positive.