Some Quantum Dynamical Semi-groups with Quantum Stochastic Dilation

We consider the GNS Hilbert space $\mathcal{H}$ of a uniformly hyper-finite $C^*$- algebra and study a class of unbounded Lindbladian arises from commutators. Exploring the local structure of UHF algebra, we have shown that the associated Hudson-Parthasarathy type quantum stochastic differential equation admits a unitary solution. The vacuum expectation of homomorphic co-cycle, implemented by the Hudson-Parthasarathy flow, is conservative and gives the minimal semi-group associated with the formal Lindbladian. We also associate conservative minimal semi-groups to another class of Lindbladian by solving the corresponding Evan-Hudson equation

Computational investigation of the influence of heating modes and moisture content on pyrolysis and ignition of live fuels

The burning of an isolated leaf-like element was computationally investigated in a series of studies, motivated by recent burning experiments performed on live leaves of manzanita (Arctostaphylos glandulosa). In this study, the relative impor- tance of heating modes, effect of fuel moisture content on pyrolysis and combustion of live fuels is explored in stages. A preliminary study was conducted on a simpli- fied one-dimensional configuration, using Gpyro. The heating sources were modeled through convection and/or radiation as boundary conditions. Results showed that the increase in radiative source temperature substantially affects ignition time; how- ever, it has marginal influence on mass loss rate and charring rate. The increase of convective heating source temperature in presence of radiation had a marginal impact on ignition time and no influence on mass loss or charring rate. Next studies were conducted in full three dimensional configurations via Gpyro-3D/FDS. The solid fuel was under the radiative heating and a 5-step chemical kinetic mechanism was used for pyrolysis. Results indicated that temperature response and thermal degradation rate was higher for lower fuel moisture content (FMC) case and ignition occurred prior to the higher FMC case. In the gas phase, high volume fraction of water vapor observed in the region close to the combustion zone as well as away from this region illustrated that evaporation and ignition occur together. In the next task of the modeling activi- ties, an improved chemistry model was used, which included hemicellulose and lignin along with cellulose and moisture. A more advanced 12-step kinetic mechanism was used for the solid phase to simulate the multi-component decomposition process in detail. The solid fuel was oriented horizontally to mimic the burning experiments of individual leaves of manzanita by the Flat Flame Burner (FFB) apparatus and was exposed to convective heating. The simulations were consistent with the experimen- tal results in terms of ignition and burnout time prediction, fire initiation and spread pattern. Local evaporation of moisture and temperature rise at the periphery of the solid fuel was observed, also a significant amount of moisture remained at the center of sample at the time of ignition indicating that different points in the domain evap- orate and pyrolyze at different times. In the final study, the effect of both convection and radiation was investigated with the fuel element oriented vertically. Evaporation occurred at a higher rate near the leading, lateral and trailing edge of the solid fuel compared to the region located at the center. This pattern of heating was either due to the flame tilt observed during simulation or due to the effect of fluid dynamics. A boundary layer growth was observed above the surface of the solid fuel which reduces the heat transfer to the region located at the center. When radiation was used along with convective heating source, the peak value of mass loss rate was 20% higher than that in convection-only case

A Study Of Quality Management In Small Organizations Providing Services Directed At People

This paper reports on a study of managerial perceptions of the implementation of total quality management (TQM). Results of a survey covering small firms in northeastern Indiana providing services directed at people are presented. Aspects discussed include the unique nature of this category of service firms, TQM deployment, tools used, successes, failures, benefits, and problems encountered. The majority of respondents indicated their firms’ commitment to TQM but a significantly smaller proportion demonstrated notable engagement with and actual implementation of a formal TQM program. Even smaller percentages had benchmarked internal quality standards, used TQM tools and quality-enhancing activities, rewarded employees for successful quality performance, and involved suppliers in their quality programs. Strategic implications of these findings are considered

A Deep Learning Approach Towards Generating High-fidelity Diverse Synthetic Battery Datasets

Recent surge in the number of Electric Vehicles have created a need to develop inexpensive energy-dense Battery Storage Systems. Many countries across the planet have put in place concrete measures to reduce and subsequently limit the number of vehicles powered by fossil fuels. Lithium-ion based batteries are presently dominating the electric automotive sector. Energy research efforts are also focussed on accurate computation of State-of-Charge of such batteries to provide reliable vehicle range estimates. Although such estimation algorithms provide precise estimates, all such techniques available in literature presume availability of superior quality battery datasets. In reality, gaining access to proprietary battery usage datasets is very tough for battery scientists. Moreover, open access datasets lack the diverse battery charge/discharge patterns needed to build generalized models. Curating battery measurement data is time consuming and needs expensive equipment. To surmount such limited data scenarios, we introduce few Deep Learning-based methods to synthesize high-fidelity battery datasets, these augmented synthetic datasets will help battery researchers build better estimation models in the presence of limited data. We have released the code and dataset used in the present approach to generate synthetic data. The battery data augmentation techniques introduced here will alleviate limited battery dataset challenges.Comment: Accepted at IEEE Transactions on Industry Application