Development Of A Micro Water Grid (MWG) Pilot Platform For Green Buildings

The objectives of this Micro Water Grid (MWG) pilot platform project are to i) address the need for reliable municipal water supplies, ii) identify and strengthen vulnerable water system elements, and iii) design an optimal micro water grid pilot platform for green buildings. This paper describes the overall context of the MWG and considers appropriate analytical methods for water demand, hydraulic analysis and decision models for optimal MWG pilot platforms. This is an on-going research project and various MWG design scenarios, along with numerical results, will be presented as the research progresses

Half-Skyrmions, Tensor Forces and Symmetry Energy in Cold Dense Matter

In a previous article, the 4D half-skyrmion (or 5D dyonic salt) structure of dense baryonic matter described in crystalline configuration in the large $N_c$ limit was shown to impact nontrivially on how anti-kaons behave in compressed nuclear matter with a possible implication on an "ice-9" phenomenon of deeply bound kaonic matter and condensed kaons in compact stars. We extend the analysis to make a further prediction on the scaling properties of hadrons that have a surprising effect on the nuclear tensor forces, the symmetry energy and hence on the phase structure at high density. We treat this problem relying on certain topological structure of chiral solitons. Combined with what can be deduced from hidden local symmetry for hadrons in dense medium and the "soft" dilatonic degree of freedom associated with the trace anomaly of QCD, we uncover a novel structure of chiral symmetry in the "supersoft" symmetry energy that can influence the structure of neutron stars.Comment: 8 pages, 4 figures; contents unchanged but expanded for a journa

Thermoeconomic Analysis of Organic Rankine Cycle Using Zeotropic Mixtures

The selection of the working fluid is an important part of design and optimization of ORC system as it effects the systems efficiency, design of ORC components, stability, safety and environmental impact. Present study aims to investigate the performance of ORC system using pure working fluids and zeotropic mixtures for low temperature geothermal heat source on the basis of thermodynamic and economic parameters of ORC system. Evaporator, expander, condenser and feed pump models are developed in MATLAB. The control volume approach is adopted for evaporator and condenser model with appropriate database of heat transfer and pressure drop correlations. For comparison, pure working fluids are taken as the base case. The ORC system with pure working fluid and zeotropic mixture under same heat and sink source conditions are optimized using multi objective genetic algorithm for maximum exergy efficiency and minimum specific investment cost. The exergy efficiency of ORC system with zeotropic mixture is improved by 14.33% compared to pure working fluid. The exergy destruction in evaporator and condenser is reduced by 24~30%. The fraction of more volatile component in zeotropic mixture effected the thermal and economic performance of ORC system, for current study the mass fraction of 40% of R245fa corresponds to optimum exergy efficiency and specific investment cost. For same condensing pressure and expander power, area of evaporator for pure working fluids and zeotropic mixture is also calculated. The required heat transfer area for zeotropic mixture is approximately 13% less than required for pure working fluid

Towards Neural Decoding of Imagined Speech based on Spoken Speech

Decoding imagined speech from human brain signals is a challenging and important issue that may enable human communication via brain signals. While imagined speech can be the paradigm for silent communication via brain signals, it is always hard to collect enough stable data to train the decoding model. Meanwhile, spoken speech data is relatively easy and to obtain, implying the significance of utilizing spoken speech brain signals to decode imagined speech. In this paper, we performed a preliminary analysis to find out whether if it would be possible to utilize spoken speech electroencephalography data to decode imagined speech, by simply applying the pre-trained model trained with spoken speech brain signals to decode imagined speech. While the classification performance of imagined speech data solely used to train and validation was 30.5 %, the transferred performance of spoken speech based classifier to imagined speech data displayed average accuracy of 26.8 % which did not have statistically significant difference compared to the imagined speech based classifier (p = 0.0983, chi-square = 4.64). For more comprehensive analysis, we compared the result with the visual imagery dataset, which would naturally be less related to spoken speech compared to the imagined speech. As a result, visual imagery have shown solely trained performance of 31.8 % and transferred performance of 26.3 % which had shown statistically significant difference between each other (p = 0.022, chi-square = 7.64). Our results imply the potential of applying spoken speech to decode imagined speech, as well as their underlying common features.Comment: 4 pages, 2 figure