Equivelar and d-Covered Triangulations of Surfaces. I

We survey basic properties and bounds for $q$-equivelar and $d$-covered triangulations of closed surfaces. Included in the survey is a list of the known sources for $q$-equivelar and $d$-covered triangulations. We identify all orientable and non-orientable surfaces $M$ of Euler characteristic $0>\chi(M)\geq -230$ which admit non-neighborly $q$-equivelar triangulations with equality in the upper bound $q\leq\Bigl\lfloor\tfrac{1}{2}(5+\sqrt{49-24\chi (M)})\Bigl\rfloor$. These examples give rise to $d$-covered triangulations with equality in the upper bound $d\leq2\Bigl\lfloor\tfrac{1}{2}(5+\sqrt{49-24\chi (M)})\Bigl\rfloor$. A generalization of Ringel's cyclic $7{\rm mod}12$ series of neighborly orientable triangulations to a two-parameter family of cyclic orientable triangulations $R_{k,n}$, $k\geq 0$, $n\geq 7+12k$, is the main result of this paper. In particular, the two infinite subseries $R_{k,7+12k+1}$ and $R_{k,7+12k+2}$, $k\geq 1$, provide non-neighborly examples with equality for the upper bound for $q$ as well as derived examples with equality for the upper bound for $d$.Comment: 21 pages, 4 figure

Investigation of boundary layer and turbulence characteristics inside the passages of an axial flow inducer

A study of the boundary layer and turbulence characteristics inside the passages of an axial flow inducer is reported. The first part deals with the analytical and experimental investigation of the boundary layer characteristics in a four bladed flat plate inducer passage operated with no throttle. An approximate analysis for the prediction of radial and chordwise velocity profiles across the passage is carried out. The momentum integral technique is used to predict the gross properties of the boundary layer. Equations are given for the exact analysis of the turbulent boundary layer characteristics using the turbulent field method. Detailed measurement of boundary layer profiles, limiting streamline angle and skin friction stress on the rotating blade is also reported. Part two of this report deals with the prediction of the flow as well as blade static pressure measurements in a three bladed inducer with cambered blades operated at a flow coefficient of 0.065. In addition, the mean velocity and turbulence measurements carried out inside the passage using a rotating triaxial probe is reported

Petrography and bulk composition of Miller Range 05035: a new lunar VLT gabbro

Miller Range (MIL) 05035 is a crystalline lunar mare gabbroic meteorite collected in Antarctica in 2005 [1]. It is an important new sample in the lunar meteorite (LM) collection as it is only one of ~8 to be classified as basaltic in nature. MIL 05035 is coarsely grained with large pyroxene grains ( 8mm) subophitically enclosing plagioclase grains ( 6mm), and accessory ilmenite, spinel, silica and sulphide phases

Pressure algorithm for elliptic flow calculations with the PDF method

An algorithm to determine the mean pressure field for elliptic flow calculations with the probability density function (PDF) method is developed and applied. The PDF method is a most promising approach for the computation of turbulent reacting flows. Previous computations of elliptic flows with the method were in conjunction with conventional finite volume based calculations that provided the mean pressure field. The algorithm developed and described here permits the mean pressure field to be determined within the PDF calculations. The PDF method incorporating the pressure algorithm is applied to the flow past a backward-facing step. The results are in good agreement with data for the reattachment length, mean velocities, and turbulence quantities including triple correlations

Industrial wastewater treatment using hydrodynamic cavitation and heterogeneous advanced Fenton processing

A combination of hydrodynamic cavitation and heterogeneous advanced Fenton process (AFP) based on the use of zero valent iron as the catalyst has been investigated for the treatment of real industrial wastewater. The effect of various operating parameters such as inlet pressure, temperature, and the presence of copper windings on the extent of mineralization as measured by total organic carbon (TOC) content have been studied with the aim of maximizing the extent of degradation. It has been observed that increased pressures, higher operating temperature and the absence of copper windings are more favourable for a rapid TOC mineralization. A new approach of latent remediation has also been investigated where hydrodynamic cavitation is only used as a pre-treatment with an aim of reducing the overall cost of pollutant degradation. It has been observed that approach of latent remediation works quite well with about 50–60% removal of TOC using only minimal initial treatment by hydrodynamic cavitation

Improving Palliative Care with Deep Learning

Improving the quality of end-of-life care for hospitalized patients is a priority for healthcare organizations. Studies have shown that physicians tend to over-estimate prognoses, which in combination with treatment inertia results in a mismatch between patients wishes and actual care at the end of life. We describe a method to address this problem using Deep Learning and Electronic Health Record (EHR) data, which is currently being piloted, with Institutional Review Board approval, at an academic medical center. The EHR data of admitted patients are automatically evaluated by an algorithm, which brings patients who are likely to benefit from palliative care services to the attention of the Palliative Care team. The algorithm is a Deep Neural Network trained on the EHR data from previous years, to predict all-cause 3-12 month mortality of patients as a proxy for patients that could benefit from palliative care. Our predictions enable the Palliative Care team to take a proactive approach in reaching out to such patients, rather than relying on referrals from treating physicians, or conduct time consuming chart reviews of all patients. We also present a novel interpretation technique which we use to provide explanations of the model's predictions.Comment: IEEE International Conference on Bioinformatics and Biomedicine 201