Ascorbic acid imprinted polymer-modified graphite electrode: A diagnostic sensor for hypovitaminosis C at ultra trace ascorbic acid level

A new kind of molecularly imprinted polymer-modified graphite electrode was fabricated by “grafting-to” approach, incorporating sol–gel technique, for the detection of acute deficiency in serum ascorbic acid level (SAAL), manifesting hypovitaminosis C. The modified electrode exhibited ascorbic acid (AA) oxidation at less positive potential (0.0 V) than the earlier reported methods, resulting in a limit of detection as low as 6.13 ng mL⁻¹ (RSD = 1.2%, S/N = 3). The diffusion coefficient (1.096 × 10⁻⁵ cm² s⁻¹), rate constant (7.308 s⁻¹), and Gibb's free energy change (−12.59 kJ mol⁻¹) due to analyte adsorption, were also calculated to explore the kinetics of AA oxidation. The proposed sensor was found to enhance sensitivity substantially so as to detect ultra trace level of AA in the presence of other biologically important compounds (dopamine, uric acid, etc.), without any cross interference and matrix complications from biological fluids and pharmaceutical samples.     

Learning first-order probabilistic models with combining rules

Many real-world domains exhibit rich relational structure and stochasticity and motivate the development of models that combine predicate logic with probabilities. These models describe probabilistic influences between attributes of objects that are related to each other through known domain relationships. To keep these models succinct, each such influence is considered independent of others, which is called the assumption of “independence of causal influences” (ICI). In this paper, we describe a language that consists of quantified conditional influence statements and captures most relational probabilistic models based on directed graphs. The influences due to different statements are combined using a set of combining rules such as Noisy-OR. We motivate and introduce multi-level combining rules, where the lower level rules combine the influences due to different ground instances of the same statement, and the upper level rules combine the influences due to different statements. We present algorithms and empirical results for parameter learning in the presence of such combining rules. Specifically, we derive and implement algorithms based on gradient descent and expectation maximization for different combining rules and evaluate them on synthetic data and on a real-world task. The results demonstrate that the algorithms are able to learn both the conditional probability distributions of the influence statements and the parameters of the combining rules.     

Thermal conductivity of ethane from 290 to 600 K at pressures up to 700 bar,including the critical region

The paper presents thermal conductivity measurements of ethane over the temperature range of 290–600 K at pressures to 700 bar including the critical region with maximum uncertainty of 0.7 to 3% obtained with a transient line source instrument. A correlation of the data is presented and used to prepare tables of recommended values that are accurate to within 2.5% in the experimental range except near saturation, and in the critical region, where the anomalous thermal conductivity values are predicted to within 5%.Nomenclature a _k , b _ij , b _k , c _i Parameters of the regression model, k=0 to n, i=0 to m, j=0 to n - P Pressure, (MPa or bar) - Q _l Heat flux per unit length (mW · m^–1) - t Time, s - T Temperature, K - T _cr Critical temperature, K - T _r Reduced temperature = T/T _cr - T _w Temperature rise of wire between times t ₁ and t ₂ K - T ^* Reduced temperature difference (T–T _cr)/T _cr - Thermal conductivity, mW · m^–1 · K^–1 - ₁ Thermal conductivity at 1 bar, mW · m^–1 · K^–1 - _bg Background thermal conductivity, mW · m^–1 · K^–1 - _cr Thermal conductivity anomaly, mW · m^–1 · K^–1 - _e Excess thermal conductivity, mW · m^–1 · K^–1 - Density, g · cm^–3 - _cr Critical density, g · cm^–3 - _r Reduced density, = / _cr - ^* Reduced density difference =(- _cr)/ _cr     

Chemical mediation of egg capsule deposition by mud snails

Mud snails (Ilyanassa obsoleta = Nassarius obsoletus = Nassa obsoleta) deposit eggs in protective capsules on hard substrata in soft bottom environments. We studied sites of egg capsule deposition and snail movement responses to odors to determine if chemoreception plays a role in deposition site selection. From results of field surveys, laboratory experiments, and field experiments, we conclude that mud snails use chemoreception for capsule deposition. Attractive odors originate from mud snail and whelk egg capsules and from living bivalves. Evidence for attractive odors from conspecifics is equivocal. Capsules are deposited on living odor sources and nearby hard substrates. We hypothesize that deposition of capsules on living substrates increases the likelihood that embryos will survive by decreasing the chance of smothering of embryos by sediments.     

Application of polymeric flocculant for enhancing settling of the pond ash particles and water drainage from hydraulically stowed pond ash

Delayed settling of the ash particles and poor drainage of water from the pond ash are the major problems faced during the hydraulic stowing of pond ash. In this study the effect of polymeric flocculant on settling of the ash particles and drainage of water during pond ash stowing are investigated. In addition, the parameters, viz. drainage and absorption of water during pond ash stowing are quantified by stowing a mine goaf model with pond ash slurries of five different concentrations added with and without flocculant. The study revealed that addition of only 5 × 10^?6 of Sodium Carboxymethyl Cellulose (Na-CMC) flocculant with the pond ash slurries during stowing offers best result in terms of quicker settling of the ash particles and enhanced water drainage from the hydraulically stowed pond ash. Besides, it resulted in drainage of more than 85% of the total water used in the initial 45 min of stowing. The improvement in drainage is caused due to coagulation and flocculation of the pond ash particles because of charge neutralization and particle–particle bridging. This study may provide a basis for estimating the drainage and absorption of water during the real pond ash stowing operation in underground mines.     

The complexity of approximately counting stable roommate assignments

We investigate the complexity of approximately counting stable roommate assignments in two models: (i) the k-attribute model, in which the preference lists are determined by dot products of “preference vectors” with “attribute vectors” and (ii) the k-Euclidean model, in which the preference lists are determined by the closeness of the “positions” of the people to their “preferred positions”. Exactly counting the number of assignments is #P-complete, since Irving and Leather demonstrated #P-completeness for the special case of the stable marriage problem (Irving and Leather, 1986 [11]). We show that counting the number of stable roommate assignments in the k-attribute model (#k-attribute SR, $k?4$) and the 3-Euclidean model (#k-Euclidean SR, $k?3$) is interreducible, in an approximation-preserving sense, with counting independent sets (of all sizes) ($\mathrm{\#}\mathit{IS}$) in a graph, or counting the number of satisfying assignments of a Boolean formula ($\mathrm{\#}\mathit{SAT}$). This means that there can be no FPRAS for any of these problems unless NP = RP. As a consequence, we infer that there is no FPRAS for counting stable roommate assignments ($\mathrm{\#}\mathit{SR}$) unless NP = RP. Utilizing previous results by Chebolu, Goldberg and Martin (2010) [3], we give an approximation-preserving reduction from counting the number of independent sets in a bipartite graph ($\mathrm{\#}\mathit{BIS}$) to counting the number of stable roommate assignments both in the 3-attribute model and in the 2-Euclidean model. $\mathrm{\#}\mathit{BIS}$ is complete with respect to approximation-preserving reductions in the logically-defined complexity class $\mathrm{\#}\mathrm{RH}{\mathrm{\Pi }}_1$. Hence, our result shows that an FPRAS for counting stable roommate assignments in the 3-attribute model would give an FPRAS for all $\mathrm{\#}\mathrm{RH}{\mathrm{\Pi }}_1$. We also show that the 1-attribute stable roommate problem always has either one or two stable roommate assignments, so the number of assignments can be determined exactly in polynomial time.     

Applications of 'TissueQuant'- a color intensity quantification tool for medical research

This paper demonstrates the use of TissueQuant - an image analysis tool for quantification of color intensities which was developed for use in medical research where the stained biological specimen such as tissue or antigen needs to be quantified. TissueQuant provides facilities for user interaction to choose and quantify the color of interest and its shades. Gaussian weighting functions are used to provide a color score which quantifies how close the shade is to the user specified reference color. We describe two studies in medical research which use TissueQuant for quantification. The first study evaluated the effect of petroleum-ether extract of Cissus quadrangularis (CQ) on osteoporotic rats. It was found that the analysis results correlated well with the manual evaluation, p < 0.001. The second study evaluated the nerve morphometry and it was found that the adipose and non adipose tissue content was maximum in radial nerve among the five nerves studied.     

Modeling of ammonothermal growth of gallium nitride single crystals

Ammonothermal growth of GaN crystals with a retrograde solubility has been modeled and simulated here using fluid dynamics, thermodynamics and heat transfer models. The nutrient is considered as a porous media bed and the flow in the porous charge is simulated using the Darcy-Brinkman-Forchheimer model. The resulting governing equations are solved using the finite volume method. For the case of retrograde solubility, the charge is put above the baffle. The temperature difference between the dissolving zone and growth zone is found smaller than that applied on the sidewall of autoclave. The baffle opening has a strong effect on the nutrient transport and supersaturation of GaN species in the growth zone.     

Artificial Neural Network Model for Predicting Stable and Unstable Regions in Cu-Zn Alloys

Processing maps are developed using the Dynamic Materials Model (DMM) and instability criterion, which help in choosing optimum process parameters for hot-working of materials. Certain high-level expertise is required to interpret and extract the information on instability regimes to be avoided during processing. In recent years, Artificial Neural Network (ANN) models have been developed to predict flow stress by using the input vector; namely, temperature, strain rate and strain. In this study, using the available Cu-Zn alloy data, ANN model has been developed to classify the hot-working process parameters, such as temperature, strain rate and flow stress for instability regime, directly from the corrected flow stress data without applying the DMM. This model uses 10 compositions of Cu-Zn system, ranging from 3% Zn to 51% Zn. The developed ANN model has been able to learn the nonlinear classifier, which separates unstable region from the stable region in the Cu-Zn alloy system with zinc content less than 40%.