Finite volume solutions for electromagnetic induction processing

A new method is presented for numerically solving the equations of electromagnetic induction in conducting materials using native, primary variables and not a magnetic vector potential. Solving for the components of the electric field allows the meshed domain to cover only the processed material rather than extend further out in space. Together with the finite volume discretisation this makes possible the seamless coupling of the electromagnetic solver within a multi-physics simulation framework. After validation for cases with known results, a 3-dimensional industrial application example of induction heating shows the suitability of the method for practical engineering calculation

Long-Term Dietary Nitrate Supplementation Does Not Prevent Development of the Metabolic Syndrome in Mice Fed a High-Fat Diet

Background. Nitric oxide (NO) is an important vascular signaling molecule that plays a role in vascular homeostasis. A reduction in NO bioavailability is thought to contribute to endothelial dysfunction, an early risk factor for both cardiovascular disease and type 2 diabetes. Dietary nitrate, through the nitrate-nitrite-NO pathway, may provide an alternate source of NO when the endogenous eNOS system is compromised. In addition to a role in the vascular system, NO may also play a role in the metabolic syndrome including obesity and glucose tolerance. Aim. To investigate the effect of long-term dietary nitrate supplementation on development of the metabolic syndrome in mice fed a high-fat diet. Methods. Following 1 week of acclimatisation, male (6-8 weeks) C57BL6 mice were randomly assigned to the following groups (10/group) for 12 weeks: (i) normal chow + NaCl (1 mmol/kg/day), (ii) normal chow + NaNO3 (1 mmol/kg/day), (iii) high-fat diet + NaCl (1 mmol/kg/day), and (iv) high-fat diet + NaNO3 (1 mmol/kg/day). Body weight and food consumption were monitored weekly. A subset of mice (5/group) underwent running wheel assessment. At the end of the treatment period, all mice underwent fasting glucose tolerance testing. Caecum contents, serum, and tissues (liver, skeletal muscle, white and brown adipose, and kidney) were collected, frozen, and stored at -80°C until analysis. Results. Consumption of the high-fat diet resulted in significantly greater weight gain that was not affected by dietary nitrate. Mice on the high-fat diet also had impaired glucose tolerance that was not affected by dietary nitrate. There was no difference in adipose tissue expression of thermogenic proteins or energy expenditure as assessed by the running wheel activity. Mice on the high-fat diet and those receiving dietary nitrate had reduced caecum concentrations of both butyrate and propionate. Conclusions. Dietary nitrate does not prevent development of the metabolic syndrome in mice fed a high-fat diet. This may be due, in part due, to reductions in the concentration of important short-chain fatty acids

Activity of chitosan and its derivatives against Leishmania major and L. mexicana in vitro

There is an urgent need for safe, efficacious, affordable and field-adapted drugs for the treatment of cutaneous leishmaniasis which affects around 1.5 million new people worldwide annually. Chitosan, a biodegradable cationic polysaccharide, has previously been reported to have antimicrobial, anti-leishmanial and immunostimulatory activities. We investigated the in vitro activity of chitosan and several of its derivatives and showed that pH of the culture medium plays a critical role on anti-leishmanial activity of chitosan against both extracellular promastigotes and intracellular amastigotes of Leishmania major and Leishmania mexicana Chitosan and its derivatives were approximately 7-20 times more active at pH 6.5 than at pH 7.5 with high molecular weight chitosan being the most potent. High molecular weight chitosan stimulated the production of nitric oxide and reactive oxygen species by uninfected and Leishmania infected macrophages in a time and dose dependent manner at pH 6.5. Despite the in vitro activation of bone marrow macrophages by chitosan to produce nitric oxide and reactive oxygen species, we showed that the anti-leishmanial activity of chitosan was not mediated by these metabolites. Finally, we showed that rhodamine-labelled chitosan is taken up by pinocytosis and accumulates in the parasitophorous vacuole of Leishmania infected macrophages

Cluster Correlation in Mixed Models

We evaluate the dependence of the cluster correlation length r_c on the mean intercluster separation D_c, for three models with critical matter density, vanishing vacuum energy (Lambda = 0) and COBE normalized: a tilted CDM (tCDM) model (n=0.8) and two blue mixed models with two light massive neutrinos yielding Omega_h = 0.26 and 0.14 (MDM1 and MDM2, respectively). All models approach the observational value of sigma_8 (and, henceforth, the observed cluster abundance) and are consistent with the observed abundance of Damped Lyman_alpha systems. Mixed models have a motivation in recent results of neutrino physics; they also agree with the observed value of the ratio sigma_8/sigma_25, yielding the spectral slope parameter Gamma, and nicely fit LCRS reconstructed spectra. We use parallel AP3M simulations, performed in a wide box (side 360/h Mpc) and with high mass and distance resolution, enabling us to build artificial samples of clusters, whose total number and mass range allow to cover the same D_c interval inspected through APM and Abell cluster clustering data. We find that the tCDM model performs substantially better than n=1 critical density CDM models. Our main finding, however, is that mixed models provide a surprisingly good fit of cluster clustering data.Comment: 22 pages + 10 Postscript figures. Accepted for publication in Ap

A Deep Neural Network Based Reverse Radio Spectrogram Search Algorithm

Modern radio astronomy instruments generate vast amounts of data, and the increasingly challenging radio frequency interference (RFI) environment necessitates ever-more sophisticated RFI rejection algorithms. The "needle in a haystack" nature of searches for transients and technosignatures requires us to develop methods that can determine whether a signal of interest has unique properties, or is a part of some larger set of pernicious RFI. In the past, this vetting has required onerous manual inspection of very large numbers of signals. In this paper we present a fast and modular deep learning algorithm to search for lookalike signals of interest in radio spectrogram data. First, we trained a B-Variational Autoencoder on signals returned by an energy detection algorithm. We then adapted a positional embedding layer from classical Transformer architecture to a embed additional metadata, which we demonstrate using a frequency-based embedding. Next we used the encoder component of the B-Variational Autoencoder to extract features from small (~ 715,Hz, with a resolution of 2.79Hz per frequency bin) windows in the radio spectrogram. We used our algorithm to conduct a search for a given query (encoded signal of interest) on a set of signals (encoded features of searched items) to produce the top candidates with similar features. We successfully demonstrate that the algorithm retrieves signals with similar appearance, given only the original radio spectrogram data. This algorithm can be used to improve the efficiency of vetting signals of interest in technosignature searches, but could also be applied to a wider variety of searches for "lookalike" signals in large astronomical datasets.Comment: 8 pages, 8 figure

A Zero Attention Model for Personalized Product Search

Product search is one of the most popular methods for people to discover and purchase products on e-commerce websites. Because personal preferences often have an important influence on the purchase decision of each customer, it is intuitive that personalization should be beneficial for product search engines. While synthetic experiments from previous studies show that purchase histories are useful for identifying the individual intent of each product search session, the effect of personalization on product search in practice, however, remains mostly unknown. In this paper, we formulate the problem of personalized product search and conduct large-scale experiments with search logs sampled from a commercial e-commerce search engine. Results from our preliminary analysis show that the potential of personalization depends on query characteristics, interactions between queries, and user purchase histories. Based on these observations, we propose a Zero Attention Model for product search that automatically determines when and how to personalize a user-query pair via a novel attention mechanism. Empirical results on commercial product search logs show that the proposed model not only significantly outperforms state-of-the-art personalized product retrieval models, but also provides important information on the potential of personalization in each product search session

Constraints on Primordial Nongaussiantiy from the High-Redshift Cluster MS1054--03

The implications of the massive, X-ray selected cluster of galaxies MS1054--03 at $z=0.83$ are discussed in light of the hypothesis that the primordial density fluctuations may be nongaussian. We generalize the Press-Schechter (PS) formalism to the nongaussian case, and calculate the likelihood that a cluster as massive as MS1054 would appear in the EMSS. The probability of finding an MS1054-like cluster depends only on \omegam and the extent of primordial nongaussianity. We quantify the latter by adopting a specific functional form for the PDF, denoted $\psi_\lambda,$ which tends to Gaussianity for $\lambda\gg 1,$ and show how $\lambda$ is related to the more familiar statistic $T,$ the probability of $\ge 3\sigma$ fluctuations for a given PDF relative to a Gaussian. We find that Gaussian initial density fluctuations are consistent with the data on MS1054 only if \omegam\simlt 0.2. For \omegam\ge 0.25 a significant degree of nongaussianity is required, unless the mass of MS1054 has been substantially overestimated by X-ray and weak lensing data. The required amount of nongaussianity is a rapidly increasing function of \omegam for 0.25 \le \omegam \le 0.45, with $\lambda \le 1$ (T \simgt 7) at the upper end of this range. For a fiducial \omegam=0.3, \omegal=0.7 universe, favored by several lines of evidence we obtain an upper limit $\lambda \le 10,$ corresponding to a $T\ge 3.$ This finding is consistent with the conclusions of Koyama, Soda, & Taruya (1999), who applied the generalized PS formalism to low (z\simlt 0.1) and intermediate (z\simlt 0.6) redshift cluster data sets.Comment: 15 pages, 11 figures, submitted to the Astrophysical Journal, uses emulateapj.st

Evolution of the Cluster Mass and Correlation Functions in LCDM Cosmology

The evolution of the cluster mass function and the cluster correlation function from z = 0 to z = 3 are determined using 10^6 clusters obtained from high-resolution simulations of the current best-fit LCDM cosmology (\Omega_m = 0.27, \sigma_8 = 0.84, h = 0.7). The results provide predictions for comparisons with future observations of high redshift clusters. A comparison of the predicted mass function of low redshift clusters with observations from early Sloan Digital Sky Survey data, and the predicted abundance of massive distant clusters with observational results, favor a slightly larger amplitude of mass fluctuations (\sigma_8 = 0.9) and lower density parameter (\Omega_m = 0.2); these values are consistent within 1-\sigma with the current observational and model uncertainties. The cluster correlation function strength increases with redshift for a given mass limit; the clusters were more strongly correlated in the past, due to their increasing bias with redshift - the bias reaches b = 100 at z = 2 for M > 5 x 10^13 h^-1 M_sun. The richness-dependent cluster correlation function, represented by the correlation scale versus cluster mean separation relation, R0-d, is generally consistent with observations. This relation can be approximated as R_0 = 1.7 d^0.6 h^-1 Mpc for d = 20 - 60 h^-1 Mpc. The R0-d relation exhibits surprisingly little evolution with redshift for z < 2; this can provide a new test of the current LCDM model when compared with future observations of high redshift clusters.Comment: 20 pages, 9 figures, accepted for publication in Ap