Dirac-K\"ahler particle in Riemann spherical space: boson interpretation

In the context of the composite boson interpretation, we construct the exact general solution of the Dirac--K\"ahler equation for the case of the spherical Riemann space of constant positive curvature, for which due to the geometry itself one may expect to have a discrete energy spectrum. In the case of the minimal value of the total angular momentum, $j=0$, the radial equations are reduced to second-order ordinary differential equations, which are straightforwardly solved in terms of the hypergeometric functions. For non-zero values of the total angular momentum, however, the radial equations are reduced to a pair of complicated fourth-order differential equations. Employing the factorization approach, we derive the general solution of these equations involving four independent fundamental solutions written in terms of combinations of the hypergeometric functions. The corresponding discrete energy spectrum is then determined via termination of the involved hypergeometric series, resulting in quasi-polynomial wave-functions. The constructed solutions lead to notable observations when compared with those for the ordinary Dirac particle. The energy spectrum for the Dirac-K\"ahler particle in spherical space is much more complicated. Its structure substantially differs from that for the Dirac particle since it consists of two paralleled energy level series each of which is twofold degenerate. Besides, none of the two separate series coincides with the series for the Dirac particle. Thus, the Dirac--K\"ahler field cannot be interpreted as a system of four Dirac fermions. Additional arguments supporting this conclusion are discussed

A Population-Based Ultra-Widefield Digital Image Grading Study for Age-Related Macular Degeneration-Like Lesions at the Peripheral Retina.

Our understanding of the relevance of peripheral retinal abnormalities to disease in general and in age-related macular degeneration (AMD) in particular is limited by the lack of detailed peripheral imaging studies. The purpose of this study was to develop image grading protocols suited to ultra-widefield imaging (UWFI) in an aged population

Probing multivalent interactions in a synthetic host-guest complex by dynamic force spectroscopy

Multivalency is present in many biological and synthetic systems. Successful application of multivalency depends on a correct understanding of the thermodynamics and kinetics of this phenomenon. In this Article, we address the stability and strength of multivalent bonds with force spectroscopy techniques employing a synthetic adamantane/β-cyclodextrin model system. Comparing the experimental findings to theoretical predictions for the rupture force and the kinetic off-rate, we find that when the valency of the complex is increased from mono- to di- to trivalent, there is a transition from quasi-equilibrium, with a constant rupture force of 99 pN, to a kinetically dependent state, with loading-rate-dependent rupture forces from 140 to 184 pN (divalent) and 175 to 210 pN (trivalent). Additional binding geometries, parallel monovalent ruptures, single-bound divalent ruptures, and single- and double-bound trivalent ruptures are identified. The experimental kinetic off-rates of the multivalent complexes show that the stability of the complexes is significantly enhanced with the number of bonds, in agreement with the predictions of a noncooperative multivalent model

Using neural networks to obtain indirect information about the state variables in an alcoholic fermentation process

This work provides a manual design space exploration regarding the structure, type, and inputs of a multilayer neural network (NN) to obtain indirect information about the state variables in the alcoholic fermentation process. The main benefit of our application is to help experts reduce the time needed for making the relevant measurements and to increase the lifecycles of sensors in bioreactors. The novelty of this research is the flexibility of the developed application, the use of a great number of variables, and the comparative presentation of the results obtained with different NNs (feedback vs. feed-forward) and different learning algorithms (Back-Propagation vs. Levenberg–Marquardt). The simulation results show that the feedback neural network outperformed the feed-forward neural network. The NN configuration is relatively flexible (with hidden layers and a number of nodes on each of them), but the number of input and output nodes depends on the fermentation process parameters. After laborious simulations, we determined that using pH and CO2 as inputs reduces the prediction errors of the NN. Thus, besides the most commonly used process parameters like fermentation temperature, time, the initial concentration of the substrate, the substrate concentration, and the biomass concentration, by adding pH and CO2, we obtained the optimum number of input nodes for the network. The optimal configuration in our case was obtained after 1500 iterations using a NN with one hidden layer and 12 neurons on it, seven neurons on the input layer, and one neuron as the output. If properly trained and validated, this model can be used in future research to accurately predict steady-state and dynamic alcoholic fermentation process behaviour and thereby improve process control performance

Instanton induced charged fermion and neutrino masses in a minimal Standard Model scenario from intersecting D-branes

String instanton Yukawa corrections from Euclidean D-branes are investigated in an effective Standard Model theory obtained from the minimal U(3)xU(2)xU(1) D-brane configuration. In the case of the minimal chiral and Higgs spectrum, it is found that superpotential contributions are induced by string instantons for the perturbatively forbidden entries of the up and down quark mass matrices. Analogous non-perturbative effects generate heavy Majorana neutrino masses and a Dirac neutrino texture with factorizable Yukawa couplings. For this latter case, a specific example is worked out where it is shown how this texture can reconcile the neutrino data.Comment: 17 pages, 3 figure

Exploratory characterization of volcanic ash sourced from Uganda as a pozzolanic material in portland cement concrete

The need for alternative cementing materials to ordinary Portland cement (OPC) has promoted characterization research on pozzolana as an important ingredient in cement production. In Uganda, natural pozzolana application in cement production is done by only two producers of Portland cement and at a small scale due to capacity constraints. The capacity constraints, together with other technological issues, continue to hinder realization of the cost reduction and improved quality benefits attributed to the use of pozzolana as a mineral admixture in Portland cement concrete. There is high abundance of natural pozzolana in Uganda in the form of volcanic ash/tuffs which, if adequately characterized, can facilitate production of different cementing materials and increase output from the various players in the cement production industry in Uganda. This paper reviews methods of pozzolana characterisation and presents preliminary research findings on application of natural pozzolana sourced from Uganda in development of Portland cement concrete. Samples collected from two different deposits in the western region of Uganda were prepared and subjected to chemical analysis and tests on compressive strength, flexural strength and flow-ability. The preliminary findings are indicative of good quality pozzolanic materials that can be applied as mineral admixture in production of Portland cement concrete