Inflationary Magnetogenesis and Anomaly Cancellation in Electrodynamics

Primordial magnetic field generated in the inflationary era can act as a viable source for present day intergalactic magnetic field of significant strength. We present a fundamental origin for such primordial generation of magnetic field, namely through anomaly cancellation of $U(1)$ gauge field in quantum electrodynamics. Addition of this term explicitly breaks conformal invariance of the theory necessary to generate magnetic field of sufficient strength. We have analysed at length the power spectrum of the magnetic field thus generated. We have also found that magnetic power spectrum has significant scale-dependance giving rise to a non-trivial magnetic spectral index, a key feature of this model. Interestingly, there exists a large parameter space where magnetic field of significant strength can be produced.Comment: v2, Revised version, 25 pages, 4 figures and 1 tabl

Fuzzy rough granular neural networks, fuzzy granules, and classification

AbstractWe introduce a fuzzy rough granular neural network (FRGNN) model based on the multilayer perceptron using a back-propagation algorithm for the fuzzy classification of patterns. We provide the development strategy of the network mainly based upon the input vector, initial connection weights determined by fuzzy rough set theoretic concepts, and the target vector. While the input vector is described in terms of fuzzy granules, the target vector is defined in terms of fuzzy class membership values and zeros. Crude domain knowledge about the initial data is represented in the form of a decision table, which is divided into subtables corresponding to different classes. The data in each decision table is converted into granular form. The syntax of these decision tables automatically determines the appropriate number of hidden nodes, while the dependency factors from all the decision tables are used as initial weights. The dependency factor of each attribute and the average degree of the dependency factor of all the attributes with respect to decision classes are considered as initial connection weights between the nodes of the input layer and the hidden layer, and the hidden layer and the output layer, respectively. The effectiveness of the proposed FRGNN is demonstrated on several real-life data sets

Evaluation of Institutional Research Productivity

The quantification of scholarly performance has become an obvious necessity in many academic pursuits. Evaluation of research output is, therefore, an integral element of R&D institutions worldwide. This paper critically scrutinizes the literature on research productivity concerning scientific institutions (include universities and departments) in an informational context. It provides a thorough review to map the quantum of knowledge relating to ‘institutional research productivity’ correlating the Indian vista. The paper also offers a few recommendations to undertake evaluative studies with caution. Thus it shows a coherent picture of this emerging area in the sociology of science

Understanding Research Productivity in the Realm of Evaluative Scientometrics

Selecting appropriate metrics and translate into the practical situation through empirical design is a cumbersome task in measuring the research productivity. A single indicator cannot work well in different situations, but selecting the'most suitable'one from dozens of indicators is very confusing. Nevertheless, establishing benchmarks in research evaluation and implementing all-factor productivity is almost impossible. Understanding research productivity is, therefore, a quintessential need for performance evaluations in the realm of evaluative scientometrics. Evaluative scientometrics endorses the measures that emerge during the decision-making process through relevant metrics and indicators expressing the organizational dynamics. Evaluation processes governed by counting, weighting, normalizing, and then comparing seem trustworthy

Understanding research productivity in the realm of evaluative scientometrics

67-69The combination of a variety of inputs (both tangible and intangible) enables the numerous outputs in varying degrees to realize the research productivity. To select appropriate metrics and translate into the practical situation through empirical design is a cumbersome task. A single indicator cannot work well in different situations, but selecting the 'most suitable' one from dozens of indicators is very confusing. Nevertheless, establishing benchmarks in research evaluation and implementing all-factor productivity is almost impossible. Understanding research productivity is, therefore, a quintessential need for performance evaluations in the realm of evaluative scientometrics. Many enterprises evaluate the research performance with little understanding of the dynamics of research and its counterparts. Evaluative scientometrics endorses the measures that emerge during the decision-making process through relevant metrics and indicators expressing the organizational dynamics. Evaluation processes governed by counting, weighting, normalizing, and then comparing seem trustworthy

Fear effect in a three-species food chain model with generalist predator

Within the framework of a food web, the foraging behavior of meso-carnivorous species is influenced by fear responses elicited by higher trophic level species, consequently diminishing the fecundity of these species. In this study, we investigate a three-species food chain model comprising of prey, an intermediate predator, and a top predator. We assume that both the birth rate and intraspecies competition of prey are impacted by fear induced by the intermediate predator. Additionally, the foraging behavior of the intermediate predator is constrained due to the presence of the top predator. It is essential to note that the top predators exhibit a generalist feeding behavior, encompassing food sources beyond the intermediate predators. The study systematically determines all feasible equilibria of the proposed model and conducts a comprehensive stability analysis of these equilibria. The investigation reveals that the system undergoes Hopf bifurcation concerning various model parameters. Notably, when other food sources significantly contribute to the growth of the top predators, the system exhibits stable behavior around the interior equilibrium. Our findings indicate that the dynamic influence of fear plays a robust role in stabilizing the system. Furthermore, a cascading effect within the system, stemming from the fear instigated by top predators, is observed and analyzed. Overall, this research sheds light on the intricate dynamics of fear-induced responses in shaping the stability and behavior of multi-species food web systems, highlighting the profound cascading effects triggered by fear mechanisms in the ecosystem

Carbon-based adsorbents from naturally available Bermuda grasses : removal of TDS and arsenic ions

In the present study, we have reported the synthesis of nano porous carbon material (GC) by the thermal treatment of the commonly available Bermuda grasses, and metal oxides doped bio-compatible polymer chitosan-GC based porous cross-linked composites (CHGCCZ) as adsorbent materials for the removal of total dissolved solids (TDS) and efficient removal of arsenic (As(V)) ions from aqueous medium, respectively. The synthesized adsorbents have been characterized by FTIR, PXRD, FESEM, TGA, and the systematic investigations have shown that the incorporation of GCs into cross-linked matrix makes them porous, more resistant to degradation, and suitable adsorption matrix for the toxic As(V) removal. The presence of As(V) ions is quantified by Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES) measurements. The amount of TDS and arsenic concentration was reduced to the minimum value of 103 ppm (average value∼119 ppm) from 414 ppm and 7.7 ppm from very high concentration of 10.15 ppm, respectively. The recyclability test has also been performed after regeneration of the CHGCCZ and the initial findings has been found to be promising. Therefore, we have systematically investigated the efficacy of TDS removal by GCs and As(V) adsorption properties of metal oxide doped cross-linked CHGCCZ composite from the aqueous system and demonstrated the regeneration process for CHGCCZ in our study