Thermal Friction as a Solution to the Hubble and Large-Scale Structure Tensions

Thermal friction offers a promising solution to the Hubble and the large-scale structure (LSS) tensions. This additional friction acts on a scalar field in the early universe and extracts its energy density into dark radiation, the cumulative effect being similar to that of an early dark energy (EDE) scenario. The dark radiation automatically redshifts at the minimal necessary rate to improve the Hubble tension. On the other hand, the addition of extra radiation to the Universe can improve the LSS tension. We explore this model in light of cosmic microwave background (CMB), baryon acoustic oscillation and supernova data, including the SH0ES $H_0$ measurement and the Dark Energy Survey Y1 data release in our analysis. Our results indicate a preference for the regime where the scalar field converts to dark radiation at very high redshifts, asymptoting effectively to an extra self-interacting radiation species rather than an EDE-like injection. In this limit, thermal friction can ease both the Hubble and the LSS tensions, but not resolve them. We find the source of this preference to be the incompatibility of the CMB data with the linear density perturbations of the dark radiation when injected at redshifts close to matter-radiation equality.Comment: 10 pages, 8 figures, 7 tables (+ 8 pages, 3 figures, 3 tables in appendix

Cosmology of dark energy radiation

In this work, we quantify the cosmological signatures of dark energy radiation—a novel description of dark energy, which proposes that the dynamical component of dark energy is comprised of a thermal bath of relativistic particles sourced by thermal friction from a slowly rolling scalar field. For a minimal model with particle production emerging from first principles, we find that the abundance of radiation sourced by dark energy can be as large as $Ω_{DER}=0.03$, exceeding the bounds on relic dark radiation by three orders of magnitude. Although the background and perturbative evolution of dark energy radiation are distinct from Quintessence, we find that current and near-future cosmic microwave background and supernova data will not distinguish these models of dark energy. We also find that our constraints on all models are dominated by their impact on the expansion rate of the Universe. Considering extensions that allow the dark radiation to populate neutrinos, axions, and dark photons, we evaluate the direct detection prospects of a thermal background comprised of these candidates consistent with cosmological constraints on dark energy radiation. Our study indicates that a resolution of $∼6  meV$ is required to achieve sensitivity to relativistic neutrinos compatible with dark energy radiation in a neutrino capture experiment on tritium. We also find that dark matter axion experiments lack sensitivity to a relativistic thermal axion background, even if enhanced by dark energy radiation, and dedicated search strategies are required to probe new parameter space. We derive constraints arising from a dark photon background from oscillations into visible photons, and find that viable parameter space can be explored with the late dark energy radiation experiment

The Cosmology of Dark Energy Radiation

In this work, we quantify the cosmological signatures of dark energy radiation -- a novel description of dark energy, which proposes that the dynamical component of dark energy is comprised of a thermal bath of relativistic particles sourced by thermal friction from a slowly rolling scalar field. For a minimal model with particle production emerging from first principles, we find that the abundance of radiation sourced by dark energy can be as large as $\Omega_{\text{DER}} = 0.03$, exceeding the bounds on relic dark radiation by three orders of magnitude. Although the background and perturbative evolution of dark energy radiation is distinct from Quintessence, we find that current and near-future cosmic microwave background and supernova data will not distinguish these models of dark energy. We also find that our constraints on all models are dominated by their impact on the expansion rate of the Universe. Considering extensions that allow the dark radiation to populate neutrinos, axions, and dark photons, we evaluate the direct detection prospects of a thermal background comprised of these candidates consistent with cosmological constraints on dark energy radiation. Our study indicates that a resolution of $\sim 6 \, \text{meV}$ is required to achieve sensitivity to relativistic neutrinos compatible with dark energy radiation in a neutrino capture experiment on tritium. We also find that dark matter axion experiments lack sensitivity to a relativistic thermal axion background, even if enhanced by dark energy radiation, and dedicated search strategies are required to probe new parameter space. We derive constraints arising from a dark photon background from oscillations into visible photons, and find that several orders of magnitude of viable parameter space can be explored with planned experimental programs such as DM Radio and LADERA.Comment: 27 pages, 16 figures, 3 table

PREVALENCE OF PERIODONTITIS IN DIABETIC AND NON-DIABETIC PATIENTS

Objective: Periodontitis is a chronic inflammatory condition characterized by destruction of periodontal tissues and resulting in loss of connectivetissue attachment, loss of alveolar bone, and formation of pathological pockets around the diseased tooth. Diabetic patients are more prone toperiodontal diseases as they are more susceptible to infections. Studies have proved that periodontitis can be considered as a complication of diabetes.Method: A total of 50 individuals will be surveyed in this study. 25 patients aged 40-60 years with diabetes and 25 non-diabetic controls in goodgeneral health aged 40-60 years. All subjects will be given clinical periodontal exam for probing depth, attachment loss, bleeding on probing, thepresence of plaque and calculus, and alveolar bone loss.Expected Outcome: Increase in periodontitis is expected to be seen in the diabetic patients as compared to the non-diabetic group. Thus, periodontitisis expected to be considered as a complication of diabetes.Result: The result showed that diabetes is a significant risk factor in diabetes.Conclusion: Increase in periodontitis is expected to be seen in the diabetic patients as compared to the non - diabetic group. Thus, periodontitis isexpected to be considered as a complication of diabetes.Keywords: Periodontitis, diabetes, probing depth, plaque, calculusÂ

A Review on Optimizing Radial Basis Function Neural Network using Nature Inspired Algorithm

Radial Basis Function (RBF) is a type of feed forward neural network .This function can be applied to interpolation, chaotic time-series modeling, control engineering, image restoration, data fusion etc. In RBF network, parameters of basis functions (such as width, the position and number of centers) in the nonlinear hidden layer have great influence on the performance of the network. Common RBF training algorithms cannot possibly find the global optima of nonlinear parameters in the hidden layer, and often have too many hidden units to reach certain approximation abilities, which will lead to too large a scale for the network and decline of generalization ability. Also, RBF neural network has the advantages of adaptive and self-learning ability, but it is difficult to determine the number of hidden layer neurons, and the weights learning ability from hidden layer to the output layer is low; these deficiencies easily lead to decreasing learning ability and recognition precision. Secondly, the Swarm Intelligence Algorithms are (Meta-Heuristic) development Algorithms, which attracted much attention and appeared its ability in the last ten years within many applications such as data mining, scheduling, improve the performance of artificial neural networks (ANN) and classification. So, in this paper the work of Artificial Bee Colony (ABC), Genetic algorithm(GA), Particle swarm optimization(PSO) and Bat algorithm(BA) have been reviewed, which optimized the RBF neural network in their own terms

Perceptions and preferences of medical students regarding teaching methods in a Medical College, Mangalore India

Introduction: In the complex setting of a medical school it becomes essential to utilize an approach to teaching and learning that is best suited to the needs of the students. In developing countries like India, where there is an exponential increase of institutions catering to medical students, it becomes a challenge to teach to large number of students per class. Hence, research is needed to identify the needs of students in relation to their day to day learning activities.Objectives: To understand the preferences and perception of medical students about the current methods of teaching, aids used for teaching and also identify barriers in learning as perceived by the students.Method: A Cross-sectional study was carried out at Kasturba Medical College, Mangalore during May 2012. Study participants included 2nd and 3rd year medical students. A semi-structured questionnaire was used to collect the information in relation to preferences and perceptions regarding teaching methods utilized for theory and clinical teaching. SPSS version 11.5 was used for analysis of data. The association between variables of interest was tested using Chi-square test.Results: A total of 286 students (56.6 % females and 43.4% males) participated with a dropout rate of 10.6%. The study revealed that 71.3% of the students had an attendance above 75%. The most preferred teaching method was Problem Based Learning (PBL) (71.4%) as students felt that it enhanced lateral thinking while Didactic Lectures was the least preferred (32.8%). The most preferred modality of teaching aid was found to be Black board preferred by 46.9% students. In learning rare signs and cases, students preferred video lectures (41%) and mannequins (75.9%) in learning clinical skills. The main barrier in theory learning identified was inappropriate teaching methods (15%) and being new to clinical posting (38.5%) in case of learning clinical skills.Conclusion: The findings of the study suggest that a combination of traditional methods with other methods such as PBL, video lectures and mannequins could be an effective way of teaching theory and clinical skills.Keywords: Perceptions and preferences, teaching methods, medical students, Indi

Hierarchical Clifford transformations to reduce entanglement in quantum chemistry wavefunctions

The performance of computational methods for many-body physics and chemistry is strongly dependent on the choice of basis used to cast the problem; hence, the search for better bases and similarity transformations is important for progress in the field. So far, tools from theoretical quantum information have been not thoroughly explored for this task. Here we take a step in this direction by presenting efficiently computable Clifford similarity transformations for quantum chemistry Hamiltonians, which expose bases with reduced entanglement in the corresponding molecular ground states. These transformations are constructed via block diagonalization of a hierarchy of truncated molecular Hamiltonians, preserving the full spectrum of the original problem. We show that the bases introduced here allow for more efficient classical and quantum computation of ground state properties. First, we find a systematic reduction of bipartite entanglement in molecular ground states as compared to standard problem representations. This entanglement reduction has implications in classical numerical methods such as ones based on the density matrix renormalization group. Then, we develop variational quantum algorithms that exploit the structure in the new bases, showing again improved results when the hierarchical Clifford transformations are used.Comment: 14 pages, 11 figure

Capturing the dynamics of a hybrid multiscale cancer model with a continuum model

Cancer is a complex disease involving processes at spatial scales from sub-cellular, like cell signalling, to tissue scale, such as vascular network formation. A number of multiscale models have been developed to study the dynamics that emerge from the coupling between the intracellular, cellular and tissue scales. Here, we develop a continuum partial differential equation model to capture the dynamics of a particular multiscale model (a hybrid cellular automaton with discrete cells, diffusible factors and an explicit vascular network). The purpose is to test under which circumstances such a continuum model gives equivalent predictions to the original multi-scale model, in the knowledge that the system details are known, and differences in model results can be explained in terms of model features (rather than unknown experimental confounding factors). The continuum model qualitatively replicates the dynamics from the multiscale model, with certain discrepancies observed owing to the differences in the modelling of certain processes. The continuum model admits travelling wave solutions for normal tissue growth and tumour invasion, with similar behaviour observed in the multiscale model. However, the continuum model enables us to analyse the spatially homogeneous steady states of the system, and hence to analyse these waves in more detail. We show that the tumour microenvironmental effects from the multiscale model mean that tumour invasion exhibits a so-called pushed wave when the carrying capacity for tumour cell proliferation is less than the total cell density at the tumour wave front. These pushed waves of tumour invasion propagate by triggering apoptosis of normal cells at the wave front. Otherwise, numerical evidence suggests that the wave speed can be predicted from linear analysis about the normal tissue steady state