Cosmological Implications of a Scale Invariant Standard Model

We generalize the standard model of particle physics such it displays global scale invariance. The gravitational action is also suitably modified such that it respects this symmetry. This model is interesting since the cosmological constant term is absent in the action. We find that the scale symmetry is broken by the recently introduced cosmological symmetry breaking mechanism. This simultaneously generates all the dimensionful parameters such as the Newton's gravitational constant, the particle masses and the vacuum or dark energy. We find that in its simplest version the model predicts the Higgs mass to be very small, which is ruled out experimentally. We further generalize the model such that it displays local scale invariance. In this case the Higgs particle disappears from the particle spectrum and instead we find a very massive vector boson. Hence the model gives a consistent description of particle physics phenomenology as well as fits the cosmological dark energy.Comment: 12 pages, no figure

Bronchiectasis in India:results from the European Multicentre Bronchiectasis Audit and Research Collaboration (EMBARC) and Respiratory Research Network of India Registry

BACKGROUND: Bronchiectasis is a common but neglected chronic lung disease. Most epidemiological data are limited to cohorts from Europe and the USA, with few data from low-income and middle-income countries. We therefore aimed to describe the characteristics, severity of disease, microbiology, and treatment of patients with bronchiectasis in India. METHODS: The Indian bronchiectasis registry is a multicentre, prospective, observational cohort study. Adult patients ( 6518 years) with CT-confirmed bronchiectasis were enrolled from 31 centres across India. Patients with bronchiectasis due to cystic fibrosis or traction bronchiectasis associated with another respiratory disorder were excluded. Data were collected at baseline (recruitment) with follow-up visits taking place once per year. Comprehensive clinical data were collected through the European Multicentre Bronchiectasis Audit and Research Collaboration registry platform. Underlying aetiology of bronchiectasis, as well as treatment and risk factors for bronchiectasis were analysed in the Indian bronchiectasis registry. Comparisons of demographics were made with published European and US registries, and quality of care was benchmarked against the 2017 European Respiratory Society guidelines. FINDINGS: From June 1, 2015, to Sept 1, 2017, 2195 patients were enrolled. Marked differences were observed between India, Europe, and the USA. Patients in India were younger (median age 56 years [IQR 41-66] vs the European and US registries; p<0\ub70001]) and more likely to be men (1249 [56\ub79%] of 2195). Previous tuberculosis (780 [35\ub75%] of 2195) was the most frequent underlying cause of bronchiectasis and Pseudomonas aeruginosa was the most common organism in sputum culture (301 [13\ub77%]) in India. Risk factors for exacerbations included being of the male sex (adjusted incidence rate ratio 1\ub717, 95% CI 1\ub703-1\ub732; p=0\ub7015), P aeruginosa infection (1\ub729, 1\ub710-1\ub750; p=0\ub7001), a history of pulmonary tuberculosis (1\ub720, 1\ub707-1\ub734; p=0\ub7002), modified Medical Research Council Dyspnoea score (1\ub732, 1\ub725-1\ub739; p<0\ub70001), daily sputum production (1\ub716, 1\ub703-1\ub730; p=0\ub7013), and radiological severity of disease (1\ub703, 1\ub701-1\ub704; p<0\ub70001). Low adherence to guideline-recommended care was observed; only 388 patients were tested for allergic bronchopulmonary aspergillosis and 82 patients had been tested for immunoglobulins. INTERPRETATION: Patients with bronchiectasis in India have more severe disease and have distinct characteristics from those reported in other countries. This study provides a benchmark to improve quality of care for patients with bronchiectasis in India. FUNDING: EU/European Federation of Pharmaceutical Industries and Associations Innovative Medicines Initiative inhaled Antibiotics in Bronchiectasis and Cystic Fibrosis Consortium, European Respiratory Society, and the British Lung Foundation

Seismic microzonation of a nuclear power plant site with detailed geotechnical, geophysical and site effect studies

This paper highlights the seismic microzonation carried out for a nuclear power plant site. Nuclear power plants are considered to be one of the most important and critical structures designed to withstand all natural disasters. Seismic microzonation is a process of demarcating a region into individual areas having different levels of various seismic hazards. This will help in identifying regions having high seismic hazard which is vital for engineering design and land-use planning. The main objective of this paper is to carry out the seismic microzonation of a nuclear power plant site situated in the east coast of South India, based on the spatial distribution of the hazard index value. The hazard index represents the consolidated effect of all major earthquake hazards and hazard influencing parameters. The present work will provide new directions for assessing the seismic hazards of new power plant sites in the country. Major seismic hazards considered for the evaluation of the hazard index are (1) intensity of ground shaking at bedrock, (2) site amplification, (3) liquefaction potential and (4) the predominant frequency of the earthquake motion at the surface. The intensity of ground shaking in terms of peak horizontal acceleration (PHA) was estimated for the study area using both deterministic and probabilistic approaches with logic tree methodology. The site characterization of the study area has been carried out using the multichannel analysis of surface waves test and available borehole data. One-dimensional ground response analysis was carried out at major locations within the study area for evaluating PHA and spectral accelerations at the ground surface. Based on the standard penetration test data, deterministic as well as probabilistic liquefaction hazard analysis has been carried out for the entire study area. Finally, all the major earthquake hazards estimated above, and other significant parameters representing local geology were integrated using the analytic hierarchy process and hazard index map for the study area was prepared. Maps showing the spatial variation of seismic hazards (intensity of ground shaking, liquefaction potential and predominant frequency) and hazard index are presented in this work

Rapid Exclusion of COVID Infection With the Artificial Intelligence Electrocardiogram

ObjectiveTo rapidly exclude severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection using artificial intelligence applied to the electrocardiogram (ECG). MethodsA global, volunteer consortium from 4 continents identified patients with ECGs obtained around the time of polymerase chain reaction–confirmed COVID-19 diagnosis and age- and sex-matched controls from the same sites. Clinical characteristics, polymerase chain reaction results, and raw electrocardiographic data were collected. A convolutional neural network was trained using 26,153 ECGs (33.2% COVID positive), validated with 3826 ECGs (33.3% positive), and tested on 7870 ECGs not included in other sets (32.7% positive). Performance under different prevalence values was tested by adding control ECGs from a single high-volume site. ResultsThe area under the curve for detection of acute COVID-19 infection in the test group was 0.767 (95% CI, 0.756 to 0.778; sensitivity, 98%; specificity, 10%; positive predictive value, 37%; negative predictive value, 91%). To more accurately reflect a real-world population, 50,905 normal controls were added to adjust the COVID prevalence to approximately 5% (2657/58,555), resulting in an area under the curve of 0.780 (95% CI, 0.771 to 0.790) with a specificity of 12.1% and a negative predictive value of 99.2%. ConclusionInfection with SARS-CoV-2 results in electrocardiographic changes that permit the artificial intelligence–enhanced ECG to be used as a rapid screening test with a high negative predictive value (99.2%). This may permit the development of electrocardiography-based tools to rapidly screen individuals for pandemic control