Study on Miniaturized Dual Polarized MIMO Antenna

MIMO (Multiple input multiple output) used multiple transmit and receive antennas to utilize multipath propagation. This method is for multiplying the capacity of a radio link. In wireless communication standards MIMO has an essential element. “MIMO” refers to a practical technique used for sending and receiving more than one data signal via multipath propagation on the same radio channel at the same time. The demand of wireless communication increases due to miniaturized Antenna and dual polarization. Compactness in system is increased by miniaturization. Data propagation rate increased due to dual polarization by differentiating propagation polarization. In this work attempt will be to conduct elaborate Study, Design and Analysis of some of the Miniaturization techniques along with bandwidth enhancement and also on Dual polarization techniques. In future the techniques will be combined and study for Miniaturized Dual Polarized MIMO Antenna will be done

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

The impact of surgical strategy and rifampin on treatment outcome in Cutibacterium periprosthetic joint infections.

BACKGROUND Cutibacterium species are common pathogens in periprosthetic joint infections (PJI). These infections are often treated with β-lactams or clindamycin as monotherapy, or in combination with rifampin. Clinical evidence supporting the value of adding rifampin for treatment of Cutibacterium PJI is lacking. MATERIALS/METHODS In this multicenter retrospective study, we evaluated patients with Cutibacterium PJI. The primary endpoint was clinical success, defined by the absence of infection relapse or new infection within a minimal follow-up of 12 months. We used Fisher's exact tests and Cox proportional hazards models to analyze the effect of rifampin and other factors on clinical success after PJI. RESULTS We included 187 patients (72.2% male, median age 67 years) with a median follow-up of 36 months. The surgical intervention was two-stage exchange in 95 (50.8%), one-stage exchange in 51 (27.3%), debridement and implant retention (DAIR) in 34 (18.2%), and explantation without reimplantation in 7 (3.7%). Rifampin was included in the antibiotic regimen in 81 (43.3%) cases. Infection relapse occurred in 28 (15.0%), and new infection in 13 (7.0%) cases. In the time-to-event analysis, DAIR (adjusted HR=2.15, p=0.03) and antibiotic treatment over 6 weeks (adjusted HR=0.29, p=0.0002) significantly influenced treatment failure. We observed a tentative evidence for a beneficial effect of adding rifampin to the antibiotic treatment - though not statistically significant for treatment failure (adjusted HR=0.5, p=0.07) and not for relapses (adjusted HR=0.5, p=0.10). CONCLUSIONS We conclude that a rifampin combination is not markedly superior in Cutibacterium PJI but a dedicated prospective multicenter study is needed