Case Study of "Wake Effect" of Adjacent Offshore Wind Farms

[Introduction] The purpose of this paper is to study the influence of real "wake effect" of adjacent offshore wind farms on generation loss. [Method] The method is established with the wake scene classification based on the actual arrangement of wind farms under different wind direction and the real wake power loss of adjacent wind farms (with a spacing of more than 20D) in operation are analyzed, based on the actual SCADA data of wind turbines in large offshore wind farms and the measured wind data of LIDAR in the same period. [Result] The results show that: for the large-scale offshore wind farms with regular arrangement, the power generation normalization of the actual SCADA data can better reflect the distribution characteristics of offshore wind energy resources and the difference of power generation capacity; Under the condition of highly centralized wind direction, the adjacent wind farms in the downwind are obviously affected by the "wake effect" of the upwind wind farm; The buffer zones with different distances of adjacent wind farms have an obvious effect on the recovery of wind speed which affected the power generating capacity. The power generating capacity can be improved but if the buffer zone can reach enough distance; In different scenes of this case, the buffer zone distance is between 23D and 44D, and the power loss of wake decreases by 27%~4%. [Conclusion] This work can provide guidance for the planning of offshore wind power base and the optimization design of large offshore wind frams

Sex differences in patients with COVID-19: a retrospective cohort study and meta-analysis

BACKGROUND: Accumulated evidence revealed that male was much more likely to higher severity and fatality by SARS-CoV-2 infection than female patients, but few studies and meta-analyses have evaluated the sex differences of the infection and progression of COVID-19 patients. AIM: We aimed to compare the sex differences of the epidemiological and clinical characteristics in COVID-19 patients; and to perform a meta-analysis evaluating the severe rate, fatality rate, and the sex differences of the infection and disease progression in COVID-19 patients. METHODS: We analyzed clinical data of patients in Changchun Infectious Hospital and Center, Changchun, Northeast China; and searched PubMed, Embase, Web of Science, and Cochrane Library without any language restrictions for published articles that reported the data of sex-disaggregated, number of severe, and death patients on the confirmed diagnosis of adult COVID-19 patients. RESULTS: The pooled severe rate and fatality rate of COVID-19 were 22.7% and 10.7%. Male incidence in the retrospective study was 58.1%, and the pooled incidence in male was 54.7%. CONCLUSION: The pooled severe rate in male and female of COVID-19 was 28.2% and 18.8%, the risky of severe and death was about 1.6folds higher in male compared with female, especially for older patients (> 50 y)

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

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

Prediction of Body Mass Index Using Concurrently Self-Reported or Previously Measured Height and Weight

<div><p>Objective</p><p>To compare alternative models for the imputation of BMI_M (measured weight in kilograms/measured height in meters squared) in a longitudinal study.</p><p>Methods</p><p>We used data from 11,008 adults examined at wave III (2001–2002) and wave IV (2007–2008) in the National Longitudinal Study of Adolescent to Adult Health. Participants were asked their height and weight before being measured. Equations to predict wave IV BMI_M were developed in an 80% random subsample and evaluated in the remaining participants. The validity of models that included BMI constructed from previously measured height and weight (BMI_PM) was compared to the validity of models that used BMI calculated from concurrently self-reported height and weight (BMI_SR). The usefulness of including demographics and perceived weight category in those models was also examined.</p><p>Results</p><p>The model that used BMI_SR, compared to BMI_PM, as the only variable produced a larger R² (0.913 vs. 0.693), a smaller root mean square error (2.07 vs. 3.90 kg/m²) and a lower bias between normal-weight participants and those with obesity (0.98 vs. 4.24 kg/m²). The performance of the model containing BMI_SR alone was not substantially improved by the addition of demographics, perceived weight category or BMI_PM.</p><p>Conclusions</p><p>Our work is the first to show that concurrent self-reports of height and weight may be more useful than previously measured height and weight for imputation of missing BMI_M when the time interval between measures is relatively long. Other time frames and alternatives to in-person collection of self-reported data need to be examined.</p></div

MSD between predicted BMI_M and actual BMI_M by weight status in the test dataset (n = 2202).

<p>(A) Abbreviations: MSD for mean signed difference; BMI for body mass index, BMI_PM is derived from measured height and weight at wave III, BMI_M is derived from measured height and weight at wave IV, BMI_SR is constructed from self-reported height and weight at wave IV. (B) MSD was calculated as the mean of predicted BMI_M minus actual BMI_M. The dashed lines in the Fig are at ±0.5 kg/m². (C) Weight status was based on BMI_M. n = 719 for normal weight group (18.5≤ BMI_M <25 kg/m²) and n = 776 for the group with obesity (BMI_M ≥30kg/m²). Results for underweight group (n = 45) and for overweight group (n = 662) were not shown.</p

R² and RMSE from regression^* of predicted BMI_M against actual BMI_M in the test dataset.

<p>R² and RMSE from regression^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0167288#t002fn002" target="_blank">*</a>} of predicted BMI_M against actual BMI_M in the test dataset.</p