An Autonomous Omnidirectional Robot

RoboCup is an international research and education initiative, which aims to foster artificial intelligence and robotics research by using competitive soccer as a standard problem. This paper presents a detailed engineering design process and the outcome for an omni-directional mobile robot platform for the Robocup Middle Size League competition. A prototype that can move omnidirectionally with kicking capability was designed, built, and tested by a group of senior students. The design included a mechanical base, pneumatic kicking mechanism, a DSP microcontroller-based control system, various sensor interfacing units, and the analysis of omnidirectional motions. The testing results showed that the system was able to move omnidirectionally with a speed of ∼2 m/s and able to kick a size 5 FIFA soccer ball for a distance of at least 5 meters

A communal catalogue reveals Earth’s multiscale microbial diversity

Our growing awareness of the microbial world’s importance and diversity contrasts starkly with our limited understanding of its fundamental structure. Despite recent advances in DNA sequencing, a lack of standardized protocols and common analytical frameworks impedes comparisons among studies, hindering the development of global inferences about microbial life on Earth. Here we present a meta-analysis of microbial community samples collected by hundreds of researchers for the Earth Microbiome Project. Coordinated protocols and new analytical methods, particularly the use of exact sequences instead of clustered operational taxonomic units, enable bacterial and archaeal ribosomal RNA gene sequences to be followed across multiple studies and allow us to explore patterns of diversity at an unprecedented scale. The result is both a reference database giving global context to DNA sequence data and a framework for incorporating data from future studies, fostering increasingly complete characterization of Earth’s microbial diversity

A communal catalogue reveals Earth's multiscale microbial diversity

Our growing awareness of the microbial world's importance and diversity contrasts starkly with our limited understanding of its fundamental structure. Despite recent advances in DNA sequencing, a lack of standardized protocols and common analytical frameworks impedes comparisons among studies, hindering the development of global inferences about microbial life on Earth. Here we present a meta-analysis of microbial community samples collected by hundreds of researchers for the Earth Microbiome Project. Coordinated protocols and new analytical methods, particularly the use of exact sequences instead of clustered operational taxonomic units, enable bacterial and archaeal ribosomal RNA gene sequences to be followed across multiple studies and allow us to explore patterns of diversity at an unprecedented scale. The result is both a reference database giving global context to DNA sequence data and a framework for incorporating data from future studies, fostering increasingly complete characterization of Earth's microbial diversity.Peer reviewe

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

Global Oceans, BAMS State of the Climate in 2021, Chapter 3

Patterns of variability in ocean properties are often closely related to large-scale climate pattern indices, and 2021 is no exception. The year 2021 started and ended with La Niña conditions, charmingly dubbed a “double-dip” La Niña. Hence, stronger-than-normal easterly trade winds in the tropical south Pacific drove westward surface current anomalies in the equatorial Pacific; reduced sea surface temperature (SST) and upper ocean heat content in the eastern tropical Pacific; increased sea level, upper ocean heat content, and salinity in the western tropical Pacific; resulted in a rim of anomalously high chlorophyll-a (Chla) on the poleward and westward edges of the anomalously cold SST wedge in the eastern equatorial Pacific; and increased precipitation over the Maritime Continent. The Pacific decadal oscillation remained strongly in a negative phase in 2021, with negative SST and upper ocean heat content anomalies around the eastern and equatorial edges of the North Pacific and positive anomalies in the center associated with low Chla anomalies. The South Pacific exhibited similar patterns. Fresh anomalies in the northeastern Pacific shifted towards the west coast of North America. The Indian Ocean dipole (IOD) was weakly negative in 2021, with small positive SST anomalies in the east and nearly-average anomalies in the west. Nonetheless, upper ocean heat content was anomalously high in the west and lower in the east, with anomalously high freshwater flux and low sea surface salinities (SSS) in the east, and the opposite pattern in the west, as might be expected during a negative phase of that climate index. In the Atlantic, the only substantial cold anomaly in SST and upper ocean heat content persisted east of Greenland in 2021, where SSS was also low, all despite the weak winds and strong surface heat flux anomalies into the ocean expected during a negative phase of the North Atlantic Oscillation. These anomalies held throughout much of 2021. An Atlantic and Benguela Niño were both evident, with above-average SST anomalies in the eastern equatorial Atlantic and the west coast of southern Africa. Over much of the rest of the Atlantic, SSTs, upper ocean heat content, and sea level anomalies were above average. Anthropogenic climate change involves long-term trends, as this year’s chapter sidebars emphasize. The sidebars relate some of the latest IPCC ocean-related assessments (including carbon, the section on which is taking a hiatus from our report this year). This chapter estimates that SST increased at a rate of 0.16–0.19°C decade−1 from 2000 to 2021, 0–2000-m ocean heat content warmed by 0.57–0.73 W m−2 (applied over Earth’s surface area) from 1993 to 2021, and global mean sea level increased at a rate of 3.4 ± 0.4 mm yr−1 from 1993 to 2021. Global mean SST, which is more subject to interannual variations than ocean heat content and sea level, with values typically reduced during La Niña, was ~0.1°C lower in 2021 than in 2020. However, from 2020 to 2021, annual average ocean heat content from 0 to 2000 dbar increased at a rate of ~0.95 W m−2, and global sea level increased by ~4.9 mm. Both were the highest on record in 2021, and with year-on-year increases substantially exceeding their trend rates of recent decades