Economics & Job Creation
“The Employment Situation — May 2021”
“It’s never too early to begin healthy eating habits”
“Driving in the snow is a team effort for AI sensors”
“Filter membrane renders viruses harmless”
“Masks, ventilation stop COVID spread better than social distancing, study shows”
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Economics & Job Creation
“The Employment Situation — May 2021”
THE EMPLOYMENT SITUATION -- MAY 2021 Total nonfarm payroll employment rose by 559,000 in May, and the unemployment rate declined by 0.3 percentage point to 5.8 percent, the U.S. Bureau of Labor Statistics reported today. Notable job gains occurred in leisure and hospitality, in public and private education, and in health care and social assistance. This news release presents statistics from two monthly surveys. The household survey measures labor force status, including unemployment, by demographic characteristics. The establishment survey measures nonfarm employment, hours, and earnings by industry. For more information about the concepts and statistical methodology used in these two surveys, see the Technical Note. Household Survey Data In May, the unemployment rate declined by 0.3 percentage point to 5.8 percent, and the number of unemployed persons fell by 496,000 to 9.3 million. These measures are down considerably from their recent highs in April 2020 but remain well above their levels prior to the coronavirus (COVID-19) pandemic (3.5 percent and 5.7 million, respectively, in February 2020). (See table A-1. See the box note at the end of this news release for more information about how the household survey and its measures were affected by the coronavirus pandemic.) Among the major worker groups, the unemployment rates declined in May for teenagers (9.6 percent), Whites (5.1 percent), and Hispanics (7.3 percent). The jobless rates for adult men (5.9 percent), adult women (5.4 percent), Blacks (9.1 percent), and Asians (5.5 percent) showed little change in May. (See tables A-1, A-2, and A-3.) Among the unemployed, the number of persons on temporary layoff declined by 291,000 to 1.8 million in May. This measure is down considerably from the recent high of 18.0 million in April 2020 but is 1.1 million higher than in February 2020. The number of permanent job losers decreased by 295,000 to 3.2 million in May but is 1.9 million higher than in February 2020. (See table A-11.) In May, the number of persons jobless less than 5 weeks declined by 391,000 to 2.0 million. The number of long-term unemployed (those jobless for 27 weeks or more) declined by 431,000 to 3.8 million in May but is 2.6 million higher than in February 2020. These long-term unemployed accounted for 40.9 percent of the total unemployed in May. (See table A-12.) The labor force participation rate was little changed at 61.6 percent in May and has remained within a narrow range of 61.4 percent to 61.7 percent since June 2020. The participation rate is 1.7 percentage points lower than in February 2020. The employment- population ratio, at 58.0 percent, was also little changed in May but is up by 0.6 percentage point since December 2020. However, this measure is 3.1 percentage points below its February 2020 level. (See table A-1.) The number of persons employed part time for economic reasons was essentially unchanged at 5.3 million in May but is 873,000 higher than in February 2020. These individuals, who would have preferred full-time employment, were working part time because their hours had been reduced or they were unable to find full-time jobs. (See table A-8.) In May, the number of persons not in the labor force who currently want a job was essentially unchanged over the month at 6.6 million but is up by 1.6 million since February 2020. These individuals were not counted as unemployed because they were not actively looking for work during the last 4 weeks or were unavailable to take a job. (See table A-1.) Among those not in the labor force who currently want a job, the number of persons marginally attached to the labor force, at 2.0 million, changed little in May but is up by 518,000 since February 2020. These individuals wanted and were available for work and had looked for a job sometime in the prior 12 months but had not looked for work in the 4 weeks preceding the survey. The number of discouraged workers, a subset of the marginally attached who believed that no jobs were available for them, was 600,000 in May, little changed from the previous month but 199,000 higher than in February 2020. (See Summary table A.) Household Survey Supplemental Data In May, 16.6 percent of employed persons teleworked because of the coronavirus pandemic, down from 18.3 percent in the prior month. These data refer to employed persons who teleworked or worked at home for pay at some point in the last 4 weeks specifically because of the pandemic. In May, 7.9 million persons reported that they had been unable to work because their employer closed or lost business due to the pandemic--that is, they did not work at all or worked fewer hours at some point in the last 4 weeks due to the pandemic. This measure is down from 9.4 million in the previous month. Among those who reported in May that they were unable to work because of pandemic-related closures or lost business, 9.3 percent received at least some pay from their employer for the hours not worked, unchanged from the previous month. Among those not in the labor force in May, 2.5 million persons were prevented from looking for work due to the pandemic. This measure is down from 2.8 million the month before. (To be counted as unemployed, by definition, individuals must be either actively looking for work or on temporary layoff.) These supplemental data come from questions added to the household survey beginning in May 2020 to help gauge the effects of the pandemic on the labor market. The data are not seasonally adjusted. Tables with estimates from the supplemental questions for all months are available online at www.bls.gov/cps/effects-of-the-coronavirus-covid-19-pandemic.htm. Establishment Survey Data Total nonfarm payroll employment increased by 559,000 in May, following increases of 278,000 in April and 785,000 in March. In May, nonfarm payroll employment is down by 7.6 million, or 5.0 percent, from its pre-pandemic level in February 2020. Notable job gains occurred in leisure and hospitality, in public and private education, and in health care and social assistance in May. (See table B-1. See the box note at the end of this news release for more information about how the establishment survey and its measures were affected by the coronavirus pandemic.) In May, employment in leisure and hospitality increased by 292,000, as pandemic-related restrictions continued to ease in some parts of the country. Nearly two-thirds of the increase was in food services and drinking places (+186,000). Employment also rose in amusements, gambling, and recreation (+58,000) and in accommodation (+35,000). Employment in leisure and hospitality is down by 2.5 million, or 15.0 percent, from its level in February 2020. In May, employment increased in public and private education, reflecting the continued resumption of in-person learning and other school-related activities in some parts of the country. Employment rose by 53,000 in local government education, by 50,000 in state government education, and by 41,000 in private education. However, employment is down from February 2020 levels in local government education (-556,000), state government education (-244,000), and private education (-293,000). Health care and social assistance added 46,000 jobs in May. Employment in health care continued to trend up (+23,000), reflecting a gain in ambulatory health care services (+22,000). Social assistance added 23,000 jobs over the month, largely in child day care services (+18,000). Compared with February 2020, employment is down by 508,000 in health care and by 257,000 in social assistance. Employment in information rose by 29,000 over the month but is down by 193,000 since February 2020. In May, job gains occurred in motion picture and sound recording industries (+14,000). Manufacturing employment rose by 23,000 in May. A job gain in motor vehicles and parts (+25,000) followed a loss in April (-38,000). Employment in manufacturing is down by 509,000 from its level in February 2020. Transportation and warehousing added 23,000 jobs in May. Employment increased in support activities for transportation (+10,000) and in air transportation (+9,000). Since February 2020, employment in transportation and warehousing is down by 100,000. Employment in wholesale trade increased by 20,000 in May, mostly in the durable goods component (+14,000). Employment in wholesale trade is down by 211,000 since February 2020. Construction employment edged down in May (-20,000), reflecting a job loss in nonresidential specialty trade contractors (-17,000). Employment in construction is 225,000 lower than in February 2020. Employment in professional and business services changed little in May (+35,000). Within the industry, employment continued to trend up in accounting and bookkeeping services (+14,000). Employment in temporary help services changed little over the month (+4,000), following a large decline in April (-116,000). Overall, employment in professional and business services is down by 708,000 since February 2020. Employment in retail trade changed little in May (-6,000). Clothing and clothing accessories stores added 11,000 jobs. Employment in food and beverage stores decreased by 26,000, following a decline of 47,000 in April. Employment in retail trade is 411,000 below its February 2020 level. In May, employment changed little in other major industries, including mining, financial activities, and other services. Average hourly earnings for all employees on private nonfarm payrolls increased by 15 cents to $30.33 in May, following an increase of 21 cents in April. Average hourly earnings of private-sector production and nonsupervisory employees rose by 14 cents to $25.60 in May, following an increase of 19 cents in April. The data for the last 2 months suggest that the rising demand for labor associated with the recovery from the pandemic may have put upward pressure on wages. However, because average hourly earnings vary widely across industries, the large employment fluctuations since February 2020 complicate the analysis of recent trends in average hourly earnings. (See tables B-3 and B-8.) In May, the average workweek for all employees on private nonfarm payrolls was 34.9 hours for the third month in a row. In manufacturing, the average workweek rose by 0.1 hour to 40.5 hours, and overtime increased by 0.1 hour to 3.3 hours. The average workweek for production and nonsupervisory employees on private nonfarm payrolls declined by 0.1 hour to 34.3 hours. (See tables B-2 and B-7.) The change in total nonfarm payroll employment for March was revised up by 15,000, from +770,000 to +785,000, and the change for April was revised up by 12,000, from +266,000 to +278,000. With these revisions, employment in March and April combined is 27,000 higher than previously reported. (Monthly revisions result from additional reports received from businesses and government agencies since the last published estimates and from the recalculation of seasonal factors.)
Employment Situation Summary (bls.gov)
“It’s never too early to begin healthy eating habits”
Researchers at Columbia University Mailman School of Public Health and Universidade Federal de Ciências da Saúde de Porto Alegre, Brazil found that when health workers were trained to promote infant healthy feeding practices to pregnant women their children consumed less fats and carbohydrates at 3 years of age and had lower measures of body fat at the age of 6. The study is the first to show that the roots for obesity start in the first year of life, after mothers stop breastfeeding. The findings are published online in the Journal of Human Nutrition and Dietetics.
“The first year after birth is a critical window for the establishment of habits that will influence health patterns throughout one’s lifetime, said Caroline N. Sangalli, in the Graduate Program in Health Sciences, Universidade Federal de Ciências da Saúde de Porto Alegre, Brazil, and first author. “The message worldwide is that to avoid obesity later in life you cannot start too early to help mothers feed their children well. And this study is proof of principle that it is possible to change a mother’s behavior.”
“Most surprising was that the mothers in our randomized trial offered ultra-processed foods, that are high in sugar and fat, as early as 6 months of age,” said Ma?rcia Vitolo, Graduate Program in Pediatrics: Child and Adolescent Health Care, Universidade Federal de Ciências da Saúde de Porto Alegre, Brazil, and co-senior author. “This behavior can be explained by cultural influences and strong marketing of processed baby foods which continues globally.”
The researchers conducted the randomized trial in Porto Alegre, Brazil, in 31 centers that provide prenatal, infant, and other primary care services to low-income families. The intervention was based on births from May 2008 to February 2009 and consisted of a training program to increase the knowledge of primary healthcare workers centered on the ‘Ten Steps for Healthy Feeding for Brazilian Children from Birth to Two Years of Age’, the Brazilian dietary guideline.
All families were informed about complementary foods that should not be offered to children under 2 years of age (i.e., cookies, snacks, soft drinks and sweets) through posters in waiting rooms. Trained interviewers measured children’s growth and other outcomes at ages 6 months, 12 months, 3 years and 6 years at subsequent home visits. Details about food types, amounts and preparation methods were also recorded.
Energy intake at all ages was lower in the intervention group compared to the control group with a statistically significant difference at age 3 years. Also, children from the intervention group at 3 years of age had lower consumption of carbohydrates and total fat than the control group and at 6 years of age had accumulated less body fat as measured by a smaller waist circumference and thinner skinfolds. “We found that the energy intake in both study groups was above the requirement across all age waves; however, the excess energy intake was less in the intervention group,” observed Sangalli, who analyzed the study results with Dr. L.H. Lumey at Columbia Mailman School of Public Health with a grant from the Brazil government. “Although the disparity was slight at the onset, in the long term, the reduced intake of 92 kcal per day adds up to 33,000 kcal per year, and changes of this magnitude could explain changes in weight gain during childhood.”
The findings were particularly striking with regard to calories from cookies and powder chocolate, important sources of carbohydrates and fats. During the health workers training, sugar, sweets, soft drinks, salty snacks, cookies and ultra-processed foods were emphasized as foods for mothers to avoid for their babies until 2 years of age.
The intervention group at 6 years of age had lower body fat on several measures but this difference was not reflected in BMI-scores, a less sensitive measure of adiposity. “However with the prevalence of overweight in the intervention group at 7 percent lower than the control group at 6 years, this does suggest a valuable public health impact — especially since estimates indicate that the reduction in 1 percent of obesity prevalence among children up to age 6 years would save $1.7 billion in medical costs,” said Vitolo.
“Many individuals including Alice Waters, Jamie Oliver, and Michelle Obama have devoted efforts to improve school lunches and eating habits of school age children to aid in the fight against obesity,” said Dr. Lumey, professor of Epidemiology and a co-senior author. “All these efforts are to be applauded and encouraged. What this study suggests is that we might have to think even earlier. Feeding practices early in life can already have a significant impact on the body size of pre-school children.”
“Driving in the snow is a team effort for AI sensors”
Nobody likes driving in a blizzard, including autonomous vehicles. To make self-driving cars safer on snowy roads, engineers look at the problem from the car’s point of view.
A major challenge for fully autonomous vehicles is navigating bad weather. Snow especially confounds crucial sensor data that helps a vehicle gauge depth, find obstacles and keep on the correct side of the yellow line, assuming it is visible. Averaging more than 200 inches of snow every winter, Michigan’s Keweenaw Peninsula is the perfect place to push autonomous vehicle tech to its limits. In two papers presented at SPIE Defense + Commercial Sensing 2021, researchers from Michigan Technological University discuss solutions for snowy driving scenarios that could help bring self-driving options to snowy cities like Chicago, Detroit, Minneapolis and Toronto.
Just like the weather at times, autonomy is not a sunny or snowy yes-no designation. Autonomous vehicles cover a spectrum of levels, from cars already on the market with blind spot warnings or braking assistance, to vehicles that can switch in and out of self-driving modes, to others that can navigate entirely on their own. Major automakers and research universities are still tweaking self-driving technology and algorithms. Occasionally accidents occur, either due to a misjudgment by the car’s artificial intelligence (AI) or a human driver’s misuse of self-driving features.
Humans have sensors, too: our scanning eyes, our sense of balance and movement, and the processing power of our brain help us understand our environment. These seemingly basic inputs allow us to drive in virtually every scenario, even if it is new to us, because human brains are good at generalizing novel experiences. In autonomous vehicles, two cameras mounted on gimbals scan and perceive depth using stereo vision to mimic human vision, while balance and motion can be gauged using an inertial measurement unit. But, computers can only react to scenarios they have encountered before or been programmed to recognize.
Since artificial brains aren’t around yet, task-specific artificial intelligence (AI) algorithms must take the wheel — which means autonomous vehicles must rely on multiple sensors. Fisheye cameras widen the view while other cameras act much like the human eye. Infrared picks up heat signatures. Radar can see through the fog and rain. Light detection and ranging (lidar) pierces through the dark and weaves a neon tapestry of laser beam threads.
“Every sensor has limitations, and every sensor covers another one’s back,” said Nathir Rawashdeh, assistant professor of computing in Michigan Tech’s College of Computing and one of the study’s lead researchers. He works on bringing the sensors’ data together through an AI process called sensor fusion.
“Sensor fusion uses multiple sensors of different modalities to understand a scene,” he said. “You cannot exhaustively program for every detail when the inputs have difficult patterns. That’s why we need AI.”
Rawashdeh’s Michigan Tech collaborators include Nader Abu-Alrub, his doctoral student in electrical and computer engineering, and Jeremy Bos, assistant professor of electrical and computer engineering, along with master’s degree students and graduates from Bos’ lab: Akhil Kurup, Derek Chopp and Zach Jeffries. Bos explains that lidar, infrared and other sensors on their own are like the hammer in an old adage. “‘To a hammer, everything looks like a nail,'” quoted Bos. “Well, if you have a screwdriver and a rivet gun, then you have more options.”
Most autonomous sensors and self-driving algorithms are being developed in sunny, clear landscapes. Knowing that the rest of the world is not like Arizona or southern California, Bos’s lab began collecting local data in a Michigan Tech autonomous vehicle (safely driven by a human) during heavy snowfall. Rawashdeh’s team, notably Abu-Alrub, poured over more than 1,000 frames of lidar, radar and image data from snowy roads in Germany and Norway to start teaching their AI program what snow looks like and how to see past it.
“All snow is not created equal,” Bos said, pointing out that the variety of snow makes sensor detection a challenge. Rawashdeh added that pre-processing the data and ensuring accurate labeling is an important step to ensure accuracy and safety: “AI is like a chef — if you have good ingredients, there will be an excellent meal,” he said. “Give the AI learning network dirty sensor data and you’ll get a bad result.”
Low-quality data is one problem and so is actual dirt. Much like road grime, snow buildup on the sensors is a solvable but bothersome issue. Once the view is clear, autonomous vehicle sensors are still not always in agreement about detecting obstacles. Bos mentioned a great example of discovering a deer while cleaning up locally gathered data. Lidar said that blob was nothing (30% chance of an obstacle), the camera saw it like a sleepy human at the wheel (50% chance), and the infrared sensor shouted WHOA (90% sure that is a deer).
Getting the sensors and their risk assessments to talk and learn from each other is like the Indian parable of three blind men who find an elephant: each touches a different part of the elephant — the creature’s ear, trunk and leg — and comes to a different conclusion about what kind of animal it is. Using sensor fusion, Rawashdeh and Bos want autonomous sensors to collectively figure out the answer — be it elephant, deer or snowbank. As Bos puts it, “Rather than strictly voting, by using sensor fusion we will come up with a new estimate.”
While navigating a Keweenaw blizzard is a ways out for autonomous vehicles, their sensors can get better at learning about bad weather and, with advances like sensor fusion, will be able to drive safely on snowy roads one day.
“Filter membrane renders viruses harmless”
Researchers at ETH Zurich are developing a new filter membrane that is highly efficient at filtering and inactivating a wide variety of air-borne and water-borne viruses. Made from ecologically sound materials, the membrane has an appropriately good environmental footprint.
Viruses can spread not only via droplets or aerosols like the new coronavirus, but in water, too. In fact, some potentially dangerous pathogens of gastrointestinal diseases are water-borne viruses.
To date, such viruses have been removed from water using nanofiltration or reverse osmosis, but at high cost and severe impact on the environment. For example, nanofilters for viruses are made of petroleum-based raw materials, while reverse osmosis requires a relatively large amount of energy.
Environmentally friendly membrane developed
Now an international team of researchers led by Raffaele Mezzenga, Professor of Food & Soft Materials at ETH Zurich, has developed a new water filter membrane that is both highly effective and environmentally friendly. To manufacture it, the researchers used natural raw materials.
The filter membrane works on the same principle that Mezzenga and his colleagues developed for removing heavy or precious metals from water. They create the membrane using denatured whey proteins that assemble into minute filaments called amyloid fibrils. In this instance, the researchers have combined this fibril scaffold with nanoparticles of iron hydroxide (Fe-O-HO).
Manufacturing the membrane is relatively simple. To produce the fibrils, whey proteins derived from milk processing are added to acid and heated to 90 degrees Celsius. This causes the proteins to extend and attach to each other, forming fibrils. The nanoparticles can be produced in the same reaction vessel as the fibrils: the researchers raise the pH and add iron salt, causing the mixture to “disintegrate” into iron hydroxide nanoparticles, which attach to the amyloid fibrils. For this application, Mezzenga and his colleagues used cellulose to support the membrane.
This combination of amyloid fibrils and iron hydroxide nanoparticles makes the membrane a highly effective and efficient trap for various viruses present in water. The positively charged iron oxide electrostatically attracts the negatively charged viruses and inactivates them. Amyloid fibrils alone wouldn’t be able to do this because, like the viral particles, they are also negatively charged at neutral pH. However, the fibrils are the ideal matrix for the iron oxide nanoparticles.
Various viruses eliminated highly efficiently
The membrane eliminates a wide range of water-borne viruses, including nonenveloped adenoviruses, retroviruses and enteroviruses. This third group can cause dangerous gastrointestinal infections, which kill around half a million people — often young children in developing and emerging countries — every year. Enteroviruses are extremely tough and acid-resistant and remain in the water for a very long time, so the filter membrane should be particularly attractive to poorer countries as a way to help prevent such infections.
Moreover, the membrane also eliminates H1N1 flu viruses and even the new SARS-CoV-2 virus from the water with great efficiency. In filtered samples, the concentration of the two viruses was below the detection limit, which is equivalent to almost complete elimination of these pathogens.
“We are aware that the new coronavirus is predominantly transmitted via droplets and aerosols, but in fact, even on this scale, the virus requires being surrounded by water. The fact that we can remove it very efficiently from water impressively underlines the broad applicability of our membrane,” says Mezzenga.
While the membrane is primarily designed for use in wastewater treatment plants or for drinking water treatment, it could also be used in air filtration systems or even in masks. Since it consists exclusively of ecologically sound materials, it could simply be composted after use — and its production requires minimum energy. These traits give it an excellent environmental footprint, as the researchers also point out in their study. Because the filtration is passive, it requires no additional energy, which makes its operation carbon neutral and of possible use in any social context, from urban to rural communities.
In addition to Mezzenga’s laboratory, scientists from several Swiss universities were involved in the work, including virus specialists from the Universities of Zurich, Lausanne and Geneva, EPFL, the University of Cagliari and the ETH spin-off BluAct, which holds the patent on this new technology.
“Masks, ventilation stop COVID spread better than social distancing, study shows”
A new study from the University of Central Florida suggests that masks and a good ventilation system are more important than social distancing for reducing the airborne spread of COVID-19 in classrooms.
The research, published recently in the journal Physics of Fluids, comes at a critical time when schools and universities are considering returning to more in-person classes in the fall.
“The research is important as it provides guidance on how we are understanding safety in indoor environments,” says Michael Kinzel, an assistant professor in UCF’s Department of Mechanical and Aerospace Engineering and study co-author.
“The study finds that aerosol transmission routes do not display a need for six feet social distancing when masks are mandated,” he says. “These results highlight that with masks, transmission probability does not decrease with increased physical distancing, which emphasizes how mask mandates may be key to increasing capacity in schools and other places.”
In the study, the researchers created a computer model of a classroom with students and a teacher, then modeled airflow and disease transmission, and calculated airborne-driven transmission risk.
The classroom model was 709 square feet with 9-foot-tall ceilings, similar to a smaller-size, university classroom, Kinzel says. The model had masked students — any one of whom could be infected — and a masked teacher at the front of the classroom.
The researchers examined the classroom using two scenarios — a ventilated classroom and an unventilated one — and using two models, Wells-Riley and Computational Fluid Dynamics. Wells-Riley is commonly used to assess indoor transmission probability and Computational Fluid Dynamics is often used to understand the aerodynamics of cars, aircraft and the underwater movement of submarines.
Masks were shown to be beneficial by preventing direct exposure of aerosols, as the masks provide a weak puff of warm air that causes aerosols to move vertically, thus preventing them from reaching adjacent students, Kinzel says.
Additionally, a ventilation system in combination with a good air filter reduced the infection risk by 40 to 50% compared to a classroom with no ventilation. This is because the ventilation system creates a steady current of air flow that circulates many of the aerosols into a filter that removes a portion of the aerosols compared to the no-ventilation scenario where the aerosols congregate above the people in the room.
These results corroborate recent guidelines from the U.S. Centers for Disease Control and Prevention that recommend reducing social distancing in elementary schools from six to three feet when mask use is universal, Kinzel says.
“If we compare infection probabilities when wearing masks, three feet of social distancing did not indicate an increase in infection probability with respect to six feet, which may provide evidence for schools and other businesses to safely operate through the rest of the pandemic,” Kinzel says.
“The results suggest exactly what the CDC is doing, that ventilation systems and mask usage are most important for preventing transmission and that social distancing would be the first thing to relax,” the researcher says.
When comparing the two models, the researchers found that Wells-Riley and Computational Fluid Dynamics generated similar results, especially in the non-ventilated scenario, but that Wells-Riley underpredicted infection probability by about 29 percent in the ventilated scenario.
As a result, they recommend some of the additional complex effects captured in Computational Fluid Dynamics be applied to Wells-Riley to develop a more complete understanding of risk of infection in a space, says Aaron Foster, a doctoral student in UCF’s Department of Mechanical and Aerospace Engineering and the study’s lead author.
“While the detailed Computational Fluid Dynamics results provided new insights into the risk variation and distance relationships, they also validated the more commonly used Wells-Riley models as capturing the majority of the benefit of ventilation with reasonable accuracy,” Foster says. “This is important since these are publicly available tools that anyone can use to reduce risk.”
The research is part of a larger overall effort to control airborne disease transmission and better understand factors related to being a super-spreader. The researchers are also testing the effects of masks on aerosol and droplet transmission distance. The work is funded in part by the National Science Foundation.
Kinzel received his doctorate in aerospace engineering from Pennsylvania State University and joined UCF in 2018. In addition to being a member of UCF’s Department of Mechanical and Aerospace engineering, a part of UCF’s College of Engineering and Computer Science, he also works with UCF’s Center for Advanced Turbomachinery and Energy Research.