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| AI is learning how to create itself Posted: 27 May 2021 02:39 AM PDT dWeb.News Article from Will Douglas Heaven dWeb.News Tech News A little stick figure with a wedge-shaped head shuffles across the screen. It moves in a half crouch, dragging one knee along the ground. It's walking! Er, sort of. Yet Rui Wang is delighted. "Every day I walk into my office and open my computer, and I don't know what to expect," he says. An artificial-intelligence researcher at Uber, Wang likes to leave the Paired Open-Ended Trailblazer, a piece of software he helped develop, running on his laptop overnight. POET is a kind of training dojo for virtual bots. So far, they aren't learning to do much at all. These AI agents are not playing Go, spotting signs of cancer, or folding proteins—they're trying to navigate a crude cartoon landscape of fences and ravines without falling over. But it's not what the bots are learning that's exciting—it's how they're learning. POET generates the obstacle courses, assesses the bots' abilities, and assigns their next challenge, all without human involvement. Step by faltering step, the bots improve via trial and error. "At some point it might jump over a cliff like a kung fu master," says Wang. It may seem basic at the moment, but for Wang and a handful of other researchers, POET hints at a revolutionary new way to create supersmart machines: by getting AI to make itself. Wang's former colleague Jeff Clune is among the biggest boosters of this idea. Clune has been working on it for years, first at the University of Wyoming and then at Uber AI Labs, where he worked with Wang and others. Now dividing his time between the University of British Columbia and OpenAI, he has the backing of one of the world's top artificial-intelligence labs. Clune calls the attempt to build truly intelligent AI the most ambitious scientific quest in human history. Today, seven decades after serious efforts to make AI began, we're still a long way from creating machines that are anywhere near as smart as humans, let alone smarter. Clune thinks POET might point to a shortcut. "We need to take the shackles off and get out of our own way," he says. If Clune is right, using AI to make AI could be an important step on the road that one day leads to artificial general intelligence (AGI)—machines that can outthink humans. In the nearer term, the technique might also help us discover different kinds of intelligence: non-human smarts that can find solutions in unexpected ways and perhaps complement our own intelligence rather than replace it. Mimicking DarwinI first spoke to Clune about the idea early last year, just a few weeks after his move to OpenAI. He was happy to discuss past work but remained tight-lipped on what he was doing with his new team. Instead of taking the call inside, he preferred to walk up and down the streets outside the offices as we talked. All Clune would say was that OpenAI was a good fit. "My idea is very much in line with many of the things that they believe," he says. "It was kind of a marriage made in heaven. They liked the vision and wanted me to come here and pursue it." A few months after Clune joined, OpenAI hired most of his old Uber team as well. Clune's ambitious vision is grounded by more than OpenAI's investment. The history of AI is filled with examples in which human-designed solutions gave way to machine-learned ones. Take computer vision: a decade ago, the big breakthrough in image recognition came when existing hand-crafted systems were replaced by ones that taught themselves from scratch. It's the same for many AI successes. One of the fascinating things about AI, and machine learning in particular, is its ability to find solutions that humans haven't found—to surprise us. An oft-cited example is AlphaGo (and its successor AlphaZero), which beat the best humanity has to offer at the ancient, beguiling game of Go by employing seemingly alien strategies. After hundreds of years of study by human masters, AI found solutions no one had ever thought of. Clune is now working with a team at OpenAI that developed bots that learned to play hide and seek in a virtual environment in 2018. These AIs started off with simple goals and simple tools to achieve them: one pair had to find the other, which could hide behind movable obstacles. Yet when these bots were let loose to learn, they soon found ways to take advantage of their environment in ways the researchers had not foreseen. They exploited glitches in the simulated physics of their virtual world to jump over and even pass through walls. Those kinds of unexpected emergent behaviors offer tantalizing hints that AI might arrive at technical solutions humans would not think of by themselves, inventing new and more efficient types of algorithms or neural networks—or even ditching neural networks, a cornerstone of modern AI, entirely. Clune likes to remind people that intelligence has already emerged from simple beginnings. "What's interesting about this approach is that we know it can work," he says. "The very simple algorithm of Darwinian evolution produced your brain, and your brain is the most intelligent learning algorithm in the universe that we know so far." His point is that if intelligence as we know it resulted from the mindless mutation of genes over countless generations, why not seek to replicate the intelligence-producing process—which is arguably simpler—rather than intelligence itself? But there's another crucial observation here. Intelligence was never an endpoint for evolution, something to aim for. Instead, it emerged in many different forms from countless tiny solutions to challenges that allowed living things to survive and take on future challenges. Intelligence is the current high point in an ongoing and open-ended process. In this sense, evolution is quite different from algorithms the way people typically think of them—as means to an end. It's this open-endedness, glimpsed in the apparently aimless sequence of challenges generated by POET, that Clune and others believe could lead to new kinds of AI. For decades AI researchers have tried to build algorithms to mimic human intelligence, but the real breakthrough may come from building algorithms that try to mimic the open-ended problem solving of evolution—and sitting back to watch what emerges. Researchers are already using machine learning on itself, training it to find solutions to some of the field's hardest problems, such as how to make machines that can learn more than one task at a time or cope with situations they have not encountered before. Some now think that taking this approach and running with it might be the best path to artificial general intelligence. "We could start an algorithm that initially does not have much intelligence inside it, and watch it bootstrap itself all the way up potentially to AGI," Clune says. The truth is that for now, AGI remains a fantasy. But that's largely because nobody knows how to make it. Advances in AI are piecemeal and carried out by humans, with progress typically involving tweaks to existing techniques or algorithms, yielding incremental leaps in performance or accuracy. Clune characterizes these efforts as attempts to discover the building blocks for artificial intelligence without knowing what you're looking for or how many blocks you'll need. And that's just the start. "At some point, we have to take on the Herculean task of putting them all together," he says. Asking AI to find and assemble those building blocks for us is a paradigm shift. It's saying we want to create an intelligent machine, but we don't care what it might look like—just give us whatever works. Even if AGI is never achieved, the self-teaching approach may still change what sorts of AI are created. The world needs more than a very good Go player, says Clune. For him, creating a supersmart machine means building a system that invents its own challenges, solves them, and then invents new ones. POET is a tiny glimpse of this in action. Clune imagines a machine that teaches a bot to walk, then to play hopscotch, then maybe to play Go. "Then maybe it learns math puzzles and starts inventing its own challenges," he says. "The system continuously innovates, and the sky's the limit in terms of where it might go." It's wild speculation, perhaps, but one hope is that machines like this might be able to evade our conceptual dead ends, helping us unpick vastly complex crises such as climate change or global health. But first we have to make one. How to create a brainThere are many different ways to wire up an artificial brain. Neural networks are made from multiple layers of artificial neurons encoded in software. Each neuron can be connected to others in the layers above. The way a neural network is wired makes a big difference, and new architectures often lead to new breakthroughs. The neural networks coded by human scientists are often the result of trial and error. There is little theory to what does and doesn't work, and no guarantee that the best designs have been found. That's why automating the hunt for better neural-network designs has been one of the hottest topics in AI since at least the 1980s. The most common way to automate the process is to let an AI generate many possible network designs, and let the network automatically try each of them and choose the best ones. This is commonly known as neuro-evolution or neural architecture search (NAS). In the last few years, these machine designs have started to outstrip human ones. In 2018, Esteban Real and his colleagues at Google used NAS to generate a neural network that beat the best human-designed networks at the time. That was an eye-opener. The 2018 system is part of an ongoing Google project called AutoML, which has also used NAS to produce EfficientNets, a family of deep-learning models that are more efficient than human-designed ones, achieving high levels of accuracy on image-recognition tasks with smaller, faster models. Three years on, Real is pushing the boundaries of what can be generated from scratch. The earlier systems just rearranged tried and tested neural-network pieces, such as existing types of layers or components. "We could expect a good answer," he says. Last year Real and his team took the training wheels off. The new system, called AutoML Zero, tries to build an AI from the ground up using nothing but the most basic mathematical concepts that govern machine learning. Amazingly, not only did AutoML Zero spontaneously build a neural network, but it came up with the most common mathematical technique that human designers use for the task. "I was quite surprised," says Real. "It's a very simple algorithm—it takes like six lines of code—but it wrote the exact six lines." AutoML Zero is not yet generating architectures that rival the performance of human-designed systems—or indeed doing much that a human designer would not have done. But Real believes it could one day. Time to train a new kind of teacherFirst you make a brain; then you have to teach it. But machine brains don't learn the way ours do. Our brains are fantastic at adapting to new environments and new tasks. Today's AIs can solve challenges under certain conditions but fail when those conditions change even a little. This inflexibility is hampering the quest to create more generalizable AI that can be useful across a wide range of scenarios, which would be a big step toward making them truly intelligent. For Jane Wang, a researcher at DeepMind in London, the best way to make AI more flexible is to get it to learn that trait itself. In other words, she wants to build an AI that not only learns specific tasks but learns to learn those tasks in ways that can be adapted to fresh situations. Researchers have been trying to make AI more adaptable for years. Wang thinks that getting AI to work through this problem for itself avoids some of the trial and error of a hand-designed approach: "We can't possibly expect to stumble upon the right answer right away." In the process, she hopes, we will also learn more about how brains work. "There's still so much we don't understand about the way that humans and animals learn," she says. There are two main approaches to generating learning algorithms automatically, but both take an existing neural network and use AI to improve it. The first approach, invented separately by Wang and her colleagues at DeepMind and by a team at OpenAI at around the same time, uses an idea called recurrent neural networks. This type of network can be trained in such a way that the activations of their neurons—roughly akin to the firing of neurons in biological brains—encode any type of algorithm. DeepMind and OpenAI took advantage of this to train a recurrent neural network to generate reinforcement-learning algorithms, which tell an AI how to behave to achieve given goals. The upshot is that the DeepMind and OpenAI systems do not learn an algorithm that solve a specific challenge, such as recognizing images, but learn a learning algorithm that can be applied to multiple tasks and adapt as it goes. It's like the old adage about teaching someone to fish: whereas a hand-designed algorithm can learn a particular task, these AIs are being made to learn how to learn by themselves. And some of them are performing better than human-designed ones. The second approach comes from Chelsea Finn at the University of California, Berkeley, and her colleagues. Called model-agnostic meta-learning, or MAML, it trains a model using two machine-learning processes, one nested inside the other. Roughly, here's how it works. The inner process in MAML is trained on data and then tested—as usual. But then the outer model takes the performance of the inner model—how well it identifies images, say—and uses it to learn how to adjust that model's learning algorithm to boost performance. It's as if you had an educational scientist watching over a bunch of teachers, each offering different learning techniques. The scientist checks which techniques help the students get the best scores and tweaks them accordingly. Through these approaches, researchers are building AI that is more robust, more generalized, and able to learn faster with less data. For example, Finn wants a robot that has learned to walk on flat ground to be able to transition, with minimal extra training, to walking on a slope or on grass or while carrying a load. Last year, Clune and his colleagues extended Finn's technique to design an algorithm that learns using fewer neurons so that it does not overwrite everything it has learned previously, a big unsolved problem in machine learning known as catastrophic forgetting. A trained model that uses fewer neurons, known as a "sparse" model, will have more unused neurons left over to dedicate to new tasks when retrained, which means that fewer of the "used" neurons will get overwritten. Clune found that setting his AI the challenge of learning more than one task led it to come up with its own version of a sparse model that outperformed human-designed ones. If we're going all in on letting AI create and teach itself, then AIs should generate their own training environments, too—the schools and textbooks, as well as the lesson plans. And the past year has seen a raft of projects in which AI has been trained on automatically generated data. Face-recognition systems are being trained with AI-generated faces, for example. AIs are also learning how to train each other. In one recent example, two robot arms worked together, with one arm learning to set tougher and tougher block-stacking challenges that trained the other to grip and grasp objects. In fact, Clune wonders if human intuition about what kind of data an AI needs in order to learn may be off. For example, he and his colleagues have developed what he calls generative teaching networks, which learn what data they should generate to get the best results when training a model. In one experiment, he used one of these networks to adapt a data set of handwritten numbers that's often used to train image-recognition algorithms. What it came up with looked very different from the original human-curated data set: hundreds of not-quite digits, such as the top half of the numeral seven or what looked like two digits merged together. Some AI-generated examples were hard to decipher at all. Despite this, the AI-generated data still did a great job at training the handwriting recognition system to identify actual digits. Don't try to succeedAI-generated data is still just a part of the puzzle. The long-term vision is to take all these techniques—and others not yet invented—and hand them over to an AI trainer that controls how artificial brains are wired, how they are trained, and what they are trained on. Even Clune is not clear on what such a future system would look like. Sometimes he talks about a kind of hyper-realistic simulated sandbox, where AIs can cut their teeth and skin their virtual knees. Something that complex is still years away. The closest thing yet is POET, the system created with Uber's Rui Wang. POET was motivated by a paradox, says Wang. If you try to solve a problem you'll fail; if you don't try to solve it you're more likely to succeed. This is one of the insights Clune takes from his analogy with evolution—amazing results that emerge from an apparently random process often cannot be re-created by taking deliberate steps toward the same end. There's no doubt that butterflies exist, but rewind to their single-celled precursors and try to create them from scratch by choosing each step from bacterium to bug, and you'd likely fail. POET starts its two-legged agent off in a simple environment, such as a flat path without obstacles. At first the agent doesn't know what to do with its legs and cannot walk. But through trial and error, the reinforcement-learning algorithm controlling it learns how to move along flat ground. POET then generates a new random environment that's different, but not necessarily harder to move in. The agent tries walking there. If there are obstacles in this new environment, the agent learns how to get over or across those. Every time an agent succeeds or gets stuck, it is moved to a new environment. Over time, the agents learn a range of walking and jumping actions that let them navigate harder and harder obstacle courses. The team found that random switching of environments was essential. For example, agents sometimes learned to walk on flat ground with a weird, half-kneeling shuffle, because that was good enough. "They never learn to stand up because they never need to," says Wang. But after they had been forced to learn alternative strategies on obstacle-strewn ground, they could return to the early stage with a better way of walking—using both legs instead of dragging one behind, say—and then take that improved version of itself forward to harder challenges. POET trains its bots in a way that no human would—it takes erratic, unintuitive paths to success. At each stage, the bots try to figure out a solution to whatever challenge they are presented with. By coping with a random selection of obstacles thrown their way, they get better overall. But there is no end point to this process, no ultimate test to pass or high score to beat. Clune, Wang, and a number of their colleagues believe this is a profound insight. They are now exploring what it might mean for the development of supersmart machines. Could trying not to chart a specific path actually be a key breakthrough on the way to artificial general intelligence? POET is already inspiring other researchers, such as Michael Dennis and Natasha Jaques at the University of California, Berkeley. They've developed a system called PAIRED that uses AI to generate a series of mazes to train another AI to navigate them. Rui Wang thinks human-designed challenges are going to be a bottleneck and that real progress in AI will require AI-generated challenges. "No matter how good algorithms are today, they are always tested on some hand-designed benchmark," he says. "It's very hard to imagine artificial general intelligence coming from this, because it is bound by fixed goals." A new kind of intelligenceThe rapid development of AI that can train itself also raises questions about how well we can control its growth. The idea of AI that builds better AI is a crucial part of the myth-making behind the "Singularity," the imagined point in the future when AIs start to improve at an exponential rate and move beyond our control. Eventually, certain doomsayers warn, AI might decide it doesn't need humans at all. That's not what any of these researchers have in mind: their work is very much focused on making today's AI better. Machines that run amok remain a far-off anti-fantasy. Even so, DeepMind's Jane Wang has reservations. A big part of the attraction of using AI to make AI is that it can come up with designs and techniques that people hadn't thought of. Yet Wang notes that not all surprises are good surprises: "Open-endedness is, by definition, something that's unexpected." If the whole idea is to get AI to do something you didn't anticipate, it becomes harder to control. "That's both exciting and scary," she says. Clune also stresses the importance of thinking about the ethics of the new technology from the start. There is a good chance that AI-designed neural networks and algorithms will be even harder to understand than today's already opaque black-box systems. Are AIs generated by algorithms harder to audit for bias? Is it harder to guarantee that they will not behave in undesirable ways? Clune hopes such questions will be asked and answered as more people realize the potential of self-generating AIs. "Most people in the machine-learning community don't ever really talk about our overall path to extremely powerful AI," he says—instead, they tend to focus on small, incremental improvements. Clune wants to start a conversation about the field's biggest ambitions again. His own ambitions tie back into his early interests in human intelligence and how it evolved. His grand vision is to set things up so that machines might one day see their own intelligence—or intelligences—emerge and improve through countless generations of trial and error, guided by algorithms with no ultimate blueprint in mind. If AI starts to generate intelligence by itself, there's no guarantee that it will be human-like. Rather than humans teaching machines to think like humans, machines might teach humans new ways of thinking. "There's probably a vast number of different ways to be very intelligent," says Clune. "One of the things that excite me about AI is that we might come to understand intelligence more generally, by seeing what variation is possible. "I think that's fascinating. I mean, it's almost like inventing interstellar travel and being able to go visit alien cultures. There would be no greater moment in the history of humankind than encountering an alien race and learning about its culture, its science, everything. Interstellar travel is exceedingly difficult, but we have the ability to potentially create alien intelligences digitally." A little stick figure with a wedge-shaped head shuffles across the screen. It moves in a half crouch, dragging one knee along the ground. It's walking! Er, sort of. Yet Rui Wang is delighted. "Every day I walk into my office and open my computer, and I don't know what to expect," he says. An artificial-intelligence researcher…Artificial intelligence, Machine learning The post AI is learning how to create itself appeared first on dWeb.News Tech News from Daniel Webster dWeb Internet Cowboy  This posting includes an audio/video/photo media file: Download Now |
| Enough to Wreck Their Rest: $10,322 for a Sleep Study Posted: 27 May 2021 02:39 AM PDT dWeb.News Article from Michelle Andrews dWeb.News Tech News  José Mendoza's snoring was bad — but the silence when he stopped breathing was even worse for his wife, Nancy. The sudden quiet would wake her and she waited anxiously for him to take another breath. If too many seconds ticked by, she pushed him hard so that he moved and started breathing again. This happened several times a week. This story also ran on NPR. It can be republished for free. Diagnosed with severe sleep apnea 15 years ago, Mendoza was prescribed a continuous positive airway pressure (CPAP) device to help him breathe easier. But the machine was noisy and uncomfortable. After a month, he stopped using it. Late in 2019, Mendoza, 61, went to an emergency department near the family's Miami home with an excruciating headache. He thought it was related to his high blood pressure, a condition sometimes linked to obstructive sleep apnea. But after a battery of tests, clinicians concluded his obstructive sleep apnea itself was likely causing his headache and cardiac problems. He needed a new CPAP machine, they said. But first, he had an at-home sleep test. Mendoza's pulmonologist said it was not detailed enough and ordered a visit to an overnight sleep lab to get extensive data. Mendoza arrived at the sleep center about 8 p.m. one night in early February and was shown into a spacious room with a sofa, a TV and a bed. After he got into his pajamas, a technician attached electrodes to his head and chest to track his brain, heart, lung and muscle activity while he slept. The technician fitted him with a CPAP with two small cannulas for his nose. Despite the unfamiliar setting and awkward equipment, Mendoza slept that night. After the study, Mendoza started using the same, more comfortable CPAP model he'd used during the study. "Now I'm not snoring. I feel more energetic. I'm not as tired as I was before," he said. The new CPAP was helping both Mendozas get a better night's sleep — until the bill came. The Patient: José Mendoza, 61, has a Humana HMO plan through the construction company where he works as a truck driver. It has a $5,000 deductible and an out-of-pocket maximum of $6,500 for covered care by in-network providers. Once his deductible is satisfied, he owes 50% in coinsurance for other billed charges. (Nancy Mendoza, who works as a social worker, and their two teenage children are covered under her employer plan.) Medical Service: An overnight sleep study at a hospital sleep center to determine the type of mask and the proper levels of airflow and oxygen needed in Mendoza's CPAP to treat his severe obstructive sleep apnea. Total Bill: $10,322, including a $9,853 outpatient charge for the sleep study and a $469 charge for the sleep specialist who interpreted the results. Humana's negotiated rate for the total was $5,419. Mendoza owed the bulk of that: $5,157, including $262 in coinsurance and $4,895 to satisfy his deductible. Humana paid $262. Service Provider: University of Miami Health System's sleep medicine facility at Bascom Palmer Eye Institute in Miami. What Gives: Sleep studies are somewhat controversial and have been flagged in the past as being overused. Not everyone who snores needs this evaluation. But with Mendoza's pauses in breathing and hypertension, he likely did. According to Dr. Vikas Saini, president of the Lown Institute, a think tank that analyzes low-value health care, sleep studies fall into a gray zone. "They are incredibly useful and necessary in certain clinical circumstances," he said. "But it's known to be one that can be overused." But how much should it cost to be monitored at home or in a hospital sleep lab? That's the question. The Office of Inspector General at the federal Department of Health and Human Services has identified billing problems for the type of sleep study Mendoza had that led to Medicare overpayments. The University of Miami Health System's total charge was high by nearly every measure, but so was the allowed amount that Humana agreed to pay the health system for the study. And because Mendoza's skimpy health plan has a deductible of $5,000, he's on the hook for paying almost all of that hefty bill. Mendoza's Humana plan agreed to pay the hospital $5,419 for the sleep study he had. That's nearly six times what Medicare would pay for the same service nationally — $920 — according to the Centers for Medicare & Medicaid Services. Private insurers typically pay higher rates than Medicare for care, but that multiple is "much higher than what other insurers would pay," said Jordan Weintraub, vice president of claims at WellRithms, a company that analyzes medical bills for self-funded companies and other clients. Consider the total facility charge of $9,853. The average charge in the United States for a sleep study of the same type is just over half that amount at $5,384, according to Fair Health, a national independent nonprofit that tracks insurance charges. Charges in the Miami area are on the high end of the national range. The average billed charges for similar hospital sleep studies in Miami range from $2,646 to $19,334, Weintraub said. So Mendoza's bill is not as high as the highest in the area, and is just under the average in Miami. "Billed charges are just completely fictitious," said Weintraub. "There's really no grounds for charging it other than that they can." More telling than what other Miami hospitals are charging for sleep studies is what the University of Miami Health System reports it actually costs the hospital to do the procedure. And that figure was just $1,154 on average in 2019, according to WellRithms' analysis of publicly available cost report data filed with CMS. That year, the hospital's average charge for the type of sleep study Mendoza had was $7,886, according to WellRithms. Mendoza doesn't pay premiums for his health plan, but his "free" coverage has a cost. The $5,000 deductible and high coinsurance leaves him woefully exposed financially if he needs medical care, as the family discovered. Nancy Mendoza's plan has a lower deductible of $1,350, but her employer charges extra to cover spouses who have coverage available to them at their own jobs. Obstructive sleep apnea is often undiagnosed, sleep medicine experts agree, and sleep studies can result in a diagnosis that leads to necessary treatment to help prevent serious problems like heart attacks and diabetes. "From that perspective, sleep testing is actually underprescribed," said Dr. Douglas Kirsch, medical director of sleep medicine at Atrium Health in Charlotte, North Carolina, who is past president of the American Academy of Sleep Medicine, a professional group. After strong growth by independent and hospital-affiliated lab-based sleep centers over several years, there's been a shift toward home-based sleep tests recently, said Charlie Whelan, vice president of consulting for health care at Frost & Sullivan, a research and consulting firm. "The entire sleep medicine field is deeply worried about a future where more testing is done at home since it means less money to be made for in-center test providers," Whelan said. Resolution: When the bill arrived, Nancy Mendoza thought it must be a mistake. José's home sleep test hadn't cost them a penny, and no one had mentioned their financial responsibility for the overnight test in the lab. She called the billing office and asked for an itemized bill. There were no complications, no anesthesia, not even a doctor present. Why was it so expensive? But what they received wasn't any more enlightening than the summary bill. She got a clear impression that if they didn't pay they'd be sent to collections. To avoid ruining their credit, they agreed to a two-year payment plan and got their first installment bill, for $214.87, in April. Nancy thinks the overall charge is too high: "It's not fair [for] people who are in the low end of the middle class." Lisa Worley, associate vice president for media relations at the University of Miami Health System, said in a statement that Mendoza "does not qualify for financial assistance because he has health insurance." Related Links But the health system's posted financial assistance policy clearly states that financial assistance is available to "underinsured individuals with a balance remaining after third party liability of $1000 or more, whose family income for the preceding 12 months is equal to or less than 300%" of the federal poverty guidelines. Under a less detailed version of the hospital policy included in one of their bills, the Mendozas meet the income threshold for "assistance provided on a sliding scale." In her statement, Worley referred to Mendoza's sleep test as an "elective service." The health system website says it "provides financial assistance for emergency and other medically necessary (non-elective) care." Mendoza's sleep study was medically necessary. The emergency department staff evaluated him and determined he needed a new CPAP to deal with serious medical problems caused by his obstructive sleep apnea. His pulmonologist concurred, as did his insurer, which preauthorized the sleep study. In a statement, Humana wrote: "With sleep studies, there can be a wide range of costs, depending on the complexity of the case and the setting." The insurer refused to comment on Mendoza's case specifically, even though the Mendozas had given permission to discuss it. The Takeaway: The Mendozas followed the rules: They used an in-network provider and got prior authorization from their insurance company for the test. Unfortunately, they are caught between two financial traps of the U.S. health care system: high-deductible health plans, which are increasingly common, and sky-high billing. With a high-deductible plan, it's crucial to try to learn what you'll owe before receiving nonemergency medical care. Ask for an estimate in writing; if you can't get one, try to shop for a different provider who will give you an estimate. Be aware that insurance plans that have zero or low premium costs may not be your best option for coverage. Once you are stuck with a high bill that hits a high deductible, remember you can still negotiate with the hospital. Find out what a more reasonable charge would be and ask for your bill to be adjusted. Also inquire about payment assistance from the hospital — most hospitals must offer this option by law (though they often do not make it easy to apply for it). If a doctor suggests a sleep study, ask if you can do one at home, and whether it's really needed. And remember: Not every snore is sleep apnea. Dan Weissmann, host of An Arm and a Leg podcast, contributed to the audio version of this story. Bill of the Month is a crowdsourced investigation by KHN and NPR that dissects and explains medical bills. Do you have an interesting medical bill you want to share with us? Tell us about it! KHN (Kaiser Health News) is a national newsroom that produces in-depth journalism about health issues. Together with Policy Analysis and Polling, KHN is one of the three major operating programs at KFF (Kaiser Family Foundation). KFF is an endowed nonprofit organization providing information on health issues to the nation. USE OUR CONTENTThis story can be republished for free (details). The University of Miami Health System charges a truck driver six times what Medicare would pay for an overnight test. The post Enough to Wreck Their Rest: $10,322 for a Sleep Study appeared first on dWeb.News Tech News from Daniel Webster dWeb Internet Cowboy  This posting includes an audio/video/photo media file: Download Now |
| Confronting Our ‘Frailties’: California’s Assembly Leader Reflects on a Year of Covid Posted: 27 May 2021 02:39 AM PDT dWeb.News Article from Samantha Young dWeb.News Tech News  SACRAMENTO, Calif. — When his 20-month-old daughter developed a rash earlier this month, Anthony Rendon did what many other parents do when their child is sick: The speaker of the California Assembly took Vienna to her pediatrician — but he did so via video from the comfort and safety of his home. This story also ran on Chico Enterprise-Record. It can be republished for free. Many Californians have relied on telehealth to connect with their health care providers during the covid-19 pandemic, but the option isn't available to everyone. That imbalance is just one of the "frailties" in America's health system that Rendon says lawmakers must address. "So many folks, when they lose their job, they're in trouble," he said. A Democrat from Los Angeles County and grandson of Mexican immigrants, Rendon led a nonprofit organization dedicated to early childhood education before his election to the Assembly in 2012. Although he hasn't authored any sweeping bills on health care, as leader of the Assembly since 2016 he has influenced which measures get a vote — and which don't. For instance, though he says he's a single-payer advocate, he angered many progressives four years ago when he blocked a bill that would have provided government-funded health care to all Californians. Rendon described the measure, approved by the state Senate, as "woefully incomplete." While that decision drew the ire of the powerful California Nurses Association union — its leader tweeted an illustration of California's iconic grizzly bear logo with a knife in its back inscribed with Rendon's name — some Capitol insiders say Rendon made the strategic decision to take the hit for his members on a politically charged issue that didn't have the votes to pass. "It's never leadership acting alone," said David Panush, a health care policy consultant who worked in state government for 35 years. "They do it on behalf of their caucuses." Rendon won his post as California's 70th Assembly speaker in part by pledging to allow his colleagues to set their own agendas in their policy committees. Under his leadership, the legislature has approved measures to expand Medicaid coverage to undocumented immigrants ages 19 to 26, protect patients from some surprise medical bills, ban the sale of flavored tobacco products, and require drug companies to report and explain drug price increases. But lawmakers rejected bills that would have taxed sugary drinks and given the state attorney general more authority over hospital consolidations. After missing nine weeks of work last year when covid shuttered the Capitol, lawmakers returned to plastic barriers on their desks, mask requirements and other safety measures. In December, Rendon's colleagues elected him to a third term as speaker. He talked with KHN's Samantha Young about his leadership role during the pandemic and his legislative priorities for the rest of this year. Q: What did you learn leading this legislative body through a pandemic as a lawmaker, a husband and a dad? First of all, we're all very fragile and we're all very resilient. It doesn't take much for our various systems to be upset and to change course. At the same time, we adjust, whether it's as a society, as a state, as an institution. In the Assembly, for example, we've almost learned how to do our business in a completely different manner, in the same way that Californians up and down the state have learned to navigate their lives in a different way. Q: How have you juggled home and work life? On the one hand, weekends are great. A lot of district events don't happen, my wife can work on her dissertation full time, and I get to take care of the baby from sunup until around dinnertime. Having worked in early childhood education for 20 years, I realize how important the first couple years are. I've spent way more time with her than I thought I would. At the same time, there's been challenges finding safe child care. Q: What weaknesses did the pandemic expose in the health care system, and what can the legislature do about it? Telehealth is great and can be very helpful but has its limitations. The pandemic really exposed the need for effective broadband throughout the state and broadband equity as well. We used to regard lack of broadband access as a rural issue. Once we sent schoolkids home, we realized there were more pervasive broadband problems. So, there's absolutely a need to do something big around broadband this year, and that's because of education and also because of health care. Q: You say you're a single-payer advocate, but under your leadership, California's coverage gains have been piecemeal. Why not just go for it and pass single-payer for everyone? Mostly because of the challenges. First of all, we would need a federal waiver. The Biden administration has already hinted that they won't do so. The president has said time and time again that he wants Obamacare to be expanded. And there's the huge price tag. There are very, very serious constitutional problems relating to the development and implementation of single-payer. Q: So, who should get coverage next? Senior undocumented immigrants are the next big group left. It's a population that obviously has tremendous challenges with respect to access and language. They tend to have a lot of preexisting conditions, a lot of other health challenges as well. So, it's important that we make sure that we cover those folks. Q: Is there anything you would have done differently, looking back on the past year? I wish we could have come up with some of the ideas for social distancing and bringing the legislature back more quickly. I think there was a sense early on in March and April [of last year] that the pandemic would run its course more quickly than it did. I remember people saying, "We'll be back in two weeks, we'll be back by midsummer, the pandemic will be gone." So, in terms of developing a lot of those plans, they came to us a little later than I wish they had. Q: How do you think vaccine distribution is going now that supply is exceeding demand? I received a phone call from a neighboring district, the president of a community college, who called me up saying, "We have all these vaccines and people have stopped showing up." We've reached this sort of plateau that's disappointing. We haven't reached this plateau because 90% of people have been vaccinated. It links directly to public health, education and information campaigns. We have to talk about the safety of the vaccine and have validators also talk about the need to get to herd immunity. Q: Along those lines, local public health departments feel that they have been underfunded for years and that they haven't had the money to do the job in this pandemic. Do you support their request for additional state funding? We need to make sure that they're adequately funded. There was a problem with respect to the pandemic. We honestly weren't ready for it. As far as these health efforts are concerned, they have to happen at the local level. The conversation has to go hand in hand with accountability measures and accountability metrics. We're not going to give folks a blank check. We know that there are vast differences in practices that a lot of the public health agencies throughout the state want to pursue, and we want to make sure that best practices are really implemented. Q: How do you negotiate with influential industries, such as hospitals, pharmaceutical companies and big labor, to get meaningful legislation passed that goes against their interests? When people boil it down to a simple question of who gives the most money, that's overly simplistic. Look at the incredible amount of work we've done here in California with respect to oil. The enviros do not give as much money to politicians as the oil companies do. But with respect to having these conversations, we take all of their input, and then the decisions, for me, are informed by what's best for the state. This story was produced by KHN, which publishes California Healthline, an editorially independent service of the California Health Care Foundation. KHN (Kaiser Health News) is a national newsroom that produces in-depth journalism about health issues. Together with Policy Analysis and Polling, KHN is one of the three major operating programs at KFF (Kaiser Family Foundation). KFF is an endowed nonprofit organization providing information on health issues to the nation. USE OUR CONTENTThis story can be republished for free (details). California Assembly Speaker Anthony Rendon says covid exposed long-standing health care inequities that must be addressed. He told KHN he wants to get more people insured, boost broadband access so more patients can use telehealth and increase funding to local health departments. The post Confronting Our 'Frailties': California's Assembly Leader Reflects on a Year of Covid appeared first on dWeb.News Tech News from Daniel Webster dWeb Internet Cowboy  |
| Aircraft Lavatory System Market Revenue to Cross USD 390 Mn by 2027: Global Market Insights Inc. Posted: 27 May 2021 02:39 AM PDT dWeb.News Article from dWeb.News Tech News  The aircraft lavatory system market for regional jets segment is poised to expand at more than 7% through 2027, led by increased regional air traffic Aircraft Lavatory System Market Growth Predicted at 7.1% Through 2027: GMI Major aircraft lavatory system market players include Jamco, B/E Aerospace CC, Diehl Aerosystems, Yokohama Aerospace, Circor Aerospace and Defence, Asia Spirit, The Nordam Group, Sanitrade, HIFraser PTY Limited and Aeroaid. SELBYVILLE, Del. – May 27, 2021 – (Newswire.com) According to the latest report “Aircraft Lavatory System Market by Aircraft (Commercial [Narrow Body, Wide Body], Regional Aircraft, Business Jets), End-user (Aircraft OEM, Aircraft MRO), Toilet Technology (Vacuum Technology, Recirculating Toilet Systems), Regional Outlook, Price Trends, Competitive Market Share & Forecast 2027” by Global Market Insights Inc., the market valuation of aircraft lavatory systems will cross $390 million by 2027. Availability of low-cost travel options along with emergence in new markets will prosper strong product demand for affordable aircraft. Expansion of regional air connectivity and the emergence of new air routes will propel the aircraft lavatory system market growth. For instance, in February 2019, Japan Airlines launched new flights between Tokyo Haneda (HND) and Manila utilizing Boeing 737-800 to further expand its regional air connectivity. Moreover, rising disposable income along with the introduction of new business and regional aircraft will lead to higher deliveries of private jets, thereby driving growth for aircraft lavatory systems during the forecast period. Request sample of this research report @ https://www.gminsights.com/request-sample/detail/3044. The regional jets segment in the aircraft lavatory system market is estimated to register a growth rate of around 7% by 2027, owing to the increased regional air traffic. The establishment of new domestic routes and old aircraft replacements will be major drivers for the market expansion of regional jets. For instance, in June 2018, Delta Air Lines from Atlanta declared its plan to buy 20 CRJ900 regional jets from Bombardier Inc. for the replacement of its older jets. The new jets will help the company to attract passengers with featured atmospheric cabins including larger lavatory, increased baggage capacity and improved aesthetics, thereby stimulating the industry revenue. OEMs segment will hold 70% of the aircraft lavatory system market share by 2027. Multi-year contracts or partnerships with the system manufacturers and suppliers for the stable supply of products will fuel product demand across OEM end-users. Additionally, the incorporation of several collaborative research & development procedures with the manufacturers for the customized lavatory system solutions to improve overall passenger experience and hygiene during travel will positively influence product penetration in the market. Further, increasing production of aircraft across the globe attributed to the growing demand of airliners and backlog order completion will induce significant growth potential in the industry over the forecast timeframe. North American aircraft lavatory system market is predicted to expand at a CAGR of over 7% through 2027. Increasing aircraft production and delivery in conjunction with rising economic conditions across the region will positively enhance air travel, which will thereby impel lavatory systems to demand across the industry over the forecast timeframe. For instance, in March 2021, United Airlines Holdings Inc. announced the buying of 25 new Boeing 737 MAX jets to bump up its position in replacement of the older fleet for a travel rebound. In addition, growing disposable incomes and regional air connectivity will also provide potential air travel opportunities for the middle-class population. Further, the presence of several manufacturers and airline operators across the region will catalyze regional growth over the coming years. Request customization of this research report @ https://www.gminsights.com/roc/3044. Key players functioning in the aircraft lavatory system market are Jamco, B/E Aerospace CC, Diehl Aerosystems, Yokohama Aerospace, Circor Aerospace and Defence, Asia Spirit, The Nordam Group, Sanitrade, HIFraser PTY Limited, and Aeroaid, with many others. Key strategies implemented by major manufacturers to strengthen their competitive edge in the market include product innovation, technological advancement, mergers & acquisitions and joint ventures. Table of Contents (ToC) of the report: Chapter 3 Aircraft Lavatory System Market Insights 3.1 Industry segmentation 3.2 Industry ecosystem analysis 3.2.1 Raw material analysis 3.2.2 Value addition at each stage 3.2.3 Distributor channel analysis 3.2.4 COVID-19 impact on distribution channel 3.2.5 Vendor matrix 3.2.5.1 List of key raw material suppliers 3.2.5.2 List of key product manufacturers/suppliers 3.3 Industry megatrends 3.4 Innovation & sustainability 3.4.1 Patent landscape 3.4.2 Technology landscape 3.4.3 Future trends 3.5 Impact of COVID-19 on aircraft industry 3.6 Industry impact forces 3.6.1 Growth drivers 3.6.2 Industry pitfalls & challenges 3.7 Regulatory landscape 3.8 Growth potential analysis 3.9 Competitive landscape, 2020 3.9.1 Company market share analysis, 2020 3.9.2 Brand analysis 3.9.3 Key stakeholders 3.9.4 Strategy dashboard 3.10 Porter’s analysis 3.11 Regional price trends (including COVID-19 impact) 3.11.1 Cost structure analysis 3.11.2 Price by aircraft 3.12 PESTEL analysis Browse Complete Table of Contents (ToC) @ https://www.gminsights.com/toc/detail/aircraft-lavatory-systems-market About Global Market Insights Inc. Global Market Insights Inc., headquartered in Delaware, U.S., is a global market research and consulting service provider offering syndicated and custom research reports, along with growth consulting services. Our business intelligence and industry research reports offer clients penetrative insights and actionable market data specially designed and presented to aid strategic decision-making. These exhaustive reports are designed via a proprietary research methodology and are available for key industries such as chemicals, advanced materials, technology, renewable energy, and biotechnology. Contact Us: Press Release Service Original Source: More Business and Technology News at https://dWeb.News The post Aircraft Lavatory System Market Revenue to Cross USD 390 Mn by 2027: Global Market Insights Inc. appeared first on dWeb.News Tech News from Daniel Webster dWeb Internet Cowboy  |
| Avamere at Mountain Ridge Director Joins UALA Board Posted: 27 May 2021 02:39 AM PDT dWeb.News Article from dWeb.News Tech News  Avamere at Mountain Ridge Director Joins UALA Board Avamere at Mountain Ridge, an assisted-living and memory-care community in South Ogden, Utah, is pleased to announce their Executive Director, Johnny DelVecchio, has been appointed to the Utah Assisted Living Association (UALA) Board. SOUTH OGDEN, Utah – May 27, 2021 – (Newswire.com) Avamere at Mountain Ridge, an assisted-living and memory-care community in South Ogden, Utah, is pleased to announce their Executive Director, Johnny DelVecchio, has been appointed to the Utah Assisted Living Association (UALA) Board. UALA’s mission statement and vision are to promote the concept of assisted living throughout the state of Utah, to increase knowledge and improve skills of members, and to assist regulatory bodies in defining standards as they apply to the management of senior housing. Thus, it’s an important role for DelVecchio — to have a voice and be a liaison between the state of Utah and assisted-living communities, including Avamere at Mountain Ridge. This is DelVecchio’s first time serving on the UALA board. Membership is based on nominations from peers in the assisted-living and healthcare industry. The UALA board reviews the nominations and makes recommendations for the best person to fill the open seats. DelVecchio was honored to be nominated by his peers and appointed to the board. He notes, “I feel honored to be nominated for this position and look forward to networking with other assisted-living communities. Here at Avamere at Mountain Ridge, we strive to be an integral part of the industry and the team to serve residents. I look forward to having a voice in matters relating to our seniors and help to make their lives better.” Everything Avamere at Mountain Ridge does centers around their residents — from activities to amenities to the care they provide. It’s all a part of their mission to enhance the life of every person they serve. Get to know about this vibrant community on Facebook. About the Avamere Family of Companies Founded in 1995 and based in Wilsonville, Oregon, the Avamere Family of Companies provides assisted living, independent living, memory care, rehabilitation, home care, transitional care, and more. Today, Avamere’s diverse companies operate in 300 locations across 20 states with over 8,000 employees. Avamere is proud to be considered one of Oregon’s most admired companies as they continue to serve their valued patients and residents. For more information, please visit avamere.com. Avamere Family of Companies 25115 SW Parkway Ave, Suite B Wilsonville, OR 97070 For press inquiries, please email press@avamere.com. ### Press Release Service Original Source: More Business and Technology News at https://dWeb.News The post Avamere at Mountain Ridge Director Joins UALA Board appeared first on dWeb.News Tech News from Daniel Webster dWeb Internet Cowboy  |
| ‘Know My Worth’ by Real OG Ice Man Indulges in A Fresh Hip Hop Arrangement Posted: 27 May 2021 01:40 AM PDT dWeb.News Article from dWeb.News Tech News  Los Angeles rapper Real OG Ice Man changes the rap scene forever with his notable potential as the artist performs unafraid in 'Know My Worth' to cement his position. Los Angeles, California May 26, 2021 (Issuewire.com) – In a world that is quickly moving towards comfort and luxury, not everyone is gifted with the same elements. Some people struggle from the beginning only to come out as the diamond from a coal mine. One such blessed talent is Real OG Ice Man. The Los Angeles rapper Real OG Ice Man has paved his path towards success and is marching towards it with utmost dedication. He grew differently and has hustled to get his music out in the world. His music is raw, straightforward, and paints a vivid picture of the artist’s most outstanding musical qualities. On his latest soundtrack ‘Know My Worth‘, the artist has aimed to share his life experiences with utmost honesty and has let his inner wordsmith do the magic. The verses are appropriate and hit hard for their relatability. The only thing that kept the artist going amidst all the turbulence is his sheer passion for the craft. The song focuses on his lyrical ability as he raps consistently to share the instances. The rich impactful verses rely on faint beats to create an impact. The artist’s journey is motivating and his powerful vocals grasp the attention at once. A strong way to draw in the listener’s attention, the rapper refuses to disappoint at any hook and unapologetically speaks his truth. Hailing from Los Angeles, Real OG Ice Man believes his music would be a source of inspiration for many others striving to make it big. His lyrical punctuality and exquisite taste in hip-hop have already started creating ripples in the hip-hop landscape. The genre of hip-hop is saturated with repetitive styles and sounds, and the rapper has emerged to introduce fresh new vibes for the audience to engage. Built under the label Cold World Productions, the song ‘Know My Worth’ magnifies his amazing skill set and is sure to open up varied opportunities for him in the coming days. Hear him on SoundCloud and YouTube and follow him on Instagram for more updates. Listen to the song ‘Know My Worth’, just visit the given below link: https://soundcloud.com/real-og-ice-man-vevo/ice-man-know-my-worth Source :Real OG Ice Man This article was originally published by IssueWire. Read the original article here. Entertainment, Music The post ‘Know My Worth’ by Real OG Ice Man Indulges in A Fresh Hip Hop Arrangement appeared first on dWeb.News Tech News from Daniel Webster dWeb Internet Cowboy  |
| Industrial Air Compressor Market to Hit $17.8 Bn by 2027: Global Market Insights, Inc. Posted: 27 May 2021 01:40 AM PDT dWeb.News Article from dWeb.News Tech News  Some of the major industrial air compressor market participants are Doosan Portable Power, Rolair Systems, Atlas Copco, Gardner Denver, Sullair, Bauer Compressors, Kaeser Kompressoren, Ingersoll-Rand, Ciasons Industrial, Aerzener Maschinenfabrik, Elgi Compressors, Hitachi, Heyner, Sullivan-Palatek, Emerson Climate Technologies, Frank Technologies, Mat Holdings, and Quincy Compressor Industrial Air Compressor Market demand to hit $17.8 bn by 2027 Industrial Air Compressor Market size is forecast to exceed USD 17.8 billion by 2027, according to a new research report by Global Market Insights Inc. SELBYVILLE, Del. – May 27, 2021 – (Newswire.com) Global Market Insights Inc. has recently added a new report on the industrial air compressor market which estimates the market valuation for industrial air compressor will cross US$17.8 billion by 2027. The rising demand for vehicles, growing oil exploration & mining activities, and development in the power & energy generation sector will boost the industry demand. Industrial air compressors are significantly utilized in most of the heavyweight applications to power pneumatic tools & equipment for delivering compressed air and for cleaning purposes. They are incorporated in almost all manufacturing activities around the globe. The global air compressor market will be driven by the rising production of automobiles and electronics in Asia Pacific. The industry growth will also be augmented by developments in the U.S. oil & gas sector. Request a sample of this research report @ https://www.gminsights.com/request-sample/detail/4474. The rapid industrial development in emerging countries of Asia Pacific and Latin America will also boost the product demand during the forecast timeframe. The increasing construction and infrastructure development activities in China, Brazil, India, and other developing countries will further contribute toward industrial compressor market growth within the future. The government agencies of various countries have formulated regulations regarding the level of noise emitted from air compressors. This can be a limiting factor in industrial air compressor market growth by 2027. The portable compressor will account for over 20% of the overall industry by 2027. Increasing product demand on account of its high operational efficiency will complement growth. The 16-75kW industrial air compressor segment held a market share of over 25% in 2020 and will grow at a CAGR over 6% by 2027. Based on application, the industrial compressor market is segregated into food & beverage, oil & gas, semiconductor & electronics, manufacturing, pharma, mining & energy, municipal & construction, agriculture, and a few other applications. The pharma application segment is expected to grow with a CAGR over 5.5%. The players competing in the industrial air compressor market include Doosan Portable Power, Rolair Systems, Atlas Copco, Gardner Denver, Sullair, Bauer Compressors, Kaeser Kompressoren, Ingersoll-Rand, Ciasons Industrial, Aerzener Maschinenfabrik, Elgi Compressors, Hitachi, Heyner, Sullivan-Palatek, Emerson Climate Technologies, Frank Technologies, Mat Holdings, and Quincy Compressor. Some of the major findings in the industrial air compressor market report include: The stationary industrial air compressor demand will be augmented by rapid industrial development in the Asia Pacific region owing to the extensive usage of compressed air in almost all manufacturing activities. Request customization of this research report at https://www.gminsights.com/roc/4474. Partial chapters of report table of contents (TOC): Chapter 3 Industrial Air Compressor Industry Insights 3.1 Industry segmentation 3.2 Industry landscape, 2017 – 2027 3.2.1 Covid-19 impact on industry landscape 3.3 Industry ecosystem analysis 3.3.1 Vendor matrix 3.3.1.1 List of key raw material/component manufacturer/suppliers 3.3.1.2 List of key product manufacturer/suppliers 3.3.1.3 List of key service providers 3.3.2 Distributor channel analysis 3.3.3 Value chain disruption analysis (COVID-19 impact) 3.3.4 Profit margin analysis 3.3.5 Value addition at each stage 3.4 Industry impact forces 3.4.1 Growth drivers 3.4.1.1 Rapid industrial growth in Asia Pacific 3.4.1.2 Technological developments in the field of air compressors 3.4.1.3 Mature manufacturing industry in the U.S. 3.4.2 Industry pitfalls & challenges 3.4.2.1 Strict regulations regarding the air compressor usage 3.4.2.2 Popularity of air compressor rental services 3.4.2.3 Strict emission norms in the U.S. 3.5 Growth potential analysis 3.6 Innovation & sustainability 3.6.1 Patent analysis 3.6.2 Technology landscape 3.6.3 Production process 3.6.4 Comparison of different equipment 3.6.5 Future trends 3.7 Regulatory trends 3.7.1 U.S. 3.7.2 Europe 3.7.3 China 3.8 Porter’s analysis 3.8.1 Global 3.8.2 Supplier power 3.8.3 Buyer power 3.8.4 Threat of new entrants 3.8.5 Industry rivalry 3.8.6 Threat of substitutes 3.9 Competitive landscape 3.9.1 Company market share analysis, 2020 3.9.1.1 Global 3.9.1.2 North America 3.9.2 Strategy dashboard 3.10 PESTEL analysis 3.11 Regional price trends 3.12 Cost structure analysis 3.12.1 R & D cost 3.12.2 Manufacturing & equipment cost 3.12.3 Raw material cost 3.12.4 Distribution cost 3.12.5 Operating cost 3.12.6 Miscellaneous cost 3.12.7 Price by lubrication 3.12.8 COVID-19 effect on pricing 3.13 COVID-19 impact on industrial air compressor demand, by application 3.13.1 Food & beverages 3.13.2 Oil and gas 3.13.3 Energy & mining 3.13.4 Semiconductor & electronics 3.13.5 Manufacturing 3.13.6 Construction & municipal 3.13.7 Pharmaceutical industry 3.13.8 Agriculture/farming 3.13.9 Others About Global Market Insights Global Market Insights, Inc., headquartered in Delaware, U.S., is a global market research and consulting service provider offering syndicated and custom research reports, along with growth consulting services. Our business intelligence and industry research reports offer clients with penetrative insights and actionable market data specially designed and presented to aid strategic decision making. These exhaustive reports are designed via a proprietary research methodology and are available for key industries such as chemicals, advanced materials, technology, renewable energy and biotechnology. Contact Us: Press Release Service Original Source: More Business and Technology News at https://dWeb.News The post Industrial Air Compressor Market to Hit $17.8 Bn by 2027: Global Market Insights, Inc. appeared first on dWeb.News Tech News from Daniel Webster dWeb Internet Cowboy  |
| A Willing Hand to Help in Fight Against COVID-19 Pandemic Posted: 27 May 2021 12:40 AM PDT dWeb.News Article from dWeb.News Tech News Nirmal provides 22 Nos of Oxygen Regulators to L&T's Oxygen Generating Units being manufactured for supply to hospitals in India. [PR.com] The post A Willing Hand to Help in Fight Against COVID-19 Pandemic appeared first on dWeb.News Tech News from Daniel Webster dWeb Internet Cowboy  |
| Save BIG on Your Next Saddle at HorseSaddleShop.com Posted: 27 May 2021 12:32 AM PDT dWeb.News Article from Daniel Webster dWeb.News Tech News Save BIG on Your Next Saddle at HorseSaddleShop.com
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