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KU Engineering professor wins $100K award to research wastewater intensification – The University of Kansas

LAWRENCE A prestigious award from the Water Research Foundation will provide the opportunity for a University of Kansas School of Engineering professor to research a breakthrough approach to improving water quality.

Belinda Sturm, professor of civil, environmental & architectural engineering, is the winner of the 2022 Paul L. Busch Award. With this $100,000 research prize, Sturm will assess how the physical, chemical and biological properties of aerobic granular sludge impact the removal of pathogens and microplastics from wastewater.

Sturms research could allow municipal wastewater treatment plants to double their capacity without the need for expanding or adding new treatment equipment.

The greatest achievement in water quality research is obtained when knowledge is put into practice to create a safer environment, Sturm said. This award will enable me to explore a new research application in collaboration with utility partners.

Wastewater from residences, businesses and other properties carries materials such as carbon, nutrients, pathogens and microplastics to water resource recovery facilities (WRRFs). While WRRFs have processes in place to remove contaminants, there is a need for more research into increasing capacity, ensuring efficiency and understanding the broader applications of existing treatment technologies.

Partnering with the city of Lawrence as well as Metro Water Recovery in Denver, Sturm will assess the removal of pathogens from wastewater due to grazing by the protozoa in biofilms, as well as the sorption of microplastics onto aerobic granular sludge granules.

This research will explore the fundamental properties of AGS while demonstrating full-scale and practical improvements for water quality, Sturm said. This research has the potential to significantly enhance wastewater treatment and further the science related to biofilms.

In addition to her work at KU, Sturm serves as director of the Kansas National Science Foundations Established Program to Stimulate Competitive Research (NSF-EPSCoR) and chair of the Water Environment Federations Municipal Design Symposium.

For 22 years, the WRF Endowment for Innovation in Applied Water Quality Research has supported the Paul L. Busch Award, providing more than $2 million in funding to researchers who are making major breakthroughs in water quality science.More information about the Paul L. Busch Award can be found on WRFs website.

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UCLA Engineers Design AI Material That Learns Behaviors and Adapts to Changing Conditions – UCLA Samueli School of Engineering Newsroom

Just like a pianist who learns to play their instrument without looking at the keys or a basketball player who puts in countless hours to throw a seemingly effortless jump shot, UCLA mechanical engineers have designed a new class of material that can learn behaviors over time and develop a muscle memory of its own, allowing for real-time adaptation to changing external forces.

The material is composed of a structural system made up of tunable beams that can alter its shape and behaviors in response to dynamic conditions. The research finding, which boasts applications in the construction of buildings, airplanes and imaging technologies among others, was published Wednesday in Science Robotics.

This research introduces and demonstrates an artificial intelligent material that can learn to exhibit the desired behaviors and properties upon increased exposure to ambient conditions, said mechanical and aerospace engineering professor Jonathan Hopkins of the UCLA Samueli School of Engineering who led the research. The same foundational principles that are used in machine learning are used to give this material its smart and adaptive properties.

When the material is placed in aircraft wings, for example, it could learn to morph the shape of the wings based on the wind patterns during a flight to achieve greater efficiency and maneuverability of the plane. Building structures infused with this material could also self-adjust the rigidity in certain areas to improve their overall stability during an earthquake or other natural or manmade disasters.

Utilizing and adapting concepts from existing artificial neural networks (ANNs), which are the algorithms that drive machine learning, the researchers developed the mechanical equivalents of ANN components in an interconnected system. The mechanical neural network (MNN), as the team called it, consists of individually tunable beams oriented in a triangular lattice pattern. Each beam features a voice coil, strain gauges and flexures that enable the beam to change its length, adapt to its changing environment in real time and interact with other beams in the system.

The voice coil, which gets its name from its original use in speakers to convert magnetic fields into mechanical motion, initiates the fine-tuned compression or expansion in response to new forces placed on the beam. The strain gauge is responsible for collecting data from the beams motion used in the algorithm to control the learning behavior. The flexures essentially act as flexible joints among the moveable beams to connect the system.

Video showing a mechanical neural network adjusting its rigidity in response to appliedforces. Credit: Flexible Research Group at UCLA

An optimization algorithm then regulates the entire system by taking the data from each of the strain gauges and determining a combination of rigidity values to control how the network should adapt to applied forces.

In order to check the validity of the strain gauge-monitored system, the research team also used cameras trained on the output nodes of the system.

Early prototypes of the system exhibited a lag between the input of the applied force and the output of the MNN response, which affected the systems overall performance. The team tested multiple iterations of the strain gauges and flexures in the beams as well as different lattice patterns and thicknesses before achieving their published design that managed to overcome the lag and accurately distribute the applied force in all directions.

Identifying the reasons why [the networks] failed to learn is important for understanding how to design MNNs that successfully learn, the researchers shared how they solved the problem through trial and error over the past five years.

Currently, the system is about the size of a microwave oven, but the researchers plan to simplify the MNN design so that thousands of the networks can be manufactured on the micro scale within 3D lattices for practical material applications. Aside from using the material in vehicles and construction materials, the researchers suggest MNNs could also be incorporated into an armor to deflect shockwaves, or in acoustic imaging technologies to harness soundwaves.

The papers lead author Ryan Lee is a mechanical and aerospace engineering doctoral student and member of Hopkins Flexible Research Group at UCLA. Erwin Mulder of the University of Twente in Enschede, Netherlands, also worked on the research, which was supported by grants from the Air Force Office of Scientific Research.

Natalie Weber contributed to this story.

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Platform Engineering: What Is It and Who Does It? – thenewstack.io

You may have heard about platform engineering, a new term adding itself to the already crowded set of roles in the development and DevOps world.

In this post, well cover platform engineering, how it differs from DevOps, why you might consider investing into platform engineering, and who needs platform engineering skills.

Platform engineering is the process of designing, building, and maintaining workflows and tools for software engineering organizations to drive consistency and speed up common tasks.

In addition, many platform engineers maintain an integrated software product called an internal developer portal, which consolidates data and knowledge fragmented across clouds, tools and teams so everyone in the engineering organization can discover essential information about their applications, services and infrastructure in one self-serve place.

Platform engineering is a new field that has arisen to meet the growing complexity of distributed software development. As software development has started involving more tools, clouds and workflows, its become increasingly difficult for software developers who are now asked to manage more different aspects that go into writing robust, secure, performant and capable applications.

Platform engineering arose to help software developers manage the complexity of developing their applications. While DevOps aspires to automate deployment and the end-to-end running of applications, in practice, this is only achievable by the largest and most talented organizations.

In reality, when organizations remove operations and implement DevOps, specific DevOps antipatterns appear. We can see some of these antipatterns in reviewing various State of DevOps reports. For example, one study shows a clustering effect where the top-performing organizations have succeeded at a you build it, you run it approach. However, the other cluster is where this pattern fails, and these organizations suffer from the following antipattern.

Platform engineering is a new field that has arisen to meet the growing complexity of distributed software development.

Senior developers often take on the role of a shadow ops organization, so the best development resources, the ones that cost the most and are better used for increasing speed and quality within the development team are unable to do the job the company hired them to do because theyre now spending their time covering live operations and other operations-related tasks like service creation.

This leads to fragmentation of ops across the organization, often with varying degrees of quality, and it depends on the amount of time the senior developer could devote to setting it up and maintaining it.

What differentiates successful organizations from those that are not? The most successful had an internal team that is responsible for an internal development portal that supports the development teams. These dedicated teams allowed delivery teams to focus on creating functional software features, not on managing dependencies, pipelines and tools.

One issue when adopting platform engineering is the tendency to build another silo. A good example would be a ticketing system where users can request features or report bugs, the requests go into the platform engineering realm, and are eventually resolved. You can combat this by focusing on enabling users to self-serve their own needs with your portal by providing accurate and relevant documentation, training sessions and pairing with users to solve their problems.

Another issue is prioritizing the right things. There are a lot of users from many different parts of your organization, so having a single feature request pipeline for those things users cannot self-serve with a committee deciding on priority is essential to servicing the needs of your organization effectively.

Keep your platform team adaptable and not stuck in the past ways of doing things. With the rapid pace of change in IT, its hard to keep up. Enablement is one way you can ease the burden on your team, but also allowing your team a consistent amount of time to train on new technologies is another.

Platform engineers are responsible for deploying, and maintaining the internal development portal. They also work with a variety of stakeholders to ensure that the portal meets its users needs.

Platform engineers typically have a deep understanding of software engineering practices and how software engineers work. In addition, platform engineers understand what the organization is attempting to deliver and the tools and workflows needed to accomplish those goals. They also have experience with a variety of DevOps tools and practices.

While a successful platform engineer needs to understand software development and DevOps tools and practices, they also need to be able to work effectively with people. Platform engineering is a team sport. They need to be able to communicate with stakeholders from all parts of the organization, from the C-suite to junior developers.

They also need to be comfortable with change. Platforms, DevOps and software engineering are constantly evolving, and the flexibility to adapt to new technologies and approaches is probably the most important skill theyll need.

Platform engineering is closely related to DevOps. Many platform engineers come from a DevOps background. DevOps is a set of practices that help businesses deliver software faster and more efficiently. It emphasizes collaboration between development and operations teams.

Platform engineering borrows many of the same principles from DevOps, including automation, continuous delivery and continuous integration.

Platform engineering differs from DevOps in that platform engineering builds the tools to help engineers and DevOps perform their tasks. Tool creation is generally not a focus for DevOps, or if tools are created, its done on an ad hoc basis.

Site reliability engineering (SRE) is another closely related field. Like platform engineering, SRE focuses on the availability and performance of software systems. SRE practitioners are often responsible for monitoring and responding to incidents. They also work with developers to prevent outages from happening in the first place. However, platform engineers are more focused on development, while SRE practitioners focus more on operations.

If your organization is developing software, theres a good chance youll need platform engineering skills to help accelerate your software development teams. Integrated development platforms are becoming more and more common, and theyre being used to help some of the most disruptive and adaptable software development teams in the world expedite their work.

Platform engineering is a team sport.

Platform engineering is something to consider for all organizations thinking about implementing DevOps. As businesses strive to deliver software faster, they need processes and engineers who can enable their software development teams, not hinder them.

The DevOps antipatterns are something to keep in mind while making your decisions. First, ask yourself if you can execute DevOps successfully. If not, then an internal developer portal and platform engineering may be the right choice for you.

An internal developer portal is a self-service application and data store that lets developers and managers track and organize everything their engineering teams build and operate.

First off, the catalog consolidates data and knowledge fragmented across clouds, tools and teams so everyone in an organization can discover essential information about their applications, services and infrastructure in one place.

This provides a variety of utility to organizations:

Platform engineering teams can build or buy internal developer portals. Building affords maximum flexibility to tailor the solution to the organizations needs; however, the level of effort associated with building and maintaining it negates the value entirely unless youre a mega-cap company. There are open source projects, including Backstage and Gimlet. The first is a DIY portal, and therefore organizations will still be required to invest heavily to attain value. Gimlet focuses on Kubernetes-based environments.

Ready-to-use internal developer portals are rapidly increasing in popularity. Configure8 is an increasingly popular option because of its end-to-end awareness of services and infrastructure, not just Kubernetes, and its advanced analytics.

Next, some platform engineering teams choose to add scaffolding services to their offering. The scaffolder consists of templates that make it simple for developers to spin up new services. This makes it easier for developers to properly launch new production services adhering to organizational standards while enjoying flexibility when needed. Scaffolders can then register the new services and related tools in the catalog so its always up to date.

If your organization is developing software, theres a good chance youll need platform engineering skills to help accelerate your software development teams.

Scaffolders are sometimes included with universal catalogs, and they can also be created separately alongside catalogs. In this case, blueprints created in a scaffolder are then merchandised inside the enterprises catalog solution via a plugin integration.

In terms of standalone tools, there are a number of open source scaffolders, such as Cookiecutter and Yeoman, and standalone commercial software solutions like Humanitec.

Its important to consider how often your enterprise will actually be creating new services versus managing and improving the existing system as you consider the relative priority of your platform engineering investments.

Finally, some platform engineering teams are investing in making it simpler to manage their infrastructure. The more infrastructure you have, across many accounts and clouds, the more value your organization will get from integrated management capabilities. These capabilities may enable you to change the size of an instance, provision different types of storage or resize Kubernetes clusters.

Many teams have invested in Infrastructure as Code (IaC) solutions such as Cloudformation and Terraform and would run changes through their normal update process while others may want a simple way to deep link into the resources running a service from within the catalog of their IDP. Much like with scaffolding, platform engineering teams should consider the frequency of these types of changes, compatibility with their IaC investments, and the number of users in the overall engineering team affected by these scenarios when planning their investments.

There are a variety of other tools and applications available for platform engineering. Here are some of the most popular ones:

Orchestration:

These are just a few of the tools and applications available for platform engineers. Platform engineering is an emerging field, so expect more tools to enter the market.

As distributed cloud systems become more commonly used and architectural patterns continue to evolve, the demand for platform engineers and internal developer portals is expected to grow.

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Terre Haute North engineering students are stepping up to help the environment and save you money on your next electric bill – WTHITV.com

TERRE HAUTE, Ind. (WTHI) - A group of Wabash Valley students is taking big steps to help our local environment and to help you save money on electric bills.

At Terre Haute North High School, 10 Civil Engineering and Architecture students are introducing their project known as "Vortex on the Wabash" to the community.

"I have lived here my whole life, and honestly it would be spectacular to be able to make a difference in the city that I have been able to live in," Logan Wenzel, one of the engineering students, said.

Logan Wenzel is just one of 10 high schoolers who took on a collaborative project at the start of the year, and it's for more than just a letter grade.

"We wanted to do this because nowhere else in the United States has really started doing this," he said. "Terre Haute would be completelydifferent and it would change everything about the way that we look at energy in Terre Haute."

Here's how the project works. Basically, the Vortex turbine generates electricity by using natural water sources, in this case, it would be the Wabash River. The water is then redirected from the river by a small channel that runs through the turbine. The water then spins the turbine, transferring it into clean energy.

The goal is to install multiple turbines to produce enough electricity to power the entire city.

"This is real world stuff," Jasen Gibbens, theCivil Engineering and Architecture teacher, said. "You are given a problem from your boss and then you have to fix the problem. I learned that early in my career and I thought, this would be the perfect opportunity to put a bunch of juniors and seniors who want to be here and want to learn and fix their communities [on a project like this}."

Students say a system like this is not only great for the environment, but it could also save residents thousands of dollars in energy costs.

"How would [people in the] state not like having to pay an electric bill? What could you do with that money instead?" Gibbens said.

And for students like Logan Wentzel, this project is more to him than helping the environment and saving money, it's also about bringing more people to the city of Terre Haute for generations to come.

"Most people that live here, especially my age, don't want to stay here, but they should, because living in Terre Haute is a lot better than what they think," Wenzel said. "This can truly help them see what we can do to help."

Now, the hope is to get this project moving forward with further discussion with city leaders soon.

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How Skill-Lync is shaping the engineering future in India – CXOToday.com

CXOToday has engaged in an exclusive interview with Mr. SuryaNarayanan PaneerSelvam, Co-Founder at Skill-lync.

One thing led to another, it was after Sarang and I completed our Masters, we realised that whatever we had learned during graduation did not help us land a job, or even keep up with our Masters coursework. This pushed us to find a solution that would make it easy for other graduating engineers to skill-up, and become more employable.

We started creating small videos on YouTube during our post-graduation and tested multiple learning modules. By 2015, we started selling our courses to US colleges, providing value-added courses, as well as started conducting live sessions. Once the colleges started to pay, we realised that there is a potential in the market which led us to collaborate with about 12 colleges from Chennai, who paid around Rs 5,000 for each student. By the end of 2015, we had more than 300 students with us. Both Sarang and I decided to quit our jobs then and return to India to focus on building Skill-Lync.

However, now, amid COVID-19 we have partnered with more than 50 colleges and corporations to provide these courses.Our mission is aligned with the Govt. of Indias clarion call of building a skilled economy as there is a shortage of high-quality engineering talent in the country that can help India ride the next industrial wave. There is a need for credible platforms such as ours created on the back of academia and the industry to ensure that the students garner the right skill set to work on the next-gen, disruptive automotive technology.

Today, we have collaborated with more than 300 colleges and corporations to provide these courses. Skill-lyncs mission is aligned with the Govt of Indias clarion call of building a skilled economy as there is a shortage of high-quality engineering talent in the country that can help India ride the next industrial wave.

2. How does it help engineering students become industry ready?

Skill-Lync has been able to establish itself as a credible partner for organisations looking at building an upskilled talent pool. We have exclusive paths to get students trained in cutting-edge content upskilling for high-quality jobs. Students from more than 80 countries have enrolled on Skill-Lync, including geographies such as the U.S., UK, UAE, Germany, Australia, Canada among others with more than 300 corporates partnerships such as Cyient, Renault-Nissan Technology Business Center India (RNTBCI), TATA Elxsi, Expleo, Segula Technologies to provide job offers to engineering students after the completing the course.

Additionally, Skill-Lync has been on an upward growth trajectory regardless of the pandemic, offering solutions that cater to the upskilling and reskilling aspect, hence it augments the skills needed for engineers to stay relevant in accordance to latest technological disruption across industries.

3. What are the other sectors you feel needs upskilling?

In a country like India having 1.3 billion population, upskilling is required in almost all sectors but mostly it is required in education, tech sectors, healthcare , BFSI, IT, retail and so on. The need for employees to constantly upskill has only steadily increased especially after Covid-19. The lockdown has turned into a necessity because of the sudden rise in online platforms offering upskilling courses.

4. Will Skill-Lync look at expanding its sectors beyond the engineering sector?

We want to dive deep into the engineering sector first and make Skill-Lync a top platform for all engineering sectors that can provide upskilling courses to make students industry ready. There are a few industries which might be our next focus point when it comes to upskilling, but at the moment, we are only looking at core engineering.

5. How has the brand grown over the years?

Over the years, Skill-Lync has expanded globally. The platform till now has upskilled over 30,000 students from 80 countries across India, Middle East, North Africa A (South East Asia), the US, Canada, Australia and Europe. Since inception, we have seen growth, and we want to bring robust results for the next two quarters to work toward our growth playbook. Till now the company has partnered with 350 companies, including Nissan, Renault, Mahindra, and Tata Elxsi, for student placements. If I talk about the target, we want to see 30% of revenue coming from other geographies within the next two years, which currently is at 12%. Covid19 has pushed many more students to look at online resources to skill & upskill themselves. We saw a 20% uptick concerning student interests.

6. How is the company making a dent in Indias $15 billion edtech industry?

There are several driving factors that have helped Skill-Lync stand out in a deeply cluttered ed-tech market. We operate within the higher education segment of the edtech industry that has seen a 75% increase in its user base, with the market size now comparable to other subdivisions such as K-12. Within higher education, Skill-Lync has carved a niche for itself wherein we are reimagining the Indian engineering education ecosystem. Our demand is augmented by the fact that despite being the 4th most opted domain at the undergraduate level in the country, the engineering curriculum & the institutions have a disconnect between what the industry demands and what the students are being taught.

7. Why is there a need to embrace upskilling in the world

The lockdown resulted in a sudden rise in online platforms offering upskilling courses. Many businesses have adopted new trends requiring frequent updates, particularly during the pandemic, encouraging people to upskill and re-skill to stand out.

For instance, the automotive industry is going through a significant change, driven by sustainability and an increasing base of conscious consumers. In addition to our coursework, we offer first-hand experience of building engineering models from scratch, thus providing a holistic view of the future of the automotive industry. Our main goal was to help the students gain insights into the future of the automotive industry which not only helps them understand challenges but provide the experience in making real products. A team of 10 students and technical engineers had built a full-scale in-house SUV prototype called -Lyncer S1. With this SUV creation, our only aim was to give more practical and hands-on knowledge to the students.

8. What are some of the benefits for Skill-Lync students?

Our courses are designed to make engineers industry-ready across core engineering domains such as mechanical, electrical, civil, computer science, electronics and biomedical engineering. We do a complete industry mapping, identifying the market gaps and the roles that corporates are looking to fill and empanel experts from Fortune 500 companies curating courses that are project-driven, thus offering deep engineering expertise to the students. We provide mock interviews for students before they face the industry experts for the job. We customise our curriculum to meet the learning outcomes that are specially curated as per the industry demand. We have built an in-house Electric Vehicle to help its students gain insights into the future of the automotive industry. The vehicle was built by a team of 6 in-house experts at Skill-Lync in collaboration with professionals allowing students to gain first-hand experience in building EV and EV prototypes independently.

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These Are the Worst Examples of Automotive Badge Engineering Ever – Jalopnik

I would like to nominate the collective badge-engineering that happened when GM killed off Saturn and Pontiac during the 2007-2008 financial crisis and subsequent bankruptcy.

The Saturn Outlook replaced the GMC Acadia (despite being the same car). Yes, GM literally rebadged a car as a replacement for its nearly-identical platform-mate. The Saturn Vue became a Buick but GM never actually shipped it to anyone and instead rebadged it a third time as the Chevrolet Captiva Sport and sold it to Hertz. The Pontiac G2, already a badge-engineered version of the Chevy Aveo, became a trim line of the Aveo in the middle of 2010. Also future Saturns all became Buicks with no regard for how theyd fit into the lineup which is how the Opel Insigna became the Buick Regal.

Lots of weird things happened when the clock ran out on Saturn and Pontiac as SamTheGeek reminds us Opels-turned-Saturns-turned-Buicks, after all. But the Saturn Outlook effectively being revived as the first-gen GMC Acadias mid-cycle refresh was one of the weirder ones I didnt notice. Kyree has the details:

You are correct , and most people dont notice that. The Saturn Outlook shared most of its shape with the GMC Acadia, but with some distinctions, like the wheel flare shape, rear light apertures, and wraparound rear windscreen. Overall, the Saturn was more chiseled.

The Saturn Outlook was discontinued with the rest of the Saturn brand in 2010. But, when GM decided to facelift the remaining three Lambda crossovers for 2013, it decided that the Saturns shape better fit GMCs then-new blocky, chiseled theme...and so quietly switched to using the Outlook tooling. Even the rear bumper cover, in its entirety, was snatched from the Outlook for the 2013 Acadia.

As far as what happened with Saturn and Buick, Im sure GM realized it was more profitable to sell the rebadged Opels as Buicks than Saturn, and they mostly fit quite well into the lineup. Buick needed compact and midsize sedans, and the Opels fit the bill nicely. The Encore was a particularly good idea.

The Cascada, however, was an interesting indulgence in a world where such cars (which also included the late 200 Convertible and Eos) were dying off left and right.

Suggested by: SamTheGeek, Kyree

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Electrical engineer will develop next-generation x-ray technology for accessible preventative healthcare – University of California, Santa Cruz

Heart disease is a leading cause of death worldwide, and catching it early can make a significant difference for prevention of future cardiovascular events. But in many places, there is limited accessibility to technology that can detect early signs of the disease, such as coronary artery calcification (CAC), when the flow of blood through the arteries is blocked by a buildup of plaque, increasing the likelihood of a heart attack.

Currently, detecting CAC requires CT scans which are costly, deliver large doses of radiation, and must be done in a hospital setting but UC Santa Cruz Associate Professor of Electrical and Computer Engineering Shiva Abbaszadeh is developing a solution that will make this preventative health care much more accessible. With a new $2 million grant from the National Institutes of Health, Abbaszadeh and her collaborator at Stanford University Assistant Professor of Radiology Adam Wang will develop new technology for detecting CAC that can be easily incorporated into routine chest x-rays, the most common medical imaging procedure.

Having an x-ray system that is easily portable can make a huge difference for some areas that might not have access to CT scans because they need a hospital environment, Abbaszadeh said. We can advance material decomposition and lesion differentiation of x-ray imaging.

The novel technology will be an advanced, dual-layer x-ray detector, producing both a traditional image of the body as well as a material-specific image which, in this case, would detect calcium. Wangs team will develop artificial intelligence algorithms to automatically detect and quantify how much calcium is present. The technology will be a drop-in solution for existing clinical procedures and doesnt require any additional radiation or scan time.

While the researchers will initially focus on CAC detection, they believe their system could be used for early detection of lung and breast cancer, tuberculosis, and other diseases.

We are developing a technology platform by combining innovations in materials science, radiation detection, circuit design, and computation to bring new capabilities to x-ray imaging, Abbaszadeh said. One application is what weve targeted coronary artery calcification but the problem is much bigger and can have a wide impact.

To create the next-generation technology for x-ray imaging, Abbaszadeh and her students will utilize the equipment in her lab at the Baskin School of Engineering to engineer the optical and electrical properties of chalcogenide material materials that contain one or more chalcogen elements. This facility, which is dedicated to developing detectors based on chalcogenide alloys of the element selenium, is the only such facility in a research setting in the country to Abbaszadeh and Wangs knowledge.

A dedicated facility for detector development presents a wide range of research opportunities for Abbaszadehs lab, as the material has properties that are well-suited for both photodetectors, used for applications ranging from medical imaging to high-energy physics.

This collaborative project will provide exciting opportunities for students in the two researchers groups to be part of a larger learning environment in which they can visit each other's labs and gain experience with all facets of their technology, including hardware engineering, AI development, and the clinical setting in which their work will be put to use.

Wang and Abbaszadeh will also collaborate with researchers at the Stanford School of Medicine, who will provide input on how to best design their systems for use in the clinical setting and provide images from real instances of CAC to train Wangs AI models. Former Stanford instructor Martin Willemink and Stanford Professor Dominik Fleischmann provided input into the development of the project, and Fleischmann, who is the director of Stanfords cardiovascular imaging division, will continue to lend his expertise. They will also collaborate with industry partners to demonstrate the performance of the new system.

Between the detector physics that Shivas working on, and the artificial intelligence algorithms we're developing at Stanford, were providing better image input information but also we will have algorithms that automatically detect and quantify how much calcium there is, Wang said. Its an improvement on multiple fronts.

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Ford F-150 Lightning winter towing tests and Engineers interview – Electrek

Ford has been taking some hits in the media on the F-150 Lightning towing range, so we thought it would be good to test it ourselves and then get the background from the people who built the Lightning, especially as we head into winter. Along with Fords PR team, we were joined by:

Heres the discussion along with firsthand towing impressions below in both New York and Detroit.

I wanted to see how much range Id lose while towing because there have been some recent videos showing only low double-digit-mile ranges coming out of the Lightning.

It is hard to quantify how much range youd lose because of a ton of significant factors, like trailer weight, aerodynamics, and efficiency as well as normal EV range factors like elevation, climate, speed, etc., which are magnified while towing a trailer.

So I did two different tows: one with an open hauling trailer with a friend in New York and one with a closed trailer in Detroit both very different experiences.

After receiving the F-150 Lightning loaner, I immediately went to a friends place to try some towing. Hes got a tiny house Airbnb north of New York City, which requires hauling loads of firewood into the mountains. As the weather gets colder, the tiny house mini split heating requires 240V power, and we wanted to see if the Lightning could power it.

His trailer is about 75 feet and weighs about 5,000 pounds loaded. Using the rear and above camera views makes hitching the trailer a breeze.

Without any outside instruction, we were able to enter the info into the Lightnings towing configurator and were off in a matter of minutes.

The trip was mostly uphill, and after about six minutes, our already dropping fast range dropped by about half, which was initially very scary. I had started the trip with about 180 miles of range and within a few miles of uphill road, we were at about 140 miles of range. The truck recalibrated us down to 68 miles of range, which was a bit scary since we had planned to try powering the house while we were there and had to make it back as well.

The truck continued to lose range quicker than we were using it until the top of the mountain where it equalized with the range. That gave us a lot of confidence to try powering the house since most of the return trip was downhill and we would be without the load of wood.

This exercise doesnt really have to do with towing, but while we were at the Tiny House, we decided to try to power the whole house, including mini split heating, using the F-150 Lightnings Pro Power on board and 240V generator plug.

It just works. Usually, this requires a generator or a very large solar/battery setup, but not only can you tow a Tiny House (or Airstream/camper) to the middle of nowhere, you can also power it and heat it with the F-150 Lightning. I think I may have sold a few F-150s on this alone.

With the heat on full blast, the two power outputs stabilized at just over a kW, meaning we could have powered this thing for a full day using about 25kWh of battery.

The interesting thing about the trip back is that we ended with just about the same range as wed started with, so we mustve regenerated close to the 10 miles of range of the trip going downhill.

In Detroit, we drove an 8,000-pound trailer, 88 feet front end, about 15 miles on the highway, with about five miles of city driving, then 15 miles to return. Initially, while on the highway, I kept it at about 55-60mph (just under 100kmph). Most of the Detroit area is quite flat, so elevation isnt a factor here, and it was about 40 degrees with rain. During this time, I saw energy usage at 1 mile/kW, which means we can extrapolate 130+ miles from the 131kWh usable battery. I would use this figure as a baseline for towing. You might get better in warmer, dryer conditions with a smaller trailer, but starting here is easy and effective, and you can always drop down to this speed when towing on the highway.

While driving in the city with stops and starts, I saw the mi/kWh go down to .9, so keep in mind that city driving with an 8,000-pound load wont necessarily save you range.

On the return trip, I tried hitting 65-70mph for brief periods, and that took the power usage down to .8 miles per kWh. So by driving just 10 mph faster, the range went from approximately 130 miles to about 100 miles.

Conclusion: Speed kills range, but it kills it even harder with a 64-square-foot front trailer. I imagine the videos where the F-150 Lightning gets only a low double-digit range are staged or at best poorly planned.

I finished with the following numbers after mixed driving:

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Ford F-150 Lightning winter towing tests and Engineers interview - Electrek

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The Disruptive Economic Impact Of Artificial Intelligence – Forbes

I firmly believe that artificial intelligence (AI) has the potential to be among the most disruptive technologies we will ever develop. So why more than 50 years since the first machine learning research is its impact still, in many ways, limited?

The Disruptive Economic Impact Of Artificial Intelligence

This is the question at the heart of a new book called Power and Prediction - The Disruptive Economics of Artificial Intelligence, co-authored by Joshua Gans, along with Ajay Agrawal and Avi Goldfarb.

I got the chance to once again catch up with Gans, holder of the Jeffrey S Skoll Chair of Technical Innovation and Entrepreneurship at Torontos Rotman School of Management.

The last time I spoke to Joshua, he had just released his first book, Prediction Machines The Simple Economics of Artificial Intelligence.

The new book picks up where that one left off. Following on from making the arguments that the true value of AI and machine learning is its ability to make predictions, the trio follow it up by setting out the theory that the true value of AI will only be realized when organizations are designed to harness it from the ground up.

To illustrate this point, the book uses the example of electricity. Many decades passed between the first practical demonstrations showed how electrical power could be harnessed and when it began to be put to widespread use.

During these years, electricity was applied to what Gans and his co-authors call point solutions switching out existing systems (such as lighting, for example) for more efficient electrical replacements.

Although this undoubtedly led to efficiencies, it wasnt until the true value of electricity was discerned (in this case, allowing power use to be decoupled from its source) that the technology became truly transformational.

Gans tells me Whereas the primary benefit of electricity was that it decoupled energy from its source, which facilitated innovation in factory design, the primary benefit of AI is that it decouples prediction from the rest of the decision-making process, which facilitates innovation in organizational design via reimagining how decisions interrelate with one another.

Decoupling prediction from the rest of the decision-making process enables a shift from merely lowering the cost of predictions to creating vastly more productive systems. It is only when this is widely understood, Gans says, that AI will achieve its transformational potential.

This means that right now, we are in "between times" for AI which happens to be the subject of the book's first chapter. This era is analogous to the period between about 1890 and the 1930s where, although we could see that electricity was hugely transformational, the systemic uses that would change the world such as the widespread electrification of factories and then homes had not yet been established.

Gans says, What we did was look back at the history of other large, transformative innovations and try to tease out lessons that we can apply to AI. There are some interesting features about the adoption of electricity by businesses. It took many decades to power machines by electricity, typically, machines were powered by some form of water generation of power, and that was a difficult thing because it needed some form of power coming into the factory at a single point.

Typically, this meant steam power a hugely inefficient method of generating kinetic energy in machinery where a large amount of the power is simply lost into the air as heat. This meant that far more power had to be generated than was needed to run the machines.

[electricity] only changed the economy when new systems developed. That change was profound and shifted power to those who controlled electricity generation and grids and to those who could use electricity at scale in mass production. You didn't want to be a manufacturer of belts and pulleys after that or a holder of downtown factory real estate," Gans explains.

Gans and his co-authors make the convincing argument that the same process is now underway with AI, and it will be those taking the initiative to design, develop, deploy and ultimately own new systems that will emerge as leaders of the new AI-powered economy.

In this book, this leads up to the interesting point that We need to ask a fairly straightforward, but potentially hard to answer question. Given what we now know about AI, how would we design our products or services or factories if we were starting from scratch?

One of the industries that the book specifically points towards as having the potential to develop these "system level" applications of AI in healthcare. So far, we have seen AI used to assist with diagnosing patients by applying machine learning technologies such as computer vision to the task of analyzing medical images like x-rays and MRI scans for signs of disease and illness. This undoubtedly has the potential to drive efficiencies and augment the work of human medical practitioners.

AI will be able to improve that diagnostic process, and you'll be able to help more patients that way," Gans says.

"But what happens if we think larger? It's not just about diagnosing; it's the recovery and treatment."

He puts forward the example of a hospital where AI is widely integrated into systems, enabling practitioners to more accurately predict patient needs. Many patients are admitted before doctors have had a chance to make a diagnosis, meaning no-one knows how long the beds they are occupying will be taken up for. This makes it very hard to assign resources and forecast demand.

A systemic use of AI could involve predicting how long every newly admitted patient would be in for. It may be able to predict when particular patients can safely be discharged and treated as outpatients and when it's safest to keep other patients in for observation until doctors understand what their condition is.

Enabling better allocation of resources thanks to systemic implementations of AI, Gans tells me, would mean the hospitals you have can be smaller. They can be more distributed, to be where people [who need them] are, things like that. Thats the kind of transformation you start thinking, ok, Im looking for sources of uncertainty, and one of the biggest sources of uncertainty in healthcare is that I have to keep patients under observation, not because I can fully articulate what might occur but just because something might occur.

So, Im very risk averse but if Im given AI, I might have more confidence and that's good for everybody."

It's an interesting argument, and the book that Gans and his co-authors have published makes a strong case for developing system-level AI applications in organizations and institutions in healthcare and beyond. Overall, it aims to address a problem that isn't unique to AI but can be seen reflected in the impact of technology in general. A quote from Robert Solow of MIT that is highlighted in the book goes, "We see the computer age everywhere but in the productivity statistics." It seems likely that the ideas that will break this status quo are out there and waiting to be implemented, and hopefully, books like this one will set us off on the path toward finding them.

You can click here to watch my interview with Joshua Gans, Jeffrey S. Skoll Chair of Technical Innovation and Entrepreneurship and Professor of Strategic Management at Toronto's Rotman School of Management.

To stay on top of the latest on the latest business and tech trends, make sure to subscribe to my newsletter, follow me on Twitter, LinkedIn, and YouTube.

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The Disruptive Economic Impact Of Artificial Intelligence - Forbes

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Skift Webinar: How Artificial Intelligence Is Reshaping Travel – Skift Travel News

In this recent webinar, FLYR Labs and Pace Revenue joined Skift to explain how travel companies can put artificial intellegence at the core of their business and technology ecosystems to keep up with customer demand, improve operational efficiencies, and drive more revenue in an increasingly volatile and unpredictable travel business.

FLYR

A recent Skift webinar, How Artificial Intelligence Is Reshaping the Travel Business, discussed how artificial intelligence (AI) and machine learning models are emerging as a means to keep up with customer demand, improve operational efficiencies, and drive more revenue in an increasingly volatile and unpredictable travel business.

In this webinar:

As travel brands aim to connect the traveler journey from end to end, they are seeking more seamless ways to incorporate and integrate data to support operations, revenue management, marketing, ancillary offers, and other commercial decisions across their organizations.

In this recent webinar, FLYR Labs and Pace Revenue joined Skift to explain how travel companies can put AI-driven insights at the core of their business and technology ecosystems. With the recent acquisition of Pace, a cutting-edge revenue and decision intelligence platform for hotels, FLYR is accelerating its ability to support new travel and transportation sectors beyond its core expertise in aviation.

The panel explored how AI is not limited to any travel vertical or specific commercial function; what to expect from a technical and tactical perspective as AI is still evolving; and how travel organizations can navigate these changes holistically in this ever-shifting environment.

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Skift Webinar: How Artificial Intelligence Is Reshaping Travel - Skift Travel News

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