Cloud Hosting

Oregon Tech engineering students win visionary award at statewide invention competition – Herald and News

July 3, 2024 | Author: admin

State Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington Washington D.C. West Virginia Wisconsin Wyoming Puerto Rico US Virgin Islands Armed Forces Americas Armed Forces Pacific Armed Forces Europe Northern Mariana Islands Marshall Islands American Samoa Federated States of Micronesia Guam Palau Alberta, Canada British Columbia, Canada Manitoba, Canada New Brunswick, Canada Newfoundland, Canada Nova Scotia, Canada Northwest Territories, Canada Nunavut, Canada Ontario, Canada Prince Edward Island, Canada Quebec, Canada Saskatchewan, Canada Yukon Territory, Canada

Zip Code

Country United States of America US Virgin Islands United States Minor Outlying Islands Canada Mexico, United Mexican States Bahamas, Commonwealth of the Cuba, Republic of Dominican Republic Haiti, Republic of Jamaica Afghanistan Albania, People's Socialist Republic of Algeria, People's Democratic Republic of American Samoa Andorra, Principality of Angola, Republic of Anguilla Antarctica (the territory South of 60 deg S) Antigua and Barbuda Argentina, Argentine Republic Armenia Aruba Australia, Commonwealth of Austria, Republic of Azerbaijan, Republic of Bahrain, Kingdom of Bangladesh, People's Republic of Barbados Belarus Belgium, Kingdom of Belize Benin, People's Republic of Bermuda Bhutan, Kingdom of Bolivia, Republic of Bosnia and Herzegovina Botswana, Republic of Bouvet Island (Bouvetoya) Brazil, Federative Republic of British Indian Ocean Territory (Chagos Archipelago) British Virgin Islands Brunei Darussalam Bulgaria, People's Republic of Burkina Faso Burundi, Republic of Cambodia, Kingdom of Cameroon, United Republic of Cape Verde, Republic of Cayman Islands Central African Republic Chad, Republic of Chile, Republic of China, People's Republic of Christmas Island Cocos (Keeling) Islands Colombia, Republic of Comoros, Union of the Congo, Democratic Republic of Congo, People's Republic of Cook Islands Costa Rica, Republic of Cote D'Ivoire, Ivory Coast, Republic of the Cyprus, Republic of Czech Republic Denmark, Kingdom of Djibouti, Republic of Dominica, Commonwealth of Ecuador, Republic of Egypt, Arab Republic of El Salvador, Republic of Equatorial Guinea, Republic of Eritrea Estonia Ethiopia Faeroe Islands Falkland Islands (Malvinas) Fiji, Republic of the Fiji Islands Finland, Republic of France, French Republic French Guiana French Polynesia French Southern Territories Gabon, Gabonese Republic Gambia, Republic of the Georgia Germany Ghana, Republic of Gibraltar Greece, Hellenic Republic Greenland Grenada Guadaloupe Guam Guatemala, Republic of Guinea, Revolutionary People's Rep'c of Guinea-Bissau, Republic of Guyana, Republic of Heard and McDonald Islands Holy See (Vatican City State) Honduras, Republic of Hong Kong, Special Administrative Region of China Hrvatska (Croatia) Hungary, Hungarian People's Republic Iceland, Republic of India, Republic of Indonesia, Republic of Iran, Islamic Republic of Iraq, Republic of Ireland Israel, State of Italy, Italian Republic Japan Jordan, Hashemite Kingdom of Kazakhstan, Republic of Kenya, Republic of Kiribati, Republic of Korea, Democratic People's Republic of Korea, Republic of Kuwait, State of Kyrgyz Republic Lao People's Democratic Republic Latvia Lebanon, Lebanese Republic Lesotho, Kingdom of Liberia, Republic of Libyan Arab Jamahiriya Liechtenstein, Principality of Lithuania Luxembourg, Grand Duchy of Macao, Special Administrative Region of China Macedonia, the former Yugoslav Republic of Madagascar, Republic of Malawi, Republic of Malaysia Maldives, Republic of Mali, Republic of Malta, Republic of Marshall Islands Martinique Mauritania, Islamic Republic of Mauritius Mayotte Micronesia, Federated States of Moldova, Republic of Monaco, Principality of Mongolia, Mongolian People's Republic Montserrat Morocco, Kingdom of Mozambique, People's Republic of Myanmar Namibia Nauru, Republic of Nepal, Kingdom of Netherlands Antilles Netherlands, Kingdom of the New Caledonia New Zealand Nicaragua, Republic of Niger, Republic of the Nigeria, Federal Republic of Niue, Republic of Norfolk Island Northern Mariana Islands Norway, Kingdom of Oman, Sultanate of Pakistan, Islamic Republic of Palau Palestinian Territory, Occupied Panama, Republic of Papua New Guinea Paraguay, Republic of Peru, Republic of Philippines, Republic of the Pitcairn Island Poland, Polish People's Republic Portugal, Portuguese Republic Puerto Rico Qatar, State of Reunion Romania, Socialist Republic of Russian Federation Rwanda, Rwandese Republic Samoa, Independent State of San Marino, Republic of Sao Tome and Principe, Democratic Republic of Saudi Arabia, Kingdom of Senegal, Republic of Serbia and Montenegro Seychelles, Republic of Sierra Leone, Republic of Singapore, Republic of Slovakia (Slovak Republic) Slovenia Solomon Islands Somalia, Somali Republic South Africa, Republic of South Georgia and the South Sandwich Islands Spain, Spanish State Sri Lanka, Democratic Socialist Republic of St. Helena St. Kitts and Nevis St. Lucia St. Pierre and Miquelon St. Vincent and the Grenadines Sudan, Democratic Republic of the Suriname, Republic of Svalbard & Jan Mayen Islands Swaziland, Kingdom of Sweden, Kingdom of Switzerland, Swiss Confederation Syrian Arab Republic Taiwan, Province of China Tajikistan Tanzania, United Republic of Thailand, Kingdom of Timor-Leste, Democratic Republic of Togo, Togolese Republic Tokelau (Tokelau Islands) Tonga, Kingdom of Trinidad and Tobago, Republic of Tunisia, Republic of Turkey, Republic of Turkmenistan Turks and Caicos Islands Tuvalu Uganda, Republic of Ukraine United Arab Emirates United Kingdom of Great Britain & N. Ireland Uruguay, Eastern Republic of Uzbekistan Vanuatu Venezuela, Bolivarian Republic of Viet Nam, Socialist Republic of Wallis and Futuna Islands Western Sahara Yemen Zambia, Republic of Zimbabwe

Here is the original post:

Oregon Tech engineering students win visionary award at statewide invention competition - Herald and News

Engineering the Maximum Mustang GTD – Design News

July 3, 2024 | Author: admin

Ford revealed plans for the Mustang GTD last summer when it announced plans for a new peak-performance version of the veteran pony car. The 800-horsepower beast is derived from the companys Mustang GT3 race car but is street-legal.

The car is the product of an unusual incidence of the life-imitates-art axiom. To race in the global GT3 sports car category in high-profile races like the 24 Hours of Le Mans, Ford engineers created the Mustang Dark Horse, whose high-performance components served as the foundation for race cars for both the GT3 and GT4 racing categories.

Ford turned to frequent partner Multimatic, not just for the Mustangs sophisticated spool valve shock absorbers, but also for the design and construction of the race cars. Ford president Jim Farley, a Mustang owner and racing enthusiast, got one look at the in-progress GT3 and asked the engineering team what it would take to make the GT3 race car legal for customers to buy and drive on the street. The Mustang GTD is that car.

With this as the GTDs back story, Ford took the occasion of the 2024 edition of the 24 Hours of Le Mans to provide the first in-person look at the GTD, with some discussion of the car by the team that created it.

As if the GTD wasnt already sufficiently track-focused, Ford announced at the race that there will be a Performance package that adds active aerodynamics and magnesium wheels. The purpose is to deliver a Nurburgring lap time of less than 7 minutes. Unlike the rules-constrained GT3 racing category, however, the GTD is comparatively unfettered, which means it can deliver even more power than the race car.

Related:Maximum Mustang: The Mustang GTD Is an 800-hp Racer for the Street

Multimatic chief technical officer and engineering guru Larry Holt was on hand at the 24 Hours of Le Mans to supervise the GT3 racing program, which finished third in the GT3 class in the race. Before the race started, Holt spoke in Fords display tent in the Manufacturers Village at the track, outlining Multimatics work to turn the Dark Horse into a GT3 and to turn a GT3 into a GTD.

He started by reviewing aerodynamic work on the car. Weve got a really unique wing stanchion that goes into the most structurally strong part of the car at the bottom of the C-pillar, he said. The [GT3] race cars got it, so we thought that would be the way to go. The gooseneck rear [wing] mounts are the way, if you look out there on the racetrack now, everybodys got their wing mounted to their top surface rather than the bottom. It is just a more efficient way to do it.

But the GT3 race car must conform to rules that are designed to level the playing field across the many manufacturers with their wide variety of body styles and engine configurations. The production GTD is free to get more advanced with its management of airflow, so the team upgraded the aerodynamic systems on the car.

Related:To Le Mans in the 2024 Ford Mustang Dark Horse

If you look at that race car, its got a single-element wing with a fixed Gurney [flap], Holt noted. This is a dual-element rear wing, and that flap, thats active. This thing generates, in its high-downforce mode, a massive amount of downforce.

Downforce is good when you need grip for cornering, but it comes at a price. Theres also a massive amount of drag, he said. So when youre out on the autobahn, and you want to go faster than everything else thats on the autobahn, you put it in [drag reduction system] mode and the flap flattens itself out.

A change in downforce and drag at the rear of the car demands a corresponding change at the front, to keep the cars grip and handling balanced. That would cause a problem with the front if you just did that, Holt explained. This [GTD] has a huge front underwing. If you look under there, its not flat. Thats got a big curvature underneath the front of the car. We open two flaps in there that screws that all up so it doesnt generate quite as much downforce.

Related:Goodyear Racing Takes on Le Mans

Multimatic made its name in racing with its dynamic spool valve shock absorbers, so naturally they devised advanced suspension for the car. We did some radical things! said Holt in video posted on Fords YouTube channel. Its got inboard suspension like a lot of prototype race cars. We put a transaxle gearbox in the back. A transaxle is what racecars have. Was that an easy thing to do? In a car that was never designed to have a transaxle? No! It was a smokin hard problem to solve.

Back at the track in France, Holt explained some of the capabilities of that inboard suspension. We have two ride heights for this car and two spring rates for this car, he said, noting that the Ford GT, which Multimatic built for Ford, also featured. When you put it in Track mode, it pulls the front down 40 [mm] and the rear down 30 [mm]. So not only do we get the center of gravity down, we also put a little rake into it, so it gives is a little bit more front [grip] so the cars really well balanced for the track.

The systems capabilities go far behind adjustable ride height, though. Then it does something else, Hold continued. It doubles the rear spring rate and takes the front spring rate up by 2.8 times. Now youve got an extremely stiff car; low, raked, big aero.

These are the kind of technologies that can produce extremely fast lap times at race tracks, even though the GTD is a street car. Fords goal is to set a new benchmark time at Germanys Nurburgring circuit with the GTD. From the lightweight carbon fiber body on every GTD to the active aerodynamics of the Performance package, weve learned from motorsport how to make the Mustang GTD excel everywhere, all in the quest for a sub-seven-minute lap of the Nurburgring, said Mustang GTD Chief Engineer Greg Goodall.

The company will make that attempt this summer, so stay tuned for the outcome.

Link:

Engineering the Maximum Mustang GTD - Design News

Prompt engineering techniques and best practices: Learn by doing with Anthropic’s Claude 3 on Amazon Bedrock … – AWS Blog

July 3, 2024 | Author: admin

You have likely already had the opportunity to interact with generative artificial intelligence (AI) tools (such as virtual assistants and chatbot applications) and noticed that you dont always get the answer you are looking for, and that achieving it may not be straightforward. Large language models (LLMs), the models behind the generative AI revolution, receive instructions on what to do, how to do it, and a set of expectations for their response by means of a natural language text called a prompt. The way prompts are crafted greatly impacts the results generated by the LLM. Poorly written prompts will often lead to hallucinations, sub-optimal results, and overall poor quality of the generated response, whereas good-quality prompts will steer the output of the LLM to the output we want.

In this post, we show how to build efficient prompts for your applications. We use the simplicity of Amazon Bedrock playgrounds and the state-of-the-art Anthropics Claude 3 family of models to demonstrate how you can build efficient prompts by applying simple techniques.

Prompt engineering is the process of carefully designing the prompts or instructions given to generative AI models to produce the desired outputs. Prompts act as guides that provide context and set expectations for the AI. With well-engineered prompts, developers can take advantage of LLMs to generate high-quality, relevant outputs. For instance, we use the following prompt to generate an image with the Amazon Titan Image Generation model:

An illustration of a person talking to a robot. The person looks visibly confused because he can not instruct the robot to do what he wants.

We get the following generated image.

Lets look at another example. All the examples in this post are run using Claude 3 Haiku in an Amazon Bedrock playground. Although the prompts can be run using any LLM, we discuss best practices for the Claude 3 family of models. In order to get access to the Claude 3 Haiku LLM on Amazon Bedrock, refer to Model access.

We use the following prompt:

Claude 3 Haikus response:

The request prompt is actually very ambiguous. 10 + 10 may have several valid answers; in this case, Claude 3 Haiku, using its internal knowledge, determined that 10 + 10 is 20. Lets change the prompt to get a different answer for the same question:

Claude 3 Haikus response:

The response changed accordingly by specifying that 10 + 10 is an addition. Additionally, although we didnt request it, the model also provided the result of the operation. Lets see how, through a very simple prompting technique, we can obtain an even more succinct result:

Claude 3 Haiku response:

Well-designed prompts can improve user experience by making AI responses more coherent, accurate, and useful, thereby making generative AI applications more efficient and effective.

The Claude 3 family is a set of LLMs developed by Anthropic. These models are built upon the latest advancements in natural language processing (NLP) and machine learning (ML), allowing them to understand and generate human-like text with remarkable fluency and coherence. The family is comprised of three models: Haiku, Sonnet, and Opus.

Haiku is the fastest and most cost-effective model on the market. It is a fast, compact model for near-instant responsiveness. For the vast majority of workloads, Sonnet is two times faster than Claude 2 and Claude 2.1, with higher levels of intelligence, and it strikes the ideal balance between intelligence and speedqualities especially critical for enterprise use cases. Opus is the most advanced, capable, state-of-the-art foundation model (FM) with deep reasoning, advanced math, and coding abilities, with top-level performance on highly complex tasks.

Among the key features of the models family are:

To learn more about the Claude 3 family, see Unlocking Innovation: AWS and Anthropic push the boundaries of generative AI together, Anthropics Claude 3 Sonnet foundation model is now available in Amazon Bedrock, and Anthropics Claude 3 Haiku model is now available on Amazon Bedrock.

As prompts become more complex, its important to identify its various parts. In this section, we present the components that make up a prompt and the recommended order in which they should appear:

The following is an example of a prompt that incorporates all the aforementioned elements:

In the following sections, we dive deep into Claude 3 best practices for prompt engineering.

For prompts that deal only with text, follow this set of best practices to achieve better results:

The Claude 3 family offers vision capabilities that can process images and return text outputs. Its capable of analyzing and understanding charts, graphs, technical diagrams, reports, and other visual assets. The following are best practices when working with images with Claude 3:

Consider the following example, which is an extraction of the picture a fine gathering (Author: Ian Kirck, https://en.m.wikipedia.org/wiki/File:A_fine_gathering_(8591897243).jpg).

We ask Claude 3 to count how many birds are in the image:

Claude 3 Haikus response:

In this example, we asked Claude to take some time to think and put its reasoning in an XML tag and the final answer in another. Also, we gave Claude time to think and clear instructions to pay attention to details, which helped Claude to provide the correct response.

Lets see an example with the following image:

In this case, the image itself is the prompt: Claude 3 Haikus response:

Lets look at the following example:

Prompt:

Claude 3 Haikus response:

Lets see an example. We pass to Claude the following map chart in image format (source: https://ourworldindata.org/co2-and-greenhouse-gas-emissions), then we ask about Japans greenhouse gas emissions.

Prompt:

Claude 3 Haikus response:

Lets see an example of narration with the following image (source: Sustainable Development Goals Report 2023, https://unstats.un.org/sdgs/report/2023/The-Sustainable-Development-Goals-Report-2023.pdf):

Prompt:

Claude 3 Haikus response:

In this example, we were careful to control the content of the narration. We made sure Claude didnt mention any extra information or discuss anything it wasnt completely confident about. We also made sure Claude covered all the key details and numbers presented in the slide. This is very important because the information from the narration in text format needs to be precise and accurate in order to be used to respond to questions.

Information extraction is the process of automating the retrieval of specific information related to a specific topic from a collection of texts or documents. LLMs can extract information regarding attributes given a context and a schema. The kinds of documents that can be better analyzed with LLMs are resumes, legal contracts, leases, newspaper articles, and other documents with unstructured text.

The following prompt instructs Claude 3 Haiku to extract information from short text like posts on social media, although it can be used for much longer pieces of text like legal documents or manuals. In the following example, we use the color code defined earlier to highlight the prompt sections:

Claude 3 Haikus response:

The prompt incorporates the following best practices:

Retrieval Augmented Generation (RAG) is an approach in natural language generation that combines the strengths of information retrieval and language generation models. In RAG, a retrieval system first finds relevant passages or documents from a large corpus based on the input context or query. Then, a language generation model uses the retrieved information as additional context to generate fluent and coherent text. This approach aims to produce high-quality and informative text by using both the knowledge from the retrieval corpus and the language generation capabilities of deep learning models. To learn more about RAG, see What is RAG? and Question answering using Retrieval Augmented Generation with foundation models in Amazon SageMaker JumpStart.

The following prompt instructs Claude 3 Haiku to answer questions about a specific topic and use a context from the retrieved information. We use the color code defined earlier to highlight the prompt sections:

Claude 3 Haikus response:

The prompt incorporates the following best practices:

In this post, we explored best prompting practices and demonstrated how to apply them with the Claude 3 family of models. The Claude 3 family of models are the latest and most capable LLMs available from Anthropic.

We encourage you to try out your own prompts using Amazon Bedrock playgrounds on the Amazon Bedrock console, and try out the official Anthropic Claude 3 Prompt Engineering Workshop to learn more advanced techniques. You can send feedback to AWS re:Post for Amazon Bedrock or through your usual AWS Support contacts.

Refer to the following to learn more about the Anthropic Claude 3 family:

David Laredo is a Prototyping Architect at AWS, where he helps customers discover the art of the possible through disruptive technologies and rapid prototyping techniques. He is passionate about AI/ML and generative AI, for which he writes blog posts and participates in public speaking sessions all over LATAM. He currently leads the AI/ML experts community in LATAM.

Claudia Cortes is a Partner Solutions Architect at AWS, focused on serving Latin American Partners. She is passionate about helping partners understand the transformative potential of innovative technologies like AI/ML and generative AI, and loves to help partners achieve practical use cases. She is responsible for programs such as AWS Latam Black Belt, which aims to empower partners in the Region by equipping them with the necessary knowledge and resources.

Simn Crdova is a Senior Solutions Architect at AWS, focused on bridging the gap between AWS services and customer needs. Driven by an insatiable curiosity and passion for generative AI and AI/ML, he tirelessly explores ways to leverage these cutting-edge technologies to enhance solutions offered to customers.

Gabriel Velazquez is a Sr Generative AI Solutions Architect at AWS, he currently focuses on supporting Anthropic on go-to-market strategy. Prior to working in AI, Gabriel built deep expertise in the telecom industry where he supported the launch of Canadas first 4G wireless network. He now combines his expertise in connecting a nation with knowledge of generative AI to help customers innovate and scale.

Read the original here:

Prompt engineering techniques and best practices: Learn by doing with Anthropic's Claude 3 on Amazon Bedrock ... - AWS Blog

Engineer charged in connection to military plane crash that left 16 service members dead – FOX13 Memphis

July 3, 2024 | Author: admin

OXFORD, Miss. - A former Warner Robins engineer has been arrested for making false statements and obstructing justice during a federal criminal investigation into a 2017 military plane crash, the U.S. Attorney's Office said in a statement.

James Michael Fisher, former Lead Propulsion Engineer at Warner Robins Logistics Center, was arrested Tuesday morning after an indictment was issued by a federal grand jury charging him with obstruction of justice and false statements during a criminal investigation.

According to the indictment, Fisher attempted to hide his past engineering decisions that may have been related to why the crash occurred. Specifically, the indictment claims that Fisher purposely concealed key engineering documents from investigators, and lied to investigators about his previous engineering decisions.

The crash of the marine transport aircraft known as "Yanky 72" occurred on July 10, 2017, near Itta Bena, Mississippi, and resulted in the deaths of fifteen Marines and one Navy Corpsman.

Fisher has been charged with two counts of making false statements and two counts of obstruction of justice. If convicted, he faces a maximum of twenty years in prison.

Originally posted here:

Engineer charged in connection to military plane crash that left 16 service members dead - FOX13 Memphis

Engineering Approved To Reconstruct Part Of Sand Bay Road – Door County Pulse

July 3, 2024 | Author: admin

Stantec project manager Skyler Witalison speaks during the June 20 Nasewaupee Town Board meeting about the company providing the town engineering services to reconstruct a section of Sand Bay Road. Photo by Kevin Boneske.

The Nasewaupee Town Board last week approved paying Stantec Consulting Services $10,500 for engineering services to reconstruct an eight-tenths of a mile section of Sand Bay Road from Sand Bay Lane to Woodlane Road.

Stantec project manager Skyler Witalison said the Wisconsin Department of Transportation has agreed under the Local Roads Improvement Program to reimburse the town up to 50% of the estimated $212,000 project, which will involve milling and repaving, replacement of all culverts and a slight reduction of the curvature in the curves.

Witalison said the project requires a professional engineer to design the project, put it out to bid and then approve the design and construction for the 20-foot-wide road with 3-foot shoulders.

He said another project the town is looking at applying for under the Agricultural Roads Improvement Program (ARIP) would improve about five miles of Idlewild Road from County Road C to Neils Road, with Stantec charging a fixed fee of $3,000 to apply for a grant.

The board agreed with Witalison to hold off on applying for that grant until he knows in July whether two other municipalities in the county are successful in their ARIP grant applications.

That will give us all a really good idea whether this is a feasible thing to move forward on or not, he said.

Town Chairman Steve Sullivan said it would cost around $1 million to improve that section of Idlewild Road, but the grant would cover 90% of that cost.

Sullivan said the criteria for awarding that grant would include how many dollars of agricultural product travel along the road.

See the article here:

Engineering Approved To Reconstruct Part Of Sand Bay Road - Door County Pulse

SIU engineering students help people with disabilities to enjoy nature – The Southern

July 3, 2024 | Author: admin

Zip Code

See the rest here:

SIU engineering students help people with disabilities to enjoy nature - The Southern

The Digital Transformation Employee Relationship Maturity Model – ENGINEERING.com

July 3, 2024 | Author: admin

Analyze current relationships between managers and employees to plan essential transformational changes.

Successful digital transformation depends on the people involved. Its not a new business concept, as managers have long understood that the relationship they have with their employees has a significant impact on overall performance. Companies with positively motivated employees have better performance than those that are demotivated. The extent and impact that employee motivation has on performance varies between companies.

In slowly changing companies, negatively motivated employees will have less impact than in those that are changing quickly. In the slowly changing organization, work is largely repeating things that have been done before and management activity is focused on directing and managing work. This is reflected in the scientific management principles established by Henry Ford and still often used today. For example, most jobs are made up of a narrow range of tasks, requiring relatively little skill, while a smaller number of professionals and managers provide direction and control.Employee participation and empowerment are weak because the form of work organization doesnt require it.

Interest in a new form of work organization, lean manufacturing, grew in western countries in the 1990s, after it was recognized that it achieved much higher levels of performance when applied in Japan. This system required higher levels of employee participation and empowerment than scientific management, because of the flexibility, teamwork, and continuous improvement needed to make it work. Failure to achieve its employee aspects is a frequent cause of failure to transition from scientific management to lean. While it was initially focused on manufacturing, elements of lean models have been introduced in most industry sectors today.

We have also seen growth in the use of agile approaches to work organization that enable more nimble response to rapid market and technological change. The agile system also requires higher levels of empowerment, innovation, and continuous improvement.Again, the system drives the role of the employee and how they are managed. Success in the human elements of this system is varied too and reflected in the success achieved with the agile approach.

Employees and digital transformation

Human elements are often cited as reasons for the failure of digital transformation activity. McKinsey reports that 70% of digital transformations fail due to employee resistance. Endava and IDC also report that 56% result in staff frustration and 50% lead to higher attrition, respectively. Only 21% of employees are engaged at work, according to a Gallup survey.

In companies that are digitally transforming (making changes based on and usually including information-based technologies), the role of employees is fundamentally important. Digital transformation often requires significant changes in the activities of employees. Working practices can change, making jobs more stimulating or boring, giving employees more or less control or discretion over their work activity. Jobs can become more or less well paid or secure and skills requirements can increase, decrease, or change altogether. For employees, the prospect of significant changes in their working lives can be a source of fear.

Digital transformation usually requires that employees are motivated towards its success. Their cooperation is necessary for specification, implementation, and sustainment of new technologies and working roles and practices. Levels of enthusiasm for and participation in continuous improvement for successful technology adoption and implementation are critical. Difficulty in achieving them in rapidly changing employment conditions is understandable.

Success in digital transformation, in most cases, requires significant change in the form of work organization used.

Employee relationships and digital transformation

For digital transformation to be successful, employees should be willing and active participants. They should feel confident about their own ability to transition due to the presence of good education and training. Trust between the employee and the organization should be based on the confidence that organizational decisions on digital transformation will be made transparently and with concern for employee welfare and quality of working life.

Involvement in continuous improvement and innovation is important for better implementation and exploitation of the technology. It is also important because it gives employees the ability to influence the changes that are happening around them. Processes should exist that allow this to happen, while also ensuring that improvement is consistent with organizational objectives.

The employee relationships necessary for digital transformation are usually significantly different than those that exist in many organizations today.

The digital transformation employee relationship maturity model

The Digital Transformation Employee Relationship Maturity Model helps organizations understand the current employee relationship and to plan activity to establish the relationships required for digital transformation.

The model has three levels:

The Traditional level is based on relationships commonly found in scientific management operating systems, but which may also linger in lean and agile systems.

The Progressive level represents what organizations pursuing a lean or agile model aspire to but dont always achieve. It is a step towards the relationship necessary for digital transformation success but is not enough.

The Transformative level represents the relationship needed for success in digital transformation.

The model is intended to be used by a group as a means of developing common understanding of and commitment to the plans you develop for change. Analysis of your current state should be undertaken by your management team. You should consider the model, collectively scoring the elements. This will identify the areas you need to develop. The action plan your team develops based on this will depend on your own conditions.

Conduct your own analysis by reviewing each of the elements of each level and allocating a score of 0 if they are non-existent through to 5 if they are fully present. This will require a candid approach from all involved. Add up the scores for each level the highest score will determine thedominant relationship approach. Next, consider the lower scoring items, from all levels, in the development of your own action plan.

The model can be applied to the organization as a whole or in individual parts. Id love to hear about your experience in applying the model.

The Digital Transformation Employee Relationship Maturity Model

Traditional

Progressive

Transformative

Peter Carr is the author and instructor of the University of Waterloo Watspeed Digital Transformation Certificate Program, available globally online, and focused on overcoming the challenges of successful technological change. The program is jointly offered with the Ontario Society of Professional Engineers.

Follow this link:

The Digital Transformation Employee Relationship Maturity Model - ENGINEERING.com

The tenured engineers of 2024 | MIT News | Massachusetts Institute of Technology – MIT News

July 3, 2024 | Author: admin

In 2024, MIT granted tenure to 12 faculty members across the School of Engineering. This years tenured engineers hold appointments in the departments of Aeronautics and Astronautics, Chemical Engineering, Civil and Environmental Engineering, Electrical Engineering and Computer Science (EECS, which reports jointly to the School of Engineering and MIT Schwarzman College of Computing), Mechanical Engineering, and Nuclear Science and Engineering.

My heartfelt congratulations to the 12 engineering faculty members on receiving tenure. These faculty have already made a lasting impact in the School of Engineering through both advances in their field and their dedication as educators and mentors, says Anantha Chandrakasan, chief innovation and strategy officer, dean of engineering, and the Vannevar Bush Professor of Electrical Engineering and Computer Science.

This years newly tenured engineering faculty include:

Adam Belay, associate professor of computer science and principal investigator at MITs Computer Science and Artificial Intelligence Laboratory (CSAIL), works on operating systems, runtime systems, and distributed systems. He is particularly interested in developing practical methods for microsecond-scale computing and cloud resource management, with many applications relating to performance and computing efficiency within large data centers.

IrmgardBischofberger,Class of 1942 Career Development Professor and associate professor of mechanical engineering,is an expert in the mechanisms of pattern formation and instabilities in complex fluids. Her research reveals new insights into classical understanding of instabilities and has wide relevance to physical systems and industrial processes. Further, she is dedicated to science communication and generates exquisite visualizations of complex fluidic phenomena from her research.

Matteo Bucciserves as theEsther and Harold E. Edgerton Associate Professor of nuclear science and engineering. His research group studies two-phase heat transfer mechanisms in nuclear reactors and space systems, develops high-resolution, nonintrusive diagnostics and surface engineering techniques to enhance two-phase heat transfer, and creates machine-learning tools to accelerate data analysis and conduct autonomous heat transfer experiments.

Luca Carlone, the Boeing Career Development Professor in Aeronautics and Astronautics, is head of the Sensing, Perception, Autonomy, and Robot Kinetics Laboratory and principal investigator at theLaboratory for Information and Decision Systems. His research focuses on thecutting edge of robotics and autonomous systems research, with a particular interest indesigning certifiable perception algorithms for high-integrity autonomous systems and developing algorithms and systems for real-time 3D scene understanding on mobile robotics platforms operating in the real world.

Manya Ghobadi, associate professor of computer science and principal investigator at CSAIL, builds efficient network infrastructures that optimize resource use, energy consumption, and availability of large-scale systems. She is a leading expert in networks with reconfigurable physical layers, and many of the ideas she has helped develop are part of real-world systems.

Zachary (Zach) Hartwig serves as theRobert N. Noyce Career Development Professor in the Department of Nuclear Science and Engineering, with a co-appointment at MITsPlasma Science and Fusion Center. His current research focuses on the development of high-field superconducting magnet technologies for fusion energy and accelerated irradiation methods for fusion materials using ion beams. He is a co-founder ofCommonwealth Fusion Systems, a private company commercializing fusion energy.

Admir Masic, associate professor of civil and environmental engineering, focuses on bridging the gap between ancient wisdom and modern material technologies. He applies his expertise in the fields of in situ and operando spectroscopic techniques to develop sustainable materials for construction, energy, and the environment.

Stefanie Mueller is the TIBCO Career Development Professor in the Department of EECS. Mueller has a joint appointment in the Department of Mechanical Engineering and is a principal investigator at CSAIL. She develops novel hardware and software systems that give objects new capabilities. Among other applications, her lab creates health sensing devices and electronic sensing devices for curved surfaces; embedded sensors; fabrication techniques that enable objects to be trackable via invisible marker; and objects with reprogrammable and interactive appearances.

Koroush Shirvan serves as theAtlantic Richfield Career Development Professor in Energy Studies in the Department of Nuclear Science and Engineering. He specializes in the development and assessment of advanced nuclear reactor technology. He is currently focused on accelerating innovations in nuclear fuels, reactor design, and small modular reactors to improve the sustainability of current and next-generation power plants. His approach combines multiple scales, physics and disciplines to realize innovative solutions in the highly regulated nuclear energy sector.

Julian Shun, associate professor of computer science and principal investigator at CSAIL, focuses on the theory and practice of parallel and high-performance computing. He is interested in designing algorithms that are efficient in both theory and practice, as well as high-level frameworks that make it easier for programmers to write efficient parallel code. His research has focused on designing solutions for graphs, spatial data, and dynamic problems.

Zachary P. Smith, Robert N. Noyce Career Development Professor and associate professor of chemical engineering, focuses on the molecular-level design, synthesis, and characterization of polymers and inorganic materials for applications in membrane-based separations, which is a promising aid for the energy industry and the environment, from dissolving olefins found in plastics or rubber, to capturing smokestack carbon dioxide emissions. He is a co-founder and chief scientist of Osmoses, a startup aiming to commercialize membrane technology for industrial gas separations.

Giovanni Traversoserves as the Karl Van Tassel (1925) Career Development Professor, an associate professor of mechanical engineering, and a gastroenterologist in the Division of Gastroenterology, Brigham and Womens Hospital (BWH), Harvard Medical School. His work focuses on the next generation of drug delivery systems that enable safe, efficient delivery of therapeutics. He also develops novel diagnostic tests and biomedical devices to support early detection of disease and drug administration.

Here is the original post:

The tenured engineers of 2024 | MIT News | Massachusetts Institute of Technology - MIT News

Additive Industries introduces metal 3D printer with adjustable build volume – ENGINEERING.com

July 3, 2024 | Author: admin

Users can scale up metal additive manufacturing via licensing service.

Metal additive manufacturing (AM) enables engineers to design and manufacture components that would be impractical or outright impossible using traditional machining and fabricating techniques. However, the technology still has a relatively high barrier to entry in the form of its upfront costs.

Additive Industries has announced a novel solution with the MetalFab 300 Flex 3D printer. The machine comes with a basic build volume of 300 x 300 x 300 mm, but through a monthly or perpetual licensing service, users can upgrade their build volume to 420 x 420 x 420 mm. Additionally, while the base model uses two 500W Yb-fiber lasers, the machine can incorporate two additional lasers through a field upgrade.

Companies today are confronted with a choice when they first enter metal additive, said Kartik Rao, strategic marketing director for Additive Industries in a press conference at RAPID + TCT 2024. Option one is that they buy a small printer to learn, develop some applications, do some material development, but when it comes to scaling, theyre stuck with a medium printer. The second option is to buy a larger printer right from the beginning, but the challenge with this is the increased financial risk.

The launch of the MetalFab 300 Flex is going to pose a profound question to customers want to adopt metal AM, which is, Why choose between the medium or large printer when you can have the best of both worlds on day one?

In addition to its unique architecture, the MetalFab 300 Flex was designed to incorporate several automated features to make it more accessible to new users, including powder handling, storage and laser calibration. Rao claimed the MetalFab 300 Flex is the most cost-affordable access to large-frame prints and with a base price of $730,000 and the option to expand the build volume, he made a compelling case.

Read the original here:

Additive Industries introduces metal 3D printer with adjustable build volume - ENGINEERING.com

IEEE-USA: Strengthening the Stance of Women in Engineering – goskagit.com

July 3, 2024 | Author: admin

Zip Code

See original here:

IEEE-USA: Strengthening the Stance of Women in Engineering - goskagit.com

Cloud Hosting

Oregon Tech engineering students win visionary award at statewide invention competition – Herald and News

Engineering the Maximum Mustang GTD – Design News

Prompt engineering techniques and best practices: Learn by doing with Anthropic’s Claude 3 on Amazon Bedrock … – AWS Blog

Engineer charged in connection to military plane crash that left 16 service members dead – FOX13 Memphis

Engineering Approved To Reconstruct Part Of Sand Bay Road – Door County Pulse

SIU engineering students help people with disabilities to enjoy nature – The Southern

The Digital Transformation Employee Relationship Maturity Model – ENGINEERING.com

The tenured engineers of 2024 | MIT News | Massachusetts Institute of Technology – MIT News

Additive Industries introduces metal 3D printer with adjustable build volume – ENGINEERING.com

IEEE-USA: Strengthening the Stance of Women in Engineering – goskagit.com

Recent Posts

Categories

Archives

Media Sites

Pages

Site admin