Manuscripts Being Accepted for ASCE-ASME Special Journal Issue...

Manuscripts Being Accepted for ASCE-ASME Special Journal Issue... Manuscripts Being Accepted for ASCE-ASME Special Journal Issue... Manuscripts Sought for ASCE-ASME Special Journal Issue on Human Performance and Decision-MakingThe guest editors of an upcoming special issue of the ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B Mechanical Engineering are currently accepting papers addressing the theme of philanthropisch performance and decision-making in complex industrialenvironments. Authors who are interested in submitting a paper for the special issue, which is expected to be published in the summerof 2019, should submit their papers electronically by Nov. 30, 2018.Complex industrial ordnungsprinzips are unquestionably subjected to major hazards, which are of great concern to businesses, governments,communities and wider stakeholder groups. For this reason, efforts are increasingly made to control unterstellung hazards and manage risks,supported by improve d computational capabilities and the application of sophisticated safety and reliability models. However, recent events such as the Air France 447 crash that caused 228 deaths and the Deepwater Horizon disaster that killed 11 people and released approximately 210 million gallons of oil into the Gulf of Mexico have shown that apparently rare or seemingly unforeseen scenarios, involving interactions between human factors, technologies and organizations, can trigger major accidents and lead to catastrophic consequences.The understanding of human behavioral characteristics, when combined with current technology aspects and organizational context, is extremely important for the safety and reliability field, in particular the role of non-technical competences, such as communication skills, leadership, decision-making capabilities and risk awareness. In addition to examining these matters, the special issue of the ASCE-ASME journal will explore contemporary regulatory approaches that shoul d go beyond rules of compliance and establish risk-based and goal-setting models in order to challenge academics and industry to develop new methods to assess human performance and anticipate critical issues.Topic areas to be covered in the special journal issue will include factors influencing human decision-making processes and human performance regulatory approaches to human factors, non-technical skills and human reliability analysis qualitative and quantitative models for assessing human reliability in high-hazard environments learning from accidents human factors in design safety culture and leadership risk perception and communication and uncertainty analysis related to human performance.The guest editors for this special issue of the ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B Mechanical Engineering are Dr. Raphael Moura, ANP, Brazil/University of Liverpool, United Kingdom, rmouraliverpool.ac.uk Dr. Luca Podofillini,Paul Scherrer Institute (PSI), Switzerland, luca.podofillinipsi.ch and Prof. Michael Beer, Leibniz University Hannover, Germany, and theUniversity of Liverpool, United Kingdom, and Tongji University, China, beerirz.uni-hannover.de.Papers can be submitted to the online peer review system for Part B. Mechanical Engineering by visiting the Journals Connect web pageat https//journaltool.asme.org/home/JournalDescriptions.cfm?JournalID=27Journal=RISK. When submitting a paper, please select SpecialIssue SI034B, and then assign the paper type as Research Paper.For more information on the ASME Journal Program, visit ASME Journals.

This simple trick will help you plan your retirement

This simple trick will help you plan yur retirementThis simple trick will help you plan your retirementIf you want to save more money, you have to create stakes for why saving money matters. Visualizing what your future will look life is one proven exercise to keep you from overspending.According to a new survey of 1,202 American adults conducted by investment management firm Capital Group, visualizing retirement can help people save up to 31% more per paycheck than those who do not think about this future.Why seeing is believing when it comes to saving moneyThe survey got half of the participants to picture the life they wanted to lead in their 60s, 70s and 80s before asking them to determine what percentage of each paycheck they wanted to save in a retirement plan. Those moments of reflection proved to be a great motivator. The visualizers said they would put down 31% more per paycheck, compared to the group that was only asked how much they wanted to save for retirement. For women and Millennials, the numbers were even higher. They said they were up to 50% more per paycheck when they visualized their retirement.We know that we need to save. The majority of Americans said they were aware they would need to self-fund their retirement. But it is easy to get caught up in the worries and obligations of the present and put off future worries around retirement for another day.Visualizing is not just tapping into the power of positive thinking, it is how our brains are wired to learn.Imagining our future helps us mentally rehearse how we want our bodies to act when it is showtime. Building a vivid picture of your future helps your brain remember to make it a present goal.One you prepare your body for action, you may feel less paralyzed by indecision. One survey found that people who keep vision boards of financial goals are almost twice as confident (59 percent vs. 31 percent) that they will achieve these goals than those who do not visualize.Images connect us more immediately and emotionally to our personal and financialgoals, and to oursetting andachievingthem. And images help us in our thinking and moving toward these goals, psychologist Barbara Nusbaum said about why visualization works. Im not surprised that people who imagine or picture their goals are better at budgeting and saving, and that these activities in themselves provide a sense of well-being.

Machine Learning Applies to Pipeline Leaks

Machine Learning Applies to Pipeline Leaks Machine Learning Applies to Pipeline Leaks Machine Learning Applies to Pipeline LeaksThe Keystone pipeline that would move crude oil from Canada through the U.S. to a refinery in Texas has been controversial, but it would only be a fraction of the more than two million miles of pipelines moving oil and gas around the country. Many existing and proposed pipelines spark the same concerns from people as Keystone the potential for leaks, especially those that go undetected for long periods of time.Existing detection systems mostly spot large problems, often visually by inspectors walking or flying over a pipeline. Internal systems commonly used in the oil and gas industry rely on computational pipeline modeling, which searches for anomalies in flow and pressure. That works well for large leaks, but falls short in finding smaller ones, of up to one percent of pipeline flow, says Maria Araujo, a manager in the mit niveau Systems Division of the So uthwest Research Institute.Even such a small percentage adds up quickly. She notes that one percent of the flow of the Keystone pipeline is in the neighborhood of 8,000 gallons per day. To improve the efficiency of detection systems, Araujo leads a kollektiv taking the technology to the next level using sensors, artificial intelligence, and deep learning. She came to the problem of leak detection while working with machine learning for autonomously driven vehicles.Sensors, cameras and hardware can be fitted to drones for inspection flyovers. Image Southwest Research InstituteWere not adapting technology, she says. Were using existing technology as building blocks. The problem is very different. With cars, youre looking for objects. Here, you look for liquids. Gasoline and diesel are transparent to the human eye. How do you differentiate between substances?Actually, the system looks for a variety of liquids. To begin tackling the challenge, the SWRI kollektiv tested four optical sens ors thermal, optical, hyperspectral and short wave infrared. They eliminated hyperspectral and short wave infrared, keeping off-the-shelf thermal and optical systems.Theres nothing unusual about using sensors for detecting leaks, but Araujo wanted to improve accuracy. So the SWRI team set out to adapt machine learning techniques, ultimately producing a multiplatform dubbed SLED, Smart Leak Detection System, that uses new algorithms to process images and identify, confirm or reject potential problems. Using feature extraction and classifier training methods, they taught computers to identify unique features across a wide range of environmental conditions.These algorithms thrive on lots of data, says Araujo. The team produced and collected thousands of images of data such as gasoline, diesel fuel, mineral oil, crude oil and water on various surfaces, including grass, gravel, dirt and hard surfaces such as concrete. The images were shot from numerous angles and under varying conditions from full sunlight to clouds and darkness. Its hard to operate under different environmental conditions, she adds. We found if you train the system under certain conditions, it gets tripped up in others, especially shading. Being able to work under shading and different temperatures was a big challenge in modifying algorithms.The ability of the system to provide a reliable fingerprint of small leaks as well as identify non-leak situations greatly increases its accuracy. Thats important because one of the biggest problems in the industry is a false alarm, says Araujo. Pipelines wind their way across long and often remote or underground rights of way. Sending work crews to remote areas and shutting a pipeline down costs a significant amount of money, and operators can dismiss alarms if there were previous false alerts, she says.SWRI further upgraded the system using deep-learning techniques. The team developed a deep convolutional neural network to process the tremendous amount of da ta to identify the hazardous liquids. Such techniques have been impractical in most cases, but advances and improvements in multi-core processing hardware are making it more common, say researchers. The final product is a fully autonomous system that can be used without human oversight, says Araujo.It can be fitted to pumping station platforms along pipeline routes, often a high-risk location because of the number of valves and equipment that can break. The SWRI team also has installed and successfully tested the system on drones that can fly over long reaches of a pipeline.We simulated pipeline leaks with a high degree of replication to the real world, she says. The work was done at SWRIs Forth Worth, TX, campus, using existing piping and systems. The initial goal was to identify the difference between water and hazardous liquids, but it exceeded expectations by differentiating between gasoline, crude oil, mineral oil and diesel, as well as water.Araujo now is working to adapt the technology to detect pipeline methane leaks in a program with the U.S. Department of Energys National Energy Technology Laboratory. The team is using infrared cameras to detect the spectral response of the gas. The deep-learning algorithm also must be reworked, a task she says is much more than an adaptation.This is a very significant tweak, she says. Now you are trying to detect a plume, something that shifts with the wind. It is a different problem.The goal is to produce an automated small-scale gaseous leak-detection system along the entire natural gas supply chain, including extraction, storage, distribution and transportation. For Further DiscussionWere using existing technology as building blocks. Gasoline and diesel are transparent to the human eye. How do you differentiate between substances?Maria Araujo, Southwest Research Institute