Thaw could release Cold War-era U.S. toxic waste buried under Greenland's ice

2016-08-09 13:13:15

OSLO Global warming could release radioactive waste stored in an abandoned Cold War-era U.S. military camp deep under Greenland's ice caps if a thaw continues to spread in coming decades, scientists said on Friday.Camp Century was built in northwest Greenland in 1959 as part of U.S. research into the feasibility of nuclear missile launch sites in the Arctic, the University of Zurich said in a statement.Staff left gallons of fuel and an unknown amount of low-level radioactive coolant there when the base shut down in 1967 on the assumption it would be entombed forever, according to the university.It is all currently about 35 meters (114.83 ft) down. But the part of the ice sheet covering the camp could start to melt by the end of the century on current trends, the scientists added."Climate change could remobilize the abandoned hazardous waste believed to be buried forever beneath the Greenland ice sheet," the university said of findings published this week in the journal Geophysical Research Letters. The study, led by York University in Canada in collaboration with the University of Zurich, estimated that pollutants in the camp included 200,000 liters (44,000 UK gallons) of diesel fuel and the coolant from a nuclear generator used to produce power."It's a new breed of political challenge we have to think about," lead author William Colgan, a climate and glacier scientist at York University, said in a statement. "If the ice melts, the camp's infrastructure, including any remaining biological, chemical, and radioactive wastes, could re-enter the environment and potentially disrupt nearby ecosystems," the University of Zurich said.The study said it would be extremely costly to try to remove any waste now. It recommended waiting "until the ice sheet has melted down to almost expose the wastes before beginning site remediation." There was no immediate comment from U.S. authorities. (Reporting By Alister Doyle; Editing by Andrew Heavens)

The Web is (Finally) a Mature Platform

2016-08-01 21:28:07

Or, why MEAN will never be LAMPI started my career as a “web developer” circa 2007. I’d dabbled before with Javascript while making sites in HTML/CSS as early as middle school, but that was my first real job as a real software developer. Back in those days, LAMP (Linux/Apache/MySQL/PHP) was about the only sensible way to build a web application. You could use Java servlets, of course, or build applications using CGI, but once PHP was stable and available and straightforward, and it quickly became the obvious choice for a huge range of applications.I’m telling you this because the other day I stumbled across this question on Quora, from an old PHP/MySQL developer who was just coming back into the game, and wanted to know what the present state of the art was. There were a huge number of different responses (of which my answer was obviously the best), but one that as conspicuously repeated was the idea of the “MEAN” stack.So Why MEAN?MEAN is a stack consisting of MongoDB for a datastore, Express.js for a backend framework, Angular.js for a frontend framework, and Node.js for a server/platform. It’s a perfectly fine technology stack, I’m sure, but to me it doesn’t seem so exceptional. After all, MongoDB has drivers for pretty much any language you would want to use, Express.js likewise has equivalents, and Angular doesn’t care a bit what language you use on the backend. Really, the whole effort just seems like a way to make Node.js development easier and/or more standardized, but it seems to have stuck for whatever reason.So why MEAN? I feel that MEAN is a meme born of developers faced with a paradox of choice. There are so many popular, proven, and acceptable ways to build an application for the web now, each with its own set of tradeoffs but all fully applicable to the general case.In the latest edition of their Web Framework Benchmarks, Techempower tested 162 frameworks, spread across 25 programming languages and running on 69 platforms. All of these are ways that people are really using to write web applications. It’s not like Express or Node is the most performant option (it does ok, tending to end up near the middle of the pack at around 10% of the most performant option) and it’s definitely not the most expressive (compare the express.js source to the flask, which tended to perform about as well).For most projects starting out, before the true performance bottlenecks of the application are even close to understood, MEAN is just one of a veritable universe of perfectly fine choices. It stands out in exactly no dimension, save the number of blog posts (and quora answers, apparently) touting it as the next great thing. MEAN is the next acceptable thing. By the time your application is starting to experience problems, you should have the enough knowledge about the nature of your issues that you can make an educated choice of datastore/language/platform/application architecture.Three years ago when I started at Tapstream, the product was entirely a Django/Postgres application. Now, the core is implemented in Java with Cassandra as a canonical datastore using a variety of big-data and web frameworks for different components, with a single-page (ha! it’s huge) Ember.js dashboard running against a Flask REST backend (although it still uses the same Postgres database as ever (ignoring the Ship-of-Theseus issues with this usage of “same”)). If your application lasts through 3 years of growth, I guarantee you’ll experience a similar transformation.So What Do I Do?If you must keep up with the joneses, in my opinion, this is the next big thing in web development. Otherwise, you should experiment with the myriad of options available and find something that works for you! The more things you try, the better the decision you’ll make when you actually have to make a decision based on real technical tradeoffs.The choice is, and always will be, yours!This original blog post was sponsored by Worklog Assistant, a JIRA time tracker. You can find the original source here. 

Fine Tune Your Polling and Batching in Mule ESB

2016-07-26 02:13:32

They say it's best to learn from others. With that in mind, let's dive into a use case I recently ran into. We were dealing with a number of legacy systems when our company decided to shift to a cloud-based solution. Of course, we had to prepare for the move — and all the complications that came with it.Use CaseWe have a legacy system built with Oracle DB using Oracle forms to create applications and lots and lots of stored procedures in the database. It's also been in use for over 17 years now with no major upgrades or changes. Of course, there have been a lot of development changes over these 17 years that taken the system close to the breaking point and almost impossible to implement something new. So, the company decided to move to CRM (Salesforce) and we needed to transfer data to SF from our legacy database. However, we couldn't create or make any triggers on our database to send real-time data to SF during the transition period.SolutionSo we decided to use Mule Poll to poll our database and get the records in bulk, then send them to SF using the Salesforce Mule connector.I am assuming that we all are clear about polling in general. If not, please refer to references at the end. Also, if you are not clear with Mule polling implementation there are few references at the bottom, too. Sounds simple enough doesn't it? But wait, there are few things to consider.What is the optimum timing of the poll frequency of your polls?How many threads of each poll you want to have? How many active or inactive threads do you want to keep?.How many polls can we write before we break the object store and queue store used by Mule to maintain your polling?What is the impact on server file system if you use watermark values of the object store?How many records can we fetch in one query from the database?How many records can we actually send in bulk to Salesforce using SFDC?These are few, if not all the considerations you have to do before implementation. The major part of polling is the WATERMARK of polling and how Mule implements the watermark in the server.Polling for Updates Using WatermarksRather than polling a resource for all its data with every call, you may want to acquire only the data that has been newly created or updated since the last call. To acquire only new or updated data, you need to keep a persistent record of either the item that was last processed, or the time at which your flow last polled the resource. In the context of Mule flows, this persistent record is called a watermark.To achieve the persistency of watermark, Mule ESB will store the watermarks in the object store of the runtime directory of a project in the ESB server. Depending on the type of object store you have implemented, you may have a SimpleMemoryObjectStore or TextFileObjectStore, which can be configured like below: Below is a simple memory object store sample: Below is text file object store sample: For any kind of object store, Mule ESB creates files in-server, and if the frequency of your polls are not carefully configured, then you may run into file storage issues on your server. For example, if you are running your poll every 10 seconds with multiple threads, and your flow takes more than 10 seconds to send data to SF, then a new object store entry is made to persist the watermark value for each flow trigger, and we will end up with too many files in the server object store.To set these values, we have consider how many records we are fetching from the database, as SF has limit of 200 records that you can send in one bulk. So, if you are fetching 2,000 records, then one batch will call SF 10 times to transfer  these 2,000 records. If your flow takes five seconds to process 200 records, including the network transfer to send data to SF and come back, then your complete poll will take around 50 seconds to transfer 2,000 records.If our polling frequency is 10 seconds, it means we are piling up the object store.Another issue that will arise is the queue store. Because the frequency and execution time have big gaps, the queue store's will also keep queuing. Again, you have to deal with too many files.To resolve this, it’s always a good idea to fine-tune your execution time of the flow and frequency to keep the gap small. To manage the threads, you can use Mule's batch flow threading function to control how many threads you want to run and how many you want to keep active.I hope few of the details may help you set up your polling in a better way.There are few more things we have to consider. What happens when error occurs while sending data? What happens when SF gives you error and can't process your data? What about the types of errors SF will send you? How do you rerun your batch with the watermark value if it failed? What about logging and recovery? I will try to cover these issues in a second blog post.Refrences:

SpaceX rocket lifts off on cargo run, then lands at launch site

2016-07-19 06:08:56

CAPE CANAVERAL, Fla. An unmanned SpaceX rocket blasted off from Florida early on Monday to send a cargo ship to the International Space Station, then turned around and landed itself back at the launch site.The 23-story-tall Falcon 9 rocket, built and flown by Elon Musk’s Space Exploration Technologies, or SpaceX, lifted off from Cape Canaveral Air Force Station at 12:45 a.m. EDT (0445 GMT).Perched on top of the rocket was a Dragon capsule filled with nearly 5,000 pounds (2,268 kg) of food, supplies and equipment, including a miniature DNA sequencer, the first to fly in space.Also aboard the capsule was a metal docking ring of diameter 7.8 feet (2.4 m), that will be attached to the station, letting commercial spaceships under development by SpaceX and Boeing Co. ferry astronauts to the station, a $100-billion laboratory that flies about 250 miles (400 km) above Earth. The manned craft are scheduled to begin test flights next year.Since NASA retired its fleet of space shuttles five years ago, the United States has depended on Russia to ferry astronauts to and from the station, at a cost of more than $70 million per person.As the Dragon cargo ship began its two-day journey to the station, the main section of the Falcon 9 booster rocket separated and flew itself back to the ground, touching down a few miles south of its seaside launch pad, accompanied by a pair of sonic booms. "Good launch, good landing, Dragon is on its way," said NASA mission commentator George Diller.Owned and operated by Musk, the technology entrepreneur who founded Tesla Motors Inc, SpaceX is developing rockets that can be refurbished and re-used, potentially slashing launch costs. With Monday’s touchdown, SpaceX has successfully landed Falcon rockets on the ground twice and on an ocean platform during three of its last four attempts.SpaceX intends to launch one of its recovered rockets as early as this autumn, said Hans Koenigsmann, the firm's vice president for mission assurance. (Reporting by Irene Klotz, Editing by Chris Michaud and Clarence Fernandez)

Tesla crash raises stakes for self-driving vehicle startups

2016-07-12 09:34:19

DETROIT/SAN FRANCISCO Concerns raised by the first reported fatality in a semi-automated car were expected to speed adoption of more sensitive technology to help vehicles see and drive themselves safely, increasing demand on the emerging autonomous vehicle technology industry, investors and analysts said.Goldman Sachs forecasts the market for advanced driver assistance systems and autonomous vehicles will grow from about $3 billion last year to $96 billion in 2025 and $290 billion in 2035. More than half of that revenue in 20 years, Goldman estimates, will come from radar, cameras and lidar, a sensor that uses laser – all tools considered essential to building vehicles that can pilot themselves.The May 7 death of Ohio technology company owner Joshua Brown in a Tesla Motors Inc (TSLA.O) Model S while the car's semi-automated Autopilot system was engaged highlighted the limitations of current automated driving systems.Tesla’s Autopilot system uses cameras and radar, but not lidar. The company said its system would have had trouble distinguishing a white semi-trailer positioned across a road against a bright sky.Industry executives and analysts told Reuters they expect the Tesla crash will spur investment in self-driving vehicle systems that combine multiple of sensors, including lidar."As we move to a higher level of autonomy in vehicles, you’re going to want to have more redundancy," which radar and lidar can provide, Dan Galves, senior vice president at vision safety system maker Mobileye NV(MBLY.N) , said in an interview. "The more sensors, the better."Carmakers have been using multiple sensors in prototypes that are in testing but not yet ready for market. A variety of technologies with overlapping capabilities is seen as a way to increase safety under a wider range of circumstances.The valuations of some self-driving startups "may even increase if there are companies that can solve some of the issues" the Tesla accident raised, said Quin Garcia, managing director of AutoTech Ventures, a Silicon Valley investment firm.Semi-automated systems such as General Motor Co's (GM.N) SuperCruise and Traffic Jam Pilot from Volkswagen AG's (VOWG_p.DE) Audi are due on the market in 2017-2018. Ford Motor Co(F.N) expects to deploy a semi-automated system, using Velodyne lidar, in 2018. Toyota Motor Corp(7203.T), which is investing more than $1 billion in such self-driving technologies as robotics and artificial intelligence, said it aims to put fully driverless cars on the road in time for the 2020 Tokyo Olympics.Delphi Automotive PLC (DLPH.N) plans to build lidar vision systems with technology from Quanergy Systems, which makes solid state lidar systems. Delphi plans to combine information from the lidar system with radar and other driver assistance technology to create a 360-degree view around a car, a company official said. Delphi has an investment in Quanergy, one of more than 50 self-driving startups that together have raised more than $800 million in investment capital in the past decade, according to a Reuters analysis of publicly available data.At least two of those startups - Quanergy which makes solid state lidar sensors, and Zoox, which is developing fully automated vehicle systems - have jumped in value to more than $1 billion each since GM's $1.2-billion acquisition earlier this year of another self-driving startup, Cruise Automation.Quanergy and Zoox hope to follow the lead of Mobileye, an Israeli supplier of vision-based safety systems to 25 global automakers, including Tesla. Co-founded in 1999 by a computer science professor at Hebrew University, Mobileye went public in 2014 and today is valued at nearly $10 billion.Mobileye plans by 2020 to offer a hardware/software system that can gather, fuse and analyze data from 20 different sensors, including cameras, lidar and radar. The company's new EyeQ5 "system on chip" will be a key component in a fully autonomous driving system that is being jointly developed with BMW AG (BMWG.DE) and Intel Corp (INTC.O) and is aimed at production in 2021.Like Mobileye, Velodyne, a leading supplier of laser-based lidar systems, works with many of the world’s top automakers, including Ford, GM, BMW, Toyota Honda Motor Co(7267.T) and Daimler AG’s (DAIGn.DE) Mercedes-Benz."Our clients want to (combine) lidar and cameras," Velodyne's Marta Hall, president of business development, told Reuters in an interview. Automakers are stepping up orders as lidar systems come down in size and price, she said. Among the potential beneficiaries of this growing interest is LeddarTech, a relatively young startup based in Canada's Quebec City. The company is providing LED-based lidar systems to French supplier Valeo (VLOF.PA), which also buys vision-based systems from Mobileye.Germany's Robert Bosch, which has been developing self-driving components and systems for more than 15 years, buys lidar from an unnamed Tier II supplier and intends to package it in a highly automated “highway pilot” system intended for series production in 2020, said spokesman Tim Wieland."Bosch sees the necessity for a sensor setup that includes radar, video and lidar," Wieland said. The three sensors "complement each other very efficiently."REGULATION AND LITIGATION WILD CARDS Regulation and litigation are two big wild cards for the autonomous driving sector.Safety regulators and industry executives have said self-driving cars ultimately could slash traffic fatalities – about 35,000 last year in the United States and more than 1.2 million globally - by up to 90 percent. But regulators are also concerned that drivers could be lulled into unsafe behavior by systems that take control for a time, but expect human operators to re-take command in an emergency.The National Highway Traffic Safety Administration is investigating the role of Autopilot in the Florida accident and another crash in Pennsylvania involving a Tesla vehicle. The agency also is expected to roll out this summer broad guidelines for deploying autonomous vehicle technology."I hope NHTSA does not overreact" to the crash, said Stefan Heck, co-founder of Nauto, another Silicon Valley self-driving startup with corporate backing. "The tradeoff is quite clear: Some safety improvement is better than none."Product liability for automated vehicles is uncharted territory. The U.S. Transportation Department has said an automated driving system could be considered the "driver" for regulatory purposes.Industry executives are betting that consumer interest in the technology will rise.A survey conducted by AlixPartners in June - before the Tesla accident was reported publicly - found that 90 percent of respondents would be interested in a self-driving car that would let the driver take the wheel from time to time. The same survey noted that nearly 80 percent of respondents would pay for the technology - including 10 percent who would spend up to $5,000.The favorable response rates are much higher than in previous surveys on self-driving technology.News of the Tesla crash "is not going to put too much of a dent in public perception" of self-driving cars, said AlixPartners' Mark Wakefield. (Reporting by Paul Lienert in Detroit and Alexandria Sage in San Francisco; Editing by Joe White and Lisa Girion)

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