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Cake day: June 17th, 2023

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  • I can’t stand this line of thinking. Where do used cars come from? From handing down new cars. You can’t buy a new used car, so someone has to keep buying new cars to supply the used car market. I’m all for prolonging the life of cars and reducing buying new cars for something newer and shinier (my daily has been two smaller 1998s in a row), but buying used “for the environment” is greenwashing your hands of the fact that it was, in fact, a newly-manufactured car at one point. Paying for a used car incentivizes people to continue buying and selling their new cars. And yeah, the manufacturing pollution sucks for a new car, but so does the operational pollution of older vehicles. What’s the break-even for the manufacturing pollution of a 2020 car vs the continued operational emissions of a 2000 car? A decade? So by 2030, buying that new 2020 might be cleaner than continuing to prop up that 2000.

    Yeah, I’m excited for a better used EV market. Saving pollution is a side effect of me needing them to be more affordable. I hope a used 2nd gen Leaf will be compatible with me in 2026. I love saving gas like it’s a competition. I hypermiled and aero-nodded a 97 Taurus. I wanted 4x4 and got a 98 Geo tracker in 2020. I needed a pickup and got a 98 4cyl Ranger in 2022. I commute half my days on a 60mpg motorcycle. I have never bought a new vehicle in 20 years of driving, but used cars aren’t manufactured as used.




  • Despite the larger size and bright colors, they’re a bit more discreet than cigarettes. Most places that ban cigarettes equally ban vapes, so they’re often concealed more. That probably has a spillover effect to areas they’re allowed. They’re only activated when sucked, so, unlike a cigarette that’s burning whether you suck it or not, most people take a puff or 3 of vape and then go a few minutes without it. It’s not as obvious as a person deliberately smoking one whole cigarette.

    But maybe they’re not around you. I think the other comments covered the locations well enough, sarcasm included. But if you suddenly smell something sweet like fruit or cereal, casually take a look for someone vaping






  • This is an excellent science fair project. This is not some hidden solution to derailment. The article is devoid of details other than the student observed great variance in truck spring condition and did extensive hypothetical testing to show it can contribute to derailment.

    A contributing factor to both frequency and severity of derailment, sure, but a rare initial cause. “Springs” are not outside the considerations of the FRA. If you ever have trouble sleeping, read an FRA derailment report of some non-catastrophic event. Back in the 90s, they went so far as to identify dampening pads - thin plastic plates in the trucks - as the cause of derailment for new heavier intermodal cars. 1" thick plastic that deformed 1/8" under the 250,000lb+ load hit a harmonic frequency that led the trucks to hunt (wobble left and right but never finding a steady center) until the wheels hopped the tracks when entering a curve.

    There is a massive test facility in Pueblo, Colorado that has the smartest engineers on the continent working and testing there. There’s straights, curves, a super loop, road crossings, bridges, and even a concrete wall for crashing. They pull rail cars wired more than your road car all to find something, anything in the data. So can resonant frequency be a big contributor to derailment? Yeah, of course. A push becomes a shove 3 cars later and hits some limit. But there’s absolutely no way to predict or control it because every car, ever load, and every piece of track is going to have some unique critical frequency.

    And, for the record, the vast majority of those “1300 derailment per day” are low speed (which the article mentions) inside yards when assembling the full train (which the article does not mention). And, while the individual root cause of other derailments is varied, a majority of them are triggered by hard braking. It takes a few minutes for the brake signal to reach the end of the train - it’s a system operated by air pressure that’s used for both signaling and applying brakes. This means when the locomotive slams the brakes, the back of the train is still trying to shove it ahead at full speed long after the locomotive passes the engineer’s original line of sight. This force can be so great it makes the rail roll out form underneath the train. Add in coupler cushions and the train can shorten itself by a few hundred feet under this pressure, shoving couplers to the sides, taking out all the cushion slack, and adding slack towards the front as the middle and rear cars apply the brakes, creating a slinky longer than a mile weighing 140 tons per link. And yes, they’ve certainly “figured out” that empty cars in front of loaded cars is a huge contributing factor to these forces, there’s nothing easy about staying competitive. This platform shows it’s well aware of how much ground rail is losing to truck transportation. By rate and by severity, trucks vastly outweigh trains in terms of damage. The problem is rail failure is much more catastrophic and gets more news. 1 fiery derailment is news. 100 fiery trucks are a statistic. Same as planes. Way safer per passenger mile, way more deadly in failure.

    And why do brakes perform so poorly? Because the benefits that come from “interchange” of rail cars between trains, lines, and companies, also comes with a massive interchangeability headache for any changes. I’m not defending the rail industry as if they’re innocent bystanders, but we should all be able to understand the difficulty in trying to convert 1.4 million freight cars to a new system for the first time in, literally, 100 years. Many of these cars reach 50 years of service before being scrapped. Someone owns the rail, someone owns the locomotive, someone owns the freight car base, someone might own the freight car body, and someone owns the cargo. These are often entirely different entities for each. You can’t really do a soft rollout, it’s all or nothing. So here, we sit, with centurion technology. Ironically, the only type that has the same loco and cars every day is the least progressive load - coal trains. It’s runs from the coal processor to the coal plant and back, nothing else. Every other freight train can be diced and hashed multiple times a day.

    It’s not all doom and gloom. The East Palestine derailment put “wayside detectors” into the general public’s lexicon. They were talking about relatively simple infrared sensors placed next to the tracks to look for hot bearings. That’s old tech. There are currently massive sheds being implemented filled with cameras that record terabytes of info for each train passing through. It can identify missing bolts, cracked springs, and other failures on the spot. Control is notified and if it’s urgent, the car can be routed to a repair shop or the train can be stopped. There’s even a type with a trolley that rolls along with each truck to image the entire circumference of the wheels, looking for chips and cracks. A stall in the rolling tech doesn’t mean a stall in the industry. Make no mistake, derailment are expensive and companies and trying damn hard to sell solutions to the rail lines.

    Passenger trains have their own headaches. Practically each line has its own design engineers reinventing the wheel. While some lines may buy existing designs (notable, the Amtrak Acela that runs from DC to Boston is a French TGV), they have their own design flares (such as doubling the number of trucks for the Acela). These trains do run as consistent units, so they tend to have intercar communication systems and hydraulic brakes, minimizing the overall braking time. That’s why their derailments usually come from speeding or collisions rather than “random” accidents.










  • XeroxCool@lemmy.worldtoTechnology@lemmy.worldWhat are your AI use cases?
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    16 days ago

    AI isn’t useless, but it’s current forms are just rebranded algorithms with every company racing to get theirs out there. AI is a buzzword for tools that were never supposed to be labeled AI. Google has been doing summary excerpts for like a decade. People blindly trusted it and always said “Google told me”. I’d consider myself an expert on one particular car and can’t tell you how often those “answers” were straight up wrong or completely irrelevant to one type of car (hint, Lincoln LS does not have a blend door so heat problems can’t be caused by a faulty blend door).

    You cite Google searches and summarization as it’s strong points. The problem is, if you don’t know anything about the topic or not enough, you’ll never know when it makes mistakes. When it comes to Wikipedia, journal articles, forum posts, or classes, mistakes are possible there too. However, those get reviewed as they inform by knowledgeable people. Your AI results don’t get that review. Your AI results are pretending to be master of the universe so their range of results is impossibly large. That then goes on to be taken is pure fact by a typical user. Sure, AI is a tool that can educate, but there’s enough it proves it gets wrong that I’d call it a net neutral change to our collective knowledge. Just because it gives an answer confidently doesn’t mean it’s correct. It has a knack for missing context from more opinionated sources and reports the exact opposite of what is true. Yes, it’s evolving, but keep in mind one of the meta tech companies put out an AI that recommended using Elmer’s glue to hold cheese to pizza and claimed cockroaches live in penises. ChatGPT had it’s halluconatory days too, it just got forgotten due to Bard’s flop and Cortana’s unwelcome presence.

    Use the other two comments currently here as an example. Ask it to make some code for you. See if it runs. Do you know how to code? If not, you’ll have no idea if the code works correctly. You don’t know where it sourced it from, you don’t know what it was trying to do. If you can’t verify it yourself, how can you trust it to be accurate?

    The biggest gripe for me is that it doesn’t understand what it’s looking at. It doesn’t understand anything. It regurgitates some pattern of words it saw a few times. It chops up your input and tries to match it to some other group of words. It bundles it up with some generic, human-friendly language and tricks the average user into believing it’s sentient. It’s not intelligent, just artificial.

    So what’s the use? If it was specifically trained for certain tasks, it’d probably do fine. That’s what we really already had with algorithmic functions and machine learning via statistics, though, right? But sparsing the entire internet in a few seconds? Not a chance.

    Edit: can’t beleive I there’d a their