When investigating the causation of disasters, the Swiss cheese model was often brought up. I know it sounds like a strange comparison to bring up cheese, but bear with me. Imagine a block of holy cheese, sliced. Some holes only go part of the way through it. Imagine an event, say you tripping up on a child's toy. This could have been prevented in a number of ways, say telling your two-year-old child to put their toys away after playing with him or looking where you're going. In the Swiss cheese model, either of these potential blockers for your painful fool would have averted disaster. But say all of those failures or holes line up. Like how you can line up the holes in slices of Swiss cheese to see all the way through. Then disaster is inevitable. Here is a more official sounding explanation. The Swiss cheese model likens human systems to multiple slices of cheese stacked side by side, in which the risk of threat becoming a reality is mitigated by the differing layers and the types of defences which are layered behind each other. This means that one failure wouldn't result in disaster. However, more multiple lapses in safety procedures and human factors line up those holes, resulting in a bigger failure of the systems placed to prevent disaster. Now I've got you thinking about tasty cheese. You might be asking, what has this got to do with anything? Well, one such incident in Tenerife in 1977 would be the perfect example of how this model works. Today, we're looking at the collision of KLM Flight 4805 and Pan Am Flight 1736. As such, I'm going to rate it here 7 on my patented plane default disaster scale, but here 9 on my legacy scale. This is the result of an impatient pilot, multiple communications failings by using non-standard phrases, radio interference, a lost plane on a runway, a terrorist attack at another airport and poor visibility due to low clouds. Each individual situation may have not led to disaster, but when added together, the collision of KLM Flight 4805 and Pan Am 1736 almost became inevitable. It is the 27th of March 1977 and members of the Canary Islands independence movement detonate a bomb in a flora shop in Gran Canaria Airport near Las Palmas, Gran Canaria, Injuring 8. As security forces scrabble to investigate, a second bomb threat is called in. Not wanting to be responsible for a massacre, the police are forced to shut down all traffic to Gran Canaria Airport whilst they search for the hidden device. With multiple planes in the air, the decision by air traffic control is to divert many aircraft to nearby Los Rodeus Airport in Tenerife, now called Tenerife North Airport, some 75km away. Los Rodeus, by the early afternoon, was a wash with aircraft far more than usually experienced at the airport. The airport had only one runway and one main taxiway running parallel to it, with four short taxiways for connecting the two. So much was the congestion on the ground that planes were blocking the taxiway, forcing aircraft to taxi on the runway before positioning themselves for takeoff, a procedure known as back-taxing. At the beginning of the shutdown, Pan Am Flight 1736's crew would have much preferred to stay in the air in a holding pattern, as at 1315 they had around two hours worth of fuel. However, the flight was ordered to land in Tenerife, much to the disappointment of her crew and passengers. Much better for the passengers' comfort to be ready to land when the all-clear was given, especially due to the long distance the flight had covered from Los Angeles with an intermediate stop at New York. The crew on board consisted of Captain Victor Grubbs aged 56, First Officer Robert Bragg aged 39, Flight Engineer George Warnes 46 and 13 flight attendants, along with 380 passengers, mostly of retirement age. The flight crew had over 30,000 hours of experience, although not all covered on the Boeing 747, the crew were confident in the plane. The Pan Am plane was quite a historically important specimen for the 747's legacy. It had operated the inaugural 747 commercial flight on the 22nd of January 1970. Not only that, but the plane had the honour on the 2nd of August 1970 to become the first 747 to be hijacked. Another plane caught out during the disruption of services to Gran Canaria Airport was KLM 4805, which, much like Pan Am 1736, had found itself diverted to Tenerife. The KLM aircraft was also a 747 and was chartered by the Holland International Travel Group and had departed from Amsterdam Airport's Shrevepole, Netherlands. The crew were also experienced but was captained by somewhat of a KLM celebrity, Jacob Godhuzen Van Zanten aged 50. Van Zanten was KLM's star pilot and head 747 training instructor. So much was his value to the company that his face was even on advertising material for the airline. His flying career started in 1947 when he gained his private pilot's licence and by 1950 he had achieved his commercial certification and began working for KLM the same year. By 1977 he had amassed over 11,000 hours of flying time, 1,500 of which were on the 747. Accompanying Van Zanten on the crew of the KLM were First Officer Class Merz and Flight Engineer William Schrader. Again, like the Pan Am flight, the team combined had several tens of thousands of hours of experience. By late afternoon, Gran Canaria Airport slowly opened up to traffic again. This meant that the much delayed and diverted aircraft could complete their journeys. Not wanting to be delayed further at its destination and not wanting to break the laws on duty crew working time, Van Zanten took the opportunity to refuel the KLM where they were. However, this would block in other aircraft including 1736 for over half an hour. Much to the annoyance of the Pan Am, which still had enough fuel on board for the final leg of its journey, the captain and First Officer left the plane to measure the gap between the KLM and the active runway, but the distance was lower than the safe minimum. They were obliged to wait. The added delay was further compounded by a Dutch family not going back to the KLM on time. Finally, at 1656, Van Zanten's KLM radioed the control tower for clearance to taxi. Canaria Airport is at 633 metres above sea level. This has an unusual side effect. Clouds at 600 metres above ground level at the nearby coast are at runway level at Los Rodeos. With clouds being blown in from the sea, this creates variable visibilities where clear visuals one minute can almost instantly drop below minimums the next. And the 27th of March was just such a day. After three minutes of confused conversation, at 1659, the instructions to proceed down the main runway and to backtrack for takeoff were received and understood by the flight crew. These instructions meant that the KLM would turn 180 degrees once at the end of the runway in preparation for takeoff. At 1702, the Pan Am called the tower for clearance to taxi. This was granted, but with the additional instructions to turn onto the third left taxiway. Pretty straightforward you might think, but when you look at the diagram of the airport, those instructions make little sense. Only two taxiways on site look viable for a 747 to make the desired turn to reach the end of the runway. The taxiways were unmarked and by now visibility on the runway was concerningly low. The Pan Am called up the tower to confirm that it was not indeed taxiway one instead of the requested three. The ATC replied abruptly, third one sir, one, two, three, third, third one. The aircraft proceeded to taxi up the main runway, marking off the taxiways to their left as they passed. Number one and two were successfully identified, but lost in a thick fog. The crew failed to identify number three. A confused conversation broke out in the cockpit, hinting that they were now lost. Meanwhile, the KLM plane contacted ATC to confirm that they had passed a fourth exit. The tower replied OK, make 180 and report ready for clearance. Almost immediately after the plane was in a position for takeoff, Van Zanten pulled back the throttles and the plane started to move. First Officer Meurs advised Van Zanten that ATC clearance had not been given and the captain replied no, I know that, go ahead and ask. In doing so, bringing the engines back to idle. At 1705, the KLM confirmed it was ready for takeoff and awaiting ATC. Soon after ATC informed the KLM what their route was to follow after takeoff, but no authorization for takeoff was actually given. The KLM replied and confirmed the information given, but added we are at takeoff, which is a non-standard message. ATC replied OK, stand by for takeoff, I will call you back. During this message, the KLM experienced radio interference, muffling the instructions. Van Zanten interrupted the co-pilot's readback with the comment, we're going. Taking ATC's non-standard use of OK as authority to proceed. The KLM released its brakes, took power and started to move down the runway. With ATC not being able to see the two planes in the thick fog, they thought all movements were still under control. The tower then contacted the Pan Am flight, saying Papa Alpha report when runway clear. To which Pan Am replied OK, we'll report when runway clear. Unbeknownst to the Pan Am and the tower that whilst this final conversation took place, the KLM had started on a head-on collision course. Hearing the communications between the tower and the Pan Am, the KLM engineer said, is he not clear that Pan American? Van Zanten replied, oh yes, but still continued with the takeoff. The KLM loomed off in the fog as it hurtled towards the Pan Am. Captain Victor Grubb shouted, there he is, the Pan Am was now near exit number four.  Captain Bragg applied the throttles and tried to turn onto the grass off the runway in an effort to avert disaster. Shortly after the KLM crew saw the Pan Am on the ground, by now it's travelling too fast to abort the takeoff. They rotated the aircraft and attempted to clear the Pan Am by getting airborne, causing a tail strike. At a speed of 160 miles per hour and at a distance of 100 metres from the Pan Am, the KLM's nose gear cleared the aircraft off the ground. But the left side engines, lower fuselage and main landing gear struck the upper part of the Pan Am's fuselage. The result was ripping apart the centre part of the Pan Am, almost directly above the wing. The right side engines crashed through the Pan Am's upper deck behind the cockpit. The momentum of the takeoff speed of the KLM led to a brief time in the air, but it had received critical damage and subsequently the plane stalled, took a sharp roll and impacted the ground 150 metres down from the Pan Am. After impact, the freshly fuelled aircraft burst into an uncontrollable fireball engulfing all within. Seeing the Inferno, rescue workers made a beeline to the site of the KLM, completely ignoring the Pan Am. In a miraculous turn of luck, the captain, first officer and flight engineer of the Pan Am 1736 had survived. This would come to haunt them for the rest of their lives. The only survivors of the disaster were 61 members of crew and passengers aboard the Pan Am. Many escaped through and now open central part of the fuselage onto the still intact wings, jumping to the relative safety of the runway. All aboard the KLM perished, most likely from the impact and fire, when the aircraft hit the ground. Although not severely deformed, the escape doors on the plane's fuselage were not operated. The 61 survivors were promptly transported to Santa Cruz hospitals. Needless to say, Los Rodeos Airport, the only operating airport on Tenerife at the time, was closed to all fixed-wing traffic. This closure would last for two days to allow some clear-up efforts. With 583 dead, 61 injured and two multi-million-dollar aircraft written off, the only questions available are how and why. Initially, KLM, upon hearing about a collision involving one of their fleet, set about assembling an investigation team. The logical person for this task would be the head of pilot training, Captain Jacob Baird-Housen Van Zanten. But the company would be shocked to find out at the helm of the disaster was their most experienced 747 pilot. The investigation into the event would gain international attention, in part due to it being a collision between a Dutch and American Airlines on Spanish soil, but also due to the entirely preventable nature of the crash. Both planes were sound mechanically and both crews had more than enough experience for operating in such conditions. However, Van Zanten did take off, disregarding all the apparent warning signs, for example, his first officer not gaining clearance for take-off and the ATC giving no explicit instructions to allow him to, which should have pointed him to stay on the ground. In the back of the captain's mind was a recently imposed law on the limits of time a crew can operate. When the rule came into force, it was relatively easy for a captain to work out how much time his crew had to work. But after December 1976, the process to work out the amount of operating time left became too complicated. This meant that crews could not work without fully knowing their remaining time and as such aired on the side of caution, as a violation could result in not just punishment from the company, but also Dutch law. This would have played on Van Zanten's mind and even on that of the crews, where exceeding the limit would result in the plane being grounded at Los Parmas and could have cost the company money and affected plans back at home. The fear of being stuck far away from home would have had an effect on the crew called fixation, an almost tunnel vision like mental state where the goal is the most important thing and all potential hazards are ignored. In the Spanish investigation into the crash, it was thought that this phenomenon along with communication areas led Van Zanten to believe that he had the authority to take off. Let's look at the communication areas that could have caused this. The Dutch investigation pointed out that during communications between the tower and both planes, background noise could be heard from a football game being listened to. This could have contributed to some confusion or distraction amongst communications. Also, the tower had to deal with a higher than normal workload. On top of that, poor visibility due to low clouds, no one could see where the other was. Also, the instructions to the Pan Am to use taxiway 3 would have resulted in a difficult manoeuvre for the crew to do, leading to confusion and eventual loss of position of the plane. During the vital clearance request stage, once the KLM had lined up, a misunderstanding led the crew to believe they had clearance. The statement by the first officer, we are ready for take off and we are awaiting ATC clearance, was responded to from the tower with your clear to Papa Alpha Beacon, which was the ATC clearance. The intention of the ATC was to inform them of their route after take off, but this was understood as you are clear to go.  Captain Van Zanten took power halfway through the read back of the first officer in the assumption that the authority had been given, which was further reinforced by hearing the word OK. His crew on board could have questioned the assumption that his seniority and experience led them to accept his decision. The main cause of the accident was the confusion in communication, where the KLM thought it had clearance for take off, or simultaneously the tower fought the plane with stationery. The final potential diversion from disaster was thwarted by pure bad luck. As the Pan Am heard the word OK from the tower, I feared the KLM could misunderstand this. In desperation they radioed saying that they were still on the runway, but this was blocked out by the tower's two second gap between the words OK and I will call you back. This created a squeal sound in the KLM cockpit, and the warning sign was missed. Multiple failures lined up at Tenerife, which each individual one wouldn't have resulted in the crash, but when each hole was matched, the outcome was inevitable. The Dutch government initially refused to accept that Van Zanten was responsible for the accident, but with relent and KLM paid the victim's family's compensation, ranging between $58,000 and $600,000. The event shook the airline industry and helped push forward the understanding of human factors, most importantly, completion bias and fixation. These issues still remain in all industries today, but the greater of one's ability to recognise these can help prevent future disasters. Las Rodas has now been relegated to domestic and smaller airplane flights. The airport has subsequently been renamed to Tenerife North. A newer, more modern airport opened in 1978 and has taken the load of international flights named Tenerife South, which by 2018 would handle over 12 million passenger journeys per year. This is a plain difficult production. All videos on the channel are creative commons attribution share alike licence. Plain difficult videos are produced by me, John, in a sunny southeastern corner of London, UK. Help the channel grow by liking, commenting and subscribing. Check out my Twitter for all sorts of photos and odds and sods, as well as hints on future videos. I've got Patreon and YouTube membership as well, so if you fancy, check them out. And all we have to say is thank you for watching.


In Arizona, there's five levels of homicide, so there's also five levels of punishment. Capital murder. This is death penalty. If the state files a notice of intent to seek death on a first-degree murder, and if the jury finds a person guilty, they can also recommend that they've found aggravating factors and then the judge can decide the sentence of either death, natural life, or 25 years to life where after 25 years of day for day, the person can petition for commutation or parole in order to get out and they can keep repetitioning every two years. Those are the three levels of punishment for capital murder and also for first-degree murder should they not give death. Now on second-degree murder, 10 years minimum day for day in prison, 16 is the presumptive, 22 is the max. So a person can get anywhere in there for second-degree murder, which is unusual because sexual contact with a minor is actually higher. Even if there's more acts with a minor, they will stack those. So we actually call those life-enders where second-degree murder, it's really just 10 to 22 years. Now with manslaughter, what is the punishment for manslaughter? 10 years minimum in prison, 10.5 presumptive, 21 maximum. Now that is not like first-degree or second-degree where it's day for day prison time. Person can actually get 85% time, meaning 10.5 years can actually be nine years. And on top of that, they have a three-month early kick-out many times. So a person can be out in eight years and nine months. That being said, nobody wants to go to prison for almost nine years. So normally on a vehicular homicide is where we've seen manslaughter. If you've been charged with that, you're going to need to hire somebody who knows what they're doing. We happen to have very good success with manslaughter, as you can see on our website jury victory section. Now in regards to negligent homicide, what is the penalty for that? Well that can be anywhere from probation with zero days in jail all the way up to probation with one year in jail flat time. Or the judge can give prison four years minimum, six years presumptive, eight years maximum. On that prison, it can be 85% time. So four years can drop down to roughly three and a half years and another three months of early kick-out for good behavior. Again, nobody wants to go to prison for over three years on a negligent homicide. Many times we're able to get negligent homicides reduced to probation with little or no jail at all. If you've been charged with any one of the homicides and you need a lawyer, contact us. We're very good at what we do.


In October 2022, Prime Minister Narendra Modi inaugurated 5G services in India and impressed upon the potential of 5G that can transform the lives of the common man by enabling innovations in diverse socioeconomic spheres. To realize this, in March 2023, Prime Minister Narendra Modi emphasized the establishment of 105G Labs. In the near future, India is going to establish 100 new 5G Labs. With this, we will get a lot of help from 5G, business models and employment potential. PM's announcement is now a reality, wherein 100 institutions have been selected for the award of 5G Labs, which consists of compact 5G network and use case devices such as drone, AR-VR headset, startup testing kit to develop use case applications related to education, health, agriculture and other sectors. 100 new 5G Labs will act as testing-combreeding grounds for upskilling local innovators such as students and startups and aid in developing new range of use case applications according to India's common man needs, further enabling India's ascent towards becoming the third largest economy in the world. Now in the presence of Honorable Prime Minister, 5G Labs are being awarded to 100 institutions in Andhra Pradesh, Arunachal Pradesh, Assam, Bihar, Chandigarh, Chattisgarh, Delhi, Goa, Gujarat, Haryana, Himachal Pradesh, Jammu and Kashmir, Jharkhand, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Manipur, Meghalaya, Mizoram, Nagaland, Odisha, Puducherry, Punjab, Rajasthan, Sikkim, Tamil Nadu, Telangana, Tripura, Uttar Pradesh, Uttarakhand, West Bengal. These Labs, India, will be a front runner in designing, developing and deploying 5G and 6G technologies. Jai Hind.


Embrace it. Embrace it all because understand something. If you want to be a CIA man, a competent, intelligent, and assertive guy, understand something.  Winners always get insulted by losers.  They're going to call you condescending.  It is going to dog. Anytime someone is a winner, they have haters.  But you know who doesn't hate them? Patriots fans. People hate the Dallas Cowboys because they're called America's team. All winners have haters. Now we understand that at the high levels, sports and entertainment, but here in the middle levels trying to get to the elite levels and to the top 10%, I think you guys understand something. People are going to come at you.  You think you're so this. You think you're so that. You're not going to say that. People who are trying to keep you down. They're not telling you that because they think less of you. They're telling you that because you have the nerve to believe in yourself and stand on what it is you believe. You're going to have men throw it at you. But what you're not going to have is some guy who is above you or better than you throwing it at you.  You're going to say, all right, that's what I mean. Do it. Be courageous. Push, push, push. That's right. Matter of fact, you're going to have winning kind of guys. They're like, yeah, yeah, do that. Do that. You know the guys who are going to throw it at you? The guys who don't want to do the work. Guys that don't want you to put, make it so hard for them.  Look at them. You're over there trying to be opportunistic and you're over there trying to schmooze and you're over there trying to glad that I'm going to keep it real. It's always to keep it real. What they really say is I want you to stay down here. I don't want you to shine so bright. It makes it hot on me. Could you could you not be so good? Could you could you not shine so bright? Could you not make me look like I'm just average and mediocre? That's what's at the basis of all this other kind of stuff. And I get so tired of guys who are smart, who are accomplished, who are capable, who have the stuff that it takes dumbing themselves down to make other people feel better. Listen to me when I tell you this. Man, there have been so many times that guys have thought if I just failed, if I just failed, they'd like me more. And what you're going to find is if you fail, you become just like the people that you think would like you more. They're going to not. They're going to still not like you. Matter of fact, they're going to resent you. They're going to say, look, I understand. Well, I'm a failure. I understand why I didn't do it. But look, he had all these opportunities. He ended up right here. You don't do them any favors by failing. You know what you do?  You go ahead and get up to the top, the top of the top, top, top. The tippy tippy top. You get your name up on Mount Rushmore. You climb Everest. You make as much. You go as high. You go as far as you can and let the haters stand outside and call you whatever outside of the gates. That's right. Outside of the gates of your mansion.  I'm a firm believer in this. That's why I was going to make notes on this. But I'm sorry. I'm just going off the cuff because I have seen this stuff so much in my life. I've had the people throw stuff like that at me. But you know what? I've never had somebody doing better than me call me into this stuff.  They're going to do it. They're going to do it. Especially the men that you are beating. Now, let's whip it up. The women that say it? The women that say it, they don't understand it. Why are they saying it?  Oh, did I say that out loud?  Or I'm going to take that back. Take it back. Let me not be hyperbolic. Arrogant is a compliment for most women.  You have courage.  You have enough to stand on it and say it directly to their face and not flinch. So embrace it. Embrace the arrogance. What's the difference between cocky and confidence? See, it's not bragging if it's true.  It's not bragging. The difference between cocky and confidence? I look at it. You can be cocky when you have the confidence that you know you're going to put the work in and get it done. There's one thing to sit around and talk about something. And you don't have the work, the hashtag show your work. You don't have the receipts. You don't have the numbers. You don't have the facts. You don't have the data. You just have the belief in yourself. Well, I'm still a proponent for that because here's the thing. Only you know whether or not you're going to do work. But if you can say you're going to do great things, you're going to be great because you know that you're going to do the work, then do it. Say it. Say it and make it and put the pressure on yourself. Put the pressure on yourself to hold yourself accountable. So when you do make the result, when you accomplish what it is you say you're going to accomplish, nobody can say anything other than, he may be arrogant. He may be cocky. He may be this. Yeah, but he also got it done. He delivered. Don't have to like him, but there's the result. It's just why I love this whole business of image. It's about outcomes. It ain't bragging if it's true. So I can say embrace the arrogance. You might as well go ahead and get to learn to like it. Learn to love it because you do no one any favors by saying, you know what? I'm going to dim my light. I'm not going to wear my suit on Saturday. I'm not going to do this. I'm not going to dress better. I'm not going to do this. I'm not going to do that. Matter of fact, I'll just sit around him and be like everybody else. Then they'll like me more. No, they won't.  I don't want you to cheat yourself to make somebody else feel comfortable. I want you to make your competition sweat. I want you to make it hard on your competition. I want you to make it hard on the women who want to be in your life because if you're going to win, you're going to get out there and do what it takes to succeed. I wonder you'll get all the results. What do you think? Client people out here are the millionaires and billionaires people who are rich and wealthy. What do you think they do? They still eat. You know what? I mean eat spam. I mean eat peanut butter and jelly because I don't want to mean forget that mess. Many people don't know how rich and wealthy people actually live but I tell you what, they don't live worrying about what people who have less than them think about them. They really don't. They may say it in public but they don't. Taiwan Lannister, the lion doesn't concern himself with the opinions of the sheep. What do you want to be? If you want to be a lion, guess what?   What are you going to do? Become a vegetarian?  Go be nice to it? Talk to it? Man, be a lion. Do what you have to do. Embrace the arrogance. It's one of your superpowers. Peace. Hey guys, whether look, style, or life, whether personal or professional, you can level up and be your personal best each and every day. If you're interested in any of that, do us both a favor. Follow the link in the description and book your one-on-one session with me. Details down in the description. Peace.


Hello, this is LaptopMedia, and today, we will show you how to open the HP Elite Book 830G9 and what's inside of it. To open this notebook, you need to undo 5 captive Philips head screws. Then, as usual, pry the bottom panel with a plastic tool, and remove it from the device. Please remember, it would be of great help to us if you just hit the like button and subscribe to our channel. That would motivate us to make even more and better videos for you. The 51.3Wh battery pack lasts for 13 hours of web browsing, or 11 hours of video playback. To take it out, unplug the battery from the motherboard, and undo all four Philips head screws. Inside, there are up to 16GB of DDR5 RAM, working at 4,800MHz. Unfortunately, it is soldered to the motherboard. Storage wise, you get 1M.2 PCI-EX4 slot, which supports Gen4 SSDs. As for the cooling, there is one heat pipe, a heat sink, and a fan. There are some cooling plates around the heat pipe, for better heat dissipation of the CPU. If you'd like to see more disassembly videos of the latest notebooks, we would highly appreciate if you hit the like button and subscribe to the channel.


Hello, my name is Antony Brotodou. I'm working in REN in France and today I'm going to show you how you can perform repeat masking on the genome. So this process of repeat masking occurs after assembling a new genome and before performing a full genome annotation of genes. So when you assemble a new genome, you get a fast-paced sequence of the genome, but often these genomes contain a lot of repeated elements that can be some very small sequences like tandem repeats of very short sequences like 80, 80, 80 that are repeated one after the other. Or you can get some interspaced repeats which are small sequences that are found in multiple places in the sequence. So it can be small ones that are named the signs, short interspaced nuclear elements, or long ones, the lines, which are long interspaced nuclear elements. So these repeats are interesting because they are part of the evolution of the genomes. They can be produced by the presence of transposons in the genome or viral insertions. And they can have direct effect on the expression of genes. The problem of these repeats is that they are troubling the software that are used to annotate the genes on the genome. So you often want to find the exact position of all these repeats on the genomes and to sort of mask them to the gene annotation software that can be used later on. So in this tutorial, we are going to perform the repeat masking on the fungal species, which is mucor musedo. So this genome was assembled following the tutorial named the fly assembly tutorial in the assembly section. I can show you here. If you go there, it's this one. So if you follow this tutorial, you will get an assembly of these species. And now and after that, you can follow the repeat masking tutorial, which is in the genome annotation section and which is here. Masking repeats with repeat mask. So there are multiple programs available to perform repeat masking. Today, we're going to use repeat mask. There are other ones like repeat modeller or repeat. This software are using different strategies. There are some software that will perform denoval annotation of repeated elements by just comparing comparing different sections of the sequence of the genomes to find all the subsequence that are repeated somewhere on the genome. And other software like repeat mask will use databases of known repeated elements that have already been found in other genomes. There are two very used databases. The first one is rep-base, but it's not free. And another one is DFAM, which is free and that we will use today in this tutorial. So that's it. We can start the tutorial just by following this. The first step is to get the assembly, so the fast day file, which is produced by the fly assembly tutorial, or you can get directly from Zenodo. So we are going to get it from Zenodo, copy this URL, and then on use galaxy.eu. So this tutorial should run perfectly on use galaxy.eu and probably other use galaxy server like .org or .org.au. So I'm going to create a new history that I will name repeat masking, and I will upload the test data here. I just paste the URL here and I will name it genome. I start and it gets added to the history. Okay, so now that the data set is green, as you can see the fast day sequence. And if you look at it, you will see the whole sequence, which is in uppercase as you can notice. Okay, so now the next step is will be to run a repeat masker on this genome. You can launch it by clicking here, or you can find it in the annotation section here. Here it is. So we're using the latest version and we will just select the genome sequence. And as I said, repeat masker is using a database. So we are going to use DFAM, the one which is bundled with repeat masker. It would be enough.  And as you can see, there's no mucor, musedo species in this list because it's a general database. So today, we are going to use to live the default, which is human. In fact, what we will do is not a perfect annotation of repeated elements. It will be a very light one, which will detect the most common repeats. For in real life, you would want to use a species as close as possible to the species you're trying to mask. But for this tutorial, it would be enough to mask the most commonly found repeated elements. And it will give good results for annotation in the following tutorial with fun annotate. Okay. And there's an important option that we need to specify here is do we want to perform soft masking or unmasking? So here we want to perform a soft masking. I execute. But just for just to show you the difference between soft and hard masking, I will launch the same tool just by clicking on this refresh button, which allows to reexecute the same job. But instead of doing soft masking, I will perform hard masking. So now we will get a result for soft masking and hard masking and we'll compare the two just when it's finished. Okay, now all the data sets are green. It took nearly half an hour to compute this repeat masking on usegalaxy.eu, but it depends on the load of the server as usual. So we are going to take a look at the output of our two repeat masking runs. Each one has four output data sets. The first one is the mask sequence here. So let's have a look. So if you remember well, the first four ones are for the soft masking and the four last ones, they are for the hard masking. So for soft masking, if you look at the sequence that is generated by repeatmasker, at first it looks exactly as in the original fast day file. But if you look closely, there are regions like these that are in a lower case and there are multiple ones. If you go a bit more down in the file, you will find other ones. I can find one here. You can see another one. That's what we call soft masking because the regions that are repeated are just put in lower case, but you still see the sequence at this position in the file. And other annotation tools that we will use in the fun annotate tutorial or maker tutorial will take into account this information that this sequence is repeated in the genome sequence. Okay, so if you look at the same output for hard masking, so data set number six here, you will see that the beginning of the sequence is the same, of course, but for all the repeated sequences, instead of having the sequence in lower case, you notice that the whole sequence is replaced by ends, the exact same number of ends as the length of the repeated sequence. So here you get one and you get, of course, other ones in the sequence everywhere. There is a repeated section. These are exactly the same regions than in the soft masking. It's just that the weight is written in the output fast A file is different. So for hard masking, you just have to remember that you lose information in a genome sequence because if you have an end, you don't know which sequence was there even if it was repeated. Often when you want to annotate the genomes to find all the location of the genes, you don't want to make a hard masking because the software for annotation often consider that a gene can be found outside repeated elements, but can sometimes overlap repeated elements. So for example, you could have a gene that starts here and continue there, and if you have end in the middle of the gene, it's really a problem because you can't guess the real sequence of the gene. If you have a soft mass sequence, if your annotation tool afterwards detects a gene that starts here and finishes here, you will get a proper protein sequence even if there is a little repeated sequence in the middle of the genes. So often you perform a soft masking step. Okay, that's the output, and this output can be used in the following fun annotate tutorial or whatever annotation tool. We can have a look at the three other outputs of repeat massacre. The first one is the repeat catalog. So that's just a list of all the repeated elements that were found in the genome with the position and the scores and the exact difference between each repeated region. You also have the output log here, which is another way of telling you exactly where repeated sequences were found with the exact sequence of the repeat, the kind of repeat, because you have a whole classification of repeats. It can be simple repeat, low complexity regions, but you will see a bit later what it can be. And you have the position, the score, and other things like that. And probably the things that is the most interesting, apart from the sequence is the repeat statistics. And there you will see very useful information. First, the total length of the genomes that you try to mask, the GC level that you can compute with other tools, which is just useful to know this information. And very important, the number of masked bases in the genome, the number of base that were found to be repeated by repeat masquerade that were masked, either soft or hard masked. So in this case, we have 48 million base pair. And in this total, there is only one million, approximately, which were detected as being repeated, which is 2.41 percent of the genome. And if you look at this region in the statistic files, that's where you will find the classification of all the repeats that were found. So you can have some short elements, long elements, LTR elements, and other kind of elements. Of course, if you look at this classification on the internet, on Wikipedia, for example, you will get a lot more information. And at the end, you get a lot of satellites repeats and simple repeats and low complexity. And if you look at the percentage, it's what represent the vast majority of repeated elements that were detected. That's probably because we use the human entry from DFAM to perform repeat masking on the genomes. Probably if we had selected a species closer to our species, we would have more repeated elements in other categories. These ones are very generic and found in any genome of the world. Okay. So 2.41 percent is quite close, which is okay for an addition later on. That's it. So if you go back to the tutorial, we've performed this step of repeat masking. There's a question, and we just insert that. We have 2.41 percent of repeated elements. And now we're ready, if you want to follow on the finite data notation tutorial, which will be in another video. Don't forget, at the end, to look at the feedback form and to give your feeling on this tutorial. It will be very useful for us to improve it in the future. Thanks for watching and listening.


So now we have the $30 being tracked perpetually and nothing's happening to the income statement Because we haven't sold it yet. This is a prior $30 from a prior transaction That's not the same thing right-click on the tab up top Let's duplicate it because now we should have another form that's actually tracking the inventory perpetually within the system so we'll go to the accounting drop-down and reports and Then let's type in up top. I'm gonna say hide this This isn't the report I wanted This is not the reports. You're looking for reports. This is gonna go to inventory item list inventory item list and This then is now tracking the inventory Internally now we only have one item the cost of three hundred of thirty dollars for it because And that normally would match what's on the balance sheet here But remember we posted something else that first transaction that wasn't tracked internally So this is the added report which is now tracking the units of inventory And it'll apply a flow assumption for us and everything But it takes a little bit more work for us to to to populate now The next thing that would happen in this process if I go to the first tab is that we would have revenue That would be generated again. Normally if you have revenue generator generated you would have an invoice form or you would have a Receive money form so for example if I made an invoice form and And we had like customer customer one Generic customer one Then down here you would have your item that would be sold now the item is now matching the This is the item that is now pulling in from the amount that we populated into the system for For the items and it's now pulling in the unit price So if I recorded an invoice then what would it do it would increase accounts receivable Which is a non-cruel account something's going to be difficult to deal with with the bank fees But we'll talk more about that later the other side would go to sales of 500 and on a perpetual inventory system the cost of goods sold would be recorded as well as the the Decrease in the inventory and if you had taxes involved it would also be applying the taxes over here as well, so You you can also imagine Being on a perpetual inventory system like I could imagine I mean sorry using the bank feeds I could imagine going into the bank accounts over here and saying that if I go into my bank account That that I'm going to see the deposits and I go into my reconcile I'm going to see the deposits that are going to clear the the the bank So let's say this was a let's say this was a a deposit from an invoice or a sale of inventory They do give you the ability to add the inventory item on the sale side here, but Again, it would be difficult usually to do that if you get a deposit That's coming through Then you're probably not going to track the inventory usually if you're selling inventory You're gonna have to make an invoice or a spend or a or a receive money form and then match Over here, so let's just show that for now if I hit the drop down again. Let's just make that invoice again if I make the invoice again and Say this is customer one customer one boom and This is gonna happen in 2022. Let's say So I'm somewhere in 2022 We'll say April or whatever of 2022 Actually, it's got to be the end of 2022 doesn't it because we had it sometime in like December Okay, and then my item is going to be that inventory item All right, so let's go ahead and record this So I'd say repro approve Invoice field explain I need a date here due date is gonna be Let's say December 31st. All right approve So now we can have some revenue on our income statement. That'll be good So if I go to my balance sheet and update that Now we're tracking accounts receivable Which is which is a non cash kind of concept So that that pulls us away from just being able to construct our books on a cash basis. We'll talk about deposits more later if I go to the income statement and update it now we've got the Sales which is recorded at that 500 and the cost of goods sold now Note cost of goods sold did not change because I didn't turn on the tracking of the inventory So let me just show you what I mean. I kind of messed it up here I'm gonna go back to the first tab and let's go to our business drop-down and if you go into your products and services and Then go into this item here I'm gonna edit the item So I turned everything on but I didn't turn on the track inventory item, which is the perpetual Tick mark you have to tick off so that it will track the inventory perpetually So if you save that it will not recreate everything retroactively So if I wanted to do this again, I'd have to go back in here and say, okay I'm going to go in here and basically Delete the transaction of the purchase of the inventory And then I'll record it again. So if I so if I go back in and say, all right this transaction right here


Hi everyone, Joe for jazby's casebreaks.com with the Giants Chiefs Redskins number block randomizer For pick your team number nine I think we just have one more number block to do if you're watching live and then we'll be in breaksville Big thanks to these folks for getting to the action if you're watching the replay of this the break itself is in a separate video So thanks to these folks getting into that three teamer Appreciate it. I think this is the right list. Yes, correct. Let's roll it. Let's randomize it Six and a six full 12 times 12th and final time after 12. We got Brady down to Javier six and 12th and final time. Good luck Six down to two Brady with three GT with three Brady with six GT with three Javier with seven Matthew G with four GT with eight last spot moja Marlin with one Greg with zero Diane with five GT with nine and JG with two. All right, there you go And there you have it Jatsby's case breaks.com. See you for the next one


Good morning guys, I'm Dhananjay Sathe, I'm a Senior Operations Engineer at Direct Eye, where I work on the platform team, and our flagship project is building our metamonitoring system, right? And let's first start up with what I call the consensus slide, and we all can come to agree on the fact that monitoring today in its current form sucks. It's perfectly broken, right? And it's a multifaceted monster.  A new beast shows up, and it's an unending thing, and when you start it off like John of Arc, you end up like that guy, you're just given up. And the problem with this is, what we believe the problem with this per se, is how we interpret monitoring and time and dimensions, and why that really matters. So going back to the basis of any monitoring system, it boils down to something like this, right? Every monitoring system has a similar construct of alerting. You have a data structure that comprises of something that gives you just enough information about what's wrong. It tells you a server, a service, possibly, which could be a host service, and a state indicating its state in certain ways, right? And of all those things, you have time, where time is just a point object. It is a time stamp that uniquely identifies when this occurred. And what that has done today is your mailbox is flooded with multiple monitoring alerts from different sources. It's often that you use Nagios, ZabEx, PENGDOM, a combination of monitoring tools to manage your monitoring systems, right? And this is an ideal Nagios, what a Nagios seller looks like. It tells you about some problem. And there are efforts going on to try and improve these interfaces, and you end up with something like Thruc, which is fairly widely used across the industry, where you are essentially playing dance-dance revolution and you have green and red lights, and every time you see a red light, you're trying to kill the red light. It's a lot like Harry and Ron in the burrow trying to weed out gnomes, and every time they spot a gnome, they throw it out. And we believe there are a few fundamental problems with the way this works. If you see what an event is at the end of a day, right? It lacks context. It just talks about itself at a particular time instant. It also cannot describe a situation. A situation to a human mind is the way we process an event, and it cannot hold state. It just tells you about its current state. It does not tell you about the history of state or how state change has occurred. And it does two or three really good things. If you take each attribute in this data structure and model it into n-dimensional space, what you essentially could do from this point was partition that space according to your needs. And this is really cool because you can do classification and clustering. Now, a very familiar construct in most monitoring systems is when you configure a check, you configure a particular escalation path for it. You configure other stuff about it. And this is not the way you think. Think of it in the way you would invest in a stock market or think of it as you would look for airline price tickets.  You're stating an intent. And the way you think about an intent could be across, so what we use in the back end is Lucene. And if you treat each attribute, you could treat each attribute as a Lucene query that part is in space and gets you something that intersects all points. It could be as random as give me, I am interested in all events, anything happening on these bunch of servers if they have a certain field in their info tag.  So this is done on the fly, right? You don't configure your checks. And what this does to you is we configure dummy checks that go into every manifest file and they just fire events into our system, right? And why this matters a lot is have a look at this example. This is a situation when you go to the beach, beach with the bumps, right? And the human definition of an event of going to the beach is discrete. It's fuzzily discrete yet it's continuous. And it contains state about itself. You have questions of like what did Tim say? Where did we go? What did we do? What did we eat? It encapsulates the conversations around it. It encapsulates the probability of someone showing up. When you make a plan to go to a certain space, you probably ask yourself questions before calling out your friends and figuring out which one of them will probably show up and which one of them will probably not show up. And humans tend to think of events and time in these situational contexts. And our brains are hardwired to solve problems about situations. You think of situations. There's a direct mapping between time as an entity and situations. And those are kind of questions you really should be asking yourself. So it's a collection of all experiences and everything around that state, right? And what if you could do this to monitoring, right? What if when I looked at a bunch of events, I didn't look at them, look at a bunch of discrete events, but I looked at it as if I'm looking at a situation, right? So this is where we came up with the incident center of the universe. And what we do is the incident is interacting with the entire system and the entire system is aware of the incident. And this is a really unique thing because when you refer to that holiday or you refer to this conference a year later, you are uniquely identifying a period in time that talks about this conference. And if you actually look at what we're doing in event systems today, it's pretty funny, right?  And there's no really unique way to identify it. You end up giving a parameter list to any other user who's trying to figure out what's happening, right? So what you do is follow this new incident center of the universe, mask away your events as timelines and state changes which make a lot of sense to the human mind, capture conversation around it. If you look at what Facebook essentially is, is an event-driven approach. When something happens in the world around you, people come together, post comments, likes, all these are events, but what you get out of it is a story. You get a timeline. You get this context of occurrence of things in the world, right? And we think about it really so naturally that even your grandmom can use Facebook today. You don't need to think in event-ed streams. And once you start doing that and get feedback and analysis, every possible query, every possible clicker user makes onto the system, every possible visit into the idea of the incident, can now be used to do really interesting things, mathematical functions, virality scores and a bunch of other things. There's an alert, the number of alerts that go out could add to its virality, right? So how do we do this? Take the center as a finite state machine and describe every other entity as a transformation function onto this. And now what you have is you've gone up from eight dimensions of an event to about 28 dimensions, both mathematical and discrete, and you can query them again, the reason I love Lucene. What I have here now is continuous time that is fuzzily discrete. So you look for patterns of events that occur and once you reach stability, the same way you would look or describe a period of illness, right? When you have a fever, you would say I had a fever three days back and now I'm okay. There's no beginning or end, but it's slightly fuzzy. And the advantages of this is, as I said, it's aware of all the changes happening across my infrastructure. It's aware of all events. I am now completely free of selecting a good monitoring system. We've actually put in systems where you could run a REST API request and we've put in stuff like KFC showed up when we were testing it. And that would still follow the same construct, right? And the system's aware of this entity and it's contextually aware. So diving in further, capture all these streams. Now, if you're trying to process events at scale, your events can be dumped into databases as immutable facts. And these are really easy to process and query for a system. And event systems work really well, but create materialized views that are good for the human context of it all. And alert based on this, do not alert based on events, and timelines of these incidents. You can define cool down periods. If I had something in the morning at 7 a.m. and it came back up at 7.15, went back down at 7.18, went back up again, I'll still continue my old escalation chain because that's still the same logical construct. But if that occurred in the evening, I'd probably still have the same context, but it would be a new escalation path because it's probably a new issue, but it's related to the same old issue, right? How do you do this? So these are some of the outputs of it. The fact that you can refer and search the space, you can actually refer back to any incident that ever happened in your infrastructure and look back at it. More interestingly, this is how we look at an incident today. You have links into your old monitoring systems. You have all your stuff coming in from Nagios or Pingdom, any other reference links into your intelligence or your Wikipedia's in your company.  Something really interesting here is this particular fact about an aggregation. So the fact that I have come to this conference is an event for me, and the fact that you've come for this conference is also an event for you, right? But what we tend to do is we tend to look at it as a single event. It's coming to a conference and you've clustered up all these ideas, all these discrete things into one big idea of a conference or even the party at the beach. And this is what our automatic system does. And as people figure patterns, they can add better rules to improve this filtering and improve these criteria, right? And people have conversation about it, right? And people act and comment. This standard construct of any monitoring system. But there's something interesting that happens out here. For every piece of data that we get as an input into this entity that we have now, we can define from qualitative data, we've come up to a quantitative score which computes the number of events, the number of references, its severity levels, people talking about it, how many people, how far up the chain were notified and using this, you can probably choose the right route when you start clustering incidents together. And in a very good, I'd say, 80 to 90% of the cases, this algorithm actually figures out what's the root cause, puts everything together, follows the chain for the root cause without involving people for each chain. So you reduce a lot of noise by doing that and that is an advantage of this idea of perceiving time. And the best part is you still have all your raw data. If you actually wanted to go and debug on the raw tab, you would actually find your event data and you could look at it. But to expect the human being to look at a bunch of event data just doesn't make any more sense. Because you're now aware of a situation. What are the other advantages of this?  When I look at a particular incident on a particular server, now in monitoring it, you can assume that other than time, the two other most important dimensions are the host name and the particular check that you're talking about. Or a host group name and the cluster check that you're talking about. And what you would do is you would find incidents with similarity signatures in the same service impacted in your host group. And the host groups, again, were not defined at the check time. They were defined by those intent queries. So I could change them on the fly without having to change my check at all. And this tells me, if I'm viewing this incident here and I'm trying to debug this, often the service is impacted across a bunch of hosts. So if I want to debug this service, I would actually probably just want to click on this one because it looks like a more important service. And it's in the same signature group. And that could probably be something that I look at and I could realign all my clusters around that field. And then I would look to solve that issue. Another interesting thing that happens is when you have freedom to control how you do these mergers as well. And on the same hosts, if you have a RabbitMQ alert, you'll actually find a bunch of RabbitMQ related checks going down and this wheel that you have here is also generated from the same scoring algorithm that was used to merge them. So you can assess qualitatively... And the advantages are, in other ways, you have statistics. And if this thing actually did have a factor where a product team was affected and you had support tickets coming into it, I think the impact of an event or an incident in infrastructure is just measuring time and measuring how many servers were affected or how many sectors were corrupted would probably be more interesting to see how many users were affected, what sort of other impacts did you have over there. And you still retain your ability to drill down into a particular issue and look at it, merge and unmudge these trees. The other thing is integration of changes. So if you follow a certain... if you have really large infrastructure that would make changes in them, then you could assess using a sum of all these scores, you could now assess how successful your change was, what was the description, what was the probable start period, how efficiently is your team actually doing your changes. And that would also show up in this incident, in the same page. And if you had a fault in your infrastructure and you did something like an RCA on it, every time there is an issue with similar signatures in a particular reference frame, all the RCA's would show up. So what this ends up doing is tomorrow when I have... when I wake up in the middle of the night or I'm in the middle of a party and I get an alert and I open this, I immediately get complete context of the system as the situation. I don't have to look through discrete sources and discrete streams to try and make sense of the situation. The situation is presented to me and I need to just act on it. And this is a very powerful thing for us. I mean, you actually stop looking at Nagyos in certain teams because of this, right? What else can you do with it? Now, this is... I talked about going from 8 to 28 dimensions, adding mathematical functions to it. So I could actually drop this issue map using an arbitrary Lucene query again. So it could be anything from a server group to a host group to incidents that add certain people involved in it to just a simple server and a service group, right? It's a Lucene query again. And I would get impacts of... and the colors of these circles would tell me what is currently happening in a certain data center that I'm running and what are the hottest issues that I need to look at? And what are the most recurring issues that tend to, you know, take up people's time on a repetitive so you probably want to get rid of them if you could? And priority ignored issues. At times, you have issues, S3 and S4 issues, that you want to fix but you don't really fix because it's not harming your production traffic in any way. So those will go down and then you could just look up this dashboard and you have time and you can clean it up, right? And you could do statistical... Right, this is one more important thing that happens out of tracking all this data is as a user, when I go to the dashboard, I can track stuff like what were the issues that interrupted my sleep, how many times was I interrupted in the last week versus this week and what were the things that actually caused me to lose sleep. And this is probably one issue that troubles every person on call than any other issue that you would have in your job. So I think it's really important to get this out of the way. The next is statistics. I define certain criteria, not particularly business SLA criteria, but criteria about how my team should actually perform and what kind of severity issues should be solved in what periods of time. And what I can do with this is I can detect how many times we breached that in a particular... how many occurrences of a breach happened in a week, right? And similarly, we could do that for weekly downtimes and MTTR statistics and a host of other things. And that's the interesting bit. When you click on any of these graph lines, you'll actually get a breach offender list or a downtime offender list and you have the issues that have caused this. They might be open still open. In most cases, they're usually closed. They've been solved. But what that helps you do is it tells you what you should probably be looking at in your infrastructure and that's a super important thing because those little penalties add up and kind of mess with your quality of service, mess with what you're trying to do. So these things are really important and it's a host of other statistics. So yeah, that is how we see monitoring and I would want to keep some time out for these questions because I think this is a fairly new approach to it. So go for it. So it wasn't clear to me. Are you offering this as a project, a service, a product, open source, business model? I... it's still an internal tool. So it was just about the... this idea is how we built our tool around. So it was about that. Understood. Okay, cool. Thank you. Great talk. Just wanted to know that when you say that, you know, you link your monitoring with the issues that were reported earlier, right? Yeah. So what's the issue tracking tool that you use? You have JIRA or something in the back end with which you link this up? No, we've just written the whole platform. Okay, cool. So it's everything happening in the platform. Oh yeah, the issue tracking for our customer services, I think use RT or JIRA. They do API calls. I'm not really sure what they use. It's a platform. They can... you can pump anything into it. Cool. And I understand this is an internal tool, but, you know, would be really helpful to get... and I came to the talk just to get the idea of how you are, you know, looking at monitoring as you said, it's a new approach. Would it be really helpful if you have a demo with dummy data set up somewhere so that, you know, you... you know, people can check it out plus, you know, it stays internal. Yeah, that might be interesting. Hey man, can you have it? Yeah, yeah. Yeah, so you have gathered a lot of data regarding services and all those things right now. I mean, you are able to visualize the entire situation of surrounding that event, right? Yeah. So, I mean, have you... Are you planning to put any auto remediation or auto healing kind of stuff in place? Potentially we could, right? So what's happened is we're sending out alerts and page or messages at endpoints. Now, what I would do is, since it's a pluggable system, I could plug in an API and at the end of the day, sending an SMS is basically calling an API endpoint. And I could add another API endpoint, but we as a team do not really handle the... I mean, in a company where you have so many businesses and so many teams doing so many things, there's no good way for us to figure out a system to auto heal stuff that you've blown up because you probably don't know what you're running out there. But we could provide you an API endpoint service that you could subscribe to and then hook into this system in a way. So that is something that could be done. Yeah, hi. I think we could do a little with the internals of the service because right now it's like you've essentially arrived at this utopian point wherein you get context-based alerts. And we... I personally have nothing that I can, you know, absorb from this, which can take my alerting system from point A to point B. Can you talk a little more about the internals? Okay. Let's see what I could talk about that. So, yeah, we really love the UNIX philosophy. We're not a monitoring system. That's what I open my talk with. We had a meta-monitoring system. And I think Bird would agree with me that... I mean, he would testify to the fact that building a monitoring system is an extremely complex task.  And we take this data and then we process it in these constructs. So I expect you to give me an event that has a minimal offer state, a server, a host name, and a timestamp, right? I need these three things to kind of build onto it, enrich my data, send it to the pipeline, construct these views. And the other interesting thing is, because of the way we've implemented it, we still have the immutable fact. So we could run our engine from the beginning of time with a completely new algorithm, a completely new bunch of things, and I would end up with a new state. It's just a materialized view. I could end up with a new state that I could then query or do whatever with, right? Backends would be elastic search, a lot of scripting into elastic search. Pipeline is a completely async pipeline, standard broker queues, processors. Potentially, potentially that would happen, right? That would happen in the case where the network would be the thing that failed first and a lot of people would be alerted and looking into it. And your aggregations would collapse into that network layer. But you do get it wrong at times. There is room for improvement over there, you could define... So the other interesting thing we do with aggregations is in most of the systems, you define an aggregation in a very specific term.  So since we've taken this approach of arbitrary queries, we could actually write an aggregation that says an S2 alert in this group and the number of those alerts is three in the last half an hour. And I have two more alerts from this other group in one hour and this query sequence overlaps and combine them all together. And you don't think of the aggregation in terms of the check, you think of the aggregation in terms of the situation. Right? So if you're talking about just the monitoring part of it, if you're using Isingar 2, you have service dependencies that you can create. So if my database goes down, I really don't need to be alerted for my app text, right? Because my database is down. So you say that this service depends on that and Isingar 2 and all these guys do that. So you can already have this in place if at all you're using those monitoring solutions. As I said, you could fire the groove, the rattlesnake example into it and it would still work. It's arbitrary strings, right? It could be a curl request. There are actually people who are using Cron jobs with curl requests as a monitoring source. So that also works. I think that's it. Cool. Thank you guys.


Hello everyone, this is Shila Ratna Banswade from Walchand Institute of Technology, Solapur. Today we are going to see the topic freehand sketch and in that we will be particularly focusing on bolts and nuts. Learning Outcomes Students will be able to describe different types of bolts and nuts. At this moment of the video, I suggest you to pause the video and remember the applications and the different types that you might have come across of the bolts and nuts. Let us move further. What are bolts and nuts? Bolt and nut together form a fastening device. There are various devices which are used or methods which are used to join different materials different components. Out of that nut and bolt arrangement is the most widely used and of low cost. The other types of joining devices or methods are riveting screws and couplings etc. But out of that bolt and nut is the most widely used device. The smaller part out of the bolt and nut is called the nut and the larger part is called as the bolt. The bolt body can be divided into two parts. The longer part is called as the shank and the top surface or the head surface is called as head which has different shapes like a square, a hexagonal or a circular with certain clamping arrangement. Square threads on the shanks are mostly preferred for better locking or better fastening applications. For nut, hexagonal shape is the most preferred one. So this is how we can define or we can state what is bolt and nuts. Let us move further. Now coming to types of bolts. The first and the widely used type of bolt is the hexagonal bolt. As you can see on the screen it has a hexagonal shape or a hexagonal head we can say. So this is the head part, this is the shank part and on the shank part there are threads over at this right hand side of the shank. The convention for the thread is shown over here. This is the most widely used bolt in most of the applications. You can see the parametric dimensions with respect to the diameter of the shank. The threading is two times of D, the diameter of shank is D, the head height is 0.75 D. The convention for external threads is shown over here in the front view and the total hexagonal size is of 2D. So this is how your typical hexagonal bolt looks like. It has wide applications in automobiles in many household equipment. Let us move to the next. This is a square headed bolt. As you can see this is the shank and this is the head. Now the head is in a square form, you can see it over here and this has a square shoulder. This part is also in a square form. When this bolt is inserted in the required application, this goes and fits into a square hole provided over there as well as this also fits into a square hole. This square shape avoids the moving of the bolt by itself. So this gives an additional clamping or fastening help for the bolt. So this is the area where square shoulder is provided. This type of bolt also comes without this square shoulder, a total shank of circular form. You can see the dimensional relation with respect to D as shown on the screen. T-headed bolt has a square shoulder as we have seen in previous type of bolt. This type of bolts are mostly used for clamping purposes on machines like drill or lathe machines to clamp the object or the job to the machine. This gives better clamping applications. You can see the parametric dimensions over here. The next type is hook bolt. Then the two surfaces to be joined out of that if one component doesn't have a space to drill a hole in it then we go for hook bolt. As you can see it has a holding kind of shape at the top at the part of the head. So the lower part is inserted into one component while the other component is hold with the previous component with the help of this holding shape. Thus this is called as hook bolt and its parametric dimensions are mentioned over here. Next eye bolt. You must have seen these kind of bolts in your workshop where you carry out your regular practicals on electric motors and etc. This kind of bolt is used to lift heavy machine components. A hole is drilled above the center of gravity of the object. Consider electric motor and in that this eye bolt is fitted. So you can lift the object by holding the bolt at this point. So it has a circular form over here to hold the bolt. This holding in turn lifts the object to which it is clamped. So mostly this type of bolts are used for lifting any heavy object. Example I repeat electric motors or any machine components found in mechanical workshops. Now let us move to next. This is a stud bolt. Till now we have seen bolts which has threads on one part whereas a head like structure on another part. Now stud is a type of bolt where both the sides of the rod have threads. So one part engages the one component to be joined and the other part is occupied by the nut and this is how clamping is done. So both the parts of the metallic rod are threaded. So you can see the thread conventions on both the parts and this is the middle part where threading is not done. Let us move to the next. Now coming to types of nut. Till now we have seen the different types of bolts. Now let us see what are the different types of nuts. The first one is hexagonal nut. Hexagonal nut is most widely preferred kind of nut. The reason is that with the help of hexagonal nut in the one sixth rotation of the spanner the nut can be moved. It can be tightened or loosened. So this is the benefit of using hexagonal nut. Next we have flange nut. Flange nut is a kind of hexagonal nut with a flange like structure attached to it. This is a single entity and a flange is found at the bottom. This type of nut is used where the hole size is big and you want to use a bolt over there. So clamping can be easily done with the help of this kind of flange nut. So it depends upon what is the hole diameter where you want to use the bolt. Next we have cap nut. Cap nut is again a type of hexagonal nut where there is a cap like structure at the top or a dome like structure over here.  And this type of nuts are used in connecting the steam pipes or locomotives. This also prevents the corrosion or leakage where these kind of nuts are used. Next we have capstone nut. Capstone nut is a cylindrical structure with holes drilled on its lateral surface. These holes are used to tighten or loose the nut with the help of a metallic rod. The metallic rod can be inserted into this and the nut can be rotated to tighten or to loose the same. Next we have slotted nut. Slotted nut is a form of ring like structure and there are serrations at the top surface as you can see on the screen. Regular spanners cannot be used to turn these kinds of nuts. These kind of nuts are used where the regular kind of spanners are very difficult to be operated. So special type of spanners are used to operate these kinds of nuts. Wing nut. Wing nut is used where frequent opening and closing of the particular component is done. These nuts can be operated with the help of fingers or it can be operated manually without the help of any spanner. So you can see there is a wing like structure with the help of which this can be rotated. These are the references. Thank you.