This is an easy activity to do, especially I do this at the beginning of the year. Actually throughout the year, but especially at the beginning, because it's pretty low-key, but it works on several different musical things. One is timbre. I'm just using sticks. Of course, these are ready-made sticks. You could just use sticks that you find on the ground, but this uses a... Timbre is the different sounds you get from an instrument. It's what makes a trumpet sound different than a flute. For sticks, you've got a rubbing sound and different types of taps. Also volume, because there's a soft component and a loud component. It just goes like this. Two little sausages frying in a pan. One goes pop, and the other goes bam. So I'll do that several times. I don't know, four or five times. The kids will usually just join me on their own as I get it. Sometimes they leave it on the floor on their hand. Be careful. Don't let them hit their fingers, because they'll be crying after that. Make sure they're out here on the ends of the sticks. It's an easy way to get kids playing without really a steady beat or anything, but you still get soft, loud, and the different sounds from the sticks. And you get a way to get them involved.


SssssssssssssssssssssssssssssssssssssssssssssssssssssssssS Usually when people talk about hidden gems or concealed ornaments or secluded trinkets or whatever, they don't usually mention sports games, and really, I can't say I blame them. I've always loved sports video games since I was a kid, but I understand that they're kind of a niche category within a niche hobby, so maybe they're not the most fun to talk about since you usually already have to be a fan of baseball, or hockey, or whatever, to enjoy them. I will contend, however, that Bad News Baseball from Tecmo is one of the better NES games that I hardly hear anybody talking about, and this one is especially hidden, so to speak, because I mean, if you want to play a baseball game on NES, you either roll with Baseball Stars, a great game that's been well established, or you go with RBI Baseball, since that one has all the real teams and players. Bad News Baseball does not have an MOB license, so no actual teams or players here, and despite the title, it has nothing to do with the Bad News Bears movie from the 70s, but it doesn't matter, because this game is still a lot of fun. The Baseball Basics are all here, it's the same pitching and hitting viewpoint as most other NES baseball games, and the fielding is just like it is in RBI Baseball, where everyone on the field moves in the same direction at the same time. The typical game modes are here too, one player versus the computer, two player versus an all-star game, and a password system. There's no season mode here, instead you just pick one team, and run the gauntlet, defeating every other team, kinda like how Tecmo Bowl is structured. Bad News Baseball features a surprising amount of depth within the game itself, with each team featuring six different bench players you can substitute at any time, complete with ratings that rank their hitting, fielding, and speed. The confusing mess you see here is actually a player rating chart for each position, CA means catcher, one is first base, two is second, O is outfield, etc. And every player gets a letter grade for each position, if they don't have a grade at all then they really suck at that position. A is arm strength, R is speed, and players get a number rating for those. Believe it or not, these ratings really matter in this game, because if you put someone in at a position that they don't belong, the game gets ruthless when it comes to fielding and throwing errors.  Now for what it's worth, to my knowledge, I don't think there's much of a wink or a nod to very many actual players like how it works in some other baseball games, like Roger Clemens MVP Baseball for instance. The rosters in this game are closer to a game like Bases Loaded, where it's completely made up players, and the third and fourth hitters in the lineup are almost always the best on your team. There's two major things that make Bad News Baseball stand out. One is the personality this game exudes, there's lots of close up shots at close plays, there's some very expressive pixel art. You see different celebrations for home runs, and that can be a shot of the opposing team looking depressed, or a ball hit so hard that it leaves Earth's orbit, or high fives from all your teammates or, uh, is that animal from the Road Warriors? What is he, a bench coach or something? Another nice touch is that each team in this game has their own theme music. I'm not sure I've come across that in an old sports game before. You even have rabbits for umpires for some reason, the same Tecmo rabbit you occasionally saw at the title screen of Tecmo games. The fielders also run like Peg Bundy for some reason, I mean geez it's like they can't decide whether to field the ball or do Conan O'Brien's hip string marionette dance. The other thing that makes this game stand out is that it cuts a quick pace. There's not a lot of downtime or dawdling around, and you can easily play a nine in a game in about 10 or 12 minutes. Bad News Baseball unfortunately has the same flaws that most other NES baseball games have, namely that the fielding just kind of sucks. One is really slow, sometimes a throw to a base will get inadvertently cut off. You can't choose a specific fielder to go after a ball, so a ball hit in between the infield and outfield can get kind of confusing. Still, I appreciate that the game has enough depth to put an actual emphasis on defense, so you can always substitute players and put better fielders in play if you want. So yeah Bad News Baseball is a fun playthrough especially with the second player. It takes the old RBI baseball style format and adds a ton of cartoony personality to the proceedings. Like when a player is thrown out he falls to the ground dazed, and when a player makes an error the game makes it look like the player took a baseball straight to the noggin. It's pretty funny. Yeah, the usual NES limitations are here, and there's no real teams or players, but this game is so pleasant and innings go by so quickly that it doesn't really matter. I would dare call this game a quote-unquote hidden gem, but I don't want Metal Jesus' lawyers coming after me. Point is, this is easily one of the better NES baseball games out there. Not quite as good as Baseball Stars, but it's still a fun time. Alright, I want to thank you for watching, and I hope you have a great rest of your day.


everyone, and welcome to the 1.30 p.m. to 2 o'clock p.m. session of the 2021 Open Simulator Community Conference. In this session, we are pleased to introduce the presentation, Hybrid Tech and Cross-Dimensional Thinking. Our speaker is James Neville, aka SIDAR Madonna. James is the owner and practitioner in applied online collaboration. His specialties include systems analysts, analysis, excuse me, social media, team operation and music composition. His research focus is the nature of virtual environments in human expression. Please check out the website found at conference.opensimulator.org for speaker bios, details of sessions and the full schedule of events. The session is being live streamed and recorded. So if you have questions or comments during the session, you may send tweets to at OpenSim CC with the hashtag OSCC21. Welcome, everyone. Let's begin the session. Thank you, Galen. For those that are wondering where I am, I am sitting on top of my first slide. Before we get started, I've been asked to share with you that I stream a music composition channel four days a week on Twitch and that I recently presented to the European Law Student Association on Privacy in the Metaverse. I will also remind you in OpenSim that you can use alt-click to zoom in on the slides. And for those on YouTube greetings, I am watching you. So say hi, sight there if you're on YouTube. Please follow my avatar as she moves from a slide to slide. First slide. Ladies and gentlemen, fellow citizens across the physical and digital worlds, thank you for your time and attention to this presentation on hybrid tech and cross-dimensional thinking sponsored by the OpenSimulator Community Conference. My online name is Sight Arm, and my offline name is James. This presentation will give examples of hybrid tech and cross-dimensional thinking using physical and digital examples from the OSCC conference, medicine, e-learning, music and training. During this presentation, I ask that every time you think of your own examples of hybrid tech and cross-dimensional thinking, you please jot them down for later sharing or even type Yoink and chat. Thank you. Definitions. Hybrid tech means combining multiple tools, thank you, Beth, to blend their strengths and balance their weaknesses relative to the needs at hand. The effective use of hybrid tech requires cross-dimensional thinking. Cross-dimensional thinking means shifting perspective again and again to try multiple tool blends to find those that best meet your use case. The guiding principle is that relevance trumps coolness. If it works, out, keep it. If it doesn't, make a note and move on. There are four key questions. One, what is the need being met? Two, what technologies are being blended? Three, how does cross-dimensional thinking come into play? Four, is it working? If you have a smartphone handy right now, I invite you to scan the QR code on my first slide for a link to the slides on this page, including my references. One, what is the need being met? Having references for later use. Two, what technologies are being blended? QR code, smartphone cam, Google Docs. Three, how does cross-dimensional thinking come into play? In a Verbella conference last year, the speaker asked us to scan the code on their screen to download their app for a live demo. I tried it and I was amazed. It worked. I looked for a QR code generator to make one for my own website. Four, is it working? If using a QR code to point people to your URL material is relevant to you, please type yoink in chat. That includes you folks on YouTube. I'm watching there too. Hello back, Michael. Please look at the diagram on my slide just below the QR code. This is another example of hybrid tech and cross-dimensional thinking. The image on the background is a 3D symbolic, thank you, Beth, symbolic model called civs and sliders. Thank you, Frank. On the right, the beads slide on rods like an abacus, adjusting the sluice bars on the left so that some kind of an optimal blend can come through. The image in the foreground is a 2D business process model. Tools, needs and perspectives flow into a blender box in the middle coming out at the right with, again, some kind of optimal blend. One, what is the need being met? Trying to express a new concept visually to gain perspective. Two, what technologies are being blended? Symbolic modeling, second life, process modeling, Google Docs. Three, how does cross-dimensional thinking come into play? Thinking in 3D as well as 2D in media as well as print. Four, is it working? I made that symbolic model a year ago. This week was the first time it reminded me of an abacus. It's still giving me new perspectives. Moving on to the next slide, please. What's your IPD? Interpupillary distance measures the distance between your eyes and is important for eyeglasses and even VR headsets. You can measure your IPD using a mirror and a ruler or you can use a phone app or you can ask your eye doctor. Which way should you go? It depends on your use case. For new eyeglasses, go with the doctor. For a VR headset, go for the mirror for now. Next slide. Thank you, Kayaker. Good idea. Look, Kayaker, alt-click me and you'll follow the slide. Thank you. I should write that down. Headsets in medicine. Here's a case where a surgeon has two technology options. The first is physical. The top left picture in this slide shows a pair of eyeglasses with glued on microscopes. This loop is used for delicate skin intestinal and cardiovascular surgery. Surgeons have these loops custom made for their personal IPD and for different zoom lengths. The picture just below shows a surgeon wearing a VR headset loop with automatic internal IPD setting and variable zoom cued by detecting the surgeon's eyes and eye commands. The headsets internal lenses and motors are shown in the pictures on the right. One, what is the need being met? Hands free zooming in and out while performing surgery. Two, what are the technologies being blended? Eye tracking, custom programming. Three, how does cross dimensional thinking come into play? This work was done as a collaborative case study between Vario who builds the headsets and Virginia Tech who provided the surgeons and programmers. Moving on to our next slide. What's your TLDR? The internet acronym for too long didn't read is TLDR and its earliest recorded use is the Urban Dictionary in 2003. The picture on the slide shows you or me when our focused attention span has been exceeded while working online. Estimates vary, but we can usually handle 10 to 20 minutes at a time. Yet even the most focused of us must break and refocus. What is the need being met? Getting and keeping your audience's attention. What technologies are being blended? Several options are listed here such as move or get a snack. Our subject has chosen power nap. Three, how does cross dimensional thinking come into play? What if the material presented were to include built in refocusing breaks? Moving on to our next slide. Biosensors in eLearning. Biosensors transform biological changes into electrical signals. The top picture in this slide shows a user who has been voluntarily wired while watching and reading material online. What is the need being met? Optimal communication of material by flat screen. What are the technologies being blended? EEG, ECG, GSRI tracking and facial coding. How does cross dimensional thinking come into play? Live biological measures of cognitive, emotional and physical engagement can be used to detect the presence or lack of sustained focused attention. Four, is it working? The bottom image summarizes zoom presentation and ebook guidelines, including annotation, animation and image choices to refocus and reengage attention for general and expert audiences. Moving on to our next slide. What's your MA? Music is a pleasing succession or combination of sounds and music awareness is our sensitivity to rhythm and melody. Music awareness shows when you hum a song or clap to a beat. Do you have a favorite song that you hum to? Do you have a favorite beat that you tap on your desk or stamp your feet to 3D in music? Many people think of composing as having only to do with music. Composing can mean any activity, putting things together in an arrangement pleasing to our senses in the physical world. This includes sounds, images, ideas, tastes, fragrances and vibrations. Thank you, Frank. In the online digital world, we are locked into ears and eyes for now. I stream a music composing channel. How I work in the metaverse has come to influence how I compose in music. And how I compose in music has come to influence how I work in the metaverse. 3D in music is near and dear to my ears and eyes. Going to stand up now and do a floor demo. Walking over here to the first set of colored objects, keyboard and drum kit. This is a color coded keyboard. When I click the shapes, they make the 12 tones scale. Thank you. And so does the front row audience. Please continue. I enjoy it. Now you can't hear it on the media stream. So front row, let me sing it for them for a minute. The front row is having a blast here, guys. You should be here YouTube. But don't worry, I sang it for you. Okay. And this is a color coded drum kit. When I click these shapes, they make six percussion sounds. The kick, the snare, the top, a hi hat, the symbol and the cowbell. You can't hear it on the media stream. So snap, snap, snap, crash, sing, sing, pump, pump. You had to have your sounds on. Somebody's playing it because I'm hearing it, but that's okay. Okay, we're going back to the top of the slide, guys. You should see the big grin on my face right now. What is the need being met? Visualizing custom intervals for composing. What are the technologies being blended? Music notation software, wave files, scripting language and frims. How does cross dimensional thinking come into play? Standard black and white keyboards are optimized for a seven note C major scale, the seven white keys. This does not work well for scale such as a seven note minor or a five note major. Nor does it tell you which are the root mode or other note functions from applied music theory. Thank you, Beth. But I had these all worked out on color diagrams on paper. I tried putting sticky tabs on my real world piano, but they were awkward to use and I still hit notes that were not supposed to be in the scale. I thought of making 3D piano kits where I could color code the notes by function and hide the ones not used. I didn't need to play the notes fast. I just needed to explore noodling among the custom intervals to find pleasing combinations. For is it working? I have now composed using eight different custom scales and feedback is positive. I've used these kits for audience demonstrations in Kitely and Second Life. Many people tell me I'm not musical. When I suggest clicking certain color combinations to see what they like, they get it. If you're interested to try these kits for yourself, pick up a copy at Expo 3 booth 21. What is the need being met? Communicating the joy of expressing beyond words in sound and image. What is the cross dimensional thinking involved? Turning music theory color diagrams on paper into 3D paper kits. Final slide. Okay, we'll need you have about 15 seconds to wrap up here. Well, thank you. Let me just skip ahead to my conclusion. My conclusion is today we defined hybrid tech is combining multiple tools to meet your needs and cross dimensional thinking has shifting perspectives when choosing what tools to blend and we pose for test questions. Applying these concepts is ongoing requires learning on learning and re learning. We sometimes do that and we sometimes resist with all our might, but we're driven by our interest to meet the needs that matter to us most. I appreciate your time and attention today. If you're interested in further moving theory into practice, come to my workshop Friday 2pm composing for the new era and operational definition of the metaverse. Thank you. Thank you, Jane aka cider for a very informative and interesting presentation. As a reminder to our audience, you will want to check out conference dot open simulator dot org to see what's coming up on the conference schedule. You won't want to miss our next session, which will begin at 2pm in this keynote region and is entitled the metaverse is coming and open sim can show the way. Also, we encourage you to visit the OSCC 21 poster Expo in the OSCC Expo 3 region to find accompanying information on presentations and explore the hyper grid tour resources in OSCC Expo 2 region along with sponsor and crowd funder booths located throughout all of the OSCC Expo regions. Thank you again to our speaker and the audience.


they are very easy to monitor so that we are going to follow again with some examples. So one of the examples I discussed last class so I'm not going to go into the details of it so I'm just giving you the examples. It is iron in an octahedral geometry with six water molecules. So what is going to happen over there? So first it is octahedral geometry so the first thing you are going to do is to draw the octahedral geometry electronic distribution. So it is iron plus two to start with so six electrons and it is going to be a high spin system because H2 is a sigma donor tides on a ligand. So six electrons are going to contribute one two three the fourth and fifth electron goes over there high spin system.  However this kind of electronic configuration also triggers the jantailer distortion. So jantailer distortion will be there and what will happen these orbitals will split up. Similarly these orbitals will split up and you are going to have this contribution and once you have jantailer distortion this is going to be stabilized this particular configuration and what will happen? These two axial ligands generally have been going to have different bond lengths compared to this square panel geometry. So there will be six outer molecules bind to the iron but two of them the axial ones are different compared to the equatorial ones. So that means previously what we say is the lattice contribution is not present that is wrong because due to this jantailer distortion now it is going to be present. So it will having both valence contribution both lattice contribution and at the end what we are going to have is a very strongly splitted line. So generally once the results is shown it is given both the values the delta value the isomer shift say it is given 1.2 millimeter per second. I am giving you the data from the original literature and the splitting the quadrupole splitting it is written as delta aq and isomer shift is written as delta this is the quadrupole splitting and this is the isomer shift. So quadrupole splitting is 3.4 millimeter per second now if I ask you to draw the MOSBUS spectroscopy how it is going to look like. So what you already know said in reality I am going to see two splitted lines like this. Now the question is what is the position of these lines on the x scale of velocity millimeter per second and this is the partial meter transmitters and over here what we are going to find out so let's say this is x this is y and we want to find x and y. So this is already given over there what is x minus y the difference between them that is delta aq value right this is given over there 3.4 millimeter per second and what is the average of x and y that means what will be the peak before it's splitted that is going to be the value of the original isomer shift so x plus y divided by 2 that will be 1.2 millimeter per second that will be the original delta value which is 1.2 millimeter per second and delta aq is 3.4 millimeter per second. So now if you solve this equation you can find x and y so I'm not doing the full mass x is minus 0.5 millimeter per second y is 2.9 millimeter per second so you can say this is minus 0.5 this is 2.9 so these are the values are getting and that is how you actually find out where the lines would be okay and before going further down the question is why the quadruple splitting happens this is because basically when you look into the excited state of i equal to 3 by 2 and the ground state of i equal to half we generally talk about this is the only signal I'm going to see so we show a line like this but in reality what happens due to this quadrupolar splitting it is for this quadrupolar moment it is going to affect only the non-spherical one that means i equal to greater than half states so over there ground state is i equal to half so it is going to remain the same i equal to half but this state is going to split up it is going to split up in plus minus half and i equal to plus minus 3 half so there are total four state possible in between 3 half plus 3 by 2 plus half minus half minus 3 by 2 and among them what happens because of this quadrupolar moment this system can differentiate between plus minus half and plus minus 3 half so plus 3 half and minus 3 half come together over here and plus half and minus half come together over here which are all generating from i equal to 3 half so over here now you can have two interactions okay so instead of one line you are going to see two lines and what is this difference this difference is the delta eq which is shown over here and what would happen if the splitting is not there this will be exactly in the middle so that is why their average value what is actually supposed to be the isomer shift okay isomer shift is before you consider the quadrupolar split where the line should be cutting the y-axis and quadrupolar splitting is after you consider the quadrupolar effect and how where the lines that is how the signals can be distributed any questions or query up to this point please go ahead if not we will go ahead and over here we are going to talk about some of the other examples and over there we will discuss the answers of the quiz tree so now take the example of an iron system bound with six ions and this is an iron plus two state so as you know cyanide carbon is actually from group 14 so it is going to be a pi acceptor sigma donor system so it is going to be a strong fill ligand so no spin complex so what will be the splitting is going to be and easy so now if you consider it is a iron two system what is going to happen we are going to see all the six electron is going to be present in the t2g level right so in this particular condition do you expect any gentler distortion the answer is no no gentler distortion because this configuration if you if you do the splitting you are not going to gain any energy so no gentler distortion so all the six ligands around this system going to be as same as it is there so there will be no lattice contribution because all the bonds will be exactly same point what about balance contribution second balance contribution is also going to be zero because it is a very symmetric t2g six easy zero system so what that t2g is already well fulfilled no asymmetry is no electrons there so also totally symmetric so all together t2g six is zero is totally symmetric system so there will be also no balance contribution so what is be the efg electric field gradient is going to be zero so that means you don't expect any quadrupolar splitting over here because your electric field around the metal ion is not creating any asymmetry and that is what actually observed when we measured the system the delta value the isomer shift for this iron system is actually minus point one three millimeter per second with delta aq value zero that means no quadrupolar splitting and you actually see a single bank like this for the fecn six system with iron at plus two of sinister so you're taking this fecn six four minus system so what I'll charge is four minus to show that there is the plus two charge of the iron and over there where it is actually cutting it is cutting at minus point one three millimeter per second compare this system with respect to the iron with four water molecules six water molecules earlier the delta value as one point two and with cyanides it's moved to minus one point three so which side it is moving from water to cyanide it is moving towards the negative side one point two two minus point one three why it is moving to negative side oxidation state remaining same only changes the spin state high spin to low spin low spin means it is moving some delay condensate out of it and that means it is actually creating more chance of the ace electron density especially the select on density to interact with the nuclear because of this shielding effect and that is increasing the high zero square value and it is multiplied with the delta r by r which is a negative term and that is why I am going to a more negative side so this is exactly the argument we have given over there so consider that weekend of the plexiglass cyanide pi donor is water keeping iron plastic so cyanide should be in the negative side water should be in the positive side and exactly that is what we observe water with one point two cyanide to minus one point three okay so that is what we are gaining now say instead of same system but I have a three minus two because now the iron is iron plus three state iron plus three step over there now if I draw it the electronic distribution so it is a d5 system it is still a low spin system because all cyanides around here one two three four five so that is how the electronic distribution is there t2g5 eg0 now you can have a gentler distortion the eg orbital there is no point of drawing the gentler distortion because there is no electrons are together so that is how it is going to look like and you are going to see some gentler distortion so what is going to happen two axial things are going to move out so you are going to see some lattice contribution all the ligand metal bonding is not going to be same so that is contribution will be non-zero or you're going to see that is contribution what about balance contribution now it is actually a t2g5 system the t2g5 eg0 system t2g5 means all the t2g orbitals are not symmetrically filled up so there is asymmetric present so balance contribution will be there so what do you expect over here you are expecting a quadrupolar splitting and in reality what we see it has a delta value of minus 0.03 millimeter per second and delta eq value close to 0.5 millimeter per second so you can draw this thing so this is going to be a very closely line two lines and that is what is going to show it over there so now the first question is where is the delta value so what are you going to do take the average of these two lines and draw in the middle and this is the value of the delta value minus 0.03 millimeter per second look into the value compare that with the previous one iron 2 it was minus 0.13 so iron 3 iron correctly yeah iron 2 is there iron 3 is in minus 0.03 so over here it is actually moved towards a little bit on the positive side compared to the iron 2 over here which is on the negative side so what is the expected thing if you have a low oxygen state that should be lie on the positive side and iron 3 which is a higher oxidation state it should lie on the more negative side but it is showing the opposite trend why it is so that is because the measurement also depends on the temperature so sometime the temperature also has a huge role to play and over here the temperature where the iron 3 is measured it's not at the same temperature as the iron 2 so that is why it is showing the data in the more positive side but if you can able to measure that at the similar temperature then the value starts shifting and at the low temperature it starts shifting close to 0.5 millimeter per second and then it shows that it is actually going to be a little bit on the further negative side which is expected for higher oxidation state so it is going to move to minus 0.5 millimeter per second so that is why you have to be very careful when you are discussing and comparing different oxidation state of a similar system like iron you have to ensure all the other experimental conditions remaining as same as possible otherwise you cannot compare them directly and over here what do you expect with respect to the splitting of the line the splitting of the line will be more sharper at lower oxidation state compared to the sorry lower temperature compared to the higher temperature okay why it is so it is again very similar to what you expect at lower temperature the electric quick gradient will be much sharper because the asymmetry will be holding it up and at higher temperature because the electron has more space to move and you are giving some thermal energy to move the electron so whatever the asymmetry you are creating around the nucleus they can get destroyed and that is why you are going to see some broad peaks at higher temperature so as you are going to lower temperature you are going to see sharper peaks so that what do you expect for iron plus 2 to iron plus 3 system in the cyanide you are expected to see the shift of the delta value keeping all the other experimental values same and the secondly is the splitting in iron 2 you are not expecting any splitting in iron 3 you expect the splitting to be there because both lattice and balance contribution will be there one more thing you're probably going to see over here in iron cyanide 3 plus system versus iron 2 H2O whole 6 system here the splitting is way too large compared to the splitting in the iron 3 why that is because if the splitting is happening with the asymmetry the asymmetry of the electrons generating from the EG level is actually affects more because the EG level shows the shifts in the DX square minus and DZ square orbital which actually creates more asymmetry around with the movement of this axial limit whereas a change in the T2G level DX Y DYZ DX they create some asymmetry but it is not as strong as it is happening in the EG level and that is also shown in the Mosba spectroscopy with respect to the quadrupole splitting quadrupole splitting is going to be much higher when the gentler distortion is happening to the EG level also if the easy level is not happening it is only happening to the T2G level your quadrupole splitting is going to be smaller okay next example now say I am taking an iron complex of the Geometry over here five of them are cyanide this is actually a nitrocyl group iron oxidation state is 2 so what is going to happen so again we are going to look into that we're going to find this is the T2G this is the EG there is a nitrocyl group is actually replaced a cyanide group now cyanide and nitrocyl both are pyrocycling even so it is going to be staying a low spin complex it is actually iron 2 D6 low spin complex so it is going to be like this now over here do you expect any gentler distortion answer is no it is a totally T2G 6 EG 0 system so do you expect any balance contribution the answer is no because it is all very symmetrically distributed T2G 6 EG 2 is easy what about lattice contribution now over here a lattice contribution will be present because all the equatorial bonds are same but one bond in the axial is cyanide one bond in axial is nitrocyl group so they are different and that difference will be shown up in the system and over here but we are going to see is the following the delta value is going to be minus 0.27 millimeter per second and delta EQ value is going to be 1.85 millimeter per second which shows this delta EQ value what you are seeing is coming from lattice contribution there is no balance contribution but lattice contribution is there and look into this value 1.85 compared to that water molecule bound system when both lattice and balance contribution was there it is 3.4 now one of them is cutting off the balance contribution is going out in the case of this complex and that is why the delta EQ value is going down you can see it is kind of additive more contribution you are getting from both lattice both balance and it is going to be contributing over there now this value of delta equal to minus 0.27 it is measured at the same condition where iron CN 6 4 minus is measured at delta equal to minus 0.13 millimeter per second so iron with all cyanides is minus 0.13 iron with five cyanides and one nitrocyl it is minus 0.27 so what you can comment on the pi acceptance property pi acceptor property comparison between nitrocyl and cyanide if we consider this delta values so you can see the delta value over here is more negative of the nitrocyl group containing system so that means why it is more negative go back to your logic that means it is going to have higher value of S electron density in the nucleus the size 0 square value is higher that means it has more S electron interacting with the nucleus that means it has less shielding that means it has less D electron so how it can happen it can happen if nitrocyl is pulling out more S D electron compared to cyanide so over there by comparing that keeping all the other systems same pi donor property wise nitrocyl is actually a little bit better than cyanide in this particular scenario and that is why the delta value is moving towards a more negative side now if you put it another particular group over here X where you are actually trying to look into how the pi acceptor property is compared to cyanides and you just have to follow that isomer shift if it is going to more positive side that means the pi acceptance property is poor if it is moving more negative side that means pi acceptor property is bad so that is how most words spectroscopy can give you such nice information about the molecular property what is happening at the molecular level of the molecule where the pi acceptance property even with one particular ligand can be distinguished okay any questions up to this point


In mathematics and science, the non-linear system is a system in which the change of the output is not proportional to the change of the input. Non-linear problems are of interest to engineers, biologists, physicists, mathematicians, and many other scientists because most systems are inherently non-linear in nature. The linear dynamical systems, describing changes in variables over time, may appear chaotic, unpredictable, or counterintuitive, contrasting with much simpler linear systems. Typically, the behavior of a non-linear system is described in mathematics by a non-linear system of equations, which is a set of simultaneous equations in which the unknowns are the functions in the case of differential equations appear as variables of a polynomial of degree higher than one or in the argument of a function which is not a polynomial of degree one. In other words, in a non-linear system of equations, the equation has to be solved cannot be written as a linear combination of the unknown variables or functions that appear in them. Systems can be defined as non-linear, regardless of whether known linear functions appear in the equations. In particular, a differential equation is linear if it is linear in terms of the unknown function and its derivatives, even if non-linear in terms of the other variables appearing in it. As non-linear dynamical equations are difficult to solve, non-linear systems are commonly approximated by linear equations linearization. This works well up to some accuracy and some range for the input values, but some interesting phenomena such as sleetons, chaos, and singularities are hidden by linearization. It follows that some aspects of the dynamic behavior of the non-linear system can appear to be counterintuitive, unpredictable or even chaotic. Although such chaotic behavior may resemble random behavior, it is in fact not random. For example, some aspects of the weather are seen to be chaotic, where simple changes in one part of the system produce complex effects throughout. This non-linearity is one of the reasons why accurate long-term forecasts are impossible with current technology.


Strange Fruit is a song recorded by Billie Holiday in 1939. It speaks graphically of the lynching of African Americans, a practice that was still happening at the time of the recording of the song. The lyrics talk of the bodies hanging from the trees as a strange fruit. The chords and melodies are dark and uncomfortable and Billie Holiday's delivery contains the shared generational pain of the results of hate and prejudice. AI can reproduce the chords, the melody, and even the tones of Billie Holiday's distinct voice, but it can't produce one thing, the humanity. The humanity lives in between the lyrics, in between the melody lines. It exists in one place, in you. It's not in a database. It can't be retrieved as knowledge because it's experience.


Hi, I'm Vanessa at the Research School of Physics today and you and today I'm going to talk to you about Uncertainties and measurement now every time you make a measurement It has a finite amount of precision and each time you do an experiment It's always going to turn out a little bit different each time And so as scientists we not only need to quantify what we're measuring We also need to quantify the error or uncertainty in that measurement and that's what we'll go through now First let's clarify the scientific use of three words error uncertainty and mistake Error is the difference between what you measure and the true value of the quantity The uncertainty in a measurement is your best estimate of the range of values You think the true value will lie within and mistakes and when you perform a measurement incorrectly Scientific methods try to minimize the possibility of mistakes as much as possible We'll discuss each of these terms in more detail in the following material


Too much cholesterol in the blood has long been considered to act as a primary risk factor for developing Alzheimer's disease, and possibly Parkinson's disease.  But wait a second, cholesterol cannot be directly exported across the blood-brain barrier, so it can't directly get into the brain, or out of the brain. Well, what if the brain has too much cholesterol and needs to get rid of some? As a safety valve, there's an enzyme in the brain that oxidizes the cholesterol, and in that form it can exit the brain and eventually the body. But here's the kicker. Although this fact means that the brain can eliminate excess amounts of these oxidation products, it could be a two-way street. It could allow toxic amounts of oxysterols, oxidized cholesterol, present in the bloodstream, to go the other way and accumulate in the brain. This is not just a theoretical concern. This elegant study showed that by measuring oxidized cholesterol levels in the blood coming off of the brain, measuring the jugular vein in the neck, compared to the levels going into the brain through the artery, you could measure the difference. And this shows that if you have too much oxidized cholesterol in your bloodstream, it can end up in your brain. This is a problem because the accumulation of oxysterols can be cytotoxic, mutagenic, atherogenic, possibly carcinogenic. In other words, toxic to cells, toxic to DNA, and contributing to heart disease and maybe cancer. Yes, samples from atherosclerotic plaques on autopsy contain 20 times more cholesterol than normal arteries, but contain 45 times higher levels of oxidized cholesterol. Cholesterol oxidation products may be up to 100 times more pathological, more toxic than unoxidized cholesterol. Contributing to not only heart disease, but potentially a variety of different major chronic diseases, including Alzheimer's. Okay, so how can we cut down on the amount of these oxysterols in our body? One way is by not eating them. Oxidized cholesterol is found in milk powders, meat and meat products, including fish, cheese, eggs, and egg products. Until recently, our understanding has been limited by the lack of testing methods to accurately analyze the amount found in various foods. Until now, found throughout the animal products. Canned tuna was surprisingly high, but ghee takes the cake. Ghee, clarified butter boiled butter, is commonly used in Indian cooking. The method of preparation appears to multiply oxidized cholesterol levels tenfold. This dietary exposure from oxidized cholesterol may help explain why the subcontinent of India is ravaged by such heart disease, even though a significant proportion of the population stays away from meat and eggs. A number of Indian dairy-based desserts are also made in a similar way. Oxidized cholesterol in the diet is a source of oxidized cholesterol in the human bloodstream, where it can then readily cross the blood-brain barrier into the brain. And this could trigger inflammation inside the brain, the build-up of amyloid. All occurring years before the impairment of memory is diagnosed. These early studies showing the build-up of oxidized cholesterol in the blood of those fed meals, rich in oxidized cholesterol, where you get the spike in your bloodstream a few hours after you eat, was done with things like powdered egg, which can be found in a lot of processed foods, but you typically don't sit down to a meal of it. You get the same thing, though, from eating normal food sources. Give some folks some salami and Parmesan cheese, which are naturally rich in cholesterol oxidation products, and later that day it's circulating throughout their bodies. And higher levels are not only associated with mild cognitive impairment, but Alzheimer's disease as well. Increased concentrations in the brain may promote cellular damage, cause nerve cell dysfunction, degeneration, and can contribute to neuroinflammation, brain inflammation, and the formation of these amyloid plaques. You can show the boost in inflammatory gene expression right in a petri dish. You can grow human nerve cells in vitro and drip a little cholesterol on, and you get a bump in inflammation, but add the same amount of oxidized cholesterol, and it gets much worse. And if you look at the changes in brain oxysterols at different stages of Alzheimer's disease on autopsy, you can see how the three main cholesterol oxidation products appear to be building up. Levels have been shown to dramatically increase in Alzheimer's disease brains, adding to the evidence that oxidized cholesterol may be a driving force behind the development of Alzheimer's disease.


All right guys, thanks for coming. We're from Devo and we're giving a talk on our detections research and the studies that we've had covering at the end more than 6,000 cloud native detections across the world, across industries. My name's David Wolf. I'm a security researcher and I'm part of the Devo SISECT organization. We're responsible for different forms of product innovation and engineering. Josh here is a detections engineer actually responsible for creating the living breathing alerts that Mike on. I'm good, boom. All right.  It's really, really, really bad. I don't even know how they got it, but please don't take a picture of that photo. No, I have a long background in offense and defense operations, technical operations, doing pen testing, going after nations adversaries and doing defensive work. So my role at Devo is to create content or create out of the box alerts. So I will recreate environments to exploit systems so I can obtain the logs. And I write some of the alerts that David's gonna be talking about. And off. He created the innovations that we researched and studied looking for optimization, migration opportunities. Interoperability was the core mission for assessing these alerts compared to other seams. How can we enable transmission in a round circuit? My background comes from an OT and an ICS environment setting financial services, connected healthcare, the extended campus and IoT. And for me, coming and seeing how detections are deployed in different organizations around the world was a new research opportunity. We didn't, the item in this part of our research portfolio came from product innovation, but it became immediately apparent that we were learning new things that we weren't expecting when it is we started digging in. This session follows typical research format. We'll give you a little bit about our research lab and our team as to why it is that we're conducting this type of research. I'll give an overview of our methods and scope. One question was how did we aggregate the alerts and where are they coming from and how did we get them and what were we looking for? And yeah. One thing I will note is the slides were prepared in advance and put out on the site. We modify those slides. So if you're following along on the ones that were originally posted, there will be some confusion in there. We're gonna get the newly revised ones uploaded to sketch.com or whatever it's called to as soon as possible, but after this talk unfortunately. So I didn't want there to be any confusion when people were trying to follow along. Fair enough. At our labs, we have three major themes that our portfolio aligns to. We have the autonomous sock, sort of the second brain for analysts and how is it can we instrument action that follows an intelligent response and reaction at enterprises and feeding into SOAR. Detection is the trigger for actual orchestration. The second major thing that we have is the augmented analysts. How can we put and enrich the data supplies and the views that analysts are taking in the sock while monitoring their data lake? How can we give something approaching actionable intelligence and at least augment with additional data that leads to new insights in the right place and at the right time? And then third, alert management. We conduct an annual research report with some thousands of survey respondents that are leaders and staff at SOCs around the world. And we're sensitive to the feedback and the pain points that our survey respondents are reporting. Alert management as it turns out is a major pain point especially cited by analysts themselves as being the primary place that they want help in the sock and that high performing socks have lots of alerts. And once we start getting into managing hundreds or thousands of alerts, it's a whole different ball game and scale and alert management become a problem. At Debo SciSec, we have a clear mission. We as researchers are researching emerging threats, reviewing the vendors and products in our ecosystem and the data supplies that all the enterprises are using and we use those supplies to solve novel customer use cases with a security focus. As a SIM provider, Debo is strongly related to visibility and analytics and enriching and deriving insight from data pipeline is a core part of SciSec's mission. We have the data scientists and security researchers and practitioners putting one-on-one together is the focus for how our organization is built. We have the three primary themes, automated sock controls and the autonomous sock, the second brain to the augmented analysts whom we enrich with context and fundamentally alert management being a third catch-all bucket to illustrate problems and the solutions that we're trying to provide to our end users. For this research project, we loosely adhered to the MITRE ATT&CK framework for the purpose of research, being three phases of assess what's there, assess the coverage, identify the gaps and then tune and acquire new defenses. So this was Detection's engineering innovation project. Like, what is our coverage? Where do we have gaps? Where do our customers have gaps? And what do we need to either tune or build in order to round out a fuller strategy and a portfolio view for our Detection's? In the SciSec research lab, our team is primarily Detection's engineers like Josh who have the focus on cloud Detection's across platform providers. We have our data scientists who are busy in the lab right now crunching all kinds of data and I'm a security researcher as an example. Real quick to David's point, one thing we will talk about is when we're talking cloud providers and the disparity of how analysts have to understand and know those cloud providers. So I just wanted to point that part out since when it was brought up about the cloud providers and the Detection's. That's all, my bad. And I hadn't said anything in a while and I wanted to say hi. Overall, research methods and scope. In the original sample were approximately 14,000 alerts after narrowing down for keying to specific customers or managed security providers. We came with 6,000 alerts that were within our sample all based in the cloud. Internationally, almost two thirds are coming from the States, one third from Amia and Asia PAC. Inside the lab, our project was focused on developing machine learning classifiers, for example, for zero trust and minor attack labeling and the generation of new kinds of alert metadata as well as identifying what type of motives attackers would have, what verbs they're executing and against which nouns. So what fundamentally assets were being targeted, especially within a cloud context. Where we have AWS and Asia and GCP is three primary providers in our data set and the ultimate attack path might be the same but the vocabulary is different. Overall, we had something on the order of just about 400 distinct customers within the data set. Let's see here. Now, when it comes to mapping the detections, the labeling it is that we generated filled in most of what we can refer to as the MITRE attack cloud matrix. And this is a composite matrix for both infrastructure and workspaces. So when we're talking cloud and when we're developing detections, there's a big difference between cloud infrastructure like AWS, Asia and GCP compared to the workspaces, cloud-based workspaces like Office 365, Google workspace, SharePoint, these types of systems. The detection portfolio that Josh and company have been developing spans both contexts of infrastructure and workspaces and we mapped our controls accordingly. Briefly in terms of scope, a few points to harvest from here are that financial services, technology companies and operational technology companies provided the bulk of our data set. We have significant presence from managed security providers, managed security service providers and the differences between how MSPs are developing custom alerts specifically compared to enterprises was interesting. It's clear that there are good reasons for an enterprise to outsource to an MSP depending on the maturity and capabilities inside. Overall, we had significant success mapping alerts and their metadata to fundamental pillars in zero trust and we had 58% of our alerts mapped to MITRE attacks successfully where we felt that the labels, that this was the ratio at which we had confidence in our MITRE attack labeling. Another key part of the research is comparing out-of-the-box detections to custom detections. We have three research themes and the first is the autonomous sock and the autonomous sock is a place where action happens, influenced by machine learning, influenced by artificial intelligence. The ultimate output is some kind of decision-making that's been automated and in the autonomous sock and using that as a research theme, some of our findings came and emerged that shed differences between how the sock in a traditional enterprise is different than that for a cloud. One key finding is that cloud defenders are significantly more likely to use out-of-the-box detections. There are multiple reasons behind this. In our interviews and our annual sock performance report, we see that cloud specialization for analysts is a huge skills problem, familiarization and understanding of what to do and why to do it per cloud makes cloud defense even more difficult for an analyst. Josh is building detections for all three major providers and just having three testing environments is enough of a pain, but to actually build the skills and the vocabulary and to understand the resources involved and how they're different is a huge skills barrier. In the end, cloud defenders are not only deploying infrastructure as code and we're at a Kubernetes conference now, the standardization and non, let's say non-intrusive customization we're supposed to be using as much boilerplate as possible whenever possible and it shows itself in the detection strategy as well compared to an enterprise, especially something like a hospital that will have exotic devices with many different types of protocols that require custom detections in comparison. MSPs and let's say non-cloud defenders and same strategies are significantly more likely to use custom queries for custom data sources, bespoke applications, for example being non-standard log sources and inputs, something that became clear as a pain point in our interviews as well as the data is that we're well past the point that organizations are defending a single cloud. It's painful. At the very least, if a company is on AWS, they are more than likely to have a second cloud. They're more than likely to be also defending Office 365, of course. They need to have a cloud detection strategy and a source strategy, both for workspaces and infrastructure and it's not just a single cloud anymore and that increases the complexity for the SOC analysts. It increases the complexity in terms of alert management and what we see here is we're at the point now that approximately one in four SOCs have a majority of their detection stack monitoring cloud has become a new normal. When it comes to out of the box and custom detections, we have a sense of on the far side, we have managed security providers being the most likely to craft custom detections. That's part of the service that they provide. They have their own libraries. They're building custom detections, rolling them out across all of their customers and it's part of the secret sauce that the MSPs bring to the equation for defending enterprises. Whereas if we go and look at, for example, in OT and ICS, we have, if we're thinking about factories, we're thinking about supply chains and transportation, we have less innovation within the SOC and we're still at basic visibility, implementing controls that are off the shelf and available. And in the case of technology, companies what we have are more standardized assets. We have fewer different types of devices, fewer kinds of IoT devices, fewer custom detections that are required. So two takeaways from the autonomous SOC. The first is that out of the box detections are key for cloud defenders. Being able to pull from a library means lower barriers, less skill, expensive skill and expertise required and a more coherent strategy for control and to end. And that as of today, cloud is a major control area for many companies, sometimes a majority of the alert and detection coverage. And this is key for any SIM and SOC automation strategy. Thanks, David. So sorry, let me just, sorry about that. Let me start off by saying real quick, I am not very comfortable with this. There started off with three people which I liked and now it's grown.  So let me get right down to it. I just had to put it out there for you. Let's talk about an augmented analyst and what it means. So augmenting an analyst is simply just providing them enriched data. You're giving them metadata and you're giving them information that they need to help make informed, educated decisions. One of those methods, every SIM out there or a SIM, however you wanna pronounce it, everyone out there, they use MITRE as part of their and we're experimenting part of augmenting the analyst. And from what we can see here is typically the analysts have more visibility towards the beginning portion or I'm sorry. Yeah, sorry, less visibility at the beginning portion. Told you, I'm not very good at this. At the beginning portion of it and more towards the end. So like persistence for cloud-based coverages, right? There are cloud-based detections. They got a lot of cloud-based detections for persistence but not a whole lot for execution in the cloud. It draws some concern because you kinda, it's a different environment than people are, we're still not wholly accustomed to working in the cloud and they may not know what to look for or they may not have the information they need. Sound, yeah, all right. Whew, sorry, thank you. All right, so let's talk about zero trust and the typical alerts that we've seen with analysts have been on devices, networks, and identities and it's not covering all the pillars of zero trust and it's been, it's something where we need to, we need to help provide better education into, I would say, or better, more assistance. And at a basic level, something that we found both in our live deployments and in our data set is that enterprises today are still operating in, let's call it an old school control model. The reality is that endpoint detection, controlling devices is a huge part of the alert stack. Not just MDM but nodes in date, like safeguarding data center is all about the devices that are there. That the campus is filled with devices and EDR solutions, antivirus solutions are some of the most mature, log generation sources that enterprises have today, especially those that have a distributed campus. And so we're coming from this device or box-based sense of control from once upon a time and before cloud. Of course we have network controls, network activity, the network pillar is key. Devices communicate with one another through a network and network controls are fundamentally different. It's not antivirus, it's not an agent, it's not a passive scan. What network controls offer are coming from firewalls, are coming from proxies, are coming from the communications between sources and destinations, clients and servers. And network controls are again coming from sort of an old school perspective of firewalls create tons of logs. The classic enterprise, the number two, the first two buckets of alerts that they're going to have are device-based and then firewall-based alerts. And of course, why do we have these alerts? At the end of the day it's users, it's people, it's identities and alerts based upon logins, authorizations, identity, authentications. And these three are three of five zero trust pillars, but those three pillars account for three quarters of all alerts in an enterprise sock. It's at the end of the day, and I think on the next slide as well, a way of conceptualizing the sock control strategy is we have these three flavors of alert and everything else. So spilling into that a little bit more is just the type of device protections that we've seen the most. And I really can't add much to what you said. So that's basically what this is. It's these two slides play into one another where this is talking about the analysts protecting, caring more about device protection. So what is device protection? It's these endpoints, it's the cloud detections. So yeah. With one note on this slide, that map to zero trust, devices, networks, and users are the resources that are primarily covered by our alert strategy. We see a bucket here that's cloud, and that's what it turns into, is that the cloud detections are fundamentally different. They have only a few types of providers. They have different kinds of assets. We're not thinking about antivirus, we're not thinking about classic firewalls and appliances that are hooked into networking gear. And cloud by itself is something of approximately 20% of all detections in the stack. So from an analyst perspective and from a strategic perspective, by mapping to zero trust, we can get classes or buckets of alerts that themselves can have specialization. Network alerts can be by protocol, and we can stack our alert strategy and detection strategy in the networking layer. We can have our DNS alerts, HTTP alerts, FTP, SSH, our remote desktop and file sharing protocols, or in a hospital or in an industrial environment. Protocol-based detections are key. Cloud is an increasing percentage in the trends that we have over time as being a percentage of the alerts. And it's a fundamentally different bucket than what zero trust is bringing to bear for the standard enterprise. So I mean, it's just, this is just elaborating further on it. It's just talking about the different cloud providers and which one's more predominant. The top takeaways that we wanted you to get from this is that the analysts need more, there's too many cloud providers out there, not enough analyst knowledge. So we need to work with the analysts to get the detections. They need to understand cloud providers. And honestly, we need to start pushing for standardization across the cloud providers. It may not be good business for them, but it's great detections for us. We've moved on from the days when an analyst just had to understand Windows and then you had one team that knew Windows and one team that knew Linux, right? Those two would talk to each other. Those days are long gone. They also need to know the cloud setup. They need to know what to look for. They need to know what credentials, where credentials shouldn't be getting stored. They need to know all these different things. And part of the purpose behind this research is to also be advocates for the analysts. Cause I mean, that's what a SIM is, right? It's supposed to advocate for them. And you can't expect someone to have AWS knowledge and come in there and work on GCP detections. And so that's the long story, sorry. That's a very big sticking point for me is the helping out the analyst and giving them the visibility that they needed in the MITRE framework.  And to add to that, poor, the result of our interviews and what it is that we're hearing from customers and also during alert migration, where we're trying to define the original strategy, what we see is that customers that are coming that have multiple clouds don't have a coherent strategy that's one for one, consistent coverage across cloud sources. GCP has a different set of controls that have been custom crafted to AWS. Workspace controls are patchy for organizations, especially after M&A, that might have multiple kinds of workspaces for users that are global and using different applications within the org. So at a strategy level, it's a patchwork and there are gaps, there are gaps in the cloud coverage and that when we were looking at the MITRE attack mapping, we were able to see, especially trapping execution in the cloud was something that was simply not happening in the control set, including the customer controls, including what the MSPs were implementing that the ease of the, there's something about creating easy controls that are obvious, but if we're trying to map a strategy that covers MITRE attack, for example, end to end, that taking that coherent strategy will illustrate in most of the customer deployments that we saw, areas where there were control gaps and that something that we were surprised to see is as a ratio of controls, how few were actually related to data exfiltration, data collection and the data pillar of zero trust is like in some models, it's at the center and what are our fundamental safeguards around data and when we're looking at visibility analytics and triggering SOAR, we saw that controls around exfiltration and data collection after we've gotten past the perimeter were weak across the board in financial services for MSPs. So we understand and what's changed in our innovation portfolio, what detections we're going to create are to help to fill in these gaps in visibility that the analysts are naturally having, as well as the MSPs to help, to help enable our MSP partner strategy. Thank you. I'm gonna have to speed through some of these things. If I skim over something that y'all that people really did want to see, just stop me. This thing is about as informal as you can get and if you don't like informal, that's fine. It's formal, don't worry about it, we're good. All right, so alert management. All right, so the purpose of this graph is to highlight a couple of things. One is in the Devo SOC report, we questioned about many different things. You can go to SOC report and see what the different types of questions are and as you can tell, I don't remember them offhand but the important thing that we wanted you to grab are the two ones that are highlighted up here about alert management. So the one on the left is alert management was critical to high-performing SOCs, some way to manage all of their alerts. And the one on the right is showing that management inside of high-performing SOCs, management doesn't care as much as analysts. So the analysts need alert management, but the management isn't too concerned about it, nearly as concerned as we would want them to be. And if your management just reverse it, you'll be happy. Everything's good. All right, so we needed to also establish a baseline. Some of you may or may not know what Zero Trust is, a lot of Bing, here it is. It's off the CISA Zero Trust maturity model, 2021. I would tell you what page to look at, but I forget. The important thing behind this one is to know that there's another step in there, as David had pointed out, where you should add the SIM in there. And going back to the other things that we were talking about, about the various pillars and what we were missing, this is what we're referencing. And as a note, so we're coming from Devo, and Devo is a SIM provider. Many of our customers are using it as a data lake to enable visibility and analytics for infrastructure for application performance monitoring for devices and machine data. Data lake use case, primary use case. Secondary use case is actually generating security decision making in action based upon a functioning data lake with full visibility. So Devo is a visibility and analytics foundational layer, or that's what we operate as a SIM. And we integrate with something like 500 vendor products that are covering the pillars of zero trust. The endpoint device protection vendor products are feeding into our visibility and analytics platform. So the coverage that we have across vendor products is from a visibility and analytics perspective. And fundamentally, we're able to see that across all functioning organizations, all pillars are represented, but we saw weaknesses with data. And in cloud, protecting the application workload. Thank you. All right, so here we're just trying to show you that when we talk about the various alerts, we noticed that in cloud detections, they were more concerned, the analysts were more concerned with visibility. You know, what can I see? Who's doing what, things like that. But in enterprises, they're more concerned about device connections, which would make sense. You know what I mean? So it's just trying to show you the criticality or the importance of what your detection should be going for and what kind of detection should be looking at based off of what your analysts, where your resources lie. Actually, another note about this is what's the difference between cloud control and traditional enterprise control is being very clear that the traditional enterprise is thinking about devices. And yes, in cloud, we can think of containers and VMs as being devices. We can think of every node in our architectural map as being devices, and that's accurate. But the device focused for actual controls and detections doesn't even come close to the traditional enterprise. Dealing with MDM, dealing with workstations and dealing with the IoT. So devices are fourth in mind for cloud defense and the cloud stock. And then we see a very real difference in workload protection. And trapping the runtime on all endpoints is an expensive undertaking for a traditional enterprise and impossible across IoT and under managed devices. But in the cloud, where we have, let's say, better access to runtime or more consistent access to runtime, we're able to generate workload detections. We're able to build native detections for application monitoring and application control. Regardless, at the end of the day, by the time, and we see it in our migrations as well, by the time we've managed to deploy our device protections and network detections, we're losing steam at the end of the actual goal of protecting the data. So we need more controls, more detections that are designed to safeguard, prevent the expiltration of data, prevent collection of data within the corporate networks, cloud or not. Thank you. So the other thing to consider is, there really, and David helped me show me when we were preparing for this presentation, really brought us to mind is that financial services aren't financial services. They're technology companies in the financial industry. And so when you're looking at detections or you're trying to target this area, then as you can see, they're moving right in line with technology between enterprise and cloud detections and they actually have a lot more cloud detections than what you would think being in the financial services, right? So I don't know about you, but ignorantly, I always like to think that my bank is somewhere in a nice little area that they control and everybody's happy, but in reality, it's somewhere in wherever, USA on a server firm. So more and more of your financial services are going to the cloud. One of the biggest things we want to talk about was logging per vendor. One thing that I did find out yesterday, I was speaking with, we were speaking with Melinda Marks over at Tech Target, I think, yeah, Tech Target. And she said that through their service, we're able to drill down to get more data about each one of the cloud service providers, AWS, Google Cloud or Azure. This is something that we pulled off from websites, is trying to understand what do they offer? Again, cloud, you can see right here the disparity in what's offered between the various providers. It's annoying. And it's also hard for you to drill down to be able to find what you need to audit, right? You don't know what you don't know. So sorry, I could spend forever on that one too. All right, so the key takeaways, and I am already over time, so I'm gonna race through, I think we're almost done anyway. Out-of-the-box detections are key in SOC automation, so in other words, you're supposed to, you need something that provides you detections written for your cloud provider, written like GCP AWS, whatever, you need those detections already built into your SIM to help your analysts and make sure that they don't start off from this ground zero. So whatever you're looking for, you need to have those out-of-the-box detections. Now, I could sell you our product, but that's not my job. But yeah, so that's one of the key takeaways, and yeah, I think it took care of the other one, so boom, right? We're good, yeah. This is just the stuff that you're supposed to learn from here. Five takeaways and five lessons learned during the course of this research. The first one was that cloud was big, and we were surprised at how many detections enterprises were using, especially in financial services to cover cloud. We were surprised to see one in four socks that a majority of their detection stack was cloud-based, safeguarding infrastructure and workspaces, and we can expect those to continue to grow, that cloud eventually dwarfs data center and campus for coverage and control coverage. We became extra sensitive to the concept of augmenting the analysts. Two innovation outputs from this research were machine learning classifiers for zero trust and for MITRE attack labeling, and what we hope for those product innovations is to augment analysts regardless of what their alerts are, what the results are, and what their data sources are with the additional enrichments that help to frame a strategy for both zero trust coverage, as well as MITRE attack end to end. Practically, we discovered that there are five kinds of detections that socks are actually implementing. Cloud is its own distinct bucket, and that, of course, devices, networks, and users as zero trust pillars form the vast majority of alerts in all enterprises, that we have a shortage of protections for application workloads, at least at a seam visibility layer, that defending the runtime is a weakness, that applications of workloads need further control, and, of course, at the end of the day, data and preventing exfiltration, controlling for data collection will help reduce impact, and that enterprises need to shore up the final stages before impact actually occurs. Out of the box for the win, we see it clearly for cloud that the extra, for multiple reasons, the extra complexity, the skills required, the challenges of multiple providers and different vocabularies, that out of the box, boilerplate detective strategy, where you can essentially piece together a strategy, as opposed to individual alerts, is the best way to support alert management. Instead of having 400 random alerts, we have five buckets with a coherent view mapped to zero trust, with pillars clearly defined, and mapped with MITRE ATT&CK in a way that, end to end, provides a coherent strategy for coverage and future developments. Using a strategy is crucial, and having a strategy at the end of the day, a focus for our research was interoperability. We have, in an ideal world with interoperability, let's say for our medical records, we go to one hospital, we go to the clinic, we go to another state, and our records are able to transfer and make the round trip and be enriched at every stage. And what it is, we're far from that, it seems. Shifting from Datadog to Splunk to Sentinel, to Devo, back and forth, we're far from transpiling our queries, from mapping alerts on a one-to-one basis. But what we can do, is we can aggregate our alerts in a way that has a coherent, bucketed strategy, and we can migrate our strategy. And this is what it is that was a practical output for our actual customers that put our research into actually moving the needle in our business processes to make migrations faster, to enable alert management in a coherent way. And then to have specializations within analysts that analysts might come from a network security background and therefore be better suited for network alerts. We're coming from a cloud background focusing on cloud alerts, and to enable specialization within the SOC. So, we went five minutes over, I apologize. No, we're five minutes over. It's, we're supposed to stop at 11.35. We, I'm available, we both will be available out there if you have any questions. There's a young lady, boom, Cass, Tess, what? Yeah, her. Caslin, she needs interviews for her podcast, so look her up. Good questions, just trying to get word out about this conference. If we can do anything to help, if I can answer any questions since I do do the detections, and I know I didn't talk much at all about that, feel free to see us, and whoever the speakers are for the next one, that's all me, my bad. All right, we're good.


Caribbean Fisherfolk Day also known as Fishermen's Feast commemorates the Feast of St. Peter, the patron saint of fishermen with the theme Better Care for a Better Share, establishing a creative small-scale fishing sector. On this day, Fisherfolk across the board are highlighted for their hard work and dedication to the fishing industry. The festivities for Fishermen's Feast included a church service, a blessing of boats, and a prize-giving ceremony for the Fishermen's Feast fishing competition. Registrar of Cooperatives and Friends Societies, Egbert Stevens, says that the Fisherfolk are at the heart of the industry, playing a critical role in the sustainable utilization of fisheries resources in the face of a changing global climatic and economic situation. Well, the Fishermen's Feast is really an opportunity for the different Fishermen's Cooperatives and for society to understand and appreciate the value of our fishermen, to give them some recognition for the hard work they're doing and the policing society how they are contributing to that. Normally Fishermen's Feast is the start of a church service. It's centered around the Feast of St. Peter and St. Paul, or Fishermen, from the Bible, and what it really does is allow the Fishermen to come out and express themselves in society that we are here, we are working, we are working in an interested society and you know please respect us and appreciate us. While the quantity and demand for fish and fish products continue to rise, both fishing and aquaculture provide a substantial source of food, employment and revenue. Secretary of the castries Fishermen's Cooperatives Society Board, Diane Matrae, explains that whilst COVID may have taken a toll on the fishing industry, it is still of great importance to come out and reflect on the change in global state of fisheries resources, the economic climate and environmental conditions within which our fishers operate. We need to be united as one body, come under one body. It's not the board that will make things happen for them. It's not the workers at the co-operative that will make things happen for them. They need to come and let us embrace each other so that things will work better for us in the future. But I am wishing them, wherever they are today, not only castries and balance bay, but the entire island, a good fisherman's feast and whatever they do, just do it in a friendship manner. Ms Matrae reiterated the Fishermen's Cooperatives commitment to working alongside the Department of Fisheries and its associate agencies to empower fisherfolk and build their resilience. From the Communications Unit of the Ministry of Agriculture, I am Anisia Antoine reporting.


So if homeostatic systems require a regulated variable in order to qualify as homeostatic, and the regulated variable has to be associated with the extracellular fluid, then of course the question is like, dude, what are some of these regulated variables? Well, I have a paper that I have been referring to often in preparation for teaching physio because homeostasis is such an important concept. And this is the information on the paper. If you want to go look it up, I'm actually going to post it in our learning management system. I'll post it in Canvas, so if you guys want to check this thing out. But there is a chart in this paper that is actually kind of awesome. It talks about a bunch of different regulated variables. It includes the range, like the physiological range that sort of functions as the set point. It talks about the sensor, where it is. So it's really an interesting reference. It might be something that would be valuable to have in your external brain, maybe not. Maybe just having it to refer to as you're doing your integration project. It just kind of gives you that big picture view. But you can see that blood oxygen levels and carbon dioxide levels, different ion concentrations, glucose concentration, body temperature, pressure, blood pressure in various aspects, and then osmolality. And this list, that's blood concentration. We'll talk about that kind of stuff in the next lecture on osmosis. This is not an exhaustive list. There are other characteristics that are, there are other regulated variables that are maintained within a homeostatic range. But these are some common ones that you might encounter. Okay, so the last thing, we're going to wrap this whole thing up with a conversation about a little bit more specifically on how does this all happen.