Johnny dollar Miniatures, you know those tiny paintings portraits usually done on porcelain. Oh, yeah, I know what you mean But I don't think I ever heard of any rhymer collection Half a dozen of them stolen from the rhymer gallery in Philadelphia cost us something over $20,000 insurance never found them Huh? Here's the thing. I just got a phone call from Wilbert rhymer. He wants to see you just as quickly as possible about those miniatures He wouldn't say Johnny, but I'll bet on it then Funny since I had nothing whatsoever to do with the case But I thought you usually had a staff man I took care that our company investigator at that time was Jerry pitcher Jerry pitcher Yeah, the one who was suspected of complicity and some of the cases he was supposed to be investigating That's the one. Well, wasn't he suspect in that robbery? Yeah, but neither we know the police could ever pin anything on him Hmm. Oh, we could do is fire him and warn the other insurance companies to look out for it Fred Any idea where Jerry pitch is operating now not the league, but nobody was sure of it. Hmm. I wonder if rhymer's suddenly got a lead on him Okay, Freddy, I'll grab the first train You'll bring you Bob Bailey in the exciting adventures of the man with the action-backed expense account America's fabulous freelance insurance investigator. It was truly Johnny dollar and now act one of yours truly Johnny dollar Expense accounts submitted by special investigator Johnny dollar the mono guarantee insurance company Philadelphia office The following isn't a count of expenses incurred during my investigation of the rhymer collection matter Expense account out of one eight twenty five train fare and incidentals Hartford to Philadelphia PA Item two seventy cents for a taxi to the rhymer galleries over on Walnut Street Mr. Rimer turned out to be short slightly gray and very very British Let's go into my office, Mr. Dollar where we can talk and disturb. Yeah sure, Mr. Rimer Well, the art business must be pretty good with a crowd like this in the store In here, please. Hey, thanks Well, the gallery is rather well patronized this morning isn't it? Who's it done for you? I don't I must say Mr. Dollar I many times wish that you would be an assigned to the case when these Priceless miniatures that I brought to me from Europe was sold in a year ago. Oh, how so? Well, it was so vastly uninsured So the man of your capabilities would have been able to find them for you Maybe you instead of that that Jerry pitcher I understand that although nobody could prove anything you suspected him of somehow being involved in that lawsuit Oh, yes, it's only because of his failure to investigate it properly. But now look here, sir This stuff this check has a direct relationship with the unusual number of people visiting the gallery this morning This check was made out by you. Yes I intend to put it in the mails before the day is over the Mono guarantee insurance company $4,050 the exact amount I received for the loss of one of the miniaturas a genuine Pellegrini But you collected something like 20,000 and all on the 6th of the stolen. Yes But now suddenly and most mysteriously the Pellegrini has been returned to me What and of course the people out there in the gallery and most of them connoisseurs would be interested in seeing it again Wouldn't that yeah, I should make sure there's a matter of fact I've already found a buyer for it. Oh, Mr. Charles Cunningham one of my regular clients. Well Just how did you happen to get this one back? Two days ago when I opened the place I found it lying inside the door on the floor under the letter slot for the morning mail Just lying there, but it was wrapped in a bit of coarse wrapping paper The sort of green dress I might use and it was tied up with a piece of dusty string No note or anything with it. Nothing. They're very mysterious. But why was it returned? Who knows? Would you call it a pretty well-known piece one that might easily be recognized? Oh, definitely and of course there were pictures of it in the papers and of the others at the time that they were stolen In other words anybody having it stood a pretty good chance of being caught if he tried to sell it But that does seem to be a logical conclusion, doesn't it? Yeah, there's always the black market. I mean abroad in Paris a Lot of fine either was stolen during the war has suddenly shown up over there Yes, and I understand that otherwise perfectly honest collectors haven't hesitated to petition it things taken from the Louvre from various museums in Germany and Alive of mr. Dollar if the thief were caught Smuggling the miniatures out of this culture. Yeah, I know what you mean But now why return these to you are one of them, Mr. Dollar. Okay, one of them, but why? Well, there is of course one possibility. Yeah Like what if the thief himself for the connoisseur one who would fully appreciate the value of the miniatures Well, I'm certain that he would never bring himself to simply destroy them that he's ought to realize He couldn't very well dispose of them without being caught So rather than destroy them he decided to give up and return them to you Well, it is a possibility isn't it, but he returned only one of them and of course this theory of yours rules out Jerry pitcher Well Yes But it's puzzling isn't it? Ah wait now the paper in the string you found wrapped around it, but where are they?  What do you mean? I? I'm afraid I threw them out to the trash mr. Dollar. It was taken away yesterday Great you might have found fingerprints on the mic control. Yeah, and they might have given us a lead on whoever sold those things in the first place I'm terribly sorry for terribly stupid of me. My reason for sending me for you I hope that by finding who returned this one you might find the rest of sure in which case your insurance company Well, he'd listen. Yeah, I would gladly return all of the money. They paid to me. I'm pretty happy Wouldn't I that now?  Well, how much we others if I can take them up Oh, I hope so Mr. Dollar or of course we we could just sit here and wait for the other five to be returned to you. I Presume your destiny, sir Yeah, I'm afraid so yes So how will you proceed? Well, I'll tell you something mr. Reimer. Yes. Yes, mr. Dollar. I haven't the least idea And now act two of yours truly Johnny dollar and the Reimer collection matter Expensive count out of two sixty cents for a cab to Fred Porter's office at Bono guarantee insurance And it was about the stolen miniatures of mr. Reimer sent for you and it was Freddie Over twenty thousand dollars worth Johnny Well, is he some idea now about who might have taken Fred one of them a palette greenie has just been returned to him What yeah, Reimer sending you a check and the amount your company paid him for that particular one Well, who returned it to him? I don't know. He doesn't know I was simply pushed through the letter slot the door of the gallery No clues no nothing Johnny whoever returned it must have been the thief or someone who knows the thief has contact with him So if you can find this person who ever brought it back We may be able to recover the other many time. Did the police really do a job when they were stolen a year ago? Yes, and the only suspect was that so-called Investigator we'd mistakenly assigned to Reimer because he seemed to know something about art. He was familiar with Reimer's place I was Jerry pitcher. That's right But they couldn't prove a thing against him and after all it was only Reimer's theory that Jerry might have been mixed up in it why I Don't know It may have been because of the sloppy way he handled his end of the investigation And don't forget it was about that time we learned to pitch his reputation wasn't all that it might have been beginning idea What Jerry might be now as I told you over the phone Johnny. It was talk that he'd skip the country Reasoning itself to be suspicious of him if you ask me I'll tell you this Fred if he did skip and if he had those miniatures with him the one place he might have gone to Listen mind if I use your phone. It's right beside you help yourself Hello operator, I want to put through a call to Paris France Paris Johnny Let's take it easy Freddie and stay close to this phone so that you can hear strange little character whose name is de Marseille But who calls himself a shock wreath a great cat His knowledge of the Paris underworld and everything that goes on in it is nothing short of fabulous Yeah, because he himself is very much a part of it of what goes on there Not a single important work of art could ever hit the black market without his knowing about it First friend Oldest and dearest friend Oh sure and I'll listen to my sack. I want some information. Maybe you can give it to me Perhaps a thousand you're out of your mind Maybe a couple a hundred if it's worth anything I'll take it easy tell me handless now listen to my sack here Well, I'm looking for a man named Jerry pitcher ever see you're here with him. You're sure Yes, I'm sure Then what about the Rimer collection of miniatures? There's some tiny paintings on porcelain. You get that sure did go on Johnny When de Marseille when did you see them? You kidding? Of course it is 200 now when Three years ago. We when they purchase for the gallery there in the United States Oh, that's a lot of help But who brought them for the Rimer gallery who purchased them. Do you know? Well, that's right Okay, 250 now tell me who Rimer himself bought them there in the black market. Oh, I see No on second thought to my sack. I'm afraid it doesn't mean a thing. I'll mail you check What was that well Okay, 400 In June What do you mean because he could not sell them they were what you call too hot Because they were known to have been so recently stolen from the gallery there in America too hot So whoever hem brought him back here All right to my sack Who is that fence that do boss on who was he trying to sell them for this man pitcher? Oh, I told you I never heard of you. Well, who then? I do not know and would you clever man like to be so on all this my dearest friend and Take some time and make you think they're nobody yet. Well, I get it. I'll send you check Only you can send it to him Fred say putting it on my expense account Well, wait a minute Johnny if he can find out who was trying to pedal the miniatures over there Are you underestimating the chargris? Well if he's seen them he knows what they're worth So what by the time he got that information you have to pay him a thousand maybe two We've been so Johnny if it leads us to the thief and the other miniatures that were stolen leads us to him or merely tells us Who he is what's the difference and I thought you were convinced of sherry pitcher. Well, who else why I? Don't know that's the police ask Grimer. Maybe I will Or maybe I got a better idea Item $4.50 for a taxi out to the Museum of Art where I finally ran down one of the curators a man by the name of Kingman Well, of course, I know the rhymer miniatures. Mr. Dollar priceless and such a shame They've been lost to the world of art stolen.  How do you mean, sir? Well, I mean the thief would never dare to try to sell them such well-known pieces Anyone anywhere would recognize them would see that he was brought to justice maybe How much would you say those six little paintings are worth about two or three years ago? Not very much, but now that their history is known. Well, this often happens with works of art, Mr Dollar, how much Mr. Kingman was somewhere I would say that is if they're ever found yes somewhere in the neighborhood of two hundred thousand dollars You're serious where among them is a body for instance in a genuine Pellegrini yes, Pellegrini Pellegrini the elder and one of our wealthy collectors mr. Charles Cunningham Would pay Listen, you know where I can find him. I believe I have his address and phone number in my office. Good. Come on But to why all this interest mr. Dollar. I'll tell you about it on the way to your office, mr. Kingman Item five ten cents for a phone call only to learn that mr. Charles Cunningham was out of town for the day so item six fourteen dollars even is for a dinner a room and some breakfast at the Bellevue Stratford Then early the next morning item seven another dime for another phone call That's right mr. Cunningham Just answer one question, please How much are you paying the rhyma galleries for a Pellegrini miniature? That's the one I mean what's the price? How much sir? Well, yeah, I know all about that. How much mr. Cunningham? Would you rather be hauled in as accessory to a fraud? Oh, it probably is much more how much? Maybe you'd better call your bank and have them stop the check Thank You mr. Cunningham. It looks as though my hunch was right. What? Goodbye, sir I need a buck for a taxi to Fred Porter's office item nine another buck for a cab for the two of us to the rhyma galleries You please tell me what this is all about Johnny. Had you got on the trail of Jerry pitcher now Freddie? I haven't even bothered trying.  I wonder his premiums do well as a matter of fact or rather large would slightly overdue now Why didn't you tell me that? What difference does it make? Well a business were good for a man like that. Wouldn't he keep up with his insurance payments? I wonder how much else he owes. What are you getting at? Money. Looks like we've arrived You know, you still haven't told me why we've come here to the gallery at the crack of dawn. Yeah, buddy Now keep the change. Yeah, man See mr. Rhymer's already opened shop Johnny. Come on. I am indeed glad to see you indeed indeed Good morning, mr. Porter. Mr. Rhymon Look here, mr. Dollar here on this tater. Why? Another of the military has a lumbar day what is lying on the floor under the mail slot just like the other that was returned Can't believe it. Yeah, let me see. No, no such and you see mr. Dollar this time I saved the string and wrap it for you.  Rhymer? When was your last trip to Europe in June? Wasn't I make many trips to Europe in order in June Yes, yes, it was but now look yeah back to your office. I want to look at your bank statements for the past year Mr. Dollar or would you rather I just call in the cops? I don't know what you're talking about real honest business You've been running on of course, but you didn't hesitate to buy stolen smuggled artworks on the Paris black market now look here I suppose I should have started thinking when you told me those miniatures were considerably underinsured What yeah, because you insured them before their true value really became known But by the time you did know what you'd already pulled the fake robbery Collected the insurance on them to keep your business going well I've had a piece you tried to sell them in Paris then but they were too hot that was in June But you still needed money didn't you would have I did not know you had to bring them back After all you'd only collected about 20,000 on them now you could sell them for 200,000 at least Is this true a pretty nice profit even after you got through returning what you collected from the insurance company mr. Rimer how to get them back Haven't mysteriously reappear one at a time and give out that cock-and-bull story about the thief not daring to sell them without being caught Well, mr. Rimer well You think If I produce the others if I if I write and sign a full confession the authorities will be more Gentle with me Son of a gun why do they do it They ever learn what's the matter with people anyhow some people it is oh Well expense account total including the trip back to high frid 3550 and Freddie don't forget a nice fee on this one as Well as a check of a chagry over there in Paris Here's truly Johnny dollar Originates in Hollywood and is written produced and directed by Jack Johnstone Herded our cast were Harry Bartell Ben right for us Lewis juniors Matthews and Marvin Miller He's sure to join us next week same time and station for another exciting story of yours truly Johnny dollar This is Dan Cumberley speaking Johnny dollar has come to you through the worldwide facilities of the United States Armed Forces radio and television service


Hi, I'm Craig from Anglin Active and today I'm going to show you how to rig soft plastics for fishing in shallow water and for fishing in deep water. In this video I used a 23cm soft plastic shad, split ring pliers, a premade stinger rig, 8mm split rings, size 7 swivels and 60lb breakin strain and a weighted cork screw jig head.  Then we're going to take the spike, the first spike on the stinger tackle, and insert it into the belly of the lure, and then see my game with the one at the back, and there we go. One soft plastic lure rigged for using in shallow water conditions. If you have bought a premade stinger rig from us in the shop, and you feel it's slightly too short for the size of bait you're using, a simple way to customise it is to add an additional swivel and split ring, so you can set the tail treble further back on the bait. This is ideal for larger baits or if fish are finicky in their tail baiting. If you'd like to fish your soft plastics deeper, you can do this by using a weighted cork screw jig head. Simply just same again with like the shallow cork screw, just simply thread on the bait like so, and then if we take our already pre rigged tandem rig from BFT, if we use the split ring pliers here to remove this, then we're going to put the split ring that we've removed from the shallow screw that is situated on the list to share, and insert it on the bottom eyelet of the weighted cork screw jig head. And same again, just take a spike, insert it into the belly of the lure, and now we have a rig soft plastic that we can now work in deeper water.


What's up everybody? JC over to jacobyscasebrakes.com. 20 21 Panini certified basketball six box hoppy brake random teams number two just sold out and again all card ship Everybody gets a random team in the NBA Second half of the case here guys So good luck. Here's a dice roller as a customer names Garrett down to Joseph and we got Hawks down to the Wizards roll We got a five and a one six times. Good luck one two three four five and Six and final time five and a one six Jesse down to Sean already six times One two three four five and six Raptors Yeah, all righty five one six six So Jesse of the Pelicans Jeremy with the Kings Arthur the Wizards Gretchen Gretchen sorry with the Grizzlies Set the wars Gretchen with the Suns Joseph You have the jazz wrong with the bucks Gretchen with the T-wills Scott with the 76ers Jeremy with the Mavericks David the Horn is knocked with the heat Jimmy Brown with the Clippers Ryan with the Nets Eric with the Celtics Luis with the Pacers Garrett You have the Bulls Corey with the Knicks Brian with the Hawks Derek with the Thunder Garrett with the Rockets Nuggets calves Jeff with the Blazers emoji of the Pistons Mike with the Magic Shea with the Spurs Derek Lakers and Sean with the Raptors, excuse so Anybody want to trade I guess don't raise guys to WC All right guys, so Joe put a little RT and an X on them. Let you know they're from the same case And here we go. We're rebelling three boxes at a time guys Really quick guys. I want to make sure these cards are Hate it when they're backwards Good luck guys We gots Dramon green red Gold team Kyrie and we got a Anthony Edwards rookie right at the top Zion We got a Kendrick none below Zion again The mighty RJ Hampton got a Kevin Durant to 99 Lulu Why Leonard you're a Lewis Lulu again Yeah, Eric Blexo Nora on the echo We got Jalen Smith 2020 Jalen Smith. You got Malcolm Brogdon Caleb Martin Supposed to be isn't a Cody martin. Is that a type old rookie? That's not and we got a rookie roll call Nico man Ricky Rubio Tyrese Maxie Jordan Noir We'll be topping the salt Russell Westbrook Kira Lewis through all the day Isaac Acaro. Oh I think twins loose, huh? Shock me And wow look at this patch autograph Saudi Bay To try pistons look at that you can sell this to clutch mojo three-color patch and autograph What's a bitch Anthony Edwards 2020 I The bottle Ramsey Coral pain Bridget for Boston and Eric and Bob Love autograph 25 Halliburton Oh God Odoka freshman fabrics for the jazz Las Palma gonna Joseph Devin Vassel Alakai Manic quickly Killian Tyrese Halliburton nice freshman our fresh faces autograph for the Kings Sacramento Kings of that one that is Jeremy Very nice came Pritchard John Derhunter Yanis Jokic We'll be topping But he healed and we got John Collins number to 99 James Wiseman 2020 And we got Devin Vassel All righty Selling that quickly It could be either that or we took out more spots JMS We'll find out though Hollywood on Sundays and Mondays I Stay late if we're busy if we're not busy, then I just head off pretty much But there's no like guaranteed till 12th type of deal, you know It's just whenever we're not busy anymore Isaac Acoro 25 for Cleveland Isaac Acoro there again We got Chris Middleton Gordon Hayward 2020 Saudi Bay, and we got out of 25 Patrick Beverly Christian Wood and Monte Morse Tyrese Maxie Josh Green all this guys isn't been just been hiding there It's whole week Anthony Edwards to 49 One color, but still for Minnesota on a Gretchen. Nice day for you, man Lamello base as well with Charlotte David does Desmond Bain out of 25 DeAngelo Russell. We got a red Cole Anthony We got a gold Brian Forbes the Spurs LeBron Malachi And a lesky got a patch here to 99 autograph Zeke With the Denver Nuggets Lamello 2020 James Wiseman. All right guys last box. Yeah, I got about 30 orders to go through guys So that's probably what I'm gonna do right now after I get done with this before I start the next break just to Get everybody up to date on what's going on Nicole Ivesovich Devin Vassel Jared Culver Austin Rivers Manu quickly thank you lose junior. Oh nice patch two out of five Pelicans going to Jesse Two out of five. All right. Hey guys. Well, there you go. Do a quick little recap So there you go. Nice out of five Lamello 2020 James Wiseman Gazeek Lamello base Anthony Edwards. We got an autograph here of Monte Morris for Denver and Isaac Acoro the 25 Rookie Roll Call We got Odoku Tyrese Halliburton and the average 2020 Bob Love Halliburne base Niko Manion Edwards base Eric Blood so and sorry bank to 25 touch autograph So there you go guys certified number two in the books Jaspies case breaks.com


Thank you very much Well, we have three interesting presentations and that in many ways reflects the puzzles and challenges That African countries face to do how to achieve Destructile transformation that they need The presentations have emphasized the role of the infrastructure human capital and innovation as the one of the premier issues that these countries face and Perhaps that we can open the session for questions and answers so we can take four questions in a row and then As our presenters to answer the question so we can take four questions. So who would like to ask? anyone Yeah, yes Thanks I have a question to Anna Moreno on the first first paper. So You started out with arguing about this export processing zones, right and then Then you You show these coefficients of variation that you see this drop in the coefficient of variation for wage employment But an increase in the coefficient for for the non-wage employment So the non-wage employment became more dispersed Across country across the country so To me, it's not entirely clear how this relates to your analysis of export processing zones So do you have something different there in mind with you with your with your analysis? So perhaps you could elaborate elaborate a little bit on this We have another can we just take four another question was around here Hello, my name is Nadia. I'm from Italy and PhD intern here. Thank you for the presentations I wanted to ask something real quick. I don't remember exactly who presented the date on the world economic forum Was that okay? I was just a very fast question on whether the people that were interviewed there are people that work in Africa or not and Then a question that Maybe should be posed to the general public of the conference at the end But I was wondering regarding the last presentation How do you feel and think about the widespread phenomenon of land grabbing which hasn't been touched at all during the conference so far and Which impact does it have on the agribusiness and on other elements that are important? Any other question? Yeah I have two questions for a demola the first one being has your group or any group identified specific Horticultural products that specific countries can develop and then the second question would be maybe your thoughts on Some diversion of resources away from Catering to local markets, which is food security issue versus Developing agribusiness which essentially caters to foreign markets, which really is not solving food security issue Maybe the last question Yes at the end of the We will have a lot more production Thank you to answer your question What I had in mind it's kind of Okay translating the Chinese model to Africa so you would have these export processing zones as a catalyzer for agglomeration For the agglomeration, so this is the idea that they're bringing to Africa. Obviously directly it applies more to coastal countries in my case, Senegal, Guinea and Tanzania But I think also to the non-costal countries through regional integration So that's why I had this as just as a reference point For the analysis, yeah, okay. Thank you very much with regards to the The lander gap Easy in Africa. Obviously that's been is a big problem particularly with regards to Agribusiness, I mean based on my experience in Penapa industry, so that was quite a lot of problem with land being taken away from small orders and This is largely controlled by the chiefs and The the regulation of the land or land administration does not take into full consideration The small order as to how they could benefit because that's just one of the problem basically I think the the next question I have is the specific do have any specific Auto-culture a product to be honest, I don't have one I mean obviously Africa have to try different product that they could benefit from and of course that links to the next question which is the lowest input in agricultural production, of course They they the the low productivity has been as a result of Lowest input in agriculture production do Africa is going to have to you know Increase the input in terms of a fertilized application Introduction of new technologies doesn't necessarily have to be biotechnology. There are other technologies such as ICT that Africa could benefit from and in terms of improving their crop put of productivity and if I may get the Someone actually mentioned something with regards to full security and agribusiness. Could you please ask that question again? I appreciate that No, I was just curious on your thoughts of because essentially if you're Doing say pineapple. This is a cash crop, right? So there may be some diversion of resources away from say production of basic commodities like rice wheat towards more Market foreign market oriented products, so do you see this as sort of Problematic if one were to expand agribusiness and make it the central point of some country's development I mean If I if if I make answer your question, I think everything boils down to the fact that African go may have to get the policy right and once they get a policy right it will target the crop that can actually benefit Farmers mode have got a loads of you know crops that small does are actually, you know Cultivating Africa they're gonna have to work with small and that's one of the problem If you don't have the identity of problem It's gonna be very difficult as to how you're gonna help them and how they can benefit from agribusiness So they have to kite them along when it comes to what they're gonna prioritize That's that's what I can say to be honest and based on that may give an example of a pineapple is working More or less walking in in Ghana is working in the hybrid schools They just need to step up the innovation program, which is fundamental to growth of this agribusiness. Thank you Thank you. Well, the Nadia's question was very simple it's generally the To do the global competitiveness report they ask people in your country is this thing working or not working What do you see as the major constraint? to do in business or to So it is yeah, it's lots of local people now That doesn't mean that foreigners don't answer because if you have multinationals in the country working there That's that country of work So But do you but they're there they're in the country is either local or operating there? Yeah, I Think your question. I didn't get your name. Sorry, but I think your question is very relevant The Gates Foundation gives Grants for huge grants for Working on you know that now they are doing the poverty reduction thing But they are focused a lot on agriculture and one of the issues is to look it's not big farms for export It's really livelihood of the small farmers and in that regard this idea of food security is extremely important I know that in Asia it's big in China food security is like a number one thing And so it is very important and I really wish that we have more research Because I don't know of any papers that really looked at this what she said is a nice research topic to see if the country What is how can you strike a balance between really starting to export all these fancy staff nice things? let's go to Europe and the US and mostly Europe and Feeding the people who really need to be fed in rural areas the next excellent question. Thanks Maybe follow-ups questions Yes at the end of the The back of the room My name is Victoria. I'm from the Africa all-party group in the UK Parliament I'm a couple of questions for Adam Ola One is I take your point about needing to Increase the amount of knowledge that's feeding into agriculture, but I wonder to what extent it's about communication of that knowledge rather than necessarily Creating new knowledge Did you look particularly in the example of the pineapples that for example the role of extension agencies or even the role that academics are playing in reaching out to the Agricultural community and the second point was you've you've focused quite Narrowly on Technological fixes and I wonder if you had investigated the role of more local forms of knowledge I think thank you. Can we just take another maybe a few questions? No Okay, so Right, I think I'm gonna I'm gonna start with a the role of local knowledge in and Penapa production obviously that's very very important. I agree with you on that basis and of course This year has to be done by the government so there are a lot of things going have to put into consideration including getting there the local farmers involved in agri-business development is very vital With regards to the role of academic to be honest I've not look at that in terms of whether they play fantastic role in development of Penapa industry in in in Ghana and Ivory Coast I think it's what looking at that's that's cute and then With regards to creation of knowledge, of course go many to create a lot of awareness in terms of Introducing new technology. That's very important and This this can only be done by you know coming up with right policy in Africa And not only coming up with right policy. He has to be implemented. That's what I actually emphasize Why are funding a program? He has to be implemented most of the policy loop quite clean on the paper in Africa But to implement is that's one of the problem to be honest. Thank you Well seems like nobody else has questions, so I'm sorry Yes, great Thank you For the contribution I mean the expose on the issue of agri-business in Africa and my question is directed to you You spoke of funding. I don't know if someone asked on that already I'm Funding and my question is from where? Is it from international donors or from the government or from the private sector? And also I would like to highlight on the case of my country Cameroon Where there is currently an issue on oil palm plantation Where it is a very critical case where The investors from the US are not finding it quite easy with the local population and The government as well with respect to the size and with environmental NGOs who are also taking into consideration the the cost on the environment so With respect to some of these challenges What are your your your opinions on investing in Africa to eradicate food security in some of these challenging periods like Tropical sub-tropical Africa. Thank you Well, maybe we are lucky to have any other question before we proceed. No rights well, I think Funding is is Very important. So how would that happen? where based on my experience basically Donors have been coming to help in terms of improving the Agri-business I've come across the case of Big Gate working with the World Bank to facilitate that in Ghana But one of the problem I found out in the literature a race lack of continuity Ampar this program they probably funded for a couple of years and after that It's not sustainable for some reason perhaps not making up profit But there is no literature that has that actually assess that impact then is what looking as right So lack of the continuity of the program either on the part of the government or the do not it's one of the one of the problem so with regards to oil plantation and and I think the government have to get it right right from beginning they have to specifically highlight what they want when it comes to You know investing or inviting the the investor Into the country. I think and they have to take The plight of the small order into consideration, which is often been Neglected. So that's one of the problems. So you have to carry it. It can't always be from, you know Top-down approach. It's got to be bottom up. That's that's what I can see to be honest Eradicating for security in Africa. Wow It's huge. I mean It's it's difficult and obviously it can be done and go may have to share a lot of interest Because as far as I'm concerned, it's just that the old thing is being done by doing all it's been done by FEO and other international organization. They've got to take a proactive approach to also info security problem rather than relying on those people from house side otherwise These guys who are coming from house side, they have their own agenda and it might not so their own purpose to be honest. Thank you Any reaction? Right, so perhaps it seems like we we are finishing ahead. So thank you very much. Okay, finally. Okay one question. Yes Yeah, please It's just a clarification. So when you are calculating a coefficient of variation to to To measure your variation You're doing it on a on a dummy type variable, right on a zero one variable Would that be a problem the levels of employment by some national units? So I take each country then Of observation pairs of national unit and easy absolute values of wage or not wait employment Was it a zero one? No, no, no, there's a value Okay, and then with that I calculate the mean and the standard deviation. So, okay Yeah, and that gives me the value of the indicator. Okay. Yeah, thanks So finally no more questions. No well, thank you very much for the presenters and


You know, we're living in a busy world. People are rushing, things are happening. One thing for sure, Alzheimer's will affect many, many people. You know that one-third of Americans over 85 are afflicted with the illness? In fact, 5.3 million Americans are living with Alzheimer's disease. Unless a cure is found, more than 16 million Americans will have the disease by 2050. Here's something, Alzheimer's disease is a sixth leading cause of death in America. One in three seniors die with Alzheimer's or other forms of dementia. So dementia is a serious problem and it affects your family, your loved one, your neighbor, your life. Dementia also affects your estate plan. Now there's two times that people with dementia or the families contact my office. It's when it's just beginning or when it's in advanced stages. It's a huge advantage to contact the office when there's just the early signs of dementia. So much can be done. Let me go into particulars. Number one, it affects your trust. Your trust takes care of you. But remember, you're trustee of your trust. You have to sign documents. You have to do trust business. Or if you have dementia, you can't do trust business because you lack the mental capacity to carry on those aspects of daily living. The second part of your estate plan that's affected by dementia is the durable power of attorney for property management and personal affairs. This is the document that allows a trusted loved one to sign for you when you have a mental condition or you're incapacitated. Bank accounts, tax forms, social security, other kinds of transactions are all affected by this durable power of attorney. You want to make sure that durable power of attorney is up to date. And finally, your advanced healthcare directive. Obviously, if you can't make intelligent decisions, someone, in case there's a medical emergency, has to make them for you. So these documents, the trust, the durable power of attorney, and the healthcare directive all need to be up to date. It's a good idea to contact the office at the early stages of dementia when we can revise those documents, review those documents, and make sure they're up to date so that you're properly taken care of or your loved one is properly taken care of. Now, if you happen to put this off and you contact the office after someone is well into that dementia, there's still stuff we can do. Now, what are we going to do? We're going to do a trust administration. We're going to put the next, the backup trustee in charge. It could be the surviving spouse. It could be a husband, a wife, it could be a son, a daughter, a best friend, a domestic partner. Whatever it is, we're going to follow the dictates of the trust and the other documents to get those people in place and instruct them on how to help manage the trust and how to care for the affected loved one.  But I can tell you this, be prepared early. Have that estate plan reviewed. There's so much to talk about. Give us a call.


So hello. Good afternoon. Good evening. Potentially good morning. It's my absolute pleasure to act as a chair for this webinar. The title is the future food safety. Can we really trust predictive analytics? I think it's a really, really good, very, very important question. So first of all, I'll introduce myself. My name is Chris Elliott. I'm professor of food safety at Queen University Belfast and also professor of food security at Tamizat University in Thailand. And I've had a very long interest, not only in predictive analytics, but actually in artificial intelligence, probably 10 years of really asking lots of questions about this kind of a emerging technology. And I think AI, there has been a lot of coverage in the media about artificial intelligence. I think just in the UK alone in the last 24 hours, we've had two really positive news stories about AI, one about the development of new antibiotics. The second was about somebody who was totally paralyzed, was able to walk again for the first time in many years. And but also we've had the media coverage about the potential catastrophic role of artificial intelligence. So I think this, this is a wonderful opportunity to talk about AI and actually demystify it to to many people. Because this is a very positive attribute and we're thinking about here is thinking about how we can prevent major food safety incidents happening. Because I think many, many people on our webinar, you know, there's, there is about 100 people registered for this one or I just kind of show you how interested and important people are thinking about not only food safety, but in ways of moving from being reactive to proactive. So this idea of thinking about how we predict the future. We jokingly called it the digital crystal ball. But actually it is about data, it's about data sciences. And I think you're going to get a lot of information about actually how these data sciences actually work and actually function, the way that models are built and how they are checked in terms of the robustness. Also just on the theme of data. It's really important to think about data in many, many different ways. How you go about collecting it. How you verify the data is accurate and correct. But also how do you trust where your data is going to, what it's going to be used for and who might get access to that data hugely important questions. And again, there will be a lot of discussions about the integrity of your data. How robust is it? How safe is your data? Really, really, I think a very, very important topic. And in this era of thinking about future food safety risks, you know, as a professor of food safety, this is a lot of what I do. We investigate big issues in terms of food safety, but we also try to think about what is coming down the line because of changes. And just to think of some of those changes that impact on food safety. I always say the number one change is our climate. And that is having a huge impact already in food safety. And we'll have a lot more impacts going forward. Do we know now what they are? Well, the answer is no. Analytics, we will get a much better idea of those changes. But also what changes is people's perceptions in terms of what is a food safety risk. Often that will be brought about by changes in regulations and changes in legislation. Food safety legislation changes virtually every day of the week in some part of the world. And generally those changes in regulations are about lowering levels or increasing new hazards into risk management plans. I think the last area that I think we need to think about in terms of future feeds food safety risks comes from something really positive. It comes from the desire of so many people to develop a truly sustainable food system. Very positive. But actually what sits behind the drive for sustainability are the unexpected consequences. And again, as a researcher, as a scientist, we are trying to think about what some of those unexpected consequences might be. In terms of our drive for circularity, there will be new risks for sure. Some existing risks will increase in severity as well. And we have to try to get ahead of those, try to understand for all of these different scenarios that I've talked about what might be happening in the future. Once we understand that. And it will not be with 100 degree of certainty. And again, we will talk about probability of events happening. But I think once we get that understanding, you can really start to change to build to modify food safety management plans. So these are the topics that we're going to talk about today. I'm incredibly interested in the subject. And we have to what I would call subject matter experts are going to impart us with with a lot of information. The first is Manus Manus Carbusis, who is the head of research and innovation at agronome agronome is a Greek AI company. This is the second one, which is fantastic by Yvonne Pfeiffer, who is global data services lead at SGS Digi Comply. So both of them are going to give us some information, some presentations, hopefully will demystify as I said some of some of the areas around predictive analytics. Your involvement as well. And your involvement will come through. Please think about any questions that you might have. Just put them into the Q&A box. I will moderate those. I will ask as many of the questions as possible to both Manus and Yvonne. And who knows, I might even try to answer one of them myself, but I'll leave that I'll leave the tough questions to Manus and Yvonne. That's for sure. But that's enough for me. Hopefully that's set the scene that's introduced what we're going to do for the next hour or so. I really hope that you enjoy it. I hope you'll find it useful and helpful. So what I'll do now is I'm going to hand over control to Manus. Manus, you're going to take us through for the next 20 minutes or so on your knowledge and experience around predictive analytics. Thank you very much Chris. Thank you for setting the stage. You touched on so many fundamental issues around safety, each of which could be a separate webinar or could actually be a project out of itself. In any case, hi everyone. I am Manus. As Chris said, I'm leading our innovation team at Agrino. And today, I hope I can help us all become that more confident that predictive analytics and food safety can work well and be applicable in real world situations with true business fund. Now, what we'll be talking about today is in part our internal work in Agrino and the food archive product, but we're also very likely to be coordinating an important European horizon project, which is called DEFRA, where we work together with HCS-DHC comply and many other excellent partners across Europe to advance the field of food risk predictions. So without further ado, let me get us started with the most provocative question I could think of. Can we predict food safety incidents before they happen? Okay. No need to vote, but do take a moment to think this through. So what is your opinion? What do you think? Is this something feasible? Can we predict food safety incidents before they happen? And hold on to this answer for the rest of the webinar and we will revisit it. Okay, so personally, I have a background in computer science and today I will be supporting a rather bold statement. There is nothing left in computer science to achieve to be able to predict a vast array of phenomena, but it is true at the same time that in many areas and in many problems, the economic and AI in general have not yet produced significant results in the real world. So if the fundamental tools are there, what are we missing? And why some of us voted yes in the previous question and some voted no. So my statement here would be that we are missing, in most cases, two things. The first one is a good working understanding of what is feasible through AI, and the other is how we achieve good cross-domain collaboration. But really, as we'll see by the end of this presentation, hopefully there is no much involved and I will do my best to explain this today. Okay, so let's start with the first of these two things, understanding what is feasible. To understand this, let's start with two concrete cases where we can try to apply AI to get predictions for food chain. So, in the first case, let's say I want to understand how Salmonella will grow in my patriotism. This is an environment I can control very precisely and account for most influential factors such as temperature humidity growth, medium, etc. I can, very meticulously, test different scenarios and no doubt result in growth. And with all this diverse data, there is a multitude of machine learning models I can use to create a rather accurate model indeed, and similar successful attempts can easily be found in the literature. But let's take a moment and think what have I managed to create. I have created a model that answers the following question. Under a given set of conditions, how will bacteria evolve in my patriotism? Okay, let's keep this in mind and let's increase our scope a bit. Let's pose a new question where we hope AI can help us answer, will I help Salmonella in my global supply chain? Instinctively, we can understand that the second problem is fat staffer, even if fundamentally we're asking about the same pathogen Salmonella and its growth and behavior. But the difference, of course, is that the factors that we need to take into consideration are much more numerous and the conditions cannot be precisely controlled to establish a clear cause of relations. So in my experience, when someone asks yes to the initial question, if we can predict food safety incidents, I suppose they're thinking closer to the predator example and when answering no to the global supply chain example. But today I would like us to start thinking in either of these two corners. So let's, one example is much more complex and impactful. It is much closer to the real world challenge we want to have an answer for. And the other has less impact, since it is a laboratory deployment, but it's also less complex. Actually, there seems to be a trade-off between complexity and impact, meaning that we cannot freely move anywhere in this space, but we are more or less confined inside this band. Let's have a look at the visualization because it can give us an interesting idea. Is there potentially a switchboard? That is specific important questions we can state that we can actually answer efficiently through a questions that have serious real-world impact and for which we can account for enough factors to create a useful predictive AI model. Next, we will look at such concrete examples moving around in this switchboard. Okay. Now let's turn to the second part. How cross-domain collaboration looks like? When structures when? Let's look at this through various working examples, questions we can answer efficiently right now, today. So to get us started, let's say I am a food company and I have multiple facilities. I have a particular dinner on the schedule and I want to kind of improve on it by incorporating a data-driven and predictive risk assessment approach. That is intervene when and where needed before it's too late. Developing a relevant AI model is an active area of research for us at Agrino and we're aligned also with the overall vision of our project. So my goal through this working example would be to showcase, hopefully in an easy to understand way, how collaboration in this cross-domain teams is structured, but also so that using AI to predict food safety risks involves no magic at all. It's a very structured process that anyone can get a feeling for and where food experts and computer scientists can work together to find really cool solutions to problem with real business mind. Okay, so let's say we put together our cross-domain team. And the first thing we do when working this cross-domain challenges is to understand why we're creating this AI model in the first place, which is not a given for many of the participants and that's understandable. So what are the things that makes us worried that we hope we can avoid or we can do better? This serves as a focus for everyone in the team and the motivation people. So in this case, the food safety experts would share with us things like constantly worried that we might miss critical, we might miss anti-critical, and then sneak up on us, leading to recalls. Furthermore, with our audit resources being so limited, it's really crucial for us to spread the map for the best outcome, and it feels like constantly juggling. Okay, next, it will be the time for the AI experts. We will look at the family of AI models that may be best fitted to the challenge given for what we're aiming for. Let's say that after enough cross-domain discussion with that app designing that we are aiming for a data-driven approach prioritizing data load, it's based on a predicted time coincidence. That is, which of my facilities is predicted to have an increased chance for food safety and stress risk related to the others, and what is an expected timeframe for this. So the food company can then audit practically and end the situation. Okay, so the computer scientist will say or think, this seems like asking how long will a given facility survive without an incident, and the time dimension seems to be important. So one thing we can try is called survival analysis. This is a particular family of AI models that use multiple factors to predict a relative increase in probability of an incident over any desired timeframe. Before we look into what survival analysis is and actually use it as an example for such deeper principles of what AI models do. Let's go back to our cross-domain team and look at the most crucial part of the whole process, which can make or break this effort. So the next thing we do is where cross-domain collaboration is most crucial. Computer scientists and food safety experts, we have to sit down together to understand the factors that influence the probability of an incident occurring. Here, most likely, we also need to narrow down our scope, the particular type of incident, so the factors can be traceable. So just remember this idea of the sweet spot. So when the food safety experts say factors, the computer scientists usually hear columns. This seems weird, but remember this along with the sweet spot idea, this I hope will be another takeaway message. So I will turn to an example now so we can understand what this means. And I know it is an obvious simplification from some of the computer scientists out there, but I have found it to enhance cross-domain collaboration a lot. So when you say factors, I say columns. Okay, let's understand this with an example. Another simple. Let's say we're working with a poultry company and they own a series of facilities in another complex supply chain. And let's say we're concerned for Salmonella cross-contamination incidents in the facilities I would like to intervene practically based on the perceived risk. First, we have to make our goal clear at this stage. It is to collect data for the factors that influence time to Salmonella incident in poultry facilities. And then we would start by monitoring some of the facilities, a representative sample, but let's say one year 365 days. The facilities will experience an incident and others will not. We also note various factors that we believe might be related to the arising of an incident. So to simplify it, let's say these factors are things like a risk score coming from sample analysis, a score for the high-reaching measure use, a score for the feed quality used if we're talking for funds, a score for equipment maintenance. For a particular facility, let's say for the first row in this column, these facilities remained Salmonella free for 112 days. It has a risk score of 10, a high-reaching measure score of 7, feed quality score of 3, equipment maintenance score of 6, and it had an incident. It had it at 112 days after the start of the stack. Okay, the factors are oversimplified, but that is all really. Food safety experts will say factors, computer scientists will say columns, columns and such a type of table. So if we can put enough columns in this table to make it wide enough and also put enough records in this table to make it long enough, then we can trust that computer scientists can find an AI model that can produce great predictions. So, and another message, even if we do not put enough columns here in this table, even if we cannot account for all the contributing factors, the AI model will still be useful. And I will return to that in a moment, but let me continue a bit with this example. So the next step involves only the computer scientists. What we do then is to construct the AI model and do what we call training. That is present the model to the previous table. It is not as easy as it sounds. Since it involves trying a lot of different arrangements, different techniques curating the table by parametrizing the motor, but it is a set process computer scientists know how to do very well. So let's not delve too much into this, but for example in this particular case, we have found that the coax proportional model works rather well. Okay. So what is the end result after all this after we have found a good AI model we have trained it with the table. What do we get. What we get at the end is a noted prioritization AI model, which is a trained a model that can usually are time records what would predict the time to incident per facility so for example. The model may say that that facility A expect an incident in the next 50 days with a probability of 75%. So then the company can do an audit and take preventive measures before the incident happens. So, let me very quickly also say a couple of things for how we can incorporate time evolving factors as well. So in this example, we can see that scores can change over time. For example, due to the introduction of measures for hygiene. This is something that there is an AI model that. And of course, an advanced formulation of the approach would be that during the training of the model, we can connect it directly with the digital food safety system of the company, taking in the raw records as they are logged related to something such as rights and feed equipment, such records and in any case mandatory other has up and other food safety management systems, and the digital locking system can make this visible. After training the model will be ready to provide the online accommodations for facility audits. Okay, so let's wonder a bit. Is this still in the sweet spot, or is it too much. This is a bit ambitious. So, let me say something again that I already alluded to. Even if we do not account for all factors, that is, we don't have all the necessary columns. Can we create a qualitatively better data informed approach. That is better than the traditional approach in many cases. Yes. For example, we can get a better data driven approach over the house based standards for auditing. So let's keep this in mind, and explore this idea a bit further. How far we can go by not accounting for all the potential factors, but getting back useful results. Okay, this is going to be a rather strange slide, but bear with me, I will try to explain as much as I can. So, let's start with a thought experiment. Let's say that in a perfect world, listeria incidents in the global supply chain are represented by this function. Okay, this is an ideal one, an ideal one, at least for an AI expert. And then I ask, how many incidents am I expecting next, even without taking any factors in the consideration. If the time series behaves so periodically, I could predict the future behavior, right. We all could know much is involved. Okay, let's keep this in mind. So one can actually construct however complex factions by adding up periodic ones. So in this example, we the sum of the three first factors generates the last one. You know something that computers can do astoundingly well. It is to find such patterns. Here we're looking at a couple of straightforward examples for a human, but an AI model can analyze in the woven levels of such patterns extremely quickly and merrily by its ability to do things extremely fast. It can find patterns with means it can find them as easily as we can extend the line in the first example. But is this magic? No, it's just very fast computations. And it has of course its limitations, but it all has incredible capabilities that are applicable in real world situations. So here is an example of an AI model fighting and using patterns over the real mysterious public incidents timeline. So let's take a closer look at this one. The actual time series of the incidents for hysteria that have gone public around the world is the black line. Now using what we call deep learning for casting models, whatever that is, we can predict how the time series will evolve in the next months. And this is the red line. These are confidence in the predictions. We also present the orange line, which is a prediction using the previous part of the black line to predict something that has already happened. So having the orange and the black lines closed intuitively means that we have good results and an increased confidence in the red line as well. So we can then ask, but why does this work so well? Because the computer has done pattern matching at an incredible scale and found patterns that exist, but we do not have enough time as humans to find patterns, much more interesting and deeper than the examples on the left. But in a sense, using the same fundamental approach as when we found patterns by looking at the time series on the left. Did we employ any magic to do it? No, it was rather straightforward. So when you do this with billions of pattern match per second, many more patterns appear straightforward and no magic at all is involved. So currently for Dakai, one can find interactive task was that nice to present the predictions of the trained AI models for many different food ingredients or food hazards tailored when users particular supply chain, however complex. I will very quickly also say a couple of things about extreme event predictions because I think I can almost hear what some of you are thinking. Can such an approach predict unexpected extreme events? For example, here is a famous case that you might remember from here back. Well, remember what we said a couple of slides before, more factors means more accuracy. To account for extreme events, you need to put more factors, more columns in your data and more records. The better your table, the more you can accurately predict. But the AI model will remain useful to read predicted lines will get qualitatively better, the more data you have, even with a small table with a few columns. But in essence, this means that adding more factors, more columns will lead to predicting more accurately, more and more extreme events that we previously could not account for the smaller table. So this is what we're currently doing in the innovation labs of I could now using more advanced AI models that can add such deeper patterns. And of course, but from that, I personally would be very happy to sit down with energy together. I would like to thank the science and food safety experts to create a little day I'm on this for your needs. Okay, very quickly, let me talk a bit about for for our larger FRI project that we're involved in and coordinating. We're along with our partners, we're doing something very important, I think, let me very quickly introduce you to the partners, it's Stockholm University from Sweden. I would also like to say to research from the Netherlands boy back from the UK, seeing our inmates from Italy, I could be from Croatia, Reina from Greece. And as with you today, I could know from Greece and this is this comply. And with such an excellent group of organizations were also looking into deeper challenges as well. And for the remaining time in my presentation, I would like very briefly to mention one of them. All the AI prediction models that we saw can be improved the more and the more diverse examples they encounter. So the wider and the longer the table the better the predictions and this has a very deep implication. Let's say we have our audit prioritization model setup, and we have a company that wants to train it and use it. We do so, and they do get back a useful model, but it is a model informed only by their own data. So in a sense they only get a deeper insight in what they already know. But in the industry, there are also other companies with their own internal facilities. If the model could be trained over their examples as well. The final model would be much more powerful in a sense covering the entire industry rather than any particular model. Of course, any individual company would be hesitant to expose sensitive data, they might say, I want a model, but what if my data are exposed, what if the data is misused and my reputation is shared. What ever we're looking to is to prove the concept that you can get the model without exposing the data. The idea is actually pretty straightforward. The model moves around the company is getting trained with the local data. No data is moved around only the model, making it stronger and informed by all participating companies. And the final model is given back role participants. The table is in the details of course, and there are many of them, but the vision is clear to create sector specific intelligence networks, build around privacy preserving AI. And if you're alluding to that. Let me say, if you would like to learn more with Chris were co organized cabbage or summit on exactly this topic of intelligence sharing. You will hear about this from all viewpoints business experts regulatory experts technical experts. We will be very happy to have you join us. This is the registration link. This is a code you can scan. And we will now send the link as well in the chat. Keep in mind that we will also have interactive brainstorming sessions small groups with any interest participants. So book your place in one of these meetings and have a say on how this important data will evolve in the future. That's all for me. Many thanks for that manless. I personally really enjoyed the presentation. I heard some questions that I have, but it actually raised some new questions which I may post you later on. So please for all of our attendees at the webinar today. There's a Q&A box just type in in your questions for manless and hopefully we'll have time to answer them later on. I think that maybe the example that you gave around Salmonella I think was really, really good. And I could, I could see so many different applications for that. So, so thanks. So what we will do now is what we'll move on to the second presentation if that's okay even what we'll ask you to give your presentation around informing regulatory decisions with food risk intelligence. Thank you. Thank you Chris and also one welcome from my side. So my name is Yvonne from STSG comply. And after we have heard from a lot of interesting things about food safety risk prediction. I will talk about how we can also do regulatory prediction for regulatory changes and why this is so important, especially for the food safety sector. Next slide please. So, if we have a look at food safety regulations, we have different challenges that we are dealing with. On the one hand, we have the regulators and the public authorities. And for them, it's really hard to implement new food safety, new food safety regulations or update office, because it's a lengthy process that requires complex decisions involving multiple stakeholders. And if they are working on a new regulation, so they actually they are not able to use the one tool that combines regulatory data with food safety data, so that they have all in place. And they have to use several sources to get all the relevant data. And even if they have the data that they need, we also are dealing with the fragmentation of the data economy, so that it's really hard to get all relevant information together. And another problem that they are dealing with is in some cases, so think about the risk assessment that were performed from the after. Not all data are available and also must be generated within the study. And on the other hand, we have the regulatory affairs specialist in the food industry. And for them, it is not easy to find all relevant information that they need to be sure that they have everything in place. And then also, if they have found them bringing all relevant information together to think about the food safety manager in a large company, and he's not only responsible for Europe, he's also responsible for China and USA, and they have to deal with all relevant kind of regulations and it's not easy to find everything that they need. And of course, they also must be up to date, follow changes in regulation, and they are responsible to detect early triggers for a new regulation. And that's not about, for example, mineral oil or PFAS. It's not a new topic. We are discussing about these topics for several years. But to detect such a trigger, and to get an understanding, what does it mean for my company, which processes must they have in place if the regulation comes. It's not so easy. And that's the reason why, next slide please, that one part of the effort project is the prediction of regulatory changes. So, and here the idea behind this is to detect these early triggers to help on the one hand the authorities and the public sector, but also the private sector to be better prepared if we have new triggers in food safety. And next, yeah. And how do we do that? So, how we want to do that. So, let's say, for this project, for this part of the project, we have the AFRA data and analytics marketplace. Let's say this is the hub of the project for this part. We want to combine regulatory data with food safety risk data. And also, by combining such data and software sources, we also attach sustainability aspects. So think about if we are all using the same database, because also low computational energy waste in real time. Within the, by using the whole AFRA network, we will have multiple participants who will attract and contribute while in data mining software and give the data to the marketplace. On the other hand, we have the data consumers. And here we also want to invoice authorities as potential consumers. And they can use our database services. And also, here we will have the financial income. We start already working on the first use case. And this use case, next slide please, is we want to have a look at pesticides and potatoes. If you think about pesticides and potatoes, even if you only think about it in Europe, it's really a complex topic because you have to monitor several pesticides which are regulated from the database. But if you're, for example, using our SGSDG compliance platform, this is the platform for food safety and regulatory compliance. Here we combine not only the regulatory part, we also have incidents and vehicles in it. And also we use the laboratory data of our laboratory network. And this also could help in the future to get a better holistic overview. And also it's helpful to follow policies and laws. So if you get an understanding about, okay, what are they talking about at AFRA, which risk assessments are they performing, then it's also easier to understand, okay, which regulations may come in the next few months. And so combining these several data will give you good overview about the actual situation. And we also started a project at DigiComply, which is called Smart Test Protocol. And here's also the idea by combining regulatory data with food safety data to give you an easier way to update your test plan. So think about the potatoes. You will have several pesticides that you have to monitor. But maybe you will also have other contaminants like heavy metals or microbiology parameters that you want to follow. Then each of these finger parameters has a total risk based on the severity and likelihood. And due to the leakage to the regulatory data and food safety data and scientific data, you will get a really good overview and understanding which changes will come and which also will affect your test plan or your product quality. But here we have to focus on the regulatory data and food safety data. But coming back to our example for this AFRA project, pesticides and potatoes, we should also have a holistic overview. We should also think about other topics like think about the European Green Deal with the Fox strategy. So here we have targets for 2030, but we have to reduce our chemical pesticides usage by 50%. And of course this also will affect the food safety topics.  So we are dealing with drought, heat and water logging on the field. And if you think about potatoes, potato plants are not stress tolerant, actually. There are some also research products to make them more stress tolerant. But if you're talking about today, they are not stress tolerant. So here we also have the consequences of poor harvest, poor quality, and of course maybe also more pathogenic terms. So what I will say is, as also Chris mentioned it in this introduction, the climate change will have a large impact on food safety. And if we want to do a prediction also for regulatory changes, we should also follow not only the regulation of food safety relevant part, but also for sustainability. And we have also should link to that weather data, for example, or other crisis that may occur. So think about the Russian Ukraine war and the supply chain interruption. This is also a topic that may affect the food safety. And yeah, this is what I wanted to present today, that if we want really to do prediction for regulatory data, it's important not only to think about food safety data. We are talking about global food risk data. And the goal for this part of the AFRA project is to have real time informing on regulatory decisions based on the global food risk data. We want to reduce computational energy and resources, create an open food intelligence network, and bringing together all the relevant stakeholders from the public and also from the private sector. And now I will hand over to Chris again and thank you for your attention. Thank you very much for that. Again, really good presentation. I think very different actually from Manos because I think you talked about very, very specific issues that are facing lots of businesses in terms of changes that are happening, the regulatory environment sustainability, and how businesses really start to think about what the future risks are associated with those so it was a really good presentation. And yeah, a number of questions I'm going to pose to both of you, but actually I'm going to start off with, there's some really good questions have come into the Q&A box and again I encourage people please ask as many questions as possible. So, there's a question from Felix, and I have to tell you Felix, you beat me to it because I was thinking about asking a very, very similar question. And this one is for Manos. And you talked about Manos about the columns, okay, and to me columns are different sets of data. And you know, I think in the example that you showed around Salmonella, I would call those direct measurements, okay, what's happening on the farm what's happening with the feed. What about indirect measurements that may well be capable of building the robustness of the models. Felix comes up with a couple of examples around say human behavior, and you know, everybody says food safety is a culture, okay, so what about human behavior aspects, what about inputting weather data as well. What are the chances of starting to put in some of those indirect columns into the model building to increase the robustness? Great. Thank you, Chris. Thank you Felix for this question. You're of course right. The more factors we can account for direct or indirect. We'll at the end improve the model. And even if in a case we have too many factors, some some of them only indirectly related to what we're trying to do. AI experts have a way of measuring which of these factors are the most relevant for the question. So there is no problem in this direction, but let me let me phrase the question a bit and say this. Even if we do not account for all the important factors, we will get a useful AI model. It might be an AI model that overlooks something. That's true. But again, it will be a useful AI model because it will have the ability to notice things that the human doing the traditional way of approaching this problem might miss. So let's not look at the, you know, at building the most powerful AI model that could be built and writing down all the possible factors. We can do great strides, even with the most the first things that we can think about the first factors the most important factors, noting them down will have a very impact. I'd like to pose another question from from our audience to have on this time. And I think it's very much involved in terms of regulations and Brendan asked the question about the sugar beet industry and currently in Europe in the UK, the harvest has been severely affected by blight and reducing yields. Part of that has been driven by regulations about about the use of agrochemicals that can be used in that particular crop. And now we're finding sugar being imported from different countries around the world, which is contributing to environmental footprint, but also have a very different regulatory framework. So is, you know, in terms of changing supply chains, and the risks associated with those changing supply chains. Is this something that you know you're already thinking about even in terms of this area of the, the regulatory environment. The two is also complicated. So if you have into even if, yeah, it's due to a regulation that you have to change your supply chain or if we have an interruption of the supply chain different types are possible. Of course, even it's not easy to have all the, let's talk about restricted substances which are regulated in the countries and in some countries, more or other active substances for pesticides are allowed in Europe. It's really not easy to follow it or to be up to date. And I think to regulate this was not work. I think, yeah, I don't know if you think about also the climate topics. We have to go hand in hand. So on the one hand, we have more regulations or restrictions using pesticides. On the other hand, we have the climate change and we have to deal with all of these topics and yeah, it's not easy to answer. Thanks very much for that. This is this is a question actually for both of you. It's okay and maybe I'll come back to you first again even there's a question about the development of AI models and in terms of fraud. So, will AI be a tool that will actually help detect fraud and supply chains, or will AI be a tool which will be helpful to the fraudsters to deceive supply chains. There's two very different approaches there. And if we are talking about fraud, we have several aspects that we have to follow. So we think about the type of fraud. So, but of course, if you select different types of data, then even if you think about, okay, how many funny, for example, it could be produced. And if you follow this number, of course, then you were able to do prediction and can can pay today. Okay, but you cannot sell so much honey because it could not be not could not be so many produced. So I think for these topics, it's easier. But if you think about the bike product, for example, it's not, not too easy. I think it's really depending on on the type of food for what we are discussing but one of what you think about it. So Manos, what's your view? Thanks Chris. This is a very interesting question because it alludes to an interesting counterbalance between having tools for a good purpose, using the same tools for a bad purpose. So, one could frame the question, okay, who has the better tools? Is it the good guys or the bad guys? The truth is everybody has the same tools. The computer science offers these models to everyone, and more or less all actors have access to the same power of tools. What is the real question here is who has access to the intelligence and who has access to the data that can train these models. And that is why it is extremely important for the good guys for the food industry to start sharing this intelligence. In a more confident way and make sure that they train their models with all the data they have as an industry sector-wide. So they create the most powerful models and nobody else has access to this data to do any harm. And on particularly this topic, we will have a very nice discussion on the summit of well Chris, where you will show two very interesting initiatives, and how they are helping exactly in that, especially in authenticity. Right. Thank you both. There's another question has been asked, and believe it or not, it's virtually identical to the question that I wrote down, in case there weren't enough questions coming in, but I will tell you there's a lot of them. Sorry, Vaughn, it's back to you again. And I think it fits in with the smart test protocol that you talked about. And certainly with changes in legislation around pesticides, bait bait fungicides herbicides and so forth. And it's likely to say, I think undesired consequences of that, we will get increased problems with mycotoxins we may get increased problems with with microbial contact agents. So is this part of what you're trying to develop in terms of this smart test protocol, or again is that something further down the line for future. So this is definitely the topic for the future so we started working with it and also firstly to connect the regulatory data with safety data, but it's totally interesting and one important part, if you think about okay how can we detect new triggers to to have the holistic overview and see okay when we are reducing substance XYZ. What impact with with that we have so think about for example, as a lane oxide, we are using it to avoid the contamination with someone else for example. And if you look at the recalls in this time where we have the as a lane oxide problem that say, and you can also see okay. We have also the detect more similar in such samples. So I think it's really interesting to see not see it per hazard, you have to see it for commodity or product and see okay which possibly has a I can have in this product and how will they affect each other. Thanks. Manos back to you. And this is a topic we have talked about quite a lot. And we're going to be talking about it quite a lot again in just a couple of weeks time. But it's about how can regulators and private sectors work better together, but what are the hurdles what are the barriers at the moment in stopping that happen happening in the food safety domain. So Chris if I understand correctly. This alludes to the problem of okay, if I share my data, am I not very deeply exposing myself to any legal life. Concerning this data. So, for from what we have seen from the field from successful initiatives, such as fin, the regulators understand the very important role that private intelligence say networks have to play so in a sense, the food industry tries to regulate itself. So we have seen at least in the UK through the example of fin that regulators understand that, and they're actually cooperating well, and within the boundaries of such a private intelligence network, and they're actually trying to be helpful. Chris actually I think this might be an interesting question for you to chip in as well. What do you think how have you seen this interplay between regulatory bodies and fin in the UK. So I think you're absolutely right. I mean there is massive tensions in terms of sharing sensitive data. Absolutely right. And it's not difficult to understand why those problems exist. It's about protecting your business protecting reputation, all sorts of different factors, and regulators love to collect data. Absolutely love it. And those really don't like to share data themselves, actually, and that's from experience. But in fin, which is for those of you who don't know it's it's food intelligence industry network, which operates in the UK, but there are many national international companies. And we have found a way we've found a wonderful mechanism of sharing data safely between companies but also between companies and regulators. And what I will do now is, this is a wonderful opportunity to advertise our first international summit on on privacy and for food risk intelligence I would really recommend that you sign up for that. There are some phenomenal presentations about this particular topic. So what I'm going to have to do now is, I think, first of all, I think all the people for asking questions are more and more questions coming into the chat box but we have a little bit run out of time to deal with those. It's really been incredibly good, because they have pinpointed some key areas of both the presentations of Manus and even. I want to thank you both for your presentations they were really really good. And personally I've learned a lot from both of them and it's got me thinking about various things but it is. You know at the outset I talked about de mystifying the whole area of artificial intelligence, and really to me is good mathematics. It is really good mathematics, good mathematical modeling, and the more data the more inputs that you have into the model, the more robust they get. I think Manus would agree with me is the predictions can look even further ahead based on the quality of the data, which again is very important, because we didn't get a chance to talk about how far ahead can you foresee a food safety risk is at one month, three months, six months, one year, and really a lot of that is about the quality of the data that goes into building the model. I think you've uncovered really nicely that the area about regulations and your regulations are changing. I think probably one of the biggest driving factors of changing regulations for food safety will be food security going forward, because many parts of the world now understand that the world's food system is not as robust as we once thought it to be. The global pandemic, the war in Ukraine has really highlighted that, and there's going to have to be, I think, much more risk assessment much more risk management around around food safety, in terms of really thinking where the priorities lie in terms of auditing in terms of testing, I think predictive analytics is going to play a huge role in that. What I'm going to do now is I'm going to say thank you to our speakers Manus Yvonne. I want to thank all of you for joining our webinar today, and all of those who asked questions and I hope I hope you all found it very helpful and useful as I did. Thank you all very much. Thank you. Have a nice evening.


So last year hundreds of young people in Bristol through a statue of the slave owner Edward Colston into the city's harbor Now Colston is rightly reviled by people as a as a governor of the Royal African Company that in his time controlled much of the transatlantic slave trade Well that company was actually established by a British monarch and the the wealth that the company acquired was piled upon the huge amounts of wealth The the monarchy today continues to sit on so you could say that in the British monarchy the British royal family We have here a living monument to all of the crimes of British imperialism and Thanks to Megan and Harry's interview over the course of the weekend We we've been given a brief glimpse into precisely how deeply this racist and colonial history runs through this institution And among many of the the revelations of which I'm sure you've heard and are aware One in particular I think shocked a lot of people and that was the fact that an unnamed Royal expressed to Harry his concern at how dark or his or her concern How dark the skin of their unborn child would be once they were finally born And all of this I think and all of the rest of the revelations have shown for millions of people that the you know All of these things that were lauded in the royal family their concern for pure pedigree is nothing more basically than racist white supremacy their celebrated cool detachment is basically a little more than a callous vengefulness and It's no wonder therefore that in this context that the complete lack of Humanity this family has shown that it's led to a certain outpouring of sympathy for Megan and Harry Now Megan and Harry will be okay They will they will be able to look after themselves and they I'm sure they will become Tremendously wealthy off of these interviews But these interviews are a real problem for British capitalism because this isn't just any old family that these people are exposing This is a family which is part and parcel of the British ruling class their racism and their callousness are not accidental They are they are part of the organic racism and callousness of the of the capitalist system of which they form a part And and the royal family are very important actually to the the British ruling class now some people Opposed the monarchy as a waste of money, but to the ruling class. It's a lot more than simply an expense Onto the British constitution the monarch actually has completely unlimited powers And it's not for nothing that they refer to it as her majesty's government and her majesty's armed forces It's actually an extremely powerful reserve weapon in the hands of the capitalist class now in normal times We're allowed to believe that this is merely a formality the powers that the monarch enjoys But in times of crisis should the interests of the British ruling class be threatened Particularly if it were threatened by the the the working class organized They would not hesitate to use these reserve powers basically And it is for this reason above all else that having seen the monarchy exposed for what it is We need to topple this feudal and racist relic relic and this this this living monument to the crimes of British imperialism In just the way the same way that the the statues of slave owners were toppled last summer That is why we as socialist appeal we raise the demand of the need for a socialist republic