Here we go. Good morning and good afternoon. Good evening. Depending on which part of the world you're based, this is going to be an extremely interesting webinar, online panel. We will be talking a lot about laboratory data, testing results, connecting different data pieces together and extracting value. I'm giving a couple of minutes to our participants to join. While I will be introducing the panel, we will zoom in the ways in which we can unlock the value that is hidden in laboratory data. But also discuss a little bit about what we mean with this seminar and how we can put data in practice to inform risk-preventing prevention. Let's start. It's all about testing, food safety testing, but not only food safety testing, testing that is generating insights related to what we find in food, in food samples, how safe is the food that we are consuming and that we are using in products, as well as additional aspects of the food samples that we test in terms of allergens, the presence of ingredients or other aspects in the food that we look at that shouldn't be there. And it's a huge business. And it's a business that is growing. While it is growing, it is also generating lots and lots and lots of data. We had this conversation a couple of months ago in one of the workshops that we had on the topic of using data to train AI models and everyone was complaining to me because they said, yes, AI is good, but we really struggled with putting the data together, finding a way, first of all, to digitize properly what we generate to testing, harmonize it, make sure that the different data pieces, they are talking about the same thing and combining them so that we can extract value from a large variety and a large volume of testing data. And this is becoming even more practical. If we look at where these insights may add value. I love this example. It's a quote from a colleague that we had this conversation, it's two or three years ago and he said, I really think that we are testing in blind. We invest so much money in food safety and fraud testing and I still feel that we are testing for things in blind. We are not sure where should we be focusing on, how should we prioritize what we are looking for. And it really made an impression on me investing such an amount of money in testing and then still struggling with extracting the value and taking real decisions. And then there is also the other dimension of having access to all the data that is being generated. Another interesting conversation that I had with a colleague describing the model that they are following that is combining internal laboratories that are generating lots and lots of testing results with laboratories from a third party that are storing the results in their own systems and he was telling me that when I'm asking this third party to provide me with access to our data, the data that we have paid for, they want to charge us. They ask for additional money so that we can understand what is the value there, what are the testing data telling us. So it can become quite a struggle putting everything in place and then using it in real decisions and that's why this panel is here. When people were registering for the webinar, many of them completed the registration questionnaire that asked them to rank these challenges in terms of importance. As you can see, many of the people in the audience they feel lost and sure in ways in which they can best combine all the data that is being generated and then integrating them in internal systems. This ranked as the number one challenge and then number two is whether they are testing for the right hazards or are we looking for something that is of high priority right now? Is there any other emerging risk that we are overlooking? And finally another challenge that has to do with the data sitting in plenty of systems, very often for parties and being very difficult to access in years. This is why this panel is here and this is why I've invited this wonderful group of panelists and I will ask you to introduce yourselves briefly and then tell us a little bit about why you're here and why did you find this panel interesting? Joel, would you like to start? Sure, absolutely. Well, thanks for having me first. That's the first thing I'd like to leave. But my name is Joel Masso. I work as Associate VP of Science Programs at Western Growers. We have a number of data type programs related to the food safety of fresh produce. Western Growers, for those who aren't aware, we're a trade association. About 50% of fresh produce in the United States originates from members who are part of our trade association. So it's effectively all the specialty crops, lettuce, citrus, nuts, those sorts of things that originate from the Western States of the United States, Western Growers represents. And from my background, I'm a quantitative microbial risk assessment person from graduate school as well as spent many years at a global testing company thinking about and helping clients use their data or accumulate data to be able to try to get to what is that holy grail of food safety management, of using your data to predict and prevent as opposed to just react to. So I'm excited to be part of this panel and to contribute. What we've learned at Western Growers with our data sharing programs, my background in microbial testing. Wonderful. Alec, how about you? Yeah, likewise. I want to thank you for having me on this panel here. So I'm currently serving as a global manager for digital quality and food safety at ADM. So it's part of my, my day to day is looking at our digital transformation and automation opportunities within the business, but also what is our analytics strategy how can we drive continuous improvement within the company of quality and food safety as well as quality control lens. So really last 15 years, my background has been in lens implementations as well as quality and food safety digital system implementations. And then what's the analytic strategy that those can drive from either mostly from a statistical quality control perspective. So really been a passion of mine for the last 15 years and really what I've lived over the course of my career. So I'm very excited to be here over the course of my career. It's been such a large breath of different products, different businesses that I've had the privilege to work in from agriculture to food and beverage to ready to eat type products. So I'm excited to be here. Wonderful. And last but not least, Yanis. Nikos, thank you very much for the intro for the very nice intro. It's a pleasure and an honor to be with the panelists that we have together today with us, with Alex and Joel. And I would like to thank them that they are here. My background is in the computer science, so I will speak more from the technology point of view. So however, I felt in love with the food safety domain and the challenges that this domain has very early in my career. So in the last for more than 10 years now, I'm working, helping the companies to digitize the risk, monitoring the risk assessment and the risk prevention workflows. And I'm very happy to be here because I see the struggle on how we can manage and how we can combine all the testing results. And I see also the opportunity of how much this can help in improving the risk prevention workforce. It's wonderful to have you, the three of you here. I think that you are the right people for this conversation and the mix that we have around the table is very interesting. Let me provide a little bit of an intro. I was trying to pull together a couple of slides to explain to our audience even the non-expert ones to explain them why this is important and why do we talk about such a large variety and a large volume in food safety or quality testing. And my favorite exercise is looking for real actual data pieces. And my even more favorite example are the certificates of analysis the typical outcome from one of the laboratories. I'm going back many, many years and I remember I recall the certificates of analysis that my dad was breaking at home because he was working on very, very relevant related topics. They looked old fashioned to me pieces of papers with numbers. I always thought computers can do it better. And here we are today looking again at old fashioned pieces of paper or information and we are having this conversation about whether and how computers can do it better. And when we talk about testing food safety plus testing we're looking about in essence understanding contamination contamination factors and the degree of contamination in different aspects and looking for different types of contaminants and hazards. So we are talking about a world that is quite diverse and broad and depending on the area in which we focus we are dealing with different types of data being generated. So here with a very brief research on the types of data that I see being generated for example for chemical contamination I saw the traditional pieces of paper or PDF files that I was showing you before with different ways of expressing what they found in terms of chemical contaminants presence and samples different versions of this data being generated up to a spreadsheet that can be automatically produced by Linz software like this table that I have here as an example very very very similar if we look at biological or microbial contamination although there we're looking at different parameters different types of contaminants different methods are being used and in many cases understanding if the contaminant that we're looking for the organism that we're looking for is the one that we are expecting or not even employees more advanced methods like PCR testing lots and lots of additional data being generated and then if we start looking at ways in which we can for example look at the authenticity of the sample if it contains what we expected to contain or what it declares that it should be containing then things can become even more sophisticated and the data types that are being generated can be even more complex thankfully computers have been employed thankfully plenty of software systems have been developed and deployed but this means that we have many of them it's a whole ecosystem different limbs, platforms or database is storing this information so this is a world that is becoming quite complex a large variety of very heterogeneous data and with many many systems in which they are stored so Yanis I will ask for your help to navigate us a little bit into how we can make this world a bit more unified or harmonized before I do this I want to get a feeling from Joel and Alex on what does testing mean in their own lives their day to day practices and in their own worlds Alex I will start with you what does testing mean for you sure so I see test results coming from multiple facets in my experience some of them come from the lab which is traditional testing I think we all see but I see a lot of test results also coming from the shop floor whether that could be micro testing that we do on the shop floor could be in line testing we have operators do could also be in line instrumentation I really need to gather data on a daily basis that data I see really supporting both our material sourcing if we think about raw material agricultural materials coming in from a grading perspective or bringing in receipts of new raw materials but I also see that from a manufacturing operations and really supporting that process I think about all of these items really from a daily basis I see thousands and thousands of test results that could be harmonized but typically it's high variability in data different formats different languages timeliness of the data is different it could be real time it could be a weeks delay depending on where it's at so that really influences the consistency in the data and some of the challenges that I see on a daily basis to understand also some of the data is structured it's documents it's bench sheets that are manually recorded at our plants so how do we get that data in order to really drive that value and unlock that value we're all looking at from either a risk management perspective or looking at our lab and facility operations local fund structure data here you say but also data that is coming with different time latencies so maybe they count as a delay that's very interesting Joel what about you yeah well thanks I'm going to approach it from the current role that I'm in as well as the data projects I'm associated with so I referenced earlier that Western growers represents a group of growers and one group specifically has kind of entered into this new paradigm of testing and how to use data and we really have a lot of outbreaks and issues associated with them when it comes to food safety and the industry continually just chips out like good agricultural practices how to implement new methods and the hard piece is always to figure out ways to assess our efforts doing anything and where potentially are the risks coming from so as we do outdoor agriculture where we have limited ability to predict or prevent the impact of the industry on the field like they're in the proverbial kind of like wild goose chase so a few years ago the industry banded together and recognized that all the efforts people were doing on microbial testing specifically so for pathogenic E. coli and salmonella and water testing so indicators there's an enormous amount of data that's going on and so there's got to be a better way we can learn something from all of these data points and transition from yay it passed my specification ship the product to what did all of this money and effort mean so western growers created with our partner from global a data sharing platform that allows the industry to anonymously submit their data and their efforts how and where that data is generated that also leads to lots of questions when you start looking at data from all these different streams is what does it really mean so this is a relatively new project it really is that new paradigm that everyone talks about of like how do we use data more productively what does microbial testing or just any kind of testing mean and aggregate as opposed to just reacting to it there's two programs that are on it there's two they're both related to leafy green production the western growers leafy green food safety data sharing program and then the leafy green marketing agreement remain test and learn program and so we're managing both of these programs for the industry they are unique and different they also are somewhat looking at the same problem and effectively you're trying to use this data to verify the context to maybe metadata or things associated with these data points that could teach us something about managing what can be a very high-risk product it is also leading to some really productive conversations that we need to be having as a kind of food safety society in respect to understanding origin of data and how you design a program I think we've been somewhat sloppy slash with testing in the sense that we do the test we get the result and move on when you start looking at data in aggregate the critical nature of like why you took the test what the test could theoretically and statistically mean and the limits of the test methods and how you got through it actually have pretty huge implications to interpreting what the results may tell you and that's what we're getting at right now which I think is it's great and there's the exercise to learn something as an industry I think that's really formative that we're learning in this project but I think probably more productive actually is the conversations that we're now having about like well we've always done a salmonella test but maybe they're all not equal or how could we standardize what we're doing such that when we are analyzing the data the first question that comes to mind isn't do we trust it and that it's really leading to a lot of really productive I would say more maturity than a lot of other produce segments as well as a lot more complexity so it's been it's an interesting exercise on many different levels so I'll stop there but that's my experience right now with testing data and culture I really like the fact that you you talk about how pooling together data from different stakeholders first of all requires a new language how can we describe the data among us it serves as a way to verify and inform the practices that we follow when we are producing but at the end of the day it puts on the table how we use testing percent this very sophisticated but also very expensive instrument amazing thank you guys but very very interesting to get a bit of a feedback from our audience as well in terms of what are they testing for so I will ask here for the help of the magic pool and I will give you a few seconds to respond to this what are you primarily testing for what kind of contamination are you afraid of what is most important for you guys in the audience lots of quality characteristics I see can you also see the results yes ok so I see lots of testing in terms of quality in terms of authenticity and then chemical biological contamination this is where I see responses coming in ok let's use this as input for our conversation you saw the result off we go Janis we need a common language and we need ways in which we can trust this huge volume variety velocity of data help us understand a little bit better how we can do this yeah I will try to do this in the next slide there are two parts in the first part I will try to describe which are the challenges and how we can overcome these challenges that we have when we try to combine the data in the second part of the few slides that I have I will show what can this unlock which business cases this can unlock and some of them are very relevant to the discussions that we have already so how many times you had the you needed to to get a trend for a risk parameter or you needed to identify the frequency of the parameter and you asked from other systems or from other databases to get an export of the data because you thought that it is easy to combine this data and answer the question that the critical question that you had in mind it happens to me many times I am very optimistic in that but still there are you cannot we cannot do that we cannot do that we cannot combine the different files that we are getting from the systems and although we have already the systems and we have already the data we cannot combine them we cannot harmonize them because that we have already mentioned some of them so first of all it's a very small part of the challenge but it's still an obstacle we have different formats of the data it can be Excel.pdf format database is API so it can be the data can be available in different formats we have both structured and unstructured information and unstructured information is a bit more challenging I will share an example as well in the next slide we have different structure information in terms of how which fields are used to describe the information the so called metadata I will also deep dive a bit about it we have different terms used to describe the same things so even if the foods there are so many food and vocabularies out there different systems are using different vocabularies and classifications and as Alex also mentioned there are different languages used so these are the main challenges that we see and although we think that the most important problem when it comes to harmonization and trying to combine the data is to harmonize the data itself the result, the actual number this is not the case here the most important issue is to harmonize the fields, the metadata that are used, the elements that are used to describe our data and we have very important elements there that are used for instance just take a very simple example shown in this example in the 1880 slide we found it at the old years of agronome when you need to describe the publication you need to have the idea of the specific publication the author, the title, the short description you need to have the date you need to have the catalog so it's very much the same thing for all the things for all the resources especially the testing resources they have so many parameters so it's about combining the metadata what is needed which is the solution when we we need to describe in a unified way the testing resources one solution is to define to specify a unified reference data model and this has two main steps the one step is to select the key attributes that we need to describe all the data the testing results and this may include the date of analysis the method that was used the type of the sample which parameters was analysed the actual results so all these are the columns that we see in a typical Excel tabular format but the second very important step is for each of these elements the metadata elements we need to define which are the values that are around are free text values around or they are controlled values and it is a specific list of values that we need to add there so these are the very important two steps to define a unified model that can help us to harmonise all the different testing resources testing the results so and how we can what we need to do is we need to do the mapping of the data that each testing source follows to the reference to this unified data model that I described so each data source follows its own data structure has its own metadata uses its own formats for units for dates for vocabularies for countries for hazards and for products and many other parameters we need to apply a mapping process that will ensure that each of these elements from the original record will be mapped and will be converted to the elements of the reference model and yes technology is very important but the question is if the technology could help in something like that yet there are semi-automated mapping tools that can significantly help and make easier this process so it is a difficult process the mapping it is a difficult process but it can be supported by technology and by even new technologies like AI as some specific example so let's start with the example of extracting the metadata and the values for each metadata element from the certificate of analysis the favorite files the favorite files so there it is even more challenging because there are totally different layouts of these certificates of analysis as you can see here there are elements that are used to describe the same things like for instance which was the parameter that was analyzed if it was aflatoxing or if it was a physical parameter and this and even the results and not only the values not only the metadata elements may be described and may be available in different languages for instance the corresponding the relevant elements and then we still have the challenge to map and transform everything to the reference model and even if we go to the easier case of do this kind of do this kind of harmonization for files that come from systems that we are using like the laboratory information example of data of a data export from a limbs A and from a limbs B you can see I'm highlighting here the column of the food that was analyzed and even the name of the element is described in a different way the values of course may be very similar in the final meaning like it's both in both cases it has to do with wheat or wheat flour the way the values that are used and the language that is used to describe this element are different so these are the challenges that we see also in practice when it comes to the harmonization so which are the what we can do and which are the typical steps that we can follow in a repeatable and reliable way so we can harmonize the different data testing results and the different resources so first of all we need to collect all the testing results in one place in one store this may be a data lake or any other technology that can be used and then after collecting and having all this in one store to transform the testing results from the original data model from the original data to the reference data model to the unified data model if we have different languages used in the testing then we can apply a step for the translation this can be assisted by machine translation or it can be it can be also post-processed by people to improve the quality and after doing that we need to map also the values from the vocabularies used in the original data models to the vocabularies that are used in the reference data models and after that in order to make them available we need to store and index this unified version of the lab tests and make them available in an easy way for the machines to get them and show them in dashboards and some good practices that I could share here from similar worlds that we have done so far is that the one thing to use the unified data model is a very important step it is also very important for the different vocabularies for all this different list of values that we have for different data elements to follow standard vocabularies and data standards and there are already standards to describe the food categories and the foods like the food x2 or the codex ZFSA classification there are also standard vocabularies used to describe for instance the chemical contaminants like the cast numbers and also it is important to use standard matrix systems to harmonize results because we need to transform the original units to the reference units so we can have something that can be compared in terms of values so these are some good practices and you talk a lot about the data layer and lots of work that has to go into the harmonization part but certainly a bit about where you see those put in practice in generating value so if we manage to harmonize all these different testing results that comes from internal or external data sources because we have both cases then we can have some very interesting use cases enabled so one of the use cases that I want to share with you the first one is that we can interact with risk assessment over aggregated external testing results in this case we can harmonize and aggregate millions of sample analysis results from sample analysis from many external data sources and there are such data sources already these are mainly results testing results surveillance results published by the authorities we can link them to search based on the metadata taxonomies that we have discussed so far and this will enable to have real-time analytics and interactive navigation per commodity hazard and geography so it is easy then to answer questions like which is the percentage of samples that were found to be above the regulatory limit for a contaminant like lead in health and spices or in specific material like cinnamon so this is the one use case the second use case is that we can combine both the external harmonized data with the internal with the company's internal harmonized testing data and we can have we can use the harmonized and aggregated millions of testing results to link them to search facet that can be used to search and have real-time analytics per commodity hazard and geography so in this case we can unlock even more value because we are taking advantage both the internal the company's internal testing data from the different testing sources but also we can see what the external lab test results are and which are the trends and emerging issues identified there so I see that this builds on the first use case and provides even more value and the third use case is focus on delivering fully the fully harmonized data streams of testing data of the combined and harmonized to internal dashboards and platforms so we have all this disconnected data testing data either these are files, databases, APIs we can collect them securely store them and harmonize all this disconnected testing data with the process that we mentioned earlier and we can make all the fully harmonized data stream available through machine interface like an API and the API can be used to integrate this harmonized data stream in internal dashboards that can have different facets as we mentioned also in the previous cases per geography, per per parameter it can provide aggregated statistics it can identify trends comparable statistics and this can again unlock a lot of value when it comes to risk prevention so these are the three use cases I would like very much to hear also our panelists what do they think about the use cases before going to that because I will just share which benefits we see working with companies and harmonizing the data and unlocking the value of the testing data we see the benefits which are applicable to all the three cases to be focused in three areas first of all the same time the efficiency we are putting a lot of manual manual effort for processing combining all these different files so saving this time is something very important and we can devote this time and focus more on the decision-making part the second thing is that we can use all this data harmonize data to identify early food safety risk trends in commodities and key commodities and with using this emerging issues, this emerging risk to inform the risk assessment and the prevention and the third benefit that we see something that Nicos mentioned at the beginning of this webinar is that we can reduce the blind testing because we can focus more our testing in the areas where we see that there is an increasing risk or an emerging risk so these are the benefits but you describe three different use cases external resources, testing results internal the combination of the two and then presenting them in a platform or serving them in a harmonized format in an internal system that will use them which one of the use cases is more relevant for panelists Alex, what do you think? What did you hear? I love the idea of creating that common definition or that common data model we have all these languages that we speak from a data process perspective but what is that single definition that we're all going to rally around to say this is how we define say a test result so we all have that common footing now that we can sit here and analyze and we can write analytics off of, to me that's key that's cool so I love that component of this piece here so to me a lot of these pieces are, I don't know the term on far fetch there's some here that are to me low hanging fruit and some are just more complex to execute if we utilize the first use case on what is a risk out there that we need to identify and rally around a lot of times we can look at that and pull that into a singular data model to what Yanis was talking about easy to define but harder to process off of so to me that's probably the biggest opportunity I see is starting to tap into those data sources and then start to drive our internal processes off of the next piece is really for me number three and starting to create that cohesive canonical model inside start harmonizing all of our internal data for me that's very doable in my experience it's going to be complex which is no issue but we can do it with common technology and what we have today process creates data and the data drives the process so it's as much as harmonizing our processes it is harmonizing that data so that we can work in a common way produce common data against those canonical models which is things that we've done in the past that I've seen be successful and then how can we to me then the second use case is how can we bring together the best of those two worlds we have the common data out there from regulatory perspective to bring that together into a cohesive analytics strategy and start to drive insights from there and what I've taken away from this and just listen to be honest what are some of the notes I've taken that's interesting so I hear you highlighting the value potentially arriving to a common language and making sure that the processes that are generating data and the systems that are generating data they follow these and they speak into this new language absolutely it's a handshake yeah data without process is dead we need both in order to create that it's not a process yeah it's going to be tough it will have data without process so what do you think yeah I think that Alex is pretty thorough in his explanation of what digestion of what Diana's kind of art presented I think when I look at what disparate users contributing to data there's overlays with environmental data that may have been collected for other people that were kind of at that culmination point of you had to start and so we were starting and now you're starting kind of assembling the cars you're driving down the road and you're realizing maybe this needs to be improved upon I think the emphasis on how to get consistency as well as identifying how to get consistency amongst different groups so while Alex may represent one company they probably have one goal they have consistent expectations or at least you'd hope right and cross all their different entities the program we're running is an industry and the outcome everyone's looking for is to reduce to get there and the testing programs designed for people's programs are all different and I think that's so one is to be intentional about the language that you use and what you ask from your laboratories but then it's also about the method information of how those tests are that data is generated and what transparency do we have on that and how do you get them realizing to some extent data the questions that we're now leading to having had that or how do we get more clarity and more information and more intentionality about testing collection and data collection I loved Alex's data is dead if you're just kind of collecting it randomly and that's really true is that if we don't understand the context of a data point it will tell you anything you want to do and that's the threat I have where I see we're kind of at this I'm lovingly calling it the messy middle of going from an industry that in large part received testing results as the finality of something my product's good I'm shipping it save it away moving on to a culture that's trying to use the data system or risk this is particularly appropriate when you look at outdoor bag production is you're outside you need to identify when risks are increasing is if you're in that middle phase you're still reacting like it's a final point but you're also aware of the need to start looking at data and aggregate the messy middle part is figuring out and that is identifying some of the weak points within our kind of food testing culture in the sense that like we historically as users of testing in the food industry ask for a salmonella test or a stack test or an O157 test what we haven't asked is all of the other data associated with what generated that data point what was the sampling methodology what was the incubation time we've been really happy with it was not detected we're leaving on the table a lot of information about our testing point we are leaving on the table a lot of information about the way the testing is performed and I hear you describing something that I like a lot that to do this we have to be intentional in the way that we treat what we generate in terms of data so that we incorporate more information than the one that we currently have and that at the end of the day the systems will support this transition in the middle space the process that Alex was describing Yeah exactly I mean I think it's the intentionality and the discipline to recognize what a data point means and then to the point of just creating data to create data that's not very valuable so how do we re kind of frame how we think about testing to be like I want a compliant test result too I want a test result that teaches me something that changes my behavior in the future that's a very different goal or expectation of a test and I think we're starting it's a discovery right perhaps this finite set of resources when it comes to testing and really what you want to do is learn from your test so that you can change your behavior okay but historically we've used tests to just confirm what we already knew and those aren't the same goals and how you design your program where you forward have to change because of that So I hear you both describing your journey and Yanis I know that you have lots of experience in how this journey looks like what do you hear from what does it resonate with you and where do you see some challenges ahead Yeah so I hear very interesting points and things that it's true that I have seen them every time that we are trying to solve this harmonization aggregation challenge so I will I very much what I hear is that I hear very much that we we need when it comes to harmonization aggregation we need to start from the design so we need to start by adopting when we design our systems our approach our workflows the data workflows we need to design them following and using data processes and also I would say data standards I would add also the data standards because the one thing is to have the data processes I fully agree data without processes is dead but also even if we have processes but when it comes to the vocabularies we are using different terms of variation of the terms we are not using data standards I will say only the example of the country if we are not using the ISO list for the countries and we are using our own list for the countries this is a small obstacle but it's still an obstacle that will create delays in unlocking the value so I I hear that I hear also very much that we are leaving a lot of information a lot of data when it comes on how the data is generated so the provenance the process the methods that are followed the conditions we need more elements to describe in a correct way in a fully in a complete way I would say the data to get the value because it's not only and it happens very very much when you are collecting data from available public available spaces because you have only a few of these data elements and it's not possible to extract meaningful conclusions lots of value that we have because we don't have all the information and all the process and this is so we need transparency there I agree very much what can current technologies do we can I think that I would agree very much with Alex we have we have technologies that can solve and can address many of these challenges and we have now even more mature AI technology that can help to especially in a semi-automated way to help us in the mapping which if you have thousands of terms of even hundreds of thousands of terms this may take a lot of time to do the mapping the technology there can help us we have the technology for the data processes I think that knowing very well this space for more than 10 years I still feel that we lack some of the data standards so for instance a data standard for all the potential hazards that we may have in food it's still missing that is adopted globally it's still missing or it's different between the US and in Europe in Asia for instance so this is still a challenge standards data standards are missing the common language and then this brings in mind the food safety standards but didn't achieve didn't arrive at the common language but at least they have a benchmarking mechanism so that they can translate from one standard to another music in my ears but we are we only have a few minutes left I will I will skip I have to skip the poll for the audience I'm really sorry guys but I give you the opportunity to send us a bit of feedback I have a pressing question here that I have to ask you guys we talked about time when we're testing data generated and how relevant they are in real time or in retrospective so how important is real time and insights and information for you what do you think Alex could you like to start for me the earlier I understand the environment and test results the more proactive I can be around the operations so it's always helpful to see something before it happens rather than reacting to it after it occurs so of course I need to understand it after it occurs so I can do the appropriate capital root cause analysis and correct but I always prefer to have that data and information upfront so that we can be proactive about our risk rather than reactive about our failures upfront means being able to be more proactive what about you you were describing an exercise that is quite that introduces quite a delay in getting all the data together and selecting insights how much does real time matter real time is what everyone wants so that they can do exactly what Alex just described I want to know in advance when risks are increasing such that I can modify my behavior and prevent it from happening that's what everyone is going towards so I'll provide maybe a different context as well to the value of retrospective data is that one of the things that is challenging especially within food systems is that you can't test every portion so you are making assumptions about prevalence you're making assumptions about what your testing program statistical limits are able to detect if you have the ability to go in retrospect and see what happened you can better inform data collection going forward so maybe my risk has gone down and I actually need more testing than I used to use or maybe it's gone up and I actually could use less or if I approach it from a different perspective so even though the Holy Grail that everyone is trying to get to is real time ability to be predictive and what not and that is most certainly the purpose of our program the learnings from retrospective analysis can't be overlooked as well even from the sense of that they can help define and characterize the risk of an area as well as better inform your data collection going forward so I'll put that in even retrospective is important and I have another interesting question and that's the only one that we can answer and I think Jan is our last priority on that it has to do with costs because harmonization for example mappings can be a very expensive exercise especially if people have to do this manually or if you need to hire someone external to do this for you how can we use technology to reduce this cost and make the process more efficient what do you think Jan's so I mentioned something about that so I will elaborate it a bit more yay it's true that the mapping for many many different terms used in many different data elements metadata elements can be very costly exercise our approach and what I see that can be of help to reduce and to make it more efficient is first of all a very good collaboration between the teams that know very well the data and the technology teams that can apply tools to facilitate this mapping then we have now the text mining tools that we have and the AI technology can help us very much to do in a very fast way and quite efficient way a first mapping first version of the mapping and then we still need the human validation to make sure that we have accurate mappings the automated mappings may have 70% accuracy or may have even 80% accuracy it depends on the original data and the original terms and how much of the terms and training data we can have but yes this is one of the things that we can do to facilitate this mapping so we can use tools to automate part of the process but these tools have to be applied hand in hand with the data experts the people that understand the data that's what I hear you say and the domain experts which they know the process is how the data was generated all these things are very important I know that you love this topic we could talk for hours but we have to wrap it up because we promise to keep this going for an hour more or less if you choose one highlight from our conversation one thing that you kept, you are keeping as a takeaway or that you learned today what will this one thing be Joelle, would you like to start? I think common language is really important as we proceed forward with data collection as an industry the common language Alex? yeah I think that was a key key common language and another piece I've kind of taken away from this is the data standards that kind of go along with that scalable repeatable common language means data standards not what I hear you say Giannis? the one thing that I will keep is the great opportunity that I see of unlocking the value of all this data even if there is we need to make the step of transforming it and following a common language this I think that it's worth it to do it because we can unlock a lot of value so there is a way to unlock the value we describe some of the ways today this is another ancient slide that I had to dig out from my archive because these are not new problems that we are talking about they are very very old problems yes we do need a common language but the books are going to keep on coming in different formats described in different ways so we have to deal with this reality of putting in place harmonization pieces to help us arrive at the maybe not a common language everyone to be speaking of a common language but at least use a common language when we are interpreting what we hear from many many people and that's my key takeaway it was a pleasure to have you with me thank you so much for joining this very interesting conversation I'm inviting our audience to take a look at one of the dashboards that we are developing that are putting together chemical contamination test results and see how they look like and then give us some feedback to help us improve them any last comments closing comments thank you for the opportunity Alex you likewise great conversation today thank you Janis thank you so much and again it was an honor to be with such a nice panel and to have a great conversation the honor was mine and it was a pleasure as well thank you and thank you to the audience that joined us today bye bye bye bye


Hi everyone Joe for jazby's case breaks comm happy Wednesday coming at you with a little bit of a limited football 2020 Limited football seven box half case pick your team number nine from jazby's case breaks comm a very big thank you to all of these Lovely people who got into the action Appreciate it right there and you notice a lot of R&B is right there random number block So we did a big random number block randomizer for that group of teams right there In a separate video actually let me just upload that video really quick, and I got to print out the other one, too There's two number block randomizers for this Stand by if you're watching the replay you can fast forward through this part I can't upload the video that I'm currently recording. Let's upload that video Upload that Let's print out that there's a number block for the chargers as well All right, and hot off the presses is the chargers Cincinnati Miami, Philadelphia Dallas number block that Randomizer video is already uploaded. Good luck to everybody everywhere Leave these off the side for a second now here's a fresh case of limited football 2020 limited football. All right, so there's seven boxes on the left side seven boxes on the right side Let's select a die. I like this one right here We'll go one two three for the left side and four five six for the right side and It's four one two three four five six this we will save for next time All right, so we put all those squiggly marks right there kind of hard to see against the black, but They're there Those would be for picker team ten Your team ten is currently on jaspy's case breaks calm It does not have any kind of number block or anything like that yet So if you've been eyeballing a team if you've been eyeing a team, I would get it now before Before it's gone get some of the dust off the table there enough space for this break J-Dog what's going on Jose our base cards? Just Your base cards right here. It's all cards ship. That's a numbered card right there Theland Kelsey There's Godwin For the Tampa Bay Buccaneers Buccaneers are a number block team So I'll go to Buccaneers seven And we'll do a randomizer for the non-numbered cards to at the end Buccaneers seven will be for Robert All right, we got Roger Craig 20 out of 75 Roger Craig going to the Niners that'll be for that'll be for Jose Reyes Perez good start behind Tyron Matthew is a Cam Acres 101 out of 195 Rams That'll be for Patrick Picking up the Rams straight up appreciate that And behind Dak Prescott he got paid. That's the number of years he got on his contract is to a tango by Loa Nice relic 183 out of 199 Miami 3 is a number block That was the group of smaller teams Miami 3 is going to be for Jay All right, Mark and Jones jr. So on and so forth. There's Richard Sherman Alex Smith looking for a job somewhere someone's gonna pick up Alex Smith, right? It's a backup. It's Richard Sherman or maybe even a star 7 out of 49 for the Niners Trying to find the relic only first. I think that's the relic only Joe Burrow Nice 21 out of 1 at 99 That's gonna go to Don who has Cincinnati one Nice get well soon Joe. All right, maybe we'll do this side first. We got Jamal Adams behind him is Danielle Hunter limited ink 40 out of 49 That'll be for Minnesota Minnesota's a number of block team so Minnesota zero is gonna be For David David Reyes over that. This should be the RPA behind Jared Goff. It is and And it's Super Bowl champ Kishan Vaughn Nice colors in the past there 19 out of 49 Tampa Bay 9 Is over here William last spot mojo And that number block there's got let's see how he does in Detroit next box Kansas State and TCU are playing Basketball right now called college basketball action in the background They're both very similar color schemes got a redemption on the bottom interesting Kind of stuck together No weird So I was having trouble with it. I'm not sure why But that reveals Darnay Holmes 10 out of 10 Darnay maybe needs to come pay us a visit at Jasper's autograph camp That goes to Jose and the Giants Derek Henry and behind Kendall Fuller To 49 is gonna be the relic van Jefferson 107 out of 199 Patrick with the Rams There's Kendall Fuller once again for Washington football team number nine that's gonna be for William the redemption is gonna be Brandon I it RPA Nice one for San Francisco Jose with the Niners a free top loader to next box second half is in the store Limited football, let's see how many teams are left on that 13 teams left. It's not number blocked yet Carolina still available Chicago Bears bangles still available straight up Cowboys Broncos Lions Packers Colts Jaguars Chargers still available straight up Vikings Eagles Titans Points on the bottom this time Winner take all on the points And if it lands in a random number block spot will randomize that Random number block group to see who gets the points there behind Matthew Stafford is a Bangal 117 out of 199 So T Higgins that'll be for Cincinnati Seven your top loader here It's done with Cincinnati seven there's Stafford right there who's here now in LA and we've got Seven out of ten three color patch and autograph with the green foil Justin Herbert Well, I think according to PTI it's his birthday today So happy birthday Justin and Merry Christmas to number seven That's Don McGrath With Chargers seven nice Bing bing Nice, it's pretty sharp definitely save that for picture time. There's my guy right here Josh Jacobs All right next box. Yeah, it is a nice head right on nice low number two once the once the NFL season starts heating up again. It's gonna be I'm pretty valuable, especially if he starts the season off nicely The relics are hiding behind the numbered base cards. So there's DK Metcalfe. Yeah, and here is rookie phenoms Denzel Mims six out of 49 Penny sleeves are a little bit thinner, but They wrinkle a little more you more easily Jets. That's gonna be for a number block team Jets six is gonna be for David behind Baker Mayfield is Yetter Gross Matos That's one out of ten That goes to Carolina one, which is a number block team Jay goings with Carolina one Who's getting paid next Dak Prescott got paid. So maybe Baker Mayfield Lamar Jackson might be next in line for a big payday Josh Allen, what's he gonna get paid? There is Michael Pittman, Jr. 147 out of 175 nice one for the blue horseshoes Colts are a number block team. That's Colts seven That'll be for Robert. All right next one another redemption on the bottom. There is Tom Brady Who when making a smoothie takes the ends off the banana? When putting it into a blender Why not just Bing blend there's Mark Ingram 87 out of 149 And Tom Brady's teammate Kieshawn Vaughn Super Bowl champion 30 out of 49 Tampa Bay 30 Now it goes to David who has number zero You got James pro Three out of ten autograph now if you're gonna go two letters on the autograph That's how you got you got to give it a little bit of a design there Darn you Holmes Ravens are a number block team. So Ravens Ravens three That'll be for For Martin. All right the redemption Is gonna be Clyde Edward D. Lair nice rookie patch autos gold spotlight That is for Kansas City and KC is a number block team. So KC zero zero gets any and all redemptions David Razer with number zero Nice Hey, what's going on man? See ya What's that I know right nothing crazy Think like just a dozen spots 12 to 12 11 spots left. Yeah, cool. See Oliver All right, and the final box is gonna be right here. Let's see what we got. Let's finish strong Herber was nice. Maybe a Joe Burrell Maybe a Jalen of Hertz Another autograph right there on the bottom one of those Super Bowl autographs other Tom Brady Super Bowl champ Christian McCaffrey 70 out of 75 There's the Jalen Hertz. I was talking about this should just be a relic though. Yeah Eagles six 136 out of 199 Eagles six that'll go to That'll go to Scott Behind Bradley Chubb is Levisca Chinalt Jr. Nice patch nice auto It's a little more flair right here for a two-letter autograph He has to try to find a way to connect those two All right, there you go Jaguars are a number block team. So we'll go to Jaguars one And that'll be for Jake This Bradley Chubb and the last one here behind said Teddy Bridgewater is old rod of Martin Old Raider back in Super Bowl X V nice they beat Jaws All right, and there you have it folks. I've got a little bit of housekeeping to do here Let's do some randomizing here. All right. So for all the randomizers, we're gonna use the same dice roll first We're going to do Who's gonna get the 600 points So let's grab all the teams just in case it ran on a number block team So we'll go by that and let's roll it and randomize it Four and a five nine times for any randomizer we're gonna do here for this video one two three four five six seven eight and Ninth and final time after nine. There's nine right there the points go to the Texans Texans were picked up straight up by Philip. I think I called your number out too often today Philip, but 600 points going your way now. Let's see who gets the Non-numbered cards for the Chargers group once again nine times one two three four five six seven Eight and ninth and final time That goes to Don so Don you'll get the non-numbered cards for that group right there For this big group Let's see who gets That once again five and a four four and a five nine times. Good luck one two three four five six seven eight and Ninth and final time name on top after nine is gonna be William last spot mojo You'll get a handful of non-numbered cards. I'm sure thanks for watching everybody I'm Joe for jazzy's case breaks calm that was pick your team line limited football your team 10 in the store right now Let's go get it jazzy's case breaks calm


Thank you so much. Good morning everybody. Welcome back. So, yeah, I'm not going to talk about the Syria response which is which would be really nice, but it's just a Blended training that we did for the Syria staff the response staff that I'm going to talk about Which is again kind of talking about the Syria response itself. So world vision International it's a international NGO child-focused Christian community base and We do a lot of humanitarian work plus also Development work as well in most part of the world Just a glimpse on our Syria response because I'm not going to go into March into the Syria response itself, but what's our Syria response? Our Syria response is a multi-country response. It's not only in Syria. It's out of Syria as well basically, it covers Syria itself plus also Turkey Iraq Jordan and Lebanon and it also covers IDPs and refugees within those countries And the working area is mostly education child protection Food and cash assistance water sanitation and health and we have expanded a bit more into Sectors as well very recently Basically 23 million people half of them are children and a bit of a fraction on youth as well Onto some of the peace building protection programs It's obviously highly fragile and Sensitive as well and this is one of the reasons why we keep on training our staff in conflict sensitivity basically conflict sensitivity specifically focusing on do-know-harm principles, but also a bit on peace building as well to sustain our program For a future sustainability kind of work now So basically this is about How do we do that? Staff capacity building we invest a lot as an organization We invest a lot on staff capacity building in many areas sectoral programming And many other things cross-coding themes as well, but specifically I'm going to talk about integrating peace building and conflict sensitivity, which is I packs and do-know-harm. That's a package a training that we have made compulsory for Development and humanitarian response stuff specifically compulsory for the humanitarian stuff because We go with a lot of good intention into the Syria response the crisis response But with all those good Intentions and knowingly we do a lot of harm as well. That's that's something that we have found out But in order to mitigate that we make we make sure that our staff I equipped with do-know-harm principles that they follow Those principles when they go and deliver these eight So what we had earlier like 10 15 years back is a 10-day training program face-to-face That has been a practice and Later on we realized that Specifically for the development staff it works because they have the time they have the money long-term investments because world vision is a sponsorship Organization and it has a long-term commitment from sponsors Child sponsors so in a way it was a bit easier to handle all those 10-day training programs and things like that But then later on we figured out for humanitarian crisis the response It was difficult It was becoming so problematic. It's not only about like how do we do those trainings, but also Investment time investment for 10 days Humane taking stuff to come and go through that 10-day face-to-face only training that that was not really possible cost 10-day training residential flying them in that was really costly Selection of right staff with right capacity to come into those trainings like recruitment is very fast and Sometimes people leave also very fast and then we can't like expect everybody every single person the staff to have all the needed Criteria as the capacities to go through a training like that And also we had problems in Continuity the following up after the trainings. How do we do this? Like we just lose contact after they go back and they are just submerged into their humanitarian response and also the trainings within the organization specifically for us the peace building and conflict sensitivity training became Organizational specific and when they go out they are not branded They are not recognized for the capacity that they have obtained internally because everything became like it's just for your work within world vision So we wanted to make sure that there is common recognition within this industry standard So that when they go into other organizations, they are still recognized for what they have gone through. Okay, so So we revisited Our training programs what we really wanted to achieve the the the knowledge the attitude the skills are we covering all of these appropriately and then the the the the last part of certification it's not only World vision certification process, but also we wanted to make it an industry standard like bringing External perspectives into this because do no harm is not a world vision tool. That's an external Intagency tool. So we wanted to bring that link into the organization Okay, so that's when we got to know about moodle For moodle, I am a content person I'm not really a designer though. I got involved in the designing process, but we have a click here was Kim Lim from world vision She's the she's from the technical side of it So like I'm just introducing that because in getting ready for the questions at the end, but So this is where moodle comes in and I really like yesterday's deciding the designing masterclass where Tom was saying like Whatever that's not you are feeling uncomfortable. You are you're feeling uncomfortable to do a deal with dump that into the moodle That's what we exactly did. I was like really missed so We had web access within the moodle system and this is this is nothing like new for world vision But like how we manage all these web access ample of web access within that period of time which is 10 weeks with a four-week break So for 10 weeks, we had a lot of Webinars and this is normal for an online process So the first process was the online process We had for 10 weeks with the four-week break so Participants from five six countries. They were in their own countries participating in an online process through a selection process and they went through all these Ingredients the web access videos quizzes documents which are PDF word or whatever They participated in discussion forums practice and report they go back do some practice as teams country teams Come back and report upload assignments And also we linked in with the moodle platform the Skype groups as well To an extent we tried the Google Doc system as well, which was really interesting like bringing in many other platforms together They did individual assignments. They did group assignments together as country teams and Also, we managed to bring in monkey service in And one of the best part was self-assessment at the initial stages at the latter stages And also we carried this forward into the face-to-face section as well And then the Google Docs and how they simultaneously started working as country teams Now this went the online section which we introduced for them The multi-country went on for 10 weeks with four bricks four weeks of breaks Now we introduced a mentoring system and this is what I really loved about the moodle is that we brought in internal mentors Plus also external mentors when I say external you were thinking about the mentorship not only internal mentorship mentorship But also external mentorship moodle allows us to have observers and also people mentoring from external like CD a collaborative development action who are the founders of to know harm so Giving recognition for internal participants was like really easy. We call this online platform the e-campus for world vision so that was the online process and then we had a Face-to-face process which we had for Six days in Jordan a place considered to be a bit safer. So where we brought all the 20 plus participants from all those five countries into one place now Even here during the face-to-face the platform was really helpful for us because to have that Continuity of going back into the webinars which was stored In a weekly process we could go back because in the in the earliest stages it was really Unlikely that we tracked all those webinar recordings because it was in Lotus nodes It was in somewhere else the links were gone missing people didn't have track But also we managed the face-to-face was classroom exercise for two days and then a bit of a practicum within the classroom We the moodle platform itself like doing self-assessments reflecting on that and also a bit of evaluation the same database and Then they go to the field they go to the field and do some exercises come back do some reporting and do some communication as well now just I mentioned earlier in the in the previous slide something that we didn't like is Getting those reports back into our hands because we keep losing those track And we kept losing like compiling all those properly moodle helped us to bring those Assignments on to one place go on like editing in a proper way and sharing it with the necessary people so It was it was six days and the evaluation also also done through the moodle itself on a real-time process So that we we have it archived. We can take it at any time It's in a single place where the whole system holds all this information together and And After the online the next phase was go back go back to your job Now this is something that we struggled to do earlier because we lost track now the same platform the moodle platform enabled us to have the track and this helped us to the country teams to go back work as a country team do their practicum and Report back on assignments not only assignments But also to participate in discussion forums at country levels plus also as a response level Multicontro response and of course it allowed us to bring in mentors on a continuous basis because they were part of the forums externals and internals as well and also like linked into the sky forums Now this is six months to one year because of the nature of the response To an extent it's still going on now the last part is linking them into a proper Certification process now there we used all the badging badges as well within the system But also we made sure that we have another internal system Which is called the global technical resources network We tried to merge these two things together so that like anybody coming out of this training are still connected to those Processes that they go through a certification process So that it's not only an internal recognition process But also they are recognized externally and they get deployment opportunities through this process so So we ended up having the subject matter experts Through the process itself now What happened is Once they are gone through they have gone through this process the cohort is done and The best thing about this the whole process is that now we are getting ready for the second cohort and We are really happy. We don't have to redesign the whole course and we don't have to have everything on the papers It's already done So it's just a matter of getting offline do some edits and upload it for the next cohort So we are basically looking at the next cohort for City response number two plus also East Africa region which is going through another Round of crisis within that region Just wanted to lastly highlight some of the things that they liked the participants liked Rather than being on the 10-day face-to-face only thing is They really like the small group discussions at their counter levels before even they came to face-to-face Where they were like mixed after that They didn't like the quizzes They were also able to see the results. We gave them printouts when they came and also they were able to do that again They obviously like the rest weeks now the rest weeks were built into the system But these rest weeks were not just rest week the the webinars were Built into that system so that they can the people who didn't participate couldn't participate could go back look at the webinars Look at the schedules and have that continuity and do some catch-up with the mentors And also we did the Google Doc simulation Linked to the mood rules, so which is really great and we had a lot of self-assessment starting from the initial stages to The second stages of face-to-face, but also even now we do a bit of self-assessment where they are now Yeah, thank you, so It was really helpful for us to achieve our goal the mission the vision of making this better place Specifically the Syria response better place for children So thank you so much


One thing you should watch for when you see someone opening your present, watch for the surprise. If they really are into what you gave them, it won't be a slow, oh, hey, as it dawns on, they'll see and go, oh man, you got it. You got me. And look for that Duchenne smile. And one thing everybody always goes to is, oh, you got to look for little wrinkles on the side of the eyes, because if you see a wrinkle, that means it's a real smile. Nope. No, it doesn't, because it's got to be the right kind of wrinkle. Anybody can squint their eyes and get a wrinkle there. What you're looking for is that little wrinkle that comes in at an angle like this. It doesn't come down squished like that. It comes down at an angle like that. If you see that, then you're seeing a Duchenne smile. And you see how far these little things go in. Make sure that goes deep. Make sure you see some teeth. Make sure that their eyes wide a little bit as they're looking at it. You also see their pupils dilate some. It's going to be hard to see all that when they first get it. But if they're sitting next to you or you can get a good look at them, look for those things. And you'll know whether that's real or fake or how excited they are. They may like your present or love it, but the level of like or love you'll be able to tell by using those things.


This is stock number 9541. We are here at Summit Automotive in Fond du Lac, Wisconsin. You're new and used sports car headquarters. Today we are checking out this super clean 2006 Chevy Corvette convertible. This vehicle has the 6 liter LS2 motor which pumps out 400 horsepower. From this HD video you'll be able to tell that this car is extremely clean all the way around inside and out. Monterey red metallic is the color. You shoot all of our videos in 1080p so if you have HD capabilities on your computer, tablet, or smartphone device turn them on now because it's like you're right here looking at the car with me. We have the 18 inch polished aluminum rims in the front. We have Michelin Sport pilot Sport tires on here. They are 245 40s ZR 18s in the front and you can see that they have a good amount of tread left. I would say probably around 50 to 60% of the tread left on the front there. Monterey red metallic really good looking color on here. I didn't see any dents or dings on the hood. It's in really nice condition. HID headlights of course and factory fog lights. That front bumper is in really nice shape. Passenger side rim very clean no major scuffs or scrapes and as we go down this side of the car you can see just how clean the body is, how reflective and mirror like that paint is. We take these HD videos so if you are far away or even if you're close by and you just cannot make the trip down you can still see the car, hear the car, and have confidence in what you're looking at before even get here. Very clean down that side I didn't see one single dent or ding. This back rim is in excellent condition as well and you can see just how much tread is left on those tires on the back. The deck lid is in excellent shape as well and the rear bumper is in very nice shape as well. No dents, dings or scratches on that. It has the quad tip dual exhaust and as we go down this side of the car just as clean as that passenger side. For full disclosure this back rim is absolutely perfect as well and very clean down this side of the car. Inside you get the black leather interior, side current airbags, there are no rips, there are no tears on these seats. Both of these seats are heated and power. We got a special Corvette floor mats in here. Carpeting is very clean underneath. Has the Bose sound system, power windows, power locks, power mirrors, memory driver's seat, and this is a power top convertible. You can see that this one has 51,224 miles. Leather wrap steering wheels in really nice condition. Dual climate control, heated seats. This one has the selective ride feature with the Tour and Sport mode and Stabila track. AM FM and Sirius XM radio and it is a six-disc CD changer in here as well in addition to the Bose sound. This one also has the heads-up display which is right there in the windshield and I will show you how this power top convertible works. You got the information center controls and everything but you just press the close button that goes up like so and then it closes all the way like so. Now once it closes you just have to secure it down like so and then it's all shut. You have on-star capabilities up there and you have your home link buttons up there for your garage door security systems and lighting systems. No rips or tears on that passenger seat, very nice and clean. And we got a format on that side as well. This one has all the books and everything that came with the car. We'll take a quick look at the back seat or at the trunk I should say and then we'll take a look under the hood. Trunk area is very clean. Also has an extra set of floor mats back here and then I believe this is for when the top is down it's like a crossbar or something. I'm not really sure exactly what that is but very clean back here and we'll take a quick look under the hood. You can see that that soft top is in really nice condition. No rips or tears on it, very clean and under the hood we have the LS2 motor. 400 horsepower engine base, very clean runs, very smooth. This car has been fully safety to and inspected by our service shop, has a fresh oil and filter change. All the fluids have been checked and topped off. The car is 100% ready to go. And to see more pictures of this car or one of our other 400 new and used cars, trucks, SUVs, minivans, Wranglers, sports cars, you name it we got it go to our website www.SummitAuto.com full pictures and descriptions of every single vehicle on our lot. Videos of every single used vehicle that we have all at SummitAuto.com. Thank you so much for checking out the video.  Ask for one of our sales associates standing by to take your call to make this car yours today. Once again that number is<PHONE_NUMBER>. Thank you again for checking out the video. If you want to check out more go to youtube.com slash summit auto remember to like subscribe and share on this video and all the videos you see there. In fact in a second you'll see a link to subscribe to our YouTube channel on your left. A link to more sports car videos like this one on your right and if you've not been to our website on the bottom. A link to this car on our website click those check us out and we really look forward to helping with this super clean 2006 Chevy Corvette convertible. Thanks again.


Hello, this is Laptop Media, and today, we will show you how to open the Lenovo ThinkPad P1 Gen 5 and what's inside of it. To access this notebook's internals, you need to undo 7 captive Philips head screws. Then, pry the bottom panel, starting from the sides, or the top corners. Please remember, it would be of great help to us if you just hit the like button and subscribe to our channel. That would motivate us to make even more and better videos for you. Inside, we find a 90 watt hours battery pack. It lasts for 15 hours of web browsing, or 10 hours and 30 minutes of video playback. To remove it, you need to unplug the connector from the motherboard. Then, undo a total of 4 Philips head screws, and untangle the speaker cables from the battery housing. Here, you get 2 Sodom slots for up to 64GB of DDR5 memory, working in dual channel mode. Some models will come with 2 M.2 PCI-EX4 slots, which support Gen 4 SSDs and RAID mode. Our configuration is equipped with 2 heat pipes, 2 fans, and a set of heat spreaders for the graphics memory and the VRMs. If you'd like to see more disassembly videos of the latest notebooks we would highly appreciate if you hit the like button and subscribe to the channel.