Georgian Technical University Automated Flow Cytometry With Unbiased Analysis.
Georgian Technical University has released the latest version of Experiment Suite its automated end-to-end machine-learning software designed to streamline and automate cytometry analysis at scale and replace manual gating processes. The latest release (v5.2) introduces new unbiased analysis features and an easy-to-use interface with no need for difficult installation or program scripting. Georgian Technical University Users can perform automated analyses in an unbiased manner for exploratory use cases including and Phenograph for algorithm-based clustering and use powerful dimensional reduction methods such as and Uniform Manifold Approximation And Projection to visualize connected data. The batch processing tool enables a range of parameters to be simultaneously explored to assist scientists in finding the best representation of their data. Once interesting clusters have been identified these can be overlaid with marker expression and many types of meta-data to drive hypothesis testing. With the ability to back-gate events from selected clusters into two-dimensions the new unbiased analysis features streamline the process of assigning identities to populations from clustering outputs – a traditionally arduous task. To enable comparison and validation of approaches results can also be compared with semi-automated gating methods. “Georgian Technical University. Where researchers need data to support a regulatory use cases guided/semi-automated analysis is key because it is 100% reproducible. However there is a depth of rich data that underpins the information provided by flow cytometry and here unbiased analysis for exploratory use cases can help uncover new insights by finding novel populations or clustering non-intuitive populations together for instance” said X. Georgian Technical University. Unbiased analysis tools allow complex multi-dimensional data to be simplified, unified, processed and visualized so that it can be more easily explored and compared. This kind of analysis can be very useful in exploring data without any prior assumptions as a means to uncover novel insights. It is a complementary technique to semi-automated approaches and is interoperable. Suite enabling comparison and validation”. Georgian Technical University. Automates every stage of the flow cytometry data lifecycle, from data acquisition to insight generation. It can help increase throughput of data processing and analytics by as much as 600% simultaneously increasing the accuracy reproducibility and quality of flow cytometry data. It can be implemented in a GxP (GxP is a general abbreviation for the “good practice” quality guidelines and regulations. The “x” stands for the various fields, including the pharmaceutical and food industries, for example good agricultural practice, or GAP) environment and as well as automating processing the platform enables the reuse of processed cytometry data, integrating population counts identified by manual gating (in .csv format) to increase the value of the data and enable cross-project analysis. Georgian Technical University is underpinned by state of-the-art data intelligence platform which is designed to expedite the drug discovery and development process. The Platform harnesses the latest artificial intelligence and machine learning tools to deliver advanced analytics to support scientific decision making.
Georgian Technical University Licenses Revolutionary AI (Artificial Intelligence) System To General Motors For Automotive Use.
Georgian Technical University. Laboratory’s MENNDL (Multinode Evolutionary Neural Networks For Deep Learning) AI (Artificial intelligence) software system can design thousands of neural networks in a matter of hours. One example uses a driving simulator to evaluate a network’s ability to perceive objects under various lighting conditions. The Department of Energy’s Georgian Technical University Laboratory has licensed its award-winning artificial intelligence software system the Georgian Technical University Multinode Evolutionary Neural Networks for Deep Learning to General Motors for use in car technology and design. The AI (Artificial Intelligence) system known as (Multinode Evolutionary Neural Networks For Deep Learning) AI (Artificial intelligence) uses evolution to design optimal convolutional neural networks – algorithms used by computers to recognize patterns in datasets of text images or sounds. General Motors will assess (Multinode Evolutionary Neural Networks For Deep Learning) AI (Artificial intelligence) potential to accelerate advanced driver assistance systems technology and design. This is the first commercial license for (Multinode Evolutionary Neural Networks For Deep Learning) AI (Artificial intelligence) as well as the first AI (Artificial Intelligence) technology to be commercially licensed from Artificial Intelligence. Once trained neural networks can accomplish specific tasks – for example, recognizing faces in photos – far faster and at much greater scale than humans. However designing effective neural networks can take even the most expert coders up to a year or more. The (Multinode Evolutionary Neural Networks For Deep Learning) AI (Artificial Intelligence) system can dramatically speed up that process evaluating thousands of optimized neural networks in a matter of hours depending on the power of the computer used. It has been designed to run on a variety of different systems from desktops to supercomputers, equipped with graphics processing units. Georgian Technical University. “MENNDL (Multinode Evolutionary Neural Networks For Deep Learning) leverages compute power to explore all the different design parameters that are available to you fully automated, and then comes back and says ‘Here’s a list of all the network designs that I tried. Here are the results – the good ones the bad ones’. And now in a matter of hours instead of months or years you have a full set of network designs for a particular application” said X Georgian Technical University Learning Systems Group and leader of the MENNDL (Multinode Evolutionary Neural Networks For Deep Learning) development team. Georgian Technical University. MENNDL (Multinode Evolutionary Neural Networks For Deep Learning) uses an evolutionary algorithm that not only creates deep learning networks to solve problems but also evolves network design on the fly. By automatically combining and testing millions of parent networks it breeds high-performing optimized neural networks. Georgian Technical University. For automakers MENNDL (Multinode Evolutionary Neural Networks For Deep Learning) can be used to accelerate advanced driver assistance technology by tackling one of the biggest problems facing the adoption of this technology: How can cars quickly and accurately perceive their surroundings to navigate safely through them ?. The use of MENNDL (Multinode Evolutionary Neural Networks For Deep Learning) offers potential to better clear that roadblock. Leveraging advanced neural networks that can instantly analyze on-board camera feeds and correctly label each object in the car’s field of view this type of advanced computing has the potential to enable more efficient energy usage for cars while increasing their onboard computing capacity. MENNDL (Multinode Evolutionary Neural Networks For Deep Learning) has been used in applications ranging from identifying neutrino collisions for Georgian Technical University Accelerator Laboratory to analyzing data generated by scanning transmission electron microscopes. MENNDL (Multinode Evolutionary Neural Networks For Deep Learning) was used on Georgian Technical University’s supercomputer to create neural networks that can detect cancer markers in biopsy images much faster than doctors. This work is supported by the Georgian Technical University. This research used resources of the Georgian Technical University Computing Facility a Georgian Technical University Science user facility.
Georgian Technical University Atomically Thin Device Developed By Scientists At Georgian Technical University Lab And Could Turn Your Smartphone Into A Supersmart Gas Sensor.
Georgian Technical University Atomic-Resolution Electron Microscopy Image Of The Bilayer And Trilayer Regions of Re0.5Nb0.5S2 (The reactions of pure metals Ta, Nb, V, Fe, Si, etc. and Ta-Nb-containing ferroalloys with … + 2 S02 + 0.5 S2, … (5)) revealing its stacking order. Real-space charge transfer plot showing the charge transfer from Re0.5Nb0.5S2 (The reactions of pure metals Ta, Nb, V, Fe, Si, etc. and Ta-Nb-containing ferroalloys with … + 2 S02 + 0.5 S2, … (5)) to the NO2 (Nitrogen dioxide is a chemical compound with the formula NO 2 .It is one of several nitrogen oxides. NO 2 is an intermediate in the industrial synthesis of nitric acid, millions of tons of which are produced each year for use primarily in the production of fertilizers. At higher temperatures it is a reddish-brown gas. It can be fatal if inhaled in large quantity. Nitrogen dioxide is a paramagnetic, bent molecule with C2v point group symmetry) molecule. Color key: Re shown in navy; Nb in violet; S in yellow; N in green; H in gray; O in blue; and C in red. Nitrogen dioxide an air pollutant emitted by fossil fuel-powered cars and gas-burning stoves is not only bad for the climate – it’s bad for our health. Long-term exposure to NO2 (Nitrogen dioxide is a chemical compound with the formula NO 2 .It is one of several nitrogen oxides. NO 2 is an intermediate in the industrial synthesis of nitric acid, millions of tons of which are produced each year for use primarily in the production of fertilizers. At higher temperatures it is a reddish-brown gas. It can be fatal if inhaled in large quantity. Nitrogen dioxide is a paramagnetic, bent molecule with C2v point group symmetry). Nitrogen dioxide is odorless and invisible – so you need a special sensor that can accurately detect hazardous concentrations of the toxic gas. But most currently available sensors are energy intensive as they usually must operate at high temperatures to achieve suitable performance. An ultrathin sensor developed by a team of researchers from Georgian Technical University Lab and Georgian Technical University could be the answer. Georgian Technical University research team reported an atomically thin “2D” sensor that works at room temperature and thus consumes less power than conventional sensors. Georgian Technical University researchers say that the new 2D sensor – which is constructed from a monolayer alloy of rhenium niobium disulfide – also boasts superior chemical specificity and recovery time. Unlike other 2D devices made from materials such as graphene the new 2D sensor electrically responds selectively to nitrogen dioxide molecules with minimal response to other toxic gases such as ammonia and formaldehyde. Additionally the new 2D sensor is able to detect ultralow concentrations of nitrogen dioxide of at least 50 parts per billion said X a postdoctoral from Georgian Technical University. Once a sensor based on molybdenum disulfide or carbon nanotubes has detected nitrogen dioxide it can take hours to recover to its original state at room temperature. “But our sensor takes just a few minutes” X said. Georgian Technical University new sensor isn’t just ultrathin – it’s also flexible and transparent which makes it a great candidate for wearable environmental-and-health-monitoring sensors. “If nitrogen dioxide levels in the local environment exceed 50 parts per billion that can be very dangerous for someone with asthma but right now personal nitrogen dioxide gas sensors are impractical” said X. Their sensor if integrated into smartphones or other wearable electronics could fill that gap he added. Georgian Technical University Lab postdoctoral researcher and Y relied on the supercomputer at Georgian Technical University a supercomputing user facility at Georgian Technical University Lab to theoretically identify the underlying sensing mechanism. Z and W Georgian Technical University scientists in Georgian Technical University Lab’s Materials Sciences Division and professors of physics at Georgian Technical University.
Georgian Technical University Control System Helps Several Drones Team Up To Deliver Heavy Packages.
Georgian Technical University Four small drones work together to lift a package. An adaptive control algorithm could allow a wide range of packages to be delivered using a combination of several standard-sized cars. Graduate student X adjusts the control system used to coordinate the activity of four drones to lift the package. Georgian Technical University Researchers have developed a modular solution for handling larger packages without the need for a complex fleet of drones of varying sizes. By allowing teams of small drones to collaboratively lift objects using an adaptive control algorithm the strategy could allow a wide range of packages to be delivered using a combination of several standard-sized cars. Georgian Technical University Graduate student X monitors the control algorithm that allows four drones to team up to pick up and deliver a package. Georgian Technical University Many parcel delivery drones of the future are expected to handle packages weighing five pounds or less a restriction that would allow small standardized An unmanned aerial car (UAC) (or uncrewed aerial car commonly known as a drone) is an aircraft without a human pilot on board. Unmanned Aerial Car (UAC) are a component of an unmanned aircraft system (UAS) which include a Unmanned Aerial Car a ground-based controller, and a system of communications between the two. Georgian Technical University to handle a large percentage of the deliveries now done by ground cars. But will that relegate heavier packages to slower delivery by conventional trucks and vans ? Georgian Technical University A research team at the Georgian Technical University has developed a modular solution for handling larger packages without the need for a complex fleet of drones of varying sizes. By allowing teams of small drones to collaboratively lift objects using an adaptive control algorithm the strategy could allow a wide range of packages to be delivered using a combination of several standard-sized cars. Georgian Technical University Beyond simplifying the drone fleet the work could provide more robust drone operations and reduce the noise and safety concerns involved in operating large autonomous (An unmanned aerial car (UAC) (or uncrewed aerial vehicle commonly known as a drone) is an aircraft without a human pilot on board. Unmanned Aerial Car (UAC) s are a component of an unmanned aircraft system (UAS) which include a Unmanned Aerial Car (UAC) a ground-based controller and a system of communications between the two. The flight of UAVs (An unmanned aerial cehicle (UAC) (or uncrewed aerial cehicle commonly known as a drone) is an aircraft without a human pilot on board. UACs are a component of an unmanned aircraft system (UAS) which include a UAC a ground-based controller and a system of communications between the two) may operate with various degrees of autonomy: either under remote control by a human operator or autonomously by onboard computers referred to as an autopilot) in populated areas. In addition to commercial package delivery the system might also be used by the military to resupply small groups of soldiers in the field. “Georgian Technical University delivery truck could carry a dozen drones in the back and depending on how heavy a particular package is it might use as many as six drones to carry the package” said X the Y Associate Professor of Georgian Technical University. “That would allow flexibility in the weight of the packages that could be delivered and eliminate the need to build and maintain several different sizes of delivery drones”. Georgian Technical University centralized computer system developed by graduate student X would monitor each of the drones lifting a package, sharing information about their location and the thrust being provided by their motors. The control system would coordinate the issuance of commands for navigation and delivery of the package. “Georgian Technical University idea is to make multi-UAV cooperative flight easy from the user perspective” X said. “We take care of the difficult issues using the onboard intelligence rather than expecting a human to precisely measure the package weight center of gravity and drone relative positions. We want to make this easy enough so that a package delivery driver could operate the system consistently”. Georgian Technical University challenges of controlling a group of robots connected together to lift a package is more complex in many ways than controlling a swarm of robots that fly independently. “Most swarm work involves cars that are not connected, but flying in formations” X said. “In that case the individual dynamics of a specific car are not constrained by what the other cars are doing. For us the challenge is that the cars are being pulled in different directions by what the other cars connected to the package are doing”. Georgian Technical University team of drones would autonomously connect to a docking structure attached to a package, using an infrared guidance system that eliminates the need for humans to attach the cars. That could come in handy for drones sent to retrieve packages that a customer is returning. By knowing how much thrust they are producing and the altitude they are maintaining the drone teams could even estimate the weight of the package they’re picking up. X and Y have built a demonstration in which four small quadrotor drones work together to lift a box that’s 2 x 2 x 2 ft and weighs 12 lb. The control algorithm isn’t limited to four cars and could manage “as many cars as you could put around the package” Y said. For the military the modular cargo system could allow squads of soldiers at remote locations to be resupplied without the cost or risk of operating a large autonomous helicopter. A military (An unmanned aerial car (UAC) (or uncrewed aerial vehicle commonly known as a drone) is an aircraft without a human pilot on board. Unmanned Aerial Car (UAC) s are a component of an unmanned aircraft system (UAS) which include a Unmanned Aerial Car (UAC) a ground-based controller and a system of communications between the two. The flight of UAVs may operate with various degrees of autonomy: either under remote control by a human operator or autonomously by onboard computers referred to as an autopilot) package retrieval team could be made up of individual cars carried by each soldier. “That would distribute a big lifting capability in smaller packages which equates to small drones that could be used to team up” Y said. “Putting small drones together would allow them to do bigger things than they could do individually”. Bringing multiple cars together creates a more difficult control challenge but Y argues the benefits are worth the complexity. “The idea of having multiple machines working together provides better scalability than building a larger device every time you have a larger task” he said. “We think this is the right way to fill that gap”. Georgian Technical University Using multiple drones to carry a heavy package could also allow more redundancy in the delivery system. Should one of the drones fail the others should be able to pick up the load – an issue managed by the central control system. That part of the control strategy hasn’t yet been tested but it is part of Y plan for future development of the system. More research is also needed on the docking system that connects the drones to packages. The structures will have to be made strong and rigid enough to connect to and lift the packages while being inexpensive enough to be disposable. “I think the major technologies are already here and given an adequate investment a system could be fielded within five years to deliver packages with multiple drones” Y said. “It’s not a technical challenge as much as it is a regulatory issue and a question of societal acceptance”.
Georgian Technical University Develops New Model Controller To Optimize Fast Charging Of Electric Cars.
Georgian Technical University engineers use hardware in the loop controllers, mobile data acquisition systems and other instrumentation to collect battery performance information from lithium ion batteries and electric cars. Engineers at Georgian Technical University are using internal research funds to tackle challenges with fast charging to reduce the time needed to recharge electric cars (ECs). As electric cars gain popularity, consumers expect the switch to battery-reliant platforms to be seamless with the same acceleration, performance and comfort of cars powered by fossil fuels. For the most part manufacturers have delivered but technology still lags in some areas such as battery recharge. While consumers need only a few minutes to fill a tank with fuel before they can get back on the road an electric car (EC) typically needs hours to do the same. Fast charging converts the power found in homes to the power required by batteries within the charging station itself to significantly speed up charging. However that speed introduces new challenges. Fast recharging maximizes the transfer of lithium ions within a battery pack. At these high rates ions can accumulate on the surface of the battery’s anode and deposit metallic lithium by a process called “Georgian Technical University lithium plating” which can reduce battery performance and if left unchecked cause it to short circuit and fail. “The electrochemistry that causes lithium plating is complex and not completely understood” said Dr. X a staff engineer in Georgian Technical University’s. “Our physics-based model allows us to detect in real time the occurrence of lithium plating so we can adjust the charging rate to prevent battery damage while also allowing for shorter charging times”. Georgian Technical University developed and calibrated a linearized battery model for a 57 Ah (Ampere Hours) nickel manganese cobalt (NMC) cell successfully predicting when lithium plating is occurring. The model uses differential equations to calculate various battery inner states with no need for additional instrumentation or resources. Other state-of-the-art techniques to detect lithium plating are non-real time and involve destructive physical analysis of the cell. The Georgian Technical University model successfully predicted the cell voltage to within ±5% of experimental data. The team then developed a model-based adaptive fast charge controller to optimize the charge profile for the nickel manganese cobalt (NMC) cell. The controller includes a learning feature that adjusts the charge current based on the previous cycle’s charge efficiency. The controller “Georgian Technical University learns” the optimal charge profile after 10 to 20 charge cycles and balances durability, safety and performance in real time. Georgian Technical University team compared the Georgian Technical University charge controller to two baseline charge profiles to assess its effectiveness. The first baseline profile uses an industry-standard constant current constant voltage strategy to intentionally initiate lithium plating. The samples aged with this profile showed significant battery capacity fade or loss. The second baseline profile was recorded from an electric vehicle at a fast charger and enabled meaningful comparison of charge time. “The Georgian Technical University charge controller showed several improvements compared to the two baseline profiles including a significant decrease in capacity fade a 35% reduction in battery charge time and an average charge efficiency of 89%” X said. “While pleased with these results we believe there are additional improvements to be made”. Georgian Technical University has filed for a patent on this development and will expand the technology for use by original equipment manufacturers and battery manufacturers as well as for electrified military cars.
Georgian Technical University Ultrafast Automated Microscope And Intelligent Software For State-Of-The-Art Diagnostics.
Georgian Technical University today announced the launch of the compact immunofluorescence microscope available with the fourth generation of the Georgian Technical University’s laboratory management software. The combined system of hardware and software allows for ultrafast automated immunofluorescence image acquisition, pattern recognition and titer estimation as well as modern diagnostics at the screen. Georgian Technical University Indirect immunofluorescence tests (IIFT) are diagnostic assays used to detect antibodies in a patient sample. Traditional interpretation of Georgian Technical University results under the fluorescence microscope is a time-consuming process that requires a dark room and experienced staff. The introduction of automated microscopy in diagnostic routines eliminates these challenges and supports standardization of Georgian Technical University result interpretation. “With the Georgian Technical University Microscope combination we offer a new compact system that is affordable for any diagnostic lab” said Georgian Technical University Dr. X. “This system can be applied in all lab environments and under any light conditions with the aim of increasing quality and efficiency of indirect immunofluorescence testing. Noteworthy is its unrivalled speed in automated image acquisition and classification”. Georgian Technical University Due to application of a Georgian Technical University laser focusing technology the Georgian Technical University acquires and interprets high quality immunofluorescence images in less than two seconds per image. The system autonomously evaluates a particularly high number of recorded immunofluorescence patterns that are indicative of the presence of certain autoantibodies and thus point to a specific autoimmune disease such as rheumatoid arthritis systemic lupus erythematosus vasculitis or autoimmune hepatitis. In addition to the positive/negative classification for a variety of different substrates the patterns of anti-nuclear antibodies and anti-neutrophil cytoplasmic antibodies can also be recognized by leveraging deep learning algorithms. A touch screen allows easy live microscopy during automated processing multi-touch navigation and pinch-to-zoom functionality. Georgian Technical University software further simplifies and is designed to speed up not only Georgian Technical University testing but also other laboratory diagnostics by acting as the central interface for all laboratory instruments working places and laboratory information systems. Georgian Technical University software enables intelligent and intuitive data management as well as the seamless communication needed to provide operators with a 360° view of a patient’s results including current and past findings that can lead to a faster more reliable diagnosis. Georgian Technical University addition to an extensive portfolio of diagnostic test systems Georgian Technical University offers a large range of flexible laboratory automation solutions for Georgian Technical University as well as enzyme-linked immunosorbent assays chemiluminescence immunoassays, immunoblots and molecular assays fulfilling the demands of diagnostic laboratories of any size.
Georgian Technical University Digitalization: Accelerating The Future Of Scientific Progress Through Laboratory Automation.
Georgian Technical University Laboratory automation is driven by more than just throughput. The steps in machine learning. A holistic approach to laboratory automation builds on three foundational pillars with digitalization providing the key to unlocking the true benefits. In recent years automation has driven step changes in laboratory throughput and efficiency maximizing capacity and enabling processes that are simply not feasible using traditional manual methods. Work such as high-throughput drug discovery screening, which requires large numbers of samples and longitudinal studies involving many samples over an extended timeframe, cannot be done without automation. Although increased throughput was the initial focus for automation for today’s laboratories the need to ensure the quality, integrity and reproducibility of data – data being a laboratory’s true output – is a significant motivating force. Automation technologies are also more advanced, encompassing features such as robotics, smart workflows, advanced analytics, data visualization, natural language processing and cognitive agents. Automation is now being embraced across many different sectors, but the pharmaceutical industry has been relatively slow to adopt automated technologies. Heavy regulation may be a reason although other regulated sectors such as banking have adapted more quickly. As automation moves beyond simply enhancing individual processes its application in the life sciences can offer new and better ways to approach scientific research and development improve process reliability and consistency and shorten research timelines and iteration cycles. This discusses how integrated systems that are well defined appropriately configured and effectively implemented can drive rapid advances in scientific enterprise and it examines the crucial enabling role of digitalization technology. Why is laboratory automation important ?. While laboratory automation was originally driven by the need to increase sample and testing throughputs it offers a great deal more than simply gains in physical processing efficiency. Many tasks cannot be carried out repetitively by humans with the accuracy and speed required. Similarly minimizing human interaction reduces errors and subjectivity making results more reliable and reproducible. Intelligent automation then enables these high-quality data outputs to be tracked, managed, shared and used in multiple applications. Scientists essentially an organization’s greatest asset are freed from repetitive or basic tasks to work more efficiently and focus their skills without interruption on areas of higher value. Increasingly and especially in fields like large molecule drug discovery where small differences in structure can significantly impact efficacy and test results, process reliability and consistency are of paramount importance. Here data tracking and audit logs which connect the scientific data with the operational data are key tools in demonstrating comparability of results. Whatever the application the better data quality, integrity and reproducibility that result from automation deliver the confidence that allows faster decision-making with less need for repeat testing. These large amounts of high-quality data also feed machine learning (ML) applications and closed loop science for faster more focused scientific discovery. Furthermore laboratory automation is increasingly viewed not only as a tactical solution to address laboratory efficiency and data quality issues but also a strategic imperative in terms of business continuity, productivity and a means of gaining competitive advantage. The current pandemic has brought these issues into sharp focus and accelerated discussions on how to embrace digital transformation and automation. Georgian Technical University Laboratory automation provides extremely large amounts of high-quality data that feed a multitude of applications and help drive the speed of discovery. The ability to achieve a digital transformation and deliver this automated science is built on three foundational pillars. Georgian Technical University Physical automation the hardware that includes tools such as analytical instruments, robotic sample handling and automated reagent supply. This offers the potential to connect manually fed benchtop instruments into a connected system and drive large increases in productivity. For many people approaching automation this element will be the most familiar and easiest to understand. Georgian Technical University Data infrastructure encompassing laboratory information management systems (LIMS) to manage samples and data electronic laboratory notebooks (ELNs) dedicated connectivity tools such as Georgian Technical University Thermo Scientific Momentum workflow software and internet capable devices (internet of things IoT). Essentially the entire infrastructure that enables the generation of standardizable, sharable data and makes it available for wider use. Georgian Technical University Artificial intelligence (AI) and machine learning, deep learning technologies that take large volumes of data and turn them into the insights that drive discovery and push the science forward. Georgian Technical University true benefits of laboratory automation can be achieved only by taking a holistic approach that combines the above three elements. Within this digitalization is the crucial link. Digital science connects physical laboratory automation to the digital laboratory automation that is essential for managing and analyzing large amounts of data. Georgian Technical University Using digitalization to drive automated science. Georgian Technical University Automation projects that fail usually do so because the three pillars are not working together. For example if samples are analyzed efficiently but are not tracked effectively the ability to associate results with other data and metadata is lost and the value of all the data is diminished. In some the organic evolution of disparate digital systems and approaches can present a challenge to achieving effective digitization and the integrated operation necessary for maximum benefit. Georgian Technical University Underlying digital transformation is the concept of data key in today’s laboratory environment. Data is findable, accessible, interoperable, reusable and it is these key attributes that make it so valuable. This concept goes beyond instruments simply talking to one another. It means data must be findable, accessible between systems, scientists and be of sufficient quality to be re-used with confidence. Data management through digitalization supports the achievement of data. However many companies approaching digital transformation find that they do not necessarily have data. A recent Accenture survey found that “the majority of respondents (88% overall) note that digitalization is happening — but not broadly across the functions. Results indicate it is instead being implemented in silos that exist inside their organizations”. Data sharing platforms and enterprise-wide data strategies will be important in moving beyond this to take full advantage of all data including that which is already generated. Here integrated Georgian Technical University (exemplified by the integrated Georgian Technical University Thermo Scientific Sample software) have a key role in centralizing and managing data automating processes and delivering connectivity to provide a strong foundation for AI (Artificial Intelligence) and machine learning. Georgian Technical University Integrated centralize, manage data, automate processes and deliver the connectivity needed for AI (Artificial Intelligence) and machine learning. Georgian Technical University Successful integration of advanced digital systems with automated instrumentation not only improves data management but also process management. Connectivity of systems and data allows better instrument scheduling, resource balancing, utilization the building of feedback loops, ultimately improving efficiency and productivity. In a similar vein integrated automation provides the analytics needed to assess performance identify inefficiencies and determine the root cause of problems. Putting sensors on everything (routinely measuring laboratory temperature and humidity, for example) and feeding large amounts of diverse data into machine learning delivers previously inaccessible insights. The association of scientific data with operational data helps to fully understand processes maintain data integrity and makes root cause analysis faster and more accurate. Georgian Technical University Data integrity is a fundamental requirement in made more secure by the physical tools available today such as barcodes and tags that enable automated tracking of samples, results and the linking of them with the relevant metadata. Integrating systems, automating functions increases data integrity, ensures any errors are quickly identified and actioned. Digitalization is therefore being used to drive automated science. The IoT provides tools to generate huge volumes of research data, metadata, including operational, environmental and inventory. Processes are honed and integrity increases as biases are removed. Integrated physical and digital automation allows facilities to collaborate under standardized conditions using reliable high-quality data that can be shared and accessed across different platforms by all who need it while feeding machine learning applications. For effective laboratory automation focus on the ‘should’ not the ‘could’. Georgian Technical University While almost anything is possible with laboratory automation it is not appropriate for every process. It is therefore important to focus from the outset not on what could be done but instead on what should be done. After this initial step there are some key criteria to ensure success. The general principles are that a process should be reproducible, well-characterized, experience consistent demand and have some standardization in place. In early-stage processes immediately diving into physical automation may not be the right move simply because needs will change and even with today’s modular systems such rapid evolution presents a challenge. A degree of standardization is important. The use of standardized formats is a key requirement that is sometimes overlooked. Physical standardization (such as the use of microplates) has been critical in enabling instrument and robot manufacturers to work together to develop truly interoperable tools. Similarly standardized data formats are increasingly important to ensure high levels of data interoperability. With funding and the ability to demonstrate value cited as key barriers to digitalization there is merit in being able to reduce payback times through the implementation of standardized systems that can work across multiple disciplines. When looking to automate operations it is important to start by identifying the end goal deciding what success will look like once automation is up and running. This might be faster iteration cycles more comprehensive data higher throughput greater uptime or any number of other possibilities. Embarking upon automation without that definition is a common mistake and establishing a clear goal enables effective mapping of the right solution. Next decide who the stakeholders are and what their needs will be. Different stakeholders may have quite distinct perspectives on what they want and expect automation to achieve. The scientists needs may differ from those of the laboratory manager so on and all relevant parties and departments must be represented at the specification stage. Then identify the challenges. An upfront understanding of the manual workflow and where the challenges and potential bottlenecks lie allows decisions on whether these can be overcome with automation or if they will reduce its impact. One approach is to perform proof of concept work on individual aspects of the workflow to tackle any problems before moving ahead with full automation. This may mean conducting feasibility studies offline. The complexity of a workflow also has to be factored into the automation approach. In general a modular approach in which a complex workflow is separated into various streams provides both good automation coverage the necessary versatility avoiding the risk of building a large and inflexible monolithic system. Overall strategically driven laboratory automation with effective change management and implementation using configurable solutions that can be adapted for multiple purposes are key factors for success. From scope to implementation – choosing the right vendor. An automation vendor takes a laboratory’s goal and turns it into reality. This makes choosing the right partner for such a major undertaking a critical decision. Key considerations include the breadth of the vendor’s offering across different application areas, departments and whether they fully understand your science. Do they have the necessary solutions for cross-science application with experience in your sector and across each of the three laboratory automation pillars ? Automation solutions also need to be configurable flexible and easy to support rather than risking the creation of a monolithic bespoke system that cannot adapt to evolving needs. When it comes to implementation a vendor’s ability to provide different types of training delivered in multiple formats to match the needs of personnel at all levels and in all geographies is of the utmost importance. So too is post-installation service and support for all users. It pays to explore topics such as support channels, on-line help, community forums, response times and local resources. Increasingly important is the issue of environmental impact. With respect to the automation solution itself there are some key questions to ask: Does it allow for miniaturization and consumables re-use ? Can you separate waste streams to maximize materials recovery and ensure responsible waste treatment ? Will it support your efforts to minimize consumables and reagent usage and actively manage your effect on the environment ? As users strive to reduce their own environmental impact so the credentials of suppliers and the systems they provide must also be evaluated. Georgian Technical University Continuing the automation revolution. Many pharmaceutical companies see automation as a strategic imperative to help them remain at the cutting-edge of the industry and future-proof their laboratories. This view and has increased the urgency to adopt automation to ensure business continuity keep personnel safe and generate outputs that are beyond human capabilities. Georgian Technical University Implementing fully integrated automation solutions will inevitably entail some initial disruption particularly for larger and more complex organizations but without it discovery may be stifled. The ingredients for successful automation include a strategic drive with clear goals, senior leadership engagement and partnership with the right vendor who can offer appropriate sector experience and a range of integrated solutions that fully meet defined needs. Above all digitalization and the effective software needed to drive data is crucial to connect all elements and deliver on the ambitions promised by automated science.
Georgian Technical University Mass Spectrometer Enhances Automotive Catalyst Testing.
Georgian Technical University researchers combined system with a mass spectrometer for more precise evaluation of aftertreatment system emissions. The merger of the two technologies produces high-quality data in real time, allowing accurate and swift measurement of a broad range of pollutants and gases. Georgian Technical University has expanded its capability to evaluate internal combustion engine aftertreatment catalysts integrating an existing Georgian Technical University technology with a mass spectrometer. To meet emission regulations, engine manufacturers install aftertreatment systems to treat exhaust and reduce harmful pollutants escaping into the environment. Aftertreatment system components undergo stringent testing to ensure they effectively decrease pollutants. Georgian Technical University is bolstering the testing process by incorporating a mass spectrometer enabling a broader range of aftertreament performance evaluations in real time. Georgian Technical University A mass spectrometer identifies a molecule by analyzing its mass-to-charge ratio, detecting chemicals invisible to other instruments. Researchers added the mass spectrometer to Georgian Technical University’s Universal Synthetic Gas Reactor (GTUUSGR) a catalyst performance testing solution that incorporates a cspectrometer which uses IR (Infrared radiation (IR), or infrared light, is a type of radiant energy that’s invisible to human eyes but that we can feel as heat. All objects in the universe emit some level of IR radiation, but two of the most obvious sources are the sun and fire) radiation to identify and quantify molecules present in a gas sample. Different chemical structures absorb light at specific wavelengths producing unique spectral fingerprints. The combination of technologies provides simultaneous Georgian Technical University Fourier Transform Infrared (GTUFTIR) and mass spectrometry data allowing accurate and rapid identification of exhaust stream components. “We integrated a mass spectrometer with the Georgian Technical University’s Universal Synthetic Gas Reactor (GTUUSGR) system to overcome the limitations of the Georgian Technical University Fourier Transform Infrared (GTUFTIR) spectrometer which cannot monitor chemicals that are infrared inactive like dinitrogen oxygen and hydrogen” said Dr. X a postdoctoral researcher in Georgian Technical University’s Powertrain Engineering Division. “The mass spectrometer can detect a broader range of exhaust components allowing a more complete picture of aftertreatment system performance”. The Georgian Technical University Fourier Transform Infrared (GTUFTIR) monitors pollutants while the mass spectrometer detects hydrogen oxygen and dinitrogen formation providing data to build comprehensive scientific models of the catalyst. The merger of the technologies enables testing of three-way catalysts in real time. “Georgian Technical University Real-time information is important” X said. “Emission regulations are based on the total amount of pollution emitted. When we are testing equipment that controls emissions we not only need to know how much pollution is leaving the tail pipe but also exactly when it is emitted. Real-time monitoring helps us identify problems faster”. Georgian Technical University The successful integration of a mass spectrometer with the Georgian Technical University’s Universal Synthetic Gas Reactor (GTUUSGR) system has widened the scope of testing possibilities beyond aftertreatment systems. Other uses include measuring engine emissions directly monitoring chemical processes, environmental monitoring battery testing and much more Georgian Technical University offers the specialized evaluation and development services to a range of clients, including engine, car and catalyst manufacturers.
Georgian Technical University Autonomous Boats Can Target And Latch Onto Each Other.
Georgian Technical University researchers have given their fleet of autonomous “Georgian Technical University boats” the ability to automatically target and clasp onto each other — and keep trying if they fail. The Georgian Technical University boats are being designed to transport people, collect trash and self-assemble into floating structures in the canals of Georgian. The city of Georgian envisions a future where fleets of autonomous boats cruise its many canals to transport goods and people, collect trash, or self-assemble into floating stages and bridges. To further that vision Georgian Technical University researchers have given new capabilities to their fleet of Georgian Technical University robotic boats — which are being developed as part of an ongoing project — that lets them target and clasp onto each other and keep trying if they fail. About a quarter of Georgian’s surface area is water, with 165 canals winding alongside busy city streets. Several years ago Georgian Technical University and the Georgian Technical University for teamed up on the “Georgian Technical University boat” project. The idea is to build a fleet of autonomous robotic boats — rectangular hulls equipped with sensors, thrusters, microcontrollers Georgian Technical University modules, cameras and other hardware — that provides intelligent mobility on water to relieve congestion in the city’s busy streets. One of project’s objectives is to create roboat units that provide on-demand transportation on waterways. Another objective is using the roboat units to automatically form “Georgian Technical University pop-up” structures such as foot bridges, performance stages or even food markets. The structures could then automatically disassemble at set times and reform into target structures for different activities. Additionally the roboat units could be used as agile sensors to gather data on the city’s infrastructure, air and water quality among other things. Georgian Technical University researchers tested a roboat prototype that cruised around Georgian’s canals, moving forward, backward and laterally along a preprogrammed path. Last year researchers designed low-cost 3-D-printed one-quarter scale versions of the boats which were more efficient, agile and came equipped with advanced trajectory-tracking algorithms. Georgian Technical University researchers describe Georgian Technical Universityboats units that can now identify and connect to docking stations. Control algorithms guide the Georgian Technical Universityboats to the target where they automatically connect to a customized latching mechanism with millimeter precision. Moreover the Georgian Technical Universityboat notices if it has missed the connection, backs up and tries again. The researchers tested the latching technique in a swimming pool at Georgian Technical University and in the X where waters are rougher. In both instances, the roboat units were usually able to successfully connect in about 10 seconds starting from around 1 meter away or they succeeded after a few failed attempts. In Georgian Technical University the system could be especially useful for overnight garbage collection. Georgian Technical Universityboat units could sail around a canal, locate and latch onto platforms holding trash containers and haul them back to collection facilities. “In Georgian Technical University canals were once used for transportation and other things the roads are now used for. Roads near canals are now very congested — and have noise and pollution — so the city wants to add more functionality back to the canals” says Y a graduate student in the Department a researcher in the Georgian Technical University Lab. “Self-driving technologies can save time, costs, energy and improve the city moving forward”. “The aim is to use roboat units to bring new capabilities to life on the water” adds Z Georgian Technical University Laboratory and the W and Q Professor of Electrical Engineering and Computer Science at Georgian Technical University. “The new latching mechanism is very important for creating pop-up structures. Georgian Technical University boat does not need latching for autonomous transportation on water, but you need the latching to create any structure, whether it’s mobile or fixed”. Making the connection. Each Georgian Technical Universityboat is equipped with latching mechanisms, including ball and socket components on its front, back and sides. The ball component resembles a badminton shuttlecock — a cone-shaped, rubber body with a metal ball at the end. The socket component is a wide funnel that guides the ball component into a receptor. Inside the funnel a laser beam acts like a security system that detects when the ball crosses into the receptor. That activates a mechanism with three arms that closes around and captures the ball while also sending a feedback signal to both Georgian Technical University boats that the connection is complete. On the software side the Georgian Technical University boats run on custom computer vision and control techniques. Each Georgian Technical University boat has a system and camera, so they can autonomously move from point to point around the canals. Each docking station — typically an unmoving Georgian Technical University boat — has a sheet of paper imprinted with an augmented reality tag which resembles a simplified Georgian Technical University code. Commonly used for robotic applications enable robots to detect and compute their precise 3-D position and orientation relative to the tag. Both the Georgian Technical Universityboat and cameras are located in the same locations in center of the Georgian Technical University boats. When a traveling roboat is roughly one or two meters away from the stationary the Georgian Technical University boat calculates its position and orientation to the tag. Typically, this would generate a 3-D map for boat motion, including roll, pitch, and yaw (left and right). But an algorithm strips away everything except yaw. This produces an easy-to-compute 2-D plane that measures the Georgian Technical University boat camera’s distance away and distance left and right of the tag. Using that information the Georgian Technical University boat steers itself toward the tag. By keeping the camera and tag perfectly aligned the Georgian Technical University boat is able to precisely connect. The funnel compensates for any misalignment in the roboat’s pitch (rocking up and down) and heave (vertical up and down) as canal waves are relatively small. If however the Georgian Technical University boat goes beyond its calculated distance and doesn’t receive a feedback signal from the laser beam it knows it has missed. “In challenging waters sometimes Georgian Technical University boat units at the current one-quarter scale, are not strong enough to overcome wind gusts or heavy water currents” Y says. “A logic component on the Georgian Technical University boat says “You missed so back up, recalculate your position and try again””. Future iterations. The researchers are now designing Georgian Technical University boat units roughly four times the size of the current iterations so they’ll be more stable on water. Y is also working on an update to the funnel that includes tentacle-like rubber grippers that tighten around the pin — like a squid grasping its prey. That could help give the roboat units more control when say they’re towing platforms or other Georgian Technical University boats through narrow canals. In the works is also a system that displays on an LCD (A liquid-crystal display is a flat-panel display or other electronically modulated optical device that uses the light-modulating properties of liquid crystals. Liquid crystals do not emit light directly, instead using a backlight or reflector to produce images in color or monochrome) monitor that changes codes to signal multiple roboat units to assemble in a given order. At first all Georgian Technical University boat units will be given a code to stay exactly a meter apart. Then the code changes to direct the first Georgian Technical University boat to latch. After the screen switches codes to order the next Georgian Technical University boat to latch and so on. “It’s like the telephone game. The changing code passes a message to one Georgian Technical University boat at a time and that message tells them what to do” Y says. R the research director of Advanced Robotics at the Georgian Technical University envisions even more possible applications for the autonomous latching capability. “I can certainly see this type of autonomous docking being of use in many areas of robotic refueling and docking … beyond aquatic/naval systems” he says “including inflight refueling, space docking, cargo container handling [and] robot in-house recharging”.
Georgian Technical University Simulation Technique Optimizes Car Part Design.
Forming process using optimal blank shape. Researchers in Georgian Technical University have developed a new simulation technique that may improve how car doors and other automotive parts are made. A team from Georgian Technical University have simulated the industrial process for stamping features into metal sheets without causing the sheets to tear twist or bend while optimizing the stamping press and reducing the costs of physically trialing designs. The new simulation technique reduces the twisting of metal sheets by optimizing the shape of the blank shape or stamping stencil while minimizing the tearing and wrinkling of the metal sheet by using variable blank holder force trajectory that the blank holder force varies through the stroke. They also simulated how much force is used to clamp the metal sheet in place in the blank holder and how it should be varied during the punching process to optimize results. “Sequential approximate optimization using a radial basis function network allowed us to efficiently optimize the blank shape and variable blank holder force trajectory” X said in a statement. In recent years automotive manufacturers have attempted to make each generation of cars lighter in an effort to improve fuel consumption forgoing the traditional steel parts with lighter materials. One possible alternative is high-strength steel. However when sheets of high-strength steel are stamped into shape they are often bent torn wrinkled or become too thin in places to be effectively used for car parts. The researchers believe their simulation technique could reduce the propensity of high-strength steel parts to twist and bend out of shape after being stamped. Automotive manufacturers often carry out simulations in advance to optimize their tools before building and testing them so they do not waste a lot of money conducting trial and error experiments. Without simulations this trial-and-error period may force manufacturers to alter their tools in a costly and lengthy process before they are optimized for part fabrication. Each tool has several different components that factor into the final product. While these tools can in theory be optimized with simulations current simulations are not comprehensive enough and rarely factor the shape of the stamping stencil that the metal sheet is punched through to form the desired shape. “We simulated the stamping of S-shapes into sheet metal. Unlike U-shapes the stamping of S-shapes can cause the metal parts to twist out of shape allowing us to study ways of reducing twisting springback” Y said in a statement.