[Gk Dubey Solutions
Gildo Santiago
Anand Dubey joined Saama Technologies in April 2017 as Business Solutions Architect, Real World Evidence and is responsible for RWE product and solution development for clinical operational groups. Prior to joining Saama, Anand worked at Genpact, a global professional services firm, where he focused on technology-enabled solutions for various groups like clinical, data sciences and commercial teams.
Pawan has more than 10 years of experience in Telecom/Software Industry in Business Analysis, Consulting in telecom OSS/BSS-Service Assurance and Fulfilment area. He has played a key role as a Business Consultant in meeting business objectives, solving business problems and furthering business success by applying e-TOM industry Standards, formulating and delivering business strategy and large-scale BSS / OSS solutions.
In addition to consulting, he is also on the advisory team of a few ventures helping accelerate their journey to commercial success and Business Growth. As a telecom evangelist of the communication, Media & Entertainment world, he often visits leading B-Schools , Engineering Insitutions as a Guest Lecturer. He has been keynote speaker at industry conferences on digital monetization, Telecom solutions, Big Data and Industrial IoT in India.
Pawan holds a Master of Science degree in Telecommunication and software engineering. He is currently working with Orange Belgium as a Principal Solution Architect in CRM Transformation and Digital division. His research interests are in the area of computer-based electronic analysis and simulation, Digital Transformation, Internet of Things architectures with applications in green automotive, internet of energy, healthcare and agriculture.
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Background: Artificial intelligence (AI) enables remote patient monitoring (RPM) which reduces costs by triaging patients to optimize hospitalization and avoid complications. The FDA regulates AI in medical devices and aims to ensure patient safety, effectiveness, and transparent AI solutions.
Methods: We searched publicly available databases on FDA-approved RPM devices. Selection criteria were established to classify a solution as AI. Technical information was analyzed on pre-identified 16 parameters for the qualified solutions.
Conclusion: The market needs more innovative RPM solutions under the De Novo category, as there are very few. The transparency is low on the technical aspect of AI algorithms. The market needs AI algorithms that can effectively classify patients rather than merely improve device functionality.
A method for creating regular arrays of surface features in the 10m to 200m range over large areas in metallic materials enables the use of treated surfaces on injection molds. This surface control is used for manufacturing products with controllable optical, tribological, heat transfer, and surface tension properties. However, the flow, filling, and solidification behavior of molten polymer at the micro scale is not well understood.
Therefore, the purpose of this study is to understand the formation of micro-structure on a polymer surface and derive a range of robust solutions for injection molding of these micro-featured surfaces. This study uses critical inputs of the molding process, mold geometry, and working materials, and determines a range of feasible solutions to recreate these micro structures without filling or de-molding defects. Five different molding parameters hypothesized as most influential in molding microstructures were varied and the resultant micro-features characterized using 3D surface profiling white light interferometry and scanning electron microscopy. Regression responses were determined and optimal value ranges for the key input parameters were identified and independent tests run to verify the results. The five parameters tested are: injection pressure, injection velocity, mold temperature, melt temperature and shape of micro structures. These tests were carried out in both HDPE and EPDM/PP alloy materials using different mold insert materials and coatings.
The tests first conducted on a flat surface were repeated on a tube surface to identify if change in geometry of component changes the formation of micro-structures.
The results obtained from these tests gave us a clear indication of how each of the above parameters controls the formation of molded microfeatures, and how parameters interact. In this case mold temperature was found to be the most critical factor, with a sensitivity of only a few degrees greatly improving the micro feature formation. The biggest concern was uniformity of micro features across the molded surface. Based on this understanding we were also able to simulate macroscopic mold filling conditions and verify homogeneous filling across the molded surface. These experimental results are used to drive a predictive tool for process planning of complete undamaged feature formation at the micro scale and uniformity at the macro scale.
It was also found that formation of structures at micro level is different from formation of structures at macro level.
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Attempt the free practice questions on Chapter 1: Suggestions for Teachers and Parents, Exercise 1: Exercise 1 with hints and solutions to strengthen your understanding. DRILL LESSONS In Mental Arithmetic BOOK 3 solutions are prepared by Experienced Embibe Experts.
Olga Dubey is CEO and Co-Founder of AgroSustain, a company founded in 2018 to reduce food waste and support sustainable food production by developing farm to fork solutions, like natural fungicides an... Read more
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