3d Cad Models Engineering LINK Download
Gertrud Inabinet
Architecture and Systems Engineering Professional Certificate has motivated me about the need of system engineering in our daily work life. No matter in which field you are working, Systems Engineering techniques and principles can be easily applied to have better credibility and reliability about your results and predictions.
The mechanisms behind cancer initiation and progression are not clear. Therefore, development of clinically relevant models to study cancer biology and drug response in tumors is essential. In vivo models are very valuable tools for studying cancer biology and for testing drugs; however, they often suffer from not accurately representing the clinical scenario because they lack either human cells or a functional immune system. On the other hand, two-dimensional (2D) in vitro models lack the three-dimensional (3D) network of cells and extracellular matrix (ECM) and thus do not represent the tumor microenvironment (TME). As an alternative approach, 3D models have started to gain more attention, as such models offer a platform with the ability to study cell-cell and cell-material interactions parametrically, and possibly include all the components present in the TME. Here, we first give an overview of the breast cancer TME, and then discuss the current state of the pre-clinical breast cancer models, with a focus on the engineered 3D tissue models. We also highlight two engineering approaches that we think are promising in constructing models representative of human tumors: 3D printing and microfluidics. In addition to giving basic information about the TME in the breast tissue, this review article presents the state-of-the-art tissue engineered breast cancer models. STATEMENT OF SIGNIFICANCE: Involvement of biomaterials and tissue engineering fields in cancer research enables realistic mimicry of the cell-cell and cell-extracellular matrix (ECM) interactions in the tumor microenvironment (TME), and thus creation of better models that reflect the tumor response against drugs. Engineering the 3D in vitro models also requires a good understanding of the TME. Here, an overview of the breast cancer TME is given, and the current state of the pre-clinical breast cancer models, with a focus on the engineered 3D tissue models is discussed. This review article is useful not only for biomaterials scientists aiming to engineer 3D in vitro TME models, but also for cancer researchers willing to use these models for studying cancer biology and drug testing.
Recent and sudden advances in the capabilities of artificial intelligence (AI) have attracted a lot of excitement and public attention. Notably, the emergence of large language models (LLMs) such as Google Bard or ChatGPT has been highlighted for their ability to create substantial volumes of natural text in a short space of time.
However, the potential uses for LLMs extend far beyond text generation, holding the significant potential to transform how engineers approach the design of projects. The ability of LLMs to analyse, interpret and even generate complex engineering documentation and instructions is an opportunity for engineers to automate repetitive and administrative tasks, giving them the ability to focus on more innovative and creative problem-solving tasks. They should therefore be actively exploring how they can harness AI and integrate it into their existing processes.
LLMs use Natural Language Processing (NLP) techniques to process input data, which allows them to interpret the semantic meanings of codes, clauses, formulas and standards. If fine-tuned correctly, this can allow models to develop accurate relationships between engineering variables and requirements within their neural networks. In turn, engineers can leverage this capability to develop systems that can automatically carry out appropriate calculations, based on the relevant formulas and clauses required for engineering design.
Just as a human would, such systems would be able to summarise proposals in a way that is simple to read and understand, including technical descriptions and suggestions. AI could act as an engineering design assistant, capable of making informed decisions and applying codified standards to repetitive types of work. This would significantly enhance productivity and efficiency in engineering workflows.
Engineers will need to be confident that the tools they use are trained on a diverse and representative data set. This underscores the critical importance of meticulous curation and thoughtful consideration when selecting data to train AI models, allowing for a more inclusive and unbiased foundation upon which AI-driven engineering design processes can be built.
Engineers will also need to be able to understand how the system has produced its outputs so that the necessary engineering due diligence can take place. This will require interpretability tools to be built into the AI system that provide design approvers with visibility into the decision-making process employed by the model. By providing transparency and insights into the internal workings of the LLM, engineers can effectively assess the validity, reliability and safety of the generated design outputs. This allows the necessary engineering rigor to be upheld, granting informed decision-making and permitting the integration of AI technologies into the engineering design process, which aligns with established standards and practices.
By combining ensemble methods and human verification, engineers can enhance the reliability and trustworthiness of LLM outputs, mitigating potential errors and promoting the responsible integration of AI into the engineering design process.
Despite these various difficulties, AI is being taken up by almost every industry, and engineering is unlikely to be an exception. Engineers can expect a continuing rate of adoption and improvement with AI-enhanced project deliveries becoming more commonplace. They will need to be conscious of the limitations of LLMs and put in place measures to mitigate them. By leveraging AI capabilities, engineers can augment their design skills and capabilities, enabling them to deliver high-quality outcomes more efficiently. The integration of AI into the engineering design process empowers engineers to tackle complex challenges, improve decision-making and enhance overall project performance.
Overall, while being mindful of the limitations, engineers can embrace the potential of AI to reshape the engineering landscape, paving the way for improved project deliveries and enhanced client satisfaction.
Leveraging industry case studies and the latest thinking from MIT, this four-course online certificate program explores the newest practices in systems engineering, including how models can enhance system engineering functions and how systems engineering tasks can be augmented with quantitative analysis.
The Core Models team owns time series algorithms that power features across the Datadog application. We are responsible for the implementation and performance of the algorithms, as well as the internal tooling to evaluate and iterate on them. Our models power features such as Watchdog Alerts and Log Anomaly Detection.
As the Engineering Manager of the Core Models team, you will lead and grow a team of Data Scientists and Software Engineers that own the implementation and development of our core time series models. These models are at the heart of Data Science efforts and are driving an increasing number of use cases.
Quite some people not using models in their daily routine have for long been questioning why all these complex models are needed and whether it is at all worth the effort. I've seen this in industry, with academic colleagues and even funding agencies. The main reason, in my humble opinion, is ignorance. The value is often not clear and given the sometimes long research and development times, especially for complex systems, it is thought that the return of investment will go through the roof. It's all about not seeing the bigger picture of how a model fits into the value chain. And likely, we modelers, are partly to blame here as we did not manage to get this message properly across. The recent revival of AI seems to be helping us to turn the tide. However, in the fields I am working in, i.e. resource recovery engineering and pharmaceutical engineering, my strange observation is that the ignorance with regard to AI does not seem to affect its acceptance. We need to be careful though that it does not end up just being an illusion as rolling out AI in these sectors will not be a walk in the park, given the vast security and health risks involved.
Hence, as I teach my students in modeling class, it is important to state why you are undertaking this challenge in the first place and how it is going to add value. It's all about expectation management. Once you convey that message clearly, the perception often completely changes. Explaining the why is not always that obvious and depends on the background of the person you are talking to. Hence, talking to academic peers is very different from convincing an industrial partner to invest in modeling. Here, I prefer the strategy to keep as many people on board as this is a sustainable strategy since it develops a "hunger for more reflex" with these people once they get the hang of it. I always like the moment when a non-modeler has an aha-moment, is on board and actually comes up with good questions to feed the model development exercise. The question then moves from "should we use models" to "what will we model first". And often this also triggers people to think with a modeler's mindset about other processes and systems.
Another illusion I observe is that many people seem to believe that AI is now going to replace mechanistic modeling and solve all problems. Are we going to ditch 4 centuries of knowledge built up in physics and engineering? That would be absurd. As history keeps telling us, it is not either black (box) or white (box), but rather shades of grey (box). It will be a blend of mechanistic and AI models in my opinion. The exact type will be depending on the modeling objective. Indeed, in some cases where data is scarce, such as when designing novel technologies, mechanistic models will be the preferred route, whereas when unraveling unknown mechanisms for knowledge buildup or operational models, machine learning or deep learning methods could assist mechanistic models to maximize their predictive power. Most often, this will result in what I tend to term hybrid models. Pure AI models in engineering are less likely as one always needs to bear in mind that data availability is a bottleneck for newly developed technologies. In order to build a sound data-driven model, not only data quantity but also data quality is important, i.e. having sufficient data in a broad operational space. If not, the model validity is limited. There might be a more profound place for AI-type models in process control.
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