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Surgery Planning and Tool Selection Using 3D Printing
Published in Harish Kumar Banga, Rajesh Kumar, Parveen Kalra, Rajendra M. Belokar, Additive Manufacturing with Medical Applications, 2023
Shagun Sharma, Chirag Ahuja, Parveen Kalra, Manarshhjot Singh, Ashish Aggarwal, Jagjit Singh Randhawa
The complete branching structure can be converted into physical replicas with the help of another revolutionary technology known as 3D printing or rapid prototyping (Markl et al., 2005). They are obtained through patients’ imaging data (CT and MRI) and fabricated using FDM and inkjet 3D-printing technology in a plethora of biocompatible and non-biocompatible materials (Naftulin et al., 2015; Sugawara et al., 2013; Ryan et al., 2015; Waran et al., 2014). These models are used as templates for pre-surgical planning and rehearsals as they are substantially held in the hands and analysed from a perception of the surgery. Its major advantage in the field of medicine is acknowledged by surgeons and physicians as an essential step for surgical planning (Arvier et al., 1994; D’Urso et al., 1998).
Performability
Published in Vivek Kale, Digital Transformation of Enterprise Architecture, 2019
ReplicationMaintain multiple copies of computations: Multiple servers in a client-server pattern are replicas of computation. The purpose of replicas is to reduce the contention that would occur if all computations took place on a single server. A load balancer is a piece of software that assigns new work to one of the available duplicate servers; criteria for assignment vary but can be as simple as round-robin or assigning the next request to the least busy server.Maintain multiple copies of data: Caching is a tactic that involves keeping copies of data (possibly one a subset of the other) on storage with different access speeds. The different access speeds may be inherent (memory versus secondary storage) or may be due to the necessity for network communication. It is system’s responsibility to choose the data to be cached. Some caches operate by merely keeping copies of whatever was recently requested, but it is also possible to predict users’ future requests based on patterns of behavior, and begin the calculations or prefetches necessary to comply with those requests before the user has made them.
Systems Modeling
Published in Devendra K. Chaturvedi, ®, 2017
To most people, the word “model” evokes images of clay cars in wind tunnels, cockpits disconnected from their airplanes to be used in pilot training, or miniature supertankers scurrying about in a swimming pool. These are examples of physical models (also called iconic models), and are not typical of the kinds of models that are of interest in operations research and system analysis. Physical models are most easily understood. They are usually physical replicas, often on a reduced scale. Dynamic physical models are used as in wind tunnels to show the aerodynamic characteristics of proposed aircraft designs. Occasionally, however, it has been found useful to build physical models to study engineering or management systems; examples include tabletop scale models of material-handling systems, and in at least one case a full-scale physical model of a fast food restaurant inside a warehouse, complete with full-scale, and, presumably hungry humans. But the vast majority of models built for such purposes are abstracted, representing a system in terms of logical or quantitative relationships that are then manipulated and changed to see how the model reacts, and thus, how the system would react— if the abstract model is a valid one. An abstract model is one in which symbols, rather than physical devices, constitute the model. The abstract model is more common but less recognized. The symbolism used can be a written language or a thought process.
Challenges when creating a cohesive digital twin ship: a data modelling perspective
Published in Ship Technology Research, 2021
Ícaro Aragão Fonseca, Henrique Murilo Gaspar
A focus on digitalization of the maritime industry has been increasing significantly, with new technologies expected to support faster completion of processes and data use during decision-making in the maritime value chain. The concept of digital twin aligns with this overall trend. Boschert and Rosen (2016) trace the origins of the digital twin to the aerospace industry, in which replicas of complex physical systems were commonly constructed, as, for example, during NASA’s Apollo space programme or by Airbus with its Iron Bird test rigs. Before system deployment, such replicas can be used to test systems integration and train crew members. During operational phases, engineers can use them to simulate operational alternatives and study issues that appear on a working aircraft by mirroring its behaviour. More recently, advances in simulation methods for engineering are expected to the enable reproduction of these practices using digital simulations, thus conceiving a digital twin system. In the early 2000s appeared the first mentions the possibility of extending product lifecycle management (PLM) platforms with data collected from the physical product in order to mirror it with the virtual counterpart (Grieves and Vickers 2016). At the same time, simulations were already used to support the operation of physical systems, even if with a relatively narrow scope. Cameron et al. (2018) cite some examples in the oil and gas sector which are analogous to a digital twin of a multiphase pipeline in the context of a broader oil and gas installation, or even to the digital twin of a valve, with components including sensors and actuators.
Photosensitizing properties of dissolved organic carbon in Canadian prairie pothole wetland ponds change in response to sunlight
Published in Canadian Water Resources Journal / Revue canadienne des ressources hydriques, 2022
Linh N. Tran, Hoang Vu, Britt D. Hall
Lastly, we further validated the accuracy of said models by testing them against a replica dataset. The replica dataset was constructed using the spare dataset from our experiments. Because of the numerous missing points in our spare set, it was insufficient to be included in the main modeling process and was thus chosen for validation purposes instead. We calculated the adjusted r2 between the available data points in the replica and the hypothesized values at given weeks. The adjusted r2 value reflects the accuracy of our model in forecasting the value of response and replicability of our study (See Supporting Information for data analysis methodology and validation with replica).