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INTRODUCTIONPrevious studies have shown that 20-30% of denture users have been dissatisfied with their dentures [1,2]. Ideal denture base material, should distribute uniform stress on the underlying tissues [3]. Several materials, such as silicone, acrylic resin, and fluoroethylene, have been used as resilient denture liners which exhibit various viscoelastic properties.To obtain stresses and displacements, FEA has been illustrated as computational tool. Since the early 1960's, the FEA has been used in the aerospace industry and later it was applied in dentistry in 1970's [4]. FEA is capable of providing detailed quantitative data at any location within a mathematical model, so is a valuable analytical tool in dentistry [4,5]. The power of FEA exists principally in its versatility with respect to a range of physical problems [5]. The structure which is to be analyzed can have a random shape, loads, and supporting conditions. In addition, the mesh can mix elements of divergent types, shapes, and physical properties. A single computer program encloses all these substantial versatility and the type, geometry, boundary conditions, and element choice are controlled by user-prepared input data [5].This study was conducted with the aim to observe and analyze the stress distribution under complete denture lined with elastic and viscoelastic liners using 3D FEA and objectives were to correlate this with a clinical study where patients were given dentures lined with elastic or viscoelastic liners to assess the effect of liners on the quality of life of patients and which liner is suitable for which type of food. MATERIALs AND METHODsThis study was conducted in in-vitro and in-vivo phases. Ethical clearance was granted by the ethical clearance committee of the Career Post-Graduate Institute of Dental Sciences, Lucknow, Uttar Pradesh, India. Phase 1: Finite Element Model (In-vitro)FEA was carried out at "Lelogistix Design" in Delhi using the following computer characteristics: Three virtual models were fabricated: One depicting a maxillary denture with an acrylic resin liner, another with a silicone liner, and a third depicting a heat-cured acrylic resin denture resting on the mucosa. The fabrication of virtual models and study of load pattern included the following steps:1. scanning the physical casts and denture models to create geometric models: The casts and dentures were made in the laboratory and in these pre-fabricated physical models of dentures and casts most appropriate defect free model was used to initiate the project. This physical model was scanned using a white-light scanner. The structured light 3D scanner (Imetric 3D GmbH) was used for contactless digitalization of the denture and cast. The scanner projects a pattern of light on the subject and assesses the pattern of deformation on the subject. The data obtained using this scanner exhibited better quality, precision, and speed than that obtained using other scanners. Instead of scanning one point at a time, the structured 3D light scanner scanned multiple ...

Health-related limitations prohibit a human from working in hazardous environments, due to which cognitive robots are needed to work there. A robot cannot learn the spatial semantics of the environment or object, which hinders the robot from interacting with the working environment. To overcome this problem, in this work, an agent is computationally devised that mimics the grid and place neuron functionality to learn cognitive maps from the input spatial data of an environment or an object. A novel quadrant-based approach is proposed to model the behavior of the grid neuron, which, like the real grid neuron, is capable of generating periodic hexagonal grid-like output patterns from the input body movement. Furthermore, a cognitive map formation and their learning mechanism are proposed using the place–grid neuron interaction system, which is meant for making predictions of environmental sensations from the body movement. A place sequence learning system is also introduced, which is like an episodic memory of a trip that is forgettable based on their usage frequency and helps in reducing the accumulation of error during a visit to distant places. The model has been deployed and validated in two different spatial data learning applications, one being the 2D object detection by touch, and another is the navigation in an environment. The result analysis shows that the proposed model is significantly associated with the expected outcomes.

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