Clone of Cardiac Action Potential Imaging System (CAPIS)

Prosthetic eyes have been used to replace fatally diseased human eyes since the beginning of the 19 th century. Even though the cosmetic aspects of the artificial eyes have come a long way since its inception, a major short coming still exists in modern designs of these eyes. Ocularists, people who custom-make the prosthetic eyes, have quite improved the cosmetic aspects of these eyes as far as color matching between the real eye and the artificial eye in a patient is concerned, both in the eye ball and the iris. Being able to utilize a dynamic pupil in an artificial eye would tremendously improve the esthetic of such eye and make them very similar to a functioning healthy human eye

Asset management of steel bridges

The deterioration of steel bridges around the world is of growing concern for bridge engineers and asset managers, as the demands on road infrastructure reach new heights. Hence, a tool that considers these heuristics can be used to validate or challenge the engineering decisions made in practice. Additionally, in a time of economic unrest and increased litigation, engineers require a means of accountability to support their remediation decisions. This paper moves through the development of a decision support system that can serve as an integrated learning tool for novice engineers, or as an accountability tool for assurance to project stakeholders. A compilation of the most commonly used remediation treatments, following an industry peer review have been used during the development stage of this system. Therefore, the decision support system constructed as part of this research, provides a valuable tool for the verification, or rejection of remediation decisions

Online distortion simulation using generative machine learning models: A step toward digital twin of metallic additive manufacturing

In the era of Industry 4.0 and smart manufacturing, Wire Arc Additive Manufacturing (WAAM) stands at the forefront, driving a paradigm shift towards automated, digitalized production. However, online simulation remains a technical barrier toward building a Digital Twin (DT) for metallic AM due to the prolonged computing time of numerical simulations and limitations in accuracy of current data-driven models. This study addresses these issues by introducing an adaptive online simulation model for predicting distortion fields, utilizing a diffusion model architecture for distortion process modelling with a Vector Quantized Variational AutoEncoder coupled with Generative Adversarial Network (VQVAE-GAN) backbone for spatial feature extraction, complemented by a Recurrent Neural Network (RNN) for time-scale result fusion. Pretrained offline with Finite Element Method (FEM) simulated distortion fields, the model successfully predicts distortion fields online using laser-scanned point clouds during the deposition process. Experimental validation on seven thin-wall structures demonstrated its superior performance, achieving a Root Mean Square Error (RMSE) below 0.9 m, outperforming FEM by 143 % and Artificial Neural Networks (ANN) based methods by 151 %, marking a significant stride towards realizing an AM-DT