Comprehensive overview of meta-heuristic algorithm applications on PV cell parameter identification

Biodiesel synthesis from Ceiba pentandra oil by microwave irradiation-assisted transesterification: ELM modeling and optimization

A comparative review on mobile robot path planning : classical or meta-heuristic methods?

In this study, we developed multiple hybrid machine-learning models to address parameter optimization limitations and enhance the spatial prediction of landslide susceptibility models. We created a geographic information system database, and our analysis results were used to prepare a landslide inventory map containing 359 landslide events identified from Google Earth, aerial photographs, and other validated sources. A support vector regression (SVR) machine-learning model was used to divide the landslide inventory into training (70%) and testing (30%) datasets. The landslide susceptibility map was produced using 14 causative factors. We applied the established gray wolf optimization (GWO) algorithm, bat algorithm (BA), and cuckoo optimization algorithm (COA) to fine-tune the parameters of the SVR model to improve its predictive accuracy. The resultant hybrid models, SVR-GWO, SVR-BA, and SVR-COA, were validated in terms of the area under curve (AUC) and root mean square error (RMSE). The AUC values for the SVR-GWO (0.733), SVR-BA (0.724), and SVR-COA (0.738) models indicate their good prediction rates for landslide susceptibility modeling. SVR-COA had the greatest accuracy, with an RMSE of 0.21687, and SVR-BA had the least accuracy, with an RMSE of 0.23046. The three optimized hybrid models outperformed the SVR model (AUC = 0.704, RMSE = 0.26689), confirming the ability of metaheuristic algorithms to improve model performance.

Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression (SVR) with GWO, BAT and COA algorithms

Operational framework for recent advances in backtracking search optimisation algorithm: A systematic review and performance evaluation

Cuckoo Search Optimization- A Review

We propose a new approach for unsupervised learning of hyperelastic constitutive laws with physics-consistent deep neural networks. In contrast to supervised learning, which assumes the availability of stress-strain pairs, the approach only uses realistically measurable full-field displacement and global reaction force data, thus it lies within the scope of our recent framework for Efficient Unsupervised Constitutive Law Identification and Discovery (EUCLID) and we denote it as NN-EUCLID. The absence of stress labels is compensated for by leveraging a physics-motivated loss function based on the conservation of linear momentum to guide the learning process. The constitutive model is based on input-convex neural networks, which are capable of learning a function that is convex with respect to its inputs. By employing a specially designed neural network architecture, multiple physical and thermodynamic constraints for hyperelastic constitutive laws, such as material frame indifference, (poly-)convexity, and stress-free reference configuration are automatically satisfied. We demonstrate the ability of the approach to accurately learn several hidden isotropic and anisotropic hyperelastic constitutive laws - including e.g., Mooney-Rivlin, Arruda-Boyce, Ogden, and Holzapfel models - without using stress data. For anisotropic hyperelasticity, the unknown anisotropic fiber directions are automatically discovered jointly with the constitutive model. The neural network-based constitutive models show good generalization capability beyond the strain states observed during training and are readily deployable in a general finite element framework for simulating complex mechanical boundary value problems with good accuracy. 

https://doi.org/10.1016/j.jmps.2022.105076

NN-EUCLID: deep-learning hyperelasticity without stress data

https://doi.org/10.1016/j.cma.2022.115225

Bayesian-EUCLID: discovering hyperelastic material laws with uncertainties

Medical dressings play an important role in the field of tissue engineering owing to their ability to accelerate the process of wound healing. Great efforts have been made to fabricate wound dressings with distinctive features for promoting wound healing. However, most of the current synthesis methods either generate dressings of uniform size or involve complex fabrication techniques, thus limiting their commercialization for the personalized dressings. We report here a dressing, which presents a paradigm shift in the design of the dressing from uniform films to a micro-patterned film. The hypothesis driving the design is the ability of the 3D patterns to provide an efficient transient matrix filling the depth of the wound rather than just providing a barrier and slight re-epithelialization. We demonstrate the use of the digital light processing 3D printing technique to generate micro-pyramid-decorated wound healing dressings with individualized design and with bio-compatible gelatin methacryloyl to contact the wounded areas. In addition to providing better adhesion to the migratory cells, the micro-pyramids also enable covalent conjugation of heparin, providing capability to sequester endogenous growth factors (GFs). Based on these advantages, the developed dressing not only adheres strongly to the wound bed but also promotes the treatment of a rat wound model by utilizing the power of endogenous GFs for tissue regeneration. Thus, it is believed that the developed dressing can break through the limitation of traditional wound treatment and be an ideal candidate for wound healing.

Light-Mediated 3D Printing of Micro-Pyramid-Decorated Tailorable Wound Dressings with Endogenous Growth Factor Sequestration for Improved Wound Healing.

The flexo process parameters play an important role in ink transfer and will lead to wastage of inks, substrate, solvents and printed stocks if not monitored and controlled. The work focuses on optimizing the flexo process parameters for 40 microns 3-layer polyethylene (PE) film with Blue Nitrocellulose (NC) ink to reduce overall manufacturing cost while maintaining the print quality for diaper application. An experimental design was conducted for the response Ink GSM (grams per square meter), ?E and Print Mottle with factors such as ink viscosity, anilox volume, plate dot shape and substrate opacity. The data was analyzed through Main Effect, Interaction Plot and Analysis of Variance (ANOVA). The regression models were developed for the response to validate the predictive ability of model. The process optimization resulted in reduction of Ink GSM, ?E and Print Mottle by 18%, 52% and 1% respectively. The ink consumption reduced by 18.26% with minimized print defects, thereby reducing the overall manufacturing cost.

/pdf/optimization-of-flexo-process-parameters-to-reduce-the-3t040eiv.pdf

Akshay Joshi

Papers

Cuckoo Search Optimization- A Review

NN-EUCLID: deep-learning hyperelasticity without stress data

Bayesian-EUCLID: discovering hyperelastic material laws with uncertainties

Light-Mediated 3D Printing of Micro-Pyramid-Decorated Tailorable Wound Dressings with Endogenous Growth Factor Sequestration for Improved Wound Healing.

Optimization of flexo process parameters to reduce the overall manufacturing cost