diffusion models

Fine-tune the stable diffusion model using mindat data to generate mineral images from textual descriptions of attribute combinations

Abstract Mindat (mindat.org) is a comprehensive mineral database that serves as a valuable resource for mineral data, encompassing a vast collection of textural descriptions and images from around the world. While the OpenMindat project accelerates knowledge discoveries in mineralogy, numerous relationships between ima...

Mathematical and computational modeling of head and neck cancer for personalized therapy

Abstract We present a coupled mathematical model for tumor progression and treatment-induced tissue remodeling in head and neck cancer. The framework links a reaction–diffusion equation for tumor cell density with finite-strain growth mechanics through a multiplicative decomposition of the deformation gradient. Radiati...

Visualization experimental study on transient cavitating flow and spray characteristics of methanol high-pressure direct injector nozzles

In the context of the green and low-carbon transformation of internal combustion engines, methanol is regarded as a promising alternative fuel owing to its high oxygen content, low carbon-to-hydrogen ratio, and excellent combustion characteristics featuring minimal particulate formation, coupled with its compatibility...

Predictive heat transfer model for fire-induced injuries in tree stems

This study introduces a physics-based mathematical model to predict the injury and mortality of tree stems during prescribed burns and in wildland fires. The two-dimensional transient model is developed in polar coordinates employing the finite volume method with an implicit time integration scheme. The model includes...

Modeling the Effect of Social Contagion in Solar Adoption through Commuter and Socioeconomic Networks

A Review of Adaptive Image Encryption Schemes Utilizing Optimized Chaotic Systems and Deep Learning Models

The encryption of images is very important in keeping visual information secret and unaltered, particularly in the case of secure transmission and storage applications. AES is one of the most extensively used cryptographic algorithms, however it does not fully use picture properties like high redundancy, strong pixel c...

Existence, Uniqueness, and Numerical Experiments for a Caputo Fractional PDE With Variable‐Exponent Diffusion in Image Denoising

ABSTRACT We propose a novel nonlinear evolution model that integrates a Caputo time‐fractional derivative with a variable exponent diffusion operator for image decomposition and noise removal. The Caputo derivative captures memory effects through a fractional order, while the spatially adaptive exponent allows the diff...

From rhizome diffusion to programmable architecture: a systems framework for controllable spatial expansion in bamboo and other clonal plants

Introduction Rhizome-driven spatial expansion in bamboo and other clonal plants is governed jointly by architectural patterning, belowground bud bank demography, hormonal crosstalk, carbon allocation, environmental plasticity and genomic regulation. This integrative review synthesises those strands and then asks a ques...

MOF–biochar composites for wastewater treatment: A review on synthesis, properties, and adsorption mechanisms

Industrial wastewater containing heavy metals, dyes, and emerging contaminants remains difficult to treat using conventional adsorbents due to limited selectivity and insufficient active sites. Recently, metal-organic framework-biochar (MOF–BC) composites have emerged as promising hybrid materials that combine the stab...

A Superconvergent Two-Grid Finite Element Method for the Keller–Segel Equations with Cross-Diffusion and Logistic Source

Deep learning-driven multisource remote sensing image fusion: Advances, challenges, and future directions

Multisource remote sensing image fusion has become an important solution to a long-standing limitation in Earth observation: individual sensors rarely provide high spatial detail, rich spectral information, reliable structural sensitivity, and frequent temporal coverage at the same time. This review examines how deep l...

PDE-Based (Partial Differential Equation) Models for Understanding Information Flow in Deep Neural Networks

Transplanting the deep neural networks into high-stakes decision-making has left folks more and more willing to have a clear mathematically-founded understanding of how they actually operate. Researchers have been advocating the use of models that are not only transparent but also mathematically sound. The existing pos...