Partial Differential Equations Meet Deep Neural Networks: A Survey.

Many problems in science and engineering can be mathematically modeled using partial differential equations (PDEs), which are essential for fields like computational fluid dynamics (CFD), molecular dynamics, and dynamical systems. Although traditional numerical methods like the finite difference/element method are widely used, their computational inefficiency, due to the large number of iterations required, has long been a challenge. Recently, deep learning (DL) has emerged as a promising alternative for solving PDEs, offering new paradigms beyond conventional methods.

Photobiomodulation Facilitates Rat Cutaneous Wound Healing by Promoting Epidermal Stem Cells and Hair Follicle Stem Cells Proliferation.

Background: Cutaneous wound healing represents a common fundamental phenomenon requiring the participation of cells of distinct types and a major concern for the public. Evidence has confirmed that photobiomodulation (PBM) using near-infrared (NIR) can promote wound healing, but the  cells involved and the precise molecular mechanisms remain elusive.

Methods: Full-thickness skin defects with a diameter of 1.

Dose-Response Relationship Between BMI and Hyperuricemia.

Objective: The purpose of the current study is to explore the demographic characteristics of hyperuricemia in China.

Study Design: The cross-sectional study was conducted, and the CHARLS dataset in 2011 was used.

Methods: Logistic regression model was used to assess the association between BMI and hyperuricemia.

Zero-Shot Learning via Structure-Aligned Generative Adversarial Network.

In this article, we propose a structure-aligned generative adversarial network framework to improve zero-shot learning (ZSL) by mitigating the semantic gap, domain shift, and hubness problem. The proposed framework contains two parts, i.e.

Dose-response relationship between working hours and hypertension: A 22-year follow-up study.

Hypertension causes a substantial burden to society. Some studies found that hypertension was associated with the working type and working hours. The purpose of the current study is to assess the dose-response relationship between working hours and hypertension.

Robust Multiobjective Optimization for Vehicle Routing Problem With Time Windows.

In this article, we focus on the vehicle routing problem (VRP) with time windows under uncertainty. To capture the uncertainty characteristics in a real-life scenario, we design a new form of disturbance on travel time and construct robust multiobjective VRP with the time window, where the perturbation range of travel time is determined by the maximum disturbance degree. Two conflicting objectives include: 1)the minimization of both the total distance and: 2)the number of vehicles.

Spiculation Sign Recognition in a Pulmonary Nodule Based on Spiking Neural P Systems.

The spiculation sign is one of the main signs to distinguish benign and malignant pulmonary nodules. In order to effectively extract the image feature of a pulmonary nodule for the spiculation sign distinguishment, a new spiculation sign recognition model is proposed based on the doctors' diagnosis process of pulmonary nodules. A maximum density projection model is established to fuse the local three-dimensional information into the two-dimensional image.

Evolutionary Algorithm for Knee-Based Multiple Criteria Decision Making.

Although numerous effective and efficient multiobjective evolutionary algorithms have been developed in recent years to search for a well-converged and well-diversified Pareto optimal front, most of these designs are computationally expensive and have to maintain a large population of individuals throughout the evolutionary process. Once the Pareto optimal front is found satisfactorily, the cognitive burden is then imposed upon decision makers to handpick one solution for implementation among a massive number of candidates even with powerful multicriteria decision-making tools. With the increase in the number of decision variables and objective functions in the face of real-world applications, these problems have become a daunting challenge.

A Network Framework for Small-Sample Learning.

Small-sample learning involves training a neural network on a small-sample data set. An expansion of the training set is a common way to improve the performance of neural networks in small-sample learning tasks. However, improper constraints in expanding training data will reduce the performance of the neural networks.