A limit analysis-based topology optimisation method for geostructure design

Geostructures, vital for the progress of civilisation, often face inefficiencies and suboptimal performance due to the lack of optimisation in current designs. Achieving cost-efficiency in geostructure design involves optimising material usage while considering practical construction aspects. While size and shape optimisations are common in geostructure design, the application of topology optimisation remains underexplored. This paper addresses this gap by introducing a novel topology optimisation method for three-dimensional geostructure design. The method integrates mixed limit analysis and density-based topology optimisation theories, allowing for two-material design focused on the ultimate bearing capacity of the geostructure. The innovation resides in aligning the applied external load in the topology optimisation process with the ultimate load that the designed geostructure can sustain. The robustness of the proposed method is exemplified through its application to the design of an embankment and soil foundation, showcasing its potential to enhance the efficiency and performance of geostructures. This research contributes to the advancement of geostructure design practices, ultimately promoting sustainable and resilient infrastructure development

Three-dimensional plasticity-based topology optimization with smoothed finite element analysis

AbstractThis paper presents a novel plasticity-based formulation for three-dimensional (3D) topology optimization of continuum structures. The proposed formulation addresses the optimization problem by combining mixed rigid-plastic analysis with density-based topology optimization, resulting in a volume minimization approach. Unlike conventional stress-constrained topology optimization methods that rely on linear elastic structural analysis, our developed formulation focuses on enhancing the loading capacity of the designed structures based on the plastic limit theory, leading to more cost-effective designs. To improve computational efficiency, we employ the smoothed finite element technique in our proposed method, enabling the utilization of linear tetrahedral elements for 3D mesh refinement. Moreover, the final formulation of our developed method can be efficiently solved using the advanced primal–dual interior point method, eliminating the need for a separate nonlinear finite element structural analysis. Numerical examples are presented to demonstrate the effectiveness of the proposed approach in offering enhanced design possibilities for continuum structures.</jats:p

Artificial intelligence applications in the diagnosis and treatment of bacterial infections

The diagnosis and treatment of bacterial infections in the medical and public health field in the 21st century remain significantly challenging. Artificial Intelligence (AI) has emerged as a powerful new tool in diagnosing and treating bacterial infections. AI is rapidly revolutionizing epidemiological studies of infectious diseases, providing effective early warning, prevention, and control of outbreaks. Machine learning models provide a highly flexible way to simulate and predict the complex mechanisms of pathogen-host interactions, which is crucial for a comprehensive understanding of the nature of diseases. Machine learning-based pathogen identification technology and antimicrobial drug susceptibility testing break through the limitations of traditional methods, significantly shorten the time from sample collection to the determination of result, and greatly improve the speed and accuracy of laboratory testing. In addition, AI technology application in treating bacterial infections, particularly in the research and development of drugs and vaccines, and the application of innovative therapies such as bacteriophage, provides new strategies for improving therapy and curbing bacterial resistance. Although AI has a broad application prospect in diagnosing and treating bacterial infections, significant challenges remain in data quality and quantity, model interpretability, clinical integration, and patient privacy protection. To overcome these challenges and, realize widespread application in clinical practice, interdisciplinary cooperation, technology innovation, and policy support are essential components of the joint efforts required. In summary, with continuous advancements and in-depth application of AI technology, AI will enable doctors to more effectivelyaddress the challenge of bacterial infection, promoting the development of medical practice toward precision, efficiency, and personalization; optimizing the best nursing and treatment plans for patients; and providing strong support for public health safety

Why does dissolving salt in water decrease its dielectric permittivity

The dielectric permittivity of salt water decreases on dissolving more salt. For nearly a century, this phenomenon has been explained by invoking saturation in the dielectric response of the solvent water molecules. Herein, we employ an advanced deep neural network (DNN), built using data from density functional theory, to study the dielectric permittivity of sodium chloride solutions. Notably, the decrease in the dielectric permittivity as a function of concentration, computed using the DNN approach, agrees well with experiments. Detailed analysis of the computations reveals that the dominant effect, caused by the intrusion of ionic hydration shells into the solvent hydrogen-bond network, is the disruption of dipolar correlations among water molecules. Accordingly, the observed decrease in the dielectric permittivity is mostly due to increasing suppression of the collective response of solvent waters.Comment: has accepted by Physical Review Letter

Systematic treatment of displacements, strains and electric fields in density-functional perturbation theory

The methods of density-functional perturbation theory may be used to calculate various physical response properties of insulating crystals including elastic, dielectric, Born charge, and piezoelectric tensors. These and other important tensors may be defined as second derivatives of the total energy with respect to atomic-displacement, electric-field, or strain perturbations, or as mixed derivatives with respect to two of these perturbations. The resulting tensor quantities tend to be coupled in complex ways in polar crystals, giving rise to a variety of variant definitions. For example, it is generally necessary to distinguish between elastic tensors defined under different electrostatic boundary conditions, and between dielectric tensors defined under different elastic boundary conditions. Here, we describe an approach for computing all of these various response tensors in a unified and systematic fashion. Applications are presented for two materials, wurtzite ZnO and rhombohedral BaTiO3, at zero temperature.Comment: 14 pages. Uses REVTEX macros. Also available at http://www.physics.rutgers.edu/~dhv/preprints/xfw_sys/index.htm

Multiple object detection of workpieces based on fusion of deep learning and image processing

A workpiece detection method based on fusion of deep learning and image processing is proposed. Firstly, the workpiece bounding boxes are located in the workpiece images by YOLOv3, whose parameters are compressed by an improved convolutional neural network residual structure pruning strategy. Then, the workpiece images are cropped based on the bounding boxes with cropping biases. Finally, the contours and suitable gripping points of the workpieces are obtained through image processing. The experimental results show that mean Average Precision (mAP) is 98.60% for YOLOv3, and 99.38% for that one by pruning 50.89% of its parameters, and the inference time is shortened by 31.13%. Image processing effectively corrects the bounding boxes obtained by deep learning, and obtains workpiece contour and gripping point information

Deep neural network for the dielectric response of insulators

We introduce a deep neural network to model in a symmetry preserving way the environmental dependence of the centers of the electronic charge. The model learns from ab-initio density functional theory, wherein the electronic centers are uniquely assigned by the maximally localized Wannier functions. When combined with the Deep Potential model of the atomic potential energy surface, the scheme predicts the dielectric response of insulators for trajectories inaccessible to direct ab-initio simulation. The scheme is non-perturbative and can capture the response of a mutating chemical environment. We demonstrate the approach by calculating the infrared spectra of liquid water at standard conditions, and of ice under extreme pressure, when it transforms from a molecular to an ionic crystal

An implicit 3D nodal integration based PFEM (N-PFEM) of natural temporal stability for dynamic analysis of granular flow and landslide problems

The particle finite element method (PFEM) is a robust approach for modelling large deformation problems with free surface evolution. The classical PFEM, however, requires variable mapping from old to new quadrature points when adopting history-dependent material models in granular flow and landslide problems. Although the nodal integration technique circumvents this issue, it makes the PFEM temporal instable in dynamic analysis when using a displacement-based formulation. In this study, we developed a new version of a three-dimensional (3D) Nodal integration based PFEM (N-PFEM) using a mixed variational principle with the final problem resolved in mathematical programming. The proposed N-PFEM not only inherits the benefit from the nodal integration scheme that no variable mapping is required for handling history-dependent models but also is naturally temporal stable requiring no ad-hoc stabilization technique. We simulated a series of benchmark problems to demonstrate its nature of temporal stability as well as other admirable features such as the volumetric-locking free property and capability for tackling extreme configuration changes. Additionally, its application to a 3D landslide with a sensitive clay layer is shown to highlight its robustness