Cultural Tourism Products: A Case Study in the Xi’an City

Nowadays, culture has been a major driver of tourism. Cultural tourism is another form of tourism by involving cultural elements. Some people traveled specifically to gain a deeper understanding of the culture or heritage of a destination. In order to satisfy tourists’ cultural needs and wants, cultural tourism products typically attracts consumers by the cultural attributes. A cultural attest is not a cultural tourism product unless it transforms itself into products that could be consumed by tourists. The market value of cultural tourism can be realized by cultural tourism products. This paper is to explore the development and evaluation of cultural tourism products. By studying a case of Xi’an in China, the paper explores how cultural tourism products work in a real world. The evaluation system examines the quality of cultural tourism products provided by the Xi’an city. The experience of developing such products can be learned through the case study

Biomaterials for Monitoring and Modulating Immune Cell Function to Target Cancer and Metastasis

Despite the improvements made in the treatment of cancer in the last 50 years, metastasis still results in up to 90% of cancer related deaths. Metastatic tumor cells are not only more aggressive but they are often less immunogenic, able to escape detection and elimination by the immune system. In addition, tumor-secreted factors induce the proliferation and activation of aberrant immune cells that further suppress the anti-tumor immune response. New findings in the fields of cancer biology and cancer immunology have enabled the development of targeted therapies and immunotherapies that act to reverse this immunosuppression. The work presented here aims take an engineering approach to build upon these findings. The development and application of biomaterial platforms that can interact with and modulate immune cells, may enable better understanding and treatment of underlying mechanisms driving cancer progression and metastasis. This work describes the application of biomaterial nanoparticles as a novel treatment for acute inflammation resulting from spinal cord injury. The nanoparticles were internalized by proinflammatory immune cells, and diverted these cells away from the injury site while reprogramming macrophages at the site to be more pro-regenerative. Increased tissue regeneration, reduced scarring, and improved functional recovery of the animal were observed with nanoparticle treatment. The immunomodulatory capabilities of nanoparticles were also tested in a model of metastatic breast cancer. The nanoparticles were found to be internalized by disease-relevant immune cells and reduced the abundance of these cells in circulation as well as at the metastatic site. The immunomodulation resulted in slower tumor growth, fewer metastatic cells at the lung, and a survival benefit when combined with anti-PD-1 therapy. This work also explores the use of implantable biomaterial scaffolds for monitoring metastasis and disease progression, and introduces a scoring metric based on gene expression of cells recruited to the scaffold to predict therapeutic outcome and the likelihood of relapse. The scaffolds were also utilized to study the dynamics of Gr1$+ cell phenotype at sites of metastasis. RNA sequencing and functional studies revealed differences in the phenotype of these cells across tissues and over time. These studies add to the existing body of knowledge of Gr1+ cells and introduces potential considerations in the development of drugs targeting these cells.PHDChemical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163265/1/zyining_1.pd

Empowering CAM-Based Methods with Capability to Generate Fine-Grained and High-Faithfulness Explanations

Recently, the explanation of neural network models has garnered considerable research attention. In computer vision, CAM (Class Activation Map)-based methods and LRP (Layer-wise Relevance Propagation) method are two common explanation methods. However, since most CAM-based methods can only generate global weights, they can only generate coarse-grained explanations at a deep layer. LRP and its variants, on the other hand, can generate fine-grained explanations. But the faithfulness of the explanations is too low. To address these challenges, in this paper, we propose FG-CAM (Fine-Grained CAM), which extends CAM-based methods to enable generating fine-grained and high-faithfulness explanations. FG-CAM uses the relationship between two adjacent layers of feature maps with resolution differences to gradually increase the explanation resolution, while finding the contributing pixels and filtering out the pixels that do not contribute. Our method not only solves the shortcoming of CAM-based methods without changing their characteristics, but also generates fine-grained explanations that have higher faithfulness than LRP and its variants. We also present FG-CAM with denoising, which is a variant of FG-CAM and is able to generate less noisy explanations with almost no change in explanation faithfulness. Experimental results show that the performance of FG-CAM is almost unaffected by the explanation resolution. FG-CAM outperforms existing CAM-based methods significantly in both shallow and intermediate layers, and outperforms LRP and its variants significantly in the input layer. Our code is available at https://github.com/dongmo-qcq/FG-CAM.Comment: This paper has been accepted by AAAI202

CFDBench: A Comprehensive Benchmark for Machine Learning Methods in Fluid Dynamics

In recent years, applying deep learning to solve physics problems has attracted much attention. Data-driven deep learning methods produce operators that can learn solutions to the whole system of partial differential equations. However, the existing methods are only evaluated on simple flow equations (e.g., Burger's equation), and only consider the generalization ability on different initial conditions. In this paper, we construct CFDBench, a benchmark with four classic problems in computational fluid dynamics (CFD): lid-driven cavity flow, laminar boundary layer flow in circular tubes, dam flows through the steps, and periodic Karman vortex street. Each flow problem includes data with different boundary conditions, fluid physical properties, and domain geometry. Compared to existing datasets, the advantages of CFDBench are (1) comprehensive. It contains common physical parameters such as velocity, pressure, and cavity fraction. (2) realistic. It is very suitable for deep learning solutions of fluid mechanics equations. (3) challenging. It has a certain learning difficulty, prompting to find models with strong learning ability. (4) standardized. CFDBench facilitates a comprehensive and fair comparison of different deep learning methods for CFD. We make appropriate modifications to popular deep neural networks to apply them to CFDBench and enable the accommodation of more changing inputs. The evaluation on CFDBench reveals some new shortcomings of existing works and we propose possible directions for solving such problems.Comment: 33 pages, 11 figures, preprin