In this paper, we present VoxelBars as an informative interface for volume visualization. VoxelBars arrange voxels into a 2D space and visually encode multiple attributes of voxels into one display. VoxelBars allow users to easily find out clusters of interesting voxels, set opacities and colors of a specific group of voxels, and achieve various sophisticated visualization tasks at voxel level. We provide results on real volume data to demonstrate the advantages of the VoxelBars over scatterplots and traditional transfer function specification methods. Some novel visualization techniques including visibility-aware transfer function design and selective clipping based on VoxelBars are also introduced.

Interpersonal ties, such as strong ties and weak ties, describe the information carried by an edge in social network. Tracking the dynamic changes of interpersonal ties can thus enhance our understanding of the evolution of a complex network. Nevertheless, existing studies in dynamic network visualization mostly focus on the temporal changes of nodes or structures of the network without an adequate support of analysis and exploration of the temporal changes of interpersonal ties. In this paper, we introduce a new visual analytics method that enables interactive analysis and exploration of the dynamic changes of interpersonal ties. The method integrates four well-linked visualizations, including a scatterplot, a pixelbar chart, a layered graph, and a node-link diagram, to allow for multi-perspective analysis of the evolution of interpersonal ties. The scatterplot created by multi-dimensional scaling can help reveal the clusters of ties and detect abnormal ties, while other visualizations allow users to explore the clusters of ties interactively from different perspectives. A case study has been conducted to demonstrate the effectiveness of our approach.

This book is a compilation of selected papers from the 14th International Symposium on Computer Science in Sport (IACSS 2023), held on September 27-30, 2023 in Hangzhou, China. The work focuses on the application of computer science and technology in the field of sports (such as intelligent data collection, data mining, visual analysis of game data, virtual reality, machine learning, computer vision, match prediction models and performance analysis). The contents make valuable contributions to academic researchers, college students, coaches and athletes, and sports management personnel (such as managers of sports associations, training bases, and professional clubs). Additionally, readers will encounter new ideas for realizing a more efficient and convenient training and exercise system.

Site-specifically patterning of biomolecules, such as DNA, peptides, proteins and virus at the submicron or nanometer resolution has been of increasing interest these years, from both scientific and technological perspectives. Such patterns with biological features and activity hold enormous promise for biological and medical applications, including high-sensitive diagnostics, genomics, proteomics, integrated biomaterials and fundamental studies of molecular and cell biology. With the development of nanofabrication technology, the past decade has witnessed the advent of a variety of promising strategies for immobilizing biomolecules. Numerous strategies with properties of well-defined feature size, high throughput, high speed, heterogeneous array, low cost and high-reserved bioactivity have been pursued. These include top-down lithographic-assisted techniques, such as nanocontact printing, nanoimprinting, dip-pen nanolithography, nanografting and electron beam lithography; and self assembly based bottom-up techniques, such as DNA-templated chemistry, colloidal lithography and block-copolymer lithography. In this chapter, research status and recent advances of the biomolecular nanopatterning are reviewed. Advantages and limitations of current approaches, as well as future opportunities and challenges are discussed.

The technique of rolling circle amplification (RCA) emerged recently as a robust tool to generate the regularly patterned DNA structures in addition to its long-recognized ability of amplifying biological signals. Here, we demonstrate the strategy of RCA-driven generation of nano-scaled cocoon-like DNA particles, designated as DNA nanoclews, for loading and delivering a small molecule anticancer drug. The characteristic biodegradability of DNA was harnessed to control the timing and location of drug release by utilizing a polymeric nanogel encapsulating DNA-degrading enzyme DNase I. This smart drug delivery system based on RCA amplicons could get internalized into targeted cells and release the cargo using the endosomal acidity as a trigger.

In this chapter, we describe the preparation of glucose-responsive vesicles (GRVs) and the fabrication of GRV-loaded microneedle-array patches for insulin delivery. The GRVs were formed of hypoxia-sensitive hyaluronic acid (HS-HA), the synthesis of which is presented in detail. We also describe the procedure to evaluate the in vivo efficacy of this smart patch in a mouse model of chemically induced type 1 diabetes through transcutaneous administration.

Gold nanomaterials with light-responsive properties can be exploited as light-triggered delivery vehicles to enhance the therapeutic efficacy of anticancer drugs. Additionally, different wavelengths of light can be utilized to achieve the combined effects of light-triggered release of therapeutics and light-induced localized heating, which results in improved anticancer efficacy. Herein, we describe methods to develop gold nanorod (AuNR) complexes that provide drug delivery or photothermal therapy when activated by ultraviolet (UV) or near-infrared (NIR) wavelengths of light, respectively. The surface functionalization of AuNRs with three key components is presented. The first component, cyclodextrin, serves to encapsulate drugs of interest. The second component, dextran-phenyl-azo-benzoic acid (DexAzo), serves as a capping agent that undergoes a conformational change upon UV light activation to expose the drugs for release. The third component is a folic acid-based targeting ligand that provides efficient delivery of the AuNR complexes to cancer cells. The dual wavelength activation of these drug-loaded AuNR complexes, which enables one to achieve highly efficient anticancer therapy through the combined effects of UV-triggered drug release and NIR-induced hyperthermia, is also described.

Diabetes mellitus (DM), also known as diabetes, is a type of common metabolic disease in which glucose is build up in the blood, caused either by pancreas's failure to produce insulin (Type 1 DM) or by insulin resistance from the body tissue (Type2 DM) (Kasuga, 2006). Diabetes currently affects an estimate 387 million people, which is equal to 8.3% of the adult population worldwide. It is also a rapid growing problem, with an expectation to rise to 592 million by 2035 (Whiting, Guariguata et al.,2011). Diabetes may lead to serious complications including cardiovascular disease, retinopathy, chronic kidney disease, and even cancer (Tai et al., 2014). Moreover, diabetes is a chronic disease and currently there is no cure with exception only in the early stage of Type 2 DM. Despite this, the diabetes-associated complications can be minimized through the tight control of blood glucose level (BGL), which requires frequent blood glucose monitoring and careful administration of insulin. Therefore, it is urgent to develop the sensitive glucose biosensor and advanced insulin delivery systems.

Identifying the appropriate parameters of a turbulence model for a class of flow usually requires extensive experimentation and numerical simulations. Therefore even a modest improvement of the turbulence model can significantly reduce the overall cost of a three-dimensional, time-dependent simulation. In this paper we demonstrate a novel method to find the optimal parameters in the Reynolds-averaged Navier–Stokes (RANS) turbulence model using high-fidelity direct numerical simulation (DNS) data. A physics informed neural network (PINN) that is embedded with the turbulent transport equations is studied, physical loss functions are proposed to explicitly impose information of the transport equations to neural networks. This approach solves an inverse problem by treating the five parameters in turbulence model as random variables, with the turbulent kinetic energy and dissipation rate as known quantities from DNS simulation. The objective is to optimize the five parameters in turbulence closures using the PINN leveraging limited data available from costly high-fidelity DNS data. We validated this method on two test cases of flow over bump. The recommended values were found to be
= 1.302,
= 1.862,
= 0.09,
= 0.75,
= 0.273; the mean absolute error of the velocity profile between RANS and DNS decreased by 22% when using these neural network inferred parameters.

For film cooling in high pressure turbines, it is vital to predict the temperature distribution on the blade surface downstream of the cooling hole. This temperature distribution depends on the interaction between the hot mainstream and the coolant jet. Deep learning techniques have been widely applied in predicting physical problems such as complex fluids dynamics. A theoretic model based on Deconvolutional Neural Network (Deconv NN) was developed to model the non-linear and high-dimensional mapping between coolant jet parameters and the surface temperature distribution. Computational Fluid Dynamics (CFD) was utilized to provide data for the training models. The input of the model includes blowing ratio, density ratio, hole inclination angle and hole diameters etc. Comparison against different methods and data set size for accuracy is conducted and the result shows that the Deconv NN is capable of predicting film cooling effectiveness on the surface in validation group with quoted error (QE) less than 0.62%. With rigorous testing and validation, it is found that the predicted results are in good agreement with results from CFD. At the end, the Sparrow Search Algorithm (SSA) is applied to optimize coolant jet parameters using the validated neural networks. The results of the optimization show that the film cooling effectiveness has been successfully improved with QE 7.35% when compared with the reference case.For film cooling in high pressure turbines, it is vital to predict the temperature distribution on the blade surface downstream of the cooling hole. This temperature distribution depends on the interaction between the hot mainstream and the coolant jet. Deep learning techniques have been widely applied in predicting physical problems such as complex fluids dynamics. A theoretic model based on Deconvolutional Neural Network (Deconv NN) was developed to model the non-linear and high-dimensional mapping between coolant jet parameters and the surface temperature distribution. Computational Fluid Dynamics (CFD) was utilized to provide data for the training models. The input of the model includes blowing ratio, density ratio, hole inclination angle and hole diameters etc. Comparison against different methods and data set size for accuracy is conducted and the result shows that the Deconv NN is capable of predicting film cooling effectiveness on the surface in validation group with quoted error (QE) less than 0.62%. With rigorous testing and validation, it is found that the predicted results are in good agreement with results from CFD. At the end, the Sparrow Search Algorithm (SSA) is applied to optimize coolant jet parameters using the validated neural networks. The results of the optimization show that the film cooling effectiveness has been successfully improved with QE 7.35% when compared with the reference case.