Simulation Studies of Pulsed Voltage Effects on Cells

This dissertation research focuses on the new field of pulsed electric field interactions with biological cells. In particular, Intracellular Electromanipulation which has important biomedical applications, is probed. Among the various aspects studied, nanosecond, high-intensity pulse induced electroporation is one phenomena. It is simulated based on a coupled scheme involving the current continuity and Smoluchowski equations. A dynamic pore model can be achieved by including a dependence on the pore population density and a variable membrane tension. These changes make the pore formation energy E(r) self-adjusting and dynamic in response to pore formation. Additionally, molecular dynamics (MD) simulations are also discussed as a more accurate, though computationally intensive, alternative. Besides inducing pores in cells, external voltages could also be used, in principle, to modulate action potential generation in nerves. The electric-field induced poration could block action potential propagation. This aspect has been studied by modifying the traditional cable model for nerves, by accounting for the increased membrane conductance and the altered membrane capacitance. This conduction block in nerves due to an electroporation related local short-circuit would be similar in concept to stopping the propagation of an air-pressure wave down a leaky pipe. This study also focuses on threshold process in cellular apoptosis induced by nanosecond, high-intensity electric pulses. In particular, the pulse number dependent cell survival trends are quantified based on a biophysical model of the cellular apoptotic processes. Time-dependent evolution of the caspase concentrations and the various molecular species are simulated. The numerical evaluations provide qualitative predictions of pulse number cell survival, the relative assessment of extrinsic and intrinsic pathways, and rough predictions of the time duration over which irreversible activation at the molecular level could be initiated by the electric pulses. Time dependent kinetics of the caspases as well as the various molecular species within the apoptotic pathway, were simulated using the rate equation model originally proposed by Bagci et al. Finally, an asymmetric electroporation model is presented. Electric pulsing pore energy and mechanical pore energy are studied. This has relevance to the flow of ions in and out of cells

Tourists’ green behavior: Co-creation and emotional experience

Based on the theory of inseparability nature of service, service dominant logic, and SOR model, this study examined the interactive relationships among tourists’ co-creation of experience, self-esteem, satisfaction with travel experience, quality of life, and green behavior. Using data collected from 493 tourists in China, the results indicated that co-creation of experience directly influenced their self-esteem, satisfaction with travel experience, and green behavior. Besides, the findings found that emotional experience (satisfaction with travel experience, self-esteem, quality of life) partially mediated the relationships between co-creation experience and green behavior. Finally, co-creation affected tourists’ green behavior through the chain mediating role of self-esteem, satisfaction with travel experience, and quality of life. Theoretical and practical implications were discussed as well

Viscous effect on interaction between shock wave and cylindrical bubble: based on the discrete Boltzmann method

The viscous effects on the interaction between a shock wave and a two-dimensional cylindrical bubble are investigated based on the discrete Boltzmann method (DBM). Besides some interesting Hydrodynamic Non-Equilibrium (HNE) behaviors, some relevant Thermodynamic Non-Equilibrium (TNE) behaviors are carefully studied. It is found that the viscosity contributes little effect on the dynamic processes in the shock compression stage but significantly influences them in the post-shock stage. A bubble with a smaller viscosity coefficient displays a stouter jet structure, can be compressed more easily, and reaches its minimum characteristic scales slower. The viscosity accelerates the average motion of the bubble, reduces the vorticity strength (circulation), and restrains the material mixing between the ambient gas and the bubble. The viscous effects on different TNE quantities/perspectives show interesting differences. These differences indicate the complexity of TNE behaviors, which still requires further understanding. The viscous effects on entropy production are also investigated. It is found that the entropy production caused by the non-organized momentum flux (NOMF) is larger than that caused by the non-organized energy flux (NOEF). As the Prandtl number increases, the entropy production $S_{\rm{NOMF}}$ increases. But the $S_{\rm{NOEF}}$ first decreases and then approaches a saturation value

Identifying influential nodes in complex contagion mechanism

Identifying influential nodes in complex networks is one of the most important and challenging problems to help optimize the network structure, control the spread of the epidemic and accelerate the spread of information. In a complex network, the node with the strongest propagation capacity is known as the most influential node from the perspective of propagation. In recent years, identifying the key nodes in complex networks has received increasing attention. However, it is still a challenge to design a metric that has low computational complexity but can accurately identify important network nodes. Currently, many centrality metrics used to evaluate the influence capability of nodes cannot balance between high accuracy and low time complexity. Local centrality suffers from accuracy problems, while global metrics require higher time complexity, which is inefficient for large scale networks. In contrast, semi-local metrics are with higher accuracy and lower time cost. In this paper, we propose a new semi-local centrality measure for identifying influential nodes under complex contagion mechanisms. It uses the higher-order structure within the first and second-order neighborhoods of nodes to define the importance of nodes with near linear time complexity, which can be applied to large-scale networks. To verify the accuracy of the proposed metric, we simulated the disease propagation process in four real and two artificial networks using the SI model under complex propagation. The simulation results show that the proposed method can identify the nodes with the strongest propagation ability more effectively and accurately than other current node importance metrics

ThumbNet: One Thumbnail Image Contains All You Need for Recognition

Although deep convolutional neural networks (CNNs) have achieved great success in computer vision tasks, its real-world application is still impeded by its voracious demand of computational resources. Current works mostly seek to compress the network by reducing its parameters or parameter-incurred computation, neglecting the influence of the input image on the system complexity. Based on the fact that input images of a CNN contain substantial redundancy, in this paper, we propose a unified framework, dubbed as ThumbNet, to simultaneously accelerate and compress CNN models by enabling them to infer on one thumbnail image. We provide three effective strategies to train ThumbNet. In doing so, ThumbNet learns an inference network that performs equally well on small images as the original-input network on large images. With ThumbNet, not only do we obtain the thumbnail-input inference network that can drastically reduce computation and memory requirements, but also we obtain an image downscaler that can generate thumbnail images for generic classification tasks. Extensive experiments show the effectiveness of ThumbNet, and demonstrate that the thumbnail-input inference network learned by ThumbNet can adequately retain the accuracy of the original-input network even when the input images are downscaled 16 times

Matching Tabular Data to Knowledge Graph with Effective Core Column Set Discovery.

Matching tabular data to a knowledge graph (KG) is critical for understanding the semantic column types, column relationships, and entities of a table. Existing matching approaches rely heavily on core columns that represent primary subject entities on which other columns in the table depend. However, discovering these core columns before understanding the table’s semantics is challenging. Most prior works use heuristic rules, such as the leftmost column, to discover a single core column, while an insightful discovery of the core column set that accurately captures the dependencies between columns is often overlooked. To address these challenges, we introduce Dependency-aware Core Column Set Discovery (DaCo), an iterative method that uses a novel rough matching strategy to identify both inter-column dependencies and the core column set. Additionally, DaCo can be seamlessly integrated with pre-trained language models, as proposed in the optimization module. Unlike other methods, DaCo does not require labeled data or contextual information, making it suitable for real-world scenarios. In addition, it can identify multiple core columns within a table, which is common in real-world tables. We conduct experiments on six datasets, including five datasets with single core columns and one dataset with multiple core columns. Our experimental results show that DaCo outperforms existing core column set detection methods, further improving the effectiveness of table understanding tasks

Key Information Retrieval to Classify the Unstructured Data Content of Preferential Trade Agreements

With the rapid proliferation of textual data, predicting long texts has emerged as a significant challenge in the domain of natural language processing. Traditional text prediction methods encounter substantial difficulties when grappling with long texts, primarily due to the presence of redundant and irrelevant information, which impedes the model's capacity to capture pivotal insights from the text. To address this issue, we introduce a novel approach to long-text classification and prediction. Initially, we employ embedding techniques to condense the long texts, aiming to diminish the redundancy therein. Subsequently,the Bidirectional Encoder Representations from Transformers (BERT) embedding method is utilized for text classification training. Experimental outcomes indicate that our method realizes considerable performance enhancements in classifying long texts of Preferential Trade Agreements. Furthermore, the condensation of text through embedding methods not only augments prediction accuracy but also substantially reduces computational complexity. Overall, this paper presents a strategy for long-text prediction, offering a valuable reference for researchers and engineers in the natural language processing sphere.Comment: AI4TS Workshop@AAAI 2024 accepted publicatio