Modeling of Electrical Conductivity and Piezoresistivity of Carbon Nanotube Based Polymer Nanocomposites

Superior electrical, thermal, and mechanical properties of carbon nanotubes (CNTs) have made them effective filler for multifunctional polymer nanocomposites (PNCs). In this thesis work, an improved model has been developed to describe the CNT networks inside polymer matrix and thereby evaluated the PNCs c and . The new model accounts for the electrical conductance contributed by the continued CNT network across the boundary of adjacent representative volume elements. It more realistically represents the interconnectivity among CNTs and enhances the evaluation of the structure-to-property relationship of PNCs . Furthermore, comprehensive investigations on the piezoresistive behaviour of PNCs have been conducted using developed modeling framework. Quantitative analyses have revealed that piezoresistivity of PNCs is predominantly governed by the three mechanisms related to the strain-induced morphological evolution of the CNT network embedded in the polymer matrix

Hypergraph Node Representation Learning with One-Stage Message Passing

Hypergraphs as an expressive and general structure have attracted considerable attention from various research domains. Most existing hypergraph node representation learning techniques are based on graph neural networks, and thus adopt the two-stage message passing paradigm (i.e. node -> hyperedge -> node). This paradigm only focuses on local information propagation and does not effectively take into account global information, resulting in less optimal representations. Our theoretical analysis of representative two-stage message passing methods shows that, mathematically, they model different ways of local message passing through hyperedges, and can be unified into one-stage message passing (i.e. node -> node). However, they still only model local information. Motivated by this theoretical analysis, we propose a novel one-stage message passing paradigm to model both global and local information propagation for hypergraphs. We integrate this paradigm into HGraphormer, a Transformer-based framework for hypergraph node representation learning. HGraphormer injects the hypergraph structure information (local information) into Transformers (global information) by combining the attention matrix and hypergraph Laplacian. Extensive experiments demonstrate that HGraphormer outperforms recent hypergraph learning methods on five representative benchmark datasets on the semi-supervised hypernode classification task, setting new state-of-the-art performance, with accuracy improvements between 2.52% and 6.70%. Our code and datasets are available.Comment: 11 page

Generating Faithful Text From a Knowledge Graph with Noisy Reference Text

Knowledge Graph (KG)-to-Text generation aims at generating fluent natural-language text that accurately represents the information of a given knowledge graph. While significant progress has been made in this task by exploiting the power of pre-trained language models (PLMs) with appropriate graph structure-aware modules, existing models still fall short of generating faithful text, especially when the ground-truth natural-language text contains additional information that is not present in the graph. In this paper, we develop a KG-to-text generation model that can generate faithful natural-language text from a given graph, in the presence of noisy reference text. Our framework incorporates two core ideas: Firstly, we utilize contrastive learning to enhance the model's ability to differentiate between faithful and hallucinated information in the text, thereby encouraging the decoder to generate text that aligns with the input graph. Secondly, we empower the decoder to control the level of hallucination in the generated text by employing a controllable text generation technique. We evaluate our model's performance through the standard quantitative metrics as well as a ChatGPT-based quantitative and qualitative analysis. Our evaluation demonstrates the superior performance of our model over state-of-the-art KG-to-text models on faithfulness

Universal Trading for Order Execution with Oracle Policy Distillation

As a fundamental problem in algorithmic trading, order execution aims at fulfilling a specific trading order, either liquidation or acquirement, for a given instrument. Towards effective execution strategy, recent years have witnessed the shift from the analytical view with model-based market assumptions to model-free perspective, i.e., reinforcement learning, due to its nature of sequential decision optimization. However, the noisy and yet imperfect market information that can be leveraged by the policy has made it quite challenging to build up sample efficient reinforcement learning methods to achieve effective order execution. In this paper, we propose a novel universal trading policy optimization framework to bridge the gap between the noisy yet imperfect market states and the optimal action sequences for order execution. Particularly, this framework leverages a policy distillation method that can better guide the learning of the common policy towards practically optimal execution by an oracle teacher with perfect information to approximate the optimal trading strategy. The extensive experiments have shown significant improvements of our method over various strong baselines, with reasonable trading actions.Comment: Accepted in AAAI 2021, the code and the supplementary materials are in https://seqml.github.io/opd

Experimental and Theoretical Study of Sandwich Panels with Steel Facesheets and GFRP Core

This study presented a new form of composite sandwich panels, with steel plates as facesheets and bonded glass fiber-reinforced polymer (GFRP) pultruded hollow square tubes as core. In this novel panel, GFRP and steel were optimally combined to obtain high bending stiffness, strength, and good ductility. Four-point bending test was implemented to analyze the distribution of the stress, strain, mid-span deflection, and the ultimate failure mode. A section transformation method was used to evaluate the stress and the mid-span deflection of the sandwich panels. The theoretical values, experimental results, and FEM simulation values are compared and appeared to be in good agreement. The influence of thickness of steel facesheet on mid-span deflection and stress was simulated. The results showed that the mid-span deflection and stress decreased and the decent speed was getting smaller as the thickness of steel facesheet increases. A most effective thickness of steel facesheet was advised

Dipolar interactions in magnetic nanowires aggregates

We investigate the role of dipolar interactions on the magnetic properties of nanowires aggregates. Micromagnetic simulations show that dipolar interactions between wires are not detrimental to the high coercivity properties of magnetic nanowires composites even in very dense aggregates. This is confirmed by experimental magnetization measurements and Henkel plots which show that the dipolar interactions are small. Indeed, we show that misalignment of the nanowires in aggregates leads to a coercivity reduction of only 30%. Direct dipolar interactions between nanowires, even as close as 2 nm, have small effects (maximum coercivity reduction of ~15%) and are very sensitive to the detailed geometrical arrangement of wires. These results strenghten the potential of magnetic composite materials based on elongated single domain particles for the fabrication of permanent magnetic materials.Comment: 7 pages, 8 figures, submitted to Journal of Applied Physic

A Critical Role of Perinuclear Filamentous Actin in Spatial Repositioning and Mutually Exclusive Expression of Virulence Genes in Malaria Parasites

SummaryMany microbial pathogens, including the malaria parasite Plasmodium falciparum, vary surface protein expression to evade host immune responses. P. falciparium antigenic variation is linked to var gene family-encoded clonally variant surface protein expression. Mututally exclusive var gene expression is partially controlled by spatial positioning; silent genes are retained at distinct perinuclear sites and relocated to transcriptionally active locations for monoallelic expression. We show that var introns can control this process and that var intron addition relocalizes episomes from a random to a perinuclear position. This var intron-regulated nuclear tethering and repositioning is linked to an 18 bp nuclear protein-binding element that recruits an actin protein complex. Pharmacologically induced F-actin formation, which is restricted to the nuclear periphery, repositions intron-carrying episomes and var genes and disrupts mutually exclusive var gene expression. Thus, actin polymerization relocates var genes from a repressive to an active perinuclear compartment, which is crucial for P. falciparium phenotypic variation and pathogenesis

Tumor-associated macrophages mediate resistance of EGFR-TKIs in non-small cell lung cancer: mechanisms and prospects

Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) are the first-line standard treatment for advanced non-small cell lung cancer (NSCLC) with EGFR mutation. However, resistance to EGFR-TKIs is inevitable. Currently, most studies on the mechanism of EGFR-TKIs resistance mainly focus on the spontaneous resistance phenotype of NSCLC cells. Studies have shown that the tumor microenvironment (TME) also mediates EGFR-TKIs resistance in NSCLC. Tumor-associated macrophages (TAMs), one of the central immune cells in the TME of NSCLC, play an essential role in mediating EGFR-TKIs resistance. This study aims to comprehensively review the current mechanisms underlying TAM-mediated resistance to EGFR-TKIs and discuss the potential efficacy of combining EGFR-TKIs with targeted TAMs therapy. Combining EGFR-TKIs with TAMs targeting may improve the prognosis of NSCLC with EGFR mutation to some extent