Through-thickness permeability study of orthogonal and angle-interlock woven fabrics

Three-dimensional (3D) woven textiles, including orthogonal and angle-interlock woven fabrics, exhibit high inter-laminar strength in addition to good in-plane mechanical properties and are particularly suitable for lightweight structural applications. Resin transfer moulding (RTM) is a cost-effective manufacturing process for composites with 3D-woven reinforcement. With increasing preform thickness, the influence of through-thickness permeability on RTM processing of composites becomes increasingly significant. This study proposes an analytical model for prediction of the through-thickness permeability, based on Poiseuille’s law for hydraulic ducts approximating realistic flow channel geometries in woven fabrics. The model is applied to four 3D-woven fabrics and three 2D-woven fabrics. The geometrical parameters of the fabrics were characterized by employing optical microscopy. For validation, the through-thickness permeability was determined experimentally. The equivalent permeability of inter-yarn gaps was found to account for approximately 90 % of the through-thickness permeability for the analysed fabrics. The analytical predictions agree well with the experimental data of the seven fabrics

Through-thickness permeability modelling of woven fabric under out-of-plane deformation

When a woven fabric is subject to a normal uniform loading, its properties such as tightness and through-thickness permeability are both altered, which relates to the fabric out-of-plane deformation (OPD) and dynamic permeability (DP). In this article, fabric OPD is analytically modelled through an energy minimisation method, and corresponding fabric DP is established as the function of loading and fabric-deformed structure. The total model shows the permeability a decrease for tight fabric and an increase for loose fabric when the uniform loading increases. This is verified experimentally by fabric OPD, static and dynamic permeabilities. Experimental tests for both permeabilities showed good agreement with the corresponding predictions, indicating the fact that tight fabric becomes denser and loose fabric gets more porous during OPD. A sensitivity study showed that an increase of fabric Young's modulus or a decrease of fabric test radius both lead to an increase of DP for tight fabric and opposite for loose fabric. The critical fabric porosity and thickness were found for inflexion of fabric DP trend during the OPD, which contributes to the optimum design of interlacing structure applied to protective textiles and composites

Pengaruh Penerapan Strategi Concept Mapping terhadap Hasil Belajar Siswa di Sekolah Dasar

This study aimed to analyze the influence of concept mapping strategy towards the learning result students in social science study on the V B grade of SD Negeri 17 Pontianak Kota. This study used an experimental method with Pre-Experimental design form using One-Group Pretest-Posttest Design. The population in this research were 68 students. The samples in this research was V B as a research class. Based on the t-test, the calculation result obtained t test (7,29) > t table (1,699) with significance level α = 5% means a significant influence using concept mapping strategy. The value of effect size (ES) is 0.39 with moderate category. It means that concept mapping strategy give a moderate effect to the learning result students in social science study on the V B grade of SD Negeri 17 Pontianak Kota

GRID: Scene-Graph-based Instruction-driven Robotic Task Planning

Recent works have shown that Large Language Models (LLMs) can promote grounding instructions to robotic task planning. Despite the progress, most existing works focused on utilizing raw images to help LLMs understand environmental information, which not only limits the observation scope but also typically requires massive multimodal data collection and large-scale models. In this paper, we propose a novel approach called Graph-based Robotic Instruction Decomposer (GRID), leverages scene graph instead of image to perceive global scene information and continuously plans subtask in each stage for a given instruction. Our method encodes object attributes and relationships in graphs through an LLM and Graph Attention Networks, integrating instruction features to predict subtasks consisting of pre-defined robot actions and target objects in the scene graph. This strategy enables robots to acquire semantic knowledge widely observed in the environment from the scene graph. To train and evaluate GRID, we build a dataset construction pipeline to generate synthetic datasets in graph-based robotic task planning. Experiments have shown that our method outperforms GPT-4 by over 25.4% in subtask accuracy and 43.6% in task accuracy. Experiments conducted on datasets of unseen scenes and scenes with different numbers of objects showed that the task accuracy of GRID declined by at most 3.8%, which demonstrates its good cross-scene generalization ability. We validate our method in both physical simulation and the real world

Inhibition of Notch1 reverses EMT and chemoresistance to cisplatin via direct downregulation of MCAM in triple-negative breast cancer cells

Resistance to chemotherapy continues to be a critical issue in the clinical therapy of triple-negative breast cancer (TNBC). Epithelial-mesenchymal transition (EMT) is thought to contribute to chemoresistance in several cancer types, including breast cancer. Identification of the key signaling pathway that regulates the EMT program and contributes to chemoresistance in TNBC will provide a novel strategy to overcome chemoresistance in this subtype of cancer. Herein, we demonstrate that Notch1 positively associates with melanoma cell adhesion molecule (MCAM), a unique EMT activator, in TNBC tissue samples both at mRNA and protein levels. High expression of Notch1 and MCAM both predicts a poor survival in basal-like/TNBC patients, particularly in those treated with chemotherapy. The expression of Notch1 and MCAM in MDA-MB-231 cells gradually increases in a time-dependent manner when exposing to low dose cisplatin. Moreover, the expressions of Notch1 and MCAM in cisplatin-resistant MDA-MB-231 cells are significantly higher than wild-type counterparts. Notch1 promotes EMT and chemoresistance, as well as invasion and proliferation of TNBC cells via direct activating MCAM promoter. Inhibition of Notch1 significantly downregulates MCAM expression, resulting in the reversion of EMT and chemoresistance to cisplatin in TNBC cells. Our study reveals the regulatory mechanism of the Notch1 pathway and MCAM in TNBC and suggesting that targeting the Notch1/MCAM axis, in conjunction with conventional chemotherapies, might be a potential avenue to enhance the therapeutic efficacy for patients with TNBC

One-Time Universal Hashing Quantum Digital Signatures without Perfect Keys

Quantum digital signatures (QDS), generating correlated bit strings among three remote parties for signatures through quantum law, can guarantee non-repudiation, authenticity, and integrity of messages. Recently, one-time universal hashing QDS framework, exploiting the quantum asymmetric encryption and universal hash functions, has been proposed to significantly improve the signature rate and ensure unconditional security by directly signing the hash value of long messages. However, similar to quantum key distribution, this framework utilizes keys with perfect secrecy by performing privacy amplification that introduces cumbersome matrix operations, thereby consuming large computational resources, causing delays and increasing failure probability. Here, we prove that, different from private communication, imperfect quantum keys with limited information leakage can be used for digital signatures and authentication without compromising the security while having eight orders of magnitude improvement on signature rate for signing a megabit message compared with conventional single-bit schemes. This study significantly reduces the delay for data postprocessing and is compatible with any quantum key generation protocols. In our simulation, taking two-photon twin-field key generation protocol as an example, QDS can be practically implemented over a fiber distance of 650 km between the signer and receiver. For the first time, this study offers a cryptographic application of quantum keys with imperfect secrecy and paves a way for the practical and agile implementation of digital signatures in a future quantum network.Comment: Comments are welcome