The Modeling of Structural-Acoustic Interaction Using Coupled FE/BE Method and Control of Interior Acoustic Pressure Using Piezoelectric Actuators

A coupled finite element (FE) and boundary element (BE) approach is presented to model full coupled structural/acoustic/piezoelectric systems. The dual reciprocity boundary element method is used so that the natural frequencies and mode shapes of the coupled system can be obtained, and to extend this approach to time dependent problems. The boundary element method is applied to interior acoustic domains, and the results are very accurate when compared with limited exact solutions. Structural--acoustic problems are then analyzed with the coupled finite element/boundary element method, where the finite element method models the structural domain and the boundary element method models the acoustic domain. Results for a system consisting of an isotropic panel and a cubic cavity are in good agreement with exact solutions and experiment data. The response of a composite panel backed cavity is then obtained. The results show that the mass and stiffness of piezoelectric layers have to be considered. The coupled finite element and boundary element equations are transformed into modal coordinates, which is more convenient for transient excitation. Several transient problems are solved based on this formulation. Two control designs, a linear quadratic regulator (LQR) and a feedforward controller, are applied to reduce the acoustic pressure inside the cavity based on the equations in modal coordinates. The results indicate that both controllers can reduce the interior acoustic pressure and the plate deflection

Game-Theoretic Analysis for a Supply Chain With Distributional and Peer-Induced Fairness Concerned Retailers

In this paper, we consider a supply chain system with one supplier and two homogeneous retailers to investigate the influence of distributional and peer-induced fairness concerns on supply chain. The Nash bargaining solution is used as distributional fairness reference and the first retailer’s monetary payoff is used as the peer-induced fairness reference. We first analyze the decision-making process of the fairness concerned retailers under a given wholesale price and make a comparison with the fairness-neutral counterparts, then we derive the supplier’s optimal wholesale price and the retailers’ corresponding optimal order quantity in equilibrium. The results show that the second retailer orders less product and receives a higher wholesale price than the first retailer. The peer-induced fairness concerned retailer is in a worse position than the distributional fairness concerned retailer

Co-supervised learning paradigm with conditional generative adversarial networks for sample-efficient classification

Classification using supervised learning requires annotating a large amount of classes-balanced data for model training and testing. This has practically limited the scope of applications with supervised learning, in particular deep learning. To address the issues associated with limited and imbalanced data, this paper introduces a sample-efficient co-supervised learning paradigm (SEC-CGAN), in which a conditional generative adversarial network (CGAN) is trained alongside the classifier and supplements semantics-conditioned, confidence-aware synthesized examples to the annotated data during the training process. In this setting, the CGAN not only serves as a co-supervisor but also provides complementary quality examples to aid the classifier training in an end-to-end fashion. Experiments demonstrate that the proposed SEC-CGAN outperforms the external classifier GAN (EC-GAN) and a baseline ResNet-18 classifier. For the comparison, all classifiers in above methods adopt the ResNet-18 architecture as the backbone. Particularly, for the Street View House Numbers dataset, using the 5% of training data, a test accuracy of 90.26% is achieved by SEC-CGAN as opposed to 88.59% by EC-GAN and 87.17% by the baseline classifier; for the highway image dataset, using the 10% of training data, a test accuracy of 98.27% is achieved by SEC-CGAN, compared to 97.84% by EC-GAN and 95.52% by the baseline classifier.Comment: 14 pages, 5 figure

Semantic Communication for Cooperative Perception based on Importance Map

Cooperative perception, which has a broader perception field than single-vehicle perception, has played an increasingly important role in autonomous driving to conduct 3D object detection. Through vehicle-to-vehicle (V2V) communication technology, various connected automated vehicles (CAVs) can share their sensory information (LiDAR point clouds) for cooperative perception. We employ an importance map to extract significant semantic information and propose a novel cooperative perception semantic communication scheme with intermediate fusion. Meanwhile, our proposed architecture can be extended to the challenging time-varying multipath fading channel. To alleviate the distortion caused by the time-varying multipath fading, we adopt explicit orthogonal frequency-division multiplexing (OFDM) blocks combined with channel estimation and channel equalization. Simulation results demonstrate that our proposed model outperforms the traditional separate source-channel coding over various channel models. Moreover, a robustness study indicates that only part of semantic information is key to cooperative perception. Although our proposed model has only been trained over one specific channel, it has the ability to learn robust coded representations of semantic information that remain resilient to various channel models, demonstrating its generality and robustness.Comment: 13 pages,22 figures;journal;submitted for possible publicatio

Black-box Backdoor Defense via Zero-shot Image Purification

Backdoor attacks inject poisoned samples into the training data, resulting in the misclassification of the poisoned input during a model's deployment. Defending against such attacks is challenging, especially for real-world black-box models where only query access is permitted. In this paper, we propose a novel defense framework against backdoor attacks through Zero-shot Image Purification (ZIP). Our framework can be applied to poisoned models without requiring internal information about the model or any prior knowledge of the clean/poisoned samples. Our defense framework involves two steps. First, we apply a linear transformation (e.g., blurring) on the poisoned image to destroy the backdoor pattern. Then, we use a pre-trained diffusion model to recover the missing semantic information removed by the transformation. In particular, we design a new reverse process by using the transformed image to guide the generation of high-fidelity purified images, which works in zero-shot settings. We evaluate our ZIP framework on multiple datasets with different types of attacks. Experimental results demonstrate the superiority of our ZIP framework compared to state-of-the-art backdoor defense baselines. We believe that our results will provide valuable insights for future defense methods for black-box models. Our code is available at https://github.com/sycny/ZIP.Comment: Accepted by NeurIPS 202

MedEdit: Model Editing for Medical Question Answering with External Knowledge Bases

Large Language Models (LLMs), although powerful in general domains, often perform poorly on domain-specific tasks like medical question answering (QA). Moreover, they tend to function as "black-boxes," making it challenging to modify their behavior. Addressing this, our study delves into model editing utilizing in-context learning, aiming to improve LLM responses without the need for fine-tuning or retraining. Specifically, we propose a comprehensive retrieval strategy to extract medical facts from an external knowledge base, and then we incorporate them into the query prompt for the LLM. Focusing on medical QA using the MedQA-SMILE dataset, we evaluate the impact of different retrieval models and the number of facts provided to the LLM. Notably, our edited Vicuna model exhibited an accuracy improvement from 44.46% to 48.54%. This work underscores the potential of model editing to enhance LLM performance, offering a practical approach to mitigate the challenges of black-box LLMs.Comment: 6 page

Progressive Failure And Life Prediction of Ceramic and Textile Composites

An engineering approach to predict the fatigue life and progressive failure of multilayered composite and textile laminates is presented. Analytical models which account for matrix cracking, statistical fiber failures and nonlinear stress-strain behavior have been developed for both composites and textiles. The analysis method is based on a combined micromechanics, fracture mechanics and failure statistics analysis. Experimentally derived empirical coefficients are used to account for the interface of fiber and matrix, fiber strength, and fiber-matrix stiffness reductions. Similar approaches were applied to textiles using Repeating Unit Cells. In composite fatigue analysis, Walker's equation is applied for matrix fatigue cracking and Heywood's formulation is used for fiber strength fatigue degradation. The analysis has been compared with experiment with good agreement. Comparisons were made with Graphite-Epoxy, C/SiC and Nicalon/CAS composite materials. For textile materials, comparisons were made with triaxial braided and plain weave materials under biaxial or uniaxial tension. Fatigue predictions were compared with test data obtained from plain weave C/SiC materials tested at AS&M. Computer codes were developed to perform the analysis. Composite Progressive Failure Analysis for Laminates is contained in the code CPFail. Micromechanics Analysis for Textile Composites is contained in the code MicroTex. Both codes were adapted to run as subroutines for the finite element code ABAQUS and CPFail-ABAQUS and MicroTex-ABAQUS. Graphic user interface (GUI) was developed to connect CPFail and MicroTex with ABAQUS

Comprehensive bioinformatics analysis reveals the role of cuproptosis-related gene Ube2d3 in myocardial infarction

BackgroundMyocardial infarction (MI) caused by severe coronary artery disease has high incidence and mortality rates, making its prevention and treatment a central and challenging aspect of clinical work for cardiovascular practitioners. Recently, researchers have turned their attention to a novel mechanism of cell death caused by Cu2+, cuproptosis.MethodsThis study integrated data from three MI-related bulk datasets downloaded from the Gene Expression Omnibus (GEO) database, and identified 16 differentially expressed genes (DEGs) related to cuproptosis by taking intersection of the 6378 DEGs obtained by differential analysis with 49 cuproptosis-related genes. Four hub genes, Dbt, Dlat, Ube2d1 and Ube2d3, were screened out through random forest analysis and Lasso analysis. In the disease group, Dbt, Dlat, and Ube2d1 showed low expression, while Ube2d3 exhibited high expression.ResultsFocusing on Ube2d3 for subsequent functional studies, we confirmed its high expression in the MI group through qRT-PCR and Western Blot detection after successful construction of a MI mouse model by left anterior descending (LAD) coronary artery ligation, and further clarified the correlation of cuproptosis with MI development by detecting the levels of cuproptosis-related proteins. Moreover, through in vitro experiments, Ube2d3 was confirmed to be highly expressed in oxygen-glucose deprivation (OGD)-treated cardiomyocytes AC16. In order to further clarify the role of Ube2d3, we knocked down Ube2d3 expression in OGD-treated AC16 cells, and confirmed Ube2d3’s promoting role in the hypoxia damage of AC16 cells by inducing cuproptosis, as evidenced by the detection of MTT, TUNEL, LDH release and cuproptosis-related proteins.ConclusionIn summary, our findings indicate that Ube2d3 regulates cuproptosis to affect the progression of MI