Layout Optimization of Two Autonomous Underwater Vehicles for Drag Reduction with a Combined CFD and Neural Network Method

This paper presents an optimization method for the design of the layout of an autonomous underwater vehicles (AUV) fleet to minimize the drag force. The layout of the AUV fleet is defined by two nondimensional parameters. Firstly, three-dimensional computational fluid dynamics (CFD) simulations are performed on the fleets with different layout parameters and detailed information on the hydrodynamic forces and flow structures around the AUVs is obtained. Then, based on the CFD data, a back-propagation neural network (BPNN) method is used to describe the relationship between the layout parameters and the drag of the fleet. Finally, a genetic algorithm (GA) is chosen to obtain the optimal layout parameters which correspond to the minimum drag. The optimization results show that (1) the total drag of the AUV fleet can be reduced by 12% when the follower AUV is located directly behind the leader AUV and (2) the drag of the follower AUV can be reduced by 66% when it is by the side of the leader AUV

A novel nonzero functional method to extended dissipativity analysis for neural networks with Markovian jumps

This paper explored the topic of extended dissipativity analysis for Markovian jump neural networks (MJNNs) that were influenced by time-varying delays. A distinctive Lyapunov functional, distinguished by a non-zero delay-product types, was presented. This was achieved by combining a Wirtinger-based double integral inequality with a flexible matrix set. This novel methodology addressed the limitations of the slack matrices found in earlier research. As a result, a fresh condition for extended dissipativity in MJNNs was formulated, utilizing an exponential type reciprocally convex inequality in conjunction with the newly introduced nonzero delay-product types. A numerical example was included to demonstrate the effectiveness of the proposed methodology

Exploring Effective Mask Sampling Modeling for Neural Image Compression

Image compression aims to reduce the information redundancy in images. Most existing neural image compression methods rely on side information from hyperprior or context models to eliminate spatial redundancy, but rarely address the channel redundancy. Inspired by the mask sampling modeling in recent self-supervised learning methods for natural language processing and high-level vision, we propose a novel pretraining strategy for neural image compression. Specifically, Cube Mask Sampling Module (CMSM) is proposed to apply both spatial and channel mask sampling modeling to image compression in the pre-training stage. Moreover, to further reduce channel redundancy, we propose the Learnable Channel Mask Module (LCMM) and the Learnable Channel Completion Module (LCCM). Our plug-and-play CMSM, LCMM, LCCM modules can apply to both CNN-based and Transformer-based architectures, significantly reduce the computational cost, and improve the quality of images. Experiments on the public Kodak and Tecnick datasets demonstrate that our method achieves competitive performance with lower computational complexity compared to state-of-the-art image compression methods.Comment: 10 page

PEO-Store: Practical and Economical Oblivious Store with Peer-to-Peer Delegation

The growing popularity of cloud storage has brought attention to critical need for preventing information leakage from cloud access patterns. To this end, recent efforts have extended Oblivious RAM (ORAM) to the cloud environment in the form of Oblivious Store. However, its impracticality due to the use of probability encryption with fake accesses to obfuscate the access pattern, as well as the security requirements of conventional obliviousness designs, which hinder cloud interests in improving storage utilization by removing redundant data among cross-users, limit its effectiveness. Thus, we propose a practical Oblivious Store, PEO-Store, which integrates the obliviousness property into the cloud while removing redundancy without compromising security. Unlike conventional schemes, PEO-Store randomly selects a delegate for each client to communicate with the cloud, breaking the mapping link between a valid access pattern sequence and a specific client. Each client encrypts their data and shares it with selected delegates, who act as intermediaries with the cloud provider. This design leverages non-interactive zero-knowledge-based redundancy detection, discrete logarithm problem-based key sharing, and secure time-based delivery proof to protect access pattern privacy and accurately identify and remove redundancy in the cloud. The theoretical proof demonstrates that the probability of identifying the valid access pattern with a specific user is negligible in our design. Experimental results show that PEO-Store outperforms state-of-the-art methods, achieving an average throughput of up to 3 times faster and saving 74% of storage space

SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs

Anomaly detection aims to distinguish abnormal instances that deviate significantly from the majority of benign ones. As instances that appear in the real world are naturally connected and can be represented with graphs, graph neural networks become increasingly popular in tackling the anomaly detection problem. Despite the promising results, research on anomaly detection has almost exclusively focused on static graphs while the mining of anomalous patterns from dynamic graphs is rarely studied but has significant application value. In addition, anomaly detection is typically tackled from semi-supervised perspectives due to the lack of sufficient labeled data. However, most proposed methods are limited to merely exploiting labeled data, leaving a large number of unlabeled samples unexplored. In this work, we present semi-supervised anomaly detection (SAD), an end-to-end framework for anomaly detection on dynamic graphs. By a combination of a time-equipped memory bank and a pseudo-label contrastive learning module, SAD is able to fully exploit the potential of large unlabeled samples and uncover underlying anomalies on evolving graph streams. Extensive experiments on four real-world datasets demonstrate that SAD efficiently discovers anomalies from dynamic graphs and outperforms existing advanced methods even when provided with only little labeled data.Comment: Accepted to IJCAI'23. Code will be available at https://github.com/D10Andy/SA

Numerical evaluation of the influence of porosity on bending properties of 2D carbon/carbon composites

Numerical simulation with progressive damage criterion is implemented to investigate the effect of porosity on the bending properties of 2D cross-ply carbon/carbon (C/C) composites. The mechanical properties of Pyrocarbon matrix regarding the change of porosity are calculated by using Mori-Tanaka approach. Combining with the stiffness degradation scheme, the ultimate bending strengths are calculated in Abaqus though a user-defined subroutine (USDFLD). Delamination is modelled by inserting cohesive elements between two adjacent plies. A good agreement is obtained when the FEM results are compared to three-point bending experiments. The FEM results show that the bending strength decreases greatly with the increase of porosity. When the porosity reaches up to 18%, the bending strength is decreased by 57%. The major fracture behaviors are interlamination delamination and continuous crack damage in 90° plies. With the increase of porosity, more severe interlamination delamination will be slightly aggravated. In addition, the increase of porosity will also accelerate the damage in 90° plies

Field-assisted sintering of low-temperature thermoelectric material BiTeSe - sintering process and part characterisation

Field-Assisted Sintering Technology (FAST), an advanced consolidation technique, was employed to synthesise low-temperature thermoelectric n-type Bi2Te2.7Se0.3 for energy harvesting applications. A systematic investigation of sintering parameters, including pressure, temperature, holding time, and heating rates, was conducted to optimise the material’s properties. Post-sintering characterisation encompassed measurements of relative density, thermal conductivity, electrical resistivity, and Seebeck coefficient. Factor analysis revealed the hierarchical influence of sintering variables, with temperature emerging as the most critical parameter, followed by pressure and holding time. The study successfully identified optimal FAST sintering conditions for Bi2Te2.7Se0.3, resulting in enhanced thermoelectric properties. This research demonstrates the efficacy of FAST in producing high-quality, low-temperature thermoelectric materials and provides valuable insights into the relationship between processing parameters and material performance

Titanium Nitride Film on Sapphire Substrate with Low Dielectric Loss for Superconducting Qubits

Dielectric loss is one of the major decoherence sources of superconducting qubits. Contemporary high-coherence superconducting qubits are formed by material systems mostly consisting of superconducting films on substrate with low dielectric loss, where the loss mainly originates from the surfaces and interfaces. Among the multiple candidates for material systems, a combination of titanium nitride (TiN) film and sapphire substrate has good potential because of its chemical stability against oxidization, and high quality at interfaces. In this work, we report a TiN film deposited onto sapphire substrate achieving low dielectric loss at the material interface. Through the systematic characterizations of a series of transmon qubits fabricated with identical batches of TiN base layers, but different geometries of qubit shunting capacitors with various participation ratios of the material interface, we quantitatively extract the loss tangent value at the substrate-metal interface smaller than $8.9 \times 10^{-4}$ in 1-nm disordered layer. By optimizing the interface participation ratio of the transmon qubit, we reproducibly achieve qubit lifetimes of up to 300 $\mu$s and quality factors approaching 8 million. We demonstrate that TiN film on sapphire substrate is an ideal material system for high-coherence superconducting qubits. Our analyses further suggest that the interface dielectric loss around the Josephson junction part of the circuit could be the dominant limitation of lifetimes for state-of-the-art transmon qubits

A biological product of Bacillus amyloliquefaciens QST713 strain for promoting banana plant growth and modifying rhizosphere soil microbial diversity and community composition

IntroductionBananas are not only an important food crop for developing countries but also a major trading fruit for tropical and semitropical regions, maintaining a huge trade volume. Fusarium wilt of banana (FWB) caused by Fusarium oxysporum f. sp. cubense is becoming a serious challenge to the banana industry globally. Biological control has the potential to offer both effective and sustainable measures for this soil-borne disease.MethodsIn order to explore the biocontrol effects of the biological agent Bacillus amyloliquefaciens QST713 strain on banana plants, two cultivars, Brazilian and Yunjiao No. 1, with varied resistance to FWB, were used in greenhouse pot experiments.ResultsResults showed that the plant height and pseudostem diameter of banana-susceptible cultivar Brazilian increased by 11.68% and 11.94%, respectively, after QST713 application, while the plant height and pseudostem diameter of resistant cultivar Yunjiao No. 1 increased by 14.87% and 12.51%, respectively. The fresh weight of the two cultivars increased by 20.66% and 36.68%, respectively, indicating that this biological agent has potential effects on plant growth. Analysis of the rhizosphere soil microbial communities of two different cultivars of banana plants showed that TR4 infection and B. amyloliquefaciens QST713 strain application significantly affected the bacterial and fungal diversity of Yunjiao No. 1, but not in the cultivar Brazilian. In addition, TR4 infection and QST713 application changed the bacterial community composition of both banana cultivars, and the fungal community composition of Yunjiao No. 1 also changed significantly. Relevance analysis indicated that the relative richness of Bacillus and Pseudomonas in the rhizosphere of both cultivars increased significantly after QST713 application, which had a good positive correlation with plant height, pseudostem girth, aboveground fresh weight, leaf length, and leaf width.DiscussionTherefore, the outcome of this study suggests that the biological agent QST713 strain has potential application in banana production for promoting plant growth and modification of soil microbial communities, particularly in the TR4-infected field