CFD-FEM simulation of water entry of aluminium flat stiffened plate structure considering the effects of hydroelasticity

In this paper, the slamming loads and structural response of an aluminium flat stiffened-plate structure during calm water entry considering the hydroelasticity effects are studied by a partitioned CFD-FEM two-way coupled method. The target structure is simplified as one segment of an idealized ship grillage structure, comprising flat plate and stiffeners. The typical numerical results are analyzed such as vertical displacement, velocity, acceleration, impact loads, and structural stress of the flexible flat bottom grillage structure considering the hydroelasticity effect and air cushion effect in different free fall height conditions. Drop test results of the same structure and other existing numerical simulation data by both coupled and uncoupled solutions in the literature are used for comparison with the present numerical simulation results. This study provides a practical means to simulate the slamming behaviour and structural response of ship structures, which is useful for predicting ship hull stiffened panel loads and related structural design

Vaccine Adjuvant Delivery Systems Constructed Using Biocompatible Nanoparticles Formed through Self-Assembly of Small Molecules

Subunit vaccines are playing a critical role in controlling numerous diseases and attracting more and more research interests due to their numerous advantages over conventional whole microbe-based vaccines. However, subunit vaccines are weak immunogens and thus have limited capacity in eliciting the humoral and cellular immunity against pathogens. Recently, nanoparticles (NPs) formed with certain small molecules through self-assembly have been employed as an effective carrier for subunit vaccines to play roles of adjuvant, delivery and stabilization of antigens, thus engendering a vaccine adjuvant-delivery system (VADS), which shows promises to overcome the hurdles in developing subunit vaccines. In particular, the small molecule-self-assembled NPs as a VADS can not only deliver vaccine ingredients to immune cells but also influence the immunoresponse toward a Th1 (type 1 T helper cell) and Th2 balanced pathway to establish both humoral and cellular immunity. This chapter describes the innovative VADSs based on the small molecule-self-assembled NPs, such as metal NPs (mNPs), emulsions, liposomes, and ISCOMs, which are elaborately designed for the development of subunit vaccines

Deep Joint Source-Channel Coding for Image Transmission With Visual Protection

Joint source-channel coding (JSCC) has achieved great success due to the introduction of deep learning (DL). Compared to traditional separate source-channel coding (SSCC) schemes, the advantages of DL-based JSCC (DJSCC) include high spectrum efficiency, high reconstruction quality, and relief of “cliff effect”. However, it is difficult to couple existing secure communication mechanisms (e.g., encryption-decryption mechanism) with DJSCC in contrast with traditional SSCC schemes, which hinders the practical usage of this emerging technology. To this end, our paper proposes a novel method called DL-based joint protection and source-channel coding (DJPSCC) for images that can successfully protect the visual content of the plain image without significantly sacrificing image reconstruction performance. The idea of the design is to use a neural network to conduct visual protection, which converts the plain image to a visually protected one with the consideration of its interaction with DJSCC. During the training stage, the proposed DJPSCC method learns: 1) deep neural networks for image protection and image deprotection, and 2) an effective DJSCC network for image transmission in the protected domain. Compared to existing source protection methods applied with DJSCC transmission, the DJPSCC method achieves much better reconstruction performance

Constituency Parsing using LLMs

Constituency parsing is a fundamental yet unsolved natural language processing task. In this paper, we explore the potential of recent large language models (LLMs) that have exhibited remarkable performance across various domains and tasks to tackle this task. We employ three linearization strategies to transform output trees into symbol sequences, such that LLMs can solve constituency parsing by generating linearized trees. We conduct experiments using a diverse range of LLMs, including ChatGPT, GPT-4, OPT, LLaMA, and Alpaca, comparing their performance against the state-of-the-art constituency parsers. Our experiments encompass zero-shot, few-shot, and full-training learning settings, and we evaluate the models on one in-domain and five out-of-domain test datasets. Our findings reveal insights into LLMs' performance, generalization abilities, and challenges in constituency parsing

Deep Joint Encryption and Source-Channel Coding: An Image Visual Protection Approach

Joint source and channel coding (JSCC) has achieved great success due to the introduction of deep learning. Compared with traditional separate source channel coding (SSCC) schemes, the advantages of DL based JSCC (DJSCC) include high spectrum efficiency, high reconstruction quality, and the relief of "cliff effect". However, it is difficult to couple encryption-decryption mechanisms with DJSCC in contrast with traditional SSCC schemes, which hinders the practical usage of the emerging technology. To this end, our paper proposes a novel method called DL based joint encryption and source-channel coding (DJESCC) for images that can successfully protect the visual information of the plain image without significantly sacrificing image reconstruction performance. The idea of the design is using a neural network to conduct image encryption, which converts the plain image to a visually protected one with the consideration of its interaction with DJSCC. During the training stage, the proposed DJESCC method learns: 1) deep neural networks for image encryption and image decryption, and 2) an effective DJSCC network for image transmission in encrypted domain. Compared with the perceptual image encryption methods with DJSCC transmission, the DJESCC method achieves much better reconstruction performance and is more robust to ciphertext-only attacks.Comment: 12 pages, 13 figure

Wireless Image Transmission Using Deep Source Channel Coding With Attention Modules

Recent research on joint source channel coding (JSCC) for wireless communications has achieved great success owing to the employment of deep learning (DL). However, the existing work on DL based JSCC usually trains the designed network to operate under a specific signal-to-noise ratio (SNR) regime, without taking into account that the SNR level during the deployment stage may differ from that during the training stage. A number of networks are required to cover the scenario with a broad range of SNRs, which is computational inefficiency (in the training stage) and requires large storage. To overcome these drawbacks our paper proposes a novel method called Attention DL based JSCC (ADJSCC) that can successfully operate with different SNR levels during transmission. This design is inspired by the resource assignment strategy in traditional JSCC, which dynamically adjusts the compression ratio in source coding and the channel coding rate according to the channel SNR. This is achieved by resorting to attention mechanisms because these are able to allocate computing resources to more critical tasks. Instead of applying the resource allocation strategy in traditional JSCC, the ADJSCC uses the channel-wise soft attention to scaling features according to SNR conditions. We compare the ADJSCC method with the state-of-the-art DL based JSCC method through extensive experiments to demonstrate its adaptability, robustness and versatility. Compared with the existing methods, the proposed method takes less storage and is more robust in the presence of channel mismatch.Comment: 13 pages, 13 figures, journal pape

2,7-Dibromo-9,9-dimethyl-9H-fluorene

The title mol­ecule, C15H15Br2, has crystallographic m2m site symmetry. As a result, all atoms, except for those of the methyl groups, are exactly coplanar. In the crystal structure, there are weak π–π inter­actions with a centroid–centroid distance of 3.8409 (15) Å between symmetry-related mol­ecules, which stack along the c axis

Controlled synthesis of mussel-inspired Ag nanoparticle coatings with demonstrated in vitro and in vivo antibacterial properties

The in-situ formation of silver nanoparticles (AgNPs) via dopamine-reduction of Ag+ has been widely utilized for titanium implants to introduce antibacterial properties. In previous studies, the preparation of AgNPs has focused on controlling the feeding concentrations, while the pH of the reaction solution was ignored. Herein, we systematically determined the influence of various pH (4, 7, 10) and Ag+ concentrations (0.01, 0.1 mg/mL) on the AgNPs formation, followed by the evaluation of the antibacterial properties in vitro and in vivo. The results revealed that an alkaline environment was favourable for AgNP formation and resulted in more particles. Although the AgNPs bearing Ti had lower biocompatibilities, it was significantly improved after 7 days of mineralization in simulated body fluid. The outstanding antibacterial property of the AgNPs was well maintained after one day and seven days of implantation. Moreover, 3D micro-CT modelling showed that the pH 10/0.1 group exhibited remarkable osteogenesis, which may be due to their strong antibacterial properties and ability to promote mineralization. Therefore, we have demonstrated that the solution pH was as important as the feeding Ag+ concentration in determining AgNP formation, and it has paved the way for developing various AgNP-loaded surfaces that could meet different antibacterial needs