Information Support Technology of Ship Survey Based on Case-based Reasoning

Recently, the significance of ship inspections hasbeen increasingly recognized because sea pollution andsafety problems are occurring more and more frequently. However, current ship inspections rely on the experience ofthe workers. Therefore, it is difficult to understand, and hence to improve, the state of ship inspections. The present problemsare that the ship inspection technical support level in China is not balanced, especially as to the current situation with low level, poor technologyin under-developed areas. In this paper, the case technology about the case-based reasoning to the ship inspection is proposed. The methods of normative inspection case representation are discussed.This is considered to be the basis of case-based reasoning. Then the tertiary case structure with the index is defined, in which the K-nearest neighbor method to calculate the similarity between caseswas used so that the ship’s inspection information can be searched effectively. In addition, animproved retrievalstrategy of the nearest neighbor method is introduced. Therefore, in the inspection site,the inspectors can acquire support information by the case bases, and then the new cases are calculated automatically. Further, a ship inspection case managementwereintroduced to improve the stability of the system. By carrying the case-based reasoning into an inspection, an inspector can generate fault information and inspection information simply and easily. Some examples of the organization and retrieval are shown at the end of the paper

DiffFashion: Reference-based Fashion Design with Structure-aware Transfer by Diffusion Models

Image-based fashion design with AI techniques has attracted increasing attention in recent years. We focus on a new fashion design task, where we aim to transfer a reference appearance image onto a clothing image while preserving the structure of the clothing image. It is a challenging task since there are no reference images available for the newly designed output fashion images. Although diffusion-based image translation or neural style transfer (NST) has enabled flexible style transfer, it is often difficult to maintain the original structure of the image realistically during the reverse diffusion, especially when the referenced appearance image greatly differs from the common clothing appearance. To tackle this issue, we present a novel diffusion model-based unsupervised structure-aware transfer method to semantically generate new clothes from a given clothing image and a reference appearance image. In specific, we decouple the foreground clothing with automatically generated semantic masks by conditioned labels. And the mask is further used as guidance in the denoising process to preserve the structure information. Moreover, we use the pre-trained vision Transformer (ViT) for both appearance and structure guidance. Our experimental results show that the proposed method outperforms state-of-the-art baseline models, generating more realistic images in the fashion design task. Code and demo can be found at https://github.com/Rem105-210/DiffFashion

A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision

Deep learning has the potential to revolutionize sports performance, with applications ranging from perception and comprehension to decision. This paper presents a comprehensive survey of deep learning in sports performance, focusing on three main aspects: algorithms, datasets and virtual environments, and challenges. Firstly, we discuss the hierarchical structure of deep learning algorithms in sports performance which includes perception, comprehension and decision while comparing their strengths and weaknesses. Secondly, we list widely used existing datasets in sports and highlight their characteristics and limitations. Finally, we summarize current challenges and point out future trends of deep learning in sports. Our survey provides valuable reference material for researchers interested in deep learning in sports applications

Silver nanoparticles confined in shell-in-shell hollow TiO2 manifesting efficiently photocatalytic activity and stability

The complete degradation of tetracycline still is a challenge for TiO2-based photocatalysts under simulated solar light irradiation. To tackle this challenge, we devise Ag nanoparticles (Ag NPs) confined in shell-in-shell hollow TiO2 photocatalyst (HTAT). This strategy mainly involves the construction of CPS@TiO2 core-shell composites, the form of TiO2 inner shell, AgNPs loading by photo-deposition, the assembly of TiO2 outer shell, and phase transition of anatase TiO2 by calcination at 450℃. All characterizations including TEM, STEM Mapping, BET, and XPS confirm the unique structure of the as-synthesized HTAT photocatalyst. As expected, the complete degradation of tetracycline (TC and TCH) can be realized by using HTAT photocatalyst under simulated solar light irradiation because its TiO2 two shells simultaneously take part in the photodegrading reaction of TC or TCH. The transformation intermediates and degradation pathway were analyzed by LC/MS. Our work effectively overcomes the disadvantages of many previously reported TiO2-based photocatalysts for the incomplete degradation of tetracycline

Hybrid Biodegradable Nanomotors through Compartmentalized Synthesis

Designer particles that are embued with nanomachinery for autonomous motion have great potential for biomedical applications; however, their development is highly demanding with respect to biodegradability/compatibility. Previously, biodegradable propulsive machinery based on enzymes has been presented. However, enzymes are highly susceptible to proteolysis and deactivation in biological milieu. Biodegradable hybrid nanomotors powered by catalytic inorganic nanoparticles provide a proteolytically stable alternative to those based upon enzymes. Herein we describe the assembly of hybrid biodegradable nanomotors capable of transducing chemical energy into motion. Such nanomotors are constructed through a process of compartmentalized synthesis of inorganic MnO2 nanoparticles (MnPs) within the cavity of organic stomatocytes. We show that the nanomotors remain active in cellular environments and do not compromise cell viability. Effective tumor penetration of hybrid nanomotors is also demonstrated in proof-of-principle experiments. Overall, this work represents a new prospect for engineering of nanomotors that can retain their functionality within biological contexts

Engineering transient dynamics of artificial cells by stochastic distribution of enzymes

Here the authors develop a coacervate micromotor that can display autonomous motion as a result of stochastic distribution of propelling units. This stochastic-induced mobility is validated and explained through experiments and theory. Random fluctuations are inherent to all complex molecular systems. Although nature has evolved mechanisms to control stochastic events to achieve the desired biological output, reproducing this in synthetic systems represents a significant challenge. Here we present an artificial platform that enables us to exploit stochasticity to direct motile behavior. We found that enzymes, when confined to the fluidic polymer membrane of a core-shell coacervate, were distributed stochastically in time and space. This resulted in a transient, asymmetric configuration of propulsive units, which imparted motility to such coacervates in presence of substrate. This mechanism was confirmed by stochastic modelling and simulations in silico. Furthermore, we showed that a deeper understanding of the mechanism of stochasticity could be utilized to modulate the motion output. Conceptually, this work represents a leap in design philosophy in the construction of synthetic systems with life-like behaviors