Olympiad delegation registration system

The purpose of this project is to design, build and implement a web application system for the Olympiad delegation registration. All the pages and user registration information will be stored in a PostgreSQL database and retrieved by JAVA Servlet and JDBC (JAVA Database Connectivity). The main purpose of this project is to provide an easy-to-register and web-base communication evironment for the National Olympic Committes (NOC) and the Organizing Committee for the Olympic Games (OCOG)

CopyScope: Model-level Copyright Infringement Quantification in the Diffusion Workflow

Web-based AI image generation has become an innovative art form that can generate novel artworks with the rapid development of the diffusion model. However, this new technique brings potential copyright infringement risks as it may incorporate the existing artworks without the owners' consent. Copyright infringement quantification is the primary and challenging step towards AI-generated image copyright traceability. Previous work only focused on data attribution from the training data perspective, which is unsuitable for tracing and quantifying copyright infringement in practice because of the following reasons: (1) the training datasets are not always available in public; (2) the model provider is the responsible party, not the image. Motivated by this, in this paper, we propose CopyScope, a new framework to quantify the infringement of AI-generated images from the model level. We first rigorously identify pivotal components within the AI image generation pipeline. Then, we propose to take advantage of Fr\'echet Inception Distance (FID) to effectively capture the image similarity that fits human perception naturally. We further propose the FID-based Shapley algorithm to evaluate the infringement contribution among models. Extensive experiments demonstrate that our work not only reveals the intricacies of infringement quantification but also effectively depicts the infringing models quantitatively, thus promoting accountability in AI image-generation tasks

The anti-sepsis activity of the components of Huanglian Jiedu Decoction with high lipid A-binding affinity

Huanglian Jiedu Decoction (HJD), one of the classic recipes for relieving toxicity and fever, is a common method for treating sepsis in China. However, the effective components of HJD have not yet been identified. This experiment was carried out to elucidate the effective components of HJD against sepsis. Thus, seven fractions from HJD were tested using a biosensor to test their affinity for lipid A. The components obtained that had high lipid A-binding fractions were further separated, and their affinities to lipid A were assessed with the aid of a biosensor. The levels of LPS in the blood were measured, and pathology experiments were conducted. The LPS levels and mRNA expression analysis of TNF-α and IL-6 of the cell supernatant and animal tissue were evaluated to investigate the molecular mechanisms. Palmatine showed the highest affinity to lipid A and was evaluated by in vitro and in vivo experiments. The results of the in vitro and in vivo experiments indicated that the levels of LPS, TNF-α and IL-6 of the palmatine group were significantly lower than those of the sepsis model group (p \u3c 0.01). The group treated with palmatine showed strong neutralizing LPS activity in vivo. The palmatine group exhibited stronger protective activity on vital organs compared to the LPS-induced animal model. This verifies that HJD is a viable treatment option for sepsis given that there are multiple components in HJD that neutralize LPS, decrease the release of IL-6 and TNF-α induced by LPS, and protect vital organs

Leveraging an Ecosystem for the Development of AI Applications

Artificial Intelligence (AI) is becoming increasingly essential for enhancing many conventional business processes and generating market opportunities. And yet, for AI to truly gain mainstream acceptance, there is a need to develop a vast array of different applications to cater to the myriad needs of the market. This, however, cannot be achieved by any AI firm in isolation. Instead, there is a need for the collectivization of a synergistic ecosystem of entities. How such an AI ecosystem is developed, however, has not been the subject of research to date. To address this gap, we conducted a case study of iFlytek, one of the most successful AI firms in China and the world. Based on our ongoing study, we developed a theoretical framework that illustrates the stages of AI ecosystem development, which can provide guidelines for other technology firms and policymakers on the orchestration and governance of market-driven AI applications

Study on semi-active particle damping technology for offshore platform truss structure

The vibration reduction device for the offshore platform truss structure has exposed to long term high-energy ultraviolet light and salt spray radiation, the traditional viscoelastic damping technology is easy to aging and failure, and its operational lifetime is limited. The semi-active particle damping technology has the advantages of high temperature resistance, anti-aging and so on, at the same time it is also able to adapt the changes of external complex load. The coupling simulation algorithm was used in this paper to study on vibration reduction performance of semi-active particle damping technology for the offshore platform truss structure. The control strategy, simulation algorithm and key parameters of semi-active particle damping are investigated to analyze the vibration reduction effect of the offshore platform truss structure with semi-active particle damper. The results have shown that the semi-active particle damping has a better effect of vibration reduction compared with the passive particle damping. In addition, the control strategy in different frequency bands has different vibration reduction effects to the system; and the simulation has a good agreement with the experimental results

Structural dividends and economic growth in China

This study aims at analyzing the impact of industrial structure upgrading on the economic growth of China. Based on panel data of 283 cities of China from 2001 to 2014, this paper uses the spatial panel SARAR to analyze the influence of industrial structure upgrading on the economic growth of China’s prefecture-level cities. The results show that China’s urban economic growth has significant spatial correlation: industrial structure upgrading is the prime reason for the economic growth and the differences in the urban economy in China, and the impact of structural dividend on economic growth is positive. However, with the further upgrading of the industrial structure, the structural dividend will become negative, i.e. there is a significant inverted “U” relationship between the industrial structure and economic growth. The study provides new empirical evidence for the new classical economic growth theory and a new research experience for a follow-up study

DoseDiff: Distance-aware Diffusion Model for Dose Prediction in Radiotherapy

Treatment planning is a critical component of the radiotherapy workflow, typically carried out by a medical physicist using a time-consuming trial-and-error manner. Previous studies have proposed knowledge-based or deep learning-based methods for predicting dose distribution maps to assist medical physicists in improving the efficiency of treatment planning. However, these dose prediction methods usuallylack the effective utilization of distance information between surrounding tissues andtargets or organs-at-risk (OARs). Moreover, they are poor in maintaining the distribution characteristics of ray paths in the predicted dose distribution maps, resulting in a loss of valuable information obtained by medical physicists. In this paper, we propose a distance-aware diffusion model (DoseDiff) for precise prediction of dose distribution. We define dose prediction as a sequence of denoising steps, wherein the predicted dose distribution map is generated with the conditions of the CT image and signed distance maps (SDMs). The SDMs are obtained by a distance transformation from the masks of targets or OARs, which provide the distance information from each pixel in the image to the outline of the targets or OARs. Besides, we propose a multiencoder and multi-scale fusion network (MMFNet) that incorporates a multi-scale fusion and a transformer-based fusion module to enhance information fusion between the CT image and SDMs at the feature level. Our model was evaluated on two datasets collected from patients with breast cancer and nasopharyngeal cancer, respectively. The results demonstrate that our DoseDiff outperforms the state-of-the-art dose prediction methods in terms of both quantitative and visual quality

A state-dependent constitutive model for coarse-grained gassy soil and its application in slope instability modelling

Free gas in sandy marine sediments is a common occurrence worldwide. A distinct feature of gassy sand is that, under undrained shearing, presence of occluded gas bubbles in the pore fluid can increase the undrained strength of sand at a relatively loose state, while reduce the strength of a relatively dense sand. Previous theoretical analyses have primarily focused on modelling the ‘beneficial’ effect of free gas on loose sand in migrating static liquefaction, with few attempts to describe the ‘detrimental’ effect of gas on dense sand under undrained loading. This study presents a state-dependent critical state model, which describes the distinct behavior of gassy sand with various states in a unified way. Comparison between the model predictions and test data of three gassy sands shows that the new model can capture the constitutive behavior of gassy marine sand over a wide range of initial states and degrees of saturation (typically between 85% and 100% for unsaturated marine sediments) using a single set of parameters. Parametric studies were performed to quantify the effects of gas (either ‘detrimental’ or ‘beneficial’) on sand with various initial states. The new model has been implemented in ABAQUS and used to simulate the stability of submarine slopes under undrained loading condition. It is found that free gas can improve and weaken the slope stability for loose and dense sand, respectively

Mycobacterial Nucleoside Diphosphate Kinase Blocks Phagosome Maturation in Murine Raw 264.7 Macrophages

bacille Calmette-Guérin (BCG), disrupt the normal function of host Rab5 and Rab7, two small GTPases that are instrumental in the control of phagosome fusion with early endosomes and late endosomes/lysosomes respectively. nucleoside diphosphate kinase (Ndk) exhibits GTPase activating protein (GAP) activity towards Rab5 and Rab7. Then, using a model of latex bead phagosomes, we demonstrated that Ndk inhibits phagosome maturation and fusion with lysosomes in murine RAW 264.7 macrophages. Maturation arrest of phagosomes containing Ndk-beads was associated with the inactivation of both Rab5 and Rab7 as evidenced by the lack of recruitment of their respective effectors EEA1 (early endosome antigen 1) and RILP (Rab7-interacting lysosomal protein). Consistent with these findings, macrophage infection with an Ndk knocked-down BCG strain resulted in increased fusion of its phagosome with lysosomes along with decreased survival of the mutant.Our findings provide evidence in support of the hypothesis that mycobacterial Ndk is a putative virulence factor that inhibits phagosome maturation and promotes survival of mycobacteria within the macrophage