Observation of high iron charge states in solar energetic particle events

The ionic charge states of Solar Energetic Particle (SEP) events provide direct information about the source plasma and the acceleration environment. In this thesis, we mainly build on charge state observation of SEP events during late 1997 to 2000 with from Solar Energetic Particle Ionic Charge Analyzer (SEPICA) on board Advanced Composition Explorer (ACE). We concentrate our effort on the high QFe (≥14) found in the SEP events, and discuss the physical principles of how these elevated charge states are produced. We statistically confirmed the energy dependent charge states found in impulsive SEP events and showed impulsive SEPs are consistent with a normal coronal source. The energy dependent charge states is consistent with stripping model of impulsive events, where the charge states are built through stripping of electrons in the dense lower corona. We found a trend between the charge states and enhancement of heavy ions and 3He at lower SEP energies, but the trend is lost at higher energies, as predicted by the stripping model. We further investigated the variation of mean charge state in impulsive SEPs and found that the elevated mean QFe in impulsive events are due to larger energy loss with stronger adiabatic deceleration. We also surveyed 89 SEP events during the time, seeking high source temperature material accelerated to SEP energies. We found that such events are very rare, and local acceleration of high temperature material may occur in a rare configuration where a second shock plows through the high temperature material of a preceding CME. Among the high QFe events, we also observed impulsive events consistent with source temperature \u3e2MK, even as high as ∼4MK based on a comparison with a model. A few events appear to be impulsive material re-accelerated in interplanetary space. We found that the original charge states of their parent events are re-distributed to different extent, thus the high Q Fe in these events, even at the lowest SEP energies, cannot be used as direct evidence of high source temperature

High Frequency and Large Dimension Volatility

Three main issues are explored in this thesis—volatility measurement, volatility spillover and large-dimension covariance matrices. For the first question of volatility measurement, this thesis compares two newly-proposed, high-frequency volatility measurement models, namely realized volatility and realized range-based volatility. It does so in the aim of trying to use empirical results to assess whether one volatility model is better than the other. The realized volatility model and realized range-based volatility model are compared based on three markets, five forecast models, two data frequencies and two volatility proxies, making sixty scenarios in total. Seven different loss functions are also used for the evaluation tests. This necessarily ensures that the empirical results are highly robust. After making some simple adjustments to the original realized range-based volatility, this thesis concludes that it is clear that the scaled realized range-based volatility model outperforms the realized volatility model. For the second research question on volatility spillover, realized range-based volatility and realized volatility models are employed to study the volatility spillover among the S&P 500 index markets, with the aim of finding out empirically whether volatility spillover exists between the markets. Volatility spillover is divided into the two categories of statistically significant volatility spillover and economically significant volatility spillover. Economically significant spillover is defined as spillover that can help forecast the volatility of another market, and is therefore a more powerful measurement than statistically significant spillover. The findings show that, in reality, the existence of volatility spillover depends on the choice of model, choice of volatility proxy and value of parameters used. The third and final research question in this thesis involves the comparison of various large-dimension multivariate models. The main contribution made by this specific study is threefold. First, a number of good performance multivariate volatility models are introduced by adjusting some commonly used models. Second, different models and various choices of parameters for these models are tested based on 26 currency pairs. Third, the evaluation criteria adopted possess much more practical implications than those used in most other papers on this subject area

Research progress of nanovaccine in anti-tumor immunotherapy

Tumor vaccines aim to activate dormant or unresponsive tumor-specific T lymphocytes by using tumor-specific or tumor-associated antigens, thus enhancing the body’s natural defense against cancer. However, the effectiveness of tumor vaccines is limited by the presence of tumor heterogeneity, low immunogenicity, and immune evasion mechanisms. Fortunately, multifunctional nanoparticles offer a unique chance to address these issues. With the advantages of their small size, high stability, efficient drug delivery, and controlled surface chemistry, nanomaterials can precisely target tumor sites, improve the delivery of tumor antigens and immune adjuvants, reshape the immunosuppressive tumor microenvironment, and enhance the body’s anti-tumor immune response, resulting in improved efficacy and reduced side effects. Nanovaccine, a type of vaccine that uses nanotechnology to deliver antigens and adjuvants to immune cells, has emerged as a promising strategy for cancer immunotherapy due to its ability to stimulate immune responses and induce tumor-specific immunity. In this review, we discussed the compositions and types of nanovaccine, and the mechanisms behind their anti-tumor effects based on the latest research. We hope that this will provide a more scientific basis for designing tumor vaccines and enhancing the effectiveness of tumor immunotherapy

Real-time source tracking for quality assurance in HDR prostate brachytherapy

High dose rate (HDR) brachytherapy is the preferred treatment option for clinically localised prostate cancer. The underlying principle is simple: temporarily inserting a highly radioactive source inside the target volume and moving it through a sequence of predefined positions for pre-calculated dwell times. HDR brachytherapy is capable of achieving a highly conformal dose distribution which cannot be matched by external beam radiation therapy

Lacome: a cross-platform multi-user collaboration system for a shared large display

Lacome is a multi-user cross-platform system that supports collaboration in a shared large screen display environment. Lacome allows users to share their desktops or application windows using any standard VNC server. It supports multi-user concurrent interaction on the public shared display as well as input redirection so users can control each other's applications. La-come supports separate types of interaction through a Lacome client for window management tasks on the shared display(move, resize, iconify, de-iconify) and for application interactions through the VNC servers. The system architecture provides for Publishers that share information and Navigators that access information. A Lacome client can have either or both, and can initiate additional Publishers on other VNC servers that may not be Lacome clients. Explicit access control policies on both the server side the client side provide a flexible framework for sharing. The architecture builds on standard cross-platform components such as VNC and JRE. Interaction techniques used in the window manager ensure simple and transparent multi-user interactions for managing the shared display space. We illustrate the design and implementation of Lacome and provide insights from initial user experience with the system.Science, Faculty ofComputer Science, Department ofGraduat

Recommended method study based on incorporating complex network ripple net

The RippleNet network models user preferences and is well applied in the recommended system. But Ripplenet didn't take into account the weight of entities in the knowledge graph, resulting in the inaccurate recommendation results. A RippleNet model incorporating the influence of the complex network nodes is proposed. After constructing the complex networks based on the knowledge maps, the maximum subnet model is extracted, the influence of the nodes in the map network is calculated, and the weight of the nodes is added to the RippleNet model as an entity. The experimental results showed that the present method increased the AUC and ACC values of RippleNet to 92.0% and 84.6%, made up for the problem that no entity influence was considered in the RippleNet network, and made the recommended results more in line with users' expectations