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BACKGROUND: Cardiac sarcomas are rare malignancies with a poor prognosis. Although angiosarcoma is the most common histological subtype, its features are poorly characterized. This study aimed to compare the clinical characteristics of the various cardiac sarcomas and the surgical techniques used and to identify factors influencing the prognosis. METHODS: Forty patients who underwent surgery for cardiac sarcomas were included; 60% of them had angiosarcoma. Clinical characteristics, tumor location, surgical techniques used, and the prognosis were compared between patients with angiosarcoma and patients with other subtypes. Kaplan–Meier curves and multivariable Cox regression were used to identify predictors of postoperative survival. RESULTS: Angiosarcomas were more likely than the other subtypes to present as pericardial effusion (85% vs 50%, P = .014). Early surgery was performed (median 24.0 days) regardless of histological subtype. The surgical technique varied according to histological subtype. Mean postoperative survival was 10 months. A positive margin (P = .13), high Ki-67 index (P = .19), younger age (P = .86), and angiosarcoma (P = .87) were identified to be potentially poor prognostic factors in univariate analyses. Cox regression identified R0 resection to be the only significant independent predictor of the prognosis after surgery (hazard ratio, 0.423, P = .039). CONCLUSIONS: Angiosarcoma differs from other subtypes of cardiac sarcoma in terms of clinical symptoms, tumor location, surgical techniques used, and prognosis. Early surgery is needed regardless of subtype. R0 resection is the only independent predictor of postoperative survival, and complete resection is usually achievable. The prognosis may be poorer in patients with a positive margin, high Ki-67 index, younger age, and angiosarcoma.

Named entity recognition is an important and challenging problem in Natural language processing. Although the past decade has witnessed major advances in entity recognition in many fields, such successes have been slow to network security field, not only because of the data in the network security field is very professional, but also due to the sensitive information in the data. To advance named entity recognition research in network security field, we introduce a large-scale Dataset for Named Entity Recognition in Threat Intelligence (DNRTI). To this end, we collect more than 300 pieces of threat intelligence. The data in DNRTI is all annotated by experts in threat intelligence interpretation using 13 object categories. The fully annotated DNRTI contains 175220 words. To build a baseline for named entity recognition in the threat intelligence field, we evaluate some deep learning model on DNRTI. Experiments demonstrate that DNRTI well represents the key information in threat intelligence and are quite challenging.

The internet data center (IDC) power system provides power guarantee for cloud computing and other information services, so its importance is self-evident. However, the occurrence time of malignant destructive events such as lightning strikes, errors in operation and cyber-attacks is unpredictable. But the loss can be minimized by formulating coping strategies in advance. So, identifying the vulnerable spots of the IDC power system come to be the key to guarantee the normal operation of information systems. Generally, the IDC power network can be modelled as a graph G, and then, the methods of finding nodes’ centrality can be applied to analyse the vulnerability. By our experience, it is not the best approach. Unlike the previous approaches, we do not solve the issue as the traditional graph problem. Instead, we fully utilize the characteristics of the IDC power network and apply reinforcement learning techniques to identify the vulnerability of the IDC power network. To our best knowledge, it is the first applying of artificial intelligence in traditional IDC power network. In this article, we propose PFEM, a parallel fault evolution model for the IDC power network, which can accelerate the process of electrical fault evolution. Moreover, we designed an algorithm which can automatically find the vulnerable spots of the IDC power network. The experiment on a real IDC power network demonstrate that the impact of vulnerable devices derived from our proposed algorithm after failure is about 5% higher than that of other algorithms, and tripping single-digit electrical devices of the IDC power system with our proposed algorithm will lead to loss of all loads.

With the rapid development of Mobile Internet and Big Data in recent years, the demand for data centers is increasing. The number of super large data centers (more than 1000 counters) has increased. This paper focus on the distribution system, who is responsible for providing a continuous, stable and uninterrupted power supply for the data center. In the event of a large-scale power outage, all business in the data center will be interrupted. Therefore, identifying which electrical equipment in the data center paly the key role in a large-scale power outage is extremely important. Considering the computational complexity of the algorithm, the validity of the vulnerable nodes, the intelligence of the algorithm, this paper proposes a two-layer fragile node identification method based on attention mechanism. The algorithm selects the topology structure and electrical parameters of the data center power distribution system, and considers the flow load of the nodes around each electrical device to form an expression vector. After the vulnerability ranking of the candidate node expression vectors, the k most vulnerable nodes are selected. The outline of the two-layer node identification method is as follows, the first layer calculates an alternative node sequence through a variety of graph-based fragile node identification methods, and the second layer re-calibrates the expression vector of each candidate node based on the attention mechanism and then sorts all candidate nodes. Through the simulation test of a real data center power distribution system, we can see that the proposed algorithm in this paper identifies the effectiveness of the vulnerable nodes in the data center power distribution system.

The research presented in this thesis is concerned with the time-dependent creep and shrinkage behaviour of steel-concrete composite curved beams with partial interaction. The research program comprises both theoretical studies and experimental investigations. Firstly, an efficient numerical method is developed for the creep and shrinkage analysis of steel-concrete composite beams curved in-plan. The flexibility of the shear connection in the radial and tangential directions at the steel-concrete interface is taken into account in the developed method. The creep and shrinkage effects of the concrete deck are considered by using the analytic Age-adjusted Effective Modulus Method. The accuracy and efficiency of the proposed approach are validated by comparing its results with available experimental results reported in the literature and with those based on an ABAQUS shell finite element model. Parametric studies are also undertaken, which demonstrate that the initial curvature and partial interaction of the beam have significant influences on the time-dependent behaviour of composite beams curved in-plan. The geometrically nonlinear time-dependent creep and shrinkage behaviour of horizontally-curved steel-concrete composite beams with partial interaction is also investigated. In order to include the effects of geometric nonlinearity, a step-by-step iterative procedure is used for the creep and shrinkage analysis. The examples presented illustrate that the geometric nonlinearity should be considered for an accurate time-dependent analysis of composite beams curved in-plan. The experimental program, which involves testing the time-dependent behaviour of a simply-supported steel-concrete composite beam that is curved in-plan, is reported. The experimental results provide benchmark data to test the validity of the theoretical model developed for the time-dependent creep and shrinkage analysis of curved composite beams, and for subsequent studies by other researchers. Finally, a numerical formulation for the nonlinear time-dependent creep and shrinkage analysis of spatially-curved steel-concrete composite beams with partial interaction is also developed. The verifications indicate that the proposed formulation for the time-dependent analysis of composite beams curved in-space is accurate. The effects of partial interaction, initial curvature and the curvature components are also illustrated with chosen examples to elucidate this complex structural behaviour.

This thesis presents the nonlinear in-plane behaviour of circular fixed arches subjected to thermal loading only. Due to the nonlinear prebuckling behaviour of arches and its effects, classical buckling theory which is founded on geometric prebuckling linearity can not predict the in-plane buckling of arches accurately. Based on a nonlinear formulation of the strain and displacement relationship and considering constant thermal distributions only, virtual work formulations are used to establish the differential equations of in-plane equilibrium and the statical boundary conditions, from which the nonlinear equilibrium equations are derived in closed form and which are suitable to use in design. By considering the adjacent buckled configurations, the differential buckling equilibrium equations are formulated from the principle of virtual work as well, and the analytical solutions for the nonlinear buckling of fixed arches are obtained. It is shown that nonlinear elastic buckling of a fixed in the plane of it curvature can not occur when it is subjected to thermal loading only, except if the arch is as a straight column. By using the algebraic representation of nonlinear in-plane equilibrium derived in this thesis, the elastic response of fixed arches at elevated temperatures and the attainment of first yield are examined in detail. The arch deflects transversely without bound in the elastic range at elevated temperatures, whereas it will yield first at the top extreme fibre of the cross section at the supports when a critical temperature is reached. The influence of several parameters such as the included angle is also considered. Based on the models of stress distributions at cross sections, the spread of yield both through the cross section and along the length of the arch is studied. It is indicated that the progress of yielding causes the first two hinges to form at the supports of the fixed arches, and then moment redistribution leads to the generation of the third hinge at the crown with an increase of temperature. Thus nonlinear plastic hinge analysis can be applied to the arch analysis under thermal loading.