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This study investigates the plastic deformation and hardening behavior of the direct-quenched ultra-high strength steel S960MC at various temperatures ranging from room temperature to 900 ℃. In this regard, the Hollomon and Voce equations are used to model the hardening behavior of the material at different temperatures. The suitability of each equation to predict the plastic flow of S960MC is evaluated based on the best resulted fit for the material. In addition, microstructural investigations are carried out to indicate the correlations between the microstructural changes, occurring in the range of room temperature to 900 ℃, and hardening behavior and governing parameters. The Hollomon approach showed deviations from the experimental results for room to intermediate temperatures; however, the Voce equation modeled the material’s strain hardening and flow behavior more successfully for the entire temperature range of room temperature–900 ℃. Additionally, there was a significant consistency between the Kocks-Mecking and Voce parameters. Dislocation interactions, dynamic strain aging, dynamic recrystallization, dynamic recovery, tempering (martensite decomposition), and austenite formation were the most influential microstructural features on the hardening behavior at various temperatures. The correlations between these microstructural features and hardening parameters were established satisfactorily for both the Hollomon and Voce approaches.

Variety of ultra-high strength steels (UHSS) with different microstructural characteristics is becoming available with continuous development of the manufacturing process in the steel industries. In order to effectively design structures made of such steel grades, a detailed knowledge of the mechanical properties is vital. Fire safety design is one of the areas in which such knowledge is essential. Welding process is indispensable in construction of steels structures with inevitable welding-induced degradation of mechanical properties of UHSSs. Thus, conducting experimental research on elevated-temperature constitutive mechanical behavior of welded joints made of UHSSs is of paramount importance. This study addresses elevated-temperature mechanical properties of as-received and as-welded S960 (manufactured via direct quenching technique) and S1100 (quenched and tempered) steel grades. A fully automated gas metal arc welding (GMAW) process with low heat input value was utilized to join the steel plates. Next, steady-state uniaxial tensile tests in the temperature range between room temperature (RT) and 900 °C were carried out. Accordingly, reduction factor-temperature relations for each tested steel in both as-received and as-welded forms are discussed and compared with several design standards, as well as with previous studies in the literature. Finally, predictive equations are proposed to estimate the elevated-temperature mechanical properties reduction factors of the tested UHSSs in as-received and as-welded forms.

Graph decomposition methods using k-core and k-truss hierarchically group vertices and edges from external to internal by degrees of vertices or tie strength of edges. As both the user engagement of nodes and the strength of relationships are important, the (k,s)-core model is proposed in the literature to discover strong communities. Nevertheless, the decomposition algorithm regarding (k,s)-core is not yet investigated. In this paper, we propose (k,s)-core algorithms to decompose a graph into its hierarchical structures considering both user engagement and tie strength. We first present the basic (k,s)-core decomposition methods. Then, we propose the advanced algorithms DES and DEK which index the support of edges to enable higher-level cost-sharing in the peeling process. In addition, effective pruning strategies are applied to DES/DEK to further enhance performance. Moreover, we build a novel index based on the decomposition result and investigate an efficient (k,s)-core query algorithm based on our index. Extensive experimental evaluations on 12 real-world datasets verify the efficiency of our proposed decomposition algorithms and show that our index-based query algorithm can speed up the state-of-the-art query algorithms by up to three orders of magnitude.

© Springer International Publishing AG, part of Springer Nature 2018. Nowadays many devices and applications in social networks and location-based services are producing, storing and using description, location and occurrence time of objects. Given a massive number of boolean top-k spatial-temporal queries and the spatial-textual message streams, in this paper we study the problem of continuously updating top-k messages with the highest ranks, each of which contains all the requested keywords when rank of a message is calculated by its location and freshness. Decreasing the ranks of existing top-k results over time and producing new incoming messages, cause continuously computing and maintaining the best results. To the best of our knowledge, there is no prior work that can exactly solve this problem. We propose two indexing and matching methods, then conduct an experimental evaluation to show the impact of parameters and analyse the models.