The Effect of Solution Time on the Microstructure of A356.2 Aluminum Alloy Provided by Rheocasting and Squeeze Casting

The evolution of the microstructure of A356.2 alloys prepared by the rheocasting and squeeze casting during solution heat treatment was investigated. In contrast with the conventional solution heat treatment process (3 hours at 540oC), a short time solution treatment process (less than 1 hour at 540oC) is applied in this paper. The results show that the rheocastings require a shorter solution time than the squeeze-castings to obtain spheroidized Si particles. After solution for 10 min, the X-ray diffraction inspection results show that the Mg2Si phase completely is dissolved in both rheocastings and squeeze-castings. However, a small amount of Mg2Si is found at the edge of the Si particle by scanning electron microscope observation. After solution for more than 20 min, the Mg2Si phase is completely dissolved. Fe-rich phases, including AlSiFeMg and AlFeSi, exist throughout the solution process. The developed T6 heat treatment with a short solution time can effectively improve production efficiency and decrease process cost for the rheocasting process. Key words: A356.2 alloy, microstructure, short solution time, rheocasting, squeeze casting

Numerical Methods for Calculating Component Modes for Geometric Mistuning Reduced-order Models

Geometric mistuning models formulated from a component mode synthesis methods often require the calculation of component modes, particularly constraint and fixed interface normal modes, during substructuring. For Integrally Bladed Rotors, these calculations are required for each sector. This paper proposes methods that reuse information garnered from solving the constraint modes of a single sector on the remaining sectors to reduce memory requirements and solution times. A mesh metamorphosis tool is used to ensure finite element models match geometry obtained from a 3D optical scanner. This tool also produces a common mesh pattern from sector-to-sector. This is exploited to produce common permutation matrices and symbolic factorizations of sector stiffness matrices that are proposed for reuse in solving subsequent constraint modes. Furthermore, a drop tolerance is introduced to remove small values during constraint mode calculation to reduce memory requirements. It is proposed to reuse this dropping pattern produced from a single sector on the remaining sectors. Approaches are then extended to a parallel processing scheme to propose effective matrix partitioning methods. Finally, information gathered during the constraint mode calculations are reused during the solution of the fixed interface normal modes to improve solution time. Results show reusing permutation matrices and symbolic factorizations from sector-to-sector improves solution time and introduces no error. Using a drop tolerance is shown to reduce storage requirements of a constraint mode matrix, while reusing the dropping pattern introduces minimal error. Similarly, reusing constraint mode information in calculating normal modes greatly improves the performance.

The Star Degree Centrality Problem: A Decomposition Approach

We consider the problem of identifying the induced star with the largest cardinality open neighborhood in a graph. This problem, also known as the star degree centrality (SDC) problem, is shown to be [Formula: see text]-complete. In this work, we first propose a new integer programming (IP) formulation, which has a smaller number of constraints and nonzero coefficients in them than the existing formulation in the literature. We present classes of networks in which the problem is solvable in polynomial time and offer a new proof of [Formula: see text]-completeness that shows the problem remains [Formula: see text]-complete for both bipartite and split graphs. In addition, we propose a decomposition framework that is suitable for both the existing and our formulations. We implement several acceleration techniques in this framework, motivated by techniques used in Benders decomposition. We test our approaches on networks generated based on the Barabási–Albert, Erdös–Rényi, and Watts–Strogatz models. Our decomposition approach outperforms solving the IP formulations in most of the instances in terms of both solution time and quality; this is especially true for larger and denser graphs. We then test the decomposition algorithm on large-scale protein–protein interaction networks, for which SDC is shown to be an important centrality metric. Summary of Contribution: In this study, we first introduce a new integer programming (NIP) formulation for the star degree centrality (SDC) problem in which the goal is to identify the induced star with the largest open neighborhood. We then show that, although the SDC can be efficiently solved in tree graphs, it remains [Formula: see text]-complete in both split and bipartite graphs via a reduction performed from the set cover problem. In addition, we implement a decomposition algorithm motivated by Benders decomposition together with several acceleration techniques to both the NIP formulation and the existing formulation in the literature. Our experimental results indicate that the decomposition implementation on the NIP is the best solution method in terms of both solution time and quality.

Breaking up prolonged sitting with a 6 min walk improves executive function in women and men esports players: a randomised trial

ObjectiveThe effect of prolonged sitting on executive function and performance in competitive esports players are unknown. This study aimed to evaluate executive function following a 6 min bout of walking or rest during prolonged gaming in competitive esports players.Methods12 men and 9 women completed three separate 2-hour gaming session days assigned in randomised order consisting of a 6 min walk break, 6 min rest break and continuous before and after each session. Postintervention participant exit survey data were also collected.ResultsThe walk condition produced a significantly faster mean solution time (7613.6±3060.5 min, p=0.02) and planning time (5369.0±2802.09, p=0.04) compared with the resting condition (9477±3547.4; 6924±3247.7) and continuous play (8200.0±3031.6; 5862.7±2860.7). The rest condition resulted in the slowest mean solution time (9477.0±3547.4) and planning time (6924.0±3247.7), with the continuous play resulting in a faster mean solution time (8200.1±3031.6) and planning time (5862.7±2860.7) than the rest condition. There was no impact on game performance in any of the conditions. However, over 70% of participants felt that the walk break improved esports performance.ConclusionsReducing sit time and breaking up prolonged sitting have acute and chronic health benefits. This study provides evidence that a 6 min walking break in the middle of 2 hours of gameplay allows gamers to have these health benefits while improving processing speed and executive function.Trial registration numberNCT04674436.

Numerical Methods for Calculating Component Modes for Geometric Mistuning Reduced-Order Models

Geometric mistuning models formulated from a component mode synthesis methods often require the calculation of component modes, particularly constraint and fixed interface normal modes, during substructuring. For Integrally Bladed Rotors, these calculations are required for each sector. This paper proposes methods that reuse information garnered from solving the constraint modes of a single sector on the remaining sectors to reduce memory requirements and solution times. A mesh metamorphosis tool is used to ensure finite element models match geometry obtained from a 3D optical scanner. This tool also produces a common mesh pattern from sector-to-sector. This is exploited to produce common permutation matrices and symbolic factorizations of sector stiffness matrices that are proposed for reuse in solving subsequent constraint modes. Furthermore, a drop tolerance is introduced to remove small values during constraint mode calculation to reduce memory requirements. It is proposed to reuse this dropping pattern produced from a single sector on the remaining sectors. Approaches are then extended to a parallel processing scheme to propose effective matrix partitioning methods. Finally, information gathered during the constraint mode calculations are reused during the solution of the fixed interface normal modes to improve solution time. Results show reusing permutation matrices and symbolic factorizations from sector-to-sector improves solution time and introduces no error. Using a drop tolerance is shown to reduce storage requirements of a constraint mode matrix. Additionally, it is shown that reusing the same dropping pattern introduces minimal error without degradation in solution times. Similarly, reusing the information from constraint modes for calculating fixed interface normal modes greatly improves the performance in a shift-and-invert technique for solving eigenvalue problems.

Study on Ultra-High Temperature Contact Solution Treatment of Al–Zn–Mg–Cu Alloys

Contact solution treatment (CST) of Al–Zn–Mg–Cu alloys can shorten solution time to within 40 s in comparison with 1800 s with traditional solution treatment using a heating furnace. Heating temperature is the key factor in solution treatment. Considering the short heating time of CST, the ultra-high solution temperature over 500 °C of Al–Zn–Mg–Cu alloys was studied in this work. The effects of solution temperatures on the microstructures and the mechanical properties were investigated. The evolution of the second phases was explored and the strengthening mechanisms were also quantitatively evaluated. The results showed that solution time could be reduced to 10 s with the solution temperature of 535 °C due to the increasing dissolution rate of the second phase and the tensile strength of the aged specimen could reach 545 MPa. Precipitation strengthening was the main strengthening mechanism, accounting for 75.4% of the total strength. Over-burning of grain boundaries occurred when the solution temperature increased to 555 °C, leading to the deterioration of the strength.

Assessing creativity independently of language: A normed Language Independent Remote Associate Task (LI-RAT)

Most creativity measures are either complex or language-dependent, hindering cross-cultural creativity assessment. We have therefore developed and tested a simple, language-independent insight task based on pictures in the style of the widely used verbal remote associate task (RAT). We demonstrate that the language-independent RAT allows assessment of different aspects of insight across large samples with different languages. It also correlates with other creativity and general problem solving tasks. The entire stimulus set, including its normative data, is made freely available. This information can be used to select items based on accuracy, mean solution time, likelihood to produce an insight, or conceptual and perceptual similarity between the pictures per item.

Aha! under pressure: Is the Aha! experience constrained by cognitive load?

The Aha! moment‒ the sudden insight sometimes reached when solving a vexing problem‒ entails a different problem-solving experience than solution retrieval reached by an analytical, multistep strategy (i.e., non-insight). To date, the (un)conscious nature of insight remains debated. We addressed this by studying insight under cognitive load. If insight and non-insight problem solving rely on conscious, effortful processes, they should both be influenced by a concurrent cognitive load. However, if unconscious processes characterize insight, cognitive load might not affect it at all. Using a dual-task paradigm, young, healthy adults (N = 106) solved 70 word puzzles under different cognitive loads. We confirmed that insight solutions were more often correct and received higher solution confidence. Importantly, as cognitive load increased, non-insight solutions became less frequent and required more solution time, whereas insightful ones remained mostly unaffected. This implies that insight problem solving did not compete for limited cognitive resources.