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AlCrFeNi multi-component alloys with excellent mechanical properties have been designed and extensively investigated in recent years. The massive fabrication of sheets will be an effective way for industrial application, where hot rolling is the inevitable process. After hot rolling, the mechanical properties could be further tailored. In this study, the effects of heat treatments on a dual-phase Al17Cr10Fe36Ni36Mo1 hot-rolled plate were systematically investigated, including: (i) annealing (700 °C, 850 °C, 1000 °C and 1150 °C for 1 h, respectively), (ii) solution and single aging (1150 °C for 1 h and 700 °C for 4 h), (iii) solution and double aging (1150 °C for 1 h, 700 °C for 4 h and 650 °C for 1 h). The B2 precipitates with varied morphologies were observed in the FCC matrix of the hot-rolled alloy after a heat treatment range from 700 °C to 1000 °C for 1 h, and the BCC particles in the B2 matrix were dissolved when the heat treatment temperature was higher than 1000 °C. The hot-rolled alloy heat treated at 700 °C for 1 h had the highest yield strength, and the hot-rolled alloy treated at 1150 °C for 1 h showed the lowest yield strength. After a solution at 1150 °C for 1 h, aging at 700 °C for 4 h and 650 °C for 1 h, the L12 phase and BCC particles were precipitated in the FCC and B2 matrices, and B2 nanoprecipitates were observed around the FCC grain boundaries. The solution and double aging alloy exhibit the tensile strength of 1365.7 ± 9.5 MPa, a fracture elongation of 14.2 ± 1.5% at 20 °C, a tensile strength of 641.4 ± 6.0 MPa, and a fracture elongation of 16.9 ± 1.0% at 700 °C, showing great potential for application. [ABSTRACT FROM AUTHOR]

An enterprise's capability is based on the quantity and collocation pattern of the heterogeneous resources it possesses. Innovation resources are the source of enterprise innovation capability. However, there is still a "black box" problem of the impact of the intensity of government support on enterprise innovation. Therefore, it is necessary to study high- or low-level enterprise innovation capability by combining the internal and external factors of the enterprise—the background characteristics of R&D personnel and the degree of government support. Based on the configuration perspective, this study uses the qualitative comparative analysis (fsQCA) method to conduct a comparative analysis of the innovation capabilities of enterprises in five time windows over six years with the longitudinal database of China's industrial enterprises whose operating income exceeds RMB 20 million from 2010 to 2015. This paper summarizes two ways of realizing high-level enterprise innovation capability: female and highly educated R&D personnel type, and highly educated R&D personnel and high government investment type. The enterprise innovation capability is affected simultaneously by multiple conditional variables, and the impact of each conditional variable on enterprise innovation capability has a trend. Further, it analyzes the impact of every antecedent variable comparing high- to low-level enterprise innovation. [ABSTRACT FROM AUTHOR]

This paper addresses performance limitations in passive clutching inertia devices due to the absence of flywheel deceleration damping. A solution introducing viscous damping to serve as the flywheel deceleration mechanism is proposed, thus forming the "CIDS" (Clutching Inertia‐Damping System). The study establishes a multi‐body motion analysis model for the CIDS within a single‐degree‐of‐freedom structure. Simulations investigate the impact of control force model parameters on the computation. The impact of additional damping on structural dynamic responses and the system's relative motion is thoroughly elucidated and revealed through dynamic simulations and multi‐body motion decomposition. The "ELS" (Equivalent Linear System) is introduced for the simplified performance assessment of CIDS. Expressions for equivalent damping and inertance are derived based on the equivalence of vibration period and energy in sinusoidal steady‐state vibration. The feasibility and accuracy of ELS for assessing CIDS's performance are validated through response analyses under harmonic and seismic excitations. Notably, displacement indicators exhibit higher precision than acceleration. When subjected to seismic excitation, selecting a more suitable excitation frequency to determine the corresponding equivalent damping can enhance the accuracy of ELS. Considering the frequency dependence of equivalent damping, a frequency‐domain‐based random response evaluation method is proposed. The influence of additional rotational damping, flywheel inertia, and structural natural period on random response is evaluated and analyzed. In CIDS with similar rotational damping and inertia, its control effect is related to the excitation. In summary, this study offers a structured framework for comprehending and evaluating the performance of CIDS, offering valuable insights into its potential engineering design, applications, and analysis. [ABSTRACT FROM AUTHOR]

Universal lesion detection (ULD) has great value in clinical practice as it can detect various lesions across multiple organs. Deep learning-based detectors have great potential but require high-quality annotated training data. In practice, due to cost, expertise requirements, and the diverse nature of lesions, incomplete annotations are encountered. Directly training ULD detectors under this condition can yield suboptimal results. Leading pseudo-label methods rely on a dynamic lesion-mining mechanism operating at the mini-batch level to address this issue. However, the quality of mined lesions is inconsistent across different iterations, potentially limiting performance enhancement. Inspired by the observation that deep models learn concepts with increasing complexity, we propose an exploratory-training-based ULD (ET-ULD) method to assess the reliability of mined lesions over time. Our approach uses a teacher-student detection model where the teacher mines suspicious lesions, which are then combined with incomplete annotations to train the student. On top of that, we design a bounding-box bank to record the mining timestamps. Each image is trained in several rounds, allowing us to get a sequence of timestamps for the mined lesions. If a mined lesion consistently appears, it is likely to be a true lesion, otherwise, it may just be a noise. This serves as a crucial criterion for selecting reliable mined lesions for retraining. Experimental results show that ET-ULD surpass existing state-of-the-art methods on two distinct lesion image datasets. Notably, on the DeepLesion dataset, ET-ULD achieved a 5.4% improvement in Average Precision (AP) over the previous methods, demonstrating its superior performance.