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Using simulated annealing, we examine a bipartitioning of small worlds obtained by adding a fraction of randomly chosen links to a one-dimensional chain or a square lattice. Models defined on small worlds typically exhibit a mean-field behavior, regardless of the underlying lattice. Our work demonstrates that the bipartitioning of small worlds does depend on the underlying lattice. Simulations show that for one-dimensional small worlds, optimal partitions are finite size clusters for any fraction of additional links. In the two-dimensional case, we observe two regimes when the fraction of additional links is sufficiently small, the optimal partitions have a stripe-like shape, which is lost for a larger number of additional links as optimal partitions become disordered. Some arguments, which interpret additional links as thermal excitations and refer to the thermodynamics of Ising models, suggest a qualitative explanation of such a behavior. The histogram of overlaps suggests that a replica symmetry is broken in a one-dimensional small world. In the two-dimensional case, the replica symmetry seems to hold, but with some additional degeneracy of stripe-like partitions.The influence of enhanced gravity on the microstructure and mechanical properties of the Al0.9CoCrFeNi high-entropy alloy, which was solidified under normal gravity (acceleration 1 g) and enhanced gravity (acceleration 140 g, acceleration 210 g, and acceleration 360 g) conditions is reported in this paper. Its solidification under enhanced gravity fields resulted in refinement of the columnar nondendritic grain structure and an increase in the area fraction of the body-centered cubic (BCC) structure phases. read more The mass transfer strengthened by enhanced gravity promoted element diffusion and enrichment, which caused changes in the composition and microstructure that, in turn, affected the mechanical properties of the alloy. The compressive strength and plasticity of the sample solidified at acceleration 360 g were equal to 2845 MPa and 36.4%, respectively, which are the highest values reported to date for Al0.9CoCrFeNi alloy.The Laplace distribution of random processes was observed in numerous situations that include glasses, colloidal suspensions, live cells, and firm growth. Its origin is not so trivial as in the case of Gaussian distribution, supported by the central limit theorem. Sums of Laplace distributed random variables are not Laplace distributed. We discovered a new mechanism leading to the Laplace distribution of observable values. This mechanism changes the contribution ratio between a jump and a continuous parts of random processes. Our concept uses properties of Bernstein functions and subordinators connected with them.Currently, deep learning has shown state-of-the-art performance in image classification with pre-defined taxonomy. However, in a more real-world scenario, different users usually have different classification intents given an image collection. To satisfactorily personalize the requirement, we propose an interactive image classification system with an offline representation learning stage and an online classification stage. During the offline stage, we learn a deep model to extract the feature with higher flexibility and scalability for different users' preferences. Instead of training the model only with the inter-class discrimination, we also encode the similarity between the semantic-embedding vectors of the category labels into the model. This makes the extracted feature adapt to multiple taxonomies with different granularities. During the online session, an annotation task iteratively alternates with a high-throughput verification task. When performing the verification task, the users are only required to indicate the incorrect prediction without giving the exact category label. For each iteration, our system chooses the images to be annotated or verified based on interactive efficiency optimization. To provide a high interactive rate, a unified active learning algorithm is used to search the optimal annotation and verification set by minimizing the expected time cost. After interactive annotation and verification, the new classified images are used to train a customized classifier online, which reflects the user-adaptive intent of categorization. The learned classifier is then used for subsequent annotation and verification tasks. Experimental results under several public image datasets show that our method outperforms existing methods.In order to study the spread of an epidemic over a region as a function of time, we introduce an entropy ratio U describing the uniformity of infections over various states and their districts, and an entropy concentration coefficient C=1-U. The latter is a multiplicative version of the Kullback-Leibler distance, with values between 0 and 1. For product measures and self-similar phenomena, it does not depend on the measurement level. Hence, C is an alternative to Gini's concentration coefficient for measures with variation on different levels. Simple examples concern population density and gross domestic product. Application to time series patterns is indicated with a Markov chain. For the Covid-19 pandemic, entropy ratios indicate a homogeneous distribution of infections and the potential of local action when compared to measures for a whole region.A novel compressed air energy storage (CAES) system has been developed, which is innovatively integrated with a coal-fired power plant based on its feedwater heating system. In the hybrid design, the compression heat of the CAES system is transferred to the feedwater of the coal power plant, and the compressed air before the expanders is heated by the feedwater taken from the coal power plant. Furthermore, the exhaust air of the expanders is employed to warm partial feedwater of the coal power plant. Via the suggested integration, the thermal energy storage equipment for a regular CAES system can be eliminated and the performance of the CAES system can be improved. Based on a 350 MW supercritical coal power plant, the proposed concept was thermodynamically evaluated, and the results indicate that the round-trip efficiency and exergy efficiency of the new CAES system can reach 64.08% and 70.01%, respectively. Besides, a sensitivity analysis was conducted to examine the effects of ambient temperature, air storage pressure, expander inlet temperature, and coal power load on the performance of the CAES system.
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