Dual Evolution Research Articles

A cryogenic incremental sheet forming process for improving the formability of AA6061 to reveal the dual enhancement effect and microstructure evolution mechanism

Based on the dual enhancement effect of hardening and plasticity in aluminum alloys at cryogenic temperatures, the cryogenic incremental sheet forming process is introduced in this paper for manufacturing complex components. Experimental results indicate that incremental sheet forming at cryogenic environments results in a remarkable enhancement of formability. The ultimate forming height of specimen at 113K presents an increase of 32.4% compared with the specimen at 295K. Meanwhile, it is confirmed that cryogenic conditions increase the work-hardening ability and reduce the microcrack generation on the specimen surface. Moreover, the mechanism of dual enhancement effect on 6061 alloy during cryogenic incremental forming was studied. At 295 K, the dislocation distribution was localized due to significant cross-slip in the specimens. It was also observed that dislocation entanglement occurred at grain boundaries, which tends to cause stress concentration and therefore reduced formability. In contrast, at 113 K, the decrease in stacking fault energy leads to suppression of cross-slip, and the uniform slip leads to a significant increase in dislocation density. As a result, the cryogenic temperature exhibits enhanced work-hardening ability and formability. The rolling texture evolves mainly along the α and β orientation lines during the forming process, the 295K specimen generates a large number of Goss textures in the final forming region due to the uneven deformation which makes it difficult for the texture to evolve further. In contrast, at 113 K the texture fully evolves and intersects in the Brass texture along the two orientation lines due to the enhanced formability.

Structural Characteristics and Evolution of the Dual Network of Patent Technology Collaboration and Innovation in China–Japan–ROK

In the context of a new round of scientific and technological revolution and industrial transformation, inter-regional international cooperation is facing fierce competition and sustainable development pressure in domestic, geopolitical, and global industrial chains and that a rational division of labor and coordination of cooperative innovation subjects, key technology nodes, and technology subgroups are of great importance to improve and upgrade the industrial and supply chain cooperation of China–Japan–ROK, as well as to enhance the efficiency of cooperation and innovation. This study uses the patented technical cooperation and innovation dual network structure analysis model and social network analysis (SNA) to analyze the dual network relationship and evolution characteristics of patent technology cooperation and innovation at different stages, based on data from 5912 invention patents applied by China, Japan, and ROK. We find that the China–Japan–ROK patent technology collaboration network is unmatched in size, and the areas of cooperation are expanding on a daily basis. However, the network’s innovation activities have not yet stabilized, and there is still room for collaborative innovation among enterprises to grow and evolve. Multinational corporations in Japan and South Korea have occupied the network’s core position at various times, forming seven key innovation groups with high-tech enterprises such as Samsung Display, Samsung Electronics, Hyundai Motor, NEC, and LEKIN as core nodes. Patents such as H01L, G02F, H04N, H01M, and G02B dominate the key technology nodes and technology subgroups, indicating that high-tech patents such as electronic information technology, semiconductors, displays, and automobile manufacturing are the primary areas of cooperation and innovation between China, Japan, and South Korea.

Dual solar-driven hydrogen evolution and alcohol oxidation with CdS quantum dot-sensitized photocatalysts prepared by the SILAR methodology

The first use of the successive ionic layer adsorption and reaction (SILAR) method to grow CdS quantum dots on TiO2 nanoparticles, achieving efficient photocatalytic alcohol oxidation and H2 evolution without catalysts or sacrificial donors.

A Co-Evolutionary Dual Niching Differential Evolution Algorithm for Nonlinear Equation Systems Optimization

A Co-Evolutionary Dual Niching Differential Evolution Algorithm for Nonlinear Equation Systems Optimization

Multistage interface engineered cobalt polysulfides core-shell nanostructures for dual energy storage devices and hydrogen evolution

Designing highly efficient electrode materials is one of the key issues for developing high performance energy storage devices and electrolytic hydrogen production. Herein, binder-free core-shell CoSx@CoNi2S4/CC nanocomposites were successfully prepared via calcination-sulfurization-electrodeposition using in-situ grown ZIF-67 nanorods as the precursor and self-sacrificial template. Attributed to the excellent electrical conductivity of the metal polysulfides core and CoNi2S4 shell and the unique nanosheet/nanoflower morphology with multi-dimensional interfaces providing rich active centers and fast transport paths, the synthesized CoSx@CoNi2S4/CC not only presented excellent energy storage performance, but also delivered good activity toward hydrogen evolution reaction. The optimal CoSx@CoNi2S4-20/CC exhibited high capacities of 985 Cg−1 (i.e., 328.3 mA h g−1; capacitance of 1970 F g−1) at 1 A g−1 and excellent rate performance (92% capacity retention at 10 A g−1). Density-functional theory calculations reveal that the metal polysulfides presented great OH adsorption ability. The assembled CoSx@CoNi2S4/CC//AC hybrid supercapacitor and CoSx@CoNi2S4/CC//Fe supercapacitor-type battery showed high energy density (50 Wh kg−1 at 799 W kg−1 and 53.3 Wh kg−1 at 800 W kg−1) and cycle stability (about 81% of the initial specific capacity at 10 A g−1 after 6000 cycles).

Adaptive dual niching-based differential evolution with resource reallocation for nonlinear equation systems

Adaptive dual niching-based differential evolution with resource reallocation for nonlinear equation systems

Optimization of terahertz MIMO array radar based on dual strategy differential evolution algorithm

Optimization of terahertz MIMO array radar based on dual strategy differential evolution algorithm

Application of fuzzy evolutionary methods for the development of dual-education projects

The article aims to present a method of specialties cases management based on models of system evolution within the frame-work of a pilot projectin institutions of professional pre-higher and higher education of Ukraine for the training of specialists in the dual form of education. The obtained data allowed determining that the methodology of project management can be successfully implemented in Ukrainian dual education. However, the Ukrainian system of dual education still lacks many elements, and some of them cannot be reproduced in the national environment, because the student himself can choose a form of education. The article pre-sents a simulation of the dual education system evolution in a specific specialty in the form of a multicriterial optimization problem. Well-known McKinsey model is adapted to determine the variant of the strategy for managing the dual education system. With the help ofsuch model, the specialties of the educational institution were ranked as candidates for investment according to the criterion of future profit. The trajectory of the specialty evolution has been developed by means of the method for assessing of qualitative factors, such as the attractiveness of the labor market and the competitive status of the educational institution. The inclusion of the specialty evolution trajectory in the integral criteria of the McKinsey model, made it possible to not onlyestablisha competitive sta-tus for the current period, but also extrapolate it without significant changes to the entire strategic period to predict the competitive status of a particular specialty. It is proposed to apply a decision support system based on the useof an apparatus of fuzzy sets and fuzzy logic to formalize decision-making in an intelligent system. The using of the fuzzy logic apparatus made it possible to adjust and clarify the strategic positions of each business unit of the dual education project portfolio. Thus, a significant practical effect was obtained, since the using of this fuzzy system allows to quickly assessingthe forecast positions of dual education projects, to evaluate the future competitive status of the educational product and the management strategy of this project

Zn and Ni dual hydrogen evolution sites integrated onto CdS for effective photocatalytic hydrogen production

Effective and affordable photocatalysts play an important role in the cost reduction of solar hydrogen production through photocatalytic water splitting. In this research, a Zn2+ and Ni2+ modified carbon hydrogen evolution cocatalyst (ZnNi-C) were synthesized and immobilized onto the surface of CdS (CdS@ZnNi-C) for photocatalytic hydrogen evolution (PHE). The preparation used an ethylenediaminetetraacetic acid disodium (EDTA)-assisted method. The hydrogen evolution rate and apparent quantum efficiency (AQE) at 420 nm for an optimized CdS@ZnNi-C photocatalyst are15.7 mmol/h/g and 31.4 %, respectively. The rate is 2.4 times and 3.7 times greater than those of Zn2+ and Ni2+ modified carbon cocatalysts (Zn-C) and (Ni-C) on CdS (CdS@Zn-C) and (CdS@Ni-C), respectively. Density functional theory calculations indicate that the elevated catalytic performance of CdS@ZnNi-C is due to the co-anchored Zn2+ and Ni2+ in carbon layers that lead to a favorable Gibbs free energy (ΔGH*) for hydrogen evolution. This approach is valuable for the design of stable and efficient photocatalysts for renewable hydrogen production.

Enhancement in the photoelectrochemical performance of BiVO4 photoanode with high (0 4 0) facet exposure

Morphology and geometrical dimensions play crucial roles in the photoelectrochemical (PEC) performance of bismuth vanadate (BiVO4) for water splitting. Decahedral BiVO4 was synthesized through a facile hydrothermal process, which exhibited superior charge injection efficiency to the nanoporous counterpart prepared by the traditional method. More importantly, the crystal size and facet proportion of BiVO4 decahedrons were facilely controlled. The charge separation efficiency can be significantly improved with a reduction in the crystal size and an increase in (040) facet exposure. A new method was developed for rate law analysis: illumination intensity-modulated oxygen evolution reaction rate versus open circuit potential difference, which suggested that the surface reaction kinetics was not affected by facet regulation. Furthermore, after decorating the FeOOH and NiOOH as dual oxygen evolution cocatalysts, an enhanced photocurrent density of 3.2mAcm-2 at 1.23V versus reversible hydrogen electrode and interfacial charge injection efficiency of 97.0% can be reached. Our work inspires the development of facet-regulated BiVO4 photoanodes with high charge injection efficiency in the PEC field and provides a feasible route to enhance its charge separation efficiency.

Rapidity evolution of the entanglement entropy in quarkonium: Parton and string duality

We investigate the quantum entanglement in rapidity space of the soft gluon wave function of a quarkonium in theories with nontrivial rapidity evolutions. We found that the rapidity evolution drastically changes the behavior of the entanglement entropy, at any given order in perturbation theory. At large ${N}_{c}$, the reduced density matrices that ``resum'' the leading rapidity logs can be explicitly constructed, and shown to satisfy Balitsky-Kovchegov-like evolution equations. We study their entanglement entropy in a simplified $1+1$ toy model and in 3D QCD. The entanglement entropy in these cases, after resummation, is shown to saturate the Kolmogorov-Sinai bound of 1. Remarkably, in 3D QCD the essential growth rate of the entanglement entropy is found to vanish at large rapidities, a result of kinematical ``quenching'' in transverse space. The one-body reduction of the entangled density matrix obeys a Balitsky-Fadin-Kuraev-Lipatov evolution equation, which can be recast as an evolution in an emergent AdS space, at large impact parameter and large rapidity. This observation allows the extension of the perturbative wee parton evolution at low $x$, to a dual nonperturbative evolution of string bits in curved ${\mathrm{AdS}}_{5}$ space, with manifest entanglement entropy in the confining regime.

Dual Closed-Loops Capacity Evolution Prediction for Energy Storage Batteries Integrated with Coupled Electrochemical Model

The health assessment for energy storage batteries matters in the context of carbon neutrality. Dual closed-loops capacity framework integrated with a reduced-order electrochemical model including triple side reactions is put forward, realizing parameter correction for health evaluation. Simplified microgrid aging experiment is formulated to test the closed-loop matching between the aging mechanism and electrochemical model relying on incremental capacity analysis. In addition, taking into account the future degradation prediction for energy storage system, the reliable capacity output afterwards acts as references for closed-loop parameter updating in empirical model to predict degradation evolution. The framework proposed implements the closed-loop dynamic updating for aging parameters with ideal error within 2%, making up for the lack of aging mechanism interpretation of accustomed empirical or data-driven black box model in the field of energy storage batteries.

Fast heuristic-based source mask optimization for EUV lithography using dual edge evolution and partial sampling.

Extreme ultraviolet (EUV) lithography is essential in the advanced technology nodes. Source mask optimization (SMO) for EUV lithography, especially the heuristic-based SMO, is one of the vital resolution enhancement techniques (RET). In this paper, a fast SMO method for EUV based on dual edge evolution and partial sampling strategies is proposed to improve the optimization efficiency and speed of the heuristic algorithm. In the source optimization (SO) stage, the position and intensity of the source points are optimized in turn. Using the sparsity of the optimized source, a partial sampling encoding method is applied to decrease the variables' dimension in optimization. In the mask optimization (MO) stage, the main features (MF) and the sub-resolution assistant features (SRAF) are optimized in turn. A dual edge evolution strategy is used in the MF optimization and the partial sampling encoding method is used in SRAF optimization. Besides, the imaging qualities at different focal planes are improved by SRAF optimization. The optimization efficiency is greatly improved by the dimensionality reduction strategies. Simulations are carried out with various target patterns. Results show the superiority of the proposed method over the previous method, especially for large complex patterns.

Zif‐Derived Electrocatalysis: Dual Evolution in Defect and Morphology of Single‐Atom Dispersed Carbon Based Oxygen Electrocatalyst (Adv. Funct. Mater. 19/2021)

Zif‐Derived Electrocatalysis: Dual Evolution in Defect and Morphology of Single‐Atom Dispersed Carbon Based Oxygen Electrocatalyst (Adv. Funct. Mater. 19/2021)

Dual evolution mixed variational analysis of multiphase flow in the subsurface

Dual evolution mixed variational formulations of multiphase flow models in the subsurface are established and analyzed. Specifically, compositional multiphase flow of a triphasic water–oil–gas multicomponent flow is treated, with a dual Gibbs oil–gas mass transfer model. Qualitative analysis of the mixed variational models is performed applying composition duality principles and methods. For multiphysics as well as parallel computing, macro-hybrid variational reformulations are considered. Dual semi-implicit time-marching schemes are presented as semi-discrete approximations for the flow-transport variational models, as well as for a precondition augmented exactly penalized dual Gibbs optimality variational formulation, which are all implementable as parallel proximal penalty–duality algorithms. In this manner, bases for fully discrete non-conforming macro-hybrid mixed finite element numerical realizations are rendered.

Dual Evolution in Defect and Morphology of Single‐Atom Dispersed Carbon Based Oxygen Electrocatalyst

AbstractThe structure design and atomic modulation of catalysts are two sides of the same coin, both of which are deemed critical factors to regulate the intrinsic electrocatalytic activity. Herein, cobalt single‐atom anchored on nitrogen‐doped graphene‐sheet@tube (CoSAs‐NGST) is derived from a novel Co, Zn‐coordinated zeolitic imidazolate framework (CoZn‐ZIF) in the presence of dicyandiamide. CoSAs‐NGST exhibited a hybrid structure with a bamboo‐like graphene tube and sheet. The atomic configuration of intrinsic defects is characterized by electron energy loss spectroscopy. The morphology differentiation from cake‐shape structure to low‐dimension hybrid not only enhances the dispersity of single atoms but also induces defect state evolution, which results in the formation of a CoN4‐rich graphene tube. Density functional theory (DFT) modeling revealed that the coupling effect on oxygen reduction reaction and oxygen evolution reaction (ORR/OER) pathways of Co‐N4‐tube and Co‐N4‐sheet is responsible for the enhanced activity of CoSAs‐NGST. In addition to the superb ORR/OER bifunctional catalytic performance, CoSAs‐NGST also demonstrates a notably small charge–discharge voltage drop of 0.93 V when applied in the rechargeable zinc–air battery outperforming Pt/C + RuO2 catalyst. The present study provides an insight into the relationship between the structure design and atomic modulation of the carbon based catalysts.

CDE-GAN: Cooperative Dual Evolution-Based Generative Adversarial Network

Generative adversarial networks (GANs) have been a popular deep generative model for real-world applications. Despite many recent efforts on GANs that have been contributed, mode collapse and instability of GANs are still open problems caused by their adversarial optimization difficulties. In this article, motivated by the cooperative co-evolutionary algorithm, we propose a cooperative dual evolution-based GAN (CDE-GAN) to circumvent these drawbacks. In essence, CDE-GAN incorporates dual evolution with respect to the generator(s) and discriminators into a unified evolutionary adversarial framework to conduct effective adversarial multiobjective optimization. Thus, it exploits the complementary properties and injects dual mutation diversity into the training, to steadily diversify the estimated density in capturing multimodes and improve generative performance. Specifically, CDE-GAN decomposes the complex adversarial optimization problem into two subproblems (generation and discrimination), and each subproblem is solved with a separated subpopulation (<i>E-Generators</i> and <i>E-Discriminators</i>), evolved by its own evolutionary algorithm. Additionally, we further propose a <i>Soft Mechanism</i> to balance the tradeoff between E-Generators and E-Discriminators to conduct steady training for CDE-GAN. Extensive experiments on one synthetic dataset and three real-world benchmark image datasets demonstrate that the proposed CDE-GAN achieves a competitive and superior performance in generating good quality and diverse samples over baselines. The code and more generated results are available at our project homepage <i><uri>https://shiming-chen.github.io/CDE-GAN-website/CDE-GAN.html</uri></i>.

VARIATION ON A THEME BY KISELEV AND NAZAROV: HÖLDER ESTIMATES FOR NONLOCAL TRANSPORT-DIFFUSION, ALONG A NON-DIVERGENCE-FREE BMO FIELD

AbstractWe prove uniform Hölder regularity estimates for a transport-diffusion equation with a fractional diffusion operator and a general advection field in of bounded mean oscillation, as long as the order of the diffusion dominates the transport term at small scales; our only requirement is the smallness of the negative part of the divergence in some critical Lebesgue space. In comparison to a celebrated result by Silvestre, our advection field does not need to be bounded. A similar result can be obtained in the supercritical case if the advection field is Hölder continuous. Our proof is inspired by Kiselev and Nazarov and is based on the dual evolution technique. The idea is to propagate an atom property (i.e., localisation and integrability in Lebesgue spaces) under the dual conservation law, when it is coupled with the fractional diffusion operator.

A Low Discrepancy Heuristic Evolution ELM and Ground Effect Theory-Based Serial Hybrid Soft Sensor Model of Floating Height in Air Cushion Furnace

In air cushion furnace, the floating height is a key process parameter which greatly affects the quality and production efficiency of high quality metal strips. However, the floating height is hard to be collected in the complex and abominable industry environment. Furthermore, due to the flow field characteristics, some important process variables are difficult to accurately calculate by traditional mechanism modeling methods. In order to accurately predict the floating height, firstly, a low discrepancy heuristic evolution ELM and ground effect theory based serial hybrid soft sensor model is proposed, which constituted by a mechanism model and two data driven models. Secondly, based on the force equilibrium equation and ground effect theory, the mechanism model is constructed, which describes the relationship between the floating height and the process variables including the jet impact angle. Thirdly, a low discrepancy heuristic evolution ELM is proposed as the data driven model to predict the jet impinging angle. In the data driven model, the novel dual mutation strategies collaboration differential evolution is proposed to guarantee the low discrepancy and physical applicability of data driven model. The effectiveness of the proposed method was validated on the self-developed air cushion experiment platform and got desirable experimental results. The research lays an important foundation for the successful implementation of monitoring and control of the strip floating process.

Cluster-based dual evolution for multivariate time series: Analyzing COVID-19.

This paper proposes a cluster-based method to analyze the evolution of multivariate time series and applies this to the COVID-19 pandemic. On each day, we partition countries into clusters according to both their cases and death counts. The total number of clusters and individual countries’ cluster memberships are algorithmically determined. We study the change in both quantities over time, demonstrating a close similarity in the evolution of cases and deaths. The changing number of clusters of the case counts precedes that of the death counts by 32 days. On the other hand, there is an optimal offset of 16 days with respect to the greatest consistency between cluster groupings, determined by a new method of comparing affinity matrices. With this offset in mind, we identify anomalous countries in the progression from COVID-19 cases to deaths. This analysis can aid in highlighting the most and least significant public policies in minimizing a country’s COVID-19 mortality rate.