Chongqing Three Gorges University (CTGU, simplified Chinese: 重庆三峡学院; traditional Chinese: 重慶三峽學院), established in 1956, is a national comprehensive university in Wanzhou District, Chongqing, at the heart of the Three Gorges areas on the Yangtze River. It presently[when?] has a total enrolment of 13,000 full-time domestic students and international students; and a staff of about 1,000, including about 300 professors and associate professors, about 400 Master's Degree or Doctor's Degree winners, and more than 60 external part-time Professors and international teachers. CTGU has 14 teaching faculties. It has more than 70 specialties for 3-year students and 4-year students, which cover eight disciplinary domains.[citation needed] It has prominent advantages in the teaching and research of the specialties of marketing, international trade, environmental protection, folk art, languages and literature, physical education, tourism, biology, chemical engineering, ethnonymics, and occupies the leading position in China in some of the specialties and subjects (As of 2009[update]).[citation needed]CTGU has established wide cooperation and exchange relationship with many universities both at home and abroad, and has developed teachers/students exchange and academic exchange.[citation needed] In 2007, CTGU established collaboratively a Confucius Institute at Community College of Denver in Colorado, USA.
Multimodal optimization problem (MMOP) seeks multiple solutions for a single objective under varying preferences. Differential Evolution (DE), known for its strong search capability, is widely applied, with niching as a key divide-and-conquer assistance technique. However, existing niching methods often fail to capitalize on population distribution knowledge accumulated through historical iterations, and are often hindered by parameter sensitivity and complex designs. To this end, we propose AMDE-GN, an adaptive multimodal DE framework with granular ball niching. It leverages a granular ball structure to adaptively partition the population from coarse-to-fine based on distance and distribution driven criteria. This regionalizes learning and search within subpopulations, effectively preserving diversity and enabling simultaneous tracking of different global optima. We further introduce an elite-led mutation strategy that maintains stochastic exploration while biasing search toward promising regions, improving convergence efficiency. In addition, AMDE-GN incorporates adaptive DE parameter control and heterogeneity-aware local search, applying differentiated refinement according to individual quality to enhance solution accuracy and achieve a better exploration-exploitation balance. Experiments on 20 multimodal benchmark problems confirm that AMDE-GN generally achieves competitive performance against state-of-the-art methods.
Two-dimensional magnetic materials with weak spin-orbit coupling would endow them with great potential for applications in low-power spintronic logic devices. In this work, the stability and magnetism of nonmetal (N, O, F, P) doped 1T-ZrS2 monolayers is systematically studied by using first principles calculations based on density functional theory. Pristine ZrS2 monolayer is a nonmagnetic semiconductor with an indirect band gap of 1.15 eV. Among the configurations of nonmetal-atom adsorption, substitutional doping, and vacancy defects, fluorine adsorption on the ZrS2 monolayer is regarded as an optimal doping strategy. At the concentration of 11.11% in F-adsorbed ZrS2, the spontaneous magnetization of F-adsorbed ZrS2 monolayer occurs at the ground state with the stable magnetic states; the magnetic moments are about 0.674 mu B, which mainly originates from the hybridization between the p-orbitals of S atoms and F atoms (0.315 mu B) and d-orbitals of Zr atoms (0.323 mu B). Moreover, the F-adsorbed ZrS2 monolayer under 0-4% strain delivers consistently low spin polarization energy with stable p-d hybridization, offering their promising potential for their practical applications in low-power spintronic devices.
This paper investigates the finite-time passivity and state estimation problem for quaternion-valued neural networks with two additive delays. By employing the Lyapunov method, several criteria are derived to ensure the finite-time passivity of the discussed system and the asymptotic stability of the error system. A novel controller is proposed to achieve finite-time passivity of the discussed system and a proportional-integral observer (PIO) strategy is adopted to tackle the state estimation problem. The direct approach is used to handle the quaternion-valued neural networks without decomposing them into real-valued or complex-valued systems, which substantially simplifies the analysis procedure. Moreover, various quaternion-valued inequalities are utilized in the analysis, contributing to reduced conservatism in the derived results. Finally, the theoretical results have been effectively demonstrated through two numerical simulation examples.
It is known that the weak adsorption of O2 on the photocatalyst surface and insufficiency of photoelectrons at the active sites generally restrict the photocatalytic reduction of O2 into H2O2. Herein we propose to design a p-type Schottky junction to address these issues. As an example, Au/MoS2 Schottky junctions have been constructed by decorating Au nanoparticles (NPs) on the surface of MoS2 hierarchical nanospheres. It is demonstrated that the coupling between Au and MoS2 results in the free electron transfer from Au (with higher Fermi level) to MoS2 (with lower Fermi level), thereby enhancing O2 adsorption on the electron-deficient Au delta+ active sites. During photocatalysis process, photoelectrons are driven from MoS2 to Au by the created interface electric field at the Au/MoS2 junction, significantly increasing the photoelectron density at the Au active sites for the photoreduction reactions. Photocatalytic experiment shows remarkable photocatalytic activity of the Au/MoS2 photocatalysts for H2O2 synthesis; particularly, the H2O2 yield rate reaches 1.06 mmol g-1 h-1 over 0.5Au/MoS2, which is increased by 2.6 times in comparison with that over MoS2 (0.41 mmol g-1 h-1). This study highlights an intriguing strategy for boosting photocatalytic synthesis of H2O2.
ABSTRACT In this paper, we investigate a distributed constrained optimisation problem over directed networks. The agents in the networks conduct local computations and communications, endeavouring to collaboratively minimise the aggregation of all locally known convex cost functions subject to a global constraint set. However, since the agents are constantly transmitting information, most existing algorithms for this problem are prone to communication burdens, especially in large‐scale networks under a limited communication bandwidth. Problems of this nature emerge in a number of applications, mostly evident in distributed classification tasks, distributed image restoration, distributed compressive sensing etc. To solve these kinds of problems, we propose an effective quantised push‐sum distributed adaptive momentum (QPS‐DADAM) algorithm. On the one hand, the QPS‐DADAM algorithm employs the random quantiser to reduce the communication overhead and avoid the channel blockage. On the other hand, the QPS‐DADAM algorithm incorporates the adaptive momentum method into the push‐sum protocol to further accelerate the convergence over directed networks. Rigorous theoretical analyses are provided to illustrate that the QPS‐DADAM algorithm converges sublinearly to the optimal solution. In addition, numerical simulations further demonstrate the efficacy of the QPS‐DADAM algorithm and the correctness of the theoretical discoveries.
