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The authors examined how the identification mechanism of the innovation performance of knowledge employees is affected by empowering leadership by influencing the organizational identification and the moderating effect of leaders on the role expectation of knowledge employees as an essential innovation subject.

The authors employed a mixed-method research approach. The authors collected data from 378 knowledge employees and managers in 20 companies in China's Yangtze River Delta cities. The authors analyzed data using multiple regression analysis forecasting methods.

The authors found that there was an inverted U-shaped relationship between empowering leadership and the innovation performance of knowledge employees; organizational identity played a partial mediating role between empowering leadership and the innovation performance of knowledge employees; role expectation of leaders on the innovation behavior of employees regulated the relationship between the organizational identity and innovation performance of knowledge employees.

This study extends the literature on empowering leadership and innovation performance. This study empirically examines the mediating effect of organizational identity between empowering leadership and innovation performance. In addition, this study empirically examines how empowered leaders' expected innovation level moderates the association between organizational identity and innovation performance.

The purpose of this study is to advance a novel evaluation index system and evaluation approach for ability of older adults in China.

This study constructed a comprehensive older adult ability evaluation index system with 4 primary indicators and 17 secondary indicators. Grey clustering analysis and entropy weight method are combined into a robust evaluation model for the ability of older adults.

The result demonstrates that the proposed grey clustering model is readily available to calculate the disability level of elderly individuals. The constructed index system more comprehensively considers all aspects of the disability of the elderly.

This study provides a quantitative method and a more reasonable index system for the determination of the disability level of the elderly.

With the development of intelligent technology, deep learning has made significant progress and has been widely used in various fields. Deep learning is data-driven, and its training process requires a large amount of data to improve model performance. However, labeled data is expensive and not readily available.

To address the above problem, researchers have integrated semi-supervised and deep learning, using a limited number of labeled data and many unlabeled data to train models. In this paper, Generative Adversarial Networks (GANs) are analyzed as an entry point. Firstly, we discuss the current research on GANs in image super-resolution applications, including supervised, unsupervised, and semi-supervised learning approaches. Secondly, based on semi-supervised learning, different optimization methods are introduced as an example of image classification. Eventually, experimental comparisons and analyses of existing semi-supervised optimization methods based on GANs will be performed.

Following the analysis of the selected studies, we summarize the problems that existed during the research process and propose future research directions.

This paper reviews and analyzes research on generative adversarial networks for image super-resolution and classification from various learning approaches. The comparative analysis of experimental results on current semi-supervised GAN optimizations is performed to provide a reference for further research.

This paper aims to present a topology optimization (TopO) method for designing heat exchangers (HEx) with two working fluids to be kept apart. The introduction of cut–cells gives rise to the cut-cell TopO method, which computes the optimal distribution of an artificial impermeability field and successfully overcomes the weaknesses of the standard density-based TopO (denTopO) by computing the fluid–solid interface (FSI) at each cycle. This allows to accurately solve the flow and conjugate heat transfer (CHT) problem by imposing exact boundary conditions on the computed FSI and results to correct performances computed without the need to re-evaluate the optimized solutions on a body-fitted grid.

The elements of an artificial impermeability distribution field defined on a background grid act as the design variables and allow topological changes to take place. Post-processing them yields two fields indicating the location of the two flow streams inside the HEx. At each TopO cycle, the FSIs computed based on these two fields are used as the cutting surfaces of the cut-cell grid. On the so-computed grid, the incompressible Navier–Stokes equations, coupled with the Spalart–Allmaras turbulence model, and the temperature equation are solved. The derivatives of the objective and constraint functions with respect to the design variables of TopO are computed by the continuous adjoint method, using consistent discretization schemes devised thanks to the “Think Discrete – Do Continuous” (TDDC) adjoint methodology.

The effectiveness of the cut-cell–based TopO method for designing HEx is demonstrated in 2D parallel/counter flow and 3D counter flow HEx operating under both laminar and turbulent flow conditions. Compared to the standard denTopO, its ability to compute FSIs along which accurate boundary conditions are imposed, increases the accuracy of the flow solver, which usually leads to optimal, rather than sub-optimal, solutions that truly satisfy the imposed constraints.

This work proposes a new/complete methodology for the TopO of two-fluid systems including CHT that relies on the cut-cell method. This successfully combines aspects from both TopO and Shape Optimization (ShpO) in a single framework thus overcoming the well-known downsides of standard denTopO regarding its accuracy or the need for a follow-up ShpO after TopO. Instead of adding the well-known Brinkman penalization terms into the flow equations, it computes the FSIs at each optimization cycle allowing the solution of the CHT problem on a cut-cell grid.

Agricultural science and technology (AST) often need the form of industry-university-research cooperation in successfully completing breakthrough agricultural technology innovations. Therefore, AST industry-university-research cooperation is not only the need of the AST development strategy in China but also the only way for the development of agricultural colleges and universities, agricultural scientific research institutions and agricultural enterprises. Among them, in the process of cooperative breakthrough agricultural technology innovation, the correct selection of partners is the basis for ensuring its effective operation.

Aiming at the time-series characteristics and information ambiguity of decision information in the dynamic selection process of AST industry-university-research partners, this research introduces the dynamic intuitionistic fuzzy multi-criteria decision-making method of time degree and orthogonal projection. On this basis, field theory is used to construct the cooperative innovation capability field model of the partners, and the threshold for the partners to enter or exit the system is designed to dynamically select and eliminate the partners.

Results show that this method fully considers the situation of cooperative innovation resources within the AST industry-university-research system and the resource complementarity of candidate partners.

Combined with examples from agricultural scientific research institutions, the applicability and superiority of the model are verified.