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This study explores the different learning practices of Chinese incubators in Chongqing and Chengdu and delves into how these “learning huddles” influence incubatees' absorptive capacity (the ability to apply knowledge) to improve their chance of success (sustainable growth).

This explorative study uses a qualitative case study approach by means of semi-structured interviews with business incubation managers and incubatees across three business incubators in Chengdu and Chongqing. The data are transcribed, coded and analyzed using an analytic map for the explanation of building and reflecting on the theoretical propositions, leading to a further understanding of the “learning huddle” mechanism.

The study finds that incubatees perceive that their absorptive capacity is increased through vicarious informal learning practices that promote access to networks and thereby builds social capital to improve their likelihood of success.

This study has limitations in sample size and design. The explorative case study approach uses a nonrandom case selection of three incubators in Chongqing and Chengdu and has a limited number of interviewees, which may lack representation of the general Chinese business incubation population and may not sufficiently be generalized beyond the sample itself.

These findings have important implications for business incubation programs. Business incubators that build learning huddles (networks) create a nurturing shared learning environment, which is suitable for incubatees to collectively absorb knowledge at the early stage of their life cycle and improve their likelihood of sustainable growth.

Since this study is limited to a Chinese context, it is also hoped that future researchers use the typology of business incubator learning practices to explore cross-culture variables, as these may influence the business incubation operations and performance.

This study adds to the discussion on how collective learning practices facilitate absorptive capacity and build social capital, which in turn improves incubatees' chance of sustainable growth and as such the authors hope that the learning practice's typology and how incubatees determine their success stimulates further research for measuring the likelihood of incubatees sustainable growth.

This paper aims to study the application of reinforcement learning (RL) in the control of an output-constrained flapping-wing micro aerial vehicle (FWMAV) with system uncertainty.

A six-degrees-of-freedom hummingbird model is used without consideration of the inertial effects of the wings. A RL algorithm based on actor–critic framework is applied, which consists of an actor network with unknown policy gradient and a critic network with unknown value function. Considering the good performance of neural network (NN) in fitting nonlinearity and its optimum characteristics, an actor–critic NN optimization algorithm is designed, in which the actor and critic NNs are used to generate a policy and approximate the cost functions, respectively. In addition, to ensure the safe and stable flight of the FWMAV, a barrier Lyapunov function is used to make the flight states constrained in predefined regions. Based on the Lyapunov stability theory, the stability of the system is analyzed, and finally, the feasibility of RL in the control of a FWMAV is verified through simulation.

The proposed RL control scheme works well in ensuring the trajectory tracking of the FWMAV in the presence of output constraint and system uncertainty.

A novel RL algorithm based on actor–critic framework is applied to the control of a FWMAV with system uncertainty. For the stable and safe flight of the FWMAV, the output constraint problem is considered and solved by barrier Lyapunov function-based control.

The purpose of this paper is to investigate passive techniques used in traditional and indigenous architecture in order to decrease energy use in the buildings and to increase thermal and users’ comfort. The city of Erbil is explored where in the rapid transformation and import of Western architectural styles and materials have resulted in ignorance of climate-responsive tradition existing in the city since thousands of years.

In order to propose a design strategy for modern residential buildings in Erbil city, a descriptive and interpretative approach is used as a methodology of this study. A literature review is done to explore the traditional use of passive techniques, and Waziran district of the city is analyzed and used as a pilot site in this study.

Due to the shortage of electric power in the city, residential buildings have limited access to electric power. Therefore, thermal comfort and reduction of the energy use in residential buildings have become vital for Erbil. The use of passive techniques in architectural design will help to reduce energy dependency.

This study is limited to residential function in Erbil. Waziran district is used for the design proposals where dwellings are in a row context. The proposals are made on a geometrical basis and plan organization; however, the selection of construction materials is not included.

There is a proposal to reduce the use of electricity, which currently has limited access in Erbil city.

On a microgravity condition, a motion of soliton might be subject to a microgravity-induced motion. There is no theory so far to study the effect of air density and gravity on the motion property. Here, the author considers the air as discrete molecules and a motion of a soliton is modeled based on He’s fractal derivative in a microgravity space. The variational principle of the alternative model is constructed by semi-inverse method. The variational principle can be used to establish the conservation laws and reveal the structure of the solution. Finally, its approximate analytical solution is found by using two-scale method and homotopy perturbation method (HPM).

The author establishes a new fractal model based on He’s fractal derivative in a microgravity space and its variational principle is obtained via the semi-inverse method. The approximate analytical solution of the fractal model is obtained by using two-scale method and HPM.

He’s fractal derivative is a powerful tool to establish a mathematical model in microgravity space. The variational principle of the fractal model can be used to establish the conservation laws and reveal the structure of the solution.

The author proposes the first fractal model for the soliton motion in a microgravtity space and obtains its variational principle and approximate solution.

The purpose of this paper is to describe the Lane–Emden equation by the fractal derivative and establish its variational principle by using the semi-inverse method. The variational principle is helpful to research the structure of the solution. The approximate analytical solution of the fractal Lane–Emden equation is obtained by the variational iteration method. The example illustrates that the suggested scheme is efficient and accurate for fractal models.

The author establishes the variational principle for fractal Lane–Emden equation, and its approximate analytical solution is obtained by the variational iteration method.

The variational iteration method is very fascinating in solving fractal differential equation.

The author first proposes the variational iteration method for solving fractal differential equation. The example shows the efficiency and accuracy of the proposed method. The variational iteration method is valid for other nonlinear fractal models as well.

This paper aims to shows the ability of the EDC model with a global reaction mechanism to describe reactions in the Eulerian simulation of a circulating fluidized bed (CFB).

The eddy dissipation concept (EDC) model is embedded in an Eulerian-Eulerian approach to simulate homogeneous reactions.

EDC_G is better than ED_FR in describing chemical reactions. The reaction of CH₄ with O₂ is faster than that of CO with O₂, and NH₃ is more liable to be converted than HCN. The combustion rate is higher than the Boudouard reaction rate of coal particles.N₂O is mainly reduced by carbon, and NO is mainly converted by carbon into N₂ and CO₂.

The EDC model with a global reaction mechanism is embedded in a multi-fluid Eulerian approach to simulate the homogeneous reactions in the coal combustion in a CFB, including combustion of volatile gases, desulfurizing reactions and NO_x reactions.

The purpose of this paper is to find the solution of classical nonlinear shallow-water wave (SWW) equations in particular to the tsunami wave propagation in crisp and interval environment.

Homotopy perturbation method (HPM) has been used for handling crisp and uncertain differential equations governing SWW equations.

The wave height and depth-averaged velocity of a tsunami wave in crisp and interval cases have been obtained.

Present results by HPM are compared with the existing solution (in crisp case), and they are found to be in good agreement. Also, the residual error of the solutions is found for the convergence conformation and reliability of the present results.

A community demonstrates the unique qualities and relationships between its members that distinguish it from other communities within a network. Network analysis relies heavily on community detection. Despite the traditional spectral clustering and statistical inference methods, deep learning techniques for community detection have grown in popularity due to their ease of processing high-dimensional network data. Graph convolutional neural networks (GCNNs) have received much attention recently and have developed into a potential and ubiquitous method for directly detecting communities on graphs. Inspired by the promising results of graph convolutional networks (GCNs) in analyzing graph structure data, a novel community graph convolutional network (CommunityGCN) as a semi-supervised node classification model has been proposed and compared with recent baseline methods graph attention network (GAT), GCN-based technique for unsupervised community detection and Markov random fields combined with graph convolutional network (MRFasGCN).

This work presents the method for identifying communities that combines the notion of node classification via message passing with the architecture of a semi-supervised graph neural network. Six benchmark datasets, namely, Cora, CiteSeer, ACM, Karate, IMDB and Facebook, have been used in the experimentation.

In the first set of experiments, the scaled normalized average matrix of all neighbor's features including the node itself was obtained, followed by obtaining the weighted average matrix of low-dimensional nodes. In the second set of experiments, the average weighted matrix was forwarded to the GCN with two layers and the activation function for predicting the node class was applied. The results demonstrate that node classification with GCN can improve the performance of identifying communities on graph datasets.

The experiment reveals that the CommunityGCN approach has given better results with accuracy, normalized mutual information, F1 and modularity scores of 91.26, 79.9, 92.58 and 70.5 per cent, respectively, for detecting communities in the graph network, which is much greater than the range of 55.7–87.07 per cent reported in previous literature. Thus, it has been concluded that the GCN with node classification models has improved the accuracy.

The purpose of this paper is to explore the similarity of the reputation of political leaders with those of their parties and to assess the claim of causal links.

A multidimensional measure of brand personality is used to measure the reputation among voters of the three main parties and their leaders in two surveys each prior to British General Elections in 2001 and 2005.

The reputations of leader and party are highly correlated, but statistically distinct in both studies. The leader's reputation appears to influence that of the party more than vice versa. However, the decline in Tony Blair's reputation between 2001 and 2005 appears to have influenced more those loyal to other parties.

Further work would be useful to compare the relative value of cognitive and affective measures of reputation, particularly in predicting voting behaviour.

The findings emphasise the role of the leader's reputation in managing that of a political party. A change of leader will, inevitably, produce a change in party reputation. The two reputations interact and monitoring such effects will require similar ways of measuring both.

Links between the reputations of organisations and their leaders have been claimed but never demonstrated empirically.

This study aims to achieve the post-stall pitching maneuver (PSPM) and decrease the deflection frequency of aircraft actuators controlled by the robust backstepping method based on event-triggered mechanism (ETM), nonlinear disturbance observer (NDO) and dynamic surface control (DSC) techniques.

To estimate unsteady aerodynamic disturbances (UADs) to suppress their adverse effects, the NDO is designed. To avoid taking the derivative of the virtual control law directly and eliminate the coupling term of the system states and dynamic surface errors in the stability analysis, an improved DSC is developed. Combined with the NDO and DSC techniques, a robust backstepping method is proposed to achieve the PSPM. Furthermore, to decrease the deflection frequency of the aircraft actuators, a state-dependent ETM is introduced.

An ETM-and-NDO-based backstepping method with an improved DSC technique is developed to achieve the PSPM and decrease the deflection frequency of aircraft actuators. And simulation results are presented to verify the effectiveness of the proposed paper.

Few studies have been conducted on the control of the PSPM in which the lateral and longitudinal attitude dynamics are coupled with each other considering the UADs. Moreover, the mechanism that can decrease the deflection frequency of aircraft actuators is rarely developed in existing research. This study proposes an ETM-and-NDO-based backstepping scheme to address these problems with satisfactory performance of the PSPM.