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The existing research rarely explains the value of authoritarian leadership in small- and medium-sized enterprises (SMEs). The purpose of this study is to explore how authoritarian leadership facilitates employee followership behaviors of the SMEs in China by considering the moderating effects of cooperative goal interdependence and leader behavioral integrity.

This research tested hypotheses with a two-wave survey from a sample of 258 respondents from SMEs in China. Hypotheses are tested using hierarchical regression analyses.

Findings of the study have indicated the instrumental function of authoritarian leadership in facilitating employee followership behavior in SMEs. In particular, this study shows that the influence of authoritarian leadership on followership behavior was positively related when employees have high cooperative goal interdependence with authoritarian leaders, and when employees perceive a leader's high behavioral integrity.

The study will help SMEs to understand that authoritarian leadership should seek optimal levels of cooperative goals with employees and integrate achievement goals into their career development strategy to enhance their followership behavior. In addition, authoritarian leadership should strive to take actions consistent with their words to guarantee the relationship between achieving goals and sharing mutual goals with employees.

The study contributes to the literature by addressing an important yet under-researched area, i.e. the effectiveness of authoritarian leadership in SMEs. Authoritarian leadership, a demanding and controlling leadership style, is often criticized by scholars. This study elaborates on a three-way interaction implied by self-determination theory in predicting followership behavior. It specifies the different roles of two situational factors (cooperative goal interdependence and leader behavior integrity) in affecting employees' followership behavior.

In this paper, a turbine blade was optimized by multidisciplinary design optimization (MDO) method. This turbine blade optimization is based on the optimization frame software iSIGHT, in which four disciplines (aerodynamics, thermal dynamics, structural mechanics and structural dynamics) have been integrated. Two commercial discipline analysis soft wares, NUMECA and ANSYS, are coupled in the platform iSIGHT. The three dimensional (3‐D) model of a blade was firstly parameterized. And then a set of parameters are chosen to optimize the blade to obtain the better overall properties. The result shows that the overall performances of the turbine blade have been improved remarkably after it is optimized by using the MDO method.

This paper aims to explore various tensions related to the diffusion and reception of the New Qing History (NQH) in China, and more specifically, it aims at underlying a recurrent tension within the core of this debate, between a Global and a Nationalist historical narrative.

The author’s focus is to analyze various texts published in China between 2006 and 2018.

The author argues that the intensity of the current debate is partly related on the one hand, to the fact that NQH undermines various aspects of China’s Nationalist teleology and territorial claims and, on the other hand, to the basic difficulty of accepting the coexistence of various historical representations that are risking to weaken contemporary’s justifications of its rising schemes.

The text presents an original reading of some important issues raised by the NQH debate.

Based on the scrap ammunition processing which is a part of the ordnance supply engineering, this paper established a decision‐making model of locations and amounts of scrap ammunition processing by the use of multiple‐objectives linear programming theory. In addition, an example was analyzed in detail to testify the model further.

This article proposes a chattering‐free sliding mode control scheme with unidirectional auxiliary surfaces (UAS‐SMC) for small miniature autonomous helicopters (Trex 250).

The proposed UAS‐SMC scheme consists of a nested sequence of rotor dynamics, angular rate, Euler angle, velocity and position loops.

It is demonstrated that the UAS‐SMC strategy can eliminate the chattering phenomenon exhibiting in the convenient SMC method and achieve a better approaching speed.

The proposed control strategy is implemented on the helicopter and flight tests clearly demonstrate that a much better performance could be achieved, compared with convenient SMC schemes.

The purpose of this paper is to develop a hybrid‐Trefftz (HT) finite element model (FEM) for simulating heat conduction in nonlinear functionally graded materials (FGMs) which can effectively handle continuously varying properties within an element.

In the proposed model, a T‐complete set of homogeneous solutions is first derived and used to represent the intra‐element temperature fields. As a result, the graded properties of the FGMs are naturally reflected by using the newly developed Trefftz functions (T‐complete functions in some literature) to model the intra‐element fields. The derivation of the Trefftz functions is carried out by means of the well‐known Kirchhoff transformation in conjunction with various variable transformations.

The study shows that, in contrast to the conventional FEM, the HT‐FEM is an accurate numerical scheme for FGMs in terms of the number of unknowns and is insensitive to mesh distortion. The method also performs very well in terms of numerical accuracy and can converge to the analytical solution when the number of elements is increased.

The value of this paper is twofold: a T‐complete set of homogeneous solutions for nonlinear FMGs has been derived and used to represent the intra‐element temperature; and the corresponding variational functional and the associated algorithm has been constructed.

With the accelerating process of urbanization, the greening areas in high density cities are becoming less and less, and the aerial greening provides a possible way for the update of the urban building headspace greening. Based on this, the application of aerial greening in the urban headspace was studied. Starting from the plant, water, landscape sketch, pavement and other construction elements, the ecological art planning and design of the urban building headspace was studied. Then, the transformation of an old factory was studied, and the rain-flood gardens and infiltration floors linked to biological corridors were introduced in the high flood risk areas. In the building headspace, a rainwater harvesting system and an air garden were designed, and the ecological planning of aerial walkway was carried out to the front abandoned viaduct. The practice proves that the introduction of ecological science and technology and art culture in the urban headspace plays an important role in purifying urban air and increasing urban greening.

Previous researches on credit scoring mainly focussed on static modeling on panel sample data set in a certain period of time, and did not pay enough attention on dynamic incremental modeling. The purpose of this paper is to address the integration of branch and bound algorithm with incremental support vector machine (SVM) ensemble to make dynamic modeling of credit scoring.

This new model hybridizes support vectors of old data with incremental financial data of corporate in the process of dynamic ensemble modeling based on bagged SVM. In the incremental stage, multiple base SVM models are dynamically adjusted according to bagged new updated information for credit scoring. These updated base models are further combined to generate a dynamic credit scoring. In the empirical experiment, the new method was compared with the traditional model of non-incremental SVM ensemble for credit scoring.

The results show that the new model is able to continuously and dynamically adjust credit scoring according to corporate incremental information, which helps produce better evaluation ability than the traditional model.

This research pioneered on dynamic modeling for credit scoring with incremental SVM ensemble. As time pasts, new incremental samples will be combined with support vectors of old samples to construct SVM ensemble credit scoring model. The incremental model will continuously adjust itself to keep good evaluation performance.

Traditional case-adaptation methods have poor accuracy, low efficiency and limited applicability, which cannot meet the needs of knowledge users. To address the shortcomings of the existing research in the industry, this paper proposes a case-adaptation optimization algorithm to support the effective application of tacit knowledge resources.

The attribute simplification algorithm based on the forward search strategy in the neighborhood decision information system is implemented to realize the vertical dimensionality reduction of the case base, and the fuzzy C-mean (FCM) clustering algorithm based on the simulated annealing genetic algorithm (SAGA) is implemented to compress the case base horizontally with multiple decision classes. Then, the subspace K-nearest neighbors (KNN) algorithm is used to induce the decision rules for the set of adapted cases to complete the optimization of the adaptation model.

The findings suggest the rapid enrichment of data, information and tacit knowledge in the field of practice has led to low efficiency and low utilization of knowledge dissemination, and this algorithm can effectively alleviate the problems of users falling into “knowledge disorientation” in the era of the knowledge economy.

This study provides a model with case knowledge that meets users’ needs, thereby effectively improving the application of the tacit knowledge in the explicit case base and the problem-solving efficiency of knowledge users.

The adaptation model can serve as a stable and efficient prediction model to make predictions for the effects of the many logistics and e-commerce enterprises' plans.

This study designs a multi-decision class case-adaptation optimization study based on forward attribute selection strategy-neighborhood rough sets (FASS-NRS) and simulated annealing genetic algorithm-fuzzy C-means (SAGA-FCM) for tacit knowledgeable exogenous cases. By effectively organizing and adjusting tacit knowledge resources, knowledge service organizations can maintain their competitive advantages. The algorithm models established in this study develop theoretical directions for a multi-decision class case-adaptation optimization study of tacit knowledge.