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The purpose of this paper is to design and implement a novel type of PCI eXtension for Instruments (PXI) bus‐based airborne data transfer equipment (DTE) test system.

First, the basic principle of PXI bus is introduced in detail. Then, the hardware and software are developed for the PXI bus‐based airborne DTE test system. Based on the description of the basic conceptions of rough set theory, a novel hybrid approach for fault diagnosis in PXI bus‐based airborne DTE test system is proposed, which is based on rough set theory, genetic algorithm and neural network. Combining with rough set theory, genetic algorithm is used to compute the reductions of the decision table. Subsequently, the condition attributes of decision table are regarded as the input nodes of neural network and the decision attributes are regarded as the output nodes of neural network correspondingly.

The exact application results are also presented to verify the feasibility and effectiveness of the developed PXI bus‐based airborne DTE test system, and the test results can also be saved automatically. The exact application results show that the various faults within the PXI bus‐based airborne DTE test system can be located on board level, and the newly developed airborne DTE test system is also easy to be extended and upgraded.

The proposed hybrid rough set theory, genetic algorithm and neural network approach could reduce the number of attributes in the decision table, simplify the structure of neural network and improve the ability of generality. The airborne DTE test system is also capable of different unit under test (UUT), which can be selected by the definite operators at the start of the test, to ensure that failures and problems are handled automatically and without intervention. This newly developed PXI bus‐based airborne DTE test system can be located on board level, and it is also very easy to be extended and upgraded. Practical implementations show that hidden errors can be effectively detected by the developed PXI bus‐based airborne DTE test system. The proposed methodology can help improve the general performance of the airborne DTE test system, and the faults can be checked with minimum time and effort. This system can enhance the army combat capability efficiently.

This paper develops a novel type of PXI bus‐based airborne DTE test system. In particular, a hybrid approach for fault diagnosis in PXI bus‐based airborne DTE test system is proposed, which is based on rough set theory, genetic algorithm and neural network. This approach provides an effective way to diagnosis the faults of the airborne DTE test system.

There is strong reaction between a company and its stakeholders on corporate social responsibility (CSR). The premise is that there should be a valid communication between them. The study researches Chinese situations on one‐way communication between company and stakeholders and builds a model on how to implement strategy on two‐way communication on CSR information between company and stakeholders according to the different characteristic of stakeholders. This paper aims to focus on the issues involved

On the basis of the analysis on stakeholder's situation using double standards, the study makes future research and builds a valid communication model between company and stakeholders.

It is found that the company can implement strategy on two‐way communication on CSR information between company and stakeholders according to different stakeholder situations in the Chinese environment. It also benefits a company's CSR performance and stakeholders' decision.

The present study provides a starting‐point for further research on communication between company and stakeholders in the Chinese situation.

The paper hightlights how companies may draw up valid strategy on two‐way communication on CSR information between company and stakeholders in order to gain better performance on CSR action and pursue stakeholders' supports.

Sparrow search algorithm (SSA) is a novel global optimization method, but it is easy to fall into local optimization, which leads to its poor search accuracy and stability. The purpose of this study is to propose an improved SSA algorithm, called levy flight and opposition-based learning (LOSSA), based on LOSSA strategy. The LOSSA shows better search accuracy, faster convergence speed and stronger stability.

To further enhance the optimization performance of the algorithm, The Levy flight operation is introduced into the producers search process of the original SSA to enhance the ability of the algorithm to jump out of the local optimum. The opposition-based learning strategy generates better solutions for SSA, which is beneficial to accelerate the convergence speed of the algorithm. On the one hand, the performance of the LOSSA is evaluated by a set of numerical experiments based on classical benchmark functions. On the other hand, the hyper-parameter optimization problem of the Support Vector Machine (SVM) is also used to test the ability of LOSSA to solve practical problems.

First of all, the effectiveness of the two improved methods is verified by Wilcoxon signed rank test. Second, the statistical results of the numerical experiment show the significant improvement of the LOSSA compared with the original algorithm and other natural heuristic algorithms. Finally, the feasibility and effectiveness of the LOSSA in solving the hyper-parameter optimization problem of machine learning algorithms are demonstrated.

An improved SSA based on LOSSA is proposed in this paper. The experimental results show that the overall performance of the LOSSA is satisfactory. Compared with the SSA and other natural heuristic algorithms, the LOSSA shows better search accuracy, faster convergence speed and stronger stability. Moreover, the LOSSA also showed great optimization performance in the hyper-parameter optimization of the SVM model.

The purpose of this paper is to propose a novel method to prepare pure Ti powder for 3D printing with tailorable particle size distribution.

The main procedures of the present method consist of gas state reaction to synthesize TiH₂ nanoparticles, agglomeration to obtain micronscale powder particles by spray drying, and densification of particle interior by heat treatment.

The prepared Ti powder has a specific bimodal particle size distribution in a range of small sizes, good sphericity and high flowability. Particularly, this new technique is capable of controlling powder purity and adjusting particle size.

To the best knowledge of the authors, the approach for preparing 3D printing metallic powders from nanoparticles has not been reported in the literature so far. This work provides a novel method that is particularly applicable to prepare 3D printing metallic powders which have small initial particle sizes and high reactivity in the air.

The purpose of this paper is to propose a control approach for small unmanned helicopters, and a novel swarm intelligence algorithm is used to optimize the parameters of the proposed controller.

Small unmanned helicopters have many advantages over other unmanned aerial vehicles. However, the manual operation process is difficult because the model is always instable and coupling. In this paper, a novel optimized active disturbance rejection control (ADRC) approach is presented for small unmanned helicopters. First, a linear attitude model is built in hovering condition according to small perturbation linearization. To realize decoupling, this model is divided into two parts, and each part is equipped with an ADRC controller. Finally, a novel Levy flight-based pigeon-inspired optimization (LFPIO) algorithm is developed to find the optimal ADRC parameters and enhance the performance of controller.

This paper applies ADRC method to the attitude control of small unmanned helicopters so that it can be implemented in practical flight under complex environments. Besides, a novel LFPIO algorithm is proposed to optimize the parameters of ADRC and is proved to be more efficient than other homogenous methods.

The model of proposed controller is built in the hovering action, whereas it cannot be used in other flight modes.

The optimized ADRC method can be implemented in actual flight, and the proposed LFPIO algorithm can be developed in other practical optimization problems.

ADRC method can enhance the response and robustness of unmanned helicopters which make it valuable in actual environments. The proposed LFPIO algorithm is proved to be an effective swarm intelligence optimizer, and it is convenient and valuable to apply it in other optimized systems.

The purpose of this paper is to improve the generation efficiency of singlet oxygen of methylene blue molecules through finely controlling their aggregation states in drug carriers.

As a photosensitiser in photodynamic therapy, methylene blue (MB) was loaded on citrate-modified hydroxyapatite (HAp) through an electrostatic interaction and followed by encapsulation of coordination complexes of tannic acid (TA) and Fe(III) ions. Ultraviolet-visible absorption spectrum of the supernatant after incubation of samples was recorded at certain time interval to investigate the release behaviour of MB. Photodynamic activity of MB was determined by the oxidation reaction of uric acid by singlet oxygen generated by MB under illumination.

Almost all MB molecules were immediately released from HAp-MB, whilst an initial burst release of MB from HAp-MB@TA was followed by a sustainable and pH-sensitised release. In comparison with HAp-MB, photocatalystic reduction of HAp-MB@TA by titanium dioxide hardly occurred under illumination, indicating the stability against reduction to leukomethylene blue in vitro. Generation efficiency of singlet oxygen by MB released from HAp-MB@TA was significantly higher than that from HAp-MB because of the control of TA and Fe(III) ions complexes on molecular structures of released MB.

A facile method was herein demonstrated to optimise the generation efficiency of singlet oxygen by controlling aggregation states of PS molecules and improve PDT efficiency to damage tumour tissues.

Field-effect transistor (FET) has excellent electronic properties and inherent signal amplification, and with the development of nanomaterials technology, FET biosensors with nanomaterials as channels play an important role in the field of heavy metal ion detection. This paper aims to review the research progress of silicon nanowire, graphene and carbon nanotube field-effect tube biosensors for heavy metal ion detection, so as to provide technical support and practical experience for the application and promotion of FET.

The article introduces the structure and principle of three kinds of FET with three kinds of nanomaterials, namely, silicon nanowires, graphene and carbon nanotubes, as the channels, and lists examples of the detection of common heavy metal ions by the three kinds of FET sensors in recent years. The article focuses on the advantages and disadvantages of the three sensors, puts forward measures to improve the performance of the FET and looks forward to its future development direction.

Compared with conventional instrumental analytical methods, FETs prepared using nanomaterials as channels have the advantages of fast response speed, high sensitivity and good selectivity, among which the diversified processing methods of graphene, the multi-heavy metal ions detection of silicon nanowires and the very low detection limit and wider detection range of carbon nanotubes have made them one of the most promising detection tools in the field of heavy metal ions detection. Of course, through in-depth analysis, this type of sensor has certain limitations, such as high cost and strict process requirements, which are yet to be solved.

This paper elaborates on the detection principle and classification of field-effect tube, investigates and researches the application status of three kinds of FET biosensors in the detection of common heavy metal ions. By comparing the advantages and disadvantages of each of the three sensors in practical applications, the paper focuses on the feasibility of improvement measures, looks forward to the development trend in the field of heavy metal detection and ultimately promotes the application of field-effect tube development technology to continue to progress, so that its performance continues to improve and the application field is constantly expanding.

Hyperspectral imaging (HSI) systems are becoming potent technologies for computer vision tasks due to the rich information they uncover, where each substance exhibits a distinct spectral distribution. Although the high spectral dimensionality of the data empowers feature learning, the joint spatial–spectral features have not been well explored yet. Gabor convolutional networks (GCNs) incorporate Gabor filters into a deep convolutional neural network (CNN) to extract discriminative features of different orientations and frequencies. To the best if the authors’ knowledge, this paper introduces the exploitation of GCNs for hyperspectral image classification (HSI-GCN) for the first time. HSI-GCN is able to extract deep joint spatial–spectral features more rapidly and accurately despite the shortage of training samples. The authors thoroughly evaluate the effectiveness of used method on different hyperspectral data sets, where promising results and high classification accuracy have been achieved compared to the previously proposed CNN-based and Gabor-based methods.

The authors have implemented the new algorithm of Gabor convolution network on the hyperspectral images for classification purposes.

Implementing the new GCN has shown unexpectable results with an excellent classification accuracy.

To the best of the authors’ knowledge, this work is the first one that implements this approach.

In the context of fire incidents within buildings, efficient scene perception by firefighting robots is particularly crucial. Although individual sensors can provide specific types of data, achieving deep data correlation among multiple sensors poses challenges. To address this issue, this study aims to explore a fusion approach integrating thermal imaging cameras and LiDAR sensors to enhance the perception capabilities of firefighting robots in fire environments.

Prior to sensor fusion, accurate calibration of the sensors is essential. This paper proposes an extrinsic calibration method based on rigid body transformation. The collected data is optimized using the Ceres optimization algorithm to obtain precise calibration parameters. Building upon this calibration, a sensor fusion method based on coordinate projection transformation is proposed, enabling real-time mapping between images and point clouds. In addition, the effectiveness of the proposed fusion device data collection is validated in experimental smoke-filled fire environments.

The average reprojection error obtained by the extrinsic calibration method based on rigid body transformation is 1.02 pixels, indicating good accuracy. The fused data combines the advantages of thermal imaging cameras and LiDAR, overcoming the limitations of individual sensors.

This paper introduces an extrinsic calibration method based on rigid body transformation, along with a sensor fusion approach based on coordinate projection transformation. The effectiveness of this fusion strategy is validated in simulated fire environments.

This paper aims to investigate the effect of transformational leadership (TL), behavioral integration of top management team (TMT) and team conflict on manager ambidexterity behavior.

Hierarchical linear modeling has been applied to test the degree of influence of TL and behavioral integration of TMT on manager ambidexterity using data collected from 60 chief executive officers (CEOs) and 322 TMT members of small- and medium-sized enterprises in the Chinese electronic commerce industry.

The results suggest the following: transformational leadership is positively associated with the behavioral integration of TMT and a high level of TMT behavioral integration strengthens the positive relationship between transformational leadership and manager ambidexterity. Also, team conflict moderates the mediating role of TMT behavioral integration in the relationship of transformational leadership to manager ambidexterity.

First, this study does not directly test whether transformational leadership encourages a focus on manager ambidexterity, although the results on behavioral integration draw attention to the usefulness of such leadership. Second, in focusing on manager ambidexterity, this paper omits key variables, especially skills and abilities.

Given that several aspects of leadership can be learned and adjusted, the findings suggest that organizations can improve their individual ambidexterity by helping the CEOs develop and display transformational leadership through training and mentoring. TMTs were found to rely mostly on the behavioral integration approach (collaborative behavior, quality of information exchange and joint decision-making) and team conflict management. Such reliance, in turn, predicts effective team behavioral coordination and subsequent manager ambidexterity.

First, this study goes beyond the current research that focuses primarily on ambidexterity at the inter-organizational alliance, firm and business unit levels. This earlier research lacks a conceptually and empirically validated understanding of ambidexterity at the level of the manager. In contrast, by investigating and examining the antecedents of manager ambidexterity behavior, the study develops an individual perspective to elucidate the ambidextrous mechanisms. Second, the study also contributes by explaining how transformational leadership relates to manager ambidexterity. To date, only limited research has disentangled how transformational leaders enhance managers’ teamwork (e.g. behavioral integration) and how such leaders affect the ambidextrous orientation of managers.