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This study aims to investigate the predictive role of team personality composition in facilitating shared leadership through team member exchange (TMX), while also to examine the moderating effect of organizational culture.

The authors conducted a two-stage online survey and selected the customer service teams, claims teams and financial teams of 26 Chinese insurance companies as the research samples. The authors finally obtained validated questionnaires from 107 teams with 457 members. The hypothesized relationships were tested using SPSS 25.0 and Mplus.

The results indicate that both team relationship-oriented and task-oriented personality composition have significant positive effects on shared leadership with team-member exchange serving as a full mediator for both paths. As a boundary condition, organizational culture (i.e. including internal integration values and external adaptation values) has a moderating effect on the influence of TMX on shared leadership.

The study investigates the predictive role of team personality composition on shared leadership, which complements the empirical studies of shared leadership antecedents in the literature. Drawing on social exchange perspective, the authors find out that TMX serves as a mediator between team personality composition and shared leadership. The authors also identify the moderating effect of organizational culture on the emergence of shared leadership. The research emphasizes the contextual boundary condition in this process.

As an increasingly important variable in the career field, career sustainability has received particular attention, yet few empirical studies have been conducted to examine its antecedents. The authors propose a moderated mediation model based on the goal-setting theory and the wise proactivity perspective for exploring when and how self-goal setting can influence career sustainability.

The authors use a time-lagged design and collect three waves of data from 1,260 teachers in basic education schools in China. The authors test the proposed hypotheses with SPSS 26.0 and Mplus 8.3.

The results show that self-goal setting positively relates to career sustainability and that career crafting plays a mediating role in this relationship. This relationship is strengthened when perceived organizational goal clarity is high.

The authors extend the application scenarios of the goal-setting theory to the field of career research and find out that self-goal setting is also a self-initiated and wise antecedent of career sustainability. From a wise proactivity perspective, the authors examine the mediating mechanism of career crafting to make positive career outcomes. Furthermore, the authors consider the impact of perceived organizational goal clarity as a boundary condition and broaden the understanding of “when to wise proactivity” from the goal-setting theory.

With the rapid growth of the Internet of Things (IoT) market and requirement, low power wide area (LPWA) technologies have become popular. In various LPWA technologies, Narrow Band IoT (NB-IoT) and long range (LoRa) are two main leading competitive technologies. Compared with NB-IoT networks, which are mainly built and managed by mobile network operators, LoRa wide area networks (LoRaWAN) are mainly operated by private companies or organizations, which suggests two issues: trust of the private network operators and lack of network coverage. This study aims to propose a conceptual architecture design of a blockchain built-in solution for LoRaWAN network servers to solve these two issues for LoRaWAN IoT solution.

The study proposed modeling, model analysis and architecture design.

The proposed solution uses the blockchain technology to build an open, trusted, decentralized and tamper-proof system, which provides the indisputable mechanism to verify that the data of a transaction has existed at a specific time in the network.

To the best of our knowledge, this is the first work that integrates blockchain technology and LoRaWAN IoT technology.

The purpose of this paper is to numerically and experimentally evaluate the effect of the protection net icing on the inlet performance of helicopter engines.

The ice shapes of the protection net at different times are first simulated by a two-dimensional (2D) icing calculation, then the porous media parameters are calculated based on the 2D ice shapes. Afterward, three-dimensional flow fields of the engine inlet with the iced net are simulated using the porous media model instead of the real protection net. The transient pressure losses of the iced protection net are calculated and tested through an icing wind tunnel test rig under different icing conditions.

Overall, the numerical results and experimental data show a good agreement. The effects of several control parameters, such as liquid water contents (LWC), water droplet diameters and airflow velocities on the pressure loss of the protection net during the icing process are analyzed in a systematic manner. The results indicate that the pressure loss increases with the increase of the LWC at the same icing time. The same trend occurs when the water droplet diameter and the airflow velocity increase.

A new method to predict the pressure loss of the iced protection net is proposed. A series of tests in an icing wind tunnel are performed to obtain the ice shapes and pressure loss of protection net during the icing process.

To save the economic cost and improve the performance of enterprises, this study aims to synthesize high performance immobilized penicillin G acylase (PGA) carriers with fast reaction speed, high recovery rate of enzyme activity and good reusability through corresponding theoretical guidance and experimental exploration.

A diblock resin was synthesized by reversible addition-fragmentation chain transfer polymerization method using N, N-diethylacrylamide (DEA) and β-hydroxyethyl methacrylate (HEMA) as functional monomers poly(N, N-diethylacrylamide)-b-poly(β-hydroxyethyl methacrylate) (PDEA-b-PHEMA) was obtained, and the effect of the ratio of DEA and HEMA on the activity of PGA was investigated, and the appropriate block ratio of DEA and HEMA was obtained. After that, the competitive rate of HEMA and glycidyl methacrylate (GMA) under the carrier preparation conditions was investigated. Based on the above work, a thermosensitive resin carrier PDEA-b-PHEMA-b-P(HEMA-co-GMA) with different target distances was synthesized, and the chemical structures and molecular weight of copolymers were investigated by hydrogen NMR (¹H NMR).

The lower critical solution temperature of the resin support decreases with the increase of the monomer HEMA in the random copolymerization; the catalytic performance study indicated that the response rate of the immobilized PGA is fast, and the recovery rate of the enzyme activity of the immobilized PGA varies with the distance between the targets. When the molar ratio of HEMA to GMA in the resin block is 8.15:1 [i.e. resin PDEA100-b-PHEMA10-b-P(HEMA65-co-GMA8)], the activity recovery rate of immobilized PGA can reach 50.51%, which was 15.49% higher than that of pure GMA immobilized PGA.

This contribution provides a novel carrier for immobilizing PGA. Under the optimal molar ratio, the enzyme activity recovery could be up to 50.51%, which was 15.49% higher than that of PGA immobilized on the carrier with nonregulated distance between two immobilization sites.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

A study of teachers in Chinese schools examined the antecedents of career sustainability with a focus more on proactive behavior from employees than internal traits, or the company’s organizational goals. Their research showed that self-goal setting has a positive influence on career sustainability and also that career crafting mediates the relationship. The effect is stronger when perceived organizational clarity is high.

The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

The purpose of this paper is to summarize the existing point cloud target detection algorithms based on deep learning, and provide reference for researchers in related fields. In recent years, with its outstanding performance in target detection of 2D images, deep learning technology has been applied in light detection and ranging (LiDAR) point cloud data to improve the automation and intelligence level of target detection. However, there are still some difficulties and room for improvement in target detection from the 3D point cloud. In this paper, the vehicle LiDAR target detection method is chosen as the research subject.

Firstly, the challenges of applying deep learning to point cloud target detection are described; secondly, solutions in relevant research are combed in response to the above challenges. The currently popular target detection methods are classified, among which some are compared with illustrate advantages and disadvantages. Moreover, approaches to improve the accuracy of network target detection are introduced.

Finally, this paper also summarizes the shortcomings of existing methods and signals the prospective development trend.

This paper introduces some existing point cloud target detection methods based on deep learning, which can be applied to a driverless, digital map, traffic monitoring and other fields, and provides a reference for researchers in related fields.

In the period of organizational change and transformation, the attitude of employees towards change has become a key factor in the success of organizational change. Based on the uncertainty management theory (UMT), the paper considers authentic leadership as an important antecedent of employee resistance to change and explores the mediating role of perceived uncertainty and the moderating role of uncertainty avoidance between authentic leadership and employee resistance to change.

The paper conducted a questionnaire survey study and a scenario experimental study. In study 1, the authors collected two stages of data from 256 employees in Central China, one month apart. In study 2, the authors designed a scenario experiment and invited 130 Chinese adults to participate.

The authors find that authentic leadership can effectively reduce employee resistance to change by reducing employee perceived uncertainty. In addition, for individuals with a higher (vs lower) degree of uncertainty avoidance, the direct impact of authentic leadership on perceived uncertainty and the indirect impact of authentic leadership on resistance to change through perceived uncertainty are both stronger (vs lower).

The presented results reveal the mechanism between authentic leadership and employee resistance to change from cognitive perspective and depict an important step toward understanding how authentic leadership and employee uncertainty avoidance interact and how they interact with employee resistance to change.

Most of the existing ground filtering algorithms are based on the Cartesian coordinate system, which is not compatible with the working principle of mobile light detection and ranging and difficult to obtain good filtering accuracy. The purpose of this paper is to improve the accuracy of ground filtering by making full use of the order information between the point and the point in the spherical coordinate.

First, the cloth simulation (CS) algorithm is modified into a sorting algorithm for scattered point clouds to obtain the adjacent relationship of the point clouds and to generate a matrix containing the adjacent information of the point cloud. Then, according to the adjacent information of the points, a projection distance comparison and local slope analysis are simultaneously performed. These results are integrated to process the point cloud details further and the algorithm is finally used to filter a point cloud in a scene from the KITTI data set.

The results show that the accuracy of KITTI point cloud sorting is 96.3% and the kappa coefficient of the ground filtering result is 0.7978. Compared with other algorithms applied to the same scene, the proposed algorithm has higher processing accuracy.

Steps of the algorithm are parallel computing, which saves time owing to the small amount of computation. In addition, the generality of the algorithm is improved and it could be used for different data sets from urban streets. However, due to the lack of point clouds from the field environment with labeled ground points, the filtering result of this algorithm in the field environment needs further study.

In this study, the point cloud neighboring information was obtained by a modified CS algorithm. The ground filtering algorithm distinguish ground points and off-ground points according to the flatness, continuity and minimality of ground points in point cloud data. In addition, it has little effect on the algorithm results if thresholds were changed.