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Research to date has yet to reach a consensus regarding the role of cross-channel consistency in omni-channel retailing. Therefore, this study aims to clarify the impact of cross-channel consistency on brand trust and loyalty by differentiating four dimensions of consistency (i.e. product, service, price and promotion consistency) and exploring the moderating roles of showrooming and webrooming motivation in these relationships.

A survey method is used to collect data. A total of 550 valid responses were obtained from multi-channel apparel brands' customers. Hypotheses were tested by employing structural equation modeling and hierarchical multiple regression analysis.

The results indicate that product and service consistency positively influence brand loyalty via brand trust, whereas price and promotion consistency do not. Furthermore, showrooming motivation negatively moderates the effects of service, price and promotion consistency on brand trust, while webrooming motivation positively moderates the effects of product, price and promotion consistency on brand trust.

This study contributes to the omni-channel retailing literature by examining the effects of different dimensions of cross-channel consistency and the moderating roles of showrooming and webrooming motivation to unravel the contradictions of previous studies. It reveals both the beneficial and dark sides of cross-channel consistency. It also extends the knowledge of brand building and cross-channel behavior in omni-channel retailing.

Varied shapes and sizes of different products with irregular rough surface and fragile properties give a challenge to traditional contact gripping. Single Bernoulli grippers are not suited to handle fragile objects as the impact of center negative pressure force could result in large deformation and stress which damage the materials, and they are also have some limitations for gripping objects with different large and small shapes. Thus, this paper aims to design a non-contact gripper for soft, rough-surfaced and fragile objects gripping with multi Bernoulli heads, which have optimal structures and parameters.

The compressed air is ejected into four Bernoulli heads through radial and long flow channels, then passes through four strip-shaped narrow gaps after fully developing in the annular cavity to provide negative pressure. Based on the mathematic model and the computational model, the key structural parameters affecting the gripping performance are selected, and parameters optimization of the gripper is performed by computational fluid dynamics simulation analysis and performance evaluation. The orthogonal method is used and L16 orthogonal array is selected for experimental design and optimization. The characteristics of the designed gripper are tested from the aspects of pressure distribution and lifting force.

From the applications in gripping different objects, the designed non-contact gripper can grip varied shapes and sizes of soft, rough-surfaced, fragile and sliced objects with little effect of torque.

In this paper, a non-contact gripper is designed for handling soft, rough-surfaced and fragile objects based on the Bernoulli principle. A systematic approach, which consists of modeling, simulation, optimization and measurement is provided for the non-contact gripper design and tests.

In order to give full play to the function of noise reduction of asphalt pavement, it is necessary to understand its internal sound absorption mechanism. Therefore, the purpose of this study is to establish a micro model of the pore structure of asphalt mixture with the help of finite element method (FEM), discuss the noise reduction mechanism of asphalt pavement from the micro perspective and analyze and evaluate the noise attenuation law of the pore structure.

The FEM was used to establish the microscopic model of the pore structure of asphalt mixture. Based on the principle of acoustics, the noise reduction characteristics of asphalt pavement were simulated. The influence of gradation and pore characteristics on the noise reduction performance of asphalt pavement was analyzed.

The results show that the open graded friction course-13 (OGFC-13) has excellent performance in noise reduction. The resonant sound absorption structure composed of its large porosity can effectively reduce the pavement noise. For asphalt concrete-13 (AC-13) and stone matrix asphalt-13 (SMA-13), the less resonant sound absorption structure makes them have poor sound absorption effect. In addition, the variation rules of noise transmission loss (TL) curve and sound absorption coefficient curve of three graded asphalt mixtures were obtained. At the same time, the peak noise reduction values of OGFC-13, AC-13 and SMA-13 were obtained, which were 650Hz, 1000Hz and 800Hz, respectively.

The results show that the simulation results can well reflect and express the experimental results. This will provide a reference for further exploring the sound absorption mechanism and its variation rule of porous asphalt pavement. It also has some positive significance for the application of low noise asphalt pavement.

The purpose of this paper is to solve the common problems of outer phenomenon and stress concentration among pneumatic networks soft actuators.

On the basis of imitating the caterpillar structure, the new soft actuator adopts the integral circular ladder structure instead of the traditional independent distributed structure as the air chamber. Through the comparison of several different structures, the parabolic in-wall curve is found to be fit for designing the optimal integrated chamber structure of the soft actuator. The curve function of each ladder chamber is computed based on the torque distribution model, aiming to decrease the terminal deformation. Meanwhile, the FEM analysis method is applied to establish the motion model of the integrated parabolic ladder soft actuator. The model’s accuracy, as well as structure’s deformation and stress, are verified.

Compared with the FEM data, the experimental data indicate that the new soft actuator has no obvious outer phenomenon, the maximum stress decreases and the stiffness increases. The new actuator is applied for designing a flexible gripper to grasp objects of different shapes and sizes. The gripper can grasp objects of 52.6 times its own mass.

The designed gripper is available for flexible production in various fields, such as capturing fruits of different sizes, soft foods or parts with complex shapes.

This paper proposes a new type soft actuator, which provides a solution for exploring the field of the soft robot. The problems of outer phenomenon and stress concentration are suppressed with pneumatic networks soft actuators.

The purpose of this study is to designed a robot odometry based on three dimensional (3D) laser point cloud data, inertial measurement unit (IMU) data and real-time kinematic (RTK) data in underground spatial features and gravity fluctuations environment. This method improves the mapping accuracy in two types of underground space: multi-layer space and large-scale scenarios.

An IMU–Laser–RTK fusion mapping algorithm based on Iterative Kalman Filter was proposed, and the observation equation and Jacobian matrix were derived. Aiming at the problem of inaccurate gravity estimation, the optimization of gravity is transformed into the optimization of SO(3), which avoids the problem of gravity over-parameterization.

Compared with the optimization method, the computational cost is reduced. Without relying on the wheel speed odometer, the robot synchronization localization and 3D environment modeling for multi-layer space are realized. The performance of the proposed algorithm is tested and compared in two types of underground space, and the robustness and accuracy in multi-layer space and large-scale scenarios are verified. The results show that the root mean square error of the proposed algorithm is 0.061 m, which achieves higher accuracy than other algorithms.

Based on the problem of large loop and low feature scale, this algorithm can better complete the map loop and self-positioning, and its root mean square error is more than double compared with other methods. The method proposed in this paper can better complete the autonomous positioning of the robot in the underground space with hierarchical feature degradation, and at the same time, an accurate 3D map can be constructed for subsequent research.

Maximum k vertex cover problem is a significant combinatorial optimization problem with various applications, such as transportation planning, social networks and sensor placement. Up to now, no practical algorithm has ever been proposed to solve this problem. Therefore, this paper aims to present an efficient local search algorithm LSKVC combining three methods for it.

First, the quick incremental evaluation method is proposed to update the related vertex scores following each addition or removal incrementally rather than recalculating them, which can speed up the algorithm. Second, the configuration checking method forbids vertices whose configuration has not changed since the last removal from being added into the candidate solution again, which can avoid the cycling problem effectively. Third, the two-stage exchange method swaps the pairs of inside and outside vertices separately rather than simultaneously, which can guarantee the tradeoff between the accuracy and complexity of the algorithm.

The proposed algorithm LSKVC is compared with the traditional GRASP algorithm and the well-known commercial solver CPLEX on DIMACS and BHOSLIB benchmarks. For the best solutions, the LSKVC algorithm is significantly superior to GRASP and CPLEX on DIMACS instances and the CPLEX solver fails, and the LSKVC algorithm slightly outperforms GRASP on the BHOSLIB instances. In addition, we undertake comparative studies of the offered methodologies and demonstrate their efficacy.

In previous research, the focus on the maximum k-vertex cover problem primarily centered around exact algorithms and approximation algorithms, with limited application of heuristic algorithms. While heuristic algorithms have been well-explored for the closely related Minimum Vertex Cover problem, they have seen limited application in the context of the maximum k-vertex cover problem. Consequently, existing algorithms designed for the Minimum Vertex Cover problem do not exhibit satisfactory performance when applied to the maximum k-vertex cover problem. In response to this challenge, we have undertaken algorithmic improvements specifically tailored to address this issue.

The purpose of this study is to solve the problem of an unknown initial position in a multi-robot raster map fusion. The method includes two-dimensional (2D) raster maps and three-dimensional (3D) point cloud maps.

A fusion method using multiple algorithms was proposed. For 2D raster maps, this method uses accelerated robust feature detection to extract feature points of multi-raster maps, and then feature points are matched using a two-step algorithm of minimum Euclidean distance and adjacent feature relation. Finally, the random sample consensus algorithm was used for redundant feature fusion. On the basis of 2D raster map fusion, the method of coordinate alignment is used for 3D point cloud map fusion.

To verify the effectiveness of the algorithm, the segmentation mapping method (2D raster map) and the actual robot mapping method (2D raster map and 3D point cloud map) were used for experimental verification. The experiments demonstrated the stability and reliability of the proposed algorithm.

This algorithm uses a new visual method with coordinate alignment to process the raster map, which can effectively solve the problem of the demand for the initial relative position of robots in traditional methods and be more adaptable to the fusion of 3D maps. In addition, the original data of the map can come from different types of robots, which greatly improves the universality of the algorithm.

The paper aims to provide an efficient meta-heuristic algorithm to solve the partial set covering problem (PSCP). With rich application scenarios, the PSCP is a fascinating and well-known non-deterministic polynomial (NP)-hard problem whose goal is to cover at least k elements with as few subsets as possible.

In this work, the authors present a novel variant of the ant colony optimization (ACO) algorithm, called Argentine ant system (AAS), to deal with the PSCP. The developed AAS is an integrated system of different populations that use the same pheromone to communicate. Moreover, an effective local search framework with the relaxed configuration checking (RCC) and the volatilization-fixed weight mechanism is proposed to improve the exploitation of the algorithm.

A detailed experimental evaluation of 75 instances reveals that the proposed algorithm outperforms the competitors in terms of the quality of the optimal solutions. Also, the performance of AAS gradually improves with the growing instance size, which shows the potential in handling complex practical scenarios. Finally, the designed components of AAS are experimentally proved to be beneficial to the whole framework. Finally, the key components in AAS have been demonstrated.

At present, there is no heuristic method to solve this problem. The authors present the first implementation of heuristic algorithm for solving PSCP and provide competitive solutions.

To make the robot that have real autonomous ability is always the goal of mobile robot research. For mobile robots, simultaneous localization and mapping (SLAM) research is no longer satisfied with enabling robots to build maps by remote control, more needs will focus on the autonomous exploration of unknown areas, which refer to the low light, complex spatial features and a series of unstructured environment, lick underground special space (dark and multiintersection). This study aims to propose a novel robot structure with mapping and autonomous exploration algorithms. The experiment proves the detection ability of the robot.

A small bio-inspired mobile robot suitable for underground special space (dark and multiintersection) is designed, and the control system is set up based on STM32 and Jetson Nano. The robot is equipped with double laser sensor and Ackerman chassis structure, which can adapt to the practical requirements of exploration in underground special space. Based on the graph optimization SLAM method, an optimization method for map construction is proposed. The Iterative Closest Point (ICP) algorithm is used to match two frames of laser to recalculate the relative pose of the robot, which improves the sensor utilization rate of the robot in underground space and also increase the synchronous positioning accuracy. Moreover, based on boundary cells and rapidly-exploring random tree (RRT) algorithm, a new Bio-RRT method for robot autonomous exploration is proposed in addition.

According to the experimental results, it can be seen that the upgraded SLAM method proposed in this paper achieves better results in map construction. At the same time, the algorithm presents good real-time performance as well as high accuracy and strong maintainability, particularly it can update the map continuously with the passing of time and ensure the positioning accuracy in the process of map updating. The Bio-RRT method fused with the firing excitation mechanism of boundary cells has a more purposeful random tree growth. The number of random tree expansion nodes is less, and the amount of information to be processed is reduced, which leads to the path planning time shorter and the efficiency higher. In addition, the target bias makes the random tree grow directly toward the target point with a certain probability, and the obtained path nodes are basically distributed on or on both sides of the line between the initial point and the target point, which makes the path length shorter and reduces the moving cost of the mobile robot. The final experimental results demonstrate that the proposed upgraded SLAM and Bio-RRT methods can better complete the underground special space exploration task.

Based on the background of robot autonomous exploration in underground special space, a new bio-inspired mobile robot structure with mapping and autonomous exploration algorithm is proposed in this paper. The robot structure is constructed, and the perceptual unit, control unit, driving unit and communication unit are described in detail. The robot can satisfy the practical requirements of exploring the underground dark and multiintersection space. Then, the upgraded graph optimization laser SLAM algorithm and interframe matching optimization method are proposed in this paper. The Bio-RRT independent exploration method is finally proposed, which takes shorter time in equally open space and the search strategy for multiintersection space is more efficient. The experimental results demonstrate that the proposed upgrade SLAM and Bio-RRT methods can better complete the underground space exploration task.

The purpose of this paper is to investigate the curvilinear relationship between perceived negative workplace gossip and target employee’s task performance, and the moderating roles of perceived organizational support (POS).

Using a sample of 275 supervisor–subordinate dyads in a two-wave survey, the authors adopted a hierarchical regression analysis to test the hypotheses.

The results revealed that there is a U-shaped relationship between perceived negative workplace gossip and task performance. Moreover, POS moderated the curvilinear relationship such that the curvilinear relationship is more pronounced among those with lower POS.

This study does not explore the mediating mechanism of how perceived negative gossip affects the target’s task performance. Moreover, as this research was conducted in a Chinese context, the question of the generalizability of the findings calls for more attention.

When the negative gossip is still in its early stages, managers should realize the potential threat to target employees and take measures to stop and minimize negative gossiping and rumormongering. Furthermore, managers should do their best to find the optimal levels of organizational support for target employees.

This study is among the first effort to understand how perceived negative gossip can influence the target employees’ performance by proposing and demonstrating a nonlinear relationship. Moreover, by illuminating how POS plays a role in the curvilinear relationship between negative gossip and task performance, the authors not only complement but also extend the literature on workplace gossip and organizational support.