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The purpose of this paper is to introduce the modeling and implementation of a novel multimode amphibious robot, which is used for patrol and beach garbage cleaning in the land–water transition zone.

Starting from the design idea of multimode motion, the robot innovatively integrates the guiding fin and wheel together, is driven by the same motor and can achieve multimodal motion such as land, water surface and underwater with only six actuated degrees of freedom. The robot dispenses with the transmission mechanism by directly connecting the servo motor with a reducer to the actuator, so it has the characteristics of simplifying the structure and reducing the quality. And to the best of the authors' knowledge, the design of the robot can be considered the minimal configuration of amphibious robots with the same locomotion capabilities.

Based on the classical assumptions of underwater dynamics analysis, this paper uses basic airfoil theory to analyze the dynamics of the robot’s horizontal and vertical motions and establishes its simplified dynamics model. Also, the underwater motion of the robot is simulated, and the results are in good agreement with the existing research results. Finally, to verify the feasibility of the robot, a prototype is implemented and fully evaluated by experiments. Experimental results show that the robot can reach the maximum speed of 2.5 m/s and 0.3 m/s on land and underwater, respectively, proving the effectiveness of the robot.

The robot has higher work efficiency with the powerful multimode motion, and its simplified structure makes it more stable while costing less.

Previous research on the factors influencing mentoring received has primarily focused on protégés' personalities and the similarity between protégés and mentors, whereas understanding on the role of protégés' skills is still limited. Drawing upon the social influence theory, this study investigated how newcomers' political skill influences newcomers' mentoring received and further affects newcomers' socialization outcomes (i.e. person-organization fit perception [P-O fit], performance proficiency and well-being).

Data were collected from 255 newcomers at a large Chinese information and technology (IT) company using a three-wave, time-lagged design.

The authors found that newcomers' political skill positively predicted mentoring received, which in turn positively affected newcomers' socialization outcomes.

These findings indicate that political skill enables newcomers to exert social influence on organizational insiders to achieve desirable socialization outcomes, enlarging both the mentoring and political skill literature.

The improved classical model makes it possible that the evaluation strategy has an optimal tendency, which reveals the purpose of this paper is to facilitate the first price sealed-bid auction more in line with the actual situation. To be more specific, there are several merits in the improvement process. On the one hand, the bid-winning probability can be improved for the bidder; on the other hand, the real market value of the subject matter can be more clearly recognized for the employer.

Bayesian estimation and grey system theory are referenced in this paper, with the use of double-parameter estimation, little historical data and expert experience. Specific implementation steps are as follows: first of all, using the double-parameter Bayesian estimation to correct the actual valuation of the bid matter v, then introducing the threat factor grey number R in the auction model, giving the improving of the optimal grey quotation and grey expectation utility under the two-party game and finally taking the aerospace component procurement as an example, simulating the bidding process of the bidding parties to arrive at the optimal bid strategy.

The improved model shows that the optimal strategy will change with the threat factor rather than a fixed value. When the threat factor grey number R follows [0.4, 0.6], the optimal quotation strategy will appear, which means quotation is higher than 50% of the bid matter's valuation.

The improved model proposed in this paper can strengthen the cost control in the Chinese commercial space process and optimize the pricing strategy for the final launch.

The modified model changes the habit that the bidder's valuation of the bid subject to mainly come from experience and to prompt the model for making full use of little historical data on the foundation of the former. It can reduce the subjective judgment error in the game results; finally, the practical cases are simulated in MATLAB at the same time, and the simulation effect is good, so we can get some more realistic conclusions on this basis.

Carbon reduction in the construction supply chain can critically affect the construction industry’s transition to an environmentally sustainable one. However, implementing carbon reduction in all parties is restricted because of the poor understanding of the drivers influencing the low-carbon construction supply chain (LCCSC). The purpose of this paper is to systematically identify the drivers of LCCSC, analyze their causality, and prioritize the importance of their management.

A decision-making analysis process was developed using an integrated decision-making trial and evaluation laboratory (DEMATEL)–analytical network process (ANP). First, the hierarchical drivers of the LCCSC were identified through a literature review. The DEMATEL method was subsequently applied to analyze the interactions between the drivers, including the direction and strength of impact. Finally, the ANP analysis was used to obtain the drivers’ weights; consequently, their priorities were established.

Various factors with complex interactions drive LCCSC. With respect to their influence relationships, incentive policy, regulatory policy, consumers’ low-carbon preference, market competition, supply chain performance, and managers’ low-carbon awareness have more significant center degrees and are cause drivers. Their strong correlations and influence on other drivers should be noticed. In terms of weights in the driver system, regulatory policy, consumers’ low-carbon preference, supply chain performance, and incentive policy are the key drivers of LCCSC and require primary attention. Other drivers, such as supply chain collaboration, employee motivation, and public participation, play a minor driving role with less management priority.

Despite some contributing studies with localized perspectives, the systematic analysis of LCCSC drivers is limited, especially considering their intricate interactions. This paper establishes the LCCSC driver system, explores the influence relationships among the drivers, and determines the key drivers. Hence, it contributes to the sustainable construction supply chain domain by enabling decision-makers and practitioners to systematically understand the drivers of LCCSC and gain management implications on priority issues with limited resources.