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The paper focuses on the issue of manual rebar-binding tasks in the construction industry, which are marked by high labor intensity, high costs and inefficient operations. The rebar-binding robots that are currently available are not fully mature. Most of them can only bind one or two nodes in one position, which leads to significant time wastage in movement. Based on a new type of rebar-binding robot, this paper aims to propose a new movement and binding control that reduces manpower and enhances efficiency.

The robot is combined with photoelectric sensors, travel switches and other sensors. It is supposed to move accurately and run in a limited area on the rebar mesh through logical judgment, speed control and position control. Machine vision is used by the robot to locate the rebar nodes and then adjusts the binding-gun position to ensure that multiple rebar nodes are bound sequentially.

By moving on the rebar mesh with accuracy, the robot meets the positioning accuracy requirements of the binding module, with experimental testing accuracy within 5 mm. Furthermore, its ability to bind four rebar nodes in one place results in a high efficiency and a binding effect that meets building standards.

The innovative design of the robot can adapt itself to the rebar mesh, move accurately to the target position and bind four nodes at that position, which reduces the number of movements on the mesh. Repetitive and heavy rebar-binding tasks can be efficiently completed by the robot, which saves human resources, reduces worker labor intensity and reduces construction overhead. It provides a more feasible and practical solution for using robots to bind rebar nodes.

There are various uncertain and nonlinear problems in hydraulic legged robot systems, including parameter uncertainty, unmodeled dynamics and external disturbances. This study aims to eliminate uncertainties and improve the foot trajectory tracking control performance of hydraulic legged robots, a high-performance foot trajectory tracking control method based on fixed-time disturbance observers for hydraulic legged robots is proposed.

First, the robot leg mechanical system model and hydraulic system model of the hydraulic legged robot are established. Subsequently, two fixed-time disturbance observers are designed to address the unmatched lumped uncertainty and match lumped uncertainty in the system. Finally, the lumped uncertainties are compensated in the controller design, and the designed motion controller also achieves fixed-time stability.

Through simulation and experiments, it can be found that the proposed tracking control method based on fixed-time observers has better tracking control performance. The effectiveness and superiority of the proposed method have been verified.

Both the disturbance observers and the controller achieve fixed-time stability, effectively improving the performance of foot trajectory tracking control for hydraulic legged robots.

This study investigates the effect of education on crop insurance knowledge within the context of noncompliance experiences. In addition, the study delves into the role of government endorsement in education, which is instructive for the implementation of future insurance promotions.

The study designs a randomized controlled trial (RCT) conducted in Jiangsu Province, China. A total of 518 sample farmers were randomly assigned to two experiments: The Education Experiment and the government’s Endorsement Experiment, respectively. After conducting a set of rigorous exogeneity tests, econometric analysis was conducted using baseline survey data and post experiment data.

Our results revealed that insurance education served as an effective tool in improving farmers’ insurance knowledge, especially their understanding of insurance mechanisms. However, this effect can be mitigated by the noncompliant insurance experience of farmers. Moreover, government-endorsed education proved to be more efficient in improving farmers’ insurance knowledge, thus highlighting the significance of building trust between insureds and insurers.

This study contributes to the literature by demonstrating that using a simple education tool, such as, brochures, can effectively improve farmers insurance knowledge. In addition, insurance mechanisms are now more urgently in need of universalization than policy information. Furthermore, by conducting the RCT, this study obtains unbiased causal inference on the effect of education on insurance knowledge and underscores the role of government endorsement in this process. In addition, the study illustrates the tradeoff between insurers’ efforts in enhancing education and regulating noncompliant insurance misconducts, which compromises education efforts. Overall, this study provides insights into the marketing strategies of insurers and government propaganda aimed at stimulating farmers’ incentives to purchase insurance.

Warehouses are under pressure to operate as efficiently as possible. In pursuit of attaining high efficiency in the order picking process, the warehouse manager must take several planning decisions, typically supported by a central planning system. However, highly centralised work erodes the autonomy of warehouse workers, interfering with worker well-being and productivity. This study holistically explores the impact of a work system with more decision autonomy for order pickers.

We conduct a unique field experiment in a real-world warehouse and use a within-subjects design to compare two work systems, one with worker autonomy and one without. 18 permanent employees participate in our study, in which we measure both psychosocial and physical well-being as well as productivity. Post-experimental interviews are conducted to delve deeper into the observed effects.

Our study illustrates that involving order pickers in operational decisions can benefit their job satisfaction and motivation without compromising productivity. Although we fail to find significance at the conventional level (α = 0.05), we do find marginally significant effects of our treatment on physical well-being aspects. Furthermore, our intervention invoked a highly positive user experience.

We show that slightly loosening tight process control results in organisational and individual benefits without endangering smooth operational flows. The warehouse in this paper acknowledged this and decided to permanently work according to this philosophy.

This study is the first to holistically explore the effects of a participatory work setting in a real-world warehouse.

Dynamically tracking the target by unmanned ground vehicles (UGVs) plays a critical role in mobile drone recovery. This study aims to solve this challenge under diverse random disturbances, proposing a dynamic target tracking framework for UGVs based on target state estimation, trajectory prediction, and UGV control.

To mitigate the adverse effects of noise contamination in target detection, the authors use the extended Kalman filter (EKF) to improve the accuracy of locating unmanned aerial vehicles (UAVs). Furthermore, a robust motion prediction algorithm based on polynomial fitting is developed to reduce the impact of trajectory jitter caused by crosswinds, enhancing the stability of drone trajectory prediction. Regarding UGV control, a dynamic vehicle model featuring independent front and rear wheel steering is derived. Additionally, a linear time-varying model predictive control algorithm is proposed to minimize tracking errors for the UGV.

To validate the feasibility of the framework, the algorithms were deployed on the designed UGV. Experimental results demonstrate the effectiveness of the proposed dynamic tracking algorithm of UGV under random disturbances.

This paper proposes a tracking framework of UGV based on target state estimation, trajectory prediction and UGV predictive control, enabling the system to achieve dynamic tracking to the UAV under multiple disturbance conditions.

Employer branding (EB) has become a powerful tool for organizations to attract employees. Recruitment communication ideally reveals the image that companies want to portray to potential employees to attract talents with the right skills and competences for the organization. This study explores the impact of EB on employer attractiveness by testing how pre-existing employee preferences interact with EB and how this interaction affects employer attractiveness.

A quasi-experiment among 289 final-year students was used to test the relationships between EB, perceived employer image, person-organization (P-O) fit and employer attractiveness, and the potential moderating variables of pre-existing preferences, in this case operationalized as locational preferences. Students are randomly assigned to four vacancies: one with and one without EB cues in two different locations: Groningen and Amsterdam. The authors used standard scales for attractiveness, perceptions of an employer and person-organization fit. The authors test the relationships using a regression analysis.

Results suggest that if respondents have previous predispositions, then their preference can be enhanced using an EB-targeted strategy. Based on these results, the authors can conclude that EB and related practices can be successful avenues for organizations in the war for talent, particularly if they reaffirm previous preferences of potential employees.

The research is original in the way it provides empirical evidence on the relationship between EB and attractiveness, particularly when previous employee preferences exist. This is of value to employers using EB as a tool to influence employer attractiveness.

This study aims to promote the anti-disturbance and tracking accuracy performance of the servo systems, in which a modified active disturbance rejection control (MADRC) scheme is proposed.

An adaptive radial basis function (ARBF) neural network is utilized to estimate and compensate dominant friction torque disturbance, and a parallel high-gain extended state observer (PHESO) is employed to further compensate residual and other uncertain disturbances. This parallel compensation structure reduces the burden of single ESO and improves the response speed of permanent magnet synchronous motor (PMSM) to hybrid disturbances. Moreover, the sliding mode control (SMC) rate is introduced to design an adaptive update law of ARBF.

Simulation and experimental results show that as compared to conventional ADRC and SMC algorithms, the position tracking error is only 2.3% and the average estimation error of the total disturbances is only 1.4% in the proposed MADRC algorithm.

The disturbance parallel estimation structure proposed in MADRC algorithm is proved to significantly improve the performance of anti-disturbance and tracking accuracy.

This study aims to increase knowledge of the ability of nutrition labels to guide consumer choices in real-life environments.

Food consumption and plate waste data were collected from two self-service restaurants (SSR) with different customer groups over six observation days: three control and three intervention (with nutrition labelling) periods. Study Group 1 consisted of vocational school students, mostly late adolescents (N = 1,710), and Group 2 consisted of spa hotel customers, mostly elderly (N = 1,807). In the experimental restaurants, the same food was served to the buffets during the control and intervention periods.

The nutrition label in the lunch buffet guides customers to eat fewer main foods and salads and to select healthier choices. Increased consumption of taste enhancers (salt and ketchup) was observed in the study restaurants after nutritional labelling. Nutrition labelling was associated with a reduction in plate waste among the elderly, whereas the opposite was observed among adolescents.

The results provide public policymakers and marketers with a better understanding of the effects of nutrition labelling on consumer behaviour. Future studies should further evaluate the effects of nutrition labelling on the overall quality of customer diets and the complex environmental, social, and psychological factors affecting food choices and plate waste accumulation in various study groups.

This paper aims to evaluate the impact of the “Postliteracy” program on the qualification of beneficiaries for socioeconomic integration. This qualification is achieved first through the consolidation of the achievements of individuals freed from illiteracy, and then through their support in creating income-generating activities by providing them with technical, economic, legal and institutional knowledge to ensure their conscious and responsible participation in local and regional development efforts.

To evaluate the impact of the “Postliteracy” program, this paper uses quasi-experimental methods with a control group (participants of the “Literacy” program 2020 / 2021) and a treatment group (participants of the “Postliteracy” program 2021 / 2022). Skill acquisition is measured through pretest and posttest evaluations using a questionnaire aligned with the National Agency for the Fight Against Illiteracy (ANLCA)-adopted curriculum. The survey occurred at the beginning and at the end of the program, providing sufficient time for skill development. The questionnaire includes three sections covering socioprofessional characteristics, technical and economic domains and legal and institutional aspects. These sections contribute to a score reflecting the acquired skills for successful socioeconomic integration.

The results of the study demonstrate that the “Postliteracy” program has a positive impact on the acquisition of competences necessary for improved socioeconomic integration of the beneficiaries. The various matching techniques reveal a score difference ranging from 12 to 14 points in favor of program participants compared to those who did not participate. The Difference-in-Differences method confirms the positive and significant impact of the program.

The findings highlight the importance of the “Postliteracy” program in national literacy policy, underlining the need to further strengthen its presence within the programs deployed by ANLCA, notably by increasing the number of beneficiaries targeted by this program. To achieve this, it would be advisable to increase the funds allocated to it within ANLCA's budget.

The originality of this work is a unique research of the case of Morocco based on a microeconometric study for which the authors evaluate the impact of adult education by applying impact evaluation methods in the field of adult literacy.

Entrepreneurial action under uncertainty has captured the interest of scholars and practitioners alike. However, this growing body of research has yet to connect entrepreneurial action with actual actions of entrepreneurs. We combine insights from effectuation theory and the psychology of entrepreneurship to investigate drivers of entrepreneurial actions involved in starting and running ventures, particularly optimism, self-efficacy and the use of causal and effectual logics.

The study employs a unique mixed-method approach combining a survey with experience sampling data. After measuring demographics, preference for effectual or causal logics and psychological variables, the 197 US entrepreneurs sampled entrepreneurs logged their daily actions in the form of asks during 60 consecutive days.

Results suggest that self-efficacy and causal logics are key drivers of entrepreneurial actions, while optimism serves as a deterrent. Interestingly, the impact of self-efficacy on actions is moderated by the entrepreneur’s experience level, measured both in years and past asking experience.

The potential of the ask as the basic mechanism for entrepreneurial actions is explored. Based on the findings, the authors offer new and fertile insights by linking psychological traits to entrepreneurial actions, causal and effectual logics and entrepreneurial expertise development.