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This study aims to address the issue of random movement and non coordination between docking mechanisms and locking mechanisms, and proposes a comprehensive dynamic docking control architecture that integrates perception, planning, and motion control.

Firstly, the proposed dynamic docking control architecture uses laser sensors and a charge-coupled device camera to perceive the pose of the target. The sensor data are mapped to a high-dimensional potential field space and fused to reduce interference caused by detection noise. Next, a new potential function based on multi-dimensional space is developed for docking path planning, which enables the docking mechanism based on Stewart platform to rapidly converge to the target axis of the locking mechanism, which improves the adaptability and terminal docking accuracy of the docking state. Finally, to achieve precise tracking and flexible docking in the final stage, the system combines a self-impedance controller and an impedance control algorithm based on the planned trajectory.

Extensive simulations and experiments have been conducted to validate the effectiveness of the dynamic docking system and its control architecture. The results indicate that even if the target moves randomly, the system can successfully achieve accurate, stable and flexible dynamic docking.

This research can provide technical guidance and reference for docking task of unmanned vehicles under the ground conditions. It can also provide ideas for space docking missions, such as space simulator docking.

The purpose of this paper is to enhance the performance of robots in peg-in-hole assembly tasks, enabling them to swiftly and robustly accomplish the task. It also focuses on the robot’s ability to generalize across assemblies with different hole sizes.

Human behavior in peg-in-hole assembly serves as inspiration, where individuals visually locate the hole firstly and then continuously adjust the peg pose based on force/torque feedback during the insertion process. This paper proposes a novel framework that integrate visual servo and adjustment based on force/torque feedback, the authors use deep neural network (DNN) and image processing techniques to determine the pose of hole, then an incremental learning approach based on a broad learning system (BLS) is used to simulate human learning ability, the number of adjustments required for insertion process is continuously reduced.

The author conducted experiments on visual servo, adjustment based on force/torque feedback, and the proposed framework. Visual servo inferred the pixel position and orientation of the target hole in only about 0.12 s, and the robot achieved peg insertion with 1–3 adjustments based on force/torque feedback. The success rate for peg-in-hole assembly using the proposed framework was 100%. These results proved the effectiveness of the proposed framework.

This paper proposes a framework for peg-in-hole assembly that combines visual servo and adjustment based on force/torque feedback. The assembly tasks are accomplished using DNN, image processing and BLS. To the best of the authors’ knowledge, no similar methods were found in other people’s work. Therefore, the authors believe that this work is original.

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.

The digital marketing landscape is rapidly evolving, but the integration of visual content still heavily depends on human expertise. Driven by the quest for innovative marketing strategies that resonate with family-oriented consumers, this study seeks to bridge this gap by applying machine learning to analyze visual content in the maternity and baby care product sector.

This study incorporates a range of machine learning techniques – including open science framework feature detection, panoptic segmentation, customized instance segmentation, and face detection calculation methods – to analyze and predict the appeal of images, thereby enhancing user engagement and parent-child intimacy.

The exploration of various ML models, such as DT, LightGBM, RIPPER algorithm, and CNNs, has offered a comparative analysis that addresses a methodological gap in the existing literature, which frequently depends on isolated model evaluations. According to our quadrant analysis with respect to engagement rate and parent-child intimacy, the selection of a model for real-world applications depends on balancing performance and interpretability.

The proposed system offers a series of actionable recommendations designed to enhance customer engagement and foster brand loyalty. This study contributes to image design in maternity and baby care marketing and provides analytical insights for recommendation systems.

Aiming at the problem of insufficient adaptability of robot motion planners under the diversity of end-effector constraints, this paper proposes Transformation Cross-sampling Framework (TC-Framework) that enables the planner to adapt to different end-effector constraints.

This work presents a standard constraint methodology for representing end-effector constraints as a collection of constraint primitives. The constraint primitives are merged sequentially into the planner, and a unified constraint input interface and constraint module are added to the standard sampling-based planner framework. This approach enables the realization of a generic planner framework that avoids the need to build separate planners for different end-effector constraints.

Simulation tests have demonstrated that the planner based on TC-framework can adapt to various end-effector constraints. Physical experiments have also confirmed that the framework can be used in real robotic systems to perform autonomous operational tasks. The framework’s strong compatibility with constraints allows for generalization to other tasks without modifying the scheduler, significantly reducing the difficulty of robot deployment in task-diverse scenarios.

This paper proposes a unified constraint method based on constraint primitives to enhance the sampling-based planner. The planner can now adapt to different end effector constraints by opening up the input interface for constraints. A series of simulation tests were conducted to evaluate the TC-Framework-based planner, which demonstrated its ability to adapt to various end-effector constraints. Tests on a physical experimental system show that the framework allows the robot to perform various operational tasks without requiring modifications to the planner. This enhances the value of robots for applications in fields with diverse tasks.

The purpose of this study was to investigate how and through which social-cognitive constructs, emotions influence healthy food shopping behaviors. Direct effects of those constructs, as well as indirect effects of consumer emotions are considered.

An altered version of the Social Cognitive Theory, including intention, socio-structural factors, outcome expectancies and self-efficacy with the addition of consumer emotions was analyzed using structural equation modeling. Data of 1,181 volunteers were collected in Germany in 2021 through an online survey.

Intention was the most important positive predictor of food choice, while socio-structural factors had the biggest impact on intentions. Those were mostly influenced by self-efficacy, which was strongly predicted by consumer emotions. Outcome expectancies did not influence the current model in any way. Consumer emotions did not directly influence intention, nor actual choice, however showed to be influencing those variables through indirect effects.

Marketers could benefit from these results by incorporating the current findings into existing marketing strategies through targeting a combination of social cognitive constructs, as well as consumer emotions to facilitate healthier food shopping behavior.

Affect has received increasing attention in regards to its impact on healthy eating behaviors in recent years. Less attention has been paid to the mechanisms through which emotions influence healthy nutrition behavior, specifically how consumer emotions influence healthy food shopping behavior.

It is inherent in human nature to pursue a fulfilling life. The art-of-living approach provides strategies to help individuals attain higher well-being. Based on current research approaches on the art-of-living, we aimed to develop, implement and evaluate an online training that enhances art-of-living and well-being scores of flight attendants.

The training focused on six art-of-living components – self-knowledge, savoring, bodily care, coping with events, positive attitude toward life and serenity. In total, 94 participants were randomly assigned to 3-day (n = 34) or 9-day (n = 30) training groups or to 2 corresponding control groups (CGs) (n = 30). Art-of-living and well-being were measured using self-reported questionnaires at pre-intervention, post-intervention and two-week follow-up.

Results showed significant pre-post differences in art-of-living and well-being scores in both experimental groups, while scores for the CGs remained stable across assessments. Intervention effects were sustained over the two-week follow-up period. We found no significant differences in efficacy between the shorter and longer training, suggesting that brief training can be effective.

These results demonstrate that well-being can be enhanced through online art-of-living training, which is promising in terms of the practical implementation of such training in resource-constrained work environments.

The presented, conducted and evaluated work intervention represents the first study to apply the multi-component approach of “art-of-living” in an online setting, comparing two trainings of varying durations. This approach offers a framework perfectly suited for future implementation in flight attendants’ work settings to increase well-being and a possible subsequent implementation in other professional groups that would benefit from online training (e.g. in a hybrid work context).

Computer vision (CV) offers a promising approach to transforming the conventional in-person inspection practices prevalent within the construction industry. However, the reliance on centralized systems in current CV-based inspections introduces a vulnerability to potential data manipulation. Unreliable inspection records make it challenging for safety managers to make timely decisions to ensure safety compliance. To address this issue, this paper proposes a blockchain (BC) and CV-based framework to enhance safety inspections at construction sites.

This study adopted a BC-enhanced CV approach. By leveraging CV and BC, safety conditions are automatically identified from site images and can be reliably recorded as safety inspection data through the BC network. Additionally, by using this data, smart contracts coordinate inspection tasks, assign responsibilities and verify safety performance, managing the entire safety inspection process remotely.

A case study confirms the framework’s applicability and efficacy in facilitating remote and reliable safety inspections. The proposed framework is envisaged to greatly improve current safety inspection practices and, in doing so, contribute to reduced accidents and injuries in the construction industry.

This study provides novel and practical guidance for integrating CV and BC in construction safety inspection. It fulfills an identified need to study how to leverage CV-based inspection results for remotely managing the safety inspection process using BC. This work not only takes a significant step towards data-driven decision-making in the safety inspection process, but also paves the way for future studies aiming to develop tamper-proof data management systems for industrial inspections and audits.

This study aims to examine how knowledge sharing contributes to organizations’ ambidexterity, their overall performance and the role of knowledge quality in this relationship. Knowledge sharing is conceptualized based on tacit and explicit dimensions, and ambidexterity is viewed as comprising exploitative and explorative capabilities.

This study uses a cross-sectional survey-based research design and structural equation modeling to test the proposed model of knowledge sharing and knowledge quality in organizational ambidexterity and the related hypotheses.

The results indicate that tacit knowledge sharing has a significant, direct impact on the exploitative and explorative capabilities of the organization and indirectly impacts both dimensions of ambidexterity (i.e. exploitative and explorative) through knowledge quality. In contrast, explicit knowledge sharing does not have a significant impact on knowledge quality and affects only the exploitative extent of ambidexterity. Both exploitative and explorative capabilities significantly impact organizational performance.

To the best of the authors’ knowledge, this study is the first study to empirically examine the role of knowledge quality in the context of knowledge sharing for ambidexterity, especially within the context of organizations in the United Arab Emirates.

The purpose of this study is to test a model of healthcare professionals’ well-being seen as a consequence of a process of motivated professional identity construction, a variable that mediates the influence of the organizational identity (utilitarian or normative) and the perceived reputation of the profession on well-being.

Cross-sectional design, based on a survey of 384 healthcare professionals. Structural equation modeling with latent variables was used to test the model.

The data provide empirical evidence supporting the proposed model. We find that organizational identity (utilitarian and normative) and perceived professional reputation positively relate to professional identity, a variable that positively relates to well-being. Professional identity mediates the relationship between organizational identity (normative and utilitarian) and perceived professional reputation and well-being. Utilitarian organizational identity and perceived professional reputation are also directly related to well-being.

This research significantly departs from the current focus of explaining the well-being of healthcare professionals by resorting mainly to individual factors and introduces organizational and institutional determinants.