Announcement regarding change in Publication Frequency

2025-01-01

Dear Mechanical Engineering Advances Community,

As we look back on 2024, Mechanical Engineering Advances (MEA, eISSN: 3029-1232) has been on a journey of robust, steady, and ongoing enhancement, enriched by the influx of many young talents. These new voices have brought a wealth of enthusiasm and innovative ideas, invigorating our journal with fresh perspectives.

To our esteemed experts who have contributed quality manuscripts, we extend our heartfelt gratitude. It is your research findings that have made our journal's content more comprehensive and at the forefront of knowledge.

To our diligent reviewers, your exceptional professionalism has been instrumental in upholding the rigorous review process of our journal's manuscripts, ensuring that our academic standards remain high.

To our editorial board members and scholars in the field, your dedication and support have been the catalyst for our progress. Your hard work and relentless efforts are pivotal to the journal's achievements.

We are deeply grateful for the support and encouragement that all scholars have shown towards our journal. As we venture into the new year, we are pleased to announce that, commencing in 2025, MEA will transition from a semi-annual to a quarterly publication schedule, with new issues slated for release in March, June, September, and December.After this adjustment, the journal will pay more attention to the in-depth review and editing of manuscripts, strictly control academic standards, and continuously enhance the academic influence of the journal.

We look forward to your continued engagement and contributions as we navigate this exciting new phase in our publication's journey.

Warm regards,

Editorial Office of Mechanical Engineering Advances (eISSN: 3029-1232)

In order to improve thermal comfort of vehicle cab, reduce driver fatigue and further improve work efficiency, researches on thermal comfort of vehicle cab are summarized. Research background of thermal comfort for vehicle cab is analyzed. And then related research progress on thermal environment in vehicle cab is studied from aspect of time and space, and thermal environment inside and outside vehicle are compared. Affecting factors of thermal comfort in vehicle cab are discussed in depth, which conclude thermophysical parameters, human physiological factors, clothing thermal resistance and other secondary factors. And thermal comfort evaluation indexes are analyzed in depth. Evaluation methods of thermal comfort in uniform environment are analyzed, related experimental research and theoretical analysis are summarized, and it also points out some problems in thermal comfort of vehicle at this stage, and also gives corresponding solutions. The future trend of thermal comfort of vehicle cab is predicted. Analysis results can provide theoretical guidance for optimization design of air conditioning supply parameters and structural parameters, and has significant meaning of improving thermal comfort of vehicle cab.

This manuscript presents a sophisticated deep neural networks (DNNs)-driven adaptive control paradigm for concurrently regulating the attitude and suppressing structural oscillations of a flexible spacecraft in a fully three-dimensional domain. By leveraging Hamilton’s principle, the spacecraft’s motion is formulated as an infinite-dimensional dynamic model described by partial differential equations, capturing the subtle interactions between rigid-body rotational maneuvers and flexible panel deformations. In contrast to traditional schemes, the proposed control methodology integrates a DNNs module to compensate for uncertain actuator anomalies and external input disturbances in real time, thereby ensuring fault tolerance under arbitrary, potentially unbounded actuator malfunctions. A rigorously constructed Lyapunov-based stability analysis corroborates that the system’s energy, angular rates, and transverse deflections remain uniformly bounded and asymptotically converge to zero, even in the face of multiple actuator failures. This theoretical guarantee stems from the synergistic interplay between the network’s representational power and the adaptive control law’s robust learning capabilities. Extensive computational experiments demonstrate the efficacy of the developed framework in orchestrating high-precision attitude stabilization while simultaneously mitigating detrimental vibrations, showcasing the superior performance and resiliency of the proposed DNNs-infused control architecture.

This study focuses on the development of a cleaning robot, covering the three main aspects of mechanical design, circuit design, and software design. First, in the area of mechanical design, we created a structure capable of agile movement and efficient cleaning to ensure the robot can operate smoothly in various environments. Second, for circuit design, we developed a microcontroller-based control system to coordinate the operation of various components, including drive motors, sensors, and image recognition modules. Furthermore, in the software design aspect, we utilized YOLO (You Only Look Once) and OpenCV technologies to enable the robot to accurately identify and classify waste during the automatic cleaning process. Finally, we conducted practical cleaning experiments to verify the robot’s ability to recognize waste and the accuracy of waste classification. The experimental results show that the cleaning robot not only possesses the ability to recognize waste but also accurately classify it, demonstrating its potential for practical applications.