Jiabao An's research while affiliated with Xi'an Jiaotong-Liverpool University and other places

This article explores the influence of temperature and humidity on human thermal comfort and exercise performance during dynamic exercise. While previous studies have investigated the relationship between exercise state and thermal comfort, few have focused on transient changes during exercise. To examine these relationships, a series of experiments were conducted in an environmental chamber with precise control over temperature and humidity conditions. Participants were selected and tested under nine different scenarios at the same running speed. Questionnaires were filled out at six different time slots, from pre-exercise till 5 min after the exercise. The predicted mean vote (PMV) model was used to estimate the average thermal comfort. The results showed that, despite a relatively constant environment, participants’ feeling of thermal comfort changed as the exercise progressed and after sweating during the post-exercise course. The sensitivity and feeling of thermal comfort varied during the whole process under different scenarios. This study provides innovative survey methods for questionnaires and objective environmental data that can be analyzed to enhance understanding of changes in thermal comfort during exercise under different environmental variables. The findings also offer suggestions for the regulation of temperature and humidity in indoor gyms, and the accuracy of the PMV model in dynamic applications is verified.

The Fourth Industrial Revolution, characterized by advancements in artificial intelligence and the emergence of technologies such as Chat Generative Pre-training Transformers (ChatGPT), has significantly impacted the manufacturing sector. This paper seeks to elucidate the foundational methodology of Industry 4.0 and explicate the transition process toward its adoption, with a focus on manufacturing and mass customization. The challenges associated with achieving high levels of both volume and mix in manufacturing processes have led to the predominance of High Mix Low Volume (HMLV) and Low Mix High Volume (LMHV) market environments. In response, mass customization has gained prominence, seeking to balance efficiency, and personalization in the production of goods and services. This paper proposes a potential approach for facilitating the transition to Industry 4.0 principles through the utilization of ChatGPT, a cutting-edge artificial intelligence tool for communication. Additionally, the study discusses the limitations associated with the ChatGPT application and outlines future prospects and expectations for its role in enhancing manufacturing flexibility and enabling mass customization.

Heat exchanger modeling has been widely employed in recent years for performance calculation, design optimizations, real-time simulations for control analysis, as well as transient performance predictions. Among these applications, the model’s computational speed and robustness are of great interest, particularly for the purpose of optimization studies. Machine learning models built upon experimental or numerical data can contribute to improving the state-of-the-art simulation approaches, provided careful consideration is given to algorithm selection and implementation, to the quality of the database, and to the input parameters and variables. This comprehensive review covers machine learning methods applied to heat exchanger applications in the last 8 years. The reviews are generally categorized based on the types of heat exchangers and also consider common factors of concern, such as fouling, thermodynamic properties, and flow regimes. In addition, the limitations of machine learning methods for heat exchanger modeling and potential solutions are discussed, along with an analysis of emerging trends. As a regression classification tool, machine learning is an attractive data-driven method to estimate heat exchanger parameters, showing a promising prediction capability. Based on this review article, researchers can choose appropriate models for analyzing and improving heat exchanger modeling.

With the rapid development of artificial intelligence and the Internet of Things, there is an explosion of available data for processing and analysis in any domain. However, signal processing efficiency is limited by the Von Neumann structure for the conventional computing system. Therefore, the design and construction of artificial synapse, which is the basic unit for the hardware-based neural network, by mimicking the structure and working mechanisms of biological synapses, have attracted a great amount of attention to overcome this limitation. In addition, a revolution in healthcare monitoring, neuro-prosthetics, and human–machine interfaces can be further realized with a flexible device integrating sensing, memory, and processing functions by emulating the bionic sensory and perceptual functions of neural systems. Until now, flexible artificial synapses and related neuromorphic systems, which are capable of responding to external environmental stimuli and processing signals efficiently, have been extensively studied from material-selection, structure-design, and system-integration perspectives. Moreover, low-dimensional materials, which show distinct electrical properties and excellent mechanical properties, have been extensively employed in the fabrication of flexible electronics. In this review, recent progress in flexible artificial synapses and neuromorphic systems based on low-dimensional materials is discussed. The potential and the challenges of the devices and systems in the application of neuromorphic computing and sensory systems are also explored.