Study on Thermal Comfort for University Classrooms in Pre- Heating Season in Xi\u27an

Thermal comfort of students in university classrooms during transition season in Xi\u27an, before heating, is studied. Indoor thermal environment parameters and outdoor weather parameters of seven typical classrooms in a university campus in Xi\u27an were measured. At the same time, the subjective questionnaires were used to know students\u27 satisfaction and expectation with various environmental factors. 992 valid questionnaires were received. Based on the data collected, the thermal comfort of occupants in classroom was discussed and a thermal comfort adaptive model was established. The results show that the range of thermal comfort acceptable to students is broader than that defined in the ASHARE standard, indicating that students have some adaptability to indoor air environment. The measured indoor thermal neutral temperature is lower than the theoretical one. There is difference between the thermal sensation vote (TSV) and the predicted mean vote (PMV). The slope of TSV cure vs. operative temperature is greater than that of PMV, indicating that under actual condition, students are more sensitive to air changes. The proposed adaptive model provided a reference for understanding the thermal comfort of university buildings under natural ventilation environment in Xi’an, helpful to improve the thermal comfort and save energy for university buildings in Xi’an

Effects of Indoor Temperature and Air Movement on Perceived Air Quality in the Natural Ventilated Classrooms

Perceived air quality is an important aspect in current guidelines and standards for indoor environment. It represents occupants’ real feeling about indoor air and affected by almost all environmental parameters, such as the temperature, the relative humidity, the air movement, and et al. Studies were conducted mainly in controlled climate chambers or air-conditioned spaces, rarely in natural ventilated spaces. In this paper, the effects of temperature and air movement on perceived air quality in natural ventilated classrooms are investigated. The indoor environmental parameters in 7 classrooms for 35 lessons are continuously measured and the students in class are asked to report their perception on the temperature, air movement, and the air quality of classrooms by filling questionnaires at once after a lesson. The number of received validated questionnaires is 992. The correlation analysis is used to investigate the effects of temperature and air movement on the perceived air quality. Results show that in natural ventilation classrooms, which are warm at temperature and moderate at humidity with an air speed lower than 0.1m/s, it is the thermal sensation rather than the temperature, enthalpy, thermal acceptability, or CO2 concentration that affects the perception of occupants for air quality. The perception for air movement influences the air quality acceptability. Increasing air movement increases the air quality acceptability. Besides, it is found that the preference of air movement is related to the air quality acceptability. When participants feel that the air movement is just suitable, the acceptability of air quality reaches the highest. When participants feel the air movement need to be adjusted, the air quality acceptability decreases

Fabrication of highly hydrophobic two-component thermosetting polyurethane surfaces with silica nanoparticles

Highly hydrophobic thermosetting polyurethane (TSU) surfaces with micro-nano hierarchical structures were developed by a simple process combined with sandpaper templates and nano-silica embellishment. Sandpapers with grit sizes varying from 240 to 7000 grit were used to obtain micro-scale roughness on an intrinsic hydrophilic TSU surface. The surface wettability was investigated by contact angle measurement. It was found that the largest contact angle of the TSU surface without nanoparticles at 102 ± 3 ° was obtained when the template was 240-grit sandpaper and the molding progress started after 45 min curing of TSU. Silica nanoparticles modified with polydimethylsiloxane were scattered onto the surfaces of both the polymer and the template to construct the desirable nanostructures. The influences of the morphology, surface composition and the silica content on the TSU surface wettability were studied by scanning electron microscopy (SEM), attenuated total reflection (ATR) infrared (IR) spectroscopy, X-ray photoelectron spectroscopy (XPS) and contact angle measurements. The surface of the TSU/SiO2 nanocomposites containing 4 wt% silica nanoparticles exhibited a distinctive dual-scale structure and excellent hydrophobicity with the contact angle above 150°. The mechanism of wettability was also discussed by Wenzel model and Cassie-Baxter model

Diffusion Enhancement for Cloud Removal in Ultra-Resolution Remote Sensing Imagery

The presence of cloud layers severely compromises the quality and effectiveness of optical remote sensing (RS) images. However, existing deep-learning (DL)-based Cloud Removal (CR) techniques encounter difficulties in accurately reconstructing the original visual authenticity and detailed semantic content of the images. To tackle this challenge, this work proposes to encompass enhancements at the data and methodology fronts. On the data side, an ultra-resolution benchmark named CUHK Cloud Removal (CUHK-CR) of 0.5m spatial resolution is established. This benchmark incorporates rich detailed textures and diverse cloud coverage, serving as a robust foundation for designing and assessing CR models. From the methodology perspective, a novel diffusion-based framework for CR called Diffusion Enhancement (DE) is proposed to perform progressive texture detail recovery, which mitigates the training difficulty with improved inference accuracy. Additionally, a Weight Allocation (WA) network is developed to dynamically adjust the weights for feature fusion, thereby further improving performance, particularly in the context of ultra-resolution image generation. Furthermore, a coarse-to-fine training strategy is applied to effectively expedite training convergence while reducing the computational complexity required to handle ultra-resolution images. Extensive experiments on the newly established CUHK-CR and existing datasets such as RICE confirm that the proposed DE framework outperforms existing DL-based methods in terms of both perceptual quality and signal fidelity

Sensor and data: key elements of human-machine interaction for human-centric smart manufacturing

The proposal of Industry 5.0 has made sustainability, human-centric and resilience the core of digital manufacturing, which also puts forward new requirements for the human-machine interaction (HMI) paradigm in human-centric smart manufacturing (HCSM). In the manufacturing scenario, the process of HMI can be divided into four parts: 1) Sensors and hardware, where the environment information and input signals are collected, 2) Data processing, where the signals are converted into data, 3) Transmission mechanism, where the data is transmitted to the processing centre, and 4) Interaction and collaboration. Among them, sensors and data are expected to become breakthrough points in optimising HMI. This is not only due to the emergence of new research, innovation and technologies but also because they are closely influenced by the new design concepts brought about by Industry 5.0. This paper analyses the latest studies and technologies in the sensor field and their possible applications in HCSM scenarios. Then, opportunities and challenges of data analysis in the HMI in Industry 5.0 are discussed. Finally, based on the design concepts and requirements of Industry 5.0, this paper demonstrates how they will become the key points for future HMI development

Human-machine interaction towards Industry 5.0: Human-centric smart manufacturing

Since the concept of Industry 5.0 was proposed, the emphasis on human–machine​ interaction (HMI) in industrial scenarios has continued to increase. HMI is part of the factory’s development towards Industry 5.0, mainly because HMI can help realise the human-centric vision. At the same time, to achieve the sustainable and resilient goals proposed by Industry 5.0, green, smart, and more advanced technologies are also considered important driving factors for factories to achieve Industry 5.0. Human-centric smart manufacturing (HCSM) factories that integrate HMI with advanced technologies are expected to become the paradigm of future manufacturing. Therefore, it is necessary to discuss technologies and research directions that may promote the implementation of HCSM in the future. In a smart factory, HMI signals will go through the process of being collected by sensors, processed, transmitted to the data analysis centre and output to complete the interaction. Based on this process, we divide HMI into four parts: sensor and hardware, data processing, transmission mechanism, and interaction and collaboration. Through a systematic literature review process, this article evaluates and summarises the current research and technologies in the HMI field and categorises them into four parts of the HMI process. Since the current usage scenarios of some technologies are relatively limited, the introduction focuses on the possible applications and problems they face. Finally, the opportunities and challenges of HMI for Industry 5.0 and HCSM are revealed and discussed