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For over half a century, traditional silicon-based integrated circuits (ICs) have been the basis of computational electronics and are widely used in computers, cell phones, and other fields. With the rapid development of human society, silicon-based semiconductor technology is approaching its physical and engineering limits. Our increasing diversity of non-traditional computing needs, such as ultra-small, ultra-fast, ultra-low-power wearables, and space radiation protection, is driving the search for new electronic materials. Semiconducting single-walled carbon nanotubes (s-SWCNTs) have many excellent electrical properties, such as high carrier mobility and high ballistic transport, making them strong candidates for new semiconductor materials in the post-Moore era. Carbon-based electronic technology has been developed for over 20 years, and the fundamental issues such as the material purification of s-SWCNTs, preparation prospects of s-SWCNT-based field-effect transistors (CNT FETs), and device physics based on CNT FETs have been basically solved. However, the chiral diversity of s-SWCNTs may lead to problems such as fluctuations in the electrical performance of CNT FETs, limiting the application of s-SWCNTs in high-end ICs. Monochiral s-SWCNTs not only have excellent electrical properties but also have a controllable structure and uniformity, which are crucial for the high-end IC of CNTs. However, some problems exist in the purity and yield of monochiral s-SWCNT preparation and the optimization of monochiral CNT FETs. Therefore, the chiral sorting of CNTs is reviewed in this paper, and the progress of polymer reprocessing in chiral separation is highlighted. Then, the research progress of monochiral CNT FETs is introduced, and possible development directions are summarized and analyzed. Finally, the application prospects of chiral-enriched s-SWCNTs include challenges and future opportunities. [ABSTRACT FROM AUTHOR]

Thermal comfort standards have suggested a number of physical indices which can be calculated from either building simulations or in situ physical monitoring to assess the long-term thermal comfort of a space. However, the prohibitively high cost of sensor technologies has limited the applications of these physical indices, and their usefulness has never been established using data collected in real buildings. This paper is the first assessment of the six types of existing indices (23 total) found in standards and five types of new indices (36 total) and their correlation with the long-term thermal satisfaction of building occupants. Correlation analyses were based on continuous thermal comfort measurements and post-occupancy evaluation surveys from four air-conditioned office buildings in Sydney, Australia. We found that the majority of existing indices, especially those based on predicted mean vote (PMV) and predicted percentage dissatisfied (PPD) metrics, have a weak correlation with thermal satisfaction. The percentage of time outside a temperature range was the best-performing index from the standards (r=-0.63). A new index based on the percentage of time that daily temperature range is greater than a threshold reported the strongest correlation (r=-0.8) with thermal satisfaction for this dataset. The results suggest that occupants’ long-term thermal comfort is influenced more by pronounced excursions beyond some acceptable temperature range and large variations in daily temperature than the average experience over time. These findings support the use of continuous monitoring technologies for long-term thermal comfort evaluation and inform potential amendments of international thermal comfort standards.

The predicted mean vote (PMV) and predicted percentage of dissatisfied (PPD) are the most widely used thermal comfort indices. Yet, their performance remains a contested topic. The ASHRAE Global Thermal Comfort Database II, the largest of its kind, was used to evaluate the prediction accuracy of the PMV/PPD model. We focused on: (i) the accuracy of PMV in predicting both observed thermal sensation (OTS) or observed mean vote (OMV) and (ii) comparing the PMV-PPD relationship with binned OTS – observed percentage of unacceptability (OPU). The accuracy of PMV in predicting OTS was only 34%, meaning that the thermal sensation is incorrectly predicted two out of three times. PMV had a mean absolute error of one unit on the thermal sensation scale and its accuracy decreased towards the ends of the thermal sensation scale. The accuracy of PMV was similarly low for air-conditioned, naturally ventilated and mixed-mode buildings. In addition, the PPD was not able to predict the dissatisfaction rate. If the PMV model would perfectly predict thermal sensation, then PPD accuracy is higher close to neutrality but it would overestimate dissatisfaction by approximately 15-25% outside of it. Furthermore, PMV-PPD accuracy varied strongly between ventilation strategies, building types and climate groups. These findings demonstrate the low prediction accuracy of the PMV–PPD model, indicating the need to develop high prediction accuracy thermal comfort models. For demonstration, we developed a simple thermal prediction model just based on air temperature and its accuracy, for this database, was higher than PMV.

Anomalous patterns in subjective votes can bias thermal comfort models built using data-driven approaches. A stochastic-based two-step framework to detect outliers in subjective thermal comfort data is proposed to address this problem. The anomaly detection technique involves defining similar conditions using a k-Nearest Neighbor (KNN) method and then quantifying the dissimilarity of the occupants’ votes from their peers under similar thermal conditions through a Multivariate Gaussian approach. This framework is used to detect outliers in the ASHRAE Global Thermal Comfort Database I & II. The resulting anomaly-free dataset produced more robust comfort models avoiding dubious predictions. The proposed method has been proven to effectively distinguish outliers from inter-individual variabilities in thermal demand. The proposed anomaly detection framework could easily be applied to other applications with different variables or subjective metrics. Such a tool holds great promise for use in the development of occupancy responsive controls for automated building HVAC systems.

Current thermal comfort standards use Predicted Mean Vote (PMV) classes as the compliance criteria despite previous critiques. The implicit assumption is that a narrower PMV range ensures higher thermal acceptability among building occupants. However, our analysis of a global database of thermal comfort field studies demonstrates that PMV classes are not appropriate design compliance criteria, and reinforces the need for a new and robust approach to thermal comfort compliance assessment. We compared two statistical methods to derive acceptable temperature ranges from occupant responses applied one to the ASHRAE Global Thermal Comfort Database II. Derived acceptable temperature ranges in real buildings (7.4K-12.2K) using this new method are wider than the current standards mandate (2K-6K). Our findings support the call for a relaxation of suggested temperature ranges in thermal comfort standards so as to minimize unnecessary space conditioning. The proposed data-driven statistical methods to determine temperature design compliance criteria are viewed as an important step forward in the age of continuous and pervasive monitoring and the associated large databases of building comfort measurements.

This work investigated the impact of periodic thickness and doping region on the doping efficiency of the P-type AlGaN superlattice. In this paper, the band structure of the simulated superlattice was analyzed. The superlattice structure of Al0.1Ga0.3N/Al0.4Ga0.6N, and the AlGaN buffer on the sapphire substrate, achieved a resistivity of ~3.3 Ω·cm. The results indicate that barrier doping and low periodic thickness offer significant advantages in introducing a reduction of the resistivity of P-type AlGaN superlattice structures. [ABSTRACT FROM AUTHOR]

Recognizing the value of open-source research databases in advancing the art and science of HVAC, in 2014 the ASHRAE Global Thermal Comfort Database II project was launched under the leadership of University of California at Berkeley’s Center for the Built Environment and The University of Sydney’s Indoor Environmental Quality (IEQ) Laboratory. The exercise began with a systematic collection and harmonization of raw data from the last two decades of thermal comfort field studies around the world. The ASHRAE Global Thermal Comfort Database II (Comfort Database), now an online, open-source database, includes approximately 81,846 complete sets of objective indoor climatic observations with accompanying “right-here-right-now” subjective evaluations by the building occupants who were exposed to them. The database is intended to support diverse inquiries about thermal comfort in field settings. A simple web-based interface to the database enables filtering on multiple criteria, including building typology, occupancy type, subjects’ demographic variables, subjective thermal comfort states, indoor thermal environmental criteria, calculated comfort indices, environmental control criteria and outdoor meteorological information. Furthermore, a web-based interactive thermal comfort visualization tool has been developed that allows end-users to quickly and interactively explore the data.

Kalman filtering is used to filter GNSS mining subsidence monitoring data in order to solve the problems of long period and high frequency GNSS mining subsidence monitoring data that are greatly affected by the external environment, high data interference noise and low data reliability. Firstly, the regression analysis method is adopted to automatically divide the monitoring data into three parts: the initial stage, the active stage and the decline stage. The subsidence in the initial stage and the decline stage is relatively stable, and the subsidence data is filtered by the standard Kalman filtering model. The add correction kalman filter model was constructed to deal with the data in the active stage of rapid subsidence change. The filter program was established by Python language, and the monitoring data of five-year and hourly interval sampling rate in a mining area in Ningxia were calculated and analyzed. The results showed that the process curve of different kalman filtering results in different stages was consistent with the measured results, and the filtering effect was good. The add correction kalman filter model can effectively process the monitoring data of mining area with large subsidence variation. The method constructed in this paper can effectively reduce the impact of data fluctuation on the subsidence result and improve the reliability of monitoring data. The research results provide a scientific basis for long-term and high-frequency settlement monitoring data processing.