Jin Zhou

This study presents an electroencephalogram (EEG)‐based framework for thermal comfort classification by examining epoch sensitivity, feature importance, and interpretability, and the integration of spectral and microstate descriptors. Ninety sessions were conducted in a climate‐controlled chamber under neutral, warm, and cool conditions. Channel‐wise relative‐power (RP) features across five frequency bands were extracted using multiple epoch lengths (2‐, 4‐, 8‐, 16‐, and 32‐s) and evaluated using three classifiers: linear Support Vector Machine (SVM), radial‐basis‐function (RBF) SVM, and Random Forest (RF). Among the tested configurations, SVM‐RBF using 16‐s epochs achieved the highest overall performance when the full‐feature set was used. When reducing feature dimensionality, RF‐based analysis identified a compact top‐15 feature subset that achieved 76.67% accuracy, comparable with full feature set. Feature importance and stability validation consistently indicated that several frontal‐temporal RP features, particularly gamma_T5, theta_T4, gamma_T4, and beta_FP1, were the most reliable contributors to thermal discrimination. Supporting this observation, interpretability analysis showed that theta_T4 was most informative for identifying neutral from warm states, whereas gamma_FP1 activity provided stronger contrast under cool conditions. Finally, combining the selected top‐15 RP features with EEG microstate temporal descriptors further improved RF accuracy to 88.57%, surpassing the SVM‐RBF baseline. Collectively, these findings demonstrate the feasibility of EEG‐based thermal comfort modelling under within‐subject settings and highlight the value of integrating spectral with microstate features, while further validation using larger populations is needed to improve cross‐subject generalizability.

Purpose of Review: Low-cost particulate matter (PM) sensors are increasingly used for indoor air quality monitoring due to their affordability and ease of deployment. However, concerns persist regarding the reliability of their built-in processing functions and the accuracy of their data. This study evaluates the performance of 30 Plantower PMS5003 sensors across three distinct indoor environments—Ex_Normal (typical occupied office space), Ex_Incense (space with anthropogenic particle emissions), and Ex_Bushfire (space affected by outdoor air pollution). The primary aim is to improve data reliability by examining the sensors’ internal processing algorithms and identifying effective calibration models. Recent Findings: Piecewise linear regression analysis revealed two key internal functions within the sensor: one for converting particle number to mass and another for adjusting based on particle type. Three calibration models—Log-Linear (LN), non-Log-Linear (nLN), and Random Forest (RF)—were evaluated. All models showed improvements over raw sensor data in terms of coefficient of determination ( r ² ), root mean square error (RMSE), mean normalized bias (MNB), and coefficient of variation (CV), with particularly notable enhancements in RMSE (up to 64%), MNB (up to 70%), and CV (over 50%). Summary: Although all three calibration models significantly improved data quality, no substantial differences were observed among them. The LN model is recommended for its simplicity and comparable performance. These findings contribute to improving algorithmic processing in low-cost sensors and offer practical guidance for end-users seeking to enhance sensor reliability in indoor air quality monitoring applications.

Different neighborhood planning concepts have evolved over a century, with the x -minute neighborhood being the most recent. Systematic investigations into the differences in their design principles are limited. This study develops an assessment framework with 11 design elements (e.g., size, density) and benchmarks the x -minute neighborhood against 18 other concepts, following a systematic review of 143 articles. The core idea of the x -minute neighborhood can be traced back to Perry's neighborhood unit concept. It shares similar characteristics with new urbanism (e.g., destinations, density) but is distinctive in its emphasis on size. The findings serve as a design repository of different concepts.