Rooftop photovoltaics (PV) and electrical vehicles (EV) have become more economically viable to residential customers. Most existing home energy management systems (HEMS) only focus on the residential occupants’ thermal comfort in terms of indoor temperature and humidity while neglecting their other behaviors or concerns. This paper aims to integrate residential PV and EVs into the HEMS in an occupant-centric manner while taking into account the occupants’ thermal comfort, clothing behaviors, and concerns on the state-of-charge (SOC) of EVs. A stochastic adaptive dynamic programming (ADP) model was proposed to optimally determine the setpoints of heating, ventilation, air conditioning (HVAC), occupant’s clothing decisions, and the EV’s charge/discharge schedule while considering uncertainties in the outside temperature, PV generation, and EV’s arrival SOC. The nonlinear and nonconvex thermal comfort model, EV SOC concern model, and clothing behavior model were holistically embedded in the ADP-HEMS model. A model predictive control framework was further proposed to simulate a residential house under the time of use tariff, such that it continually updates with optimal appliance schedules decisions passed to the house model. Cosimulations were carried out to compare the proposed HEMS with a baseline model that represents the current operational practice. The result shows that the proposed HEMS can reduce the energy cost by 68.5% while retaining the most comfortable thermal level and negligible EV SOC concerns considering the occupant’s behaviors.

Between 2010 and 2014, the National Fire Protection Association (NFPA) estimated that female firefighters experienced 1260 injuries on the fireground each year. Previous research attributed some of these injuries to ill-fitting fire personal protective equipment (PPE). Therefore, in this mixed-method paper, the authors explored the relationship between fire PPE and injuries, and how they related to sizing and fit. To achieve this aim, data were collected from manufacturer-provided web communications regarding sizing and fit, user surveys (n = 74), and 1:1 interviews (n = 31) with U.S. female firefighters. The data considered how the size and fit standards established by the NFPA and how leading fire PPE manufacturers’ interpretation of standards impacted fit for female firefighters. Interview and survey data pinpointed experiences with the PPE sizing processes that led to poor fit. The data also identified previously undocumented knowledge gaps between NFPA size standards, commercialized products, and processes used by manufacturers and firehouses to fit female practitioners. The study discovered several opportunities to improve the size and fitting process women experienced when acquiring new turnout gear. With effective fire industry partnerships and future research, women can experience fewer injuries, improved comfort, and work performance with their PPE while establishing equality with their male counterparts.

Obtaining accurate body measurements is a critical step when designing products to fit the human body. Compared to traditional manual methods, 3D body scanning has fundamentally enhanced the accessibility of the body, however, the datasets extracted from 3D body scans often have missing values. Recently, the applications of data-driven machine learning (ML) methods in anthropometrics studies and clothing-related work have been increasing. However, there has been limited research on exploring if missing data and difficult-to-extract measurements from 3D scans could be predicted accurately and efficiently by using ML methods. Therefore, this exploratory study investigates the potential use of four mainstream ML methods in improving the usefulness of a 3D body scan dataset.

The 3D body scan technology has recently innovated the way of measuring human bodies and generated a large volume of body measurements. However, one inherent issue that plagues the use of the resultant database is the missing data usually caused by using automatic data extractions from the 3D body scans. Tedious extra efforts have to be made to manually fill the missing data for various applications. To tackle this problem, this paper proposes a machine learning (ML)-based approach for 3D body measurement estimation while considering the measurement (feature) importance. The proposed approach selects the most critical features to reduce the algorithm input and to improve the ML method performance. In addition, a Bayesian search is further used in fine-tuning the hyperparameters to minimize the mean square error. Two distinct ML methods, i.e., Random Forest and XGBoost, are used and tested on a real-world dataset that contains 3D body scans of 212 participants in the Kansas-Missouri area of the United States. The results show the effectiveness of the proposed methods with roughly 3% of Mean Absolute Percentage Errors in estimating the missing data. The two ML methods with the proposed hybrid feature selection and the Baysian search are comprehensively compared. The comparative results suggest that the Random Forest method performs better than the XGBoost counterpart in filling missing 3D body measurements.