Linking human-building interactions in shared offices with personality traits

Occupant behavior influences office building energy performance. The level of human-building interactions (HBIs) in shared offices strongly influences building energy use and occupant well-being. This study explored the link between occupant personality types and their behaviors of sharing energy and environment control systems and interactions with their colleagues. Inspired by the Five-Factor Model (FFM), we classified HBI behaviors into four dimensions: willingness to share control, knowledge of control, group decision behavior, and adaptive strategies. These four variables can be mapped to the four personality traits proposed by the FFM: agreeableness, openness, extraversion, and conscientiousness. Our cluster analysis identified six behavioral patterns: average (17.7%), reserved (15.3%), environmentally friendly (16.6%), role model (24.2%), self-centered (17.2%), and mechanist (9.0%). We further applied association rules, a widely utilized machine learning technique, to discover how demographics, building-related contextual factors, and perception-attitudinal factors influence HBI behaviors. Country, control feature accessibility, and group dynamics were found to be the three most influential factors that determine occupants’ HBI behaviors. The study provides insights about building design and operation, as well as policy to promote socially and environmentally desirable HBI behaviors in a shared office environment

Reconstruction of 5D5D Cosmological Models From Equation of State of Dark Energy

We consider a class of five-dimensional cosmological solutions which contains two arbitrary function $\mu(t)$ and $\nu(t)$. We found that the arbitrary function $\mu(t)$ contained in the solutions can be rewritten in terms of the redshift $z$ as a new arbitrary function $f(z)$. We further showed that this new arbitrary function $f(z)$ could be solved out for four known parameterized equations of state of dark energy. Then the $5D$ models can be reconstructed and the evolution of the density and deceleration parameters of the universe can be determined.Comment: 10 pages, 4 eps figures, ws-ijmpd.cls styl

Visualizing urban microclimate and quantifying its impact on building energy use in San Francisco

Weather data at nearby airports are usually used in building energy simulation to estimate energy use in buildings or evaluate building design or retrofit options. However, due to urbanization and geography characteristics, local weather conditions can differ significantly from those at airports. This study presents the visualization of 10-year hourly weather data measured at 27 sites in San Francisco, aiming to provide insights into the urban microclimate and urban heat island effect in San Francisco and how they evolve during the recent decade. The 10-year weather data are used in building energy simulations to investigate its influence on energy use and electrical peak demand, which informs the city's policy making on building energy efficiency and resilience. The visualization feature is implemented in CityBES, an open web-based data and computing platform for urban building energy research

Coil combination using linear deconvolution in k-space for phase imaging

Background: The combination of multi-channel data is a critical step for the imaging of phase and susceptibility contrast in magnetic resonance imaging (MRI). Magnitude-weighted phase combination methods often produce noise and aliasing artifacts in the magnitude images at accelerated imaging sceneries. To address this issue, an optimal coil combination method through deconvolution in k-space is proposed in this paper. Methods: The proposed method firstly employs the sum-of-squares and phase aligning method to yield a complex reference coil image which is then used to calculate the coil sensitivity and its Fourier transform. Then, the coil k-space combining weights is computed, taking into account the truncated frequency data of coil sensitivity and the acquired k-space data. Finally, combining the coil k-space data with the acquired weights generates the k-space data of proton distribution, with which both phase and magnitude information can be obtained straightforwardly. Both phantom and in vivo imaging experiments were conducted to evaluate the performance of the proposed method. Results: Compared with magnitude-weighted method and MCPC-C, the proposed method can alleviate the phase cancellation in coil combination, resulting in a less wrapped phase. Conclusions: The proposed method provides an effective and efficient approach to combine multiple coil image in parallel MRI reconstruction, and has potential to benefit routine clinical practice in the future

Cross-source sensing data fusion for building occupancy prediction with adaptive lasso feature filtering

Fusing various sensing data sources can significantly improve the accuracy and reliability of building occupancy detection. Fusing environmental sensors and wireless network signals are seldom studied for its computational and technical complexity. This study aims to propose an integrated adaptive lasso model that is able to extract critical data features for environmental and Wi-Fi probe dual sensing sources. Through rapid feature extraction and process simplification, the proposed method aims to improve the computational efficiency of occupancy detecting models. To validate the proposed model, an onsite experiment was conducted to examine two occupancy data resolutions, (real-time and four-level occupancy resolutions). The results suggested that, among all twelve features, eight features are most relevant. The mean absolute error of the real-time occupancy can be reduced to 2.18 and F1_accuracy is about 84.36% for the four-level occupancy