Evaluation of the impact of phase change humidity control material on energy performance of office buildings

Phase change humidity control material (PCHCM) is a new kind of composite made of high performance PCM microcapsules and diatomite. The PCHCM composite can moderate the hygrothermal variations by absorbing or releasing both heat and moisture and significantly reduce the peak/valley values of indoor temperature and relative humidity. In this paper, a novel model is developed to evaluate the energy performance of office buildings with PCHCM. The model is validated by a series of experiments, and then applied to investigate the effect of PCHCM on energy consumption in different typical climates worldwide (i.e. Beijing, Paris, Atlanta, and Guangzhou). Results show that high values of energy efficiencies can be obtained in the climates which characterized by a wide amplitude of temperature and humidity difference all day along (Paris and Atlanta). Noteworthy, the highest potential energy saving rate could be up to 19.57% for the office building in Paris

Deep learning-based method to accurately estimate breast tissue optical properties in the presence of the chest wall

SIGNIFICANCE: In general, image reconstruction methods used in diffuse optical tomography (DOT) are based on diffusion approximation, and they consider the breast tissue as a homogenous, semi-infinite medium. However, the semi-infinite medium assumption used in DOT reconstruction is not valid when the chest wall is underneath the breast tissue. AIM: We aim to reduce the chest wall\u27s effect on the estimated average optical properties of breast tissue and obtain accurate forward model for DOT reconstruction. APPROACH: We propose a deep learning-based neural network approach where a convolution neural network (CNN) is trained to simultaneously obtain accurate optical property values for both the breast tissue and the chest wall. RESULTS: The CNN model shows great promise in reducing errors in estimating the optical properties of the breast tissue in the presence of a shallow chest wall. For patient data, the CNN model predicted the breast tissue optical absorption coefficient, which was independent of chest wall depth. CONCLUSIONS: Our proposed method can be readily used in DOT and diffuse spectroscopy measurements to improve the accuracy of estimated tissue optical properties

Difference imaging from single measurements in diffuse optical tomography: A deep learning approach

SIGNIFICANCE: Difference imaging, which reconstructs target optical properties using measurements with and without target information, is often used in diffuse optical tomography (DOT) in vivo imaging. However, taking additional reference measurements is time consuming, and mismatches between the target medium and the reference medium can cause inaccurate reconstruction. AIM: We aim to streamline the data acquisition and mitigate the mismatch problems in DOT difference imaging using a deep learning-based approach to generate data from target measurements only. APPROACH: We train an artificial neural network to output data for difference imaging from target measurements only. The model is trained and validated on simulation data and tested with simulations, phantom experiments, and clinical data from 56 patients with breast lesions. RESULTS: The proposed method has comparable performance to the traditional approach using measurements without mismatch between the target side and the reference side, and it outperforms the traditional approach using measurements when there is a mismatch. It also improves the target-to-artifact ratio and lesion localization in patient data. CONCLUSIONS: The proposed method can simplify the data acquisition procedure, mitigate mismatch problems, and improve reconstructed image quality in DOT difference imaging

Development of a Procedure for Estimating the Parameters of Mechanistic Emission Source Models from Chamber Testing Data

In order to evaluate the impacts of volatile organic compounds (VOCs) emissions from building materials on the indoor pollution load and indoor air quality beyond the standard chamber test conditions and test period, mechanistic emission source models have been developed in the past. However, very limited data are available for the required model parameters including the initial concentration (Cm0), in-material diffusion coefficient (Dm), partition coefficient (Kma), and convective mass transfer coefficient (km). In this study, a procedure is developed for estimating the model parameters by using VOC emission data from standard small chamber tests. Multivariate regression analysis on the experimental data are used to determine the parameters. The Least Square and Global search algorithm with multi-starting points are used to achieve a good agreement in the normalized VOC concentrations between the model prediction and experimental data. To verify the procedure and estimate its uncertainty, simulated chamber test data are first generated by superposition of different levels of “experimental uncertainties” on the theoretical curve of the analytical solution to a mechanistic model, and then the procedure is used to estimate the model parameters from these data and determine how well the estimates converged to the original parameter values used for the data generation. Results indicated that the mean value of the estimated model parameters Cm0 was within -0.04%+/-2.47% of the true values if the “experimental uncertainty” were within +/-10% (a typical uncertainty present in small-scale chamber testing). The procedure was further demonstrated by applying it to estimate the model parameters from real chamber test data. Wide applications of the procedure will result in a database of mechanistic source model parameters for assessing the impact of VOC emissions on indoor pollution load, and for evaluating the effectiveness of various IAQ design and control strategies

Understanding Android Obfuscation Techniques: A Large-Scale Investigation in the Wild

In this paper, we seek to better understand Android obfuscation and depict a holistic view of the usage of obfuscation through a large-scale investigation in the wild. In particular, we focus on four popular obfuscation approaches: identifier renaming, string encryption, Java reflection, and packing. To obtain the meaningful statistical results, we designed efficient and lightweight detection models for each obfuscation technique and applied them to our massive APK datasets (collected from Google Play, multiple third-party markets, and malware databases). We have learned several interesting facts from the result. For example, malware authors use string encryption more frequently, and more apps on third-party markets than Google Play are packed. We are also interested in the explanation of each finding. Therefore we carry out in-depth code analysis on some Android apps after sampling. We believe our study will help developers select the most suitable obfuscation approach, and in the meantime help researchers improve code analysis systems in the right direction

Effect of Physical Exercise on College Students’ Life Satisfaction: Mediating Role of Competence and Relatedness Needs

This study examined the effect of physical exercise on the life satisfaction among college students. On the basis of the Basic Psychological Need Theory, the mediating roles of competence and relatedness needs satisfaction and their differences among college students in physical education (PE) majors and non-PE majors were explored. The sample included 1,012 college students who were selected to participate in an online survey. Major findings were as follows: (1) The total effect of physical exercise commitment on college students’ life satisfaction was marginally significant while that of physical exercise adherence was not significant; (2) The effect of physical exercise commitment was observed exclusively through the mediating role of relatedness need satisfaction, while that of physical exercise adherence was through both competence and relatedness needs satisfaction; (3) In terms of differences caused by major, only one mediation path, that was, physical exercise → competence need satisfaction → college students’s life satisfaction was significant among PE majors. This study thus enriched the empirical research on the benefits of physical exercise to individual mental health, highlighted the particularity of college students majoring in PE, and provided targeted and sensible suggestions for the design of physical exercise intervention programs