An Investigation of Optimal Pressurization for Buildings in Hot and Humid Climates

Buildings in a hot and humid climate usually are kept at a positive pressurization level to avoid infiltration induced issues such as mold growth within building envelopes. This dissertation combines existing models of infiltration and mold growth to predict the influence of pressurization level on the risk of mold growth. The simulation results indicate that a 3 meter high unpressurized building in College Station, TX with 22⁰C indoor temperature set-pint will experience an annual increase in mold index, and 1.5 Pa positive pressurization should theoretically eliminate the long-term risk of an increasing mold index on all walls. The model also indicates that only 1 Pa pressurization is required if the same building is moved to Fort Worth, TX and no pressurization is required if it is moved to Atlanta, GA. Furthermore, a field experiment indicates that the conventional pressurization system fails to pressurize each floor of the eight-floor Harrington Tower building equally due to stack effect; extra make-up air is required to compensate the leaked air through the over-pressurized floors which results in extra energy consumption. An Internal Fan Balancing Pressurization System is proposed to solve this problem. The building energy simulation results suggest that the annual energy cost savings from using the proposed system can range from 3.7% to 6.7% of the total utility bill depending on different assumptions. To verify the feasibility of the proposed system, a scaled three-floor model is developed; on the scale model the Internal Fan Balancing System is able to reduce 28% to 32% of required make-up air flow by keeping better pressurization levels

Speech Dereverberation Based on Integrated Deep and Ensemble Learning Algorithm

Reverberation, which is generally caused by sound reflections from walls, ceilings, and floors, can result in severe performance degradation of acoustic applications. Due to a complicated combination of attenuation and time-delay effects, the reverberation property is difficult to characterize, and it remains a challenging task to effectively retrieve the anechoic speech signals from reverberation ones. In the present study, we proposed a novel integrated deep and ensemble learning algorithm (IDEA) for speech dereverberation. The IDEA consists of offline and online phases. In the offline phase, we train multiple dereverberation models, each aiming to precisely dereverb speech signals in a particular acoustic environment; then a unified fusion function is estimated that aims to integrate the information of multiple dereverberation models. In the online phase, an input utterance is first processed by each of the dereverberation models. The outputs of all models are integrated accordingly to generate the final anechoic signal. We evaluated the IDEA on designed acoustic environments, including both matched and mismatched conditions of the training and testing data. Experimental results confirm that the proposed IDEA outperforms single deep-neural-network-based dereverberation model with the same model architecture and training data

Supervised Collective Classification for Crowdsourcing

Crowdsourcing utilizes the wisdom of crowds for collective classification via information (e.g., labels of an item) provided by labelers. Current crowdsourcing algorithms are mainly unsupervised methods that are unaware of the quality of crowdsourced data. In this paper, we propose a supervised collective classification algorithm that aims to identify reliable labelers from the training data (e.g., items with known labels). The reliability (i.e., weighting factor) of each labeler is determined via a saddle point algorithm. The results on several crowdsourced data show that supervised methods can achieve better classification accuracy than unsupervised methods, and our proposed method outperforms other algorithms.Comment: to appear in IEEE Global Communications Conference (GLOBECOM) Workshop on Networking and Collaboration Issues for the Internet of Everythin

Antidepressant Effects on Insulin Sensitivity and Proinflammatory Cytokines in the Depressed Males

Growing evidence suggests that mood disorder is associated with insulin resistance and inflammation. Thus the effects of antidepressants on insulin sensitivity and proinflammatory responses will be a crucial issue for depression treatment. In this study, we enrolled 43 non-diabetic young depressed males and adapted standard testing procedures to assess glucose metabolism during 4-week hospitalization. Before and after the 4-week antidepressant treatment, participants underwent oral glucose tolerance test (OGTT) and frequently sampled intravenous glucose tolerance test (FSIGT). Insulin sensitivity (SI), glucose effectiveness (SG), acute insulin response, and disposition index (DI) were estimated using the minimal model method. The plasma levels of C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), and adiponectin were measured. The Hamilton depression rating scale (HAM-D) total scores were reduced significantly during the course of treatment. There were no significant changes in the parameters of SI, SG, and DI. Compared to drug naïve status, the level of plasma IL-6 was significantly elevated (0.77 to 1.30 pg/ml; P = .001) after antidepressant therapy. However, the concentrations of CRP, TNF-α, and adiponectin showed no differences during the course of treatment. The results suggest that antidepressants may promote stimulatory effect on the IL-6 production in the early stage of antidepressant treatment

On the Momentum Dependence of the Flavor Structure of the Nucleon Sea

Difference between the $\bar u$ and $\bar d$ sea quark distributions in the proton was first observed in the violation of the Gottfried sum rule in deep-inelastic scattering (DIS) experiments. The parton momentum fraction $x$ dependence of this difference has been measured over the region $0.02 < x < 0.35$ from Drell-Yan and semi-inclusive DIS experiments. The Drell-Yan data suggested a possible sign-change for $\bar d(x)-\bar u(x)$ near $x \sim 0.3$, which has not yet been explained by existing theoretical models. We present an independent evidence for the $\bar d(x)-\bar u(x)$ sign-change at $x \sim 0.3$ from an analysis of the DIS data. We further discuss the $x$-dependence of $\bar d(x)-\bar u(x)$ in the context of meson cloud model and the lattice QCD formulation.Comment: 5 pages, 5 figures, final versio