Life cycle assessment of white roof and sedum-tray garden roof for office buildings in China

White roof (WR) and Sedum lineare tray garden roof (STGR) have been convinced to improve the energy-efficiency and provide various benefits for conventional impervious grey roofs. Some national and local standards have standardized and recommended these technologies in existing building retrofits, however, they do not include assessment and choice of a particular roof retrofit in different climates. This paper presents a 40-year life-cycle cost analysis (LCCA) of an office building roof retrofitted by adding either WR or STGR over an existing grey roof in five cities, located in four Chinese climate zones. The LCCA find that the WR retrofits exhibit positive life-cycle net savings (NS) in warm winter zones, ranging 5.7–35.1 CNY/m 2 , and STGR retrofits have negative NS of -81.3– -16.7 CNY/m 2 in all climate zones. The NS of both WR and STGR generally tend to improve as one moves from the coldest cities to the warmest cities. LCCA results suggest that adding new building codes concerning crediting or prescribing WR and STGR retrofits into office buildings with grey roofs in hot summer climate zones and warm winter zone in China, respectively. And featured by more specific requirements, the localized Technical Norms help promote the implementation of new building codes

Neutrino Masses, Lepton Flavor Mixing and Leptogenesis in the Minimal Seesaw Model

We present a review of neutrino phenomenology in the minimal seesaw model (MSM), an economical and intriguing extension of the Standard Model with only two heavy right-handed Majorana neutrinos. Given current neutrino oscillation data, the MSM can predict the neutrino mass spectrum and constrain the effective masses of the tritium beta decay and the neutrinoless double-beta decay. We outline five distinct schemes to parameterize the neutrino Yukawa-coupling matrix of the MSM. The lepton flavor mixing and baryogenesis via leptogenesis are investigated in some detail by taking account of possible texture zeros of the Dirac neutrino mass matrix. We derive an upper bound on the CP-violating asymmetry in the decay of the lighter right-handed Majorana neutrino. The effects of the renormalization-group evolution on the neutrino mixing parameters are analyzed, and the correlation between the CP-violating phenomena at low and high energies is highlighted. We show that the observed matter-antimatter asymmetry of the Universe can naturally be interpreted through the resonant leptogenesis mechanism at the TeV scale. The lepton-flavor-violating rare decays, such as $\mu \to e + \gamma$, are also discussed in the supersymmetric extension of the MSM.Comment: 50 pages, 22 EPS figures, macro file ws-ijmpe.cls included, accepted for publication in Int. J. Mod. Phys.

Learning Bayesian Networks using the Constrained Maximum a Posteriori Probability Method

Purely data-driven methods often fail to learn accurate conditional probability table (CPT) parameters of discrete Bayesian networks (BNs) when training data are scarce or incomplete. A practical and efficient means of overcoming this problem is to introduce qualitative parameter constraints derived from expert judgments. To exploit such knowledge, in this paper, we provide a constrained maximum a posteriori (CMAP) method to learn CPT parameters by incorporating convex constraints. To further improve the CMAP method, we present a type of constrained Bayesian Dirichlet priors that is compatible with the given constraints. Combined with the CMAP method, we propose an improved expectation maximum algorithm to process incomplete data. Experiments are conducted on learning standard BNs from complete and incomplete data. The results show that the proposed method outperforms existing methods, especially when data are extremely limited or incomplete. This finding suggests the potential effective application of CMAP to real-world problems. Moreover, a real facial action unit (AU) recognition case with incomplete data is conducted by applying different parameter learning methods. The results show that the recognition accuracy of respective recognition methods can be improved by the AU BN, which is trained by the proposed method