Soy-based infant formulas have been in use for >30 y. These formulas are manufactured from soy protein isolates and contain significant amounts of phytoestrogens of the isoflavone class. As determined by HPLC, the isoflavone compositions of commercially available formulas are similar qualitatively and quantitatively and are consistent with the isoflavone composition of soy protein isolates. Genistein, found predominantly in the form of glycosidic conjugates, accounts for >65% of the isoflavones in soy-based formulas. Total isoflavone concentrations of soy-based formulas prepared for infant feeding range from 32 to 47 mg/L, whereas isoflavone concentrations in human breast milk are only 5.6 +/- 4.4 microg/L (mean +/- SD, n = 9). Infants fed soy-based formulas are therefore exposed to 22-45 mg isoflavones/d (6-11 mg x kg body wt(-1) x d(-1)), whereas the intake of these phytoestrogens from human milk is negligible (<0.01 mg/d). The metabolic fate of isoflavones from soy-based infant formula is described. Plasma isoflavone concentrations reported previously for 4-mo-old infants fed soy-based formula were 654-1775 microg/L (mean: 979.7 microg/L: Lancet 1997:350;23-7), significantly higher than plasma concentrations of infants fed either cow-milk formula (mean +/- SD: 9.4 +/- 1.2 microg/L) or human breast milk (4.7 +/- 1.3 microg/L). The high steady state plasma concentration of isoflavones in infants fed soy-based formula is explained by reduced intestinal biotransformation, as evidenced by low or undetectable concentrations of equol and other metabolites, and is maintained by constant daily exposure from frequent feeding. Isoflavones circulate at concentrations that are 13,000-22,000-fold higher than plasma estradiol concentrations in early life. Exposure to these phytoestrogens early in life may have long-term health benefits for hormone-dependent diseases.

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Identification approaches applied to semi-physical thermal network structures, so called gray-box modeling approaches, are popular in building science for energy audits, retrofit analysis and advanced building controls, e.g. model predictive control. However conventional identification approaches applied to thermal networks fail when there are significant unmeasured heat gains that influence building responses. This paper presents a method to obtain improved gray-box building models from closed loop data having significant unmeasured disturbances. The method estimates both physical parameters of a building thermal network model and also a disturbance model that characterizes the unmeasured inputs. The performance of the algorithm is demonstrated using numerical and experimental results.

8Penetration of advanced building control techniques into the market has been slow since buildings are unique and site-9 specific controller design is costly. In addition, for medium-to large-sized commercial buildings, HVAC system 10 configurations can be very complex making centralized control infeasible. This paper presents a general multi-agent control 11 methodology that can be applied to building energy system optimization in a "plug-and-play" manner. A multi-agent 12 framework is developed to automate the controller design process and reduce the building-specific engineering efforts. To 13 support distributed decision making, two alternative consensus-based distributed optimization algorithms are adapted and 14implemented within the framework. The overall multi-agent control approach was tested in simulation with two case studies: 15 optimization of a chilled water cooling plant and optimal control of a direct-expansion (DX) air-conditioning system serving 16 a multi-zone building. In both cases, the multi-agent controller was able to find near-optimal solutions and significant energy 17 savings were achieved. 18Keyword: Multi-agent control; Building energy system optimization; Distributed optimization; HVAC component 19 coordination 20 INTRODUCTION 21More than 40% of the primary energy usage in the United States is related to energy consumption in buildings [1] and if 22 buildings are not operated properly, a significant amount of energy is wasted. The energy savings opportunities for optimal 23 building controls are becoming widely recognized leading to growing research efforts in the past few years. However, the 24 deployment of advanced controls in buildings has been progressing very slowly due to several reasons: (1) buildings are 25 unique in terms of both building construction and heating, ventilation and air-conditioning (HVAC) system configuration, 26 which makes building-specific controller design costly; (2) optimal control of complex building energy systems is difficult 27

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