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Recent studies suggest that research participants show reduced distortion of their taboo attitudes and behaviors when they take part in Internet-based procedures from outside the laboratory. We explored whether such procedures would reduce distortion in the assessment of racial bias. In Study 1, White participants who completed the study in the laboratory evaluated Black targets more favorably than White targets. This unexpected “outgroup-favoring” pattern occurred in both pencil-and-paper and Internet versions of the study, showing that modality did not produce it; but when participants worked outside the laboratory via the Internet, this pattern disappeared. Study 2 replicated the above findings and further indicated that the reduced distortion in Internet-based studies was due to the removal of the experimenter rather than removing the participants from the laboratory environment. The implications of these findings for the study of controlled processes of prejudice and the nature of Internet-based social communication are discussed.

A comprehensive optimization model that can determine the most cost-effective and environmentally sustainable production pathways in an integrated processing network is needed, especially in the bioconversion space. We develop the most comprehensive bioconversion network to date with 193 technologies and 129 materials/compounds for fuels production. We consider the tradeoff between scaling capital and operating expenditures (CAPEX and OPEX) as well as life cycle environmental impacts. Additionally, we develop a general network-based modeling framework with nonconvex terms for CAPEX. To globally optimize the nonlinear program with high computational efficiency, we develop a specialized branch-and-refine algorithm based on successive piecewise linear approximations. Two case studies are considered. The optimal pathways have profits from 2$12.9 to $99.2M/yr, and emit 791 ton CO 2 -eq/yr to 31,571 ton CO 2 -eq/yr. Utilized technologies vary from corn-based fermentation to pyrolysis. The proposed algorithm reduces computational time by up to three orders of magnitude compared to general-purpose global optimizers. V C 2014 American Institute of Chemical Engineers AIChE J, 61: 530-554, 2015Green steps denote final solution steps, yellow denote checking steps, red steps denote that another iteration is needed, and blue denotes solving an MILP with the SOS1 formulation. UB 5 upper bound, LB 5 lower bound. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.] 540Hardwood is converted via gasification and subsequent upgrading to gasoline. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

Surfaces that resist protein adsorption are important for many bioanalytical applications. Bovine serum albumin (BSA) coatings and multi-arm poly(ethylene glycol) (PEG) coatings display low levels of non-specific protein adsorption and have enabled highly quantitative single-molecule (SM) protein studies. Recently, a method was developed for coating a glass with PEG-BSA nanogels, a promising hybrid of these two low-background coatings. We characterized the nanogel coating to determine its suitability for SM protein experiments. SM adsorption counting revealed that nanogel-coated surfaces exhibit lower protein adsorption than covalently coupled BSA surfaces and monolayers of multi-arm PEG, so this surface displays one of the lowest degrees of protein adsorption yet observed. Additionally, the nanogel coating was resistant to DNA adsorption, underscoring the utility of the coating across a variety of SM experiments. The nanogel coating was found to be compatible with surfactants, whereas the BSA coating was not. Finally, applying the coating to a real-world study, we found that single ligand molecules could be tethered to this surface and detected with high sensitivity and specificity by a digital immunoassay. These results suggest that PEG -BSA nanogel coatings will be highly useful for the SM analysis of proteins.

A bioconversion product and process network converts different types of biomass to various fuels and chemicals via a plethora of technologies. Reliable bioconversion processing pathways should be designed considering the effect of uncertain parameters, such as biomass feedstock price and biofuel product demand. Given a large-scale bioconversion product and process network of 194 technologies and 139 materials/compounds, we propose a two-stage adaptive robust mixed-integer nonlinear programming problem. The model allows for decisions at the design and operational stages to be made sequentially and considers budgets of uncertainty to control the level of robustness. Nonlinearity in this model appears in the first-stage objective function, and the second-stage problem is a linear program. We efficiently solve the proposed problem with a tailored algorithm. The robust optimal solutions corresponding to various uncertainty budgets show that the minimum total annualized cost is more sensitive to biofuel demand uncertainty compared to biomass feedstock price uncertainty.

Featured Application: The specific application of this work is that it improves management of solid biofuel supply chain.Its potential application is for integrated product distribution management where product quality control and traceability can be integrated to increase customer satisfaction and resource utilization, and reduce logistics cost as well as environmental impact.Abstract: Agricultural pruning biomass is one of the important resources in Europe for generating renewable energy. However, utilization of the agricultural residues requires development of efficient and effective logistics systems. The objective of this study was to develop smart logistics system (SLS) appropriate for the management of the pruning biomass supply chain. The paper describes the users' requirement of SLS, defines the technical and functional requirements and specifications for the development of SLS, and determines relevant information/data to be documented and managed by the SLS. This SLS has four major components: (a) Smart box, a sensor unit that enables measurement of data such as relative humidity, temperature, geographic positions; (b) On-board control unit, a unit that performs route planning and monitors the recordings by the smart box; (c) Information platform, a centralized platform for data storing and sharing, and management of pruning supply chain and traceability; and (d) Central control unit, an interface linking the Information platform and On-board control unit that serves as a point of administration for the whole pruning biomass supply chain from harvesting to end user. The SLS enables the improvement of performance of pruning biomass supply chain management and product traceability leading to a reduction of product loss, increased coordination of resources utilisation and quality of solid biofuel supply, increased pruning marketing opportunity, and reduction of logistics cost. This SLS was designed for pruning biomass, but could also be adapted for any type of biomass-to-energy initiatives.

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