Social support is believed to be a universally valuable resource for combating stress, yet Asians and Asian Americans report that social support is not helpful to them, resist seeking it, and are underrepresented among recipients of supportive services. We distinguish between explicit social support (seeking and using advice and emotional solace) and implicit social support (focusing on valued social groups) and show that Asians and Asian Americans are psychologically and biologically benefited more by implicit social support than by explicit social support; the reverse is true for European Americans. Our discussion focuses on cultural differences in the construal of relationships and their implications for social support and delivery of support services.

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Stress is implicated in the development and progression of a broad array of mental and physical health disorders. Theory and research on the self suggest that self-affirming activities may buffer these adverse effects. This study experimentally investigated whether affirmations of personal values attenuate physiological and psychological stress responses. Eighty-five participants completed either a value-affirmation task or a control task prior to participating in a laboratory stress challenge. Participants who affirmed their values had significantly lower cortisol responses to stress, compared with control participants. Dispositional self-resources (e.g., trait self-esteem and optimism) moderated the relation between value affirmation and psychological stress responses, such that participants who had high self-resources and had affirmed personal values reported the least stress. These findings suggest that reflecting on personal values can keep neuroendocrine and psychological responses to stress at low levels. Implications for research on the self, stress processes, health, and interventions are discussed.

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An analytical method was developed to quantitatively determine the asphaltene content in petroleum crude oils by Fourier transform infrared spectroscopy (FT-IR). Asphaltenes are a solubility class of compounds found in crude oils. They are black to dark brown solids defined by their insolubility in n-alkane solvents. The structure of asphaltene molecules is polynuclear aromatic rings with alkyl side chains and heteroatoms such as nitrogen, oxygen, and sulfur attached. Asphaltenes are known to cause oil well plugging and irreversible catalyst deactivation in refineries. The asphaltene content of 50 crude oils from a wide array of geochemical conditions was determined by the standard n-pentane insolubles method. FT-IR spectra of the 50 crude oils were collected using an attenuated total reflectance cell. A partial least squares model was generated to predict the amount of asphaltenes from 42 of the crude oils. The model was shown to have an R 2 value of 0.95 and a standard error of estimate of 0.92 wt %. An independent prediction set of eight crude oils was used to test the validity of the model. The prediction set was shown to have an R 2 value of 0.96 and a standard error of prediction of 0.99 wt %. The FT-IR method compares favorably with the current laboratory method in terms of results, is faster, and uses no solvents.

We report for the first time a direct comparison of the three most common vibrational analysis techniques for the determination of individual BTEX components (benzene, toluene, ethylbenzene, ortho-xylene, meta-xylene, and para-xylene) in blended commercial gasolines. Partial least-squares (PLS) regression models were constructed for each BTEX component by using each of the three spectroscopic techniques. A minimum of 120 types of blended gasolines were used in the training set for each BTEX component. Leave-one-out validation of the training sets yields lower standard errors for Raman and mid-IR spectroscopies when compared to near-IR for all six BTEX components. In general, mid-IR has slightly lower standard errors than Raman. These trends are upheld when the models are tested by using independent test sets with a minimum of 40 types of blended gasolines (all of which differ in composition from the training set).

A Fourier transform Raman spectrometer was used to collect the Raman spectra of 208 commercial petroleum fuels. The individual motor and research octane numbers (MON and RON, respectively) were determined experimentally using the industry standard ASTM knock engine method. Partial least-squares regression analysis was used to build regression models which correlate the Raman spectra of 175 of the fuels with the experimentally determined values for MON, RON, and pump octane number (the average of MON and RON) of the fuels. Each of the models was validated using leave-one-out validation.The standard errors of validation are 0.415, 0.535, and 0.410 octane units for MON, RON, and pump octane number, respectively. By comparing the standard error of validation to the standard deviation for the experimentally determined octane numbers, it is evident that the accuracy of the Raman determined values is limited by the accuracy of the training set used in creating the models. The Raman regression models were used to

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