The present study applies an allostatic load framework to an examination of the relationship between maternal psychosocial risk factors and maladaptive parenting behaviors. Specifically, the implications of low socioeconomic status and maternal depressive symptoms for maternal sympathovagal functioning during young children’s distress were examined, as well as whether that functioning was, in turn, associated with maternal insensitivity, hostility, intrusiveness, and disengagement during mother–child dyadic interaction. Consistent with an allostatic framework, three patterns of sympathovagal functioning were expected to emerge: normative arousal, hyperarousal, and hypoarousal profiles. Furthermore, meaningful associations between maternal psychosocial risk factors, maladaptive parenting behaviors, and the three profiles of sympathovagal functioning were anticipated. Participants included 153 mother–toddler dyads recruited proportionately from lower and middle socioeconomic status backgrounds. Mothers’ sympathovagal response to their child’s distress was assessed during the Strange Situation paradigm, and mothers’ parenting behavior was assessed during a dyadic free-play interaction. As hypothesized, normative arousal, hyperarousal, and hypoarousal profiles of maternal sympathovagal functioning were identified. Maternal depressive symptomatology predicted the hyperarousal profile, whereas socioeconomic adversity predicted hypoarousal. Moreover, allostatic load profiles were differentially associated with problematic parenting behaviors. These findings underscore the role of physiological dysregulation as a mechanism in the relationship between proximal risk factors and actual maladaptive parenting behaviors.

In this work, we present a method for the selection of a subset of nodes in a wireless sensor network whose application is to reconstruct the image of a (spatially) bandlimited physical value (e.g., temperature). The selection method creates a sampling pattern based on blue noise masking and guarantees a near minimal number of activated sensors for a given signal-to-noise ratio. The selection method is further enhanced to guarantee that the sensor nodes with the least residual energy are the primary candidates for deselection, while enabling a tradeoff between sensor selection optimality and balanced load distribution. Simulation results show the effectiveness of these selection methods in improving signalto-noise ratio and reducing the necessary number of active sensors compared with simpler selection approaches.

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