Understanding the link between low self-esteem and depression

Although it is well documented that low self-esteem and depression are related, the precise nature of the relation has been a topic of ongoing debate. We describe several theoretical models concerning the link between self-esteem and depression, and review recent research evaluating the validity of these competing models. Overall, the available evidence provides strong support for the vulnerability model (low self-esteem contributes to depression), weaker support for the scar model (depression erodes self-esteem), and little support for alternative accounts such as the diathesis-stress model. Moreover, the vulnerability model is robust and holds across gender, age, affective-cognitive versus somatic symptoms of depression, European background versus Mexican-origin participants, and clinical versus nonclinical samples. Research on further specifications of the vulnerability model suggests that the effect is (a) partially mediated by rumination, (b) not influenced by other characteristics of self-esteem (i.e., stability and contingency), and (c) driven predominantly by global rather than domain-specific self-esteem. The research has important theoretical implications because it counters the commonly repeated claim that self-esteem has no long-term impact. Moreover, the research has important practical implications, suggesting that depression can be prevented, or reduced, by interventions that improve self-esteem.

A reliable, traffic-adaptive and energy-efficient link layer for wireless sensor networks

The paper presents a link layer stack for wireless sensor networks, which consists of the Burst-aware Energy-efficient Adaptive Medium access control (BEAM) and the Hop-to-Hop Reliability (H2HR) protocol. BEAM can operate with short beacons to announce data transmissions or include data within the beacons. Duty cycles can be adapted by a traffic prediction mechanism indicating pending packets destined for a node and by estimating its wake-up times. H2HR takes advantage of information provided by BEAM such as neighbour information and transmission information to perform per-hop congestion control. We justify the design decisions by measurements in a real-world wireless sensor network testbed and compare the performance with other link layer protocols.