Positive Beliefs about Post-Event Processing in Social Anxiety Disorder

Background: Post-event processing (PEP) is an important maintenance factor of social anxiety disorder (SAD). This study examined psychometric properties of the Positive Beliefs about Post-Event Processing Questionnaire (PB-PEPQ; Fisak & Hammond, 2013), which measures metacognitive beliefs about PEP. Method: Participants receiving treatment for SAD (n = 71) and other anxiety and related disorders (n = 266) completed self-report questionnaires at several timepoints. Results: Confirmatory factor analysis did not support the PB-PEPQ's proposed unidimensional model. Subsequent exploratory factor analysis yielded a three-factor structure consisting of engaging in PEP to (1) review negative events (Negative scale), (2) review positive events (Positive scale), and (3) better understand one's social anxiety (Understand scale). Within the SAD subsample, PB-PEPQ scales demonstrated good internal consistency (α = 0.83–0.85) and test–retest reliability (r = 0.65–0.78). Convergent and criterion validity of the PB-PEPQ Negative scale were supported. PB-PEPQ scale scores were significantly higher within the SAD group, as compared with the other groups (generalised anxiety disorder, panic disorder and agoraphobia, posttraumatic stress disorder, and obsessive-compulsive disorder), supporting the scales’ discriminative validity. Conclusion: Findings support the reliability and validity of the PB-PEPQ in a clinical sample and reveal the measure's multifactorial structure.

Processing Complex Events in Fog-Based Internet of Things Systems for Smart Agriculture

The recent growth of the Internet of Things’ services and applications has increased data processing and storage requirements. The Edge computing concept aims to leverage the processing capabilities of the IoT and other devices placed at the edge of the network. One embodiment of this paradigm is Fog computing, which provides an intermediate and often hierarchical processing tier between the data sources and the remote Cloud. Among the major benefits of this concept, the end-to-end latency can be decreased, thus favoring time-sensitive applications. Moreover, the data traffic at the network core and the Cloud computing workload can be reduced. Combining the Fog computing paradigm with Complex Event Processing (CEP) and data fusion techniques has excellent potential for generating valuable knowledge and aiding decision-making processes in the Internet of Things’ systems. In this context, we propose a multi-tier complex event processing approach (sensor node, Fog, and Cloud) that promotes fast decision making and is based on information with 98% accuracy. The experiments show a reduction of 77% in the average time of sending messages in the network. In addition, we achieved a reduction of 82% in data traffic.