Posttraumatic stress and depressive symptoms in children after the Wenchuan earthquake

Background: Many studies have reported the comorbidity of posttraumatic stress disorder (PTSD) and depression in children. However, the underlying relationship between PTSD and depression remains unclear. Objective: This study examines the relationship between PTSD and depressive symptoms in children who survived the Wenchuan earthquake in China. Methods: In total, 301 children were assessed at four months and then followed up at 29, 40 and 52 months after the disaster. The ages of the children ranged from 9.6–14.6 years old, and the sample included 157 males and 144 females. The children were assessed by using the University of California at Los Angeles PTSD reaction index for DSM-IV for PTSD symptoms and the Children’s Depression Inventory for depressive symptoms. Results: Comorbid PTSD and depressive symptoms were prevalent in 4.0, 3.3, 3.7 and 5.1% of the participants at times 1, 2, 3 and 4, respectively. The cross-lagged analysis indicated that PTSD symptoms at time 1 predicted depressive symptoms at time 2; depressive symptoms at time 1 predicted PTSD symptoms at time 2; depressive symptoms at time 2 predicted PTSD symptoms at time 3; and depressive symptoms at time 3 predicted PTSD symptoms at time 4. The findings also showed that being female, poor parental relationships and trauma exposure were risk factors for PTSD or depressive symptoms. Conclusions: The results suggest that the causal relationship between PTSD and depressive symptoms changes over time; the effects of PTSD symptoms tend to decrease, while those of depressive symptoms tend to increase. Two stages of the relationship between PTSD and depressive symptoms were observed, namely, that PTSD and depressive symptoms first influenced each other and then that depressive symptoms predicted PTSD. The results of our study also suggest that females with poor parental relationships and a high degree of trauma exposure are more likely to require intervention

Hiding the Source Based on Limited Flooding for Sensor Networks

Wireless sensor networks are widely used to monitor valuable objects such as rare animals or armies. Once an object is detected, the source, i.e., the sensor nearest to the object, generates and periodically sends a packet about the object to the base station. Since attackers can capture the object by localizing the source, many protocols have been proposed to protect source location. Instead of transmitting the packet to the base station directly, typical source location protection protocols first transmit packets randomly for a few hops to a phantom location, and then forward the packets to the base station. The problem with these protocols is that the generated phantom locations are usually not only near the true source but also close to each other. As a result, attackers can easily trace a route back to the source from the phantom locations. To address the above problem, we propose a new protocol for source location protection based on limited flooding, named SLP. Compared with existing protocols, SLP can generate phantom locations that are not only far away from the source, but also widely distributed. It improves source location security significantly with low communication cost. We further propose a protocol, namely SLP-E, to protect source location against more powerful attackers with wider fields of vision. The performance of our SLP and SLP-E are validated by both theoretical analysis and simulation results

The relationship between PTSD and depressive symptoms among children after a natural disaster: A 2-year longitudinal study

This study examined the relationship between posttraumatic stress disorder (PTSD) and depressive symptoms among children who survived the Lushan earthquake. Three hundred thirty-three children (154 males, 179 females) were assessed for acute stress disorder (ASD) and depressive symptoms at 2 weeks (T1), and their PTSD and depressive symptoms were recorded at 1.5 (T2), 6 (T3), 12 (T4) and 24 (T5) months after the earthquake. The results showed that ASD predicted PTSD and depressive symptoms from T1 to T2, and PTSD symptoms predicted depressive symptoms from T2 to T5, but not vice versa. Depressive symptoms predicted avoidance from T1 to T5; in turn, avoidance predicted depressive symptoms from T3 to T5. Hyperarousal and intrusive symptoms had an effect on depressive symptoms between T1 and T2, and depressive symptoms predicted hyperarousal and intrusion from T2 to T3; after 12 months they did not significantly predict each other. The results suggest that PTSD symptoms generally precede depressive symptoms, and that hyperarousal and intrusive symptoms are major symptoms of PTSD soon after trauma events, while avoidance symptoms are major symptoms of PTSD late after trauma events. The relationship between PTSD and depressive symptoms mostly fits the interactive model.</p

Improved imputation of rule sets in class association rule modeling: application to transportation mode choice

Predicting transportation mode choice is a critical component of forecasting travel demand. Recently, machine learning methods have become increasingly more popular in predicting transportation mode choice. Class association rules (CARs) have been applied to transportation mode choice, but the application of the imputed rules for prediction remains a long-standing challenge. Based on CARs, this paper proposes a new rule merging approach, called CARM, to improve predictive accuracy. In the suggested approach, first, CARs are imputed from the frequent pattern tree (FP-tree) based on the frequent pattern growth (FP-growth) algorithm. Next, the rules are pruned based on the concept of pessimistic error rate. Finally, the rules are merged to form new rules without increasing predictive error. Using the 2015 Dutch National Travel Survey, the performance of suggested model is compared with the performance of CARIG that uses the information gain statistic to generate new rules, class-based association rules (CBA), decision trees (DT) and the multinomial logit (MNL) model. In addition, the proposed model is assessed using a ten-fold cross validation test. The results show that the accuracy of the proposed model is 91.1%, which outperforms CARIG, CBA, DT and the MNL model

Association rules and prediction of transportation mode choice: Application to national travel survey data

Predicting transportation mode choice is a classic challenge of travel behavior research. Over the years, different theoretical concepts and modeling approaches have been applied. This paper elaborates the application of class association rules (CARs) and examines their predictive performance using data extracted from the 2015 National Dutch Travel Survey. To solve the problem how to activate rules that have high confidence but low support, the information gain (IG) concept is introduced in the model building process. The modeling process in this study first involves extracting frequent items from the data using the FP-Growth algorithm and deriving CARs from these frequent items. Next, the IG statistic is used to construct a novel model (named CARIG), which consists of a set of decision rules that formally represent behavioral scripts, for predicting individuals’ transportation mode choice. The performance of CARIG is compared with the performance of conventional class-based association rules (CBA), decision trees (DT), a convolutional neural network (CNN) and a logistic regression (LR) model. In addition, a 10-fold cross validation test using a grid search parameter optimization method is conducted to validate the proposed approach. The results show that the proposed method is promising in predicting transportation mode choices observed in the national travel survey data

Association rules and prediction of travel choices: a case study of transportation mode choice

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