Teachers' recognition of school bullying according to background variables and type of bullying

How teachers identify and judge school bullying may affect their willingness to intervene in bullying situations and influence their strategies for doing so. This study aimed to investigate whether there were significant differences in teachers' identification of bullying incidents according to background variables (gender, teaching experience, and education level). The participants of this study were 150 primary school and middle school teachers in Taiwan, A 24-item Recognition of Bullying incidents Questionnaire (RBIQ) was used in this study to explore whether teachers can identify physical, verbal, and relational scenarios as bullying or non-bullying incidents. A mixed-model two way ANOVA was used to analyze this data. Results revealed that teachers' teaching experiences significantly interacted with behavioral types, and teachers' education levels also sigm candy interacted with behavioral types. In addition, no gender differences in the identification of bullying were observed. Overall, teachers were more likely to identifi physical bullying incidents than relational ones. The results of this study suggest that teachers should participate in training to help them identify bullying incidents, particularly when these involve relational bullying

Cascading failures in coupled networks with both inner-dependency and inter-dependency links

We study the percolation in coupled networks with both inner-dependency and inter-dependency links, where the inner- and inter-dependency links represent the dependencies between nodes in the same or different networks, respectively. We find that when most of dependency links are inner- or inter-ones, the coupled networks system is fragile and makes a discontinuous percolation transition. However, when the numbers of two types of dependency links are close to each other, the system is robust and makes a continuous percolation transition. This indicates that the high density of dependency links could not always lead to a discontinuous percolation transition as the previous studies. More interestingly, although the robustness of the system can be optimized by adjusting the ratio of the two types of dependency links, there exists a critical average degree of the networks for coupled random networks, below which the crossover of the two types of percolation transitions disappears, and the system will always demonstrate a discontinuous percolation transition. We also develop an approach to analyze this model, which is agreement with the simulation results well.Comment: 9 pages, 4 figure

Bacterial infection in association with snakebite: A 10-year experience in a northern Taiwan medical center

BackgroundMicrobiological data of secondary wound infections following snakebites is rarely reported in Taiwan. The objective of this study was to assess the secondary wound infection after venomous snakebites.MethodsWe conducted a 10-year retrospective survey on patients admitted for venomous snakebites and microbiological data of wound cultures at a medical center in northern Taiwan.ResultsBetween April 2001 and April 2010, 231 patients who experienced snakebites were included. Male predominated, accounting for 62.3% (144). The age range of patients was 4–95 years. Ninety-five (41.1%) people were bitten by Trimeresurus mucrosquamatus, followed by Tstejnegeri, and cobra. A total of 61 pathogens were obtained from 21 patients. Thirty-nine (63.9%) isolates were gram-negative bacteria, 14 (23%) gram-positive pathogens, and 8 (13.1%) anaerobic pathogens. There were 17 patients bitten by cobra in these 21 patients. Morganella morganii and Enterococcus species were the most common pathogens identified in the wound cultures.ConclusionCobra bite causes more severe bacterial infection than other kinds of snakebites. Oral amoxicillin/clavulanate plus ciprofloxacin or parenteral piperacillin/tazobactam alone can be the choices for empirical or definitive treatment, and surgical intervention should be considered for established invasive soft tissue infections

Online Updating of Statistical Inference in the Big Data Setting

We present statistical methods for big data arising from online analytical processing, where large amounts of data arrive in streams and require fast analysis without storage/access to the historical data. In particular, we develop iterative estimating algorithms and statistical inferences for linear models and estimating equations that update as new data arrive. These algorithms are computationally efficient, minimally storage-intensive, and allow for possible rank deficiencies in the subset design matrices due to rare-event covariates. Within the linear model setting, the proposed online-updating framework leads to predictive residual tests that can be used to assess the goodness-of-fit of the hypothesized model. We also propose a new online-updating estimator under the estimating equation setting. Theoretical properties of the goodness-of-fit tests and proposed estimators are examined in detail. In simulation studies and real data applications, our estimator compares favorably with competing approaches under the estimating equation setting.Comment: Submitted to Technometric