Childhood emotional trauma and cyberbullying perpetration among emerging adults: a multiple mediation model of the role of problematic social media use and psychopathology

Research suggests that a small minority of social media users experience problems as a result of their online use. The purpose of the present study was to examine the association of cyberbullying perpetration and problematic social media use with childhood emotional trauma, Cluster B (narcissistic, histrionic, antisocial, and borderline) personality traits, dissociative experiences (DEs), depression, and self-esteem in a nonclinical undergraduate sample. A total of 344 university students volunteered to complete a questionnaire that included measures on the aforementioned dimensions. Thirty-eight percent of the participants had emotional neglect and 27% had emotional abuse, while 44% of them demonstrated at least one cyberbullying perpetration behavior. Results indicated that cyberbullying perpetrators had higher scores on problematic social media use, dissociative experiences, Cluster B traits, depression and childhood emotional trauma, and lower on self-esteem. Path analysis demonstrated that, while adjusting for gender and age, childhood emotional trauma was directly and indirectly associated with cyberbullying perpetration via Cluster B traits. Moreover, depression and dissociation were directly associated with problematic social media use. The findings of this study emphasize the important direct role of childhood emotional trauma and pathological personality traits on cyberbullying perpetration

ICAR: endoscopic skull‐base surgery

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Direction finding with a uniform circular array via single snapshot processing

In this work a new algorithm for multiple emitter direction finding by using a uniform circular array is proposed. The algorithm is based on single snapshot processing, and therefore, it has no restriction on the coherency of the sources. The problem formulation is based on the transformation of the snapshot. The transformed sequence is formed by taking the discrete Fourier transform of the snapshot and weighting it suitably. It contains the so-called distortion terms, which are taken into account by using an iterative correction scheme to improve the estimation accuracy. The convergence is achieved in a few steps, and a significant performance improvement is observed when the distortion terms are taken into account. The proposed bearing estimation algorithm is based on the linear prediction method developed in this study, in which the prediction filter coefficients are found by replacing the weighted data matrix by a specified rank approximation, which is obtained by its singular-value decomposition. The direction of arrival estimates are obtained from the angular locations of the prediction-error filter zeros. It is observed through computer simulations that the algorithm performance is improved as compared to that of the forward-backward linear prediction (FBLP) and the modified FBLP methods by choosing an appropriate rank for the approximating matrix. The root-mean-square errors are close to the Cramer-Rao bounds in most cases, where the aforementioned methods fail to work. (C) 1997 Elsevier Science B.V

Direction finding with a circularly rotated antenna

In this work, a new algorithm for multiple emitter direction finding by using a single antenna moving along a circular trajectory is proposed. The problem is formulated by taking the Doppler frequency shift, caused by the movement of the antenna. into account, and by assuming that the information, hidden in the incoming signals, does not change in the observation duration. The proposed direction finding algorithm is, therefore, based on single snapshot processing and also on the linear prediction method developed in [1,2]. It has no restriction on the correlation of the incoming signals since it performs a single snapshot processing. The algorithm performance is investigated through computer simulations and it is observed that the root-mean-square errors of the direction of arrival estimates are less than one degree in most cases. Therefore, it seems reasonable to use the proposed direction finding algorithm in some particular applications, for instance, in identifying the environment for calibration purposes

Marine litter prediction by artificial intelligence

Artificial intelligence techniques of neural network and fuzzy systems were applied as alternative methods to determine beach litter grading, based on litter surveys of the Antalya coastline (the Turkish Riviera). Litter measurements were categorized and assessed by artificial intelligence techniques, which lead to a new litter categorization system. The constructed neural network satisfactorily predicted the grading of the Antalya beaches and litter categories based on the number of litter items in the general litter category. It has been concluded that, neural networks could be used for high-speed predictions of litter items and beach grading, when the characteristics of the main litter category was determined by field studies. This can save on field effort when fast and reliable estimations of litter categories are required for management or research studies of beaches-especially those concerned with health and safety, and it has economic implications. The main advantages in using fuzzy systems are that they consider linguistic adjectival definitions, e.g. many/few, etc. As a result, additional information inherent in linguistic comments/refinements and judgments made during field studies can be incorporated in grading systems