The “net” of the Internet: Risk Factors for Cyberbullying among Secondary-School Students in Greece

Cyberbullying is one of the most common threats an adolescent might face online with serious negative consequences at the social, emotional and educational level. Despite the intense study of the phenomenon over the past decade, most researchers ask for new directions in research that will focus on risk and protective factors. This study attempted to investigate the predictive effect of internet use, parental mediation, school-bullying and victimisation, gender and empathy on cyberbullying and cybervictimisation in two measurements, 4 months apart. Data collection was conducted using a self-report questionnaire. Four hundred and forty secondary school students, aged 12–14, participated in the study. The students were randomly selected from six public schools located in the Greater Metropolitan Area of Thessaloniki in Greece. Results showed that involvement in traditional bullying as a victim or as a perpetrator is the factor with the highest predictability for cyberbullying and cybervictimisation, which also remains stable across time. This finding points to the urgent need for designing and implementing programmes against all forms of bullying both offline and on line.     

The differential predictive validity of the Youth Level of Service/Case Management Inventory: the role of gender

Most criminogenic risk assessments are developed and empirically validated on samples of boys or samples comprised of mostly boys, and subsequently applied to girls. Using a sample of male (n = 1,267) and female (n = 453) probationers, the purpose of the current study was to examine the potentially differential performance of the Youth Level of Service/Case Management Inventory (YLS/CMI) and the instrument’s eight domains for female juvenile offenders. Findings revealed gender differences in the predictive validity of the YLS/CMI in which girls scored significantly higher on the risk assessment, but recidivated at significantly lower rates than boys two years following the administration of the assessment. The predictive validity of the instrument was impacted by these gender differences in which the YLS/CMI total score was a better predictor of recidivism for boys (area under the curve (AUC) = .623) than girls (AUC = .565). The only subscales that significantly predicted recidivism for girls were the family (AUC = .607) and personality (AUC = .572) domains, whereas all of the subscales significantly predicted recidivism for boys. Directions for future research as well as gender-responsive assessment, programming, and policy implications are discussed.     

The detection of clandestine graves in an arid environment using thermal imaging deployed from an unmanned aerial vehicle

The Middle East is one of the world regions that has frequently suffered from armed conflicts that resulted in mass burials. However, the detection of clandestine graves in such an arid environment by deploying remote sensing payload on unmanned aerial vehicles (UAVs) has received little attention. The present study used a UAV equipped with a thermal sensor aimed at narrowing down the search area of possible gravesites in the arid climate of Kuwait. The enclosed research area, which includes both control and experimental mass graves, was imaged for 18 months. The variation in topsoil temperature and soil moisture between the graves and their surroundings was evaluated. The results of the analysis demonstrated the effectiveness of thermal imaging techniques in detecting heat produced from buried sheep carcasses and detecting the change in grave soil moisture for our research environment for 7 and 10 months, respectively. The buried animals significantly influenced the topsoil temperature (p = 0.044), while the height from which the images were captured had an insignificant effect on the measured temperature within the range tested (p = 0.985). Furthermore, there was a negative correlation (−0.359) between grave temperature and the calculated soil moisture. The results from these cost- and time-effective search methods presented in this study confirm their potential for the detection of burial sites in an arid environment.     

Determination of vehicle speed from recorded video using the open-source software Kinovea

Video devices of different kind often record traffic accidents, including vehicle-pedestrian collisions and hit-and-run accidents. In these cases, the vehicle speed is valuable information because it can assist the investigators in an accident reconstruction. This paper examines the use of Kinovea, an open-source video annotation tool designed for sport analysis, to estimate vehicle speed in forensic videos. Kinovea does not require a complex methodology, and it can be used to make the calculation easily. A series of vehicle driving experiments using an appropriately calibrated speed radar system (so called Scout Speed) were carried out, and measurements were compared with the estimated speed. In controlled conditions, the comparison of Scout reference speed and calculated average vehicle speed by means of Kinovea found an average difference of 0.43 km/h, with a margin of error of ±0.64 km/h. In addition, further preliminary tests were carried out to check the reliability of the measurements under lower resolution conditions. Also, in these cases the calculations were in line with the ground truth. Therefore, in the tested conditions, Kinovea demonstrated to be an easy and reliable tool available for forensic video examiners. Further tests need to be conducted in order to address the applicability of the measurement technique with true CCTV/surveillance video recordings.     

Development and validation of a LC–MS/MS method for analysis of vortioxetine in postmortem specimens. First data from an authentic case

Vortioxetine is an antidepressant recently licensed in USA and EU for the treatment of major depressive disorder. Neither fatal case due to overdose nor data about postmortem concentrations on blood or other specimens have been reported. The aims of this study were the development and validation of a method for vortioxetine analysis by Liquid Chromatography Tandem Mass Spectrometry (LC–MS/MS) in postmortem samples and its application in an authentic case. The method was validated and applied on blood, vitreous humor, bile, brain, liver, kidney, and gastric content. After protein precipitation, the supernatant was directly injected into LC–MS/MS. Analysis was carried out by Multiple Reaction Monitoring (MRM) mode. The authentic case concerned a 38 years-old woman, affected by depression, who was found hanged at home. The method determined an acceptable sensitivity, selectivity, linearity, precision, and accuracy for all matrices. No interference was shown for all matrices. The matrices do not significantly reduce the peak intensity of vortioxetine. No carryover was shown. Toxicological analysis of the authentic case showed vortioxetine in blood (234 ng/ml), vitreous humor (10.5 ng/ml), brain (490 ng/g), lung (479 ng/g), liver (3751 ng/g), kidney (798 ng/g), bile (2267 ng/ml) and gastric content (253 ng/ml). Our case suggests that even at blood concentrations of vortioxetine equal to 234 ng/ml, the subject was able to stage and carry out the hanging. Vortioxetine concentrations found in the other cadaveric samples (biological fluids, organs, and gastric content) may be helpful to evaluate further similar comparable cases.     

Death by compaction in a robotic dumpster

Robotic waste management automation systems are increasingly utilized around the globe as a user-friendly, ecological and hygienic alternative to waste disposal, simultaneously reducing the volume and frequency of waste collection. The present paper aims to report a case of death by compaction in an automated robotic waste management system and review relevant literature on compactor-related fatalities. The unusual fatal accident involved a 37-year-old male citizen who deliberately entered the robotic dumpster to retrieve his accidentally discarded medication. Upon entering the robotic chamber, the compaction system was automatically activated, although all safety measures were in full operation. This paper highlights the lack of standardized reporting on compactor-related mortality, which however is of importance both in terms of its social impact and public safety. These deaths occur rarely, in scattered areas of the globe, associated with various situations (e.g., occupational fatalities, homeless people, infanticide) and trash compactors of different types, technology, age, maintenance quality, warning signage and safety interlocks. In all previously reported fatalities, the technology involved was considerably older (as in conventional waste containers, garbage compaction trucks, chute-fed compactors in buildings, etc.). The case described in the present report is a rare event in both literature and international news coverage as it encompasses a fully automated technology intended for unsupervised, direct use by the public being located in public spaces where all citizens can interact with it. The aim of this report is to raise awareness and improve knowledge about safety parameters so that similar incidents can be avoided in the future.     

Deep learning for sex determination: Analyzing over 200,000 panoramic radiographs

The objective of this study is to assess the performance of an innovative AI-powered tool for sex determination using panoramic radiographs (PR) and to explore factors affecting the performance of the convolutional neural network (CNN). The study involved 207,946 panoramic dental X-rays and their corresponding reports from 15 clinical centers in São Paulo, Brazil. The PRs were acquired with four different devices, and 58% of the patients were female. Data preprocessing included anonymizing the exams, extracting pertinent information from the reports, such as sex, age, type of dentition, and number of missing teeth, and organizing the data into a PostgreSQL database. Two neural network architectures, a standard CNN and a ResNet, were utilized for sex classification, with both undergoing hyperparameter tuning and cross-validation to ensure optimal performance. The CNN model achieved 95.02% accuracy in sex estimation, with image resolution being a significant influencing factor. The ResNet model attained over 86% accuracy in subjects older than 6 years and over 96% in those over 16 years. The algorithm performed better on female images, and the area under the curve (AUC) exceeded 96% for most age groups, except the youngest. Accuracy values were also assessed for different dentition types (deciduous, mixed, and permanent) and missing teeth. This study demonstrates the effectiveness of an AI-driven tool for sex determination using PR and emphasizes the role of image resolution, age, and sex in determining the algorithm's performance.     

Assessment of post-mortem submersion interval using total aquatic decomposition scores of drowned human cadavers

Investigating drowning-related deaths remains a significant problem for forensic personnel all over the world. The previously published decomposition scoring method like the total aquatic decomposition (TAD) score promises to estimate the correct post-mortem submersion interval (PMSI) in aquatic habitats through the assessment and calculation of the decomposition rate and accumulated degree days (ADD). The current study comprised of 53 drowned death cases belonging to various districts of Haryana from May 2016 to August 2017. The regression and Pearson's correlation indicated a significant correlation between the TAD scores and the actual ADD (calculated through water temperatures) (r² = 0.917) and between the actual and the estimated ADDs (calculated through TAD scores used by Heaton et al. [21]) (r = 0.9585). The results indicated that the estimated ADD tends to over predict the PMSI compared to the actual ADD. It is further confirmed by paired t-test, which showed the mean of actual ADD (mean = 349) to be significantly lower than the mean of estimated ADD (mean = 663). Moreover, these methods will help forensic investigators and researchers formulate region-specific regression equations for PMSI estimation.     

Through the Party Lens: How Citizens Evaluate TV Electoral Spots

Party affiliation is considered one of the most important factors explaining voters' party choice, but also a strong intervening variable when it comes to the effectiveness of electoral advertising. The question raised in this study is to what extent party affiliation explains voters' judgments of electoral advertising, which was investigated by using data carried out during the Swedish general election campaign 2010. The results show that party affiliation still functions as a filter when voters are exposed to electoral advertising. The findings are suggested to be understood against the background of cognitive dissonance theory and selective exposure according to which people try to avoid a state of cognitive dissonance by avoiding information that conflicts with their attitudes.     

Correction to: A Two-Way Street? Reciprocal Associations Between Parental Warmth and Hostility with Substance Use Among Justice-Involved Adolescents

Journal of Youth and Adolescence -