Dangerous Behavior Research Articles

Restraint and Seclusion Procedures With Children With Autism Spectrum Disorder

Physical and mechanical restraints and seclusion are currently used as emergency procedures in public schools across the United States. Students who exhibit dangerous, aggressive, or self-injurious behaviors are more likely to be restrained or secluded in school. Since many students with autism spectrum disorder exhibit one or more of these behaviors, they are even more likely to be subjected to these restrictive interventions. Policy on restrictive interventions is not outlined in the Individuals with Disabilities Education Improvement Act (IDEIA) of 2004, the federal special education law, leaving states to determine their own policies. Issues surrounding current policy and law on restraint and seclusion, as well as recommendations, are discussed.

Data analysis and interpretation as a preventive base on the epidemiology of road accidents in Albania

Road accidents are a serious problem of the modern world. They are one of the leading causes of injuries and are the third most common cause of death. Every year, more than a million people, adults and children, die on the streets and several million are injured. The mortality rate due to injuries from road accidents reaches 2.2% of all deaths in the world. The study presents the epidemiology of road accidents with special emphasis on the main issues of road safety in Albania, in relation to the dangerous behavior of road users (non-compliance with traffic rules). Despite the various measures that have been taken to improve safety on Albanian roads, the number of dead and injured in road accidents is still high, and the losses borne by society are high. To improve road safety in Albania, it is necessary to continue the multi-action plan to systematically advance the level of road safety.

Seclusion in a Moroccan Adult Psychiatry Unit

Despite significant advances in the pharmacological treatment of mental disorders, seclusion is still used in daily practice for patients hospitalized in Moroccan psychiatric institutions. Staff generally prefers to avoid using these procedures, as they limit the patient's freedom and undermine his or her dignity. However, it is sometimes necessary to do so to manage extreme episodes of dangerous behavior that threatens the safety of the patient and those around them. We aim in this study to identify the factors that impact the average duration of seclusion and to deduce, if possible, recommendations and measures to optimize or even reduce this time. This is a one-year retrospective study, conducted in the psychiatric department of IBN ROCHD University hospital in Casablanca-Morocco. The study includes data from 169 patients admitted during the entire year of 2018. Multivariate logistic regression analysis was employed to identify factors associated with the duration of seclusion. Based on the results of our study, it appears that the only socio- demographic factor that has a significant impact on the duration of the isolation is marital status. Also, for patients with a personal psychiatric history, the isolation was shorter. Furthermore, it was observed that patients who self-harmed resided longer in seclusion rooms. Regarding the medical diagnosis, it seems that schizoaffective disorder is the only diagnosis that leads to a longer period of isolation than others. Post-seclusion observations indicate that patients whose condition has deteriorated are placed in an extended placement. Although seclusion is not part of the patient's standard treatment, such an intervention may be implemented as an emergency treatment for patients exhibiting behavior that is dangerous to themselves or others. In addition, it would also be necessary to consider Morocco’s reality with its cultural aspects but also to deal with the lack of human and material resources from which our health care system suffers

Dissociative Identity Disorder in a pregnant woman: a case report

Dissociative Identity Disorder is a rare diagnosis, resulting from criteria present in the Diagnostic and Statistical Manual of Mental Disorders five edition that characterize this disorder as a chronic and uncommon psychiatric condition, which cures with the disruption of the patient's identity, which may form two or more. Amnesias, headaches, divergent and dangerous behaviors are present and described over the years as severely affecting patients, in addition to other major psychiatric disorders. We report the case of a patient with dissociative identity disorder during her second pregnancy.

Risk-Taking Behaviors Considering Internet Gaming Disorder among Iranian University Students: A Latent Class Analysis.

Dangerous behaviors adversely affect the health of adolescents and young adults. This study aimed to identify the subgroups of college students based on the parameters of risky behavior and analyze the impact of demographic factors and internet gaming disorder (IGD) belonging to each class. A cross-sectional study. The study was conducted on 1355 students through a multi-stage random sampling method in 2020. A survey questionnaire was used to collect data, and all students completed 1294 sets of questionnaires. The data were analyzed using t test and latent class analysis (LCA) through SPSS and PROC LCA in SAS 9.2 software. Three latent classes have been identified as low-risk (75%), tobacco smoker (8%), and high-risk (17%). There was a high possibility of risky behavior in the third class. Marital status (being single) (OR = 2.28, 95% CI: 1.19-4.37), unemployment (having no job) along with education (OR = 1.56, 95% CI: 1.04-2.33), and IGD (OR = 1.06, 95% CI: 1.04-1.09) increased the risk of inclusion in the tobacco smoker class. Moreover, unemployment (having no job) along with education (OR = 1.43, 95% CI: 1.11-1.84) increased the chance of being in the high-risk class. According to the findings of this study, 25% of the students were tobacco smokers or were in the high-risk class. The results of this study may help develop and evaluate preventive strategies that simultaneously take into account different behaviors.

Взаимосвязь предикторов иррационального поведения с индивидуально-типологическими и личностными особенностями

A number of studies in cognitive psychology indicate that human behavior is largely governed by irrational factors (heuristics, cognitive biases, attitudes). These factors influence vital decisions, which can subsequently affect a person’s life and behavior in a broad temporal perspective. There is a lack of research on identifying the individual-typological and personal characteristics that make the irrational behavior more likely to occur. Our research is aimed at revealing the interrelation of predictors of irrational behavior (superstitiousness, “prelogical” defense, unconstructive, categorical, personally superstitious and esoteric thinking, naive optimism), individual-typological and personality characteristics of respondents. We used I. R. Abitov’s questionnaire, J. Tobacyk’s Paranormal Belief Scale adapted by D. S. Grigoriev, I. Y. Stoyanova’s Beliefs and Superstitions Questionnaire, and S. Epstein’s Constructive Thinking Inventory (adapted by S. N. Enikolopov and S. V. Lebedev) to reveal the expression of irrational behavior predictors. The individual-typological characteristics were identified using the Temperament Accentuation Test and the Individual-Typological Questionnaire, while the personal characteristics were identified using the G. V. Zalewski Psychological Rigidity Questionnaire and the C. Scheyer and M. Carver Test for Optimism. It was found that a decrease in the constructive thinking is associated with the expression of such characteristics as shyness, aggression, neuroticism (anxiety), sensitivity to external influences, various manifestations of rigidity, emotional instability and pessimism. It was also found that the expression of these characteristics, as well as pessimism and aversion to changes in self-esteem and personal position, is associated with a tendency to show categorical (“black and white”) thinking. Naive optimism is more often shown by respondents with such characteristics as hyperthymia and increased social activity. The relationship of personality-superstitious thinking with pessimism, fear of new situations, shyness, neuroticism (anxiety), and a tendency to exaggerate one’s difficulties were found. The novelty of our study lies in the discovery of complexes of characteristics that can increase the likelihood of irrational behavior. Reviewing the literature on the topic of the research, we did not find any works made in the mainstream of such an approach. The complexes of individual-typological and personal characteristics revealed in the given research can be considered as “targets” of psychological correction and prevention to decrease the risk of socially dangerous behavior.

Dangerous Driving Behavior Recognition Based on Hand Trajectory

Dangerous driving behaviors in the process of driving will produce road traffic safety hazards, and even cause traffic accidents. Common dangerous driving behavior includes: eating, smoking, fetching items, using a handheld phone, and touching a control monitor. In order to accurately identify the dangerous driving behaviors, this study first uses the hand trajectory data to construct the dangerous driving behavior recognition model based on the dynamic time warping algorithm (DTW) and the longest common sub-sequence algorithm (LCS). Secondly, 45 subjects’ hand trajectory data were obtained by driving simulation test, and 30 subjects’ hand trajectory data were used to determine the dangerous driving behavior label. The matching degree of hand trajectory data of 15 subjects was calculated based on the dangerous driving behavior recognition model, and the threshold of dangerous driving behavior recognition was determined according to the calculation results. Finally, the dangerous driving behavior recognition algorithm and neural network algorithm are compared and analyzed. The dangerous driving behavior recognition algorithm has a fast calculation speed, small memory consumption, and simple program structure. The research results can be applied to dangerous driving behavior recognition and driving distraction warning based on wrist wearable devices.

Treating elopement without extinction in a preschool setting

AbstractElopement is a prevalent and dangerous behavior that is common with children with autism spectrum disorder (ASD). Due to safety, logistics, and effort on the part of stakeholders, recent research has emphasized the need for treatments that do not include extinction or response blocking. Using a multiple‐baseline‐across‐functions design, we successfully decreased elopement with functional communication training, without additional treatment components, for one function but needed to add an additional treatment component (response blocking) for the second function for a 4 year‐old boy with ASD who engaged in elopement in a preschool setting. We highlight contributions to the literature on elopement as well as directions for future research.

Evaluation of road safety policies and their enforcement in Mexico City, 2015–2019: an interrupted time-series study

BackgroundMexico City approved new road safety policies in 2015, which included lower speed limits and higher fines for traffic offences. In 2019, economic fines were replaced by a point penalty...

Stress can lead to an increase in smartphone use in the context of texting while walking.

Texting while walking (TWW) is a dangerous behavior that can lead to injury and even death. While several studies have examined the relationship between smartphone use and stress, to our knowledge no studies have yet investigated the relationship between stress and TWW. The objective of the present study was to investigate this relationship by examining the effects of stress on TWW, the effects of TWW on subsequent stress, and the effect of stress on multitasking performance. A total of 80 participants completed two sequential tasks in a laboratory while they walked on a treadmill and responded to a biological motion stimulus imitating the movement of another pedestrian. In the unrestricted task, participants were given the choice to use their personal phones. In the controlled task, they carried a text conversation with a research assistant while they walked and responded to the stimulus. Stress was measured via questionnaire and saliva collection for measure of cortisol (a stress hormone) before and after each task. Results show that greater psychological stress and cortisol variations were associated with a greater number of phone uses during the unrestricted task. Greater phone use during the unrestricted task was associated with lower subsequent psychological stress in women and total time of phone use was correlated with subsequent cortisol levels. Stress measured before the controlled task had no effect on multitasking performance, but participants with moderate performance were those with the highest cortisol levels. Our results suggest that stress could be a precursor to TWW and that it could affect a pedestrian’s ability to stay safe when using their smartphone.

Machine Learning Human Behavior Detection Mechanism Based on Python Architecture

Human behavior is stimulated by the outside world, and the emotional response caused by it is a subjective response expressed by the body. Humans generally behave in common ways, such as lying, sitting, standing, walking, and running. In real life of human beings, there are more and more dangerous behaviors in human beings due to negative emotions in family and work. With the transformation of the information age, human beings can use Industry 4.0 smart devices to realize intelligent behavior monitoring, remote operation, and other means to effectively understand and identify human behavior characteristics. According to the literature survey, researchers at this stage analyze the characteristics of human behavior and cannot achieve the classification learning algorithm of single characteristics and composite characteristics in the process of identifying and judging human behavior. For example, the characteristic analysis of changes in the sitting and sitting process cannot be for classification and identification, and the overall detection rate also needs to be improved. In order to solve this situation, this paper develops an improved machine learning method to identify single and compound features. In this paper, the HATP algorithm is first used for sample collection and learning, which is divided into 12 categories by single and composite features; secondly, the CNN convolutional neural network algorithm dimension, recurrent neural network RNN algorithm, long- and short-term extreme value network LSTM algorithm, and gate control is used. The ring unit GRU algorithm uses the existing algorithm to design the model graph and the existing algorithm for the whole process; thirdly, the machine learning algorithm and the main control algorithm using the proposed fusion feature are used for HATP and human beings under the action of wearable sensors. The output features of each stage of behavior are fused; finally, by using SPSS data analysis and re-optimization of the fusion feature algorithm, the detection mechanism achieves an overall target sample recognition rate of about 83.6%. Finally, the research on the algorithm mechanism of machine learning for human behavior feature classification under the new algorithm is realized.

Hazard Trend Identification Model Based on Statistical Analysis of Abnormal Power Generation Behavior Data

In order to solve the problem of abnormal data identification for key indicators with the deepening and development of power enterprise reform, this paper proposes a method of dangerous trend identification model based on a statistical analysis of abnormal power generation behavior data. The method includes a data access scheme, feature extraction scheme, and anomaly detection algorithm. The experimental results show that the proportion of users whose electricity consumption behavior conforms to the peak period electricity consumption > normal period electricity consumption > valley period electricity consumption exceeds 90%. More than 85% of users’ electricity consumption behavior is in line with the proportion of electricity consumption that is less than 0.25 in millet hours. The proportion of users whose fluctuation coefficient of electricity consumption in the valley period is less than 1 exceeds 85%, and 99.9% of users’ fluctuation coefficient of electricity consumption in the valley period is less than 5, which proves that abnormal power generation behavior data and abnormal power consumption data can bring early warning to some dangerous power consumption behaviors. The statistical analysis model of abnormal power generation behavior data can play a positive role in the identification of dangerous trends.

Sleep architecture and emotional inhibition processing in adolescents hospitalized during a suicidal crisis.

BackgroundSuicide is the second leading cause of death in adolescents. Sleep disturbances could alter inhibitory processes and contribute to dangerous behaviors in this critical developmental period. Adolescents in suicidal crisis have been shown to have lighter sleep compared to healthy controls. Additionally, suicidal adolescents have lower neural resources mobilized by emotionally charged inhibition processing. The present exploratory study aimed to determine how sleep architecture in suicidal adolescents may relate to inhibition processing in response to emotional stimuli.MethodsTen adolescents between 12 and 17 years of age with a diagnosis of major depressive disorder and who attempted suicide were recruited while hospitalized for a suicidal crisis in a psychiatric inpatient unit. Event-related potentials (ERPs) were recorded prior to bedtime during a Go/NoGo task involving pictures of sad, happy, and neutral faces. Polysomnography was then recorded throughout the night. Pearson correlations were conducted to investigate how inhibition performance and ERP parameters reflecting inhibition processing (i.e., P3d and N2d derived from difference waveform calculated as NoGo minus Go trials) relate to sleep architecture.ResultsPoorer inhibition accuracy in response to emotional stimuli was significantly correlated with shorter REM sleep latency, higher REM sleep, and more frequent nocturnal awakenings. The P3d in response to sad faces was negatively correlated with NREM2 sleep and positively correlated with NREM3 sleep. No such association with the P3d was found for happy or neutral stimuli. There were no significant correlations for the N2d.ConclusionAltered sleep in adolescents with depression who are in a suicidal crisisis associated with behavioral inhibition difficulties and fewer neural resources mobilized by inhibitory processes in emotionally charged contexts. This highlights the importance of addressing sleep disturbances while managing suicidal crises in adolescents.

Taking prevalence effects on the road: Rare hazards are often missed.

Previous work has shown that, in many visual search and detection tasks, observers frequently miss rare but important targets, like weapons in bags or abnormalities in radiological images. These prior studies of the low-prevalence effect (LPE) use static stimuli and typically permitted observers to search at will. In contrast, many real-world tasks, like looking for dangerous behavior on the road, only afford observers a brief glimpse of a complex, changing scene before they must make a decision. Can the LPE be a factor in in dynamic, time-limited moments of real driving? To test this, we developed a novel hazard-detection task that preserves much of the perceptual richness and complexity of hazard detection in the real world, while allowing for experimental control over event prevalence. Observers viewed brief video clips of road scenes recorded from dashboard cameras and reported whether they saw a hazardous event. In separate sessions, the prevalence of these events was either high (50% of videos) or low (4%). Under low prevalence, observers missed hazards at more than twice the rate observed in the high-prevalence condition. Follow-up experiments demonstrate that this elevation of miss rate at low prevalence persists when participants were allowed to correct their responses, increases as hazards become increasingly rare (down to 1% prevalence) and is resistant to simple cognitive intervention (participant prebriefing). Together, our results demonstrate that the LPE generalizes to complex perceptual decisions in dynamic natural scenes, such as driving, where observers must monitor and respond to rare hazards.

YOLOv5-AC: Attention Mechanism-Based Lightweight YOLOv5 for Track Pedestrian Detection.

In response to the dangerous behavior of pedestrians roaming freely on unsupervised train tracks, the real-time detection of pedestrians is urgently required to ensure the safety of trains and people. Aiming to improve the low accuracy of railway pedestrian detection, the high missed-detection rate of target pedestrians, and the poor retention of non-redundant boxes, YOLOv5 is adopted as the baseline to improve the effectiveness of pedestrian detection. First of all, L1 regularization is deployed before the BN layer, and the layers with smaller influence factors are removed through sparse training to achieve the effect of model pruning. In the next moment, the context extraction module is applied to the feature extraction network, and the input features are fully extracted using receptive fields of different sizes. In addition, both the context attention module CxAM and the content attention module CnAM are added to the FPN part to correct the target position deviation in the process of feature extraction so that the accuracy of detection can be improved. What is more, DIoU_NMS is employed to replace NMS as the prediction frame screening algorithm to improve the problem of detection target loss in the case of high target coincidence. Experimental results show that compared with YOLOv5, the AP of our YOLOv5-AC model for pedestrians is 95.14%, the recall is 94.22%, and the counting frame rate is 63.1 FPS. Among them, AP and recall increased by 3.78% and 3.92%, respectively, while the detection speed increased by 57.8%. The experimental results verify that our YOLOv5-AC is an effective and accurate method for pedestrian detection in railways.

Development of Steering Control System for Autonomous Vehicle

Automation can help us to reduce the number of crashes on our roads. Through research it is identified that 94 percent of the accidents that occur are because of driver behavior or error as a factor and self-driving vehicles can help reduce driver error. High levels of automation have the potential to reduce risky and dangerous driver behavior and prevent accidents. The main aim is to convert the manual operated steering of the vehicle into fully autonomous steering. The objective of Steering Control System is to control the vehicle’s steering while the vehicle is in motion and also to take accurate decisions while making a turn from the given inputs. The main purpose to develop a steering system for the autonomous vehicle is to replace the manual steering of the vehicle into driverless steering. The steering control is responsible for the vehicle’s steering i.e., at what desired angle the vehicle need to turn. A PID (Proportional Integral Derivative) controller and an encoder is basically used to control the system based on the necessary conditions and requirement’s. For the vehicle’s steering the encoder is used to generate pulses when the steering wheel is turned so that those pulse values can be sent to the DC Motor which is attached to the front axle which is responsible for the vehicle to turn. This autonomous vehicle is a Level-4 automation system and the benefit of this automation is that the vehicle can be even operated in manual mode whenever it’s necessary.

Preliminary evidence of the efficacy of the Reducing Aggressive Driving (RAD) program

Introduction: Aggressive driving contributes to crashes, which often result in serious or fatal injuries. Efforts to reduce road trauma need to include strategies to reduce emotional and aggressive driving. Thus far, solutions have not comprehensively addressed the reasons why drivers become aggressive. This study provides preliminary evidence of the effectiveness of the Reduce Aggressive driving (RAD) program in improving driver behavior. The RAD is based on group discussion, feedback, and goal setting to encourage more positive responses to triggers for aggressive driving. The aim of this study was to evaluate the delivery of the RAD and its impact on driver anger and aggression. Method: A total of 94 drivers, ranging in age from 18 to 74 years (Mean = 38; SD = 15; 56% males) attended one two-hour online RAD session during which they identified triggers for their aggression and developed individual strategies to avoid aggressive driving. Most (87%) participants agreed that the RAD helped them generate realistic strategies to avoid aggressive driving. A subset of 67 participants provided self-reported anger and aggressive driving tendencies one month, and four months after the RAD. Results: When these were compared to baseline measures taken before participation in the RAD, decreases across all measures were observed. Thus, anger and aggressive driving significantly decreased one month after the RAD, and these decreases were maintained at the four month follow up; providing evidence of the effectiveness of the RAD in reducing these dangerous behaviors. Further research is needed to objectively measure changes in behavior to and support broader roll-out of the RAD program.

Are Republicans and Conservatives More Likely to Believe Conspiracy Theories?

A sizable literature tracing back to Richard Hofstadter’s The Paranoid Style (1964) argues that Republicans and conservatives are more likely to believe conspiracy theories than Democrats and liberals. However, the evidence for this proposition is mixed. Since conspiracy theory beliefs are associated with dangerous orientations and behaviors, it is imperative that social scientists better understand the connection between conspiracy theories and political orientations. Employing 20 surveys of Americans from 2012 to 2021 (total n = 37,776), as well as surveys of 20 additional countries spanning six continents (total n = 26,416), we undertake an expansive investigation of the asymmetry thesis. First, we examine the relationship between beliefs in 52 conspiracy theories and both partisanship and ideology in the U.S.; this analysis is buttressed by an examination of beliefs in 11 conspiracy theories across 20 more countries. In our second test, we hold constant the content of the conspiracy theories investigated—manipulating only the partisanship of the theorized villains—to decipher whether those on the left or right are more likely to accuse political out-groups of conspiring. Finally, we inspect correlations between political orientations and the general predisposition to believe in conspiracy theories over the span of a decade. In no instance do we observe systematic evidence of a political asymmetry. Instead, the strength and direction of the relationship between political orientations and conspiricism is dependent on the characteristics of the specific conspiracy beliefs employed by researchers and the socio-political context in which those ideas are considered.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11109-022-09812-3.

Are We Sure We Fully Understand What an Infodemic Is? A Global Perspective on Infodemiological Problems.

Infodemic is defined as an information epidemic that can lead to engaging in dangerous behavior. Although the most striking manifestations of the latter occurred on social media, some studies show that dismisinformation is significantly influenced by numerous additional factors, both web-based and offline. These include social context, age, education, personal knowledge and beliefs, mood, psychological defense mechanisms, media resonance, and how news and information are presented to the public. Moreover, various incorrect scientific practices related to disclosure, publication, and training can also fuel such a phenomenon. Therefore, in this opinion article, we seek to provide a comprehensive overview of the issues that need to be addressed to bridge the gap between science and the public and build resilience to the infodemic. In particular, we stress that the infodemic cannot be curbed by simply disproving every single false or misleading information since the belief system and the cultural or educational background are chief factors regarding the success of fake news. For this reason, we believe that the process of forming a critical sense should begin with children in schools (ie, when the mind is more receptive to new ways of learning). Furthermore, we also believe that themes such as scientific method and evidence should be at the heart of the university education of a future scientist. Indeed, both the public and scientists must be educated on the concepts of evidence and validity of sources, as well as learning how to dialogue appropriately with each other. Finally, we believe that the scientific publishing process could be greatly improved by paying reviewers for their work and by ceasing to pursue academic success at all costs.

Investigating the Impact of Mounted Mobile Phones on Driving Behavior

Because the use of handheld mobile phones while driving is considered dangerous behavior, mounted mobile phones have emerged as an alternative. This study compared use of mounted with handheld mobile phones while driving and investigated the impact of mounted mobile phones on driving behavior. The behavior of receiving and making calls, and reading/sending text messages using mobile phones mounted on windshields, dashboards, vents, and cupholders while driving was observed. It was found that mounted mobile phones have less distraction potential than handheld mobile phones; however, they can distract the driver to the same degree, depending on the mounting position. In conclusion, a key factor that determines the potential distraction level of a mounted mobile phone while driving is the mounting position. The hands-free law should consider the mounting position of the mobile phone, rather than focus exclusively on manual interaction.