Dangerous Behavior Research Articles

Socio-Economic Determinants of Human Negligence in Wildfire Incidence: A Case Study from Pakistan’s Peri-Urban and Rural Areas

This study aims to examine the socio-economic determinants of human negligence in wildfire occurrences across Pakistan’s peri-urban and rural regions. Increasingly frequent and severe wildfires, driven by climate change, socio-economic conditions, and human negligence, have become a pressing issue. Rising global temperatures and changing precipitation patterns have created drier conditions, while unsafe human activities—such as improper disposal of flammable materials and unsafe agricultural burning—further escalate wildfire risks. These issues are particularly pronounced in Pakistan, where high poverty levels, limited resources, and low education contribute to dangerous behaviors. Weak governance and poor policy enforcement further exacerbate the problem. Data were collected using a cross-sectional survey from 500 participants across five regions. Multiple regression analysis revealed that higher poverty levels significantly increased negligence, whereas higher education and improved access to resources reduced it. Structural Equation Modeling (SEM) highlighted the critical roles of local governance, policy enforcement, and community engagement in mitigating wildfires. Correlation analysis indicated an inverse relationship between wildfire risk awareness and negligent behavior. Chi-square tests demonstrated a strong connection between wildfires and migration patterns, underscoring the socio-economic instability caused by these events. Finally, linear regression showed that wildfires significantly impact regional climate indicators, emphasizing the need for integrated management strategies. This study offers valuable insights into the socio-economic factors driving wildfires in Pakistan and provides guidance for developing targeted mitigation strategies.

The Influence of Cyber Victimization, Internet Use, and Perception of Cyberbullying-on-Cyberbullying Perpetration among Korean Adults: A National Sample-Based Study

Background: Although cyberbullying has emerged as a serious problem even among adults, most researches have been conducted on the adolescents. We aimed to verify the independent effects of cyber victimization, internet use, and the perception of cyberbullying-on-cyberbullying perpetration in South Korea adults. Methods: The data of 1500, 20s to 50s Korea adults from the 2019 Survey on the Cyberbullying conducted by the National Information Society Agency were used. Results: A hierarchical multiple regression analysis indicated that cyber victimization and internet use were positively related to cyberbullying perpetration. In particular, the perception of cyberbullying was negatively associated with cyberbullying perpetration. Conclusion: This study is an early effort to verify the influence of the perception of cyberbullying-on-cyberbullying perpetration. Educating that cyberbullying is an illegal and dangerous behavior is important to prevent cyberbullying perpetration.

A pre-action indication of making stop/go decisions at flashing-light-controlled grade crossings based on drivers’ EEG information: A driving simulation experiment with audio-warning assistance

This study found that the electroencephalogram (EEG) information is a prior indication for sensing drivers’ imminent actions. It is possible to get to know the drivers’ intentions before their next moves, providing huge potentials of early interventions on the occurrence of accidents. A driving simulation experiment was conducted to identify EEG patterns during pre-action decision-making, assess their roles in stop/go decisions, and evaluate the impact of warnings on EEG signals. Four driving groups were divided based on drivers’ stop/go decisions and the influence of dilemma-zone effect, which could reflect drivers’ abilities to stop safely at the onset of flashing red light. The results revealed significant variations in EEG signals among the four driving groups, highlighting EEG as a sensitive pre-action indication. EEG changes indicated that with warning assistance, drivers have enhanced situational awareness, are able to make a stop/go decision in advance, and have less workload on visual search when approaching grade crossings. Such EEG patterns could interpret drivers’ stop/go decisions with a highest accuracy of 97.11%, and could identify potentially dangerous behaviors. These insights have implications for pre-event intervention based on EEG monitoring using non-contact devices.

Update and validation of the Beliefs about Losing Control Inventory-II (BALCI-II): a psychometric investigation

ABSTRACT The Beliefs about Losing Control Inventory (BALCI) was developed to assess negative beliefs about losing control in obsessive-compulsive disorder (OCD). Since its creation, research and theoretical work support negative beliefs about losing control as a potential transdiagnostic construct. The present study revised and expanded the original BALCI to be more inclusive of control-related concerns beyond those that would be expected in OCD (e.g. concerns about losing control over how one comes across to others in social anxiety disorder; SAD). Undergraduate students (N = 440) completed a questionnaire battery including the BALCI-II item pool. An exploratory factor analysis of the 32-item BALCI-II supported a four-factor solution. Three of the identified factors capture the feared consequences of losing control: 1) overwhelming emotions, 2) dangerous behaviour, and 3) madness. The fourth factor captures inflated beliefs about probability/severity of those losses. The BALCI-II was found to have good convergent and divergent validity, good to excellent internal, and retest reliability and was shown to have predictive utility in both OCD and SAD, above and beyond existing disorder-specific maladaptive belief domains. Results suggest the BALCI-II is an improvement over the previous version and supports the relevance of these beliefs beyond OCD.

Predictive Model of Risk Behaviour in the University Student Community

Aim. The study aimed to attempt to explain the probable causality of undertaken risk behaviours in a group of university students, from the perspective of various predictors (which included low individual and social resources, as well as other risk behaviours). The choice of predictors was dictated, among other things, by the assumptions of risk behaviour theory and gateway theory. Methods. The research was conducted among university students from many cities in Poland. Empirical data were collected using the survey method. The tool was a survey questionnaire, constructed from the author's scale and the Resilience Scale by Gail Wagnild in the Polish adaptation by Janusz Surzykiewicz and Karol Konaszewski (2019). The data were statistically analysed. The target procedure became path analysis. Results. The study revealed several plausible predictive factors for specific risk behaviours of students. Analyses included behavioural categories such as the use of psychoactive substances and behaviour associated with them, risky sexual contact, low physical and psychosocial health care, dangerous behaviour in daily life, and disclosure of images and private data on the Internet. Each form of risky behaviour was situated in the model. The predictive factors in the path structure were identified as, among others, low social and cultural resources and low levels of resilience. It also appeared that some risk behaviours may be predictors for others. Conclusion. The study reveals that not only syndromes of risk behaviours, but even entire risk pathways creating the possibility to infer probable causality, are noticeable in the group of university students.

Study of the effects of different mechanical mishaps or Traumatic Brain Injury(TBI) on brain region: A Review

The following concerns and measures of neurotrauma are discussed here: It expands more on the protection role that the skull encompasses regarding the encephalon together with a detailed explanation of the effects of head injuries and their implications, which may include coma or death as a result of fractures or dysfunctions. The text gives descriptions of the incidence, especially in car-related accidents, and details about TBI such as the symptoms and the issues likely to occur as a result of the injury. The paper also draws attention to certain dangerous behaviors; such as; non-utilization of seat belts, driving when drunk, no helmet usage, and inadequate supervision to eliminate traumatic brain injury. Among the changes in management of the TBIs whether surgical or through medication, the text lastly highlights the fact that more emphasis has been placed on preventing this condition than on finding a cure for it.

Supporting driver performance in closely spaced tunnel-interchange structure: Traffic control devices and effects on driving behavior

Objectives With the rapid development of expressways in the mountainous regions of southwestern China, closely spaced tunnel-interchange structures have inevitably emerged due to topographical constraints and environmental limitations. Given the unfavorable road geometry and rapid cross-section transitions, drivers face significant safety concerns. This study aims to investigate drivers’ safety performance at closely spaced tunnel-interchange sections and determine how safety risks can be mitigated through improved traffic control devices design. Methods Thirty-nine participants conducted an experimental study in a fixed-base simulator. The test scenario was modeled on the Xingyan Freeway-S3801 and accurately reproduced in the simulator. For each safety performance metric, the driving simulator experiments yielded a dataset with 780 observations. To address the idiosyncratic variation due to individual driver differences, a series of linear mixed effects models (LMM) were developed to analyze drivers’ behavior responses. Results In closely spaced tunnel-interchange sections, a general impairment of both longitudinal and lateral performance was observed. This study identified potential critical impact variables in traffic control device systems. According to the LMM results: (a) Removing the 0.5 km interchange ramp exit advance guide sign located in the tunnel exit area reduces dangerous behavior in the corresponding impact area. (b) Replacing the 0.5 km interchange ramp exit advance guide sign with arrow pavement markers as an information source supports improved driver performance, promoting driver safety. (c) Adding tunnel exit distance signs within tunnels is recommended to enhance situation awareness for drivers. Conclusions This study addresses the scientific issues related to traffic control devices setup for closely spaced tunnel-interchange sections, focusing on identifying potential critical impact variables. The findings provide guidance on the design of traffic control devices for such sections and support revisions to national engineering standards.

Fifty Years of Psychiatric Classification and Epidemiology Interactions: What is a Mental Disorder?

Psychiatric clinical diagnostic formulation has evolved over time. The changes alter our understanding and our ability to provide a public health perspective on the epidemiology of mental disorders in large populations. Epidemiology is an important perspective and set of tools to assess prevalence, treated prevalence, untreated prevalence, individual risks for mental disorders, and possible links to the etiology of disorders by following the trails of environmental exposures, biological measures, interpersonal dynamics, and genetic risk factors. As communities develop health-care services to treat individuals with mental disorders, knowledge about their prevalence and treatment requirements is also important. Since severe mental disorders may require institutional care, the diagnostic criteria used may either protect an individual’s liability for dangerous behavior (i.e. the insanity defense) or be used to control political and social dissidents. The criteria may also be used to determine evidence-based treatment options and eligibility for disability benefits. In this paper, using my vantage points as a physician scientist and public health officer, with leadership positions in national federal and professional mental health organizations, I address the developments in these areas over the past 50 years that have influenced institutional positions in the U.S. National Institute of Mental Health, the World Health Organization, the American Psychiatric Association, and the Center for the Study of Traumatic Stress. These perspectives may aid the next generation of investigators to advance the epidemiological and mental disorder classification scientific fields.

Virtual criminal encroachments on sexual freedom or inviolability in the space of the Metaverse

The article substantiates the identity between the psychosomatic reactions of the human biological body and the information received by the user in the Metaverse through the extended reality technology. It is proved that the psychological connection between the user and his/her avatar depends not so much on the similarity between them, but on the efforts and resources spent on creating such an image. It is substantiated that users of virtual worlds to some extent associate or identify themselves with their virtual bodies or characters, and a significant part of the virtual experience is perceived as a very real experience. The sense of one's own virtual presence can be so compelling that it can affect the degree of self-identity of a person, and therefore virtual forms of sexual or other abuse can be experienced as physical and directly participate in the mechanism of causing socially dangerous harm. The author considers certain ways of encroaching on sexual freedom or sexual inviolability, in particular, fraud, body blending using deepfake technology, taking possession of an avatar, forcing it to perform certain actions, overt verbal and graphic actions, covert actions, causing tactile sensations without the consent of another person, socially dangerous behaviour of bots and algorithms that control the space of a certain Metaverse with the help of artificial intelligence. Based on the study, the author proposes to supplement the Criminal Code of Ukraine with new provisions aimed at preventing criminal offences against sexual freedom or sexual inviolability in the virtual space of the Metaverse. For the purposes of legal regulation, the author proposes to distinguish certain parts of the Metaverse and, according to the criteria of control and functional purpose, to divide them into closed (individual), corporate (collective) and public spaces.

Motivations for hazing in collegiate settings: Employing situational strength to identify, disrupt, and construct

AbstractHazing is motivated by numerous psychosocial phenomena embedded in the broader cultural milieu. The identification and deconstruction of these motivators presents an especially unique pathway to eliminating harmful and dangerous behaviors. The application of situational strength to hazing prevention offers a robust and applicable framework to help administrators and professionals impactfully disrupt hazing and reshape the larger campus and organizational ecology to support healthier joining experiences.

Disability, recruitment and job retention: Identifying barriers and facilitators from the employers' point of view.

Despite increased public awareness of the professional, integration of people with disabilities, they encounter more difficulties than their non-disabled counterparts in accessing employment. The aim of this study was to gain a better understanding of the barriers and facilitators to hiring and retaining people with disabilities from the perspective of employers in the private and public sector in France. This was a qualitative study using semi-structured interviews with forty-two employers from public structures or private companies. The Consolidated Criteria for Reporting Qualitative Research was used as a guideline to secure accurate and complete reporting of the study. Among barriers we found that 1. Stereotypes persist about disability, still associated with lower productivity and dangerous behaviour; 2. Disabilities were associated with costs that were considered too high; 3. Disabilities management was seen as an administrative burden. 4. The bumps of inclusion. We also highlighted facilitators such as 1. A public sector is a more favourable environment 2. Employer's personal motivation to hire people with disabilities; 3. The support by Job Coaches was seen as a powerful facilitator. The decision to hire people with disabilities depends on many personal, societal and organisational factors. This study has contributed to a better understanding of their interrelationships and could be useful in developing more effective strategies for the inclusion of people with disabilities.

Emergency Department Utilization Due to Challenging Behavior in Children and Adolescents Diagnosed with Autism Spectrum Disorder.

Children and adolescents with autism spectrum disorder (ASD) are at a greater risk of seeking emergency department (ED) services during behavioral crises, such as acute aggression, suicidal or homicidal ideation, self-injury, or other types of challenging behavior (e.g., pica, dangerous behaviors). Research demonstrates children and adolescents with ASD often return to the ED due to challenging behavior, suggesting that gaps in care exist (e.g., follow-up appointments, referrals). However, the current knowledge basis is largely based on data from other countries. Given the unique landscape of healthcare in the United States, it is prudent to elucidate characteristics of children and adolescents with ASD who are seeking emergency care due to challenging behavior, as well as systems-level factors that both contribute to our understanding of challenging behavior and ASD in ED settings. In this study, we focus on frequency and characteristics of children and adolescents with ASD presenting to the ED with challenging behavior over the course of a 6-year period in the Midwest region of the United States. Clinical implications for ED staff are discussed.

Examining the impact of knowledge, supervision, and work fatigue on unsafe actions among warehouse workers

Unsafe actions are dangerous behaviors that can cause work accidents, resulting in injury or death. Warehouses are high-risk areas for such incidents due to the presence of unsafe actions during work processes. This study aims to explore the relationship between knowledge, supervision, and work fatigue with unsafe actions among warehouse workers at PT Cipta Krida Bahari Samarinda. This quantitative study employs a cross-sectional design, with a sample size of 50 workers using the total sampling technique. The independent variables are knowledge, supervision, and work fatigue, while the dependent variable is unsafe action. Data analysis utilized univariate and bivariate (chi-square) methods. Results indicated that 54 percent of workers engaged in unsafe actions, 60 percent had good knowledge, 56 percent reported good workplace supervision, and 54 percent experienced fatigue. Bivariate analysis showed no significant relationship between knowledge (p=0.203) and work fatigue (p=0.168) with unsafe actions. However, a significant relationship was found between supervision (p=0.019) and unsafe actions. It is recommended that the company enhance supervision practices and enforce strict penalties for violations to minimize unsafe actions among workers.

NREM parasomnia-related behaviors and adverse childhood experiences

PurposeTo assess the prevalence, types, sociodemographic factors, and reported dangerous activities of sleep-related behaviors likely representing NREM parasomnia episodes, as well as their association with adverse childhood experiences in Hungary. MethodsCross-sectional survey of 1000 adults (aged ≥18 years) representing the Hungarian population, using a non-probability quota sampling with a random walk method and a structured face-to-face interview. A multi-criterion weighting procedure was applied to correct bias along the main sociodemographic variables to the data available. Binary logistic regression estimated the odds of NREM parasomnia-related behaviors associated with sociodemographic factors and adverse childhood experiences. ResultsThe prevalence of NREM parasomnia-related behaviors was 2.7 %, and self-reported sleep-eating was 0.1 % of the population (4.6 % of parasomnia-like activities). For middle-aged adults, the odds of sleep ambulation were significantly lower than for younger adults (OR 0.3; P = 0.03). A participant's family occurrence of reported parasomnia-like activity increased their odds of having it by more than 7 times (OR 7.1; P < 0.001). Nine participants out of those 27 people reporting NREM parasomnia-related behavior episodes, reported childhood adverse experiences, increasing the odds of parasomnia-related behavior by more than six times (OR 6.2; P < 0.001) compared to those not reporting it. ConclusionThis is the first population survey in Hungary on adult sleep-related behaviors likely representing NREM parasomnia episodes and the potential association with childhood traumatic events preceding them. The related dangerous behaviors call for safety measures and prevention. The significant association between adverse childhood events and NREM parasomnia-related behaviors needs further analysis.

Prevention of Occupational Accidents and Occupational Diseases with Supervised Machine Learning Algorithms: Different Sector Applications

Abstract − The Occupational health and safety is a discipline that prevents work accidents and occupational diseases with a proactive method. For employee health, countries have legal responsibilities within the scope of international conventions, and employers have national responsibilities. It is obligatory for employers to carry out risk assessments, provide occupational safety trainings, carry out inspections, employ occupational safety specialists and workplace physicians, and record all work regard work safety. In countries, inspections are carried out with labor inspectors and private companies provide occupational safety services. However, it is difficult for the authorities to monitor occupational safety in large industrial facilities such as petrochemicals and refineries, where the flow of workers, materials and work equipment is too much and very fast. As workplace capacity, number of employees and material flow increase, the type and number of work accidents and occupational diseases also increase. Artificial intelligence technologies facilitate these follow-ups. The purpose of this article is to investigate the proactive prevention of the factors that cause work accidents and occupational diseases with supervised machine learning algorithms in different sectors. A literature search was conducted on sciencedirect, scopus, googlescholar databases. It has been examined what kind of algorithms are used in which sectors. According to the studies in the literature and applications in different sectors, the data collected with sensors and stored with cloud computing are fed to the relevant supervised machine learning algorithms that have been trained and tested before, and the factors that cause work accidents and occupational diseases are determined in advance. In addition to sound, image, health, location and environment data, physical parameters such as distance, level and pressure are monitored instantly with sensors. Managers are warned when a dangerous situation or behavior is detected in and threshold values are exceeded. In addition to employee and vehicle location tracking, predictive maintenance is provided by monitoring the performance of work and production vehicles. With the decrease in occupational accidents and diseases, occupational safety performance increases and costs decrease.

Клинико-психологические факторы в контексте правосознания больных шизофренией, совершивших общественно опасные деяния

&lt;p style="text-align: justify;"&gt;The work is aimed at the study of clinical and psychological factors that determine the legal consciousness of schizophrenia patients who have committed SDA. The materials of an empirical study obtained on a sample of schizophrenia patients undergoing compulsory treatment in a psychiatric hospital are presented. The study involved male 62 patients aged 18 to 35 years. Methods of pathopsychological research: Dembo&amp;mdash;Rubinstein&amp;rsquo;s &amp;ldquo;Self-assessment&amp;rdquo;, MMPI, &amp;ldquo;The level of subjective control&amp;rdquo; by Rotter, &amp;ldquo;Ways of coping behavior&amp;rdquo; by Lazarus, pathopsychological functional tests &amp;mdash; &amp;ldquo;Exclusion of objects&amp;rdquo;, &amp;ldquo;Pictogram&amp;rdquo;. The pathopsychological features reflecting significant differences in the patients&amp;rsquo; legal consciousness have been established. New psychological information confirms the need to personalize psychological support during compulsory treatment. The key aspects of psychological work aimed at the prevention of socially dangerous behavior are the consideration of the attitude of patients to the disease and to the committed offense, as well as their individual psychological characteristics in order to reduce maladaptive and criminogenic behavioral stereotypes and the transformation of defects of legal consciousness.&lt;/p&gt;

DBN-protected material Enhanced intrusion prevention sensor system defends against cyber attacks in the IoT devices

By linking objects globally, the Internet of Things (IoT) has transformed technology with the goal of achieving an unparalleled degree of intelligence that will benefit mankind in many areas. Resilient applications in safety, healthcare, and industrial processes rely heavily on continuous connectivity and interaction with surrounding objects. But the IoT ecosystem's enormous number of businesses and apps significantly raises the possibility of unwanted access, raising worries about cyberattacks. It is important to protect symmetrical networks used in modern communication from these dangers. With an emphasis on a sophisticated intrusion detection and prevention system based on Deep Belief Symmetrical Networks (DBNs), this study investigates cutting edge techniques and tactics for preventing security breaches. Our research specifically investigates possibly dangerous behaviour within IoT symmetrical networks and attempts to determine its source. We present a DBN-protected material improved symmetrical intrusion prevention sensor system that improves IoT device security. We improve the system's capacity to identify and prevent cyber-attacks by exploiting DBNs. We compare the suggested method's performance to industry-standard Intrusion Detection Systems (IDS) algorithms and Domain Generation Algorithms (DGAs) to assess its effectiveness. We create results that demonstrate the usefulness of our method in fighting against cyber-attacks in the IoT environment through rigorous research and testing. This study advances the development of safe IoT Symmetrical devices and encourages the full realization of their promise in allowing a connected and intelligent world.

An Integrated Approach to the Spanish Driving Behavior Questionnaire (SDBQ) in the City of Cuenca, Ecuador

Traffic collisions are the seventh leading cause of death in Ecuador, with reckless driving being one of the main causes. Although there are statistical data on traffic crashes, there has not yet been a comprehensive investigation of the causes. Therefore, the main objective of this study is to investigate unsafe driving behavior using a modified version of the Spanish Driving Behavior Questionnaire (SDBQ) adapted for Ecuador. The 34-item SDBQ we used has four main dimensions: lapses, errors, violations, and aggressive driving. To apply the SDBQ, a stratified random probability sample of 470 drivers with valid driver’s licenses aged 18–69 was used. Of the drivers, 68.8% were male, while 33.2% were female. We used a chi-square test and descriptive statistics to analyze the data for the SDBQ application items. Finally, four generalized linear Poisson models were used. The results show that taxi drivers have the highest scores on three of the four main dimensions of the SDBQ and male drivers are more likely than female drivers to cause traffic accidents. Drivers are also more likely to cause traffic accidents if they drive more hours per day. This research is the first of its kind to analyze driver behavior-based solutions in Ecuador to reduce traffic accidents. The error factor is the most critical outcome of dangerous behavior in the city of Cuenca. The SDBQ aims to foster a culture of safety and sustainability by promoting road safety measures through legislation and traffic regulations.

An Appreciative Analysis of Selected Songs

This article examines how Tanzanian artists, Wagosi wa Kaya and Mr. Ebbo as an example, have defied expectations and confounded critics by employing humour in their songs to caution against risky behaviour. Popular Tanzanian songs have typically been accused for a number of years by the government and the general public of ‘destroying’ Tanzanian values, especially by promoting dangerous activity. These kinds of songs are usually banned, and the writers’ performance licences revoked by the government. Although many musicians have faced criticism for allegedly inciting dangerous behaviour through their music, Mr Ebbo and the Wagosi wa Kaya music group, distinguished musicians, have been notable exceptions. They have composed superb songs that contradict these charges. Using thematic and content analyses and informed by the Theory of Planned Behaviour, the article emphasises how important it is for other people to value these and similar musicians in order to support them and push more musicians to write songs that are constructive and morally sound so that society can reap the benefits of the reintroduction of social values.

Detection of Dangerous Human Behavior by Using Optical Flow and Hybrid Deep Learning

Dangerous human behavior in the driving sense may cause traffic accidents and even cause economic losses and casualties. Accurate identification of dangerous human behavior can prevent potential risks. To solve the problem of difficulty retaining the temporal characteristics of the existing data, this paper proposes a human behavior recognition model based on utilized optical flow and hybrid deep learning model-based 3D CNN-LSTM in stacked autoencoder and uses the abnormal behavior of humans in real traffic scenes to verify the proposed model. This model was tested using HMDB51 datasets and JAAD dataset and compared with the recent related works. For a quantitative test, the HMDB51 dataset was used to train and test models for human behavior. Experimental results show that the proposed model achieved good accuracy of about 86.86%, which outperforms recent works. For qualitative analysis, we depend on the initial annotations of walking movements in the JAAD dataset to streamline the annotating process to identify transitions, where we take into consideration flow direction, if it is cross-vehicle motion (to be dangerous) or if it is parallel to vehicle motion (to be of no danger). The results show that the model can effectively identify dangerous behaviors of humans and then test on the moving vehicle scene.