Examining the Hypothesis of Common Factors Shared by Different Addictive Behaviors and Gender Effects on Propensity to Addiction Type

Background: From the two facts reported by previous research: 1) frequent co-occurrence of more than one addictive behavior, 2) childhood adversities identified as origins of different types of addictive behaviors, it is assumed that all types of addictive behaviors, regardless of substance, behavioral, or relationship, share common factors which have not yet been proven by epidemiological research. The Shorter PROMIS Questionnaire (SPQ) was previously developed to assess 16 types of addictive behaviors. Its factor structure, however, has not been fully investigated. Confirming the factor structure will enable us to hypothesize the common factor(s) shared by all, or if not all, most types of addictive behaviors. Aims: This study aimed at 1) examining the factor structure of the SPQ, 2) confirming the reliability of the questionnaire, and 3) examining the impacts of gender and age on each addictive behavior. Methods: Data obtained from 232 Japanese adults who completed all items of the SPQ were used for the analyses. After confirming the one-factor structure model for each of the 16 subscales, the validity of the one-factor structure of the SPQ was evaluated using Confirmatory Factor Analysis (CFA), by adapting 16 subscale scores as observed variables. If its validity was not confirmed, another model which showed better compatibility to the data was explored. The reliability of the SPQ as well as that of all 16 subscales was evaluated. Also, the impacts of gender and age on each subscale score were examined. Results: The one-factor structure for each of the 16 subscales was confirmed. The compatibility of the SPQ one-factor model was not acceptable. The best fit model was a bi-factor model in which one main factor was shared by all 16 subscales, and three factors were shared by some specific addictive behaviors. Male respondents were more likely than female respondents to show high scores in Alcohol, Tobacco, Gambling, Sex, and Recreational Drugs, and low scores only in Shopping. Respondents’ age did not impact any of the 16 subscale scores. Conclusion: It was demonstrated that there are common factors shared by all different types, as well as selected types of addictive behaviors, by conducting CFAs of the SPQ. Reliability was proven for the SPQ and for all 16 subscales. Male respondents were more likely to show physically hedonic addictive behaviors.

The Role of Impulsivity and Addiction in Gambling Behaviors: How Applied Neuroscience Research Can Help in the Monitoring and Management of Mental Health Issues

One in five Americans experience symptoms associated with at least one mental health disorder every year. These include behavioral addictions, which have long been overlooked despite their similar neural bases with substance addictions. Gambling addiction, a type of behavioral addiction, deserves particular attention given the significant negative effects, this addiction has on financial and interpersonal health. The purpose of this paper is to review the available literature concerning the behavioral and neural processes involved in gambling addiction, including: the anticipation of reward, the role of dopamine, and the neural substrates of the decision-making processes involved in gambling addiction. Market research has also identified solutions that integrate applied neuroscience and self-tracking systems to monitor and manage mental health issues associated with gambling addiction. The authors then propose a gambling treatment-focused mobile app solution that addresses outstanding issues with a special design aimed at reversing plasticity in order to relieve the effects of gambling addiction. .

P300 change and cognitive behavioral therapy in subjects with Internet addiction disorder A 3-month follow-up study

Event-related potential studies of cognitive function in addiction behaviors have focused on the P300 event-related potential component. The current study investigated the association between P300 component and Internet addiction disorder. We found that individuals with Internet addiction disorder exhibited significantly longer P300 latencies than controls （N2： P = 0.035; P3a： P = 0.031 P3b： P = 0.043） and similar P300 amplitudes compared to control participants. After 3 months of cognitive behavioral therapy, P300 latencies decreased significantly in the P3a and P3b （P3a： P = 0.045; P3b： P = 0.062）. These results suggest that deficits in cognitive function may be involved in Internet addiction disorder, and that clinical psychological treatment may be effective.

Nicotine Addiction Models and Evaluation of Animal Behavior

Nicotine is one of the most widely concerning components of cigarette smoke.Long-term intake of nicotine can lead to nicotine dependence that affects higher brain functions,which may involve anxiety,learning and memory impairments,and abnormal decision-making.To facilitate analyzing the molecular mechanisms of nicotine dependence,it is common to establish rodent nicotine dependence models to better understand the physiological changes.This paper summarizes and evaluates the application of animal behavior evaluation experiments in nicotine-dependent animal models,in order to provide a reliable reference for researchers to establish rodent nicotine-dependent models for evaluation.

Validity,Reliability,and Measurement Invariance of the Thai Smartphone Application-Based Addiction Scale and Bergen Social Media Addiction Scale

Background:In recent years,there has been increased research interest in both smartphone addiction and social media addiction as well as the development of psychometric instruments to assess these constructs.However,there is a lack of psychometric evaluation for instruments assessing smartphone addiction and social media addiction in Thailand.The present study evaluated the psychometric properties and gender measurement invariance of the Thai version of the Smartphone Application-Based Addiction Scale(SABAS)and Bergen Social Media Addiction Scale(BSMAS).Method:A total of 801 Thai university students participated in an online survey from January 2022 to July 2022 which included demographic information,SABAS,BSMAS,and the Internet Gaming Disorder Scale-Short Form(IGDS9-SF).Results:Confirmatory Factor Analyses(CFAs)found that both the SABAS and BSMAS had a one-factor structure.Findings demonstrated adequate psychometric properties of both instruments and also supported measurement invariance across genders.Moreover,scores on the SABAS and BSMAS were correlated with scores on the IGDS9-SF.Conclusion:The results indicated that the SABAS and BSMAS are useful psychometric instruments for assessing the risk of smartphone addiction and social media addiction among Thai young adults.

Unveiling the brain’s symphony:exploring the necessity and sufficiency of neural networks in behavior control

Since the pioneering work by Broca and Wernicke in the 19th century,who examined individuals with brain lesions to associate them with specific behaviors,it was evident that behaviors are complex and cannot be fully attributable to specific brain areas alone.Instead,they involve connectivity among brain areas,whether close or distant.At that time,this approach was considered the optimal way to dissect brain circuitry and function.These pioneering efforts opened the field to explore the necessity or sufficiency of brain areas in controlling behavior and hence dissecting brain function.However,the connectivity of the brain and the mechanisms through which various brain regions regulate specific behaviors,either individually or collaboratively,remain largely elusive.Utilizing animal models,researchers have endeavored to unravel the necessity or sufficiency of specific brain areas in influencing behavior;however,no clear associations have been firmly established.

Machine Learning Techniques in Predicting Hot Deformation Behavior of Metallic Materials

In engineering practice,it is often necessary to determine functional relationships between dependent and independent variables.These relationships can be highly nonlinear,and classical regression approaches cannot always provide sufficiently reliable solutions.Nevertheless,Machine Learning(ML)techniques,which offer advanced regression tools to address complicated engineering issues,have been developed and widely explored.This study investigates the selected ML techniques to evaluate their suitability for application in the hot deformation behavior of metallic materials.The ML-based regression methods of Artificial Neural Networks(ANNs),Support Vector Machine(SVM),Decision Tree Regression(DTR),and Gaussian Process Regression(GPR)are applied to mathematically describe hot flow stress curve datasets acquired experimentally for a medium-carbon steel.Although the GPR method has not been used for such a regression task before,the results showed that its performance is the most favorable and practically unrivaled;neither the ANN method nor the other studied ML techniques provide such precise results of the solved regression analysis.

Increased excitatory amino acid transporter 2 levels in basolateral amygdala astrocytes mediate chronic stress–induced anxiety-like behavior

The conventional perception of astrocytes as mere supportive cells within the brain has recently been called into question by empirical evidence, which has revealed their active involvement in regulating brain function and encoding behaviors associated with emotions.Specifically, astrocytes in the basolateral amygdala have been found to play a role in the modulation of anxiety-like behaviors triggered by chronic stress. Nevertheless, the precise molecular mechanisms by which basolateral amygdala astrocytes regulate chronic stress–induced anxiety-like behaviors remain to be fully elucidated. In this study, we found that in a mouse model of anxiety triggered by unpredictable chronic mild stress, the expression of excitatory amino acid transporter 2 was upregulated in the basolateral amygdala. Interestingly, our findings indicate that the targeted knockdown of excitatory amino acid transporter 2 specifically within the basolateral amygdala astrocytes was able to rescue the anxiety-like behavior in mice subjected to stress. Furthermore, we found that the overexpression of excitatory amino acid transporter 2 in the basolateral amygdala, whether achieved through intracranial administration of excitatory amino acid transporter 2agonists or through injection of excitatory amino acid transporter 2-overexpressing viruses with GfaABC1D promoters, evoked anxiety-like behavior in mice. Our single-nucleus RNA sequencing analysis further confirmed that chronic stress induced an upregulation of excitatory amino acid transporter 2 specifically in astrocytes in the basolateral amygdala. Moreover, through in vivo calcium signal recordings, we found that the frequency of calcium activity in the basolateral amygdala of mice subjected to chronic stress was higher compared with normal mice.After knocking down the expression of excitatory amino acid transporter 2 in the basolateral amygdala, the frequency of calcium activity was not significantly increased, and anxiety-like behavior was obviously mitigated. Additionally, administration of an excitatory amino acid transporter 2 inhibitor in the basolateral amygdala yielded a notable reduction in anxiety level among mice subjected to stress. These results suggest that basolateral amygdala astrocytic excitatory amino acid transporter 2 plays a role in in the regulation of unpredictable chronic mild stress-induced anxiety-like behavior by impacting the activity of local glutamatergic neurons, and targeting excitatory amino acid transporter 2 in the basolateral amygdala holds therapeutic promise for addressing anxiety disorders.

Relationship between parenting stress and behavioral and emotional problems in preschool children: A mediation effect analysis

BACKGROUND Emotional reactions,such as anxiety,irritability,and aggressive behavior,have attracted clinical attention as behavioral and emotional problems in preschool-age children.AIM To investigate the current status of family rearing,parental stress,and behavioral and emotional problems of preschool children and to analyze the mediating effect of the current status of family rearing on parental stress and behavioral/emo-tional problems.METHODS We use convenience sampling to select 258 preschool children in the physical examination center of our hospital from October 2021 to September 2023.The children and their parents were evaluated using a questionnaire survey.Pearson's correlation was used to analyze the correlation between child behavioral and emotional problems and parental stress and family rearing,and the structural equation model was constructed to test the mediating effect.RESULTS The score for behavioral/emotional problems of 258 preschool children was(27.54±3.63),the score for parental stress was(87.64±11.34),and the score for parental family rearing was(31.54±5.24).There was a positive correlation between the behavioral and emotional problems of the children and the“hostile/mandatory”parenting style;meanwhile,showed a negative correlation with the“support/participation”parenting style(all P<0.05).The intermediary effect value between the family upbringing of parents in parental stress and children's behavior problems was 29.89%.CONCLUSION Parental family upbringing has a mediating effect between parental stress and behavioral and emotional problems of children.Despite paying attention to the behavioral and emotional problems of preschool-age children,clinical medical staff should provide correct and reasonable parenting advice to their parents to promote the mental health of preschool-age children.

Relationships among Sedentary Time,Electronic Product Addiction,and Depression in Adolescents during the COVID-19 Epidemic:A Cross-Lagged Study

Objective:This study was conducted to explore the relationships among sedentary behavior(SB),electronic product addiction(EPA),and depression(D)in adolescents during the COVID-19 epidemic.Methods:A total of 604 adolescents(including 309 girls and 295 boys aged 12-18)were selected from Qufu City,Shandong Province,China for three rounds of investigation.The model was constructed using AMOS 23.0 software,and cross-lagged analysis was conducted.Results:SB at T1 can significantly positively predict SB and EPA at T2(p<0.05).EPA at T1 can significantly positively predict SB and D at T2(p<0.05).Physical activity level and SB at T2 can significantly predict SB and EPA at T3(p<0.05).EPA at T2 can significantly predict SB,EPA,and D at T3(p<0.05).Conclusions:SB and EPA are predictive factors for D.Moreover,SB can significantly positively predict D and indirectly predict D through the mediating effect of EPA.

Comparison of brain functions between healthy participants and methamphetamine users with various addiction histories:Data analysis based on EEG and fNIRS

The electroencephalogram(EEG)rhythm and functional near-infrared spectroscopy(fNIRS)activation levels have not been compared between a healthy control group(HCG)and methamphetamine user group(MUG)with different addiction histories.This study used 64-electrode EEG and fNIRS to conduct an experiment that analyzed the resting and craving states.The EEG and fNIRS data of 56 participants were collected,including 14 healthy participants,14 methamphetamine users with an addiction history of 0.5–5 years,14 users with an addiction history of 5–10 years,and 14 users with an addiction history of 10–15 years.Isolated effective coherence(iCoh)within the brain network was used to process the EEG data.Statistical analysis was performed to compare differences in iCoh among the delta,theta,alpha,beta,and gamma bands and explore oxyhemoglobin activation levels in the ventrolateral prefrontal cortex,dorsolateral prefrontal cortex,orbitofrontal cortex,and frontopolar prefrontal cortex(FPC)of the control group.Finally,the Kmeans,Gaussian mixed model(GMM),linear discriminant analysis(LDA),support vector machine(SVM),Bayes,and convolutional neural networks(CNN)algorithms were used to classify methamphetamine users based on drug and neutral images.A 3-class accuracy was achieved.Changes in EEG and fNIRS activation levels of HCG and MUG with varied addiction histories were demonstrated.

A semi-analytical pressure and rate transient analysis model for inner boundary and propped fractures exhibiting dynamic behavior under long-term production conditions

The loss of hydrocarbon production caused by the dynamic behavior of the inner boundary and propped fractures under long-term production conditions has been widely reported in recent studies.However,the quantitative relationships for the variations of the inner boundary and propped fractures have not been determined and incorporated in the semi-analytical models for the pressure and rate transient analysis.This work focuses on describing the variations of the inner boundary and propped fractures and capturing the typical characteristics from the pressure transient curves.A generalized semi-analytical model was developed to characterize the dynamic behavior of the inner boundary and propped fractures under long-term production conditions.The pressure-dependent length shrinkage coefficients,which quantify the length changes of the inner zone and propped fractures,are modified and incorporated into this multi-zone semi-analytical model.With simultaneous numerical iterations and numerical inversions in Laplace and real-time space,the transient solutions to pressure and rate behavior are determined in just a few seconds.The dynamic behavior of the inner boundary and propped fractures on transient pressure curves is divided into five periods:fracture bilinear flow(FR1),dynamic PFs flow(FR2),inner-area linear flow(FR3),dynamic inner boundary flow(FR4),and outer-area dominated linear flow(FR5).The early hump during FR2 period and a positive upward shift during FR4period are captured on the log-log pressure transient curves,reflecting the dynamic behavior of the inner boundary and propped fractures during the long-term production period.The transient pressure behavior will exhibit greater positive upward trend and the flow rate will be lower with the shrinkage of the inner boundary.The pressure derivative curve will be upward earlier as the inner boundary shrinks more rapidly.The lower permeability caused by the closure of un-propped fractures in the inner zone results in greater upward in pressure derivative curves.If the permeability loss for the dynamic behavior of the inner boundary caused by the closure of un-propped fractures is neglected,the flow rate will be overestimated in the later production period.

Cluster DetectionMethod of Endogenous Security Abnormal Attack Behavior in Air Traffic Control Network

In order to enhance the accuracy of Air Traffic Control(ATC)cybersecurity attack detection,in this paper,a new clustering detection method is designed for air traffic control network security attacks.The feature set for ATC cybersecurity attacks is constructed by setting the feature states,adding recursive features,and determining the feature criticality.The expected information gain and entropy of the feature data are computed to determine the information gain of the feature data and reduce the interference of similar feature data.An autoencoder is introduced into the AI(artificial intelligence)algorithm to encode and decode the characteristics of ATC network security attack behavior to reduce the dimensionality of the ATC network security attack behavior data.Based on the above processing,an unsupervised learning algorithm for clustering detection of ATC network security attacks is designed.First,determine the distance between the clustering clusters of ATC network security attack behavior characteristics,calculate the clustering threshold,and construct the initial clustering center.Then,the new average value of all feature objects in each cluster is recalculated as the new cluster center.Second,it traverses all objects in a cluster of ATC network security attack behavior feature data.Finally,the cluster detection of ATC network security attack behavior is completed by the computation of objective functions.The experiment took three groups of experimental attack behavior data sets as the test object,and took the detection rate,false detection rate and recall rate as the test indicators,and selected three similar methods for comparative test.The experimental results show that the detection rate of this method is about 98%,the false positive rate is below 1%,and the recall rate is above 97%.Research shows that this method can improve the detection performance of security attacks in air traffic control network.

Evidence for the Reliability and Validity of the Arabic Version of the Student Risk Screening Scale for Internalizing and Externalizing Behaviors (SRSS-IE)

School-based universal screening for behavioral/emotional risk is a necessary first step to providing services in an educational setting for students with emotional and behavioral disorders (EBDs). Psychometric properties are critical to making decisions about choosing a screening instrument. The purpose of the present study was to examine the psychometric properties of the student risk screening scale for internalizing and externalizing behaviors (SRSS-IE). Participants included 3145 students and their teachers. Item-level analyses of the current sample supported the retention of all items. The internal consistency of the SRSS items ranged from 0.83 to 0.85. Convergent validity between the SRSS-IE and a well-established screening tool, the strength and difficulties questionnaire (SDQ), was found for the total score (r = 0.70). Additionally, the results of this study demonstrate strong social validity, suggesting the SRSS-IE to be a useful and functional screening tool. We conclude that the SRSS-IE is a valid and reliable instrument for assessing the level of emotional and behavioral difficulties among elementary students.

Redefining gaming addictive behavior:influence of media,psychological dimensions and clinical implications

This review critically examines gaming addiction within the contexts of media psychology and addiction theory.It outlines a continuum of gaming behavior,from casual play to addiction,characterized by loss of control,prioritizing gaming over other activities,and negative life consequences.The inclusion of gaming disorder in International Classification of Disease 11 and its provisional status in Diagnostic and Statistical Manual of Mental Disorder 5 highlight growing clinical and societal recognition.The review explores neural correlates of gaming addiction,such as activation in reward-related brain regions,drawing parallels with substance addiction.It highlights how media and marketing promote gaming behaviors potentially leading to addiction,raising ethical concerns about game design and advertising.The review systematically analyzes the negative physical,mental,social,and occupational impacts of gaming addiction.It advocates for a balanced approach emphasizing awareness,therapeutic interventions,and responsible media practices,while also proposing areas for future research and policy development to mitigate the risks of excessive gaming.

Microglia in drug addiction:A perspective from neuroimmunopharmacology

Drug addiction refers to a state of dependence that arises from habitual drug intake and can result in specific withdrawal symptoms upon cessation.The most commonly abused substances include psychostimulants,cannabinoids,and opioids.When drugs are consumed,they stimulate the release of dopamine,a neurotransmitter crucial for the pleasure and reward centers of the brain.With repeated drug use,the brain undergoes various changes,leading to tolerance,dependence,and addiction(Lüscher et al.,2020).The mechanisms involved in drug addiction are highly complex and involve diverse cell types within the brain.

Influence of heat treatment on microstructure,mechanical and corrosion behavior of WE43 alloy fabricated by laser-beam powder bed fusion

Magnesium(Mg)alloys are considered to be a new generation of revolutionary medical metals.Laser-beam powder bed fusion(PBF-LB)is suitable for fabricating metal implants withpersonalized and complicated structures.However,the as-built part usually exhibits undesirable microstructure and unsatisfactory performance.In this work,WE43 parts were firstly fabricated by PBF-LB and then subjected to heat treatment.Although a high densification rate of 99.91%was achieved using suitable processes,the as-built parts exhibited anisotropic and layeredmicrostructure with heterogeneously precipitated Nd-rich intermetallic.After heat treatment,fine and nano-scaled Mg24Y5particles were precipitated.Meanwhile,theα-Mg grainsunderwent recrystallization and turned coarsened slightly,which effectively weakened thetexture intensity and reduced the anisotropy.As a consequence,the yield strength and ultimate tensile strength were significantly improved to(250.2±3.5)MPa and(312±3.7)MPa,respectively,while the elongation was still maintained at a high level of 15.2%.Furthermore,the homogenized microstructure reduced the tendency of localized corrosion and favoredthe development of uniform passivation film.Thus,the degradation rate of WE43 parts was decreased by an order of magnitude.Besides,in-vitro cell experiments proved their favorable biocompatibility.

BCCLR:A Skeleton-Based Action Recognition with Graph Convolutional Network Combining Behavior Dependence and Context Clues

In recent years,skeleton-based action recognition has made great achievements in Computer Vision.A graph convolutional network(GCN)is effective for action recognition,modelling the human skeleton as a spatio-temporal graph.Most GCNs define the graph topology by physical relations of the human joints.However,this predefined graph ignores the spatial relationship between non-adjacent joint pairs in special actions and the behavior dependence between joint pairs,resulting in a low recognition rate for specific actions with implicit correlation between joint pairs.In addition,existing methods ignore the trend correlation between adjacent frames within an action and context clues,leading to erroneous action recognition with similar poses.Therefore,this study proposes a learnable GCN based on behavior dependence,which considers implicit joint correlation by constructing a dynamic learnable graph with extraction of specific behavior dependence of joint pairs.By using the weight relationship between the joint pairs,an adaptive model is constructed.It also designs a self-attention module to obtain their inter-frame topological relationship for exploring the context of actions.Combining the shared topology and the multi-head self-attention map,the module obtains the context-based clue topology to update the dynamic graph convolution,achieving accurate recognition of different actions with similar poses.Detailed experiments on public datasets demonstrate that the proposed method achieves better results and realizes higher quality representation of actions under various evaluation protocols compared to state-of-the-art methods.

Integrated ribosome and proteome analyses reveal insights into sevoflurane-induced long-term social behavior and cognitive dysfunctions through ADNP inhibition in neonatal mice

A growing number of studies have demonstrated that repeated exposure to sevoflurane during development results in persistent social abnormalities and cognitive impairment.Davunetide,an active fragment of the activity-dependent neuroprotective protein(ADNP),has been implicated in social and cognitive protection.However,the potential of davunetide to attenuate social deficits following sevoflurane exposure and the underlying developmental mechanisms remain poorly understood.In this study,ribosome and proteome profiles were analyzed to investigate the molecular basis of sevoflurane-induced social deficits in neonatal mice.The neuropathological basis was also explored using Golgi staining,morphological analysis,western blotting,electrophysiological analysis,and behavioral analysis.Results indicated that ADNP was significantly down-regulated following developmental exposure to sevoflurane.In adulthood,anterior cingulate cortex(ACC)neurons exposed to sevoflurane exhibited a decrease in dendrite number,total dendrite length,and spine density.Furthermore,the expression levels of Homer,PSD95,synaptophysin,and vglut2 were significantly reduced in the sevoflurane group.Patch-clamp recordings indicated reductions in both the frequency and amplitude of miniature excitatory postsynaptic currents(mEPSCs).Notably,davunetide significantly ameliorated the synaptic defects,social behavior deficits,and cognitive impairments induced by sevoflurane.Mechanistic analysis revealed that loss of ADNP led to dysregulation of Ca^(2+)activity via the Wnt/β-catenin signaling,resulting in decreased expression of synaptic proteins.Suppression of Wnt signaling was restored in the davunetide-treated group.Thus,ADNP was identified as a promising therapeutic target for the prevention and treatment of neurodevelopmental toxicity caused by general anesthetics.This study provides important insights into the mechanisms underlying social and cognitive disturbances caused by sevoflurane exposure in neonatal mice and elucidates the regulatory pathways involved.

Association of preschool children behavior and emotional problems with the parenting behavior of both parents

BACKGROUND Parental behaviors are key in shaping children’s psychological and behavioral development,crucial for early identification and prevention of mental health issues,reducing psychological trauma in childhood.AIM To investigate the relationship between parenting behaviors and behavioral and emotional issues in preschool children.METHODS From October 2017 to May 2018,7 kindergartens in Ma’anshan City were selected to conduct a parent self-filled questionnaire-Health Development Survey of Preschool Children.Children’s Strength and Difficulties Questionnaire(Parent Version)was applied to measures the children’s behavioral and emotional performance.Parenting behavior was evaluated using the Parental Behavior Inventory.Binomial logistic regression model was used to analyze the association between the detection rate of preschool children’s behavior and emotional problems and their parenting behaviors.RESULTS High level of parental support/participation was negatively correlated with conduct problems,abnormal hyperactivity,abnormal total difficulty scores and abnormal prosocial behavior problems.High level of maternal support/participation was negatively correlated with abnormal emotional symptoms and abnormal peer interaction in children.High level of parental hostility/coercion was positively correlated with abnormal emotional symptoms,abnormal conduct problems,abnormal hyperactivity,abnormal peer interaction,and abnormal total difficulty scores in children(all P<0.05).Moreover,paternal parenting behaviors had similarly effects on behavior and emotional problems of preschool children compared with maternal parenting behaviors(all P>0.05),after calculating ratio of odds ratio values.CONCLUSION Our study found that parenting behaviors are associated with behavioral and emotional issues in preschool children.Overall,the more supportive or involved the parents are,the fewer behavioral and emotional problems the children experience;conversely,the more hostile or controlling the parents are,the more behavioral and emotional problems the children face.Moreover,the impact of fathers’parenting behaviors on preschool children’s behavior and emotions is no less significant than that of mothers’parenting behaviors.