• Journal of Preventive Medicine and Public Health
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• v.40 no.2
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• pp.145-149
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• 2007

Objectives : This study characterized the extent to which youth depressive symptoms, parental alcohol problems, and parental drinking account for differences in alcohol-related problems among a large sample of adolescent females. Methods : The stratified sample consists of 2077 adolescent females from twelve female-only high schools located in a large metropolitan city in the Republic of Korea. Students completed a questionnaire about alcohol use and alcohol problems, their parents' alcohol problems, and a number of risk and protective factors. Data were analyzed using tobit regression analyses to better characterize the associations among variables. Results : Almost two-thirds of students who consume alcohol had experienced at least one to two alcohol-related problems in their lives and 54.6% reported at least one current symptom of depression, with nearly one-third reporting two depressive symptoms. Two-thirds of the students indicated that at least one parent had an alcohol-related problem, and that approximately 29% had experienced several problems. Results of tobit regression analyses indicate that youth alcohol-related problems are positively associated with depressive symptoms (p<0.01) and parent drinking problems (p<0.05). Parental drinking is no longer significant when the variable parental attention is added to the model. Decomposition of the tobit parameters shows that for every unit of increase in depressive symptoms and in parent drinking problems, the probability of a youth experiencing alcohol problems increases by 6% and 1%, respectively. For every unit of increase in parental attention, the probability of youth experiencing drinking problems decreases by 5%. Conclusions : This study presents evidence that alcohol-related problems and depressive symptoms are highly prevalent among adolescent females. Although a comprehensive public health approach is needed to address drinking and mental health problems, different interventions are needed to target factors associated with initiation of alcohol problems and those associated with increased alcohol problems among those who already began experiencing such problems.

• Journal of Arbitration Studies
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• v.26 no.4
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• pp.43-62
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• 2016

The international arbitration system has been a useful method of settling disputes arising from international transactions. Arbitration provides the opportunity for the parties to choose a fair and neutral forum and to participate in the selection of the decision maker and the rules that will be applied. Because arbitration is a creature of contract, there is no agreement to arbitrate if there is no contract. An arbitration clause should be designed to fit the circumstances of the transaction and the parties' needs. The parties draft an arbitration clause with insufficient attention to the transaction to which it relates. Insufficient attention to arbitration agreement has caused the delay of arbitration procedure or even the inability to arbitrate. Therefore the parties pay sufficient attention to the underlying transaction so that the arbitration clause can be tailored to their particular requirements and to possible disputes that may reasonably be anticipated.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.23 no.1
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• pp.15-25
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• 2020

Prevalence of pediatric obesity has increased worldwide in the last 20 years. Obese children suffer not only physical complications but also mental health problems such as depression, attention deficit hyperactivity disorder (ADHD), and eating disorders, as well as psychosocial impairments, such as school adjustment problems, bullying, and low self-esteem. Recently, there have been some studies on the association of mental health problems and pediatric obesity. In the treatment of pediatric obesity, many previous studies suggest multidisciplinary treatment. However, cognitive behavioral therapy (CBT) has attracted attention because obese children are accompanied by body image distortion, emotion dysregulation, and difficulties in stimulus control. This review is a narrative summary of the recent studies on mental health problems and CBT in pediatric obesity. The relationship between depression/anxiety and pediatric obesity is still inconsistent but recent studies have revealed a bidirectional relation between depression and obesity. Additionally, some studies suggest that obese children may have eating disorder symptoms, like loss of control eating, and require therapeutic intervention for pediatric obesity treatment. Furthermore, impulsivity and inattention of ADHD symptom is thought to increase the risk of obesity. It has also been suggested that CBT can be very effective for mental health problems such as depression, impulsivity, and body image distortion, that may coexist with pediatric obesity, and use of multimedia and application can be useful in CBT.

• Journal of Information Processing Systems
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• v.19 no.1
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• pp.130-138
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• 2023

The traffic flow in an urban area is affected by the date, weather, and regional traffic flow. The existing methods are weak to model the dynamic road network features, which results in inadequate long-term prediction performance. To solve the problems regarding insufficient capacity for dynamic modeling of road network structures and insufficient mining of dynamic spatio-temporal features. In this study, we propose a novel traffic flow prediction framework called shared spatio-temporal attention convolution optimization network (SSTACON). The shared spatio-temporal attention convolution layer shares a spatio-temporal attention structure, that is designed to extract dynamic spatio-temporal features from historical traffic conditions. Subsequently, the graph optimization module is used to model the dynamic road network structure. The experimental evaluation conducted on two datasets shows that the proposed method outperforms state-of-the-art methods at all time intervals.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.1-14
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• 2024

Time-series forecasting is extensively used in the actual world. Recent research has shown that Transformers with a self-attention mechanism at their core exhibit better performance when dealing with such problems. However, most of the existing Transformer models used for time series prediction use the traditional encoder-decoder architecture, which is complex and leads to low model processing efficiency, thus limiting the ability to mine deep time dependencies by increasing model depth. Secondly, the secondary computational complexity of the self-attention mechanism also increases computational overhead and reduces processing efficiency. To address these issues, the paper designs an efficient multi-layer attention-based time-series forecasting model. This model has the following characteristics: (i) It abandons the traditional encoder-decoder based Transformer architecture and constructs a time series prediction model based on multi-layer attention mechanism, improving the model's ability to mine deep time dependencies. (ii) A cross attention module based on cross attention mechanism was designed to enhance information exchange between historical and predictive sequences. (iii) Applying a recently proposed sparse attention mechanism to our model reduces computational overhead and improves processing efficiency. Experiments on multiple datasets have shown that our model can significantly increase the performance of current advanced Transformer methods in time series forecasting, including LogTrans, Reformer, and Informer.

• Journal of Korea Multimedia Society
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• v.25 no.1
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• pp.124-134
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• 2022

Based on existing research, the psychological state of Chinese students has become a very significant issue that needs to be resolved. In addition to paying attention to the daily life and study of Chinese students, the psychological problems of Chinese students are also worthy of attention. At the same time, if the existing psychological problems are not resolved in time, serious consequences may result. Based on the ART(Attention Restoration Theory) theory, this article uses VR (Virtual Reality) content as a medium, uses 3D modeling software to build a healing scene that helps Chinese students improve their psychological and emotional state, and presents it in a VR device. To achieve the purpose of improving the psychological and emotional state of Chinese students. According to experimental tests, the VR recovery scene constructed by this method can help improve the psychological mood of Chinese international students who already have subliminal depression. The results of independent sample T-tests after data analysis experiments show that after the intervention of the experiment, the depression of the experimental group is significantly improved compared to the control group. It is proved that the method in this study is effective for the mentality and emotion of Chinese international students who have subliminal depression. There is a significant improvement effect.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2552-2570
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• 2022

Underwater images usually have various problems, such as the color cast of underwater images due to the attenuation of different lights in water, the darkness of image caused by the lack of light underwater, and the haze effect of underwater images because of the scattering of light. To address the above problems, the channel attention mechanism, strengthen-operate-subtract (SOS) boosting mechanism and gated fusion module are introduced in our paper, based on which, an underwater image recovery network is proposed. First, for the color cast problem of underwater images, the channel attention mechanism is incorporated in our model, which can well alleviate the color cast of underwater images. Second, as for the darkness of underwater images, the similarity between the target underwater image after dehazing and color correcting, and the image output by our model is used as the loss function, so as to increase the brightness of the underwater image. Finally, we employ the SOS boosting module to eliminate the haze effect of underwater images. Moreover, experiments were carried out to evaluate the performance of our model. The qualitative analysis results show that our method can be applied to effectively recover the underwater images, which outperformed most methods for comparison according to various criteria in the quantitative analysis.

• Journal of the Korean Society of Physical Medicine
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• v.16 no.2
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• pp.101-113
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• 2021

PURPOSE: The purpose of this study was to investigate the differences and correlation between body composition, exercise performance, and behavior based on playing football in childhood. METHODS: 16 subjects who played football in childhood participated in the study. Body composition and exercise performance were measured, and problem behavior was assessed for each of them. All subjects were asked to play football 50 min/day, one day/week for 8-weeks. RESULTS: Muscle mass, muscular strength, balance, and cardiopulmonary endurance, anxiety depression, atrophy depression, attention problems, rule violations, DSM somatization problems, DSM rebellious behavior problems, and sociality significantly increased after 8-weeks. There was a negative (-) correlation between anxiety depression and atrophy depression, and DSM somatization problem and muscular strength, attention problem and balance, and rule violation and cardiopulmonary endurance, after playing football. CONCLUSION: These results confirmed that playing football in childhood had a positive effect on body composition, and that exercise performance and problem behavior were related.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.11 no.2
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• pp.282-289
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• 2000

This study was designed to examine the validity of HPR subscale in Korean Personality Inventory for Children(KPI-C) and Attention Problems subscale in Korean Child Behavior Checklist(K-CBCL) as diagnostic tool for Attention-Deficit/Hyperactivity Disorder(ADHD). Nineteen ADHD-1 type, twenty-three ADHD-H type, sixteen Neurosis, and fifteen normal children with the age from 6 to12 were selected based on DSM-IV, and their responses of the KPI-C and CBCL were analyzed. Omnibus F-test results showed that there were significant differences in the F scores of HPR and Attention Problems T scores(p<.05). But in Posthoc analysis, the HPR and AP scores in three clinical groups were significantly higher than in normal group, but there was no group difference among three clinical groups(p<.05). These results shows that HPR subscale and Attention Problems subscale may be useful tools for screening clinical groups(vs normal group) but there was a limit to the clinical validity of two subscales as diagnostic tools for the subtypes of ADHD.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.47-54
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• 2023

For Bengali music emotion classification, deep learning models, particularly CNN and RNN are frequently used. But previous researches had the flaws of low accuracy and overfitting problem. In this research, attention-based Conv1D and BiGRU model is designed for music emotion classification and comparative experimentation shows that the proposed model is classifying emotions more accurate. We have proposed a Conv1D and Bi-GRU with the attention-based model for emotion classification of our Bengali music dataset. The model integrates attention-based. Wav preprocessing makes use of MFCCs. To reduce the dimensionality of the feature space, contextual features were extracted from two Conv1D layers. In order to solve the overfitting problems, dropouts are utilized. Two bidirectional GRUs networks are used to update previous and future emotion representation of the output from the Conv1D layers. Two BiGRU layers are conntected to an attention mechanism to give various MFCC feature vectors more attention. Moreover, the attention mechanism has increased the accuracy of the proposed classification model. The vector is finally classified into four emotion classes: Angry, Happy, Relax, Sad; using a dense, fully connected layer with softmax activation. The proposed Conv1D+BiGRU+Attention model is efficient at classifying emotions in the Bengali music dataset than baseline methods. For our Bengali music dataset, the performance of our proposed model is 95%.