In recent decades, social network sites (SNSs) have become more popular and SNSs addiction has become a serious social problem. However, few studies have examined the effect of a person's internal working model (IWM) on addiction, which determine how attachment processes operate throughout the life course. This study aims at investigating the influence of IWMs and the psychological state, particularly loneliness and interpersonal trust, and of gender differences on addiction of SNSs in Japanese university students. Participants were 284 undergraduates in Japan (130 males, 154 females) who were evaluated with an original questionnaire about SNSs addiction, the UCLA Loneliness Scale, Interpersonal Trust Scale, and Internal Working Model Scale. We conducted multiple regression analyses by setting the entry method, one of which was forced entry, to examine the relationship between the dependent variable (SNSs addiction) and the independent variables (other factors) in each gender. The results show that SNSs addiction is influenced by an ambivalent attachment style (males, β = .19; females β = .36) and utilization time (males, β = .32; females β = .32) in both genders. To compare gender differences, we examined the structural equation modeling. The results show that only the influence of an ambivalent attachment style is significantly different between males and females (z = 5.04, p < .01), suggesting that such an attachment style predicts SNSs addiction. Because females tend to use SNSs as interaction tools, those with a high ambivalent style may become preoccupied with peer group membership. To prevent * Corresponding author. A. Fujimori et al. 1833 SNSs addiction, it is important that children form stable attachment relationships with parents/ caregivers when young. Regarding clinical implications, if counseling or psychotherapy is employed for people with SNSs addiction, it is important to assess the attachment style, and that therapy work toward changing an ambivalent style to a stable one.

Since it is supposed that the number of patients with dementia will increase as populations age in the near future, it is important to prevent dementia. In the present study, we examined whether acupuncture and life style improvements are able to enhance cognitive function.The subjects who worry about being forgetful were recruited and divided at random into two groups ; a group receiving acupuncture with transcutaneous electrical acupuncture-point stimulation (TEAS) and improvements in life style (20 subjects ; group A) and a group undergoing improvements in life style alone (20 subjects ; group B) for 12 weeks. The results showed that the cognitive functions assessed via a Mini-Mental State Examination (MMSE) and the Wechsler Memory Scale-Revised, sleep time, and sleep efficiency were improved in all subjects included in groups A and B after the interventions. There were significant pre-to post-intervention differences in MMSE and sleep efficiency in group A only. It was found that these interventions increased NK cells, NK activity and B cell numbers, and decreased T cell and helper T cell numbers. Thus, acupuncture and improvement of life style could enhance cognitive function and may be useful for the prevention of dementia.acupuncture, TEAS, cognitive function, actigraphy, immunity

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<div class="section abstract"><div class="htmlview paragraph">For car-following models, the car-following characteristics differ depending on the vehicle type, such as passenger cars, motorcycles, and trucks. Therefore, constructing a model for each category is essential. To that end, various modeling methods have been proposed; however, herein, we particularly focused on the long short-term memory (LSTM), which is the best method for forecasting long-term time-series data.[<span class="xref">1</span>, <span class="xref">2</span>] The objective of this study was to construct a car-following model for each vehicle category using the LSTM and to evaluate the model accuracy for each vehicle category. In this study, US-101 and I-80 data provided by the next-generation simulation (NGSIM), which is based on natural traffic flow data, were used. In the NGSIM, only car-following situations were selected as car-following data, and these were classified into the vehicle type: motorcycles, passenger cars, and trucks. The classified data were then used to construct LSTM-based car-following models for each category. As the evaluation of this model was dependent on the amount of data, the model was built according to the amount of data for motorbikes, which was the lowest. A merged integrated model was also constructed by mixing the data obtained from all vehicle type, and the accuracy of the model was compared with that of the model for each vehicle category. Thus, this study investigated the impact of car-following models constructed for each vehicle category on the model accuracy using the LSTM. The results showed that for passenger cars and motorcycles, the RMSE values of the vehicle-specific models were smaller than those of the integrated model and the accuracy of the models was better than that of the integrated model. In future, we intend to construct car-following models for each vehicle characteristic.</div></div>