Couple conflict observed: Emotions in Belgium and Japan.

In the present study, we examined cultural variation in couples' emotions during disagreement. We coded the emotions of 58 Belgian and 80 Japanese couples using the Specific Affect Coding System. We observed more anger and domineering, but less fear/tension and other-validation in Belgian than in Japanese couples. Moreover, in Japanese couples, culturally typical emotions were associated with higher conflict resolution and relationship satisfaction. The findings suggest meaningful cultural differences in couples' observed emotions during disagreement, as they can be understood from the prevailing relationship ideals in each culture. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Assessing streetscape greenery with deep neural network using Google Street View.

The importance of greenery in urban areas has traditionally been discussed from ecological and esthetic perspectives, as well as in public health and social science fields. The recent advancements in empirical studies were enabled by the combination of 'big data' of streetscapes and automated image recognition. However, the existing methods of automated image recognition for urban greenery have problems such as the confusion of green artificial objects and the excessive cost of model training. To ameliorate the drawbacks of existing methods, this study proposes to apply a patch-based semantic segmentation method for determining the green view index of certain urban areas by using Google Street View imagery and the 'chopped picture method'. We expect that our method will contribute to expanding the scope of studies on urban greenery in various fields.

Extracting multiple layers of social networks through a 7-month survey using a wearable device: a case study from a farming community in Japan.

As individuals are susceptible to social influences from those to whom they are connected, structures of social networks have been an important research subject in social sciences. However, quantifying these structures in real life has been comparatively more difficult. One reason is data collection methods-how to assess elusive social contacts (e.g., unintended brief contacts in a coffee room); however, recent studies have overcome this difficulty using wearable devices. Another reason relates to the multi-layered nature of social relations-individuals are often embedded in multiple networks that are overlapping and complicatedly interwoven. A novel method to disentangle such complexity is needed. Here, we propose a new method to detect multiple latent subnetworks behind interpersonal contacts. We collected data of proximities among residents in a Japanese farming community for 7 months using wearable devices which detect other devices nearby via Bluetooth communication. We performed non-negative matrix factorization (NMF) on the proximity log sequences and extracted five latent subnetworks. One of the subnetworks represented social relations regarding farming activities, and another subnetwork captured the patterns of social contacts taking place in a community hall, which played the role of a "hub" of diverse residents within the community. We also found that the eigenvector centrality score in the farming-related network was positively associated with self-reported pro-community attitude, while the centrality score regarding the community hall was associated with increased self-reported health. Supplementary Information: The online version contains supplementary material available at 10.1007/s42001-022-00162-y.