In this study, we conducted a study on a city park where the Designated Manager System was introduced and where a profit facility (cafe) was developed by Park-PFI. The target park is Shikishima Park in Maebashi City, Gunma Prefecture, a large-scale park. The first purpose of this study is to verify a system that automatically measures the walking behavior of park users. An automatic camera and a pedestrian tracking system were used. Automatic measurement was performed at the place where the cafe was to be opened, and the pedestrian traffic and walking behavior could be grasped. The walking behavior that can be measured is the pedestrian's walking locus, walking distance, walking distance, and walking speed. The second purpose is to grasp the intention of using the cafe and the evaluation of the user about the park management. According to a web questionnaire survey, 63% of the people intend to use the cafe, and 36% of the people who use the park more frequently by opening a cafe. The more people who use the park, the higher the intention to use the cafe. From the above, we obtained valuable information to verify the effects of the designated manager system and Park-PFI.
The purpose of this study is to evolve the Swedish Weight Sounding test (Hereinafter referred to as SWS test) results from the conventional "relative index indicating the hardness of the ground" to "values with clear physical meaning". For this purpose, we analyzed the results of the edge penetration box shear test simulating a SWS test, the tri-axial compression test, and the SWS test using a full-scale embankment. The results indicated that the Nsw value and the torque value in the SWS test were not indicators of soil shear strength but indicators of soil volume compression characteristics. In addition, the shape of the screw point in the SWS test was simulated as "screw", and the acting force was considered from a mechanical engineering point of view. The results showed that the SWS test did not "screw in" the screw point, but penetrated by "compressing" the ground by rotating the screw point.
Electroencephalogram (EEG) measurement, being an appropriate approach to understanding the underlying mechanisms of the major depressive disorder (MDD), is used to discriminate between depressive and normal control. With the advancement of deep learning methods, many studies have designed deep learning models to improve the classification accuracy of depression discrimination. However, few of them have focused on designing a convolutional filter to learn features according to EEG activity characteristics. In this study, a novel convolutional neural network named HybridEEGNet that is composed of two parallel lines is proposed to learn the synchronous and regional EEG features, and further differentiate normal controls from medicated and unmedicated MDD patients. A ten-fold cross validation method is used to train and test the model. The results show that HybridEEGNet achieves a sensitivity of 68.78%, a specificity of 84.45%, and an accuracy of 79.08% in three-category classification.
Intrinsically disordered regions (IDRs) can be predicted by computer programs. In this work, we pursued what factors provide basis for predicting IDRs. We conducted a random forest analysis to obtain degrees of contribution of each of the amino acid residues for the predictions. The results suggested that the contribution of proline is remarkably larger than other residues. Next, we analyzed the distribution of proline residues around the boundaries between IDRs and structural domains (SDs), disclosing that proline residues notably overrepresent in the SD sides of the boundaries. This result can contribute to develop more accurate prediction programs and to understand the structural nature of intrinsically disordered proteins.
As the focus of the study in this year，we introduce a method of using a motion capture system to measure and evaluate the motion range of a wearable exoskeleton. The exoskeleton is developed by us for supporting the wearer’s upper extremity． In order to avoid hindering the wearer’s movement and completely achieve all intended upper limb movements, the flexion and extension movements of the elbow and shoulder joints are actively powered to support the wearer while the other two degrees of freedom (DoFs) of the shoulder joints are freely passive． We further demonstrate the experimental results that validate the effectiveness of the power assistance using surface myoelectric potential．
To convert ferulic acid into 4-vinylguaiacol (4-VG), Saccharomyces cerevisiae must have intact PAD1 and FDC1 genes. British-type top-fermenting yeast strains have nonsense mutations in both of these genes; whereas, bottom-fermenting yeast strains have a nonsense mutation in their S. cerevisiae-type FDC1, and have lost their S. eubayanus-type FDC1 and PAD1 genes. Here, top-fermenting yeast transformants in which wild-type PAD1 and FDC1 derived from the laboratory yeast S. cerevisiae S288C were inserted in the genome, exhibited ferulic acid decarboxylation activity. Similarly, bottom-fermenting yeast transformants expressing wild-type S. cerevisiae-type FDC1 or S. eubayanus-type FDC1 also exhibited ferulic acid decarboxylation activity. Thus, the lack of ferulic acid decarboxylation activity in bottom-fermenting yeast is due to mutation of the S. cerevisiae-type FDC1 gene, coupled with absence of the S. eubayanus-type FDC1 gene.