High-Resolution Magnetic Force Microscopy Using Carbon Nanotube Probes Fabricated Directly by Microwave Plasma-Enhanced Chemical Vapor Deposition

Carbon nanotubes (CNTs) have been successfully grown on the tip apex of an atomic force microscopy (AFM) cantilever by microwave plasma-enhanced chemical vapor deposition (MPECVD). Both scanning electron microscopy (SEM) and transmission electron microscopy (TEM) observations reveal that the diameter of the CNTs is ∼30 nm and the magnetic particles with diameter of ∼20 nm, which was used as catalyst for the CNT growth, exist on the top. This CNT probe has been applied to magnetic force microscopy (MFM) on the ultrahigh-density magnetic recording media with 1200 kilo flux change per inch (kfci)

Recent Trends in Sensor-based Activity Recognition

This seminar introduces recent trends in sensor-based activity recognition technology. Technology to recognize human activities using sensors has been a hot topic in the field of mobile and ubiquitous computing for many years. Recent developments in deep learning and sensor technology have expanded the application of activity recognition to various domains such as industrial and natural science fields. However, because activity recognition in the new domains suffers from various real problems such as the lack of sufficient training data and complexity of target activities, new solutions have been proposed for the practical problems in applying activity recognition to real-world applications in the new domains. In this seminar, we introduce recent topics in activity recognition from the viewpoints of (1) recent trends in state-of-the-art machine learning methods for practical activity recognition, (2) recently focused domains for human activity recognition such as industrial and medical domains and their public datasets, and (3) applications of activity recognition to the natural science field, especially in animal behavior understanding.Maekawa T., Xia Q., Otsuka R., et al. Recent Trends in Sensor-based Activity Recognition. Proceedings - IEEE International Conference on Mobile Data Management 2023-July, 36 (2023); https://doi.org/10.1109/MDM58254.2023.00018

Occurrences of metamorphosed ultramafic rock and associating rocks in Howard Hills, Enderby Land, East Antarctica: Evidence of partial melting from geochemical and isotopic characteristics

Large blocks of metamorphic rocks with mafic to ultramafic compositions were discovered in felsic gneiss at the central part of northern Howard Hills in Enderby Land. The ultramafic core is separated from the felsic gneiss by a mantle of pyroxene granulite. We can recognize from mineral assemblages and chemical compositions that the metamorphic rocks experienced ultrahigh temperature (UHT) metamorphism. Rubidium-strontium and samarium neodymium analytical data from the metamorphic rocks yield apparent ages of about 2.65 Ga within analytical error on isochron diagrams. Metamorphic rocks with mafic to ultramafic compositions are enriched in incompatible elements and have high Sr isotope ratios, resulting in some samples in improbable Nd model ages. This is attributed to enrichment of compatible elements and/or depletion of incompatible elements during metamorphism. We conclude that these metamorphic rocks experienced partial melting during UHT metamorphism. Pyroxene granulite was produced as a residual material after partial melting of LILE-enriched protoliths with high Sr isotope ratios

Psychological resilience is correlated with dynamic changes in functional connectivity within the default mode network during a cognitive task

Resilience is a dynamic process that enables organisms to cope with demanding environments. Resting-state functional MRI (fMRI) studies have demonstrated a negative correlation between resilience and functional connectivities (FCs) within the default mode network (DMN). Considering the on-demand recruitment process of resilience, dynamic changes in FCs during cognitive load increases may reflect essential aspects of resilience. We compared DMN FC changes in resting and task states and their association with resilience. Eighty-nine healthy volunteers completed the Connor–Davidson Resilience Scale (CD-RISC) and an fMRI with an auditory oddball task. The fMRI time series was divided into resting and task periods. We focused on FC changes between the latter half of the resting period and the former half of the task phase (switching), and between the former and latter half of the task phase (sustaining). FCs within the ventral DMN significantly increased during “switching” and decreased during “sustaining”. For FCs between the retrosplenial/posterior cingulate and the parahippocampal cortex, increased FC during switching was negatively correlated with CD-RISC scores. In individuals with higher resilience, ventral DMN connectivities were more stable and homeostatic in the face of cognitive demand. The dynamic profile of DMN FCs may represent a novel biomarker of resilience

Potential of Mid-tropospheric Water Vapor Isotopes to Improve Large-Scale Circulation and Weather Predictability

Recent satellite techniques have uncovered detailed tropospheric water vapor isotope patterns on a daily basis, yet the significance of water isotopes on weather forecasting has remained largely unknown. Here, we perform a proof‐of‐concept observing system simulation experiment to show that mid‐tropospheric water isotopes observed by the Infrared Atmospheric Sounding Interferometer (IASI) can substantially improve weather forecasts through non‐local impacts on the convective heating structure and large‐scale circulation. Assimilating IASI isotopes can improve wind, humidity, and temperature fields by more than 10% at mid‐troposphere compared to only assimilating conventional non‐isotopic observations. These improvements are about two‐thirds of assimilating simultaneous IASI water vapor observations. The improvements can be attributed more to thermodynamic (phase change) effects than dynamic (transport) effects of water isotopes. Furthermore, isotopic observations produce additional 3%–4% improvements to the fields constrained by the conventional observations and simultaneous IASI water vapor observations, demonstrating the unique characteristics of water isotopes. Plain Language Summary Accurate weather forecasting has tremendous socio‐economic benefits by saving lives from natural hazards and affecting numerous sectors including water resources, energy, and agriculture. Although recent satellite techniques have enabled observing detailed water isotope patterns (e.g., HDΟ and Η$_{2}$$^{18}$O) in the atmosphere, it has not been incorporated in operational weather forecasting. Here, we show that water isotopes can substantially improve weather forecasts by improving the heating structure and large‐scale circulation. Satellite‐observed isotopes can improve wind, humidity, and temperature fields up to 3%–4% compared to utilizing conventional non‐isotopic observations and concurrent water vapor observations by the same satellite. We anticipate that our results will facilitate further modeling developments in isotopic processes and benefit the societal sectors by improving operational weather forecasting

The Japanese version of the Generalized Problematic Internet Use Scale 2 (GPIUS2): Psychometric evaluation and analysis of the theoretical model

BACKGROUND: The Generalized Problematic Internet Use Scale 2 (GPIUS2) is a self-administered questionnaire that evaluates problematic internet use (PIU) from a multidimensional perspective. We analysed the psychometric properties and adequacy of the theoretical model of Japanese version of the GPIUS2. METHODS: This study included 291 healthy Japanese adults (median age = 25 years; interquartile range 22-43 years; 128 women) who completed the GPIUS2 and several other questionnaires evaluating the degree of PIU, self-esteem, depression, and impulsivity. RESULTS: Exploratory factor analysis (EFA) revealed a similar factor structure between the original and Japanese versions of the GPIUS2, with only minor differences in item composition. Higher-order confirmatory factor analyses revealed a good overall fit for the factorial model suggested by EFA, indicating adequate construct validity. The model showed acceptable internal consistency. Partial correlation analyses between GPIUS2 and other measures, with age as a control variable, revealed good convergent validity. Finally, structural equation modelling showed a good fit to the data, supporting the cognitive-behavioural model of Caplan (2010). CONCLUSIONS: The Japanese version of the GPIUS2 has good psychometric properties and the theoretical model of the original GPIUS2 is applicable to Japanese adults

The Key Role of Heavy Precipitation Events in Climate Model Disagreements of Future Annual Precipitation Changes in California

Climate model simulations disagree on whether future precipitation will increase or decrease over California, which has impeded efforts to anticipate and adapt to human-induced climate change. This disagreement is explored in terms of daily precipitation frequency and intensity. It is found that divergent model projections of changes in the incidence of rare heavy (\u3e60 mm day−1) daily precipitation events explain much of the model disagreement on annual time scales, yet represent only 0.3% of precipitating days and 9% of annual precipitation volume. Of the 25 downscaled model projections examined here, 21 agree that precipitation frequency will decrease by the 2060s, with a mean reduction of 6–14 days yr−1. This reduces California\u27s mean annual precipitation by about 5.7%. Partly offsetting this, 16 of the 25 projections agree that daily precipitation intensity will increase, which accounts for a model average 5.3% increase in annual precipitation. Between these conflicting tendencies, 12 projections show drier annual conditions by the 2060s and 13 show wetter. These results are obtained from 16 global general circulation models downscaled with different combinations of dynamical methods [Weather Research and Forecasting (WRF), Regional Spectral Model (RSM), and version 3 of the Regional Climate Model (RegCM3)] and statistical methods [bias correction with spatial disaggregation (BCSD) and bias correction with constructed analogs (BCCA)], although not all downscaling methods were applied to each global model. Model disagreements in the projected change in occurrence of the heaviest precipitation days (\u3e60 mm day−1) account for the majority of disagreement in the projected change in annual precipitation, and occur preferentially over the Sierra Nevada and Northern California. When such events are excluded, nearly twice as many projections show drier future conditions