Musashino University

Depression is a leading cause of global disability, and subthreshold cases contribute substantially to the burden. Cognitive behavioural therapy (CBT) is effective for depressive symptoms, but its impact on social functioning in subthreshold depression remains unclear. This study evaluated the effects of smartphone-based CBT on social functioning and personal health records among individuals with subthreshold depression and examined whether a personalised and optimised therapy (POT) algorithm enhanced these outcomes. We performed a secondary analysis of the RESiLIENT trial (n=5361). Participants were stratified by baseline Patient Health Questionnaire-9 (PHQ-9) scores (0-4 and 5-14) and randomised to nine CBT app groups or a self-check group. Outcomes included social functioning and health records. A mixed-effects model for repeated measures was used to analyse the outcomes. The POT algorithm selected CBT skills tailored to individual characteristics. Depressive symptoms improved in both PHQ-9 groups compared with those of the self-check group, but gains in social functioning were modest, observed mainly in the PHQ-9 score ≤4 group. No consistent benefits were seen for health behaviours, job resignation, sick leave or healthcare costs. However, in a simulated randomised comparison using 10-fold cross-validated datasets, the POT algorithm had potentially beneficial effects for productivity (standardised mean difference (SMD)=0.16, 95% CI 0.03 to 0.29) and social adjustment (SMD=-0.13, 95% CI -0.26 to 0.00). Smartphone CBT effectively reduced depressive symptoms but showed limited impact on social functioning and healthcare costs. Personalised optimisation may modestly improve productivity and adjustment, suggesting its promise for enhancing social outcomes. Personalised tailoring of CBT skills may strengthen the effects of digital interventions on social functioning. UMIN000047124.

Tian et al. (2025) have made a valuable contribution to dairy science by investigating strain-level variation in diacetyl production by Lactiplantibacillus plantarum, a key bacterium influencing dairy flavor. Their study revealed substantial phenotypic diversity among 351 isolates and identified strain WJ108 as a particularly high producer, highlighting the potential for targeted strain selection. However, their conclusion regarding distinct volatile signatures, based on principal component analysis (PCA), warrants further discussion.

Assessing travel-related Quality of Life (QoL) is a significant challenge due to its reliance on subjective human perception, which is difficult to measure. Traditional studies are often constrained by the limited scope of costly, manual data collection. The significant contribution of this research is the introduction of a new framework that can analyze tourist perception on a large and systematic scale. The novelty of this work lies in the utilization of Google Street View (GSV) as a large-scale visual data source, coupled with the use of a sequence-based model to analyze travel routes, which allows for the simulation of the ever-changing travel experience better than the analysis of single, static images. This framework was applied to a dataset in Phuket, consisting of 456 driving routes, totaling 4,560 sequential images. Visual features were extracted from this image data using Object Detection and Semantic Segmentation techniques and then used to build prediction models with LSTM and KNeighborsRegressor for analysis in conjunction with QoL scores from questionnaires. The analysis revealed key insights into tourist perception patterns, identifying a significant divergence between the culturally homogeneous group of Thai tourists and the highly diverse group of foreign tourists. Furthermore, it was found that tangible quality of life dimensions (e.g., Material Well-being) was perceived more consistently than abstract dimensions (e.g., Emotional Well-being). In conclusion, this research shifts the paradigm of route analysis from focusing solely on efficiency to a human-centric process, providing a new tool for urban planners and stakeholders to understand and improve the quality of the journey in a more profound way.

Evaluation equations for ultimate shear capacity and allowable shear strength of SFRC shear walls were proposed, and their validity was examined using the results of previous experiments and analysis. It was confirmed that the experimental maximum shear capacity could be evaluated accurately and on the safe side by using an equation that accumulates the shear strength that steel fibers can bear to the shear capacity equation (truss-arch mechanism) for RC shear walls. In addition, it was confirmed that the experimental maximum capacity could be simulated using a finite element analysis model that simplified the SFRC material properties.

It is challenging for a computer to comprehend dynamic characteristics and variables of a human user; however, conceptual modeling can serve as a language to describe these dynamic aspects. In this research, we aim to model the human user’s varying input and output modalities in human-computer interaction, making the human appear as part of the system architecture. This allows technologies to adapt to the user’s different input and output modalities. As a first step, we model a class diagram to depict a systemic view of interaction. Secondly, we formulated a state machine diagram to describe the behavior of the user and IT artifact in the context of accessible interaction. These models contribute to accessible interaction between humans and computers, where the computer either changes or adjusts its own input and output modalities so that the human can receive them and provide their own outputs according to their abilities.

This paper proposes a new conceptual framework for modeling human action. There are too many aspects for describing human behavior, however, the paper proposes only seven dimensions that describe human experience. The first four dimensions describe physical characteristics consisting of 3D position and time. The remaining three dimensions describe physiological, psychological, and Social characteristics respectively. This model can be applied to the full range of human experiences. The methods for measuring each parameter of the seven dimensions are discussed. The process is exemplified in the Data Sensorium setup.

The Paris Agreement of 2015 has prompted countries to accelerate their efforts to become carbon neutrality efforts, which meant reducing CO2 emissions to virtually zero. Limiting global warming to less than 1.5°C by 2050 rely on technologies that remove CO2 from the atmosphere faster than humans release it. This implies that CO2 will be removed at a rate of 1-30 gigaton per year by 2050. Carbon Capture Storage / Sequestration (CCS) and Utilization (CCU) are concepts and technologies that collect emitted CO2 store it permanently underground, or recycle it as energy or chemicals for use in manufacturing and other economic activities. CCS and CCU have been discussed globally, but have not reached local and practical levels. Currently planned large-scale CCS requires significant government investment and new technological developments for capture, transport, and storage / sequestration, therefore implementation is expected to start in the second half of 2030 towards the 2050 goal. The need to start acting now where possible rather than waiting for the distant future, makes it important to implement CCS and CCU on a small scale and build towards future scale-up as an immediate solution. This study proposes a support method and system to help companies that emit large amounts of CO2 such as power plants, cement, petrochemicals, and steel industries, to decide how to treat their CO2 emissions in the context of decarbonization. In this study, a Simple, Measurable, Attainable, Relative, and Time-Bound (SMART) decision support method and Direct Air Capture Location and Cost Simulator (DLCS) system were developed to provide a solution to the Negative Emission 5W1H “What, Who, Which, When, Why, and How” from the perspective of a company that emits CO2. A prototype model with parameter settings was proposed based on knowledge gained from practical experience. The functionality of the SMART method and DLCS system was confirmed by applying sample data from the actual data of the ‘Tokyo Region’ as a Proof of Concept (PoC). In this PoC, characteristics of direct air capture which is a critical technology for negative emissions, were verified. The core of the SMART and DLCS model entails combinatorial optimization, distance calculation, cost estimation, and market projection including constraint solution.