弘前大学

The effects of pyrolysis-stage operating conditions on intermediate evolution and subsequent gasification performance remain insufficiently understood in two-stage biomass pyrolysis–gasification. Pyrolysis temperature and pyrolysis residence time were examined in a lab-scale system through independent pyrolysis and coupled gasification experiments. At a residence time of 10 min, increasing pyrolysis temperature from 500 to 700 °C reduced the char yield from 39.4 to 22.8 wt% and increased the dry N2-free pyrolysis gas yield from 0.15 to 0.26 Nm3/kg. After gasification at approximately 850 °C, the final dry N2-free gas yield ranged from 0.94 to 1.05 Nm3/kg and the H2/CO ratio remained within approximately 1.7–1.8, indicating that the influence of pyrolysis temperature was attenuated during downstream gasification. At 600 °C, increasing the pyrolysis residence time from 6.7 to 20 min reduced the tar yield from 42.1 to 32.3 wt% and increased the pyrolysis gas yield from 0.17 to 0.26 Nm3/kg. Among these residence-time conditions tested at 600 °C, 10 min gave the highest final gas yield, carbon conversion efficiency, and cold gas efficiency, reaching 0.99 Nm3/kg, 78.0%, and 72.6%, respectively. These results identify the pyrolysis residence time in the pyrolysis stage as an important operating parameter for optimizing the two-stage process.

Improving cognitively healthy survival is important for achieving healthy aging. Therefore, it would be valuable to estimate the future risk of either incident dementia or death in community-dwelling older adults. This study aimed to develop a set of risk prediction models for either incident dementia or death that can be applied according to data availability across diverse clinical settings, using longitudinal data from community-dwelling older Japanese adults. A total of 8,334 participants aged ≥65 years were prospectively followed up from 2016 to 2023. Logistic regression was used to develop multivariable prediction models. The developed models were translated into simplified scoring systems based on the β coefficients. The discrimination abilities of the models were assessed by C-statistics, and the calibration was assessed by calibration plots. During the follow-up period, 1,151 participants developed either dementia or death. In the multivariable model using potential predictors commonly available in the primary care setting, age, male sex, formal education ≤9 years, diabetes mellitus, use of lipid-lowering agents, leanness, history of stroke, history of heart disease, history of respiratory disease, history of cancer, current smoking, no regular exercise habit, and low frequency of social interactions were selected as predictors. Adding cognitive status, depressive symptoms, apolipoprotein E-ε4 carrier status, and brain MRI markers to the model further enhanced its predictive performance. The developed models and simplified scores showed good discrimination and calibration. Risk stratification with our models may be useful for assessing the risks of incident dementia and death and, consequently, for prolonging cognitively healthy survival.

There is a growing demand for lead-free, highly transparent, high-performance X-ray shielding materials that are both flexible and contain high concentrations of metals. This work demonstrates a flexible, transparent composite made from sodium polytungstate (SPT) incorporated into a matrix consisting of polyvinyl alcohol (PVA) and sorbitol at a concentration of 59 wt.%. The unique particle-free fabrication of this material enables homogeneous distribution of the SPT without the need for surfactants, surface modifications, or ultrasonic processing. This composite exhibits 90% transmittance at 600 nm along with exceptional flexibility and a density of 2.56 g cm-3. Tungsten ions in the matrix undergo hydrogen bonding with -OH groups in the PVA and sorbitol. These bonds are confirmed by shifts in the & horbar;OH stretching peak in infrared spectra. This composite provides an X-ray shielding value of 0.14 mmPb at a thickness of 1.53 mm, significantly exceeding the value of 0.09 mmPb at a thickness of 1.70 mm for a lead-containing commercial product. This performance surpasses that of state-of-the-art transparent shielding materials, thus offering an unprecedented combination of softness, clarity, and radiation protection. The material developed in this work could have applications in the fields of medicine, nuclear energy, aerospace, and optoelectronics.

The purpose of this study was to investigate the relationships between exercise habits and muscle strength, function, and volume for residents of a snowy region. This study was a cross-sectional, retrospective study using data from the Iwaki Health Promotion Project Health checkup program held between 2015 and 2019 (n = 1885); the final analyzed sample was 1401. The participants were divided into four groups based on their active season of exercise in non-winter and winter: all seasons (Active–Active, n = 131, median 53 years), except winter (Active–None, n = 83, 61 years), winter only (None–Active, n = 31, 57 years), and nothing all seasons (None–None, n = 1156, 48 years). Analysis of covariance was used to compare outcomes (gait speed, grip strength, and skeletal muscle mass index) between age or sex category. Grip strength was significantly lower in None–None (33.0 kg) than in Active–Active (34.6 kg) in young and middle-aged adults. In females, gait speed was significantly lower in None–None (2.48 m/sec) than in Active–Active (2.64 m/s) and in Active–None (2.68 m/s), and skeletal muscle mass index was significantly lower in Active–None (25 kg/m2) than in Active–Active (26.0 kg/m2). Exercise habits throughout the year had a positive impact on grip strength in young and middle-aged adults among residents of a snowy region in Japan. Also, maintaining year-round exercise including winter was associated with positive effects on gait speed and muscle volume in females.

This paper presents an automated framework for generating high-fidelity bridge information models based on the IFC4x3 standard. Addressing the challenges of manual modeling and data consistency in bridge engineering, the proposed solution enables the seamless transformation of structured design data (e.g., Excel tables) as input into detailed and semantically rich IFC models as output through programmatic generation. The workflow integrates a JSON intermediary layer to facilitate flexible data exchange and supports the rapid assembly of complex bridge components, including main girders, secondary beams, connections, bolts, stiffeners, and diaphragms. A key innovation lies in the systems' parameter-driven geometry generation, which allows for efficient adjustment and iteration of bridge designs. The framework ensures both geometric precision and semantic completeness, providing the geometric and semantic foundation for downstream workflows including structural modeling, asset management, and maintenance planning. Furthermore, the architecture is designed with future AI integration in mind, enabling large language models to interact with and modify bridge parameters via natural language commands. Case studies on steel plate girder and box girder bridges demonstrate the systems' capability to handle intricate structural details and generate models swiftly, with performance scaling linearly with complexity. While current limitations include a focus on steel structures and reliance on comprehensive metadata, the paper outlines future directions such as expanding to other bridge types, implementing automated design rule checks, and enhancingAI-driven design support. Overall, this research advances the digitalization and automation of bridge modeling, providing a robustfoundation for intelligent design, analysis, and lifecycle management within the civil engineering domain.