This study uses propensity score matching (PSM) to quantitatively evaluate the impact of Japan's Location Optimization Plan (LOP) on residential land prices. Despite the growing attention given to compact city development policies, rigorous empirical evaluations of the effectiveness of the LOP using causal inference methods remain limited. Stable land prices are crucial for sustainable urban development. Stable land prices ensure fair property valuations and promote investment confidence. Land price stability also affects infrastructure planning. The analysis uses data from Hachioji City, where the LOP has been implemented for over a decade, and compares it with data from other densely populated areas within Tokyo Prefecture. By applying nearest neighbor and kernel matching techniques, the study isolates the effects of the policy from confounding variables. The results demonstrate that land prices in Hachioji City decreased by approximately 6701.32 JPY/m2 compared to other areas in Tokyo. The results suggest that LOP implementation is associated with more moderate land price changes in Hachioji than in comparable areas. The findings contribute to our understanding of how spatial planning policies influence land markets in the context of demographic decline. The findings also offer methodological foundations for future policy assessments in similar contexts.
This study links mobility styles with carsharing intentions and next-generation vehicle preferences in urban Japan. An online survey of licensed drivers in Tokyo Metropolis, Kanagawa Prefecture, and Osaka Prefecture captured mode use frequencies and stated preferences by travel purpose. Cluster analysis identified three mobility styles: private-car users, public-transport users, and a diversified, high-carsharing group. The diversified profile combined high carsharing participation with high private-car ownership, indicating that carsharing can complement rather than replace existing car access. Random Forest results indicate that age, car usage frequency, and income best distinguish carsharing users from non-users. Carsharing preferences varied substantially by stated-use context. For daily and business trips, most respondents prefer carsharing stations within 400 m and short distances; for tourism and hometown visiting, they accept longer access to carsharing stations and longer trips. Compact and Kei cars (Japanese light vehicles) in Japan dominate daily use, while minivans and SUVs gain importance for leisure. Gasoline and hybrid vehicles are most preferred, with limited willingness to pay extra for electric vehicles. The findings support segment- and purpose-specific carsharing station placement, fleet composition, and gradual electrification strategies in mature, transit-rich metropolitan areas.
Over more than a century following the discovery of superconductors, extensive research has been conducted to leverage their properties, particularly in fundamental materials science applications. Nonetheless, significant challenges still exist that hinder the comprehensive production of superconductors, including the optimal critical temperature (Tc) and current density (Jc), concerns regarding the purity of superconductor phases, sintering temperature, complexities in crystal growth, and various high-cost fabrication-related issues. Recent significant breakthroughs in artificial intelligence (AI) approaches have provided disruptive solutions, such as machine learning (ML), to address these fundamental issues. Thus, ML approaches can be employed to address the issues associated with superconductivity and serve as a means to achieve optimal conditions for superconductors and their applications. ML methodologies can deliver rapid, efficient, and precise solutions for intricate and nonlinear technological, manufacturing, and economic challenges in the domain of superconductivity. This paper initially presents the notion of AI and the often employed ML techniques. A comprehensive conceptual overview is provided for studies employing ML methods aimed at properties enhancement, condition monitoring, and the structural analysis of existing superconductors, along with other pertinent applications. This subject overview is organized into three primary topics: fundamental application utilizing ML, databases used by ML models, and our main focus which is the optimization techniques used alongside with ML in superconductors. Furthermore, the difficulties associated with using ML methodologies in superconductivity and their applications are presented. Ultimately, prospective developments regarding the integration of ML approaches with superconducting for various applications are examined.
Photoacoustic (PA) imaging enables high-contrast visualization of blood vessels; however, accurate 3-D vascular imaging requires volumetric information across a wide frequency band. Sparse-element 2-D matrix array transducers can enable rapid and cost-effective 3-D imaging. However, when used with conventional universal back projection (UBP) techniques, these systems often suffer from image degradation. Model-based learning (MBLr), which integrates physical models with deep learning, has emerged as a promising approach to address this limitation and improve image quality from sparse sensor configurations. In this study, we performed simulation-based analyses of 3-D PA images to investigate the extent to which MBLr can achieve high contrast using a sparse, cost-effective matrix array and to examine the mechanism underlying its image quality improvements. Quantitative image quality metrics showed that MBLr provided significant improvements compared with conventional UBP reconstruction methods. Specifically, a sparse 16 × 16 array (Case S) reconstructed using MBLr outperformed both the baseline 32 × 32 array (Case B) and the ultra-wideband 32 × 32 array (Case U). Spatial frequency analysis revealed that MBLr enhanced high-frequency recovery in the in-plane (x and y) directions and improved low-frequency components in the axial (z) direction. These improvements enabled substantially enhanced 3-D vascular visualization with superior vessel delineation, contrast, and structural preservation. Overall, our findings demonstrate that MBLr enables high-quality volumetric imaging with sparse array transducer, providing important insights for the design of cost-effective PA imaging systems.
Urban expansion and population growth intensify the Urban Heat Island (UHI) effect, increasing energy demands for cooling and amplifying public health risks, particularly when compounded by extreme weather events such as heatwaves. This study introduces a conceptual framework for Urban Digital Twins (UDTs) to assess and mitigate UHI impacts by integrating advanced urban informatics, high-resolution atmospheric modelling, and 3D visualisation. Using Sofia, Bulgaria, as a case study, the framework incorporates energy consumption data and street-level urban features, including tree cover and solar radiation, combined with the Weather Research and Forecasting (WRF) model simulations to characterise the urban thermal environment. The outputs are translated into public health metrics, such as the Wet Bulb Globe Temperature (WBGT), and visualised in 3D to identify high-risk areas, thereby informing decision-making for residents and policymakers. The modelling system was rigorously calibrated and validated through case studies, demonstrating strong accuracy in simulating urban thermal dynamics and associated health risks. Results highlight critical zones in central Sofia where WBGT exceeded 25.2 °C at 1600 LST on 22 August 2018, indicating moderate-to-high daily activity levels and potential discomfort for residents. The proposed framework supports climate-sensitive urban planning by linking surface thermal exposure, energy use, and public health vulnerability, and demonstrates scalability for application across diverse urban environments under current and future climate conditions.
