Rissho University (立正大学, Risshō Daigaku), one of the oldest universities in Japan, was founded in 1580, when a seminary was established as a learning center for young monks of the Nichiren shu.The university's name came from the Rissho Ankoku Ron, a thesis written by Nichiren, a prominent Buddhist priest of the Kamakura period. Rissho University enrolls approximately 11,900 students. It has 14 undergraduate departments and 6 graduate school research departments on two separate campuses..
This study provides an assessment of warm season (May to September) heatwave characteristics and their trends across eight cities in Japan, using daily maximum (T max) and minimum (T min) temperature records spanning the period 1955 to 2022. T max and T min heatwaves were defined using a relative threshold approach and described in relation to their annual frequency, duration, and cumulative heat and exceedance probabilities. Man-Kendall trend tests and spline regression techniques were applied to establish the nature of trends and to estimate the rates of change and acceleration of annual heatwave related cumulative heat. Findings confirm that heatwaves are an intrinsic feature of Japan's warm season climate, with annual exceedance probabilities greater than 60% for T max heatwaves and 70% for T min heatwaves across all eight locations. A pronounced north-south gradient was identified, with southern locations experiencing more frequent and longer-lasting events, greater cumulative heat, and faster rates of heat accumulation, reflecting the influence of extended warm seasons and likely persistent subtropical high-pressure systems. Strong evidence was found for an increase in annual heatwave frequency and duration and cumulative heat for both daytime and nocturnal heatwaves, especially for recent decades, with the trend in nocturnal heat particularly noticeable. Overall study findings contribute to a growing international literature on the evolving nature of heatwaves and their differential expression across space and time, which bears implications for the field of heatwave risk management under current and future climate.
Aging societies have created a need to accelerate knowledge transfer between workers, and rapidly improve the skills of novice workers. This paper proposes a scheduling – based strategy that accounts for skill improvement in a mechanical engineering design activity of an Engineer-to-Order (ETO) production system. A metaheuristic-based solution is proposed, using a tabu-search based algorithm. A posteriori Pareto optimization is used to derive non-dominated solutions for each iteration. Furthermore, a hybrid neighbourhood structure is proposed, with neighbourhoods designed to address the objective functions. A paired assignment method, based on the Cognitive Load Theory (CLT) is suggested as a method to implement rapid skill development. The proposed algorithm is validated against the results of an exact brute force method, and the proposed hybrid neighbourhood is evaluated against existing neighbourhood structures proposed in literature. To evaluate the effectiveness of the proposed scheduling method under various jobs, engineers, and workload conditions, multiple tests are conducted over a span of 6 months. The results are compared with those simulated for the current scheduling practices in ETO design engineering activities. The findings indicate that over the long term, the proposed method leads to better skill improvement in all scenarios. Additionally, it outperforms existing practice in terms of total tardiness under most conditions, except in situations with the highest workload settings or workplaces with a higher proportion of expert-level engineers.
A systematic framework for constructing optimized interpolating operators strongly coupled to QCD two-particle states is developed, which is achieved by incorporating interhadron spatial wave functions. To efficiently implement these operators in lattice QCD, a novel quark smearing technique utilizing noise vectors is proposed. Applied to the Ω c c c Ω c c c system, these optimized operators prove superior to combinations of limited plane-wave operators, enabling the resolution of distinct eigenstates separated by only ∼ 5 MeV near the threshold 2 m Ω c c c ≃ 9700 MeV . This exceptional resolving power opens new possibilities for studies of a wide range of hadronic systems in QCD.
This study addresses truck congestion at Japanese agricultural wholesale markets by proposing a novel simulation-driven scheduling system that integrates optimisation and Generative AI (GenAI)-based interaction interface. Congestion caused by upstream scheduling constraints and limited unloading space results in excessive waiting, increased CO $ _2 $ 2 emissions, and deteriorating labour conditions. In the proposed system, a genetic algorithm under fairness constraints is used to minimise total operational and waiting times by utilising real-time data on truck size, shipment content, unloading duration, and spatial constraints. Additionally, a GenAI-based interaction system provides dynamic instructions to drivers, enhancing their compliance. Simulation experiments using data reflecting actual market conditions demonstrated that, compared to the conventional first-come-first-served method, the proposed system can reduce total unloading time by nearly 10% and average waiting time by over 20%. Furthermore, by reducing truck idling during peak hours, the system contributes to measurable reductions in truck idling-related CO $ _2 $ 2 emissions, reinforcing its practical relevance for sustainable logistics operations. Overall, the proposed system enhances logistics efficiency and labour conditions, providing an empirical digital transformation paradigm for agricultural wholesale markets.
