Suffering earthquake (EQ) disasters, Japan has developed intensive networks of seismographs and achieved probabilistic long-/medium-term forecasting of big EQs. However, the great disaster EQs of 1995 and 2011 were never predicted. After these two EQs, Seismology Society of Japan and the government decided to avoid investigating the short-term prediction of EQs, considering it impossible. Earthquake Prediction Society of Japan has been challenging this issue since 2014 and has found a light very recently, especially by observing electromagnetic precursor phenomena. The present paper first clarifies the requirements for the observation of precursor phenomena to be useful for a future short-term prediction alert system. Using these criteria, various research presentations are reviewed and three new presentations are found promising. Especially, M. Tsutsui observed the variation of DC electric field deep underground every second with high S/N ratio. The electric field generated by piezoelectric effect is transmitted through the earth’s crust without being disturbed by noise on the ground. In the case of EQ (on 2021/5/1, M6.8, 750 km away), the signal showed a drastic (±) variation for 46 min. 1.5 h before EQ, a sharp spike at the time of EQ, and a drastic variation for 68 min. 8.5 h after EQ. The data are excellent, showing the correlation with the EQ and containing much information about the EQ rupture process. This method is at the stage of single observation site, overcoming various practical difficulties. The present paper proposes to initiate a collaborative research project within EPSJ with several research groups and observation sites. The project will certainly step up to prove the correlation with EQs, find the method to predict EQs with 3 factors (where, when, and how large), and establish a system for short-term EQ prediction alert. The present approach is based on the TRIZ philosophy and experimental science in general and will serve Japan and all over the world.
The extraction of benzoic acid from natural resins such as Styrax holds considerable industrial significance, given its widespread use in pharmaceuticals, food, and cosmetics. This study introduces an approach to enhance the extraction process by designing a novel installation in this respect. The design roadmap integrates Generative AI with neuro-symbolic AI algorithms. We employ a neuro-symbolic AI framework that merges AI's generative capabilities for initial design conceptualization with symbolic reasoning, enriched with TRIZ principles and Complex Systems Design Thinking (CSDT) methodologies. This combination aids in navigating complex problem-solving scenarios and promoting an innovative solution. Environmental issues are integrated throughout the design process to ensure that the solution also meets eco-sustainability objectives. Results indicate that the novel design markedly enhances the extraction efficiency of benzoic acid, reduces energy consumption, and lowers waste production. The design's adaptability for industrial applications has been validated, with future enhancements aimed at incorporating real-time monitoring AI systems for dynamic adjustments based on raw material variability.
This paper explores the application of generative AI for systematic and inventive problem-solving in engineering design. Utilizing a multi-directional prompting approach, the study investigates the ability of AI chatbots to generate and evaluate innovative concepts based on numerous elementary solution principles. The research involved two sets of experiments with graduate and undergraduate students solving seven engineering design problems. The findings indicate that while generative AI can quickly generate a large number of ideas, it often over-estimates the feasibility and usefulness of its solutions and tends to create overly complex designs. Comparisons of AI evaluations with those conducted by human participants revealed significant differences, highlighting the need for human oversight to ensure practical and contextually relevant outcomes. The experiments also revealed performance differences among various AI models, confirming a bias in self-assessment. Despite these challenges, integrating generative AI with multi-directional prompting using elementary inventive principles, primarily based on TRIZ methodology, proved effective in fostering innovative solutions. The study highlights the potential of simultaneously applying different AI models alongside human expertise to leverage the strengths of both large language models and human technical creativity.
With the planet facing escalating degradation due to mass production and increasing carbon emissions and waste, the buildings sector is at the core of innovation to build or optimize existing buildings, particularly in terms of adopting the most appropriate technologies, considering user behavior, and reducing carbon emissions. This research emphasizes the significance of a comprehensive and inventive approach in addressing the challenges of contemporary building sector practices. Using the Inventive De-sign Methodology (IDM), we propose a decision-support method for an evaluation tool for simulating and calculating the life-cycle costs, energy efficiency, and environmental impact of buildings. This tool is named NUKOSI RE. TRIZ is combined with the Design of Experiments Analysis to identify the most critical parameters of NUKOSI RE and avoid compromises between them. By better understanding the relationships (contradic-tions) among various parameters and recognizing the limitations of current models, we can help decision-makers pave the way for more effective and sustainable construction measures in the future.
Currently, AI is mainly used in the TRIZ innovation process to find solutions to well-defined technical problems using TRIZ tools such as Inventive Principles, Standards and, occasionally, Functional Oriented Search (FOS). In practice, however, problem solving is usually the least time-consuming part of the innovation process, with most effort normally spent defining the overall goal of the innovation, identifying and analyzing the initial problem, selecting the best solution from the set of solutions found, and justifying its feasibility. Therefore, AI would be much more useful if it were introduced into these labor-intensive parts of the TRIZ innovation process as well, and undoubtedly AI developers will eventually try to automate the entire innovation process. The objectives of this paper are (1) to assess the current effectiveness of using AI in real TRIZ projects, (2) to predict the most likely sequence of future AI implementation in different parts of the TRIZ innovation process, and (3) to identify related challenges. The objectives are achieved by analyzing the composition and timing of various activities in a typical TRIZ project and applying the Trend of Decreasing Human Involvement to these activities, where a TRIZ project is considered a technological process that transforms an initial, poorly formulated problem into a viable solution/product. These results can be used by AI and TRIZ specialists to create a roadmap for integrating AI and TRIZ to produce a fully automated innovation process that is applicable to technical systems and, potentially, to business systems.
