Ishida Takeshi

The Tiger Pufferfish (Takifugu rubripes) is a staple in Japanese cuisine, with over ten species of the Takifugu genus found in the surrounding seas. Given that certain parts of the pufferfish are toxic, they are predominantly prepared by trained professionals. Furthermore, species within the Takifugu genus are susceptible to hybridization, leading to an increase in hybrid numbers. However, identifying these hybrids is a challenging and time-consuming task, even for experts. To address this, we developed a transfer learning model using the pretrained VGG16 model to differentiate between pufferfish species. The VGG16 model, commonly used in image recognition, is built on convolutional neural networks. We also implemented Gradient-weighted Class Activation Mapping (Grad-CAM) for visual interpretation of the model. Grad-CAM generates a heat map that highlights the areas focused on by the AI model in the image, allowing us to identify factors contributing to misjudgment and make further improvements. We used seven species from the Takifugu genus (excluding hybrids), and approximately 15 colored images of each species were prepared for machine learning. The results showed that our model was able to distinguish between pufferfish species with relatively high accuracy, although some misclassification occurred among species with similar body patterns. The Grad-CAM results revealed that the model was able to distinguish body patterns, but some misclassifications occurred due to gravel and background objects being recognized as patterns.

Learners of thermodynamics learn a basic thermodynamic state quantity “entropy” which is challenging to understand owing to multiple reasons. First, entropy is explained using multiple defining equations； intuitively understanding the meaning from the equations can be difficult. Second, entropy is often explained in terms of “clutter” and “disorder” of energy； however, the correspondence between these concepts and the defining equation is not obtained intuitively. Therefore, in this study, we considered a virtual lattice space in which gas molecules are arranged and developed a model that enables intuitive understanding and quantitative calculations using defining equations. Specifically, the model was implemented in spreadsheet software with 100 gas molecules in a virtual space of 100 lattices. The model showed that even such a simple model can define thermodynamic quantities and quantify the number of cases Win Boltzmann’s equation from the viewpoint of the arrangement of molecules in lattice space. This is a tool that can calculate and quantitatively examine all entropy from multiple entropy-defining equations. This calculation sheet shows that the calculated values of entropy by the Sackur–Tetrode equation and Boltzmann’s equation are almost the same. Furthermore, the entropy difference calculated using the thermodynamic defining equation dS = dQ/T was also consistent with the values by other equations. Therefore, the model can specifically calculate the values of various entropy-defining equations.