## 2 Methodology
### ANN Modeling
#### 2.3.4 Practical Application of Real-Time Conformity Monitoring  
The evaluation of the ANN model demonstrates the accuracy of micro-environmental prediction for the empty enclosure, which represents the worst-case scenario for hygothermal buffering compared to enclosures containing paper-based collections. However, assessing the performance of the ANN model in practical applications is essential. This part of the study aims to assess the robustness of real-time prediction for monitoring micro-environmental conformity in practical scenarios where enclosures with paper-based collections are used. Validation Experiment  
The trial was carried out in a test room, 5 m long, 3 m wide, and 3 m tall (Figure 7). Inside the room, the air distribution was created to match that of the library's air-conditioning configuration, with air supplied from the top and returned from the bottom, setting the velocity of supply air at approximately 0.2 m/s to prevent dust buoyancy. The macro-environment was controlled to fall within the range of 16-32 \({}^{\circ}\)C and 30-80 %RH, with an accuracy of approximate \(\pm\)2 \({}^{\circ}\)C and \(\pm\)10 %RH. The operational points were set at 20 \({}^{\circ}\)C and 55 %RH for this trial. These conditions represent the common NLS operating environment, which exhibits minimal seasonal variation. The trial was carried out in a controlled chamber 5 m long, 3 m wide, and 3 m tall (Figure 7). Inside this room, the conditioned air went into the room space through a void above a grid ceiling and then returned at a low level through three vents 0.1 m above the floor. The desk panel was meant to separate two airflow zones. The upper zone was inside the mainstream, while the lower one experienced more turbulence. These configuration efforts were to replicate the air distribution of the real scenario for validation purposes. Two enclosures were strategically placed in the two zones to account for temperature and humidity fluctuations across varying spatial gradients inside the space. They were filled with 70% full newspapers to represent the most common use of these enclosures. Additionally, two temperature and humidity loggers (loggers 2 and 4 in Figure 7) were placed inside these enclosures to record the micro-environment conditions. Another two loggers (loggers 1 and 3 in Figure 7) were positioned near these enclosures to monitor the macro-environment. Their measurement accuracies are \(\pm\)1 \({}^{\circ}\)C (within the range of 5-60 \({}^{\circ}\)C) and \(\pm\)3 %RH (within the range of 20-80 %RH). The sampling interval for all loggers was set at 15 min, consistent with the interval used in the BMS (Building Management System). Parallel Prediction  
To predict the micro-environment within various enclosures positioned differently, we replicated the trained ANN for each of the sensors located in the macro-environment. The trained ANN models were to act as parallel predictors in a BMS (Figure 8). The ANNs were fed with the latest 24 h macro-environmental data, consisting of 96 data points with 15 min sampling intervals from the on-site monitoring, to predict the micro-environmental condition. The maximum fluctuations and associated bands in the predicted results were calculated. Simultaneously, a real-time measurement of micro-environment was conducted to assess the prediction in the trial test. Analyzing environmental data helps identify risks specific to the collections with various materials, but this inherent diversity makes this process complex [48]. Therefore, the ANN prediction was based on the worst-case scenario or the least buffering effect, an empty box with no collections inside to further moderate the micro-environmental fluctuations. This resulted in larger predicted fluctuations and bands compared to real-time measurements, which understandably led to deviations between the predicted and measured data.