Biodegradation of aliphatic polyurethane foams in soil: Influence of amide linkages and supramolecular structure

Polyurethane (PU) foams are classified as physically nonrecyclable thermosets. The current effort of sustainable and eco-friendly production makes it essential to explore methods of better waste management, for instance by modifying the structure of these frequently used polymers to enhance their microbial degradability. The presence of ester links is known to be a crucial prerequisite for the biodegradability of PU foams. However, the impact of other hydrolysable groups (urethane, urea and amide) occurred in PU materials, as well as the supramolecular structure of the PU network and the cellular morphology of PU foams, is still relatively unexplored. In this work, fully aliphatic PU foams with and without hydrolyzable amide linkages were prepared and their aerobic biodegradation was investigated using a six-month soil burial test. Besides the variable chemical composition of the PU foams, the influence of their different supramolecular arrangement and cellular morphologies on the extent of biodegradation was also evaluated. Throughout the soil burial test, the release of carbon dioxide, and enzyme activities of proteases, esterases, and ureases were measured. At the same time, phospho-lipid fatty acids (PLFA) analysis was conducted together with an assessment of microbial community composition achieved by analysing the genetic information from the 16S rRNA gene and ITS2 region sequencing. The results revealed a mineralization rate of 30-50 % for the PU foams, indicating a significant level of degradation as well as indicating that PU foams can be utilized by soil microorganisms as a source of both energy and nutrients. Importantly, microbial biomass remained unaffected, suggesting that there was no toxicity associated with the degradation products of the PU foams. It was further confirmed that ester linkages in PU foam structure were easily enzymatically cleavable, while amide linkages were not prone to degradation by soil microorganisms. In addition, it was shown that the presence of amide linkages in PU foam leads to a change in the supramolecular network arrangement due to increased content of hard segments, which in turn reduces the biodegradability of PU foam. These findings show that it is important to consider both chemical composition and supramolecular/macroscopic structure when designing new PU materials in an effort to develop environmentally friendly alternatives.

Calibration of pedestrian ingress model based on CCTV surveillance data using machine learning methods

Machine learning methods and agent-based models enable the optimization of the operation of high-capacity facilities. In this paper, we propose a method for automatically extracting and cleaning pedestrian traffic detector data for subsequent calibration of the ingress pedestrian model. The data was obtained from the waiting room traffic of a vaccination center. Walking speed distribution, the number of stops, the distribution of waiting times, and the locations of waiting points were extracted. Of the 9 machine learning algorithms, the random forest model achieved the highest accuracy in classifying valid data and noise. The proposed microscopic calibration allows for more accurate capacity assessment testing, procedural changes testing, and geometric modifications testing in parts of the facility adjacent to the calibrated parts. The results show that the proposed method achieves state-of-the-art performance on a violent-flows dataset. The proposed method has the potential to significantly improve the accuracy and efficiency of input model predictions and optimize the operation of high-capacity facilities.

Analysis of the use of behavioral data from virtual reality for calibration of agent-based evacuation models

Agent-based evacuation modeling represents an effective tool for making predictions about evacuation aspects of buildings such as evacuation times, congestions, and maximum safe building capacity. Collection of real behavioral data for calibrating agent-based evacuation models is time-consuming, costly, and completely impossible in the case of buildings in the design phase, where predictions about evacuation behavior are especially needed. In recent years evacuation experiments conducted in virtual reality (VR) have been frequently proposed in the literature as an effective tool for collecting data about human behavior. However, empirical studies which would assess validity of VR-based data for such purposes are still rare and considerably lacking in the agent-based evacuation modeling domain. This study explores opportunities that the VR behavioral data may bring for refining outputs of agent evacuation models. To this end, this study employed multiple input settings of agent-based evacuation models (ABEMs), including those based on the data gathered from the VR evacuation experiment that mapped out evacuation behaviors of individuals within the building. Calibration and evaluation of models was based on empirical data gathered from an original evacuation exercise conducted in a real building (N = 35) and its virtual twin (N = 38). This study found that the resulting predictions of single agent models using data collected in the VR environment after proposed corrections have the potential to better predict real-world evacuation behavior while offering desirable variance in the data outputs necessary for practical applications.