This work aims to explore the use of machine learning techniques, particularly clustering and cluster evolution tracking, to analyze travel patterns in public transportation in a city and provide valuable insights for urban transit planning and optimization. Clustering involves identifying and grouping similar objects, such as passengers with different ticket types, and distinguishing them from dissimilar objects in other groups. Over time, groups can change, so tracking this change can provide more detailed and valuable insights than analyzing data in aggregates. Clustering and cluster evolution tracking can reveal groups of passengers that are more or less affected by changes such as seasonality or fare increases. We propose a framework called DUGET (Dynamic User Grouping and Evolution Tracking), which clusters anonymized users based on their ticket choices and temporal travel patterns using a multi-step approach. The clusters are then tracked over time using Jaccard similarity based on memberships, allowing for the analysis and visualization of changes. Our experiments using a real-world public transportation dataset collected in Stockholm, Sweden, show the feasibility of tracking change over time in public transportation by examining passenger behavior as a temporal aggregate. The framework we propose is generalizable and can be used for future projects to understand trends in groups of objects.

In wood science, accurate segmentation of non-cellular elements in microscopy images is critical for assessing wood quality and understanding growth patterns. Yet, it is challenging due to the complex morphology of wood components. This work explores the development of a semi-supervised deep learning model for segmenting non-cellular elements in wood microscopy images of Norway spruce, an essential source for construction materials in Europe, addressing the challenge of manual annotation’s labor intensity and expertise requirement. The segmentation model employs advanced deep learning architectures, including Convolutional Neural Networks and a Vision Transformer, to capture the intrinsic patterns embedded in wood structures. We proposed a Pixel-level Guided Mean-Teacher (PG-MT) framework as an improvement to the Mean-Teacher semi-supervised learning technique. Our framework enables pixel-level guided correction to enhance segmentation accuracy and model robustness with limited labeled datasets. Our experimental evaluations show that the proposed PG-MT framework improved the Dice score for medullary ray segmentation by 0.95% and the IoU score by 1.14% over the Uncertainty-Aware Mean-Teacher (UA-MT) framework. Additionally, the integration with laboratory instruments emphasizes the model’s effectiveness in accurately estimating cross-sectional cell wall thickness, demonstrating a strong correlation with X-ray measurements. This result validates the model’s practical applicability in laboratory settings, enhancing the analysis of wood properties. This work provides a robust semi-supervised DL framework for segmenting non-cellular elements in wood microscopy images, significantly reducing the annotation burden and paving the way for more automated and precise wood property analysis.

Deteriorating air quality is a growing concern that has been linked to many health-related issues. Its monitoring is a good first step to understanding the problem. However, it is not always possible to collect air quality data from every location. Various data interpolation techniques are used to assist with populating sparse maps with more context, but many of these algorithms are computationally expensive. This work introduces a three-step Chain Mail algorithm that uses kriging (without any modifications to the base algorithm) and achieves up to ×100 execution time improvement with minimal accuracy loss (relative RMSE of 3%) by running concurrent interpolation executions. This approach can be described as a multiple-step parallel interpolation algorithm that includes specific regional border data manipulation for achieving greater accuracy. It does so by interpolating geographically defined data chunks in parallel and sharing the results with their neighboring nodes to provide context and compensate for lack of knowledge of the surrounding areas. Combined with a serverless cloud architecture, this approach opens doors to interpolating large data sets in a matter of minutes while remaining cost-efficient. The effectiveness of the three-step Chain Mail approach depends on the equal point distribution among all nodes and the resolution of the parallel configuration. In general, it offers a good balance between execution speed and accuracy.