Abstract

Abstract
Data-driven models have been recognized as powerful tools to support Structural Health Monitoring (SHM). This paper contributes to the literature by exploring two data-driven approaches to detect damage through changes in a set of variables that assess the condition of the structure, and accommodates the challenge that may arise due to the influence of environmental and operational variabilities. This influence is reflected in the response of the structure and can reduce the probability of detecting damage in a structure or increase the probability of signaling false positives. This paper conducts a comparative study between a machine learning detection approach (supported by linear regression, random forest, support vector machine, and neural networks) and a cointegration approach, with the aim of detecting damage as early as possible. This study also contributes to the literature by evaluating the merits of the damage detection methods using real data collected from a small-scale structure. The structure is analyzed in a reference state and a perturbed state in which damage is emulated. The results show that both approaches are able to detect damage within the first 24 h, without ever signaling false positives. The cointegration based approach can notably detect damage after 10 h and 15 minutes, while the machine learning approach takes 20 h 30 m to detect damage.

Abstract

Abstract
Subgroup discovery (SD) aims at finding significant subgroups of a given population of individuals characterized by statistically unusual properties of interest. SD on event logs provides insight into particular behaviors of processes, which may be a valuable complement to the traditional process analysis techniques, especially for low -structured processes. This paper proposes a scalable and efficient method to search significant SD rules on frequent sequences of events, exploiting their multidimensional nature. With this method, it is intended to identify significant subsequences of events where the distribution of values of some target aspect is significantly different than the same distribution for the entire event log. A publicly available real -life event log of a Dutch hospital is used as a running example to demonstrate the applicability of our method. The proposed approach was applied on a real -life case study based on the public transport of a medium size European city (Porto, Portugal), for which the event data consists of 133 million smartcard travel validations from buses, trams and trains. The results include a characterization of mobility flows over multiple aspects, as well as the identification of unexpected behaviors in the flow of commuters (public transport). The generated knowledge provided a useful insight into the behavior of travelers, which can be applied at operational, tactical and strategic business levels, enhancing the current view of the transport services to transport authorities and operators.

Abstract
Miniaturized three-electrode electrochemical sensors (MES) are widely used in the advancement of innovative technologies for remote sensing applications. MESs consist of conductive electrodes that are applied onto an inert solid substrate using various techniques, such as photolithography, electroplating, and screen printing. Typical MES systems comprise working (WE) and counter (CE) electrodes based on gold (Au), paired with a reference electrode (RE) based on silver (Ag). This configuration is commonly selected due to Au's high conductivity, low resistance, and compatibility with robust organothiol chemistries, especially for the WE. Moreover, Ag is often preferred for REs owing to its low toxicity, stability, and high conductivity. Nevertheless, in uncontrolled environments outside of cleanrooms, both Au and Ag surfaces are prone to atmospheric contamination, resulting in significant sensor variability and compromised analytical performance. Therefore, it is crucial to integrate a pre-processing stage into the sensor manufacturing process to guarantee the quality and cleanliness of MES electrode surfaces for sensor functionalization and precise electrochemical measurements. Considering the potential negative effects of methods tailored for a specific electrode material on another material, this study extensively investigates 18 different treatment methods for MESs incorporating Au CEs and WEs, along with Ag REs. Employing a multi-parametric analysis, this study aims to identify the most effective treatment for a variety of electrode materials, thereby improving analytical accuracy and reproducibility for subsequent MES (bio)sensor applications. Miniaturized three-electrode electrochemical sensors (MES) are essential for advancing remote sensing technologies. However, the inherent morpho-chemical heterogeneity of built-in electrodes challenges MES analytical performance. This study investigates treatments for the different electrode materials, providing new methods to enhance quality control, analytical accuracy, and reproducibility in MES biosensing applications. image

Abstract
As sports analytics evolve to include a broad spectrum of data from diverse sources, the challenge of integrating heterogeneous data becomes pronounced. Current methods struggle with flexibility and rapid adaptation to new data formats, risking data integrity and accuracy. This paper introduces PlayField, a framework designed to robustly handle diverse sports data through adaptable configuration and an automated API. PlayField ensures precise data integration and supports manual interventions for data integrity, making it essential for accurate and comprehensive sports analysis. A case study with ZeroZero demonstrates the framework's capability to improve data integration efficiency significantly, showcasing its potential for advanced analytics in sports.