Abstract

Abstract
At the practical level, the static stability constraint is one of the most important constraints in practical pallet loading problems, such as air cargo palletizing. Approaches to modeling static stability, which range from base support and mechanical equilibrium calculations to physical simulation, differ in workflow, focus, and assumptions, so choosing the right static stability approach has a substantial impact on the quality of the solution and, ultimately, on loading security. To date, little research has investigated the structural differences between approaches. The aim of this paper is to integrate knowledge and shed light on the applicability of the different approaches for the practical scenario of air cargo palletizing. We tackle this problem through (1) a reformulation and extension of static stability toward loading stability, (2) a conceptual analysis of current approaches, and (3) benchmarking that employs an independent multibody simulation on multiple heterogeneous datasets. Our results show that all approaches are prone to structure errors and vary significantly in their premises and information usage. Further, full base support is revealed to be the most restrictive approach by far, while physical simulation achieves the greatest accuracy. Given the trade-off between accuracy and runtime, the mechanical equilibrium approach is a good choice, while partial base support performs best for lower support values.

Abstract
Pain is a highly subjective phenomenon that depends on multiple factors. The common methods used to evaluate pain require the person to be awakened and cooperative, which may not always be possible. Moreover, such methods are subject to non-quantifiable influences, namely the impact of an individual's emotional state on how pain is perceived or how negative emotions may exacerbate pain perception, while positive emotions may attenuate it. The goal of this study was to conduct a novel protocol for pain induction with emotional elicitation and assess its feasibility. In this protocol, the physiological responses were monitored, and collected, through Electrocardiogram, Electrodermal Activity, and surface Electromyogram signals. Along the protocol, the pain perception was evaluated using a 0-10 numerical rating scale and by registering the time from the pain stimulus beginning to the Pain and Tolerance Thresholds. This study comprised three emotional sessions, negative, positive, and neutral, which were performed through videos of excerpts of terror, comedy, and documentary films, respectively, followed by pain induction using the Cold Pressor Task (CPT). A total of 56 participants performed the study, with a CPT mean time of about 91.70 +/- 39.64 s among all the sessions. The conducted protocol was considered feasible and safe as it allowed the collection of physiological data, pain, and questionnaires' reports from 56 participants, without any harm to them. Moreover, the collected data can be further used to assess how emotional conditions influence pain perception and to provide better emotion-calibrated pain recognition systems based on physiological signals.

Abstract

Abstract

Abstract
The public transportation sector generates large volumes of sensor data that, if analyzed adequately, can help anticipate failures and initiate maintenance actions, thereby enhancing quality and productivity. This work contributes to a real-time data-driven predictive maintenance solution for Intelligent Transportation Systems. The proposed method implements a processing pipeline comprised of sample pre-processing, incremental classification with Machine Learning models, and outcome explanation. This novel online processing pipeline has two main highlights: (i) a dedicated sample pre-processing module, which builds statistical and frequency-related features on the fly, and (ii) an explainability module. This work is the first to perform online fault prediction with natural language and visual explainability. The experiments were performed with the Metropt data set from the metro operator of Porto, Portugal. The results are above 98 % for f-measure and 99 % for accuracy. In the context of railway predictive maintenance, achieving these high values is crucial due to the practical and operational implications of accurate failure prediction. In the specific case of a high f-measure, this ensures that the system maintains an optimal balance between detecting the highest possible number of real faults and minimizing false alarms, which is crucial for maximizing service availability. Furthermore, the accuracy obtained enables reliability, directly impacting cost reduction and increased safety. The analysis demonstrates that the pipeline maintains high performance even in the presence of class imbalance and noise, and its explanations effectively reflect the decision-making process. These findings validate the methodological soundness of the approach and confirm its practical applicability for supporting proactive maintenance decisions in real-world railway operations. Therefore, by identifying the early signs of failure, this pipeline enables decision-makers to understand the underlying problems and act accordingly swiftly.