Abstract
Semantic segmentation consists of predicting a semantic label for each image pixel. While existing deep learning approaches achieve high accuracy, they often overlook the ordinal relationships between classes, which can provide critical domain knowledge (e.g., the pupil lies within the iris, and lane markings are part of the road). This paper introduces novel methods for spatial ordinal segmentation that explicitly incorporate these inter-class dependencies. By treating each pixel as part of a structured image space rather than as an independent observation, we propose two regularization terms and a new metric to enforce ordinal consistency between neighboring pixels. Two loss regularization terms and one metric are proposed for structural ordinal segmentation, which penalizes predictions of non-ordinal adjacent classes. Five biomedical datasets and multiple configurations of autonomous driving datasets demonstrate the efficacy of the proposed methods. Our approach achieves improvements in ordinal metrics and enhances generalization, with up to a 15.7% relative increase in the Dice coefficient. Importantly, these benefits come without additional inference time costs. This work highlights the significance of spatial ordinal relationships in semantic segmentation and provides a foundation for further exploration in structured image representations.

Abstract

Abstract
AbstractNon-pharmaceutical interventions (NPIs), including measures such as lockdowns, travel limitations, and social distancing mandates, play a critical role in shaping human mobility, which subsequently influences the spread of infectious diseases. Using COVID-19 as a case study, this research examines the relationship between restrictions, mobility patterns, and the disease’s effective reproduction number (Rt) across 13 European countries. Employing clustering techniques, we uncover distinct national patterns, highlighting differences in social compliance between Northern and Southern Europe. While restrictions strongly correlate with mobility reductions, the relationship between mobility and Rtis more nuanced, driven primarily by the nature of social interactions rather than mere compliance. Additionally, employing XGBoost regression models, we demonstrate that missing mobility data can be accurately inferred from restrictions, and missing infection rates can be predicted from mobility data. These findings provide valuable insights for tailoring public health strategies in future crisis and refining analytical approaches.

Abstract
This paper examines the role of digital twins (DTs) in promoting sustainability within seaport operations and logistics. DTs have emerged as promising tools for enhancing seaport performance. Despite the recognized potential of DTs in seaports, there is a paucity of research on their practical implementation and impact on seaport sustainability. Through a systematic literature review, this study seeks to elucidate how DTs contribute to the sustainability of seaports and to identify future research and practical applications. We reviewed and categorized 68 conceptual and practical digital applications into ten core areas that effectively support economic, social, and environmental objectives in seaports. Furthermore, this paper proposes five preliminary potential applications for DTs where practical implementations are currently lacking. The primary findings indicate that DTs can enhance seaport sustainability by facilitating real-time monitoring and decision-making, improving safety and security, optimizing resource utilization, enhancing collaboration and communication, and supporting the development of the seaport ecosystem. Additionally, this study addresses the challenges associated with DT implementation, including high costs, conflicting stakeholder priorities, data quality and availability, and model validation. The paper concludes with a discussion of the implications for seaport managers and policymakers. © The Author(s) 2024.

Abstract

Abstract
Traumatic Brain Injury (TBI) is a form of brain injury caused by external forces, resulting in temporary or permanent impairment of brain function. Despite advancements in healthcare, TBI mortality rates can reach 30%–40% in severe cases. This study aims to assist clinical decision-making and enhance patient care for TBI-related complications by employing Artificial Intelligence (AI) methods and data-driven approaches to predict decompensation. This study uses learning models based on sequential data from Electronic Health Records (EHR). Decompensation prediction was performed based on 24-h in-mortality prediction at each hour of the patient's stay in the Intensive Care Unit (ICU). A cohort of 2261 TBI patients was selected from the MIMIC-III dataset based on age and ICD-9 disease codes. Logistic Regressor (LR), Long-short term memory (LSTM), and Transformers architectures were used. Two sets of features were also explored combined with missing data strategies by imputing the normal value, data imbalance techniques with class weights, and oversampling. The best performance results were obtained using LSTMs with the original features with no unbalancing techniques and with the added features and class weight technique, with AUROC scores of 0.918 and 0.929, respectively. For this study, using EHR time series data with LSTM proved viable in predicting patient decompensation, providing a helpful indicator of the need for clinical interventions. © 2025 Elsevier Ltd