Search Results (117)

A long-standing challenge for the structural health monitoring (SHM) community is the masking effect of environmental variability, typically addressed by orthogonal projection (OP)-based data normalization to isolate the influence of environmental variability and enable structural anomaly detection. However, conventional OP techniques, such as principal component analysis, rely on clean and complete data, and their performance degrades in the presence of outliers or missing entries. To overcome this limitation, this paper proposes an integrated approach that combines OP with noisy low-rank matrix completion (NLRMC). The main advantage of NLRMC is its ability to couple low-rank and sparse decomposition with matrix completion, simultaneously handling data corruption and missingness to recover incomplete datasets and enable robust anomaly detection. By incorporating novelty-indicator extraction, a fully online, unsupervised anomaly-detection procedure is established. Validation on a vibration-based SHM dataset from the KW51 railway bridge confirms that the NLRMC-OP approach achieves reliable detection of operational state changes before and after retrofitting, even under both data corruption and missing scenarios. This study advances the usability of SHM data and facilitates efficient decision-making, while also highlighting the broader significance of leveraging the low-rank data structure in AI-enabled operation and maintenance of civil infra-structure. Full article

To address the computational efficiency challenges in two-dimensional (2D) direction-of-arrival (DOA) estimation, a two-stage framework integrating the Nyström approximation with subspace decomposition techniques is proposed in this paper. The methodology strategically integrates the Nyström approximation with subspace decomposition techniques to bridge the critical performance–complexity trade-off inherent in high-resolution parameter estimation scenarios. In the first stage, the Nyström method is applied to approximate the signal subspace while simultaneously enabling construction of a reduced rank covariance matrix, which effectively reduces the computational complexity compared with eigenvalue decomposition (EVD) or singular value decomposition (SVD). This innovative approach efficiently derives two distinct signal subspaces that closely approximate those obtained from the full-dimensional covariance matrix but at substantially reduced computational cost. The second stage employs a sophisticated subspace-based estimation technique that leverages the principal singular vectors associated with these approximated subspaces. This process incorporates an iterative refinement mechanism to accurately resolve the paired azimuth and elevation angles comprising the 2D DOA solution. With the use of the Nyström approximation and reduced rank framework, the entire DOA estimation process completely circumvents traditional EVD/SVD operations. This elimination constitutes the core mechanism enabling substantial computational savings without compromising estimation accuracy. Comprehensive numerical simulations rigorously demonstrate that the proposed framework maintains performance competitive with conventional high-complexity estimators while achieving significant complexity reduction. The evaluation benchmarks the method against multiple state-of-the-art DOA estimation techniques across diverse operational scenarios, confirming both its efficacy and robustness under varying signal conditions. Full article

Artificial intelligence (AI) offers several opportunities in wildfire management, particularly for improving short- and long-term fire occurrence forecasting, spread modeling, and decision-making. When properly adapted beyond research into real-world settings, AI can significantly reduce risks to human life, as well as ecological and economic damages. However, despite increasingly sophisticated research, the operational use of AI in wildfire contexts remains limited. In this article, we review the main domains of wildfire management where AI has been applied—susceptibility mapping, prediction, detection, simulation, and impact assessment—and highlight critical limitations that hinder practical adoption. These include challenges with dataset imbalance and accessibility, the inadequacy of commonly used metrics, the choice of prediction formats, and the computational costs of large-scale models, all of which reduce model trustworthiness and applicability. Beyond synthesizing existing work, our survey makes four explicit contributions: (1) we provide a reproducible taxonomy supported by detailed dataset tables, emphasizing both the reliability and shortcomings of frequently used data sources; (2) we propose evaluation guidance tailored to imbalanced and spatial tasks, stressing the importance of using accurate metrics and format; (3) we provide a complete state of the art, highlighting important issues and recommendations to enhance models’ performances and reliability from susceptibility to damage analysis; (4) we introduce a deployment checklist that considers cost, latency, required expertise, and integration with decision-support and optimization systems. By bridging the gap between laboratory-oriented models and real-world validation, our work advances prior reviews and aims to strengthen confidence in AI-driven wildfire management while guiding future research toward operational applicability. Full article

The emergence of quantum computing poses significant threats to conventional public-key cryptography, driving the urgent need for quantum-resistant cryptographic solutions. While quantum key distribution addresses secure key exchange, its dependency on symmetric keys and point-to-point limitations present scalability constraints. Quantum Asymmetric Encryption (QAE) offers a promising alternative by leveraging quantum mechanical principles for security. This paper presents the first practical implementation of a QAE protocol on IBM Quantum devices, building upon the theoretical framework originally proposed by Yoon et al. We develop a generalized Grover Search Algorithm (GSA) framework that supports non-standard initial quantum states through novel diffusion operator designs, extending its applicability beyond idealized conditions. The complete QAE protocol, including key generation, encryption, and decryption stages, is translated into executable quantum circuits and evaluated on both IBM Quantum simulators and real quantum hardware. Experimental results demonstrate significant scalability challenges, with success probabilities deteriorating considerably for larger systems. The 2-qubit implementation achieves near-perfect accuracy (100% on the simulator, and 93.88% on the hardware), while performance degrades to 78.15% (simulator) and 45.84% (hardware) for 3 qubits, and declines critically to 48.08% (simulator) and 7.63% (hardware) for 4 qubits. This degradation is primarily attributed to noise and decoherence effects in current Noisy Intermediate-Scale Quantum (NISQ) devices, highlighting the limitations of single-iteration GSA approaches. Our findings underscore the critical need for enhanced hardware fidelity and algorithmic optimization to advance the practical viability of quantum cryptographic systems, providing valuable insights for bridging the gap between theoretical quantum cryptography and real-world implementations. Full article

In many real-world control tasks, agents operate under partial observability, where access to complete state information is limited or corrupted by noise. This poses significant challenges for reinforcement learning algorithms, as methods relying on full states or long observation histories can be computationally expensive and less robust. Four-Phase Rapid Motor Adaptation (FRMA) is a reinforcement learning framework designed to address these challenges in high-frequency control tasks under partial observability. FRMA proceeds through four sequential stages: (i) full-state pretraining to establish a strong initial policy, (ii) auxiliary hidden-state prediction for LSTM memory initialization, (iii) aligned latent representation learning to bridge partial observations with full-state dynamics, and (iv) latent-state policy fine-tuning for robust deployment. Notably, FRMA leverages full-state information ($s_t$) only during training to supervise latent representation learning, while at deployment it requires only short sequences of recent observations and actions. This allows agents to infer compact and informative latent states, achieving performance comparable to policies with full-state access. Extensive experiments on continuous control benchmarks show that FRMA attains near-optimal performance even with minimal observation–action histories, reducing reliance on long-term memory and computational resources. Moreover, FRMA demonstrates strong robustness to observation noise, maintaining high control accuracy under substantial sensory corruption. These results indicate that FRMA provides an effective and generalizable solution for partially observable control tasks, enabling efficient and reliable agent operation when full state information is unavailable or noisy. Full article

Urban river-crossing corridors serve as critical bottlenecks within urban transportation networks, where traffic management during construction periods directly influences urban operational efficiency and socioeconomic activities. Traditional management approaches based on empirical judgment exhibit fundamental limitations when confronting large-scale infrastructure construction projects, including low prediction accuracy, delayed response times, and insufficient systematic coordination. This survey aims to synthesize existing data-driven approaches, identify research gaps, and establish a roadmap for intelligent traffic management advancement. Unlike previous surveys focusing on individual technologies, this review constructs a complete technical chain from data sensing to intelligent decision-making and systematically reveals implementation pathways for paradigm transformation. The research establishes technical architecture encompassing data sensing, intelligent analysis, predictive warning, and decision support systems, while elucidating the application mechanisms of cutting-edge technologies such as multi-source data fusion, artificial intelligence, and digital twins in urban traffic management. Through analysis of six representative engineering case studies from China, the United States, Republic of Korea, Russia, and Europe, including bridge construction, emergency repair, and highway reconstruction projects, the investigation reveals that data-driven approaches not only achieve improvements in technical performance but also facilitate fundamental paradigm shifts in traffic management philosophy from passive response to proactive prevention, and from localized optimization to systematic coordination. The findings enable policymakers to develop standardized frameworks for data-driven traffic systems, assist urban planners in selecting appropriate technologies based on project characteristics, and guide engineers in implementing integrated traffic management solutions during critical infrastructure construction. Full article

This study investigates the influence of freeze–thaw (F–T) cycles on the seismic fragility of double-column bridge piers. Mechanical tests were conducted on standard concrete specimens subjected to 0, 25, 50, 75, and 100 F–T cycles using an HC-HDK9/F rapid freeze–thaw testing machine. The experimental results were used to calibrate and validate the applicability of the selected concrete constitutive model. A nonlinear finite element model of a double-column bridge pier was developed in the OpenSees platform, incorporating material degradation parameters corresponding to varying F–T cycles. Incremental dynamic analysis (IDA) was performed to derive seismic demand curves and quantify fragility corresponding to multiple damage states. The results indicate that the failure probability of the piers increases significantly with the number of F–T cycles, particularly for slight and moderate damage levels. In the low to moderate peak ground acceleration (PGA) range, the exceedance probabilities for slight and moderate damage states show a sharp rise, highlighting the sensitivity of early-stage damage to F–T degradation. It is worth noting that under the extreme condition of PGA = 1.0 g and 100 freeze–thaw cycles, the piers still exhibit a certain degree of redundancy against severe and complete damage, which to some extent reflects the certain rationality of the current seismic design in freeze–thaw environments. These findings underscore the robustness of current seismic design provisions in cold regions and provide theoretical and data-driven support for performance assessment, service life prediction, and maintenance planning of bridges exposed to freeze–thaw environments. Full article

This qualitative action research case study explored how a blended literacy learning intervention combining the flipped classroom model with youth-selected multimodal texts influenced sixth-grade Academic Intervention Services (AIS) students’ comprehension of figurative language. The study was conducted over four months in a New York State middle school and involved seven students identified as at-risk readers. Initially, students engaged with teacher-created instructional videos outside of class and completed analytical activities during class time. However, due to low engagement and limited comprehension gains, the intervention was revised to incorporate student autonomy through the selection of multimodal texts such as graphic novels, song lyrics, and YouTube videos. Data was collected through semi-structured interviews, journal entries, surveys, and classroom artifacts, and then analyzed using inductive coding and member checking. Findings indicate that students demonstrated increased the comprehension of figurative language when given choice in both texts and instructional videos. Participants reported increased motivation, deeper engagement, and enhanced meaning-making, particularly when reading texts that reflected their personal interests and experiences. The study concludes that a blended literacy model emphasizing autonomy and multimodality can support comprehension and bridge the gap between in-school and out-of-school literacy practices. Full article

A significant aerodynamic interference effect exists between parallel bridges. In this study, a proposed long-span cable-stayed bridge, near which is an existing truss-arch bridge, was considered as the background. The wind characteristics at the proposed bridge site and the wind-induced responses of the bridge deck were investigated with and without the influence of an upstream bridge. The results showed that under aerodynamic interference of the upstream bridge, the downstream bridge site exhibited a noticeable change in the mean wind speed profile within the height range of the main girder and arch. The turbulence intensities significantly increased, especially for u and w components. The integral scales decreased remarkably, and the wind speed spectra redistributed toward higher frequencies. For the wind-induced responses, the mean displacements of the downstream bridge all decreased; in contrast, the buffeting and peak displacements all increased in both the maximum single cantilever state and the completed state, while the variation in buffeting response was much more significant and dominated the peak response. Moreover, under the interference of the upstream bridge, the buffeting displacement spectra redistributed toward high frequencies. This research acts as an effective tool for achieving secure bridge design and finding a better balance between design constraints. Full article

The Forensic Anthropology Research Facility (FARF) at Texas State University (TXST), San Marcos, TX, USA, is a leading human taphonomy facility (HTF), dedicated to advancing forensic science through the study of human decomposition. This systematic review synthesizes 15 scholarly outputs comprising 7 peer-reviewed journal articles and 8 dissertations centered on arthropod-associated research undertaken at FARF, with particular emphasis on its contribution to forensic entomology. The analyzed body of literature is organized into six overarching thematic domains: (1): refining postmortem interval (PMI) estimation; (2): developmental biology of forensic arthropods; (3): arthropod behavior and forensic implications; (4): Taxonomy and systematics; (5): microbial–arthropod interactions; and (6): forensic decomposition scenarios with arthropod involvement. Key contributions from these studies include refined methodologies for PMI estimation, the systematic revision of forensically relevant arthropods and identification of accidental arthropod activity. Additionally, studies at FARF have incorporated interdisciplinary approaches bridging entomology, microbiology and ecology. The semi-arid, subtropical environment and large open natural range of FARF provides some unique regional and specific insights concerning decomposition. This entomological review on FARF is the first to be completed concerning any HTF and adds to the knowledge of forensic evidence involving arthropods. Full article

Accurate and automated segmentation of gliomas from Magnetic Resonance Imaging (MRI) is crucial for effective diagnosis, treatment planning, and patient monitoring. However, the aggressive nature and morphological complexity of these tumors pose significant challenges that call for advanced segmentation techniques. This review provides a comprehensive analysis of Deep Learning (DL) methods for glioma segmentation, with a specific focus on bridging the gap between research performance and practical clinical deployment. We evaluate over 80 state-of-the-art models published up to 2025, categorizing them into CNN-based, Pure Transformer, and Hybrid CNN-Transformer architectures. The primary objective of this paper is to critically assess these models not only on their segmentation accuracy but also on their computational efficiency and suitability for real-world medical environments by incorporating hardware resource considerations. We present a comparison of model performance on the BraTS datasets benchmark and introduce a suitability analysis for top-performing models based on their robustness, efficiency, and completeness of tumor region delineation. By identifying current trends, limitations, and key trade-offs, this review offers future research directions aimed at optimizing the balance between technical performance and clinical usability to improve diagnostic outcomes for glioma patients. Full article

Erythrocyte (RBC) aging involves significant structural and nanomechanical alterations crucial to their function. This study aims to bridge the gap between analyses based on statistical morphometric parameters, e.g., membrane roughness, and those based on point-dependent nanomechanical properties, e.g., stiffness or Young’s modulus. Using Atomic Force Microscopy, we investigated morphology, membrane roughness, and nanomechanical properties on the very same RBCs under dehydrated (air) and hydrated (physiological buffer) conditions. The cells were studied at different stages of in vitro aging: one, seven, and 12 days. Our results quantitatively show that across dehydration, as well as along the aging pathway, RBCs become progressively more rigid while their membrane roughness decreases, a trend observed in both environments. Notably, the differences between the hydrated and dehydrated states were large in young cells but diminished when erythrocytes aged. Despite these parallel trends, high-resolution mapping on the nanoscale revealed that roughness and Young’s modulus do not correlate, indicating that these parameters are linked to different properties. In conclusion, this work provides a comprehensive protocol for a biophysical description of RBC aging and establishes that the simultaneous measurement of membrane roughness and nanomechanical properties offers a complementary approach, yielding a more complete characterization of cellular properties. Full article

We introduce a quantum integrated-information measure $\Phi$ for multipartite states within the Relational Quantum Dynamics (RQD) framework. $\Phi \left(\rho \right)$ is defined as the minimum quantum Jensen–Shannon distance between an n-partite density operator $\rho$ and any product state over a bipartition of its subsystems. We prove that its square root induces a genuine metric on state space and that $\Phi$ is monotonic under all completely positive trace-preserving maps. Restricting the search to bipartitions yields a unique optimal split and a unique closest product state. From this geometric picture, we derive a canonical entanglement witness directly tied to $\Phi$ and construct an integration dendrogram that reveals the full hierarchical correlation structure of $\rho$. We further show that there always exists an “optimal observer”—a channel or basis—that preserves $\Phi$ better than any alternative. Finally, we propose a quantum Markov blanket theorem: the boundary of the optimal bipartition isolates subsystems most effectively. Our framework unites categorical enrichment, convex-geometric methods, and operational tools, forging a concrete bridge between integrated information theory and quantum information science. Full article

A significant advancement in Neural Network (NN) research is the integration of domain-specific knowledge through custom loss functions. This approach addresses a crucial challenge: How can models utilize physics or mathematical principles to enhance predictions when dealing with sparse, noisy, or incomplete data? Physics-Informed Neural Networks (PINNs) put this idea into practice by incorporating a forward model, such as Partial Differential Equations (PDEs), as soft constraints. This guidance helps the networks find solutions that align with established laws. Recently, researchers have expanded this framework to include Bayesian NNs (BNNs) which allow for uncertainty quantification. However, what happens when the governing equations of a system are not completely known? In this work, we introduce methods to automatically select PDEs from historical data in a parametric family. We then integrate these learned equations into three different modeling approaches: PINNs, Bayesian-PINNs (B-PINNs), and Physical-Informed Bayesian Linear Regression (PI-BLR). To assess these frameworks, we evaluate them on a real-world Multivariate Time Series (MTS) dataset related to electrical power energy management. We compare their effectiveness in forecasting future states under different scenarios: with and without PDE constraints and accuracy considerations. This research aims to bridge the gap between data-driven discovery and physics-guided learning, providing valuable insights for practical applications. Full article

Background: Economic logistics in fire and rescue services is a critical determinant of operational readiness, fiscal sustainability, and resilience to large-scale emergencies. Despite its strategic importance, logistics remains under-researched in Central and Eastern European contexts, where legacy governance structures and EU-funded modernization coexist with systemic inefficiencies. This study focuses on the Slovak Fire and Rescue Service (HaZZ) as a case to explore how economic logistics systems can be restructured for greater performance and value. Objective: The objective of this paper was to evaluate the structure, performance, and reform potential of the logistics system supporting HaZZ, with a focus on procurement efficiency, lifecycle costing, digital integration, and alignment with EU civil protection standards. Methods: A mixed-methods design was applied, comprising the following: (1) Institutional analysis of governance, budgeting, and legal mandates based on semi-structured expert interviews with HaZZ and the Ministry of Interior officers (n = 12); (2) comparative benchmarking with Germany, Austria, the Czech Republic, and the Netherlands; (3) financial analysis of national logistics expenditures (2019–2023) using Total Cost of Ownership (TCO) principles, completed with the visualization of cost trends and procurement price variance through original heat maps and time-series graphs. Results: The key findings are as follows: (1) HaZZ operates a formally centralized but practically fragmented logistics model across 51 district units, lacking national coordination mechanisms and digital infrastructure; (2) Maintenance costs have risen by 42% between 2019 and 2023 despite increasing capital investment due to insufficient lifecycle planning and asset heterogeneity; (3) Price variance for identical equipment categories across regions exceeds 30%, highlighting the inefficiencies in decentralized procurement; (4) Slovakia lacks a national Logistics Information System (LIS), unlike peer countries which have deployed integrated digital platforms (e.g., CELIS in the Czech Republic); (5) Benchmarking reveals high-impact practices in centralized procurement, lifecycle-based contracting, regional logistics hubs, and performance accountability—particularly in Austria and the Netherlands. Impacts: Four high-impact, feasible reforms were proposed: (1) Establishment of a centralized procurement framework; (2) national LIS deployment to unify inventory and asset tracking; (3) adoption of lifecycle-based and performance-based contracting models; (4) development of regional logistics hubs using underutilized infrastructure. This study is among the first to provide an integrated economic and institutional analysis of the Fire and Rescue Service logistics in a post-socialist EU member state. It offers a structured, transferable reform roadmap grounded in comparative evidence and adapted to Slovakia’s hybrid governance model. The research bridges gaps between modernization policy, procurement law, and digital public administration in the context of emergency services. Full article