Monther Rashed Alfuraidan

Transportation digital twins depend on continuous streams of sensor data to maintain accurate representations of evolving traffic conditions. In practice, however, sensing infrastructures are routinely affected by missing observations, communication interruptions, and measurement noise, limiting the reliability of downstream monitoring and operational analysis.This study develops a graph signal processing framework for reliability and observability analysis in transportation digital twins. Traffic measurements are represented as graph signals over a sensor network whose edges capture both spatial proximity and statistical similarity between sensing locations. Missing-data recovery and denoising are formulated through Laplacian-based regularization, yielding spatially coherent reconstruction of degraded traffic measurements. To better represent heterogeneous traffic interactions, a hybrid graph construction combining road-network distances with empirical traffic correlation is also investigated.Beyond reconstruction, the framework introduces graph-based indicators for evaluating sensor criticality, local redundancy, and network observability through spectral properties of the graph Laplacian. Experiments conducted on the PEMS-BAY dataset show that graph-based reconstruction remains stable under both random and structured missing-data scenarios, with the hybrid graph consistently producing the strongest reconstruction performance among the graph-based models considered. The analysis further shows that vulnerability in the sensor network is governed more strongly by uneven local redundancy than by loss of global connectivity.The proposed framework provides an interpretable approach for improving reliability of sensor network in transportation digital twins, while also supporting infrastructure-oriented analysis related to sensing resilience, redundancy planning, and network monitoring.

Let u , v be strings over an alphabet Σ with |Σ| ≥ 2. Let L ( u , v ) be the Levenshtein distance and, when | u | = | v |, let H ( u , v ) be the Hamming distance. For equal-length strings, L ( u , v ) ≤ H ( u , v ). For u ∈ Σ*, define hl( u ): = max _{| v |=| u |} (H( u , v )−L( u , v )). Ruth and Lladser [ Discrete Math. 346 (2023) Paper No. 113310] showed that if the run-length of u is at most 2, then hl( u ) = 0; conversely, hl( u ) = 0 implies ρ ( u ) ≤ 2. For p ∈ {2, 3}, we characterize all strings u with hl( u ) = | u | – p . We also identify two types of strings u such that hl( u ) = | u | – 4.

Extracting seismic velocities from recorded seismic data requires converting the shot gathers to mid-point gathers, calculating the velocity spectrum, and picking the velocity values. In this paper, we propose to use graph theory to extract the seismic velocity values directly from the mid-point gathers. We use spectral data of a weighted graph model for approximating the seismic velocity. We develop a regression model to predict the seismic velocity from the largest eigenvalue of the graph representing the physical system. The approach is tested on a synthetic seismic data that represent a typical near-surface geological situation. The method was able to predict the seismic velocity of the second layer with 99.4% accuracy.

We give a new minimization theorem for equilibrium problems on a quasi-weighted graph. This result generalizes the graphical version of the Ekeland’s variational principle for equilibrium problems on weighted graphs (Alfuraidan and Khamsi in Proc Am Math Soc 9:33–40, 2022).

In this paper, we give a graphical version of the Ekeland’s variational principle (EVP) for equilibrium problems on weighted graphs. This version generalizes and includes other equilibrium types of EVP such as optimization, saddle point, fixed point and variational inequality ones. We also weaken the conditions on the class of bifunctions for which the variational principle holds by replacing the strong triangle inequality property by a below approximation of the bifunctions.

We present a complete description of strongly regular graphs admitting a distance-transitive group of automorphisms. Parts of the list have already appeared