Search Results (141)

In the LoRa (long-range) wide area network (LoRaWAN), Class A devices employ a pure ALOHA random access mechanism. Under large-scale access and bursty traffic conditions, severe packet collisions are likely, which reduces throughput and increases the packet loss rate. To address these issues, herein, we propose a collision mitigation scheme integrating the extended Kalman filter (EKF) with nonorthogonal multiple access (NOMA). First, a nonlinear state-space model is constructed to capture the dynamic evolution of backlog nodes and the uncertainty of traffic arrivals. The backlog node number is modeled as the hidden state, while newly arrived and successfully decoded packets are incorporated into the state-transition equation. At the gateway, decoded packet counts and channel occupancy are treated as observations based on which a nonlinear mapping between system state and observable features is established. The EKF is then applied to recursively predict and correct, enabling real-time estimation of the backlog state. Accordingly, an adaptive backoff strategy is designed to adjust transmission probability based on the estimated optimal load. Furthermore, to mitigate packet loss caused by collisions, a power-domain NOMA scheme with successive interference cancelation (SIC) is introduced. Signals transmitted with different spreading factors (SFs) are decoupled into approximately independent processing branches by exploiting inter-SF quasi-orthogonality. To account for imperfect inter-SF orthogonality, cross-SF residual coupling coefficients are introduced to characterize leakage interference. For transmissions sharing the same SF, overlapping packets are successively decoded and recovered through a NOMA-SIC mechanism jointly constrained by the SINR-based decoding threshold, the power-domain separation requirement, the maximum number of resolvable SIC layers, and residual SIC interference. Accordingly, the proposed receiver architecture enhances the decoding and recovery capability for collided LoRa packets. Simulation results demonstrate that, under medium-to-high traffic loads, the proposed scheme significantly improves throughput and access success rate while effectively reducing collision probability and packet loss, thereby enhancing the overall robustness and efficiency of the LoRa network. Full article

Surface seismic data often suffer from limited bandwidth and uneven illumination, which degrade PSDM (prestack depth migration) in deep and structurally complex settings. VSP (vertical seismic profiling), particularly DAS-VSP, provides a higher signal-to-noise ratio and richer high-frequency content near the wellbore but has a limited lateral imaging aperture. To exploit the complementary strengths of these two observation systems, we propose an OVT domain (offset vector tile) joint Kirchhoff prestack depth migration workflow that integrates surface seismic and VSP data within a unified depth domain framework. The workflow includes wavelet (amplitude–phase) matching, consistent datuming, joint well–surface tomographic velocity model building using both surface CIG (common image gather) residual moveout and VSP first-arrival constraints, efficient travel time table construction based on 3D eikonal solvers, OVT domain joint migration, azimuth-dependent CIG depth correction for anisotropy, and ray-based illumination compensation for amplitude balancing. Synthetic tests demonstrate that the proposed method improves reflector continuity and increases the effective bandwidth of the joint image relative to surface-only PSDM. A field application in the northwest Sichuan Basin further shows that the joint imaging better matches well synthetics in the target interval, increasing the correlation coefficient from 0.753 (surface-only) and 0.738 (VSP-only) to 0.787 (joint) while reducing inter-azimuth CIG depth residuals to within 3 m after anisotropy correction. These results indicate that OVT domain joint imaging can enhance thin-bed resolution and near-well structural delineation, providing a practical multi-source data fusion solution for high-fidelity depth imaging in complex reservoirs. Full article

Under the trend of intelligent transportation and connected vehicles, real-time control plays a vital role in improving bus system efficiency. Existing bus control strategies typically treat buses as homogeneous points and achieve system equilibrium by maintaining consistent headways. However, this simplification overlooks differences in dynamic responses and the evolution of powertrain lifespan arising from vehicle heterogeneity. It converts the sparse constraint problem, which is intended to ensure timely arrival, into a hard constraint on the vehicle trajectory over the entire time horizon, thereby excessively restricting individual optimal evolutionary paths and causing the optimization process to become trapped in a local optimum. To this end, this paper proposes SMATD3, a multi-agent cooperative control algorithm that accounts for vehicle heterogeneity. By adopting a centralized training and decentralized execution paradigm and avoiding the specification of a fixed inter-vehicle spacing target, the algorithm enables each vehicle to adaptively adjust its speed control strategy according to its own dynamic characteristics, thereby achieving the coordinated optimization of system equilibrium and individual objectives. The simulation results indicate that the proposed method can effectively suppress bus tailgating and achieve the coordinated multi-objective optimization of operational stability, passenger travel efficiency, energy consumption, and battery health. From a sustainability perspective, improved headway regularity and service reliability can enhance public transit attractiveness and support mode shift, while smoother energy use and reduced battery degradation lower lifecycle impacts. Full article

Urban intersection traffic signals play a crucial role in managing traffic flow and ensuring road safety. However, traditional actuated signal controllers make phase-switching decisions based on limited local traffic information, without leveraging network-wide context from navigation services. In this paper, we propose CATS, a Context-Aware Traffic Signal control system that jointly optimizes intersection signal control and road navigation for Connected and Automated Vehicles (CAVs). CATS integrates two key components: a Best-Combination CTR (BC-CTR) scheme and the Self-Adaptive Interactive Navigation Tool (SAINT). BC-CTR enhances the original Cumulative Travel-Time Responsive (CTR) scheme through a two-step selection procedure: it first identifies the phase with the highest cumulative travel time (CTT) and then selects the compatible phase combination with the greatest group CTT, providing an explicit improvement over the single-combination evaluation of the original CTR that allows for a more accurate response to real-time intersection demand. SAINT provides congestion-aware route guidance via a congestion-contribution step function, directing vehicles away from congested segments while signal timings simultaneously adapt to incoming traffic. Under a 100% CAV penetration setting, SUMO-based simulations across moderate-to-heavy traffic conditions (vehicle inter-arrival times of 5 to 9 s) show that CATS reduces the mean end-to-end travel time by up to 23.72% and improves the throughput by up to 93.19% over three baselines (fixed-time navigation with enhanced signal control, congestion-aware navigation with original signal control, and fixed-time navigation with original signal control), confirming that the co-design of navigation and signal control produces complementary benefits. Full article

Service Level Agreement (SLA) compliance in hospital transport processes is essential in terms of patient safety, service continuity, and resource efficiency. However, transport requests occur as irregular events, limiting the applicability of equally spaced time-series assumptions. The presented study jointly addresses two complementary objectives in an event-based framework: predicting the interarrival time between consecutive transport requests (next-event forecasting) and forecasting the total request count within forward SLA horizons (forward-count forecasting). Machine learning methods such as Ridge Regression, Extra Trees, and Histogram-based Gradient Boosting, as well as deep learning architectures such as Long Short-Term Memory and Gated Recurrent Unit, were compared under different time horizons and adaptive history windows on time-stamped transport request records from the operational system supporting a private hospital in Turkey, including patient, specimen, and material transport requests. Results indicate that deep learning methods yield lower errors in demand count prediction at short time horizons; as the horizon lengthens, machine learning performs similarly and even outperforms in some cases; and as the history window increases, the prediction error for the next request occurrence systematically decreases. The lowest mean absolute error values in request counts were obtained for demand forecasting within a 30 min time window; 2.10 for material transport, 3.88 for patient transport, and 2.84 for specimen transport. Additionally, R² value reached 0.98 for next-event forecasting with a rolling-memory window of 20 events. Overall, the findings suggest that hospital transport demand is substantially predictable and that event-based forecasting can support SLA-oriented staffing, task dispatching, and delay mitigation. Full article

Encrypted traffic classification is essential for network management and security, yet payload inspection is ineffective under modern protocols such as Transport Layer Security (TLS) and Quick UDP Internet Connections (QUIC). Existing metadata-based methods perform well for coarse-grained tasks but often fail to distinguish structurally similar applications because they model temporal behavior only implicitly or coarsely. We propose the Bi-Directional Directed Temporal Graph (BiDT), a framework based on a Directed Temporal Interaction Graph (DTIG) and a Bi-Directional GraphSAGE (BiGraphSAGE). The DTIG represents packets as nodes and explicitly encodes inter-arrival times (IATs) as directed edge attributes, preserving both causal structure and communication rhythm. The BiGraphSAGE then aggregates temporal interaction features from forward and backward perspectives. We evaluated the BiDT on the VNAT benchmark and validated it on ISCX-VPN. On the challenging 10-class VNAT dataset, the BiDT achieves 98.57% accuracy and outperforms strong baselines, including complete separation of easily confused protocols such as SCP and SFTP. The results on ISCX-VPN further confirm the effectiveness of the proposed design. These findings show that explicit temporal edge modeling is effective for fine-grained encrypted traffic classification. Full article

Waveguide-like propagation in elongated underground environments—utility corridors, logistics tunnels—generates dense multipath that can cause the earliest or strongest resolvable channel impulse response (CIR) component to originate from a specular reflection rather than the direct line-of-sight (LOS) path. In the single-anchor CIR-tap-based implementations common to practical ultra-wideband (UWB) systems, baseline estimators such as phase-difference-of-arrival (PDOA) and MUSIC rely on selecting a single dominant CIR component, producing large angle-of-arrival (AoA) errors whenever the selected path is a reflection. We propose a multipath credibility selection (MCS) AoA estimator, MCS-AoA, that does not require explicit LOS/NLOS classification. The algorithm scores each resolvable CIR component with four credibility factors—amplitude significance, time-of-flight (TOF) consistency, inter-baseline phase–geometry agreement, and cross-baseline coherence—and fuses retained candidates into a credibility-weighted spatial covariance matrix for 2D MUSIC search. Field experiments on a custom five-channel coherent UWB platform compare MCS-AoA against six baselines—PDOA, MUSIC, MVDR/Capon, TLS-ESPRIT, PwMUSIC, and DNN-AoA. In an underground corridor (5–40 m), MCS-AoA achieves an azimuth/elevation MAE of 1.00°/1.46°, outperforming all baselines (PDOA: 2.26°/2.49°; MUSIC: 1.76°/2.40°; next-best PwMUSIC: 1.44°/2.17°); in a logistics tunnel (5–80 m), it achieves a 1.19° overall azimuth MAE. Simulations corroborate these gains, with a 0.71° azimuth RMSE at 80 m (69.3% reduction over PDOA) and 86.6% of estimates falling within 1°. Full article

The paper explores an advanced single-server M/G/1 retrial queueing model that employs a push-out service with two unique classes of customers, i.e., transient (priority) customers and recurrent customers. The arrivals of customers are Poisson process. The service time of customers and retrial time of transit customers are follow general probability distributions. The inter-retrial time of the recurrent customer is exponentially distributed. The system also includes feedback behavior of transit customers and probabilistic push-out of repeat customers. Closed-form formulae are obtained expressing steady-state distributions of important system states using supplementary variable technique (SVT) and probability generating functions (PGFs). The impact of parameters is shown with the help of numerical experiments, and the Beetle Antennae Search (BAS) algorithm is used to optimise the performance of the system. These results are useful in designing and optimization of priority-based service systems such as cloud computing systems, communication networks, and real-time task scheduling systems. Full article

This paper investigates Direction-of-Arrival (DOA) estimation of Long-Range Navigation-C (Loran-C) signals using an Ultra-Short Baseline (USBL) receiving array. Two least-squares angle estimation approaches based on inter-element delay measurements are examined, including Correlation-based Least-Squares (Corr-LS) and a Zero-Crossing-based Least Squares (ZC-LS). In both methods, relative delays are extracted only within the local array and subsequently mapped to azimuth through a geometric least squares formulation; the approach is, therefore, distinct from distributed time difference-of-arrival (TDOA) localization. For comparison, the Multiple Signal Classification (MUSIC) algorithm is implemented as a covariance-based DOA estimator that operates without explicit delay extraction. Experiments were conducted using Loran-C transmissions from the Xuancheng, Xi’an, and Rongcheng stations, with 100 valid pulse groups collected for each station. Statistical analysis using boxplots shows that Corr-LS exhibits the largest variance due to broadened or shifted correlation peaks, particularly under skywave–groundwave interference. ZC-LS reduces both variance and bias by exploiting the deterministic zero-crossing structure of the Loran-C waveform. MUSIC produces the most concentrated azimuth estimates but requires a well-conditioned covariance matrix and substantially higher computational costs. The results demonstrate that ZC-LS achieves a favorable balance among angular accuracy, robustness, and real-time feasibility, making it suited for compact Loran-C receivers and complementary navigation applications in GNSS-challenged environments. Full article

Background/Objectives: The optimal transport strategy for patients with traumatic brain injury (TBI) remains debated, particularly in trauma systems where inter-hospital transfer is common. Whether secondary transfer independently influences mortality after risk adjustment is unclear. This study aimed to evaluate the association between admission route and in-hospital mortality among patients with TBI at a major trauma center (MTC). Methods: This retrospective observational study included 417 patients with TBI and an Abbreviated Injury Scale (AIS) head score ≥ 3 (direct admission: 245; inter-hospital transfer: 172). Severe TBI was defined as a total Glasgow Coma Scale (GCS) score ≤ 8 or the need for advanced airway management. Multivariable logistic regression was performed to assess whether admission route was independently associated with in-hospital mortality after adjustment for age, physiological status at MTC arrival, and injury severity. Subgroup analysis was conducted in patients with severe TBI. Results: Crude mortality was higher in the direct admission group than in the transfer group (40.8% vs. 26.7%; p = 0.003), despite significantly longer injury-to-trauma center arrival times in transferred patients (219.0 vs. 44.0 min). In multivariable analysis, admission route was not independently associated with mortality in the overall cohort (adjusted odds ratio [aOR] 0.75; 95% CI 0.44–1.28; p = 0.298) or in the severe TBI subgroup (n = 233; aOR 0.88; 95% CI 0.47–1.67; p = 0.705). Increasing age and lower GCS motor scores were consistently associated with higher mortality in both analyses. Conclusions: Inter-hospital transfer was not independently associated with increased in-hospital mortality among patients with TBI. After consideration of patient age and neurological severity, initial stabilization at a nearby hospital followed by transfer may be an acceptable transport strategy for patients who present with physiological instability requiring immediate resuscitative interventions. Full article

Broadcast-based flooding in wireless ad hoc networks is subject to the broadcast storm problem, characterized by excessive transmissions, collisions, and link losses. While randomized network coding (RNC) enhances resilience against packet losses, efficient buffer management and adaptive transmission strategies are essential. This paper proposes novel buffering mechanisms and adaptive coding strategies to improve data unit reception rates in RNC-based broadcast flooding. Our buffering mechanism combines Last-In-First-Out (LIFO) and Least Recently Used (LRU) discard policies. When buffers are full, it prioritizes the discarding of stale, incomplete buffers based on elapsed time since the last coded block arrival, thereby overcoming First-In-First-Out (FIFO) limitations that prematurely discard buffers before sufficient coded blocks have accumulated. Our adaptive coding dynamically adjusts transmitted coded packets based on data unit duplication rates without inter-node coordination, reducing blocks during high duplication and increasing them under difficult reception conditions. Simulation experiments using OMNeT++ and INET framework for Vehicular Ad Hoc Networks demonstrate that LIFO+LRU buffering significantly increases the received data units and prevents redundant reception, while adaptive coding further improves reception rates under challenging conditions. Full article

Background/Objectives: Emergency department length of stay (ED LOS) is a key indicator reflecting emergency department crowding, patient safety, and healthcare resource efficiency. Among injured patients, ED LOS may be prolonged depending on injury severity and disposition pathways (admission and inter-hospital transfer). This nationwide study using the Korean National Emergency Department Information System (NEDIS) aimed to (1) describe the distribution and determinants of ED LOS among injured patients and (2) quantify the mediating effects of disposition (admission and transfer) on the association between injury severity measured by the International Classification of Diseases-based Injury Severity Score (ICISS) and ED LOS. Methods: We analyzed NEDIS injury-related ED visit records collected from the date of IRB approval through 12 January 2026. We conducted a retrospective observational study using NEDIS data. Of 1,048,575 injury-related ED visits, 1,035,484 visits with valid ED LOS and eligible records were included after excluding missing key variables and implausible time values. ED LOS was calculated in minutes using arrival and departure timestamps. Injury severity was assessed using ICISS (primary: based on 15 diagnoses; sensitivity: based on 20 diagnoses). Determinants of ED LOS were evaluated using gamma regression with a log link. Disposition was categorized as discharge, admission, and inter-hospital transfer; admission and transfer were modeled as binary mediators. Causal mediation analyses estimated the average causal mediation effect (ACME), average direct effect (ADE), total effect, and proportion mediated. Multiple sensitivity analyses (outlier handling, missing-data approaches, alternative log-linear modeling, and EMS arrival subgroup analyses) assessed robustness. Results: The median ED LOS was 150 min (IQR 90–260). ED LOS differed substantially by disposition: 120 min for discharged patients, 420 min for admitted patients, and 360 min for transferred patients. Overall, 17.9% of visits had an ED LOS ≥ 6 h, and prolonged stays were concentrated among admitted (≥6 h: 55.0%) and transferred (≥6 h: 45.0%) patients. In gamma regression, a 0.05 decrease in ICISS (greater severity) was associated with longer ED LOSs in the unadjusted model (Ratio 1.34) and remained significant in the fully adjusted model (Ratio 1.12, 95% CI 1.11–1.13). Admission and transfer were strong determinants of ED LOS in the final model (ratios of 2.35 and 2.05, respectively). In mediation analyses, admission mediated 36.8% of the severity–ED LOS association (ACME 0.085; ADE 0.146), and transfer mediated 14.3% (ACME 0.033; ADE 0.198). Findings were consistent across sensitivity analyses. Conclusions: In this nationwide cohort of injured patients, ED LOS showed a right-skewed distribution, with prolonged stays concentrated in admission and transfer pathways. Injury severity (ICISS) was independently associated with longer ED LOS, and a substantial proportion of this association was mediated through admission and transfer. Reducing ED LOS among severely injured patients likely requires not only streamlining diagnostic and treatment processes but also system-level interventions targeting output-stage bottlenecks, including inpatient bed operations/boarding management and transfer coordination. Full article

Climate warming can alter reproductive timing of species, yet the capacity for phenological adjustment in long-lived species, particularly marine ones, remains elusive. Using 17 years of monitoring data from one of the largest loggerhead turtle (Caretta caretta) populations, we investigated the environmental drivers of reproductive phenology and output. We found that warmer sea surface temperatures (SST) in both the feeding ground and the nesting ground advanced the start, peak, and end of the nesting season. We provide evidence for waves of arrival at the nesting ground, suggesting more turtles produce fewer clutches than previously thought. Inter-nesting intervals were shorter during episodes of higher SST, particularly in larger females, likely underpinned by metabolic scaling variation in reproductive pacing. Conversely, remigration intervals lengthened over time in all size classes, reflecting the detected continuous decrease in productivity in the feeding ground. As a result of reduced ocean productivity, both clutch size and clutch frequency also declined over the study period. Moreover, the declining trend in body size further reduces reproductive output, as smaller females produce smaller clutch sizes. Overall, we show that sea turtle population dynamics correlate with environmental parameters. The sustained decline in reproductive output underscores the need to mitigate the impacts of climate warming on the foraging area to safeguard this population, which, given its size, holds global significance. Full article

Extreme movements in financial time series pose challenges for risk management and forecasting, particularly when their timing is irregular and difficult to anticipate. This study aims to develop a probabilistic framework for detecting and predicting such events using daily Bitcoin returns as a case study. We first identify extreme positive and negative return events using the Isolation Forest algorithm and estimate their empirical recurrence patterns using a dynamic frequency table to derive baseline parametric probabilities. A 7-day Hawkes excitation kernel is then applied to capture short-run self-exciting dynamics, and both components are integrated using logistic regression to produce real-time probability forecasts. The results show that positive events occur more frequently than negative ones and that prediction accuracy improves over time: Brier scores, which measure the accuracy of probabilistic predictions, decrease as additional event data accumulate, and log loss values exhibit a consistent downward trend. Overall, by combining anomaly detection, empirical inter-arrival estimation, and excitation dynamics into a unified structure, the proposed framework offers a transparent and adaptable tool for forecasting extreme events in the financial market. Full article

Network operators increasingly rely on abstracted telemetry (e.g., flow records and time-aggregated statistics) to achieve scalable monitoring of high-speed networks, but this abstraction fundamentally constrains the forensic and security inferences that can be supported from network data. We present a design-time audit framework that evaluates which threat hypotheses become non-supportable as network evidence is transformed from packet-level traces to flow records and time-aggregated statistics. Our methodology examines three evidence layers (L0: packet headers, L1: IP Flow Information Export (IPFIX) flow records, L2: time-aggregated flows), computes a catalog of 13 network-forensic artifacts (e.g., destination fan-out, inter-arrival time burstiness, SYN-dominant connection patterns) at each layer, and maps artifact availability to tactic support using literature-grounded associations with MITRE Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK). Applied to backbone traffic from the MAWI Day-In-The-Life (DITL) archive, the audit reveals selectiveinference loss: Execution becomes non-supportable at L1 (due to loss of packet-level timing artifacts), while Lateral Movement and Persistence become non-supportable at L2 (due to loss of entity-linked structural artifacts). Inference coverage decreases from 9 to 7 out of 9 evaluated ATT&CK tactics, while coverage of defensive countermeasures (MITRE D3FEND) increases at L1 (7 → 8 technique categories) then decreases at L2 (8 → 7), reflecting a shift from behavioral monitoring to flow-based controls. The framework provides network architects with a practical tool for configuring telemetry systems (e.g., IPFIX exporters, P4 pipelines) to reason about and provision the minimum forensic coverage. Full article