Search Results (1,242)

Among the many quasiprobability representations of quantum mechanics, the family of Kirkwood–Dirac (KD) representations has come to the foreground in recent years. Each such KD representation is determined by the choice of two complementary complete sets of commuting observables $\widehat{A}$ and $\widehat{B}$ with respect to which it is Born-compatible, meaning that it correctly reproduces their Born probabilities for every state. In this paper, we identify what property uniquely characterizes the KD representations among all such $\widehat{A}$ and $\widehat{B}$ Born-compatible quasiprobability representations. For that purpose, we first define a natural notion of a quantum conditional expectation of an observable $\widehat{X}$, given an observable $\widehat{Y}$, in a state $\widehat{\rho }$, as a best estimator, and we show that it has the basic properties generally expected of a conditional expectation. We then show that only the KD representations provide a notion of conditional, expectation given $\widehat{B}$ (or given $\widehat{A}$) that coincides with the above quantum conditional expectation. As a byproduct of our analysis, we show a state-dependent no-go theorem. We prove that, if the quantum conditional expectation of an observable $\widehat{X}$, given an observable $\widehat{Y}$ in a state $\widehat{\rho }$ admits an anomalous value (meaning a value lying outside the interval $[x_{min},x_{max}]$), then there cannot exist a Born-compatible joint probability distribution $\mu (x,y)$ for $\widehat{X}$ and $\widehat{Y}$ in the state $\widehat{\rho }$ for which the associated conditional probability $\mu \left(x\right|y)$ yields a conditional expectation that coincides with the quantum conditional expectation. We further apply our findings to revisit a standard model for phase estimation in quantum metrology. We show in particular that, within the real sector of a given KD representation, the classical Fisher information of this phase estimation problem vanishes identically. Full article

Many studies require evidence that a new treatment improves efficacy and maintains or improves safety. Composite endpoints can obscure trade-offs and complicate interpretation. We propose a single-stage hypothesis test that directly evaluates two binary endpoints against a concurrent control, offering a transparent alternative to composite endpoints. The test rejects only if the observed improvements on both endpoints exceed pre-specified paired thresholds. The joint distribution of efficacy and safety is modeled with a four-category multinomial, yielding probabilities for all the outcome combinations. This enables exact computation of rejection probabilities and identification of least-favorable parameter configurations to control type I error at the nominal level while retaining adequate power. Design tables map the target significance level and power, together with predefined effect sizes for each endpoint, to the required sample size and decision thresholds. Simulations and one case study illustrate design selection and interpretation. The proposed test provides an exact and practical tool for early-phase trials with dual binary endpoints, particularly when efficacy and safety must be evaluated simultaneously. Full article

Indoor visible light communication (VLC) has attracted increasing attention as a promising wireless access technology because of its large unlicensed bandwidth and dual functionality of illumination and data transmission. However, practical VLC systems are vulnerable to line-of-sight (LoS) blockage caused by user mobility, human shadowing, and indoor obstacles, which may degrade link reliability and service continuity. Although hybrid VLC/RF networks can improve robustness by using RF transmission as a backup link, excessive RF fallback under severe optical blockage may overload the bandwidth-limited RF interface and reduce the service quality of RF-associated users. To address this issue, this paper investigates a risk-aware illumination-constrained resource allocation scheme for hybrid VLC/RF indoor networks under random optical blockage. A unified system model is developed by considering Lambertian optical propagation, random optical blockage, RF backup transmission, and working-plane illumination constraints. Based on this model, a joint user association and power allocation problem is formulated under QoS, transmit-power, and illumination requirements. The proposed scheme evaluates VLC service utility under blockage uncertainty, controls RF fallback to avoid excessive backup-link loading, allocates VLC/RF transmission power, and performs illumination feasibility adjustment to preserve the required lighting level. Simulation results show that, under severe blockage conditions, the proposed scheme reduces the outage probability to approximately 0.26, compared with 0.68 for VLC-only transmission and 0.47 for threshold-based VLC/RF switching. For a 20-user network, the proposed scheme achieves an average sum rate of approximately 277 Mbps, maintains a 100% illumination compliance ratio, and achieves higher energy efficiency than the benchmark schemes. Further RF backup analysis shows that the proposed scheme can maintain the service quality of RF-associated users by avoiding excessive RF fallback. These results demonstrate the effectiveness of the proposed framework for reliable and illumination-feasible hybrid VLC/RF indoor communication. Full article

Topographic ridges are widely used in wildfire interpretation, suppression planning, and potential control-line design, but the claim that final fire boundaries preferentially follow ridge crests is rarely tested against local terrain availability. Remote-sensing-based burn mapping and DEM-derived terrain metrics now make this question testable at cohort scale, although most Korean wildfire studies have focused on ignition, occurrence probability, or fire-risk prediction rather than final-perimeter geometry. We therefore tested whether 118 final wildfire perimeters in the Republic of Korea (2018–2025) were non-randomly associated with ridge lines derived independently from a 30 m SRTM DEM. Sentinel-2 pre- and post-fire imagery and official fire metadata were used to generate burn masks and perimeters, which were sampled every 20 m and compared with ridge networks using a proximity endpoint (R30) and a joint distance-orientation endpoint (Aθ) under a local translate-and-rotate null model. Most fires were both more ridge-proximal and more strongly ridge-aligned than their local null perimeters, and the directional signal was the stronger of the two (mean enrichment 2.3, versus 1.5 for proximity alone). A valley-inclusive comparator showed no comparable pattern, indicating an association specific to ridges rather than to terrain lines in general. The directional signal was robust to ridge continuity, spatial scale, null design, and the exclusion of road-adjacent ridges. Because the analysis uses final mapped perimeters rather than time-resolved fire fronts, it documents a ridge-specific geometric association rather than proof that ridges stopped individual fires. These results provide an observational benchmark for terrain representation in fire-spread models. Full article

In practical quantum communication, quantum channels are inevitably affected by noise and decoherence, leading to their degradation into non-maximally entangled or even mixed states. As a result, conventional quantum teleportation schemes based on non-maximally entangled channels are inherently probabilistic and cannot simultaneously achieve unit fidelity and unit success probability. To address this issue, we exploit the structural degrees of freedom of high-dimensional partially entangled channels and construct an asymmetric joint measurement basis matched to the Schmidt-coefficient distribution of the channel, thereby proposing a controlled multi-output perfect quantum teleportation scheme. First, based on a three-dimensional partially entangled five-qutrit channel, a controlled two-output teleportation model for unknown single-qubit states is established. Perfect transmission with both unit fidelity and unit success probability is achieved through the controller’s projective measurement, the sender’s asymmetric joint measurement, and the receivers’ corresponding local recovery operations. On this basis, the scheme is generalized to arbitrary d-dimensional partially entangled channels and further extended from the two-output configuration to the multi-output scenario. Our analysis shows that, when the two largest Schmidt coefficients of the channel are equal, deterministic perfect teleportation with both unit fidelity and unit success probability can still be achieved using non-maximally entangled resources. The proposed scheme is more consistent with realistic quantum communication environments and provides a theoretical foundation for efficient and controllable quantum information distribution in complex quantum networks. Full article

Aiming at the direction-of-arrival (DOA) estimation problem in pulse-disturbed and wireless transmission environments, this paper presents a variational Bayesian DOA estimation approach utilizing a Hidden Markov Model (HMM). First, a method for joint sparsity of signals and noise is used to build a hierarchical Bayesian structure and, by means of mixed-noise modeling, to simulate practical scenarios. Next, a Forward-Backward algorithm for Hidden Markov Models is used to model the changes in noise state over time and thus capture the temporal correlation of impulse disturbances. Finally, it computes the posterior probability via variational inference and iteratively adjusts the arrival angle for higher accuracy. Simulation results show that, in the presence of mixed-noise conditions, this scheme has achieved relatively accurate direction-of-arrival (DOA) estimation with lower computational costs compared to other Bayesian learning methods. Full article

As renewable energy sources become increasingly integrated into interconnected power networks, system operating points (OPs) undergo frequent and unpredictable shifts. However, conventional delays in updating equivalent model parameters during these OP transitions often compromise modeling accuracy. To address this challenge, this study proposes an online dynamic OP clustering method for interconnected grids featuring wind and photovoltaic generation. First, an equivalent model for renewable-integrated interconnected grids is established. Subsequently, a dynamic OP clustering strategy is developed; this strategy combines an offline construction phase utilizing joint probability distributions and data clustering with an online update mechanism that dynamically adjusts cluster boundaries via membership calculations. This approach enables real-time clustering, effectively minimizing equivalence errors and adapting swiftly to ongoing network variations. Simulation results based on the China–Mongolia interconnected power grid demonstrate that the proposed method significantly outperforms traditional static approaches in both equivalence accuracy and computational adaptability. By delivering precise, real-time network equivalents, this approach provides robust support for practical grid operations, including online security assessment, optimal power dispatching, and transient stability analysis, thereby contributing to the long-term stability and sustainability of modern power systems. Full article

The joint probabilistic modeling of environmental variables is essential for the design and analysis of offshore structures, as it enables the representation of dependence between parameters and the realistic estimation of combined events. This article presents a comparative evaluation between the Conditional Modeling Approach (CMA) and eight families of parametric copulas (Gaussian, Student’s t, Gumbel, Clayton, Frank, Joe, BB1, and Plackett) for the joint modeling of significant wave height ($H_s$) and peak period ($T_p$). Three datasets from the Brazilian coast were analyzed, encompassing a broad spectrum of dependence coefficient levels (Pearson’s coefficient, Kendall’s tau, and Spearman’s rho), ranging from high values to near-zero, including a scenario with domain-varying dependence across the $T_p$ domain. The results demonstrate that the CMA is the most robust model across all regimes, with some limitations only in the domain-varying scenario and in rank-domain residuals at low dependence coefficients. Parametric copulas perform satisfactorily solely in scenarios with high and moderate-high magnitude dependence coefficients, with the Gaussian copula standing out. At low dependence magnitudes, all copulas produce structures close to statistical independence, which shows that low dependence coefficients do not characterize the full dependence structure between $H_s$ and $T_p$. Full article

Background/Objectives: Aztreonam/avibactam is a promising treatment option for serious infections caused by metallo-β-lactamase-producing carbapenem-resistant Enterobacterales (MBL-CRE). However, the labeled regimen is operationally demanding because it requires frequent, prolonged infusions, and the recommended loading dose does not match the commercially available vial strength. This population pharmacokinetic (PopPK)-based Monte Carlo simulation study aimed to optimize aztreonam/avibactam dosing across renal function strata while maintaining pharmacokinetic/pharmacodynamic (PK/PD) target attainment. Methods: Published PopPK models for aztreonam and avibactam were reconstructed and applied in Monte Carlo simulations. Virtual adult patients (body weight 70 kg) were stratified into five renal function groups according to creatinine clearance (CrCL): 101–120, 81–100, 51–80, 31–50, and 15–30 mL/min. Simulated scenarios varied infusion duration, dosing interval, maintenance dose, and loading strategy. Prespecified PK/PD targets were 60% fT > MIC (the percentage of dosing interval that free drug concentration remains above the minimum inhibitory concentration) for aztreonam (MIC 8 mg/L) and 50% fT > CT (the percentage of dosing interval that free drug concentration remains above the critical threshold concentration) for avibactam (CT 2.5 mg/L). A joint probability of target attainment (PTA) ≥ 90% was considered acceptable. Results: Regimen performance differed across renal function strata. For patients with CrCL > 80 mL/min, the labeled q6h regimen infused over 3 h remained the most robust option, whereas shortening the infusion to 1 h or 2 h reduced target attainment. In the CrCL 51–80 and 31–50 mL/min subgroups, both q6h/3 h and q6h/2 h regimens generally achieved acceptable PTA. However, in the CrCL 31–50 mL/min subgroup receiving q6h/2 h administration, omitting a loading dose was associated with reduced early avibactam exposure. In the CrCL 15–30 mL/min subgroup, a simplified half-vial regimen (0.75/0.25 g q8h/2 h) provided PTA comparable to that of the complex labeled reduced-dose regimen. Across loading dose scenarios, omission of the loading dose was best supported in the CrCL 51–80 mL/min subgroup, whereas retaining the labeled loading dose remained the more prudent approach in the CrCL 31–50 mL/min subgroup when a 2 h infusion was used. Conclusions: PopPK-guided, renal function-stratified simplification of aztreonam/avibactam dosing may improve clinical practicality without materially compromising PK/PD target attainment in selected patient subgroups. A 2 h infusion appears a reasonable alternative for patients with CrCL 31–80 mL/min, and a 0.75/0.25 g q8h/2 h half-vial regimen may be considered a plausible exploratory option for patients with CrCL 15–30 mL/min. These findings support more feasible administration strategies, but prospective clinical validation remains necessary. Full article

The Inner Mongolia reach of the Yellow River faces compound “low flow, high sediment” extremes under reservoir regulation, threatening flood and ice-flood safety in ways that traditional mean-based or correlation-based methods fail to quantify. This study integrates POT-GPD extreme value theory with a vine copula-CoVaR framework using daily data (1951–2023) from four stations. The financial CoVaR concept was adapted to rivers through three hydrological modifications: a 5-day hydrodynamic lag, redefinition of the baseline to the downstream unconditional VaR, and semi-parametric tail modeling. Bootstrap confidence intervals (n = 1000) and a sensitivity analysis to the upstream–downstream lag (τ = 3–7 days) and the period cutoff (1984–1990) were used to assess robustness. Bayangol exhibits the highest Expected Shortfall (ES₉₅ = 0.0329 kg·s·m⁻⁶). The Bayangol → Toudaoguai path is the only persistent positive risk transmission link, with ΔCoVaR showing a directionally consistent increase of 253% from the natural period (1951–1986) to the regulated period (1987–2023); by contrast, ΔCoVaR from Dengkou to Toudaoguai remains near zero or negative when assessed under the conventional bivariate framework. A three-dimensional vine copula analysis, conducted independently for the pre- and post-reservoir periods, reveals a qualitative reversal of compound extreme spillover that is masked when the two periods are pooled. While the bivariate analysis identifies Bayangol → Toudaoguai as the only persistent positive spillover route at the annual scale, the 3D vine analysis unpacks the compound extreme mechanism at the daily scale. Under the joint compound extreme condition (upstream Q and S each ≥ Q₉₀), the conditional VaR₉₅ of downstream sediment concentration shifts from systematically negative in P1 (ΔVaR₉₅ = −4.75 kg·m⁻³ at the 90th-percentile threshold, indicating natural attenuation) to systematically positive in P2′ (ΔVaR₉₅ = +4.70 kg·m⁻³, +86.9% relative increase, indicating amplification). The same reversal is observed for the tail mean (ΔES₉₅), is preserved across four compound extreme thresholds (Q₇₅–Q₉₀), and is robust to the choice of period cutoff (28/28 cases reverse across seven candidate cutoffs). Bidirectional counterfactual simulations indicate that the copula shift from tail independence (Clayton) to tail dependence (Gaussian) alone elevates extreme concurrence probability by 58% (from 2.21% to 3.49%), while marginal distribution changes contribute negligibly (≤0.1 percentage points). Structural deterioration of water–sediment coordination therefore dominates risk amplification. The copula-CoVaR framework offers a candidate tool that requires further validation with large samples for tail risk assessment in heavily regulated fluvial systems. Full article

In complex detection environments, unknown detection probability and clutter rate hinder accurate tracking of cluster targets. To address this issue, this paper proposes a novel multi-sensor adaptive generalized labeled multi-Bernoulli (MS-AGLMB) filter. Specifically, we consider interactions among cluster members and adopt a virtual leader–follower model to describe cluster kinematics. Given unknown environmental parameters, we employ an adaptive cardinalized probability hypothesis density (CPHD) filter to estimate the detection probability and clutter rate in real time. Furthermore, we use Gibbs sampling to efficiently truncate GLMB association hypotheses, obtaining the posterior density and solving the multi-sensor measurement partitioning problem. A joint prediction and update strategy enables simultaneous estimation of target trajectories, detection probability, clutter rate, and cluster structure. Simulation results demonstrate that the proposed algorithm achieves greater robustness in scenarios with time-varying detection probability and clutter rate, outperforming comparison filters in cluster target tracking. Full article

Integrating renewable energy into modern grids while reducing carbon emissions represents a critical challenge for achieving “dual carbon” objectives. This paper proposes a two-stage stochastic optimization scheduling model for integrated energy systems (IES) that accounts for dynamic carbon emission factors and spatiotemporal uncertainty in wind power. First, a dynamic carbon emission factor model is developed to reflect real-time grid operational status and marginal power generation characteristics, replacing the conventional fixed-factor approach and enabling precise guidance for low-carbon electricity procurement strategies. Second, a Copula-based joint probability distribution model is established to capture complex temporal and spatial correlations in multi-wind-farm clusters, from which representative scenarios are generated and reduced through advanced pruning techniques. The scheduling model minimizes total operating costs and tiered carbon trading costs via mixed-integer quadratic programming (MIQP) and Benders decomposition. Case studies demonstrate that the proposed approach reduces daily operating costs by 6.4% (from 2.069 to 1.936 million yuan) and total carbon emissions by 8.4% (from 1051.8 to 963.2 tonnes) compared to conventional static-factor methods. Further, by accurately characterizing wind power uncertainty, the model achieves wind power absorption rates exceeding 90%, reducing curtailment from 272 kWh to 75 kWh and improving renewable energy utilization from 57.5% to 92%. The results validate that dynamic carbon factors and spatiotemporal correlation modelling effectively enhance both low-carbon performance and economic efficiency in IES dispatch, offering theoretical and practical guidance for achieving carbon-neutral energy system operations. Full article

The design of coastal protection structures requires design parameters that accurately represent the hydrodynamic conditions along the coast. Currently, these input variables are based on univariate probability models, which do not consider the joint probability of water level and statistical wave parameters such as significant wave height. Bivariate probability modeling using copula models offers an alternative. Copulas can be used to describe the dependencies between water level and significant wave height and to compute joint probabilities of occurrence. First, various copulas are fitted to samples of physically consistent combinations of water level and significant wave height extracted from storm surge events along the German Baltic Sea coast of Mecklenburg-Western Pomerania. Next, the most appropriate copula model is used to compute design combinations of water level and significant wave height for selected return periods. The bivariate design parameters are compared with the univariate ones in a simplified design example for wave run-up on a dike. The validation of various models shows that the Frank copula best describes the dependence structure. The bivariate design parameters obtained for the same return periods are lower than those determined using the univariate method. The available data only allow a limited application of the copulas for engineering design in the study area. Nevertheless, copulas have the potential to replace univariate methods for determining design parameters and thus contribute to more reliable and cost-efficient coastal protection structure design. Full article

Urban pluvial flood risk in industrial zones is intensifying under climate change, yet the joint influence of digital elevation model (DEM) resolution, surface roughness heterogeneity, and infiltration capacity on simulation accuracy remains insufficiently characterized. This study presents a comprehensive sensitivity analysis combining five DEM resolutions (0.5, 1, 2, 5, and 10 m), six rainfall scenarios (10- to 200-year return periods plus the observed event of 10 July 2024), and three infiltration rates (5, 10, and 20 mm h⁻¹), yielding 90 simulation cases executed with the open-source GPU solver SynxFlow on an NVIDIA A100 80GB GPU. A spatially distributed Manning’s roughness field (n_M = 0.013–0.100 s m^−1/3) was derived from the Ministry of Environment land cover product, replacing the conventional uniform-roughness assumption. Model performance was assessed against seven validation gauges (five flooded, two no-flood controls) compiled from contemporaneous news reports, using the 25 m × 25 m patch-maximum simulated depth at each gauge and probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI). The 0.5 m baseline achieved POD = 0.80, FAR = 0.20, and CSI = 0.67 at the 5 cm depth threshold. Coarsening the grid reduced peak depth by up to 37% and flooded area by 5%, with the most rapid degradation occurring between 2 m and 5 m. A 2 m grid retained area error within 2% and volume error within 1% while delivering an approximately 33-fold runtime reduction relative to the 0.5 m baseline; the 10 m grid achieved up to ~1400× speedup, spanning three orders of magnitude across the resolution range. Resolution sensitivity intensified under higher rainfall and lower infiltration, confirming that “adequate” resolution is conditional on event severity. A tiered resolution selection matrix linking application scale, target accuracy, and computational cost is proposed to support evidence-based flood adaptation planning for industrial zones. Full article

Infrared small target detection (IRSTD) is a task in target detection and computer vision that remains challenging but also critical. The cause of its complexity and difficulty lies in the inherent features of this class of targets, as most of the dataset has extreme class imbalance, weak classification contrast, and complex noise clutter in the background. Focusing on these existing issues, this work proposes CG-IRNet, a structure-aware detection framework that integrates multi-scale feature aggregation with Structure–Confidence Hybrid (SCH) loss, which integrates an augmented variant of confidence-aware Scale–Location Sensitive (SLS) loss with instance-wise structural supervision and a confidence-guided background suppression mechanism, which are all targeted towards enhancing localization consistency while largely reducing false alarms. In addition to these, a frequency-aware feature refinement module is incorporated to strengthen small target saliency under highly cluttered scenes. This work included a series of extensive experiments across three benchmark datasets included in SIRST, namely IRSTD-1K, NUAA-SIRST, and NUDT-SIRST. These experiments demonstrate a superior trade-off between detection probability (Pd) and false alarm rate. On IRSTD-1K, CG-IRNet achieves 65.09 mIoU and reduces the false alarm rate to 30.992 × 10⁻⁶, which is significantly lower than SCTransNet (55.74 × 10⁻⁶) at the same detection probability (93.27%). On NUAA-SIRST and NUDT-SIRST, the proposed method achieves 96.95% and 98.62% detection probability, respectively, while maintaining competitive or lower false alarm rates under challenging background conditions. These outcomes effectively demonstrate the improvements achieved in this work and the effectiveness of the proposed confidence-guided suppression and structure-aware optimization. Also included in the group of experiments performed in this work is the ablation study on model hyperparameters and qualitative analyses, which further confirm the joint improvements contributed by the proposed structural supervision and confidence-aware design, particularly in regimes where a low false alarm rate is the goal of optimization. Full article