Multiparty quantum key agreement (MQKA) enables n ≥ 3 mutually distrustful users to establish a shared secret key through collaborative quantum protocols. In this paper, we provide a comprehensive review where we argue that MQKA is best understood as a design space organized along three orthogonal but tightly coupled axes: (1) network architecture, which determines how quantum states flow between participants; (2) quantum resources, which encode the physical degrees of freedom used for implementation; and (3) security model, which defines trust assumptions about devices and infrastructure. Rather than treating MQKA as a linear sequence of isolated protocols, we develop this three-axis perspective to reveal recurrent patterns, sharp trade-offs, and unexplored design spaces. We classify MQKA protocols into structural families, map them to underlying quantum resources, and analyze how different security models shape fairness and collusion resistance. We further identify open challenges in composable security frameworks, network native integration, device-independent implementations, and propose a research roadmap toward hybrid-resource, bosonic-code-encoded, and fairness-aware MQKA suitable for the future quantum internet deployments in the post-NISQ era.
This study screens construction and demolition waste (CDW)-based geopolymer composites containing a fixed graphene oxide (GO) dosage and varying graphene (G) contents to examine mechanical, electrochemical, and cradle-to-gate environmental trade-offs. Reduced-size specimens were tested in compression, while powder-coated carbon-paper electrodes were evaluated by cyclic voltammetry in a three-electrode configuration. Low graphene loading retained a compressive strength of approximately 15.2 MPa for G1. Across G0–G4, the apparent CV-derived gravimetric capacitance increased from approximately 280 to 460 F/g at 5 mV/s. These values are reported as uncorrected screening estimates because the bare carbon-paper background was not subtracted and the deposited active mass was not independently verified. G5 was excluded from candidate consideration because its compressive strength decreased to approximately 2 MPa, representing an approximately 87% reduction relative to G0. The cradle-to-gate global warming potential, reported per 1 m3 of bulk composite, increased from approximately 478 kg CO2-eq for the plain geopolymer to 1219 kg CO2-eq for G1 and 5924 kg CO2-eq for G5, primarily because of G and GO production. G1 represented the most favorable screening compromise among the graphene-containing formulations, but its strength limits it to non-structural or semi-structural material-screening contexts. The study identifies formulation-level trade-offs and future testing requirements rather than demonstrating a structural supercapacitor or a field-ready energy-storage material.
Multiple sclerosis (MS) is a demyelinating disorder of the central nervous system where retinal layer thinning, measured via optical coherence tomography (OCT), has emerged as a promising biomarker. However, the development of automated, explainable, and efficient computational frameworks for OCT-based MS detection remains an unmet need. In this study, we propose a novel multi-stage graph-based automatic segmentation approach to delineate seven different retinal layers in macular-centered OCT B-scans. From the segmented layers, thickness profiles were extracted to feed into custom convolutional neural network (CNN) for MS vs. healthy control classification. Proposed custom CNN achieved an accuracy of 94%, sensitivity of 92%, and specificity of 97%, outperforming several pre-trained established architectures (e.g., VGG16, ResNet). Statistical analysis (one-way ANOVA) revealed significant thinning in six out of seven retinal layers in MS patients compared to controls (p<0.05). Grad-CAM explanation confirmed that the model focused on anatomically feasible regions, particularly the ILM and NFL-GCL complex. Integration of novel automatic segmentation, validated thickness biomarkers, light-weight custom CNN establishes an explainable, automated pipeline for OCT B-scan based MS detection. Proposed framework demonstrates a strong potential for clinical translation as a non-invasive, imaging-based diagnostic aid for MS detection.
Stochastic models are widely employed to describe system degradation and thereby assess reliability and safety. Most existing models are based on Wiener or Gamma processes, whose increments are Gaussian or Gamma distributed, respectively. Degradation data often exhibit jumps and asymmetry. Damage corresponds to increasing degradation and decreasing health indicators, such as battery capacity; recovery reverses these directions. This study investigates the potential of the Variance Gamma (VG) process as the basis for a new degradation model. The VG process has increments following a variance-gamma distribution, a normal variance-mean mixture with Gamma mixing, which provides flexibility to capture both skewness and heavy tails. To account for realistic nonmonotonic and state-dependent dynamics, a transformed VG process, defined through a monotone state-space mapping, is introduced, inducing dependent increments in the observed degradation. Reliability-related quantities are investigated through first-passage approximations of the second kind, allowing tractable approximations of failure-time and Remaining Useful Life (RUL) distributions under the transformed model. A simulation study illustrates parameter estimation, validates the proposed approximations, and examines the propagation of parameter-estimation uncertainty into reliability and RUL predictions. The proposed model is further applied to NASA lithium-ion battery degradation data, with parameters estimated by maximum likelihood and its fitting performance compared with transformed Wiener and standard VG models. The proposed Transformed Variance Gamma (TVG) process provides a flexible and interpretable framework for modeling degradation, simultaneously incorporating asymmetry, tail heaviness, stochastic variability, and uncertainty quantification, with potential applications in reliability assessment and maintenance decision-making.
Sustainable aviation fuel (SAF) deployment is commonly judged by blend percentage, although blend fraction alone does not determine climate cost-effectiveness. This study retains a transparent plant-to-passenger framework that propagates SAF production economics through blend pricing, flight expenditure, annual airline exposure, passenger-normalized burden, lifecycle CO2e reduction, average abatement cost, and carbon-value break-even. The linear benchmark shows that when unit fuel price and lifecycle emission factor are independent of deployment scale and mix linearly with SAF fraction, average abatement cost is mathematically blend-invariant; this identity is treated as a baseline property of the accounting model rather than as a paradox or stand-alone novelty. The operational analysis is now anchored to the current 50% ASTM D7566 HEFA-SPK blend limit, with higher fractions used only as explicitly notional extensions where retained. At 50% SAF, the reference market case adds USD 3250 per flight and USD 15.29 per effective passenger while avoiding 6.50 tCO2e per flight; the average abatement cost remains USD 500 tCO2e−1. The reference production model retains an LCOSAF of USD 2355 t−1, while additional boundary tests show where the linear identity ceases to hold: a bounded feedstock-supply response that raises feedstock price by 15% at 50% blend increases modeled LCOSAF to about USD 2532 t−1 and average abatement cost to USD 628 tCO2e−1. A coupled lifecycle sensitivity links yield, hydrogen carbon intensity, and electricity carbon intensity to the same production scenarios, co-product accounting is tested using revenue-credit, economic-allocation, and energy-allocation treatments, and Sobol indices confirm feedstock price, yield, and CAPEX as the dominant variance contributors. The resulting framework preserves the original plant-to-passenger transmission logic while distinguishing deployment scale from cost-effectiveness, identifying the assumptions that break linear blend invariance, and placing the cost–carbon results in a certification and policy context relevant to aviation deployment.
