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.
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.
Hydrogen is a promising clean, renewable energy source with zero carbon waste, generated via an electrochemical conversion system. The key to electrocatalytic water splitting lies in the semi-reactions, such as OER/ HER, and overall water splitting, yet remains hindered by their sluggish kinetics. To overcome this challenge, the development of an appropriate, competent, low-cost, and efficient bifunctional electrocatalyst for hydrogen and oxygen evolution is highly desirable. In this perspective, we adopted a hydrothermal method to design an efficient CeSe2-Cu2O/g-C3N4 heterostructure as a bifunctional electrocatalyst. The layered structure of roughly spherical nanoparticles and the higher redox potentials of Ce3+/Ce4+ and Cu1+/Cu2+ on the catalyst surface created heterojunction interfaces for active centres and strong electronic interactions for robust charge-transport rates, which accelerate the OER and HER activity with impressive electro-catalytic potential of 202 and 89 mV at standard current density by using 1.0 mol alkaline electrolyte. Besides, the anionic edge sites in the structure of CeSe2-Cu2O/g-C3N4 optimised the adsorption of reactants and intermediates, thereby demonstrating superior stability of 60 h and 80 h for OER and HER, respectively. In the overall water electrolysis test, the CeSe2-Cu2O/g-C3N4 electrolyser exhibits excellent stability for 40 h at 1.53 V, maintaining 10 mA cm-2. The present work proposes a new approach to designing an efficient and stable bifunctional catalyst to enhance electrocatalytic water splitting performance by coupling g-C3N4 via an anionic bimetallic strategy.
In traditional last-mile attended home delivery, stochastic service failures, primarily driven by uncertain customer presence, incur additional operational costs for the logistics provider and cause customer inconvenience. This stochasticity also poses a major challenge to collaborative truck-drone routing with simultaneous pickup and delivery. In such systems, retained payloads from service failures accelerate drone energy consumption, thereby disrupting vehicle synchronization and compromising flight feasibility. In this paper, we introduce a new problem, the collaborative truck-drone pickup-and-delivery routing under uncertain customer presence (CTD-PDRUCP). To capture the non-linear payload-energy-uncertainty coupling, we model and solve this problem through a unified two-stage, proactive-reactive framework. Specifically, the offline proactive stage constructs risk-averse routes utilizing an expectation-based surrogate recourse operator, whereas the online reactive stage performs rapid dynamic route re-optimization upon service failures. This framework is efficiently powered by the integration of Ant Colony Optimization and Adaptive Large Neighborhood Search (ACO-ALNS). Extensive computational experiments, including a real-world case study, and Monte Carlo simulations demonstrate the effectiveness of the proposed framework in terms of solution quality and robustness. Finally, our sensitivity analyses reveal that expanding the drone-eligible customer ratio yields diminishing returns and that higher drone speeds offer negligible cost savings due to payload and endurance bottlenecks, providing valuable managerial insights for collaborative last-mile delivery.
Photovoltaic modules experience gradual degradation and sudden failures that reduce energy yield, reliability, and safety, motivating the use of imaging-based diagnostic techniques. Imaging approaches enable the detection of electrical and physical defects that are often invisible through conventional visual inspection. Studies have reported that microcracks, hotspots, potential-induced degradation, and light-induced degradation can noticeably affect PV module reliability. This review therefore examines both established field-deployed diagnostic techniques (technology readiness level > 5) and emerging approaches that are under development (technology readiness level <5). Most existing studies focus on the development or evaluation of individual imaging techniques, while some review articles discuss multiple methods without providing detailed comparisons between conventional and emerging diagnostic approaches across different fault types. As a result, a systematic comparison of the diagnostic capabilities of the available imaging modalities remains limited. To address this gap, this review presents a structured study and fault-centric benchmarking of various imaging-based PV inspection techniques, emphasizing fault visibility and diagnostic relevance across imaging modalities rather than relying solely on reported accuracy metrics. Additionally, a hybrid scope–mapping systematic review methodology is applied, in which peer-reviewed studies are screened, classified, and synthesized based on fault type and technological maturity. Based on results reported in the literature, machine learning-assisted infrared thermography has achieved detection accuracies of 94–98%, while deep learning-based electroluminescence methods have reported accuracies of up to 97.8%. Ultraviolet fluorescence techniques have demonstrated crack detection rates exceeding 91% and inspection throughput up to 10–15 times higher than near-infrared inspection under comparable operating conditions. These performance values originate from different studies, datasets, and experimental conditions and are therefore intended to illustrate representative capabilities rather than enable direct comparison between techniques. Emerging approaches such as daylight luminescence and magnetic-field-based diagnostics are also gaining attention, although their broader use remains limited by operational complexity and signal-to-noise challenges.
