Search Results (6,645)

Pollen viability is a crucial determinant of reproductive success in plants. Given the enormous threat posed to the common ash (Fraxinus excelsior L.) by ash dieback, it is important to investigate the potential disease’s effects on pollen viability and germination. Thus, we conducted an analysis of these pollen characteristics across three distinct forest stands in southern Bavaria, with up to 23 ash trees per study site. These ash trees exhibited varying degrees of ash dieback-related damage symptoms, enabling us to assess differences between mildly and severely affected trees (via Mann–Whitney-U/Wilcoxon tests, complemented by linear mixed-effects modelling). Pollen viability was assessed using the TTC test, while pollen germination capacity was evaluated on a sucrose–agar medium. Our findings revealed no statistically significant differences in pollen viability between mildly affected and severely diseased trees, as indicated by both the TTC test and pollen germination assay when applying non-parametric analyses (Mann–Whitney U and Kruskal–Wallis tests). Nevertheless, a consistent tendency towards higher pollen viability was observed in healthier ash trees. When accounting for the hierarchical structure of the data using linear mixed-effects modes, tree vitality showed a significant effect on pollen viability, whereas a substantial proportion of the observed variation was explained by interannual differences. These results indicate that ash trees generally retain the capacity to produce viable pollen across different levels of disease severity, but vitality-related effects are subtle and context-dependent. However, severely diseased trees produced few or no flowers, substantially reducing the likelihood that their pollen contributes to fertilization. We therefore conclude that ash dieback primarily limits reproductive success in common ash mainly by reducing flower and pollen production, whereas pollen viability itself is strongly driven by interannual differences. Consequently, no consistent pattern of declining pollen viability with increasing disease severity emerged. Full article

Background: Risk stratification in non-ischemic dilated cardiomyopathy (DCM) remains challenging because left ventricular ejection fraction (LVEF) and late gadolinium enhancement (LGE) do not fully capture the underlying myocardial substrate. Septal native T1 mapping provides a quantitative assessment of diffuse myocardial abnormalities and may contribute to myocardial tissue characterization within a multiparametric CMR framework. Methods: This retrospective single-center study included 45 consecutive patients with non-ischemic DCM referred for clinically indicated CMR at Perrino Hospital, Brindisi, Italy, between November 2023 and November 2025. All examinations were performed using a standardized CMR protocol including cine imaging, LGE, and native T1 mapping on a 1.5-T Siemens Healthineers scanner. Septal native T1 was used as the primary mapping parameter because of its established reproducibility and robustness for myocardial tissue characterization. Patients were followed for a composite endpoint including all-cause mortality, major ventricular arrhythmic events, appropriate ICD therapy, and hospitalization for heart failure. Endpoint coding was verified, and all analyses were performed using the final validated dataset. Results: During a median follow-up of 15 months, 14 patients (31.1%) experienced the composite endpoint. Patients with events had lower LVEF (27.1 ± 7.8% vs. 48.3 ± 10.5%; p < 0.001), higher LVEDVi (142.6 ± 28.5 vs. 110.6 ± 23.4 mL/m²; p = 0.001), and higher septal native T1 values among patients with available T1 measurements (1047.5 ± 25.0 vs. 1031.5 ± 24.3 ms; p = 0.065). ROC analysis identified a septal native T1 threshold of 1042 ms for prediction of the composite endpoint, with an exploratory AUC of 0.70. Event-free survival was lower in patients with septal native T1 ≥ 1042 ms. Given the limited number of events, all regression and hierarchical analyses should be interpreted as exploratory and hypothesis-generating. Conclusions: Higher septal native T1 values were observed in patients experiencing adverse clinical outcomes; however, native T1 was not independently associated with the composite endpoint in exploratory Cox regression analyses. Full article

The Terziali is a shear-hosted orogenic gold prospect located in the Central Anatolian Crystalline Complex, Türkiye. This study focuses on soil geochemistry, element correlations, background and threshold values, and evaluates exploration implications over a survey area of 35.5 km². A total of 1826 soil samples were collected from the B horizon using a grid of 100 × 50 m and were analyzed using ICP-AES, ICP-MS, and fire assay techniques. Statistical techniques of median + 2MAD threshold calculations, descriptive statistics, Kolmogorov–Smirnov tests, correlation analysis, hierarchical clustering, and Q–Q plots were carried out to identify geochemical anomalies. The data demonstrate Au threshold (28 ppb) and peak concentration (460 ppb), non-normal distributions characterized by strong positive skewness, revealing the outliers linked to mineralization. Soil geochemistry indicates a moderate association between Au and As in the four-acid dataset (r = 0.465), although the correlations between Au and Sb and Ag and W are relatively weak. The spatial analysis indicates that Au anomalies are predominantly linked to the NW–SE-oriented Demirli Thrust Fault. As displays extensive dispersion halos surrounding the gold anomalies; it establishes itself as an efficient pathfinder element. Conversely, Sb and W exhibit unique anomaly patterns, whereas Ag patterns are weak and dispersed. The Terziali prospect provides a substantial geochemical framework for identifying structurally controlled orogenic gold systems in Central Anatolia and the western Tethyan metallogenic belt. Full article

Small-scale island communities whose livelihoods depend on aquaculture are increasingly vulnerable under interacting climatic and non-climatic stressors. Conventional indicator-based assessments are useful for describing the level of vulnerability, but many empirical assessments remain less able to explain how multiple stressors are mediated through local social–ecological structures and feedback processes to produce different vulnerability patterns. This study aims to explain how vulnerability is formed in island aquaculture communities by linking social–ecological system structures with vulnerability processes and by examining empirically informed feedback pathways. Drawing on evidence from three island aquaculture communities in southeastern China, household survey data were first used to classify community types through hierarchical clustering. Semi-structured interviews, field observations, and documentary materials were then qualitatively coded to develop empirically informed conceptual causal loop diagrams (CLDs) for each type. Key variables and recurring feedback pathways were identified through loop-based structural analysis and cross-case comparison. The analysis indicates that vulnerability formation in island aquaculture communities is associated with recurring reinforcing feedbacks within local social–ecological system structures, through which multiple climatic, ecological and socio-economic stressors are translated into differentiated vulnerability outcomes. Across the case communities, resource overexploitation and marine pollution reinforce an ecology–livelihood degradation loop, while labor outmigration erodes social capital, disrupts intergenerational knowledge transmission, and weakens collective action and adaptive capacity, exacerbating socio-ecological vulnerability. At the same time, dominant stressors, key drivers, and feedback configurations vary across community types, generating divergent vulnerability trajectories and highlighting the context-dependent nature of vulnerability dynamics. These results suggest that governance interventions targeting isolated stressors or relying on static vulnerability analyses are insufficient where reinforcing feedbacks dominate. Effective adaptation strategies should explicitly target critical feedback pathways and strengthen stabilizing processes. By integrating social–ecological systems thinking with vulnerability analysis, this study provides a feedback-oriented approach for diagnosing vulnerability formation and supports more feedback and context-sensitive governance in small-scale island aquaculture communities. Full article

Background: The growing complexity of liquid biopsy in precision oncology demands a structured classification framework that can accommodate its expanding multi-omic scope. As the field has matured from early Tumor Microemboli research—focused on multicellular clusters of circulating tumor cells (CTCs) that drive high-efficiency metastasis—to the broader systemic analysis of the “Tumor Microenvironment” (TME) encompassing malignant and non-malignant components, the need for a hierarchical taxonomy has become evident. Objective: To integrate these diverse data streams into a coherent clinical framework, a multi-tiered classification system is needed. This review proposes a foundational roadmap that formally distinguishes the systemic ecosystem from its physical and functional subsets and highlights their clinical utility in therapeutic decision-making. Proposed Taxonomy: We advocate for the adoption of Circulating Tumor Microenvironment (cTME) as the inclusive term for the systemic environment, encompassing non-cellular factors such as ctDNA, extracellular vesicles, and biophysical attributes. Conversely, physical cellular clusters should be strictly classified as Circulating Tumor Emboli (CTE). Crucially, we define Circulating Tumor-Associated Cells (C-TACs) as the functional cellular subset within the cTME, encompassing single CTCs, CTE, and supporting non-malignant cells like CTECs and CAFs. Clinical Applications: Establishing this distinction allows for the seamless integration of molecular profiling (NGS) and functional assays. We highlight emerging evidence that C-TACs may serve as the primary substrate for Chemo-Response Profiling (CRP), with early proof-of-concept studies reporting high concordance with clinical outcomes that still await independent prospective confirmation. Furthermore, preliminary evidence suggests that identifying these functional units, particularly perioperative CTE, may help predict the efficacy of adjuvant chemotherapy in early-stage malignancies, although this remains to be confirmed in prospective studies. Conclusions: Adopting this unified taxonomy may help advance precision oncology. By recognizing the cTME as the superordinate ecosystem and C-TACs as its functional executors, clinicians may be better positioned to interpret multi-modal liquid biopsy data, providing a conceptual roadmap for integrating these technologies into platforms for personalized cancer management. We emphasize that this framework is intended to be hypothesis-generating and that its clinical applications require prospective validation before routine adoption. Full article

Background: Carbapenem-resistant K. pneumoniae (CRKP) represents a major challenge in hospitalized patients because of its association with healthcare exposure, restricted antimicrobial options, and adverse clinical outcomes. Microbiological isolation alone does not define invasive disease; therefore, clinical interpretation requires separation of colonization, localized infection, invasive infection, and carbapenem-resistant Enterobacterales (CRE)-associated sepsis. This study evaluated epidemiological features, resistance phenotypes, treatment adequacy, and clinical outcomes among hospitalized adults with K. pneumoniae isolates, using a clinical framework that distinguishes colonization from active infection and invasive disease. Methods: This single-center retrospective observational cohort study included 157 consecutive adults admitted between January and July 2025 to a tertiary-care hospital with at least one microbiologically confirmed K. pneumoniae isolate recovered from clinical specimens and/or CRE surveillance rectal swabs. Isolates were assigned hierarchically to four mutually exclusive phenotypic groups: carbapenem-susceptible K. pneumoniae (CSKP), extended-spectrum beta-lactamase (ESBL)-producing carbapenem-susceptible K. pneumoniae (ESBL), carbapenem-resistant non-carbapenemase-producing K. pneumoniae (CRKP), and carbapenemase-producing K. pneumoniae (CP-KP). A prespecified secondary analysis compared carbapenem-resistant isolates (CRKP + CP-KP) with non-carbapenem-resistant isolates (CSKP + ESBL). Clinical adjudication distinguished colonization-only cases, non-invasive infection, bloodstream infection, device-associated infection, and CRE-associated sepsis; ventilator-associated pneumonia (VAP) was considered when source data allowed reliable attribution. Sepsis was defined according to Sepsis-3 criteria; quick Sequential Organ Failure Assessment (qSOFA) was used only as a bedside screening tool. Statistical tests were selected according to variable type, distribution, and expected cell counts. Results: The cohort comprised 157 unique patients, with a median age of 71 years (interquartile range [IQR], 61–76). Current CRE colonization was documented in 79/154 patients with available colonization status (51.3%). Complete-case in-hospital mortality was higher in the carbapenem-resistant group (CRKP + CP-KP, n = 46) than in the non-carbapenem-resistant group (CSKP + ESBL, n = 111): 11/42 (26.2%) versus 5/108 (4.6%; Fisher exact odds ratio (OR) 7.31, 95% confidence interval (CI) 2.36–22.65; p < 0.001); overall complete-case mortality was 16/150 (10.7%). Multivariable logistic regression for carbapenem resistance (N = 150; five prespecified covariates; events per variable (EPV) = 9.0) identified age 65 years or older (adjusted odds ratio [aOR] 3.78, 95% CI 1.32–10.86), recent hospitalization within 30 days (aOR 2.56, 95% CI 1.16–5.63), and current colonization (aOR 2.96, 95% CI 1.24–7.05) as independent predictors. CRE-associated sepsis was excluded a priori because of definitional circularity with the case definition. Male sex showed a non-significant protective trend (aOR 0.50, 95% CI 0.22–1.12). CRE-associated sepsis showed a strong bivariate association with carbapenem resistance (OR 9.90, 95% CI 3.91–25.09; p < 0.001), and this association is reported descriptively because the variable was excluded from the multivariable model owing to definitional circularity. Model performance was acceptable, with area under the curve (AUC) 0.77, Hosmer–Lemeshow p = 0.95, and Nagelkerke R² = 0.25. Of 99 molecularly characterized isolates, OXA-48-like was detected in 78 (78.8%), NDM in 71 (71.7%), KPC in 6 (6.1%), and NDM + OXA-48-like dual production in 54 (54.5%); VIM and IMP were uniformly negative. Conclusions: In this high-risk hospital cohort, carbapenem resistance in K. pneumoniae was associated with advanced age, recent healthcare exposure, current CRE colonization, and a pronounced unadjusted mortality signal. Interpretation of sepsis and mortality requires explicit separation of colonization from active infection and invasive disease. These findings support intensified CRE surveillance, source-specific clinical interpretation, rapid resistance detection, and risk-adapted empirical antimicrobial strategies in high-risk hospital settings. Full article

Accurate assessment of winding condition for power transformers is critical for ensuring the stable operation of modern power systems. Vibration signal has been regarded as an effective and promising evaluator for winding diagnosis. While on-line vibration monitoring offers the continuous, non-invasive and in-service assessment for winding condition, establishing precise correlations between the variable vibration patterns and specific winding condition remains challenging. To this end, an off-line vibration response analysis (VRA) technique was presented in the paper. Specifically, vibration frequency response (VFR) curves, indicating the winding response, were first obtained when the transformer was excited by the developed vibration response testing system, consisting of constant current variable-frequency power supply, intermediate transformer, accelerometers, data acquisition, control and analysis system. The VFR curves were then quantitatively and comprehensively described through four kinds of correlation indices. Finally, hierarchical integration strategy was proposed to aggregate those indices into quantitative criterion for condition assessment. The proposed method was validated on a real transformer under both normal and fault conditions, demonstrating superior performance. Notably, a 10% decrease in the evaluation criterion indicates an incipient winding looseness, while a reduction of 25% or more suggests severe looseness, prompting timely maintenance recommendations. Full article

Autoregressive models with exogenous variables (ARX) constitute a fundamental class of dynamic regression models used extensively for time series analysis across a wide range of applications. A pervasive limitation of the existing Bayesian analyses of ARX models is their near-exclusive reliance on the Gaussian error assumption, which is routinely violated in empirical applications exhibiting heavy-tailed innovations, distributional outliers, or excess kurtosis. To address this deficiency, we develop a rigorous Bayesian estimation framework for these models whose errors are drawn from the scale-mixtures of normal (SMN) family, which is a rich, symmetric, heavy-tailed class of distributions. Exploiting the hierarchical stochastic representation of the SMN family through observation-specific latent scale-mixing variables, the ARX model is embedded in an augmented data structure that restores Gaussian conditional structure. Under three distinct prior formulations—namely, normal-gamma, Zellner’s g-prior, and Jeffreys’ prior—we derive closed-form full conditional posterior distributions for the ARX coefficient vector and the error scale parameter, which follow multivariate normal and inverse-gamma distributions, respectively. In addition, for the SMN-specific shape parameters, we derive the full conditional posteriors for each distribution in the family, and some of them are non-standard distributions handled by embedding Metropolis-Hastings steps within the Gibbs sampler. The resulting hybrid MCMC algorithm is validated through a comprehensive simulation study spanning three ARX model configurations and all three SMN special cases. A real macroeconomic application to US consumer price inflation demonstrates the practical utility of the framework, confirming heavy-tailed residuals and yielding precise, well-calibrated posterior estimates. Full article

Recent advancements in Vision-Language Models (VLMs) have enabled robotic systems to leverage model-based understanding and reasoning over visual and linguistic inputs, offering a promising approach for interpreting user intent in human–robot interaction (HRI). In particular, deictic expressions commonly used in object handovers, such as “take this” and “give me that”, cannot be fully interpreted through language alone and require a comprehensive understanding of the speaker’s perspective and the environment. This study proposes a prompt-driven vision-language framework for deictic interpretation in human–robot handover. The system integrates a pre-trained VLM with a hierarchical prompt that decomposes reasoning into intent classification, spatio-temporal grounding, and output self-validation, enabling accurate identification of target objects and goal locations without model fine-tuning. Experimental results demonstrate 100% command interpretation accuracy across multiple interaction scenarios, including pick-and-place tasks, robot-to-human and human-to-robot handovers, and temporal deictic commands. Notably, the system operates under a prompt–command language mismatch, accurately interpreting Korean commands while being guided by English-based prompts. Analysis across progressive system configurations further demonstrates that structured prompting plays a critical role in reasoning performance. These results highlight the effectiveness of a prompt-driven approach for deictic interpretation and spatio-temporal grounding, providing a practical training-free framework for HRI. Full article

The development of sustainable electrocatalysts for clean energy by modifying biomass-derived activated carbon with nitrogen and transition metals is presented. Activated carbon (AWC) material was obtained using alder wood char as a precursor, while nitrogen and cobalt or copper nanoparticles were incorporated with the aim of creating efficient materials for hydrazine oxidation (HzOR) and direct hydrazine–hydrogen peroxide fuel cells (DHHPFC, N₂H₄–H₂O₂). The composition, structure, and surface morphology of the created materials were examined using X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), energy-dispersive X-ray analysis (EDX), and inductively coupled plasma optical emission spectroscopy (ICP-OES). The activity of the AWC, AWC–Co–N, and AWC–Cu–N catalysts for HzOR was investigated using cyclic voltammetry (CV) and linear sweep voltammetry (LSV). N₂H₄–H₂O₂ fuel-cell tests were performed by applying the catalysts as both the anode and cathode. It was found that all materials retained a hierarchical porous carbon framework, while metal incorporation altered surface compactness. Cobalt doping produced well-dispersed Co nanoparticles and abundant Co–N–C coordination sites, whereas Cu introduction resulted in moderately compact structures with uniformly distributed Cu-based nanoparticles. Electrochemical measurements demonstrated that both metal dopants enhanced HzOR activity, with the catalytic performance following the order of AWC–Co–N > AWC–Cu–N > AWC. Fuel-cell testing further confirmed this trend: AWC–Co–N achieved the highest maximum power density (30.4 mW cm⁻²), outperforming AWC–Cu–N (17.7 mW cm⁻²). These results identify AWC–Co–N as a highly effective bifunctional electrocatalyst for DHHPFCs. Full article

Renal-resident macrophages (RMs) are essential regulators of kidney homeostasis and repair, yet the mechanisms governing RM niche regeneration after acute depletion remain poorly defined. To overcome these limitations, we have developed an inducible human CD59- intermedilysin (hCD59-ILY) ablation system, enabling rapid, specific, and reversible depletion of targeted macrophage populations, and subsequent replenishment of RMs, followed by longitudinal scRNA-seq analysis of kidneys at baseline and days 1, 3, and 7 post-ablation. RM ablation triggered a rapid and sustained upregulation of Cx3cl1, predominantly in proximal tubular epithelial cells (PTC1/PTC2), establishing a persistent chemotactic niche signal that coincided with macrophage repopulation. Regenerating RMs transitioned from inflammatory/stress-associated states toward metabolically active and proliferative phenotypes enriched in glycolysis, oxidative phosphorylation, MYC, and cell-cycle programs, with attenuation of canonical inflammatory pathways. Cell–cell communication analysis revealed an early burst of intercellular signaling at day 1, followed by progressive normalization, with fibronectin (Fn1), osteopontin (Spp1), chemokine (Ccl), and amyloid precursor protein (App) axes emerging as key mediators of niche restoration. Transcriptional network analysis identified a conserved regulatory module (Tfe3, Mitf, Hif1a, Myc, Gabpa, Rcor1) coordinating macrophage differentiation and regenerative programming, linking metabolic adaptation to lineage reconstitution. Sub-clustering revealed five dynamically shifting RM subsets with distinct inflammatory, remodeling, proliferative, and surveillance states, reflecting a hierarchical regeneration process. Functional validation using clodronate-mediated depletion in Secreted Phosphoprotein 1 (Spp1) (Opn)-deficient mice demonstrated impaired macrophage repopulation, establishing osteopontin as a critical regulator of RM regeneration. Together, these data define a coordinated epithelial–immune circuit in which Cx3cl1-driven chemotaxis, Spp1-dependent signaling, and a core transcriptional network orchestrate macrophage niche reconstitution and kidney repair following acute immune cell ablation. Full article

The certification information of wheat varieties contains valuable breeding knowledge and plays an important role in germplasm resource management and breeding research. However, most certification information is stored in unstructured text form, making it difficult to support efficient knowledge acquisition and utilization. To address this issue, this paper investigates joint entity-relation extraction from wheat variety certification texts and its application to knowledge graph construction. Specifically, building on existing character-word fusion methods, we propose the Joint Entity-Relation Extraction for Wheat Variety Certification Texts (JERE-WVCT) to address indiscriminate incorporation of candidate features in character-word fusion that obscures key features, a lack of differentiated weight assignment for features, and severe imbalance across relation types. Within JERE-WVCT, a deep character-word fusion mechanism based on hierarchical filtering and ranking is designed to enhance the representation of domain-specific entities. In addition, relation labels are incorporated as prior knowledge to alleviate the impact of relation type imbalance and improve the model’s triple extraction capability. Experimental results show that JERE-WVCT achieves an F1 score of 96.78% on the wheat variety certification corpus, outperforming all baseline models. Based on the extracted triples, a wheat variety knowledge graph is constructed, and exploratory variety relationship analysis is conducted as a downstream application. The results demonstrate the effectiveness of the proposed model for structured knowledge acquisition and support graph-based exploration of wheat variety information. Full article

Background/Objectives: Medication-related osteonecrosis of the jaw (MRONJ) is a serious complication in osteoporosis patients undergoing tooth extraction. This study aimed to develop and evaluate an interpretable, machine learning–derived triage score for rapid risk stratification at the initial dental visit. Methods: This retrospective study included 850 osteoporosis patients (443 MRONJ, 407 controls) in the derivation cohort and 559 independent multicenter MRONJ cases for external evaluation. A reference random forest model identified a hierarchical feature structure, which was translated into an additive integer-weighted scoring system through systematic hyperparameter optimization. Structural tipping points were identified using isotonic regression and first discrete derivative analysis. Internal performance was further characterized by sensitivity, specificity, PPV, NPV, calibration slope and intercept, the Hosmer–Lemeshow test, decision curve analysis, bootstrap optimism correction, and subgroup analyses. External evaluation assessed three-tier distribution concordance and case capture rates with non-inferiority testing. Results: The reference random forest achieved an AUC of 0.792. The final MRONJ triage score (range 0–17) incorporated six binary predictors with mutually exclusive drug route categories. The triage score preserved discriminative performance (AUC 0.772; ΔAUC = 0.020; p = 0.149). Two tipping points at scores 7 and 14 defined three risk tiers: low (0–6; 20.9%), moderate (7–13; 55.3%), and high (≥14; 83.5%). At the moderate-risk threshold (≥7), the score achieved sensitivity 90.3% (95% CI 87.2–92.7%) and specificity 45.0% (40.2–49.8%); at the high-risk threshold (≥14), specificity rose to 91.4% and PPV to 83.1%. Calibration was adequate (slope 0.994; intercept 0.0006; Hosmer–Lemeshow p = 0.381), and decision curve analysis demonstrated higher net benefit than reference strategies across all clinically relevant threshold probabilities. The bootstrap optimism-corrected AUC was 0.778, and discriminative performance remained stable across age, route, duration, and site subgroups (AUC range 0.70–0.79). In the external cohort, the case capture rate at the ≥7 threshold was non-inferior (83.4% vs. 88.0%; Δ = −4.6%; margin −10%). Conclusions: The MRONJ triage score demonstrated stable discrimination and reproducible case capture in an independent multicenter cohort. By relying on six variables obtainable at the initial dental visit, this framework may have the potential to reduce unnecessary tertiary referrals and support safer clinical decision-making, although this benefit was not directly demonstrated and requires confirmation in prospective implementation studies. Full article

As quantum communication evolves from point-to-point to multi-user networks, existing scalable schemes for multi-party quantum key distribution increasingly reveal their limitations in complex scenarios. These limitations are primarily manifested as a reliance on a central node, which results in insufficient hierarchical control capabilities of the system. Furthermore, existing schemes cannot enforce communication controllability at the quantum physical layer, making authorized communication difficult. To address these challenges, we propose a GHZ-state-based quantum-layer authorized hierarchical multi-user quantum key distribution scheme. By exploiting the global correlation of multipartite entanglement, authorization is mapped onto constraints imposed by managers’ measurement outcomes on receivers’ correlation structures, enabling hierarchically controlled key establishment. Specifically, any two users dynamically act as managers, whose measurements act as entanglement constraints dictating whether remaining users successfully establish a shared secret key. Theoretical analysis demonstrates the scheme guarantees correctness and consistency of key generation. Simulation results further indicate good robustness against typical types of channel noise, enabling stable controlled quantum key distribution within low-noise regimes. This work shifts communication authorization from classical mechanisms at the application layer to the quantum physical layer, providing a promising approach for secure quantum communication in complex networks. Full article

City networks refer to the connections of physical or virtual flows among cities at different spatial scales, including population migration networks, economic networks, information networks and innovation networks. This concept has gradually evolved into an important paradigm for understanding the regional spatial structures. Based on Baidu Index data within the Yangtze River Economic Belt (YREB) in China, this paper constructs an information network and investigates its connection patterns. Using social network analysis, the structural differentiation of the information network is further investigated at both the overall and subregional scales. The results show that the connection patterns of the information network exhibit an obvious hierarchical structure, with the complexity of the spatial pattern gradually increasing from the upstream to the downstream regions. Furthermore, the structural assessment results suggest that the information network is characterized by high agglomeration, high mobility, high hierarchy and low disassortativity. These findings indicate that the information network in the YREB is dominated by several highly developed core city clusters. However, the inherently closed structure resulting from these characteristics may not be sufficiently counterbalanced by low disassortativity. Under sudden disturbances, such a structural configuration may exhibit limited adaptability, delayed response capacity, and slow reorganization and learning processes, thereby weakening structural resilience. This study provides a deeper understanding of intercity relationships within the YREB and offers policy implications for enhancing structural resilience across the Yangtze River Basin. Full article