Search Results (3,156)

Discrete-Variable Quantum Key Distribution (DV-QKD) provides a mechanism for establishing secret keys between legitimate parties using quantum-state transmission and authenticated classical post-processing. In this work, the underlying quantum layer follows a biased-basis decoy-state BB84 model using phase-randomized weak coherent pulses, while additional implementation-level mechanisms are integrated to support Cyber-Physical System (CPS) communication. Exponential Double Delta Encoding-based Merkle Tree (EDDE-MT) is employed as a receiver-side detection-record integrity mechanism for detecting deletion, insertion, reordering, or modification of records relative to an authenticated committed detection-event batch. It does not establish the completeness of the original TCSPC acquisition, detect records omitted before commitment, detect physical photon loss, or increase the information-theoretic secrecy of the QKD key. Time-Correlated Single Photon Counting (TCSPC) is used for detection-event and timing acquisition, while 2’s Complement Cyclic Redundancy Check-based Low-Density Parity Check (2CCRC-LDPC) supports error reconciliation. Following privacy amplification, the legitimate parties retain matching copies of the distilled QKD key locally. Discrete Variable Quantum Mellin Transform Cryptography (DVQMTC) uses fresh, non-reused segments of this privacy-amplified key for application-layer payload protection; the Mellin-transform component is treated only as implementation-level preprocessing and not as a cryptographic key-generation mechanism. Side-channel monitoring is performed using Gini Cramer’s V Correlation-Stationary Wavelet Transform (GCVC-SWT), Helical Valley-Principal Component Analysis (HV-PCA), and an Entmax-based hyperbolic Tangent exponential Linear Unit-Bidirectional Long Short-Term Memory (E-TeLU-Bi-LSTM) classifier. On the AES-HD benchmark, E-TeLU-Bi-LSTM achieved 99.24% classification accuracy; this value represents benchmark-level classification performance and is not interpreted as experimental validation of physical side-channel protection in a deployed DV-QKD system. Frequency Division Multiple Access (FDMA) and the Halton Quasi-Sequence-Invasive Weed Optimization Algorithm (HQS-IWOA) are further incorporated as classical network-resource segmentation and load-management mechanisms and do not modify the composable QKD security bound. The contribution of the work is therefore positioned as a system-level engineering integration of QKD key establishment, detection-record integrity, reconciliation, application-layer data protection, side-channel monitoring, and network-resource management for CPS. The information-theoretic secrecy claim remains restricted to the underlying finite-key decoy-state BB84 procedure under the stated security assumptions; no new QKD security theorem, formally new cryptographic primitive, or experimentally validated physical quantum communication capability is claimed. Full article

This paper presents a simulation-based cyber-quantum Internet of Things (IoT) security framework for modeling, securing, and detecting attacks in QKD-secured IoT communication environments. The proposed framework integrates heterogeneous IoT traffic generation, gateway-assisted routing, edge processing, QKD key-pool management, Quantum-Synchronized Ephemeral Encryption (Q-SEE), cross-layer adversarial attack injection, and AI-based multiclass detection. Unlike conventional IoT intrusion datasets that mainly capture packet- or flow-level abnormalities, the generated dataset represents the joint behavior of IoT sessions, network delay, queue pressure, QKD state, key consumption, encryption-mode transitions, ciphertext metadata, and cyber-quantum risk. A Python/SimPy/NetworkX simulation was developed using 80 IoT devices, 3 gateways, 2 edge servers, 4 cyber-quantum control-plane nodes, and 1 adversarial orchestrator. The final simulation produced 46,351 records with 76 features covering normal traffic, five traditional IoT attacks, and six novel cyber-quantum attacks, including QKD key-pool starvation, QBER camouflage, false QKD-health injection, encryption downgrade induction, queue–key coupling, and multi-vector cyber-quantum orchestration. Q-SEE adaptively selects among QKD-OTP, QKD-synchronized AES-256 ephemeral mode, PQC fallback, degraded mode, and blocked mode according to QBER, secret key rate, key availability, device criticality, downgrade pressure, and risk. A leakage-aware Quantum-Aware Kolmogorov–Arnold Network (QKAN) was then trained using deployable cyber-quantum evidence. The final nonrisk QKAN achieved 98.79% test accuracy, 98.61% macro-F1, 98.85% weighted-F1, and 99.78% macro-AUC, demonstrating effective detection of traditional and cyber-quantum IoT attacks. Full article

Land-use change (LUC) and salinization interact synergistically to regulate depth-dependent fractionation and biological mediation of soil organic carbon (SOC) in vulnerable agroecosystems. Unlike previous syntheses addressing these drivers separately, the present review integrates them within a depth-resolved biological framework to reveal their combined effects on fraction-specific distribution under contrasting anthropogenic and ionic regimes. In the topsoil (0–30 cm), LUC and salinity synergistically collapse fungal networks, suppress carbon use efficiency, and restructure microbial communities to accelerate particulate organic matter (POM) turnover and impair mineral-associated organic matter (MAOM) formation. In the subsoil (>30 cm), salinity-driven clay dispersion and pore occlusion restrict oxygen diffusion and carbon accessibility, while LUC-induced loss of deep-rooting vegetation reduces carbon supply to mineral-associated pools. These depth-decoupled mechanisms render subsoil MAOM relatively resilient to direct ionic stress but highly vulnerable to land-use legacy, a distinction rarely represented in existing conceptual models. The evidence highlights key management implications, including restoring biological complexity in topsoil through reduced tillage, mycorrhizal re-establishment, and osmotic stress alleviation; conserving subsoil carbon by restoring deep-rooting vegetation and maintaining favorable ionic conditions for organo-mineral stabilization; and using depth-specific biomarkers, including enzymatic stoichiometry, fungal-to-bacterial ratios to detect SOC vulnerability before measurable losses occur. Future research should prioritize depth-explicit monitoring and integrated biological–physicochemical approaches to improve predictions of SOC dynamics. The resulting framework provides a mechanistic basis for depth-differentiated carbon management in salinizing landscapes. Full article

The increasing interconnection of smart-grid operational technology, industrial-edge services, and utility information systems creates a critical need for resilient and continuously monitored industrial cyber-physical communication. Although quantum key distribution (QKD) can strengthen session-key establishment for advanced metering infrastructure, distributed energy resources, substation automation, supervisory control, and utility-core services, practical QKD deployments remain vulnerable to implementation-level side-channel attacks that can compromise the cryptographic protection layer without directly targeting conventional network packets. This paper presents a QKD-secured industrial smart-grid cyber-physical system framework for simulating and detecting adaptive side-channel attacks. The proposed 36-node industrial communication architecture integrates AMI devices, DER controllers, PMU and substation automation components, industrial-edge gateways, QKD modules, key-management services, SCADA and utility-core servers, security-operation-center components, and adversarial access points. A 100,000-record cyber-quantum dataset is generated across 12 operating conditions comprising normal communication and 11 adaptive QKD side-channel attacks: detector blinding, time shift, wavelength switching, Trojan-horse probing, photon-number splitting, decoy-state spoofing, RNG bias, calibration manipulation, local-oscillator manipulation, synchronization spoofing, and combined adaptive quantum hacking. Each scenario introduces coupled primary and secondary perturbations across optical, detector, timing, synchronization, randomness, calibration, photon-statistical, leakage, key-generation, encryption, and industrial-network-performance features. To support intelligent industrial security monitoring, the proposed Quantum-aware Mamba–Kolmogorov–Arnold Network (Q-MambaKAN) organizes device, network, QKD, side-channel, encryption, and risk evidence into an ordered cyber-quantum representation processed through selective state-space learning, side-channel attention, nonlinear KAN mapping, adaptive fusion, and multi-task prediction heads. Results show that the QBER increases from 0.071 during normal operation to 0.426 under combined adaptive quantum hacking, while encryption success decreases from 98.1% to 0%. Q-MambaKAN achieves a 99.48% binary detection accuracy, a 99.70% binary F1-score, a 97.60% multiclass macro-F1, and a risk RMSE of 0.021. Full article

This paper proposes a location error-tolerant data embedding technique for display-to-camera (D2C) communication systems. The method is designed to enable robust data transmission while maintaining high image fidelity, facilitating simultaneous digital content display and data communication. To address the common issue of alignment and localization inaccuracies in D2C systems, the proposed approach defines specific regions of interest to ensure robustness against object detection model location errors. The architecture employs an expanding and contracting network structure for the encoder to achieve seamless data integration, while the decoder utilizes a computationally efficient “thin” structure for rapid data extraction. To improve performance in diverse environments, various distortion models were integrated into the system training. The system’s effectiveness was evaluated by measuring the bit error rate under conditions of Gaussian noise, blur, simulated localization errors, and real-world distortions. Image quality was validated using peak signal-to-noise ratio and the structural similarity index measure. The results indicate that the proposed technique maintains superior image quality and achieves reliable data recovery even in the presence of significant localization errors. These findings suggest that the approach provides a stable and effective solution for practical mobile-based D2C communication. Full article

Plant growth-promoting bacteria (PGPB) are increasingly deployed as biofertilizers, yet the link between an inoculant’s genomic potential and its realized effect on the plant is rarely assessed within an integrative framework that jointly captures the rhizosphere microbiome, plant phenotype, and strain genome. Two Bacillus strains—B. halotolerans 1453 and B. pumilus 630—were applied to wheat and soybean in a factorial pot experiment (2 strains × 2 application methods × 3 frequencies + control, 3–4 replicates). Rhizosphere samples (n = 67 after filtering) were profiled by 16S rRNA sequencing with PICRUSt2 functional prediction and compositional validation (Aitchison PERMANOVA, ALDEx2, ANCOM-BC2). The PGPB gene repertoire was characterized by genome mining (481 marker genes, 14 categories). Wheat phenotype (six traits) and soybean height were analyzed with models appropriate for count data (Negative Binomial and binomial GLMs) for treatment-vs.-control comparisons, and with factorial ANOVA for decomposition into main effects and interactions. Crop identity was the dominant factor shaping both microbiome structure and function (PERMANOVA R² = 14.7% taxonomically and R² = 7.8% functionally, both p < 0.001), with biologically meaningful taxonomic differences between wheat and soybean; strain, application count and method had no significant effect on community composition (R² < 4% each), and co-occurrence networks showed no reliable differences between crops once read depth and sample size were controlled for. Despite this neutrality at the microbiome level, inoculation significantly increased wheat spike count (NB-GLM, all 12 treatments vs. control, padj 0.0002–0.031), ear weight, and stem count, with application count the strongest source of variability and a pronounced strain × application count. Strain 1453 outperformed 630 in spike count (+23.1%, p = 0.012) and ear weight (+20.4%, p = 0.023); we hypothesize that this may be related to its more complete DNRA pathway (narGHI + nirB-nirD) and biocontrol genes (bacE, srfAA). Strain 630 produced a less pronounced effect than strain 1453 but was subject to smaller fluctuations across replicates (CV ≈ 16–21% vs. ≈24–26% for 1453), which may reflect better resilience to environmental fluctuations, possibly due to its confirmed rsbV/rsbW stress-tolerance regulon. Rhizosphere microbiome composition differed clearly by crop (wheat vs. soybean) but showed no detectable response to strain, application method, or application count. Despite this lack of a microbiome signal, inoculation significantly increased wheat spike count and ear weight, with the magnitude and stability of this effect differing by strain. We hypothesize that this strain-dependent difference relates to underlying genomic differences—particularly in nitrogen metabolism (DNRA pathway) and stress-tolerance genes—though this link has not been tested directly and remains a hypothesis for future work. Full article

Rumor events on social media generate opposing camps whose interaction structure is not captured by spreading models or content detectors. This study describes the camp and bridging structure of three rumor events on Sina Weibo, selected from confirmed cases published by the platform’s rumor-refutation channel. Each event is represented as a multilayer interaction network built from repost, comment, and mention relations. Camps are detected by modularity-based community assignment, and overlap is measured through a fractional membership distribution over communities. Three information-theoretic quantities characterize the structure. In the three events, membership entropy separates committed users from bridging users. Structure-to-stance mutual information measures the alignment between interaction communities and text stance. Cross-layer mutual information measures the consistency of camps across interaction types. A co-opetition matrix of mean edge sentiment describes cooperation within camps and competition between camps and identifies alliance structure. In the three events, membership entropy is bimodal, structure-to-stance mutual information is positive and above a permutation null, and the co-opetition matrix has positive diagonal entries. The three events show three distinct temporal patterns, namely a persistent standoff, a hardening toward a single camp after an official correction, and a reversal with an alliance between two camps. The patterns are recovered under a look-ahead-free temporal scheme. In the three events, bridging users hold higher betweenness centrality than non-bridging users. The results describe cross-camp bridging structure in three rumor cascades and connect the structure to a co-opetition reading of camp relations. Full article

Urban spatial structure is foundational for planning and management, yet its dynamic and multi-scale nature remains difficult to capture. Travel-flow networks derived from trajectory data provide a useful lens for revealing intra-urban interactions, but conventional community detection methods often overlook the scaling effect of travel distance and intra-day temporal dynamics. To address this limitation, this study proposes a novel framework to delineate urban structure at different scales, which identifies breakpoints in travel distances where interaction patterns shift. The method performs recursive community identification on the traffic flow, which is stratified by equal-distance intervals: it first detects communities using flows within a short-distance threshold, aggregates them into super-nodes, and then considers the short-distance flows and longer-distance flows between these communities and re-detects the communities at this level. When the community structure changes significantly, the flows from the previous distance range are excluded from further analysis, and the previous communities are treated as nodes for the next level, so that hierarchical organization is preserved and scale-specific interactions are isolated. Applying this method to Beijing taxi trajectory data across five time periods, a four-level spatial community structure is revealed: street-scale communities (short-distance flows), district-scale communities (medium-distance flows), urban–rural-scale communities (long-distance flows), and metropolitan-scale communities formed by ultra-long-distance flows. Significant intra-day variations are also identified, with morning-peak and night-time structures differing notably from other periods. The findings offer a multi-scale perspective on urban spatial organization and provide implications for traffic management and urban planning. Full article

Background/Objectives: Population aging has emerged as a major global challenge, particularly in Asian countries experiencing rapid demographic transitions. Among them, South Korea represents one of the fastest aging societies in the world, having rapidly transitioned into a super-aged society. As aging populations expand worldwide, increasing attention has been directed toward well-dying as an important component of quality of life, end-of-life care, and social well-being in later life. This study aims to identify the major research trends and knowledge structures of well-dying studies by applying text net-work analysis and LDA-based topic modeling. Methods: A total of 91 Korean academic studies related to well-dying published between 2016 and March 2026 were collected from publicly accessible scholarly databases and analyzed via keyword frequency, degree centrality, and community detection analyses, as well as LDA-based topic modeling. Results: The results showed that keywords such as “death,” “awareness,” “education,” “older adults,” “medical care,” and “life-sustaining treatment” played central roles in the knowledge network. Community analysis revealed that well-dying research has evolved into interconnected domains involving psychological preparations for death, hospice and palliative care, legal and ethical decision-making, and community-based aging policies. Topic modeling further identified four major themes: (1) psychological well-being and death preparation in later life, (2) social and policy approaches to well-dying, (3) well-dying education and healthcare perceptions, and (4) legal and ethical issues surrounding life-sustaining treatment decisions. Conclusions: The findings suggest that well-dying research is expanding from individual psychological adaptation to broader social, medical, legal, and policy dimensions. As one of the world’s fastest-aging societies, the Korean case provides meaningful implications for other countries facing accelerated population aging and highlights the importance of integrated well-dying policies and community-based support systems in super-aged societies. Full article

Rapid, reliable, and energy-efficient data collection is essential for disaster response, where terrestrial communication networks may be disrupted or unavailable. Unmanned Aerial Vehicles (UAVs) provide a flexible means of collecting critical sensing data, but their operation is constrained by limited onboard energy, stochastic wireless conditions, complex three-dimensional environments, and stringent latency requirements. This paper presents a structured multi-UAV framework that separates mission optimisation into spatial, temporal, and safety layers. In the spatial layer, a three-dimensional Travelling Salesman Problem with Neighbourhoods (3D-TSPN) formulation enables UAVs to collect data by entering valid sensing regions rather than visiting exact sensor coordinates. An Age of Information (AoI)-aware Genetic Algorithm (GA) optimises the sensor-visitation sequence, while Rapidly Exploring Random Tree Connect (RRT-Connect) generates obstacle-aware feasible paths in the three-dimensional environment. In the temporal layer, a Lyapunov-based controller selects between local processing and binary offloading to a single Mobile Edge Computing (MEC) node according to queue backlog, processing delay, energy consumption, information freshness, wireless-link feasibility, and task deadlines. In the safety layer, continuous-time conflict detection and bounded temporal or spatial adjustments are used to monitor and mitigate inter-UAV and obstacle-related risks. The framework is evaluated under stochastic wireless, mobility, computation, and obstacle conditions using 20 independent random seeds. Across the corresponding 20 proposed-policy runs, it achieves a 100% mission-validity rate, complete sensor coverage, no dropped tasks, and zero final collision or near-miss events. Compared with planning-oriented and MEC-oriented baselines, the proposed framework achieves lower information age, average delay, processing delay, energy consumption, and system cost under the evaluated conditions, while maintaining reliable multi-UAV coordination. The layered design also clarifies the contribution of each component: 3D-TSPN provides spatial flexibility, the AoI-aware GA improves route sequencing, RRT-Connect supports obstacle-aware path feasibility, Lyapunov control enables queue-aware processing decisions, and safety monitoring supports coordinated multi-UAV operation. These results indicate that integrating spatial planning, computation control, and safety coordination within a clearly separated layered architecture can provide an effective solution for multi-UAV disaster-response data collection in complex three-dimensional environments. Full article

Accurate characterization of picosecond broadband pulse signals is important for radio-frequency electronics, radar detection and high-speed communications. However, bandwidth-limited oscilloscopes attenuate high-frequency components, broaden rising edges, reduce peak amplitudes and introduce ringing distortion. Conventional regularized deconvolution relies heavily on manual parameter tuning and is vulnerable to noise, while Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) models suffer from long training time and insufficient capability to capture global temporal dependencies. Here we present a waveform reconstruction method integrating convolutional neural networks, Transformer and learnable physics-guided deconvolution constraints. The model simultaneously extracts local waveform details and models global temporal correlations, which improves training efficiency and noise robustness, and restores pulse rising edges, peak amplitudes and overall waveform morphology with higher precision. The proposed method offers a low-cost software solution to realize high-precision measurement of high-speed pulse signals without hardware upgrades. Full article

Large-water fisheries are central to China’s freshwater aquaculture, but their sustainability is threatened by eutrophication from traditional high-density stocking. Polyculture of grass carp and crucian carp offers an ecologically sound alternative; nevertheless, its direct effects on plankton communities require further study. We conducted monthly sampling from July to December 2023 (over two seasons within a single aquaculture cycle) in polyculture ponds and used eDNA metabarcoding (18S V9) with high-throughput sequencing. We detected 288 planktonic taxa across 28 phyla and 290 genera based on eDNA sequence assignment. Alpha diversity (Chao1, Shannon, Simpson) peaked in autumn and was lowest in winter, with Chao1 showing greater seasonal variation. Beta diversity revealed season as the dominant factor (R² = 0.287, p = 0.012), with significant summer–winter divergence. Null-model analysis indicated stochastic processes (56.3%) slightly exceeded deterministic filtering (43.7%), and stochasticity was higher in summer (67.2%), suggesting fish bioturbation and grazing create heterogeneous microhabitats. Co-occurrence networks were dominated by positive links (87.3%), with Cryptomonas as a central hub, implying mutualism and niche complementarity. This study is the first to integrate eDNA metabarcoding with community assembly theory to elucidate seasonal succession and interactions in this polyculture. Our findings confirm that polyculture sustains diversity and stability via top-down (grazing) and bottom-up (nutrient release) effects, providing eDNA-based taxonomic evidence for management of large-water aquaculture. Full article

Alliance network embeddedness provides firms with access to external knowledge and resources, yet its innovation implications vary across firms and network contexts. This study examines how network structure and combinations of embedding characteristics are associated with corporate innovation performance. Based on 335 firm-level observations from Chinese listed biopharmaceutical manufacturing firms, the study first identifies heterogeneous alliance network environments through community detection and K-Means clustering. Four network types are identified: dyadic, ringlike, star, and complex alliances. Classification and regression trees (CART) are then employed to extract interpretable, threshold-based decision rules linking network embedding characteristics to high and non-high innovation performance. The results show that no single network characteristic is consistently associated with innovation performance across alliance types. In dyadic alliances, moderate cooperation intensity is associated with high innovation performance, whereas ringlike alliances exhibit conditional associations involving cooperation intensity and partner centrality. Star alliances are characterized by configurations involving cooperation breadth and network position, while complex alliances exhibit more multidimensional combinations of structural and relational conditions. The findings indicate that the innovation relevance of alliance network embeddedness is network-type-specific and configuration-dependent. As a complementary robustness analysis, fuzzy-set qualitative comparative analysis broadly supports several core configurational patterns identified by CART, while also revealing alternative configurations, particularly in complex alliances. The study demonstrates that understanding alliance network embeddedness requires attention to network context, empirical thresholds, and combinations of network characteristics rather than isolated network attributes. Full article

Large yellow croaker (Larimichthys crocea) is an economically important marine fish species in China, yet regional variation in its gut microbial community structure and functional characteristics remains insufficiently understood. This study aimed to characterize and compare the gut microbiota of farmed large yellow croaker across five representative aquaculture regions. Intestinal microbial communities were characterized using 16S rRNA gene sequencing, followed by analyses of microbial diversity, taxonomic composition, predicted bacterial phenotypes, co-occurrence networks, and environmental associations. The five cultured populations shared a broadly similar microbial community structure dominated by Proteobacteria, Bacteroidota, and Firmicutes, whereas the relative abundance of several dominant taxa varied among regions. Alpha-diversity differences were mainly associated with community evenness and dominance rather than estimated richness. Although regional samples partially overlapped in ordination analyses, the aquaculture region explained 41.4% of the variation in gut microbial composition, indicating significant region-associated differentiation. Predicted microbial characteristics were comparatively similar among cultured populations, despite variation in taxonomic composition, while microbial network organization also differed across regions. Environmental variables showed clearer associations with water microbiota than with gut microbiota, and local aquaculture water represented a detectable but limited potential source of intestinal microorganisms. Overall, farmed large yellow croaker maintained a shared gut microbial community structure but exhibited region-associated variation in community composition and predicted bacterial phenotypes. These findings provide a regional baseline for understanding gut microbial variation in farmed large yellow croaker under different aquaculture conditions. Full article

Acinetobacter baumannii is a critical priority ESKAPE pathogen whose resistome co-occurrence architecture, temporal dynamics, and One Health distribution remain poorly characterized. A total of 20,739 genomes (2000–2025; NCBI, PubMLST, BV-BRC) were annotated using CARD-RGI v6.0.5, AMRFinderPlus v4.2.7, and ResFinder v4.7.2. A co-occurrence network was constructed using Jaccard filtering (≥10%; >0.30) with Bayesian bootstrap and Louvain. Temporal trends were assessed by linear regression with Benjamini–Hochberg (2003–2024), and One Health compartments were assessed using balanced PERMANOVA (Bray–Curtis, 999 permutations). The network comprised 41 nodes and 82 edges with a small-world topology; five communities and nine hub genes were identified. Eighteen ARGs showed significant trends (FDR); six last-resort determinants (blaOXA-23-like, blaNDM, armA, ftsI, msr(E), mph(E)) increased in prevalence. The compartment explained significant variance in the resistome (R² = 0.210; p < 0.001). abaF showed the highest One Health convergence (0.814), and blaNDM was detected in 52 countries. The A. baumannii resistome exhibits a convergent architecture of co-resistance: a small-world network preserved in One Health compartments for more than two decades. The rise in six last-resort determinants signals a population-based transition toward broader profiles, establishing a framework for integrated antimicrobial resistance surveillance. Full article