Search Results (814)

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

The emergence of resistance-associated mutations in coronavirus main protease (Mpro) poses a significant challenge to the development of broad-spectrum antiviral therapeutics. In this study, we improved and accelerated a mutation-aware machine learning (ML) framework to predict the binding score of Nirmatrelvir analogue ligands against wild-type and mutant MERS-CoV Mpro. A library of 15,889 Nirmatrelvir derivatives generated through systematic scaffold modification was docked against the wild-type and five variants of the Mpro, producing a total of 95,334 structural and docking score datasets of these protein–ligand complexes. During the ML model development phase, ligand effects were learned from RDKit molecular descriptors and graph-based representations, and the mutation-induced effects were captured through delta-encoded physicochemical properties (hydrophobicity, charge, aromaticity, and polarity) of the active-site residues. Among the evaluated models, the CatBoost regressor tree-based algorithm achieved the lowest mean absolute error (MAE) value of 0.23 Kcal/mol and an R² of 0.87. Further improvement was achieved by creating a weighted ensemble model combining the CatBoost regressor, XGBoost and LightGBM regressor, resulting in a prediction accuracy with a MAE of 0.19 Kcal/mol and an R² of 0.90 relative to docking scores. Model robustness was further evaluated through random-, ligand group- and scaffold group- K-fold cross-validation along with their Y-randomization. Moreover, the models were also tested with a new set of 1000 structurally diverse compounds. SHAP analysis was conducted, which identified 20 molecular descriptors critical for accurate predictions. The ensemble model accurately predicted the binding affinities of Nirmatrelvir and its four analogues (E1–E4), reproducing the experimental pIC50 trend and correctly identifying the most potent inhibitors. The ensemble model also showed consistent performance across all MERS-CoV Mpro variants, S147Y, S142G, L144A, S142G/S147Y, and S142G/L144A/S147Y, demonstrating its potential for rapidly discovering mutation-resistant antiviral drugs. Full article

The Lower Cretaceous fan-delta sandstones in the North Yellow Sea Basin underwent a distinctive polyphase burial trajectory, offering a natural laboratory to investigate path-dependent diagenesis and its impact on reservoir quality evolution in superimposed rift basins. Integrating petrographic, cathodoluminescence, SEM, and quantitative diagenetic analysis, this study reveals an anomalously compaction-dominated diagenetic regime wherein mechanical compaction accounted for 32.1% porosity loss (ICOMPACT ~0.8) compared to only 7.4% by cementation. This anomaly is attributed to a path-dependent mechanism as follows: a >60 Ma erosional hiatus arrested early calcite cementation, leaving sandstones mechanically metastable and vulnerable to intensified anomalous re-compaction triggered by rapid reburial since ~37 Ma during the Himalayan tectonic phase (since 66 Ma). The paragenetic sequence progresses from early poikilotopic calcite precipitation to late-stage microquartz, ferroan carbonates, and illitization, with intermediate feldspar dissolution generating secondary porosity. Critically, reservoir quality exhibits strong facies-dependent heterogeneity driven by divergent diagenetic pathways. Proximal matrix-supported gravels (Gcm/Gmm) are destroyed by pseudomatrix formation, whereas channelized sandstones (St/Sp) with localized early cement frameworks are buffered against compaction and preserve enhanced porosity (12%–18%) through subsequent dissolution. Consequently, this polyphase burial history fundamentally decouples reservoir quality from maximum burial depth. Effective reservoir prediction requires a paradigm shift from conventional depth-porosity transforms to integrated diagenetic facies analysis, validated by static petrophysical and well-log signatures (e.g., elevated Th/K ratios). These findings establish a transferable genetic model for evaluating porosity preservation in analogous Mesozoic polyphase-reactivated rift basins. Full article

The study evaluated the effect of an Ascophyllum nodosum-based biofertilizer applied under three treatments: foliar (FA, 0.5 g/L every 15 days), root (RA, 0.5 g/L every 30 days), and combined (F&RA, 0.5 g/L, Foliar every 15 days and Root every 30 days) compared to a control (C) in aeroponic cultivation of Phaseolus vulgaris L. var. Opus. Environmental conditions were continuously monitored using an IoE-based system, ensuring consistent microclimatic characterization throughout the experimental period. A non-parametric statistical approach was applied due to non-normal data distribution. Most growth and yield variables did not show statistically significant differences; however, significant treatment effects were observed for leaf temperature, root temperature, calcium, iron, and manganese. Significant differences among treatments (α = 0.05) were identified using Kruskal–Wallis test for calcium (p = 0.0002), iron (p = 0.0067), and manganese (p = 0.0439), while other micronutrients showed no statistical differences. Descriptive statistics indicated moderate shifts in central tendency, particularly for calcium and iron, with higher values observed in the combined treatment (F&RA). Effect size analysis using Cliff’s delta (δ) revealed moderate to large differences for calcium, iron, and manganese, although these estimates were interpreted cautiously given the unequal replication among treatments. Spearman correlation analysis showed moderate to strong associations, although none were statistically significant (p > 0.05). Overall, results indicate limited effects of the biofertilizer on growth and yield variables but treatment-associated changes in selected nutritional components, suggesting element-dependent responses to biofertilizer application. Full article

The transition toward carbon neutrality demands not only an understanding of the complex dynamics within energy–economy–environment (3E) systems but also the ability to strategically enhance their resilience against external shocks. Here, we introduce a bidirectional coupled framework that integrates macro-level supernetwork topology with micro-level agent-based modeling (ABM) to diagnose structural vulnerabilities and optimize systemic performance. Applied to 41 cities in the Yangtze River Delta (YRD) from 2010 to 2023, our framework reveals a persistent core–periphery spatial disparity in coupling coordination, underpinned by four distinct network layers—energy flow, economic linkage, environmental impact, and policy synergy—whose densities vary by an order of magnitude. Through coupled evolutionary simulations, we quantify system resilience as a tripartite metric of robustness, adaptability, and recovery, identifying a systemic structural weakness: a robust recovery capacity is offset by substantially lower robustness. To address this, we deploy a genetic algorithm to solve for optimal investment allocation under a budget constraint, demonstrating that a targeted, hub-centric strategy yields a higher marginal resilience gain than uniform distribution. Furthermore, embedding multi-agent reinforcement learning (MARL) and social learning into policy scenario simulations shows that unified environmental standards and a carbon-inclusive mechanism effectively eliminate regulatory arbitrage and accelerate low-carbon behavioral diffusion, improving system-wide robustness by 35% under extreme climate shocks. This work delivers a closed-loop, transferable analytical framework that transforms structural diagnosis into actionable optimization, offering a scientific basis for coordinated regional decarbonization strategies. Full article

The striped mullet (Mugil cephalus) and white mullet (Mugil curema) support artisanal fishing at the mouth of the Soto La Marina River, Gulf of Mexico, an area of great ecological value within the Laguna Madre and Rio Bravo Delta Protected Natural Area, the Terrestrial Priority Region RTP-83, and the Marine Priority Region RMP-44. Given the absence of historical catch and effort time series—a typical constraint of data-limited fisheries—a length-based frequency approach was used to estimate growth parameters, mortality rates, and the exploitation rate (E = F/Z). During 2018–2019, 1134 specimens of M. cephalus and 339 of M. curema were sampled. Due to sexual dimorphism in M. cephalus, analyses were performed separately for females, males, and combined sexes, while M. curema was analyzed with sexes combined. Growth (L∞, k) and mortality (Z, M, F) parameters for combined sexes were: M. cephalus (562 mm, 0.14 year⁻¹; 3.72, 0.21, 3.51 year⁻¹) and M. curema (329 mm, 0.15 year⁻¹; 1.46, 0.25, 1.21 year⁻¹). Exploitation rates (E) substantially exceeded Gulland (E = 0.5) and Patterson (E = 0.4) reference points: M. cephalus females (0.891), males (0.915), combined sexes (0.944), and M. curema (0.828). It is concluded that both stocks show strong evidence of growth overfishing, with exploitation rates well above established reference points, revealing a disconnect between the area’s conservation designations and the actual condition of the resource. Full article

Improving regional economic resilience is a point addressed in the sustainable development goals (SDGs; i.e., SDG 8 and SDG 11). The Yangtze River Delta (YRD) has demonstrated excellent economic resilience during the COVID-19 pandemic, largely due to the persistent green transition of the YRD in the past two decades. This paper uses a single-case method combined with the perspective of evolutionary economic geography to systematically investigate the process of green transition in the YRD (2000–2023) at both vertical and horizontal levels and proposes an integrated multi-dimensional system framework to reveal the collaborative logic of the overall green transition action and the internal mechanism of enhancing economic resilience in the YRD. The findings indicate that the combination of external factors such as contradiction change, magnifying crises, economic stabilization, and policy steering has driven the historical inevitability of green transition in China. Under such conditions, the YRD not only completed development in terms of primitive accumulation of space (coordinated development, i.e., chassis), industry (orderly upgrade, i.e., engine), and governance (equal supply, i.e., lubricant) earlier but also ensured the stability of this triangle, injecting sustained strong momentum into the rapid recovery of the economy under the impact. The solidification of green concepts further enhances the sustainability and strength of the YRD’s economic resilience. These findings provide beneficial experience on how to resume production after the pandemic or lay out cities in developing countries that are still in rapid urbanization in advance. Full article

The outbreak of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has emerged as one of the most significant global health crises in recent history. Coronaviruses are a diverse group of RNA viruses classified into alpha, beta, gamma, and delta genera, with SARS-CoV-2 belonging to the beta-coronavirus family. The virus exhibits high transmissibility and causes a wide spectrum of clinical manifestations ranging from mild respiratory symptoms to severe complications such as acute respiratory distress syndrome, multi-organ failure, and death, particularly among elderly and immunocompromised individuals. Structurally, SARS-CoV-2 possesses a large single-stranded RNA genome encoding major structural proteins, including spike (S), envelope (E), membrane (M), and nucleocapsid (N) proteins, which play critical roles in host-cell recognition and viral infection. Understanding the molecular mechanisms of virus–host interactions, especially protein–protein interactions (PPIs), is essential for uncovering viral pathogenesis and identifying potential therapeutic targets. Traditional experimental techniques for PPI detection, such as yeast two-hybrid and affinity purification methods, are often expensive, labor-intensive, and prone to inaccuracies. Consequently, computational approaches based on machine learning (ML) and deep learning (DL) have gained significant attention for efficient and scalable PPI prediction. These methods use diverse biological information, including protein sequences, structural features, genomic data, Gene Ontology annotations, and interaction networks, to model complex biological relationships. This survey reviews computational approaches to PPI prediction, highlighting ML- and DL-based techniques, methodological advances, performance evaluation practices, and limitations that affect benchmark comparability. It also discusses biological databases and data sources commonly used in PPI studies and explicitly considers how models trained in coronavirus-centered settings may generalize to other viral families with different mechanisms of host interaction. Full article

Grounded in the principles of wave dynamics, this study employs the wave function expansion approach to mathematically describe how plane P- and SV-waves scatter around a composite tunnel lining embedded in an unbounded medium. To establish the transient analytical framework for the dual-layer structure subjected to blast excitations, we integrate Fourier integral transforms alongside the Heaviside step and Dirac delta functions. We systematically analyze how the tunnel’s transient dynamic stress concentration factor (DSCF) responds to variations in the shear modulus ratio, as well as the specific characteristics of the incoming waves (i.e., wave type and dimensionless pulse duration). Furthermore, a seismic mitigation strategy featuring a “soft-exterior, rigid-interior” configuration is theoretically explored. The analytical outcomes theoretically indicate that short-duration transient waves provoke severe dynamic stress concentrations within the lining, with SV-waves posing a markedly greater threat to structural integrity than P-waves. Analyses reveal that the peak dynamic stress concentration (DSCFmax) primarily localizes at the tunnel’s crown and invert. Interestingly, altering the pulse duration does not significantly shift this spatial distribution pattern. Ultimately, analytical results suggest that adopting the “soft-exterior, rigid-interior” design and optimizing the thickness of the primary support can substantially alleviate these stress concentrations, providing preliminary theoretical guidance for vibration attenuation. Full article

Convergent evolution in protein antigens is common across pathogens, including SARS-CoV-2; the most likely reason is the need to evade the selective pressure exerted by previous infection- or vaccine-elicited immunity. There is a pressing need for automated analysis of convergent mutations. We developed ConvMut, a tool to identify patterns of recurrent mutations in SARS-CoV-2 evolution; we exploited the granular phylogeny-based lineage hierarchy developed by PANGO, allowing us to observe deltas, i.e., groups of mutations that are acquired with respect to the immediately upstream tree nodes. Deltas comprise amino acid substitutions, insertions, and deletions. ConvMut can perform individual protein analysis to identify the most common single mutations acquired independently in a given subtree. Lineages are then gathered into clusters according to user-selected sets of shared mutations. An interactive graph orders the evolutionary steps of clusters, details the acquired amino acid change for each sublineage, and allows us to trace the evolutionary path until a selected lineage. ConvMut also supports frequency analysis for a given nucleotide or amino acid changes at a given residue across a selected phylogenetic subtree. ConvMut facilitates the exploration of convergent evolutionary trends in SARS-CoV-2, providing insights that could support the development of broadly effective anti-Spike monoclonal antibodies and Spike-based vaccines. Full article

Introduction: Mediterranean coastal wetlands are highly dynamic ecosystems that support diverse fish communities and are often of high conservation value. The Ebro Delta is one of the most important coastal wetlands in the Western Mediterranean, and knowledge of fish assemblages is essential for its effective conservation and management. Environmental DNA (eDNA) metabarcoding provides a non-invasive approach that can potentially complement conventional surveys for fish biodiversity monitoring. Objective: This study aimed to evaluate the potential of eDNA metabarcoding as a complementary tool to conventional fyke net surveys for fish biodiversity monitoring in the Mediterranean coastal wetlands. Methodology: In 2022, fish assemblages were surveyed across 12 areas of the Ebro Delta using eDNA metabarcoding (12S MiFish) and conventional fyke net sampling. Results were compared with a 22-year historical dataset. Results: A total of 27 fish taxa were detected, 13 of which were exclusive to eDNA, 11 were shared between methods, and three were recorded only by fyke nets. The reliability of eDNA metabarcoding was supported by the detection of endangered species, such as Anguilla anguilla and Apricaphanius iberus; ubiquitous taxa, such as Atherina boyeri and Pomatoschistus microps; and invasive species, such as Gambusia holbrooki and Cyprinus carpio. Detection of invasive species was maximized using eDNA. While eDNA revealed higher species richness than fyke nets, community composition differed significantly between methods, reflecting distinct detection patterns. eDNA preferentially detected non-benthic species, whereas fyke nets were more robust for benthic taxa detection. eDNA recovered most historically recorded species but failed to detect some taxa, such as Misgurnus anguillicaudatus and Sardina pilchardus. Despite richness differences, the two methods provided complementary views of fish assemblages, highlighting method-specific detection limitations and opportunities. Conclusions: eDNA does not fully replace conventional surveys and their combined use improves the detection of threatened and invasive species, better supporting conservation and management. Full article

In Chile, Eucalyptus globulus stands out as a significant forest species, yielding around 2 million tonnes of bark; this by-product is a valuable source of phenolic compounds. This research evaluated the valorization of E. globulus bark using alkali-assisted extraction (AAE) and obtained extracts intended to protect the wood against fungal degradation and ultraviolet (UV) radiation. The chemical and thermal properties of the extracts were characterized using total phenolic content (TPC), antioxidant capacity, FTIR spectroscopy, LC-LTQ-Orbitrap-MS, and thermal analyses (TGA and DSC). Pine wood samples were impregnated using the Bethel process, and their absorption, retention, leaching, UV resistance, gloss, and antifungal efficacy were evaluated. The AAE showed an extraction yield of 8.79%, almost double that of aqueous extraction, with a phenolic content of 970 mg GAE/100 g dry bark and good antioxidant capacity. The MS/MS analysis tentatively identified low-molecular-weight organic acids, phenolic acids, a hydrolyzable tannin derivative, ellagic acid, methylated flavonol glycosides, and an iridoid non-phenolic metabolite. Thermal analysis indicated greater stability of the alkaline extracts, with a mass loss of less than 10% up to 200 °C, and significant degradation between 220 and 300 °C. Leaching tests showed a lower release of polyphenols from alkali-treated wood, indicating reduced mobility and/or greater retention of the extractives within the wood structure. Biological assays demonstrated effective inhibition of stain fungi and strong resistance to brown rot. Furthermore, UV aging tests showed less color change (Delta E*) and greater resistance to surface degradation. These results demonstrate the potential of alkaline extracts from E. globulus bark as sustainable additives for wood protection. Full article

The subcellular localization of long noncoding RNAs (lncRNAs) is a central determinant of their function, yet its molecular determinants remain incompletely defined, and most existing predictors rely on the primary sequence. Because RNA-binding proteins (RBPs) are the proximal effectors of RNA compartmentalization, this study tested whether the composition of RBPs bound to a lncRNA is predictive of its nuclear or cytoplasmic localization. Enhanced crosslinking and immunoprecipitation (eCLIP) occupancy for 139 RBPs in K562 cells was integrated with the cytoplasmic–nuclear relative concentration indices (CN-RCIs) derived from matched subcellular fractionation, and localization was modeled under chromosome-grouped cross-validation with nested regularization. RBP-occupancy composition predicted localization beyond the transcript size and total binding amount (incremental cross-validated coefficient of determination, delta-R-squared = 0.17; receiver-operating-characteristic area under the curve, AUC = 0.73, a moderate-strength association; Freedman–Lane permutation, p = 0.005). This increment persisted (delta-R-squared = 0.12; p = 0.005) against an expanded baseline that additionally absorbed the transcript abundance, intron content and exon number, indicating predictive information that is not reducible to these transcript features, and the classifier was well calibrated (Brier score = 0.10; expected calibration error = 0.02). The signed coefficient profile separated RBP function systematically: factors acting in nuclear processes (splicing, 3′-end processing, and nuclear-matrix association) carried negative, nuclear-direction weights, whereas factors acting in cytoplasmic processes (translation and messenger RNA stability) carried positive, cytoplasmic-direction weights (Mann–Whitney p = 0.013). The profile generalized across cell lines: a K562-trained model predicted HepG2 localization (transfer AUC = 0.71 using 76 shared RBPs), and HepG2 reproduced the association independently (AUC = 0.77). The association is correlational and of moderate strength; it is presented as an interpretable, RBP-occupancy-based complement to sequence-based predictors of lncRNA localization. Full article

Background/Objectives: Impaired kidney function is associated with persistently elevated cardiac troponin levels, complicating evaluation of suspected non-ST-segment elevation acute coronary syndrome (NSTE-ACS). The comparative performance of high-sensitivity cardiac troponin T (hs-cTnT) and I (hs-cTnI) across sampling intervals in this population remains uncertain. We aimed to identify a kidney function-adapted assay-sampling protocol combination for suspected NSTE-ACS that may support collaborative pathways between nephrologists and acute care clinicians. We therefore assessed kidney function-specific diagnostic and prognostic performance using 0/1 h and 0/3 h protocols. Methods: We conducted a prospective observational cohort study of adults presenting with suspected NSTE-ACS at a tertiary emergency department between March 2019 and December 2020. Patients were stratified according to kidney function at presentation using estimated glomerular filtration rate (eGFR). Impaired kidney function was operationally defined as eGFR < 60 mL/min/1.73 m². Serial hs-cTnT and hs-cTnI concentrations were measured at 0, 1, and 3 h and interpreted using assay-specific thresholds and delta criteria. Diagnostic performance for NSTE-ACS and prognostic performance for 30-day major adverse cardiovascular events (MACEs) were evaluated. Results: Among 140 patients, 58 (41%) had impaired kidney function. Baseline hs-cTnT and hs-cTnI concentrations were significantly higher in patients with impaired kidney function across all sampling time points. In this group, the 0/3 h protocol demonstrated superior diagnostic performance compared with the 0/1 h protocol for both assays. Using 0/3 h testing, hs-cTnI achieved the highest sensitivity (88.6%; 95% CI, 49.2–95.3), whereas hs-cTnT showed the highest negative predictive value (92.2%; 95% CI, 76.2–94.6). In patients with preserved kidney function, both assays demonstrated high specificity and positive predictive value with the 0/3 h protocol. Prognostic discrimination for 30-day MACEs also improved with a 0/3 h strategy, particularly in patients with impaired kidney function. Conclusions: In patients with impaired kidney function and suspected NSTE-ACS, extending troponin testing to 3 h improves diagnostic accuracy and short-term prognostic performance, supporting kidney function-adapted troponin strategies in emergency and nephrology care. Full article

Rising heatwaves threaten urban sustainability, necessitating a shift toward heat resilience. This study examines 38 cities across China’s three major coastal urban agglomerations (2016–2024) to quantify dynamic resilience responses. Utilizing a dual-threshold identification method and the Baidu Search Index to construct a Standardized Stress Index (SSI), the research evaluates urban heat vulnerability (UHV) through an exposure–sensitivity–adaptive capacity framework while applying NMF and machine learning models (XGBoost/SHAP) to analyze spatiotemporal heterogeneity. The results show that heatwave pressures peaked in 2022–2023, with Jing–Jin–Ji’s UHV evolving from localized clusters toward regional homogenization. Regional UHV profiles reveal that Jing–Jin–Ji is constrained by population pressures, the Yangtze River Delta (YRD) by resource allocation, and the Pearl River Delta by industrial attributes; notably, the YRD’s systematic coordination effectively offsets structural vulnerability. Furthermore, the optimized XGBoost model achieves strong predictive performance (R² = 0.673), revealing that core factors like summer heat exposure intensity (SHE, 25.65% importance) trigger sharp non-linear surges in social stress upon crossing critical inflection thresholds (e.g., SHE at −0.10). The conclusion will lead to the formulation of differentiated, forward-looking climate adaptation strategies to enhance urban resilience across major regions. Full article