Search Results (2,885)

An earthquake lethality model was employed to assess the casualty distribution in Yunnan, Guizhou, and Sichuan provinces, taking into account the ground motion acceleration with different 50-year exceedance probabilities. When the probability is 63%, fatalities are predominantly concentrated in central and south-western Yunnan, as well as central, southern, and western Sichuan. At a 10% probability, the peaks of the casualties are observed in southern, eastern, and central Sichuan. In Yunnan (excluding the northwest and southeast regions), the casualty density exhibits unevenness, whereas Guizhou experiences relatively low casualties (except in the eastern and western mountainous areas). Xichang incurs the most substantial losses, followed by Lancang. Xundian, Songming, and Dongchuan demonstrate a high propensity for fatalities, and the risk is relatively high in the vicinity of the Longjiang and Nujiang faults. If a destructive earthquake occurs near these areas within the next 50 years, the probability of a Level-I emergency response exceeds 10%. When the ground motion acceleration doubles (especially when the exceedance probability drops to 2% in 50 year and 0.1% in a year), the predicted number of casualties remains relatively stable. However, the grid of the casualty population exhibits a higher degree of spatial concentration of casualties, and the disaster-affected area expands. There exists no linear correlation between earthquake-induced fatalities and the ground motion level. When the 50-year exceedance probability decreases from 63% to 10%, the casualty rate may increase by several dozen times. Full article

This paper addresses the imaging crosstalk problem in 2D spectra from the LAMOST Phase II upgrade, caused by increased fiber density. We propose HyperDecouple_Net, a hypernetwork-based decoupling algorithm designed to overcome key limitations of existing deep learning models, including overlapping-layer collapse and structural distortion. The method integrates an adaptive overlapping-layer enhancement module, a dual-scale hypernetwork differential decoupling module, and a linear consistency constraint module. Additionally, we introduce LAMOST-SD-2026, a public dataset comprising 15,500 linearly superimposed spectral samples with ground-truth labels, derived from real LAMOST Phase I observations. Experimental results on this dataset show that HyperDecouple_Net achieves superior performance, with a PSNR_A of 12.71 dB, PSNR_B of 10.87 dB, SSIM_B of 0.3895, and SAM of 0.4841, outperforming both traditional methods (e.g., NMF, ICA) and recent deep learning approaches. The proposed method can be directly integrated into the LAMOST Phase II preprocessing pipeline, offering a robust solution for high-precision spectral decoupling and supporting the scientific output of the survey. Full article

Automated detection of archaeological stone cairns using high-resolution satellite imagery offers a scalable approach for documenting vulnerable heritage landscapes in the Ennedi Massif, where extensive and remote terrain limits traditional field survey, and rapid documentation is required. This study presents a GIS and deep learning framework based on the YOLOv8 model to identify and map stone cairns using Google Satellite RGB imagery at 28.5 cm spatial resolution. Ground-truth data collected via GPS field survey were used to train and validate YOLOv8n. The study area was divided into two regions with contrasting terrain and illumination conditions to evaluate model transferability. The training region included 149 verified cairns, while the independent test region included 103 cairns. Early stopping reduced overfitting, reaching mAP50 of 99.5% and mAP50–95 of 94.3%. A density-based spatial clustering algorithm was applied to merge overlapping detections and generate circular cairn representations. On the test set, the model achieved 83.5% precision, recall, and F1-score, indicating stable performance under the selected operational configuration. Comparison with YOLOv5n showed slightly higher localization accuracy for YOLOv8n, while YOLOv5n yielded marginally higher precision and F1-score. Overall, the framework provides a non-invasive tool for large-scale archaeological prospection and heritage monitoring in remote desert environments. Full article

Mobile RFID inventory in libraries must be planned and evaluated under noisy observations, configuration-dependent read regimes, and incomplete supervision. This paper presents an uncertainty-aware analytics framework for robot-assisted RFID inventory using the public RFID Location dataset. The framework has three phases. Phase 1 converts irregular list-encoded logs into atomic RFID events and quantifies how operating configuration changes read density and signal variability. Phase 2 performs map-constrained Bayesian shelf inference by synchronizing RFID reads with robot trajectory and antenna geometry and by fusing RSSI and carrier phase over feasible shelf candidates. Phase 3 translates posterior spread and non-convergence into proxy review workload and cost, enabling configuration comparison and certainty–throughput trade-off analysis when strict EPC-to-item linkage is unavailable. Across 688,073 aligned RFID observations, the pipeline produces 18,190 posterior tag estimates from five inventory runs. The empirical results show strong run dependence: the best run achieves a mean posterior spread of 0.906 m with a convergence rate of 0.553, whereas a degraded run reaches only 0.004 convergence with a mean spread above 2.1 m. Because EPC-to-item linkage is unavailable, these values are posterior concentration and workload indicators rather than ground-truthed localization-accuracy metrics. A saved phase-weight ablation further shows that adding phase information substantially sharpens posterior concentration relative to an RSSI-only baseline. Under the proxy workload model, autonomous-S1-P30 provides the most favorable balance among posterior certainty, scan effort, and implied review burden. Full article

Using a sample of 297 prefecture-level cities in China from 2010 to 2022 and drawing on green patent transfer data, this study constructs a directed weighted network and applies social network analysis, a modified gravity model, and quadratic assignment procedure (QAP) regression to examine the spatial structural evolution, node topology characteristics, and driving factors of China’s green technology transfer (GTT) network. The results show that: (1) From 2010 to 2022, the number of nodes grew from 249 to 292, network coverage increased from 83.8% to 98.3%, and the number of edges expanded by a factor of 14.47. Network density and average degree also rose markedly. The spatial structure evolved from an initially sparse and fragmented configuration into a polycentric complex network centered on the Beijing–Tianjin–Hebei region, the Yangtze River Delta, and the Chengdu–Chongqing economic circle. (2) In terms of node topology, the intermediary and control capacities of cities exhibit dynamic changes, with central and western cities gaining growing influence within the network. (3) Cohesive subgroup analysis identifies four functional blocks, revealing a multi-level technology spillover path of “core—secondary—regional—peripheral.” (4) QAP regression further identifies the digital economy, geographic location, high-speed rail mileage, industrial structure, and government environmental concern as key drivers of network formation and evolution. This study offers a new perspective on understanding cross-regional green technology transfer and provides theoretical grounding and policy references for promoting regional collaborative innovation and green low-carbon development. Full article

In the development of deep shale gas horizontal wells, precise geo-steering relies heavily on downhole sensor networks to acquire extensive formation and engineering parameters. Coiled tubing (CT) provides a promising acoustic waveguide for downhole sensing systems, but conventional acoustic sources rely on gravity-induced interfacial preload. Under highly deviated or horizontal well conditions, the loss of the axial gravity component may induce contact–nonlinearity instability, resulting in waveform distortion and spectral pollution. To address this limitation, a constant-stiffness preloading method based on elastic compliance control is proposed, together with a modal reconstruction strategy achieved by removing high-density tungsten blocks. A fluid–solid coupled dynamic model incorporating contact nonlinearity is established to reveal the dynamic separation mechanism of the acoustic source interface under varying gravity-vector conditions. Wave spring assemblies are then used to reconstruct the mechanical boundary and physically suppress time-domain clipping. Full-scale ground circulation experiments on a 1500 ft CT string show that the proposed method decouples acoustic-source performance from wellbore trajectory. Waveform asymmetry is reduced from 18.4% to 2.1%, and total harmonic distortion decreases from 12.5% to 1.8%. In addition, the first-order longitudinal natural frequency is shifted from 420 Hz to 2850 Hz, avoiding low-frequency pump noise and achieving a 12 dB SNR improvement. This physical-layer gain provides an optimized signal baseline for receiver-end demodulation algorithms. Ultimately, this study provides a robust physical-layer solution for acoustic telemetry in complex deep-earth environments, advancing the reliability of data interaction in downhole sensing systems. Full article

Introduction: The sea lamprey (Petromyzon marinus) is an anadromous jawless fish that migrates between marine and freshwater environments, spawning once in rivers before dying. It is distributed along both coasts of the North Atlantic. In Asturias (northern Spain), P. marinus is classified as Vulnerable, and a conservation plan is currently under development. In the present study, Critical Conservation Areas (CCAs) are defined as river reaches requiring urgent protection due to their high ecological value for the species. Objective: This study aimed to identify key reproductive and larval habitats (spawning grounds and silt–sand banks) to support the designation of CCAs for the conservation of the sea lamprey in Asturias. Methodology: Fieldwork was conducted in the main salmonid rivers of Asturias (Deva, Nalón, Narcea, Navia, Eo, and Sella basins). During spring 2025 and 2026, habitats were characterized and spawning sites identified using transects. In autumn 2025, larvae were sampled by electrofishing within defined areas (10–30 m²), measured and weighed, and densities were extrapolated using an inverse distance weighting (IDW) model. Basin use and critical areas were assessed based on species distribution and habitat quality. Results: A total of 1886 larvae were recorded across 27 river sections, of which 1366 were measured. Sea lamprey was present in 74.1% of sections, whereas brook lamprey (Lampetra planeri) was detected only in the Deva basin. Mean larval size was 9.5 ± 3.7 cm and 2.3 ± 2.19 g, with marked spatial variability. The highest larval densities occurred in the Eo (21.5 ind/m²) and Narcea rivers (15.1 ind/m²). Additionally, 94 spawning records were identified, and 65% of the 83 assessed sections were classified as good or very good habitat. Conclusions: A total of 31 CCAs (~300 km) were proposed, including 97 km classified as “of interest”, 21 km as “important”, and 180 km as “highly important”. Habitat use was greatest in sinuous middle river reaches with riffles, pools, and fine sediment deposits. Transversal barriers, dredging, and channel simplification were identified as the main drivers of habitat loss. These findings provide a robust scientific basis for conservation planning and management of the species in Asturias. Full article

Plant functional traits are central to regulating ecosystem multifunctionality (EMF), yet how coordinated above- and below-ground resource acquisition strategies mediate the effects of forest structural diversity on EMF remain insufficiently understood, particularly in typical south subtropical forests. Here, we applied a trait-based framework to disentangle the pathways linking forest structural diversity to EMF through plant resource acquisition strategies. Typical south subtropical forests were sampled for community-level leaf and root traits, including leaf total nitrogen and total phosphorus content, specific leaf area, leaf dry matter content, root diameter, specific root length, root tissue density, root total nitrogen and root total phosphorus content. EMF was quantified using 13 indicators associated with carbon storage, litter decomposition, primary productivity, and nutrient cycling, evaluated using both averaging and multi-threshold approaches. Principal component analysis was used to summarize trait variation along major functional axes representing the leaf and root economics spectra, and structural equation modeling was employed to quantify direct and trait-mediated pathways linking forest structural diversity to EMF. We found pronounced variation in EMF among forest types, with multifunctionality increasing along the classical fast-slow plant economics spectrum. Communities dominated by fast-growing species exhibited consistently higher EMF than those dominated by slow-growing species, with below-ground traits showing stronger associations with EMF than above-ground traits. In contrast, EMF was unrelated to the root collaboration gradient, suggesting that alternative below-ground foraging strategies contributed little to multifunctionality. Moreover, the positive effects of structural diversity on EMF were indirectly mediated through both leaf and root conservation gradients. Notably, the relative importance of these trait-mediated pathways was threshold-dependent. Root conservation gradients dominated EMF at low multifunctionality thresholds, whereas leaf conservation gradients became increasingly important at higher thresholds. Our findings show that forest structural diversity enhances ecosystem multifunctionality through coordinated leaf and root strategies. By revealing trait-mediated links between biodiversity and EMF, this study clarifies how community composition and species turnover shape multifunctionality in typical south subtropical forests. Full article

Although fine-grained spatial knowledge of the urban population distribution is fundamental for effective urban management, traditional census data lack sufficient resolution. Current disaggregation methods often struggle to probabilistically fuse heterogeneous data, such as noisy mobile signaling and building attributes, while ensuring hierarchical consistency between micro-level predictions and macro-level ground truth. To address these gaps, this study proposes a Bayesian-informed hierarchical learning (BIHL) model framework for building-level population estimation. The methodology integrates three distinct layers: (1) a data-driven prior model using a LightGBM ensemble to generate initial probabilistic estimates and uncertainty weights; (2) an enhanced neural network posterior estimator featuring a multi-branch architecture—incorporating Zone Bias Embedding and Zone Interaction networks—to capture non-linear urban dynamics and spatial heterogeneity; and (3) a constrained optimization layer utilizing a hierarchical loss function that enforces strict consistency between aggregated building estimates and official census data through dynamic curriculum learning. Through empirical validation in Haidian District, Beijing, it is demonstrated that the BIHL framework significantly outperforms baseline models (MLR, Random Forest, and LightGBM), achieving a Mean Absolute Percentage Error (MAPE) of 11.36%. This study confirms that incorporating building-level spatial locations and residential categories is vital for mitigating “spatial smoothing” and systematic under-prediction in high-density areas. This framework provides a robust, high-fidelity solution for generating residential population layers, which are essential for city planning. Full article

Radio occultation (RO) has become a key technique for monitoring the ionosphere by deriving electron density (Ne) profiles and total electron content (TEC) from GNSS signals. This study assesses the newly deployed PlanetiQ GNOMES constellation by validating its ionospheric Ne profiles and profile-based TEC against collocated measurements from ionosondes and the COSMIC-2 mission under both quiet and disturbed geomagnetic conditions. Data matching for the statistical validation uses conservative spatial thresholds of less than 1° in latitude and longitude and temporal limits of 30 min for ionosondes and 1 h for COSMIC-2, supported by a dedicated sensitivity analysis, whereas storm-time case studies apply tighter temporal collocation and explicit control of the ray path geometry. Quantitative agreement is evaluated using root mean square error (RMSE), mean and absolute mean differences, correlation coefficients, regression analysis, and normalized percentage differences for key F-layer parameters, including the maximum Ne of the F2 layer (NmF2), the peak height of the F2 layer (hmF2), and the critical frequency of the F2 layer (foF2), along with altitude-dependent Ne profiles. PlanetiQ shows strong consistency with ionosonde profiles, with RMSE ranging from 2.94 × 10⁴ to 2.76 × 10⁵ el/cm³, correlations typically exceeding 0.90, and normalized absolute mean differences often near or below about 10–20%, although lower correlations of about 0.31 and 0.69 are found at Poker Flat and Awase, respectively, reflecting complex local structures and regional variability. Comparisons with COSMIC-2 during quiet conditions yield RMSE values between 7.06 × 10⁴ and 2.16 × 10⁵ el/cm³, correlations from 0.94 to 0.99, and percentage differences that generally remain within a few tens of percent, while storm-time analyses show RMSE between 1.12 × 10⁵ and 3.70 × 10⁵ el/cm³ with correlations from 0.80 to 0.99, confirming robust agreement across a wide range of geophysical conditions. Regression results demonstrate slopes near 1.00 and correlation coefficients above 0.90 for NmF2 and foF2 between PlanetiQ and both ionosondes and COSMIC-2, whereas hmF2 exhibits larger scatter, particularly during geomagnetic disturbances; additional binning by spatial and temporal separation indicates that temporal mismatches generally have a stronger impact on discrepancies than horizontal distance. Overall, the results demonstrate that PlanetiQ ionospheric RO data provide accurate and consistent measurements of key ionospheric parameters, comparable to those from COSMIC-2 and ionosondes, and can reliably complement existing observing systems for monitoring ionospheric variability and space-weather impacts. Full article

Urban vitality research treats food-and-beverage venues as aggregate point-of-interest counts and existing morphometric classification frameworks operate at the building, block or neighbourhood scale, leaving the commercial ground-floor interface without a programme-specific typology. This study develops GATE (Ground-floor Architectural Typology at the street Edge), a 22-variable morphological framework operating at the venue–street–interface scale, and applies it to 85 interfaces across eleven commercial arteries in the core of a mid-sized Turkish city. Ward hierarchical clustering yields a single dendrogram read at macro (k = 3) and micro (k = 7) resolutions, validated through Kruskal–Wallis tests that separate 17 and 19 of the 22 variables, respectively. Three macro types emerge: narrow-fronted apartment-ground-floor venues, detached garden-plot pavilion venues and vertically organised transparent-fronted venues. Space Syntax Integration, Choice and Shannon diversity produce no significant relationship with pedestrian density in the aggregate. Type stratification points to a resolution-dependent moderator effect: the apartment-ground-floor type shows negative Integration and positive Choice coupling, while the transparent vertical type shows positive Integration coupling, producing a directional pattern consistent with Simpson’s paradox. GATE provides one of the first programme-specific venue-level morphological frameworks and establishes an explicit quantitative mapping of the Panerai–Castex analytical typology with future multi-city applications to test its generalisability. Full article

This study investigates the electrical properties of gold mine tailings from the Soweto mining region to assess their potential as a low-cost and sustainable backfill material for grounding systems. Samples were collected from historical mine dumps, oven-dried at 70 °C for 24 h to determine dry density and baseline moisture content, and reconstituted to controlled moisture levels of 5–25% by mass. Bulk electrical resistivity was measured using the Wenner four-electrode method in accordance with ASTM G57-06. The results reveal a strong inverse correlation between moisture content and resistivity. At low moisture content (≈5%), resistivity exceeded measurable limits, indicating poor ionic conduction, whereas increasing moisture content led to a substantial reduction in resistivity, reaching an average value of approximately 10 Ω at 25% moisture due to improved pore water continuity and ionic mobility. These findings demonstrate that moisture-conditioned gold mine tailings can achieve electrical performance comparable to that of conventional grounding enhancement materials while offering notable economic and environmental benefits. Owing to their local availability and waste re-utilisation potential, the tailings present a technically feasible and environmentally responsible solution for improving earthing performance in high-resistivity soils. Further work should examine long-term field performance, corrosion effects, and leaching behaviour. Full article

In this paper, we investigate the dynamics of quantum correlations in the ground-state hyperfine manifold of the hydrogen atom subjected to a static external magnetic field and local pure dephasing. The electron–proton spin pair is modeled as a bipartite two-qubit system evolving under the combined effects of hyperfine coupling, Zeeman splitting, and a Lindblad master equation that describes Markovian dissipative processes. Employing exact analytical solutions for the time-dependent density matrix elements (derived in the Markovian open-system framework), we quantify entanglement persistence via concurrence and state stability via Uhlmann fidelity with respect to the initial preparation. For an initial Werner state, numerical results reveal that the external magnetic field substantially modifies the system dynamics: Both concurrence and fidelity exhibit pronounced dependence on the Zeeman parameter, producing field-controlled oscillations, delayed entanglement sudden death, and altered decoherence rates. This behavior originates from Zeeman-induced lifting of hyperfine degeneracies, symmetry breaking of the isotropic Werner state, and redistribution of populations and coherences. Unlike previous studies that treat hyperfine interactions, Zeeman splitting, or decoherence in isolation, the present work provides a unified analytical treatment that simultaneously incorporates all three mechanisms. The findings underscore the competition between coherent hyperfine coupling and environmental noise and open new pathways for precision spectroscopy and robust quantum information protocols based on atomic spin degrees of freedom. Full article

In this study, spent coffee grounds (SCGs) were incorporated into polylactic acid (PLA) filaments and 3D-printed parts to investigate their effects on thermal, physical, and mechanical properties. Differential scanning calorimetry showed that SCG addition slightly reduced the glass transition temperature of PLA while markedly increasing its crystallinity, whereas thermogravimetric analysis revealed a moderate decrease in degradation onset temperature that remained well above the processing and printing temperatures, ensuring safe fabrication. Tensile testing indicated that SCG incorporation led to noticeable reductions in filament strength and stiffness, whereas the elongation at break was only weakly affected because of counteracting plasticization effects. For the printed parts, SCGs imparted a dark brown coloration, decreased density, and increased moisture uptake due to their porous and hydrophilic nature, while tensile, flexural, and impact strengths were reduced and the tensile modulus and elongation at break remained statistically similar across the 0–20 wt% range. These findings indicate that SCGs can be effectively incorporated to tailor the crystallinity, color, and density of PLA-based 3D-printed composites, albeit with trade-offs in strength and impact performance. Full article

The utilization of construction and demolition waste (CDW) as a supplementary cementitious material (SCM) represents a promising strategy for reducing cement consumption, minimizing environmental impacts, and promoting sustainable waste valorization. In this study, hybrid recycled powder was produced from mixed CDW obtained from a Portuguese recycling facility and processed through mechanical grinding to achieve particle size characteristics comparable to Portland cement. The ground powder was subsequently thermally activated at 600 °C and evaluated as a partial replacement for Portland cement in concrete. Concrete mixtures were prepared with recycled powder replacement contents of 5%, 15%, 25%, and 35%. The physical, mechanical, and durability properties of the concrete were investigated, including density, water absorption, compressive strength, carbonation and chloride penetration resistance. The results indicate that thermally activated recycled powder can be successfully incorporated as a partial cement replacement while maintaining satisfactory mechanical and durability performance. These findings demonstrate that thermally activated hybrid recycled powder derived from mixed CDW has significant potential as a sustainable SCM, contributing to reduced cement consumption and supporting the development of low-carbon concrete. Full article