Search Results (2,698)

Tunnel excavation in ground comprising a soft upper stratum and a hard lower stratum posed significant challenges, particularly during invert construction using drilling-and-blasting methods, which could disturb the surrounding rock and the installed support system. To address these issues, this study evaluates the feasibility of an alternative support scheme that eliminates the invert by employing expanded arch feet, thereby utilizing the self-bearing capacity of the underlying hard stratum. Both physical model tests and numerical simulations were conducted to compare the structural performance of a conventional lining with an invert against the proposed expanded arch foot lining. Based on the strain measurements and earth pressure cell readings from the physical model tests, together with the internal force and contact pressure results obtained from the numerical simulations, the axial force, bending moment, and contact pressure between the lining and surrounding rock were compared for the two lining schemes. The safety factors were further calculated using the axial force and bending moment at representative sections. The results showed qualitatively consistent trends between the tests and simulations. Compared with the conventional invert lining, the expanded arch foot lining increased the axial force and bending moment at the arch foot and sidewall by 7.95% and 29.60%, respectively, while slightly reducing those at the crown by 4.44% and 3.09%. This indicates that part of the structural demand is transferred from the crown to the arch foot and sidewall, where the enlarged sections provide greater bearing capacity. The contact pressure distributions of the two lining schemes were different. Owing to its closed structural form, the conventional invert lining produced a more favorable contact pressure condition at the sidewall. In contrast, the expanded arch foot lining showed higher safety factors at the arch foot and sidewall, with increases of 89.80% and 36.00% in the model tests and more than 140% in the numerical simulations. Meanwhile, the internal force distributions of the two lining schemes remained generally comparable. These findings indicated that the lining with expanded arch feet provided sufficient structural capacity at critical sections and could serve as a feasible alternative to the conventional lining with an invert, while avoiding the construction-related disadvantages associated with invert excavation. Full article

Road networks providing access to forest stands, including integral parts such as timber yards, are subjected to significant loads from timber-transporting vehicles. These loads act on the road surfaces, causing them to deform. This study measured and compared the extent of deformation at two timber yards in the mountain forests of southern Poland in the Western Carpathians. These surfaces were constructed as: crushed stone (timber yard in the Ustroń Forest District) and earth (Forest Experimental Station of the University of Agriculture in Kraków). The measurements were carried out using photogrammetric techniques based on aerial surveys by an unmanned aerial vehicle. The measurements were based on networks of reference points with coordinates in local coordinate systems. During the period between the flights, timber was being stored, handled and transported at both sites. The forest administration provided data on the volume of timber delivered to and removed from the storage yard, as well as basic information on the transport vehicles. Soil samples were taken from the surface of both storage yards for laboratory analysis to illustrate working conditions—the soil type, current moisture content, organic matter content and filtration coefficient (from the soil particle size distribution curve) were determined. Digital terrestrial model (DTM) rasters obtained from both aerial surveys at each storage yard were used to calculate differential rasters, which were analysed by plotting cross-sections at selected locations and directions and by calculating the volume of changes in surface geometry between the survey dates. The observed depths of ruts reach 0.4 m at the Ustroń storage yard and 0.5 m at the LZD storage yard, whilst changes involving the displacement of soil from the ruts above the previous surface level are 0.3 m at the Ustroń storage yard and 0.4 m at the LZD storage yard. Greater deformation was observed on the earth surface (with a high organic content and poorer drainage) than on the crushed stone surface, even though the latter was covered by an uncleared layer of mud and waste left over from timber handling. Given the two-site, single-cycle design of this study, this pattern is consistent with—but cannot on its own confirm—a stabilising effect of surface reinforcement; differences in soil moisture, organic content and observation period between the two sites may also have contributed and could not be separated from the effect of surface type alone. Full article

Earth Observation (EO) analysis increasingly relies on large and heterogeneous satellite datasets, yet developing EO workflows often requires specialized expertise in data selection, geospatial programming, and cloud-based processing. Recent advances in Large Language Models (LLMs) offer new opportunities for natural-language interaction with EO systems, although challenges related to transparency, reproducibility, and domain-specific reasoning remain. This study presents SeaScope, an explainable AI framework that integrates LLMs, Retrieval-Augmented Generation (RAG), scientific knowledge retrieval, and Google Earth Engine (GEE) to transform natural-language requests into transparent and executable EO workflows. The framework combines knowledge retrieval, code generation, cloud execution, provenance tracking, and interactive visualization within a unified environment. A pilot implementation is demonstrated through maritime and coastal monitoring applications, including oil spill detection, vessel monitoring, water quality assessment, floating debris detection, and air quality analysis. Multiple state-of-the-art LLMs are evaluated under both RAG and non-RAG configurations using representative EO case studies. The results indicate substantial differences among model families and show that retrieval augmentation can significantly improve workflow generation quality and reliability for capable models, while providing more limited benefits for smaller models. The proposed framework demonstrates the potential of explainable AI agents to support transparent, reproducible, and scalable EO analysis. Full article

To address the insufficient load-bearing capacity and overall stiffness of timber truss roofs in brick–timber and earth–timber structures in rural areas, as well as the requirements for preserving traditional architectural characteristics, a steel–timber composite roof system was proposed. The system was developed through rational integration of timber and steel components to enhance the overall mechanical performance of the structure. At the joint level, flexural performance tests were conducted on cramp-iron joint, gusset–plate joint, and steel–timber joint. The moment–rotation relationships, failure modes, and ductility characteristics of the three joint types were systematically investigated. Based on the experimental results, a trilinear moment–rotation model was established. Furthermore, a finite element model of the roof structure was established using SAP2000 (26.2.0), and the stress distribution and load–displacement responses under horizontal static loading were analyzed through numerical simulation. The influence of different joint configurations on the mechanical performance of the roof structure was evaluated from an overall structural perspective. The results demonstrated that the peak bending moment of the steel–timber joint was increased by approximately 163.50% and 3.74% compared with those of the cramp-iron joint and gusset–plate joint, respectively. The ductility coefficient was enhanced by approximately 9.33% and 62.91%, respectively. In the finite element model of the roof structure, the peak load of the roof system with the steel–timber joint was increased by approximately 114.96% and 9.54%, while the corresponding displacement capacity was improved by approximately 76.62% and 43.41%, compared with the other two roof systems, respectively. Future studies will focus on further evaluating the seismic performance of steel–timber composite roof systems through cyclic loading experiments, dynamic response analysis, and full-scale structural validation, thereby providing a more comprehensive understanding of their long-term applicability in earthquake-prone rural buildings. Full article

Monitoring forest degradation using medium-resolution optical sensors often results in an underestimation of the actual ecological impacts, limiting conservation strategies in threatened regions. We evaluated the forest disturbance dynamics (2004–2025) in the Permanent Production Forests of Tahuamanu, Madre de Dios, Peru. We processed multitemporal Landsat images in Google Earth Engine to map change trajectories by applying Spectral Mixture Analysis to derive the Normalized Difference Fraction Index (NDFI) integrated with a stratified area estimator. This approach yielded overall accuracies of ≥93%. Our findings show that structural degradation is replacing deforestation as the main driver of forest alteration. By 2025, the footprint of this disturbance, which silently affects the understory, had quadrupled the extent of deforestation, a trend evidenced by the stratified adjustment that revealed over 10,000 hectares of structural damage previously hidden under the label of intact forest, demonstrating the typical omission bias of passive sensors. Relying on raw maps means underestimating biomass lost to understory degradation. To address this systematic bias, it is necessary to operationalize the NDFI model along with a rigorous stratified estimation. With this combined approach, a scalable, cost-effective, and statistically robust framework is offered to monitor the subtle degradation that traditional mapping systems overlook. To our knowledge, this is the first long-term, area-corrected assessment of forest disturbance within Peru’s oldest formal timber concessions, and it shows that degradation persists and accelerates even under a regulated management model. Full article

Heavy metal fluoride glasses (HMFGs), particularly fluorozirconate glass systems such as ZBLAN have attracted considerable attention due to their unique physical properties, including low phonon energies, wide transparency from the UV to the mid-IR, and high rare-earth ion doping solubility, making them promising materials for photonic applications. This review provides an overview of fluoride glass synthesis methods, structural characteristics, and physical properties of fluorozirconate glasses, with emphasis on glass processing conditions, thermal, mechanical, and optical properties. The structural characteristics are discussed in terms of zirconium–fluorine polyhedral networks and their compositional dependence, while physical properties are analyzed, including glass transition behavior, crystallization tendency, elastic moduli, and infrared transmission. Rare-earth doped Er³⁺, Ho³⁺, and Tm³⁺ ZBLAN glasses are also discussed, which exhibit efficient emissions in the near and mid-IR spectral regions. Although significant progress has been achieved, limitations related to thermal stability, mechanical strength, and incomplete understanding of structure–property relationships persist. Future research should therefore focus on compositional optimization and predictive structural modeling to enable the design of improved fluoride glasses for various applications. Full article

In multi-infeed UHVDC receiving-end grids, transformer DC-bias risk is affected not only by the magnitude of grounding-electrode current, but also by the AC-grid topology, substation grounding condition, voltage-level-dependent current limits, and polarity coordination among grounding electrodes. Conventional single-electrode or magnitude-only assessments may therefore fail to identify the substations that require priority mitigation. This paper proposes a full-registry high-risk substation identification and targeted mitigation framework for transformer DC bias in multi-infeed UHVDC receiving-end grids. A field–circuit coupling model is established using the earth-resistivity model, grounding-electrode parameters, substation grounding parameters, transformer winding DC resistances, and AC-grid topology. To address earth-resistivity uncertainty, a measurement-based correction procedure is introduced and verified by an engineering field-measurement case. On this basis, the receiving-end grid is represented as a weighted complex network, and high-risk substations are identified by jointly considering network importance, voltage-dependent limits, multi-mode DC-bias exposure, and over-limit severity. A case study with 898 substations is carried out under four representative grounding-electrode operating modes. The results show that CJ single-electrode operation produces no over-limit substation, whereas YZ single-electrode, same-polarity two-electrode, and opposite-polarity two-electrode operation produce 2, 3, and 2 over-limit substations, respectively. Polarity coordination changes the risk pattern: same-polarity operation mainly aggravates the UHV substations NY UHV and ZMD UHV, whereas opposite-polarity operation relieves them but concentrates the 500 kV risk at SMPP and ZT. Guided by the high-risk ranking, installing 2 Ω neutral-point resistors at only two over-limit 500 kV substations reduces the currents at ZT and SMPP from 5.26 A and 5.68 A to 2.55 A and 0.58 A, respectively, bringing all evaluated substations within their limits and avoiding system-wide retrofitting. Full article

Soil moisture is a key forecast variable of land surface models. Direct assimilation of microwave brightness temperature data to optimize soil moisture initial fields is an effective approach to improve the simulation accuracy of soil moisture. However, most existing direct assimilation methods adopt physical radiative transfer models as observation operators, and their complex parametric errors greatly restrict the improvement in assimilation performance. This study introduces a high-precision MLP (Multilayer Perceptron)-based surrogate radiative transfer model as the observation operator. Combined with the Simplified Extended Kalman Filter (SEKF), it develops a direct radiance data assimilation system for the Common Land Model (CoLM). Assimilation experiments are conducted using brightness temperature data from the Microwave Radiation Imager (MWRI) onboard the FY-3D satellite. Their performance over China’s land areas is systematically assessed through comparison with the assimilation scheme based on the Community Microwave Emission Model (CMEM). The results show that the MLP-based assimilation scheme can effectively improve soil moisture simulation accuracy, yet the improvement varies across vegetation types: grassland areas achieve the largest error reduction (10.2%), while semidesert areas present the most prominent increase in the correlation coefficient (53.9%). Compared with the CMEM scheme, the MLP scheme exhibits better error stability and produces generally improved assimilation effects; specifically, in semidesert areas, the error decreases by 9.4%, and the correlation coefficient increases by 62.8%. This study demonstrates that deep learning-based observation operators have strong application potential for land surface data assimilation under complex physical mechanisms. Full article

Recent advances in numerical modelling, remote sensing, and artificial intelligence are bringing a transformation in our ability to assess sediment transport. Nevertheless, climate change is fundamentally altering sediment production, transport, and deposition through intensifying hydrological extremes, sea-level rise, changing storm regimes, cryosphere degradation, and increasing human modification of sediment pathways. These interacting drivers challenge conventional sediment transport assessment, which has largely evolved within separate fluvial, estuarine, coastal, and marine disciplines and often lacks an integrated perspective capable of representing source-to-sink sediment connectivity under non-stationary environmental conditions. Although significant advances have been made in process-based numerical modelling, Earth observation, and artificial intelligence (AI), these approaches are commonly reviewed independently, limiting their collective application to regional climate-responsive sediment assessment. This review examines state-of-the-art process-based numerical models, observational tools, and machine-learning approaches for sediment transport from source-to-sink. A transparent benchmarking scheme is used to compare leading modelling systems (e.g., AdH, SRH-2D, FLO-2D, HEC-RAS, TELEMAC, Delft3D, EFDC, SCHISM, XBeach, ROMS), highlighting differences in dimensionality, sediment-process representation, computational demands, and climate-scenario readiness. Remote sensing (optical, SAR, LiDAR, UAV) and AI/ML/DL methods (e.g., random forests) are reviewed as complementary tools that enhance model parametrization, improve validation, and address uncertainty in data-limited regions. A reproducible bibliometric synthesis based on Dimensions.ai records (2000–2026) reveals accelerating growth in sediment-transport research, with strong recent expansion in coastal, estuarine, and data-driven modelling applications. Major challenges include cohesive sediment physics, cross-environment coupling, limited long-term validation datasets, and the need for scalable workflows compatible with climate-model forcing. In this manuscript, we analyse and propose a future roadmap for near-term integration of satellite–field data streams, medium-term development of hybrid physics–AI models, and long-term coupling of sediment modules within Earth-system and regional climate frameworks. Collectively, this review provides a foundation for next-generation, climate-responsive sediment transport assessment supporting sustainable river basin and coastal management. Full article

The emergence of biological membranes was a critical step in the origin of cellular life because compartmentalization enabled molecular concentration, selective interactions, and increasingly complex chemical evolution. While fatty acids are widely considered the primary constituents of primitive membranes, the origin of the hydrophilic molecular scaffolds required for more stable amphiphilic systems remains unresolved. In this review, we propose a new conceptual framework in which low-molecular-weight polyols—including ethylene glycol, glycerol, tetritols, and related sugar alcohols—served as key molecular intermediates linking abiotic carbohydrate chemistry with the emergence of proto-lipids and protomembranes during a pre-phosphate stage of Earth history. Experimental and theoretical studies indicate that abiotic carbon chemistry can generate abundant polyols capable of esterification, etherification, hydrogen bonding, and reversible complexation with borate species. We hypothesize that borate-mediated stabilization of sugars and polyols promoted molecular selection, while sulfur-rich geochemical environments supplied chemically diverse amphiphiles and redox-active reaction networks. Building upon these observations, we propose a pH-dependent evolutionary model in which acidic sulfur-rich environments favored sulfo-protolipids, near-neutral environments promoted mixed polyol–fatty acid membranes, and alkaline boron-rich systems facilitated borate-associated amphiphiles and dynamic supramolecular membrane organization. We further suggest that borate-cross-linked polyol hydrogels acted as transitional soft-matter systems connecting molecular synthesis, membrane self-assembly, compartmentalization, and the emergence of proto-informational assemblies. Modern glycolipids, sulfolipids, archaeal ether lipids, and calditol-containing tetraether membranes are discussed as structural analogues, rather than direct evolutionary descendants, supporting the chemical versatility of polyol-based membrane architectures. Although the proposed evolutionary framework remains hypothetical, it integrates current knowledge from prebiotic organic chemistry, membrane biophysics, boron coordination chemistry, sulfur geochemistry, and systems chemistry into a unified and experimentally testable model for the evolution of proto-lipids, protomembranes, and early protocellular organization. Full article

Software-defined satellite networks (SDSNs) enhance resource utilization and flexibility in space-based networks by leveraging a global view and programmability. A highly reliable control plane is essential to sustain network operations. However, the highly dynamic topology and physical failures in Low Earth Orbit (LEO) environments can cause satellite node outages or inter-satellite link disruptions, leading to control plane interruptions and local load imbalances. To address this, we propose a switch migration mechanism for failure recovery and establish a multi-objective migration model that jointly optimizes control link delay, controller load variance, and normalized migration ratio. To accommodate distinct dynamic characteristics such as frequent topology changes, failure-intensive periods, and stable periods, we design two algorithms: a robust migration algorithm, DNSGA-II, which features population diversity maintenance and environmental awareness, and an efficient migration algorithm, IHAOAVOA, which integrates strong global exploration with powerful local exploitation. Simulation results show that IHAOAVOA rapidly converges under large-scale failures, achieving millisecond-level delay recovery and low normalized migration ratio overhead during failure-intensive periods, while DNSGA-II focuses on long-term load balancing and system stability during stable periods, effectively suppressing localized controller overload. By adopting IHAOAVOA during topology fluctuations or high-failure phases to reduce delay, and switching to DNSGA-II during stable phases to optimize load distribution, the overall network robustness can be improved under the evaluated failure scenarios. This work provides effective support for achieving highly reliable control in SDSNs under failure scenarios. Full article

Effective conservation of the Near Threatened (NT) black-necked crane (Grus nigricollis) requires standardized frameworks for organizing multi-scale biodiversity data. This study proposes the Essential Biodiversity Variables (EBV) system as an optimal structure for archiving and sharing biodiversity data such as Habitat Suitability Index (HSI). Developed by the Group on Earth Observations Biodiversity Observation Network (GEO BON), the EBV framework is an emerging system offering a robust solution for standardizing data exchange. Based on 483,592 valid location records of 106 black-necked cranes using satellite telemetry, we apply species distribution models and demonstrate how the multi-dimensional EBV architecture accommodates distinct life-stage preferences: breeding sites favor mid-elevations modulated by temperature; migration staging relies on precipitation regimes; and wintering grounds are driven by moisture availability and the avoidance of human-modified landscapes. The EBV NetCDF (Network Common Data Form) format functions as a self-describing hypercube that captures spatial, temporal, and life-stage dimensions while ensuring metadata transparency. This integration facilitates critical applications, including the identification of priority conservation areas and climate vulnerability assessments, thereby bridging the gap between species-specific modeling and global biodiversity monitoring standards. Full article

Per- and polyfluoroalkyl substances (PFASs) are persistent synthetic chemicals of global concern. While most research has focused on their occurrence and toxicity, far less attention has been paid to their impacts on the global carbon cycle. This review synthesizes current evidence on how PFASs influence carbon cycling across soils, aquatic systems, and the atmosphere. In soils, PFASs alter organic carbon inputs by affecting plant biomass and root exudates and shift microbial community composition and enzyme activities, thereby modulating organic matter decomposition. In aquatic ecosystems, PFASs biologically impair carbon sequestration by inhibiting plankton, and abiotically interact with extracellular polymeric substances to prolong the cycling of dissolved organic carbon. The atmosphere acts as a key mediator as follows: thermal treatment of PFASs generates perfluorocarbons, potent greenhouse gases that exacerbate global warming and further disturb carbon cycling. Despite clear disruptive effects, major knowledge gaps remain. Future research should use quantitative structure–property relationship modeling to assess PFAS alternatives (e.g., PFHxS), and employ advanced molecular tracking (e.g., isotopic labeling, NanoSIMS) and machine learning to unravel nonlinear PFAS–carbon dynamics. Improved detection technologies are needed to identify greenhouse gas byproducts from PFAS thermal treatment. Ultimately, deploying high-resolution flux observation networks and integrating PFAS dynamics into Earth system models and carbon-accounting frameworks are critical for predicting carbon–climate feedback and supporting global carbon neutrality goals. Full article

Cultural heritage sites are affected by environmental and anthropogenic pressures that require decision-analysis methods capable of combining heterogeneous datasets while accounting for uncertainty. Multi-Criteria Decision Analysis (MCDA), particularly when integrated with Geographic Information Systems (GIS) and Earth Observation (EO) data, is widely used in geospatial analysis involving multiple, often conflicting criteria. This review examines the evolution, application domains, and methodological challenges of MCDA in cultural heritage risk assessment. The literature indicates a predominant reliance on weighting-based methods, especially the Analytic Hierarchy Process (AHP) combined with GIS-based weighted overlay techniques, while uncertainty treatment, temporal monitoring, validation, and multi-threat applications remain limited. Three illustrative applications show that asset-level, regional susceptibility, and historic-urban frameworks address complementary decision needs but differ in their data, expertise, and institutional requirements. Recent developments show a trend to combine MCDA with fuzzy logic, machine learning, and uncertainty modeling, although methodological consistency across these approaches remains uneven. The findings suggest that multi-criteria risk assessment for cultural heritage may depend less on introducing new analytical techniques and more on improving the integration of existing methods. Incorporating repeatable environmental observations, sensitivity analyses, multi-threat assessment, and stakeholder participation may support a more coherent and reproducible approach to heritage-risk assessment. Full article

Rare earth elements (REEs) are critical for advanced manufacturing and clean energy, yet their separation remains extremely challenging due to the nearly identical ionic radii of adjacent lanthanides. Conventional solvent extraction, ion exchange, and precipitation methods are limited by their high reagent consumption, slow kinetics, poor selectivity, and environmental burdens. Nanofiltration (NF) offers a green and efficient alternative—operating in the aqueous phase with low energy demand and continuous high throughput. This review systematically summarizes NF-based REE separation. We first elucidate the fundamental mechanisms (size exclusion, Donnan exclusion, dielectric exclusion, and complexation enhancement), and discuss how lanthanide hydration chemistry underpins these synergistic effects. Membrane materials, from commercial to biomimetic, are critically surveyed, with an emphasis on strategies to overcome the trade-off between permeability and selectivity. The impacts of operating conditions and solution chemistry are analyzed, and NF applications ranging from single REE systems to real leachates are assessed. A comparative evaluation positions NF against conventional technologies. Key challenges remain: poor adjacent REE selectivity, membrane fouling, performance loss at high salinity, chemical instability, and a gap between model and real feeds. Future directions include designing high-selectivity membranes, integrating machine learning optimization, establishing standardized protocols, and realizing closed-loop process integration. Full article