TerraMosaic Daily Digest: Feb 21, 2026
Daily Summary
Across 54 selected studies, the most consequential advances sharpen the mechanistic links between forcing, deformation, and failure—and then translate them into design decisions. Seismic and volcanic hazard papers move beyond simplified geometries: discrete‐element back‐analysis reproduces Merapi’s 2020 flank acceleration without invoking wholesale collapse, while 3‑D basin simulations show how basin structure modulates amplification and liquefaction onset. In parallel, infrastructure-focused studies operationalize these insights, including tension‑pile strategies to prevent tunnel flotation in liquefiable ground and copula‑linked bi‑hazard (shaking + landslides) risk optimization for railway alignment.
Hydroclimate work tightens the loop from drivers to impacts to monitoring. Urban flood prediction improves when spatio‑temporal rainfall signatures are learned jointly (Bayesian‑optimized BiLSTM‑U‑Net), and large‑ensemble climate simulations indicate a strengthened, east‑shifted MJO teleconnection that could nearly double Southeast U.S. precipitation anomalies and increase persistence of extremes—conditions that elevate both flooding and rainfall‑triggered slope failure risk. On the observation side, SWOT‑centered sensor fusion delivers near‑daily reservoir water levels and resolves low‑amplitude seiche dynamics in atoll lagoons, providing the temporal density needed for compound‑event attribution. Complementary process studies refine sediment and contaminant pathways, from rare‑earth‑element provenance tracing in major rivers to coupled hydro‑mechanical models of pollutant transport in engineered liners.
Key Trends
- 3‑D structure and inelastic deformation are becoming default assumptions: Case studies show that basin geometry and plasticity can shift both amplification and liquefaction thresholds, while discrete‑element models can reproduce complex volcanic flank kinematics without prescribing failure a priori.
- Coupled hazards are being encoded directly into infrastructure design workflows: Rather than screening shaking and earthquake‑induced landslides separately, new planning frameworks link fragility with copulas and propagate uncertainty through optimization; parallel mitigation work targets liquefaction-driven tunnel uplift with tension‑pile systems.
- SWOT is catalyzing a step-change in water‑hazard observability: KaRIn enables two‑dimensional mapping of subtle seiche oscillations, and multi‑sensor fusion pushes reservoir water‑level products toward near‑daily cadence—an enabling layer for flood/drought diagnostics.
- Spatio‑temporal learning is replacing static predictors for floods and extremes: Deep architectures that jointly encode rainfall sequences and spatial context improve urban flood prediction, while large‑ensemble climate experiments diagnose how teleconnections (e.g., MJO) may intensify persistent precipitation extremes.
- Material and transport pathways are being traced with higher specificity: Rare‑earth‑element fingerprints quantify upstream sediment contributions, and pore‑ to field‑scale transport studies clarify how roughness, eddies, and coupled consolidation–advection processes govern contaminant migration in subsurface and engineered barriers.
Selected Papers
This digest features 54 selected papers from 550 deduplicated papers analyzed (out of 2280 raw papers scanned) across multiple journals. Each paper has been evaluated for its relevance to landslide and broader geohazard research and includes links to the original publications.
1. Insights Into the 2020 Instability Crisis of Merapi Through Numerical Modeling
Core Problem: The unprecedented 2020 instability crisis of Merapi volcano's northwestern flank, with significant displacement and fear of collapse, required understanding the role of magma-filled fracture pressurization in driving the deformation.
Key Innovation: Developed discrete element method numerical models that successfully reproduce the observed inelastic deformation and kinematics of Merapi's northwestern flank, demonstrating how magma pressure can cause significant plastic deformation and sliding without leading to a major flank collapse, which is crucial for understanding volcanic hazards.
2. Development of flood hazard model based on land use and land cover change 1973–2023 in Cikapundung watershed, West Java Province
Core Problem: Changes in Land Use and Land Cover (LULC) due to urbanization and deforestation disrupt ecosystem patterns, decrease soil infiltration capacity, and exacerbate flood hazards in the Cikapundung watershed.
Key Innovation: Development of a flood hazard model based on LULC changes from 1973–2023, providing a tool to assess and understand flood risks in the Cikapundung watershed.
3. The 3D basin effect on the seismic response estimation at sites with liquefiable layers: Case study of Volos basin (Thessaly, Central Greece)
Core Problem: Traditional 1D layered models may inadequately capture the complex seismic response and liquefaction initiation at sites within 3D basin structures, leading to underestimation of hazard.
Key Innovation: Demonstrated through 3D numerical modeling and non-linear analysis for the Volos basin that the 3D basin structure significantly amplifies ground motion, affects its duration and frequency content, and highly impacts the initiation of liquefaction, proving its superiority over 1D models for accurate seismic hazard estimation in such environments.
4. Preventing seismic tunnel flotation in liquefiable soil: a numerical Investigation of a tension pile system
Core Problem: Underground structures in liquefiable soils are vulnerable to earthquake-induced uplift (flotation), and conventional ground improvement methods are costly and disruptive.
Key Innovation: Developed a robust 2D dynamic numerical modeling approach to evaluate the tensile demand of tension piles for preventing tunnel flotation in liquefiable ground, validated against centrifuge tests, offering a cost-efficient alternative for preliminary design.
5. Distributionally robust optimization with bi-hazard seismic risks evaluation for railway planning and design
Core Problem: Existing automated railway design methods evaluate seismic risks from ground shaking and earthquake-induced landslides separately, leading to unreliable estimates of their combined seismic risks and model uncertainties.
Key Innovation: Proposal of a bi-hazard seismic risks evaluation model that derives joint structural fragility by linking fragility probabilities via a copula function and aggregating them through a damage state matrix. This is integrated with a Distributionally Robust Optimization (DRO) model and a least-cost railway design model, solved by an improved particle swarm optimization algorithm, to account for model uncertainties and minimize combined cost-risk.
6. How Fault Zone Fabric Controls the Hydro‐Mechanical Behavior and Evolution of Critical State Shearing in Clay Shale
Core Problem: Understanding how fault zone fabric controls the hydro-mechanical behavior and evolution of critical state shearing in clay-rich formations, which is crucial for earthquake mechanics and subsurface applications.
Key Innovation: Direct experimental evidence from fully hydro-mechanically coupled triaxial tests on preserved fault material from Opalinus Clay, showing distributed, steady-state shearing, stress-dependent volumetric response, and the absence of cohesion in scaly clay fabric, reducing shear strength below intact rock.
7. From physical regulation to biochemical dominance: erosion-driven shifts in plant regulation of aggregate stability in Mollisols
Core Problem: Erosion-induced soil structure degradation is a critical threat, and while vegetation restoration helps, how plant-mediated mechanisms of aggregate stability evolve along erosion gradients remains poorly understood, hindering targeted restoration strategies.
Key Innovation: Demonstrated that dominant plant-mediated mechanisms of soil aggregate stabilization shift along erosion gradients (from physical root-pore regulation to physico-biochemical synergy to biochemical dominance), proposing a mechanism-driven plant-erosion matching evaluation framework to optimize restoration strategies for degraded soils.
8. Bayesian-optimized BiLSTM-U-Net framework for urban flood prediction with spatio-temporal feature integration
Core Problem: Existing urban flood prediction models often analyze spatial or temporal features in isolation and use empirically selected input features, limiting their ability to model complex spatio-temporal drivers and potentially introducing noise.
Key Innovation: A Bayesian-optimized BiLSTM-U-Net framework that integrates multi-feature selection with spatio-temporal modeling. It uses RNNs for temporal rainfall sequences, CNNs for spatio-temporal feature extraction/fusion, and Bayesian optimization for optimal input feature selection, achieving high accuracy in urban flood prediction.
9. Response of buried pipe to passive dynamic vehicular load in sand-laden slope
Core Problem: Limited understanding exists regarding the response of buried pipelines in soil slopes to passive dynamic vehicular loads, particularly how setback distance, proximity to the slope crest, and burial depth influence pipe displacement due to lateral soil movement.
Key Innovation: Experimentally and numerically investigated the response of buried pipes in sand-laden slopes to passive cyclic loading, identifying that pipes in the middle section of the slope exhibit the most significant displacement due to peak shear stresses, and demonstrating the effectiveness of geogrid reinforcements in reducing pipe displacement.
10. SWOT Sheds Light on Seiche Oscillations Within Atoll Islands
Core Problem: The lack of detailed, two-dimensional observations of low-amplitude seiche oscillations in relatively small water bodies like atoll lagoons, hindering understanding of their role in coastal erosion and flooding.
Key Innovation: Demonstrating the unprecedented ability of the SWOT satellite's KaRIn instrument to visualize two-dimensional seiche-like structures in atoll lagoons, combining satellite remote sensing with in-situ measurements and theoretical modeling to investigate their spatial and spectral properties and their contribution to coastal erosion and flooding.
11. Changes in MJO Teleconnections in the Southeast U.S. Under Global Warming in the CESM2 Large Ensemble
Core Problem: Understanding how Madden-Julian Oscillation (MJO) teleconnections and their influence on extreme weather, particularly precipitation, will change over North America, specifically the Southeast U.S., under global warming.
Key Innovation: Using a large climate model ensemble (CESM-LENS2) to project that in a warmer climate, the MJO teleconnection strengthens and shifts eastward, with a new Rossby wave source emerging over the SEUS, nearly doubling SEUS precipitation anomalies and substantially increasing persistent extreme events.
12. Accumulation pattern, source apportionment and risk assessment of soil heavy metals across diverse land use types: Insights from the northern foothills of Qinling mountains, China
Core Problem: Understanding the accumulation patterns, sources, and risks of heavy metal (HM) contamination across diverse land use types within urban-natural ecotones, and how land use influences these factors.
Key Innovation: Systematically investigated HM contamination, sources (using PMF), and ecological/health risks across four land use types in the Qinling Mountains, revealing land use-driven spatial heterogeneity, identifying Cd as the largest contaminant, and highlighting natural geological processes as dominant risk contributors, providing insights for land use-specific pollution control.
13. Size-dependent shear behavior of backfill–rock interfaces constructed from in-situ 3D morphology: experimental and numerical study
Core Problem: The size effect on the shear behavior of backfill-rock interfaces is not fully understood, which is critical for predicting failure mechanisms of backfill materials and ensuring interface stability in underground mining operations.
Key Innovation: Experimental and numerical study demonstrating that while peak/residual shear strengths are size-independent, peak shear/normal displacements and fracture energy increase linearly with specimen size. The study establishes a mechanistic link among mechanical parameters, failure behavior, and energy dissipation, providing theoretical insights for interface stability and backfill design in underground mining.
14. Radiosonde‐to‐Space (R2S) Atmospheric Specifications: Bridging Observations and Models for Infrasound Propagation
Core Problem: Discrepancies between existing atmospheric models (G2S) and observations hinder accurate prediction of infrasound signal arrival, which is crucial for event interpretation in real-world scenarios, including geohazard monitoring.
Key Innovation: Development of Radiosonde-to-Space (R2S) atmospheric specifications by merging direct radiosonde observations of the lower atmosphere with G2S profiles aloft, demonstrating that incorporating real-time lower-atmosphere observations can significantly alter propagation predictions and improve event interpretation.
15. Eddy‐Controlled Anomalous Transport in Rough Conduits: Physics‐Based Parameterization and Distributed Modeling
Core Problem: Existing solute transport models for non-Fickian migration (anomalous transport) in rough karst conduits, caused by eddies trapping solutes, often lack clear physical backgrounds for their parameters, hindering accurate prediction of pollutant movement.
Key Innovation: Designing a series of rough conduits to quantitatively evaluate eddy influence on solute transport, proposing a new method for eddy zone identification, summarizing the quantitative relationship between flow velocities, roughness, and eddy area proportion, and developing a new physics-based solute transport model that clarifies parameter meanings and accurately captures breakthrough curves.
16. Experimental and numerical investigation of mooring line failure dynamic responses for a 15 MW hybrid floating offshore wind turbine
Core Problem: The transient dynamic response of large Floating Offshore Wind Turbines (FOWTs) following sudden mooring line failure, and the subsequent platform motions and tension redistribution, are not fully understood or experimentally validated.
Key Innovation: Designs a novel 15 MW FOWT platform with a taut mooring system, conducts 1:50 scale model tests and numerical simulations of mooring line failures, providing experimentally validated insights into transient response mechanisms and establishing a framework for mooring system safety design and risk assessment.
17. Quantifying Surface Downward Shortwave Radiation and Its Direct and Diffuse Components Using Fengyun-4A AGRI Observations
Core Problem: Limited retrieval of direct and diffuse components of surface downward shortwave radiation (Rs), despite its fundamental importance for surface energy budgets and biogeochemical cycles, and a need for higher spatial resolution and precision.
Key Innovation: A framework integrating machine learning (random forest) with traditional physical models to retrieve instantaneous Rs and its direct (Rdirect) and diffuse (Rdiffuse) components at 4-km spatial resolution over China using Fengyun-4A AGRI satellite observations, achieving high accuracy (R=0.98, RMSE=17.13 W/m2 for Rs) and outperforming existing products in resolution and precision.
18. A Feature Tracking and Trajectory Selection Based Rotation Axis Estimation Method for Small Bodies Using Optical Remote Sensing Images From the Approach Phase
Core Problem: Challenges in robustly and efficiently estimating the rotation axis of small bodies from optical remote sensing images during the approach phase due to limited image availability, weak surface textures, uncertain observation geometries, tracking errors, unreliable trajectories, and dependence on accurately known rotation periods.
Key Innovation: A rotation-axis estimation method for small bodies based on image feature tracking (sparse optical flow), adaptive trajectory selection, and a geometry-based optimization model (genetic algorithm) that identifies the correct rotation axis solution without requiring prior knowledge of the rotation period, significantly outperforming existing algorithms (errors below 3° in 89% of cases) and validated with in-orbit data.
19. Increasing trends in the nocturnal and compound hot extremes in Eastern Mediterranean cities (1960–2022)
Core Problem: Traditional studies of hot extremes primarily focus on maximum air temperature, often overlooking the impact of minimum air temperature and the combined effect of daytime and nighttime thermal conditions (compound hot extremes), leading to an incomplete understanding of increasing heatwave trends.
Key Innovation: Examination of increasing trends in nocturnal (tropical nights) and compound hot extremes, as well as three types of heat waves (nighttime, daytime, compound), in Eastern Mediterranean cities (1960–2022), revealing significant increases in frequency and intensity, particularly in urbanized areas.
20. An experimental and numerical study on the influence of inherent and induced anisotropy of a fine-grained soil
Core Problem: Existing numerical models (e.g., modified Cam Clay, anisotropic visco-hypoplastic model) struggle to accurately capture the effects of inherent and induced anisotropy in reconstituted fine-grained soils, which is crucial for predicting their mechanical behavior.
Key Innovation: Experimentally investigated inherent and induced anisotropy of kaolin samples through triaxial tests, and extended the anisotropic visco-hypoplastic model by incorporating a cross-isotropic elastic stiffness formulation, systematically evaluating parameters essential for accurately describing stiffness anisotropy in fine-grained soils.
21. Quantitative analyses of the Yarlung Zangbo River sediment contributions: Evidence from rare-earth elements
Core Problem: The relative contributions of local fluvial erosion versus upstream transport processes to river system sediments remain a core controversy, lacking a robust quantitative framework for distinction.
Key Innovation: Established a quantitative framework using rare-earth elements (REEs) to distinguish sediment sources in the Yarlung Zangbo River, identifying δEu as a robust tracer and quantifying that upstream contributes 85–87.7% of sediments downstream, enhancing understanding of sediment characteristics and material migration.
22. Estimation of multi-depth soil water storage properties by inversion of crop model: Effect of type, frequency, and timing of observations
Core Problem: Reliable estimation of multi-depth soil water storage properties (SWPs) is crucial for land-surface process modeling, but such information is often limited.
Key Innovation: Evaluates the potential of surface soil moisture (SM1), surface soil temperature (TS1), Leaf Area Index (LAI), and Evapotranspiration (ET) for estimating multi-depth SWPs using inverse modeling with a STICS crop model. It identifies optimal variable combinations and observation strategies for accurate SWP estimation, reducing reliance on large datasets.
23. Integrating SWOT With Multi‐Source Satellite Observations for Near‐Daily Reservoir Water Level Monitoring
Core Problem: Traditional satellite altimetry faces multiple challenges, such as limited coverage, infrequent observations, and weather-related issues, hindering high-frequency and accurate reservoir water level monitoring critical for assessing climate variability and anthropogenic regulation.
Key Innovation: Development of a proof-of-concept framework integrating multi-source satellite data, with the SWOT mission as the primary source, to generate high-resolution, near-daily reservoir water level timeseries, significantly enhancing observation frequency (3.2–8.1 times increase) and maintaining high accuracy (R2>0.90, MAE 0.11-0.46m) by standardizing water levels across sensors and tracks.
24. Numerical research on the impact of built-in rectangular poles on the dynamic characteristics of rectangular liquid tanks
Core Problem: Conventional pure-water Tuned Liquid Dampers (TLDs) have limited energy dissipation capacity, often insufficient for structural vibration control requirements, and large TLD tanks for offshore platforms need robust internal components for stability.
Key Innovation: Proposes an innovative TLD configuration with built-in rectangular poles, improves a CFD two-phase flow solver (CLS-VOF), systematically investigates parameter effects on sloshing, and develops an equivalent mechanical model using PSO to estimate dynamic parameters for TLD design.
25. Spatiotemporal synchronization-aware cross-domain mission planning for air-sea-underwater heterogeneous unmanned swarms
Core Problem: Mission planning for cross-domain heterogeneous unmanned swarms in multi-dimensional maritime operations is challenged by platform disparities and strict spatiotemporal synchronization requirements, making task assignment and path planning tightly coupled and complex.
Key Innovation: Designs a single-stage cooperative planning framework for air-sea-underwater swarms, introducing a recursive temporal deduction mechanism and a Q-learning enhanced Genetic Algorithm (COGAQ) with specific encoding and search strategies, providing a precise and efficient solution for spatiotemporal coordination in complex maritime scenarios like search and rescue and ocean monitoring.
26. Analysis of the impact of an underwater shaking table and its IWAC on surface waves
Core Problem: Multi-disaster coupling simulation, particularly seismic-wave-current coupling, requires understanding how experimental equipment like underwater shaking tables influence the flow field and surface waves for accurate hazard replication.
Key Innovation: Investigating and quantifying the impact of horizontal and vertical motions of an underwater shaking table and its IWAC on surface waves under different water depths, providing a basis for designing and applying such tables for multi-disaster coupling simulations.
27. A Resource-Efficient Cardiac Arrhythmia Detection Using Nonlinear Dynamics in Optimized Delay State Networks
Core Problem: Traditional cardiac arrhythmia detection methods often fail to capture subtle temporal phase drifts or require extensive computational resources and handcrafted features, limiting early diagnosis and real-time applicability, especially in resource-constrained environments.
Key Innovation: A novel methodology combining Reconstructed Phase Space (RPS) analysis with an optimized Delay State Network (DSN) that leverages the entire Phase Space Structure as direct input, employing a single nonlinear node with delayed feedback to emulate multiple virtual nodes, significantly reducing hardware demands and achieving high accuracy (99.3%) with resource efficiency suitable for edge deployment.
28. Cloud removal in remote sensing images using the G2R framework and SSMamba
Core Problem: Existing multi-temporal cloud removal methods in remote sensing are limited by temporal heterogeneity when using cloud-free images as training targets, and current networks lack sufficient capability for modeling spatio-spectral dynamics and cross-dimensional interactions.
Key Innovation: A novel Generate-to-Remove (G2R) self-supervised framework that uses the cloudy image itself as a supervision target to introduce temporal constraints, combined with a Spectral–Spatial Mamba network (SSMamba) for coordinated representation learning and efficient feature interaction, achieving accurate cloud removal and improved detail restoration.
29. The Middle Pleistocene Great Lakes period in Otindag Dune Field revealed by optically stimulated luminescence dating
Core Problem: Intense wind erosion in the Otindag Dune Field has made it challenging to preserve sediments, resulting in a gap in long-term climate change research for the region, particularly regarding Middle Pleistocene lacustrine phases.
Key Innovation: Utilized SAR thermally transferred optically stimulated luminescence (TT-OSL) dating to establish a stratigraphic chronological framework extending back to 606 ka, revealing Middle Pleistocene warm-humid and cold-dry climate phases and paleo-lake evolution, updating previous records and highlighting tectonic and monsoon controls.
30. Influence of ground motion characteristics on the seismic response of large reinforced-concrete water tanks with nonlinear fluid–structure interaction
Core Problem: Insufficient understanding of the seismic behavior of large reinforced concrete water tanks under nonlinear fluid-structure interaction, particularly regarding ground-motion frequency content and intensity.
Key Innovation: Development of a fully coupled three-dimensional nonlinear numerical model (Coupled Eulerian–Lagrangian method) to investigate seismic response, revealing that both ground-motion frequency content and intensity significantly influence hydrodynamic pressure and structural demand, with high-frequency excitations producing substantially smaller structural deformations. Provides insights for performance-based seismic design.
31. Salt Precipitation and Clogging During Gas Injection in Fractured Porous Media
Core Problem: Salt precipitation and clogging during gas injection in fractured porous media significantly impacts injectivity during subsurface gas storage, and the underlying mechanisms, especially pore-scale effects, are not fully understood.
Key Innovation: Investigated salt precipitation and clogging in fractured micromodels, revealing that aggregated salt clogs gas channels while bulk salt has less impact, and increased injection rate weakens negative impacts. This deepens understanding of salt-induced injectivity impairment.
32. Bayesian updating of LRFD resistance factors of driven PPC piles from dynamic pile load tests
Core Problem: Current LRFD codes provide limited guidance on updating resistance factors (RFs) using dynamic pile load testing (DPLT), and the calibration process for RFs under varying site conditions and testing levels is underexplored.
Key Innovation: Proposes a novel Bayesian interface framework (BIF) with four methods to update LRFD RFs using DPLT data, accounting for site variability, demonstrating significant increases in RFs even with limited DPLT and improved design reliability.
33. Modeling ship movements under automatic mooring system with consideration of harbor oscillations
Core Problem: Conventional mooring systems struggle to provide adequate stability and safety for increasing vessel sizes, especially under severe sea conditions like long-period waves and harbor oscillations, leading to operational inefficiencies and safety concerns.
Key Innovation: Presents a numerical model for an automatic mooring system based on a PD controller, demonstrating its ability to effectively suppress vessel horizontal motion under harbor oscillation conditions, thereby enhancing port safety and operational efficiency in extreme sea states compared to traditional systems.
34. Continuous missing original data imputation method for bottom-sitting acoustic ocean wave meter
Core Problem: The continuous absence of original data in bottom-sitting acoustic ocean wave observations significantly affects the accuracy of wave parameters, with traditional methods struggling with multivariate coupling, trend, and transient coexistence.
Key Innovation: Proposes a time series multiple intelligent imputation model that employs multi-scale component decomposition (SSA), back propagation neural network fitting (BPNF) for trend data, and integrated gated recurrent unit (GRU) with dynamic time warping (DTW) for transient data.
35. ConvNeXt-PIV: A modern convolutional framework for particle image velocimetry
Core Problem: Conventional CNN-based PIV networks struggle to capture global structures and large-scale motions in complex flows, while Transformer-based models incur substantial computational and memory costs.
Key Innovation: Proposes ConvNeXt-PIV, an encoder built on the ConvNeXt module using depthwise convolution and large kernels to expand the receptive field, improving accuracy and efficiency in estimating velocity fields for complex flows.
36. Distributed optimal formation control for UAV-USV multiagent systems: A FEWNN-based RL approach with self-adjusting prescribed performance
Core Problem: The formation tracking control problem for UAV-USV systems requires an optimal controller that can handle lumped uncertainty and guarantee steady-state performance and precise trajectory tracking, while traditional methods have limitations in prescribed performance constraints.
Key Innovation: Designs a fuzzy Elman wavelet neural network (FEWNN)-based reinforcement learning (RL) optimal controller with a self-adjusting prescribed performance function (SAPF) to constrain tracking errors and adaptively adjust error boundaries for UAV-USV formation tracking control.
37. Repaint High-Density Surface Electromyography Signal Using Denoising Diffusion Probabilistic Model
Core Problem: Signal loss and channel corruption in High-density surface electromyography (HD-sEMG) limit its reliability and practical applications, with conventional interpolation methods failing for multiple adjacent affected channels.
Key Innovation: A novel HD-sEMG signal reconstruction approach based on the denoising diffusion probabilistic model (DDPM) with a repaint strategy, leveraging a U-Net structure with spatiotemporal embedding to achieve high-fidelity reconstruction (lowest nRMSE, highest PSNR) without prior knowledge of corruption patterns, outperforming existing methods.
38. Top-Down Coarse-to-Fine Cascade Network for High-Precision Cluster Infrared Small Target Detection
Core Problem: Current deep-learning methods struggle with high-precision cluster infrared small target detection (CIRSTD) due to independent single-target modeling, leading to feature coupling, dim targets being submerged by background clutter, and dynamic cluster shapes.
Key Innovation: A Coarse-to-Fine Cascade IRSTD (C2IRSTD) network that employs an adaptive regional attention mechanism and a coarse cluster extraction module for overall features, combined with an Inner Fine Distinction module (integrating Gaussian and Scharr filters) to amplify dim target saliency and mitigate missed detections and adjacent target coupling.
39. Morphology-adaptive Au-Ag nanowire elastronics for integrated FlexoSERS and bioelectrical sensing
Core Problem: Need for versatile, conformable, and durable multimodal optical-electrical sensing platforms for applications like wearable health monitors and human-machine interfaces.
Key Innovation: Development of a morphology-adaptive Au-Ag nanowire elastronic platform that enables high-sensitivity, uniform, and durable FlexoSERS and stable bioelectrical sensing (ECG, EMG) across various 1D-3D geometries.
40. Electroluminescent perovskite QD–based neural networks for energy-efficient and accelerate multitasking learning
Core Problem: The need for more energy-efficient and accelerated multitasking learning in neuro-inspired AI systems for applications like robotics and autonomous vehicles.
Key Innovation: Established an MT learning framework using a dual-output electroluminescent synaptic device array based on perovskite quantum dots, which concurrently processes PSC and PSEL signals to perform multiple tasks with significant computational speed improvements and reduced energy consumption.
41. The aquifer mapping and nomenclature guidelines for the Bengal Basin, Bangladesh
Core Problem: Inconsistent and varied aquifer nomenclature in the Bengal Basin hinders standardized groundwater investigation, research, interpretation, and management, creating ambiguity and limiting data comparability across studies and agencies.
Key Innovation: Establishment of a standardized, physiography-based aquifer mapping and nomenclature framework for the Bengal Basin, utilizing 5,000 borehole logs, 3D modeling, and interpolated maps, which identifies seventeen distinct hydrostratigraphic units and introduces a uniform three-part coding system to enhance groundwater resource assessment and governance.
42. Short time and local spatial scale estimation of sedimentary volumes during the Little Ice Age from sparse data: Case of a Rhône delta palaeochannel (France)
Core Problem: Accurately estimating sediment volumes from sparse data in complex fluvial morphologies at fine spatial and temporal scales.
Key Innovation: Evaluation of Discrete Smooth Interpolation (DSI) and kriging with curvilinear anisotropy for reconstructing 3D deposit thickness, showing DSI's ability to reconstruct detailed features from minimal data and revealing the disproportionate geomorphological impact of short-term LIA floods.
43. Quantification of the active width in gravel-bed rivers: The effect of morphology, flood magnitude and survey frequency
Core Problem: Difficulty in directly measuring instantaneous bedload active width in gravel-bed rivers and understanding its relationship to morphological active width across different morphologies, flood magnitudes, and survey frequencies.
Key Innovation: Using a physical model and field data to quantify active width in relation to dimensionless stream power and morphology, assessing differences between bedload and morphological active widths, and confirming the control of stream power and survey timespan on morphological active width.
44. A novel transformer-based CO2 retrieval framework incorporating prior constraint and hierarchical features injection: assessment of transferability for Tansat-2
Core Problem: Traditional methods for satellite CO2 retrieval are time-consuming, and deep learning models struggle with accuracy and extrapolation to unseen high values, especially for next-generation large-swath satellites.
Key Innovation: Developed a Transformer-based framework integrating prior constraint and hierarchical features for high-precision CO2 retrieval, demonstrating robust extrapolation capabilities and transferability to new satellite data (Tansat-2).
45. Characterizing full-waveform laser returned intensity and bidirectional reflectance factor of forest stands using discrete point cloud data
Core Problem: Quantifying the respective contributions of forest structural and spectral properties to full-waveform laser returned intensity (LRI) and bidirectional reflectance factor (BRF) for forest biodiversity dynamics.
Key Innovation: Presents a physics-based framework to simulate full-waveform LRI and BRF by explicitly integrating forest structural complexity (point clouds) and spectral heterogeneity (Monte-Carlo ray-tracing with leaf-level optics), validated against GEDI and TECIS observations.
46. Integrating flux footprint, random forest, and SHAP for interpretable hourly carbon flux upscaling: Development and application in the Qilian mountains watershed
Core Problem: Conventional eddy covariance (EC) upscaling methods for regional carbon fluxes are limited to daily/monthly resolutions, missing sub-daily dynamics, and machine learning frameworks often lack interpretability regarding environmental drivers.
Key Innovation: Introduces an integrated framework combining flux footprint modeling, Random Forest, and SHAP algorithms to upscale GPP and Reco hourly with high spatial resolution, providing interpretability of environmental drivers and generating a long-term hourly carbon flux dataset for the Qilian Mountains.
47. Divergent GPP dynamics in alpine and temperate grasslands: Hierarchical climatic controls across the Qinghai-Tibetan and Mongolian Plateaus
Core Problem: Understanding the spatiotemporal variations and climate change responses of GPP in alpine and temperate grasslands, and bridging tower-based observations with large-scale remote sensing estimates while maintaining interpretability.
Key Innovation: Developed a novel Random Forest Regression-Light Use Efficiency-Solar Induced Fluorescence (RFR-LUE-SIF) model that integrates machine learning with physiological principles and satellite SIF, providing a scalable, data-driven, and physiologically consistent approach for assessing grassland carbon dynamics and their hierarchical climatic responses.
48. Spatiotemporal averaging resolution of high importance within Earth-observation-based light use efficiency models of gross primary production
Core Problem: The relationship between GPP and absorbed photosynthetically active radiation (APAR) in light use efficiency models shifts from asymptotic to linear depending on spatiotemporal averaging resolution, which is not well understood.
Key Innovation: Investigated the critical spatiotemporal scales at which the GPP-APAR relationship converts from asymptotic to linear, demonstrating that higher resolutions (half-hourly to daily, smaller pixel sizes) require an asymptotic relationship, while lower resolutions (monthly/weekly, larger pixel sizes) favor a linear one, providing guidance for selecting appropriate models based on satellite sensor resolution.
49. Internal solitary wave parameters from SWOT KaRIn sea surface topography: a case study in the Tropical Atlantic
Core Problem: Assessing essential Internal Solitary Wave (ISW) parameters like amplitude and wavelength from satellite sea surface manifestations has been challenging, and weakly nonlinear theories (KdV) often underestimate wave amplitudes and fit surface expressions poorly.
Key Innovation: Employed an inversion method based on a fully nonlinear equation with continuous stratification, using SWOT KaRIn sea surface height anomaly as a constraint, to accurately determine ISW parameters (amplitude, wavelength, phase speed, velocity field), demonstrating superior performance over KdV theory in a case study.
50. Even after thirty years, cropland abandonment in a semi-arid region has not fully restored the soil properties
Core Problem: Limited information exists on the long-term impact (thirty years) of converting degraded grasslands to conventional agricultural lands and then back to primary vegetation on soil quality and recovery in semi-arid regions.
Key Innovation: Demonstrated that even after thirty years of cropland abandonment, soil characteristics (fertility indices, microbial parameters, fauna, microflora, bulk density, porosity, aggregate stability) in semi-arid mountainous regions have not been fully restored, suggesting the need for natural amendments to accelerate regeneration.
51. Dynamic vegetation responses to climate change and human activities in the eastern North China region over the past 3,500 years: Pollen record from the Caofeidian Wetland
Core Problem: The relative importance and temporal variability of climate change and human activities in driving ecosystem and regional landscape changes during the late Holocene in the eastern North China region are not fully understood.
Key Innovation: Used REVEALS and Rate of Change (RoC) analysis on a pollen record to quantitatively reconstruct vegetation dynamics and precipitation, identifying two abrupt vegetation change events (at ~2.5 ka BP and ~220 cal yr BP) driven by prolonged drought, sea level decline, and intensified human activities, providing new evidence for coastal wetland ecosystem responses.
52. Corner film flow during forced imbibition in porous media: Insights from microfluidics experiment and phase-field simulation
Core Problem: Understanding the pore-scale competition between corner flow and main meniscus flow during forced imbibition in heterogeneous and homogeneous porous media is crucial for subsurface fluid displacement processes, but the mechanisms linking microscopic and macroscopic behaviors are not fully elucidated.
Key Innovation: Synergistic integration of theoretical analysis, phase-field simulations, and microfluidic experiments to establish a theoretical relation between critical precursor corner film advancement length and capillary number, identifying three imbibition regimes and linking microscopic pore-scale mechanisms to macroscopic imbibition patterns, with implications for CO2 sequestration and subsurface hydrology.
53. A numerical model for consolidation-induced contaminant transport in PVD-treated dredged sludge
Core Problem: Existing models for consolidation-induced contaminant transport in PVD-treated dredged sludge inadequately account for realistic boundaries and changes in soil properties, leading to inaccurate assessments of pollution risks and environmental monitoring.
Key Innovation: Developed a numerical model for consolidation-induced contaminant transport in PVD-treated dredged sludge that simulates contaminant transport under self-weight nonlinear consolidation, demonstrating how PVDs accelerate contaminant removal and how engineering parameters influence concentration variations.
54. Numerical manifold method for the transient HMC fully coupled model in triple-layer composite liners
Core Problem: Simulating leachate migration in triple-layer composite liners (GMB/GCL/CCL) is challenging due to material inhomogeneity and the drawbacks of traditional FEM in weakly discontinuous porous media, especially regarding interface continuity for coupled variables.
Key Innovation: Developed a Numerical Manifold Method (NMM) for a fully coupled three-field (u-p-c) model in composite liners, overcoming FEM limitations by constructing approximations that exactly satisfy interface continuity conditions for displacement, pore pressure, and pollutant concentration, accurately simulating solute migration.