Search Results (517)

Liquid Time-Constant networks (LNNs), recurrent models with adaptive, input-dependent time constants, were evaluated for water level prediction in an urban drainage system. The architecture was assessed on a ten-year measurement dataset from the Bellinge catchment and benchmarked against gated recurrent unit (GRU), long short-term memory (LSTM), temporal convolutional network (TCN), and multilayer perceptron (MLP) baselines. The best-performing LNN variant achieved the highest mean predictive accuracy in the benchmark (Nash–Sutcliffe efficiency, NSE = 0.849), with particularly accurate representation of the continuous response of the gravity-driven part of the network without substantial degradation during flash-flood events. The analysis showed that the adaptive time constants help distinguish system-wide hydraulic processes from local control actions while providing interpretable diagnostics of the model’s internal response scale. The principal challenge for the LNN was the intermittent operation of the pumping station; isolating the pump pathway in the dual-branch DB-LNN variant mitigated this performance degradation while preserving predictive accuracy at the remaining sensors. The ablation analysis indicated that architectural separation of the processing pathways reduced interference between the continuous hydraulic dynamics and the local switching dynamics of the threshold-controlled facility. Full article

Unconsolidated sandstone gas reservoirs often suffer from near-wellbore plugging, permeability contrast, and sand production after acid stimulation. This study evaluates a staged chemical process that couples limited cationic polymer sand stabilization with reaction-induced self-diverting acidizing, instead of direct premixing or simultaneous co-injection of acid and sand control fluids. Residual acid viscosity tests, core flooding experiments, dynamic sand production tests, scanning electron microscopy/energy-dispersive spectroscopy, and low-field nuclear magnetic resonance measurements were used to investigate viscosity building, diversion, permeability retention, and pore structure evolution. The 5 wt% EAB self-diverting acid exhibited the strongest reaction-induced viscosity-building behavior, reaching a residual acid viscosity of 134 mPa·s. The selected HCl + HF acid system produced a clear temporary-plugging response during core flooding, and the optimized 0.2 wt% cationic polyacrylamide stabilizer increased the critical sand production flow rate to 1.8 mL·min⁻¹ while maintaining acceptable permeability recovery. Microscopic evidence indicates improved pore connectivity without large-scale skeletal collapse. A comprehensive synergy index showed that medium-permeability cores (300–500 mD) achieved the best balance among acid diversion, sand stabilization, and permeability retention. The proposed process provides a laboratory-scale foundation for coupled stimulation and sand control design. Full article

This proposed study presents an in-situ experimental approach for investigating anode-side water dynamics through detailed analysis of pressure-drop data measured across multiple segments of bipolar plate flow channels. A theoretical model was first developed to predict pressure-drop profiles and liquid-water distribution in parallel multiphase flow channels. Based on the model, a bipolar plate with integrated multi-point pressure sensors was designed to enable real-time, spatially resolved monitoring of gas–liquid dynamics and provide advanced diagnostic capabilities. The proposed system was successfully implemented in a single Proton Exchange Membrane Fuel Cell (PEMFC) with an active area of 282 cm², which allows accurate identification of the timing and location of flooding events during operation. Experimental results showed that inadequate water management can cause flooding under steady-state conditions, while water accumulation is more prominent during startup and load-reduction processes. Additionally, the effects of reactant flow rate, humidity, and circulating water temperature on water distribution within the channels were systematically evaluated. Based on these findings, a closed-loop diagnostic and control framework is proposed by enabling dynamic adjustment of operating parameters according to pressure-drop characteristics for real-time optimization of water management and improved fuel cell performance. Full article

In the Ramis River basin of the Peruvian Andes, flood events have become increasingly frequent and intense due to climate variability. However, the basin has limited hydro-meteorological observation records, and its flood generation mechanisms are extremely complex. This situation not only hinders the accurate identification of flood-prone areas, but also limits the effective implementation of flood risk management measures. This study sets three core objectives: to assess flood sensitivity across the basin, identify the dominant factors that influence flood sensitivity, and verify the flood detection performance of multispectral indices. The study adopts two core methods. First, a multi-criteria framework that integrates the Analytic Hierarchy Process (AHP) and Geographic Information System (GIS) is used, incorporating seven flood-related environmental factors and one precipitation triggering variable. Second, the performance of four spectral indices—NDVI, NDWI, SAVI, and MSAVI2 is verified through Spearman correlation analysis, Moran’s I index, and the random forest algorithm. The study finds that landform and geology are the core factors controlling flood sensitivity, with weights of 0.35 and 0.23, respectively. Moderately flood-sensitive areas account for the largest share of the basin, reaching 66% and covering 236.54 km². The flood extent estimated by the spectral indices ranges from 36.73 km² to 101.87 km². Among these indices, NDVI has the strongest spatial correlation with flood-prone areas. The random forest model used in this study has an AUC of 0.6935 and an overall accuracy of 63.51%. The analytical framework proposed in this study is applicable to data-scarce Andean River basins, and the combined use of multispectral indices can provide support for flood risk management and decision-making in this region. Full article

Full-field water saturation is central to waterflood surveillance but cannot be observed continuously across a reservoir, whereas well histories provide sparse dynamic evidence. We formulate target-time saturation reconstruction as a mapping from static geology, well locations, scheduled controls, simulated multi-well production responses, and development time to a two-dimensional saturation field. The water-balance-guided saturation network (WB-SatNet) combines a U-Net spatial pathway, a fixed-order gated recurrent unit (GRU) history encoder, explicit time conditioning, and a closed-boundary water-storage consistency term. Experiments used 400 geological realizations, 800 simulation runs, six target times, realization-wise train/validation/test splitting, and three random seeds. On the held-out test set, WB-SatNet achieved a mean absolute error (MAE) of 0.01175, a coefficient of determination (${\mathrm{R}}^2$) of 0.98145, a structural similarity index measure (SSIM) of 0.99177, a flooded-area intersection over union (IoU) of 0.95523, and a global storage-consistency error of 0.00944. Its mean MAE was 6.31% lower than that of TCN-U-Net, the strongest temporal convolutional network baseline. Target-time, component-ablation, flooded-area, storage-consistency, history-window, noise, and operating-regime analyses support a monitoring-oriented interpretation. These results indicate that WB-SatNet provides an effective framework for production-history-conditioned water-saturation reconstruction and waterflood state monitoring in the investigated setting. Full article

Silicon (Si)-based amendments can regulate soil nitrogen (N) cycling and reduce gaseous N losses, but the effect of nano-Si particle size on mineral N dynamics and nitrous oxide (N₂O) emissions under flooded tropical paddy soil conditions remains unclear. This study aimed to elucidate how nano-Si particle size regulates mineral N dynamics and N₂O emissions, with particular emphasis on whether smaller particles promote ammonium N (NH₄⁺-N) dominance and more effectively mitigate N₂O emissions than larger particles. A 30-day flooded incubation experiment was conducted using tropical paddy soil amended with 15 or 50 nm nano-Si particles, each applied at 67 mg kg⁻¹ dry soil, alongside an unamended control. Mineral N dynamics, extracellular enzyme activities, N-cycling functional genes, and N₂O emissions were evaluated. Compared with the control, 15 nm and 50 nm nano-Si significantly increased NH₄⁺-N concentration by 16.4% and 5.25% while reducing nitrate N (NO₃⁻-N) concentration by 40.8% and 23.0%, respectively. The NO₃⁻-N/NH₄⁺-N ratio decreased significantly by 49.8% and 22.1%, indicating a particle-size-dependent shift toward NH₄⁺ dominance. The 15 nm treatment significantly enhanced β-N-acetylglucosaminidase and leucine aminopeptidase activities, supporting organic N turnover and NH₄⁺-N accumulation. It also significantly reduced the abundance of the nitrite reductase gene nirS, involved in denitrification, whereas the N₂O reductase gene nosZ, responsible for N₂O reduction, showed no consistent treatment-specific response. Consistently, cumulative N₂O emissions decreased significantly by 33.6% and 18.2% under 15 nm and 50 nm treatments, respectively. These results indicate that both nano-Si treatments significantly shifted mineral N dynamics toward NH₄⁺-N dominance and reduced cumulative N₂O emissions compared with the control. These responses were consistently stronger under the 15 nm treatment than under the 50 nm treatment, demonstrating that smaller nano-Si particles more effectively limit NO₃⁻-N accumulation, promote NH₄⁺-N retention, and mitigate N₂O emissions in flooded tropical paddy soil. Nevertheless, direct measurements of N-transformation rates, comparisons with conventional Si sources, and field-scale validation are required before practical application. Full article

Water flooding in low-permeability, highly heterogeneous reservoirs often causes a rapid increase in water cut and inefficient pressure maintenance because injected water preferentially flows through high-permeability channels. In this study, a field-scale compositional simulation model was established to evaluate CO₂ water-alternating-gas (WAG) enhanced oil recovery in a mature waterflooded reservoir in the Daqing Oilfield. The model was constrained by geological data, experimentally tuned pressure–volume–temperature (PVT) behavior, relative-permeability measurements, and slim-tube tests. The minimum miscibility pressure (MMP) of the CO₂-oil system was estimated to be 19.8 MPa. An 187-month production history was matched using field oil rate, water production, water cut, and reservoir-pressure data. At the current development stage, the reservoir has an oil recovery of 23.6%, an average water cut of 61.34%, and an average reservoir pressure of approximately 6.9 MPa. A 30-year prediction was then performed to compare continued water flooding with several CO₂-WAG development strategies. Sensitivity analyses were conducted for the pressure-restoration level, pre-injection fluid, well-pattern conversion, slug size, and gas/water slug-size ratio. Continued water flooding increased the final oil recovery to only 28.4% and resulted in a water cut of 92.8%. Sequential scenario screening identified a best-performing case among the tested scenarios, consisting of CO₂ pre-injection to restore the average reservoir pressure to 11 MPa, conversion to a staggered line-drive well pattern, a slug size of 0.025 PV, and a gas/water slug-size ratio of 1:1. Under this sequentially selected case, the end-of-forecast oil recovery reached approximately 57.24%, which was the highest value among the cases evaluated in this study and was 28.84 percentage points higher than continued water flooding. The predicted recovery is conditional on the adopted geological, relative-permeability, EOS, and history-matching assumptions. Because the designed average reservoir pressure is below the measured MMP and local pressure above the MMP was not demonstrated, the modeled process is consistently interpreted as immiscible CO₂-WAG. The predicted recovery improvement is interpreted as being associated with pressure support, gas-mobility control, improved sweep efficiency, and compositional CO₂–oil interactions represented by the model, including CO₂ dissolution, oil swelling, and viscosity reduction. The contribution of this work is a field-scale, experimentally constrained workflow for selecting CO₂-WAG operating parameters in mature waterflooded low-permeability reservoirs; CO₂ storage performance should be quantified separately in future work. This study provides an experimentally constrained and history-validated field-scale workflow for identifying a best-performing CO₂-WAG operating case among the tested scenarios in mature waterflooded low-permeability reservoirs. Full article

IoT sensor deployments increasingly export measurement streams to edge gateways and content-addressed storage such as IPFS, but access control decisions must be enforced without disclosing sensor owner policies, requester attributes, or stale data versions. Existing blockchain, CP-ABE, and zero-knowledge approaches reduce parts of this leakage, yet they can still expose public policy structure, accept stale Merkle proofs after sensor stream updates, overload provers when policies grow, or leave IPFS gateways vulnerable to bandwidth abuse. This paper proposes zk-Guard-R, a policy-hidden and replay-safe zk-SNARK access control framework for privacy-preserving IoT sensor data sharing. zk-Guard-R replaces public sparse policy matrices with MiMC-Merkle policy commitments verified inside the proof, separates long-lived logical sensor policy roots from frequently updated physical IPFS data roots, binds every proof to an on-chain nonce, and decouples attribute possession from policy interpretation through a bounded stack-based policy interpreter. Numeric sensor-access predicates are represented through committed values and range check gadgets, while an off-chain verification gateway couples accepted proofs with payment channel vouchers before releasing encrypted IPFS chunks. The design contribution is separated from the measured prototype: the full protocol specifies a bounded policy interpreter, whereas the present gnark prototype evaluates the core committed policy, committed attribute, range check, data root, nonce, Solidity verifier, and gateway-metering mechanisms. We implement a gnark BN254/Groth16 research prototype and benchmark it against a matrix-public zk-Guard prototype, a blockchain ABAC baseline, an IoT token/HMAC baseline, and a CP-ABE-style cryptographic-work proxy. For 128 attributes, the zk-Guard-R prototype with MiMC-Merkle commitments uses 425,574 R1CS constraints, generates proofs in 3.12 s, verifies in 0.73 ms, and uses 641 MB peak Go heap allocation. A three-run repeat of the 128-attribute configuration gives a proof-generation mean of 2.80 s with a 0.54 s standard deviation on the same local host, illustrating the runtime variability of prover measurements. We also deploy the generated Solidity verifier on a local Anvil EVM and measure 241,942 gas for a successful verification transaction, and we evaluate a local Kubo/IPFS gateway under valid, replayed, and voucher-limited flood requests. The results show that zk-Guard-R shifts substantial but measurable work to the prover while improving policy confidentiality, freshness, and gateway metering for IPFS-backed IoT sensor data sharing. Full article

The complexity of micrometer-scale pore-throat structures in sandstone reservoirs strongly controls the occurrence state and mobilization degree of residual oil after water-flooding. To clarify the differences in residual oil occurrence between pure oil-zone and transition-zone reservoirs and their microscopic controlling mechanisms, sandstone samples were collected from the SII oil layer group, which belongs to the Upper Cretaceous Yaojia Formation, in the Nanqi area of the Daqing Oilfield, northern Songliao Basin, NE China, and were investigated. Mercury intrusion capillary pressure (MICP), two-dimensional nuclear magnetic resonance (2D NMR), laser scanning confocal microscopy (LSCM), micro-computed tomography (micro-CT), X-ray diffraction (XRD), wettability measurement and fractal analysis were integrated to systematically characterize the pore-throat architecture, mineral composition, seepage capacity, and residual oil occurrence of the two reservoir types. The results show that the pore-throat radius distributions are mainly unimodal. In the pure oil-zone samples, the pore-throat distribution is highly consistent with the corresponding permeability contribution curve, whereas evident deviations occur in some transition-zone samples. Large and medium pore throats exert the most significant control on seepage capacity, and the difference in fractal characteristics is mainly reflected by D1, the fractal dimension of large pore throats. The transition-zone reservoirs generally exhibit moderate to strong water-wet characteristics. Owing to the development of fine pore throats and strong capillary forces, water is prone to retention within pore-throat spaces, resulting in pronounced water-blocking and Jamin effects. After water-flooding, the pure oil-zone reservoirs exhibit lower residual oil saturation, with residual oil occurring mainly in a bound state; in contrast, the transition-zone reservoirs show higher residual oil saturation and relatively high proportions of free and semi-bound residual oil. Mineral composition further modifies pore-throat complexity and residual oil occurrence. D1 is negatively correlated with feldspar content, indicating that increased feldspar content helps improve the pore-throat structure, but positively correlated with clay mineral content, suggesting that clay minerals enhance structural complexity. In the transition-zone reservoirs, kaolinite and illite–smectite mixed-layer minerals are relatively well developed. Their velocity-sensitive and water-sensitive effects readily induce pore-throat blockage and increased flow resistance, which are important causes of residual oil enrichment and difficult oil mobilization in the transition zone. Full article

Neighborhood quality is the hardest hedonic component to measure and is usually proxied by coarse census aggregates. I ask whether complaint composition reveals infrastructure quality that markets capitalize, or if it merely reflects unequal engagement and urbanicity. I link 556,882 geolocated complaints from Thailand’s Traffy Fondue platform (January 2025 to June 2026) to 8553 for-sale condominiums across Bangkok, geocoding each project with two independent services (76% building precision, cross-validated to 45 m), and measure the local complaint environment within 250 to 1000 m rings. Across the 45 active districts, price tracks complaint composition strongly, but the fingerprint is collinear with urban density and collapses once complaint density is controlled. The central contribution is a within-area test: conditional on district and sub-district fixed effects, condominiums in a locally more flood-prone micro-location sell for less, 4.4% per standard deviation, strengthening with radius and with geocoding precision, and surviving false-discovery-rate correction. Complaint-mix diversity is likewise capitalized. Footpath share is positively signed but does not survive correction: complaint composition is two-dimensional, some categories indexing disamenity, others urban intensity. Regional PM_2.5 haze carries no within-city signal. Citizen complaints are thus a spatially resolved fingerprint of local environmental quality, capitalizable only where the signal is local. Full article

The accumulation of sediment in rivers and lakes can elevate riverbeds, leading to a reduction in river channel flood-carrying capacity, impairment of water conservancy project benefits, and destruction of aquatic ecosystems. Systematic dredging projects are required in the routine maintenance of river channels to ensure flood control safety and ecological health. This study utilized a successfully constructed efficient composite engineering bacterial strain system to conduct field pilot-scale validation in a closed river environment with a water volume of 10,000 cubic meters. Various indicators such as sediment thickness, organic matter, COD (chemical oxygen demand), total nitrogen, and total phosphorus were measured, along with metagenomic analysis, to explore the application effectiveness and feasibility of microbial composite strain technology in river sediment remediation. The results demonstrated that the composite strain technology effectively reduced the thickness and pollutant content of river sediment. The average sediment thickness significantly decreased from 32 cm to 22 cm, with a 10 cm thick mineralized layer forming within two weeks. The reductions in organic matter content, COD content, total nitrogen, and total phosphorus content reached 60.03%, 47.73%, 37.36%, and 29.16%, respectively. Metagenomic analysis revealed that the biodiversity of the treated river channel was higher than that of parallel and control channels at both the phylum and genus levels. The experimental results are useful for optimizing river sediment remediation. Full article

Invasive alien species (IAS) are an important but insufficiently integrated risk in sustainable infrastructure development, particularly in projects involving earthworks, soil movement, habitat disturbance and prolonged construction. Their spread may cause biodiversity loss, ecosystem degradation, higher maintenance costs, project delays and long-term deterioration of land-use sustainability. This study identifies critical stages of infrastructure project implementation associated with elevated IAS risk and proposes management measures supporting prevention, early detection, rapid response and post-intervention monitoring. The research used a comparative multiple-case study of three large infrastructure projects in Poland implemented under FIDIC conditions: flood embankment reconstruction, railway line modernisation and express road construction. A Before–After approach combined analysis of pre-construction environmental documentation, baseline ecological inventories, field verification after IAS detection, control measures and monitoring outcomes. Japanese knotweed (Reynoutria japonica Houtt.) was analysed as a model species because of its high regeneration potential and relevance to soil management. The analysed cases indicate that elevated IAS risk may occur during site preparation, earthworks, topsoil stripping and storage, soil reuse, finishing, landscaping and utility relocation. The study proposes a practical IAS risk classification framework to support risk identification, preventive action and monitoring intensity with the potential to support sustainability performance and reduce ecological and economic impacts. Full article

The newly constructed Luofengshan Tunnel obliquely underpasses the operating Hanyicun railway tunnel, requiring an integrated assessment of deformation response and service safety. This study combined field monitoring, ultrasonic shear-wave reflection imaging, and three-dimensional numerical simulation to evaluate track-bed settlement, lining deformation, floor integrity, lining stress, and structural safety. The complete third-party monitoring record showed that vertical deformation dominated the tunnel response. The final maximum cumulative values were −17.44 mm for manual lining settlement, −14.91 mm for automated lining settlement, and −11.30 mm for track-bed settlement; the corresponding maximum absolute values of horizontal displacement and convergence were 1.82 mm and 1.40 mm, respectively. Same-period comparisons showed that deformation increments decreased after breakthrough, and post-lining flood-season monitoring showed monthly rates below 1 mm/month. Ultrasonic imaging detected no evident voids or collapse within 2.0–2.2 m below the floor. Numerical simulation showed that incorporating measured initial deformation increased the maximum compressive stress from 4.95 MPa to 6.26 MPa, whereas subsequent underpass excavation and train loading increased it slightly to 6.28 MPa and 6.33 MPa, respectively. The final minimum safety factor was 3.18, indicating that the underpass impact remained controllable. Full article

The probability estimation of agrometeorological damage risk is the core technical support for consolidating the agricultural disaster prevention and reduction system and ensuring the sustainable development of national agriculture. It has been widely applied in agricultural production and agricultural insurance. Based on the crop planting area data, as well as the damaged crop area data (damage-affected, damage-stricken, and dead harvest) of 31 provinces and municipalities from 1980 to 2018, this study creatively builds a comprehensive damage strength index. After that, this study obtains accurate risk probability estimation results of five meteorological damage types by using the parameters of the nonparametric normal information diffusion model. The results show that the risk probability of comprehensive meteorological damage is the largest, followed by drought, flood, wind and hail, and freezing. Flood in Hubei, drought in NeiMenggol, windstorm and hailstorm in Qinghai, freezing damage in Hainan, and comprehensive meteorological damage in NeiMenggol have the highest risk probability. The regions where various meteorological damage occur show different distribution characteristics, which is closely related to the latitude and longitude and topography of China. These findings indicate that it is necessary to understand the overall patterns of agrometeorological damage risks and consider their internal heterogeneity, in order to take targeted prevention and control measures to avoid systemic risks in agricultural production and safeguard sustainable and high-quality agricultural development. Full article

Natural and human-induced disasters can cause significant loss of life and property, particularly at the building level, highlighting the need for effective early detection, real-time monitoring, and rapid post-disaster response. Current disaster management approaches largely rely on citizen reports and manual observations, which may lead to delays and inefficient resource allocation, especially in large-scale events. This study proposes a secure, modular, multi-layer disaster monitoring and decision-support architecture that integrates sensor-based building-edge monitoring units deployed at both the building and apartment levels with a central emergency monitoring system. The architecture comprises three main layers: edge sensing, secure cellular communication, and central decision-making. Building-edge monitoring units collect data related to structural motion and inclination indicators, fire, flooding, and gas leaks, perform preliminary processing, and transmit aggregated data securely to the central system. Communication security is ensured through a certificate-based authentication mechanism supported by a dedicated certificate authority, reducing the risk of unauthorized access and fraudulent data injection. The central system performs automated event detection and separately evaluates physical building condition and communication status, enabling prioritized response planning. To evaluate feasibility, a two-building prototype was implemented and tested through scenario-based experiments involving two independently operating building-edge monitoring units connected to the same central monitoring system. The prototype demonstrated concurrent secure data acquisition and central aggregation from two buildings; however, district- and regional-scale performance requires further validation through larger-scale controlled load tests and field deployments. Under laboratory conditions, the prototype demonstrated sensor-data acquisition, authenticated transmission, and centralized event classification. End-to-end latency and building-edge monitoring unit power consumption were also measured; however, the prototype was not validated under environmental conditions representative of real disasters. Overall, the findings suggest that sensor-based, secure, and centralized monitoring systems may complement traditional disaster management approaches. Full article