Search Results (105)

Flooding is one of the most common and destructive natural disasters worldwide, and projections indicate that its intensity will increase across various climate regions during this century. South Sudan is particularly vulnerable due to a combination of factors, including hydrological releases from Lake Victoria, local rainfall patterns, and wetland retention dynamics. These factors raise important questions regarding the hydrological connectivity between Lake Victoria and the Nile system. This study examined how upstream hydrological conditions impact flood dynamics in South Sudan’s flood-prone regions, specifically in the states of Jonglei and Unity along the River Nile. To statistically estimate flood propagation lag time from Lake Victoria to the Sudd wetland, we used Cyclone Global Navigation Satellite System (CYGNSS) remote sensing data and water-level altimetry from both Lake Victoria and the River Nile at Mangalla. The analytical methods included moving block bootstrap (MBB) cross-correlation and Gaussian process (GP) modeling. Furthermore, we validated the event-based propagation and inundation patterns using flood event reports from the Displacement Tracking Matrix (DTM). The findings indicate that the statistical propagation signals took approximately 106 days during the wet season (95% confidence interval [CI]: 60–150 days) and 134 days during the dry season (95% CI: 75–195 days) for the downstream water level response to reach the River Nile at Mangalla, and 3–4 weeks to reach the adjacent floodplains downstream. Residual stationarity diagnostics showed augmented Dickey–Fuller (ADF) statistics below −7 across the analyzed propagation pathways, indicating statistically stationary lag-adjusted residual behavior. Consistent temporal correspondence between inferred flood arrival windows and independently reported DTM flood-impact periods provides cautious support for the hydrological plausibility of the estimated propagation structure. Full article

Particulate-reinforced polymer composites (PRPCs) are susceptible to cracking under tensile loading, severely limiting their service life. Here, we propose a pressure-gradient-driven infiltration method that rapidly repairs narrow (<10 μm) cracks in a highly filled PRPC (95 wt.% BaSO₄/5 wt.% fluororubber). Microstructural evidence confirms that the adhesive completely fills the tortuous crack and forms a continuous adhesive–matrix interface capable of supporting load transfer. Semi-circular bend (SCB) testing demonstrates a substantially higher peak load and increased apparent structural stiffness after repair under the present semi-circular bend configuration, indicating apparent mechanical enhancement beyond simple load-bearing recovery. Digital image correlation (DIC) and fracture morphology show that repair suppresses notch-tip strain localization, reduces the strain concentration factor, shifts the failure-controlling zone away from the original notch tip, and deflects the crack propagation path. Phase-field simulations further show that the post-repair load-bearing capacity is governed by the adhesive–matrix interfacial strength; once this strength approaches or exceeds the tensile strength of the intact PRPC (~8.3 MPa), the repaired crack path is stabilized, enabling peak-load enhancement while suppressing damage localization along the original crack path and shifting failure to adjacent weaker regions. Overall, this work establishes a promising crack repair approach for highly filled PRPCs, while the underlying interface-controlled mechanism provides guidance for adhesive selection and repair design. Full article

To address wellbore instability and the technical challenges associated with high-density water-based drilling fluid loss control in deep shale gas formations of the Sichuan-Chongqing region in China, a novel nano-micro sealant designated CLG-Seal was synthesized via molecular structural optimization. The molecular structure of newly developed CLG-Seal exhibits distinct core–shell structural characteristics. The inorganic nano-silica constitutes the rigid core of CLG-Seal, which guarantees its plugging performance. The hydrophobically associating polymer which is coated on the surface of nano-silica constructs the flexible shell of CLG-Seal, endowing the CLG-Seal with excellent gel-forming capacity, adhesion film-forming capacity, deformability and perfect dispersibility. Transmission electron microscopy and scanning electron microscopy were employed to characterize the morphology of the CLG-Seal nanomicron-scale plugging agent. The sealing performance and underlying mechanisms of CLG-Seal were subsequently evaluated via particle plugging apparatus tests, displacement experiments, and etched glass micromodel simulations. Field trials conducted in the third section of Well WY3-2-3HF validated the application effectiveness of this agent in drilling fluid systems. The results indicate that the nano-micro sealant CLG-Seal exhibits a median particle size of D₅₀ is 146 nm, which can be modulated by adjusting the synthesis conditions. The nano-micro sealant CLG-Seal significantly mitigates fluid loss in low-permeability microfractures and fissures. Notably, a concentration of merely 3% is sufficient to achieve optimal nano-micro plugging performance. The results of the mechanism study indicate that while the CLG-Seal particles are close to each other, the polymer chains with flexible long chain structure which are coated on the surface of nano-silica constructs tend to be intertwined, forming a cross-linked network structure of gel film, thereby increasing the interaction between nano-micron particles and forming an impermeable plugging film. In addition, due to the nanoscale effect, the CLG-Seal has a strong tendency to adsorb onto the surface of shale rock through hydrogen bonding with the shale matrix. The hydrophobically associating polymer with high elastic modulus and excellent mechanical properties can enhance the pressure-bearing capacity of the filter cake through elastic deformation. Therefore, these nano-micron particles can form a strong sealing film on the filter cake and at the micropores of shale rock, thereby creating a dense mud cake on the outside of the shale formation. Field trial results demonstrate that the incorporation of the nano-micro sealant CLG-Seal into the drilling fluid for the third section of Well WY3-2-3HF reduced the PPA fluid loss to 4.6 mL. This value represents a substantial reduction compared to adjacent wells and signifies a remarkable improvement over the drilling fluids previously employed in the Longmaxi Formation of this block. Furthermore, the treated drilling fluid exhibited a superior filtration control pressure capacity of 10.5 MPa. The operation was completed successfully without any lost circulation or wellbore instability, and achieved a drilling footage of 42 h with an average penetration rate of 7.81 m/h. The mud weight was reduced by approximately 0.08–0.10 g/cm³ compared to offset wells. These results confirm the excellent application efficiency of the newly developed CLG-Seal in field operations. Full article

To address the erosion-induced failure of large-caliber gun barrels under extreme thermochemical coupling, this study systematically investigates the microstructural evolution of multi-layered gradient regions along the radial direction of 32CrNi3MoV steel under extreme thermochemical cycling. Leveraging SEM, EBSD, TKD, and double-beam aberration-corrected TEM, combined with JMatPro thermodynamic simulations, the phase transitions, crystallographic characteristics, and substructural evolution spanning from the bore surface to the matrix are elucidated. The results demonstrate that a three-layer gradient structure forms along the radial direction. The topmost layer is a chemically stabilized metastable austenite diffusion layer with a thickness of 1.5–4.0 μm. which is attributed to the suppression of martensitic transformation due to C/N interstitial diffusion lowering the MS temperature. The observed high-density dislocation tangles and stacking faults within this austenite diffusion layer result from thermal mismatch stresses during rapid thermal cycling. The subsurface region is a martensitic transformation layer with a thickness of 70–97 μm, exhibiting a substructural gradient from nanostructured high-density twinned martensite to refined lath martensite. Thermodynamic analysis indicates that rapid heating (≈10⁵ °C/s) facilitates significant austenite nucleation and growth during the reverse phase transformation, subsequently forming nanostructured martensitic grains via non-equilibrium transformation during rapid cooling. Adjacent to this is a matrix tempering layer extending approximately 160 μm. Nanoindentation hardness profiling reveals that the peak radial hardness (≈1000 HV) occurs within the fine-grained martensitic zone approximately 40 μm from the surface. In contrast, the tempered layer exhibits reduced hardness (≈400 HV) compared to the original matrix (≈500 HV). This is primarily attributed to transient high-temperature over-tempering effects, which induces carbide coarsening and the loss of solid solution strengthening, alongside the softening of prior austenite grain boundaries. This study clarifies the micro-to-nanoscale evolution of the barrel microstructure, providing critical theoretical insights for understanding erosion mechanisms and improving lifetime predictions. Full article

Against the background of increasing pressure on water environmental protection and regional industrial transformation, water environmental governance (WEG) is jointly shaped by environmental regulation and industrial agglomeration. However, the mechanisms underlying this relationship remain insufficiently examined. Based on provincial-level data from China, this study uses fixed-effects models and spatial econometric models to examine the effects of environmental regulation on WEG. The results show a clear threshold pattern. When the environmental regulation index exceeds 0.3, its positive association with WEG begins to emerge. Basin-location analysis indicates that downstream regions may require stronger environmental regulation to improve WEG. Spatial analysis reveals positive spillover effects under both the contiguity weight matrix and the basin-adjacency weight matrix. Mechanism analysis further shows that environmental regulation is negatively associated with WEG through the specialized agglomeration of pollution-intensive industries. It is also positively associated with WEG through upstream and downstream linkage agglomeration in the clean industrial chain. Future research could further explore micro-level mechanisms and cross-regional linkages to provide deeper evidence for improving WEG. Full article

Coal-mine-gas-monitoring data exhibits missing phenomena due to the harsh underground operating environment. Accurate imputation of missing values in gas-monitoring sequences serves as a key data foundation for guaranteeing the continuity of gas data, enhancing the reliability of disaster early warning, and improving the accuracy of mine safety situation analysis and judgment. Aiming at the prevalent random and segmented missing issues in coal-mine-gas-monitoring time-series data, and the limitation that existing imputation methods struggle to accurately capture the nonlinear spatiotemporal correlations and long-range temporal dependencies of such data, this study proposes a missing data imputation method for coal mine gas time-series data based on the Spatio-Temporal Graph Attention Network—Echo State Network (ST-GAT-ESN). Firstly, this method extracts temporal features of the gas concentration sequence using a Gated Recurrent Unit (GRU). Subsequently, it models multiple monitoring points as graph nodes through a Graph Attention Network (GAT), constructs an adjacency matrix based on airflow propagation relationships, and adaptively learns the spatial dependency weights between monitoring points to realize the deep fusion of spatiotemporal features. Finally, it designs a dual-channel Echo State Network (ESN), synchronously inputs the spatiotemporal fusion features of the missing regions before and after, efficiently fits the nonlinear evolutionary trend of the data by virtue of the echo state property of the reservoir, and solves the output layer weights through ridge regression to achieve accurate imputation of missing values. Experimental results demonstrate that, compared with the single-ST-GAT-ESN, ESN, and ARIMA models, the proposed method achieves the optimal imputation performance in both random and segmented missing scenarios within the missing rate range of 5–50%. The three evaluation metrics—Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE)—are reduced by 30–80% compared with the benchmark models. Moreover, the imputation curve achieves the best fitting performance with the ground-truth curve at a 50% segmented missing rate. This study confirms that the ST-GAT-ESN model effectively enhances the adaptability and robustness to complex missing patterns via spatiotemporal collaborative modeling and a dual-channel fusion mechanism, providing a high-precision and highly stable technical solution for ensuring the integrity of coal-mine-gas-monitoring data, and also provides theoretical references and engineering insights for the missing-value processing of industrial time-series monitoring data. Full article

Background/Objectives: Most brain magnetic resonance imaging (MRI) is performed in supine position, although posture may influence cerebrovascular signal characteristics through gravity-related physiological changes. However, posture-dependent vascular signal alterations on low-field MRI have not been sufficiently quantified. This study aimed to quantify posture-related internal carotid artery (ICA) signal alterations using low-field MRI by comparing seated and supine images with intensity-, noise-, and texture-based metrics. Methods: Nine healthy adults (20–69 years old; one female) underwent 0.25 T tilting MRI in supine and seated postures. 3D gradient echo T1-weighted images were obtained. The bilateral ICA regions of interest (ROI) and adjacent reference ROI were manually delineated. The signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), signal intensity ratio (SIR), gray-level co-occurrence matrix (GLCM) texture features (contrast, correlation, energy, and homogeneity) were extracted and compared between postures using Wilcoxon signed-rank tests. Results: Seated posture produced significantly higher ICA signal intensity metrics than the supine posture, with increased SNR (median 17.11 vs. 13.48), CNR (median 21.94 vs. 18.36), and SIR (median 10.84 vs. 9.54) (p = 0.004). GLCM texture analysis demonstrated a significant decrease in contrast in the seated position (median 62.01 vs. 145.92; p = 0.004), whereas correlation, energy, and homogeneity showed no significant between-posture differences. Conclusions: Low-field MRI was sensitive to posture-dependent ICA signal alterations. ICA-based metrics may provide quantitative markers of gravity-related cerebrovascular adaptation. Full article

This study aims to assess the evolution of land cover in the Lam Phra Phloeng (LPP) watershed and predict future land use patterns. By employing the Gray Level Co-occurrence Matrix (GLCM) and several spectral indices, high classification accuracy (>92%) was achieved using the Random Forest (RF) algorithm. Based on classified land use maps from 2003 and 2023, future land use predictions for 2030, and 2050 were generated using the CA-Markov chain model. The predictions suggest a gradual trend toward deforestation and the expansion of croplands, driven by population growth and increased anthropogenic activity in the region. The Sediment Delivery Ratio (SDR) model, part of the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) suite, was used to simulate soil loss in the LPP watershed. The results indicate minimal soil loss in vegetated areas and significant erosion in regions adjacent to water bodies, primarily due to rainfall erosivity. This research highlights the social, ecological, and economic implications of land use change. Furthermore, best management practices (BMPs) are identified as effective strategies for land restoration and erosion reduction. The study also discusses three widely adopted soil erosion control techniques, providing recommendations for reforestation and erosion mitigation programmes. Full article

The widespread application of road deicing salts in northern regions has led to elevated salinity in roadside soils and adjacent watersheds. Phytoremediation offers a cost-effective and sustainable approach for mitigating salt contamination, but its success depends on utilizing plant species that can both tolerate and remove salt under roadside conditions. To systematically identify high-potential candidates from the large inventory of salt-tolerant plants in North America, we developed a quantitative decision matrix incorporating criteria related to ecological safety, establishment potential on disturbed soils, aboveground biomass production, biomass use-value, and salt uptake capacity. Thirteen of the highest-ranked species were subsequently evaluated for sodium (Na⁺) and chloride (Cl⁻) uptake in a controlled greenhouse study under saline and non-saline conditions. The greatest total salt uptake was observed in common sunflower (Helianthus annuus) (35.6 mg Na⁺ and 100.2 mg Cl⁻ plant⁻¹) and pitseed goosefoot (Chenopodium berlandieri) (18.6 mg Na⁺ and 76.0 mg Cl⁻ plant⁻¹), while perennial species including tall fescue turfgrass (Lolium arundinaceum), showy goldenrod (Solidago speciosa), and weeping alkaligrass (Puccinellia distans) also demonstrated substantial uptake combined with greater long-term suitability for roadside management. Overall, this study presents a quantitative framework for phytoremediation species selection and provides experimental evidence supporting both annual and perennial species for mitigating deicing salt contamination through environmentally sustainable, low-input roadside management strategies. Full article

The composition of air pollutants in industrial complexes differs from that of general urban areas, often containing more hazardous substances that pose significant health risks to both workers and residents nearby. In this study, PM_2.5 and its 29 chemical components (eight ions, two carbon species, and 19 trace elements) were measured and analyzed at five monitoring sites adjacent to the Yeosu and Gwangyang industrial complexes from August 2020 to December 2024. Chemical characterization and source identification were conducted. The average PM_2.5 concentration was 18.63 ± 9.71 μg/m³, with notably higher levels observed during winter and spring. A low correlation (R = 0.56) between elemental carbon (EC) and organic carbon (OC) suggests a dominance of secondary aerosols. The charge balance analysis of [NH₄⁺] with [SO₄²⁻], [NO₃⁻], and [Cl⁻] showed slopes below the 1:1 line, indicating that NH₄⁺ is capable of neutralizing these anions. Positive matrix factorization (PMF) identified eight contributing sources—biomass burning (10.4%), sea salt (11.8%), suspended particles (7.1%), industrial sources (4.6%), Asian dust (5.2%), steel industry (21.8%), secondary nitrate (16.4%), and secondary sulfate (22.7%). These findings provide valuable insights for the development of targeted mitigation strategies and the establishment of effective emission control policies in industrial regions. Full article

Surface water is essential for sustaining ecosystems and supporting human socio-economic development, yet pollution from urbanization increasingly threatens its ecological sustainability. The accurate prediction of dissolved oxygen (DO), as an important indicator of water quality, is crucial for water resource protection. To address the methodological gaps in current research, we propose a hybrid deep learning model (GCG) that integrates spatiotemporal correlations to enhance DO prediction accuracy through the systematic exploitation of latent data dependencies. This study proposes a three-stage modeling framework: (1) A novel adjacency matrix construction methodology based on Pearson correlation coefficients is developed to quantify spatial correlations between monitoring stations, enabling spatial feature aggregation via graph convolutional networks (GCNs); (2) the spatially enhanced features are subsequently processed through 1D convolutional neural networks (CNNs) to capture temporal local patterns; (3) model performance is comprehensively evaluated using four metrics: R², RMSE, MAE, and MAPE. The proposed model was implemented for DO prediction in Lake Taihu, China. Experimental results demonstrate that compared to conventional adjacency matrix construction methods, the Pearson correlation-based adjacency matrix confers advantages, achieving at least a 5% reduction in RMSE and over 10% improvement in MAE and MAPE. Furthermore, the GCG model outperformed the comparison model, with an R² enhancement of 8%, while reducing RMSE and MAE by over 70% and 60%, respectively. These results validate the model’s effectiveness in mining spatiotemporal correlations for regional water quality forecasting, offering a reliable tool toward sustainable water monitoring and ecosystem-based management. Full article

Gel treatments have been widely applied to control water production in oil and gas reservoirs. However, for water shutoff in dense gas reservoirs, most gel-based treatments focus on individual wells rather than the entire reservoir, exhibiting limited treatment depth, poor durability, and inadequate repeatability Notably, formation damage is a primary consideration in treatment design—most dense gas reservoirs have a permeability of less than 1 mD, making them highly susceptible to damage by formation water, let alone viscous polymer gels. Constrained by well completion methods, gelant can only be bullheaded into deep gas wells in most scenarios. Due to the poor gas/water selective plugging capability of conventional gels, the injected gelant tends to enter both gas and water zones, simultaneously plugging fluid flow in both. Although several techniques have been developed to re-establish gas flow paths post-treatment, treating gas-producing zones remains risky when no effective barrier exists between water and gas strata. Additionally, most water/gas selective plugging materials lack sufficient thermal stability under high-temperature and high-salinity (HTHS) gas reservoir conditions, and their injectivity and field feasibility still require further optimization. To address these challenges, treatment design should be optimized using non-selective gel materials, shifting the focus from directly preventing formation water invasion into individual wells to mitigating or slowing water invasion across the entire gas reservoir. This approach can be achieved by placing large-volume gels along major water flow paths via fully watered-out wells located at structurally lower positions. Furthermore, the drainage capacity of these wells can be preserved by displacing the gel slug to the far-wellbore region, thereby dissipating water-driven energy. This study evaluates the viability of placing gels in fully watered-out wells at structurally lower positions in an edge-water drive gas reservoir to slow water invasion into structurally higher production wells interconnected via numerous microfractures and high-permeability streaks. The gel system primarily comprises polyethyleneimine (PEI), a terpolymer, and nanofibers. Key properties of the gel system are as follows: Static gelation time: 6 h; Elastic modulus of fully crosslinked gel: 8.6 Pa; Thermal stability: Stable in formation water at 130 °C for over 3 months; Injectivity: Easily placed in a 219 mD rock matrix with an injection pressure gradient of 0.8 MPa/m at an injection rate of 1 mL/min; and Plugging performance: Excellent sealing effect on microfractures, with a water breakthrough pressure gradient of 2.25 MPa/m in 0.1 mm fractures. During field implementation, cyclic gelant injections combined with over-displacement techniques were employed to push the gel slug deep into the reservoir while maintaining well drainage capacity. The total volumes of injected fluid and gelant were 2865 m³ and 1400 m³, respectively. Production data and tracer test results from adjacent wells confirmed that the water invasion rate was successfully reduced from 59 m/d to 35 m/d. The pilot test results validate that placing gels in fully watered-out wells at structurally lower positions is a viable strategy to protect the production of gas wells at structurally higher positions. Full article

Sabina vulgaris is a keystone shrub species endemic to arid northwestern China, renowned for its exceptional drought tolerance, sand fixation capabilities, and critical role in desert ecosystem stability. This study investigates the impact of coal mining activities on the spatiotemporal dynamics of S. vulgaris shrublands in the ecologically fragile Mu Us Sandy Land, focusing on the Longde Coal Mine adjacent to the Shenmu S. vulgaris Nature Reserve. Utilizing seven periods (2013–2025) of 2 m resolution Gaofen-1 (GF-1) satellite imagery spanning 12 years of mining operations, we implemented a deep learning approach combining UAV-derived hyperspectral ground truth data and the SegU-Net semantic segmentation model to map shrub distribution via GF-1 data with high precision. Classification accuracy was rigorously validated through confusion matrix analysis (incorporating the Kappa coefficient and overall accuracy metrics). Results reveal contrasting trends: while the S. vulgaris Protection Area exhibited substantial expansion (e.g., Southern Section coverage grew from 2.6 km² in 2013 to 7.88 km² in 2025), mining panels experienced significant degradation. Within Panel 202, coverage declined by 15.4% (58.4 km² to 49.5 km²), and Panel 203 showed a 18.5% decrease (3.16 km² to 2.57 km²) over the study period. These losses correlate spatially and temporally with mining-induced groundwater depletion and land subsidence, disrupting the shrub’s shallow-root water access strategy. The study demonstrates that coal mining drives fragmentation and coverage reduction in S. vulgaris communities through mechanisms including (1) direct vegetation destruction, (2) aquifer disruption impairing drought adaptation, and (3) habitat fragmentation. These findings underscore the necessity for targeted ecological restoration strategies integrating groundwater management and progressive reclamation in mining-affected arid regions. Full article

The reliability of embedded processors in safety- and mission-critical domains is increasingly threatened by radiation-induced soft errors, particularly multiple-cell upsets (MCUs) that simultaneously corrupt adjacent cells in external SDRAM. While prior studies on the LEON3 processor have largely focused on single-event upsets (SEUs) in internal SRAM structures, they overlook MCU effects in off-chip SDRAM, a critical gap that limits fault coverage and compromises system-level reliability assessment in modern high-density embedded systems. This paper presents an SDRAM-based fault injection framework using FPGA emulation to evaluate the impact of MCUs on the LEON3 soft-core processor, with faults directly injected into the external memory subsystem where data corruptions can rapidly propagate into system-level failures. The methodology injects spatially correlated two-bit MCUs directly into SDRAM during realistic workload execution. Three architecturally diverse benchmarks were analyzed, each representing a distinct computational workload: a numerical (matrix multiplication), signal-processing (FFT), and a cryptographic (AES-128 encryption) application, chosen to capture arithmetic-intensive, iterative, and control-intensive execution profiles, respectively. The results reveal a distinct workload-dependent vulnerability profile. Matrix multiplication exhibited >99.99% fault activation, with outcomes overwhelmingly dominated by data store errors. FFT showed >97% activation in steady-state execution, following an initial phase sensitive to alignment and data access exceptions. AES displayed 88.12% non-propagating faults, primarily due to injections in inactive memory regions, but remained exposed to critical memory access violations and control-flow exceptions that enable fault-based cryptanalysis. These findings demonstrate that SEU-only models severely underestimate real-world MCU risks and underscore the necessity of selective, workload-aware fault-tolerance strategies: lightweight ECC for cryptographic data structures, alignment monitoring for signal processing, and algorithm-based fault tolerance (ABFT) for numerical kernels. This work provides actionable insights for hardening LEON3-based systems against emerging multi-bit threats in radiation-rich and adversarial environments. Full article

The inner mitochondrial membrane proteins ATP/ADP carrier protein 1 (AAC1) and Uncoupling protein 1 (UCP1) belong to the SLC25 mitochondrial carrier family. AAC1 is responsible for ATP/ADP exchange, while UCP1-dependent proton transport, which also requires small molecules known as activators, is the basis of brown fat thermogenesis. Arachidonic acid (AA) is an endogenous activator capable of inducing proton transport in both proteins. As such, both AAC1- and UCP1-dependent proton transport are potential targets of weight loss drugs. While AAC1 structures have long been available, only recently have structures of UCP1 been determined. Unfortunately, no AA-bound structure of either protein is available. To explore their interactions with AA, we performed molecular dynamics (MD) simulations of both proteins. Six parallel simulations of each protein were run with an average length of just over 6 μs, for a total of 75 μs of aggregate simulation across both proteins. AA bound deeply between transmembrane helix (TM) helices or in the central cavity of AAC1 in 14 events and between TM helices of UCP1 in 6 events. All AA involved in these deep binding events came from the intermembrane space-facing (C) leaflet. In AAC1, AA most often bound between TM1/TM2 and TM5/TM6. In four cases the fatty acid bound at the bottom of the central cavity rather than in an interhelical groove. In UCP1, all but one deeply bound AA sat between TM5 and TM6. No AA fully entered the cavity as observed in AAC1. In addition to entering the proteins, AAs were enriched around them in the surrounding membrane adjacent to the TM helices. While both protein structures exhibit hydrophobic stretches separating the intermembrane space (IMS) from the matrix, water wires formed through both AAC1 and UCP1, connecting the bulk water in both regions. Grotthuss shuttling along water wires has been proposed as a possible mechanism of AAC1/UCP1-dependent proton transport, but water wires are not present in experimental structures and have not previously been reported in MD simulations. Calculations of electric potentials along these water wires find a large 0.75–1 V electrostatic barrier along water wires through AAC1 and a substantially smaller such barrier of ~0.5 V through UCP1. Full article