Search Results (496)

Flood control briefings are critical emergency response documents that provide timely decision support for urban safety and regional development under climate change challenges. However, existing large language models (LLMs) face significant difficulties in domain-specific adaptation, content controllability, and logical consistency when processing complex water conservancy data. This study aims to develop a robust automated text generation method that ensures high accuracy and logical rigor for flood prevention in the Lixiahe region. We propose an IP-CoT method that integrates Chain-of-Thought (CoT) reasoning for structured information extraction and an Inverse Prompting (IP) mechanism with beam search to optimize content relevance using the DeepSeek-R1 model. Validated on a constructed dataset comprising flood control records from the Lixia River network from 2010 to 2024, the proposed method achieved an accuracy rate of 95.32% in the verification of emotional attributes, which is 2% to 15% higher than most traditional models. Additionally, in the verification of thematic attributes, fluency and diversity were improved, showing significant enhancements compared to the baseline model. This approach significantly enhances the quality and efficiency of domain-specific text generation, providing a reliable intelligent solution for modernizing regional flood control decision-making systems. Full article

Titanium and its alloys are widely used in endoprostheses. The naturally formed titanium dioxide film on titanium surfaces improves chemical stability and enhances implant biocompatibility. However, oxidised titanium surfaces may also promote bacterial adhesion and biofilm formation, contributing to implant-associated infections. Therefore, surface modification represents a key strategy for controlling microbial–implant interactions. This article focuses widely used titanium alloy Ti-6Al-4V treated with a laser beam, which induces surface colour changes as a result of oxide formation. Laser processing enables controlled formation of micro- and nanoscale features, structural reconstructions, and defects that may influence the surface electrical charge and, consequently, cell immobilisation. Thus, the surface colour, electrical potential, and cell immobilisation capacity are likely interrelated. From a manufacturing perspective, titanium oxide colouring facilitates quality control and process reproducibility, as surface colour provides a rapid, non-destructive visual indicator of oxide thickness and treatment consistency. This study aims to identify correlations among surface colour, electrical potential, and cell immobilisation capacity on laser-treated titanium alloys. A relationship between the optical properties, electronic structure, and biological response of laser-processed titanium oxide films is established. Specifically, the blue colour saturation of the oxide film is inversely correlated with the electron work function. A more saturated blue corresponds to a lower work function, indicating a higher positive surface charge density. This shift is attributed to changes in electron affinity, likely resulting from laser-induced structural reconstruction and defect formation within the oxide layer. The proposed changes in electronic structure are supported by modifications in the electronic density of states, analysed using near-threshold photoelectron spectroscopy. The biological response is directly linked to these physical changes: enhanced immobilisation of yeast (Saccharomyces cerevisiae) cells on the treated alloy surface correlates with the electron work function. These results may assist in the development of controlled titanium oxide surfaces with enhanced biocompatibility. Full article

The spatial response of rectangular pulse heterodyne phase-sensitive optical time-domain reflectometry ($\varphi$-OTDR) to an acoustic event is characterized by a windowing function rather than a point-like sensitivity. This effect degrades the system’s spatial resolution and introduces systematic errors in array signal processing. This work presents modeling analysis and a mitigation strategy for this fundamental limitation. The spatial windowing effect is modeled as a point spread function (PSF) derived from physical mechanisms and system parameters, including the pulse width, gauge length, and intra-pulse intensity dynamics. The PSF model is validated against measurements under near-ideal conditions using a fiber-coupled tuning fork. A Wiener filter-based deconvolution method is utilized to invert the windowed spatial response towards a point-like response. The effectiveness of this inversion is demonstrated through enhanced spatial resolution and accurate reconstruction of two-dimensional wavefront geometry. Furthermore, the impact of this effect on array signal processing is quantitatively evaluated. The results demonstrate that the proposed method effectively suppresses systematic errors in wavefield analysis, and specifically enhances the accuracy and confidence of steered response power—phase transform (SRP-PHAT) spatial spectrum estimation. This study provides a systematic framework for understanding, quantifying, and inverting the spatial response in $\varphi$-OTDR, enabling accurate and interpretable acoustic field sensing. Full article

Production profiling is essential for optimizing production strategies in oil and gas wells. Conventional production logging tools provide only discrete, time-limited measurements and face operational challenges in long or complex horizontal wells. Distributed fiber-optic sensing (DTS/DAS) enables continuous, full-wellbore monitoring but each sensing modality has limitations when used alone: DTS interpretation is influenced by wellbore disturbances and thermal hysteresis, while DAS acoustic energy does not always correspond to actual inflow zones. This study proposes a joint interpretation method integrating DTS-based temperature inversion with DAS frequency-band energy and apparent velocity analysis. DTS data are processed using a coupled wellbore–formation heat-transfer model to obtain segmental flow rates, while DAS data are analyzed using short-time Fourier transform, cross-correlation, and Hough transform to extract positive and negative apparent velocities indicating fluid migration directions. Field results show that high-production intervals at 4126–4486 m correlate with positive apparent velocities, whereas medium-/low-production and shut-in stages exhibit persistent negative velocities linked to backflow and reinjection. The combined interpretation effectively distinguishes reservoir inflow from wellbore flow by jointly constraining thermal response and flow direction, thereby reducing uncertainties associated with single-method analysis. Full article

During the process of damage identification and safety-state evaluation of cable-stayed bridges, the cable tension should also be incorporated into common monitoring, which usually includes displacement and strain. However, the testing process of cable tension is complicated, and the disassembly, installation and maintenance of the cable tension meter are higher priced and difficult. To improve the efficiency of damage evaluation regarding cable-stayed bridges, information-entropy theory is introduced and the curvature entropy index of the difference in the influence line of displacement is proposed. To obtain effective data parameters for damage evaluation, first, the dynamic disturbance in the displacement time-history response is removed through variational modal decomposition, and the multi-axle effect of vehicles is regularized, so as to identify the measured influence line of displacement of cable-stayed bridges. Second, the peak value of the curvature entropy index of the difference in the influence line of displacement under varied damage degrees of stay cables is extracted to construct the inverse fitting formula of damage degree. The entropy value of the measured influence line of displacement is then substituted into a PSO-BP neural network, so as to obtain the damage degree of the corresponding position of the measured data regarding the influence line of displacement of bridges. Finally, the health status of stay cables is evaluated using the information-entropy parameters of the influence line of displacement. The theoretical model and actual data are used for testing, and the research results show that: (1) the location and degree of cable damage can be effectively located and quantified by using the curvature entropy index of the difference in the influence line of displacement, and (2) the cable health index of the cable-stayed bridge tested by actual data is 96.73%, consistent with the conclusion of on-site technical evaluation. Full article

Uranium ore preconcentration is a critical step in achieving environmentally sustainable uranium mining and reducing the operational load of hydrometallurgical processing systems. Conventional radioactive sorting systems predominantly employ a “single-ore-particle intermittent measurement” mode. Under continuous ore flow and high-throughput operating conditions, however, the radiation fields of adjacent ore particles inevitably overlap, which results in gamma-counting interference and blurred ore-segment boundaries, thereby limiting sorting accuracy and system capacity. To address these challenges, this study established a convolutional model that describes the relationship between ore-grade distribution and gamma-response characteristics under continuous ore flow conditions. On this basis, a deconvolution-based method for uranium ore grade calculation was proposed, and an adaptive determination strategy for the characteristic parameter α was introduced to improve grade estimation accuracy and enable reliable identification of ore-segment boundaries. The experimental results showed that, for uranium grades ranging from 0.05% to 0.18% and ore-segment lengths of 16–40 cm, the relative errors between the inverted and true grades of individual segments were all less than 10%. Compared with conventional intermittent measurement and identification schemes, the proposed method achieves stable and accurate grade inversion under conditions of overlapping radiation fields in continuous ore segments. Full article

In arid northwest China, water scarcity is the primary constraint on agricultural sustainability. Accurate prediction of soil moisture under vegetation is essential for optimizing water use and enabling precision irrigation. Furthermore, water and nitrogen management are often studied in isolation, and their spatiotemporal synergy in regulating soil moisture remains unclear, which hinders the development of optimized coupled strategies. To address this, this study integrated UAV hyperspectral (450–950 nm), multispectral remote sensing, and ground sensor networks to systematically conduct field experiments covering three irrigation levels: full irrigation (W1) at 100% of maintaining soil moisture content; mild deficit irrigation (W2), with soil moisture content set at three-quarters of W1; and severe deficit irrigation (W3), with soil moisture content set at half of W1 and three nitrogen application rates (N1: 350, N2: 250, and N3: 150 kg/ha) in a field experiment. Through sensitive band extraction and spectral index optimization, triple-band indices (RES: Reflectance Extraction Index, MSR: Moisture Sensitive Ratio Index, two novel triple-band spectral indices developed based on Kubelka–Munk and Hapke models) were innovatively developed to enhance signals and suppress noise. Random Forest algorithms were employed to construct soil moisture inversion models for different soil layers. Rigorous comparative analysis comprehensively evaluated performance differences between hyperspectral and multispectral technologies in the indirect retrieval of soil moisture based on crop physiological response and detecting soil moisture at varying depths (10–100 cm). The results indicate that the 450–760 nm visible band represents the optimal spectral region for soil moisture detection. The two indices (MSR and RES) constructed within this range demonstrated prediction correlations 18–32% higher than traditional indices. Hyperspectral technology exhibited comprehensive advantages, particularly in monitoring deep soil layers (>80 cm) (R² = 0.49 vs. 0.18 for multispectral). The spatiotemporal dynamics of soil moisture are primarily governed by irrigation intensity, while nitrogen fertilizers indirectly influence water redistribution through physiological processes such as root architecture regulation, rather than directly altering soil water-holding capacity. This study demonstrates the efficacy of a UAV-based hyperspectral system for precision soil moisture monitoring in vegetated farmland, and it provides a critical scientific basis for optimizing water–nitrogen management and enhancing water use efficiency in arid agriculture. Full article

The advancement of large-scale marine infrastructure demands increasingly accurate prediction of settlement in deep-water foundations. The caisson is an important type of deep-water foundation whose additional settlement induced by superstructure construction directly impacts the overall safety of the project. This study focuses on the main tower foundation of the Changtai Yangtze River Bridge, recognized as the world’s largest deep-water caisson foundation. A three-dimensional finite element model was developed using the hardening soil model with small-strain stiffness (HSS) constitutive model to simulate the settlement response of the caisson foundation throughout the entire superstructure construction process. The model’s reliability was verified through systematic comparison with field monitoring data. Furthermore, an inversion analysis was conducted on the initial shear modulus ($G_0^{\mathrm{ref}}$), the most sensitive parameter of the HSS model, based on the measured data. The results reveal that its optimal value exhibits significant load dependency, varying according to the construction stage. Accordingly, practical strategies for parameter determination are proposed: a fixed-value method ($G_0^{\mathrm{ref}}$ = 2$E_{\mathrm{ur}}^{\mathrm{ref}}$) suitable for conventional design and a more precise stage-specific value method. Both approaches markedly enhance the settlement prediction accuracy, particularly under high-load conditions. The findings offer valuable insights for the refined design and safety assessment of similar deep-water mega-foundation projects. Full article

The coupling of physical transport and phase-transfer processes represents a fundamental mechanism governing metal cycling in estuarine systems under tidal oscillations. Taking Quanzhou Bay as a model system, we conducted continuous observations and sample collection at the river channel (Q1), the turbidity maximum zone (Q2), and the outer bay channel (Q3). The metals (Al, Ti, Ba, Cu, Mn, and Zn) were measured by ICP-MS to systematically investigate the distribution, transport, and inter-media transfer across multiple water layers under varying estuarine processes. Our findings demonstrate that particulate metal concentrations in Quanzhou Bay exhibit strong synchrony with suspended sediment concentrations (SSC) over tidal cycles, displaying a distinct sediment-following pattern controlled by alternating end members. Particulate metal fluxes during flood and ebb-tides generally followed the hierarchy Q1 > Q2 >> Q3. Notably, stations Q1 and Q2 were dominated by flood-tide fluxes with net transport directed landward, whereas Q3 was characterized by ebb tide dominance with net flux directed seaward—revealing a spatial division of labor between “inner bay retention/reallocation” and “outer bay channel export”. In contrast, dissolved metals exhibited marked element-specific responses to tidal forcing: Al and Ti increased during flood tides at stations Q1 and Q2, while Ba and Cu showed opposite trends, and Mn and Zn displayed more conservative behavior. Concurrently, solid/liquid partition coefficient (logKd) values for Al, Ti and Ba, Cu exhibited inverse patterns over tidal cycles, suggesting divergent adsorption–desorption regulation under identical hydrodynamic conditions that drives differential phase-transfer dynamics. These disparities likely reflect intrinsic chemical properties and source variations among the elements. This study elucidates, at the tidal timescale, the coupled processes of “alternating end-member control—estuarine filter modulation—concurrent channelized export and inner bay retention” in Quanzhou Bay, providing critical process-level insights for metal flux quantification and bay pollution remediation initiatives in an ecological restoration project. Full article

In the process of using the freezing method to uncover coal from stone gates, the thermal evolution profiles of the coal body during the freezing process tend to be complex due to the presence of gas and moisture. To investigate the temperature response of coal containing gas under different freezing temperature conditions, a self-developed low-temperature freezing test system for coal containing water and gas was used to conduct freezing and cooling tests at different freezing temperatures (−5 °C to −30 °C). The temperature changes at various measuring points inside the coal over time were monitored in real time, and the temperature distribution, cooling law, and strain evolution process of the coal in the axial and radial directions were analyzed. The experimental results show that the cooling process of the center point of the coal can be divided into four stages: rapid cooling, extremely slow temperature drop, relatively slow cooling, and stable constant temperature. The time required to reach the stable constant temperature stage is inversely proportional to the freezing temperature, and corresponding prediction formulas have been established based on this. The standardized coal briquettes exhibit a gradient distribution characteristic of gradually increasing temperature from outside to inside in both axial and radial directions, with the radial temperature distribution being well matched by an exponential decay model. The strain of coal is affected by both thermal shrinkage and ice-induced expansion. The occurrence time of frost heave is positively correlated with freezing temperature, while the strain of frost heave is negatively correlated with freezing temperature. The axial frost heave effect is significantly stronger than the radial effect, but the radial frost heave occurs slightly earlier than the axial effect. This study reveals the thermal-mechanical coupling response mechanism of gas-containing coal during the low-temperature freezing process, and the research results can provide theoretical support for parameter optimization and engineering application of low-temperature freezing anti-outburst technology. Full article

Seismic wavefield simulation is the primary technique used to study the effects of vertical transverse isotropy (VTI) on the propagation of Rayleigh waves. However, conventional Rayleigh wave dispersion equations are based on isotropic assumptions and cannot be applied to the dispersion characteristics of multi-layered VTI media. Based on the Rayleigh wave potential functions in VTI media, this study derives inhomogeneous wave equations governing the Rayleigh wave potentials. These equations exhibit a distinctive duality; the particular solution associated with the inhomogeneous term in the P-wave equation coincides exactly with the solution of the homogeneous SV-wave equation. Compared to existing methods, the solution to the wave equations does not require decoupling. Using conventional exponential-form potential function solutions, this study realizes the analytical computation of Rayleigh wave inhomogeneous wave equations in VTI media and establishes a dispersion equation for multi-layered VTI media. The reliability of the method is verified through mathematical back substitution and numerical validation. To further explore the dispersion characteristics of Rayleigh waves in VTI media, a three-layered model is designed, and the dispersion response features under different VTI parameters are computed, indicating the high sensitivity of the dispersion curves to changes in any of the five VTI parameters. This paper presents a non-decoupled recursive analytical method for computing Rayleigh wave wavefields and dispersion curves in VTI media. The approach requires solving only a second-order inhomogeneous boundary-value differential equation and adopts the standard exponential potential representation used for isotropic media. This makes the method more practical and yields a fast, convenient algorithm for seismic parameter inversion and data processing in VTI media. Full article

Tunnel ahead prospecting is important for reducing construction risks associated with faults, fractured zones, and cavities ahead of the tunnel face, but controlled active-source surveys are often impractical during continuous TBM operation. TBM drilling-noise records provide persistent passive excitation; however, strong nonstationarity and narrowband tonal contamination can hinder stable retrieval of interpretable impulse-like responses. We propose an adaptive impulse reconstruction algorithm that couples CEEMDAN-based mode screening with MDD interferometry. CEEMDAN screening suppresses quasi-stationary tonal components while preserving coherent propagation-related wavefields, producing effective signals suitable for interferometric processing. The MDD stage is stabilized using band-limited inversion, phase-only whitening, and a multi-reference strategy. Numerical experiments with a 3D elastic tunnel model indicate that the proposed workflow yields a more compact and laterally coherent virtual-source gather than correlation-based baselines (CC and PHAT-CC) and single-reference deconvolution interferometry, supporting reflection-oriented interpretation beyond simple wavelet compression. Field measurements from an operating TBM tunnel, together with a hammer-impact benchmark, are consistent with the feasibility of the workflow under real tunneling conditions and with physically plausible moveout behavior in the reconstructed gathers. Full article

Due to the weak electrical response characteristics of groundwater nitrate contamination, traditional monitoring and remediation assessment methods are limited by low spatiotemporal resolution, high cost, and strong subjectivity. To address this issue, this study proposed an integrated technical framework combining field detection, laboratory-controlled experiments, and remediation process monitoring, aiming to explore the application potential of Electrical Resistivity Tomography (ERT) in nitrate pollution monitoring and remediation evaluation. First, ERT survey lines (L1 and L2) were deployed at a chemical-contaminated site in Luzhou, Sichuan Province, and groundwater samples were collected. Coupled with hydrochemical analysis, the feasibility of ERT for identifying nitrate plumes was verified. Second, a quantitative response model between nitrate concentration and resistivity was established through Miller box experiments, and a multi-line layout was optimized via sand tank experiments to mitigate boundary effects and improve monitoring accuracy. Finally, grouped sand tank experiments involving electroactive bacteria (EAB) and magnetite were conducted. Combined with 16S rRNA sequencing, the coupling mechanism between ERT electrical responses and biogeochemical processes was elucidated. The results showed that the low-resistivity anomaly zones identified by field ERT were accurately consistent with the high-nitrate contamination zones, and Piper diagrams confirmed that nitrate-related ions were the primary cause of the low-resistivity anomalies. The power function quantitative model established by the Miller box experiment (y = 1021.97x^−0.74, R² = 0.9589) enabled the indirect inversion of nitrate concentrations, with a small deviation between theoretical and measured values in the deep layer (16–18 m). The optimized layout of one main and three auxiliary survey lines effectively characterized the spatiotemporal migration of the contamination plume. Under high-water level conditions, the ternary system of nitrate–magnetite–EAB exhibited the strongest low-resistivity response. Microbial analysis indicated that electroactive groups (e.g., Pseudomonas and Flavobacterium) enriched in the EAB group were the core drivers of enhanced electrical conductivity. The integrated ERT monitoring technology system constructed in this study realizes the visual identification of nitrate plumes and dynamic tracking of remediation processes, providing technical support for the precise monitoring and in situ remediation of nitrate contamination in agricultural non-point sources and industrial sites. Full article

To effectively manage and conserve ecosystems, it is crucial to understand how vegetation changes over time and space and what drives these changes. The Normalized Difference Vegetation Index (NDVI) is a key measure of plant growth that is highly sensitive to climate variations. Despite its importance, there has been limited research on vegetation changes in the upper sections of the Irtysh River. In our study, we combined various datasets, including NDVI, temperature, precipitation, soil moisture, elevation, and land cover. We conducted several analyses, such as Theil–Sen median trend analysis, Mann–Kendall trend and mutation tests, partial correlation analysis, the geographical detector model, and wavelet analysis, to reveal the region’s pronounced warming and moistening trend in recent years, the response relationship between NDVI and the climate, and the primary drivers influencing NDVI variations. We also delved into the spatiotemporal evolution of NDVI and identified key factors driving these changes by analyzing atmospheric circulation patterns. Our main findings are as follows: (1) Between 1901 and 2022, the area’s temperature rose by 0.018 °C/a, with a noticeable increase in the rate of warming around 1990; precipitation increased by 0.292 mm/a. From 1950 to 2022, soil moisture exhibited a steady increase of 0.0002 m³ m⁻³/a. Spatial trend distributions indicated that increasing trends in temperature and precipitation were evident across the entire region, while trends in soil moisture showed significant spatial variation. (2) During 1982 to 2022, the vegetation greening trend was 0.002/10a, indicating a gradual improvement in vegetation growth in the study area. The spatial distribution of monthly average NDVI values revealed that the main growing season of vegetation spanned April to November, with peak NDVI values occurring in June–August. Combined with serial partial correlation and spatial partial correlation analysis, temperatures during April to May effectively promoted the germination and growth of vegetation, while soil moisture accumulation from June to August (or January to August) effectively met the water demand of vegetation during its growth process, with a significant promoting effect. Geographical detector results demonstrate that temperature exhibits the strongest explanatory power for NDVI variation, whereas land cover has the weakest. The synergistic promotional effect of multiple climatic factors is highly pronounced. (3) Wavelet analysis revealed that the periodic characteristics of NDVI and climate variables over a 2–15-year timescale may have been associated with the impacts of atmospheric circulation. Taking NDVI and climatic factors from June to August as an example, before 2000, temperature was the dominant influencing factor, followed by precipitation and soil moisture; after 2000, precipitation and soil moisture became the primary drivers. The North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) were the primary atmospheric circulation patterns influencing vegetation variability in the region. Their effects were reflected in the inverse relationship observed between NAO/AO indices and NDVI, with typical phases of high and low NDVI closely corresponding to shifts in NAO and AO activity. This study helps us to understand how plants have been changing in the upper parts of the Irtysh River. These insights are critical for guiding efforts to develop the area in a way that is sustainable and beneficial for the environment. Full article

Background and Objectives: Medication adherence is a key determinant of treatment effectiveness in early-stage breast cancer, particularly during long-term systemic therapies. As breast cancer is increasingly diagnosed at younger ages, a growing number of women continue to carry active parenting responsibilities during treatment. However, the associations between parenting-related psychosocial factors and medication adherence remain insufficiently explored. This study aimed to examine the associations between parenting stress, spiritual well-being, and medication adherence in women with early-stage breast cancer who maintain active parenting roles. Materials and Methods: This multicenter, cross-sectional study included 432 women with early-stage (I–III) breast cancer receiving active systemic therapy across nine oncology centers. Parenting stress was assessed using the Parenting Stress Scale (PSS), spiritual well-being using the Functional Assessment of Chronic Illness Therapy–Spiritual Well-Being Scale (FACIT-Sp-12), and medication adherence using the 6-item Modified Morisky Adherence Scale (MMAS-6). Spearman correlation analyses and multivariable linear regression models were used to evaluate associations between variables. Mediation analysis was performed using Hayes’ PROCESS macro (Model 4) with 5000 bootstrap samples to assess statistical mediation. Results: Parenting stress was positively associated with poorer medication adherence (ρ = 0.248, p < 0.01), whereas spiritual well-being was negatively associated with non-adherence (ρ = −0.225, p < 0.01). Parenting stress showed a strong inverse association with spiritual well-being (ρ = −0.597, p < 0.01). In multivariable regression analyses, both parenting stress and spiritual well-being were independently associated with medication adherence (β = 0.180, p = 0.002 and β = −0.199, p = 0.001, respectively). Mediation analysis demonstrated a significant indirect statistical association between parenting stress and medication adherence through spiritual well-being (indirect effect = 0.0155), consistent with partial statistical mediation. Conclusions: Medication adherence among women with early-stage breast cancer and active parenting responsibilities is associated with psychosocial context in addition to clinical factors. Parenting stress is associated with poorer adherence, whereas greater spiritual well-being is associated with better adherence within a statistical mediation framework. These findings generate hypotheses for future longitudinal and interventional studies. Full article