Search Results (940)

With the growing demand for antibiotic-free and sustainable poultry production, plant-derived antimicrobials have emerged as promising alternatives. However, a systematic understanding of the combined effects of oregano essential oil (OEO) and Macleaya cordata extract (MCE) on the broiler gut microbiome remains lacking. This study employed an integrated “structure–function–phenotype” framework to investigate the individual and combined (OMS) effects of OEO and MCE on gut microecological remodeling and its coupling with host growth, metabolic, and immune phenotypes. A total of 960 one-day-old broiler chicks were individually weighed and then randomly allocated to four treatments using body-weight-stratified randomization, with 6 replicate pens per treatment and 40 birds per pen, to ensure similar initial body weight across groups. Over a 42-day trial, we evaluated growth performance, serum biochemistry, antioxidant status, and immune parameters. Compared to the control, the OMS treatment significantly enhanced average daily feed intake (ADFI) and average daily gain (ADG), increased serum total protein (TP), and decreased blood urea nitrogen (BUN), triglycerides (TG), total cholesterol (TC), and alkaline phosphatase (ALP). However, the feed-to-gain ratio (F/G) was also higher in the OMS group, indicating that the improvement in growth rate did not translate into enhanced feed efficiency but was primarily driven by increased feed consumption. OMS also improved overall antioxidant capacity and key enzyme activities, elevated immunoglobulin levels, and reduced pro-inflammatory cytokines. Notably, OMS maintained Lactobacillus dominance, enriched Bacteroides, Enterococcus, and Butyricicoccus, and reduced Escherichia–Shigella. Functional predictions via PICRUSt2 suggested enhanced metabolic pathways related to antioxidant and immune functions; however, these results represent inference-based predictions and should be interpreted cautiously. Overall, the combination of OEO and MCE exerted synergistic benefits on growth, physiological health, and gut microbiota, supporting its potential as a phytogenic strategy for antibiotic-free broiler production. Full article

Elevated ambient temperature is the primary abiotic element that possibly diminishes production and economic profitability in the chicken industry. The current study evaluated the role of dietary addition of Spirulina platensis (SP) and humic acid (HA) in mitigating the effects of acute cyclic heat stress on growth performance, meat quality, immune status, and intestinal morphology in broiler chickens. Five hundred three-day-old male Ross 308 broiler chicks (average body weight 101.42 ± 3.22 g) were assigned to five experimental groups, each with 10 replicates (10 birds per replicate). The chicks in the first group received a basal diet and were maintained in thermoneutral conditions (NEG CON). The remaining four groups received either a basal diet only (POS CON) or a basal diet added with SP (2 g/kg of feed; SP group), HA (5 g/kg of feed; HA group), or a mix of SP and HA by the same doses (SP+HA group). The four groups were exposed to acute cyclic heat stress (36 °C ± 2 °C) from the 22nd to the 25th day of age for 6 h/day. The HA group showed increased body weight and body weight gain, and improved feed conversion ratio compared with other groups (p < 0.001). The addition of SP and HA improved sensory characteristics and reduced the dripping losses of the breast muscles. The phagocytic % and phagocytic index were higher in the SP group compared with the NEG and POS CON. The serum levels of triiodothyronine and thyroxine were higher in the HA and SP+HA groups compared with the control groups. The serum concentrations of interleukin-10, complement 3, and lysozymes, as well as the liver concentrations of HSP90A and HSP90B, were higher in the SP, HA, and SP+HA groups compared with the NEG and POS CON. The duodenal villous height and width were significantly greater in the HA group compared to the other groups. Spleen histomorphology in the SP and HA groups was better than that of the POS CON. The HA group showed up-regulation in the immune expression of clusters of differentiation 3 (CD3) and 20 (CD20) proteins in the spleen tissues. In conclusion, both HA and SP, individually but not in combination, mitigated the detrimental effects of acute cyclic heat stress on growth and immunity. Humic acid addition provided the most pronounced improvements in performance and intestinal morphology. Further studies are warranted to clarify the biochemical interactions between SP and HA under different stress intensities. Full article

Variation in individual animal feed intake is influenced by factors such as bunk management, digestive physiology, social hierarchy, and health status. While previous research has primarily examined feeding behavior in cattle offered high-concentrate diets, limited information exists regarding these relationships in high-forage systems. Residual Feed Intake (RFI), defined as the difference between an animal’s actual and expected feed intake based on metabolic mid-test body weight and average daily gain, serves as a key measure of feed efficiency. Animals with negative RFI values are classified as more efficient, whereas those with positive values are less efficient. This study investigated associations between feeding behavior and feed efficiency in yearling purebred Angus bulls (n = 232) and heifers (n = 58) consuming forage-based diets using a Vytelle feeding system. Upon arrival, bulls averaged 350.3 ± 3.6 kg and heifers averaged 287.5 ± 5.0 kg, with a subsequent 14-day acclimation followed by a 49-day ad libitum feeding period. In bulls, RFI was positively correlated with bunk visits (r = 0.34, p < 0.0001) and negatively correlated with duration (r = −0.16, p = 0.0124). In heifers, daily visits were negatively correlated with intake (r = −0.88, p < 0.0001), and RFI was negatively associated with RADG (r = −0.53, p < 0.0001). Full article

Accurate daily runoff forecasting is essential for flood control and water resource management, yet existing models struggle with the seasonal non-stationarity and inter-basin variability of runoff sequences. This paper proposes a Season-Aware Ensemble Forecasting (SAEF) method that integrates SVM, LSSVM, LSTM, and BiLSTM models to leverage their complementary strengths in capturing nonlinear and non-stationary hydrological dynamics. SAEF employs a seasonal segmentation mechanism to divide annual runoff data into four seasons (spring, summer, autumn, winter), enhancing model responsiveness to seasonal hydrological drivers. An Improved Arctic Puffin Optimization (IAPO) algorithm optimizes the model weights, improving prediction accuracy. Beyond numerical gains, the framework also reflects seasonal runoff generation processes—such as rapid rainfall–runoff in wet seasons and baseflow contributions in dry periods—providing a physically interpretable perspective on runoff dynamics. The effectiveness of SAEF was validated through case studies in the Dongjiang Hydrological Station (China), the Elbe River (Germany), and the Quinebaug River basin (USA), using four performance metrics (MAE, RMSE, NSEC, KGE). Results indicate that SAEF achieves average Nash–Sutcliffe Efficiency Coefficient (NSEC) and Kling–Gupta efficiency (KGE) coefficients of over 0.92, and 0.90, respectively, significantly outperforming individual models (SVM, LSSVM, LSTM, BiLSTM) with RMSE reductions of up to 58.54%, 55.62%, 51.99%, and 48.14%. Overall, SAEF not only strengthens predictive accuracy across diverse climates but also advances hydrological understanding by linking data-driven ensembles with seasonal process mechanisms, thereby contributing a robust and interpretable tool for runoff forecasting. Full article

Corrosion of carbon steel in acidic media remains a critical challenge during acidizing operations. This study evaluates carrot and rosemary extracts—individually and in combination—as green corrosion inhibitors for carbon steel in 1 M HCl. Inhibition performance was assessed using weight loss, potentiodynamic polarization (PDP), electrochemical impedance spectroscopy (EIS), SEM/EDS, and adsorption isotherms. Weight-loss measurements showed inhibition efficiencies of 59.5% (carrot) and 85.7% (rosemary) at 800 ppm, while their 30/70 mixture achieved a markedly higher efficiency of 99.6%. PDP results confirmed this trend, with corrosion current density decreasing from 892 μA/cm² (blank) to 13.4 μA/cm² for the mixture, corresponding to 98.5% efficiency. In addition, EIS analysis revealed a substantial increase in charge-transfer resistance from 41.1 ohm.cm² (blank) to 174.9 ohm.cm² (carrot), 266.9 ohm.cm² (rosemary), and 1868.1 ohm.cm² for the 30/70 mixture, confirming superior barrier formation. Moreover, temperature-dependent tests showed only a 5% efficiency loss for the mixture and an average 6% decrease for the single extracts between 25–45 °C, indicating good thermal stability. Also, SEM images demonstrated severe surface damage in the blank sample, while carrot-, rosemary-, and mixture-treated surfaces showed progressively smoother morphologies. EDS analysis confirmed this trend, with Fe content increasing from 65.78% (blank) to 90.16% (carrot), 91.88% (rosemary), and 94.59% for the mixture. Furthermore, FTIR and GC–MS identified oxygenated functional groups and major phytochemicals responsible for adsorption. Adsorption data followed the Langmuir model, and Gibbs free energy values from −25 to −31 KJ/mol indicated spontaneous mixed physisorption–chemisorption. Overall, the 30/70 carrot–rosemary mixture consistently achieved the highest corrosion protection across all tests, confirming strong synergistic adsorption and demonstrating its potential as a high-performance, eco-friendly inhibitor for acidic environments. Full article

Background: Differences in total daily energy expenditure (TDEE) across sports, sex, and skill level support the need for sport- and athlete-specific energy intake recommendations. The purpose of the current review was to examine TDEE and related markers of energy expenditure across field-based team sports. A secondary aim was to evaluate physical activity levels (PAL), calculated as TDEE divided by resting metabolic rate (RMR), and their utility in estimating energy needs within team sports. Methods: The review was limited to studies that included the field-based team sports of rugby or soccer and reported energy expenditure data using doubly labeled water (DLW). A literature review identified 11 studies meeting criteria. Weighted means (Xw) and standard deviations (SDw) were calculated for each variable when pooled across each sport category. Results: Rugby (4417 ± 654 kcal·d⁻¹) had a higher average TDEE than soccer (3157 ± 331 kcal/day; p < 0.001). When normalized to body mass, rTDEE was similar between sports (rugby: 49.5 ± 1.3 kcal·kg⁻¹·day⁻¹; soccer: 49.3 ± 1.8 kcal·kg⁻¹·day⁻¹; p = 0.967). PAL values were significantly higher in rugby (2.2 ± 0.4) compared to soccer (1.7 ± 0.2; p = 0.004). RMR was also greater in rugby (2136 ± 322 kcal·d⁻¹) compared to soccer (1835 ± 208 kcal·d⁻¹; p = 0.04). Conclusions: Rugby athletes exhibited higher TDEE values than soccer athletes, reflecting greater absolute energy demands. However, similar relative TDEE values suggest that differences in body size and composition likely contribute to the observed differences in absolute expenditure. These findings underscore the importance of individualized nutrition strategies within team sports and highlight PAL as a useful metric to contextualize energy requirements. Full article

The nutria or coypu (Myocastor coypus M.) is endemic to South America. However, this species invaded Central Europe in the 19th century. The rapid spread of the invasive nutria across Central Europe triggered countless ecological conflicts with the local species. In the current research, we surveyed two populations in Slovakia and compared the reproductive performance of this species to its reproductive performance in other countries, where it is native. A total of 69 nutria were harvested from the wild in 2022–2024. The result of the postmortem analysis reveals no intersexual differences in the body weight and length. A visual inspection of the uterus among 25 female specimens confirmed that 16 (64%) were pregnant, including 3 individuals with the body size characteristics of young nutrias. The pregnancy rate was estimated at 90% (9 out of 10) in spring and 47% (7 out of 15) in autumn. The average number of embryos per individual was 6.94 ± 2.22 for all females, and it was 6.27 ± 2.05 in spring and 8.8 ± 1.64 in autumn, with no significant difference between seasons (p > 0.05). No clear relationship between the Body Mass Index (BMI) and the fertility of females, i.e., the number of embryos, was observed. Our results highlight the crucial need to raise public awareness about the invasive characteristics of the species and contribute to the establishment of an effective intervention techniques, including the establishment of the necessary legal framework for eradicating emerging populations across Central European habitats. Full article

In the context of complex scenarios, identifying the posture of individuals is a critical technology in the fields of intelligent surveillance and autonomous driving. However, existing methods face challenges in effectively balancing real-time performance, occlusion, and recognition accuracy. To address this issue, we propose a lightweight hybrid model, referred to as YOLO-SwinTransformer, in this study. This model utilizes YOLOv8’s CSP Darknet as the primary network to achieve efficient multi-scale feature extraction. It integrates the Path Aggregation Network aggregation (PANet) and HRNet with high-resolution multi-scale feature extraction, enhancing cross-level semantic information interaction. The primary innovation of this model is the design of a modified Swin Transformer posture identification module, incorporating the Spatial Locality-Aware Module (SLAM) to enhance local feature extraction, achieving a combined modeling of space attention and time-series continuity. This effectively addresses the challenges posed by occlusion and video distortion in identifying posture. Additionally, we have extended the CIoU Loss and weighted mean square error loss functions to improve posture identification strategies, enhancing the precision of key points. Ultimately, extensive experimentation with both the COCO dataset and the self-built realistic road dataset demonstrated that the YOLO-SwinTransformer model achieved a state-of-the-art Average Precision (AP) of 84.9% on the COCO dataset, representing a significant 12.8% enhancement over the YOLOv8 baseline (72.1% AP). More importantly, on our challenging self-built real-world road dataset, the model achieved 82.3% AP (a 13.7% improvement over the baseline’s 68.6% AP), proving its superior robustness in complex occlusion and low-light scenarios. The model’s size is 27.3 M, and its lightweight design enables 39–41 FPS of real-time processing on edge devices, providing a feasible solution for intelligent monitoring and autonomous driving applications with high precision and efficiency. Full article

The basic reproduction number ($R_0$) is an epidemiological metric that represents the average number of new infections caused by a single infectious individual in a completely susceptible population. The methodology for calculating this metric is well-defined for numerous model types, including, most prominently, Ordinary Differential Equations (ODEs). The basic reproduction number is used in disease modeling to predict the potential of an outbreak and the transmissibility of a disease, as well as by governments to inform public health interventions and resource allocation for controlling the spread of diseases. A Petri Net (PN) is a directed bipartite graph where places, transitions, arcs, and the firing of the arcs determine the dynamic behavior of the system. Petri Net models have been an increasingly used tool within the epidemiology community. However, no generalized method for calculating $R_0$ directly from PN models has been established. Thus, in this paper, we establish a generalized computational framework for calculating $R_0$ directly from Petri Net models. We adapt the next-generation matrix method to be compatible with multiple Petri Net formalisms, including both deterministic Variable Arc Weight Petri Nets (VAPNs) and stochastic continuous-time Petri Nets (SPNs). We demonstrate the method’s versatility on a range of complex epidemiological models, including those with multiple strains, asymptomatic states, and nonlinear dynamics. Crucially, we numerically validate our framework by demonstrating that the analytically derived $R_0$ values are in strong agreement with those estimated from simulation data, thereby confirming the method’s accuracy and practical utility. Full article

1-Butyl-3-methylimidazolium (bmim) ionic liquids (ILs) are widely used for lignocellulose fractionation, yet their role extends beyond mere solvents. This study revealed that bmim-based ILs act as active chemical reagents, modifying the lignin structure in an anion-dependent manner. Thermal treatment (80–150 °C) of spruce dioxane lignin with [bmim]OAc, [bmim]Cl, and [bmim]MeSO₄ resulted in two distinct transformation pathways. In [bmim]MeSO₄, acidic catalysis dominates, leading to lignin condensation (increase in weight-average molecular weight, M_w, to 15.2 kDa at 150 °C) and intense sulfur incorporation (up to 9.9%) via anion-derived methylation/sulfation. Conversely, [bmim]OAc promotes depolymerization (decrease in M_w to 3.6 kDa) and efficient covalent bonding of the bmim cation to lignin (up to 10.8 cations per 100 aromatic units and a 6.5% nitrogen content at 150 °C), preventing condensation. Two-dimensional NMR and HPLC-HRMS analyses revealed the formation of a C–C bond between the C₂ atom of the imidazole ring and the α-carbon of the phenylpropane lignin fragments and allowed for the identification of a number of individual nitrogen-containing lignin oligomers in the [bmim]OAc-treated samples. Their formation likely proceeds via nucleophilic addition of the N-heterocyclic carbene (NHC), derived from the bmim cation by deprotonation with the highly basic acetate anion, to aldehyde groups. The action of [bmim]Cl primarily induces acid-catalyzed transformations of lignin with minimal covalent modification. These findings redefine imidazolium ILs as reactive media in biorefining, where their covalent interactions can influence the properties of lignin but complicate its native structure and the recyclability of the IL. Full article

Rapid warming and expanding heat seasons are reshaping electricity demand in cities, with basin-type megacities like Chengdu facing amplified risks due to calm-wind, high-humidity conditions and fast-growing digital infrastructure. This study develops a Transformer-based, multi-model downscaling framework that integrates outputs from 17 CMIP6 global climate models (GCMs), dynamically re-weighted through self-attention to generate city-scale temperature projections. Compared to individual models and simple averaging, the method achieves higher fidelity in reproducing historical variability (correlation ≈ 0.98; RMSD < 0.05 °C), while enabling century-scale projections within seconds on a personal computer. Downscaled results indicate sustained warming and a seasonal expansion of cooling needs: by 2100, Chengdu is projected to warm by ~2–2.5 °C under SSP2-4.5 and ~3.5–4 °C under SSP3-7.0 (relative to a 2015–2024 baseline). Using a transparent, temperature-only Cooling Degree Day (CDD)–load model, we estimate median summer (JJA) electricity demand increases of +12.8% under SSP2-4.5 and +20.1% under SSP3-7.0 by 2085–2094, with upper-quartile peaks reaching +26.2%. Spring and autumn impacts remain modest, concentrating demand growth and operational risk in summer. These findings suggest steeper peak loads and longer high-load durations in the absence of adaptation. We recommend cost-aware resilience strategies for Chengdu, including peaking capacity, energy storage, demand response, and virtual power plants, alongside climate-informed urban planning and enterprise-level scheduling supported by high-resolution forecasts. Future work will incorporate multi-factor and sector-specific models, advancing the integration of climate projections into operational energy planning. This framework provides a scalable pathway from climate signals to power system and industrial cost management in heat-sensitive cities. Full article

Electro-hydraulic servo fatigue testing machines are susceptible to concurrent degradation and failure of multiple components during high-frequency, high-load, and long-duration cyclic operations, posing significant challenges for online health monitoring. To address this, this paper proposes a multi-state recognition method based on spatiotemporal feature fusion and bidirectional cross-attention. The method employs a Bidirectional Temporal Convolutional Network (BiTCN) to extract multi-scale local features, a Bidirectional Gated Recurrent Unit (BiGRU) to capture forward and backward temporal dependencies, and Bidirectional Cross-Attention (BiCrossAttention) to achieve fine-grained bidirectional interaction and fusion of spatial and temporal features. During training, GradNorm is introduced to dynamically balance task weights and mitigate gradient conflicts. Experimental validation was conducted using a real-world multi-sensor dataset collected from an SDZ0100 electro-hydraulic servo fatigue testing machine. The results show that on the validation set, the cooler and servo valve achieved both accuracy and F1-scores of 100%, the motor-pump unit achieved an accuracy of 98.32% and an F1-score of 97.72%, and the servo actuator achieved an accuracy of 96.39% and an F1-score of 95.83%. Compared to single-task models with the same backbone, multi-task learning improved performance by approximately 3% to 4% for the hydraulic pump and servo actuator tasks, while significantly reducing overall deployment resources. Compared to single-task baselines, multi-task learning improves performance by 3–4% while reducing deployment parameters by 75%. Ablation studies further confirmed the critical contributions of the bidirectional structure and individual components, as well as the effectiveness of GradNorm in multi-task learning for testing machines, achieving an average F1-score of 98.38%. The method also demonstrated strong robustness under varying learning rates and resampling conditions. Compared to various deep learning and fusion baseline methods, the proposed approach achieved optimal performance in most tasks. This study provides an effective technical solution for high-precision, lightweight, and robust online health monitoring of electro-hydraulic servo fatigue testing machines under complex operating conditions. Full article

Background/Objectives: Type 2 diabetes mellitus (T2DM) is increasingly recognized as a contributor to skeletal fragility despite patients often having a normal or even elevated bone mineral density (BMD), a phenomenon described as the “T2DM bone paradox.” This study aimed to use DEXA screening to explore how metabolic and demographic factors, particularly body mass index (BMI), age, sex, and glycated hemoglobin (HbA1c), influence Bone Mineral Density (BMD) among Saudi adults, a population where diabetes and obesity are highly prevalent. Methods: A retrospective cross-sectional study was conducted among 89 adults (mean age 61.1 years; 82% female) who underwent dual-energy X-ray absorptiometry (DEXA) at King Fahad Specialist Hospital in Tabuk, Saudi Arabia. Bone mineral density was evaluated at the lumbar spine, femoral neck, and total hip. Correlation and multiple regression analyses were conducted to assess how age, sex, body mass index (BMI), and glycated hemoglobin (HbA1c) were related to BMD T-scores. Results: The prevalence of osteopenia and osteoporosis was 43.8% and 23.6%, respectively, with women and older adults showing the highest rates of low bone mass. Participants had a mean age of 61.1 ± 12.1 years, average BMI of 32 kg/m², and mean HbA1c of 6.6 ± 1.8%. Females showed slightly lower T-scores at all skeletal sites compared with males (lumbar spine −1.81 vs. −1.55; femoral neck −1.15 vs. −0.76; total hip −0.62 vs. −0.12), indicating greater bone loss in women. BMI was consistently and positively associated with BMD across all skeletal sites (p < 0.05), whereas age and female sex were negative predictors at the femoral neck and hip. HbA1c showed a paradoxical positive relationship with BMD at weight-bearing sites, reflecting the complexity of metabolic effects on bone quality. The models explained up to 28% of the variance in BMD. Conclusions: Individuals with higher level BMI tended to have better bone mass, while older age and female sex were related to decreased BMD. The positive association between HbA1c and BMD supports the concept of the “diabetic bone paradox” and emphasizes the value of combining the evaluation of both metabolic and skeletal factors when assessing fracture risk in Middle Eastern populations. Full article

In recent years, differential synthetic aperture radar interferometry (DInSAR) has been widely used to monitor ground deformation induced by mineral resource exploitation. Compared with conventional DInSAR, InSAR time series (TS-InSAR) techniques offer significantly improved monitoring accuracy. However, their results still remain strongly influenced by atmospheric delays. To address this and discuss the applicability of tropospheric delay correction methods over mining areas, this study applied multiple correction strategies to distributed scatterer InSAR (DS-InSAR), including the Linear, ERA5, GACOS, spatio-temporal filtering method, and their adaptive weighted fusion approach. Meanwhile, an improved Common Scene Stacking (CSS) InSAR tropospheric delay correction method has been proposed. These methods’ performance have been evaluated by the quantitative comparisons of the corrected interferometric phases and by in situ measurements. The results indicated that the adaptive fusion method outperformed any individual model included, where spatio-temporal filtering should be applied with caution, as it may undermine part of the deformation signal. The effectiveness of ERA5 and GACOS is limited due to their resolution mismatch with that of the SAR images. On the other hand, the improved CSS method achieved the best results over the study area, with an average reduction of 32.22% in the RMSE of the interferometric phase, resulting in an RMSE below 8 mm on average and as low as 5 mm over certain areas. Thus, over local mining areas with large-magnitude and ground deformation, the improved CSS outperforms all the other compared methods, where it can effectively mitigate atmospheric delays while preserving the deformation signals. Full article

The tidal mudflat of the South Yellow Sea is characterized by complex sediment environments that preserve rich paleoenvironmental signals, making it an important area for understanding land–sea interactions and promoting sustainable coastal development. Thus, accurate identification of sediment sequences and layer thicknesses becomes crucial for interpreting sediment dynamics and paleoenvironmental reconstruction. While borehole data have elucidated local sediment facies, their spatially discontinuous nature hinders a holistic reconstruction of regional depositional history. To overcome this limitation, ground-penetrating radar (GPR) surveys were conducted across the tidal mudflat of the South Yellow Sea, enabling systematic correlation between radar reflection patterns and sediment architectures. Based on the relationship between the dielectric permittivity and wave velocity, short-time Fourier transform (STFT) was applied to derive the peak-weighted average frequency in the frequency domain for individual soil layers, revealing its dependence on dielectric properties. Sediment interfaces and layer thicknesses were determined using three methods: the radar image waveform method, the Hilbert spectrum instantaneous phase method, and the generalized S-transform time–frequency analysis method. The results indicate the following: (1) GPR enables high-fidelity imaging of subsurface stratigraphy, successfully resolving three distinct radar facies: F1: high-amplitude, horizontal, continuous reflections with parallel waveforms; F2: moderate-to-high-amplitude, sinuous continuous reflections with parallelism; and F3: medium-amplitude, discontinuous chaotic reflections. (2) All three methods effectively characterize subsurface soil stratification, but positioning accuracy decreases systematically with depth. Excluding anomalous errors at one site, the relative error for most layers within the 1 m depth is below 15%, and remains ≤25% at the 1–2 m depth. Beyond the 2 m depth, reliable stratification becomes unattainable due to severe signal attenuation. (3) Comparative analysis demonstrates that the Hilbert spectral instantaneous phase method significantly enhances GPR signals, achieving an optimal performance with positioning errors consistently below 5 cm for most soil layers. The application of this approach along the tidal mudflat of the South Yellow Sea significantly enhances the precision of sediment layer boundary identification. Our analysis systematically interpreted radar facies, demonstrating the effectiveness of the Hilbert spectrum instantaneous phase method in delineating soil stratification. These findings offer reliable technical support for interpreting GPR data in comparable sediment environments. Full article