Search Results (2,886)

Soil organic carbon (SOC) plays a critical role in climate regulation, soil fertility, and ecosystem resilience, making its accurate spatial quantification essential for sustainable land management and greenhouse gas (GHG) reporting. However, mapping SOC in heterogeneous agroforestry systems remains challenging due to vegetation cover and landscape complexity. In this study, we develop and evaluate a hybrid bare soil modelling- Digital Soil Mapping supported by ML framework to generate high-resolution soil properties predictions in Mediterranean agroforestry systems (Extremadura, Spain). A dual modelling approach was implemented, combining (i) Bare Soil modelling using Sentinel-2 multi-temporal reflectance composites and (ii) Digital Soil Mapping (DSM) supported by environmental covariates (climate, terrain, vegetation) following the SCORPAN framework. Machine learning models, namely Quantile Regression Forests (QRF) and Extreme Gradient Boosting (XGBoost), were applied and optimised using automated hyperparameter tuning (FLAML). A total of 107 LUCAS topsoil samples and 36 complementary points from the Forest ICP Level I were used for calibration and validation, with a 70/30 train–test split. Results show that Sentinel-2-based modelling can effectively capture SOC spatial variability in bare soil conditions, while DSM improves predictions in vegetated areas. Model performance reached R² values up to 0.76 (QRF, pH) and RMSE as low as 0.03 (XGBoost, N), with uncertainty quantified using the Prediction Interval Ratio (PIR) and performance further supported by RPIQ values up to 3.15. However, prediction accuracy remains sensitive to vegetation structure and sample density. The proposed framework provides a scalable and uncertainty-aware approach for SOC mapping, supporting Tier-3 GHG inventories and emerging Monitoring, Reporting, and Verification (MRV) systems. The results highlight the importance of integrating multi-source datasets and hybrid modelling strategies for reliable SOC estimation in complex landscapes. Full article

Contemporary deep learning-based image steganography techniques for embedding images within images are hindered by inadequate utilization of frequency-domain features and limited steganographic security, restricting their effectiveness in practical privacy protection contexts. To mitigate these limitations, we introduce a frequency–spatial dual-path fusion-based deep steganography approach, termed FS-Stego. This method incorporates a frequency–spatial dual-path architecture within the generator network. Specifically, the frequency-domain processing module facilitates feature embedding in the complex domain, while the spatial-domain processing module maintains the image’s structural integrity, thereby enabling the co-optimization of multi-dimensional features. Second, an adaptive fusion module is developed to dynamically adjust the weights of the two paths, while residual connections and attention mechanisms are utilized to mitigate feature loss. Third, a multi-objective loss function is implemented to simultaneously optimize the quality of the stego images and the reconstruction accuracy of the secret images. The proposed method utilizes three open-source datasets as cover images and the LFW dataset as the secret images. Experimental results demonstrate that, compared to existing deep steganographic techniques, the stego and recovered images achieve superior peak signal-to-noise ratios (PSNR) and structural similarity (SSIM). Regarding model efficiency, the number of parameters is reduced to below 0.98 million, significantly enhancing practical performance. The proposed method ensures high-quality image recovery while maintaining steganographic security, thereby offering an effective solution for privacy protection. Full article

Petroleum exploitation in the Niger Delta has caused widespread contamination of mangrove ecosystems, yet studies that integrate total petroleum hydrocarbons (TPH) and metals in mangrove water are still very limited. This study presents the first dual-pollutant baseline assessment of TPH and five priority metals (Cd, Cr, Pb, Ni, Zn) in Bille mangrove water, a severely oil-impacted system supporting about 50,000 residents. Water samples were collected from six sites along a contamination gradient (flow station, pipeline passage, old bunkering site) and analyzed for TPH (C8–C40) and metals. All concentrations are reported in mg/L for direct comparability with World Health Organization (WHO) drinking-water guidelines and United States Environmental Protection Agency (USEPA) thresholds. TPH concentrations ranged from 0.18 to 57.66 mg/L, with Site 3 (pipeline passage) showing levels about 320-fold higher than reference sites and exceeding the WHO drinking-water guideline (0.05 mg/L) by up to 1153-fold. Cadmium (0.040–0.350 mg/L) and nickel (0.055–0.561 mg/L) exceeded WHO drinking-water guidelines (Cd 0.003 mg/L; Ni 0.07 mg/L) by 13–117- and up to 8-fold, respectively. Health risk assessment, using USEPA Risk Assessment Guidance for Superfund (RAGS) protocols, revealed a total cancer risk of 4.15 × 10⁻³ at Site 3, 41-fold above the USEPA acceptable threshold of 1 × 10⁻⁴, and extreme non-carcinogenic risk (Hazard Index = 20.03–25.51) at petroleum-infrastructure sites; cadmium contributed 86–88% of both carcinogenic and non-carcinogenic effects. Ecological risk indices classified Site 3 as extreme (Potential Ecological Risk Index = 722, against the Håkanson PERI = 600 “very-high-risk” threshold), mainly driven by cadmium (Er = 310–350) and nickel (Er = 140–150). Source apportionment using the Carbon Preference Index, enrichment factors, and strong TPH–metal correlations (r > 0.88, p < 0.01) clearly identified petroleum operations as the dominant contamination source. This work demonstrates the critical importance of integrated multi-pollutant assessments in petroleum-degraded mangrove water for guiding environmental protection and public-health interventions. Full article

Multi-terminal voltage-source-converter-based HVDC (VSC-MTDC) systems are increasingly used to integrate renewable energy and interconnect asynchronous AC grids, but conventional fixed-coefficient droop control cannot simultaneously limit DC-voltage deviations, reduce operating losses, and preserve converter power margins during operating-point switching. This paper hypothesizes that a rule-based fuzzy adjustment of the droop slope can provide smooth multi-objective coordination without inter-station communication. A dual Mamdani fuzzy controller is developed: one controller adjusts the weighting between loss-oriented and power-margin-oriented droop coefficients according to converter power margin, while the other introduces a voltage-deviation correction according to DC-bus voltage. The controller is implemented and verified in a five-terminal MMC-based VSC-MTDC model built in PSCAD/EMTDC, where simulation data are generated under heavy-load, light-load, and power-reference switching scenarios using specified line and converter parameters. Compared with conventional droop control, the proposed strategy improves power-margin utilization, reduces operating-point discontinuities, and raises the minimum DC voltage from 370.2 kV to 381.4 kV in the severe switching case. The results confirm that fuzzy-slope droop control can achieve smoother operating-point switching and better coordinated optimization among voltage stability, operating loss, and converter reserve margin. Full article

In satellite-denied environments such as urban canyons, tunnels, and underground parking facilities, achieving high-precision autonomous positioning for vehicles remains a critical challenge. Although high-precision inertial measurement units (IMUs) can provide accurate dead reckoning, their deployment is limited by cost, size, and power consumption, making low-cost, microelectromechanical systems IMUs (MIMUs) an attractive alternative solution. However, the single MIMU suffers from substantial measurement noise and bias instability, leading to rapid error divergence that cannot sustain long-term autonomous navigation. To address the above issues, this paper proposes an autonomous positioning system based on a wheel-mounted MIMU array (Wheel-AINS). The system adopts a differential layout in which multiple low-cost MIMU chips are installed at the center of each of the left and right rear wheels, forming redundant sensor arrays. By differentially fusing symmetrically mounted chips, common-mode noise and zero bias are effectively canceled while the wheel rotation provides natural rotational modulation. The fused gyroscope outputs and known wheel radius are then used to estimate the vehicle forward speed, replacing traditional odometers. The estimated wheel speed and vehicle kinematic constraints are then integrated within a Kalman filter framework to suppress the error divergence of the inertial navigation system. A dedicated embedded hardware prototype with multi-chip synchronous acquisition and wireless transmission was developed. Three groups of urban road tests with total distances of 0.85 km, 2.14 km, and 2.49 km were conducted. The results indicate that the average position drift rate of the Wheel-AINS is 0.50%, and the average heading RMSE is 12.2°. The closure error of the 2.49 km trajectory is 10.43 m, reduced by approximately 80% compared with a single MIMU. The ablation experiment reveals that the MIMU array fusion module is the primary source of accuracy improvement, reducing the position RMSE from 155.0 m to 10.1 m, while the dual-wheel distance constraint further optimizes the position RMSE to 8.2 m, but increases the heading RMSE from 13.3° to 13.6°. This demonstrates that the proposed method can substantially improve autonomous positioning accuracy while maintaining a notably low system cost, providing a viable technical pathway for long-endurance vehicle navigation in satellite-denied environments. Full article

AI-generated images (AIGIs) often contain perceptual defects that differ from the distortions commonly studied in conventional no-reference image quality assessment (NR-IQA). This work investigates image-only AIGC image quality assessment, where no prompt text is used and the quality score must be inferred from visual evidence such as artifacts, structure, and semantic plausibility. We propose a dual-pathway wavelet-attention framework built on a Swin Transformer V2-Base backbone. The artifact pathway employs a Noise Perceptive Attention Module (NPAM) with fixed Haar wavelet decomposition to describe generation-related sub-band degradation cues, whereas the image-perception pathway models semantic, structural, and contextual quality evidence using multi-scale attention, global–local spatial-channel attention, and pyramid pooling. The two pathways are integrated through adaptive fusion and a spatially weighted regression head with an auxiliary global prediction. Experiments on AGIQA-1K, AGIQA-3K, and AIGCIQA2023 demonstrate competitive in-domain performance, including SRCC values of 0.8418 on AGIQA-3K and 0.8445 on the quality dimension of AIGCIQA2023. The evaluation further covers individual module ablations, score-fusion variants, seed stability, qualitative error analysis, and cross-database transfer, revealing both the contribution of the proposed components and the remaining difficulty of source-disjoint generalization. Full article

FDM-printed polylactic acid (PLA) composites containing 5 and 10 wt% obsidian powder sourced from the Kars region of Eastern Anatolia (Turkey) were produced via twin-screw masterbatch extrusion and subsequent single-screw filament dilution. Mechanical (tensile, three-point flexure, notched Charpy impact, Shore D), physical (density), thermal (simultaneous TGA/DSC) and microstructural (macroscopic fractography and SEM at 100×–1000×) characterizations were performed on FDM-printed specimens. Young’s modulus rose monotonically by +9.0% at 5 wt% and +18.2% at 10 wt%, while ultimate tensile strength decreased by 12.4% and 17.3%, respectively. The flexural modulus increased by +15.2% at 5 wt% and plateaued at 10 wt% (+16.7%), whereas the flexural strength decreased by only 3.5% at 10 wt%, indicating that flexure-mode loading is markedly more tolerant of obsidian filler than axial tension. Shore D hardness rose by +2.11 points from 0 to 5 wt% with saturation thereafter. TGA showed a dual thermal effect: T₅ and T₁₀ dropped by 5–6 °C from 5 to 10 wt%, while the main decomposition rate decreased by ~46% and the decomposition interval widened from 9.7 to 23.5 °C, indicating a barrier/heat-shielding effect of dispersed silicate particles. SEM revealed a continuous ductile → transitional → brittle progression with increasing obsidian content; extended interfacial debonding lines at 10 wt% identified weak unmodified filler/matrix coupling as the principal performance-ceiling factor. Density measurements indicated a ~3–6% residual void fraction consistent with the inter-bead voids observed by SEM. To the authors’ knowledge, this is the first systematic study of obsidian as a reinforcing filler in PLA; the 5 wt% composition is identified as a strong candidate for esthetic, flexure-dominant, and low-load structural applications. Full article

Engineering computer graphics education frequently exhibits a gap between procedural CAD software (e.g. CATIA 2022) training and the strategic engineering reasoning required by industrial practice. This paper documents a holistic redesign of two advanced CAD courses. The study is framed within the Scholarship of Teaching and Learning (SoTL) tradition as a practitioner-led reflective case study. The redesign integrates four pedagogical mechanisms within an enterprise-CAD context: authentic problem-based learning, dual-layered asynchronous video scaffolding, software-agnostic heuristics (including pre-modelling cognitive mapping), and cognitive apprenticeship. The analysis triangulates three institutional data sources: quantitative Course Experience Survey indicators, qualitative student response themes, and twelve consecutive years of cohort-level academic performance records (2013–2024). The 2022 intervention iteration coincided with a marked elevation in academic performance. Grades reached approximately two standard deviations above the historical baseline. Concurrently, qualitative themes highlighted perceived industrial relevance and platform-portable confidence. However, performance in the post-intervention iterations (2023 and 2024) partially regressed. While scores remained above the historical mean, they did not sustain the 2022 peak. This pattern indicates partial sustainment, rather than evidence of a stable or definitive sustained pedagogical effect. This case is reported as descriptive rather than inferential. While the observed patterns align strongly with theoretical predictions, they do not establish definitive causal effects. Ultimately, the primary contribution of this study lies in documenting the integrated operationalization of these four mechanisms. Furthermore, it highlights longitudinal pedagogical sustainability as a critical, under-examined dimension that single-iteration evidence systematically obscures. Full article

In the context of global climate change and China’s dual-carbon strategy, this analysis examines how land-use transition is associated with land-use carbon effects in China’s resource-based cities. From the perspective of urban development stages, an analytical framework is built by linking development stage, land-use structure, and carbon source–sink structure. Using 262 resource-based cities from 2011 to 2023, we estimate land-use-related carbon emissions, carbon sequestration, and net land-use carbon effects with the carbon emission coefficient method and analyze their spatiotemporal patterns and driving factors using GeoDetector. The results show clear differences among city types. Mature cities form the largest group. Growth cities show the fastest expansion of impervious surfaces, while regenerative cities present signs of ecological recovery. This suggests that land-use transition is not simply the expansion of impervious surfaces, but a stage-dependent process of structural change. Land-use carbon effects also differ across stages. Mature cities maintain high and stable carbon-source effects. Growth cities exhibit increasing carbon-source effects, declining cities show reduced emissions but limited improvement in the carbon source–sink structure, and regenerative cities show improved carbon-sink capacity under ecological restoration. Overall, net land-use carbon effects follow a rise–decline–rebound pattern and show clear spatial heterogeneity and visually apparent clustering patterns. Population size has strong explanatory power, while interactions between socioeconomic and land-use factors further shape spatial differences. These results support stage-specific low-carbon transition strategies. Full article

Background: Electroencephalography (EEG) signals are widely used for monitoring anesthesia depth during surgery. Current commercial indicators are largely closed-source and may reflect dynamic changes with some delay. Methods: This study proposes a multitask deep learning model for continuous Bispectral Index (BIS) estimation, binary anesthesia-state classification, and prediction of transitions toward light anesthesia at different time intervals. Dual-channel EEG signals from 5471 surgical cases in the VitalDB dataset were divided into 60 s windows. Short-Time Fourier Transform (STFT) captured instantaneous frequency changes to transform the signal into a two-dimensional map. A ResNet-SE architecture incorporating Squeeze-and-Excitation blocks was used to identify EEG features associated with anesthesia depth. Results: A Mean Absolute Error of 3.27 and a Root Mean Square Error of 5.48 were obtained in anesthesia depth estimation. Light anesthesia classification achieved an AUC of 0.99 on the internal test set. Conclusions: The proposed multitask model enables the assessment of anesthesia depth and transitions toward light anesthesia using EEG signals. Full article

The oncocytic variant of adrenocortical carcinoma (OACC) represents an exceptional type of adrenal malignancy, with heterogenous presentation. Currently, the genetic and molecular spectrum remains an open matter. A 20-year-old adult was accidentally found with a 7.2 cm adrenal tumor and underwent an open right adrenalectomy with OACC confirmation. Post-adrenalectomy positron emission tomography/computed tomography was negative. Immunohistochemistry was positive for calretin, inhibin, steroidogenic factor 1; Ki67 of 20%. Microsatellite instability was 7.61. Lin–Weiss–Bisceglia score showed 2 major criteria [mitoses 6/50 HPF + positive atypical mitoses], the reticuline algorithm (disrupted reticuline network + mitoses 6/50 HPF) was consistent for a malignant behavior, the Helsinki score was of 48. Next generation sequencing identified a likely pathogenic variant of CEL gene (heterozygote, c.539-2A>G) in peripheral blood and two pathogenic variants in the tumor: exon 48, NF1 gene [c.7159_7164del p.(N2387_F2388del)] and exon 6, TP53 gene [c.596delG p.(G199Efs*48)]. Polycystic ovary syndrome type A has been diagnosed as teenager with no phenotype change before the tumor detection. After surgery, oocyte retrieval and cryopreservation upon ovarian stimulation protocol (OSP) was performed before starting mitotane therapy. To the best of our knowledge, this is a novel genetic configuration in OACC with an impact on prognosis to be determined. Hyperandrogenemia stands on a dual source (potential CEL-driven insulin resistance for the ovary and OACC-originating for the adrenal glands). Also, this is the first case to receive OSP in OACC, noting that a tailored multidisciplinary management is mandatory. Full article

The dense tree canopy in the complex orchard environment obstructs wireless positioning signals and generates NLOS interference, which reduces the positioning accuracy of agricultural mobile robots. This study investigates a localization method for agricultural mobile robots based on two UWB tags and an electronic compass. By analyzing the NLOS interference factors and error sources of UWB, a method for NLOS interference suppression and positioning correction employing two UWB tags tightly coupled with heading angle was proposed. The construction of the heading angle L2IB system and its comprehensive process were also introduced as follows. The proposed method constructs candidate localization domains for dual UWB tags based on multilateration and integrates the inter-tag distance and heading-angle constraints within an L2IB framework to suppress NLOS-induced errors and estimate the robot center position. Experiments were performed under four simulated scenarios, namely line-of-sight (LOS), single-anchor occlusion, multi-anchors occlusion, and single-tag occlusion. The proposed method was compared with the centroid and least-squares methods. The results demonstrate that the L2IB method effectively improves localization accuracy under NLOS conditions. Specifically, in the single-tag NLOS interference scenario, the MAE, RMSE, and maximum localization error were 3.7, 4.0, and 6 cm, respectively. These results indicated that the system could meet the positioning needs of most NLOS environments in the orchard. Therefore, the proposed method exhibits feasibility and provides a new alternative for high-precision localization of mobile robots in orchards under NLOS conditions. Full article

In the context of the “dual-carbon” target, the large-scale integration of renewable energy sources leads to an increased risk of transient voltage instability at the high voltage direct current (HVDC) transmission receiving end. The HVDC transmission system possesses fast and accurate power regulation capability. After a fault occurs near the inverter station, reducing the DC current enables the reactive power from the compensation devices to be released and injected into the receiving-end power grid, thereby providing emergency voltage support for the receiving-end grid. To reduce control costs, an optimization model constrained by transient voltage violation is established, and the DC current modulation is acquired via an online solution. To maintain system stability and meet the requirements of online applications, it is crucial to rapidly solve the optimization model based on the grid operating mode and contingency information to update the emergency control strategy table in the special protection system (SPS). Conventional global orthogonal collocation (GOC) and adaptive orthogonal collocation (AOC)-based solution methods transform the optimization model in the continuous time domain into a nonlinear programming (NLP) problem for solution, which addresses the low efficiency of traditional rolling optimization. However, the GOC- and AOC-based solution methods improve the discretization accuracy of the model by pursuing global uniform densification of collocation points, making it difficult to balance solution accuracy and solution efficiency. To this end, this paper proposes an efficient interval partition dynamic adaptive orthogonal collocation (IP-DAOC)-based solution method. Firstly, the overall optimization time window is interval-partitioned into multiple initial intervals, and an interval-partitioned transient voltage stability emergency control optimization model is established. Furthermore, the interval length and the number of collocation points are dynamically adjusted according to the curvature of interpolation polynomials at collocation points in different intervals. Finally, after interval adjustment, the dynamic equations discretized in adjacent intervals are made continuous by reconstructing the differential matrix. This solution method reduces the total number of collocation points, thereby decreasing the scale of the NLP problem and narrowing the search space, significantly improving solution efficiency while ensuring solution accuracy. To verify the effectiveness of the proposed solution method, simulations are carried out on a modified IEEE 14-bus system. The results are compared with those of the traditional GOC- and AOC-based solution methods, which further demonstrate the superiority of the proposed solution method. Full article

Fine particulate matter with a diameter ≤2.5 μm (PM_2.5) pollution poses a global public health crisis, demonstrating significant threats to human health. This study focused on the strategically important Chengdu-Chongqing Economic Circle in western China, systematically comparing the toxic effects of urban and rural PM_2.5 across five levels. PMF and regression analysis were used to identify source contributions, dual-omics to pinpoint key molecules, and epidemiological data with a GAM model to assess health risks. Findings demonstrate that rural PM_2.5 possesses greater biotoxicity than its urban counterpart. Cytotoxicity in urban and rural PM_2.5 originated from road dust/vehicle emissions and biomass burning, respectively. Subsequently, integrated omics and molecular biology analyses identify kinesin family member 20A (KIF20A) as a shared key target, which mediates toxicity induced by both urban and rural PM_2.5. Finally, epidemiological analysis reveals that females and ≥65 years old exhibit relatively high sensitivity to urban PM_2.5 exposure trends, with rhinitis showing a comparatively higher impact among various related diseases. The novelty of this work lies in its pioneering application of a multi-tiered investigative approach. This approach spans “environmental samples-cellular mechanisms-population health” within the Chengdu-Chongqing economic circle context, systematically elucidating common and distinct respiratory health risk of urban and rural PM_2.5. This work offers a vital scientific foundation for advancing region-specific, precise air pollution prevention and control measures. Full article

Direct grid-connected wireless charging based on direct AC–AC conversion is attractive for electric vehicles (EVs) because it can reduce power conversion stages and improve charger compactness. In matrix-converter-based wireless power transfer (WPT) systems, the grid-frequency AC voltage can be directly converted into high-frequency AC voltage without using bulky DC-link electrolytic capacitors. However, the removal of the intermediate energy-storage stage also makes the EV wireless charger more sensitive to grid-voltage fluctuation. For an LCC-S compensated WPT system, the voltage-source output characteristic makes the charging-side voltage sensitive to grid-voltage disturbance, resulting in severe MC output-current and battery charging-current overshoot. This transient overcurrent may threaten both the power converter and the EV battery charging process. In this paper, a dual-frequency state-space model is developed for the matrix-converter-based electrolytic-capacitor-less LCC-S WPT system to analyze the disturbance propagation from the grid side to the high-frequency resonant stage and the EV battery side. Based on the model, the current-overshoot suppression capability and bandwidth limitation of the conventional dual closed-loop control strategy are investigated. To further enhance transient current protection, a grid-voltage feedforward strategy is proposed to compensate for the disturbance before severe current overshoot is formed. Finally, experimental results verify that the proposed method effectively suppresses the MC output-current and battery charging-current overshoot under grid-voltage fluctuation, thereby improving the grid-disturbance resilience and dynamic safety of direct grid-connected EV wireless charging systems. Full article