Search Results (5,110)

The interlinking converter, an important device in a hybrid AC-DC microgrid, undertakes the task of power distribution between the AC sub-microgrid and DC sub-microgrid. To address the limitations of traditional bidirectional droop control in islanding mode, particularly the lack of consideration for regulation priority between AC frequency and DC voltage, this paper proposes an adaptive bidirectional droop control strategy. By introducing an adaptive weight coefficient based on normalized AC frequency and DC voltage, the strategy prioritizes regulating larger deviations in AC frequency or DC voltage. Interlinking converter action thresholds are set to avoid unnecessary frequent starts and stops. Finally, a hybrid AC-DC microgrid system in islanding mode is established in the Matlab/Simulink 2024b simulation platform to verify the effectiveness of the proposed control strategy. Full article

Background and Objectives: Accurate noninvasive differentiation between hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) remains a clinical challenge. This study aimed to assess the dignostic performance of apparent diffusion coefficient (ADC) values from diffusion-weighted MRI in distinguishing between HCC and ICC, with histological confirmation as the gold standard. Materials and Methods: A retrospective analysis was performed on 61 patients (41 HCC, 20 ICC) who underwent liver MRI and percutaneous biopsy between 2019 and 2024. ADC values were measured from diffusion-weighted sequences (b-values of 0, 500, and 1000 s/mm²), and regions of interest were placed over solid tumor areas. Statistical analyses included t-tests, one-way ANOVA, and ROC curve analysis. Results: Mean ADC values did not differ significantly between HCC (1.09 ± 0.19 × 10⁻³ mm²/s) and ICC (1.08 ± 0.11 × 10⁻³ mm²/s). ROC analysis showed poor discriminative ability (AUC = 0.520; p = 0.806). In HCC, ADC values decreased with lower differentiation grades (p = 0.008, η² = 0.224). No significant trend was observed in ICC (p = 0.410, η² = 0.100). Immunohistochemical markers such as CK-7, Glypican 3, and TTF-1 showed significant diagnostic value between tumor subtypes. Conclusions: ADC values have limited utility for distinguishing HCC from ICC but may aid in HCC grading. Immunohistochemistry remains essential for accurate diagnosis, especially in poorly differentiated tumors. Further studies with larger cohorts are recommended to improve noninvasive diagnostic protocols. Full article

Speaker profiling systems are often evaluated on a single corpus, which complicates reliable comparison. We present a fully reproducible evaluation pipeline that trains Convolutional Neural Networks (CNNs) and Long-Short Term Memory (LSTM) models independently on three speech corpora representing distinct recording conditions—studio-quality TIMIT, crowdsourced Mozilla Common Voice, and in-the-wild VoxCeleb1. All models share the same architecture, optimizer, and data preprocessing; no corpus-specific hyperparameter tuning is applied. We perform a detailed preprocessing and feature extraction procedure, evaluating multiple configurations and validating their applicability and effectiveness in improving the obtained results. A feature analysis shows that Mel spectrograms benefit CNNs, whereas Mel Frequency Cepstral Coefficients (MFCCs) suit LSTMs, and that the optimal Mel-bin count grows with corpus Signal Noise Rate (SNR). With this fixed recipe, EfficientNet achieves 99.82% gender accuracy on Common Voice (+1.25 pp over the previous best) and 98.86% on VoxCeleb1 (+0.57 pp). MobileNet attains 99.86% age-group accuracy on Common Voice (+2.86 pp) and a 5.35-year MAE for age estimation on TIMIT using a lightweight configuration. The consistent, near-state-of-the-art results across three acoustically diverse datasets substantiate the robustness and versatility of the proposed pipeline. Code and pre-trained weights are released to facilitate downstream research. Full article

Purpose: To assess the performance of 2D T2-weighted (w) Turbo Spin Echo (TSE) MRI reconstructed with a deep learning (DL)-powered super resolution reconstruction (SRR) algorithm combining compressed sensing (CS) denoising and resolution upscaling for high-resolution hippocampal imaging in patients with (epileptic) seizures and suspected hippocampal pathology. Methods: A 2D T2w TSE coronal hippocampal sequence with compressed sense (CS) factor 1 (scan time 270 s) and a CS-accelerated sequence with a CS factor of 3 (scan time 103 s) were acquired in 28 patients. Reconstructions using the SRR algorithm (CS 1-SSR-s and CS 3-SSR-s) were additionally obtained in real time. Two readers graded the images twice, based on several metrics (image quality; artifacts; visualization of anatomical details of the internal hippocampal architecture (HIA); visibility of dentate gyrus/pes hippocampi/fornix/mammillary bodies; delineation of gray and white matter). Results: Inter-readout agreement was almost perfect (Krippendorff’s alpha coefficient = 0.933). Compared to the CS 1 sequence, the CS 3 sequence significantly underperformed in all 11 metrics (p < 0.001-p = 0.04), while the CS 1-SRR-s sequence outperformed in terms of overall image quality and visualization of the left HIA and right pes hippocampi (p < 0.001-p < 0.04) but underperformed in terms of presence of artifacts (p < 0.01). Lastly, relative to the CS 1 sequence, the CS 3-SRR-s sequence was graded worse in terms of presence of artifacts (p < 0.003) but with improved visualization of the right pes hippocampi (p = 0.02). Conclusion: DL-powered SSR demonstrates its capacity to enhance imaging performance by introducing flexibility in T2w hippocampal imaging; it either improves image quality for non-accelerated imaging or preserves acceptable quality in accelerated imaging, with the additional benefit of a reduced scan time. Full article

Transportation infrastructure is a fundamental driver of economic growth and regional connectivity; and the supply of this infrastructure is often assumed to reduce spatial disparities. This study investigates the impact of transportation accessibility on regional disparities in land prices across South Korea from 2010 to 2019. Using spatial econometric models and geographically weighted regression (GWR), this study evaluates how variations in transportation networks influence land price differentials between regions. The results confirm that transportation accessibility positively affects land prices; but GWR coefficients reveal substantial regional variations in the extent to which accessibility improvements drive land price growth. Furthermore, while the overall distribution of transportation accessibility remained relatively stable, its influence on land price appreciation varied significantly, contributing to a widening gap in land values between regions. These findings underscore the critical role of transportation infrastructure in shaping regional inequalities and highlight the need for more equitable transportation policies to mitigate spatial disparities and promote balanced regional development Full article

The chiral nonlinear Schrödinger equation (CNLSE) serves as a simplified model for characterizing edge states in the fractional quantum Hall effect. In this paper, we leverage the generalization and parameter inversion capabilities of physics-informed neural networks (PINNs) to investigate both forward and inverse problems of 1D and 2D CNLSEs. Specifically, a hybrid optimization strategy incorporating exponential learning rate decay is proposed to reconstruct data-driven solutions, including bright soliton for the 1D case and bright, dark soliton as well as periodic solutions for the 2D case. Moreover, we conduct a comprehensive discussion on varying parameter configurations derived from the equations and their corresponding solutions to evaluate the adaptability of the PINNs framework. The effects of residual points, network architectures, and weight settings are additionally examined. For the inverse problems, the coefficients of 1D and 2D CNLSEs are successfully identified using soliton solution data, and several factors that can impact the robustness of the proposed model, such as noise interference, time range, and observation moment are explored as well. Numerical experiments highlight the remarkable efficacy of PINNs in solution reconstruction and coefficient identification while revealing that observational noise exerts a more pronounced influence on accuracy compared to boundary perturbations. Our research offers new insights into simulating dynamics and discovering parameters of nonlinear chiral systems with deep learning. Full article

In this study, we systematically investigated the adsorption behavior of a titanium-based metal–organic framework (MOF) sensing layer on five primary alcohol homologs using the quartz crystal microbalance (QCM) technique. Unexpectedly, response signals were significantly enhanced for molecules exceeding the framework’s pore dimensions, contradicting conventional molecular sieving models. Further investigations revealed that the adsorption time constant (${\tau }_a$) is linearly proportional to the molecular diameter ($R^2=0.952$) and the integral response (AUC) increases almost exponentially with the molecular weight ($R^2=0.891$). Although the effective diffusion coefficient ($D_{\mathrm{eff}}$) decreases with increasing molecular size ($D_{\mathrm{eff}}\propto d^{-5.96}$, $R^2=0.981$), the normalized diffusion hindrance ratio ($D_{\mathrm{eff}}/D_{\mathrm{gas}}$) decreases logarithmically with an increasing diameter. Larger responses result from stronger host–guest interactions with the framework despite significant diffusion limitations for larger molecules. These findings demonstrate the synergistic regulation of adsorption and diffusion in MOF-QCM systems. Our investigation experimentally elucidates the ’size-selectivity paradox’ in microporous sensing interfaces and establishes a quantitative framework for optimizing sensor performance through balanced control of diffusion kinetics and interfacial interactions in similar materials. Full article

This study aims to address the challenges posed by uneven energy amplitude and a low signal-to-noise ratio (SNR) in the total focus imaging of coarse-crystalline elastic anisotropic materials. A novel method for acoustic field correction vector-coherent total focus imaging, based on the materials’ properties, is proposed. To demonstrate the effectiveness of this method, a test specimen, an austenitic stainless steel nozzle weld, was employed. Seven side-drilled hole defects located at varying positions and depths, each with a diameter of 2 mm, were examined. An ultrasound simulation model was developed based on material backscatter diffraction results, and the scattering attenuation compensation factor was optimized. The acoustic field correction function was derived by combining acoustic field directivity with diffusion attenuation compensation. The phase coherence weighting coefficients were calculated, followed by image reconstruction. The results show that the proposed method significantly improves imaging amplitude uniformity and reduces the structural noise caused by the coarse crystal structure of austenitic stainless steel. Compared to conventional total focus imaging, the detection SNR of the seven defects increased by 2.34 dB to 10.95 dB. Additionally, the defect localization error was reduced from 0.1 mm to 0.05 mm, with a range of 0.70 mm to 0.88 mm. Full article

We propose an efficient Nyström-based unitary subspace method for low-complexity two-dimensional (2D) direction-of-arrival (DOA) estimation in uniform rectangular array (URA) signal processing systems. The conventional high-resolution DOA estimation methods often suffer from excessive computational complexity, particularly when dealing with large-scale antenna arrays. The proposed method addresses this challenge by combining the Nyström approximation with a unitary transformation to reduce the computational burden while maintaining estimation accuracy. The signal subspace is approximated using a partitioned covariance matrix, and a real-valued transformation is applied to further simplify the eigenvalue decomposition (EVD) process. Furthermore, the linear prediction coefficients are estimated via a weighted least squares (WLS) approach, enabling robust extraction of the angular parameters. The 2D DOA estimates are then derived from these coefficients through a closed-form solution, eliminating the need for exhaustive spectral searches. Numerical simulations demonstrate that the proposed method achieves comparable estimation performance to state-of-the-art techniques while significantly reducing computational complexity. For a fixed array size of $M=N=20$, the proposed method demonstrates significant computational efficiency, requiring less than $50\%$ of the running time compared to conventional ESPRIT, and only $6\%$ of the time required by ML methods, while maintaining similar performance. This makes it particularly suitable for real-time applications where computational efficiency is critical. The novelty lies in the integration of Nyström approximation and unitary subspace techniques, which jointly enable efficient and accurate 2D DOA estimation without sacrificing robustness against noise. The method is applicable to a wide range of array processing scenarios, including radar, sonar, and wireless communications. Full article

To address the challenges of model uncertainty, strong nonlinearities, and controller tuning in high-precision trajectory tracking for hydraulic servo systems, this paper proposes a hierarchical GA-PID-MPC fusion strategy. The architecture integrates three functional layers: a Genetic Algorithm (GA) for online parameter optimization, a Model Predictive Controller (MPC) for future-oriented planning, and a Proportional–Integral–Derivative (PID) controller for fast feedback correction. These modules are dynamically coordinated through an adaptive cost-aware blending mechanism based on real-time performance evaluation. The MPC module operates on a linearized state–space model and performs receding-horizon control with weights and horizon length $\theta =[q,r,T_p]$ tuned by GA. In parallel, the PID controller is enhanced with online gain projection to mitigate nonlinear effects. The blending coefficient $\sigma \left(t\right)$ is adaptively updated to balance predictive accuracy and real-time responsiveness, forming a robust single-loop controller. Rigorous theoretical analysis establishes global input-to-state stability and $H_{\infty }$ performance under average dwell-time constraints. Full article

Accurate carbon price forecasting is essential for market stability, risk management, and policy-making. To address the nonlinear, non-stationary, and multiscale nature of carbon prices, this paper proposes a forecasting framework integrating secondary decomposition, two-stage feature selection, and dynamic ensemble learning. Firstly, the original price series is decomposed into intrinsic mode functions (IMFs), using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). The IMFs are then grouped into low- and high-frequency components based on multiscale entropy (MSE) and K-Means clustering. To further alleviate mode mixing in the high-frequency components, an improved variational mode decomposition (VMD) optimized by particle swarm optimization (PSO) is applied for secondary decomposition. Secondly, a two-stage feature-selection method is employed, in which the partial autocorrelation function (PACF) is used to select relevant lagged features, while the maximal information coefficient (MIC) is applied to identify key variables from both historical and external data. Finally, this paper introduces a dynamic integration module based on sliding windows and sequential least squares programming (SLSQP), which can not only adaptively adjust the weights of four base learners but can also effectively leverage the complementary advantages of each model and track the dynamic trends of carbon prices. The empirical results of the carbon markets in Hubei and Guangdong indicate that the proposed method outperforms the benchmark model in terms of prediction accuracy and robustness, and the method has been tested by Diebold Mariano (DM). The main contributions are the improved feature-extraction process and the innovative use of a sliding window-based SLSQP method for dynamic ensemble weight optimization. Full article

To address the modeling complexity and multi-objective collaborative optimization challenges in multi-depot and multiple unmanned aerial vehicle (UAV) delivery task planning, this paper proposes a bi-layer planning framework, which comprehensively considers resource constraints, multi-depot coordination, and the coupling characteristics of path execution. The novelty of this work lies in the seamless integration of an enhanced genetic algorithm and tailored swarm optimization within a unified two-tier architecture. The upper layer tackles the task assignment problem by formulating a multi-objective optimization model aimed at minimizing economic costs, delivery delays, and the number of UAVs deployed. The Enhanced Non-Dominated Sorting Genetic Algorithm II (ENSGA-II) is developed, incorporating heuristic initialization, goal-oriented search operators, an adaptive mutation mechanism, and a staged evolution control strategy to improve solution feasibility and distribution quality. The main contributions are threefold: (1) a novel ENSGA-II design for efficient and well-distributed task allocation; (2) an improved PSO-based path planner with chaotic initialization and adaptive parameters; and (3) comprehensive validation demonstrating substantial gains over baseline methods. The lower layer addresses the path planning problem by establishing a multi-objective model that considers path length, flight risk, and altitude variation. An improved particle swarm optimization (PSO) algorithm is proposed by integrating chaotic initialization, linearly adjusted acceleration coefficients and maximum velocity, a stochastic disturbance-based position update mechanism, and an adaptively tuned inertia weight to enhance algorithmic performance and path generation quality. Simulation results under typical task scenarios demonstrate that the proposed model achieves an average reduction of 47.8% in economic costs and 71.4% in UAV deployment quantity while significantly reducing delivery window violations. The framework exhibits excellent capability in multi-objective collaborative optimization. The ENSGA-II algorithm outperforms baseline algorithms significantly across performance metrics, achieving a hypervolume (HV) value of 1.0771 (improving by 72.35% to 109.82%) and an average inverted generational distance (IGD) of 0.0295, markedly better than those of comparison algorithms (ranging from 0.0893 to 0.2714). The algorithm also demonstrates overwhelming superiority in the C-metric, indicating outstanding global optimization capability in terms of distribution, convergence, and the diversity of the solution set. Moreover, the proposed framework and algorithm are both effective and feasible, offering a novel approach to low-altitude urban logistics delivery problems. Full article

Plow loosening machines are essential agricultural machinery in the agricultural production process. Improving the surface strengthening process and extending the working life of the plow teeth of the plow loosening machine are of great significance. In this paper, the preparation of Stellite6/Cu composite coating on the surface of 316L steel substrate intended for strengthening the plow teeth of a plow loosening machine using laser cladding technology was studied. The influence of different laser process parameters on the microstructure and properties of Stellite6/Cu composite coating was investigated. The composite coating powder was composed of Stellite6 powder with a different weight percent of copper. Microstructural analysis, phase composition, elemental distribution, microhardness, wear resistance, and corrosion resistance of the composite coatings on the plow teeth were analyzed using scanning electron microscopy (SEM), X-ray diffraction (XRD), microhardness testing, energy dispersive spectroscopy (EDS), friction and wear testing, and electrochemical workstation measurements. The results showed that (1) When the laser power was 1000 W, the average hardness of the prepared Stellite6/Cu composite layer achieved the highest hardness, approximately 1.36 times higher than the average hardness of the substrate, and the composite coating prepared exhibited the best wear resistance; (2) When the scanning speed was 800 mm/min, the composite coating exhibited the lowest average friction coefficient and the optimal corrosion resistance in a 3.5% wt.% NaCl solution with a self-corrosion current density of −7.55 µA/cm²; (3) When the copper content was 1 wt.%, the composite coating achieved the highest average hardness with 515.2 HV, the lowest average friction coefficient with 0.424, and the best corrosion resistance with a current density of −8.878 µA/cm². Full article

Light Detection and Ranging (LiDAR) provides three-dimensional information that can be used to extract tree parameter measurements such as height (H), canopy volume (CV), canopy diameter (CD), canopy area (CA), and tree stand density. LiDAR data does not directly give diameter at breast height (DBH), an important input into allometric equations to estimate biomass. The main objective of this study is to estimate tree DBH using existing allometric models. Specifically, it compares three global DBH pantropical models to calculate DBH and to estimate the aboveground biomass (AGB) of the Lake Broadwater Forest located in Southeast (SE) Queensland, Australia. LiDAR data collected in mid-2022 was used to test these models, with field validation data collected at the beginning of 2024. The three DBH estimation models—the Jucker model, Gonzalez-Benecke model 1, and Gonzalez-Benecke model 2—all used tree H, and the Jucker and Gonzalez-Benecke model 2 additionally used CD and CA, respectively. Model performance was assessed using five statistical metrics: root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), percentage bias (MBias), and the coefficient of determination (R²). The Jucker model was the best-performing model, followed by Gonzalez-Benecke model 2 and Gonzalez-Benecke model 1. The Jucker model had an RMSE of 8.7 cm, an MAE of −13.54 cm, an MAPE of 7%, an MBias of 13.73 cm, and an R² of 0.9005. The Chave AGB model was used to estimate the AGB at the tree, plot, and per hectare levels using the Jucker model-calculated DBH and the field-measured DBH. AGB was used to estimate total biomass, dry weight, carbon (C), and carbon dioxide (CO₂) sequestered per hectare. The Lake Broadwater Forest was estimated to have an AGB of 161.5 Mg/ha in 2022, a Total C of 65.6 Mg/ha, and a CO₂ sequestered of 240.7 Mg/ha in 2022. These findings highlight the substantial carbon storage potential of the Lake Broadwater Forest, reinforcing the opportunity for landholders to participate in the carbon credit systems, which offer financial benefits and enable contributions to carbon mitigation programs, thereby helping to meet national and global carbon reduction targets. Full article

Perca schrenkii populations are experiencing significant declines, yet comprehensive morphological studies are still lacking. Understanding the relationship between morphological traits and body weight is crucial for conservation and breeding programs. We analyzed 13 morphological traits in 100 P. schrenkii specimens from Hamsigou Reservoir using correlation analysis, path analysis, and principal component analysis (PCA). Body weight exhibited the highest variability (CV = 39.76%). Strong correlations were observed between body weight and body length (R = 0.942), total length, and body width. A four-variable regression model explained 94.1% of body weight variation, with body length showing the strongest direct effect (path coefficient = 0.623). The first three principal components accounted for 76.687% of the total variance. Our findings demonstrate that BL, BW, BD, and ES can effectively predict body weight, providing valuable insights for the conservation and selective breeding of P. schrenkii. Full article