Search Results (9,084)

Shoreline change detection from remote sensing imagery remains challenging in environments subject to water level fluctuations, as remotely sensed shoreline positions reflect instantaneous hydrodynamic states rather than true geomorphic change. In the Great Lakes, seasonal and short-term water level variations can produce apparent shoreline shifts unrelated to sediment dynamics. Reliable calibration with bathymetry and water level data can mitigate this effect, but such data are often unavailable or difficult to obtain for many coastal and lacustrine systems worldwide. To address this limitation, we proposed a morphology-based framework that quantifies geometric change between successive shoreline curves using a discrete Fréchet distance, a modified Euclidean distance and a Union distance metric. Rather than relying solely on cross-shore displacements, the approach leverages shape similarity to differentiate water-level-driven shifts from true morphological change. We evaluated the framework across three spatial scales (100 m, 500 m, and 1000 m) along 125 km of southwestern Lake Michigan coastline using 2010 and 2020 aerial imagery, benchmarking against water-level-calibrated DSAS erosion hotspots. The Fréchet distance improved monotonically with scale, achieving strong agreement at 1000 m (F1 = 0.84, Spearman $\rho$ = 0.79) but limited reliability at 100 m. While individual morphology-based metrics appeared competitive with or inferior to uncalibrated DSAS at each scale, the union of both distances substantially outperformed uncalibrated DSAS at management-relevant scales (F1 of 0.64 vs. 0.50 at 500 m and 0.79 vs. 0.42 at 1000 m), reflecting the complementary nature of shape-based and displacement-based detection. The Patient Rule Induction Method (PRIM) further identified gentle nearshore slopes and moderate separation from engineered structures as the geomorphic conditions under which the morphology-based and calibrated erosion indicators converged most closely (in-box F1 = 0.92 at 1000 m and 0.72 at 500 m). These results suggest that the proposed framework, particularly the complementary union of both metrics, provides a practical, calibration-free alternative for multiscale shoreline change screening in lacustrine and microtidal, data-limited environments, while local-scale applications still benefit from explicit water-level correction. Full article

To address the issues of high working power consumption and poor structural stability of current ploughing equipment under conditions of straw coverage and heavy clay soil, a 1LFT-450D variable width reversible plough (VWRP) with resistance reduction function is designed. Based on the shark shield scale, a bionic resistance reduction plough body was designed. Through theoretical analysis, the turnover mechanism (TM) and the working width adjustment mechanism (WWAM) were designed, and their main structural parameters were determined. Further research was conducted on key components using simulation software. The discrete element method (DEM) simulation results indicated that arranging bionic ribs on the plough breast achieved the best resistance reduction effect compared with the ploughshare tip and ploughshare. Meanwhile, relative to the conventional plough body, the designed bionic plough body exhibited average reductions in resistance and energy consumption of 12.55% and 12.34%, respectively. The soil bin test further verified the resistance reduction performance of the designed bionic plough body. The kinematic performance of the TM and the WWAM was analyzed using RecurDyn, and their reliability and stability were verified through the mechanism performance test. The results of the field operation performance test showed that under the conditions of forward speed of 8–10 km·h⁻¹ and working width of 1320–2000 mm, the operation performance of the designed VWRP satisfied the requirements of relevant standards. This study can provide a theoretical reference for the resistance reduction optimization of agricultural machinery soil-engaging parts and the design of new ploughs. Full article

This paper investigates the secrecy energy efficiency (SEE) maximization problem in a downlink multiple-input single-output (MISO) wireless communication system assisted by an intelligent reflecting surface with movable elements (ME-IRS). Unlike a conventional IRS, which has fixed-position elements, the proposed ME-IRS enables dynamic adjustment of element positions to exploit additional spatial degrees of freedom for performance enhancement. However, such flexibility introduces new challenges due to the strong coupling among transmit beamforming, IRS phase shifts, and element positions, as well as the additional power consumption caused by element movement. To address these issues, we formulate an SEE maximization problem by jointly optimizing the transmit beamforming, phase shift matrix, and element positions. The resulting problem is highly non-convex owing to the fractional objective function and coupled variables. To address this challenge, an efficient alternating optimization (AO) framework is developed by leveraging semidefinite relaxation (SDR), successive convex approximation (SCA), and gradient-based methods. Simulation results demonstrate that the proposed ME-IRS configuration significantly outperforms conventional fixed-position and discrete-position IRS configurations in terms of SEE, providing valuable insights into the impact of movable region size and system parameters. Full article

Uneven seed spacing, skewed stalk posture, and inconsistent planting depth remain major challenges in horizontal sugarcane planting. To address these issues, a semi-automatic transverse sugarcane planter integrating a supply–buffer–discharge seeder and multiple soil-engaging components was developed. The seed placement process and the interaction between stalk discharge and soil disturbance were investigated through Discrete Element Method (DEM) simulations and experiments. First, the working principle and key component parameters of the whole machine were determined. It integrated the processes of soil crushing, furrowing, seeding, ridge covering. In addition, a dynamic analysis was conducted on the inter-particle disengagement effect during the two-step seed filling process of lifting and discharging. Secondly, a discrete element simulation model for the entire process of soil-engaging seed arrangement operations was established for the machine. The effects of forward speed and seed outlet position were studied using a discrete element method (DEM) simulation model that coupled soil disturbance flow with stalk-seed discharge behaviour. Furthermore, a response surface methodology (RSM) experiment was performed on the seeding test bench to quantify the effects of guiding parameters on seed placement uniformity. The determination coefficient (R²) of the established regression model exceeded 0.9, indicating high prediction accuracy. The optimal collaborative parameter combination was optimized as follows: forward speed of 1.2 m·s⁻¹, buffer inclination angle of 55°and supply roller speed of 26 r·min⁻¹. After verification, the seed placement uniformity coefficient of the seeder reached 91.8 ± 1.4%, which met the expected accuracy requirements for horizontal planting. Full article

Despite significant advancements in communication systems, inherent limitations persist in providing reliable data transmission for emerging applications with massive data exchanges. Semantic communication offers promising solutions by extracting and transmitting meaningful information rather than raw bit sequences. However, it faces challenges from high mobility and dynamic channel conditions in wireless environments. In this paper, we design a ground-to-air network architecture that integrates a rotary-wing unmanned aerial vehicle (UAV) and ground terminals to maximize semantic transmission efficiency while maintaining low energy consumption. This approach leverages the high mobility of the UAV for flexible deployment and the data reduction capabilities of semantic communication. Therefore, we formulate a multi-objective optimization problem to simultaneously balance the total semantic transmission rate and the UAV propulsion energy consumption by jointly optimizing the UAV hovering position, semantic encoding lengths, and resource block (RB) allocation. The problem is complex, with mixed continuous and discrete variables, which necessitates an advanced optimization method. To address these challenges, we propose a novel greedy-enhanced adaptive multi-objective cellular genetic algorithm (GEAMOCell), which utilizes an adaptive neighborhood selection mechanism to balance exploration and exploitation, and employs a crowding-guided archive feedback mechanism to maintain population diversity. The simulation results demonstrate that the proposed GEAMOCell algorithm outperforms baseline algorithms in terms of convergence, semantic transmission rate, and energy efficiency. Full article

The accurate and rapid prediction of hazard gas dispersion fields in urban environments is essential for guiding emergency sensor deployment and enabling real-time risk assessment. However, the computational cost associated with Computational Fluid Dynamics (CFD) simulations hinders their use as real-time forward models, while simplified Gaussian plume models lack the fidelity to resolve building obstruction effects. This study proposes a morphology-guided conditional Generative Adversarial Network (cGAN) framework designed to achieve real-time gas dispersion field modeling in urban environments with complex building configurations. The urban area is discretized into 50 × 50 m grid cells, each characterized by six morphological parameters describing building geometry. K-means clustering categorizes these cells into distinct morphological types. High-fidelity dispersion datasets are then generated for each type using Lattice Boltzmann Method (LBM) simulations. Each sample encodes building geometry, release location, wind speed, and time as multi-channel input images, with the corresponding gas dispersion concentration field is recorded as the output. Two cGAN architectures, Image-to-Image Translation (Pix2Pix) and its high-resolution variant (Pix2PixHD), are employed to learn the mapping from input features to dispersion fields. Model performance is evaluated using four complementary metrics: Fraction within a Factor of Two (FAC2) for prediction accuracy, Normalized Root Mean Square Error (NRMSE) for precision, Fractional Bias (FB) for systematic error, and Structural Similarity Index (SSIM) for spatial pattern fidelity. A case study is conducted across a 1176 km² urban district in China. The results demonstrate that under varying wind speeds (0.5–1.5 m/s) and temporal scales (5–60 s), and across five morphological categories, the Pix2PixHD-based model achieves 92.5% prediction accuracy and reproduces 97.6% of the spatial patterns. The proposed framework accelerates computation by approximately 18,000 times compared to traditional CFD, reducing inference time to under 0.1 s per scenario. This sub-second capability enables real-time concentration field estimation for emergency management, and provides a physically informed, computationally feasible forward model that can potentially support sensor-based gas source localization and detection network planning in complex urban environments. Full article

Monitoring surface subsidence in mining areas is essential for geological disaster early warning and safe production. Existing geometric difference methods heavily rely on the local consistency of multi-temporal point clouds. When horizontal displacement and vertical subsidence are coupled, horizontal movements often cause local misalignments, leading to spatial deviations and discrete anomalies in vertical estimations. To address this issue, this paper proposes DL-C2C, a deep learning model for subsidence extraction from bi-temporal ground point clouds. Within a unified framework, the model introduces horizontal displacement as an auxiliary constraint into the vertical solving process, effectively improving the stability of vertical subsidence estimation through continuous cross-temporal alignment and correlation updating. For feature extraction, DL-C2C employs a PointConv multi-scale pyramid combined with a proposed scale-adaptive Transformer to enhance cross-scale information interaction under sparse and non-uniform sampling conditions. Furthermore, the network constructs dynamic local associations through iterative alignment within a recursive framework, and introduces diffusion-based residual correction at the fine-scale stage to compensate for detail errors at subsidence basin boundaries and in data-missing regions. Experiments on simulated and real-world datasets—covering aeolian sand and mountainous gully landforms—demonstrate that the method achieves mining 3D error (M3DE) of 0.16 cm and 0.22 cm in simulated scenarios. In real-world mining area validations, compared to existing methods, DL-C2C significantly reduces discrete anomalous points, yields an error distribution closer to zero, and exhibits superior performance in boundary transition continuity and non-subsidence area stability. In conclusion, this model provides reliable technical support for large-scale, high-precision intelligent monitoring of geological disasters in mining areas. Full article

Vibration test fixtures are widely used to evaluate the dynamic characteristics of structures. However, their performance is often limited by their excessive weight and unintended resonances. Conventional optimization methods, such as genetic algorithms, have been applied to improve fixture design; however, they often require considerable computational effort and are inefficient for problems involving discrete design variables. To address these limitations, this study proposes a rib thickness optimization method based on an orthogonal array. The novelty of the proposed method lies in the introduction of an influence value that simultaneously reflects lightweighting effect and first natural frequency change. The proposed method generates orthogonal arrays for rib-thickness configurations, performs modal analyses, and applies analysis of means based on this influence value to identify ribs with low structural influence for thickness reduction. Its effectiveness was validated through comparison with a genetic algorithm under identical conditions. The results showed that the orthogonal array achieved rib reduction patterns similar to those of the genetic algorithm while requiring only 0.84% of the analyses and 1.14% of the computation time required by the genetic algorithm. These findings demonstrate that the orthogonal array provides an efficient and practical alternative for rib thickness optimization in vibration test fixtures. Full article

Acute Lymphoblastic Leukemia (ALL) is a highly prevalent hematological malignancy, especially in children, for whom precise and prompt subtype identification is essential to establish suitable treatment protocols. Current deep learning-based computer-aided diagnosis (CAD) methods for identifying ALL are hindered by numerous drawbacks, such as a dependence on solely spatial feature depictions, elevated feature dimensions, computationally extensive deep learning architectures, inadequate multi-layer feature utilization, and poor interpretability. This paper introduces LUMINA-Net, a custom, lightweight, and interpretable deep learning CAD for the automated identification and subtype diagnosis of ALL using microscopic blood smear pictures. LUMINA-Net makes four principal contributions: first, it integrates a self-attention module within a lightweight custom Convolution Neural Network (CNN) to effectively capture long-range spatial relationships across clinically pertinent cytological patterns while preserving a compact design. Second, it employs a Discrete Wavelet Transform (DWT)-based wavelet pooling layer that decreases feature dimensions by up to 96.875% while enhancing the obtained depictions with spatial-spectral information. Third, it utilizes a multi-layer feature fusion strategy that combines wavelet-pooled features from two deep layers with a third fully connected layer to create a discriminating multi-scale feature vector. Fourth, it incorporates Gradient-weighted Class Activation Mapping as a dedicated explainability process to furnish clinicians with apparent visual explanations for each classification decision. Withoit the need for image enhancement or segmentation preprocessing, LUMINA-Net outperforms the competing state-of-the-art methods on the same dataset, achieving a peak accuracy of 99.51%, specificity of 99.84%, and sensitivity of 99.51% on the publicly available Kaggle ALL dataset. This demonstrates that LUMINA-Net has the potential to be a dependable, effective, and clinically interpretable CAD tool for ALL diagnosis. Full article

Background: Conventional analyses of physiological recovery often rely on discrete metrics that assume independence across time points, thereby ignoring intrinsic temporal continuity and masking substantial interindividual heterogeneity. This proof-of-concept study assesses the efficacy of Functional Data Analysis (FDA) as a promising framework for characterizing individual response dynamics following a functional threshold power (FTP) test. Methods: Physiological time-series data (including blood lactate, heart rate, blood pressure, and glucose levels) collected from 21 trained cyclists (10 professionals, 11 amateurs) were represented as functional objects using FDataGrid on the original sampling grid (0, 3, 5, 10, 20 min), without basis expansion or smoothing. We conducted unsupervised functional clustering (K-means; Fuzzy K-means) and supervised classification (Maximum Depth with Modified Band Depth, K-Nearest Neighbors, Nearest Centroid, functional QDA with parametric Gaussian covariance). Model performance was estimated via Repeated Stratified 5-Fold Cross-Validation with 10 repetitions (50 folds), reporting accuracy, balanced accuracy (mean ± SD), 95% CIs, permutation p-values, and sensitivity/specificity from aggregated confusion matrices. Results: Lactate (CL) and diastolic blood pressure (DBP) provided useful and statistically significant discrimination across several classifiers (e.g., KNN, Nearest Centroid, functional QDA), whereas heart rate showed modest discriminative value and glucose intermediate performance. Unsupervised analyses revealed distinct lactate recovery profiles and graded membership for hemodynamic/metabolic variables, supporting the value of FDA for resolving heterogeneity beyond group-average trends. Conclusions: FDA offers a feasible and informative approach for classifying recovery phenotypes while preserving temporal structure. Findings are promising but should be interpreted with caution due to the small sample size, sparse time points, and the need for external validation in larger, independent cohorts before translation into routine decision-making. Full article

The increasing penetration of Doubly Fed Induction Generator (DFIG)-based wind energy systems raises major concerns regarding voltage stability and Fault Ride-Through (FRT) capability under grid disturbances and wind speed variations. This paper proposes a coordinated control framework for a grid-connected DFIG system, where a Static Synchronous Compensator (STATCOM) based on discrete-time deadbeat current control is integrated with a Supercapacitor Energy Storage System (SCES) connected to the DC link through a bidirectional DC-DC converter governed by cascaded Active Disturbance Rejection Control (ADRC). The deadbeat-controlled STATCOM provides fast reactive current injection for voltage support during sag and swell events, while the cascaded ADRC enhances DC-link voltage regulation and suppresses rotor-speed oscillations. Comprehensive MATLAB/Simulink simulations are carried out under variable wind speed and severe grid disturbances up to 80% voltage sag and 50% voltage swell. For voltage regulation, the proposed method is compared with SVC and PI-based STATCOM. In addition, SCES control performance is evaluated by comparing PI, single ADRC, and cascaded ADRC in terms of DC-link voltage overshoot, undershoot, and ripple. The results show clear improvements in voltage response and transient performance. Under a 20% voltage sag, the proposed deadbeat-controlled STATCOM significantly improves the dynamic response, where the undershoot is reduced from 0.125 p.u. (with SVC) to 0.04 p.u., and the settling time is shortened from 0.04 s to 0.025 s. Under a severe 80% sag, the overshoot is limited to 0.02 p.u., compared with 0.13 p.u. for the SVC and 0.15 p.u. for the PI-based STATCOM. Similarly, under a 50% voltage swell, the overshoot is reduced to 0.20 p.u., compared with 0.46 p.u. for the SVC and 0.27 p.u. for the PI-based STATCOM. Regarding the DC-link performance under 80% sag, the proposed cascaded ADRC-based SCES limits the overshoot and undershoot to 6 V and 2 V, respectively, compared with 39 V and 32 V for the PI-based SCES. These results confirm the superior damping, disturbance rejection, and FRT enhancement achieved by the proposed strategy. Full article

Integrated sensing and communications (ISAC) requires tight coordination between spatial signal design and multiple-access strategies to balance communication throughput and sensing accuracy under shared spectral and hardware constraints. However, existing ISAC frameworks with rate-splitting multiple access (RSMA) typically rely on fixed antenna arrays and decoupled optimization, which fundamentally limit their ability to adapt to fast channel variations and dynamic sensing requirements. This paper introduces a fluid antenna-enabled RSMA-assisted ISAC architecture, in which movable antenna ports are exploited as a new spatial degree of freedom to enhance adaptability in both communication and sensing operations. Fluid antenna systems (FAS) are deployed at both the base station and user terminals, allowing dynamic port selection that reshapes the effective channel and sensing beampattern in real time. We formulate a joint sum-rate maximization problem subject to explicit sensing-quality constraints, capturing the coupled impact of antenna port selection, RSMA rate allocation, and multi-beam transmit design. The proposed framework maximizes the communication sum-rate while ensuring that the sensing functionality satisfies a predefined sensing quality constraint. This constraint-based ISAC formulation guarantees that sufficient sensing power is directed toward the target while optimizing communication performance. The resulting optimization involves strongly coupled discrete and continuous decision variables, rendering conventional optimization methods ineffective. To address this challenge, a hierarchical deep reinforcement learning (HDRL) framework is developed, where an upper-layer deep Q-network (DQN) determines discrete antenna port selection and a lower-layer twin delayed deep deterministic policy gradient (TD3) algorithm optimizes continuous beamforming and rate-splitting parameters. Numerical results demonstrate that the proposed approach significantly improves system performance, achieving higher communication sum-rate while satisfying sensing requirements under dynamic propagation conditions. Full article

With the rapid growth of data, feature selection has become essential for improving machine learning performance. However, most existing unsupervised feature selection methods rely on greedy strategies, which often lead to suboptimal solutions. Moreover, traditional information–theoretic approaches are primarily designed for discrete data and require discretization when applied to continuous data, potentially causing information loss. To address these issues, this paper proposes a global unsupervised feature selection method based on fuzzy mutual information (UFS-FMI). The proposed method integrates fuzzy set theory with information measures to quantify feature relevance and redundancy, and formulates a fractional optimization model. A combination of projection neural networks and kWTA neural networks is employed to achieve global optimization. Experimental results on nine UCI benchmark datasets demonstrate that UFS-FMI consistently outperforms several representative methods in terms of classification accuracy, clustering accuracy, and normalized mutual information (NMI). In particular, on datasets such as Movement_libras, Ionosphere, and Control, the proposed method achieves significantly improved classification performance, confirming its effectiveness and robustness. Full article

Supergiants are luminous post-main-sequence massive stars whose effective temperatures ($T_{\mathrm{eff}}$) are key inputs for stellar evolution and feedback studies. We present a photometry-based procedure to derive $T_{\mathrm{eff}}$ for a sample of galactic supergiants of spectral types B and A by fitting the spectral energy distributions (SEDs) in the UV-to-mid-IR range to ATLAS9 model spectra converted into synthetic photometry using the corresponding passband transmission profiles while simultaneously solving for the line-of-sight extinction. The SEDs were constructed from published data taken in different photometric systems (Johnson or Kron–Cousins $UBVRI$, Strömgren $uvby$, $JHK$ magnitudes from various sources, and AllWISE) and supplemented with UV TD-1 fluxes for brighter stars. The interstellar extinction law is based on Cardelli, Clayton & Mathis approximation assuming a total-to-selective ratio $R_V=A_V/E(B-V)=3.1$. The best-fitting parameters are obtained by minimizing a covariance-weighted ${\chi }^2$ statistic in logarithmic flux space over a grid of $A_V$ values and a discrete model grid. We test the method on 20 targets and find generally good agreement with published literature temperature estimates. The main limitations are non-simultaneous photometry for possibly variable objects and the residual coupling between temperature and reddening in broadband SED fitting. This study is intended as a methodological demonstration on a pilot sample rather than a definitive parameter catalog. Full article

This article proposes a novel time-space two-grid high-order compact difference scheme for the one-dimensional nonlinear Schrödinger equation subject to Dirichlet boundary conditions. In comparison with the fully nonlinear compact difference scheme, the proposed methodology combines a small-scale nonlinear fourth-order compact difference algorithm on a time-space coarse grid and a large-scale linearized correction compact difference algorithm on a fine grid. In contrast to the time two-grid compact difference method, the proposed scheme applies the two-grid technique in both the spatial and temporal domains, thereby further improving computational efficiency. Solutions from the coarse grid are projected onto the fine grid via a temporally linear and spatially cubic Lagrange interpolation operator. Unconditional stability and optimal convergence rates, which are fourth-order in space and second-order in time, are proven in both the discrete $L^2$ and $L^{\infty }$ norms, without any constraints on the grid ratio. In addition to the standard techniques of the energy method, a discrete Sobolev inequality and an a priori error estimate are employed to demonstrate stability and high-order convergence. Finally, the theoretical results are validated through numerical experiments, which confirm the robustness and reliability of the proposed approach. A single-soliton experiment demonstrates that, compared with the fully nonlinear compact difference scheme, the proposed method achieves a significant reduction in CPU time while maintaining a comparable level of accuracy. Additional experiments further illustrate the algorithm’s effectiveness in simulating two-soliton interactions and soliton birth. These findings establish the proposed scheme as a highly efficient alternative to conventional nonlinear approaches. Full article