Search Results (13,954)

Reliable cropland, forestland, and grassland maps support resource assessment and ecological management in arid and semi-arid Central Asia. Existing land-cover products often delineate these classes differently, vary in reliability across classes and locations, and may share the same errors even when they agree. This study formulates multi-product fusion as a pixel- and class-specific reliability decision problem. To address this problem, we propose a reliability-adaptive fusion framework, the Discrepancy-Aware Reliability-Adaptive Fusion Network (DRAFNet), using 2020 maps from three global 30 m land-cover products—FROM-GLC Plus, GLC-FCS30D, and GlobeLand30—and variables representing aridity, temperature, precipitation, elevation, and slope. Unlike fixed-weight fusion methods and segmentation models that use the source products only as input channels, DRAFNet retains the categorical source decisions and adjusts each contribution according to its estimated reliability for the assigned class and location. Weight removed from an unreliable source is transferred to a residual expert, which provides an alternative prediction when the source products are unreliable or share the same error. Voting entropy and geo-environmental variables provide contextual information for this decision. On independent test samples from the five Central Asian countries, DRAFNet achieved an overall accuracy (OA) of 0.8275, a Kappa coefficient of 0.7698, a mean intersection over union (mIoU) of 0.7046, and a macro-averaged F1 score (Macro F1) of 0.8241. These values were 0.95–1.38 percentage points higher than those of U-Net++, the strongest benchmark. Local comparisons indicated more coherent spatial patterns and clearer boundaries in areas of pronounced disagreement. The mean and median absolute log-ratio deviations from area statistics reported by the Food and Agriculture Organization of the United Nations (FAO) were 0.618 and 0.450, respectively, both lower than those of the source products. These results support land-resource assessment and ecological management in Central Asia. Full article

Computational imaging shifts part of the aberration correction from optical hardware to algorithms, offering a viable path toward compact, simplified systems. However, overexposed regions—often caused by phenomena such as water-body reflections—can readily induce severe ringing artifacts in reconstructed images. To address this problem, we propose a Ringing-perceptive Cooperative Reconstruction Network (RPCR-Net). This network integrates a learned Wiener filter and a field-of-view shared kernel prediction network (FOV-KPN) for feature extraction and innovatively incorporates a combined regularization mechanism that leverages a Local Maximum Gradient Prior and a multi-scale ringing measurement model within its loss function to suppress artifacts while preserving details. Validated on a constructed overexposed image dataset, RPCR-Net improves the Peak Signal-to-Noise Ratio (PSNR) from 29.08 dB to 37.06 dB and the Structural Similarity Index Measure (SSIM) from 0.8795 to 0.9549. Experiments on real-world scenes further confirm its capability to suppress ringing artifacts while maintaining visual quality. The proposed method can generate high-quality images such as image reconstruction and robustness improvement in optical systems. Full article

Closed droplet contours provide the geometric basis for area estimation in vision-based droplet observation. In OLED inkjet printing, droplets are deposited into pixel wells with predefined geometry; projected area is therefore a primary geometric quantity for assessing whether the deposited liquid sufficiently fills the well or risks overflow. This work formulates closed-contour sampling under a fixed sampling budget as an area-driven sampling problem. A leading-order analysis of the local arc–chord area error shows that the dominant cubic term depends jointly on curvature and segment length. Minimization of the resulting leading-order area-error functional yields an asymptotically optimal area-driven sampling density proportional to the cube root of curvature, together with a sampling-budget estimate under a target area-error tolerance. The derived sampling density is implemented on the fitted closed contour through cumulative-weight inversion. Experiments on random closed curves and the droplet dataset provide a systematic quantitative comparison with representative methods under identical fixed-budget settings, complemented by statistical analysis and evaluations of geometric fidelity, sensitivity, and computational efficiency. The proposed method achieves lower area estimation error under the tested sampling budgets, with the improvement being most pronounced at lower sampling budgets, while the reported geometric-fidelity metrics show no disproportionate degradation of contour fidelity. These results demonstrate the effectiveness of area-driven sampling for closed-contour area estimation under limited sampling budgets. Full article

Accurate semantic segmentation is essential for long-term fixed-view monitoring, where seasonal, illumination, weather, and environmental changes alter local appearance while the global scene layout remains relatively stable. To address this structure–appearance modeling problem, we propose SPMES, a Semantic Prior-Guided Period-Aware Multi-Expert Segmentation framework. SPMES comprises a Global Expert that learns stable semantic-prior maps from the complete training set, a Semantic-Guided Fusion Module that injects these priors into the input representation, and period-specific experts that model recurring appearance characteristics according to acquisition time. Experiments on a long-term fixed-view monitoring dataset collected for this study evaluate SPMES with U-Net, U-Net++, U²-Net^†, Swin-Unet, and Mamba-UNet. Compared with the corresponding baselines, SPMES obtains better point estimates across all six evaluation metrics for each backbone, although the magnitude of the changes varies across architectures and metrics. Ablation studies show that individual configurations exhibit metric- and backbone-dependent effects, whereas the complete integration of global semantic priors, semantic-guided fusion, and period-specific learning provides the best overall balance. These results support the effectiveness and backbone-level compatibility of SPMES within the studied monitoring setting. Full article

For the application of unmanned aerial vehicle (UAV)-based passive target localization, the positions of the UAVs play an important role because different UAV configurations provide distinct TDOA measurement geometries. In addition, the uncertainty of the target position affects the localization performance of different UAV configurations. Focusing on the problem of target localization by UAVs, this paper studies a UAV path optimization method for passive target localization considering target-position uncertainty. First, a passive localization signal model is established, and the TDOA method based on the Chan algorithm is deployed for target passive localization. Second, the Cramer–Rao lower bound (CRLB) for the Chan–TDOA localization method is derived as the criterion of the UAVs’ path optimization. To consider target-position uncertainty, the global CRLB is calculated within the uncertainty region of the target position instead of only applying the traditional single-point CRLB. Third, to improve computational efficiency, an analytical approximation of the global CRLB is derived from a second-order Taylor expansion instead of repeatedly calculating the multiple integral terms. By combining this objective with the PSO algorithm, the UAVs’ configuration is searched and applied at each time step. Finally, numerical simulations are performed to verify the validity and effectiveness of the proposed analytical global CRLB path-optimization method. Full article

In engineering R&D organizations, resource contention among autonomous projects creates complex scheduling challenges in distributed multi-project environments. Traditional coordination mechanisms often prioritize global efficiency while overlooking inter-project fairness, which can lead to stakeholder resistance and execution delays. This study proposes a transparent decision support approach for the Distributed Resource-Constrained Multi-Project Scheduling Problem (DRCMPSP). We develop a two-stage scheduling mechanism that integrates Multi-Criteria Decision-Making (MCDM) into the global coordination process. First, an enhanced Chaotic Genetic Algorithm (CGA) with elitism generates local schedules. Second, global resource conflicts are resolved using MCDM methods. Inter-project fairness is implemented by limiting each project’s relative objective deterioration and by evaluating the dispersion of the resulting project-level burdens. Validation through an Unmanned Aerial Vehicle R&D case study and extensive experiments shows that the TOPSIS-based mechanism achieves a significantly lower standard deviation than the auction-based mechanism under high resource contention. The approach supports transparent and fairness-aware conflict resolution by making trade-offs among project-level outcomes explicit to managers. Full article

To address the problems in hull form optimization where an increase in design variables requires approximation models to be rebuilt from scratch and historical CFD samples are insufficiently utilized, this paper proposes an incremental approximation model construction method. Based on the additive decomposition property of high-dimensional model representation, this method breaks through the rigid structure limitations of traditional approximation models. When dimension expansion occurs in the design space, it fully inherits existing low-order component models and historical sample point databases, requiring only local supplementary sampling and incremental construction for new variables and their strong coupling terms, thereby achieving adaptive cross-dimensional updates of the approximation model. The method is validated through numerical test functions and a container ship hull line resistance optimization case study. Results show that, compared with traditional full-dimensional approximation models, this method reduces full-space resampling overhead and maintains high prediction accuracy while reducing CFD sample requirements, providing an efficient modeling approach for ship hydrodynamic optimization in high-dimensional dynamic spaces. Full article

Asthma is one of the most prevalent chronic respiratory diseases worldwide, and environmental pollutants such as desert dust play a significant role in triggering respiratory sensitization and aggravating asthma symptoms. In this study, a mathematical model is proposed to investigate the dynamics of dust-induced asthma progression. The population is divided into unaware susceptible individuals, aware susceptible individuals, dust-sensitized individuals, and asthmatic individuals, while two additional variables describe dust concentration in the human population and the environment. Fundamental qualitative properties of the model, including positivity, boundedness, existence of equilibria, and stability conditions, are established. It is shown that the asthma-free equilibrium is locally and globally asymptotically stable whenever the dust-induced asthma threshold is below unity, whereas a unique asthma-persistent equilibrium exists when the dust-induced asthma threshold exceeds unity. To reduce the burden of asthma, an optimal control problem is formulated by incorporating three time-dependent interventions: awareness campaigns, medical intervention for dust-sensitized individuals, and environmental dust reduction. Pontryagin’s Maximum Principle is employed to characterize the optimal controls and derive the corresponding adjoint system. Numerical simulations, performed using the forward–backward sweep method, demonstrate that the proposed interventions significantly reduce the number of asthmatic individuals compared with the uncontrolled case. Furthermore, a cost-effectiveness analysis based on the Average Cost-Effectiveness Ratio (ACER) and Incremental Cost-Effectiveness Ratio (ICER) is conducted to compare seven intervention strategies. The results indicate that the medical intervention strategy is the most economically attractive option, while the combined strategy involving all controls achieves the largest reduction in asthma burden. Full article

Automatic modulation recognition (AMR) is essential for cognitive radio and intelligent wireless communication, yet its performance degrades markedly under low signal-to-noise ratio (SNR) conditions because weak local structures are corrupted and class boundaries become less separable. To address this problem, we propose ADGNet, an automatic modulation recognition network based on adaptive wavelet enhancement and dynamic graph construction. ADGNet adopts a dual-branch architecture in which the main branch extracts time-domain amplitude–phase features from raw in-phase/quadrature sequences, while the auxiliary branch combines short-time Fourier transform features with adaptive wavelet features to capture complementary frequency–energy distributions and multi-scale transient details. A dual-domain adaptive encoder then recalibrates and fuses the two branches, suppressing redundant and noise-contaminated responses. In addition, a G2 dynamic topology module constructs sample-adaptive top-k adjacency matrices from node features and fuses them with the original graph structure to improve temporal relation modeling. Experiments on RadioML2016.10a and RadioML2016.10b show average accuracies of 64.23% and 69.69%, respectively. On RadioML2016.10b, ADGNet achieves 63.48% average accuracy from −12 dB to 0 dB, compared with 59.51% for the baseline. With approximately 0.13 million parameters, ADGNet provides a favorable balance between low-SNR robustness and model complexity. Full article

The complex dispersion and modal sensitivity of an axisymmetric transverse magnetic surface wave supported by a dielectric-coated perfectly conducting cylinder are investigated. Starting from Maxwell’s equations, the boundary-value problem is reduced to a nonlinear complex dispersion equation for the longitudinal propagation constant $\beta$. A numerical framework combining zero-level localization of the real and imaginary parts of the dispersion function, nonlinear root refinement, numerical clustering, and adaptive continuation in the complex coating permittivity is used to identify and track a selected spectral branch. One- and two-parameter computations characterize the mapping $\epsilon \mapsto \beta \left(\epsilon \right)$ over prescribed subsets of the complex-permittivity plane. At fixed $\Im \epsilon$, increasing $\Re \epsilon$ increases $\Re \beta$ and decreases $\Im \beta$, whereas at fixed $\Re \epsilon$, increasing $\Im \epsilon$ increases both components of $\beta$ in the investigated parameter range. The rectangular-grid, concentric-circle, and radial-beam experiments show that the spectral response is smooth on the considered parameter sets but non-affine, coupled, and direction-dependent. The corresponding longitudinal electric field is reconstructed, normalized, and phase-aligned along the tracked branch. Difference fields, radial localization measures, a global modal distance, and a normalized correlation coefficient show that the same qualitative radial TM mode is retained throughout the sampled parameter domain, while its propagation constant and spatial localization vary continuously with the complex coating permittivity. Full article

In confined waters such as cylindrical aquaculture cages and ponds, underwater robots for cleaning, harvesting and other tasks that use single-beam mechanical scanning sonar for positioning and navigation are susceptible to multipath interference in complex water environments. Meanwhile, under extreme sea conditions, the severe attitude swaying of the robot and the slow-scanning characteristic of the sonar superimpose on each other, causing range stretching and helical deformation of the acoustic point cloud. To address these problems, this paper analyzes the deformation mechanism of single-beam sonar and proposes a spatiotemporal joint deformation compensation and localization-mapping method. First, an attitude-derived probabilistic confidence model is introduced as a lightweight robustness safeguard to characterize the geometric reliability of sonar echoes and reduce the contribution of low-confidence measurements during subsequent registration. Second, a beam-level spatiotemporal joint de-deformation algorithm is designed: the slant range in polar coordinates is flattened to eliminate nonlinear swaying deformation, and a beam-level displacement back-estimation based on the beam time offset and feedback velocity is employed to remove helical misalignment, thereby enhancing the underlying correction capability for dynamic deformation processes. Finally, a lightweight SLAM architecture that integrates keyframe-based dynamic sub-maps is constructed, where a confidence-weighted ICP is used to estimate the planar position with the heading provided by the compass and provide velocity-based closed-loop feedback, effectively mitigating the problem of global matching divergence caused by underlying dynamic deformations. Real-data-driven semi-physical disturbance tests based on measured pool data show that, under the injected ±45° roll disturbance and translational drift, the proposed method reduces the maximum point-to-reference error MaxAE from 1.059 m to 0.098 m. The reported mean internal registration residual decreases from 0.275 m for the traditional navigation odometry SLAM to 0.158 m for the proposed method, corresponding to a numerical reduction of approximately 42.5%. Under the evaluated conditions, the proposed method effectively mitigates point-cloud deformation and registration instability caused by robot swaying and slow-scanning sonar, while confidence weighting is retained as an auxiliary robustness mechanism for handling low-confidence correspondences. Full article

Text-based person search (TPS) suffers from cross-modal informational skewness: pedestrian images are high-dimensional and redundancy-prone, while textual descriptions are sparse, incomplete, and sometimes inaccurate. To address the low alignment accuracy and poor robustness caused by the inherent uneven information distribution of visual and textual modalities in TPS, this paper proposes a unified Partition-based Information Rebalancing (PaIR) framework to realize balanced optimization and precise alignment of cross-modal information from both global content and local part dimensions. The framework adopts the CLIP dual-modal encoder for basic feature extraction and constructs a parallel global–local dual representation system to compensate for the lack of fine-grained spatial information in single global features. To eliminate modal redundancy and noise interference, a dual-modal noise suppression module is designed to filter invalid redundant information through visual foreground–background separation and textual token weight screening, while introducing adversarial constraints and orthogonal constraints to purify effective features. On this basis, a part balance alignment module is built to complete human semantic part decomposition and soft matching alignment for dual-modal features. Aiming at the common part semantic missing problem in textual descriptions, a visual part correlation affinity matrix is utilized for semantic associative completion to balance the information density of dual modalities. Finally, a global–local joint alignment strategy integrates hierarchical features and bidirectional cross-modal attention interaction to eliminate global–local semantic discontinuity and enhance fine-grained cross-modal matching capability. Extensive experiments on three public benchmarks demonstrate that PaIR consistently improves multiple baselines. Full article

The construction of large-scale buildings often faces extended production cycles due to inefficiencies in scheduling processes. To address this challenge, a timed Petri net model was developed to analyze and optimize construction scheduling. Based on the Petri net transition sequence, a scheduling optimization model was proposed. To solve the model efficiently, an improved brainstorming optimization (BSO) algorithm was introduced. Compared with the classical BSO, two targeted enhancements were introduced: a problem-specific encoding and decoding method for Petri net transition sequences to ensure solution feasibility and an embedded simulated annealing local search mechanism to prevent premature convergence in later iterations. The proposed methodology was validated using real-world data from a large high-speed railway hub foundation pit construction project. Results demonstrated a significant reduction of 531 working hours in the total scheduling time, representing a 15.47% improvement in scheduling efficiency compared to traditional sequential scheduling methods. This approach not only shortened the scheduling cycle and enhanced production efficiency but also offered an innovative solution to address scheduling issues in complex construction processes. Full article

Breast cancer relapse in bone is a significant clinical problem that is experienced in ~70–80% of patients with late-stage breast cancer. This condition commonly occurs 5–10+ years following surgical removal of the primary tumour. The long latency seen prior to relapse in bone is a result of tumour cell dormancy. Once disseminated to the bone, tumour cell interaction with the bone metastatic niche (endosteal niche and endovascular cells) maintains cells in a dormant state until changes to the local environment activate the niche to support outgrowth. Amassing evidence suggests that cytokines are key regulators of the bone metastatic niche, controlling bone homing and metastatic outgrowth. Pro-inflammatory cytokines, including IL-1β, IL-6, IL-8, TGFβ and RANKL, play crucial roles in attracting tumour cells to bone. Furthermore, these cytokines act in conjunction with IFN, VEGF, TGF, PTHrP, FGF, OPG and various chemokines to regulate expansion of the niche, facilitating tumour cell escape from dormancy and promoting the “vicious cycle of bone metastasis”. Here, we review the current literature to provide an up-to-date understanding of how interactions between cytokine signalling cascades regulate the bone metastatic niches to promote homing, dormancy or metastatic outgrowth of breast cancers. Because breast cancers are predominantly osteolytic, this review focuses on dormancy and metastatic outgrowth associated with lytic disease in addition to current advances in novel therapeutics aimed at preventing this condition through targeting dormant cells. Full article

Cloud task scheduling is a critical component of cloud computing systems because it directly affects resource allocation, workload distribution, execution efficiency, and service cost. However, many metaheuristic algorithms suffer from population diversity loss, premature convergence, and an inadequate balance between global exploration and local exploitation when solving complex and large-scale optimization problems. To address these limitations, this study develops an Enhanced Human Urbanization Algorithm (EHUA) for numerical optimization and cloud task scheduling. Inspired by the collaborative resource-allocation behavior of modern logistics networks, three coordinated mechanisms are reformulated within the adventurer–city–citizen structure of the original Human Urbanization Algorithm: a logistics-hub-guided adaptive exploration mechanism, a supply–demand-based dynamic redistribution mechanism, and a cooperative logistics delivery exploitation mechanism. These mechanisms reduce excessive dependence on a single capital, adaptively regulate city search ranges, and strengthen citizen-level solution refinement. The performance of EHUA is evaluated on the CEC2014 and CEC2020 benchmark suites using convergence analysis, box plots, numerical statistics, Wilcoxon signed-rank tests, Friedman rankings, and ablation experiments. EHUA obtains the best mean fitness values on 20 of the 30 CEC2014 functions under both 30- and 50-dimensional settings, on 8 of the 10 CEC2020 functions at 10 dimensions, and on all 10 functions at 20 dimensions, demonstrating strong overall competitiveness and repeatability without implying universal superiority on every problem. EHUA is further applied to cloud task scheduling under workload scales ranging from 100 to 10,000 tasks. Considering comprehensive cost, monetary cost, execution time, and load cost, the proposed method consistently achieves low comprehensive scheduling costs and maintains favorable trade-offs among individual objectives as the workload increases. These results indicate that EHUA provides an effective and scalable optimization framework for complex benchmark problems and cloud task scheduling applications. Full article