Search Results (73)

The Online Droplet Inspection system plays a vital role in closed-loop control for OLED inkjet printing. However, generating realistic synthetic droplet images for reliable restoration and precise measurement of droplet parameters remains challenging due to the complex, multi-factor degradation inherent to microscale droplet imaging. To address this, we propose a physics-informed degradation model, Diffraction–Gaussian–Motion–Noise (DGMN), that integrates Fraunhofer diffraction, defocus blur, motion blur, and adaptive noise to replicate real-world degradation in droplet images. To optimize the multi-parameter configuration of DGMN, we introduce the MISABO (Multi-strategy Improved Subtraction-Average-Based Optimizer), which incorporates Sobol sequence initialization for search diversity, lens opposition-based learning (LensOBL) for enhanced accuracy, and dimension learning-based hunting (DLH) for balanced global–local optimization. Benchmark function evaluations demonstrate that MISABO achieves superior convergence speed and accuracy. When applied to generate synthetic droplet images based on real droplet images captured from a self-developed OLED inkjet printer, the proposed MISABO-optimized DGMN framework significantly improves realism, enhancing synthesis quality by 37.7% over traditional manually configured models. This work lays a solid foundation for generating high-quality synthetic data to support droplet image restoration and downstream inkjet printing processes. Full article

The spread of infectious diseases is inherently linked to human social behavior, characterized by complexity, diversity, and openness. Intelligent agents in computer science provide a powerful framework for capturing such dynamics, enabling complex epidemic patterns to emerge from simple local rules. These agents exhibit self-organization, adaptability, and self-optimization, making them well suited for individual-level modeling. Agent-based models (ABMs) have shown promising results in epidemic simulation and policy evaluation. However, current implementations often suffer from simplistic behavioral assumptions and rigid interaction mechanisms, limiting their realism and flexibility. This paper first reviews the current landscape of epidemic modeling approaches. It then analyzes the underlying mechanisms of advanced intelligent agents, highlighting their modeling capabilities. The study focuses on four key advantages of intelligent agent-based modeling and elaborates on three critical roles these agents play in evaluating and optimizing intervention strategies. Full article

The ecological assessment of rivers under the Water Framework Directive (WFD) requires the use of biological quality elements (BQEs) across defined abiotic types of rivers. However, limited evidence exists on how well biological indices perform across multiple typological classes, particularly under the influence of complex, overlapping stressors. This study evaluated the diagnostic performance of four biological indices (IO—diatoms, MIR—macrophytes, MMI_PL—benthic macroinvertebrates, and EFI + PL—fish) in 16 river sites in southern Poland. These were classified into four abiotic types (5, 6, 12, and 17) and subjected to varying levels of human pressure. Biological, physical and chemical, and hydromorphological data were collected along environmental gradients including conductivity, nutrient enrichment, and habitat modification. Statistical analyses were used to evaluate patterns in community composition and index responsiveness. The IO and MMI_PL indices were the most consistent and sensitive in distinguishing between reference and degraded river conditions. MIR and EFI + PL were more variable, especially in lowland rivers, and showed stronger associations with habitat structure and oxygen levels. Conductivity emerged as a key driver of biological responses across all BQEs, with clear taxonomical shifts observed. The results support the need to consider both typological context and local environmental variation in ecological classification. The findings underscore the need for typology-aware, pressure-specific biomonitoring strategies that combine multiple organism groups and integrate continuous environmental variables. Such approaches can enhance the ecological realism and diagnostic accuracy of river assessment systems, supporting more effective water resource management across diverse hydroecological contexts. Full article

Unbounded dynamic scene reconstruction is crucial for applications such as autonomous driving, robotics, and virtual reality. However, existing methods struggle to reconstruct dynamic scenes in unbounded outdoor environments due to challenges such as lighting variation, object motion, and sensor limitations, leading to inaccurate geometry and low rendering fidelity. In this paper, we proposed Gaussian-UDSR, a novel 3D Gaussian-based representation that efficiently reconstructs and renders high-quality, unbounded dynamic scenes in real time. Our approach fused LiDAR point clouds and Structure-from-Motion (SfM) point clouds obtained from an RGB camera, significantly improving depth estimation and geometric accuracy. To address dynamic appearance variations, we introduced a Gaussian color feature prediction network, which adaptively captures global and local feature information, enabling robust rendering under changing lighting conditions. Additionally, a pose-tracking mechanism ensured precise motion estimation for dynamic objects, enhancing realism and consistency. We evaluated Gaussian-UDSR on the Waymo and KITTI datasets, demonstrating state-of-the-art rendering quality with an 8.8% improvement in PSNR, a 75% reduction in LPIPS, and a fourfold speed improvement over existing methods. Our approach enables efficient, high-fidelity 3D reconstruction and fast real-time rendering of large-scale dynamic environments, while significantly reducing model storage overhead. Full article

In late imperial China, a type of painting known as “panoramic maps” (shengjing tu 聖境圖, literally “sacred realm maps”) depicted Buddhist sacred sites. Often surviving as woodblock prints, examples from Mount Wutai and Mount Putuo are particularly representative. Previous research has often viewed these images as pilgrimage guides or focused on the relationship between pictorial perspectives and actual geography. This study centers on panoramic maps of Mount Wutai and Mount Putuo, examining both vertical and horizontal layouts, to offer a preliminary understanding of this genre. This study argues that: (1) Unlike urban maps, panoramic maps emphasize significant monasteries and landscape features, incorporating local legends and historical narratives, thus possessing strong narrative qualities. (2) These images likely functioned as pilgrimage souvenirs. Diverging from practical roadmaps, their primary goal was not strict realism but rather to convey the site’s sacredness and associated information through landscape painting conventions, allowing viewers to perceive its sacredness. (3) The woodblock print medium facilitated affordable reproduction, accelerating the circulation of the sacred site’s significance among the populace and aiding in its promotion. This research contends that the panoramic maps primarily function as folk landscape paintings reflecting the sacred site, capable only of approximating the relative positions of features. The widespread adoption of late-period woodblock printing enabled the low-cost reproduction and dissemination of the sacredness inherent in these Buddhist landscapes, constructing idealized spatial representations shaped by religious belief and geomantic principles. Full article

The Ca Mau Peninsula (CMP), the southernmost region of the Mekong Delta, is increasingly threatened by groundwater salinization, posing severe risks to both the freshwater supply and land sustainability. This study develops a three-dimensional, density-dependent groundwater flow and salinity transport model to investigate salinization dynamics across the CMP’s complex multi-aquifer system. Unlike previous studies that largely rely on model calibration, this research introduces a novel approach by systematically deriving the spatial distribution of longitudinal dispersivity based on sediment characteristics. Moreover, detailed land use mapping is integrated to assign spatially and temporally variable Total Dissolved Solids (TDS) values to the uppermost layers, thereby enhancing the model realism in areas where monitoring data are limited. The model was utilized not only to simulate the regional salinity evolution, but also to critically evaluate conceptual hypotheses related to the mechanisms driving groundwater salinization. Results reveal a strong influence of seasonal and land use factors on salinity variability in the upper aquifers, while deeper aquifers remain largely stable, affected primarily by paleosalinity and localized pumping. This integrated modeling approach contributes to a better understanding of regional-scale groundwater salinization and highlights both the potential and the limitations of numerical modeling under data-scarce conditions. The findings provide a valuable scientific basis for adaptive water resource management in vulnerable coastal zones. Full article

Image super-resolution (SR) models based on the generative adversarial network (GAN) face challenges such as unnatural facial detail restoration and local blurring. This paper proposes an improved GAN-based model to address these issues. First, a Multi-scale Hybrid Attention Residual Block (MHARB) is designed, which dynamically enhances feature representation in critical face regions through dual-branch convolution and channel-spatial attention. Second, an Edge-guided Enhancement Block (EEB) is introduced, generating adaptive detail residuals by combining edge masks and channel attention to accurately recover high-frequency textures. Furthermore, a multi-scale discriminator with a weighted sub-discriminator loss is developed to balance global structural and local detail generation quality. Additionally, a phase-wise training strategy with dynamic adjustment of learning rate (Lr) and loss function weights is implemented to improve the realism of super-resolved face images. Experiments on the CelebA-HQ dataset demonstrate that the proposed model achieves a PSNR of 23.35 dB, a SSIM of 0.7424, and a LPIPS of 24.86, outperforming classical models and delivering superior visual quality in high-frequency regions. Notably, this model also surpasses the SwinIR model (PSNR: 23.28 dB → 23.35 dB, SSIM: 0.7340 → 0.7424, and LPIPS: 30.48 → 24.86), validating the effectiveness of the improved model and the training strategy in preserving facial details. Full article

To address the temporal jitter with low segmentation accuracy and the lack of high-precision transformations for specific object classes in video generation, we propose the fish body pattern sync-style network for ornamental fish videos. This network innovatively integrates dynamic texture transfer with instance segmentation, adopting a two-stage processing architecture. First, high-precision video frame segmentation is performed using Mask2Former to eliminate background elements that do not participate in the style transfer process. Then, we introduce the wavelet-gated styling network, which reconstructs a multi-scale feature space via discrete wavelet transform, enhancing the granularity of multi-scale style features during the image generation phase. Additionally, we embed a convolutional block attention module within the residual modules, not only improving the realism of the generated images but also effectively reducing boundary artifacts in foreground objects. Furthermore, to mitigate the frame-to-frame jitter commonly observed in generated videos, we incorporate a contrastive coherence preserving loss into the training process of the style transfer network. This enhances the perceptual loss function, thereby preventing video flickering and ensuring improved temporal consistency. In real-world aquarium scenes, compared to state-of-the-art methods, FSSNet effectively preserves localized texture details in generated videos and achieves competitive SSIM and PSNR scores. Moreover, temporal consistency is significantly improved. The flow warping error index decreases to 1.412. We chose FNST (fast neural style transfer) as our baseline model and demonstrate improvements in both model parameter count and runtime efficiency. According to user preferences, 43.75% of participants preferred the dynamic effects generated by this method. Full article

I conducted an integrative literature review by utilizing theoretical and methodological elements of critical realism (i.e., the distinction between ontology and epistemology) to evaluate the significance of triple-helix intermediaries. This review involved examining all published research on smart cities in “elite” ABS (Chartered Association of Business Schools) journals (4, 4*). My findings indicate that the philosophical foundations of the examined literature are predominantly grounded on “positivism”, “postmodernism”, “interpretivism”, and “pragmatism”, without delving into the ontological reinforcement of capitalist institutions through innovation creation and diffusion—a central concern of critical realism. I argue that this oversight stems from the prevailing “paradigm” within these “elite” journals, which often excludes historical and critical perspectives. In response, I propose a reoriented intermediary, the Triple-Helix Business Clinic, grounded in critical-realist assumptions. This new theoretical framework can guide practical policy development aimed at reinforcing business innovation and driving broader socioeconomic progress. Full article

Urban heat island (UHI) effects pose significant challenges to sustainable urban development, necessitating innovative modeling techniques to optimize urban morphology for thermal resilience. This study integrates the Pix2Pix and CycleGAN architectures to generate high-fidelity urban morphology models aligned with local climate zones (LCZs), enhancing their applicability to urban climate studies. This research focuses on eight major Chinese coastal cities, leveraging a robust dataset of 4712 samples to train the generative models. Quantitative evaluations demonstrated that the integration of CycleGAN with Pix2Pix substantially improved structural fidelity and realism in urban morphology synthesis, achieving a peak Structural Similarity Index Measure (SSIM) of 0.918 and a coefficient of determination (R²) of 0.987. The total adversarial loss in Pix2Pix training stabilized at 0.19 after 811 iterations, ensuring high convergence in urban structure generation. Additionally, CycleGAN-enhanced outputs exhibited a 35% reduction in relative error compared to Pix2Pix-generated images, significantly improving edge preservation and urban feature accuracy. By incorporating LCZ data, the proposed framework successfully bridges urban morphology modeling with climate-responsive urban planning, enabling adaptive design strategies for mitigating UHI effects. This study integrates Pix2Pix and CycleGAN architectures to enhance the realism and structural fidelity of urban morphology generation, while incorporating the LCZ classification framework to produce urban forms that align with specific climatological conditions. Compared to the model trained by Pix2Pix coupled with LCZ alone, the approach offers urban planners a more precise tool for designing climate-responsive cities, optimizing urban layouts to mitigate heat island effects, improve energy efficiency, and enhance resilience. Full article

Despite significant challenges, the text-guided remote sensing image generation method shows great potential in many practical applications such as generative adversarial networks in remote sensing tasks; generated images still face challenges such as low realism, face challenges, and unclear details. Moreover, the inherent spatial complexity of remote sensing images and the limited scale of publicly available datasets make it particularly challenging to generate high-quality remote sensing images from text descriptions. To address these challenges, this paper proposes the RSVQ-Diffusion model for remote sensing image generation, achieving high-quality text-to-remote-sensing image generation applicable for target detection, simulation, and other fields. Specifically, this paper designs a spatial position encoding mechanism to integrate the spatial information of remote sensing images during model training. Additionally, the Transformer module is improved by incorporating a short-sequence local perception mechanism into the diffusion image decoder, addressing issues of unclear details and regional distortions in generated remote sensing images. Compared with the VQ-Diffusion model, our proposed model achieves significant improvements in the Fréchet Inception Distance (FID), the Inception Score (IS), and the text–image alignment (Contrastive Language-Image Pre-training, CLIP) scores. The FID score successfully decreased from 96.68 to 90.36; the CLIP score increased from 26.92 to 27.22, and the IS increased from 7.11 to 7.24. Full article

The wind farm layout optimization problem (WFLOP) aims to maximize wind energy utilization efficiency and mitigate energy losses caused by wake effects by optimizing the spatial layout of wind turbines. Although Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) have been widely used in WFLOP due to their discrete optimization characteristics, they still have limitations in global exploration capability and optimization depth. Meanwhile, the Differential Evolution algorithm (DE), known for its strong global optimization ability and excellent performance in handling complex nonlinear problems, is well recognized in continuous optimization issues. However, since DE was originally designed for continuous optimization scenarios, it shows insufficient adaptability under the discrete nature of WFLOP, limiting its potential advantages. In this paper, we propose a Fractional-Order Difference-driven DE Optimization Algorithm called FODE. By introducing the memory and non-local properties of fractional-order differences, FODE effectively overcomes the adaptability issues of advanced DE variants in WFLOP’s discreteness while organically applying their global optimization capabilities for complex nonlinear problems to WFLOP to achieve more efficient overall optimization performance. Experimental results show that under 10 complex wind farm conditions, FODE significantly outperforms various current state-of-the-art WFLOP algorithms including GA, PSO, and DE variants in terms of optimization performance, robustness, and applicability. Incorporating more realistic wind speed distribution and wind condition data into modeling and experiments, further enhancing the realism of WFLOP studies presented here, provides a new technical pathway for optimizing wind farm layouts. Full article

The quantum electromagnetic (EM) field is formulated in the Weyl–Wigner representation (WW), which is equivalent to the standard Hilbert space one (HS). In principle, it is possible to interpret within WW all experiments involving the EM field interacting with macroscopic bodies, the latter treated classically. In the WW formalism, the essential difference between classical electrodynamics and the quantum theory of the EM field is just the assumption that there is a random EM field-filling space, i.e., the existence of a zero-point field with a Gaussian distribution for the field amplitudes. I analyze a typical optical test of a Bell inequality. The model admits an interpretation compatible with local realism, modulo a number of assumptions assumed plausible. Full article

The increasing adoption of 2D/3D laser line sensors in industrial and research applications necessitates accurate and efficient simulation tools for tasks such as surface inspection, dimensional verification, and quality control. This paper presents a novel algorithm developed in MATLAB for simulating the measurements of any 2D/3D laser line sensor on STL CAD models. The algorithm uses a modified fast-ray triangular intersection method, addressing challenges such as overlapping triangles in assembly models and incorporating sensor resolution to ensure realistic simulations. Quantitative analysis shows a significant reduction in computation time, enhancing the practical utility of the algorithm. The simulation results exhibit a mean deviation of 0.42 mm when compared to real-world measurements. Notably, the algorithm effectively handles complex geometric features, such as holes and grooves, and offers flexibility in generating point cloud data in both local and global coordinate systems. This work not only reduces the need for physical prototyping, thereby contributing to sustainability, but also supports AI training by generating accurate synthetic data. Future work should aim to further optimize the simulation speed and explore noise modeling to enhance the realism of simulated measurements. Full article

In response to the shortcomings of current spatiotemporal prediction models, which frequently encounter difficulties in temporal feature extraction and the forecasting of medium to high echo intensity regions over extended sequences, this study presents a novel model for radar echo extrapolation that combines a translator encoder-decoder architecture with a spatiotemporal dual-discriminator conditional generative adversarial network (STD-TranslatorNet). Initially, an image reconstruction network is established as the generator, employing a combination of a temporal attention unit (TAU) and an encoder–decoder framework. Within this architecture, both intra-frame static attention and inter-frame dynamic attention mechanisms are utilized to derive attention weights across image channels, thereby effectively capturing the temporal evolution of time series images. This approach enhances the network’s capacity to comprehend local spatial features alongside global temporal dynamics. The encoder–decoder configuration further bolsters the network’s proficiency in feature extraction through image reconstruction. Subsequently, the spatiotemporal dual discriminator is crafted to encapsulate both temporal correlations and spatial attributes within the generated image sequences. This design serves to effectively steer the generator’s output, thereby augmenting the realism of the produced images. Lastly, a composite multi-loss function is proposed to enhance the network’s capability to model intricate spatiotemporal evolving radar echo data, facilitating a more comprehensive assessment of the quality of the generated images, which in turn fortifies the network’s robustness. Experimental findings derived from the standard radar echo dataset (SRAD) reveal that the proposed radar echo extrapolation technique exhibits superior performance, with average critical success index (CSI) and probability of detection (POD) metrics per frame increasing by 6.9% and 7.6%, respectively, in comparison to prior methodologies. Full article