Search Results (6,073)

The integration of artificial intelligence (AI) into industrial processes is a promising method for enhancing operational efficiency and quality control. In particular, contact lens manufacturing requires specialized artificial intelligence technologies owing to stringent safety requirements. This study introduces a novel approach that employs polar coordinate transformation and a customized deep learning model, the Polar Sequence-guided Attention Network (PSgANet), to improve the accuracy of defect detection in the rim-connected zone (RCZ) of contact lenses. PSgANet is specifically designed to process polar coordinate-transformed image data by integrating sequence learning and attention mechanisms to maximise the capability for detecting and classifying defective patterns. This model converts irregularities along the edges of contact lenses into linear arrays via polar coordinate transformation, enabling a clearer and more consistent identification of defective regions. To achieve this, we applied sequence learning architectures such as GRU, LSTM, and Transformer within PSgANet and compared their performances with those of conventional models, including GoogleNetv4, EfficientNet, and Vision Transformer. The experimental results demonstrated that the PSgANet models outperformed the existing CNN-based models. In particular, the LSTM-based PSgANet achieved the highest accuracy and balanced precision and recall metrics, showing up to a 7.75% improvement in accuracy compared with the traditional GoogleNetv4 model. These results suggest that the proposed method is an effective tool for detecting and classifying defects within the RCZ during contact lens manufacturing processes. Full article

High-precision 3D object detection is pivotal for autonomous driving. However, voxel-based two-stage detectors still struggle with small and non-rigid objects due to the misalignment between classification confidence and localization accuracy, and the loss of fine-grained spatial context during feature flattening. To address these issues, we propose the Varifocal Fine-grained Refinement Network (VFR-Net). We introduce Varifocal Loss (VFL) to learn IoU-aware scores for prioritizing high-quality proposals, and a Fine-Grained Refinement Attention (FGRA) Module to capture local geometric details via self-attention before flattening. Extensive experiments on the KITTI and ONCE datasets demonstrate that VFR-Net consistently outperforms the Voxel R-CNN baseline, improving the overall mAP by +1.12% on KITTI and +2.63% on ONCE. Specifically, it achieves AP gains of +1.81% and +1.28% for pedestrians and cyclists on KITTI (averaged over Easy/Moderate/Hard), and +6.53% and +1.50% on ONCE (Overall). Full article

Recent advances in satellite observations have expanded the use of Sea Surface Temperature (SST) and Sea Surface Height (SSH) data in climate and oceanography, yet their low spatial resolution limits fine-scale analyses. We propose HiT_DS, a modular hierarchical Transformer framework for high-resolution downscaling of SST and SSH fields. To address challenges in multiscale feature representation and physical consistency, HiT_DS integrates three key modules: (1) Enhanced Dual Feature Extraction (E-DFE), which employs depth-wise separable convolutions to improve local feature modeling efficiently; (2) Gradient-Aware Attention (GA), which emphasizes dynamically important high-gradient structures such as oceanic fronts; and (3) Physics-Informed Loss Functions, which promote physical realism and dynamical consistency in the reconstructed fields. Experiments across two dynamically distinct oceanic regions demonstrate that HiT_DS achieves improved reconstruction accuracy and enhanced physical fidelity, with selective module combinations tailored to regional dynamical conditions. This framework provides an effective and extensible approach for oceanographic data downscaling. Full article

The accurate simulation of sub-GeV particle detectors is essential for interpreting experimental data and optimizing detector design. This work identifies and addresses several critical aspects in modeling such detectors, taking as a case study the High-Energy Particle Detector (HEPD-02), a space-borne instrument developed within the CSES-02 mission to measure electrons in the ∼3–100 MeV range, protons and light nuclei in the ∼30–200 MeV/n. The HEPD-02 instrument consists of a silicon tracker, plastic and LYSO scintillator calorimeters, and anticoincidence systems, making it a representative example of a complex low-energy particle detector operating in Low Earth Orbit. Key challenges arise from replicating intricate detector geometries derived from CAD models, selecting appropriate hadronic physics lists for low-energy interactions, and accurately describing the detector response—particularly quenching effects in scintillators and digitization in solid-state tracking planes. Particular attention is given to three critical aspects: the precise CAD-level geometry implementation, the impact of hadronic physics models on the detector response, and the parameterization of scintillation quenching. In this study, we present original solutions to these challenges and provide data–MC comparisons using data from HEPD-02 beam tests. Full article

Accurate day-ahead photovoltaic (PV) power forecasting is essential for secure operation and scheduling in power systems with high PV penetration, yet its performance is often constrained by the coarse spatial resolution of operational numerical weather prediction (NWP) products at the plant scale. To address this issue, this paper proposes an attention-enhanced CNN–LSTM forecasting framework integrated with a spatial downscaling strategy. First, seasonal and diurnal characteristics of PV generation are analyzed based on theoretical irradiance and historical power measurements. A CNN–LSTM network with a channel-wise attention mechanism is then employed to capture temporal dependencies, while a composite loss function is adopted to improve robustness. We fuse multi-source meteorological variables from NWP outputs with an attention-based module. We also introduce a multi-site XGBoost downscaling model. This model refines plant-level meteorological inputs. We evaluate the framework on multi-site PV data from representative seasons. The results show lower RMSE and higher correlation than the benchmark models. The gains are larger in medium power ranges. These findings suggest that spatially refined NWP inputs improve day-ahead PV forecasting. They also show that attention-enhanced deep learning makes the forecasts more reliable. Quantitatively, the downscaled meteorological variables consistently achieve lower normalized MAE and normalized RMSE than the raw NWP fields, with irradiance-related errors reduced by about 40% to 55%. For day-ahead PV forecasting, using downscaled NWP inputs reduces RMSE from 0.0328 to 0.0184 and MAE from 0.0194 to 0.0112, while increasing the Pearson correlation to 0.995 and the CR to 98.1%. Full article

As an emerging immersive media format, point clouds (PC) inevitably suffer from distortions such as compression and noise, where even local degradations may severely impair perceived visual quality and user experience. It is therefore essential to accurately evaluate the perceived quality of PC. In this paper, a no-reference point cloud quality assessment (PCQA) method that uses complexity-driven patch sampling and an attention-enhanced Swin-Transformer is proposed to accurately assess the perceived quality of PC. Given that projected PC maps effectively capture distortions and that the quality-related information density varies significantly across local patches, a complexity-driven patch sampling strategy is proposed. By quantifying patch complexity, regions with higher information density are preferentially sampled to enhance subsequent quality-sensitive feature representation. Given that the indistinguishable response strengths between key and redundant channels during feature extraction may dilute effective features, an Attention-Enhanced Swin-Transformer is proposed to adaptively reweight critical channels, thereby improving feature extraction performance. Given that traditional regression heads typically use a single-layer linear mapping, which overlooks the heterogeneous importance of information across channels, a gated regression head is designed to enable adaptive fusion of global and statistical features via a statistics-guided gating mechanism. Experiments on the SJTU-PCQA dataset demonstrate that the proposed method consistently outperforms representative PCQA methods. Full article

The sliding window method is a commonly used data processing in time series forecasting tasks, and determining the appropriate window size is a crucial step in constructing predictive models. However, the current setting of window size parameters is often based on empirical knowledge, making the scientific determination of the optimal sliding window size highly significant. This paper proposes an algorithm for optimizing window size based on sample entropy, which is applicable not only to the original undecomposed sequences but also effectively to the decomposed sequences. The proposed algorithm has been validated using the open-source Elia grid data across multiple model architectures, including recurrent (GRU/LSTM) and attention-based (Transformer) networks. Experimental results demonstrate that the algorithm effectively determines an optimal window size of 106. The optimized window consistently leads to superior prediction performance, with the CEEMD-GRU model achieving a MAPE of 0.256, RMSE of 22.529, and MAE of 18.186—representing reductions of over 5% compared to the undecomposed benchmark. Furthermore, the enhancement is more significant for decomposed sequences, and the algorithm’s efficacy is validated across different neural network architectures (e.g., LSTM, GRU, Transformer), confirming its practical utility and generalizability. Full article

Organic optoelectronic devices receive appreciable attention due to their low cost, ecology, mechanical flexibility, band-gap engineering, brightness, and solution process ability over a broad area. In this study, we designed and studied organic light-emitting diodes (OLEDs) consisting of an assembly of natural dyes, extracted from noble fir leaves (evergreen) and blue hydrangea flowers mixed with poly-methyl methacrylate (PMMA) as light emitters. We experimentally demonstrate the effective conversion of blue light emitted by an inorganic laser/photodiode into longer-wavelength red and green tunable photoluminescence due to the excitation of natural dye–PMMA nanostructures. UV-visible absorption and photoluminescence spectroscopy, ellipsometry, and Fourier transform infrared methods, together with optical microscopy, were performed for confirming and characterizing the properties of light-emitting diodes based on natural dyes. We highlighted the optical and physical properties of two different natural dyes and demonstrated how such characteristics can be exploited to make efficient LED devices. A strong pure red emission with a narrow full-width at half maximum (FWHM) of 23 nm in the noble fir dye–PMMA layer and a green emission with a FWHM of 45 nm in blue hydrangea dye–PMMA layer were observed. It was revealed that adding monolayer MoS₂ to the nanostructures can significantly enhance the photoluminescence of the natural dye due to a strong correlation between the emission bands of the inorganic–organic emitters and back mirror reflection of the excitation blue light from the monolayer. Based on the investigation of two natural dyes, we demonstrated viable pathways for scalable manufacturing of efficient hybrid OLEDs consisting of assembly of natural-dye polymers through low-cost, purely ecological, and convenient processes. Full article

Plasma-activated water (PAW) has gained attention across agricultural, medical, cosmetic, and sterilization fields due to its production of reactive oxygen and nitrogen species (ROS and RNS). Although PAW has been primarily explored for seed germination and sterilization in agriculture, its role as a nutrient source and physiological regulator remains less understood. In this study, PAW generated by a surface dielectric barrier discharge (SDBD) system contained approximately 1000 ppm nitrate (NO₃⁻) and was designated as PAW1000. Diluted PAW solutions were applied to sprout crops—wheat (Triticum aestivum), barley (Hordeum vulgare), radish (Raphanus sativus), and broccoli (Brassica oleracea var. italica)—grown under hydroponic and soil-based conditions. PAW100 and PAW200 treatments enhanced growth, increasing fresh biomass by up to 26%, shoot length by 22%, and root length by 18%, depending on the species. In silico analysis identified nitrogen-responsive transcripts among several autophagy-related genes. Consistent with this, fluorescence microscopy of Arabidopsis thaliana GFP-StATG8 lines revealed increased autophagosome formation following PAW treatment. The growth-promoting effect of PAW was diminished in atg4 mutants, indicating that autophagy contributes to plant responses to PAW-derived ROS and RNS. Together, these findings demonstrate that diluted PAW generated by SDBD enhances biomass accumulation in sprout crops, and that autophagy plays a regulatory role in mediating PAW-induced physiological responses. Full article

The enteric nervous system (ENS) is a critical component of the gut–brain axis, playing a pivotal role in gastrointestinal homeostasis and systemic health. Emerging evidence suggests that ENS dysfunction precedes central neurodegenerative disorders. Progesterone, known for its neuroprotective and anti-inflammatory properties in the central nervous system (CNS), has received growing attention for its potential role in ENS physiology. This study aimed to map the expression of nuclear and membrane-bound progesterone receptors in the human ENS, considering regional intestinal, sex, and age variations. Immunofluorescence and Reverse Transcription-Polymerase Chain Reaction (RT-PCR) were used to evaluate receptor distribution in anatomically distinct intestinal regions. Consistent expression of classical nuclear progesterone receptors (PR-A/B) and the non-classical Progesterone receptor membrane component 1 (PGRMC1) in myenteric ganglion cells across all intestinal segments was observed. RT-PCR confirmed the expression of PR-A/B, PGRMC1, mPRα, and mPRβ, with regional variations. Sex-specific patterns were evident along with age-related downregulation. Our findings provide a detailed characterization of progesterone receptor expression in human ENS, highlighting sex- and age-dependent regulation. The identification of progesterone signaling within the myenteric plexus suggests a hormonal influence in gut–brain communication. Targeting ENS progesterone receptors may open novel therapeutic avenues to modulate neurodegenerative CNS disorders via peripheral intervention along the gut–brain axis. Full article

This study presents a texture-aware image synthesis framework designed to generate material-consistent façades using adversarial learning. The proposed architecture incorporates a mask-guided channel-wise attention mechanism that adaptively merges segmentation information with texture statistics to reconcile structural guiding with textural fidelity. A thorough comparative analysis was performed utilizing three internal variants—Vanilla GAN, Wasserstein GAN (WGAN), and WGAN-GP—against leading baselines, including TextureGAN and Pix2Pix. The assessment utilized a comprehensive multi-metric framework that included SSIM, FID, KID, LPIPS, and DISTS, in conjunction with a VGG-19 based perceptual loss. Experimental results indicate a notable divergence between pixel-wise accuracy and perceptual realism; although established baselines attained elevated PSNR values, the suggested Vanilla GAN and WGAN models exhibited enhanced perceptual fidelity, achieving the lowest LPIPS and DISTS scores. The WGAN-GP model, although theoretically stable, produced smoother but less complex textures due to the regularization enforced by the gradient penalty term. Ablation investigations further validated that the attention mechanism consistently enhanced structural alignment and texture sharpness across all topologies. Thus, the study suggests that Vanilla GAN and WGAN architectures, enhanced by attention-based fusion, offer an optimal balance between realism and structural fidelity for high-frequency texture creation applications. Full article

The photovoltaic field has seen significant development in recent years, with continuously expanding installation capacity and increasing grid integration. However, due to the intermittency of solar energy and meteorological variability, PV output power poses serious challenges to grid security and dispatch reliability. Traditional forecasting methods largely rely on modeling historical power and meteorological data, often neglecting the consideration of cloud movement, which constrains further improvement in prediction accuracy. To enhance prediction accuracy and model interpretability, this paper proposes a dual-branch attention-based PV power prediction model that integrates physical features from ground-based cloud images. Regarding input features, a cloud segmentation model is constructed based on the vision foundation model DINO encoder and an improved U-Net decoder to obtain cloud cover information. Based on deep feature point detection and an attention matching mechanism, cloud motion vectors are calculated to extract cloud motion speed and direction features. For feature processing, feature attention and temporal attention mechanisms are introduced, enabling the model to learn key meteorological factors and critical historical time steps. Structurally, a parallel architecture consisting of a linear branch and a nonlinear branch is adopted. A context-aware fusion module adaptively combines the prediction results from both branches, achieving collaborative modeling of linear trends and nonlinear fluctuations. Comparative experiments were conducted using two years of engineering data. Experimental results demonstrate that the proposed model outperforms the benchmarks across multiple metrics, validating the predictive advantages of the dual-branch structure that integrates physical features under complex weather conditions. Full article

The nonholonomic constraints of fixed-wing UAVs, characterized by coupled heading-curvature feasibility and asymmetric costs, fundamentally deviate from classical Euclidean routing assumptions. While standard neural combinatorial optimization (NCO) architectures could theoretically incorporate Dubins costs via reward signals, such naive adaptations lack the capacity to explicitly model the intrinsic SE(2) geometric invariance and directional asymmetry of fixed-wing motion, leading to suboptimal generalization. To bridge this gap, we propose a Dubins-Aware NCO framework. We design a dual-channel embedding to decouple asymmetric physical distances from rotation-stable geometric features. Furthermore, we introduce a Rotary Phase Encoding (RoPhE) mechanism that theoretically guarantees strict SO(2) equivariance within the attention layer. Extensive sensitivity, ablation, and cross-distribution generalization experiments are conducted on tasks spanning varying turning radii and problem variants with instance scales of 10, 20, 36, and 52 nodes. The results consistently validate the superior optimality and stability of our approach compared with state-of-the-art DRL and NCO baselines, while maintaining significant computational efficiency advantages over classical heuristics. Our results highlight the importance of explicitly embedding geometry-physics consistency, rather than relying on scalar reward signals, for real-world fixed-wing autonomous scheduling. Full article

Remote photoplethysmography (rPPG) enables non-contact acquisition of human physiological parameters using ordinary cameras, and has been widely applied in medical monitoring, human–computer interaction, and health management. However, most existing studies focus on estimating specific physiological metrics, such as heart rate and heart rate variability, while paying insufficient attention to reconstructing the underlying rPPG waveform. In addition, publicly available datasets typically record facial videos accompanied by fingertip PPG signals as reference labels. Since fingertip PPG waveforms differ substantially from the true photoplethysmography (PPG) signals obtained from the face, deep learning models trained on such datasets often struggle to recover high-quality rPPG waveforms. To address this issue, we collected a new dataset consisting of palm-region videos paired with wrist-based PPG signals as reference labels, and experimentally validated its effectiveness for training neural network models aimed at rPPG waveform reconstruction. Furthermore, we propose a generative adversarial network (GAN)-based pulse-wave synthesis framework that produces high-quality rPPG waveforms by denoising the mean green-channel signal. By incorporating time-domain peak-aware loss, frequency-domain loss, and adversarial loss, our method achieves promising performance, with an RMSE (Root Mean Square Error) of 0.102, an MAPE (Mean Absolute Percentage Error) of 0.028, a Pearson correlation of 0.987, and a cosine similarity of 0.989. These results demonstrate the capability of the proposed approach to reconstruct high-fidelity rPPG waveforms with improved morphological accuracy compared to noisy raw rPPG signals, rather than directly validating health monitoring performance. This study presents a high-quality rPPG waveform reconstruction approach from both data and model perspectives, providing a reliable foundation for subsequent physiological signal analysis, waveform-based studies, and potential health-related applications. Full article

Background: Adult females with Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD) are frequently underdiagnosed due to gender bias, overlapping symptoms, and limited awareness among healthcare professionals. The scarcity of research on this subject—particularly in the UK context—underscores the need for further investigation. Accordingly, the aim of this study was to explore the lived experiences of adult females receiving a late diagnosis of ASD and/or ADHD and to identify key barriers within the UK diagnostic pathway. This study addresses a critical knowledge gap by examining the factors contributing to delayed diagnosis within the United Kingdom. Study Design and Method: The study employed a qualitative approach, utilising an anonymous online questionnaire survey comprising nine open-ended questions. Responses were obtained from 52 UK-based females aged 35–65 years who had either received or were awaiting a diagnosis of ASD and/or ADHD. Data were analysed thematically within a constructivist framework. Findings: The analysis revealed three overarching themes: (i) limited understanding and lack of empathy among healthcare professionals, (ii) insufficient post-diagnostic support, with most participants reporting no follow-up care, and (iii) a complex, protracted diagnostic process, often involving waiting periods exceeding three years. Gender bias and frequent misdiagnosis were recurrent issues, contributing to significant psychological distress. These findings underscore the need for systemic reforms and align closely with gaps identified in the existing literature. Conclusions: The findings emphasise the urgent need for gender-sensitive diagnostic frameworks, enhanced professional training, and a person-centred approach to care. Key recommendations include shortening diagnostic waiting times, strengthening healthcare professionals’ knowledge base, and ensuring equitable and consistent post-diagnostic support. Full article