Search Results (20,603)

Enhancing TiO₂ performance is essential for advancing photocatalysis, environmental remediation, and energy conversion technologies. In this work, nanosized TiO₂ was modified with biochar (BC) derived from red sea algae at different loadings (0, 5, 10, and 15 wt%). Structural analysis confirmed that TiO₂ maintained its crystalline framework while biochar introduced additional amorphous features and modified surface morphology. Optical measurements revealed a redshift in the absorption edge and tunable bandgap values (3.28–3.72 eV), accompanied by increases in refractive index and extinction coefficient, indicating enhanced light–matter interactions. Electrochemical studies demonstrated that the TiO₂/5 wt% BC composite exhibited the lowest charge-transfer resistance and highest peak current, reflecting superior conductivity. Photocatalytic tests showed that TiO₂/5 wt% BC achieved nearly 84% degradation of methylene blue within 150 min under visible-light irradiation, with stable reusability over multiple cycles. These findings demonstrate that moderate biochar incorporation (5 wt%) optimally enhances the physicochemical, electrochemical, and photocatalytic properties of TiO₂, making it a promising candidate for wastewater treatment, solar-driven catalysis, and sustainable energy applications. Full article

This study introduces GAT-CAMDA, a novel framework for the structural health monitoring (SHM) of composite materials under temperature-induced variability, leveraging the powerful feature extraction capabilities of Graph Attention Networks (GATs) and advanced domain adaptation (DA) techniques. By combining Maximum Mean Discrepancy (MMD) and Correlation Alignment (CORAL) losses with a domain-discriminative adversarial model, the framework achieves scalable alignment of feature distributions across temperature domains, ensuring robust damage detection. A simple yet at the same time efficient data augmentation process extrapolates damage behaviour across unmeasured temperature conditions, addressing the scarcity of damaged-state observations. Hyperparameter optimisation via Optuna not only identifies the optimal settings to enhance model performance, achieving a classification accuracy of 95.83% on a benchmark dataset, but also illustrates hyperparameter significance for explainability. Additionally, the GAT architecture’s attention demonstrates the importance of various sensors, enhancing transparency and reliability in damage detection. The dual use of Optuna serves to refine model accuracy and elucidate parameter impacts, while GAT-CAMDA represents a significant advancement in SHM, enabling precise, interpretable, and scalable diagnostics across complex operational environments. Full article

Camouflage detection in hyperspectral imaging is hindered by the spectral similarity between artificial materials and natural vegetation. This study proposes a non-destructive classification framework integrating optimized sample partitioning, spectral preprocessing, and residual deep learning to address this challenge. Hyperspectral data of camouflage fabrics and natural grass (389.06–1005.10 nm) were acquired and preprocessed using principal component analysis, standard normal variate (SNV) transformation, Savitzky–Golay (SG) filtering, and derivative-based enhancement. The Sample set Partitioning based on joint X–Y distance (SPXY) algorithm was applied to improve representativeness of training subsets, and several classifiers were constructed, including support vector machine (SVM), random forest (RF), k-nearest neighbors (KNN), convolutional neural network (CNN), and residual network (ResNet). Comparative evaluation demonstrated that the SPXY-ResNet model achieved the best performance, with 99.17% accuracy, 98.89% precision, and 98.82% recall, while maintaining low training time. Statistical analysis using Kullback–Leibler divergence and similarity measures confirmed that SPXY improved distributional consistency between training and testing sets, thereby enhancing generalization. The confusion matrix and convergence curves further validated stable learning with minimal misclassifications and no overfitting. These findings indicate that the proposed SPXY-ResNet framework provides a robust, efficient, and accurate solution for hyperspectral camouflage detection, with promising applicability to defense, ecological monitoring, and agricultural inspection. Full article

Soluble solids content is the most important attribute related to the quality and price of apples. The objective of this study was to detect the soluble solids content (SSC) in ‘Fuji’ apples using hyperspectral imaging combined with a deep learning algorithm. The hyperspectral images of 570 apple samples were obtained and the whole region of apple sample hyperspectral data was collected and preprocessed. In addition, a method involving multi-attention convolutional neural network (MA-CNN) is proposed, which extracts spectral and spatial features from hyperspectral images by embedding channel attention (CA) and spatial attention (SA) modules in a convolutional neural network. The CA and SA modules help the network adaptively focus on important spectral–spatial features while reducing the interference of redundant information. Additionally, the Bayesian optimization algorithm (BOA) is used for model hyperparameter optimization. A comprehensive evaluation is conducted by comparing the proposed model with CA-CNN models, SA-CNN, and the current mainstream models. Furthermore, the best prediction performances for detecting SSC in apple samples were obtained from the MA-CNN model, with an ${\mathrm{R}}_{\mathrm{p}}^2$ value of 0.9602 and an RMSEP value of 0.0612 °Brix. The results of this study indicated that the MA-CNN algorithm combined with hyperspectral imaging technology can be used as an effective method for rapid detection of apple quality parameters. Full article

Deaf and hearing-impaired individuals rely on sign language, a visual communication system using hand shapes, facial expressions, and body gestures. Sign languages vary by region. For example, Arabic Sign Language (ArSL) is notably different from American Sign Language (ASL). This project focuses on creating an Arabic Sign Language Recognition (ArSLR) System tailored for healthcare, aiming to bridge communication gaps resulting from a lack of sign-proficient professionals and limited region-specific technological solutions. Our research addresses limitations in sign language recognition systems by introducing a novel framework centered on ResNet50ViT, a hybrid architecture that synergistically combines ResNet50’s robust local feature extraction with the global contextual modeling of Vision Transformers (ViT). We also explored a tailored Vision Transformer variant (SignViT) for Arabic Sign Language as a comparative model. Our main contribution is the ResNet50ViT model, which significantly outperforms existing approaches, specifically targeting the challenges of capturing sequential hand movements, which traditional CNN-based methods struggle with. We utilized an extensive dataset incorporating both static (36 signs) and dynamic (92 signs) medical signs. Through targeted preprocessing techniques and optimization strategies, we achieved significant performance improvements over conventional approaches. In our experiments, the proposed ResNet50-ViT achieved a remarkable 99.86% accuracy on the ArSL dataset, setting a new state-of-the-art, demonstrating the effectiveness of integrating ResNet50’s hierarchical local feature extraction with Vision Transformer’s global contextual modeling. For comparison, a fine-tuned Vision Transformer (SignViT) attained 98.03% accuracy, confirming the strength of transformer-based approaches but underscoring the clear performance gain enabled by our hybrid architecture. We expect that RAFID will help deaf patients communicate better with healthcare providers without needing human interpreters. Full article

The separable heat pipe, with its highly efficient heat transfer and flexible layout features, has become an innovative solution to the heat dissipation problem of batteries, especially suitable for the directional heat dissipation requirements of high-energy-density battery packs. However, most of the number–value models currently studied examine the flow of refrigerant working medium within the pump as an isentropic or isothermal process and are unable to effectively analyze the heat transfer characteristics of different internal regions. Based on the laws of energy conservation, momentum conservation, and mass conservation, this study establishes a steady-state mathematical model of the pump-driven microchannel-separated heat pipe. The influence of factors—such as the phase state change in the working medium inside the heat exchanger, the heat transfer flow mechanism, the liquid filling rate, the temperature difference, as well as the structural parameters of the microchannel heat exchanger on the steady-state heat transfer and flow performance of the pump-driven microchannel-separated heat pipe—were analyzed. It was found that the influence of liquid filling ratio on heat transfer quantity is reflected in the ratio of change in the sensible heat transfer and latent heat transfer. The sensible heat transfer ratio is higher when the liquid filling is too low or too high, and the two-phase heat transfer is higher when the liquid filling ratio is in the optimal range; the maximum heat transfer quantity can reach 3.79 KW. The decrease in heat transfer coefficient with tube length in the single-phase region is due to temperature and inlet effect, and the decrease in heat transfer coefficient in the two-phase region is due to the change in flow pattern and heat transfer mechanism. This technology has the advantages of long-distance heat transfer, which can adapt to the distributed heat dissipation needs of large-energy-storage power plants and help reduce the overall lifecycle cost. Full article

Background: Complicated pneumonia (CP) in children presents in various forms—including empyema, necrotizing pneumonia (NP), necrotizing pneumonia with pleural effusion (NP + PE), and parapneumonic pleural effusion (PPE)—and is associated with significant morbidity despite advances in antimicrobial therapy. This study aimed to describe and compare the clinical characteristics, laboratory findings, antibiotic use, and outcomes across different CP subtypes in hospitalized children and to assess the impact of prior antibiotic use on presentation and treatment outcomes. Methods: This retrospective observational study included 58 children admitted with CP to tertiary hospitals in Jordan. Patients were categorized into four subtypes: empyema (n = 4), NP (n = 4), NP + PE (n = 17), and PPE (n = 33). Demographic data, clinical features, laboratory results, antibiotic regimens, and clinical outcomes were analyzed. Multivariable regression was used to identify predictors of prior antibiotic use. Results: Fever and cough were the most common symptoms (96.6%). Over 40% of patients had received antibiotics prior to admission. Those pre-treated had significantly longer symptom duration (8.2 vs. 4.5 days, p < 0.001), longer hospitalization (18.2 vs. 14.6 days, p = 0.023), and more frequent chest tube insertion (66.7% vs. 35.3%, p = 0.019). Streptococcus pneumoniae was the most common organism isolated in culture-positive cases. Vancomycin-based regimens were the most frequently used treatments. Univariate regression analysis showed that patients with prior antibiotic use had significantly higher odds of longer hospitalization duration (OR = 1.11, p = 0.028) and chest tube insertion (OR = 3.67, p = 0.021). Conclusions: Complicated pneumonia in children remains a diverse and clinically significant condition. The findings demonstrate that prolonged symptom duration prior to hospitalization and certain clinical interventions were associated with prior antibiotic exposure. These results provide insight into local disease patterns and prescribing behaviors, which may help inform strategies to optimize antimicrobial stewardship and improve care pathways for affected children. Full article

Posttraumatic growth (PTG) and resilience should have distinct features due to their theoretical background, and yet their respective relationships with optimism have been consistently positive. Their relationships with pessimism have been understudied, which obscures how PTG and resilience may conceptually differ. We hypothesize that the differences may emerge whether optimism and pessimism are evaluated as cognitive expectancies or dispositional personality traits. The current study examined how optimism and pessimism would be distinctly associated with PTG and resilience, depending on whether optimism and pessimism reflect dispositional personality traits or cognitive expectancies. Midwestern United States university students (N = 347) completed an in-person survey that included measures examining optimism and pessimism as personality traits and a cognitive task estimating the likelihood of positive and negative future events happening to them and happening to others and re-estimating after obtaining novel information (i.e., belief update), in addition to PTG and resilience. Results indicated that dispositional optimism was positively associated with both PTG and resilience, whereas dispositional pessimism was negatively associated with only resilience. Furthermore, higher expectancy of positive events to be happening in the future was mostly associated with PTG whereas lower expectancy of negative events to be happening in the future was mostly associated with resilience. In addition, the perception that positive events would be more likely to happen to them than to others was only associated with resilience. Findings regarding the relationships with adjusted cognitive expectancies (i.e., belief update) were mixed. The current findings reveal potential distinctions between PTG and resilience by highlighting that they may have asymmetrical relationships with optimism and pessimism, depending on whether optimistic/pessimistic characteristics are considered as personality traits or cognitive expectations of positive and negative future events. Full article

With the rapid expansion of the civil aviation industry, the surge in flight numbers has led to increasingly pronounced issues of air route congestion and flight conflicts. 4D trajectory prediction, by dynamically adjusting aircraft paths in real time, can prevent air route collisions, alleviate air traffic pressure, and ensure flight safety. Therefore, this paper proposes a combined model—GAT-BiGRU-TPA—based on the Spatio-Temporal Graph Neural Network (STGNN) framework to achieve refined 4D trajectory prediction. This model integrates Graph Attention Networks (GAT) to extract multidimensional spatial features, Bidirectional Gated Recurrent Units (BiGRU) to capture temporal dependencies, and incorporates a Temporal Pattern Attention (TPA) mechanism to emphasize learning critical temporal patterns. This enables the extraction of key information and the deep fusion of spatio-temporal features. Experiments were conducted using real trajectory data, employing a grid search to optimize the observation window size and label length. Results demonstrate that under optimal model parameters (observation window: 30, labels: 4), the proposed model achieves a 45.72% reduction in mean Root Mean Square Error (RMSE) and a 43.40% decrease in Mean Absolute Error (MAE) across longitude, latitude, and altitude compared to the optimal baseline BiLSTM model. Prediction accuracy significantly outperforms multiple mainstream benchmark models. In summary, the proposed GAT-BiGRU-TPA model demonstrates superior accuracy in 4D trajectory prediction, providing an effective approach for refined trajectory management in complex airspace environments. Full article

Cross-view geo-localization allows an agent to determine its own position by retrieving the same scene from images taken from dramatically different perspectives. However, image matching and retrieval face significant challenges due to substantial viewpoint differences, unknown orientations, and considerable geometric distribution disparities between cross-view images. To this end, we propose a cross-view geo-localization framework based on novel view synthesis that generates pseudo aerial-view images from given street-view scenes to reduce the view discrepancies, thereby improving the performance of cross-view geo-localization. Specifically, we first employ 3D Gaussian splatting to generate new aerial images from the street-view image sequence, where COLMAP is used to obtain initial camera poses and sparse point clouds. To identify optimal matching viewpoints from reconstructed 3D scenes, we design an effective camera pose estimation strategy. By increasing the tilt angle between the photographic axis and the horizontal plane, the geometric consistency between the newly generated aerial images and the real ones can be improved. After that, the DINOv2 is employed to design a simple yet efficient mixed feature enhancement module, followed by the InfoNCE loss for cross-view geo-localization. Experimental results on the KITTI dataset demonstrate that our approach can significantly improve cross-view matching accuracy under large viewpoint disparities and achieve state-of-the-art localization performance. Full article

Background/Objectives: Breast cancer remains one of the leading causes of mortality among women, necessitating continued advancements in diagnostic methods to enhance early detection and treatment outcomes. This review explores the current landscape of breast cancer classification, focusing on machine learning (ML), deep learning (DL), and hybrid/ensemble models. Methods: A systematic search following PRISMA guidelines identified 50 eligible studies published between 2018 and 2025. Studies were included based on their use of mammogram datasets and implementation of computer-aided diagnosis methods for classification. Models were compared in terms of preprocessing, feature extraction, optimization strategies, and classification performance. Results: Representative high performing models illustrate the strengths and limitations of each approach. In ML, an optimized ELM achieved 100% accuracy on MIAS. DL methods such as Vision Transformers also reached 100% accuracy on DDSM, outperforming conventional CNNs. Hybrid models, particularly IEUNet++, achieved 99.87% accuracy, offering robust multi class classification. Conclusions: While ML and DL approaches can achieve near perfect accuracy, they typically focus on binary classification tasks and require extensive preprocessing, feature extraction, and optimization. In contrast, hybrid methods provide comparable or superior performance while simultaneously addressing multi-classification with fewer handcrafted steps, highlighting their robustness. These findings underscore the need for innovative solutions that balance model accuracy, interpretability, and resource efficiency. By addressing these challenges, future classification systems can better support early breast cancer detection and improve patient outcomes. Full article

Aiming at the problems of complex image background and numerous interference factors faced by visual navigation systems in orchard environments, this paper proposes an orchard navigation line recognition method based on U-Net. Firstly, the drivable areas in the collected images are labeled using Labelme (a graphical tool for image annotation) to create an orchard dataset. Then, the Spatial Attention (SA) mechanism is inserted into the downsampling stage of the traditional U-Net semantic segmentation method, and the Coordinate Attention (CA) mechanism is added to the skip connection stage to obtain complete context information and optimize the feature restoration process of the drivable area in the field, thereby improving the overall segmentation accuracy of the model. Subsequently, the improved U-Net network is trained using the enhanced dataset to obtain the drivable area segmentation model. Based on the detected drivable area segmentation mask, the navigation line information is extracted, and the geometric center points are calculated row by row. After performing sliding window processing and bidirectional interpolation filling on the center points, the navigation line is generated through spline interpolation. Finally, the proposed method is compared and verified with U-Net, SegViT, SE-Net, and DeepLabv3+ networks. The results show that the improved drivable area segmentation model has a Recall of 90.23%, a Precision of 91.71%, a mean pixel accuracy (mPA) of 87.75%, and a mean intersection over union (mIoU) of 84.84%. Moreover, when comparing the recognized navigation line with the actual center line, the average distance error of the extracted navigation line is 56 mm, which can provide an effective reference for visual autonomous navigation in orchard environments. Full article

The growing use of machine learning (ML) and artificial intelligence across sectors has shown strong potential to improve decision-making processes. However, the adoption of ML by non-technical professionals remains limited due to the complexity of traditional development workflows, which often require software engineering and data science expertise. In recent years, low-code and no-code platforms have emerged as promising solutions to democratize ML by abstracting many of the technical tasks typically involved in software engineering pipelines. This paper investigates whether these platforms can offer a viable alternative for making ML accessible to non-expert users. Beyond predictive performance, this study also evaluates usability, setup complexity, the transparency of automated workflows, and cost management under realistic “out-of-the-box” conditions. This multidimensional perspective provides insights into the practical viability of LC/NC tools in real-world contexts. The comparative evaluation was conducted using three leading cloud-based tools: Amazon SageMaker Canvas, Google Cloud Vertex AI, and Azure Machine Learning Studio. These tools employ ensemble-based learning algorithms such as Gradient Boosted Trees, XGBoost, and Random Forests. Unlike traditional ML workflows that require extensive software engineering knowledge and manual optimization, these platforms enable domain experts to build predictive models through visual interfaces. The findings show that all platforms achieved high accuracy, with consistent identification of key features. Google Cloud Vertex AI was the most user-friendly, SageMaker Canvas offered a highly visual interface with some setup complexity, and Azure Machine Learning delivered the best model performance with a steeper learning curve. Cost transparency also varied considerably, with Google Cloud and Azure providing clearer safeguards against unexpected charges compared to Sagemaker Canvas. Full article

Accurate forecasting of springback continues to pose a significant challenge in sheet metal forming processes. The present paper presents a numerical model designed for the precise prediction of springback, allowing for a deeper understanding of plasticity behavior during cold forming operations in sheet metals. The key contribution of this model is the introduction of a non-associated anisotropic constitutive model featuring nonlinear mixed isotropic–kinematic hardening. This model is derived from Hill’48 quadratic function and it was implemented into ABAQUS 6.13 software environment through the user defined UMAT subroutine. For improved precision, kinematic hardening parameters specific to 5083 aluminum sheet metal were meticulously derived from cyclic shear experiments. Our results demonstrate the model’s strong capability in predicting springback during the U-bending operation, achieving remarkable accuracy. The design of experiments DOE is used as a statistical method to optimize the number of experiments and analyze the effects of key input factors. In this study, sheet thickness, punch speed, and sampling angle relative to the rolling direction (RD) are examined at different levels to assess their impact on folding force and springback. The strong agreement between experimental results and theoretical predictions confirms the accuracy and reliability of the proposed models in estimating folding force and springback. Full article

Caffeic acid phenethyl ester (CAPE), a polyphenol from propolis, is well recognized for its anti-inflammatory, antioxidant, antimicrobial, and osteogenic properties. This study aimed to develop macroporous bone scaffolds composed of a chitosan/agarose matrix reinforced with nanohydroxyapatite and enriched with stable CAPE derivatives to enhance their biomedical potential for applications in bone tissue engineering and regenerative medicine. A comprehensive evaluation of microstructural and biological properties of the produced scaffolds was conducted. The fabricated scaffolds exhibited high porosity (49–60%) with interconnected pores and compressive strength (1.2–1.8 MPa), closely resembling cancellous bone and indicating suitability for bone regeneration. They were biocompatible, promoted osteoblast adhesion, proliferation, and differentiation, and supported apatite deposition on their surfaces, demonstrating strong bioactivity and potential for implant osseointegration. Importantly, the scaffolds did not trigger excessive production of reactive oxygen or nitrogen species, suggesting a low risk of inflammatory responses. Additionally, CAPE-enriched scaffolds inhibited biofilm formation by Staphylococcus aureus and Staphylococcus epidermidis, reducing the risk of implant-associated infections. In summary, these CAPE-modified scaffolds integrate optimal microstructural and biological features, such as reducing oxidative stress and inhibiting biofilm formation, and thus offer a promising strategy for enhancing bone repair and regeneration in clinical applications. Full article