Search Results (1,616)

In this study, we introduce the generalized Repunit sequence and its hybrid quaternion extension derived from a parametric recurrence relation that preserves the base-10 structure of classical Repunit numbers. Fundamental properties of the proposed sequences, including the characteristic equation, generating function, and Binet-type formula, are systematically investigated. Several algebraic identities, such as bilinear index-reduction formulas, are established to demonstrate the internal structure and consistency of the construction. Numerical experiments and graphical analyses are conducted to examine the structural behavior of the generalized Repunit sequence and its hybrid quaternion counterpart. While the scalar Repunit sequence exhibits regular and predictable growth, the hybrid quaternion extension displays significantly higher structural complexity and variability. Density distributions, contour plots, histogram representations, and discrete variation measures confirm the presence of enhanced diffusion and local irregularity in the quaternion-based structure. These statistical, graphical, and numerical findings indicate that generalized Repunit hybrid quaternion sequences possess properties that are relevant to encoding, masking, and preprocessing mechanisms in applied mathematical and computational frameworks. However, this work does not propose a complete cryptographic algorithm, nor does it claim compliance with established cryptographic security standards such as NIST SP 800-22. The results should therefore be interpreted as pre-cryptographic indicators that motivate further research toward rigorous security evaluation, algorithmic development, and broader applications in areas such as coding theory, signal processing, and nonlinear dynamical systems. Full article

Color-to-grayscale conversion is a fundamental preprocessing task with widespread applications in digital printing, electronic ink displays, medical imaging, and artistic photo stylization. A primary challenge in this domain is to simultaneously preserve global luminance distribution and local contrast. To address this, we propose an adaptive conversion method centered on a novel objective function that integrates information entropy with Edge Content (EC), a metric for local gradient information. The key advantage of our approach is its ability to generate grayscale results that maintain both rich overall contrast and fine-grained local details. Compared with previous adaptive linear methods, our approach demonstrates superior qualitative and quantitative performance. Furthermore, by eliminating the need for computationally expensive edge detection, the proposed algorithm provides an effective solution to the color-to-grayscale conversion. Full article

In selective laser melting (SLM), real-time visual inspection of powder spreading quality is essential for maintaining dimensional accuracy and mechanical performance. However, reflections from metallic chamber walls introduce non-uniform illumination and reduce local contrast, hindering reliable defect detection. To overcome this problem, a chamber-reflection-aware image enhancement method is proposed, integrating a physical reflection model with a dual-channel deep network. A Gaussian-based curved-surface reflection model is first developed to describe the spatial distribution of reflective interference. The enhancement network then processes the input through two complementary channels: a Retinex-based branch to extract illumination-invariant reflectance components and a principal components analysis (PCA)-based branch to preserve structural information. Furthermore, a noise-aware loss function is designed to suppress the mixed Gaussian–Poisson noise that is inherent in SLM imaging. Experiments conducted on real SLM monitoring data demonstrate that the proposed method significantly improves contrast and defect visibility, outperforming existing enhancement algorithms in peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and natural image quality evaluator (NIQE). The approach provides a physically interpretable and robust preprocessing framework for online SLM quality monitoring. Full article

Vegetation segmentation in Very High-Resolution (VHR) satellite imagery has become an essential task for ecological monitoring, supporting diverse applications such as large-scale vegetation conservation and detailed segmentation of small local areas. In particular, multi-class vegetation segmentation, which distinguishes various vegetation types beyond simple binary segmentation of vegetation and non-vegetation, enables detailed analysis of subtle ecosystem changes and has gained increasing importance. However, the annotation of VHR satellite imagery requires extensive time and effort, resulting in a lack of datasets for vegetation segmentation, especially those including multi-class annotations. To address this limitation, this study proposes MultiVeg, a deep learning dataset based on VHR satellite imagery for detailed multi-class vegetation segmentation. MultiVeg includes preprocessed 0.5 m resolution images collected by the KOMPSAT-3 and 3A satellites from 2014 to 2023, covering diverse environments such as urban, agricultural, and forest regions. Each image was carefully annotated by experts into three semantic classes, which are Background, Tree, and Low Vegetation, and validated through a structured quality check process. To verify the effectiveness of MultiVeg, seven representative semantic segmentation models, including convolutional neural network and Transformer-based architectures, were trained and comparatively analyzed. The results demonstrated consistent segmentation performance across all classes, confirming that MultiVeg is a high-quality and reliable dataset for deep learning-based multi-class vegetation segmentation research using VHR satellite imagery. The MultiVeg will be publicly available through GitHub (release v1.0), serving as a valuable resource for advancing deep leaning-based vegetation segmentation research in the remote sensing field. Full article

Cough sounds are a key acoustic indicator for detecting respiratory diseases in laying hens, which have become increasingly prevalent with the intensification of poultry housing systems. As an important early signal, cough sounds play a vital role in disease prevention and precision health management through timely recognition and spatial localization. In this study, an improved BiLSTM–TDOA method was proposed for the accurate recognition and localization of laying hen cough sounds. Nighttime audio data were collected and preprocessed to extract 81 acoustic features, including formant parameters, MFCC, LPCC, and their first and second derivatives. These features were then input into a BiLSTM-Attention model, which achieved a precision of 97.50%, a recall of 90.70%, and an F1-score of 0.9398. An improved TDOA algorithm was then applied for three-dimensional sound source localization, which resulted in mean absolute errors of 0.1453 m, 0.1952 m, and 0.1975 m along the X, Y, and Z axes across 31 positions. The results demonstrated that the proposed method enabled accurate recognition and 3D localization of abnormal vocalizations in laying hens, which will provide a novel approach for early detection, precise control, and intelligent health monitoring of respiratory diseases in poultry houses. Full article

Understanding how land tenure and socio-economic pressures shape landscape transformation is critical for sustainable management in biodiversity-rich regions. This study examines three decades (1987–2017) of land use and land cover (LU&LC) change in the Ngerenyi area of the Taita Hills, Kenya, by integrating multispectral Landsat analysis with household survey data. Harmonized pre-processing and supervised classification of four LU&LC classes, agriculture, built-up areas, high-canopy vegetation, and low-canopy vegetation, achieved overall accuracies above 80% and Kappa values exceeding 0.75. Transition modeling using the Minimum Information Loss Transition Estimation (MILTE) approach, combined with net-versus-swap metrics, revealed persistent decline and fragmentation of high-canopy vegetation, cyclical transitions between agriculture and low-canopy vegetation, and the near-irreversible expansion of built-up areas. Low-canopy vegetation exhibited the highest dynamism, reflecting both degradation from canopy loss and natural regeneration from fallowed cropland. Household surveys (n = 141) identified agricultural expansion, charcoal production, fuelwood extraction, and population growth as the dominant perceived drivers, with significant variation across tenure categories. The population in Taita Taveta County increased from 205,334 in 2009 to 340,671 in 2019, reinforcing documented pressures on land resources and woody biomass. As part of the Eastern Arc biodiversity hotspot, the landscape’s diminishing high-canopy patches underscore the importance of conserving undisturbed vegetation remnants as ecological baselines and biodiversity refuges. The findings highlight the need for tenure-sensitive, landscape-scale planning that integrates private landowners, regulates subdivision, promotes agroforestry and alternative energy options, and safeguards remaining high-canopy vegetation to enhance ecological resilience while supporting local livelihoods. Full article

Mesh generation is a critical preprocessing step in Computational Fluid Dynamics. In ship hydrodynamics, existing mesh generation methods lack adaptability to complex hull surface geometries, necessitating repeated optimization. To address these issues, a new hybrid mesh generation strategy was proposed, integrating Non-Uniform Rational B-Spline surface interpolation, advancing front technique, hull surface curvature features, and mesh quality evaluation parameters. Firstly, the ship hull surface was partitioned into multiple regions, and each region was assigned a specific mesh type. Subsequently, the adaptively sized mesh was generated based on local curvature variations. Finally, the angle skewness was employed as an objective function to improve the mesh quality. In addition, considering the actual ship model as an example, the mesh generated by our method and conventional Laplacian smoothing method were used to perform first-order potential flow simulations, and the results were compared against the convergence values. The results indicated that our method has lower root mean square errors in computing the total non-viscous force, steady drift force and ship hull free floating Response Amplitude Operator. This method is applicable to numerical simulations of the ship potential flow, providing high-quality hull meshes for hydrodynamic analysis. Full article

The demand for real-time land cover mapping from high-resolution remote sensing (HR-RS) imagery motivates lightweight segmentation models running directly on satellites. By processing on-board and transmitting only fine-grained semantic products instead of massive raw imagery, these models provide timely support for disaster response, environmental monitoring, and precision agriculture. Many recent methods combine convolutional neural networks (CNNs) with Transformers to balance local and global feature modeling, with convolutions as explicit information aggregation modules. Such heterogeneous hybrids may be unnecessary for lightweight models if similar aggregation can be achieved homogeneously, and operator inconsistency complicates optimization and hinders deployment on resource-constrained satellites. Meanwhile, lightweight Transformer components in these architectures often adopt aggressive channel compression and shallow contextual interaction to meet compute budgets, impairing boundary delineation and recognition of small or rare classes. To address this, we propose SatViT-Seg, a lightweight semantic segmentation model with a pure Vision Transformer (ViT) backbone. Unlike CNN-Transformer hybrids, SatViT-Seg adopts a homogeneous two-module design: a Local-Global Aggregation and Distribution (LGAD) module that uses window self-attention for local modeling and dynamically pooled global tokens with linear attention for long-range interaction, and a Bi-dimensional Attentive Feed-Forward Network (FFN) that enhances representation learning by modulating channel and spatial attention. This unified design overcomes common lightweight ViT issues such as channel compression and weak spatial correlation modeling. SatViT-Seg is implemented and evaluated in LuoJiaNET and PyTorch; comparative experiments with existing methods are run in PyTorch with unified training and data preprocessing for fairness, while the LuoJiaNET implementation highlights deployment-oriented efficiency on a graph-compiled runtime. Compared with the strongest baseline, SatViT-Seg improves mIoU by up to 1.81% while maintaining the lowest FLOPs among all methods. These results indicate that homogeneous Transformers offer strong potential for resource-constrained, on-board real-time land cover mapping in satellite missions. Full article

A large share of the global population now lives in urban areas, which creates growing challenges for city life. Local authorities are seeking ways to enhance urban livability, with transportation emerging as a major focus. Developing smart public transit systems is therefore a key priority. Accurately estimating travel times is essential for managing transport operations and supporting strategic decisions. Previous studies have used statistical, mathematical, or machine learning models to predict travel time, but most examined these approaches separately. This study introduces a hybrid framework that combines statistical regression models and machine learning algorithms to predict public bus travel times. The analysis is based on 1410 bus trips recorded between November 2021 and July 2022 in Kayseri, Turkey, including detailed meteorological and operational data. A distinctive aspect of this research is the inclusion of weather variables—temperature, humidity, precipitation, air pressure, and wind speed—which are often neglected in the literature. Additionally, sensitivity analyses are conducted by varying k values in the K-nearest neighbors (KNN) algorithm and threshold values for outlier detection to test model robustness. Among the tested models, CatBoost achieved the best performance with a mean squared error (MSE) of approximately 18.4, outperforming random forest (MSE = 25.3) and XGBoost (MSE = 23.9). The empirical results show that the CatBoost algorithm consistently achieves the lowest mean squared error across different preprocessing and parameter settings. Overall, this study presents a comprehensive and environmentally aware approach to travel time prediction, contributing to the advancement of intelligent and adaptive urban transportation systems. Full article

In the context of advancing sustainable local building materials, this study evaluates the mechanical impact of polypropylene (PP) fiber reinforcement on adobe bricks, specifically addressing the novel variable of fiber addition timing relative to the traditional 45-day biological fermentation process. Two experimental scenarios were investigated: fiber addition before fermentation and fiber addition after fermentation. In the pre-fermentation scenario, the unreinforced control specimen achieved the highest mean compressive strength (1.92 MPa), followed by reduced values of 1.66 MPa (0.25%), 1.60 MPa (0.50%), and 1.55 MPa (1.00%). In the post-fermentation scenario, the control recorded 1.81 MPa, while the PP-reinforced mixtures reached 1.73 MPa (0.25%), 1.65 MPa (0.50%), and 1.77 MPa (1.00%). Across both stages, PP fibers consistently decreased in strength due to weak bonding at the fiber–soil interface, as their hydrophobic nature disrupts the fermentation-derived biopolymer network formed by straw decomposition. Overall, this study highlights the limitations of synthetic fiber reinforcement within biologically stabilized adobe and contributes to the ongoing development of sustainable earthen construction systems. Full article

Anomaly Detection (AD) in telecommunication networks is critical for maintaining service reliability and performance. However, operational networks present significant challenges: high-dimensional Key Performance Indicator (KPI) data collected from thousands of network elements must be processed in near real time to enable timely responses. This paper presents an unsupervised approach leveraging Mahalanobis Distance (MD) to identify network anomalies. The MD model offers a scalable solution that capitalizes on multivariate relationships among KPIs without requiring labeled data. Our methodology incorporates preprocessing steps to adjust KPI ratios, normalize feature distributions, and account for contextual factors like sample size. Aggregated anomaly scores are calculated across hierarchical network levels—cells, sectors, and sites—to localize issues effectively. Through experimental evaluations, the MD approach demonstrates consistent performance across datasets of varying sizes, achieving competitive Area Under the Receiver Operating Characteristic Curve (AUC) values while significantly reducing computational overhead compared to baseline AD methods: Isolation Forest (IF), Local Outlier Factor (LOF) and One-Class Support Vector Machines (SVM). Case studies illustrate the model’s practical application, pinpointing the Random Access Channel (RACH) success rate as a key anomaly contributor. The analysis highlights the importance of dimensionality reduction and tailored KPI adjustments in enhancing detection accuracy. This unsupervised framework empowers telecom operators to proactively identify and address network issues, optimizing their troubleshooting workflows. By focusing on interpretable metrics and efficient computation, the proposed approach bridges the gap between AD and actionable insights, offering a practical tool for improving network reliability and user experience. Full article

Joining high-strength, cold-drawn Cu-Mg alloy conductors is a critical challenge for ensuring the reliability of high-speed railway catenary systems. This study investigates the evolution of mechanical properties and microstructure in Cu-0.43 wt% Mg alloy wires joined by multi-pass butt-upset cold welding without special surface preparation. High-integrity joints were achieved, exhibiting a peak tensile strength of 624 MPa (~96% of the base material’s strength). After four upsetting processes, the tensile strength of the weld can reach 90% of the original strength, and the gains from subsequent upsetting processes are negligible. Microstructural analysis revealed the joining process is governed by localized severe shear deformation, which forges a distinct gradient microstructure. This includes a transition zone of fine, equiaxed-like grains formed by dynamic recrystallization/recovery, and a central zone featuring a nano-laminar structure, high dislocation density, and deformation twins. A multi-stage dynamic bonding mechanism is proposed. It progresses from initial contact via thin film theory to bond consolidation through a “mechanical self-cleaning” process, where extensive radial plastic flow effectively expels surface contaminants. This work clarifies the fundamental bonding principles for pre-strained, high-strength alloys under multi-pass cold welding, providing a scientific basis to optimize this heat-free joining technology for industrial applications. Full article

Mine-induced surface subsidence threatens infrastructure and can trigger cascading geohazards, so accurate and computationally efficient monitoring and forecasting are essential for early warning. We integrate Persistent Scatterer InSAR (PS-InSAR) time series with a Bayesian-optimized CNN–LSTM designed for spatiotemporal prediction. The CNN extracts spatial deformation patterns, the LSTM models temporal dependence, and Bayesian optimization selects the architecture, training hyperparameters, and the most informative exogenous drivers. Groundwater level and backfilling intensity are encoded as multichannel inputs. Endpoint anchoring with affine calibration aligns the historical series and the forward projections. PS-InSAR indicates a maximum subsidence rate of 85.6 mm yr⁻¹, and the estimates are corroborated against nearby leveling benchmarks and FLAC^3D simulations. Cross-site comparisons show acceleration followed by deceleration after backfilling and groundwater recovery, which is consistent with geological engineering conditions. A symmetry-aware preprocessing step exploits axial regularities of the deformation field through mirroring augmentation and documents symmetry-breaking hotspots linked to geological heterogeneity. These choices improve generalization to shifted and oscillatory patterns in both the spatial CNN and the temporal LSTM branches. Short-term forecasts from the BO–CNN–LSTM indicate subsequent stabilization with localized rebound, highlighting its practical value for operational planning and risk mitigation. The framework combines automated hyperparameter search with physically consistent objectives, reduces manual tuning, enhances reproducibility and generalizability, and provides a transferable quantitative workflow for forecasting mine-induced deformation in complex goaf systems. Full article

Periapical radiographs remain the first-line imaging modality in endodontics due to accessibility and low radiation dose, whereas cone-beam computed tomography (CBCT) is reserved for inconclusive cases or suspected anatomical complexity. We propose a physics- and geometry-aware preprocessing pipeline coupled with sliding-window Viterbi tracking to enhance canal visibility and recover plausible root canal trajectories directly from routine periapical images. The pipeline standardizes row-wise brightness, compensates for the cone-like tooth density profile (Tukey window), and suppresses noise prior to dynamic-programming inference, requiring only minimal operator input (two-point orientation and region of interest). In a retrospective evaluation against micro-computed tomography (micro-CT)/CBCT reference anatomy, the approach accurately localized canals on periapicals under study conditions, suggesting potential as a rapid, chairside aid when 3D imaging is unavailable or deferred. Full article

Background/Objectives: Thymoma is a rare epithelial neoplasm originating from the thymus gland, and its accurate detection and classification using computed tomography (CT) images remain diagnostically challenging due to subtle morphological similarities with other mediastinal pathologies. This study presents a deep learning (DL)-based model designed to improve diagnostic accuracy for both thymoma detection and subtype classification (benign vs. malignant). Methods: The proposed approach integrates a pre-trained VGG16 network for efficient feature extraction—capitalizing on its capacity to capture hierarchical spatial features—and an MLP-Mixer-based feature enhancement module, which effectively models both local and global feature dependencies without relying on conventional convolutional mechanisms. Additionally, customized preprocessing and post-processing methods are employed to enhance image quality and suppress redundant data. The model’s performance was evaluated on two classification tasks: distinguishing thymoma from healthy cases and discriminating between benign and malignant thymoma. Comparative analysis was conducted against state-of-the-art DL models including ResNet50, ResNet34, SEResNeXt50, InceptionResNetV2, MobileNetV2, VGG16, InceptionV3, and DenseNet121 using metrics such as F1 score, accuracy, recall, and precision. Results: The model proposed in this study obtained its best performance in thymoma vs. healthy classification, with an accuracy of 97.15% and F1 score of 80.99%. In the benign vs. malignant task, it attained an accuracy of 79.20% and an F1 score of 78.51%, outperforming all baseline methods. Conclusions: The integration of VGG16’s robust spatial feature extraction and the MLP-Mixer’s effective feature mixing demonstrates superior and balanced performance, highlighting the model’s potential for clinical decision support in thymoma diagnosis. Full article