Search Results (5,566)

Mediterranean meadows of Posidonia oceanica are chronically exposed to complex mixtures of environmental contaminants, including metals and trace elements derived from coastal urbanization, maritime traffic, and industrial activities. This study aimed to assess metal-related toxicity in P. oceanica by integrating multi-element burden analysis with a panel of oxidative stress biomarkers. Concentrations of a wide suite of elements were quantified in samples of internal (juvenile), intermediate, and external (adult) leaves, reflecting the ontogenetic structure of the plant. Oxidative responses were evaluated using five biomarkers [i.e., hydrogen peroxide (H₂O₂), lipid peroxidation (TBARS), superoxide dismutase (SOD), glutathione S-transferase (GST), and catalase (CAT)] measured on each leaf compartment. Biomarker data were standardized and integrated into a merged Stress Index summarizing overall physiological toxicity. Associations between individual elements, the sum of all measured elements (ΣallElements), the Stress Index, and single biomarkers were explored using Pearson correlation analysis. Juvenile leaves exhibited the highest Stress Index values, elevated H₂O₂ and TBARS, and marked activation of SOD and GST, indicating early oxidative toxicity. Intermediate leaves showed a trend toward increased CAT activity, not reaching statistical significance, along with minimal damage, suggesting effective detoxification, whereas adult leaves accumulated higher levels of Fe, Ni, and Pb, but displayed moderate stress responses. Overall, leaf-class structure strongly modulated both exposure and toxicological response. The integration of ΣAllElements with multi-biomarker indices provides a robust framework for diagnosing metal-related toxicity in P. oceanica under realistic multi-element exposure scenarios. Full article

Binder jetting relies on the infiltration of binder droplets into a porous powder bed, where the spatial arrangement of droplets critically influences feature formation and structural integrity. In particular, the role of droplet spacing in regulating infiltration behavior remains insufficiently understood. In this study, droplet infiltration is investigated using a reconstructed three-dimensional powder bed combined with a Volume of Fluid (VOF) model. Both single- and dual-droplet configurations are examined to isolate the effect of droplet spacing on spreading, merging, and capillary-driven penetration. The results show that droplet spacing governs the redistribution of liquid flow between lateral spreading and vertical infiltration. Three distinct regimes are identified as spacing decreases: independent infiltration at large spacing, cooperative merging at intermediate spacing, and over-penetration at small spacing. These regimes reflect a transition from isolated droplet behavior to strongly coupled infiltration within the pore network. An optimal spacing of approximately 150 μm is found to balance spreading and penetration, enabling continuous deposition with controlled infiltration depth. Experimental measurements show good agreement with numerical predictions, with an average deviation of 8.66%. The present study clarifies the mechanism by which droplet spacing controls infiltration behavior and provides practical guidance for parameter selection in binder jetting processes. Full article

Accurate detection of ship targets in complex marine environments is fundamental to ensuring maritime security and safeguarding maritime rights. With the increasing diversity of vessel types and configurations, achieving precise identification of multiple ship classes amidst dynamic interference and cluttered backgrounds has emerged as a formidable challenge in marine surveillance. To address three pervasive issues in ship target detection—namely, high false-negative rates for small targets, inadequate feature discrimination, and imprecise localization—this paper proposes AK-DSAM-YOLOv13, a multi-scale detection algorithm specifically tailored for complex marine scenarios. Built upon the YOLOv13n architecture, the proposed algorithm implements integrated optimizations across the backbone network, neck structure, and loss function. First, a lightweight cross-scale feature extraction module, AKC3k2, is constructed by incorporating Alterable Kernel Convolutions (AKConv) to reconstruct the feature extraction path, thereby significantly enhancing the representation of multi-scale targets. Second, a Dynamic Up-Sampling Dual-Stream Attention Merging (DyDSAM) structure is designed, which integrates the DySample operator with a Dual-Stream Attention Mechanism (DSAM) to effectively suppress background clutter and improve feature fusion accuracy. Third, an Accuracy-Intersection-over-Union (AIoU) loss function is introduced to jointly optimize overlap area, center distance, and aspect ratio, enhancing localization robustness for small-scale objects. Experimental results on the self-built CM-Ships dataset, as well as the public SeaShips and McShips datasets, demonstrate that AK-DSAM-YOLOv13 significantly outperforms baseline models in detection accuracy, recall, and generalization capability while maintaining a low computational overhead. This research provides an efficient and reliable technical framework for intelligent maritime visual monitoring in complex environments. Full article

Meaningful partitioning of 2D binary shapes remains a challenging problem in shape analysis because many existing methods rely mainly on local geometric rules or skeleton simplification, which often struggle to separate the main body of a shape from its protruding parts in a perceptually meaningful way. This limitation becomes more evident in shapes with thin limbs, branching structures, or irregular extensions, where preserving topology while achieving human-consistent decomposition is difficult. We present a fully automatic framework for the hierarchical partitioning of 2D binary shapes into semantically meaningful core bodies and protruding limbs (tails). The pipeline begins by generating candidate structural lines through multi-directional centroid tracking along horizontal, vertical, and diagonal (±45°) bands. Three direction-specific Sugeno fuzzy controllers first evaluate these lines based on normalized length, angular alignment, and minimum distance to the boundary. A second pair of fuzzy systems then classifies segments as either tails or core parts using thickness statistics derived from the distance transform. For ambiguous merged tail groups, iterative midpoint splitting is applied until stable labeling is achieved. High-curvature boundary corners are then detected via signed turning-angle analysis, and candidate cutting rays are assessed through exact region splitting, tail area measurement, and label purity analysis. An adaptive third-stage fuzzy controller ranks these candidates according to cut length, purity, and area. The highest-scoring non-overlapping cuts are executed iteratively, progressively peeling peripheral parts while preserving the overall topology and symmetry of the shape. The proposed framework is evaluated on a targeted subset of 32 categories from the 2D Shape Structure Dataset Results on this evaluated subset indicate that the method produces coherent and topologically consistent partitions, with competitive agreement with the available human-annotated references. This training-free framework provides an interpretable tool for 2D shape analysis, with potential applications in object recognition, computer animation, and symmetry studies. Full article

Infrastructure projects of significant scale face persistent challenges in data coordination, scheduling, and cost control. Although individual digital tools are widely adopted in the construction sector, empirical evidence on their coherent systemic integration within a unified management cycle remains limited. This explanatory case study addresses that gap by examining ^{Section 3} of the Sibiu–Făgăraș Motorway (17.61 km, 27 structures) in Romania—an ongoing TEN-T project. Evidence was collected during the active construction phase (January 2022–December 2024) from Common Data Environment (CDE) logs, BIM/BrIM model outputs, drone photogrammetry datasets, schedule and payment records, and Business Intelligence (BI) dashboards. The study demonstrates how six digital applications—CDE, model-based fabrication (LOD 400), 3D coordination, 4D/5D simulation, reality capture, and BI dashboards—were operationalized as a closed-loop Plan–Do–Check–Act (PDCA) cycle, functioning as a human-in-the-loop digital twin for project Please check if this address is duplicate with aff .1 or not. If so, please merged them into one and revise the author’s associated number and ensure that each number in numerical order. delivery. Illustrative operational indicators observed during implementation include an estimated 20% reduction in coordination-related RFIs, a 15% reduction in steel fabrication material waste, a reduction in payment validation cycle time from 15 days to approximately 2 days, and a 40% improvement in cash flow stability through data-driven activity re-sequencing. These findings suggest that systemic digital integration, rather than isolated tool adoption, supports more proactive and sustainability-aligned infrastructure project control. Full article

The interpretation of geophysical multi-attribute surveys is often subjective and complicated by large datasets, prompting the need for automated fusion methods that preserve structures and enhance anomalies. This study introduces an image fusion approach that combines the non-subsampled contourlet transform (NSCT) with the New Metric Parameter (NMP) rule to integrate multi-source polarizability and resistivity data for copper–nickel exploration. Using NSCT, source images are decomposed into multi-scale, multi-directional low- and high-frequency sub-bands. Low-frequency components are fused through dynamic weighting, while high-frequency components are merged using the NMP rule. The sensitivity to key parameters—such as low-frequency weight, grid size, and grid angle—was assessed using field data. Results indicate that NSCT + NMP fusion enhances spatial resolution and boundary definition of anomalies, effectively merging low resistivity with high polarizability signals. Quantitative field validation shows that 82.43% of the gabbroic mineralization zone has a judging coefficient below 0.45, confirming the fusion accuracy. Optimal parameter choices include dynamically adjusted low-frequency weights, a grid size that balances detail and noise suppression, and a 45° square grid for directional neutrality. This method offers a practical strategy for joint multi-physical data analysis and improved spatial recognition of mineralized bodies in exploration. Full article

Background/Objectives: Bimanual movements with a 90° relative phase are typically unstable but can be facilitated by Lissajous visual feedback, which integrates the movements of the two hands into a single visual representation. We examined whether such visual integration leads to a unified sensorimotor representation by testing whether unilateral tactile stimulation suppresses motor output bilaterally during bimanual force production. Methods: Fifteen healthy participants produced rhythmic bimanual index finger flexion with a 90° relative phase under two feedback conditions: Lissajous feedback and individual visual feedback. In each trial, vibrotactile stimulation was applied to either hand or not applied at one of four phases of the force cycle. Force trajectory error and post-stimulus electromyographic (EMG) activity of the first dorsal interosseous muscle were analyzed. Results and Discussion: Lissajous feedback reduced force trajectory error compared with individual feedback. Tactile stimulation did not produce bilateral suppression of motor output. This indicates that visual integration of bimanual movements does not lead to global bilateral suppression of motor output induced by unilateral tactile stimulation. A significant reduction in post-stimulus EMG amplitude was observed only when the right hand was stimulated during one phase of the Lissajous feedback task. This suppression may reflect the unmasking of the tactile stimulus-induced inhibition within sensorimotor processes in the left hemisphere when visual feedback of the two hands is merged into a single representation. Full article

Crude fidaxomicin contains difficult-to-separate impurities, and conventional dual-step purification usually requires intermediate concentration and transfer, which increases process complexity and may aggravate product loss or degradation. To address this challenge, this study exploits the complementary selectivity of methanol/water (80/20, v/v) and acetonitrile/water (70/30, v/v) binary mobile phases and proposes two purification processes based on step-gradient twin-column recycling chromatography, namely spatial integration and system integration. In the spatial integration strategy, dual-stage separations that are conventionally performed in separate chromatographic systems are sequentially integrated into a single twin-column recycling system in combination with on-line heart-cutting, thereby eliminating intermediate off-line processing steps. In contrast, the system integration strategy merges the two binary mobile phases in defined proportions to construct a single ternary mobile phase composed of methanol/acetonitrile/water (37.5/37.5/25, v/v/v), enabling one-step complete separation. The results demonstrate that the spatial integration strategy, employing binary mobile-phase switching, produces fidaxomicin with a purity of 99.9%, recoveries ranging from 75.27% to 78.77%, and productivities ranging from 307.22 to 328.82 g·L⁻¹·day⁻¹, regardless of the switching sequence. The system integration strategy, based on one-step elution with the ternary mobile phase, achieves the same product purity of 99.9% without mobile-phase switching, with a recovery of 70.41% and a productivity of 246.33 g·L⁻¹·day⁻¹. These results confirm the applicability and flexibility of both integrated strategies for fidaxomicin purification, while indicating that the spatial integration strategy provides better overall preparative performance and the system integration strategy offers a simpler one-step operation. Full article

Hydraulic fracturing of unconventional reservoirs requires accurate fracture monitoring for treatment optimization. Low-frequency distributed acoustic sensing (LF-DAS) in neighbor wells provides dense strain-rate observations, but gauge-length averaging limits spatial resolution and merges closely spaced fracture features. This study formulates gauge-length averaging as an explicit convolution operator and develops a regularized inversion method combining Tikhonov smoothing, a recursive prior, and L-curve parameter selection, supported by a semi-analytical multi-fracture forward model. On a synthetic benchmark, the method advances the effective resolution from the 10 m gauge-length scale to the 1 m sample-spacing scale, recovering fracture count in all hit-window time slices (versus 32% for raw data), achieving Pearson correlation of 0.80 versus 0.29, with peak-position error reduced by 47%. Noise-sensitivity analysis indicates a practical SNR floor near 20 dB, and Wiener-filter comparison confirms 1.5–2.7× correlation and 1.5–2.3× peak-count advantages across tested noise levels. Field application to HFTS-2 B1H stages 22 and 23 reveals previously hidden tensile features consistent with higher local fracture density. With per-stage processing in seconds and no extra sensing hardware, the method is well suited for near-real-time deployment. Full article

Digital management of modern railway bridges, a substantial part of high-speed railway networks, is often hindered by manual interpretation of construction drawings for Building Information Modeling (BIM). While individual technologies like optical character recognition (OCR) and neural networks are well-established, their generic application often fails on complex engineering documents. To address this, a domain-adaptive automatic recognition and semantic interpretation framework is proposed for railway bridge construction drawings. The novelty of this work lies in a specialized hybrid data fusion strategy that intelligently merges vector CAD file parsing with morphology-denoised OCR, resolving spatial and semantic conflicts. Furthermore, a back-propagation (BP) neural network is explicitly adapted to classify the extracted text into specific engineering categories, overcoming the challenges of dense layouts and overlapping symbols. Finally, the framework achieves end-to-end integration by transforming these semantic entities directly into structured, IFC-compatible BIM parameters. Evaluated on 250 real-world drawings, the framework achieved an average F1-score of 91.0% in semantic classification and improved processing efficiency by 6.5 times compared to manual methods. Moreover, 93.8% of the extracted entities achieved strict BIM parameter correctness, defined as seamless mapping to Revit IFC attributes without manual intervention. Full article

Cultivated land, as a vital resource for human sustenance, requires region-specific protection strategies worldwide. Semantic segmentation technology for agricultural land remote sensing imagery offers a scientific foundation and decision-making support for cultivated land protection through accurate identification and dynamic monitoring. In China, the fragmented distribution, small parcel sizes, complex terrain, and indistinct boundaries of cultivated land pose challenges to the intelligent interpretation of high-resolution remote sensing (HRRS) imagery. Conventional semantic segmentation methods often struggle to address these complexities. To address this issue, we propose a hybrid network called STAR-Net (Swin Transformer Auxiliary Residual Structure) for semantic segmentation of agricultural land in HRRS imagery whose encoder integrates a Global-Local Feature Fusion Module to effectively merge complementary information from both branches. A Multi-Scale Aggregation Module within the decoder facilitates the fusion of shallow spatial details and deep semantic cues, enhancing the model’s ability to discriminate objects at varying scales. Using the LoveDA dataset, we show that STAR-Net generates the highest Intersection over Union (IoU) on the “Barren” and “Forest”, achieving the improvement of 9.88% and 7.05% respectively, while delivering comparable IoU performance on other categories. Overall performance improved by 0.46% in mIoU compared to state-of-the-art models. Across all target categories, the method also achieves the greatest count of leading segmentation metrics. Full article

This paper introduces a novel framework by merging the concepts of non-Archimedean generalized Menger spaces and ($\varkappa$-$ϝ$)-weak proximal contractions. Extending the best proximity point concept to a triple of sets, we establish new existence theorems for these contractions without requiring the probabilistic P-property, representing a meaningful advancement beyond prior findings, which is a significant generalization of existing results. The study leverages two control functions ($\varkappa$ and $ϝ$) within the contraction condition to derive optimal approximate solutions to fixed-point equations for non-self mappings. Consequently, our core results not only extend but also unify a range of established theorems within classical probabilistic and G-metric spaces. We present a significant application to theoretical computer science by proving that a self-mapping acting on infinite words possesses a unique fixed point. Full article

The seedlings of the young Atlas cedar (Cedrus atlantica Manetti) are very important for the regeneration and restoration of forest populations of this endemic species, which inhabits a very fragmented area in the highest mountains of North Africa (Algeria and Morocco). There is very minimal information on the diseases that are afflicting these young plants. In this work, Alternaria strains CHP2, S4.2, and SP1.1 were isolated from different plants and subjected to identification and pathogenicity testing. The infected plants developed clear symptoms of light brown disease spots on the leaves with a yellowish or chlorotic halo around them, which gradually developed to a yellowing of the plantlets and their complete drying. Some spots merged to form large areas of necrosis which covered an average of 80% of the plantlets. The impact of the infection on plant physiology was determined using measurements of photosynthetic pigments, which showed reductions of 46.28% in chlorophyll and 59.90% in carotenoids in strains SP1.1 and CHP2, respectively. Molecular characterization of the ITS region of the isolates revealed that strains CHP2 and S4.2 showed high sequence similarity to reference sequences of Alternaria spp., including taxa related to A. destruens and A. murispora, although species-level identification remains tentative. These findings highlight the growing relevance of fungal pathogens in forest regeneration under global climate change. By revealing the pathogenic role of Alternaria species, this study contributes to sustainable forest management and conservation strategies in changing environments. Full article

Peste des Petits Ruminants (PPR) is a highly contagious viral disease of small ruminants and remains a major threat to food security and rural livelihoods across Africa, the Middle East, and Asia. In the Mediterranean, uneven outbreak reporting and intense spatial clustering hinder the identification of regions where environmental and anthropogenic conditions favor disease occurrence. This study applied an interpretable machine-learning ecological niche modeling framework to characterize PPR spatial suitability in North Africa. A merged outbreak dataset (n = 744) was compiled from the Food and Agriculture Organization (FAO) EMPRES-i and the World Animal Health Information System (WAHIS) databases for 2005–2026. Outbreak locations were linked to environmental and anthropogenic predictors, spatially thinned, and paired with randomly sampled pseudo-absences at a 1:1 ratio. After correlation-based screening and Boruta feature selection, four classifiers were compared under five-fold spatial block cross-validation: a generalized linear model (GLM), a support vector machine (SVM), Random Forest (RF), and extreme gradient boosting (XGBoost). All models showed good discriminatory performance. Random Forest (RF) and extreme gradient boosting (XGBoost) yielded the highest area under the receiver operating characteristic curve value (AUC = 0.94). Random Forest achieved the highest specificity, XGBoost achieved the highest sensitivity, and the support vector machine showed the most even sensitivity–specificity tradeoff among the machine-learning classifiers. Sheep density, mean diurnal temperature range, temperature seasonality, and human population density were consistently the dominant drivers. Predicted PPR suitability based on reported outbreaks was concentrated along the North African coastal belt and low across most arid inland regions. These findings suggest that passive surveillance is likely to be most informative in coastal production systems where host density, environmental suitability, and reporting opportunity overlap. At the same time, areas of lower reported-outbreak suitability should not be interpreted as disease-free and may require complementary active surveillance approaches. Full article

Arrhythmia is a common and potentially life-threatening cardiovascular condition. Photoplethysmography (PPG) has emerged as a noninvasive alternative to electrocardiography for cardiac rhythm monitoring, yet most PPG-based methods remain limited to binary classification. In this study, a new deep learning approach is suggested for categorizing six arrhythmia types from PPG data: sinus rhythm (SR), premature ventricular contraction (PVC), premature atrial contraction (PAC), ventricular tachycardia (VT), supraventricular tachycardia (SVT), and atrial fibrillation (AF). The raw PPG signal is enhanced by extracting its first and second derivatives to capture morphological features not readily apparent in the original signal. A hybrid architecture, VGG-BiLSTM, is utilized, merging VGG convolutional layers for spatial features extraction with bidirectional long short-term memory layers for modeling temporal dependencies. A stratified data splitting strategy is further adopted to address class imbalance across arrhythmia types. A publicly available dataset containing 46,827 PPG segments from 91 individuals was employed to assess the effectiveness of the suggested technique. The method yielded an overall accuracy, sensitivity, specificity and F1 score of 88.7%, 78.5%, 97.6% and 80.5% correspondingly. Full article