Search Results (2,754)

Spatial heterogeneity in soil deposition poses a significant challenge to accurate geotechnical characterization, which is essential for sustainable infrastructure development. This study presents an advanced geotechnical data-driven mapping framework, based on a monotonized and augmented formulation of Shepard’s inverse distance weighting (IDW) algorithm, implemented through the Google Earth Engine (GEE) platform. The approach is rigorously evaluated through a comparative analysis against the classical IDW and Kriging techniques using standard key performance indices (KPIs). Comprehensive field and laboratory data repositories were developed in accordance with international geotechnical standards (e.g., ASTM). Key geotechnical parameters, i.e., standard penetration test (SPT-N) values, shear wave velocity (Vs), soil classification, and plasticity index (PI), were used to generate high-resolution geospatial models for a previously unmapped region, thereby providing essential baseline data for building infrastructure design. The results indicate that the augmented IDW approach exhibits the best spatial gradient conservation and local anomaly detection performance, in alignment with Tobler’s First Law of Geography, and outperforms Kriging and classical IDW in terms of predictive accuracy and geologic plausibility. Compared to classical IDW and Kriging, the augmented IDW algorithm achieved up to a 44% average reduction in the RMSE and MAE, along with an approximately 30% improvement in NSE and PC. The difference in spatial areal coverage was found to be up to 20%, demonstrating an improved capacity to model spatial subsurface heterogeneity. Thematic design maps of the load intensity (LI), safe bearing capacity (SBC), and optimum foundation depth (OD) were constructed for ready application in practical design. This work not only establishes the inadequacy of conventional geostatistical methods in highly heterogeneous soil environments but also provides a scalable framework for geotechnical mapping with accuracy in data-poor environments. Full article

In this paper, we propose a deterministic epidemic model for malware propagation in wireless mobile environments, taking into account the presence of hotspots. Conventional epidemic models for wireless mobile environments typically assume that mobile hosts randomly move in a given area. However, in real-world scenarios, hotspots such as restaurants and universities often attract large gatherings of people. Therefore, the assumption of purely random movement is not suitable for modeling these scenarios. To realistically represent the movement patterns of mobile hosts, we incorporate the influence of hotspots into our model. Specifically, we formulate a system of Ordinary Differential Equations (ODEs) that captures the infection dynamics in scenarios where one or more hotspots affect host mobility. Our model introduces non-uniform movement probabilities based on cosine similarity, allowing us to reflect the tendency of mobile hosts to cluster around specific locations. By applying these movement probabilities to the ODEs, our epidemic model provides a more accurate representation of malware spreading behavior in hotspot-affected environments. We investigate how malware propagates through mobile hosts and compare the dynamics between the conventional and our proposed models. Numerical experiments demonstrate that our epidemic model effectively captures malware spreading behavior in mobile scenarios with hotspots. Full article

The present paper treats the problem of steady laminar MHD flow of an incompressible viscous fluid for mixed convection stagnation-point flow over a vertical stretching sheet in the presence of an externally magnetic field. By means of the Optimal Auxiliary Functions Method (OAFM), the resulting nonlinear ODEs are semi-analytically solved. The impact of various physical parameters, such as the velocity ratio parameter A, the Prandtl number $Pr$, and the Hartmann number $Ha$, on the behavior of velocity and temperature profiles is analyzed. Both assisting ($\lambda >0$) and opposing ($\lambda <0$) flows are considered. The influence of these parameters is tabulated and graphically presented. The originality of this work lies in the development of effective semi-analytical solutions and in the excellent agreement between these solutions and the corresponding numerical solutions. This highlights the accuracy of the proposed method applied to steady laminar MHD flow. A comparative analysis underlines the advantages of the OAFM compared to the iterative method. The obtained results confirm that the OAFM represents a competitive mathematical tool to explore a large class of nonlinear problems with applications in engineering. Full article

The oscillatory properties of pendular motion, along with the associated energetic conditions, are used to induce analytical functions capable of simultaneously describing the angular position and velocity. To describe the angular position of a generic pendulum, for very large amplitudes of oscillation, we used the numerical solutions obtained from the numerical resolution of the differential equation of motion. The solver software needed was built using the LabView 2019 platform, but any other ODE solver containing peak and valley detectors can be used. The fitting software and plots were performed with the ORIGIN 7.0 program, but also other equivalent programs can be used. For a non-damped pendulum, an analytical model is proposed, built from simple trigonometric functions, but containing the important physical information of the dependence between the period and amplitude of oscillation. The application of the proposed model, using the numerical solutions of the non-approximated differential equation of motion, shows very good agreement, less than 0.01%, for large amplitudes, up to 3π/4. Full article

Background: Autologous osteochondral transplantation (AOT)—the transfer of hyaline cartilage with its underlying subchondral bone—is well established for focal osteochondral lesions, yet evidence for larger (>200 mm²) defects is limited. We assessed clinical and functional outcomes of AOT in patients with osteochondral knee lesions exceeding 200 mm². Methods: In this retrospective cohort study, 52 patients underwent AOT for full-thickness osteochondral defects of the femoral condyles or patellofemoral joint. All lesions were ≥200 mm² and treated with a standardized press-fit technique using one to four overlapping cylindrical grafts. Pain and knee function were evaluated preoperatively and at 3, 6, and 12 months postoperatively with the Visual Analogue Scale (VAS), Tegner–Lysholm Knee Score (TLKS), and Knee Society Score (KSS). Results: Mean defect size was 224.4 ± 84.5 mm². The VAS improved from 6.32 ± 1.1 preoperatively to 0.72 ± 0.6 at 12 months (p < 0.001). The TLKS rose from 58.6 ± 11.4 to 95.0 ± 6.8 and the KSS from 63.8 ± 12.2 to 97.4 ± 4.9 during the same period (both p < 0.001). Most gains occurred within the first 3–6 months and were sustained at 12 months. No major surgical complications were observed, and outcomes were unaffected by age, sex, or graft number/size. Conclusions: AOT is a safe, effective option for large osteochondral knee defects (>200 mm²), offering rapid, durable pain relief and excellent functional recovery while preserving native joint structures. Accurate donor site reconstruction and precise graft placement in the weight-bearing zone appear critical for optimal results. Longer-term prospective studies are needed to confirm durability and refine patient-selection criteria. Full article

Oxide dispersion-strengthened (ODS) alloys are regarded as one of the most promising materials for Generation IV nuclear fission systems, owing to their exceptional attributes such as high strength, corrosion resistance, and irradiation tolerance. The traditional methods for fabricating oxide dispersion-strengthened (ODS) alloys are both time-consuming and costly. In contrast, additive manufacturing (AM) technologies enable precise control over material composition and geometric structure at the nanoscale, thereby enhancing the mechanical properties of components while reducing their weight. This novel approach offers significant advantages over conventional techniques, including reduced production costs, improved manufacturing efficiency, and more uniform distribution of oxide nanoparticles. This review begins by summarizing the state of the art in Fe-based and Ni-based ODS alloys fabricated via traditional routes. Subsequently, it examines recent progress in the AM of ODS alloys, including Fe-based, Ni-based, high-entropy alloys, and medium-entropy alloys, using powder bed fusion (PBF), directed energy deposition (DED), and wire arc additive manufacturing (WAAM). The microstructural characteristics, including oxide particle distribution, grain morphology, and alloy properties, are discussed in the context of different AM processes. Finally, critical challenges and future research directions for laser-based AM of ODS alloys are highlighted. Full article

Edge detection of space targets is vital in aerospace applications, such as satellite monitoring and analysis, yet it faces challenges due to diverse target shapes and complex backgrounds. While deep learning-based edge detection methods dominate due to their powerful feature representation capabilities, they often suffer from large parameter sizes and lack explicit geometric prior constraints for space targets. This paper proposes a novel edge detection method for satellite targets based on knowledge distillation, namely STED-KD. Firstly, a multi-stage distillation strategy is proposed to guide a lightweight, fully convolutional network with fewer parameters to learn key features and decision boundaries from a complex teacher model, achieving model efficiency. Next, a shape prior guidance module is integrated into the student branch, incorporating geometric shape information through shape prior model construction, similarity metric calculation, and feature reconstruction, enhancing adaptability to space targets and improving detection accuracy. Additionally, a curvature-guided edge loss function is designed to ensure continuous and complete edges, minimizing local discontinuities. Experimental results on the UESD space target dataset demonstrate superior performance, with ODS, OIS, and AP scores of 0.659, 0.715, and 0.596, respectively. On the BSDS500, STED-KD achieves ODS, OIS, and AP scores of 0.818, 0.829, and 0.850, respectively, demonstrating strong competitiveness and further confirming its stability. Full article

Early testicular development is vital for adult male fertility but remains highly vulnerable to stress during the suckling stage. Fermented Chinese chive (Allium tuberosum) is known for its antioxidant and immunomodulatory properties, yet its role in testicular development remains unclear. In this study, Songliao Black piglets received 3‰ fermented Chinese chive (LK group) mixed with starter feed and compared to a control (OD group). Testicular samples at weaning (28 days) underwent transcriptomic and metabolomic analyses. Although no significant differences were observed in gross testicular morphology, the LK group significantly increased individual (13.85%) and litter (15.11%) weaning weights (p < 0.05), with elevated serum triglycerides, total cholesterol, and a 32.2% rise in IgG levels (p < 0.05). Integrated analysis identified 76 shared pathways, including ferroptosis, insulin resistance, PI3K-Akt, MAPK, and cAMP signaling. Upregulated genes in the LK group were mainly related to energy metabolism, antioxidant defense, immune regulation, steroidogenesis, and neuroendocrine signaling, suggesting improved metabolic activity, reduced oxidative stress, and accelerated reproductive maturation. Molecular docking indicated that kaempferol and isorhamnetin from Chinese chive bind strongly to proteins involved in testicular development. Overall, fermented Chinese chive supplementation enhances early testicular development in suckling piglets via integrated modulation of metabolic, immune, and signaling pathways, providing a nutritional strategy to optimize reproductive potential in breeding boars. Full article

This work presents an image processing algorithm for the segmentation of the personalized mapping of retinal nerve fiber layer (RNFL) bundle trajectories in the human retina. To segment RNFL bundles, preprocessing steps were used for noise reduction and illumination correction. Blood vessels were removed. The image was fed to a maximum–minimum modulation algorithm to isolate retinal nerve fiber (RNF) segments. A modified Garway-Heath map categorizes RNF orientation, assuming designated sets of orientation angles for aligning RNFs direction. Bezier curves fit RNFs from the center of the optic disk (OD) to their corresponding end. Fundus images from five different databases (n = 300) were tested, with 277 healthy normal subjects and 33 classified as diabetic without any sign of diabetic retinopathy. The algorithm successfully traced fiber trajectories per fundus across all regions identified by the Garway-Heath map. The resulting trace images were compared to the Jansonius map, reaching an average efficiency of 97.44% and working well with those of low resolution. The average mean difference in orientation angles of the included images was 11.01 ± 1.25 and the average RMSE was 13.82 ± 1.55. A 24-2 visual field (VF) grid pattern was overlaid onto the fundus to relate the VF test points to the intersection of RNFL bundles and their entry angles into the OD. The mean standard deviation (95% limit) obtained 13.5° (median 14.01°), ranging from less than 1° to 28.4° for 50 out of 52 VF locations. The influence of optic parameters was explored using multiple linear regression. Average angle trajectories in the papillomacular region were significantly influenced (p < 0.00001) by the latitudinal optic disk position and disk–fovea angle. Given the basic biometric ground truth data (only fovea and OD centers) that is publicly accessible, the algorithm can be customized to individual eyes and distinguish fibers with accuracy by considering unique anatomical features. Full article

Purpose: To investigate genotype–phenotype correlations in PRPH2-retinopathies in a cohort of 36 patients from the Oxford Eye Hospital and report on novel pathogenic variants. Methods: Clinical data, including best corrected visual acuities (BCVA), fundus autofluorescence (FAF), and optical coherence tomography (OCT) imaging, were analysed. Genetic testing was performed using next-generation sequencing (NGS). Results: In this cohort, 26 different PRPH2 variants, including 8 novel variants, were identified. Variants were clustered in the D2 loop of the protein. A diverse range of phenotypes were observed: pseudo-Stargardt pattern dystrophy (PSPD) (47.2%), adult-onset vitelliform macular dystrophy (AVMD) (22.2%), pattern dystrophy (PD) (25.0%), atypical macular dystrophy (2.8%), and retinitis pigmentosa (RP) (2.8%). The mean age of symptom onset was 44.0 ± 14.4 years. Mean BCVA was 0.20 ± 0.54 logMAR OD and 0.14 ± 0.29 logMAR OS at baseline and 0.33 ± 0.40 logMAR OD and 0.32 ± 0.40 logMAR OS after a mean follow up duration of 6.0 ± 3.2 years (range 1–11 years). A thickened ellipsoid zone (EZ) was noted in 34/36 patients with a mean EZ thickness of 44.3 ± 11.3 µm OD and 42.7 ± 11.6 µm OS. No clear genotype–phenotype correlations were observed. Conclusions: The significant phenotypic range described in this study is consistent with the previously reported phenotypic variability in PRPH2 retinopathy and emphasises the complexity of establishing genotype–phenotype correlations in this disease. The thickness of the EZ on OCT may serve as a useful biomarker in distinguishing PRPH2 retinopathy from other phenocopies. These findings contribute to improved understanding of PRPH2 retinopathy and help inform diagnosis and genetic counselling. Full article

Welsh onion (Allium fistulosum L.), a globally significant vegetable, flavoring agent, and phytomedicine resource, has remained unavailable with established transient expression platforms for functional genomic investigations. To address this critical methodological limitation, we present systematically optimized protocols for both Agrobacterium-mediated hairy root transformation and protoplast transient expression systems, achieving significant advances in transformation efficiency for this species. Through systematic optimization of key parameters, including Agrobacterium rhizogenes (A. rhizogenes) strain selection (with Ar.Qual demonstrating superior performance), explant type efficacy, bacterial suspension optical density (OD₆₀₀ = 0.3), and acetosyringone induction concentration (100 μM), we established a highly efficient stem disc infection methodology, achieving 88.75% hairy root induction efficiency. Subsequent optimization of protoplast isolation protocols identified the optimal enzymatic digestion conditions: 6-h dark digestion of young leaves using 1.0% (w/v) Cellulase R-10, 0.7% (w/v) Macerozyme R-10, and 0.4 M mannitol, yielding 3.3 × 10⁶ viable protoplasts g⁻¹ FW with 90% viability. System functionality validation through PEG-mediated transient transformation demonstrated successful green fluorescent protein (GFP) reporter gene expression, confirmed by fluorescence microscopy. As the first documented transient expression platforms for Welsh onion, these protocols enable essential molecular investigations, including in planta promoter activity profiling, subcellular protein localization, and CRISPR-based genome-editing validation. This methodological breakthrough overcomes previous technical constraints in Welsh onion molecular biology, providing critical tools for accelerated gene functional characterization in this agriculturally important species. Full article

Diabetic retinopathy (DR), a critical ocular disease that can lead to blindness, demands early and accurate diagnosis to prevent vision loss. Current automated DR diagnosis methods face two core challenges: first, subtle early lesions such as microaneurysms are often missed due to insufficient feature extraction; second, there is a persistent trade-off between model accuracy and efficiency—lightweight architectures often sacrifice precision for real-time performance, while high-accuracy models are computationally expensive and difficult to deploy on resource-constrained edge devices. To address these issues, this study presents a novel deep learning framework integrating depthwise separable convolution and a multi-view attention mechanism (MVAM) for efficient DR diagnosis using retinal images. The framework employs multi-scale feature fusion via parallel 3 × 3 and 5 × 5 convolutions to capture lesions of varying sizes and incorporates Gabor filters to enhance vascular texture and directional lesion modeling, improving sensitivity to early structural abnormalities while reducing computational costs. Experimental results on both the diabetic retinopathy (DR) dataset and ocular disease (OD) dataset demonstrate the superiority of the proposed method: it achieves a high accuracy of 0.9697 on the DR dataset and 0.9669 on the OD dataset, outperforming traditional methods such as CNN_eye, VGG, and UNet by more than 1 percentage point. Moreover, its training time is only half that of U-Net (on DR dataset) and VGG (on OD dataset), highlighting its potential for clinical DR screening. Full article

This study investigates the spatiotemporal optimization of small-scale emergency infrastructure in high-density urban environments, using nucleic acid testing sites in Macao as a case study. The objective is to enhance emergency responsiveness during future public health crises by aligning infrastructure deployment with dynamic patterns of population influx. A behaviorally informed spatial decision-making framework is developed through the integration of kernel density estimation, point-of-interest (POI) distribution, and origin–destination (OD) path simulation based on an Ant Colony Optimization (ACO) algorithm. The results reveal pronounced temporal fluctuations in testing demand—most notably with crowd peaks occurring around 12:00 and 18:00—and highlight spatial mismatches between existing facility locations and key residential or functional clusters. The proposed approach illustrates the feasibility of coupling infrastructure layout with real-time mobility behavior and offers transferable insights for emergency planning in compact urban settings. Full article

Urban wastewater is composed of nutrients such as nitrogen and phosphorus, organic matter, heavy metals, pathogens, and micropollutants. If untreated, these contribute to eutrophication and environmental degradation. Microalgae-based bioremediation offers a sustainable solution, showing promise for pollutant removal and high-value bioproduct generation. This study evaluates the efficacy of Galdieria sulphuraria ACUF 427 in treating urban wastewater, with a focus on nutrient removal and phycocyanin production at different optical densities (OD 2, OD 4, and OD 6). Nutrient removal rates (RRs) were analysed for ammonium nitrogen (N-NH₄⁺), ammonia nitrogen (N-NH₃), phosphate phosphorus (P-PO₄³⁻), and chemical oxygen demand (COD). The RR for N-NH₄⁺ increased with optical density, reaching 7.49 mg/L/d at an optical density of 6. Similar trends were observed for N-NH₃ and P-PO₄³⁻, with peak removal at OD 6. COD removal remained high across all ODs, though differences between OD 4 and OD 6 were not statistically significant. Significant variations (p < 0.05) in nutrient removal were noted across the ODs, except for COD between OD 4 and OD 6. Biomass growth and phycocyanin production were significantly higher in the wastewater compared to the control (Allen Medium), with the most effective performance observed at an optical density (OD) of 6. Maximum growth rates were 0.241 g/L/d at OD 6, 0.178 g/L/d at OD 4, and 0.120 g/L/d at OD 2. These results highlight the potential of G. sulphuraria as an agent for wastewater bioremediation and the production of high-value compounds, particularly at elevated cell densities, where we achieved superior nutrient removal and biomass production. Full article

The Zigui Basin, located in the Three Gorges Reservoir Area, has developed numerous landslides due to its interlayering of sandstone and mudstone, geological structure, and reservoir operations. This study identifies a fourth type of landslide failure mode: an oblique-dip slope wedge (OdSW) landslide, based on the Wanshuitian landslide. Following four heavy rainfall events from 3 to 13 July 2024, this landslide exhibited significant deformation on the 17th and was completely destroyed within 40 min. The dimensions of the landslide were 350 m in length, 160 m in width, and 20 m in thickness, with a volume estimated at 8.0 × 10⁵ m³. The characteristics of landslide deformation and the changes in moisture content within the shallow slide body were ascertained using unmanned aerial vehicles, moisture meters, and mobile phone photography. The landslide was identified to have occurred within the weathered residual layer of mudstone, situated between two sandstone layers, with the eastern boundary defined by an inclined rock layer. Upon transitioning into the accelerated deformation stage, the landslide initially exhibited uniform overall sliding deformation, culminating in accelerated deformation destruction. The dip structure created terrain disparities, resulting in a step-like terrain on the left bank and gentler slopes on the right bank, with interbedded soil and rock in a shallow layer, because the interlayered soft and hard geological conditions caused varied weathering and erosion patterns on the riverbank slopes. The interbedded weak–hard stratum layer fostered the development of the oblique-dip slope wedge landslide. Based on the improved Green–Ampt model, we developed a stability prediction methodology for an oblique-dip slope wedge landslide and determined the rainfall infiltration depth threshold of the Wanshuitian landslide (9.8 m). This study aimed not merely to sharpen the evolution mechanism and stability prediction of the Wanshuitian landslide but also to formulate more effective landslide-monitoring strategies and emergency management measures. Full article