Search Results (1,969)

Background: recently, regenerative medicine has introduced a new branch of science that facilitates the repair of damaged tissues and organs in acute myocardial infarction. This study explores the role of the C-type natriuretic peptide (CNP) system in myocardial infarction (MI) and its modulation by Apelin-13 functionalized patches (A-13p). Methods: using an experimental rat model of ischemia/reperfusion, the rats were divided into four groups: Sham, Infarct, Sham with A-13p, and Infarct with A-13p. Cardiac tissue from the infarct, border, and remote zones was analyzed for CNP and its receptors’ mRNA expression via Real-Time PCR. Results: histological analysis, 4 weeks post A-13p implantation, showed no damage from A-13p implantation in either MI or Sham groups, with reduced left ventricle wall thinning in the Infarct group treated with A-13p. CNP mRNA expression was higher in the infarcted groups (p = ns), especially in the border/infarct zone (BZ + IZ), compared to the Sham group (p = 0.05). NPR-B receptor expression was higher in the RZ than in (BZ + IZ), both in the absence (p = 0.02) and presence of patches (p = 0.01), while NPR-C expression was lower. No significant differences were observed in VEGF mRNA levels across the groups. Conclusions: the findings suggest that the CNP system is involved in MI and that A-13p modulates CNP expression, highlighting CNP as a potential target for therapeutic strategies aimed at regulating vascular remodeling and angiogenesis in MI treatment. Full article

The sustainability of resources and ecological integrity are significantly influenced by land use and land cover change (LULCC) dynamics, particularly in ecotonal semi-arid regions where biome transitions are highly sensitive to anthropogenic disturbance and climatic variability. This study aims to assess historical LULCC dynamics and spatial reconfiguration across nine classes (grassland, shrubland, wetlands, forestland, waterbodies, farmed land, built-up land, bare land, and mines/quarries) in the C5 Secondary Drainage Region of South Africa over the three periods 1990–2014, 2014–2022, and 1990–2022. Using the South African National Land Cover datasets and the TerrSet liberaGIS v20.03 Land Change Modeller, this research applied post-classification comparison, transition matrices, asymmetric gain–loss metrics, and patch-based landscape analysis to quantify the magnitude, direction, source–sink dynamics, and spatial reconfiguration of LULCC. Results showed that between 1990 and 2014, Shrubland expanded markedly (+49.1%), primarily at the expense of Grassland, Wetlands, and Bare land, indicating bush encroachment and hydrological stress. From 2014 to 2022, the trend reversed as Grassland increased substantially (+261.2%) while Shrubland declined sharply (−99.3%). Forestland also regenerated extensively (+186%) along riparian corridors, and Waterbodies expanded more than fivefold (+384.6 km²). Over the long period between 1990 and 2022, Built-up land (+30.6%), Cultivated land (+16%), Forestland (+140%), Grassland (+94.4%), and Waterbodies (+25.6%) increased, while Bare land (−58.1%), Mines and Quarries (−56.1%), Shrubland (−98.9%), and Wetlands (−82.5%) decreased. Asymmetric analysis revealed strongly directional transitions, with early Grassland-to-Shrubland conversion likely driven by grazing pressure, fire suppression, and climate variability, followed by a later Shrubland-to-Grassland reversal consistent with fire, herbivory, and ecotonal climate sensitivity. LULC dynamics in the C5 catchment show class-specific spatial reconfiguration, declining landscape diversity (SHDI 1.3 → 0.9; SIDI 0.7 → 0.43), and patch metrics indicating urban and cultivated fragmentation, shrubland loss, and grassland consolidation. Based on these quantified trajectories, we recommend targeted catchment-scale land management, shrubland restoration, and monitoring of anthropogenic hotspots to support ecosystem services, hydrological stability, and sustainable land use in ecotonal regions. Full article

Isogeometric analysis (IGA) is a numerical methodology for solving differential equations by employing basis functions that preserve the exact geometry of the domain. This approach is based on a class of mathematical functions known as NURBS (Non-Uniform Rational B-Splines). Representing a domain with NURBS entities often requires multiple patches, especially for complex geometries. Bivariate NURBS, defined as tensor products, enforce global refinements within a patch and, in multi-patch models, these refinements are propagated to other model patches. The use of T-Splines with extraordinary points offers a solution to this issue by enabling local refinements through unstructured meshes. The analysis of T-Spline models is performed using a Bézier extraction technique that relies on extraction operators that map Bézier functions to T-Spline functions. When generating a T-Spline model, careful attention is required to ensure that all T-Spline functions are linearly independent—a necessary condition for IGA—in order to form T-Splines that are suitable for analysis. In this sense, this work proposes a methodology to automate the generation of bidimensional unstructured meshes for IGA through T-Splines with extraordinary points. An algorithm for generating unstructured finite element meshes, based on domain decomposition of quadrilateral patches, is adapted to construct T-Spline models. Validation models demonstrate the methodology’s flexibility in generating locally refined isogeometric models. Full article

Although extensive evidence notes a nonlinear relationship between urban greenspace and wellbeing, the conditional role of spatial patterns in this relationship has rarely been examined. To address this gap, this study investigates whether and how landscape metrics moderate the nonlinear association between greenspace coverage and life satisfaction (LS) in urban China. Using nationally representative data from the 2015 wave of the Chinese Social Survey (N = 4319 across 321 subdistricts), this study combines individual-level LS scores with high-resolution GlobeLand30 land use data. Moderated quadratic regression models and formal endpoint slope and turning point tests are applied to identify both the shape and dynamics of the greenspace–wellbeing relationship. The analysis reveals a robust U-shaped curve: LS is lowest at moderate greenspace levels and higher at both low and high extremes. Critically, spatial pattern features, including aggregation index, Euclidean nearest neighbor distance, patch density, and patch richness, significantly moderate this relationship. The turning point of the U-shape moves rightward with greater aggregation and leftward with higher fragmentation or richness. While visual presentation indicates that the curve flips at low patch isolation, further statistical analyses indicate insufficient curve steepness. These findings support that the “more is better” argument should be extended to consider both greenspace quantity and spatial configuration in urban planning for optimal wellbeing outcomes. Full article

Accurate and interpretable detection of pulmonary emphysema on low-dose computed tomography (LDCT) remains a critical challenge for large-scale screening and population health studies. This work proposes a quality-controlled and interpretable deep learning pipeline for emphysema assessment using ResNet-152 embeddings. The pipeline integrates automated lung segmentation, quality-control filtering, and extraction of 2048-dimensional embeddings from mid-lung patches, followed by analysis using logistic regression, LASSO, and recursive feature elimination (RFE). The embeddings are further fused with quantitative CT (QCT) markers, including %LAA, Perc15, and total lung volume (TLV), to enhance robustness and interpretability. Bootstrapped validation demonstrates strong diagnostic performance (ROC-AUC = 0.996, PR-AUC = 0.962, balanced accuracy = 0.931) with low computational cost. The proposed approach shows that ResNet embeddings pretrained on CT data can be effectively reused without retraining for emphysema characterization, providing a reproducible and explainable framework suitable as a research and screening-support framework for population-level LDCT analysis. Full article

This study presents a compact bulb-shaped ultra-wideband microstrip patch antenna designed for microwave imaging applications, more specifically, breast tumor detection. Traditional antenna design methods for medical applications are time-consuming. The proposed antenna, designed in CST Microwave Studio 2019 on a Rogers RT 5880 substrate with a slotted ground plane, achieves a bandwidth of 11.1 GHz, a gain of 6.2 dBi, and an efficiency above 80%. In response to the limitations of conventional antenna design approaches, this study introduces a novel machine learning-based approach to accelerate the design process, where a custom CatBoost model predicts key dimensions—feedline width, large circle radius, and small circle radius, based on the performance metrics such as resonant frequency, minimum reflection coefficient, bandwidth, real and imaginary part of impedance. The model achieves a cross-validation score of 95.13% with a mean absolute error of 0.0166 mm, outperforming conventional machine learning approaches. Shapley Additive exPlanations analysis is applied to interpret feature contributions. A prototype is fabricated using the prediction of a machine learning model. The bulb-shaped antenna structure, wide operational bandwidth, consistent gain, and strong sensitivity to tissue dielectric variations enhance its effectiveness for breast tumor detection compared with conventional antennas. Furthermore, experiments with a breast phantom confirmed the prototype’s suitability for detecting dielectric contrasts in tissue, establishing a foundation for machine learning-assisted antenna design in medical imaging. Full article

Rapid urbanization has exacerbated urban-stormwater challenges, highlighting the critical need for coordinated surface-water and groundwater management through rainfall recharge. However, current sponge city construction methods often overlook the crucial role of underground aquifers in regulating the water cycle and mostly rely on simplified engineering approaches. To address these limitations, this study proposes a spatial optimization framework for urban-scale sponge systems that integrates a hydrodynamic model (FVCOM), geographic information systems (GIS), and Monte Carlo simulations. This framework establishes a comprehensive evaluation system that synergistically integrates surface water inundation depth, geological lithology, and groundwater depth to quantitatively assess sponge city suitability. The FVCOM was employed to simulate surface water inundation processes under extreme rainfall scenarios, while GIS facilitated spatial analysis and data integration. The Monte Carlo simulation was utilized to optimize the spatial layout by objectively determining factor weights and evaluate result uncertainty. Using Shenzhen City in China as a case study, this research combined the “matrix-corridor-patch” theory from landscape ecology to optimize the spatial structure of the sponge system. Furthermore, differentiated planning and management strategies were proposed based on regional characteristics and uncertainty analysis. The research findings provide a replicable and verifiable methodology for developing sponge city systems in high-density urban areas. The core value of this methodology lies in its creation of a scientific decision-making tool for direct application in urban planning. This tool can significantly enhance a city’s climate resilience and facilitate the coordinated, optimal management of water resources amid environmental changes. Full article

Drastic cropland expansion and its internal structural changes have had an obvious impact on agricultural landscapes and ecosystem services. However, a prolonged investigation of this effect is still lacking in China’s grain-producing bases, such as Sanjiang Plain. To address this issue, half a century of study on the ‘land trajectory migration–landscape evolution–ecological effect,’ covering the period 1970–2020, was elucidated using the synergistic methodology of spatial analysis technology, the reclamation rate algorithm, the landscape indicator, and the newly established ecosystem service improvement model. Satellite observation results indicate that the cropland area exhibited a substantial expansion trend from 23,672.69 km² to 42,856.17 km² from 1970 to 2020, representing a net change of +19,183.48 km² and a huge growth rate of 81.04%, which led to an obvious improvement in the level of agricultural cultivation. Concurrently, the internal structure of the cropland underwent dramatic restructuring, with rice fields increasing from 6.46% to 53.54%, while upland fields decreased from 93.54% to 46.46%. In different regions, spatially heterogeneous improvements of 2.64–52.47% in agricultural cultivation levels across all cities were observed. From 1970 to 2020, the tracked cropland center of gravity trajectories exhibited a distinct biphasic pattern, initially shifting westward and then followed by a southward transition, accumulating a displacement of 19.39 km². As for the evolved agricultural landscapes, their integrity has improved (SHDI = −0.08%), accompanied by increased connectivity (CON = +8.82%) and patch edge integrity (LSI = −15.71%) but also by reduced fragmentation (PD = −48.14%). Another important discovery was that the evaluated ecosystem services continuously decreased from 2337.84 × 10⁸ CNY in 1970 to 1654.01 × 10⁸ CNY in 2020, a net loss of −683.84 × 10⁸ CNY and a huge loss rate of 33.65%, accompanied by a center–periphery gradient pattern whereby degradation propagated from the low-value central croplands to the high-value surrounding natural covers. These discoveries will play a significant role in guiding farmland structure reformation, landscape optimization, and ecosystem service improvement. Full article

(1) Forest fragmentation and associated edge effects can strongly modify the diversity and distribution of soil invertebrates, yet their responses in temperate floodplain forests remain poorly understood. We investigated myriapod (centipede and millipede) assemblages in a fragmented forest mosaic in the protected landscape area Litovelské Pomoraví (Czech Republic), focusing on the role of stand age, ecotones and key microhabitat variables. (2) Myriapods were sampled continuously during two years using pitfall traps arranged along transects crossing four neighboring patches (clear-cut with seedlings, 10-year-old stand, 87-year-old and 127-year-old Querco–Ulmetum forests). Species diversity was quantified using the Shannon–Wiener index, and patterns were analyzed by t-tests, canonical correspondence analysis and generalized additive models. (3) We collected over six thousand individuals (10 centipede and 10 millipede species). Diversity peaked in old-growth stands and adjacent ecotones, and two of the three ecotones supported particularly high species abundances. Litter cover and thickness, stand age, and the structure of the herb and shrub layers were the most important predictors of species distributions. Dominant species (e.g., Glomeris tetrasticha Brandt, 1833, Lithobius mutabilis L. Koch, 1862, L. forficatus (Linnaeus, 1758)) showed contrasting habitat preferences, reflecting niche differentiation along microhabitat and stand-age gradients. (4) Our findings indicate that conserving a fine-grained mosaic of stand ages, together with structurally complex forest interiors and ecotones, is essential for maintaining myriapod diversity and the ecosystem functions they provide in Central European forests. Full article

Hydrogen permeation parameters of PA12 were obtained through high-pressure hydrogen permeation experiments conducted under various temperature and pressure conditions. The temperature-dependent mechanism governing the hydrogen permeation behavior of PA12 was further examined using dynamic mechanical analysis (DMA). A multi-field coupled numerical model was established and validated against the experimental results. Based on the validated numerical approach, the hydrogen permeation behavior of a type IV hydrogen storage vessel with local reinforcement was investigated. The results show that both temperature and pressure have a significant influence on the hydrogen permeation performance of PA12. When the temperature is below the glass transition temperature ($\mathit{Tg}$) of PA12 (48.34 °C), the diffusion coefficient remains low, whereas temperatures above the $\mathit{Tg}$ led to a marked increase in the diffusion coefficient. In addition, the local reinforcement patch effectively prolongs the time required to reach steady-state permeation, reduces the hydrogen permeation flux before and after steady state, and enhances the overall resistance to hydrogen permeation of the type IV vessel. As the diffusion coefficient of the liner material increases, the hydrogen diffusion rate increases substantially, leading to greater hydrogen accumulation in the dome region and higher permeation levels both before and after steady state. These findings provide theoretical guidance and design references for optimizing the hydrogen-resistant performance of type IV hydrogen storage vessels. Full article

Under the intensifying water shortages in the vegetation season, early identification of Ips typographus L. damage is crucial for preventing wide outbreaks, which undermine the economic potential of commercial stands of Norway spruce (Picea abies Karst.) across Europe. For this purpose, remote sensing based on satellite images is considered one of the most efficient methods, particularly in homogenous and wide forested landscapes. However, under highly heterogeneous seminatural managed forest landscapes in lowland Central and Northern Europe, as illustrated by the eastern Baltic region and Latvia in particular, the efficiency of such an approach can lack the desired accuracy. Hence, the identification of smaller damage patches by I. typographus, which can act as a source of wider outbreaks, can be overlooked, and situational awareness can be further aggravated by infrastructure artefacts. In this study, the accuracy of satellite imaging for the identification of I. typographus damage was evaluated, focusing on the occurrence of false positives and particularly false negatives obtained from the comparison with UAV imaging. Across the studied landscapes, correct or partially correct identification of damage patches larger than 30 m² occurred in 73% of cases. Still, the satellite image analysis of the highly heterogeneous landscape resulted in quite a common occurrence of false negatives (up to one-third of cases), which were related to stand and patch properties. The high rate of false negatives, however, is crucial for the prevention of outbreaks, as the sources of outbreaks can be underestimated, burdening prompt and hence effective implication of countermeasures. Accordingly, elaborating an analysis of satellite images by incorporating stand inventory data could improve the efficiency of early detection systems, especially when coupled with UAV reconnaissance of heterogeneous landscapes, as in the eastern Baltic region. Full article

The increasing severity of extreme weather events necessitates rapid methods for post-disaster damage assessment. Current remote sensing datasets often lack the spatial resolution required for a detailed evaluation of critical waterfront infrastructure, which is vulnerable during hurricanes. To address this limitation, we introduce the Flood and Waterfront Infrastructure Segmentation Dataset (FWISD), a new dataset constructed from high-resolution unmanned aerial vehicle imagery captured after a major hurricane, comprising 3750 annotated 1024 × 1024 pixel image patches. The dataset provides semantic labels for 11 classes, specifically designed to distinguish between intact and damaged structures. We conducted comprehensive experiments to evaluate the performance of both convolution and Transformer-based models. Our results indicate that hybrid models integrating Transformer encoders with convolutional decoders achieve a superior balance of contextual understanding and spatial precision. Regression analysis indicates that the distance to water has the maximum influence on the detection success rate, while comparative experiments emphasize the unique complexity of waterfront infrastructure compared to homogenous datasets. In summary, FWISD provides a valuable resource for developing and evaluating advanced models, establishing a foundation for automated systems that can improve the timeliness and precision of post-disaster response. Full article

With the increasing complexity of cyber threats and the inefficiency of traditional vulnerability management, artificial intelligence has been increasingly integrated into cybersecurity. This review provides a comprehensive evaluation of AI-powered strategies including machine learning, deep learning, and large language models for identifying cybersecurity vulnerabilities and supporting automated patching. In this review, we conducted a synthesis and appraisal of 29 peer-reviewed studies published between 2019 and 2024. Our results indicate that AI methods substantially improve the precision of detection, scalability, and response speed compared with human-driven and rule-based approaches. We detail the transition from conventional ML categorization to using deep learning for source code analysis and dynamic network detection. Moreover, we identify advanced mitigation strategies such as AI-powered prioritization, neuro-symbolic AI, deep reinforcement learning and the generative abilities of LLMs which are used for automated patch suggestions. To strengthen methodological rigor, this review followed a registered protocol and PRISMA-based study selection, and it reports reproducible database searches (exact queries and search dates) and transparent screening decisions. We additionally assessed the quality and risk of bias of included studies using criteria tailored to AI-driven vulnerability research (dataset transparency, leakage control, evaluation rigor, reproducibility, and external validation), and we used these quality results to contextualize the synthesis. Our critical evaluation indicates that this area remains at an early stage and is characterized by significant gaps. The absence of standard benchmarks, limited generalizability of the models to various domains, and lack of adversarial testing are the obstacles that prevent adoption of these methods in real-world scenarios. Furthermore, the research suggests that the black-box nature of most models poses a serious problem in terms of trust. Thus, XAI is quite pertinent in this context. This paper serves as a thorough guide for the evolution of AI-driven vulnerability management and indicates that next-generation AI systems should not only be more accurate but also transparent, robust, and generalizable. Full article

Microneedle (MN) technologies have emerged as a groundbreaking platform for transdermal and intradermal drug delivery, offering a minimally invasive alternative to oral and parenteral routes. Unlike passive transdermal systems, MNs allow the permeation of hydrophilic macromolecules, such as peptides, proteins, and vaccines, by penetrating the stratum corneum barrier without causing pain or tissue damage, unlike hypodermic needles. Recent advances in materials science, microfabrication, and biomedical engineering have enabled the development of various MN types, including solid, coated, dissolving, hollow, hydrogel-forming, and hybrid designs. Each type has unique mechanisms, fabrication techniques, and pharmacokinetic profiles, providing customized solutions for a range of therapeutic applications. The integration of 3D printing technologies and stimulus-responsive polymers into MN systems has enabled patches that combine drug delivery with real-time physiological sensing. Over the years, MN applications have grown beyond vaccines to include the delivery of insulin, anticancer agents, contraceptives, and various cosmeceutical ingredients, highlighting the versatility of this platform. Despite this progress, broader clinical and commercial adoption is still limited by issues such as scalable and reliable manufacturing, patient acceptance, and meeting regulatory expectations. Overcoming these barriers will require coordinated efforts across engineering, clinical research, and regulatory science. This review thoroughly summarizes MN technologies, beginning with their classification and drug-delivery mechanisms, and then explores innovations, therapeutic uses, and translational challenges. It concludes with a critical analysis of clinical case studies and a future outlook for global healthcare. By comparing technological progress with regulatory and commercial hurdles, this article highlights the opportunities and limitations of MN systems as a next-generation drug-delivery platform. Full article

Three-dimensional (3D) printing has transformed the development of microneedle arrays (MNAs) by enabling exceptional control over their geometry, distribution, materials, and functionality in a single-step, customizable process. This review represents a design-centric framework that organizes recent advancements in four interconnected levers: (i) individual microneedle (MN) geometry and size; (ii) patch-level MN distribution and multi-array architectures; (iii) computer-aided design (CAD), finite element analysis (FEA), computational fluid dynamics (CFD), and artificial intelligence/machine learning (AI/ML)-driven optimization; and (iv) manufacturing constraints and emerging solutions for scalability and reproducibility. Outcomes show that small changes in the radius of the MN’s tip, the MN’s aspect ratio, the MN’s internal lattice architecture, and the spacing of the array can dramatically influence their insertion force, mechanical reliability, payload capacity, and therapeutic coverage. Now, digital tools can bridge the design and experimental outcomes, while novel morphologies, hybrid materials, and theranostic integrations are expanding the clinical potential of MNs. The remaining challenges, resolution-versus-throughput trade-offs, biocompatibility, batch-to-batch consistency, and lack of testing standardization are examined alongside promising directions in high-throughput 3D printing, stimuli-responsive materials, and closed-loop systems. Finally, rational, model-guided design strategies are positioning 3D-printed MNAs as versatile platforms for painless, patient-specific drug delivery, diagnostics, and personalized medicine. Full article