Search Results (21,223)

Hurricane incidents have become increasingly frequent along the coastal United States and have had a negative impact on the mangrove forests and their ecosystem services across the southeastern region. Mangroves play a key role in providing coastal protection during hurricanes by attenuating storm surges and reducing erosion. However, their resilience is being increasingly compromised due to climate change through sea level rises and the greater intensity of storms. This article examines the role of remote sensing tools in studying the impacts of hurricanes on mangrove forests in the coastal United States. Our results show that various remote sensing tools including satellite imagery, Light detection and ranging (LiDAR) and unmanned aerial vehicles (UAVs) have been used to detect mangrove damage, monitor their recovery and analyze their 3D structural changes. Landsat 8 OLI (14%) has been particularly useful in long-term assessments, followed by Landsat 5 TM (9%) and NASA G-LiHT LiDAR (8%). Random forest (24%) and linear regression (24%) models were the most common modeling techniques, with the former being the most frequently used method for classifying satellite images. Some studies have shown significant mangrove canopy loss after major hurricanes, and damage was seen to vary spatially based on factors such as proximity to oceans, elevation and canopy structure, with taller mangroves typically experiencing greater damage. Recovery rates after hurricane-induced damage also vary, as some areas were seen to show rapid regrowth within months while others remained impacted after many years. The current challenges include capturing fine-scale changes owing to the dearth of remote sensing data with high temporal and spatial resolution. This review provides insights into the current remote sensing applications used in hurricane-prone mangrove habitats and is intended to guide future research directions, inform coastal management strategies and support conservation efforts. Full article

Recently, Vision Transformers (ViTs) have been actively applied to fine-grained visual recognition (FGVR). ViT can effectively model the interdependencies between patch-divided object regions through an inherent self-attention mechanism. In addition, patch selection is used with ViT to remove redundant patch information and highlight the most discriminative object patches. However, existing ViT-based FGVR models are limited to single-scale processing, and their fixed receptive fields hinder representational richness and exacerbate vulnerability to scale variability. Therefore, we propose MultiScale Patch Selection (MSPS) to improve the multiscale capabilities of existing ViT-based models. Specifically, MSPS selects salient patches of different scales at different stages of a MultiScale Vision Transformer (MS-ViT). In addition, we introduce Class Token Transfer (CTT) and MultiScale Cross-Attention (MSCA) to model cross-scale interactions between selected multiscale patches and fully reflect them in model decisions. Compared with previous Single-Scale Patch Selection (SSPS), our proposed MSPS encourages richer object representations based on feature hierarchy and consistently improves performance from small-sized to large-sized objects. As a result, we propose M2Former, which outperforms CNN-/ViT-based models on several widely used FGVR benchmarks. Full article

Soil consolidation, particularly in fine-grained soils like clay, is crucial in predicting settlement and ensuring the stability of structures. Additionally, the compressibility of fine-grained soils is of critical importance not only in civil engineering but also in various other fields of study. The compression index (C_c), derived from soil properties such as the liquid limit (LL), plastic limit (PL), plasticity index (PI), water content (w), initial void ratio (e₀), and specific gravity (G_s), plays a vital role in understanding soil behavior. This study employs machine learning algorithms—the random forest regressor (RFR), gradient boosting regressor (GBR), and AdaBoost regressor (ABR)—to predict the C_c values based on a dataset comprising 915 samples. The dataset includes LL, PL, W, PI, G_s, and e₀ as the inputs, with C_c as the output parameter. The algorithms are trained and evaluated using metrics such as the coefficient of determination (R²), mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE). Hyperparameter optimization is performed to enhance the model performance. The best-performing model, the GBR model, achieves a training R² of 0.925 and a testing R² of 0.930 with the input combination [w, PL, LL, PI, e₀, G_s]. The RFR model follows closely, with a training R² of 0.970 and a testing R² of 0.926 using the same input combination. The ABR model records a training R² of 0.847 and a testing R² of 0.921 under similar conditions. These results indicate superior predictive accuracy compared to previous studies using traditional statistical and machine learning methods. Machine learning algorithms, specifically the gradient boosting regressor and random forest regressor, demonstrate substantial potential in predicting the C_c value for fine-grained soils based on multiple soil parameters. This study involves leveraging the efficiency and effectiveness of these algorithms in geotechnical engineering applications, offering a promising alternative to traditional oedometer testing methods. Accurately predicting the compression index can significantly aid in the assessment of soil settlement and the design of stable foundations, thereby reducing the time and costs associated with laboratory testing. Full article

Animal bones, particularly from cattle after slaughter, are commonly discarded, posing environmental challenges and highlighting the need for sustainable valorization. This study investigated the effect of enzyme and organic acid treatment on physicochemical properties, particle size, microstructure and safety of meat-bone paste (MBP). Two samples were prepared: a control (MBP-C) without enzyme treatment and an experimental sample (MBP-E) treated with pepsin and ascorbic acid. Results showed that the enzyme reaction rate increased from 0.004 mmol/min at 60 min to 0.014 mmol/min at 120–180 min before declining to 0.006 mmol/min at 480 min, suggesting substrate depletion or product inhibition. Temperature greatly influenced reaction rates, peaking at 0.0129 mmol/min at 30 °C, with significant declines at higher temperatures due to enzyme denaturation. The enzyme’s kinetic performance was proportional to the pepsin concentration, demonstrating enhanced catalytic efficiency at higher enzyme concentrations. Particle size analysis revealed that enzyme treatment significantly reduced bone particle size, with 86.33% of particles measuring between 0.05 and 0.2 mm, compared to 86.4% between 0.25 and 0.75 mm in the untreated sample. Microscopy confirmed these findings, showing an average particle size reduction from 0.21 mm to 0.052 mm after enzyme treatment. Physicochemical analysis revealed no significant differences in chemical composition between the two samples. However, enzyme-treated MBP-E exhibited a lower pH (5.9) compared to MBP-C (7.02), attributed to the addition of ascorbic acid. Water-binding capacity significantly increased in MBP-E (82.54% vs. 77.28%), indicating enhanced hydration and collagen loosening during enzymatic action. Enzyme treatment significantly reduced the total viable count and eliminated pathogenic bacteria (E. coli, Listeria, Salmonella), improving MBP safety. These findings highlight the potential of this approach for valorizing animal bones as a valuable food ingredient while promoting sustainable waste management practices. Full article

The influence of the parameters of accelerated carbonization in a 99.9% CO₂ environment on the hardening kinetics of blended cement with 15 wt% opoka additive, the physical and mechanical properties of the resulting products, the mineralogical composition, and the amount of absorbed CO₂ were investigated. Sedimentary rock opoka was found to have opal silica and calcite as its predominant constituent parts. Therefore, these properties determine that it serves as an extremely suitable raw material and a source of both SiO₂ and CaO. The strength properties of the mortars (blended cement/standard sand = 1:3) were similar or even better than those of samples based on Ordinary Portland cement (OPC): the compressive strength exceeded 50 MPa under optimal conditions. In blended cement, some of the pores are filled with fine-dispersed opoka, which can lead to an increase in strength. By reducing the amount of OPC in mixtures, the negative impact of its production on the environment is reduced accordingly. Using XRD, DSC, and TG methods, it was determined that replacing 15 wt% of OPC clinker with opoka does not affect the mineralogy of the crystalline phases as the same compounds are obtained. After determining the optimal parameters for sample preparation and hardening, in accordance with the obtained numbers, concrete pavers of industrial dimensions (100 × 100 × 50 mm) were produced. Their strength indicators were even ~10% better. Full article

Diffusion models have attracted considerable scholarly interest for their outstanding performance in generative tasks. However, current style transfer techniques based on diffusion models still rely on fine-tuning during the inference phase to optimize the generated results. This approach is not merely laborious and resource-demanding but also fails to fully harness the creative potential of expansive diffusion models. To overcome this limitation, this paper introduces an innovative solution that utilizes a pretrained diffusion model, thereby obviating the necessity for additional training steps. The scheme proposes a Feature Normalization Mapping Module with Cross-Attention Mechanism (INN-FMM) based on the dual-path diffusion model. This module employs soft attention to extract style features and integrate them with content features. Additionally, a parameter-free Similarity Attention Mechanism (SimAM) is employed within the image feature space to facilitate the transfer of style image textures and colors, while simultaneously minimizing the loss of structural content information. The fusion of these dual attention mechanisms enables us to achieve style transfer in texture and color without sacrificing content integrity. The experimental results indicate that our approach exceeds existing methods in several evaluation metrics. Full article

Molybdenum is an important material in modern industry, widely used in extreme environments such as rocket engine nozzles and microelectrodes due to its high melting point, excellent mechanical properties, and thermal conductivity. However, as a difficult-to-machine metal, traditional machining methods struggle to achieve the desired microstructures in molybdenum. Electrochemical machining (ECM) offers unique advantages in manufacturing fine structures from hard-to-machine metals. Studies have shown that molybdenum exhibits a fast corrosion rate in alkaline or acidic solutions, posing significant environmental pressure. Therefore, this study investigates the electrochemical machining of molybdenum in neutral salt solutions to achieve high-precision microstructure fabrication. First, the polarization curves and electrochemical impedance spectroscopy (EIS) of molybdenum in NaNO3 solutions of varying concentrations were measured to determine its electrochemical reaction characteristics. The results demonstrate that molybdenum exhibits good electrochemical reactivity in NaNO3 solutions, leading to favorable surface erosion morphology. Subsequently, a mask electrochemical machining technique was employed to fabricate arrayed microstructures on the molybdenum surface. To minimize interference between factors, an orthogonal experiment was used to optimize the parameter combination, determining the optimal machining process parameters. Under these optimal conditions, an array of micro-groove structures was successfully fabricated with an average groove width of 110 μm, a depth-to-width ratio of 0.21, an aspect ratio of 9000, and a groove width error of less than 5 μm. Full article

Background: The survival benefit of surgical revascularization in multivessel coronary artery disease is well understood, though it can be modified by left ventricular dysfunction. Chronic exposure to air pollutants has gained more attention recently as a possible non-traditional morbidity and mortality cardiovascular risk factor. This study identified possible 5-year mortality risk factors related to postoperative left ventricular performance, including air pollutants. Patients: There were 283 patients (244 (86%) males) with a median age of 65 (60–70) years enrolled in the retrospective analysis. All patients were referred for off-pump coronary artery revascularization due to chronic coronary syndrome that presented as a multivessel coronary artery disease. They were divided into three groups depending on the postoperative course of left ventricular fraction (LVEF 50% or more (169 patients), LVEF between 41 and 49% (61 patients), and LVEF 40% or less (53 patients)). Results: The overall survival rate was 84% (237 patients) in a median follow-up time of 5.3 (4.8–6.1) years. The median (Q1–Q3) chronic air pollution exposures for the analyzed group were 19.3 (16.9–22.4) μg/m³ for fine particles such as PM_2.5, 25.8 (22.5–29.4) μg/m³ for coarse particles such as PM₁₀, and 12.2 (9.7–14.9) μg/m³ for nitric dioxide (NO₂). The mortality in the first group (LVEF at least 50%) was 23 (13.6%), in the second group (LVEF 41–49%) was 9 (15%), and in the third group (LVEF 40% or less) was 14 (26%). The multivariable regression analysis for the five-year mortality risk in the first group revealed the predictive value of dyslipidemia (HR: 3.254, 95% CI: 1.008–10.511, p = 0.049). The multivariable regression analysis for five-year mortality risk in the second group revealed the predictive value of dyslipidemia (HR: 3.391, 95% CI: 1.001–11.874, p = 0.050) and PM_2.5 (HR: 1.327, 95% CI: 1.085–1.625, p = 0.006). In the third group (severely decreased LVEF), chronic PM_2.5 exposure was found to be significant (HR: 1.518, 95% CI: 1.50–2.195, p = 0.026) for 5-year mortality prediction. Conclusions: Traditional risk factors, such as dyslipidemia, are pivotal in the 5-year mortality risk following surgical revascularization. Chronic exposure to ambient air pollutants such as PM_2.5 may be an additional risk factor in patients with left ventricular dysfunction. Full article

When loose, saturated sands and non-plastic silts are subjected to undrained cyclic loading, they will generate positive pore pressures. This increase in pore pressures leads to a decrease in effective stress with a corresponding decrease in shear strength and increase in liquefaction susceptibility. For combinations of sand and non-plastic silt, the threshold fines content can be defined as the non-plastic silt fines content at which the soil changes from sand-like behavior to silt-like behavior. Soils below the threshold fines content behave like sands and soils above the threshold fines content behave like silts. During cyclic triaxial and cyclic direct simple shear tests performed on specimens of sand and silt prepared to the same relative density but different fines contents, two rates of pore pressure generation were observed. When compared at five cycles of loading, soils with silt contents above the threshold fines content were found to produce pore pressure ratios as much as 50% higher than those observed for soils with silt contents below the threshold fines content. When evaluated in terms of cycles, cycle ratio, and dissipated energy ratio, the rate of pore pressure generation was found to be more rapid for soils above the threshold fines content than for soils below the threshold fines content. Full article

Although fine dust is linked to numerous health issues, including cardiovascular, neurological, respiratory, and cancerous diseases, research on its effects on oral health remains limited. In this study, we investigated the protective effects of mature hemp stem extract-induced exosomes (MSEIEs) on periodontal cells exposed to fine dust. Using various methods, including microRNA profiling, PCR, flow cytometry, immunocytochemistry, ELISA, and Alizarin O staining, we found that MSE treatment upregulated key microRNAs, such as hsa-miR-122-5p, hsa-miR-1301-3p, and hsa-let-7e-5p, associated with vital biological functions. MSEIEs exhibited three primary protective functions: suppressing inflammatory genes while activating anti-inflammatory ones, promoting the differentiation of periodontal ligament stem cells (PDLSCs) into osteoblasts and other cells, and regulating LL-37 and MCP-1 expression. These findings suggest that MSEIEs have potential as functional biomaterials for applications in pharmaceuticals, cosmetics, and food industries. Full article

This work presents two pre-processing patches to automatically correct the residual local misalignment of datasets acquired by very/extremely high resolution (VHR/EHR) satellite multispectral (MS) scanners, one for, e.g., GeoEye-1 and Pléiades, featuring two separate instruments for MS and panchromatic (Pan) data, the other for WorldView-2/3 featuring three instruments, two of which are visible and near-infra-red (VNIR) MS scanners. The misalignment arises because the two/three instruments onboard GeoEye-1 / WorldView-2 (four onboard WorldView-3) share the same optics and, thus, cannot have parallel optical axes. Consequently, they image the same swath area from different positions along the orbit. Local height changes (hills, buildings, trees, etc.) originate local shifts among corresponding points in the datasets. The latter would be accurately aligned only if the digital elevation surface model were known with sufficient spatial resolution, which is hardly feasible everywhere because of the extremely high resolution, with Pan pixels of less than 0.5 m. The refined co-registration is achieved by injecting the residue of the multivariate linear regression of each scanner towards lowpass-filtered Pan. Experiments with two and three instruments show that an almost perfect alignment is achieved. MS pansharpening is also shown to greatly benefit from the improved alignment. The proposed alignment procedures are real-time, fully automated, and do not require any additional or ancillary information, but rely uniquely on the unimodality of the MS and Pan sensors. Full article

Wool, a natural fibre derived from sheep, can present a challenge to wool processing and manufacturing industries because of the variation in fibre traits. Genetic improvement offers one solution to this challenge, and having a better understanding of the genes that affect wool fibre traits is therefore important. Here, we describe ovine KRTAP19-3, a new member of the KAP19 gene family. Phylogenetic analysis revealed its relationship to other known KRTAP19 gene sequences, and an analysis of the nucleotide sequence variation in KRTAP19-3 from 288 sheep of a variety of breeds revealed six unique variant sequences. Among these variants, eleven single nucleotide polymorphisms (SNPs) were detected, with six located in the coding region. Three of these coding region SNPs were non-synonymous and would result in amino acid changes. Associations were observed between the presence of specific sequence variants in Chinese Tan sheep and wool trait variation, particularly an increase in fibre diameter variability in the heterotypic hair fibres. These findings enhance our understanding of the genes that encode sheep wool proteins. Full article

Magnetotelluric (MT) forward modeling is essential in geophysical exploration, enabling the investigation of the Earth’s subsurface electrical conductivity. Traditional finite difference methods (FDMs) typically use uniform grids, which can be computationally inefficient and fail to accurately capture complex geological structures. This study addresses these challenges by introducing a non-uniform grid-based FDM for MT forward modeling. The proposed method optimizes computational resources by varying grid resolution, offering finer grids in areas with complex geology and coarser grids in more homogeneous regions. We apply this method to both typical synthetic models and a complex fault structure case study, demonstrating its capability to accurately resolve subsurface features while reducing computational costs. The results highlight the method’s effectiveness in capturing fine-scale details that are often missed by uniform grid approaches. The conclusions drawn from this study suggest that the non-uniform grid FDM not only improves the accuracy of MT modeling but also enhances its efficiency, making it a valuable tool for geophysical exploration in challenging environments. Full article

Background: The long-term effects on neonates born of COVID-19-positive pregnancies are still unclear. Congenital gastrointestinal malformations (CGIMs) often require urgent surgical intervention and antibiotic coverage. We aim to compare the health status at one-year post-surgical follow-up of cases of CGIM born of COVID-19-positive pregnancies to those of non-COVID-19 pregnancies. Methods: We conducted a comparative, observational study of 41 patients who underwent surgery at our hospital for congenital gastrointestinal malformations in 2022. They were initially treated with antibiotics and parenteral nutrition, which was later replaced with enteral nutrition gradually after the surgery. We then analyzed the data related to their growth and development during their 12-month follow-up visit at our outpatient clinic. We classified the children born of COVID-19-positive mothers as Group 1 (n = 14) and those born of mothers without COVID-19 symptoms or with unconfirmed status as Group 2 (n = 33). Results: Forty-one patients showed up for a one-year follow-up (between 11 and 13 months of life). Hence, the final Group 1 comprised 12 and Group 2 comprised 29 children. The patients were categorized based on their anatomical location. Of the cohort, 56.09% were preemies, and 43.91% were full-term newborns. We used seven parameters to evaluate both groups based on growth and developmental milestones: verbal skills, cognitive development, weight gain, height achieved, fine motor movements, gross motor movements, and social/emotional behavior. Group 1 children showed a significant decrease in height and weight compared to Group 2 children. In Group 1, 83.33% of patients were prescribed antibiotics, while only 10.34% in Group 2 were in the same situation. There were no cases of malabsorption syndrome in Group 2, but 16.66% of patients in Group 1 had it, with patients being operated on for duodenal malformations. None of the infants had necrotizing enterocolitis, post-surgical complications, or sepsis. All the children received antibiotics to prevent infection before and after surgery. No mortality was noted. Conclusions: In our one-year follow-up study, it was seen that even after surgical correction of congenital gastrointestinal malformations, children born of COVID-19-positive pregnancies can suffer serious growth and developmental delays, and gastrointestinal health issues might be more common. Since the long-term effects of COVID-19-positive pregnancies are not yet clear, larger cohort-based studies are required in this domain. Antibiotics destroy gut microbiota, especially in cases of gastrointestinal malformations and surgical resections. Growth and developmental milestones can not only be affected by CGIMs but also be further delayed by COVID-19 infections. Full article