Search Results (2,221)

Friction and wear characteristics play a critical role in the functionality and durability of prosthetic sockets, which are essential components in lower-limb prostheses. Traditionally, these sockets are manufactured from bulk polymers or composite materials reinforced with advanced carbon, glass, and Kevlar fibres. However, issues of accessibility, affordability, and sustainability remain, particularly in less-resourced regions. This study investigates the potential of self-reinforced polymer composites (SRPCs), including poly-lactic acid (PLA), polyethylene terephthalate (PET), glass fibre (GF), and carbon fibre (CF), as sustainable alternatives for socket manufacturing. The tribological behaviour of these self-reinforced polymers (SrPs) was evaluated through experimental friction tests, comparing their performance to commonly used materials like high-density polyethylene (HDPE) and polypropylene (PP). Under varying loads and rotational speeds, HDPE and PP exhibited lower coefficients of friction (COF) compared to SrPLA, SrPET, SrGF, and SrCF. SrPLA recorded the highest average COF of 0.45 at 5 N and 240 rpm, while SrPET demonstrated the lowest COF of 0.15 under the same conditions. Microscopic analysis revealed significant variations in wear depth, with SrPLA showing the most profound wear, followed by SrCF, SrGF, and SrPET. In all cases, debris from the reinforcement adhered to the steel ball surface, influencing the COF. While these findings are based on friction tests against steel, they provide valuable insights into the durability and wear resistance of SRPCs, a crucial consideration for socket applications. This study highlights the importance of tribological analysis for optimising prosthetic socket design, contributing to enhanced functionality and comfort for amputees. Further research, including friction testing with skin-contact scenarios, is necessary to fully understand the implications of these materials in real-world prosthetic applications. Full article

Background/Objectives: Thyroid autoimmunity (TAI) affects about 15% of women of reproductive age and can negatively affect pregnancy outcomes. One possible mechanism for pregnancy complications can be attributed to a disturbed process of placentation caused by thyroid antibodies. To test this hypothesis, placental hormones and angiogenic factors in pregnant women with TAI were evaluated. Methods: Fifty-eight hypothyroid women positive for TPOAb/TgAb, thirty-three hypothyroid women negative for TPOAb/TgAb, and thirty-nine healthy controls were enrolled in this study. Maternal thyroid function tests were established every month throughout pregnancy, and angiogenic placental factors, pro-angiogenic placental growth factor (PlGF); two anti-angiogenic factors, soluble vascular endothelial growth factor receptor 1 (sFlt-1) and soluble endoglin (sEng); and placental hormones, estradiol, progesterone, and hCG, were determined during each trimester. Results: Obstetrical and neonatal outcomes did not differ between the groups. However, several detrimental effects of thyroid antibodies were observed. These included a positive correlation between TgAb and the sEng/PlGF ratio in the first trimester and positive correlations between TPOAb and sFlt-1 and between TgAb and the sFlt-1/PlGF ratio in the third trimester. TgAbs in the first trimester was a risk factor for gestational hypertension and preeclampsia. Conclusions: Our study indicates that TPOAbs and TgAbs can exert a direct harmful effect on placentation, leading to disturbances in the production of placental angiogenic factors and, consequently, to an increased risk of gestational hypertension and preeclampsia. Full article

The GF-7 satellite, China’s inaugural sub-meter-level stereoscopic mapping satellite, has been deployed for a wide range of applications, including natural resource investigation, environmental monitoring, fundamental surveying, and the development of global geospatial information resources. The satellite’s stable platform and reliable imaging systems are crucial for achieving high-quality imaging and precise attitude measurements. However, the satellite’s operation is affected by both internal and external factors, which induce vibrations in the satellite platform, thereby affecting image quality and mapping accuracy. To address this challenge, this paper proposes a novel method for constructing a satellite platform vibration model based on geographic location information. The model is developed by integrating composite data from star sensors and gyroscopes (gyro) with subsatellite point location data. The experimental methodology involves the composite processing of gyro data and star sensor optical axis angles, integration of the processed data through time-matching and normalization, and denoising of the integrated data, followed by trigonometric fitting to capture the periodic characteristics of platform vibrations. The positions of the satellite substellar points are determined from the satellite orbit data. A rigorous geometric imaging model is then used to construct a vibration model with geographic location correlation in combination with the satellite subsatellite point positions. The experimental results demonstrate the following: (1) Over the same temporal range, there is a significant convergence in the waveform similarities between the gyro data and the star sensor optical axis angles, indicating a strong correlation in the jitter information; (2) The platform vibration exhibits a robust correlation with the satellite’s geographic location along its orbit. Specifically, the model reveals that the GF-7 satellite experiences the maximum vibration amplitude between 5° S and 20° S latitude during its ascending phase, and the minimum vibration amplitude between 5° N and 20° N latitude during the descending phase. The model established in this study offers theoretical support for optimizing satellite attitude and mitigating platform vibrations. Full article

Solar energy plays a crucial role in mitigating greenhouse gas emissions in the context of global climate change. However, its deployment for green electricity generation can significantly influence regional climate and vegetation dynamics. While prior studies have examined the impacts of solar power plants on vegetation, the accuracy of these assessments has often been constrained by the availability of publicly accessible multispectral, high-resolution remotely sensed imagery. Given the abundant solar energy resources and the ecological significance of the Tibetan Plateau, a thorough evaluation of the vegetation effects associated with solar power installations is warranted. In this study, we utilize sub-meter resolution imagery from the GF-2 satellite to reconstruct the fractional vegetation cover (FVC) at the Gonghe solar thermal power plant through image classification, in situ sampling, and sliding window techniques. We then quantify the plant’s impact on FVC by comparing data from the pre-installation and post-installation periods. Our findings indicate that the Gonghe solar thermal power plant is associated with a 0.02 increase in FVC compared to a surrounding control region (p < 0.05), representing a 12.5% increase relative to the pre-installation period. Notably, the enhancement in FVC is more pronounced in the outer ring areas than near the central tower. The observed enhancement in vegetation growth at the Gonghe plant suggests potential ecological and carbon storage benefits resulting from solar power plant establishment on the Tibetan Plateau. These findings underscore the necessity of evaluating the climate and ecological impacts of renewable energy facilities during the planning and design phases to ensure a harmonious balance between clean energy development and local ecological integrity. Full article

Glyphosate (GH) wastewater potentially poses hazards to human health and the aquatic environment, due to its persistence and toxicity. A highly superhydrophilic and stable graphite felt (GF)/polydopamine (PDA)/titanium dioxide nanotubes (TiO₂-NT)/SnO₂/Ru anode was fabricated and characterized for the degradation of glyphosate wastewater. Compared to control anodes, the GF/PDA/TiO₂-NT/SnO₂/Ru anode exhibited the highest removal efficiency (near to 100%) and a yield of phosphate ions of 76.51%, with the lowest energy consumption (0.088 Wh/L) for degrading 0.59 mM glyphosate (GH) at 7 mA/cm² in 30 min. The exceptional activity of the anode may be attributed to the co-activation of lattice oxygen in TiO₂-NT and SnO₂ by coupled Ru, resulting in a significant amount of •O₂⁻ and oxygen vacancies as active sites for glyphosate degradation. After electrolysis, small molecular acids and inorganic ions were obtained, with hydroxylation and dephosphorization as the main degradation pathways. Eight cycles of experiments confirmed that Ru doping prominently enhanced the stability of the GF/PDA/TiO₂-NT/SnO₂/Ru anode due to its high oxygenophilicity and electron-rich ability, which promoted the generation and utilization efficiency of active free radicals and defects-associated oxygen. Therefore, this study introduces an effective strategy for efficiently co-activating lattice oxygen in SnO₂ and TiO₂-NT on graphite felt to eliminate persistent organophosphorus pesticides. Full article

Oxidative stress (OS) plays a crucial role in placental pathogenesis and pregnancy-related complications. This review explores OS’s impact on placental development and function, focusing on novel biomarkers for the early detection of at-risk pregnancies and emerging therapeutic strategies. We analyzed recent research on OS in placental pathophysiology, examining its sources, mechanisms, and effects. While trophoblast invasion under low-oxygen conditions and hypoxia-induced OS regulate physiological placental development, excessive OS can lead to complications like miscarriage, preeclampsia, and intrauterine growth restriction. Promising OS biomarkers, including malondialdehyde, 8-isoprostane, and the sFlt-1/PlGF ratio, show potential for the early detection of pregnancy complications. Therapeutic strategies targeting OS, such as mitochondria-targeted antioxidants, Nrf2 activators, and gasotransmitter therapies, demonstrate encouraging preclinical results. However, clinical translation remains challenging. Future research should focus on validating these biomarkers in large-scale studies and developing personalized therapies to modulate placental OS. Emerging approaches like extracellular vesicle-based therapies and nanomedicine warrant further investigation for both diagnostic and therapeutic applications in pregnancy-related complications. Integrating OS biomarkers with other molecular and cellular markers offers improved potential for the early identification of at-risk pregnancies. Full article

High-precision extraction of mangrove areas is a crucial prerequisite for estimating mangrove area as well as for regional planning and ecological protection. However, mangroves typically grow in coastal and near-shore areas with complex water colors, where traditional mangrove extraction algorithms face challenges such as unclear region segmentation and insufficient accuracy. To address this issue, in this paper we propose a new algorithm for mangrove identification and extraction based on Orthogonal Matching Filter–Weighted Least Squares (OMF-WLS) target spectral information. This method first selects GF-6 remote sensing images with less cloud cover, then enhances mangrove feature information through preprocessing and band extension, combining whitened orthogonal subspace projection with the whitened matching filter algorithm. Notably, this paper innovatively introduces Weighted Least Squares (WLS) filtering technology. WLS filtering precisely processes high-frequency noise and edge details in images using an adaptive weighting matrix, significantly improving the edge clarity and overall quality of mangrove images. This innovative approach overcomes the bottleneck of traditional methods in effectively extracting edge information against complex water color backgrounds. Finally, Otsu’s method is used for adaptive threshold segmentation of GF-6 remote sensing images to achieve target extraction of mangrove areas. Our experimental results show that OMF-WLS improves extraction accuracy compared to traditional methods, with overall precision increasing from 0.95702 to 0.99366 and the Kappa coefficient rising from 0.88436 to 0.98233. In addition, our proposed method provides significant improvements in other metrics, demonstrating better overall performance. These findings can provide more reliable technical support for the monitoring and protection of mangrove resources. Full article

In order to solve the problems that existing tomato maturity detection methods struggle to take into account both common tomato and cherry tomato varieties in complex field environments (such as light change, occlusion, and fruit overlap) and the model size being too large, this paper proposes a lightweight tomato maturity detection model based on improved YOLO11, named GFS-YOLO11. In order to achieve a lightweight network, we propose the C3k2_Ghost module to replace the C3K2 module in the original network, which can ensure a feature extraction capability and reduce model computation. In order to compensate for the potential feature loss caused by the light weight, this paper proposes a feature-refining module (FRM). After embedding each feature extraction module in the trunk network, it improves the feature expression ability of common tomato and cherry tomato in complex field environments by means of depth-separable convolution, multi-scale pooling, and channel attention and spatial attention mechanisms. In addition, in order to further improve the detection ability of the model for tomatoes of different sizes, the SPPFELAN module is also proposed in this paper. In combining the advantages of SPPF and ELAN, multiple parallel SPPF branches are used to extract features of different levels and perform splicing and fusion. To verify the validity of the method, this study constructed a dataset of 1061 images of common and cherry tomatoes, covering tomatoes in six ripened categories. The experimental results show that the performance of the GFS-YOLO11 model is significantly improved compared with the original model; the P, R, mAP50, and MAP50-95 increased by 5.8%, 4.9%, 6.2%, and 5.5%, respectively, and the number of parameters and calculation amount were reduced by 35.9% and 22.5%, respectively. The GFS-YOLO11 model is lightweight while maintaining high precision, can effectively cope with complex field environments, and more conveniently meet the needs of real-time maturity detection of common tomatoes and cherry tomatoes. Full article

Space-based observations of the total lunar eclipse on 21 January 2019 were conducted using the geostationary Earth-orbiting satellite Gaofen-4 (GF-4). This study represents a pioneering effort to address the observational gap in full-disk lunar eclipse photometry from space. With its high resolution and ability to capture the entire lunar disk, GF-4 enabled both quantitative and qualitative analyses of the variations in lunar brightness, as well as spectra and color changes, across two spatial dimensions, from the whole lunar disk to resolved regions. Our results indicate that before the totality phase of the lunar eclipse, the irradiance of the Moon diminishes to below approximately 0.19% of that of the uneclipsed Moon. Additionally, we observed an increase in lunar brightness at the initial entry into the penumbra. This phenomenon is attributed to the opposition effect, providing scientific evidence for this unexpected behavior. To investigate detailed spectral variations, specific calibration sites, including the Chang’E-3 landing site, MS-2 in Mare Serenitatis, and the Apollo 16 highlands, were analyzed. Notably, the red-to-blue ratio dropped below 1 near the umbra, contradicting the common perception that the Moon appears red during lunar eclipses. The red/blue ratio images reveal that as the Moon enters Earth’s umbra, it does not simply turn red; instead, a blue-banded ring appears at the boundary due to ozone absorption and the lunar surface composition. These findings significantly enhance our understanding of atmospheric effects on lunar eclipses and provide crucial reference information for the future modeling of lunar eclipse radiation, promoting the integration of remote sensing science with astronomy. Full article

The energy sector relies on numerical model output forecasts for operational purposes on a short-term scale, up to 10 days ahead. Reducing model errors is crucial, particularly given that coarse resolution models often fail to account for complex topography, such as that found in Costa Rica. Local circulations affect wind conditions at the level of wind turbines, thereby impacting wind energy production. This work addresses a specific need of the Costa Rican Institute of Electricity (ICE) as a public service provider for the energy sector. The developed methodology and implemented product in this study serves as a proof of concept that could be replicated by WMO members. It demonstrates a product for wind speed forecasting at wind power plants by employing a novel strategy for model input selection based on large-scale indicators leveraging artificial intelligence-based forecasting methods. The product is developed and implemented based on the full-value chain framework for weather, water, and climate services for the energy sector introduced by the WMO. The results indicate a reduction in the wind forecast RMSE by approximately 55% compared to the GFS grid values. The conclusion is that combining coarse model outputs with regional climatological knowledge through AI-based downscaling models is an effective approach for obtaining reliable local short-term wind forecasts up to 10 days ahead. Full article

This study explores a novel approach to obtaining 3D printed strain sensors, focusing on how changing the printing conditions can produce a different piezoresistive response. Acrylonitrile butadiene styrene (ABS) filled with different weight concentrations of carbon nanotubes (CNTs) was printed in the form of dog bones via fused filament fabrication (FFF) using two different raster angles (0–90°). Scanning electron microscopy (SEM) and atomic force microscopy (AFM) in TUNA mode (TUNA-AFM) were used to study the morphological features and the electrical properties of the 3D printed samples. Tensile tests revealed that sensitivity, measured by the gauge factor (G.F.), decreased with increasing filler content for both raster angles. Notably, the 90° orientation consistently showed higher sensitivity than the 0° orientation for the same filler concentration. Creep and fatigue tests identified permanent damage through residual electrical resistance values. Additionally, a cross-shaped sensor was designed to measure two-dimensional deformations simultaneously, which is applicable in the robotic field. This sensor can monitor small and large deformations in perpendicular directions by tracking electrical resistance variations in its arms, significantly expanding its measuring range. Full article

The Environmental Trace Gases Monitoring Instrument (EMI-II) onboard the Chinese GaoFen-5B (GF5B) and DaQi-1 (DQ1) satellites is the successor of the previous EMI onboard the Chinese GaoFen-5 (GF5) satellite, and has a higher spatial resolution and a better signal-to-noise ratio. The GF5B and DQ1 were launched in September 2021 and April 2022, respectively. As part of China’s ultraviolet-visible hyperspectral satellite instrument series, the EMI-II aims to conduct network observations of pollution gases globally in the morning and early afternoon. In this study, NO₂ data were retrieved from the EMI-II payloads on the GF5B and DQ1 satellites using the Differential Optical Absorption Spectroscopy (DOAS) algorithm. The two satellites were consistently compared, and the results showed strong consistency on various spatial and temporal scales (R² > 0.8). In four representative regions worldwide, NO₂ data from the EMI-II exhibited good spatial consistency with those from the TROPOMI. The correlation coefficient (R²) of the total vertical column density (VCD) between the EMI-II and TROPOMI exceeded 0.85, and that of the tropospheric NO₂ VCD exceeded 0.57. Compared with single-satellite observations, the dual-satellite network of the GF5B and DQ1 can effectively increase the observation frequency. On a daily scale, dual-satellite observations can reduce the impact of cloud coverage by 6–8% compared to single-satellite observations, and there are two valid observations of nearly 50% of the world’s regions. Additionally, the differences between the two satellites can reflect the NO₂ diurnal variations, which demonstrates the potential for studying pollutant gas diurnal variations. Full article

This study investigated the performance of recycled waste fishing net sheets (WSs) as a sustainable strengthening material for reinforced concrete (RC) slabs. The primary challenge addressed is the debonding failure caused by the low bond strength at the WS-to-concrete interface. To analyze this, two full-scale RC slabs—one with and one without strengthening—were cast and tested under a four-point bending setup. Finite element (FE) models incorporating existing bond–slip laws were developed using the ABAQUS software to simulate the strengthened slab’s behavior. A sensitivity analysis was performed to assess the impact of bond–slip parameters on the failure mechanism. Experimental results indicated that the WS-strengthened slab enhanced the RC slab capacities by 15% in yield load and 13% in initial stiffness. Furthermore, the maximum shear stress of 0.5τ_max or interfacial fracture energy of 0.2G_f, compared to values proposed by Monti et al., enabled the simulation of the global response observed in the experiment. Full article

Understanding the complex link between inflammation, gut health, and dietary amino acids is becoming increasingly important in the pathophysiology of inflammatory bowel disease (IBD). This study tested the hypothesis that a leucine-rich diet could attenuate inflammation and improve gut health in a mouse model of IBD. Specifically, we investigated the effects of a leucine-rich diet on dextran sulfate sodium (DSS)-induced colitis in germ-free (GF) SAMP1/YitFC (SAMP) mice colonized with human gut microbiota (hGF-SAMP). hGF-SAMP mice were fed one of four different diets: standard mouse diet (CHOW), American diet (AD), leucine-rich AD (AD + AA), or leucine-rich CHOW diet (CH + AA). Body weight, myeloperoxidase (MPO) activity, gut permeability, colonoscopy scores, and histological analysis were measured. Mice on a leucine-rich CHOW diet showed a decrease in fecal MPO prior to DSS treatment as compared to those on a regular diet (p > 0.05); however, after week five, prior to DSS, this effect had diminished. Following DSS treatment, there was no significant difference in gut permeability, fecal MPO activity, or body weight changes between the leucine-supplemented and control groups. These findings suggest that while a leucine-rich diet may transiently affect fecal MPO levels in hGF-SAMP mice, it does not confer protection against DSS-induced colitis symptoms or mitigate inflammation in the long term. Full article

This review aimed to identify newly discovered bacteria from individuals with periodontal/peri-implant diseases and organize them into new clusters (GF-MoR complexes) to update Socransky’s complexes (1998). For methodological development, the PCC (Population, Concept, Context) strategy was used for the focus question construction: “In patients with periodontal and/or peri-implant disease, what bacteria (microorganisms) were detected through laboratory assays?” The search strategy was applied to PubMed/MEDLINE, PubMed Central, and Embase. The search key terms, combined with Boolean markers, were (1) bacteria, (2) microbiome, (3) microorganisms, (4) biofilm, (5) niche, (6) native bacteria, (7) gingivitis), (8) periodontitis, (9) peri-implant mucositis, and (10) peri-implantitis. The search was restricted to the period 1998–2024 and the English language. The bacteria groups in the oral cavity obtained/found were retrieved and included in the GF-MoR complexes, which were based on the disease/condition, presenting six groups: (1) health, (2) gingivitis, (3) peri-implant mucositis, (4) periodontitis, (5) peri-implantitis, and (6) necrotizing and molar–incisor (M-O) pattern periodontitis. The percentual found per group refers to the number of times a specific bacterium was found to be associated with a particular disease. A total of 381 articles were found: 162 articles were eligible for full-text reading (k = 0.92). Of these articles, nine were excluded with justification, and 153 were included in this review (k = 0.98). Most of the studies reported results for the health condition, periodontitis, and peri-implantitis (3 out of 6 GF-MoR clusters), limiting the number of bacteria found in the other groups. Therefore, it became essential to understand that bacterial colonization is a dynamic process, and the bacteria present in one group could also be present in others, such as those observed with the bacteria found in all groups (Porphyromonas gingivalis, Tannarela forsythia, Treponema denticola, and Aggregatibacter actinomycetemcomitans) (GF-MoR’s red triangle). The second most observed bacteria were grouped in GF-MoR’s blue triangle: Porphyromonas spp., Prevotela spp., and Treponema spp., which were present in five of the six groups. The third most detected bacteria were clustered in the grey polygon (GF-MoR’s grey polygon): Fusobacterium nucleatum, Prevotella intermedia, Campylobacter rectus, and Eikenella corrodens. These three geometric shapes had the most relevant bacteria to periodontal and peri-implant diseases. Specifically, per group, GF-MoR’s health group had 58 species; GF-MoR’s gingivitis group presented 16 bacteria; GF-MoR’s peri-implant mucositis included 17 bacteria; GF-MoR’s periodontitis group had 101 different bacteria; GF-MoR’s peri-implantitis presented 61 bacteria; and the last group was a combination of necrotizing diseases and molar–incisor (M-I) pattern periodontitis, with seven bacteria. After observing the top seven bacteria of all groups, all of them were found to be gram-negative. Groups 4 and 5 (periodontitis and peri-implantitis) presented the same top seven bacteria. For the first time in the literature, GF-MoR’s complexes were presented, gathering bacteria data according to the condition found and including more bacteria than in Socransky’s complexes. Based on this understanding, this study could drive future research into treatment options for periodontal and peri-implant diseases, guiding future studies and collaborations to prevent and worsen systemic conditions. Moreover, it permits the debate about the evolution of bacterial clusters. Full article