Search Results (4,773)

Monitoring sediment concentration in water bodies is crucial for assessing water quality, ecosystems, and environmental health. However, physical sampling and sensor-based approaches are labor-intensive and unsuitable for large-scale, continuous monitoring. This study employs machine learning models to estimate suspended sediment concentration using images captured in natural light, named RGB, and near-infrared (NIR) conditions. A controlled dataset of approximately 1300 images with SSC values ranging from 1000 mg/L to 150,000 mg/L was developed, incorporating temperature, time of image capture, and solar irradiance as additional features. Random forest regression and gradient boosting regression were trained on mean RGB values, red reflectance, time of captured, and temperature for natural light images, achieving up to 72.96% accuracy within a 30% relative error. In contrast, NIR images leveraged gray-level co-occurrence matrix texture features and temperature, reaching 83.08% accuracy. Comparative analysis showed that ensemble models outperformed deep learning models like Convolutional Neural Networks and Multi-Layer Perceptrons, which struggled with high-dimensional feature extraction. These findings suggest that using machine learning models and RGB and NIR imagery offers a scalable, non-invasive, and cost-effective way of sediment monitoring in support of water quality assessment and environmental management. Full article

The displacement of bridge towers is relatively large under strong wind action. Changes in tower displacement can reflect the usage status of the bridge towers. Therefore, it is necessary to conduct performance warning research on tower displacement under strong wind action. In this paper, the triple standard deviation method, multiple linear regression method, and interpolation method are used to preprocess monitoring data with skipped points and missing anomalies. An improved multi-rate data fusion method, validated using simulated datasets, was applied to correct monitoring data at bridge tower tops. The fused data were used to feed predictive models and generate structural performance alerts. Spectral analysis confirmed that the fused displacement measurements achieve high precision by effectively merging the low-frequency GPS signal with the high-frequency accelerometer signal. Structural integrity monitoring of wind-loaded bridge towers used modeling residuals as alert triggers. The efficacy of this proactive monitoring strategy has been quantitatively validated through statistical evaluation of alarm accuracy rates. Full article

Digital technologies could facilitate green development by enhancing energy efficiency. However, existing research on coupling coordination between digital infrastructure and green development remains scarce. To fill this research gap, this study analyzes the spatio-temporal variations and barriers of coupling coordination. An evaluation index system is established and then the coupling relationship and the barrier factors between digital infrastructure and green development are analyzed. A provincial analysis is conducted by using data from China. The results in the study indicate (1) coupling coordination between digital infrastructure and green development exhibits a relatively low state, characterized by an overall upward trend; (2) noteworthy disparities are observed in the spatio-temporal pattern of the coupling coordination degree, reflecting the overall evolutionary trend from low to high coupling coordination, along with the characteristics of positive spatial correlation and high spatial concentration; and (3) obstacle factors are analyzed from the aspects of digital infrastructure and green development, emphasizing the construction of mobile phone base stations and investment in pollution control, among other aspects. This study contributes valuable insights for improvement paths for digital infrastructure and green development, offering recommendations for optimizing strategies to promote their coupled development. Full article

Urochloa brizantha (Brizantha) is cultivated under varying altitudinal and management conditions. Twelve full-sun (monoculture) plots and twelve shaded (silvopastoral) plots were established, proportionally distributed at 170, 503, 661, and 1110 masl. Evaluations were conducted 15, 30, 45, 60, and 75 days after establishment. The conservation and integration of trees in silvopastoral systems reflected a clear anthropogenic influence, evidenced by the preference for species of the Fabaceae family, likely due to their multipurpose nature. Although the altitudinal gradient did not show direct effects on soil properties, intermediate altitudes revealed a significant role of CaCO₃ in enhancing soil fertility. These edaphic conditions at mid-altitudes favored the leaf area development of Brizantha, particularly during the early growth stages, as indicated by significantly larger values (p < 0.05). However, at the harvest stage, no significant differences were observed in physiological or productive traits, nor in foliar chemical components, underscoring the species’ high hardiness and broad adaptation to both soil and altitude conditions. In Brizantha, a significant reduction (p < 0.05) in stomatal size and density was observed under shade in silvopastoral areas, where solar radiation and air temperature decreased, while relative humidity increased. Nonetheless, these microclimatic variations did not lead to significant changes in foliar chemistry, growth variables, or biomass production, suggesting a high degree of adaptive plasticity to microclimatic fluctuations. Foliar ash content exhibited an increasing trend with altitude, indicating greater efficiency of Brizantha in absorbing calcium, phosphorus, and potassium at higher altitudes, possibly linked to more favorable edaphoclimatic conditions for nutrient uptake. Finally, forage quality declined with plant age, as evidenced by reductions in protein, ash, and In Vitro Dry Matter Digestibility (IVDMD), alongside increases in fiber, Neutral Detergent Fiber (NDF), and Acid Detergent Fiber (ADF). These findings support the recommendation of cutting intervals between 30 and 45 days, during which Brizantha displays a more favorable nutritional profile, higher digestibility, and consequently, greater value for animal feeding. Full article

This article analyzes the operating principle of the BMM sensor emitter in order to improve the accuracy of the wireless determination of the BMM sensor coordinates under a massif of destroyed rock in the context of the problem of determining the shift of rocks during gold ore mining. Using numerical simulations, FEM has been developed to develop digital models reflecting individual cases of the propagation of the magnetic field of the emitter located in various geological conditions and positions relative to the rock surface and the vertical axis. The accuracy of determining the coordinates of the radio beacon in the rock has been analyzed, and data on the deviation of the coordinates of the peaks of the magnetic field strength from the radio beacon axis have been obtained in cases of a heterogeneous composition of the rock massif, the influence of the deviation of the emitter axis angle from the vertical, the influence of the unevenness of the collapse relief, and the influence of the superposition of fields from different radiation sources. A study has been carried out to determine the direction of the radio beacon search based on the resulting vector of the emitter’s magnetic field strength. Full article

Barium chloride (BaCl₂), a known environmental pollutant, induces organ-specific oxidative stress through disruption of redox homeostasis. This study evaluated the protective effects and safety profile of sodium copper chlorophyllin (SCC) and ascorbic acid (ASC) against BaCl₂-induced oxidative damage in the liver and brain of mice using a two-phase experimental protocol. Animals received either SCC (40 mg/kg), ASC (160 mg/kg), or their combination for 14 days prior to BaCl₂ exposure (150 mg/L in drinking water for 7 days), allowing evaluation of both preventive and therapeutic effects. Toxicological and behavioral assessments confirmed the absence of systemic toxicity or neurobehavioral alterations following supplementation. Body weight, liver and kidney indices, and biochemical markers (Aspartate Aminotransferase (ASAT), Alanine Aminotransferase (ALAT), creatinine) remained within physiological ranges, and no anxiogenic or locomotor effects were observed. In the brain, BaCl₂ exposure significantly increased SOD (+49%), CAT (+66%), GPx (+24%), and GSH (+26%) compared to controls, reflecting a robust compensatory antioxidant response. Although lipid peroxidation (MDA) showed a non-significant increase, SCC, ASC, and their combination reduced MDA levels by 42%, 37%, and 55%, respectively. These treatments normalized antioxidant enzyme activities and GSH, indicating an effective neuroprotective effect. In contrast, the liver exhibited a different oxidative profile. BaCl₂ exposure increased MDA levels by 80% and GSH by 34%, with no activation of SOD, CAT, or GPx. Histological analysis revealed extensive hepatocellular necrosis, vacuolization, and inflammatory infiltration. SCC significantly reduced hepatic MDA by 39% and preserved tissue architecture, while ASC alone or combined with SCC exacerbated inflammation and depleted hepatic GSH by 71% and 78%, respectively, relative to BaCl₂-exposed controls. Collectively, these results highlight a differential, organ-specific response to BaCl₂-induced oxidative stress and the therapeutic potential of SCC and ASC. SCC emerged as a safer and more effective agent, particularly in hepatic protection, while both antioxidants demonstrated neuroprotective effects when used individually or in combination. Full article

Background/Objectives: Quality of life (QOL) is a multifaceted concept involving a variety of factors which define the overall well-being of individuals. Food security, which implies a resilient food system, is one factor that is central to the calculus of the QOL status of a community considering that food is a staple of life. Advancing food security as a strategy for attaining sustained improvement in community QOL hinges on recognizing that food security is embedded in a matrix of other factors that work with it to generate the QOL the community experiences. The lived experience of the community defines the community’s QOL value matrix and the relative position of food security in that value matrix. Our thesis is that the role of food security in the lived experience of low-income communities depends on the position food security is accorded relative to other factors in the QOL value matrix of the community. Methods: This study employed a multimethod approach to define the QOL value matrix of low-income Guilford County residents, identifying the relative position of the value components and demographic segments based on priority ranking. First, an in-depth interview was conducted and then a telephone survey (280 sample) was used for collecting data. The ISAC Analysis Procedure and Best–Worst Scaling methods were used to identify and rank components of the QOL value matrix in terms of their relative impact on QOL. Results: The analysis revealed that spiritual well-being is the most important contributor to QOL, with a weight of 0.23, followed by access to health services (0.21) and economic opportunities (0.16), while food security has a moderate impact with 0.07. Conclusions: These findings emphasize the need for targeted policy interventions that consider the specific needs of different demographic segments to effectively improve QOL and inform the design of resilient food systems that reflect the lived experiences of low-income communities. Food security policies must be integrated with broader quality of life interventions, particularly for unemployed, low-educated, and single individuals, to ensure that a resilient food system effectively reduces inequities and address community-specific vulnerabilities. Full article

A novel reflective metasurface (RMS) is proposed in this paper. The MS measures 128 × 128 × 2.794 mm³ and consists of a six-layer vertically stacked structure, with a liquid crystal (LC) cavity in the middle layer. A dual fan-shaped direct current (DC) bias circuit is designed to minimize the interaction between the radio frequency (RF) signal and the DC source, allowing control of the LC dielectric constant via bias voltage. This enables multi-band operation to improve communication capacity and quality for x-band devices. The polarization conversion (PC) structure employs an orthogonal anisotropic design, utilizing logarithmic functions to create two pairs of bowtie microstrip patches for linear-to-circular polarization conversion (LCPC). Simulation results show that for x-polarized incident waves, with an LC dielectric constant of ε_r = 2.8, left- and right-handed circularly polarized (LHCP and RHCP) waves are achieved in the frequency ranges of 8.15–8.46 GHz and 9.84–12.52 GHz, respectively. For ε_r = 3.9, LHCP and RHCP are achieved in 9–9.11 GHz and 9.86–11.81 GHz, respectively, and for ε_r = 4.6, they are in 8.96–9.11 GHz and 9.95–11.51 GHz. In the case of y-polarized incident waves, the MS reflects the reverse CP waves within the same frequency ranges. Measured results show that at ε_r = 2.8, the axial ratio (AR) is below 3 dB in the frequency ranges 8.16–8.46 GHz and 9.86–12.48 GHz, with 3 dB AR relative bandwidth (ARBW) of 3.61% and 23.46%, respectively. For ε_r = 4.6, the AR < 3 dB in the frequency range of 9.78–11.34 GHz, with a 3 dB ARBW of 14.77%. Finally, the measured and simulated results are compared to validate the proposed design, which can be applied to various applications within the corresponding operating frequency band. Full article

Enhancing market integration levels is crucial for advancing sustainable regional collaborative development and achieving ecological protection and high-quality development goals within the Yellow River Basin, fostering a balance between economic efficiency, social equity, and environmental resilience. This study analyzed the retail price data of goods from prefecture-level cities in the Yellow River Basin from 2010 to 2022, employing the relative price method to measure the market integration index. Additionally, it examined the temporal and spatial evolution patterns and driving factors using the Dagum Gini coefficient and panel regression models. The results indicate the following. (1) The market integration index of the Yellow River Basin shows a fluctuating upward trend, with an average annual growth rate of 9.8%. The spatial pattern generally reflects a situation where the east is relatively high and the west is relatively low, as well as the south being higher than the north. (2) Regional disparities are gradually diminishing, with the overall Gini coefficient decreasing from 0.153 to 0.104. However, internal differences within the downstream and midstream areas have become prominent, and contribution rate analysis reveals that super-variable density has replaced between-group disparities as the primary source. (3) Upgrading the industrial structure and enhancing the level of economic development are the core driving forces, while financial support and digital infrastructure significantly accelerate the integration process. Conversely, the level of openness exhibits a phase-specific negative impact. We propose policy emphasizing the need to strengthen development in the upper reach of the Yellow River Basin, further improve interregional collaborative innovation mechanisms, and enhance cross-regional coordination among multicenter network nodes. Full article

Methyl groups can be obtained either from the diet (labile methyl groups) or produced endogenously (methylneogenesis) via one-carbon (C1-) metabolism as S-adenosylmethionine (SAM). The essential nutrients folate and choline (through betaine) are metabolically entwined to feed their methyl groups into C1-metabolism. A choline-deficient diet in rats produces a 31–40% reduction in liver folate content, 50% lower hepatic SAM levels, and a doubling of plasma homocysteine. Similarly, folate deficiency results in decreased total hepatic choline. Thus, sufficient intakes of both folate and choline (or betaine) contribute to safeguarding the methyl balance in the body. A significant amount of choline (as phosphatidylcholine) is produced in the liver via the SAM-dependent phosphatidylethanolamine methyltransferase. Experimental studies using diets deficient in several methyl donors have shown that supplemental betaine was able to rescue not only plasma betaine but also plasma folate. Fasting plasma homocysteine concentrations are mainly determined by folate intake or status, while the effect of choline or betaine on fasting plasma homocysteine is minor. This appears to contradict the finding that approximately 50% of cellular SAM is provided via the betaine-homocysteine methyltransferase (BHMT) pathway, which uses dietary choline (after oxidation to betaine) or betaine to convert homocysteine to methionine and then to SAM. However, it has been shown that the relative contribution of choline and betaine to cellular methylation is better reflected by measuring plasma homocysteine after a methionine load test. Choline or betaine supplementation significantly lowers post-methionine load homocysteine, whereas folate supplementation has a minor effect on post-methionine load homocysteine concentrations. This review highlights the interactions between folate and choline and the essentiality of choline as a key player in C1-metabolism. We further address some areas of interest for future work. Full article

Understanding the molecular basis of endometrial receptivity is crucial for improving implantation outcomes in assisted reproduction, especially for patients with recurrent implantation failure (RIF). This study investigates the timing relationship between microRNA (miRNA) and messenger RNA (mRNA) profiles in the endometrium using simultaneously the endometrial receptivity array (ERA) and the microRNA receptivity assay (MIRA) in 100 RIF patients undergoing euploid blastocyst transfer. The concordance rate between ERA and MIRA was 72% (Kappa = 0.50), suggesting partial overlap in profiling. Patients were stratified by the timing sequence of miRNA relative to mRNA into Fast, Equal, and Slow groups. Those with delayed miRNA expression (Slow group) had significantly lower pregnancy rates (54.5%) than those with synchronous or leading miRNA expression (81.9% and 94.1%, respectively; p = 0.031). Moreover, the Slow group exhibited higher prior implantation failure counts and altered expression in 15 miRNAs, many involved in aging-related pathways. These findings highlight that asynchronous miRNA–mRNA profiles may reflect impaired receptivity and suggest that miRNA-based staging adds valuable diagnostic insight beyond mRNA profiling alone. Dual assessment of mRNA and miRNA profiles may offer additional diagnostic insight into endometrial receptivity but requires further validation before clinical application. Full article

Bruising in ‘Korla’ pears represents a prevalent phenomenon that leads to progressive fruit decay and substantial economic losses. The detection of early-stage bruising proves challenging due to the absence of visible external characteristics, and existing deep learning models have limitations in weak feature extraction under complex optical interference. To address the postharvest latent damage detection challenges in ‘Korla’ pears, this study proposes a collaborative detection framework integrating structured-illumination reflectance imaging (SIRI) with multi-order gated attention mechanisms. Initially, an SIRI optical system was constructed, employing 150 cycles·m⁻¹ spatial frequency modulation and a three-phase demodulation algorithm to extract subtle interference signal variations, thereby generating RT (Relative Transmission) images with significantly enhanced contrast in subsurface damage regions. To improve the detection accuracy of latent damage areas, the MOGA-UNet model was developed with three key innovations: 1. Integrate the lightweight VGG16 encoder structure into the feature extraction network to improve computational efficiency while retaining details. 2. Add a multi-order gated aggregation module at the end of the encoder to realize the fusion of features at different scales through a special convolution method. 3. Embed the channel attention mechanism in the decoding stage to dynamically enhance the weight of feature channels related to damage. Experimental results demonstrate that the proposed model achieves 94.38% mean Intersection over Union (mIoU) and 97.02% Dice coefficient on RT images, outperforming the baseline UNet model by 2.80% with superior segmentation accuracy and boundary localization capabilities compared with mainstream models. This approach provides an efficient and reliable technical solution for intelligent postharvest agricultural product sorting. Full article

Background/Objectives: The number needed to vaccinate (NNV) is a metric commonly used to evaluate the public health impact of a vaccine as it represents the number of individuals that must be vaccinated to prevent one case of disease. Traditional calculations may underestimate vaccine benefits by neglecting indirect effects and duration of protection (DOP), resulting in NNV overestimation. This study evaluated the NNV for the pediatric 20-valent pneumococcal conjugate (PCV20) US immunization program, as compared to PCV13, with a unique approach to NNV. Methods: A multi-cohort, population-based Markov model accounting for indirect effects was employed to calculate the NNV of PCV20 to avert a case of pneumococcal disease, invasive pneumococcal disease (IPD), hospitalized non-bacteremic pneumonia (NBP), ambulatory NBP, and otitis media (OM), as well as to prevent antibiotic-resistant cases and antibiotic prescriptions. Results: The mean NNV over a 25-year time horizon to prevent one case of pneumococcal disease was 6, with NNVs of 854 for IPD, 106 for hospitalized NBP, 25 for outpatient NBP, and 9 for OM, 11 for a course of antibiotic, and 4 for resistant disease. The mean NNV per year decreased over time, reflecting the DOP and increasing indirect effects over time. Conclusions: This study presents a novel approach to NNVs and shows that relatively few vaccinations are required to prevent disease. The decrease in NNV over time highlights the necessity of including DOP and indirect effects in NNV calculations, ensuring a more realistic assessment of a vaccine’s impact. Full article

Phosphorus (P) availability is a major constraint on plant growth in many forest ecosystems, yet the strategies by which different tree species acquire and utilize various forms of soil phosphorus remain poorly understood. This study investigated how coexisting tree species with contrasting mycorrhizal types, specifically arbuscular mycorrhizal (AM) and ectomycorrhizal (ECM) associations, respond to different phosphorus forms under field conditions. An in situ root bag experiment was conducted using four phosphorus treatments (control, inorganic, organic, and mixed phosphorus) across four subtropical tree species. A comprehensive set of fine root traits, including morphological, physiological, and mycorrhizal characteristics, was measured to evaluate species-specific phosphorus foraging strategies. The results showed that AM species were more responsive to phosphorus form variation than ECM species, particularly under inorganic and mixed phosphorus treatments. Significant changes in root diameter (RD), root tissue density (RTD), and acid phosphatase activity (RAP) were observed in AM species, often accompanied by higher phosphorus accumulation in fine roots. For example, RD in AM species significantly decreased under the Na₃PO₄ treatment (0.94 mm) compared to the control (1.18 mm), while ECM species showed no significant changes in RD across treatments (1.12–1.18 mm, p > 0.05). RTD in AM species significantly increased under Na₃PO₄ (0.030 g/cm³) and Mixture (0.021 g/cm³) compared to the control (0.012 g/cm³, p < 0.05), whereas ECM species exhibited consistently low RTD values across treatments (0.017–0.020 g/cm³, p > 0.05). RAP in AM species increased significantly under Na₃PO₄ (1812 nmol/g/h) and Mixture (1596 nmol/g/h) relative to the control (1348 nmol/g/h), while ECM species showed limited variation (1286–1550 nmol/g/h, p > 0.05). In contrast, ECM species displayed limited trait variation across treatments, reflecting a more conservative acquisition strategy. In addition, trait correlation analysis revealed stronger coordination among root traits in AM species. And AM species exhibited high variability across treatments, while ECM species maintained consistent trait distributions with limited plasticity. These findings suggest that AM and ECM species adopt fundamentally different phosphorus acquisition strategies. AM species rely on integrated morphological and physiological responses to variable phosphorus conditions, while ECM species maintain stable trait configurations, potentially supported by fungal symbiosis. Such divergence may contribute to functional complementarity and species coexistence in phosphorus-limited subtropical forests. Full article

This study employs k-means clustering to analyze local organizations responsible for land improvement in Greece, identifying four distinct groups with consistent geographic patterns but divergent financial and operational characteristics. By integrating unsupervised machine learning with spatial analysis, the research offers a novel perspective on irrigation water pricing and cost recovery. The findings reveal that organizations located on islands, despite high water costs due to limited rainfall and geographic isolation, tend to achieve relatively strong financial performance, indicating the presence of adaptive mechanisms that could inform broader policy strategies. In contrast, organizations managing extensive irrigable land or large volumes of water frequently show poor cost recovery, challenging assumptions about economies of scale and revealing inefficiencies in pricing or governance structures. The spatial coherence of the clusters underscores the importance of geography in shaping institutional outcomes, reaffirming that environmental and locational factors can offer greater explanatory power than algorithmic models alone. This highlights the need for water management policies that move beyond uniform national strategies and instead reflect regional climatic, infrastructural, and economic variability. The study suggests several policy directions, including targeted infrastructure investment, locally calibrated water pricing models, and performance benchmarking based on successful organizational practices. Although grounded in the Greek context, the methodology and insights are transferable to other European and Mediterranean regions facing similar water governance challenges. Recognizing the limitations of the current analysis—including gaps in data consistency and the exclusion of socio-environmental indicators—the study advocates for future research incorporating broader variables and international comparative approaches. Ultimately, it supports a hybrid policy framework that combines data-driven analysis with spatial intelligence to promote sustainability, equity, and financial viability in agricultural water management. Full article