Search Results (9,099)

Cucumber, a high-yielding crop commonly grown in facility environments, is particularly susceptible to nitrogen (N) deficiency due to its rapid growth and high nutrient demand. This study used cucumber as its experimental subject and established a spectral dataset of leaves under four nutritional conditions, normal supply, nitrogen deficiency, phosphorus deficiency, and potassium deficiency, aiming to develop an efficient and robust method for quantifying N in cucumber leaves using Raman spectroscopy (RS). Spectral data were preprocessed using three baseline correction methods—BaselineWavelet (BW), Iteratively Improve the Moving Average (IIMA), and Iterative Polynomial Fitting (IPF)—and key spectral variables were selected using 4-Dimensional Feature Extraction (4DFE) and Competitive Adaptive Reweighted Sampling (CARS). These selected features were then used to develop a N content prediction model based on Partial Least Squares Regression (PLSR). The results indicated that baseline correction significantly enhanced model performance, with three methods outperforming unprocessed spectra. A further analysis showed that the combination of IPF, 4DFE, and CARS achieved optimal PLSR model performance, achieving determination coefficients (R²) of 0.947 and 0.847 for the calibration and prediction sets, respectively. The corresponding root mean square errors (RMSEC and RMSEP) were 0.250 and 0.368, while the residual predictive deviation (RPDC and RPDP) values reached 4.335 and 2.555. These findings confirm the feasibility of integrating RS with advanced data processing for rapid, non-destructive nitrogen assessment in cucumber leaves, offering a valuable tool for nutrient monitoring in precision agriculture. Full article

The resilience concept is well established in engineering, but the quantitative studies of vessel impact resilience for bridge structures remain limited. This paper presents an integrated framework for assessing vessel impact resilience under combined rebar corrosion and vessel collision effects. First, a corroded reinforced concrete bridge is considered for nonlinear static analysis to quantify initial corrosion damage and for nonlinear dynamic analysis to evaluate post-impact function loss. Then, recovery for each damage state is modeled by using both negative exponential and triangular recovery functions to estimate restoration times and to obtain a vessel impact resilience index. The results show that increasing corrosion severity markedly reduces resilience capacity. Furthermore, resilience indices obtained from the negative exponential function generally exceed those from the triangular function, and this improvement becomes more significant at lower resilience levels. Resilience indices calculated by using negative exponential and triangular recovery functions show negligible differences when the concrete bridge is in the uncorroded initial state and the vessel impact velocity is below 1.5 m/s. However, as reinforcement corrosion increases, the maximum discrepancy between these two recovery functions also increases, reaching a value of 67% at a corrosion level of 15.0%. From the numerical results obtained from a case study, it is important to select an appropriate recovery model when assessing vessel impact resilience. For rapid initial restoration followed by slower long-term recovery, the negative exponential model yields greater resilience gains compared to the triangular model. The proposed method thus provides an effective tool for engineers and decision makers to evaluate and improve the vessel impact resilience of aging bridges under the combined corrosion and impact effects. This proposes a quantitative metric for resilience-based condition assessment and maintenance planning. Full article

The global spread of Mpox virus (MPXV) underscores the urgent need for rapid, field-deployable diagnostic tools, especially in low-resource settings. We evaluated a loop-mediated isothermal amplification (LAMP) assay, termed LAMPOX, developed by Coris BioConcept. The assay was tested in three formats—two liquid versions and a dried, ready-to-use version—targeting only the ORF F3L (Liquid V1) or both the ORF F3L and N4R (Liquid V2 and dried) genomic regions. Analytical sensitivity and specificity were assessed using 60 clinical samples from confirmed MPXV-positive patients. Sensitivity on clinical samples was 81.7% for Liquid V1 and 88.3% for Liquid V2. The dried LAMPOX assay demonstrated a sensitivity of 88.3% and a specificity of 100% in a panel of 112 negative controls, with most positive samples detected in under 7 min. Additionally, a simplified sample lysis protocol was developed to facilitate point-of-care use. While this method showed slightly reduced sensitivity compared to standard DNA extraction, it proved effective for samples with higher viral loads. The dried format offers key advantages, including ambient-temperature stability and minimal equipment needs, making it suitable for point-of-care testing. These findings support LAMPOX as a promising tool for rapid MPXV detection during outbreaks, especially in resource-limited settings where traditional PCR is impractical. Full article

This article presents a comprehensive assessment of mudflow risk in the Talgar River basin through the application of Multi-Criteria Decision Analysis (MCDA) methods and numerical modeling using the Rapid Mass Movement Simulation (RAMMS) environment. The first part of the study involves a spatial assessment of mudflow hazard and susceptibility using GIS technologies and MCDA. The key condition for evaluating mudflow hazard is the identification of factors influencing the formation of mudflows. The susceptibility assessment was based on viewing the area as an object of spatial and functional analysis, enabling determination of its susceptibility to mudflow impacts across geomorphological zones: initiation, transformation, and accumulation. Relevant criteria were selected for analysis, each assigned weights based on expert judgment and the Analytic Hierarchy Process (AHP). The results include maps of potential mudflow hazard and susceptibility, showing areas of hazard occurrence and risk impact zones within the Talgar River basin. According to the mudflow hazard map, more than 50% of the basin area is classified as having a moderate hazard level, while 28.4% is subject to high hazard, and only 1.8% falls under the very high hazard category. The remaining areas are categorized as very low (4.1%) and low (14.7%) hazard zones. In terms of susceptibility to mudflows, 40.1% of the territory is exposed to a high level of susceptibility, 35.6% to a moderate level, and 5.5% to a very high level. The remaining areas are classified as very low (1.8%) and low (15.6%) susceptibility zones. The predictive performance was evaluated through Receiver Operating Characteristic (ROC) curves, and the Area Under the Curve (AUC) value of the mudflow hazard assessment is 0.86, which indicates good adaptability and relatively high accuracy, while the AUC value for assessing the susceptibility of the territory is 0.71, which means that the accuracy of assessing the susceptibility of territories to mudflows is within the acceptable level of model accuracy. To refine the spatial risk assessment, mudflow modeling was conducted under three scenarios of glacial-moraine lake outburst using the RAMMS model. For each scenario, key flow parameters—height and velocity—were identified, forming the basis for classification of zones by impact intensity. The integration of MCDA and RAMMS results produced a final mudflow risk map reflecting both the likelihood of occurrence and the extent of potential damage. The presented approach demonstrates the effectiveness of combining GIS analysis, MCDA, and physically-based modeling for comprehensive natural hazard assessment and can be applied to other mountainous regions with high mudflow activity. Full article

With the rapid pace of urbanization, cities are increasingly facing severe challenges related to environmental pollution, ecological degradation, and climate change. Extreme climate events—such as heatwaves, droughts, heavy rainfall, and wildfires—have intensified public concern about sustainability, environmental protection, and low-carbon development. Ensuring environmental preservation while maintaining residents’ quality of life has become a central focus of urban governance. In this context, evaluating green indicators and predicting urban greenness is both necessary and urgent. This study incorporates international frameworks such as the EU Green City Index, the European Green Capital Award, and the United Nations Sustainable Development Goals to assess urban sustainability. The Extreme Gradient Boosting (XGBoost) algorithm is employed to predict the green level of cities and to develop multiple optimized models. Comparative analysis with traditional models demonstrates that XGBoost achieves superior performance, with an accuracy of 0.84 and an F1-score of 0.81. Case study findings identify “Greenhouse Gas Emissions per Person” and “Per Capita Emissions from Transport” as the most critical indicators. These results provide practical guidance for policymakers, suggesting that targeted regulations based on these key factors can effectively support emission reduction and urban sustainability goals. Full article

The combination of scaffold materials and bioactive factors is a promising strategy for promoting bone defect repair in tissue engineering. Previous studies have shown that osteoglycin (OGN) is highly expressed in the bone repair process using deer antler as an animal model of bone defects. It suggests that OGN may be a key active component involved in the bone repair process. The aim of this study was to investigate whether deer OGN (dOGN) could effectively promote bone regeneration. We successfully expressed dOGN using the E. coli pET30a system and evaluated its biological activity through cell proliferation and migration assays. At a concentration of 5 μg/mL, dOGN significantly promoted cell proliferation and migration. We then incorporated dOGN onto decellularized antler cancellous bone (DACB) scaffolds and assessed their osteogenic potential both in vitro and in vivo. The results indicated that dOGN loading enhanced cell proliferation, adhesion, and osteogenic activity. In vivo experiments confirmed that the dOGN-DACB scaffold significantly improved bone regeneration compared to DACB alone. This study demonstrates that dOGN-loaded DACB scaffolds hold great potential for clinical applications in treating critical-sized bone defects by mimicking the rapid regenerative properties of deer antlers. Full article

The demand for eco-friendly nanomaterial synthesis has increased interest in biological approaches. Yeast-mediated biosynthesis of gold nanoparticles (AuNPs) offers a sustainable alternative with potential biotechnological applications. This study developed a rapid screening method to identify oleaginous yeast strains able to synthesize AuNPs. A collection of 114 oleaginous yeasts from the LIBBA laboratory was screened. UV–Vis spectroscopy at 530–560 nm was used to assess nanoparticle formation, identifying 20 strains that effectively synthesize AuNP. Electron microscopy confirmed the presence of intracellular and extracellular nanoparticles, with variations in size and morphology. This screening and optimization approach effectively identified promising yeast candidates and refined biosynthesis conditions, providing a foundation for industrial-scale nanoparticle production. Full article

With the rapid development of video streaming services, adaptive bitrate (ABR) algorithms have become a core technology for ensuring optimal viewing experiences. Traditional ABR strategies, predominantly rule-based or reinforcement learning-driven, typically employ uniform quality assessment metrics that overlook users’ subjective preference differences regarding factors such as video quality and stalling. To address this limitation, this paper proposes an adaptive video bitrate selection system that integrates preference modeling with reinforcement learning. By incorporating a preference learning module, the system models and scores user viewing trajectories, using these scores to replace conventional rewards and guide the training of the Proximal Policy Optimization (PPO) algorithm, thereby achieving policy optimization that better aligns with users’ perceived experiences. Simulation results on DASH network bandwidth traces demonstrate that the proposed optimization method improves overall Quality of Experience (QoE) by over 9% compared to other mainstream algorithms. Full article

Against the backdrop of multifaceted strategies to combat climate change, understanding soil erosion’s role in carbon cycling is critical due to terrestrial carbon pool vulnerability. This study integrates bibliometric methods with visualization tools (CiteSpace, VOSviewer) to analyze 3880 Web of Science core publications (1994–2024, inclusive), constructing knowledge graphs and forecasting trends. The results show exponential publication growth, shifting from slow development (1994–2011) to rapid expansion (2012–2024), aligning with international climate policy milestones. The Chinese Academy of Sciences led productivity (519 articles), while the US demonstrated major influence (H-index 117; 52,297 citations), creating a China–US bipolar research pattern. It was also found that Dutch journals dominate this research field. A keyword analysis revealed a shift from erosion-driven carbon transport to ecosystem service assessments. Emerging hotspots include microbial community regulation, climate–erosion feedback, and model–policy integration, though developing country collaboration remains limited. Future research should prioritize isotope tracing, multiscale modeling, and studies in ecologically vulnerable regions to enhance global soil carbon management. This study provides a novel analytical framework and forward-looking perspective for the soil erosion research on soil carbon cycling, serving as an extension of climate change mitigation strategies. Full article

A novel near-infrared (NIR) squaraine-based chemosensor, SQ-68, has been designed and synthesized for the sensitive and selective detection of Cu²⁺ and Ag⁺ ions, offering a compact solution for multi-analyte sensing. SQ-68 demonstrates high selectivity, with its performance influenced by the solvent environment: It selectively detects Cu²⁺ in acetonitrile and Ag⁺ in an ethanol–water mixture. Upon binding with either ion, SQ-68 undergoes significant absorption changes in the NIR region, accompanied by visible color changes, enabling naked-eye detection. Spectroscopic studies confirm a 1:1 binding stoichiometry with both Cu²⁺ and Ag⁺, accompanied by hypochromism. The detection limits are 0.09 μM for Cu²⁺ and 0.38 μM for Ag⁺, supporting highly sensitive quantification. The sensor’s practical applicability was validated in real water samples (sea, lake, and tap water), with recovery rates ranging from 73–95% for Cu²⁺ to 59–99% for Ag⁺. These results establish SQ-68 as a reliable and efficient chemosensor for environmental monitoring and water quality assessment. Its dual-analyte capability, solvent-tunable selectivity, and visual detection features make it a promising tool for rapid and accurate detection of heavy metal ions in diverse aqueous environments. Full article

Asphalt plant reclaimed powder is a common solid waste in road engineering. Reusing reclaimed powder as filler holds significant importance for environmental protection and resource conservation. The key factors affecting the feasibility of reclaimed powder reuse are its acidity/alkalinity and cleanliness. Traditional evaluation methods, such as the methylene blue test and plasticity index, can assess reclaimed powder properties to guide its recycling. However, these methods suffer from inefficiency, strong empirical dependence, and high variability. To address these limitations, this study proposes a rapid and precise evaluation method for reclaimed powder properties based on Fourier transform infrared spectroscopy (FTIR). To do so, five field-collected reclaimed powder samples and four artificial samples were evaluated. Scanning electron microscopy (SEM), X-ray fluorescence spectroscopy (XRF), and X-ray diffraction (XRD) were employed to characterize their microphase morphology, chemical composition, and crystal structure, respectively. Subsequently, FTIR was used to establish correlations between key acidity/alkalinity, cleanliness, and multiple characteristic peak intensities. Representative infrared characteristic peaks were selected, and a quantitative functional group index (Is) was proposed to simultaneously evaluate acidity/alkalinity and cleanliness. The results indicate that reclaimed powder primarily consists of tiny, crushed stone particles and dust, with significant variations in crystal structure and chemical composition, including calcium carbonate, silicon oxide, iron oxide, and aluminum oxide. Some samples also contained clay, which critically influenced the reclaimed powder properties. Since both filler acidity/alkalinity and cleanliness are affected by clay (silicon/carbon ratio determining acidity/alkalinity and aluminosilicate content affecting cleanliness), this study calculated four functional group indices based on FTIR absorption peaks, namely the Si-O-Si stretching vibration (1000 cm⁻¹) and the CO₃²⁻ asymmetric stretching vibration (1400 cm⁻¹). These indices were correlated with conventional testing results (XRF for acidity/alkalinity, methylene blue value, and pull-off strength for cleanliness). The results show that the Is index exhibited strong correlations (R² = 0.89 with XRF, R² = 0.80 with methylene blue value, and R² = 0.96 with pull-off strength), demonstrating its effectiveness in predicting both acidity/alkalinity and cleanliness. The developed method enhances reclaimed powder detection efficiency and facilitates high-value recycling in road engineering applications. Full article

Preventing progressive collapse induced by accidental events poses a critical challenge in the design and construction of resilient structures. While substantial progress has been made in planar structures, the progressive collapse mechanisms of precast concrete spatial structures—particularly regarding the effects of precast slabs—remain inadequately explored. This study develops a refined finite element modeling approach to investigate progressive collapse mechanisms in fully bonded prestressed precast concrete (FB-PPC) spatial frames, both with and without precast slabs. The modeling approach was validated against available test data from related sub-assemblies, and applied to assess the collapse performance. A series of pushdown analyses were conducted on the spatial frames under various column removal scenarios. The load–displacement curves, slab contribution, and failure modes under different conditions were compared and analyzed. A simplified energy-based dynamic assessment was additionally employed to offer a rapid estimation of the dynamic collapse capacity. The results show that when interior or side columns fail, the progressive collapse process can be divided into the beam action stage and the catenary action (CA) stage. During the beam action stage, the compressive membrane action (CMA) of the slabs and the compressive arch action (CAA) of the beams work in coordination. Additionally, the tensile membrane action (TMA) of the slabs strengthens the CA in the beams. When the corner columns fail, the collapse stages comprise the beam action stage followed by the collapse stage. Due to insufficient lateral restraints around the failed column, the development of CA is limited. The membrane action of the slabs cannot be fully mobilized. The contribution of the slabs is significant, as it can substantially enhance the vertical resistance and restrain the lateral displacement of the columns. The energy-based dynamic assessment further reveals that FB-PPC spatial frames exhibit high ductility and residual strength following sudden column removal, with dynamic load–displacement curves showing sustained plateaus or gentle slopes across all scenarios. The inclusion of precast slabs consistently enhances both the peak load capacity and the residual resistance in dynamic collapse curves. Full article

The accumulation of end-of-life tires and the rapid increase in demolition activities pose significant environmental and waste-management challenges. The redevelopment of construction materials incorporating this waste is a potentially promising strategy for minimizing environmental impact while promoting the principles of a circular economy. This study investigates the performance of self-compacting concrete (SCC) incorporating up to 20% rubber aggregates (sand and gravel) and 40% recycled concrete aggregate (RCA) for non-structural applications. A series of tests was conducted to assess fresh and hardened properties, including flowability, compressive strength, tensile strength, flexural strength, water absorption, and density. The results indicated that increasing RCA content reduced density and compressive strength, while tensile and flexural strengths were only moderately affected. Response surface methodology (RSM), utilizing a Box–Behnken design, was employed to optimize compressive, tensile, and flexural strength responses. Statistical analysis was used to identify the optimal mix proportions, which balance the mechanical performance and sustainability of SCC with recycled components. Mixtures incorporating moderate rubber content—specifically, 5–5.5% sand rubber and 0–6% coarse rubber—and 40% recycled-concrete aggregate (RCA) achieved the highest predicted performance, with compressive strength ranging from 20.00 to 28.26 MPa, tensile strength from 2.16 to 2.85 MPa, and flexural strength reaching 5.81 MPa, making them suitable for sidewalks and walkways. Conversely, mixtures containing higher rubber proportions (5.5–20% sand rubber and 20% coarse rubber) combined with the same RCA level (40%) showed the lowest mechanical performance, with compressive strength between 5.2 and 10.08 MPa, tensile strength of 1.05–1.41 MPa, and flexural strength from 2.18 to 3.54 MPa. These findings underscore the broad performance range achievable through targeted optimization. They confirm the viability of recycled materials for producing environmentally friendly SCC in non-structural applications. Full article

The accelerated evolution of remote sensing technology has intensified the demand for real-time tile map generation, highlighting the limitations of conventional mapping approaches that rely on manual cartography and field surveys. To address the critical need for rapid cartographic updates, this study presents a novel multi-stage generative framework that synergistically integrates Generative Adversarial Networks (GANs) with Diffusion Denoising Models (DMs) for high-fidelity map generation from remote sensing imagery. Specifically, our proposed architecture first employs GANs for rapid preliminary map generation, followed by a cascaded diffusion process that progressively refines topological details and spatial accuracy through iterative denoising. Furthermore, we propose a hybrid attention mechanism that strategically combines channel-wise feature recalibration with coordinate-aware spatial modulation, enabling the enhanced discrimination of geographic features under challenging conditions involving edge ambiguity and environmental noise. Quantitative evaluations demonstrate that our method significantly surpasses established baselines in both structural consistency and geometric fidelity. This framework establishes an operational paradigm for automated, rapid-response cartography, demonstrating a particular utility in time-sensitive applications including disaster impact assessment, unmapped terrain documentation, and dynamic environmental surveillance. Full article

Background: Acute mountain sickness (AMS) is a prevalent and potentially life-threatening condition caused by rapid exposure to high-altitude hypoxia, affecting pulmonary and neurological functions. Tongqiao Jiuxin Oil (TQ), a traditional Chinese medicine formula composed of aromatic and resinous ingredients such as sandalwood, agarwood, frankincense, borneol, and musk, has been widely used in the treatment of cardiovascular and cerebrovascular disorders. Clinical observations suggest its potential efficacy against AMS, yet its pharmacological mechanisms remain poorly understood. Methods: The chemical profile of TQ was characterized using UHPLC-Q-Exactive Orbitrap HRMS. Network pharmacology was applied to predict the potential targets and pathways involved in AMS. A rat model of AMS was established by exposing animals to hypobaric hypoxia (~10% oxygen), simulating an altitude of approximately 5500 m. TQ was administered at varying doses. Physiological indices, oxidative stress markers (MDA, SOD, GSH), histopathological changes, and the expression of hypoxia- and apoptosis-related proteins (HIF-1α, VEGFA, EPO, Bax, Bcl-2, Caspase-3) in lung and brain tissues were assessed. Results: A total of 774 chemical constituents were identified from TQ. Network pharmacology predicted the involvement of multiple targets and pathways. TQ significantly improved arterial oxygenation and reduced histopathological damage in both lung and brain tissues. It enhanced antioxidant activity by elevating SOD and GSH levels and reducing MDA content. Mechanistically, TQ downregulated the expression of HIF-1α, VEGFA, EPO, and pro-apoptotic markers (Bax/Bcl-2 ratio, Caspase-3), while upregulated Bcl-2, the anti-apoptotic protein expression. Conclusions: TQ exerts protective effects against AMS-induced tissue injury by improving oxygen homeostasis, alleviating oxidative stress, and modulating hypoxia-related and apoptotic signaling pathways. This study provides pharmacological evidence supporting the potential of TQ as a promising candidate for AMS intervention, as well as the modern research method for multi-component traditional Chinese medicine. Full article