Search Results (215)

This study aimed to control rapid localized corrosion and inflammation of biodegradable magnesium implants by developing a pH-responsive mPEG-PLGA coating loaded with dexamethasone (Dex). The mPEG-PLGA layer was designed to selectively degrade in alkaline conditions, thereby moderating pH elevation at the implant surface while enabling controlled Dex release. By varying the molecular weight of mPEG and PLGA, the degradation rate and microsphere size were tunable, allowing adjustment of the drug release profile. Among the tested coating solution concentrations (1.5–7.5 mg/mL), the formulation with 3 mg/mL Dex yielded a final cumulative release concentration of 0.02 mg/mL over a two-week period, which suppressed inflammatory responses in RAW 264.7 macrophages with minimal cytotoxicity, while enhancing BMP-2 and RUNX2 expression in mesenchymal stem cells. In a rat femur defect model, Mg implants coated with mPEG-PLGA containing 3 mg/mL Dex significantly increased bone volume and bone mineral density and reduced early TNF-α expression, accompanied by continuous new bone formation and strong BSP-positive osseointegration. These findings suggest that the proposed pH-responsive mPEG-PLGA/Dex coating provides a promising strategy to simultaneously regulate corrosion, attenuate inflammation, and promote bone regeneration around magnesium implants. Full article

Background: Aspiration pneumonia (AP) remains a major cause of morbidity and mortality, yet non-invasive tools for monitoring lung injury in preclinical models are limited. Lung ultrasound (LUS) is widely used clinically, but existing murine scoring systems lack anatomical resolution and have not been validated for aspiration-related injury. Methods: We developed the Modified Lung Edema Ultrasound Score (MLEUS), a region-structured adaptation of the Mouse Lung Ultrasound Score (MoLUS), designed to accommodate the heterogeneous and gravity-dependent injury patterns characteristic of murine AP. Male C57BL/6 mice were assigned to sham, 6 h, 24 h, or 48 h groups. Regional LUS findings were compared with histological injury scores and wet-to-dry (W/D) ratios. Inter-rater reliability was assessed using the intraclass correlation coefficient (ICC). Results: Global LUS–histology correlation was weak (ρ = 0.33, p = 0.114). In contrast, regional performance varied markedly. The right upper (RU) zone showed the strongest correspondence with histological injury (r = 0.55, p = 0.005), whereas right and left diaphragmatic regions demonstrated minimal association. LUS abnormalities were detectable as early as 6 h, preceding clear histological progression. Inter-rater reliability was good (ICC = 0.87). Conclusions: MLEUS provides a reproducible, region-specific framework for evaluating aspiration-induced lung injury in mice. Although global correlations with histology were limited, region-dependent analysis identified that the RU zone as a reliable acoustic window for concurrent injury assessment. Early ultrasound changes highlight the sensitivity of LUS to dynamic aeration and interstitial alterations rather than cumulative tissue damage. These findings support the use of LUS as a complementary, non-invasive physiological monitoring tool in small-animal respiratory research and clarify its methodological scope relative to existing scoring frameworks. Full article

Long-distance natural gas pipelines with internal corrosion defects are susceptible to fatigue failure under operational pressure fluctuations, posing significant risks to infrastructure integrity and safety. To address this, the present study employs a finite element methodology, utilizing Ansys Workbench to model pipelines of various specifications with parametrically defined corrosion defects, and nCode DesignLife to predict fatigue life based on Miner’s linear cumulative damage theory. The S-N curve for X70 steel was directly adopted, while a power-function model was fitted for X80 steel based on standards. A cleaned real-world pressure-time history was used as the load spectrum. Parametric analysis reveals that defect depth is the most influential factor, with a depth coefficient increase from 0.05 to 0.25, reducing fatigue life by up to 67.5%, while the influence of defect width is minimal. An empirical formula for fatigue life prediction was subsequently developed via multiple linear regression, demonstrating good agreement with simulation results and providing a practical tool for the residual life assessment and maintenance planning of in-service pipelines. Full article

Low-cost RGB intraoral cameras are accessible alternatives to intraoral scanners; however, generating panoramic images is challenging due to narrow fields of view, textureless surfaces, and specular highlights. This study proposes a robust stitching framework and identifies optimal acquisition parameters to overcome these limitations. All experiments were conducted exclusively on a mandibular dental phantom model. Geometric consistency was further validated using repeated physical measurements of mandibular arch dimensions as ground-truth references. We employed a deep learning-based approach using SuperPoint and SuperGlue to extract and match features in texture-poor environments, enhanced by a central-reference stitching strategy to minimize cumulative drift errors. To validate the feasibility in a controlled setting, we conducted experiments on dental phantoms varying working distances (1.5–3.0 cm) and overlap ratios. The proposed method detected approximately 19–20 times more valid inliers than SIFT, significantly improving matching stability. Experimental results indicated that a working distance of 2.5 cm offers the optimal balance between stitching success rate and image detail for handheld operation, while a 1/3 overlap ratio yielded superior geometric integrity. This system demonstrates that robust 2D dental mapping is achievable with consumer-grade sensors when combined with advanced deep feature matching and optimized acquisition protocols. Full article

This study presents a quantitative simulation analysis of hydrogen-enriched methane (HENG) storage with nitrogen as the cushion-gas in a depleted gas reservoir by varying three key operational parameters: the injection/withdrawal period, hydrogen blending ratio (5–20%), and injection depth. Ten injection–withdrawal cycles were modeled for each scenario, and performance was evaluated using cycle-averaged and cumulative hydrogen purity, recovery factors, and the mixing zone size. Extending the injection period increased hydrogen purity to 20.00–20.26% and reduced nitrogen to 0.001–0.003%, but recovery decreased from 65.63 to 53.83–41.09% due to enhanced dispersion and residual trapping. The blending ratio was the dominant control: 20% blending yielded 19.9–20.0% purity with nitrogen as low as 0.00–0.03%, whereas 5–10% blending produced lower purity but minimized nitrogen production to 0.97–1.08%. Injection depth affected nitrogen recovery more than purity, increasing from 0.72–1.20% (upper) to 1.46–1.61% (lower), along with thicker mixing zones. Final mixing zone size ranged from 3176 to 5546 blocks, with smaller zones consistently linked to higher purity and lower nitrogen breakthrough. The shut-in period further reduced nitrogen recovery from 6.49 to 1.33% and stabilized mixing behavior. Overall, minimizing late-cycle mixing zone thickness is essential for maintaining HENG storage performance. Although this study provides quantitative insights into HENG operational strategies, the use of a homogeneous grid and simplified fluid properties limits representation of geological heterogeneity and reactive processes. Future work will incorporate heterogeneity and reaction modeling into field-scale simulations to validate and refine these operating strategies for practical deployment. Full article

Environmental regulations serve as a critical determinant of industrial competitiveness in the global market. Recent policy shifts have driven a gradual convergence of rural environmental standards with urban norms, fostering a dynamic landscape of “top-down competition” between urban and rural regulatory frameworks. While the economic consequences of regional regulatory disparities are well-documented, the specific impacts of this regulatory convergence remain insufficiently explored. To address this gap, this study constructs a novel index to measure the convergence of environmental regulations between urban districts and rural counties at the prefecture level. Utilizing an unbalanced panel dataset of 5600 county-level timber processing enterprises, the Heckman two-stage model is employed for empirical analysis. The results demonstrate that the convergence of urban and rural environmental regulations significantly enhances both the export probability and export intensity of county-level firms, with these effects exhibiting persistence and cumulative growth over time. These findings remain robust across a series of validation tests, including instrumental variable estimation, double machine learning, and alternative model specifications. Mechanism analysis reveals that regulatory convergence promotes exports primarily by improving access to green credit and enhancing peer quality within the industry. Furthermore, heterogeneity tests indicate that the positive effects are most pronounced for start-ups and firms in the decline stage, as well as for enterprises located in eastern China, those outside the Yangtze River Economic Belt, and those subject to minimal government intervention. This study provides critical micro-level evidence that helps enterprises navigate the evolving policy landscape and supports the formulation of strategies to boost export trade amidst the integration of environmental regulations. Full article

Monitoring the stability of hydraulic structures such as dams and reservoirs in seismically active regions is essential for ensuring their safety and operational reliability. This study presents a comprehensive geospatial approach combining lineament analysis and geodynamic zoning to assess the structural stability of the Voroshilov and Priyut reservoirs located in the Almaty region, Kazakhstan. A regional lineament map was generated using ASTER GDEM data, while ALOS PALSAR data were used for detailed local analysis. Lineaments were extracted and analyzed through automated processing in PCI Geomatica. Lineament density maps and azimuthal rose diagrams were constructed to identify zones of tectonic weakness and assess regional structural patterns. Integration of lineament density, GPS velocity fields, InSAR deformation data, and probabilistic seismic hazard maps enabled the development of a detailed geodynamic zoning model. Results show that the studied sites are located within zones of low local geodynamic activity, with lineament densities of 0.8–1.2 km/km², significantly lower than regional averages of 3–4 km/km². GPS velocities in the area do not exceed 4 mm/year, and InSAR analysis indicates minimal surface deformation (<5 mm/year). Despite this apparent local stability, the 2024 Voroshilov Dam failure highlights the cumulative effect of regional seismic stresses (PGA up to 0.9 g) and localized filtration along fracture zones as critical risk factors. The proposed geodynamic zoning correctly identified the site as structurally stable under normal conditions but indicates that even low-activity zones are vulnerable under cumulative seismic loading. This demonstrates that an integrated approach combining remote sensing, geodetic, and seismic data can provide quantitative assessments for dam safety, predict potential high-risk zones, and support preventive monitoring in tectonically active regions. Full article

This study screens the permeability of volatile organic compounds (VOCs) through edible films made of candelilla wax and guar gum, offering new insights into their role as aroma and moisture barriers. Four formulations (0.2–0.4% wax, 0.4–0.8% gum, and 0.2–0.3% glycerol) were tested using a fractional factorial design. VOC fluxes (one ester, two aldehydes, two terpenes, and one lactone) were monitored via headspace solid-phase microextraction coupled to gas chromatography–mass spectrometry (HS-SPME/GC-MS) in a diffusion cell and modeled kinetically. Wax-rich matrices compacted the network, reducing initial VOC transmission by up to 60%, while glycerol fine-tuned micromobility and selectivity. The formulation containing 0.4% wax, 0.8% gum, and 0.2% glycerol minimized time-dependent flux acceleration and reduced the cumulative permeability of both polar (hexanal) and non-polar (limonene) markers by 80%. Aroma loss decreased across all blends, correlating with improved water vapor control. These results establish quantitative criteria for developing sustainable edible coatings that balance aroma retention, water-barrier performance, and mechanical flexibility. Full article

Improving nutrient use efficiency and minimizing environmental pollution from excessive fertilization require appropriate nutrient management supported by continuous monitoring of soil nutrient levels during crop growth. As only a few real-time sensors for the measurement of soil nutrients are available, this study evaluated the potential of electrical conductivity (EC) sensors, which reflect the ionic concentrations of the soil solution, for real-time estimation of plant-available nutrient levels. Nitrogen and potassium were sequentially supplied to achieve cumulative application rates of 25–300% of the nutrient uptake-based fertilization rate. The relationship between cumulative fertilization rate and accumulated sensor-based EC increase was described using linear, polynomial, and nonlinear saturation models. Sensor EC increased linearly from 25 to 125% of the nutrient uptake-based fertilization rate, while higher application rates were better explained by the nonlinear saturation equation. Sensor-based EC showed strong correlation with soil ammonium nitrogen (NH₄⁺-N), indicating that the sensor effectively reflected nutrient dynamics. In open-field pepper soil, fertigation-induced increases in sensor EC followed the patterns predicted by both the linear and nonlinear saturation models established in the laboratory. These results demonstrate that EC sensors can be used for real-time monitoring of soil nutrient levels and may contribute to efficient nutrient management in open-field cultivation. Full article

In modern shipbuilding, the structural complexity of ship components and the constrained workspace make robotic grinding prone to collisions. To improve safety and stability, this paper proposes a collision-free posture optimization method for ship-component operations. First, forward and inverse kinematic models are established, and postures along the path are organized into a directed graph. Feasible postures are then identified under joint-limit and singularity constraints. Directed bounding boxes and the GJK collision detection algorithm are applied to construct a collision-free posture set. An improved A* algorithm is then introduced. It incorporates a multi-source heuristic based on joint-space geometric distance and a safety-distance penalty to compute an optimal posture sequence with minimal joint deviation. This design promotes smooth transitions between consecutive postures. Simulation results show that the proposed method avoids robot–workpiece interference in constrained environments and improves obstacle avoidance and motion smoothness. Compared with the standard A* algorithm, the proposed approach reduces search time by 15.8% and increases the minimum safety distance by nearly fivefold. Compared with a non-optimized posture sequence, cumulative joint variation is reduced by up to 92.5%. The joint amplitude range decreases by an average of 41.2%, and the standard deviation of joint fluctuations decreases by 37.8%. The proposed method provides a generalizable solution for robotic measurement, assembly, and machining in complex and confined environments. Full article

Natamycin, a polyene macrolide antifungal, has long been used as a food preservative and is the only Food and Drug Administration (FDA)-approved topical treatment for fungal keratitis. While its safety is supported by specific ergosterol interaction and minimal systemic absorption, current research mainly focuses on short-term effects, often overlooking long-term, developmental, and microbiome-related impacts. In food applications, questions remain about cumulative exposure and potential disruptions to gut microbiota. For ophthalmology, advanced delivery methods like nanocarriers and hydrogels enhance drug penetration but may alter pharmacokinetics and pose formulation challenges. Regulatory approvals have historically depended on established safe use and limited toxicological data, emphasizing the need for more systematic evaluations. Zebrafish (Danio rerio) represent a promising yet underutilized model for addressing significant gaps in research, particularly in the realms of microbiome studies, ocular health, developmental processes, and multigenerational effects. When paired with omics technologies, zebrafish facilitate comprehensive system-level mapping of drug-induced outcomes. This review consolidates existing evidence and positions zebrafish as a vital translational link between in vitro assays, mammalian models, and clinical practice. Additionally, it proposes a framework to ensure the effective and scientifically supported use of natamycin in both food and medicinal applications. Full article

This paper proposes a computationally efficient framework for estimating first-passage probabilities of nonlinear structures under stochastic seismic excitations. The methodology integrates Optimal Latinized Partially Stratified Sampling (OLPSS) with the Random Function Spectral Representation Method (RFSRM) to generate a minimal yet optimal set of samples in the low-dimensional input space. Each sample corresponds to a representative nonstationary ground motion time history, which is then used to drive nonlinear dynamic analyses. The extreme values of the structural responses are extracted, and their distribution tails are accurately modeled using the Shifted Generalized Lognormal Distribution (SGLD), whose parameters are efficiently estimated via an extrapolation method. This allows for the construction of the probability density function (PDF) and cumulative distribution function (CDF) of the extreme responses, from which the failure probabilities and reliability indices are calculated. The proposed framework is rigorously validated against the Monte Carlo simulation (MCS) benchmarks using two illustrative examples, including a nonlinear single-degree-of-freedom (SDOF) system and a three-story shear building model. The results demonstrate that the proposed method achieves excellent accuracy in estimating failure probabilities and reliability indices, while significantly reducing the number of required simulations and thereby confirming its high efficiency and accuracy for rapid performance-based seismic assessment. Full article

We propose a distribution-aware framework for unsupervised outlier detection that transforms multivariate data into one-dimensional neighborhood statistics and identifies anomalies through fitted parametric distributions. This directly addresses central difficulties of high-dimensional data—including sparsity of observations, the concentration of pairwise distances, hubness phenomena in nearest-neighbor graphs, and general effects of the curse of dimensionality that degrade classical distance-based scoring. Supported by the Cumulative Distribution Function (CDF) Superiority Theorem and validated through Monte Carlo simulations, the method connects distributional modeling with Receiver Operating Characteristic–Area Under the Curve (ROC–AUC) consistency and produces interpretable, probabilistically calibrated scores. Across 23 real-world datasets, the proposed parametric models demonstrate competitive or superior detection accuracy with strong stability and minimal tuning compared with baseline non-parametric approaches. The framework is computationally lightweight and robust across diverse domains, offering clear probabilistic interpretability and substantially lower computational cost than conventional non-parametric detectors. These findings establish a principled and scalable approach to outlier detection, showing that statistical modeling of neighborhood distances can achieve high accuracy, transparency, and efficiency within a unified parametric framework. Full article

This study formulates predictive maintenance of complex technical systems as a constrained multi-layer probabilistic optimization problem that unifies four interdependent analytical paradigms. The mathematical framework integrates: (i) Weibull reliability modelling with parametric lifetime estimation; (ii) Bayesian posterior updating for dynamic adaptation under uncertainty; (iii) nonlinear machine-learning inference for data-driven pattern recognition; and (iv) ontology-based semantic reasoning governed by logical axioms and domain-specific constraints. The four layers are synthesized through a formal orchestration operator, defined as a sequential composition, where each sub-operator is governed by explicit mathematical constraints: Weibull cumulative distribution functions, Bayesian likelihood-posterior relationships, gradient-based loss minimization, and description logic entailment. The system operates within a cognitive digital twin architecture, with orchestration convergence formalized through iterative parameter refinement until consistency between numerical predictions and semantic validation is achieved. The framework is validated through a case study on aircraft wheel-hub crack prediction. The mathematical formulation establishes a rigorous analytical foundation for cognitive predictive maintenance systems applicable to safety-critical technical systems including aerospace, energy infrastructure, transportation networks, and industrial machinery. Full article

Ultra-shallow prefabricated underpass tunnel technology has been widely adopted in urban transportation construction owing to its advantages of rapid construction and minimal environmental impact. However, the deformation behavior of tunnel joints under long-term vehicular dynamic loads remains unclear, which constrains the reliability and durability of this technology. To address this, this study focuses on a large cross-section tunnel with five bidirectional lanes. A combined methodology of “refined numerical simulation + long-term cyclic loading model tests” was employed to systematically investigate the dynamic response and cumulative deformation patterns of tunnel joints under different burial depths (3 m, 5 m, and 8 m) and prestress levels (0–0.5 MPa). First, based on the analysis of structural bending moment distribution, various division principles such as zero-moment points and maximum-moment points were compared, leading to the determination of a joint layout scheme primarily adopting a two-segment division. On this basis, a refined numerical model integrating pavement excitation and vehicle dynamic coupling was established, supplemented by a model test with 2 million loading cycles, to reveal the deformation mechanism of joints under both moving vehicle loads and long-term loading. The results indicate the following: (1) burial depth is the decisive factor controlling overall joint deformation—increasing the depth from 3 m to 8 m can reduce the maximum joint opening and slip by approximately 60%; (2) prestress serves as a key measure for restraining joint opening and ensuring waterproofing performance, with its effect being particularly pronounced under shallow burial conditions; (3) based on the dynamic attenuation coefficient, the concept of “sensitive burial depth” (approximately 3.7 m) is proposed, providing a quantitative criterion for identifying tunnels susceptible to surface traffic loads; (4) the recommended two-segment structural division scheme effectively controls deformation while considering construction convenience and waterproofing reliability. The methodological framework of “numerical simulation + model testing” established in this study can provide theoretical support and engineering reference for the long-term performance design and assessment of ultra-shallow prefabricated tunnels. Full article