Search Results (2,109)

Background/Objectives: Vesicular Stomatitis Virus (VSV)-vectored vaccines represent a versatile platform for the development of vaccines against infectious diseases. Replication-competent recombinant VSV vectors in which the native glycoprotein (G) is replaced with a heterologous antigen (rVSVΔG) are widely used and have demonstrated clinical success. In addition to antigen-specific responses, these vaccines induce humoral immunity directed against vector components, which primarily reflects vector exposure and contributes to the overall characterization of vaccine-induced immunity. Standardized assays for quantifying such vector-directed responses are therefore of increasing importance. Methods: We developed and qualified a quantitative enzyme-linked immunosorbent assay (ELISA) for the detection of human IgG antibodies against VSV-Nucleoprotein (N). Assay development included optimization of antigen coating, blocking conditions, and detection reagents. A 10-point standard curve was established using pooled human serum, and assay performance was evaluated by assessing dynamic range, sensitivity, cut point, dilutional linearity, precision, robustness, and sample stability. Results: The optimized assay utilized a coating concentration of 2 μg/mL VSV-N antigen (100 ng/well) and 1% casein as the blocking buffer. The assay demonstrated a dynamic range of 0.33–41.66 arbitrary units per milliliter (AU/mL) with excellent curve fit (R² > 0.996). The cut point was established at an OD₄₅₀ of 0.286. Precision across intra-assay, inter-assay, and inter-operator evaluations met predefined acceptance criteria (≤25% CV). The assay was robust across different plate washers and readers and maintained performance following up to three freeze–thaw cycles. Conclusions: This study describes a robust and reproducible ELISA for quantifying anti-VSV-N IgG responses. The assay provides a fit-for-purpose tool for assessing vector-directed humoral immunity and supports standardized immunogenicity evaluations across VSV-vectored vaccine studies. Full article

Rotary steerable drilling technology is fundamentally aimed at achieving precise wellbore trajectory control. As a representative directional tool, a dynamic push-the-bit RSS generates steering force during rotary drilling through the interaction between its extendable steering pads and the borehole wall, and it is distinguished from static push-the-bit RSS by the rotational friction that develops at the pad–wall interface. To further clarify the influence of friction on the resultant steering force and the build-up rate, this study develops a steering-force optimization model that explicitly incorporates tangential friction, validates the model, and then conducts numerical simulations to examine how PDC bit design parameters and formation properties affect the build-up rate. The results indicate that the friction-aware optimization model can achieve a higher build-up rate. Quantitatively, relative to the friction-free allocation model that is commonly used as the baseline in push-the-bit BUR prediction, the friction-aware formulation increases the final lateral displacement from approximately 28.4 to 30.6 mm in the analytical comparison (+7.7%) and from approximately 24.3 to 26.9 mm in the full-scale finite-element comparison (+10.7%) over the same steering-force action time. In soft formations with a low internal friction angle, a bit design combining a moderate gauge-protection dimension, an appropriate inner cone angle, and a large crown radius can effectively enhance lateral cutting and steering-force transmission, thereby improving build capability and trajectory stability. These findings provide a theoretical basis for improving build-rate efficiency in push-the-bit rotary steerable drilling systems. Full article

Tree Bark-Derived bio-based polymers are promising renewable materials for sustainable coatings, surface protection, adhesives, and functional films. This review aims to clarify how harvesting processes affect raw-material quality and coating performance. The materials discussed include Raw Lacquer, pine resin-derived rosin, turpentine, and tree gums. Key harvesting factors, such as incision depth, tapping frequency, collection method, environmental conditions, and tree physiological status, can influence yield stability, impurity content, enzyme activity, viscosity, chemical composition, and batch consistency. These changes further affect film formation, curing behavior, adhesion, barrier properties, corrosion resistance, water sensitivity, and durability. Traditional manual harvesting is flexible but labor-intensive, skill-dependent, and difficult to standardize. Recent precision and intelligent harvesting technologies, including controlled-depth cutting, low-damage incision, multi-sensor perception, adaptive trajectory planning, and closed collection, provide new approaches for improving harvesting efficiency, reducing contamination, protecting tree health, and supplying coating-grade raw materials. This review establishes a framework linking feedstock characteristics, harvesting parameters, raw-material quality, and coating film performance, and outlines future directions for sustainable, automated, and low-damage harvesting to support high-quality bio-based coatings. Full article

Monocrystalline silicon, with its high refractive index, high infrared transmittance, and excellent dimensional stability, serves as a key optical component in high-energy laser systems, infrared imaging, and guidance fields. Its processing quality directly affects the performance indicators of related systems. To address the challenges of nanoscale precision shape and property control during processing, methods such as ultra-precision cutting, magnetorheological polishing, laser micromachining, ion beam processing, plasma etching, and chemical–mechanical polishing have been adopted to improve the surface shape accuracy and repair defects of monocrystalline silicon components. This paper reviews the research progress of key technologies, including nanoscale precision surface shape control manufacturing technology, nanoscale precision property control generation methods, and combined processes for its nanoscale shape and property control, providing technical support for achieving nanoscale precision shape and property control manufacturing of monocrystalline silicon components. Full article

Polyamide (PA) films have been widely utilized in high-precision medical devices and aerospace components, while laser precision cutting technology has significantly broadened their application scope. Although ultraviolet (UV) picosecond lasers are effective for high-precision cutting of PA films, their cutting mechanism and the optimization method for the process remain to be elucidated. First, the mechanism of UV picosecond laser cutting of PA films was investigated through a simulation of the thermal degradation process and analysis of the solid/gas byproduct composition. The results indicate that the photochemical reaction primarily dominates the process, with the photothermal effect contributing synergistically. Second, a cutting quality evaluation framework was established, with the kerf width and heat-affected zone (HAZ) width as its primary metrics, followed by an orthogonal experiment. The experimental results revealed the influence of process parameters on the cutting quality, and it was determined that an optimal process parameter combination exists, identified as 80 mm/s, 1.67 W, and three times (cutting speed, laser power, repetition number of cutting). Under this optimal configuration, narrow kerf (23.6 ± 2.7 μm) and HAZ (28.4 ± 3.3 μm) were achieved. Full article

Seawater intrusion remains a critical threat to coastal groundwater, where subsurface cut-off walls are commonly used for mitigation. This study employs 2D variable-density numerical modeling to investigate the impacts of hydraulic conductivity anisotropy (rk = 0.02–50) and seasonal inland groundwater fluctuations on residual saltwater desalination, quantified by means of RRSM and RRSL. Our results revealed that rk is inversely correlated with final desalination efficiency. Lower rk values (0.02–0.1) achieve exhaustive salt removal despite requiring longer flushing durations. Conversely, higher rk values significantly suppress efficiency and induce violent oscillations in desalination rates under seasonal forcing. A critical failure mechanism was identified: intensified vertical flow dynamics allow saltwater to “overtop” the barrier during low inland groundwater stages, triggering severe secondary intrusion. These findings underscore that conventional cut-off wall designs may be inadequate under dynamic boundaries, necessitating taller barrier configurations and precise anisotropy assessments to ensure long-term functional resilience in coastal aquifer management. Full article

To support drilling gas influx control, saline aquifer CO₂ sequestration and CCUS development under the dual carbon goals, this study proposes a high-precision calculation method for the solubility of high-temperature, high-pressure, CO₂-rich natural gas in formation water. An activity–fugacity coupling model is established: fugacity coefficients of gas components are solved via the dimensionless Helmholtz free energy equation of state, and liquid-phase activity coefficients are characterized by the Pitzer electrolyte model. Comparative experiments with three natural gas and three formation water samples are carried out at 393.15–453.15 K and 5–100 MPa to analyze the influences of temperature, pressure, salinity and CO₂ content on solubility for model verification. The overall relative error between calculated and experimental data is below 10% (max 4.5%). Solubility rises rapidly with pressure then plateaus, declines with salinity, and grows with CO₂ content; CO₂ solubility far exceeds that of alkanes. This efficient, widely applicable model cuts engineering costs and guides safe oil-gas exploitation and CCUS deployment. Full article

Consistent laser cutting quality is one of the problems associated with the nonlinearity of relationships between process parameters and output responses. This problem acquires particular importance when it comes to cutting advanced nanocomposites, which requires precise tuning. Despite the wide adoption of intelligent modelling, few studies have investigated the comparative efficiency of various approaches based on the use of the same dataset. In this research, the effectiveness of three models—Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Fuzzy Logic System (FLS)—was tested on experimental data related to the CO₂ laser cutting of ABS/CNT nanocomposites. Input parameters included laser power and cutting speed, whereas HAZ width, kerf width, and surface electrical resistivity were used as output data. Data was split into training, testing, and validation datasets; models were created using supervised machine learning. Model performance was evaluated using Root Mean Square Error (RMSE). Analysis of results showed that ANN demonstrated acceptable predictive capabilities, yielding correlation coefficients (R) close to 1 (≈0.99) and RMSE values of 0.2956 for HAZ, 0.2061 for kerf width, and 2.3655 for surface electrical resistivity. Prediction by means of FLS was able to identify general tendencies; however, it produced RMSE values of 0.4741 for HAZ, 0.6297 for kerf width, and 1.9258 for surface electrical resistivity. Finally, the ANFIS model proved to be the most reliable model, yielding the lowest RMSE values for HAZ (0.2784), kerf width (0.0450), and surface electrical resistivity (0.0905). In conclusion, this research shows that ANFIS can be used effectively for building models predicting laser cutting processes; therefore, it represents an approach worth using in future investigations in this field. Full article

Conventional defect inspection for warp-knitted lace relies on manual work and negative-sample-based training, resulting in low efficiency, frequent false detections and poor adaptability. This study presents a novel AI visual inspection system centered on positive-sample learning, which is built upon a five-layer 5G + Industrial Internet distributed architecture. Supported by modified looms, high-precision imaging devices and an optimized YOLOv5s model, the system accomplishes intelligent defect detection. A positive-sample self-learning paradigm and dual-model collaboration mechanism are proposed to reduce the demand for negative samples and cut labeling expenses. The integration of CBAM, FPN + PAN structure, self-supervised learning and hybrid loss further strengthens the recognition performance for subtle defects under complex patterns. Industrial tests show that the system reaches a grid-level classification accuracy of 95% and a frame-level detection rate over 98%, with a detection speed of 30 m/min. It reduces labor costs and product reject rates by 40% and 30% correspondingly while running stably in real production. This method breaks the constraints of traditional training modes, provides a scalable intelligent solution for the digital upgrading of the warp-knitted lace industry, and promotes the high-quality development of textile manufacturing. Full article

To address the challenge of hand intrusion detection in frozen tuna cutting operations where operators wear thermal-insulating gloves, this study proposes a hand detection method based on dual-domain background modeling with absolute accuracy constraints. To tackle issues arising from low-resolution infrared arrays, such as defective pixels, random noise, and complex low-temperature backgrounds, a data preprocessing pipeline integrating defective pixel correction, exponential moving average (EMA), and median filtering is developed. A dual-domain background suppression (DDBS) strategy, combining spatial-domain and temporal-domain models with sensor absolute accuracy constraints, is employed to extract hand foregrounds under complex thermal conditions. Temperature thresholding, connected-component analysis, and hole-filling are further applied to effectively separate hands from frozen tuna. An experimental platform incorporating a Raspberry Pi 4B and an MLX90640 sensor was constructed, and a dataset comprising 1173 infrared frames was collected for validation purposes. Experimental results demonstrate that the proposed method achieves an accuracy of 94.12%, precision of 91.69%, recall of 97.55%, and F1-score of 94.53% for hand intrusion detection, with an average processing time of approximately 1.84 ms per frame. This provides a cost-effective, real-time solution for hand safety monitoring in frozen food processing operations. Full article

The Irish Association for Cancer Research (IACR) Annual Conference 2025, held in Belfast, showcased cutting-edge developments across the cancer research landscape. This report summarizes key presentations, highlighting innovations in drug delivery, exercise interventions, artificial intelligence and computational biology, cancer stem cell plasticity, and translational research approaches. The meeting emphasised mechanistically informed strategies, multi-modal therapies, and the integration of patient-relevant models to improve therapeutic outcomes. The sessions collectively underscored the importance of combining biological, chemical, and physical approaches, as well as emerging tools in precision oncology, to address therapeutic resistance and enhance patient care. Full article

The reliance on traditional or manual phenotyping creates significant operational bottlenecks in rice breeding due to its resource-intensive and time-consuming nature. This review focused on the significance of high-throughput phenotyping (HTP) as a promising technology that enables rapid, accurate, and non-destructive phenotyping of large populations. HTP has great potential to accelerate rice breeding by revolutionizing phenomics. This review examined the various applications of HTP in rice research, phenomics, and breeding. The use of HTP in rice has been substantiated through a range of cutting-edge technologies, such as drones, imaging systems, and sensor networks, that facilitate precise monitoring of key traits at various growth stages, assessment of responses to biotic and abiotic stresses, and the identification of genes or quantitative trait loci (QTLs) associated with essential characteristics. Also, this review discussed HTPs’ contributions to current rice breeding programs and documented notable challenges in scaling them. This review offers insights into optimizing HTP strategies to advance rice research, phenomics, and rice breeding. Full article

This study conducts a multi-objective optimization of an impact pruner for coniferous plantation trees, using Prince Rupprecht’s larch (Larix principis-rupprechtii Mayr) in North China as a case study. The objective is to establish an impact cutting mechanics model and to construct an impact cutting platform. This study utilizes the Box–Behnken principle, with the cutting speed (v), cutter wedge angle (β), and cutting clearance (L) as influencing factors and the cutting energy consumption (Y₁), total equipment energy consumption (Y₂), and specific cutting area (S) as evaluation indexes. The cutting parameters were optimized using a mathematical model for multi-objective optimization. The experimental results indicate that the factors influencing target Y₁ were ranked as β, L, and v, while the factors influencing target Y₂ were ranked as β, v, and L, and the factors influencing target S were ranked as L, β, and v. Field tests demonstrated that the optimization reduced the cutting energy consumption by up to 16.90% and improved the cutting quality by up to 19.28%. These gains directly translate to improved operational efficiency and economic value in forestry management. The optimal parameters corresponding to these improvements are v = 2.15 m·s⁻¹, β = 20°, and L = 5 mm, resulting in Y₁ = 36.10 J, Y₂ = 3351.01 J, and S = 3.45. These results demonstrate the feasibility and efficiency of the impact pruning method for Larix principis-rupprechtii in coniferous plantation forests. By combing mechanism analysis with multi-objective optimization, this study proposes a solution that can improve the pruning quality of coniferous plantation trees, reduce the energy consumption of impact pruning machines, enhance tree health, and serve as a measure to prevent pests and diseases, contributing to the advancement of artificial forest plant protection technology. Full article

Blood-based biomarkers offer a promising “biochemical imaging” approach for acute ischemic stroke (AIS) management, providing objective and accessible tools to complement conventional neuroimaging. This narrative review synthesizes recent advances in biomarkers derived from multiple neurovascular unit (NVU) compartments, including glial fibrillary acidic protein (GFAP), S100 calcium-binding protein B (S100B), ubiquitin carboxy-terminal hydrolase L1 (UCH-L1), neuron-specific enolase (NSE), neurofilament light chain (NfL), matrix metalloproteinase-9 (MMP-9), Claudin-5, Occludin, brain-derived neurotrophic factor (BDNF), interleukin-33 (IL-33), tumor necrosis factor-alpha (TNF-alpha), PARK7/DJ-1, glycogen phosphorylase BB (GP-BB), and circulating microRNAs. We focus on their stage-specific clinical utility across three scenarios: (1) ultra-early differentiation between ischemic stroke and intracerebral hemorrhage in prehospital and emergency settings; (2) dynamic prediction and monitoring of hemorrhagic transformation after reperfusion therapies; and (3) assessment of infarct burden, neurorepair potential, and long-term functional outcomes. Despite their promise, clinical translation remains hindered by assay platform heterogeneity, lack of standardized cut-off values, limited cost-effectiveness data, and insufficient prospective validation adjusted for key covariates such as age and renal function. We further discuss multi-marker panel construction, including strategies to address biomarker collinearity and overfitting. Future directions emphasize stage-specific panels, point-of-care testing devices, and artificial intelligence algorithms to advance precision medicine in stroke care. Full article

To address the demand for high-precision, high-efficiency, and standardized hydrographic information in intelligent shipping, this study systematically investigates key technologies for high-resolution hydrographic data parsing and intelligent information services. Focusing on the East China Sea, a space–air–ground integrated monitoring data access system is established. A hybrid data assimilation method combining four-dimensional variational (4D-Var) and ensemble Kalman filter is adopted to realize quality control, deep fusion, and optimal state estimation of multi-source heterogeneous hydrographic observations. A hybrid tidal harmonic response model is further developed to improve the refined forecasting accuracy of tide levels and ocean currents. A hierarchically decoupled system architecture is designed, and modules for data production, sharing, exchange, and visualization are developed in compliance with the international S-100 standard. By integrating hybrid spatiotemporal indexing, multi-level caching, and intelligent query optimization, the system achieves low-latency and high-concurrency service capabilities. Experimental results show that, compared with conventional models, the proposed framework reduces tidal forecast RMSE by approximately 15.8% under extreme weather, raises the continuity index of current vectors to 0.93, and cuts the S-100 product generation latency to less than 30 s. This research establishes a full-chain technical system from data parsing and product generation to intelligent services, providing a reliable technical support platform for ship intelligent navigation, dynamic route planning, and maritime safety assurance. Full article