Browse Topic: Research and development
This study presents a data-driven approach for strengthening aviation safety by integrating human factors assessment with modern predictive modeling techniques. The work focuses on understanding how human performance, operational conditions, and system-level interactions collectively influence safety risk, and how these interactions can be quantified to support improved design and decision-making. Unlike previous studies that address human factors or predictive modeling in isolation, this research offers a unified framework that links causal human factors indicators with statistical modeling, feature extraction, and machine learning based risk estimation. The novelty of this work lies in the structured pipeline that transforms raw categorical and narrative human factors information into measurable predictors that can be analyzed using structural modeling and machine learning. The methodology includes data preparation, dimensionality reduction, latent pattern discovery, dependence
Compliance verification in aerospace systems often relies on labor-intensive workflows that demand extensive manual effort to produce structured review documentation and requirement matrices. These processes can span dozens of hours per review, are vulnerable to inconsistencies due to non-standardized annotations, and depend heavily on individual interpretation of fragmented technical sources. With a growing backlog of review tasks and a steady influx of new requests, the need for scalable automation has become increasingly important. This study presents a modular automation framework designed to streamline compliance assessments through intelligent document parsing, requirement extraction, and matrix generation. The system integrates optical character recognition, computer vision, and natural language processing techniques to process both scanned and digital documents. By digitizing data across multiple hardware configurations and automating extraction from diverse technical records
Stricter environmental legislation is driving ever-more-demanding performance targets for gasoline particulate filters (GPFs). This study constructs a multi-scale filtration model based on fractal characteristics, taking into account particle size distribution and particle deposition, to investigate the influence of the microstructure of porous media on GPF performance and analyze the impact of structural parameters on capture efficiency and pressure drop. The results show that: (1) Increasing the wall thickness can improve the capture efficiency and pressure drop, and a thicker wall has a stronger inertial interception capacity for larger particles. (2) A reduction in porosity markedly alters both filtration efficacy and flow pressure drop. For particles in the intermediate size range (0.1-0.5 μm), the capture efficiency of a low-porosity structure is more sensitive to the diffusion deposition of small particles, while the inertial collision efficiency of large particles is higher. (3
In the field of measuring carbon emissions from road traffic, the carbon emission factor method has remarkable advantages in terms of standardization, operational simplicity, and adaptability. Backed by the IPCC international standard framework, this method offers convenient access to a dynamic factor database and incorporates an adaptive adjustment mechanism for real-world scenarios, such as technological advancements and regional disparities. Against this backdrop, this study employs the carbon emission factor method to establish refined measurement models based on load capacity and fuel consumption, respectively. These models are then applied to quantify carbon emissions from trucks on specific sections of the G30 highway in Xinjiang. The load-based model calculates emissions by integrating truck axle weight and driving distance, while the fuel-based model analyzes fuel consumption data in conjunction with driving mileage. A comparison of the two models in terms of measurement
MyDefence has officially opened its U.S. counter uncrewed aircraft systems (C UAS) manufacturing and innovation facility in Oklahoma City, marking a major step in the company's expansion of its North American production footprint. The latest MyDefence facility, which became operational in February, strengthens the company's ability to support U.S. and allied defense customers with domestically produced counter drone technologies while reinforcing supply chain resilience, regulatory compliance, and lifecycle support. The opening comes amid rapid growth in the scale, diversity, and technical sophistication of uncrewed aerial system threats. Advances in autonomy, range, payload integration, and - critically -radio frequency (RF) employment have increased demand for counter UAS solutions that can evolve as quickly as the threat itself.
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