Explore the vast range of titles available.

The quality of each component in the aerospace industry is integral to the safety of the entire system, with the quality inspection of electrical connectors being a critical aspect of ensuring safety. This paper researches a machine vision-based electrical connector pin automatic identification and quality inspection method for actual engineering needs. Due to the issues of sample scarcity and high computational power consumption associated with deep learning methods, our method uses the captured pin images and employs a template-matching-based algorithm for the automatic recognition of pins and calculation of their skew degree. The correct rate of pin identification is 100%, and skewed pin detection accuracy is better than 0.05 mm. The proposed method can promptly address the current issues of low efficiency, high rate of missed inspections, and chaotic management in the quality detection of electrical connector pins.

In this paper a new shear connector called V-shaped angle shear connector for steel–concrete composite system is proposed. This shear connector was proven to improve some mechanical properties of shear connectors, including high shear transfer, uplift resistance, sufficient ductility, and strength degradation resistance under cyclic loading, as well as to being cost effective compared with similar shear connectors, such as C-shaped channel and angle shear connectors. A total of 14 push-out tests were performed on composite beams with these connectors under monotonic and low cyclic loading. The failure mode, shear resistance, and ductility of the push-out specimens were investigated. The study also comprises of finite element and parametric analysis using an effective numerical model of the experimental push-out tests using the program ABAQUS. The finite element models were validated against the test results presented in experimental tests. Results showed that V-shaped angle shear connector has excellent behavior in terms of both shear strength and ductility. In addition, high resistance under cyclic loading was exhibited since the shear resistance of this connector was almost similar in both monotonic and cyclic loadings. Finite element results show good agreement with experimental results. The results discussed on the ductility and strength of this connector with different size and slope of inclination. In addition, the channel and angle shear connectors were compared with V-shaped angle shear connectors. V-shaped angle shear connectors behave much better than other similar connectors, such as normal angle shear connectors, and are superior to channel shear connectors in most specimens.

This is a numerical study to investigate the behavior of novel stiffened angle shear connectors embedded in solid concrete slabs at both ambient and elevated temperatures. An advanced nonlinear finite element model is developed and validated with available experimental work by Nouri, K., et al. 2021. Additionally, parametric studies are performed to evaluate the variations in concrete strength and the connector’s dimensions. The results indicate that the ultimate strength of the stiffened angle shear connector drops by 92% in 1050 °C. Comparing studies show the strength of the stiffened shear connector at 700–850 °C is equivalent to the ordinary C-shaped shear connectors. The stiffened shear connector is more ductile at elevated temperatures as compared to ambient temperatures. The shear strength raised to 66% and 159.7% by increasing the height and width of the stiffened shear connector, respectively. Furthermore, the height of the stiffened shear connector is crucial to enhance the shear strength capacity as compared to the ordinary C-shaped shear connector.

Indeed, understanding the behavior of headed stud shear connectors in composite steel and concrete construction is essential for ensuring structural integrity and optimal performance. This research focuses on the sensitivity and parametric assessment of the behavior of headed stud shear connectors in composite steel and concrete construction using ensemble machine learning techniques. The study aims to uncover hidden correlations and patterns in the data using a detailed database from 464 push tests, where connectors are welded within the ribs of both trapezoidal and re-entrant steel decks. These patterns provide insights into the performance of shear connectors under various conditions, including different welding methods. The application of ensemble machine learning offers an opportunity to understand complex relationships between variables that may not be immediately evident through conventional analysis. Within the study context, eight types of ensemble machine learning models are implemented and applied to estimate the shear capacity of shear studs and conduct feature importance and partial dependence analysis. The outcomes of this research contribute to a deeper understanding of the factors influencing the performance of shear connectors, providing valuable input for structural design and evaluation in composite construction practices. As a result, this research not only enriches the current academic discourse on shear connectors but also offers pragmatic insights for professionals in the field, thereby bridging the gap between theoretical research and real-world applications in composite construction practices.

The stability and reliability of the MT ferrule polishing process are the foundation for ensuring and improving the performance of optical fiber connectors. Aiming at the difficulty of evaluating process reliability caused by multiple processes and influencing factors in the MT ferrule polishing process, a process reliability analysis method based on the GO methodology is proposed. By decomposing the key processes of the MT ferrule polishing process, identifying the failure modes and influencing factors of each process, a GO methodology-based reliability model is constructed. Finally, combined with the data of actual production cases, quantitative analysis is carried out to calculate the reliability of the process and the contribution degree of key failure links, verifying the feasibility of the proposed method.

The reliability of electrical connectors directly influences the success of aerospace launches, and retracted pin detection is a critical inspection step for quality assurance. This study presents an improved method for detecting retracted pins using binocular vision technology. First, based on the analysis of the stereo-matching process, a feature pixel extraction method utilizing the grassfire algorithm and a feature pixel matching method employing global perspective transformation is proposed. Second, after obtaining the three-dimensional coordinates of the feature points, the Random Sample Consensus algorithm is employed for retracted pin detection. Experimental results demonstrate that the proposed algorithm achieves a precision rate and recall rate exceeding 90% for detecting electrical connectors with retracted pins; especially, with a recall rate of 80%, the precision rate reaches 96%, and with a precision rate of 74.4%, the recall rate reaches 96.7%.

IntroductionThe International Organization for Standardization (ISO) developed the ISO 80369 engineering standards to regulate the design of small-bore connectors for clinical applications, preventing tubing misconnections and wrong-route errors. Among these standards, ISO 80369-6, or NRFit™, specifically addresses neuraxial and major regional anaesthesia applications. The traditional Luer connector, widely used across medical fields, poses a significant risk due to its compatibility across multiple applications, leading to potential administration errors. The transition to NRFit™ aims to enhance patient safety by eliminating the possibility of misconnections.MethodsA survey was conducted to assess the awareness of healthcare staff regarding the key design differences between NRFit™ and Luer lock syringes/connectors. The survey focused on three main features: the NRFit™ connector’s yellow colour coding, its 20% smaller diameter compared to Luer connectors, and its flush tip design (unlike the Luer tip, which extends beyond the collar). A total of 30 respondents participated in the survey.ResultsThe survey revealed limited awareness among staff regarding the unique features of NRFit™ connectors. None of the respondents correctly identified all three key differences. Only 3% of participants correctly identified two features (colour coding and smaller diameter), while 23% identified one feature (colour coding). A significant majority (73%) failed to identify any of the key differences, and 50% provided non-related answers. Common responses included mentions of colour coding, diameter differences, and ease of use, but many answers were inaccurate or unrelated to the specific design features of NRFit™.ConclusionNRFit™ specialized small-bore connector design represents a critical improvement in medical practice, significantly enhancing patient safety by preventing wrong-route medication administration. Successful implementation of NRFit™ within healthcare settings requires careful planning and collaboration with all stakeholders. Training and education initiatives are necessary to improve staff awareness and facilitate the transition from Luer lock systems.ReferencesB. Braun USA. (n.d.). NRFit™ Design Information. Retrieved from https://www.bbraunusa.comISMP. (n.d.). NRFit™: A Global Fit for Neuraxial Medication Safety. Retrieved from https://www.ismp.orgNHS England. (2017). Resources to Support Safe Transition to NRFit™. Retrieved from https://www.england.nhs.ukBritish Journal of Nursing. (n.d.). Changing Practice for Neuraxial Applications Using NRFit™.

Shear connectors play a prominent role in the design of steel-concrete composite systems. The behavior of shear connectors is generally determined through conducting push-out tests. However, these tests are costly and require plenty of time. As an alternative approach, soft computing (SC) can be used to eliminate the need for conducting push-out tests. This study aims to investigate the application of artificial intelligence (AI) techniques, as sub-branches of SC methods, in the behavior prediction of an innovative type of C-shaped shear connectors, called Tilted Angle Connectors. For this purpose, several push-out tests are conducted on these connectors and the required data for the AI models are collected. Then, an adaptive neuro-fuzzy inference system (ANFIS) is developed to identify the most influencing parameters on the shear strength of the tilted angle connectors. Totally, six different models are created based on the ANFIS results. Finally, AI techniques such as an artificial neural network (ANN), an extreme learning machine (ELM), and another ANFIS are employed to predict the shear strength of the connectors in each of the six models. The results of the paper show that slip is the most influential factor in the shear strength of tilted connectors and after that, the inclination angle is the most effective one. Moreover, it is deducted that considering only four parameters in the predictive models is enough to have a very accurate prediction. It is also demonstrated that ELM needs less time and it can reach slightly better performance indices than those of ANN and ANFIS.