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Abstract Studies on 3D-printed porous bone scaffolds mostly focus on materials or structural parameters, while the repair of large femoral defects needs to select appropriate structural parameters according to the needs of different parts. In this paper, a kind of stiffness gradient scaffold design idea is proposed. Different structures are selected according to the different functions of different parts of the scaffold. At the same time, an integrated fixation device is designed to fix the scaffold. Finite element method was used to analyze the stress and strain of homogeneous scaffolds and the stiffness gradient scaffolds, and the relative displacement and stress between stiffness gradient scaffolds and bone in the case of integrated fixation and steel plate fixation. The results showed that the stress distribution of the stiffness gradient scaffolds was more uniform, and the strain of host bone tissue was changed greatly, which was beneficial to the growth of bone tissue. The integrated fixation method is more stable, less stress and evenly distributed. Therefore, the integrated fixation device combined with the design of stiffness gradient can repair the large femoral bone defect well.

The stability of the forming layer shape is a critical factor that impacts the final quality of sample morphology in wire and arc additive manufacturing (WAAM). This paper investigates the forming process and control methods to identify ways to optimize the process and improve the quality of the final product. The study aims to enhance the quality and precision of samples produced by the WAAM process by establishing a morphology control method based on image feedback. The focus is on real-time image acquisition using a CCD and simultaneous extraction of forming process parameter characteristics. Using a central composite experimental design, a prediction model is developed to estimate key process parameters and feature sizes of deposits, including deposition height and width. To further analyze the dynamic characteristics of feature sizes of deposited layers, step response identification is conducted using three process parameters as the system input: forming speed, arc current, and wire feeding speed. The experiments are designed to determine the weight of each process parameter and achieve a high level of response speed and precision. The findings indicate that the feature size of the deposited layer is most sensitive to changes in forming speed, followed by wire feeding speed, while arc current has the least impact on feature size. After verification, the monitoring of the deposition height and width was found to be in good agreement with the prediction model, with an accuracy of over 90%. The results of this study can be used for size measurement and optimization of large aviation aluminum alloy components using WAAM technology and to improve the quality of products produced using these processes.

AbstractRecent enhancements in computational capacity provide an opportunity for harnessing the enormous amount of reservoir data already acquired and extracting useful information for hydrocarbon exploration, development, and production. This article reports a three-step clustering technique to determine well groups based on subsurface geological heterogeneity using feature extraction, hierarchical ensemble clustering, and spatial mapping. The first step of the presented methodology is to group the wells into different clusters based on the formation rock composition and property features extracted from well logs using the expectation maximization algorithm. The one-dimensional (1D) stacking pattern of each well log curve is expressed through a two-dimensional (2D) transformation scheme. Thus, the clustering can capture the vertical stacking patterns of well logs, which is essential for reservoir heterogeneity characterization. This base clustering process generated a feature matrix which is further grouped through the hierarchical ensemble clustering in a latent space of well logs in the second step. Through the ensemble clustering, different clustering proposals obtained from the base clustering are integrated corroboratively to reflect a comprehensive feature of all studied logs. In the third step, the spatial clustering is performed based on the ensemble results, considering the spatial distances between well locations in the target area. The results of the 2D spatial map may provide insights into the sedimentary depositional environment in terms of the lateral geological heterogeneity features. Therefore, the proposed clustering technique can present a fast geological modeling method to integrate geological heterogeneity features presented in multiple well logs, which is not yet fully utilized in traditional geomodeling approaches. The results can also support further reservoir studies, such as petrophysical modeling, reservoir modeling, and fluid flow simulation studies.

Brake-by-wire (BBW) technology is crucial in driverless cars. The BBW technology, which has a faster reaction time and greater stability, can improve passenger safety in driverless cars. BBW technology refers to the removal of some complicated mechanical and hydraulic components from the traditional braking system in favor of using wires to transmit braking signals, which improves braking performance. Firstly, this paper summarized BBW’s development history as well as its structure, classification, and operating principles. Subsequently, various control strategies of the BBW system were analyzed, and the development trend and research status of the motor brake-control strategy and wheel-cylinder pressure-control strategy in the braking force-distribution strategy were analyzed respectively, and the brake fault-tolerance technology and regenerative-braking technology were also analyzed and summarized. Finally, this paper summarized the various technologies of BBW, taking the electromechanical brake (EMB) in the braking system as an example to discuss the current challenges and the way forward.

Fires in university laboratories often lead to serious casualties and property damage, and traditional sensor-based fire detection techniques suffer from fire warning delays. Current deep learning algorithms based on convolutional neural networks have the advantages of high accuracy, low cost, and high speeds in processing image-based data, but their ability to process the relationship between visual elements and objects is inferior to Transformer. Therefore, this paper proposes an improved YOLOX target detection algorithm combining Swin Transformer architecture, the CBAM attention mechanism, and a Slim Neck structure applied to flame smoke detection in laboratory fires. The experimental results verify that the improved YOLOX algorithm has higher detection accuracy and more accurate position recognition for flame smoke in complex situations, with APs of 92.78% and 92.46% for flame and smoke, respectively, and an mAP value of 92.26%, compared with the original YOLOX algorithm, SSD, Faster R-CNN, YOLOv4, and YOLOv5. The detection accuracy is improved, which proves the effectiveness and superiority of this improved YOLOX target detection algorithm in fire detection.

Due to the complex environment of the university laboratory, personnel flow intensive, personnel irregular behavior is easy to cause security risks. Monitoring using mainstream detection algorithms suffers from low detection accuracy and slow speed. Therefore, the current management of personnel behavior mainly relies on institutional constraints, education and training, on-site supervision, etc., which is time-consuming and ineffective. Given the above situation, this paperproposes animprovedYouOnlyLookOnce version7 (YOLOv7) to achieve the purpose of quickly detecting irregular behaviors of laboratory personnel while ensuring high detection accuracy. First, to better capture the shape features of the target, deformable convolutional networks (DCN) is used in the backbone part of the model to replace the traditional convolution to improve the detection accuracy and speed. Second, to enhance the extraction of important features and suppress useless features, this paper proposes a new convolutional block attention module_efficient channel attention (CBAM_E) for embedding the neck network to improve the model's ability to extract features from complex scenes. Finally, to reduce the influence of angle factor and bounding box regression accuracy, this paper proposes a new a-SCYLLA intersection over union (a-SIoU) instead of the complete intersection over union (CIoU), which improves the regression accuracy while increasing the convergence speed. Comparison experiments on public and homemade datasets show that the improved algorithm outperforms the original algorithm in all evaluation indexes, with an increase of 2.92% in the precision rate, 4.14% in the recall rate, 0.0356 in the weighted harmonic mean, 3.60% in the mAP@0.5 value, and a reduction in the number of parameters and complexity. Compared with the mainstream algorithm, the improved algorithm has higher detection accuracy, faster convergence speed, and better actual recognition effect, indicating the effectiveness of the improved algorithm in this paper and its potential for practical application in laboratory scenarios. [ABSTRACT FROM AUTHOR]

The estimation of groundwater released from acquitards is important for the groundwater resource management. In this study, a governing equation of drawdown variation within the acquitard, which undergoes nonlinear consolidation, was obtained based on the consolidation theory of Gibson et al. (1967, https://doi.org/10.1680/geot.1967.17.3.261). Then, analytical solutions of drawdown variations within an acquitard were derived for a single aquifer underlying an acquitard, as well as for the case when there are aquifers above and below an acquitard. Drawdown solutions were obtained for both the cases when there was a sudden hydraulic head decline in the confined aquifer(s). The characteristics of the groundwater dynamics and water balance in the acquitard have been analyzed with the analytical solution. A laboratory experiment was conducted to verify the analytical solution, and a type‐curve fitting method was presented to calculate the acquitard's hydraulic parameters. Our study revealed that the cumulative water released from the acquitard predicted by Gibson's theory (1967) is less than that obtained through Terzaghi's theory (1943). Groundwater released from the acquitard increases with increasing compression index, acquitard thickness, and drawdown of the adjacent aquifer, and with decreasing initial void ratio and initial effective stress. The time for the drawdown to reach steady state increases with increasing compression index, initial void ratio, and acquitard thickness, and with decreasing initial effective stress and hydraulic conductivity. [ABSTRACT FROM AUTHOR]

In almost all quantum applications, one of the key steps is to verify that the fidelity of the prepared quantum state meets expectations. In this Letter, we propose a new approach solving this problem using machine-learning techniques. Compared to other fidelity estimation methods, our method is applicable to arbitrary quantum states, the number of required measurement settings is small, and this number does not increase with the size of the system. For example, for a general five-qubit quantum state, only four measurement settings are required to predict its fidelity with ±1% precision in a nonadversarial scenario. This machine-learning-based approach for estimating quantum state fidelity has the potential to be widely used in the field of quantum information. [ABSTRACT FROM AUTHOR]