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Solid propellants (SPs), as a high-energy material, are commonly used in military and industrial power systems, such as solid rocket and missiles. The SPs, however, confronts severe difficulties of inevitable defects while being made, thus bringing about the significance of inspection. However, the literatures before typically tackled this problem separately, which subsequently combines different models for the variety of defect patterns. Despite of the effectiveness, this act of matters usually brings excessive complexity and additionally computational burden. In this article, we managed to solve this problem in an integrated framework, which unite both the size detection task and shape detection task at the same time, but with different training strategies. To be specific, our framework is mostly consisted of two stage. Firstly, the SPs region is output using a semantic segmentation network, and size measurements are completed with traditional image processing to determine the size defects of the SPs. Then, the depth features of the segmentation network are combined with the semantic segmentation map to make a spatial attention mechanism, which is input to the deep classifier to complete the shape defect detection. The focus of model is gradually shifted from the segmentation task to the classification task as the number of training sessions increases by introducing dynamic balancing factors. The experimental results show that the multi-task learning approach can greatly improve the generalization and robustness of the model, and the accuracy and speed are improved for appearance defect detection of SPs.

Diabetic kidney disease (DKD) is the most common complication of type 2 diabetes mellitus (T2DM), and its pathogenesis is not yet fully understood and lacks noninvasive and effective diagnostic biomarkers. In this study, we performed urine metabolomics to identify biomarkers for DKD and to clarify the potential mechanisms associated with disease progression.We applied a liquid chromatography-mass spectrometry-based metabolomics method combined with bioinformatics analysis to investigate the urine metabolism characteristics of 79 participants, including healthy subjects (n = 20), T2DM patients (n = 20), 39 DKD patients that included 19 DKD with microalbuminuria (DKD + micro) and 20 DKD with macroalbuminuria (DKD + macro).Seventeen metabolites were identified between T2DM and DKD that were involved in amino acid, purine, nucleotide and primarily bile acid metabolism. Ultimately, a combined model consisting of 2 metabolites (tyramine and phenylalanylproline) was established, which had optimal diagnostic performance (area under the curve (AUC) = 0.94). We also identified 19 metabolites that were co-expressed within the DKD groups and 41 metabolites specifically expressed in the DKD + macro group. Ingenuity pathway analysis revealed three interaction networks of these 60 metabolites, involving the sirtuin signaling pathway and ferroptosis signaling pathway, as well as the downregulation of organic anion transporter 1, which may be important mechanisms that mediate the progression of DKD.This work reveals the metabolic alterations in T2DM and DKD, constructs a combined model to distinguish them and delivers a novel strategy for studying the underlying mechanism and treatment of DKD.

Fused plus wire arc additive manufacturing is a low cost and high efficiency manufacturing technology for large scale metal parts. Aiming at the difficulty and low accuracy of component size information acquisition under strong arc light, this paper designs a laser bandpass composite filter system based on the intensity of the spectral characteristics. The component size features are extracted by image processing algorithms such as edge detection, threshold division, and line fitting. Through the adaptive control of the forming speed, the deposition width deviation is automatically corrected. The results show that under strong arc light, the feature size accuracy monitoring reaches 91.79%, and the clear image of deposition layer can be obtained effectively. This method can realize the effective forming with the deposition width of 5.5–10 mm and can accurately change the deposition layer width on demand. The dynamic control value error of the deposition feature size is less than 0.4 mm, that is, the control system can effectively ensure the component surface accuracy.

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.

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.

Recent 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.

The quantification of the spall zone size remains the significant challenging area in health monitoring of rolling element bearings (REBs). Size estimation for the spall zone of a rolling element bearing (REB) is playing a crucial role in bearing performance degradation assessments, damage severity evaluation and remaining useful life prediction. The conventional algorithms of spall zone size estimation are focused on establishing a robust signal processing technique to measure the width of the spall zone based on the analysis of vibrations. In this paper, a new analytical model is proposed for size estimation of the spall zone located on the outer race of a REB. An investigation into the kinematic mechanism of the passing process of the rolling element over the spall zone is carried out, the excitation mechanism of the entry and exit responses excited by a spall located on the REB outer race is studied and the contact models of rolling element-spall interaction are presented in this paper. The dual-impulse time separation (DTS) is determined from the pre-whitened vibration signal. As a result, a spall zone size estimation model which is a function of the DTS and the relationship of rolling element-spall zone interaction is developed based on the Hertzian contact theory. Experiments for four sizes of seeded spall are carried out to validate the proposed model for quantitatively estimating the spall zone size. All of the results which are given by the proposed analytical model present a good agreement with the real size of the spall width of the REB. Comparison between the estimated values of spall zone size calculated from the proposed model and the actual spall zone sizes of the rolling element bearing shows that the proposed model of spall zone size estimation is effective in quantifying the size of the bearing spall.

In quantum information science, a major task is to find the quantum models that can outperform their classical counterparts. Automaton is a fundamental computing model that has wide applications in many fields. It has been shown that the quantum version of automaton can solve certain problem using a much smaller state space compared to the classical automaton. Here we report an experimental demonstration of an optical quantum automaton, which is used to solve the promise problems of determining whether the length of an input string can be divided by a prime number P with no remainder or with a remainder of R. Our quantum automaton can solve such problem using a state space with only three orthonormal states, whereas the classical automaton needs no less than P states. Our results demonstrate the quantum benefits of a quantum automaton over its classical counterpart and paves the way for implementing quantum automaton for more complicated and practical applications.

To employ a quantum device, the performance of the quantum gates in the device needs to be evaluated first. Since the dimensionality of a quantum gate grows exponentially with the number of qubits, evaluating the performance of a quantum gate is a challenging task. Recently, a scheme called quantum gate verification (QGV) has been proposed, which can verifies quantum gates with near-optimal efficiency. In this paper, we implement a proof-of-principle optical experiment to demonstrate this QGV scheme. We show that for a single-qubit quantum gate, only $\sim300$ samples are needed to confirm the fidelity of the quantum gate to be at least $97\%$ with a $99\%$ confidence level using the QGV method, whereas, at least $\sim3000$ samples are needed to achieve the same result using the standard quantum process tomography method. The QGV method validated by this paper has the potential to be widely used for the evaluation of quantum devices in various quantum information applications.

Nonlocality plays a fundamental role in quantum information science. Recently, it has been theoretically predicted and experimentally demonstrated that the nonlocality of an entangled pair may be shared among multiple observers using weak measurements with moderate strength. Here we devise an optimal protocol of nonlocality sharing among three observers and show experimentally that nonlocality sharing may be also achieved using weak measurements with near-maximum strength. Our result sheds light on the interplay between nonlocality and quantum measurements and, may find applications in quantum steering, unbounded randomness certification and quantum communication network., Comment: 6 pages, 4 figures

Quantum mechanics provides a disembodied way to transfer quantum information from one quantum object to another. In theory, this quantum information transfer can occur between quantum objects of any dimension, yet the reported experiments of quantum information transfer to date have mainly focused on the cases where the quantum objects have the same dimension. Here we theoretically propose and experimentally demonstrate a scheme for quantum information transfer between quantum objects of different dimensions.By using an optical qubit-ququart entangling gate, we observe the transfer of quantum information between two photons with different dimensions, including the flow of quantum information from a four-dimensional photon to a two-dimensional photon and vice versa.The fidelities of the quantum information transfer range from 0.700 to 0.917, all above the classical limit of 2/3. Our work sheds light on a new direction for quantum information transfer and demonstrates our ability to implement entangling operations beyond two-level quantum systems., 12 pages, 10 figures

The dual-impulse behavior, which is produced by the spall located on the surface of the bearing raceway, is closely related to the size of the spall zone. This allows to establish an accurate mechanism of quantitative fault diagnosis and spall size estimation. However, dynamic model of dual-impulse behavior, which couples the nonlinear excitation force and the impact force induced by the interaction between the rolling element and the spall zone, have not been proposed before, especially under the elastohydrodynamic lubrication (EHL) condition. To address this issue, the corresponding dual-impulse excitation mechanism is investigated and explained, and the force produced by the collision of the rolling element with the trailing edge of the spall zone is derived. A coupled nonlinear dynamic model of the rolling element bearing (REB) with a spall on the inner race is proposed in this paper, in which the displacement and excitation force originating from the passing process of the rolling element over the spall zone are modeled as a piecewise function. The transient collision force excited by the strike of the rolling element on the trailing edge of the spall area is introduced, and the contact between the rolling elements and both the bearing races under EHL condition is modeled. To validate the proposed model, the dual-impulse time spacing (DITS) is analysed, and the vibration responses and its corresponding envelope spectra under simulation and experimental conditions are studied theoretically and experimentally. The simulated results agree well with the experimental results.

The Einstein–Podolsky–Rosen (EPR) paradox is one of the milestones in quantum foundations, arising from the lack of a local realistic description of quantum mechanics. The EPR paradox has stimulated an important concept of “quantum nonlocality,” which manifests itself in three types: quantum entanglement, quantum steering, and Bell’s nonlocality. Although Bell’s nonlocality is more often used to show “quantum nonlocality,” the original EPR paradox is essentially a steering paradox. In this work, we formulate the original EPR steering paradox into a contradiction equality, thus making it amenable to experimental verification. We perform an experimental test of the steering paradox in a two-qubit scenario. Furthermore, by starting from the steering paradox, we generate a generalized linear steering inequality and transform this inequality into a mathematically equivalent form, which is friendlier for experimental implementation, i.e., one may measure the observables only in the x , y , or z axis of the Bloch sphere, rather than other arbitrary directions. We also perform experiments to demonstrate this scheme. Within the experimental errors, the experimental results coincide with theoretical predictions. Our results deepen the understanding of quantum foundations and provide an efficient way to detect the steerability of quantum states.

The Jilh formation (JILH) is a tight, predominantly dolomitic Triassic formation occurring from 8,000 ft true vertical depth (TVD) to 10,000 ft TVD across the Ghawar field in Saudi Arabia. Drillers experienced extremely high pressured saltwater, oil and/or gas flows in about 20% of the wells that traverse it. The highest recorded flow rate from the JILH was 25,000 barrels per day (BPD) of saltwater and the highest mud weight required to control the JILH flow was 164 pounds per cubic foot (pcf) (22 ppg). The majority of the observed flows have been saltwater, which required a mud weight of 120 pcf to 155 pcf (16 ppg to 20.7 ppg) to control the well. This unpredictable nature of the JILH flow has not only mystified the drillers, but also plagued them across the Ghawar field. If abnormal pressure is encountered in the JILH, a casing string is required to isolate it prior to drilling the Khuff gas reservoirs. If abnormal JILH pressure could be reliably predicted, 80% of the Khuff gas wells could be drilled with a "Slim Hole" casing design resulting in substantial reductions in overall well costs. This paper presents a recent pilot study for predicting abnormal pressure in the JILH in the central area of the Ghawar field, Saudi Arabia. The 3D pore pressure distribution is estimated using a state-of-the-art integrated 3D pore pressure modeling software (PP3D), which is a combination of basin modeling and data inversion techniques. All available data sets (geological, logs and drilling reports) in the area of interest were used to conduct extensive log analyses. This study identified the main cause of abnormal pressure in the JILH as fluid migration from the Sudair shale, due to formation compaction during burial. Well developed anhydrite stringers at the bottom of the base Jilh Dolomite (BJDM) blocked fluid migration to the JILH from the Sudair shale. Abnormal pressure exists in areas where these anhydrite layers did not develop well. The pore pressure along well trajectories of four wells was blind tested with the predicted 3D pore pressure distribution. The average difference in pore pressure for these blind tested wells was −4.5 pcf (−0.6 ppg). Pore pressure in eight additional wells was also blind tested and good agreement between the actual and predicted pressures was found (average difference of −0.75 pcf i.e., −0.1 ppg). It was concluded that the accuracy of the model prediction would be adequate to optimize the drilling plans and casing programs in future wells.