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In diverse realms of research, such as holographic optical tweezer mechanical measurements, colloidal particle motion state examinations, cell tracking, and drug delivery, the localization and analysis of particle motion command paramount significance. Algorithms ranging from conventional numerical methods to advanced deep-learning networks mark substantial strides in the sphere of particle orientation analysis. However, the need for datasets has hindered the application of deep learning in particle tracking. In this work, we elucidated an efficacious methodology pivoted toward generating synthetic datasets conducive to this domain that resonates with robustness and precision when applied to real-world data of tracking 3D particles. We developed a 3D real-time particle positioning network based on the CenterNet network. After conducting experiments, our network has achieved a horizontal positioning error of 0.0478 μm and a z-axis positioning error of 0.1990 μm. It shows the capability to handle real-time tracking of particles, diverse in dimensions, near the focal plane with high precision. In addition, we have rendered all datasets cultivated during this investigation accessible.

Generally, excellent mechanical properties of Mg alloys are desired, but their rapid degradation properties are seldom utilized. Petroleum fracturing techniques are required to take full advantage of this rapid degradation. Therefore, we have prepared an as-extruded Mg–6.0Gd–1.2Cu–1.2Zr (wt.%) alloy and treated it with peak aging to analyze its potential as a degradable fracture ball. The results show that the as-extruded alloy mainly consists of an α-Mg matrix, second phase, and large elongated α-Mg grains (LEGs). After aging, the LEGs undergo static recrystallization, which improves the mechanical properties of the alloy, and a lamellar long period stacking ordered (LPSO) phase is observed. Under simulated underground temperature conditions (93 °C), the ultimate tensile strength and elongation of both as-extruded and as-aged alloys are over Ȧ MPa and 11.1%, respectively, and the ultimate compressive strength and elongation of both alloys are over 336 MPa and 16.9%, respectively. The corrosion rate of the as-extruded alloy in 3 wt.% KCl solution at 93 °C reaches 1660.8 mm/y by mass loss test, and that of the as-aged alloy increases to 1955.1 mm/y. The atomic force microscope analysis result confirms that the second phase shows the highest corrosion potential, followed by the lamellar LPSO phase and α-Mg matrix. The as-extruded and as-aged Mg–6.0Gd–1.2Cu–1.2Zr alloy with good mechanical properties and a high corrosion rate in this work shows promising potential for degradable fracturing ball applications.

In recent years, Mg alloys have attracted increased attention for biomedical application owing to their good biodegradability, biocompatibility, and biomechanical properties. However, rapid corrosion is still one of the most common limitations for their implanted application. In this work, the microstructure and corrosion behavior of the solution- and aging-treated Mg-xGd-1Zn-0.4Zr (x = 3, 6, and 9 wt%, denoted as GZ31K, GZ61K, and GZ91K, respectively) alloys were studied using optical microscope (OM), scanning electron microscope (SEM), immersion tests, electrochemical tests, and quasi in situ corrosion method. The results show that block Gd-rich precipitates and needle-like Zr-Zn-rich precipitates are formed as well as α-Mg matrix. With the increase in Gd content, the precipitates increase and the grain size first reduces and then increases. Corrosion experiment results show that the GZ61K alloy has the best corrosion resistance and the GZ91K alloy shows the highest corrosion rate among the three alloys in simulated body fluid (SBF). It is found that α-Mg is preferentially corroded and the precipitates have better corrosion resistance as compared to the α-Mg matrix. The GZ61K alloy with the corrosion rate of 0.23 mm/y in SBF shows a promising prospect for biomedical application.

In this work, the effects of Sb doping on the electrical conductivity of fine-grain 0.9(K0.5Na0.5)(Nb1−xSbx)O3-0.1Bi(Ni2/3Nb1/3)O3 (KNNSx-BNN) ceramics were systemically investigated. It was found that the grain size decreases from ~900 nm (x = 0) to ~340–400 nm (x = 0.06–0.08), and then increases again to ~700 nm (x = 0.10). This is because the solubility limit of Sb doping is about 0.08 in this ceramic, and more Sb doping will facilitate the grain growth as the sintering aids. Impedance and conductivity analyses reveal that the grain resistance and its activation energy show a similar changing tendency with grain size, while grain boundary conductivity steadily increases after Sb doping. In this process, the grain contribution on ceramic conductivity changes with grain size variation, and grain boundary contribution becomes more obvious with increasing doping content. The reduction in grain size, improvement in grain boundary density and doping ions entering into the grain boundary should contribute to the evolution of electrical conductivity properties after Sb doping in KNN-based ferroelectric ceramics.

Domain adaptation-based bearing fault diagnosis methods have recently received high attention. However, the extracted features in these methods fail to adequately represent fault information due to the versatility of the work scenario. Moreover, most existing adaptive methods attempt to align the feature space of domains by calculating the sum of marginal distribution distance and conditional distribution distance, without considering variable cross-domain diagnostic scenarios that provide significant cues for fault diagnosis. To address the above problems, we propose a deep convolutional multi-space dynamic distribution adaptation (DCMSDA) model, which consists of two core components: two feature extraction modules and a dynamic distribution adaptation module. Technically, a multi-space structure is proposed in the feature extraction module to fully extract fault features of the marginal distribution and conditional distribution. In addition, the dynamic distribution adaptation module utilizes different metrics to capture distribution discrepancies, as well as an adaptive coefficient to dynamically measure the alignment proportion in complex cross-domain scenarios. This study compares our method with other advanced methods, in detail. The experimental results show that the proposed method has excellent diagnosis performance and generalization performance. Furthermore, the results further demonstrate the effectiveness of each transfer module proposed in our model.

Accurate estimation of lithium-ion battery state-of-health (SOH) is important for the safe operation of electric vehicles; however, in practical applications, the accuracy of SOH estimation is affected by uncertainty factors, including human operation, working conditions, etc. To accurately estimate the battery SOH, a hybrid neural network based on the dilated convolutional neural network and the bidirectional gated recurrent unit, namely dilated CNN-BiGRU, is proposed in this paper. The proposed data-driven method uses the voltage distribution and capacity changes in the extracted battery discharge curve to learn the serial data time dependence and correlation. This method can obtain more accurate temporal and spatial features of the original battery data, resulting higher accuracy and robustness. The effectiveness of dilated CNN-BiGRU for SOH estimation is verified on two publicly lithium-ion battery datasets, the NASA Battery Aging Dataset and Oxford Battery Degradation Dataset. The experimental results reveal that the proposed model outperforms the compared data-driven methods, e.g., CNN-series and RNN-series. Furthermore, the mean absolute error (MAE) and root mean square error (RMSE) are limited to within 1.9% and 3.3%, respectively, on the NASA Battery Aging Dataset.