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A deep learning-based cooperative fault diagnosis method is proposed in this study. The proposed method aims to improve the reliability of contemporary fault diagnosis methods for the multipermanent magnet synchronous motor (PMSM) drive system of four-wheel-independent-drive electric vehicles (4WID-EVs). Specifically, a novel 1-D signal to 2-D RGB image conversion method is proposed to transform the fault diagnosis task into an image classification problem, and the impressive image classification capability of the convolutional neural network (CNN) is utilized to achieve a highly accurate fault diagnosis function. Following this, CNNs of various architectures are trained using the constructed RGB image dataset, and the fault diagnosis accuracy under different architectures is compared. In addition, the adaptability of the trained network is increased by utilizing two adaptive factors to ensure that the trained CNN can be compatible with all untrained combinations of angular speed and load torque. Furthermore, by combining the designed single fault diagnosis method with directed-graph theory, a comprehensive evaluation criteria-based cooperative diagnosis method is proposed, which further improves the accuracy significantly. The effectiveness of the proposed fault diagnosis method is demonstrated via adequate comparison.

The interleaved parallel topology of the dc–dc converter can increase the load capacity and reduce the ripple. However, the multiphase control system requires many current sensors, which increases the software/hardware cost. To solve this problem, this article proposes a dual time-scale model predictive control (DMPC) algorithm based on a single current sensor. First, the current estimator replaces current sensors to measure the unbalanced branch currents and reduce hardware complexity. Then, a DMPC algorithm is proposed to reduce the parallel computational complexity of the controller. Furthermore, a parameter identification module dynamically updates the current estimator and controller to further increase the system’s accuracy. This method reduces the software/hardware complexity while ensuring control effectiveness. The validity of the proposed algorithm is verified on a three-phase buck converter.

This paper investigates the event-triggered fixed-time multi-lane fusion control for vehicle platoon systems with distance keeping constraints where the vehicles are spread in multiple lanes. To realize the fusion of vehicles in different lanes, the vehicle platoon systems are firstly constructed with respect to a two-dimensional (2-D) plane. In case of the collision and loss of effective communication, the distance constraints for each vehicle are guaranteed by a barrier function-based control strategy. In contrast to the existing results regarding the command filter techniques, the proposed distance keeping controller can constrain the distance tracking error directly and the error generated by the command filter is coped with by adaptive fuzzy control technique. Moreover, to offset the impacts of the unknown system dynamics and the external disturbances, an unknown input reconstruction method with asymptotic convergence is developed by utilizing the interval observer technique. Finally, two relative threshold triggering mechanisms are utilized in the proposed fixed-time multi-lane fusion controller design so as to reduce the communication burden. The corresponding simulation results also verify the effectiveness of the proposed strategy.

Ionospheric tomography is a popular technique for ionosphere imaging. Though dozens of models have been proposed in the past decades, their inflexibilities in constraints, dependencies on background, and lacks of constraint on dark voxels remain headache. Several means were taken in our developed model, named EOF-based multiscale tomographic model (EMST). In EMST, a series of submodels characterized by different scales are established. A new scale is continuously added to the previous model to capture the structures of different scales by taking each EOF as a new scale. The captured structures are then consolidated by using them to restrain the subsequent models. This ensures a rigid but also a flexible model. Then, the background dependency problem is alleviated by an initial model that can give a rough estimation on the ionosphere without any background. Finally, the vertical total electron content (VTEC) map is used to additionally restrain the voxels, especially the dark voxels, in EMST. A few tests were conducted to validate our model, compare it with the Farzaneh model, and discuss the means. Results show that our model outperforms the reference model in vertical profile, F2 layer peak density (NmF2), slant total electron content (STEC), and VTEC comparisons. Its average improvements over the Farzaneh model reach 4.93%, 28.69%, and 40.89% in NmF2, STEC, and VTEC, respectively. Besides, evidence also shows that the EMST model is less dominated by the prior model and can be benefited from the multiscale strategy and the VTEC constraint.

The energy storage capacity of electric vehicles (EVs) enables them to have the potential to form an uninterruptible power system (UPS) when the power supply is interrupted in the community. To solve the problems related to the reasonable power distribution and accurate voltage tracking control for EVs accessed community UPS, an integrated cooperative control strategy based on the multi-agent system is proposed in this paper. First, an improved power distribution strategy based on the $P$-$f$ droop control is developed to realize the state-of-charge balancing of EVs having different capacities and voltages. Then, a cooperative voltage tracking control strategy for EV energy storage units is developed to realize the accurate voltage tracking control, and a second-order sliding mode differentiator is designed to estimate the derivative of the virtual control law. Besides, the unknown part of the model is approximated by the fuzzy logic system, and the neighborhood synchronization error is upper-bounded by the prescribed performance control. The integrated cooperative control strategy designed is completely distributed. Results show the effectiveness of the proposed integrated cooperative control strategy.

Microfluidic technologies have been widely used for single-cell studies as they provide facile, cost-effective, and high-throughput evaluations of single cells with great accuracy. Capturing single cells has been investigated extensively using various microfluidic techniques. Furthermore, cell retrieval is crucial for the subsequent study of cells in applications such as drug screening. However, there are no robust methods for the facile release of the captured cells. Therefore, we developed a stretchable microfluidic cell trapper for easy on-demand release of cells in a deterministic manner. The stretchable microdevice consists of several U-shaped microstructures to capture single cells. The gap at the bottom edge of the microstructure broadens when the device is stretched along its width. By tuning the horizontal elongation of the device, ample space is provided to release particle/cell sizes of interest. The performance of the stretchable microdevice was evaluated using particles and cells. A deterministic release of particles was demonstrated using a mixture of 15 μm and 20 μm particles. The retrieval of the 15 μm particles and the 20 μm particles was achieved with elongation lengths of 1 mm and 5 mm, respectively. Two different cell lines, T47D breast cancer cells and J774A.1 macrophages, were employed to characterise the cell release capability of the device. The proposed stretchable micro cell trapper provided a deterministic recovery of the captured cells by adjusting the elongation length of the device. We believe that this stretchable microfluidic platform can provide an alternative method to facilely release trapped cells for subsequent evaluation.

In this paper, a high-performance nonlinear control technique is presented to address the robust anti-disturbance speed tracking problem for permanent magnet synchronous motors (PMSM) in electric vehicles. In the first step, the dynamic model of PMSM is established, and the controller is designed based on backstepping control. Additionally, consider eliminating the differential noise of the virtual controller and approving the virtual control rate and its derivative through the command filter in order to avoid complicated high-order derivatives. Then, based on the PMSM model, an improved model reference adaptive system (MRAS) is proposed to account for unknown parameter perturbation. Besides, in order to improve the anti-interference capability of the system during operation, a second-order nonlinear disturbance observer model is developed, and the robustness of the system is improved by combining the model with linear sliding mode control. Finally, the stability of the system is established using Lyapunov stability criteria, and the hardware-in-the-loop platform is established to demonstrate that the controller in this paper has excellent anti-interference abilities and robustness.

Based on vehicle-to-grid technology, electric vehicles can be used as power sources in the case of power failure. With the aim to reduce voltage overshoot and improve the anti-disturbance ability of the vehicle-to-grid inverter, a high-performance voltage control strategy based on funnel control and finite-time disturbance observer is developed. First, the dynamic model of the inverter in dq-frame is established, and the lumped disturbance including the unmodeled part is considered. Next, a novel funnel variable is proposed to ensure that the voltage tracking error can be stabilized within the prescribed funnel boundary, and thus enhance the transient performance. Then, a novel finite-time disturbance observer is designed to estimate the lumped disturbance in the system such as load fluctuations, and improve the anti-disturbance ability of the controller. Moreover, the second-order sliding mode differentiator is introduced to estimate the derivative of the virtual control law and eliminate the explosion of complexity problem in the derivation process. Finally, the finite-time stability of the proposed voltage control strategy is analyzed via the Lyapunov theory. The effectiveness of the proposed control strategy is verified by two cases.

Antimicrobial resistance (AMR) is a long-term public health challenge worldwide, and it is increasingly recognized to be a heteroresistance phenomenon in an isogenic bacterial population. When the minority population of resistant bacteria with strong AMR is not handled in time, such a subpopulation can be enriched leading to the further development of bacterial AMR. However, conventional AMR studies based on ensemble-averaged data from a large population fail to characterize the bacterial heterogeneity. In this work, we develop a method using plasmonic colloidosomes and matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) to study single bacterial cell AMR. The plasmonic colloidosomes act simultaneously as bacteria containers and sample spots for MALDI-TOF MS detection. Cells of β-lactamase-producing Escherichia coli(E. coli) are trapped in colloidosome containers (∼200 μm in diameter) in the presence of antibiotic drug ampicillin (AMP). Benefiting from the fast reaction kinetics in microcompartments, the hydrolysis product of AMP by bacteria can be detected by MALDI-TOF MS within 40 min. The colloidosomes as MALDI sample spots also benefit sensitive detection and accurate quantification of AMP and its hydrolysis product. It was found that even an isogenic population could consist of a mixture of bacteria that have different resistance degrees to antibiotics. Taking the β-lactamase-producing E. colias an example, 20% of the bacterial individuals have relatively strong activity in hydrolyzing AMP. It is expected that the colloidosome-based platform would reveal a prospective application in full characterization of single bacterial cell AMR.

Current transient analysis predominantly relies on zero-dimensional/one-dimensional tools, proficient at capturing aerothermodynamic variations across critical engine stations but insufficient for analyzing the internal flow field evolution during transients. Addressing this gap, the study presents an enhanced quasi-three dimensional (quasi-3D) transient simulation technique that integrates component volume effects, offering a significant leap from the preceding quasi-3D transient simulation method based on quasi-steady assumption. By embedding the component volume effects on density, momentum, and energy within the physical temporal dimension of the Navier-Stokes equations, the refined quasi-3D transient model achieves a closer representation of physical phenomena. Validation against a single-shaft turbofan engine’s experimental data confirms the model’s accuracy. Average errors for key performance indicators, including shaft speed, thrust, mass flow rate, and critical component exit temperature and pressure, remain below 0.41%, 5.69%, 2.55%, 3.18% and 0.67%, respectively. Crucially, the model exposes a discernible temporal lag in the compressor outlet pressure and temperature response due to volume effects—previously unquantified in quasi-3D transient simulations. And further exploration of the meridional flow field emphasizes the consequential role of volumes in transient flow field evolution. Incorporating volume effects within quasi-3D transient simulations enhances engine modeling and is pivotal for precise transient analysis in engine design and optimization.

Excitonic devices operate based on excitons, which can be excited by photons as well as emitting photons and serve as a medium for photon-carrier conversion. Excitonic devices are expected to combine the advantages of both the high response rate of photonic devices and the high integration of electronic devices simultaneously. However, because of the neutral feature, exciton transport is generally achieved via diffusion rather than using electric fields, and the efficient control of exciton flux directionality has always been difficult. In this work, a precisely designed one-dimensional periodic nanostructure (1DPS) is used to introduce periodic strain field along with resonant mode to the WS2monolayer, achieving exciton oriented diffusion with a 7.6-fold exciton diffusion coefficient enhancement relative to that of intrinsic, while enhancing the excitonic emission intensity by a factor of 10 and reducing exciton saturation threshold power by 2 orders of magnitude. Based on the analysis of the density functional theory (DFT) and the finite-element method (FEM), we attribute the anisotropy of exciton diffusion to exciton funneling induced by periodic potentials, which do not require excessive potential height difference for an efficient oriented diffusion. As a result of resonant emission, the exciton diffusion is dragged into the nonlinear regime owing to the high exciton density close to saturation, which improves the exciton diffusion coefficient and diffusion anisotropy more appreciably.

Antimicrobial resistance (AMR) is an urgent threat to public health. Rapid bacterial identification and AMR tests are important to promote personalized treatment of patients and to limit the spread of AMR. Herein, we explore the utility of plasmonic colloidosomes in bacterial analysis based on mass spectrometry (MS) and Raman scattering. It is found that colloidosomes can provide a rigid micrometer-size platform for bacterial culture and analysis. Coupled with matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS, this platform enables bacterial identification at the species level with cell counts as low as 50, >100 times more sensitive than the standard method of MALDI-TOF MS based bacterial identification. Coupled with Raman scattering, it can distinguish single bacterial cells at the strain level and recognize AMR at the single-cell level. These reveal the broad potential of the platform for flexible and versatile bacterial detection and typing.

Classical throughflow methodologies, due to their oversimplified assumptions regarding dimensionality reduction, result in a significant reliance on empirical inputs. This paper introduces a rigorous dimensionality reduction approach, building upon Adamczyk’s average-passage approach, wherein four averaging operators are incorporated into the Navier–Stokes equations. This method dispels the conventional assumptions of time averaging and passage periodicity inherent in primary equations, simultaneously addressing circumferential non-uniformity in the flow field through the inclusion of high-order source terms in the equations. The study employs a time-marching numerical method to solve the averaged equations and offers a comprehensive discussion on the influence and role of high-order source terms, derived from three-dimensional unsteady Reynolds-averaged Navier–Stokes results, in throughflow solutions. The transonic fan case’s results presented in this paper underscore the critical significance of high-order source terms in throughflow calculations, particularly concerning temporal and circumferential fluctuations. At the design point, considering temporal correlations reveals the wake recovery effect, resulting in a 3% increase in flow capacity, alongside heightened pressure ratio and efficiency. Conversely, incorporating circumferential correlations, intricately associated with steady flow structure, induces a 5% reduction in flow capacity, accompanied by a decrease in pressure ratio and efficiency. This paper advocates for the accurate modeling of high-order source terms and identifying computational error sources to improve the precision of throughflow solutions, though the actual modeling of these terms is not the focus of this paper.