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A novel hierarchical direct yaw moment controller is designed to enhance the lateral stability of the four-wheel-drive electric vehicle. The adaptive sliding mode control (ASMC) technique in the upper-layer controller is employed to compute an additional yaw moment. The lower-layer controller distributes this yaw moment into each independent wheel by utilizing model predictive control allocation (MPCA). The proposed MPCA aims to mitigate the performance deterioration induced by in-wheel motor dynamics and optimize the power consumption stemming from the additional yaw moment. Co-simulation and hardware-in-the-loop (HIL) test is conducted to verify the performance of the proposed controller. Validation results show that the proposed hierarchical ASMC-MPCA controller outperforms the sliding mode control MPCA (SMC-MPCA) and the integrated nonlinear model predictive control (NMPC) with the lowest root-mean-square errors (RMSEs)of yaw rate, sideslip angle, lateral deviation, and lowest power consumption. Additionally, the chattering phenomenon in SMC-MPCA can be suppressed effectively by adaptively estimating the parameter uncertainties. The proposed ASMC-MPCA controller also consumes less computational resources than the NMPC and SMC-MPCA, which indicates that the ASMC-MPCA is more suitable for an automotive onboard controller. The comparison between hierarchical and integrated controller frameworks also shows that the hierarchical framework is more suitable for production vehicles under non-powerful vehicle control units.

The latest electric vehicles (EVs) are becoming increasingly complex and resource-intensive owing to their advanced functional capabilities and autonomy. In this study, EVs are considered as cyber-physical systems (CPS), and a novel event-triggered sliding mode controller (ET-SMC) with a hierarchical framework is developed to enhance lateral stability and reduce communication resource consumption in EVs through torque vectoring control (TVC). The high-level controller of the ET-SMC is designed based on a nonlinear vehicle model, and its stability is rigorously proven using the Lyapunov and input-to-state stable (ISS) theorems. Additionally, a traditional periodic sliding mode controller (SMC) is developed as a benchmark controller. The Sine-with-Dwell (SwD) test scenario, an internationally recognized method for verifying lateral stability, is employed to evaluate the control performance using a hardware-in-the-loop (HIL) test bench. The experimental results demonstrate that the ET-SMC can not only maintain similar control performance in enhancing lateral stability but also reduce controller communication resource consumption by approximately 30% compared to the periodic SMC in this typical driving scenario. These results show that the proposed control algorithm is promising for future EVs.

This article focuses on solving the optimal control problem for magnetorheological fluid (MRF)-based semiactive suspension (SAS) systems with input saturation and time-varying delay. A robust switched H$\infty$ method is proposed in this work based on the Takagi–Sugeno (T–S) fuzzy theory. A novel hybrid model that incorporates both the fluid flow mechanism (FFM) and hysteresis phenomenon model (HPM) is adopted to separate the passive and active components of the MRF damper. Under such a framework, the convex and reciprocally convex approaches are used to transform the features of input saturation and time-varying delay into linear matrix inequality conditions. Furthermore, a Lyapunov–Krasovskii function is employed to guarantee the stability of the MRF–SAS system subject to input saturation and time-varying delay. Finally, the effectiveness of the proposed method is validated via a numerical example of an MRF–SAS system. The results show that the proposed controller indicates good dynamic performance for the MRF–SAS system with input saturation and time-varying delay.

Offline reinforcement learning (ORL) has been getting increasing attention in robot learning, benefiting from its ability to avoid hazardous exploration and learn policies directly from precollected samples. Approximate policy iteration (API) is one of the most commonly investigated ORL approaches in robotics, due to its linear representation of policies, which makes it fairly transparent in both theoretical and engineering analysis. One open problem of API is how to design efficient and effective basis functions. The broad learning system (BLS) has been extensively studied in supervised and unsupervised learning in various applications. However, few investigations have been conducted on ORL. In this article, a novel incremental ORL approach with sparse broad critic approximation (BORL) is proposed with the advantages of BLS, which approximates the critic function in a linear manner with randomly projected sparse and compact features and dynamically expands its broad structure. The BORL is the first extension of API with BLS in the field of robotics and ORL. The approximation ability and convergence performance of BORL are also analyzed. Comprehensive simulation studies are then conducted on two benchmarks, and the results demonstrate that the proposed BORL can obtain comparable or better performance than conventional API methods without laborious hyperparameter fine-tuning work. To further demonstrate the effectiveness of BORL in practical robotic applications, a variable force tracking problem in robotic ultrasound scanning (RUSS) is investigated, and a learning-based adaptive impedance control (LAIC) algorithm is proposed based on BORL. The experimental results demonstrate the advantages of LAIC compared with conventional force tracking methods.

The active suspension systems have attracted increasing attentions because of its advantages in improving automotive dynamic performance. This paper focuses on the fuzzy robust non-fragile H2/H∞control problem for the nonlinear active suspension system with the time-varying actuator delay and control uncertainty. Firstly, a Takagi-Sugeno fuzzy suspension model is established to describe the highly nonlinear components in the suspension system. Then, a series of linear matrix inequalities are derived based on a novel parameter Lyapunov function to guarantee the asymptotic stability of the suspension systems. Also, a fuzzy H2/H∞controller is derived for the proposed fuzzy suspension model with the parallel distributed compensation method. Thirdly, a co-design criterion is proposed to obtain the fuzzy controller with the time varying delay and control uncertainty simultaneously. Finally, both simulations and experiments are carried out to demonstrate the effectiveness and robustness of the proposed controller.

This paper focuses on the combined path following (PF) and direct yaw-moment control (DYC) problem of unmanned electric vehicles (UEVs) based on an event-triggered Takagi–Sugeno (T–S) fuzzy method. First, to describe the parameter uncertainties and nonlinear tire dynamics of UEVs, a T–S fuzzy dynamic model is developed for performance analysis and controller design of the vehicle lateral dynamic system. Second, as the signal transmission inevitably suffers from delay and data dropouts because of the limited network bandwidth, an event-triggered scheme is constructed to complete the communication scheduling and save the communication resource. Third, considering that there is an unavoidable asynchronous constraint between the fuzzy system and the fuzzy controller due to the communication delay, a hierarchical event-triggered fuzzy controller design approach is given via a new Lyapunov functional. Moreover, a differential distribution scheme is introduced to obtain the desired torque of each tire of the vehicle. Finally, experimental results validate that the presented controller in this paper is able to improve the path following accuracy by 61.93% with a resource economization 92.04% compared with the existing method.

The consideration of reliability in controller design is able to avoid the potential actuator faults from inappropriate strategies. This work presents an optimal reliability-critical controller to avoid potential actuator faults by developing a probabilistic adaptive dynamic programming (ADP) algorithm with the estimation of fault interruption. The proposed algorithm distinguishes from existing ADPs in that the structural reliability is considered in policy iteration, endowing the resultant controller with the capacity to avoid potential actuator faults. The algorithm relaxes the generalized damage energy-Hamilton–Jacobi–Bellman equation to a reliability-critical problem, which is solved by proposing a probabilistic policy iteration method. Instead of studying the stability regardless of physical damage, the effect of physical damage is considered in the system stability in the form of structural reliability, and the probabilistic policy iteration guarantees the optimal relation between the stability and structural reliability. Finally, the effectiveness of the proposed algorithm is verified by conducting experimental tests.

Transcription factors are essential regulators of various physiological and pathological processes. However, detecting transcription factor-DNA binding activities is often time-consuming and labor-intensive. Homogeneous biosensors that are compatible with mix-and-measure protocols have the potential to simplify the workflow for therapeutic screening and disease diagnostics. In this study, we apply a combined computational-experimental approach to investigate the design of a sticky-end probe biosensor, where the transcription factor-DNA complex stabilizes the fluorescence resonance energy transfer signal of the donor-acceptor pair. We design a sticky-end biosensor for the SOX9 transcription factor based on the consensus sequence and characterize its sensing performance. A systems biology model is also developed to investigate the reaction kinetics and optimize the operating conditions. Taken together, our study provides a conceptual framework for the design and optimization of sticky-end probe biosensors for homogeneous detection of transcription factor-DNA binding activity.

This article investigates the issue of fixed-time path-following control for autonomous ground vehicles (AGVs) with completely unknown parameters subject to preset error constraints. Before converging the path-following errors, a fixed-time prescribed performance control (PPC) protocol is developed to guarantee the selected nonsingular sliding manifold evolving within the preset boundaries. Then, the fixed-time PPC target for the preview error of the AGV can be achieved by satisfying the parameter inequality. Furthermore, the designed adaptive laws can ensure the control accuracy without knowledge of vehicle parameters. Finally, the hardware-in-the-loop tests of multiple maneuvering conditions demonstrate that the proposed fixed-time PPC protocol can provide superior comprehensive path-following performance for the AGV over the conventional fixed-time controller, and the preview error can never violate the prescribed performance constraints.

This article proposes an observer-based discrete-time cascaded control (ODCC) strategy for lateral stabilization of Steer-by-Wire (SbW) vehicles with consideration of uncertainties and disturbances. First, for the observation of the sideslip angle and yaw rate, an information fusion-based unscented Kalman filter (IFUKF) is designed to reduce the negative effect from the variation of the parameters; Second, aiming to eliminate the errors of control variables for lateral stabilization of SbW vehicles, a discrete-time sliding mode predictive control (DSMPC) is presented to deal with matched and mismatched uncertainties and input constraint; Third, to reduce the tracking error between the actual front wheel steering angle and the desired one generated by the DSMPC, a combination of discrete-time fast terminal sliding mode and active disturbance rejection control is proposed to tackle the problems of parameter uncertainties and disturbances in the SbW system. Performance evaluations are conducted via both software-in-the-loop and hardware-in-the-loop to examine the availability and practicability of the ODCC strategy.

In this article, a generalized fuzzy subset (GFS) method is proposed to assess the time-varying multistate reliability of the perturbation failure coupling measurement system. First, a perturbation-failure coupling mechanism is designed to define the propagation chain of perturbations so as to integrate all the possible perturbations as the inputs of the GFS method. Second, to assess the time-varying multistate reliability, a GFS reliability model is constructed based on the composite limit state. Furthermore, the concept of the uncertain subset boundary is presented to conduct the reliability assessment via a group of embedded interval type-2 fuzzy sets. To address the deficiency of the GFS reliability model, a data-driven strategy is designed to establish the implicit relation between the limited expert knowledge and the membership function. Finally, the experimental tests are carried out to examine the superiority of the GFS method, and parametric studies are also conducted to study the reliability of the PFCM system.

This article introduces a robust H-infinity fuzzy static output feedback (RHF-SOF) control with norm-bounded strategy to enhance the path-following performance for the four-wheel-independent-driven electric vehicles (FWID-EVs). Firstly, taking the parameter uncertainties including vehicle mass, moment of inertia, longitudinal velocity, and tire cornering stiffness into account, a fuzzy optimization model is constructed to approximate the nonlinear characteristic of the vehicle system using both the parameter uncertainty reduction strategy and polytope reduction strategy. Secondly, with consideration of the difficulty of the online lateral velocity measurement, a Lyapunov stability-based RHF-SOF control with the gain scheduling method is proposed to control the variables of the path following. Thirdly, since the actuator saturation can deteriorate the control performances, a norm-bounded strategy is designed to address the nonlinear saturation problem. The proposed controller is verified and evaluated through the hardware-in-the-loop experimental test with different maneuvers.

The rotor-bearing system of large rotating machinery has multiple bearings with complex vibration correlations, which significantly affect the effectiveness of intelligent diagnosis in industrial production. In this article, a new framework of fault diagnosis for the rotor with multiple bearings is proposed. The framework is composed of two parts: 1) instantaneous orbit feature fusion image construction; 2) the deep convolutional network based on transfer learning. The multivariate complex variational mode decomposition (MCVMD) is adopted to decompose the complex-valued signals of multiple bearings, which can make full use of the joint information between signals by considering the axis orbit of each bearing simultaneously. To our best knowledge, it is the first attempt of applying MCVMD to the field of fault diagnosis. Then, multiple orbit features are derived from the decomposed signals to reflect the transient state of vibration. Finally, the fusion feature images, constructed by the orbit features of multiple bearings, can exhaustively present the overall status of the rotor-bearing system. Parameter transfer is used for the deep convolutional network to solve the time-consuming training problem. The experiment and verification is carried out on three steam turbines and the pumped storage unit. The results demonstrate that the proposed method outperforms the existing approaches based on the original signal, frequency, or time-frequency features.

With the consideration of actuator faults, including the unknown steering mechanism misalignments and motor traction losses, this article presents a fixed-time control protocol to follow reference paths and velocities for autonomous ground vehicles (AGVs) with preset performance constraints. To provide sufficient large boundaries for the initial states, the hyperbolic tangent function is employed to predefine the constraints with respect to the path-following and velocity control performance. Based on the homeomorphic mapping and barrier Lyapunov theorem, the fixed-time prescribed performance control (PPC) objective-integrated fault-tolerant scheme can be achieved for the controlled AGV. In comparison to three different fixed-time controllers without the fault-tolerant or PPC scheme, the hardware-in-the-loop (HIL) test results demonstrate that the proposed control protocol can always provide superior control performance for the AGV under various maneuvering conditions.

Selective laser melting (SLM) is a powder-based additive manufacturing technology that can be used to fabricate high-density components with complex geometry. Several studies have investigated the process parameters that affect surface quality. However, most researchers have ignored the importance of the scanning strategy. In this study, the Taguchi method was used to investigate the relation between warpage and fundamental parameters (laser power, scanning speed, overlap, and scanning angle) to fabricate stable and undistorted specimens. Moreover, several scanning strategies (offset scanning, line scanning, meander scanning, meander scanning with hatch vector, and lightning scanning) were applied to explore the influences on surface quality. The results revealed that meander scanning and lightning scanning generated consistent specimens without large deformation. The process parameters, such as an increased 45° scanning direction and 30% overlap, optimized the surface quality. A lower scanning speed (500 mm/s) could generate lower Ra of specimens, with the exception of lightning scanning with an increased 45° scanning direction. This study may contribute to the growing understanding of the scanning strategy in SLM.

In recent years, system of systems resilience has been widely studied. System of systems has obvious resilience properties when considering dynamic reconfiguration in the following four parts: avoidance, survival, adaption and recovery. System of systems can be downgraded and recovered by reconfiguring resources to keep the performance output enough to satisfy the threshold under internal failure or external shocks. In other words, because of dynamic reconfiguration, system of systems has obvious characteristics of resilience. In this study, first, a new resilience model for systems and system of systems based on the performance threshold is proposed. Second, military system of systems is decomposed hierarchically, including system of systems–level, platform-level and system-level top-down, respectively. Third, a radar network of military system of systems is taken as a typical case. A performance model for a radar network under internal or external shocks is established based on the linear-Gauss-Poisson process in system of systems, and its parameters are discussed in detail. Finally, a typical 5-node radar network of formation air defense military system of systems is taken as an example to demonstrate proposed models and methods. The reliability and resilience loss are achieved by considering internal failure or external shocks, which can serve as a reference for evaluating and improving the effectiveness of system of systems.

Ball screws play a critical role in high-quality precision manufacturing. The use of machine learning and artificial intelligence for the diagnosis of machines’ health status is increasingly pertinent. The processing of big data originating from machine sensors is crucial. However, installing multiple sensors on the object requiring diagnosis may be costly. A sensorless strategy using built-in signals to determine the conditions of a hollow ball screw was deployed. Moreover, we evaluated the most discriminative parameters among fusion sensor signals by using Fisher’ criteria. A support vector machine (SVM) as diagnostic tool was used. In the absence of prominent characteristic features in data, the conventional SVM cannot classify the data well. To address this concern, we constructed a feature engineering for distinguishing features from the raw data to facilitate the SVM classification process well. In addition, we validated the physical phenomenon in realistic ball screw conditions through feature extraction. Experimental results demonstrated the average diagnostic accuracy levels for the ball screw preload, pretension, cooling system, and table payload were 98.91%, 94.08%, 91.69%, and 93.5%, respectively, after feature engineering was applied successfully.

Industry 4.0 accelerates the growth of unmanned technology that reduces the labor cost and creates high automation in manufacturing system. The automated guided vehicle which is capable of transferring materials or executing tasks without human intervention becomes a necessary system for modern unmanned factories. The study explores the guidance and control design to accomplish the common task of path-following control for unmanned ground vehicles (UGV). A complete design method is presented that includes the lateral-directional autopilot, the vector field guidance for path-following, and multi-sensor fusion. The lateral-directional autopilot produces the low-level control action, the higher level guidance indicates the course direction of UGV at every spatial point based on the lateral path error, and the accurate UGV position relies on the estimate obtained by dynamically fusing sensors with extended Kalman filter. The design parameters in every stage are analyzed theoretically first and then fine-tuned in practice. The process is clearly described in this study, and the field test results are discussed in details to verify the performance of the proposed method and demonstrate the superiority over others.

This study employed wearable inertial sensors integrated with an activity-recognition algorithm to recognize six types of daily activities performed by humans, namely walking, ascending stairs, descending stairs, sitting, standing, and lying. The sensor system consisted of a microcontroller, a three-axis accelerometer, and a three-axis gyro; the algorithm involved collecting and normalizing the activity signals. To simplify the calculation process and to maximize the recognition accuracy, the data were preprocessed through linear discriminant analysis; this reduced their dimensionality and captured their features, thereby reducing the feature space of the accelerometer and gyro signals; they were then verified through the use of six classification algorithms. The new contribution is that after feature extraction, data classification results indicated that an artificial neural network was the most stable and effective of the six algorithms. In the experiment, 20 participants equipped the wearable sensors on their waists to record the aforementioned six types of daily activities and to verify the effectiveness of the sensors. According to the cross-validation results, the combination of linear discriminant analysis and an artificial neural network was the most stable classification algorithm for data generalization; its activity-recognition accuracy was 87.37% on the training data and 80.96% on the test data.

This article investigates the reliable fuzzy sampled-data control issue of nonlinear vehicle suspension systems subject to actuator faults. Considering the high nonlinear characteristics in the springs and the shock absorbers, the Takagi–Sugeno fuzzy method is utilized to describe the nonlinear suspension system. To deal with the discrete phenomenon of nonuniform sampling, an input-delay is employed to convert the continuous-discrete suspension system into a continuous-time form. Motivated by the above system model, a Lyapunov functional candidate is employed to obtain sufficient conditions that can meet system stability and performance requirements simultaneously. Furthermore, a novel two-step optimization algorithm is proposed for the sampled-data controller design in the structure of output feedback. Finally, experimental tests are implemented via an active suspension test rig, and the test results illustrate that the proposed method contributes greatly to improve the ride comfort, handling performance, and road holding capability simultaneously.

This paper concerns the robust gain-scheduling control issue for autonomous path following systems with stochastic network-induced delay. Firstly, to effectively approximate the highly nonlinear tire dynamics, the linear fractional transformation formulations are employed to describe the tire cornering stiffness with a norm-bounded uncertainty. Secondly, by taking the data dropout and delay encountered in signal computation and transmission into account, a more generalized lumped delay form is proposed to unify the time-varying data dropout and network-induced delay. Moreover, a Markovian process is presented to describe the lumped delay as a stochastic distribution. Thirdly, to address the issue of varying vehicle velocity, a linear parameter varying model is established to capture vehicle lateral behaviors. Based on the stochastic stability theory, a new robust gain-scheduling path following control method is proposed for the autonomous vehicles. Finally, the experimental study is presented to bridge the gap between the theoretical and practical investigations on path following control of autonomous vehicles, and results validate the superior performance of the proposed method compared with existing works.

With high demands for novel technologies in the marine mechatronic instrumentality and the limitations of the present hydraulic pumps, a novel bidirectional seawater hydraulic internal ball gear pump is proposed to accomplish the function of fluid power transmission and control. The proposed hydraulic pump can be used for hydraulic buoyancy control systems of underwater vehicles and different marine mechatronic equipment. In this paper, the model, the meshing and the flow characteristics of the internal ball gear pump are developed. The radial unbalance force of the pump, leakage, and flow pulsation are also analyzed. The new type of eccentric shaft-hole rotary sealing designed in this paper can eliminate axial leakage and is expected to improve volumetric efficiency, working pressure, and power density. Simulation and experimental results show that: (1) by using the concave/convex ball gears rather than the existing gears, the concave ball gear drives the convex ball ring to rotate, realizing energy transfer and conversion; (2) compared with the water hydraulic internal gear pump, the internal ball gear pump has excellent performance. Experimental results also show that when the speed is 1500 rpm, the maximum operating pressure can reach 7 MPa. At 2 MPa, its volumetric efficiency and flow can reach 75% and 0.189L/min, respectively. The outcomes of this study show that the proposed seawater hydraulic internal ball gear pump is effective and has a lot of potential.

Solubilizing, self-propelling droplets have emerged as a rich chemical platform for the exploration of active matter, but isotropic droplets rely on spontaneous symmetry breaking to sustain motion. The introduction of permanent asymmetry, e.g., in the form of a biphasic Janus droplet, has not been explored as a comprehensive design strategy for active droplets, despite the widespread use of Janus structures in motile solid particles. Here, we uncover the chemomechanical framework underlying the self-propulsion of biphasic Janus oil droplets solubilizing in aqueous surfactant. We elucidate how droplet propulsion is influenced by the degree of oil mixing, droplet shape, and oil solubilization rates for a range of oil combinations. In addition, spatiotemporal control over droplet swimming speed and orientation is demonstrated through the application of thermal gradients applied via joule heating and laser illumination. We also explore the interactions between collections of Janus droplets, including the spontaneous formation of spinning multi-droplet clusters.

Cell reprogramming has wide applications in tissue regeneration, disease modelling and personalized medicine. In addition to biochemical cues, mechanical forces also contribute to the modulation of the epigenetic state and a variety of cell functions through distinct mechanisms that are not fully understood. Here we show that millisecond deformation of the cell nucleus caused by confinement into microfluidic channels results in wrinkling and transient disassembly of the nuclear lamina, local detachment of lamina-associated domains in chromatin and a decrease of histone methylation (histone H3 lysine 9 trimethylation) and DNA methylation. These global changes in chromatin at the early stage of cell reprogramming boost the conversion of fibroblasts into neurons and can be partially reproduced by inhibition of histone H3 lysine 9 and DNA methylation. This mechanopriming approach also triggers macrophage reprogramming into neurons and fibroblast conversion into induced pluripotent stem cells, being thus a promising mechanically based epigenetic state modulation method for cell engineering.

The intricate interactions between host and microbial communities hold significant implications for biology and medicine. However, traditional microbial profiling methods face limitations in processing time, measurement of absolute abundance, detection of low biomass, discrimination between live and dead cells, and functional analysis. This study introduces a rapid multimodal microbial characterization platform, Multimodal Biosensors for Transversal Analysis (MBioTA), for capturing the taxonomy, viability, and functional genes of the microbiota. The platform incorporates single cell biosensors, scalable microwell arrays, and automated image processing for rapid transversal analysis in as few as 2 h. The multimodal biosensors simultaneously characterize the taxon, viability, and functional gene expression of individual cells. By automating the image processing workflow, the single cell analysis techniques enable the quantification of bacteria with sensitivity down to 0.0075%, showcasing its capability in detecting low biomass samples. We illustrate the applicability of the MBioTA platform through the transversal analysis of the gut microbiota composition, viability, and functionality in a familial Alzheimer’s disease mouse model. The effectiveness, rapid turnaround, and scalability of the MBioTA platform will facilitate its application from basic research to clinical diagnostics, potentially revolutionizing our understanding and management of diseases associated with microbe-host interactions.

The electronically controlled semi-active air suspension systems have been widely used to improve the vehicle ride comfort and road holding performance by adjusting the damper stiffness. This work focuses on the design of a damping force controller to enhance the ride comfort, road holding and stability under the presence of unknown air spring pressure. Firstly, an improved skyhook suspension reference model is developed to generate the desired dynamic criteria (i.e. vehicle body acceleration, pitch and roll angles). Secondly, by employing the backstepping technique, a novel damping force controller is proposed to approach the desired dynamic criteria. Thirdly, a parameter estimation method is also designed to estimate the air spring pressure to obtain the air spring force. Comparative studies are carried out among passive suspension systems, and the semi-active air suspension systems with the proposed model reference damping force control, traditional backstepping control and existing sliding mode control. Numerical results show that a significant improvement of ride comfort can be observed with the semi-active air suspension system based on the proposed model reference backstepping controller.

The vehicle lateral stability control is a challenging problem due to the tire nonlinearity and the immeasurable sideslip angle. Thus, an adaptive model predictive control (AMPC) scheme based on interacting multiple model (IMM) vehicle sideslip angle observer is proposed in this paper. First, the observer is composed of the Extended Kalman filter (EKF) and the Unscented Kalman filter (UKF), which improves the real-time performance as well as the observation accuracy. Then, a multi-objective controller based on adaptive vehicle model prediction is designed using model predictive control algorithm. This controller aims to achieve a balance between the actuation and state constraints. The T-S fuzzy algorithm is used to observe the tire cornering stiffness and design an adaptive vehicle model. By utilizing the appropriate objective function and a quadratic programing solver, the controller output is obtained to achieve vehicle stability control. Finally, the effectiveness of the designed AMPC scheme under various working conditions is verified by Carsim-Simulink joint simulation platform and hardware-in-the-loop (HIL) test.

Collective cancer invasion with leader–follower organization is increasingly recognized as a predominant mechanism in the metastatic cascade. Leader cells support cancer invasion by creating invasion tracks, sensing environmental cues and coordinating with follower cells biochemically and biomechanically. With the latest developments in experimental and computational models and analysis techniques, the range of specific traits and features of leader cells reported in the literature is rapidly expanding. Yet, despite their importance, there is no consensus on how leader cells arise or their essential characteristics. In this Perspective, we propose a framework for defining the essential aspects of leader cells and provide a unifying perspective on the varying cellular and molecular programmes that are adopted by each leader cell subtype to accomplish their functions. This Perspective can lead to more effective strategies to interdict a major contributor to metastatic capability.

This paper proposes a novel sampled-data asynchronous fuzzy output feedback control approach for active suspension systems in restricted frequency domain. In order to better investigate uncertain suspension dynamics, the sampled-data Takagi-Sugeno (T-S) fuzzy half-car active suspension (HCAS) system is considered, which is further modelled as a continuous system with an input delay. Firstly, considering that the fuzzy system and the fuzzy controller cannot share the identical premises due to the existence of input delay, a reconstructed method is employed to synchronize the time scales of membership functions between the fuzzy controller and the fuzzy system. Secondly, since external disturbances often belong to a restricted frequency range, a finite frequency control criterion is presented for control synthesis to reduce conservatism. Thirdly, given a full information of state variables is hardly available in practical suspension systems, a two-stage method is proposed to calculate the static output feedback control gains. Moreover, an iterative algorithm is proposed to compute the optimum solution. Finally, numerical simulations verify the effectiveness of the proposed controllers.

This paper proposes a static-output-feedback based robust fuzzy wheelbase preview control algorithm for uncertain active suspensions with time delay and finite frequency constraint. Firstly, a Takagi-Sugeno (T-S) fuzzy augmented model is established to formulate the half-car active suspension system with consideration of time delay, sprung mass variation and wheelbase preview information. Secondly, in view of the resonation between human's organs and vertical vibrations in the frequency range of 4-8 Hz, a finite frequency control criterion in terms of $H_{\infty}$ norm is developed to improve ride comfort. Meanwhile, other mechanical constraints are also considered and satisfied via generalized $H_{2}$ norm. Thirdly, in order to maintain the feasibility of the controller despite of some state variables are not online-measured, a two stage approach is adopted to derive a static output feedback controller. Finally, numerical simulation results illustrate the excellent performance of the proposed controller.

The emergence of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) threatens the health of the global population and challenges our preparedness for pandemic threats. Previous outbreaks of coronaviruses and other viruses have suggested the importance of diagnostic technologies in fighting viral outbreaks. Nucleic acid detection techniques are the gold standard for detecting SARS-CoV-2. Viral antigen tests and serological tests that detect host antibodies have also been developed for studying the epidemiology of COVID-19 and estimating the population that may have immunity to SARS-CoV-2. Nevertheless, the availability, cost, and performance of existing viral diagnostic technologies limit their practicality, and novel approaches are required for improving our readiness for global pandemics. Here, we review the principles and limitations of major viral diagnostic technologies and highlight recent advances of molecular assays for COVID-19. In addition, we discuss emerging technologies, such as clustered regularly interspaced short palindromic repeats (CRISPR) systems, high-throughput sequencing, and single-cell and single-molecule analysis, for improving our ability to understand, trace, and contain viral outbreaks. The prospects of viral diagnostic technologies for combating future pandemic threats are presented.

Bladder cancer is an increasingly common malignancy, and muscle invasive bladder cancer is associated with particularly high rates of morbidity and mortality. The morphologic and molecular diversity of bladder cancer poses significant challenges in elucidating the invasion mechanisms responsible for disease progression. Furthermore, conventional invasion assays do not provide a physiological context for studying bladder cancer invasion within 3D microenvironments and have limited ability to capture the contribution of cellular phenotypic heterogeneity to disease progression. Here, we describe the development of a 3D microtumor invasion model suitable for the analysis of cellular phenotypic heterogeneity in cell lines and primary tumor cells from bladder cancer patients. This model incorporates a self-assembly approach for recapitulating features of bladder cancer invasion in 3D microenvironments and probing the invasive cell subpopulations. The gene expression profiles of invading microtumors were analyzed by incorporating a gold nanorod-locked nucleic acid biosensor. The incorporation of the single cell biosensor and transient gene knockdown into the system revealed the formation of invasive leader cells with upregulated Delta-like ligand 4 (DLL4) expression as well as the role of NOTCH1-DLL4 signaling in collective bladder cancer invasion. The involvement of DLL4 expressing cells in bladder cancer invasion was also observed in patient samples obtained from transurethral resection. Collectively, our study demonstrates a 3D microtumor invasion model for investigating intracellular heterogeneity of bladder cancer invasion and analyzing patient derived samples toward personalized medicine applications.

With the development of the controllable suspension systems, the mono-tube hydraulic adjustable damper has attracted great public attention with the advantages such as good heat dissipation, less power, fast response, durable, reliable, and simple structure. However, the unknown regulating mechanism modeling impedes the practical application of the mono-tube hydraulic adjustable damper. To model the regulating mechanism, this paper analytically studies the behavior of the mono-tube hydraulic adjustable damper via developing an analytical model and thermal effect equations for the use of engineering design. Then, the mono-tube hydraulic adjustable damper is tested in an integral shock absorber testing system to verify the accuracy of model and equations. On the basis of the verified analytical model and thermal effect equations, a compensation system with gray neural network algorithm is originally designed to model the regulating mechanism of the mono-tube hydraulic adjustable damper, thus achieving the desired damping force adaptively and accurately at various working conditions by obtaining the required rotary angle of the adjustment rod. The simulation results and experimental results show that the characteristic analyses of mono-tube hydraulic adjustable damper are reliable. Meanwhile, the simulation results of the gray neural network algorithm also indicate that the proposed compensation system can provide an exact regulating mechanism model for the mono-tube hydraulic adjustable damper and the proposed gray neural network algorithm is superior to the traditional neural network algorithm.

Collective cell migration is vital to tissue remodeling in wound repair, development, and cancer invasion. Nevertheless, studies on collective cell migration have largely focused on epithelial growth and repair mechanisms and have only recently expanded to explore coordinated metastatic cancer and smooth muscle cell behaviors. The regulatory mechanisms of smooth muscle cell collective migration, such as leader–follower organization and mechanosensitivity, remain poorly understood. In this study, we demonstrate the involvement of leader cells during collective smooth muscle cell migration using dynamic cell tracking and single cell gene expression analysis. Engineered wound models, including ingrowth, outgrowth, and straight edge geometries, along with traction force microscopy and finite element stress mapping reveal that smooth muscle leader cells are enhanced at the wound edge when the intercellular tension near the cell wound boundary is reduced. Pharmacological perturbation further supports the notion that mechanical force negatively regulates the formation of leader cells. The mechanical regulation of collective smooth muscle cell migration via the formation of leader cells may lead to novel treatment strategies for pathogenic smooth muscle cell conditions in the future.

Understanding how the mechanical microenvironment affects cardiomyocyte development is crucial to the creation of in vitro models for studying heart physiology and pathophysiology. This knowledge will also facilitate the design of biomaterials and tissue scaffolds utilized in the generation of functional tissue constructs for regenerative medicine and drug screening applications. Here, plasma lithography patterning of elastomeric substrates is exploited for creating microtissues composed of neonatal cardiomyocytes and investigating their attributes in different mechanical microenvironments. Restriction of the cellular outgrowth in line patterns results in cardiomyocytes developing into multicellular clusters and collectively adapting to geometric confinement and substrate stiffness. Immunofluorescence microscopy, video microscopy, and force spectroscopy show that the size and shape of the cardiomyocyte clusters, as well as sarcomere length, fiber alignment, beating amplitude, and beating frequency of the cardiomyocytes, are regulated by the microenvironmental cues. Computational analysis reveals that the mechanical stress at the cluster-substrate interface strongly correlates with the characteristics of the cardiomyocytes. Taken together, our results underscore a collective mechanoadaptation scheme in cardiac development.

Collective cell migration plays a pivotal role in development, wound healing, and metastasis, but little is known about the mechanisms and coordination of cell migration in 3D microenvironments. Here, we demonstrate a 3D wound healing assay by photothermal ablation for investigating collective cell migration in epithelial tissue structures. The nanoparticle-mediated photothermal technique creates local hyperthermia for selective cell ablation and induces collective cell migration of 3D tissue structures. By incorporating dynamic single cell gene expression analysis, live cell actin staining, and particle image velocimetry, we show that the wound healing response consists of 3D vortex motion moving toward the wound followed by the formation of multicellular actin bundles and leader cells with active actin-based protrusions. Inhibition of ROCK signaling disrupts the multicellular actin bundle and enhances the formation of leader cells at the leading edge. Furthermore, single cell gene expression analysis, pharmacological perturbation, and RNA interference reveal that Notch1-Dll4 signaling negatively regulates the formation of multicellular actin bundles and leader cells. Taken together, our study demonstrates a platform for investigating 3D collective cell migration and underscores the essential roles of ROCK and Notch1-Dll4 signaling in regulating 3D epithelial wound healing.

The functions of RNA are tightly regulated via diverse intracellular mechanisms. However, probing the complex dynamics of endogenous RNA in live cells is a challenging task. In the present study, a DNA transformer is designed for visualizing the abundance, distribution, and mobility of endogenous mRNAs in live human cells. The transformable tetrahedral DNA (T-TED) probe has a flexible hinge structure and is programmed to conform into a 3D tetrahedron upon binding with the target mRNA. By incorporating Förster resonance energy transfer (FRET) imaging, super-resolution localization, and single particle tracking, the T-TED biosensor is applied for investigating the dynamics of Delta-like ligand 4 (Dll4) mRNA, which encodes a transmembrane protein, in human pulmonary microvascular endothelial cells. The data reveal unprecedented subpopulations of Dll4 mRNA with distinct mobility organized spatially in association with the endoplasmic reticulum and microtubule networks. The ability to monitor the dynamics of endogenous RNA in live human cells will provide a useful tool for studying the functions and regulation of RNA.

This article proposes an integrated sliding mode control–two-point wheelbase preview strategy for semi-active air suspension system with gas-filled adjustable shock absorber. First of all, a vehicle suspension model with rolling lobe air spring and gas-filled adjustable shock absorber is built, following with a road input model for the front wheel. By describing the detailed structure and working process of the gas-filled adjustable shock absorber, the regulating mechanism between the stepper motor and the designed gas-filled adjustable shock absorber is established. Subsequently, the sliding mode control algorithm is applied to generate the desired damping force with the real-time state of the vehicle. Moreover, to predetermine the road profile for the rear wheel, a two-point wheelbase preview approach is proposed and its superiority is also illustrated as compared with the conventional single-point wheelbase preview approach. To evaluate the performance of the proposed system, numerical analysis is conducted with other three comparative schemes, namely, passive suspension system, active suspension system with H infinity control, and sliding mode control–controlled semi-active air suspension system with adjustable shock absorber. Simulation results show that the integrated sliding mode control–two-point wheelbase preview strategy can be successfully utilized in the semi-active air suspension system with stepper motor-driven gas-filled adjustable shock absorber, and the vehicle performance with the proposed system can be greatly improved.

Bacterial infection is a leading cause of morbidity and mortality (from infants to the elderly) and accounts for more than $20 billion in healthcare costs in the United States each year. The pathogens responsible for many of the common infectious diseases, such as urinary tract infection (UTI) and ventilator-associated infections (VAIs), have proven to be highly adept in acquiring mechanisms of antimicrobial resistance. The use of broad-spectrum antibiotics by healthcare providers and the infiltration of antibiotics in the environment have accelerated the selection and growth of resistant pathogens. To further exacerbate the problem, the need for new antibiotics has far outpaced the development of new classes of antibiotics by the pharmaceutical industry (only two new classes of antibiotics have reached the market in the last 20 years), in large part due to prohibitive cost and historically poor return on investment to develop new antibiotics. Consequently, clinicians have limited treatment options, particularly in the neediest patients. To tackle this major global health issue, we are developing novel technological approaches for rapid definitive clinical microbiological analysis. These technologies will improve the clinical management of bacterial infections and reduce the improper use of antibiotics in current practice, hopefully limiting the spread of drug-resistant organisms.

Recently, multilayer extreme learning machine (ML-ELM) was applied to stacked autoencoder (SAE) for representation learning. In contrast to traditional SAE, the training time of ML-ELM is significantly reduced from hours to seconds with high accuracy. However, ML-ELM suffers from several drawbacks: 1) manual tuning on the number of hidden nodes in every layer is an uncertain factor to training time and generalization; 2) random projection of input weights and bias in every layer of ML-ELM leads to suboptimal model generalization; 3) the pseudoinverse solution for output weights in every layer incurs relatively large reconstruction error; and 4) the storage and execution time for transformation matrices in representation learning are proportional to the number of hidden layers. Inspired by kernel learning, a kernel version of ML-ELM is developed, namely, multilayer kernel ELM (ML-KELM), whose contributions are: 1) elimination of manual tuning on the number of hidden nodes in every layer; 2) no random projection mechanism so as to obtain optimal model generalization; 3) exact inverse solution for output weights is guaranteed under invertible kernel matrix, resulting to smaller reconstruction error; and 4) all transformation matrices are unified into two matrices only, so that storage can be reduced and may shorten model execution time. Benchmark data sets of different sizes have been employed for the evaluation of ML-KELM. Experimental results have verified the contributions of the proposed ML-KELM. The improvement in accuracy over benchmark data sets is up to 7%. [ABSTRACT FROM PUBLISHER]

Organic–inorganic halide perovskites have attracted increasing interest for solar-energy harvesting because of the simple fabrication process, high efficiency, and low cost. In this work, we systematically investigate the structural and electronic properties, and stability of two-dimensional (2D) hybrid organic–inorganic perovskites (HOIPs) based on density-functional-theory calculations. We explore a general rule to predict the bandgap of the 2D HOIP: its bandgap decreases as the thickness increases and the size of metal atom decreases as well as that of the halide atom increases. We find that effective mass of hole increases as the thickness of 2D HOIP increases. Importantly, the 2D HOIPs exhibit high stability on the resistance of water and oxygen than bulk HOIPs due to high positive adsorption energy. Our results confirm that the 2D HOIPs may be excellent alternatives to the unstable bulk HOIPs in solar energy harvesting with improved performance due to suitable bandgap, small carrier effective mass, and high resistance to water and oxygen molecules.

Supplemental Digital Content is available in the text.

Bloodstream infections are major contributing factors of morbidity and mortality among children. Precise and timely identification of causative agents can improve the clinical management and outcome of the infection, potentially saving lives. Electrochemical biosensors previously described by Gao et al. (2017) have the potential to deliver greater speed and discrimination. However, to date there are no data that determine whether the age of the host would cause bacteria to demonstrate different growth characteristics, or whether pediatric samples would behave differently using this electrochemical biosensor. The importance of this knowledge gap is clear: the preclinical testing phase of this line of research is limited by the relative lack of pediatric healthy blood volunteers to complete this work. Therefore, in this study we have applied this novel technology to diagnose bacteria spiked into pediatric blood and compared directly with adult blood samples. Only 180 µL of blood was utilized from both adult and pediatric volunteers and inoculated with Escherichia coli67, and the signals generated at different time points were compared. We were able to demonstrate that the signals generated by adult and pediatric blood were not significantly different with this detection technology.

Bloodstream infections are major contributing factors of morbidity and mortality among children. Precise and timely identification of causative agents can improve the clinical management and outcome of the infection, potentially saving lives. Electrochemical biosensors previously described by Gao et al. (2017) have the potential to deliver greater speed and discrimination. However, to date there are no data that determine whether the age of the host would cause bacteria to demonstrate different growth characteristics, or whether pediatric samples would behave differently using this electrochemical biosensor. The importance of this knowledge gap is clear: the preclinical testing phase of this line of research is limited by the relative lack of pediatric healthy blood volunteers to complete this work. Therefore, in this study we have applied this novel technology to diagnose bacteria spiked into pediatric blood and compared directly with adult blood samples. Only 180 µL of blood was utilized from both adult and pediatric volunteers and inoculated with Escherichia coli67, and the signals generated at different time points were compared. We were able to demonstrate that the signals generated by adult and pediatric blood were not significantly different with this detection technology.

This technical note describes an experimental method to determine the formability limits by fracture in sheet metal forming. The method makes use of laboratory test specimens commonly utilized in the mechanical, fracture and formability characterization of sheet materials and involves determination of the gauge length strains at the cracked regions of the specimens after testing. The presentation explains how measurements are made and what calculations need to be performed in order to determine the fracture forming limit by tension in the principal strain space. The method is applied to Titanium grade 1 sheets, and the fracture forming limit is validated by subsequent experiments with single point incremental forming.

Accurate and timely detection of bacterial pathogens will improve the clinical management of infections. Herein, we demonstrate an electrochemical biosensor that directly detects bacteria in human blood samples, resulting in the rapid diagnosis of a bloodstream infection. The multiplex biosensor detects the species-specific sequences of the 16S ribosomal RNA of bacteria for pathogen identification in physiological samples without preamplification. The analytical performance characteristics of the biosensor, including the limit of detection and probe cross-reactivity, are evaluated systematically. The feasibility of the biosensor for a diagnosis of a bloodstream infection is demonstrated by identifying bacterial clinical isolates spiked in whole blood and blood culture samples that were tested positive for bacteria. The electrochemical biosensor correctly identifies all the species in the samples with 100% concordance to microbiological analysis.

This paper proposes a new diagnostic framework, namely, probabilistic committee machine, to diagnose simultaneous-fault in the rotating machinery. The new framework combines a feature extraction method with ensemble empirical mode decomposition and singular value decomposition, multiple pairwise-coupled sparse Bayesian extreme learning machines (PCSBELM), and a parameter optimization algorithm to create an intelligent diagnostic framework. The feature extraction method is employed to find the features of single faults in a simultaneous-fault pattern. Multiple PCSBELM networks are built as different signal committee members, and each member is trained using vibration or sound signals respectively. The individual diagnostic result from each fault detection member is then combined by a new probabilistic ensemble method, which can improve the overall diagnostic accuracy and increase the number of detectable fault as compared to individual classifier acting alone. The effectiveness of the proposed framework is verified by a case study on a gearbox fault detection. Experimental results show the proposed framework is superior to the existing single probabilistic classifier. Moreover, the proposed system can diagnose both single- and simultaneous-faults for the rotating machinery while the framework is trained by single-fault patterns only.

Alternating current (AC) electrokinetics is a collection of processes for manipulating bulk fluid mass and embedded objects with AC electric fields. The ability of AC electrokinetics to implement the major microfluidic operations, such as pumping, mixing, concentration, and separation, makes it possible to develop integrated systems for clinical diagnostics in nontraditional health care settings. The high conductivity of physiological fluids presents new challenges and opportunities for AC electrokinetics–based diagnostic systems. In this review, AC electrokinetic phenomena in conductive physiological fluids are described followed by a review of the basic microfluidic operations and the recent biomedical applications of AC electrokinetics. The future prospects of AC electrokinetics for clinical diagnostics are presented.