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This paper proposes a novel sliding mode control (SMC) algorithm for direct yaw moment control of four-wheel independent drive electric vehicles (FWID-EVs). The algorithm integrates adaptive law theory, fractional-order theory, and nonsingular terminal sliding mode reaching law theory to reduce chattering, handle uncertainty, and avoid singularities in the SMC system. A sequential quadratic programming (SQP) method is also proposed to optimize the yaw moment distribution under actuator constraints. The performance of the proposed algorithm is evaluated by a hardware-in-the-loop test with two driving maneuvers and compared with two existing SMC-based schemes together with the cases with the change of vehicle parameters and disturbances. The results demonstrate that the proposed algorithm can eliminate chattering and achieve the best lateral stability as compared with the existing schemes. [Display omitted] • A novel AFONT-SMC is proposed to achieve fast global convergence and precise tracking. • An adaptive law is proposed to avoid singularity and strengthen the system robustness. • A new boundary of full vehicle is studied to ensure the controllability of FWID-EVs. • Two new actuator constraints are studied to improve torque distribution in FWID-EVs. [ABSTRACT FROM AUTHOR]

• A novel magnetorheological fluid damper inverse model is constructed based on the radial basis function neural network. • The filter performance function is employed in the semi-active suspension system as the reference state. • A unified frame is developed to study the semi-active suspension system with various disturbances. The magnetorheological fluid semi-active suspension system has been widely employed in vehicles. In general, the output force of the magnetorheological fluid damper varies with service time and temperature, which will cause the controller performance to degrade or even fail. To conquer this problem, this study proposes a compensatory backstepping strategy for the magnetorheological fluid suspension system. First, the normalized phenomenological model is introduced to simulate the nonlinear characteristics of the magnetorheological fluid damper. Second, the adaptive radial-basis function neural network is employed to construct the inverse model of the magnetorheological fluid damper. Next, the reference state is calculated by the filter performance function to trade off the vehicle handling stability and ride comfort. Finally, the voltage-force compensator is constructed to improve the robustness of the controller under input time delay, external noise disturbance, and actuator failures. Furthermore, the performances of the magnetorheological fluid damper, inverse magnetorheological fluid model, and compensatory backstepping controller are simulated under bump and random excitations. It is found that the proposed compensatory backstepping controller shows good improvement in the semi-active suspension system with time delay, noise disturbance, and actuator failure. [ABSTRACT FROM AUTHOR]

This work solves the robust passive fault-tolerant control problem for autonomous electric vehicles based on an adaptive event triggered mechanism. Firstly, given the system uncertainties from the tire dynamics and the longitudinal speed, the T–S fuzzy model method is used to approximate the vehicle lateral dynamics. Secondly, taking the communication constraints caused by band-limited networks into account, an adaptive event-triggered scheme is introduced in the process of the control design. Moreover, the asynchronous constraint of the weight function between the controller and system is considered. Thirdly, considering that the actuator faults are inevitably encountered in the control system, a robust passive fault-tolerant control method is proposed to improve vehicle performances. Finally, simulations are carried out to illustrate the effectiveness and robustness of the proposed approach. • A fuzzy vehicle dynamic model is constructed with asynchronous constraints. • An improved AET mechanism is designed to reduce the transmitted number of signals. • The actuator faults are described by a polytope with finite vertices. • A new Lyapunov function is developed to solve the problem of limited delay range. [ABSTRACT FROM AUTHOR]

For the purpose of improving the lateral stability in the path following process for the distributed drive electric vehicles subject to multiple actuator/sensor faults, a new fault diagnosis and fault tolerant control method is proposed in this paper through the integration of the active front steering (AFS) and direct yaw-moment control (DYC). Firstly, considering the actuator/sensor faults and parameter uncertainties together, a Takagi–Sugeno fuzzy model is established to describe the integrated AFS/DYC system with nonlinear dynamics. Secondly, because the fault information plays a key role in the fault tolerant control, a fuzzy estimation observer is presented to diagnose actuator/sensor faults online. Thirdly, considering the high cost of the measurement sensors for lateral speed, it is hard to measure the complete state of the vehicle. Thus, in the frame of dynamic output feedback structure, a fuzzy fault tolerant control method is proposed to improve the path following performance and guarantee the dynamic stability. Finally, the hardware-in-the-loop experimental results of two typical driving maneuvers show that the proposed control method has a superior performance in the improvement of the driving stability and active safety. [ABSTRACT FROM AUTHOR]

A nanotube assisted microwave electroporation (NAME) technique is demonstrated for delivering molecular biosensors into viable bacteria for multiplex single cell pathogen identification to advance rapid diagnostics in clinical microbiology. Due to the small volume of a bacterial cell (~femtoliter), the intracellular concentration of the target molecule is high, which results in a strong signal for single cell detection without amplification. The NAME procedure can be completed in as little as 30 minutes and can achieve over 90% transformation efficiency. We demonstrate the feasibility of NAME for identifying clinical isolates of bloodborne and uropathogenic pathogens and detecting bacterial pathogens directly from patient's samples. In conjunction with a microfluidic single cell trapping technique, NAME allows single cell pathogen identification and antimicrobial susceptibility testing concurrently. Using this approach, the time for microbiological analysis reduces from days to hours, which will have a significant impact on the clinical management of bacterial infections. A nanotube assisted microwave electroporation (NAME) technique is demonstrated for rapid microbiological analysis of pathogenic bacteria. NAME delivers molecular biosensors into viable bacteria for multiplex single cell pathogen identification and antimicrobial susceptibility testing. The technique determines the species in as little as 30 minutes and perform antimicrobial susceptibility testing in less than 3 hours. Unlabelled Image [ABSTRACT FROM AUTHOR]

Abstract As an effective approach of suppressing vibrations, the constrained layer damping (CLD) has drawn wide attention from the automotive and aerospace industries. However, most of the existing investigations focus on the beam structures with CLD and few studies have been done on the plate structures with CLD. Considering the practical applications, this work studies the finite element (FE) modeling of plate structures with CLD by considering the shear and extension strains in all of three layers. To reduce the computational cost and ensure the accuracy, a simplified single-layer equivalent method is originally proposed to model the plate structure with CLD based on the equivalent material properties. In this method, the equivalent material properties are obtained by defining a new equation which includes the equivalent bending stiffness. By nonlinear regression of these responses at resonance frequencies, the equivalent bending stiffness can be obtained, and the plate structure with CLD can be regarded as a regular single-layer plate for modeling. The simulation result shows that the proposed simplified single-layer equivalent method using single-layer equivalent material properties is efficient and accurate for modeling plate structures with CLD. [ABSTRACT FROM AUTHOR]

This research explores the influence of renewable fuels, including three kinds of biodiesel along with ethanol on the physical properties and structural characteristics of particulate matter (PM) emitted from a diesel engine in comparison with pure diesel. After adding 10 vol% of grape seed biodiesel, coffee biodiesel and eucalyptus oil into diesel, three biodiesel blended fuels (10% grape seed biodiesel (DGs10), 10% spent coffee ground biodiesel (DC10) and eucalyptus oil biodiesel (DEu10)) were produced and tested in this study. Besides, one ethanol blend containing 9 vol% of ethanol and 1 vol% of biodiesel (blend stabilizer) was also tested to do the comparison. In the present study, scanning transmission electron microscope (STEM) and scanning electron microscope (SEM) were employed for analyzing the microstructure, nanostructure and electron diffraction pattern of PM. Raman spectrometer (RS) was also used for the analysis of structural characterization of PM. In addition, several experimental instruments like microbalance, measuring cup, viscometer, oxygen bomb calorimeter and Gas Chromatography-Mass Spectrometer (GC-MS) were employed to detect the fuel properties, including density, heating value, viscosity, composition and cetane number. A conclusion can be drawn that both biodiesel blends and ethanol blend have a changing effect on the PM properties compared to pure diesel, where biodiesel blends have a slightly weaker influence than ethanol blend. Regarding the biodiesel blends, DGs10 has more impact than DC10 and DEu10 in changes of PM properties, particularly in the reduction of PM mass, making it a good candidate for renewable fuel for diesel engines. [Display omitted] • Analyzing physical properties and structural characteristics of particle matter (PM). • Examining PM of diesel engine fueled with diesel, three biodiesel blends and ethanol. • Biodiesel and ethanol can impact the environment and human health through PM changes. • Ethanol blend has more influence than biodiesel blends on the PM properties changes. • DGs10 has more influence than DC10 and DEu10 on the PM properties changes. [ABSTRACT FROM AUTHOR]

Dual-injection engines, which allow real-time control and injection of two different fuels, are capable of varying the ratio of biofuel blends at different engine operating conditions for optimal engine performance. However, while many experiments have been carried out on these engines to demonstrate their advantages, very few studies have focused on the corresponding air–fuel ratio (AFR) control strategy. In order to achieve stable engine operation, it is essential to maintain transient AFR during the change of fuel blend ratio. Therefore, this study proposes an adaptive controller for AFR control of dual-injection engines. The proposed controller is designed based on a recently developed machine learning method called extreme learning machine, and its stability is verified with Lyapunov analysis. Simulations have been performed on an industry-level engine simulation software to verify the controller. Since dual-injection engines are not available in the market, a spark-ignition engine has been retrofitted for dual-injection operation so that the proposed controller can be implemented and evaluated experimentally. Both simulation and experiment results show that the proposed controller can effectively regulate the AFR to desired level. The results also show that the proposed controller outperforms the engine built-in AFR controller, indicating its significance for dual-injection engines. [ABSTRACT FROM AUTHOR]

An online extreme learning machine (ELM) based modeling and optimization approach for point-by-point engine calibration is proposed to improve the efficiency of conventional model-based calibration approach. Instead of building hundreds of local engine models for every engine operating point, only one ELM model is necessary for the whole process. This ELM model is firstly constructed for a starting operating point, and calibration of this starting point is conducted by determining the optimal parameters of the model. This ELM model is then re-used as a base model for a nearby target operating point, and optimization is performed on the model to search for its best parameters. With a design of experiment strategy on the best parameters obtained, new measurements from the target operating point can be collected and used to update the model. By repeating the optimization and model update procedures, the optimal parameters for the target point can be found after several iterations. By using the model of this target point as the base model for another nearby operating point and repeating the same process again, calibration for all the operating points can be done online efficiently. The contribution of the proposed method is to save the number of experiments in the calibration process. To verify the effectiveness of the proposed approach, experiments on a commercial engine simulation software have been conducted. Three variants of online ELM are utilized in the model update process for comparison. The results show that engine calibration can be carried out with much fewer measurements and time using the proposed approach, and the initial training free online ELM is the most efficient online modeling method for this application. [ABSTRACT FROM AUTHOR]

Although many combinations of biofuel blends are available in the market, it is more beneficial to vary the ratio of biofuel blends at different engine operating conditions for optimal engine performance. Dual-injection engines have the potential to implement such function. However, while optimal engine calibration is critical for achieving high performance, the use of two injection systems, together with other modern engine technologies, leads the calibration of the dual-injection engines to a very complicated task. Traditional trial-and-error-based calibration approach can no longer be adopted as it would be time-, fuel- and labor-consuming. Therefore, a new and fast calibration method based on sparse Bayesian extreme learning machine (SBELM) and metaheuristic optimization is proposed to optimize the dual-injection engines operating with biofuels. A dual-injection spark-ignition engine fueled with ethanol and gasoline is employed for demonstration purpose. The engine response for various parameters is firstly acquired, and an engine model is then constructed using SBELM. With the engine model, the optimal engine settings are determined based on recently proposed metaheuristic optimization methods. Experimental results validate the optimal settings obtained with the proposed methodology, indicating that the use of machine learning and metaheuristic optimization for dual-injection engine calibration is effective and promising. [ABSTRACT FROM AUTHOR]

The automotive engine is prone to various faults due to its complex structure and running conditions. Development of a fast response and accurate intelligent system for fault diagnosis of automotive engines is therefore greatly urged. The engine fault diagnosis faces challenges because of the existence of simultaneous-faults which are multiple single-faults appear concurrently. Another challenge is the high cost in acquiring the exponentially increased simultaneous-fault signals. Traditional signal-based engine fault diagnostic systems may not give reliable diagnostic results because they usually rely on single classifier and one kind of engine signal. To enhance the reliability of fault diagnosis and the number of detectable faults, this research proposes a new diagnostic framework namely probabilistic committee machine (PCM). The framework combines feature extraction (empirical mode decomposition and sample entropy), a parameter optimization algorithm, and multiple sparse Bayesian extreme learning machines (SBELM) to form an intelligent diagnostic framework. Multiple SBELM networks are built to form different domain committee members. The members are individually trained using air ratio, ignition pattern and engine sound signals. The diagnostic result from each fault detection member is then combined by using a new probabilistic ensemble method, which can improve the overall diagnostic accuracy and increase the number of detectable faults as compared to individual classifier. 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 automotive engines while the system is trained by single-fault patterns only. [ABSTRACT FROM AUTHOR]

While many standards have been stipulated to control vehicular emissions, current inspection program for examining the engine health of in-use vehicles is practically ineffective and time-consuming. In particular, in-use vehicles are only required for inspection yearly, but huge amount of emissions may have been produced from malfunctioned engines daily. A new wireless inspection and notification system (WINS) is therefore proposed to monitor the vehicle engine health on the street in situ . The principle of WINS is to wirelessly examine some of the engine parameters through radio frequency identification (RFID) and traffic lights. RFID tags are installed on vehicles to collect the engine health information, whereas RFID interrogators are installed on traffic lights for wireless data transmission. Experiments were carried out to evaluate the effectiveness of the proposed WINS, and the results show that the proposed WINS is more convenient and economical than traditional vehicle inspection system. Moreover, as there are more than hundreds of traffic lights in the traffic network of a city, a maximum spanning tree (MAXST) algorithm is proposed to determine the suitable number of RFID devices required in the network so that the implementation cost, system loading and missing rate can be optimized. Different from the typical spanning tree algorithm in operational research, the MAXST algorithm has a domain-specific rule and weight calculation method for this application. To verify the methodology, simulations on the traffic networks of Shenzhen, New York and London were conducted. Results show that only 25–40% of traffic lights of the traffic networks are necessary for installation of RFID interrogators, with a rate of 2–7% that the vehicle owners may be able to escape the location of RFID interrogators. [ABSTRACT FROM AUTHOR]

This study presents the optimization of biodiesel engine performance that can achieve the goal of fewer emissions, low fuel cost and wide engine operating range. A new biodiesel engine modeling and optimization framework based on extreme learning machine (ELM) is proposed. As an accurate model is required for effective optimization result, kernel-based ELM (K-ELM) is used instead of basic ELM because K-ELM can provide better generalization performance, and the randomness of basic ELM does not occur in K-ELM. By using K-ELM, a biodiesel engine model is first created based on experimental data. Logarithmic transformation of dependent variables is used to alleviate the problems of data scarcity and data exponentiality simultaneously. With the K-ELM engine model, cuckoo search (CS) is then employed to determine the optimal biodiesel ratio. A flexible objective function is designed so that various user-defined constraints can be applied. As an illustrative study, the fuel price in Macau is used to perform the optimization. To verify the modeling and optimization framework, the K-ELM model is compared with a least-squares support vector machine (LS-SVM) model, and the CS optimization result is compared with particle swarm optimization and experimental results. The evaluation result shows that K-ELM can achieve comparable performance to LS-SVM, resulting in a reliable prediction result for optimization. It also shows that the optimization results based on CS is effective. [ABSTRACT FROM AUTHOR]

Practical automotive engine fault diagnosis in an automotive service center is usually performed by analyzing the qualitative symptom descriptions provided by the vehicle owner. However, it is a non-trivial and time-consuming procedure because; (i) the qualitative symptom descriptions are usually collected through a questionnaire containing binary, numerical, and vague data that are difficult to digest; (ii) the engine malfunctioning may not be caused by a single-fault only, but several single-faults simultaneously (i.e., simultaneous-faults). Therefore, automotive mechanic usually requires several days or even weeks to diagnose and fix the engine. To improve this non-trivial and time-consuming procedure of engine fault diagnosis for the mechanic, a new framework of simultaneous-fault diagnosis is proposed in this paper by integrating fuzzification, pairwise probabilistic multi-label classification, and decision-by-threshold. This framework is called fuzzy and probabilistic simultaneous-fault diagnosis (FPSD). Compared to traditional frameworks, FPSD requires much fewer training cases of costly simultaneous-faults while it can probabilistically diagnose both unseen single-faults and simultaneous-faults based on qualitative symptom descriptions. To evaluate the performance of FPSD, a comparative study was conducted over common classification techniques. Experimental results show that the proposed framework can effectively alleviate the aforementioned issues. [ABSTRACT FROM AUTHOR]

Abstract: Rapid detection of bacterial pathogens is critical toward judicious management of infectious diseases. Herein, we demonstrate an in situ electrokinetic stringency control approach for a self-assembled monolayer-based electrochemical biosensor toward urinary tract infection diagnosis. The in situ electrokinetic stringency control technique generates Joule heating induced temperature rise and electrothermal fluid motion directly on the sensor to improve its performance for detecting bacterial 16S rRNA, a phylogenetic biomarker. The dependence of the hybridization efficiency reveals that in situ electrokinetic stringency control is capable of discriminating single-base mismatches. With electrokinetic stringency control, the background noise due to the matrix effects of clinical urine samples can be reduced by 60%. The applicability of the system is demonstrated by multiplex detection of three uropathogenic clinical isolates with similar 16S rRNA sequences. The results demonstrate that electrokinetic stringency control can significantly improve the signal-to-noise ratio of the biosensor for multiplex urinary tract infection diagnosis. From the Clinical Editor: Urinary tract infections remain a significant cause of mortality and morbidity as secondary conditions often related to chronic diseases or to immunosuppression. Rapid and sensitive identification of the causative organisms is critical in the appropriate management of this condition. These investigators demonstrate an in situ electrokinetic stringency control approach for a self-assembled monolayer-based electrochemical biosensor toward urinary tract infection diagnosis, establishing that such an approach significantly improves the biosensor's signal-to-noise ratio. [Copyright &y& Elsevier]

• A novel BLS stacking framework is proposed for the accurate diagnosis of GIM. • A parallel attention module is proposed to boost the classifiers in Level-0. • A BLS-based approach is proposed in Level-1. • The proposed BLS2-MSA obtained better results on GIM diagnosis. Gastric intestinal metaplasia (GIM) is a pre-malignant lesion of gastric cancer, which is the fourth leading cause of cancer-related mortalities. The accurate diagnosis and effective treatment of GIM can decrease the incidence of gastric cancer. Traditionally, GIM diagnosis is conducted through upper endoscopy imaging, which is highly dependent on endoscopists' experience, and the diagnostic results may fluctuate with their discrepant skills or potential fatigue. Thus, computer-aided diagnosis (CAD) of GIM with high accuracy is urgently needed, while currently there is no such computer system in commercial market. In this paper, a novel broad learning system stacking framework with multi-scale attention (BLS2-MSA) is proposed, which contains Level-0 for preliminary diagnosis and Level-1 for final decision. In Level-0 of the BLS2-MSA, there are five classifiers, four of which are constructed using multi-scale features from the backbone neural network with the proposed parallel attention module, and the other classifier adopts a standard TL method only. In Level-1 of the BLS2-MSA, a broad learning system-based incremental updating approach is first proposed to boost the performance of classifiers in Level-0. Experimental results show that the True Positive Rate (TPR), the True Negative Rate (TNR), the Positive Predictive Value (PPV), the Accuracy (ACC), the F 1 and the Area Under ROC Curve (AUC) of the BLS2-MSA are 93.6 % , 91.2 % , 93.6 % , 93.2 % , 93.6 and 0.931 respectively, and the diagnostic results demonstrate that the BLS2-MSA could perform competitively compared with skilled endoscopists. All of these indicate that the proposed method enables an accurate and reliable GIM diagnosis. [ABSTRACT FROM AUTHOR]

• A chest CT dataset contains multiple types of pneumonia is available for the public. • Established and analyzed a multi-scale attention network for pneumonia detection. • Spatial attention block can enhance the performance of convolutional neural networks. • Multiclass focal loss can effectively handle the data imbalance issue. The quick and precise identification of COVID-19 pneumonia, non-COVID-19 viral pneumonia, bacterial pneumonia, mycoplasma pneumonia, and normal lung on chest CT images play a crucial role in timely quarantine and medical treatment. However, manual identification is subject to potential misinterpretations and time-consumption issues owing the visual similarities of pneumonia lesions. In this study, we propose a novel multi-scale attention network (MSANet) based on a bag of advanced deep learning techniques for the automatic classification of COVID-19 and multiple types of pneumonia. The proposed method can automatically pay attention to discriminative information and multi-scale features of pneumonia lesions for better classification. The experimental results show that the proposed MSANet can achieve an overall precision of 97.31%, recall of 96.18%, F1-score of 96.71%, accuracy of 97.46%, and macro-average area under the receiver operating characteristic curve (AUC) of 0.9981 to distinguish between multiple classes of pneumonia. These promising results indicate that the proposed method can significantly assist physicians and radiologists in medical diagnosis. The dataset is publicly available at https://doi.org/10.17632/rf8x3wp6ss.1. [ABSTRACT FROM AUTHOR]

Abstract: This study aims to determine optimal biodiesel ratio that can achieve the goals of fewer emissions, reasonable fuel economy and wide engine operating range. Different advanced machine learning techniques, namely ELM (extreme learning machine), LS-SVM (least-squares support vector machine) and RBFNN (radial-basis function neural network), are used to create engine models based on experimental data. Logarithmic transformation of dependent variables is used to alleviate the problems of data scarcity and data exponentiality simultaneously. Based on the engine models, two optimization methods, namely SA (simulated annealing) and PSO (particle swarm optimization), are employed and a flexible objective function is designed to determine the optimal biodiesel ratio subject to various user-defined constraints. A case study is presented to verify the modeling and optimization framework. Moreover, two comparisons are conducted, where one is among the modeling techniques and the other is among the optimization techniques. Experimental results show that, in terms of the model accuracy and training time, ELM with the logarithmic transformation is better than LS-SVM and RBFNN with/without the logarithmic transformation. The results also show that PSO outperforms SA in terms of fitness and standard deviation, with an acceptable computational time. [Copyright &y& Elsevier]

Abstract: Modern automotive engines are controlled by an electronic control unit (ECU), and engine power performance is significantly affected by the selection of both ECU parameters and engine components. The engine performance tuning is usually done by a trial-and-error method. In the current literature, very little research has considered the selection of engine parts because engine parts are complicated objects that are usually represented as nominal data. These data are meaningless values in terms of computation. This paper presents a novel multiple-input/output least-squares support vector machine plus one-of-n remapping method for modelling engine power performance using both numerical (ECU parameters) and nominal data (candidate engine parts). The Quasi-Newton method, a genetic algorithm and particle swarm optimisation are then applied to the engine model to determine the optimal engine setup automatically. A simple binary code synthesis rule is also proposed to optimise the nominal variable. Both experimental and simulation results show that the proposed methodology can successfully yield an optimal engine setup. [Copyright &y& Elsevier]

Abstract: Hysteresis effect degrades the positioning accuracy of a piezostage, and hence the nonlinearity has to be suppressed for ultrahigh-precision positioning applications. This paper extends least squares support vector machines (LS-SVM) to the domain of hysteresis modeling and compensation for a piezostage driven by piezoelectric stack actuators. A LS-SVM model is proposed and trained by introducing the current input value and input variation rate as the input data set to formulate a one-to-one mapping. By adopting the radial basis function (RBF) as kernel function, the LS-SVM model only has two free hyperparameters, which are optimally tuned by resorting to Bayesian inference framework. The effectiveness of the presented model is verified as compared with two state-of-the-art approaches, namely, Bouc–Wen model and modified Prandtl–Ishlinskii (MPI) model. In addition, the LS-SVM inverse model based feedforward control combined with an incremental proportional–integral–derivative (PID) feedback control is designed to compensate the hysteresis nonlinearity. Experimental results show that the LS-SVM model based hybrid control scheme is superior to the Bouc–Wen model and MPI model based ones as well as either of the stand-alone controllers. The rate-dependent hysteresis is suppressed to a negligible level, which validates the effectiveness of the constructed controller. Owing to a simple procedure, the proposed LS-SVM based approach can be applied to modeling and control of other types of hysteretic systems as well. [Copyright &y& Elsevier]

Abstract: Whenever there is any fault in an automotive engine ignition system or changes of an engine condition, an automotive mechanic can conventionally perform an analysis on the ignition pattern of the engine to examine symptoms, based on specific domain knowledge (domain features of an ignition pattern). In this paper, case-based reasoning (CBR) approach is presented to help solve human diagnosis problem using not only the domain features but also the extracted features of signals captured using a computer-linked automotive scope meter. CBR expert system has the advantage that it provides user with multiple possible diagnoses, instead of a single most probable diagnosis provided by traditional network-based classifiers such as multi-layer perceptions (MLP) and support vector machines (SVM). In addition, CBR overcomes the problem of incremental and decremental knowledge update as required by both MLP and SVM. Although CBR is effective, its application for high dimensional domains is inefficient because every instance in a case library must be compared during reasoning. To overcome this inefficiency, a combination of preprocessing methods, such as wavelet packet transforms (WPT), kernel principal component analysis (KPCA) and kernel K-means (KKM) is proposed. Considering the ignition signals captured by a scope meter are very similar, WPT is used for feature extraction so that the ignition signals can be compared with the extracted features. However, there exist many redundant points in the extracted features, which may degrade the diagnosis performance. Therefore, KPCA is employed to perform a dimension reduction. In addition, the number of cases in a case library can be controlled through clustering; KKM is adopted for this purpose. In this paper, several diagnosis methods are also used for comparison including MLP, SVM and CBR. Experimental results showed that CBR using WPT and KKM generated the highest accuracy and fitted better the requirements of the expert system. [Copyright &y& Elsevier]

Urinary tract infection (UTI) is among the most common bacterial infections and poses a significant healthcare burden. The standard culture-based diagnosis of UTI has a typical delay of two to three days. In the absence of definitive microbiological diagnosis at the point of care, physicians frequently initiate empirical broad-spectrum antibiotic treatment, and this has contributed to the emergence of resistant pathogens. Biosensors are emerging as a powerful diagnostic platform for infectious diseases. Paralleling how blood glucose sensors revolutionized the management of diabetes, and how pregnancy tests are now conducted in the home, biosensors are poised to improve UTI diagnosis significantly. Biosensors are amenable to integration with microfluidic technology for point-of-care (POC) applications. This review focuses on promising biosensor technology for UTI diagnosis, including pathogen identification and antimicrobial susceptibility testing, and hurdles to be surpassed in the translation of biosensor technology from bench to bedside. [ABSTRACT FROM AUTHOR]

Abstract: Cellular processes are regulated by various mechanical and physical factors in their local microenvironment such as geometric confinements, cell–substrate interactions, and cell–cell contact. Systematic elucidation of these regulatory mechanisms is crucial for fundamental understanding of cell biology and for rational design of biomedical devices and regenerative medicine. Here, we report a generally applicable plasma lithography technique, which performs selective surface functionalization on large substrate areas, for achieving long-term, stable confinements with length scales from 100 nm to 1 cm toward the investigation of cell–microenvironment interactions. In particular, we applied plasma lithography for cellular confinement of neuroblastomas, myoblasts, endothelial cells, and mammary gland epithelial cells, and examined the motion of mouse embryonic fibroblasts in directionality-confined environments for studying the effect of confinements on migratory behavior. In conjunction with live cell imaging, the distance traveled, velocity, and angular motion of individual cells and collective cell migration behaviors were measured in confined environments with dimensions comparable to a cell. A critical length scale that a cell could conceivably occupy and migrate to was also identified by investigating the behaviors of cells using confined environments with subcellular length scales. [ABSTRACT FROM AUTHOR]

Electrothermal flow is a promising technique in microfluidic manipulation toward laboratory automation applications, such as clinical diagnostics and high-throughput drug screening. Despite the potential of electrothermal flow in biomedical applications, relatively little is known about electrothermal manipulation of highly conductive samples, such as physiological fluids and buffer solutions. In this study, the characteristics and challenges of electrothermal manipulation of fluid samples with different conductivities were investigated systematically. Electrothermal flow was shown to create fluid motion for samples with a wide range of conductivity when the driving frequency was greater than 100kHz. For samples with low conductivities (below 1S/m), the characteristics of the electrothermal fluid motions were in quantitative agreement with the theory. For samples with high conductivities (greater than 1S/m), the fluid motion appeared to deviate from the model as a result of potential electrochemical reactions and other electrothermal effects. These effects should be taken into consideration for electrothermal manipulation of biological samples with high conductivities. This study will provide insights in designing microfluidic devices for electrokinetic manipulation of biological samples toward laboratory automation applications in the future. [Copyright &y& Elsevier]

Many components in biological matrices influence the result of an analysis, affecting assay sensitivity and reproducibility. Improved matrix management becomes critical as requirements for higher assay sensitivity and increased process throughput become more demanding. There are several robotic laboratory automation systems that are commercially available, which serve to minimize matrix interference by performing purification and extraction protocols. However, there is an unmet need of inline matrix effect reduction solutions to reduce the processing time and cost for automated sample preparation. In microfluidics, effective matrix management is essential for developing fully integrated systems capable of meeting these requirements. This review surveys current biological matrix management techniques for liquid chromatography-tandem mass spectrometry (LC-MS/MS) methods and binding assays with a view toward building automatable processes. For some systems, simple sample-preparation methods, such as dilution and protein precipitation (PPT), are sufficient, whereas other systems require labor-intensive methods, such as liquid-liquid extraction (LLE) and solid-phase extraction (SPE). To achieve high throughput, PPT, LLE, and SPE have been adopted to 96-well-plate format. Online SPE has also been coupled with LC-MS/MS to automate sample preparation and analysis of urine, plasma, and serum matrices. However, offline processing of whole blood is still required to obtain plasma and serum. The ultimate goal of implementing sample preparation to reduce matrix effects within untreated sample is to achieve reproducibility and sensitivity required by the application; therefore, inline sample preparation integrated with molecular analysis will be highly significant for laboratory automation. Electrokinetic methods have the potential of handling whole-blood, urine, and saliva samples and can be incorporated into microfluidic systems for full automation. Optimization of analysis conditions and the use of appropriate standards have likewise assisted in reducing or correcting matrix effects and will also be discussed. [Copyright &y& Elsevier]

Abstract: The automotive engine performance is greatly affected by the calibration of its electronic control unit (ECU). The method for ECU calibration is traditionally done by trial-and-error. This traditional method consumes a large amount of time and money. To resolve this problem, case-based reasoning (CBR) is employed, so that an existing and effective ECU setup can be adapted to fit another similar class of engines. The adaptation procedure is done through a more sophisticated step called case-based adaptation (CBA) (). CBA is an effective knowledge management tool, which can interactively learn the expert adaptation knowledge. The paper briefly reviews the methodologies of CBR and CBA. Then the application to ECU calibration is described via a case study. With CBR and CBA, the efficiency of calibrating an ECU can be enhanced. A prototype system has also been developed to verify the usefulness of CBR in ECU calibration. [Copyright &y& Elsevier]

Cartridge-based microfluidics is a promising technology for clinical diagnostics. By miniaturizing the fluid-handling processes required for genomic and proteomic analyses, reagent and specimen volume is minimized along with the size of the system. We demonstrate an automated microfluidic system capable of performing six multiplexed genomic and proteomic analyses simultaneously, by means of an integrated electrochemical sensor and embedded controls. [Copyright &y& Elsevier]

Emerging molecular studies have shown that the transcription factor NF-E2-related factor (Nrf2) plays an essential role in cancer chemoprevention. Here, we report the development of a molecular biosensor for rapid detection of antioxidant-responsive element (ARE)-bound Nrf2 protein. The development will provide a molecular assay for high-throughput screening of chemopreventive compounds. Specifically, a double-stranded DNA probe is designed based on the ARE sequence. One of the DNA strands is labeled with a fluorophore on the 5′ end and the complementary strand is labeled with a quencher on the 3′ end. A single-stranded DNA competitor is also designed. The existence of the Nrf2 stabilizes the fluorescent probes and delays the competitor from separating the fluorophore-quencher complex. Therefore, the concentration of the Nrf2 proteins can be measured quantitatively based on the fluorescence intensity. The molecular binding scheme was demonstrated using purified p50 and the detection of endogenous Nrf2 was demonstrated using whole-cell lysates treated with sulforaphane. The assay can easily be incorporated into an automated platform for high-throughput screening of chemopreventive compounds targeting Nrf2. [Copyright &y& Elsevier]

Abstract: Automotive engine power and torque are significantly affected with effective tune-up. Current practice of engine tune-up relies on the experience of the automotive engineer. The engine tune-up is usually done by trial-and-error method, and then the vehicle engine is run on the dynamometer to show the actual engine output power and torque. Obviously, the current practice costs a large amount of time and money, and may even fail to tune up the engine optimally because a formal power and torque model of the engine has not been determined yet. With an emerging technique, least squares support vector machines (LS-SVM), the approximated power and torque model of a vehicle engine can be determined by training the sample data acquired from the dynamometer. The number of dynamometer tests for an engine tune-up can therefore be reduced because the estimated engine power and torque functions can replace the dynamometer tests to a certain extent. Besides, Bayesian framework is also applied to infer the hyperparameters used in LS-SVM so as to eliminate the work of cross-validation, and this leads to a significant reduction in training time. In this paper, the construction, validation and accuracy of the functions are discussed. The study shows that the predicted results using the estimated model from LS-SVM are good agreement with the actual test results. To illustrate the significance of the LS-SVM methodology, the results are also compared with that regressed using a multilayer feed forward neural networks. [Copyright &y& Elsevier]

Precise measurement of the mechanical properties of a cell provides useful information about its structural organization and physiological state. It is interesting to understand the effect of individual components on the mechanical properties of the entire cell. In this study, we investigate the influence of the cytoskeletal actin on the viscoelastic properties of a cell. Actin-specific agents, including latrunculin A and jasplakinolide, are used to alter the organization of the cytoskeletal actin. Brassica oleracea protoplasts are treated with the drugs and deformed under an external electric potential. The relaxation processes of single protoplasts after electrodeformation are measured. The data are analyzed by a model-independent spectrum recovery algorithm. Two distinct characteristic time constants are obtained from the relaxation spectra. Treatment with latrunculin A increases both of the relaxation time constants. The longest relaxation times for control, latrunculin A treated, and jasplakinolide treated cells are determined to be 0.28, 1.0, and 0.21 s, respectively. [Copyright &y& Elsevier]

• A computer-aided diagnose system is proposed to diagnose gastrointestinal metaplasia. • The system is based on multi-feature fusion method. • A new attention fusion module is proposed to generate the final features. • Transfer learning and regularization method are used to address the limited data. Gastrointestinal metaplasia (GIM) is a disease that is closely related to early gastric cancer. The early diagnosis of GIM can effectively avoid gastric cancer. Traditionally, GIM diagnosis is done through human analysis of endoscopy imaging, which is time-consuming and exhausting. Computer aided diagnosis of GIM is urgently needed but currently there is no such computer system in commercial market. Considering the complex features of gastroscopic images, and different pixels contain different weight information of color and texture features, a novel multi feature fusion method composed of new feature module (FM) and attention feature module (AFM) is proposed. First, a residual deep network is used as the base framework to build FM combined with high and low-level features, which can make up for the deficiency of single high-level features. Then, the RGB image, HSV (Hue Saturation Value) image, and LBP (Local Binary Pattern) features are considered as 3-way inputs of the proposed model. In other words, the deep features of the endoscopy image are extracted respectively from image pixels, colors, and texture. Finally, these deep features are sent into a novel AFM to generate the final features for GIM recognition. AFM first adaptively learns feature weights through attention mechanism, and then fuses the above three types of features. Experimental results show that the proposed method achieves a high recognition accuracy of 90.28% under a dataset of 1050 images collected from a local hospital. In addition, the proposed method is superior to single-featured networks and existing method in term of recognition accuracy. [ABSTRACT FROM AUTHOR]

The literature reveals that fossil fuels will continue to be the main energy source for transportation fleets up to 2050, despite their non-renewability and the emissions of pollutants resulting from their use. Until the full adoption of non-fossil and zero-emission vehicles (e.g., electric vehicles), the application of renewable liquid fuels could be the best choice due to the latter's positive effects such as reduced consumption of fossil fuels and reduced emissions of pollutants. Alcohols, among others, have been widely tested as alternative fuels in diesel engines, however, there is no comprehensive information about their impacts on particulate matter (PM) which has a direct influence on human health, the environment, and emission catalyst's efficiency. This review comprehensively explores the impact of lower/higher alcohols, from methanol to decanol, in dual-fuel mode on the PM physicochemical properties emitted from diesel engines under different alcohol ratios and operating conditions. It is observed that alcohols change the PM physical properties by reducing primary particle size/number, radius of gyration, fractal dimension, and fringe length and increasing fringe separation distance and tortuosity compared to those of diesel PM. Regarding the chemical properties, alcohols reduce polycyclic aromatic hydrocarbons, benzo[a]pyrene equivalent, organic carbon (OC), elemental carbon (EC), ions, and soot ignition temperature and increase OC/EC ratio and soot oxidation reactivity. Thus, each 10% of alcohol, by volume, has the potential to reduce PM mass, number, and size by about 17, 20, and 10%, while lower alcohols and blended mode have more impact than higher alcohols and fumigation mode, respectively. [Display omitted] • Impact of alcohols on particulate matter (PM) properties from CI engines are reviewed. • PM structure and chemical properties have strong effect on PM mass, number, and size. • Alcohol fuels can significantly change the physicochemical properties of diesel PM. • Lower alcohols have more impact than higher alcohols on PM properties. • Alcohols in blended format have more impact than fumigated format on PM reduction. [ABSTRACT FROM AUTHOR]

Recently, multilayer extreme learning machine (ML-ELM) and hierarchical extreme learning machine (H-ELM) were developed for representation learning whose training time can be reduced from hours to seconds compared to traditional stacked autoencoder (SAE). However, there are three practical issues in ML-ELM and H-ELM: (1) the random projection in every layer leads to unstable and suboptimal performance; (2) the manual tuning of the number of hidden nodes in every layer is time-consuming; and (3) under large hidden layer, the training time becomes relatively slow and a large storage is necessary. More recently, issues (1) and (2) have been resolved by kernel method, namely, multilayer kernel ELM (ML-KELM), which encodes the hidden layer in form of a kernel matrix (computed by using kernel function on the input data), but the storage and computation issues for kernel matrix pose a big challenge in large-scale application. In this paper, we empirically show that these issues can be alleviated by encoding the hidden layer in the form of an approximate empirical kernel map (EKM) computed from low-rank approximation of the kernel matrix. This proposed method is called ML-EKM-ELM, whose contributions are: (1) stable and better performance is achieved under no random projection mechanism; (2) the exhaustive manual tuning on the number of hidden nodes in every layer is eliminated; (3) EKM is scalable and produces a much smaller hidden layer for fast training and low memory storage, thereby suitable for large-scale problems. Experimental results on benchmark datasets demonstrated the effectiveness of the proposed ML-EKM-ELM. As an illustrative example, on the NORB dataset, ML-EKM-ELM can be respectively up to 16 times and 37 times faster than ML-KELM for training and testing with a little loss of accuracy of 0.35%, while the memory storage can be reduced up to 1/9. [ABSTRACT FROM AUTHOR]

This paper presents a self-evolving fuzzy model-based controller for a generic hypersonic vehicle (HV). The self-evolving fuzzy model can dynamically evolve its rule base through evaluating the influence of a rule and then is introduced to reconstruct the unknown dynamics so that the changing dynamics and unknown uncertainties of the HV are integrated. The control law is designed using the fuzzy identified model instead of the actual HV one. The stability analysis of the whole control system is presented from the Lyapunov function and shows that the tracking errors converge to zero. The bounds of the control inputs are considered in this study and ensured by means of the stable projection-type parameter adaptation laws. To reduce the computation complexity, only the parameters of a ‘winner’ rule closest to the current state are adjusted while those of other rules maintain unvaried. This differs from the existing studies in which the parameters of all rules need to be adjusted. The simulation results under the nominal conditions and parameter uncertainties demonstrate the superior performance of the proposed controller. [ABSTRACT FROM AUTHOR]