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Background: Coronavirus papain-like proteases (PLpros) play a crucial role in virus replication and the evasion of the host immune response. Infectious bronchitis virus (IBV) encodes a proteolytically defective remnant of PL1pro and an active PL2pro. However, the function of PL1pro in IBV remains largely unknown. This study aims to explore the effect of PL1pro on virus replication and underlying mechanisms. Results: The recombinant viruses rIBV-ΔPL1pro and rIBV-ΔPL1pro-N were obtained using reverse genetic techniques through the deletion of the IBV PL1pro domain and the N-terminal conserved sequence of PL1pro (PL1pro-N). We observed significantly lower replication of rIBV-ΔPL1pro and rIBV-ΔPL1pro-N than wild-type IBV. Further investigation revealed that the lack of PL1pro-N in IBV decreased virus resistance to interferon (IFN) while also inducing host immune response by enhancing the production of IFN-β and activating the downstream STAT1 signaling pathway of IFNs. In addition, the overexpression of PL1pro-N significantly suppressed type I IFN response by down-regulating the expressions of genes in the IFN pathway. Conclusions: Our data demonstrated that IBV PL1pro plays a crucial role in IBV replication and the suppression of host innate immune responses, suggesting that IBV PL1pro could serve as a promising molecular target for antiviral therapy. [ABSTRACT FROM AUTHOR]

Microbes in nature often live in dense and diverse communities exhibiting a variety of spatial structures. Microbial range expansion is a universal ecological process that enables populations to form spatial patterns. It can be driven by both passive and active processes, for example, mechanical forces from cell growth and bacterial motility. In this review, we provide a taste of recent creative and sophisticated efforts being made to address basic questions in spatial ecology and pattern formation during range expansion. We especially highlight the role of motility to shape community structures, and discuss the research challenges and future directions. [ABSTRACT FROM AUTHOR]

• A novel inpainting method is proposed for the generation of fine grained details. • A novel loss for preserving edges can ease the difficult of edge generation. • The combination of the proposed constraints can make inpainting model generate sharp and detailed results. With the emergence of Generative Adversarial Networks, great progress has been made in image inpainting. However, most existing methods can produce plausible results, but fail to generate finer textures and structures. This is mainly due to the fact that (1) the generation of finer content in the masked region of an image is not constrained enough during network training, and (2) many different alternative pixels are exist to fill in the masked regions, making it very difficult for the inpainting network to generate reasonable sharp edges. To address these issues, we propose a Scale Enhanced GAN (SE-GAN) model which combines the constraints of large- and small-scale receptive fields of our tailor-made discriminators to achieve fine-grained constraint on image details, a novel edge loss to further ensure the sharpness of the generated image. Experiments on multiple datasets including faces(CelebA-HQ), textures(DTD), buildings(Facade) and natural images(ImageNet, Places2) show that our approach can generate higher quality inpainting results with more details than previous methods. [ABSTRACT FROM AUTHOR]

Dyes pose significant risks for aquatic environments and biological health in general owing to their non-biodegradable nature, carcinogenicity, and toxicity. The effective treatment of dye wastewater has become an important research topic. In this study, acrylic polymers (AP) loaded with magnetic iron manganese oxides (MIMO) (AP/MIMO) were prepared and used for the first time for the adsorption of methylene blue (MB). Carbon in AP/MIMO exists predominantly in the C–H and C–C forms, with its content reaching 50.7%. Oxygen and nitrogen in AP/MIMO exist mainly in the –C O- and -N-C forms, with contents of up to 41.5% and 73.3%, respectively. MB removal by AP/MIMO was consistent with the pseudo-second-order kinetic model (R2 = 0.99), equilibrium was achieved within 20 min, and the highest MB capacity of 2611.23 mg g−1 was predicted by the Langmuir isotherm model (R2 = 0.91–0.94). AP/MIMO exhibited excellent MB adsorption performance in the pH range of 4–10, with a removal efficiency higher than 99.0% (MB = 100 mL 1000 mg L−1; AP/MIMO = 50 mg). Thermodynamic indicators, such as positive entropy (Δ S 0 ; 98.30 J⋅mol−1⋅K−1), negative Gibbs free energy (Δ G 0 ; −29.40, −28.50, and −27.50 KJ⋅mol−1), and positive enthalpy (Δ H 0 ; 2.30 KJ⋅mol−1), demonstrated that MB removal by AP/MIMO was autonomous, favorable, and endothermic. In addition, the integration of experimental results and theoretical calculations verified that electrostatic interactions were the primary mechanism for MB adsorption at carboxyl sites on AP/MIMO. The total interaction energy between AP and MB was −310.43 kJ⋅mol−1, and the electrostatic effect had a decisive contribution to the MB adsorption, with a value of up to −341.06 kJ⋅mol−1. AP and MB were most likely bound by –COO and S atoms. Overall, AP/MIMO exhibits high adsorption capacity and shows potential as a high-performance magnetic polymer for MB removal. [Display omitted] • AP/MIMO were prepared by bulk polymerization combined with chemical precipitation method. • MB removal by AP/MIMO was autonomous, favorable, and endothermic. • The highest MB capacity of 2611.23 mg g−1 was predicted by the Langmuir model. • The electrostatic interaction energy was −341.06 kJ mol−1. • AP and MB were most likely bound by –COO and S atoms. [ABSTRACT FROM AUTHOR]

The switched shunting resistor circuit is a classical and practical passive equalization scheme that nevertheless suffers from the voltage imbalance after the charging process. It results from the discrepancy of the shunting resistor and the physical characteristics of supercapacitor (SC) cells. In this paper, the distributed voltage equalization control strategies with the proportional-integral (PI) observer for the switched shunting resistor circuit are analyzed and compared. First, the charging and equalizing system is modeled as the switched system and the system observability is proved. The decentralized control and consensus control are analyzed and their shortages are presented. Then, the leader–follower cooperative control is proposed to improve the charging time while keeping the equalization performance. Meanwhile, the PI observer is designed to estimate the internal capacitance voltage of an SC precisely to avoid the effect of equal series resistance. The control convergence is proved by using the Lyapunov stability and switched system theory. The experimental results show that the proposed method has much superiority and wide applicability than the existing methods for equalizing voltages of SCs. [ABSTRACT FROM AUTHOR]

The example-based super-resolution (SR) methods can be mainly categorized into two classes: the internal SR methods and the external SR methods. The internal SR methods only use samples obtained from a single low resolution (LR) input, while the external SR methods only utilize an external database. The complementary information included in internal and external samples is rarely taken into account. This paper presents a novel extraction and learning method about the complementary information between external samples and internal samples, and then the learned complementary information is used to improve the single image SR performance. Firstly, we construct an initial high resolution (HR) image via sparse coding over the learned dictionary pair with external samples. Secondly, we propose an adaptive sample selection scheme (ASSS) to acquire the mixed samples. Thirdly, we present a novel adaptive mixed samples ridge regression (AMSRR) model to effectively learn the complementary information included in the mixed samples. Finally, we optimize the SR image. Extensive experimental results validate the effectiveness of the proposed algorithm comparing with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Currently, in most traditional VSLAM (visual SLAM) systems, static assumptions result in a low accuracy in dynamic environments, or result in a new and higher level of accuracy but at the cost of sacrificing the real–time property. In highly dynamic scenes, balancing a high accuracy and a low computational cost has become a pivotal requirement for VSLAM systems. This paper proposes a new VSLAM system, balancing the competitive demands between positioning accuracy and computational complexity and thereby further improving the overall system properties. From the perspective of accuracy, the system applies an improved lightweight target detection network to quickly detect dynamic feature points while extracting feature points at the front end of the system, and only feature points of static targets are applied for frame matching. Meanwhile, the attention mechanism is integrated into the target detection network to continuously and accurately capture dynamic factors to cope with more complex dynamic environments. From the perspective of computational expense, the lightweight network Ghostnet module is applied as the backbone network of the target detection network YOLOv5s, significantly reducing the number of model parameters and improving the overall inference speed of the algorithm. Experimental results on the TUM dynamic dataset indicate that in contrast with the ORB–SLAM3 system, the pose estimation accuracy of the system improved by 84.04%. In contrast with dynamic SLAM systems such as DS–SLAM and DVO SLAM, the system has a significantly improved positioning accuracy. In contrast with other VSLAM algorithms based on deep learning, the system has superior real–time properties while maintaining a similar accuracy index. [ABSTRACT FROM AUTHOR]

Since the single image super-resolution (SR) is an extremely ill posed problem, we introduce a novel auto-regressive moving average (ARMA) model-based regularization term into the spare representation-based framework to deal with it in this paper. In our framework, we have a dual regularization. Firstly, we use the ARMA models trained from external samples to establish a regularization term. ARMA model-based regularization serves as a local constraint. Secondly, we introduce the nonlocal (NL) self-similarity as another regularization term. Both the local and the NL regularizations are unified into the sparse representation-based framework. Finally, extensive experiments verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

With the acceleration of urbanization in the world, urban traffic congestion has become an urgent challenge in most cities. Adaptive traffic signal control is the most approved control method to solve the problem, and accurate real-time traffic information is critical to this solution. This paper presents distributed cooperative reinforcement learning-based traffic control that integrates V2X networks’ dynamic clustering algorithm. To obtain traffic flow information accurately and instantaneously, it is important to improve the cluster stability in V2X networks. A dynamic clustering algorithm is proposed based on the enhanced affinity propagation. The proposed clustering algorithm introduces the initial cluster partition to maintain a proper cluster size and adds the lane and destination factors to improve the cluster's stability. The algorithm can provide efficient and accurate traffic state information to traffic signal controls. By integrating the clustering algorithm, a cooperative reinforcement learning control scheme is proposed to balance the traffic load. To address the tough dimensionality curse of reinforcement learning, a distributed mechanism for intersection cooperation is introduced, and a fast gradient-descent function approximation method is proposed to improve the controls’ real-time performance. The proposed intelligent traffic control scheme that integrates the stable clustering algorithm can effectively improve the traffic throughput, reduce the average waiting time, and avoid congestion. Numerical simulations on real scenarios validate the performance of the proposed approach. [ABSTRACT FROM PUBLISHER]

Using the sensor nodes to achieve target tracking is a challenging problem in resource-limited wireless sensor networks. The tracking nodes are usually required to consume much energy to improve the tracking performance. In this paper, an energy-efficient node scheduling method is proposed to minimize energy consumption while ensuring the tracking accuracy. Firstly, the Kalman-consensus filter is constructed to improve the tracking accuracy and predict the target position. Based on the predicted position, an adaptive node scheduling mechanism is utilized to adjust the sample interval and the number of active nodes dynamically. Rather than using traditional search algorithm, the scheduling problem is decomposed to decouple the sample interval and number of nodes. And the node index is mapped into real domain to get closed-form solution to decide the active nodes. Thus, the NP-complete nature is avoided in the proposed method. The proposed scheduling method can keep the tracking accuracy while minimizing energy consumption. Simulation results validate its effective performance for target tracking in wireless sensor networks. [ABSTRACT FROM AUTHOR]

Cognitive radio has emerged as a promising solution to address the problems posed by coming spectrum scarcity for the inherently resource-constrained sensor networks. Reliability and energy consumption are key objectives for spectrum sensing in cognitive sensor networks. In this paper, a fast differential evolution algorithm is proposed to optimize the energy consumption and spectrum sensing performance jointly. By constructing a comprehensive performance metric, the joint optimization is transferred to a multiobjective optimal problem, in which the sleeping schedule and censoring mechanism are taken into consideration. The main objective of the proposed algorithm is to minimize the network energy consumption subjected to constraints on the detection performance by optimally deriving the censoring and sleeping probabilities. To accelerate the convergence speed and maintain the diversity, the algorithm utilizes the advantages of opposite-based learning for generating the initial population and a tournament scheme in mutation step. In the crossover step, a control parameters dynamic adjustment scheme is applied to make a trade-off between exploration and exploitation. Finally, a selection mechanism is introduced for generating a well-distributed Pareto optimal front. The simulation results show that the proposed algorithm can reduce the average energy consumption of cognitive sensor node, while improving the global probability of spectrum sensing. [ABSTRACT FROM AUTHOR]

Extraction of lane markings is still a challenge due to the poor quality of lane markings and interferences from external circumstances. In this paper, we utilize line segments as low-level features to detect lane markings on structured road scenes. Our novel algorithm can be highlighted in four items as follows. Firstly, a road surface region is reasonably located using adaptive segmentation method, and then most of the interferences from the external circumstances are eliminated by Laplacian filter. Secondly, the line segments detected by the line segment detector are applied to represent the structural information of lanes. Thirdly, non-lane candidate markings are removed through orientation and vanishing point constraints. Finally, the lanes are accurately extracted from the remaining candidate lane marking. Experimental results on complex structured road scenarios in urban streets are shown and the effectiveness and robustness of our novel algorithm are underpinned by the experimental results. [ABSTRACT FROM PUBLISHER]

Abstract: Due to the fact that natural images are inherently sparse in some domains, sparse representation has led to interesting results in image acquiring, representing, and compressing high-dimensional signals. Based on the experiences and learned priors in sparse domain from low and high resolution images, the typical ill-posed inverse problem of image super-resolution is effectively solved by the l 1-norm optimization techniques. However, how to reasonably combine the sparse representation theory and the feature of natural images is still a critical issue for performances improvements of image super-resolution algorithms. Considering the fact that the different morphologic features in natural images should be regularized by different constrains in sparse domain, in this paper we present a novel sparse representation algorithm with reasonable morphologic regularization for single image super-resolution. Extensive experimental results on various natural images validate the superiority of the proposed algorithm in terms of qualitative and quantitative performance. [Copyright &y& Elsevier]

Abstract: A novel multi-morphology image super-resolution algorithm via sparse representation is proposed in this paper. Firstly, the observed low resolution image is decomposed into a linear combination of morphological components by morphological component analysis. Secondly, for effective representation of different components, each dictionary pair is learned from the low resolution component and corresponding high resolution component training sets. Then, each high resolution morphological component is generated by combine the sparse coefficients of low resolution morphological component with the corresponding high resolution component dictionary. Finally, fusing all high resolution morphological components yields the final high resolution image. Experimental results on various natural and intelligent transport systems surveillance images demonstrate the superiority of the proposed method in terms of qualitative and quantitative performance. [Copyright &y& Elsevier]

It is well known that dimethyl sulphoxide (DMSO) increases membrane permeability, which makes it widely used as a vehicle to facilitate drug delivery across biological membranes. However, the mechanism of how DMSO increases membrane permeability has not been well understood. Recently, molecular dynamics simulations have demonstrated that DMSO can induce water pores in biological membranes, but no direct experimental evidence is so far available to prove the simulation result. Using FluxOR Ti+ influx assay and intracellular Ca2+ imaging technique, we studied the effect of DMSO on Ti+ and Ca2+ permeation across cell membranes. Upon application of DMSO on CHO-K1 cell line, Ti+ influx was transiently increased in a dose-dependent manner. The increase in Ti+ permeability induced by DMSO was not changed in the presence of blockers for K+ channel and Na+-K+ ATPase, suggesting that Ti+ permeates through transient water pores induced by DMSO to enter into the cell. In addition, Ca2+ permeability was significantly increased upon application of DMSO, indicating that the transient water pores induced by DMSO were non-selective pores. Furthermore, similar results could be obtained from RAW264.7 macrophage cell line. Therefore, this study provided experimental evidence to support the prediction that DMSO can induce transient water pores in cell membranes, which in turn facilitates the transport of active substances across membranes. [ABSTRACT FROM AUTHOR]

Abstract: Paraffin oil-in-water nano-emulsions stabilized by Tween 80/Span 80 were prepared using the emulsion inversion point method at different emulsification temperatures. Nano-emulsions with droplet size below 200 nm were formed above a critical surfactant-to-oil ratio of 0.20 at 50 °C. The main destabilization mechanism of the systems was found to be Ostwald ripening. An interesting phenomenon was that the Ostwald ripening rate declined as the surfactant concentration rose. Furthermore, flocculation was also found to contribute to the instability of the nano-emulsions, especially for those with low surfactant concentrations. Study on the electrophoretic properties of emulsion droplets revealed a negative value of the zeta potential, which was strongly dependent on the pH of the systems. [Copyright &y& Elsevier]

This paper develops a model predictive multi-objective control framework based on an adaptive cruise control (ACC) system to solve the energy allocation and battery state of charge (SOC) maintenance problems of hybrid electric vehicles in the car-following scenario. The proposed control framework is composed of a car-following layer and an energy allocation layer. In the car-following layer, a multi-objective problem is solved to maintain safety and comfort, and the generated speed sequence in the prediction time domain is put forward to the energy allocation layer. In the energy allocation layer, an adaptive equivalent-factor-based consumption minimization strategy with the predicted velocity sequences is adopted to improve the engine efficiency and fuel economy. The equivalent factor reflects the extent of SOC variation, which is used to maintain the battery SOC level when optimizing the energy. The proposed controller is evaluated in the New York City Cycle (NYCC) driving cycle and the Urban Dynamometer Driving Schedule (UDDS) driving cycle, and the comparison results demonstrate the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

Driving style recognition plays a key role in ensuring driving safety and improving vehicle traffic efficiency. With the development of sensing technology, data-driven methods are more widely uesd to recognize driving style. However, adequately labeling data is difficult for supervised learning methods, while the classification accuracy is not sufficiently approved for unsupervised learning methods. This paper proposes a new driving style recognition method based on Tri-CatBoost, which takes CatBoost as base classifier and effectively utilizes the semi-supervised learning mechanism to reduce the dependency on data labels and improve the recognition ability. First, statistical features were extracted from the velocity, acceleration and jerk signals to fully characterize the driving style. The kernel principal component analysis was used to perform nonlinear feature dimension reduction to eliminate feature coupling. CatBoost is an ensemble of symmetric decision trees whose symmetry structure endows it fewer parameters, faster training and testing, and a higher accuracy. Then, a Tri-Training strategy is employed to integrate the base CatBoost classifiers and fully exploit the unlabeled data to generate pseudo-labels, by which the base CatBoost classifiers are optimized. To verify the effectiveness of the proposed method, a large number of experiments are performed on the UAH DriveSet. When the labeling ratio is 50%, the macro precision of Tri-CatBoost is 0.721, which is 15.7% higher than that of unsupervised K-means, 1.6% higher than that of supervised GBDT, 3.7% higher than that of Self-Training, 0.7% higher than that of Co-training, 1.5% higher than that of random forest, 6.7% higher than that of decision tree, and 4.0% higher than that of multilayer perceptron. The macro recall of Tri-CatBoost is 0.744, which is also higher than other methods. The experimental results fully demonstrate the superiority of this work in reducing label dependency and improving recognition performance, which indicates that the proposed method has broad application prospects. [ABSTRACT FROM AUTHOR]

This paper investigates a dual exclusive channel model in which each manufacturer distributes its goods through a single exclusive retailer, but two goods are substitute. The decision rule between two channels is Nash game in Case 1, while it is Stackelberg game in Case 2. From manufacturer Stackelberg (MS), retailer Stackelberg (RS), and Nash game (VN) theoretic perspectives, nine game models are developed to examine the effect of product substitutability and relative channel status on pricing decisions at both horizontal competition and vertical competition levels. The analysis suggests that the type of price leadership scenarios, the level of product substitutability, and the relative channel status play a significant role in decision making. For instance, in case 1, the symmetric leadership (two manufacturers or two retailers are leaders) is always the dominant strategy and equilibrium for either two manufacturers or two retailers regardless of product substitutability and relative channel status. Nevertheless, the asymmetric leadership may lead channel members to encounter a prisoner's dilemma if the relative channel status is small. By contrast, in Case 2, the symmetric leadership is not the unique dominant strategy for either two manufacturers or two retailers. In contrast to many earlier results, we also show that whether the first-mover and the late-mover advantages exist, depending on the level of related channel status. [ABSTRACT FROM AUTHOR]

The high-speed train operation process is highly nonlinear and has multiple constraints and objectives, which lead to a requirement for the automatic train operation (ATO) system. In this paper, a hybrid model predictive control (MPC) framework is proposed for the controller design of the ATO system. Firstly, a piecewise linear system with state and input constraints is constructed through piecewise linearization of the high-speed train’s nonlinear dynamics. Secondly, the piecewise linear system is transformed into a mixed logical dynamical (MLD) system by introducing the auxiliary binary variables. For the transformed MLD system, a hybrid MPC controller is designed to realize the precise control under hard constraints. To reduce the online computation complexity, the explicit control law is computed offline by employing the mixed-integer linear programming (MILP) technique. Simulation results validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Summary: This article deals with state estimation of complex nonlinear discrete fractional‐order systems with unknown noise statistics by means of an adaptive fractional‐order Unscented Kalman filter (AFUKF). Firstly, in order to alleviate the communication burden of fractional‐order Unscented Kalman filter, short‐term memory effect is utilized to decide an appropriate memory length. Then aiming at the problem of filtering divergence and accuracy degradation caused by unknown statistical characteristics of noise, based on the maximum a posterior (MAP) principle, a noise statistical estimator is introduced to estimate and correct the statistical characteristics of noise in real‐time. Finally, the unbiasedness of the proposed algorithm is analyzed to verify that the estimated mean and covariance of noise are unbiased. The effectiveness and accuracy of AFUKF are demonstrated via simulation experiments. [ABSTRACT FROM AUTHOR]

This paper focuses on the periodic event-triggered consensus problem for multiagent systems under undirected graph. In the periodic event-triggered control, the event condition is tested only periodically to decide when to exert the control behaviors and when to broadcast the sampling data. Two sets of event-triggered strategies combining with sampled-data control are designed based on an exponential decay function and a quadratic Lyapunov function, respectively. Some useful theorems for these two periodic event-triggered strategies about the choice of period are derived to guarantee the asymptotical stability of the closed-loop multiagent systems. In addition, this paper gives a sufficient condition about the bounds of communication delay for the multiagent systems. It is rigorously proved that the overall system will achieve the consensus under the proposed strategy if the period and time delay satisfy the theorems. Finally, we extend these solutions to general linear dynamical systems. Simulation results are presented to show the effectiveness. [ABSTRACT FROM AUTHOR]

Battery energy management systems have been studied in control communities for many years. This paper proposes a new perspective by integrating control and scheduling for battery-powered autonomous systems. This is motivated by the observations that battery closed-loop control can significantly improve the DC-bus stability but reduce the battery durability, while load scheduling can considerably improve the battery durability by smoothing the load power. In view of the above findings, we propose a proactive energy management strategy for the battery energy management system to improve both the DC-bus stability and battery durability. We first analyze the schedulability of load tasks to check if they are schedulable. Then, the power of schedulable loads is scheduled with an active load scheduling algorithm to smooth the fluctuation of battery current and extend the battery lifetime. Thereafter, the scheduled load power is integrated with a feedforward control to restrain DC-bus voltage fluctuation. In addition to the classical sporadic/periodic load tasks model, we propose a new aperiodic load task model to characterize the load power triggered by events in practical applications. An experimental platform is built to verify the effectiveness of the proposed proactive energy management method. Experimental results show that the proposed proactive energy management method can suppress the 15.71% DC-bus voltage fluctuation and reduce the 8.93% battery current fluctuation, extending the battery lifetime by 7.57% compared to existing energy management strategies. • A proactive energy management strategy integrated scheduling and control is proposed. • An aperiodic load model is proposed for characterizing events-triggered loads. • An active scheduling method based fixed-priority is proposed to guarantee real-time. • Li-ion battery lifetime and DC-bus voltage regulation are improved. • An experiment platform is built to verify the performance of the proposed method. [ABSTRACT FROM AUTHOR]

Zinc oxide nanoparticles (Nano-ZnO) are widely used in sunscreens, clothes, medicine and electronic devices. However, the potential risks of human exposure and the potential for adverse health impacts are not well understood. Previous studies have demonstrated that exposure to Nano-ZnO caused liver damage and hepatocyte apoptosis through oxidative stress, but the molecular mechanisms that are involved in Nano-ZnO-induced hepatotoxicity are still unclear. Endoplasmic reticulum (ER) is sensitive to oxidative stress, and also plays a crucial role in oxidative stress-induced damage. Previous studies showed that ER stress was involved in many chemical-induced liver injuries. We hypothesized that exposure to Nano-ZnO caused oxidative stress and ER stress that were involved in Nano-ZnO-induced liver injury. To test our hypothesis, mice were gavaged with 200 mg/kg or 400 mg/kg of Nano-ZnO once a day for a period of 90 days, and blood and liver tissues were obtained for study. Our results showed that exposure to Nano-ZnO caused liver injury that was reflected by focal hepatocellular necrosis, congestive dilation of central veins, and significantly increased alanine transaminase (ALT) and aspartate transaminase (AST) levels. Exposure to Nano-ZnO also caused depletion of glutathione (GSH) in the liver tissues. In addition, our electron microscope results showed that ER swelling and ribosomal degranulation were observed in the liver tissues from mice treated with Nano-ZnO. The mRNA expression levels of ER stress-associated genes (grp78, grp94, pdi-3, xbp-1) were also up-regulated in Nano-ZnO-treated mice. Nano-ZnO caused increased phosphorylation of RNA-dependent protein kinase-like ER kinase (PERK) and eukaryotic initiation factor 2α (eIF2α). Finally, we found that exposure to Nano-ZnO caused increased ER stress-associated apoptotic protein levels, such as caspase-3, caspase-9, caspase-12, phosphorylation of JNK, and CHOP/GADD153, and up-regulation of pro-apoptotic genes (chop and bax). These results suggest that oxidative stress and ER stress-induced apoptosis are involved in Nano-ZnO-induced hepatotoxicity in mice. [ABSTRACT FROM AUTHOR]

It is a challenge to maintain a safe and efficient tracking for multiple high-speed trains under the moving block operational mode. In this paper, a novel cooperative tracking control based on a consensus algorithm and artificial potential field theory is proposed to realize the train tracking within a distance range. A tunable artificial potential function is first designed to dynamically adjust the distance between adjacent high-speed trains with real-time train states. By regulating the parameters of the artificial potential function, the safety distance can be adjusted according to the required tolerance deviation of the actual distance. Under the proposed strategy, each high-speed train operates with the desired speed and tracks the preceding one with an adjustable distance range. Numerical train operational cases are investigated to illustrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

The track settlement has a great influence on the safe operation of high-speed trains. The existing track settlement measurement approach requires sophisticated or expensive equipments, and the real-time performance is limited. To address the issue, an ultra-high resolution track settlement detection method is proposed by using millimeter wave radar based on frequency modulated continuous wave (FMCW). Firstly, by constructing the RCS statistical feature data set of multiple objects in the track settlement measurement environment, a directed acyclic graph-support vector machine (DAG-SVM) based method is designed to solve the problem of track recognition in multi-object scenes. Then, the adaptive chirp-z-transform (ACZT) algorithm is used to estimate the distance between the radar and the track surface, which realizes automatic real-time track settlement detection. An experimental platform has been constructed to verify the effectiveness of the proposed method. The experimental results show that the accuracy of track classification and identification is at least 95%, and the accuracy of track settlement measurement exceeds 0.5 mm, which completely meets the accuracy requirements of the railway system. [ABSTRACT FROM AUTHOR]

Pneumatic brake systems are crucial for the operation of trains. However, due to switching characteristics of on/off solenoid valves, the precise pressure control and low switching activities of valves are difficult to guarantee simultaneously during braking. To address the issue, a hybrid model predictive control (MPC) method is proposed for implementing the multi‐objective optimisation in this paper, i.e. the precise pressure tracking and the valve switching reduction. In order to model the hybrid behaviour caused by continuous dynamics of the pressure and discrete features of solenoid valves, the mixed logic dynamical system theory is applied. Then, the braking control problem is recast as a hybrid MPC problem, which jointly optimises the pressure tracking precision and the valve switching. A mixed integer linear program is formulated to solve the MPC problem in an efficient way. Both simulation and experiment results show that the accurate pressure control and low switching activities of valves can be achieved by the proposed method when compared with existing methods. [ABSTRACT FROM AUTHOR]

In geo-distributed cloud storage systems, data replication has been widely used to serve the ever more users around the world for high data reliability and availability. How to optimize the data replica placement has become one of the fundamental problems to reduce the inter-node traffic and the system overhead of accessing associated data items. In the big data era, traditional solutions may face the challenges of long running time and large overheads to handle the increasing scale of data items with time-varying user requests. Therefore, novel offline community discovery and online community adjustment schemes are proposed to solve the replica placement problem in a scalable and adaptive way. The offline scheme can find a replica placement solution based on the average read/write rates for a certain period of time. The scalability can be achieved as 1) the computation complexity is linear to the amount of data items and 2) the data-node communities can evolve in parallel for a distributed replica placement. Furthermore, the online scheme is adaptive to handle the bursty data requests, without the need to completely override the existing replica placement. Driven by real-world data traces, extensive performance evaluations demonstrate the effectiveness of our design to handle large-scale datasets. [ABSTRACT FROM AUTHOR]

This paper proposed a cubic spline interpolation-based path planning method to maintain the smoothness of moving the robot's path. Several path nodes were selected as control points for cubic spline interpolation. A full path was formed by interpolating on the path of the starting point, control points, and target point. In this paper, a novel chaotic adaptive particle swarm optimization (CAPSO) algorithm has been proposed to optimize the control points in cubic spline interpolation. In order to improve the global search ability of the algorithm, the position updating equation of the particle swarm optimization (PSO) is modified by the beetle foraging strategy. Then, the trigonometric function is adopted for the adaptive adjustment of the control parameters for CAPSO to weigh global and local search capabilities. At the beginning of the algorithm, particles can explore better regions in the global scope with a larger speed step to improve the searchability of the algorithm. At the later stage of the search, particles do fine search around the extremum points to accelerate the convergence speed of the algorithm. The chaotic map is also used to replace the random parameter of the PSO to improve the diversity of particle swarm and maintain the original random characteristics. Since all chaotic maps are different, the performance of six benchmark functions was tested to choose the most suitable one. The CAPSO algorithm was tested for different number of control points and various obstacles. The simulation results verified the effectiveness of the proposed algorithm compared with other algorithms. And experiments proved the feasibility of the proposed model in different dynamic environments. [ABSTRACT FROM AUTHOR]

After the successful construction and operation experience of the 10 MW high-temperature gas-cooled reactor (HTR-10), a high-temperature gas-cooled pebble-bed modular (HTR-PM) demonstration plant is under construction in Shidao Bay, Rongcheng City, Shandong province, China. An online gross γ monitoring instrument has been designed and placed at the exit of the helium purification system (HPS) of HTR-PM and is used to detect the activity concentration in the primary circuit after purification. The source terms in the primary loop of HTR-PM and the helium purification process were described. The detailed configuration of the gross γ monitoring instrument was presented in detail. The Monte Carlo method was used to simulate the detection efficiency of the monitoring system. Since the actual source terms in the primary loop of HTR-PM may be different than the current design values, a sensitivity analysis of the detection efficiency was implemented based on different relative proportions of the nuclides. The accuracy and resolution of the NaI(Tl) detector were discussed as well. [ABSTRACT FROM AUTHOR]

Gadolinium is a soluble neutron poison for ensuring criticality safety of nuclear facility. A neutron induced gamma activation device was developed for the on-line measurement of gadolinium. The experimental device consisted of an 241Am–Be neutron source, six 3He detectors and a liquid scintillation detector. The size of sample container was optimized by using Monte Carlo simulations. Aqueous sample containing gadolinium nitrate were conducted with the device to obtain the calibration curve, and neutron self-shielding effect was also studied to correct the non-linear response. The results showed the minimum detectable concentration (MDC) of gadolinium was 0.426 mg/L. Two test samples were conducted to evaluate the performance of the device. The results demonstrated that the discrepancies were within 10%, which indicate the developed system can be successfully used for on-line monitoring of gadolinium. • Neutron induced gamma activation technique was applied for the on-line measurement of gadolinium. • An 241Am–Be neutron source, a liquid scintillation detector and six 3He detectors were used to develop the device. • The neutron self-shielding effect was corrected by using Monte Carlo simulations and experimental data. [ABSTRACT FROM AUTHOR]

Encouraged by our previous findings and in continuation of our ongoing study project in designing and synthesis of novel Nur77-targeting anti-cancer agents, a series of 5-((4-(pyridin-3-yl)pyrimidin-2-yl)amino)-1H-indole-2-carboxamide derivatives were designed, synthesized and biologically evaluated as potent Nur77 modulators. Among synthesized compounds, 8b maintained good potency against different liver cancer cell lines and other types of cancer cell lines while exhibiting lower toxicity than the positive compound celastrol. Moreover, 8b displayed excellent Nur77-binding activity, superior to the lead compound 10g and comparable to the reference compound celastrol. The cytotoxic action of 8b towards cancer cells was associated with its induction of Nur77-mitochondrial targeting and Nur77-dependent apoptosis. Notably, 8b has good in vivo safety and anti-hepatocellular carcinoma (HCC) activity. Altogether, this study reveals that 8b is a novel Nur77 modulator with great promise for further research. [Display omitted] • New 5-((4-(pyridin-3-yl)pyrimidin-2-yl)amino)-1H-indole-2-carboxamide derivatives are synthesized. • 8b is a good binder of Nur77 (K D = 354 nM) with broad-spectrum antitumor activity. • 8b induces Nur77-targeting mitochondria and Nur77-dependent apoptosis. • 8b displays a better safety than reference compound celastrol in vitro and in vivo. • 8b effectively inhibits tumor growth in a mice hepatoma HepG2 xenograft model. [ABSTRACT FROM AUTHOR]

[Display omitted] • 35 New Ph-C-DIM+Cl- derivatives were designed and synthesized, and A11, B5 and B15 were identified as new Nur77 modulators. • A11, B5 and B15 showed excellent pro-apoptosis activity in colon cancer cell lines HCT116 and SW620, while showed non-significant cytotoxicity against normal colon cell line NCM460. • A11, B5 and B15 bind Nur77 with high affinity, with a K d of 34 nM, 19 nM and 16 nM, respectively. • A11, B5 and B15 induced apoptosis of cancer cells by activating the Nur77/Bcl-2 apoptotic pathway. Nur77, an orphan nuclear receptor, is implicated in regulating diverse cellular biological processes including apoptosis and inflammation. We previously identified BI1071 (DIM-C-pPhCF 3 +MeSO 3 -), an oxidized methanesulfonate salt of (4-CF 3 -Ph-C-DIM), was a direct ligand of Nur77, which could activate the Nur77-Bcl-2 apoptotic pathway. To obtain more effective compounds targeting the Nur77-mediated apoptotic pathway, we designed and synthesized a series of BI1071 analogs by introducing various substituent groups in the indolyl-rings of BI1071. Structure-activity relationship study identified A11, B5 and B15 as improved analogs with stronger binding affinity to Nur77 and enhanced apoptotic activity compared to BI1071. Nur77-binding studies demonstrated that A11, B5 and B15 bind to Nur77 with a K d of 34 nM, 19 nM and 16 nM, respectively. Furthermore, mechanism studies showed that A11, B5 and B15 induced apoptosis through utilizing the Nur77-Bcl-2 pathway. [ABSTRACT FROM AUTHOR]

Acute lung injury (ALI) is characterized by tissue damage that leads to pulmonary epithelial membrane dysfunction and macrophage activation. Currently however, the exact mechanism by which the initial mediators of mouse lung epithelial (MLE-12) cells induce inflammation remines unclear. We constructed co-culture systems of MLE-12 cells with mouse macrophage cells RAW246.7 which were realized by the supernatant and Transwell chamber. In previous study, we successfully constructed an influenza A virus-induced MLE-12 cells model. Extracellular Vesicles (EVs) from cells supernatant were isolated by differential ultracentrifugation and confirmed by transmission electron microscopy. High-throughput sequencing results showed that MLE-12 cells stimulated by influenza A virus had higher level of miR-1249–5p. The results were validated by RT-qPCR analysis. The research aimed to investigate the roles and mechanisms of miR-1249–5p in ALI. RAW246.7 cells were transfected with miR-1249–5p mimic/inhibitor. The concentrations of TNF-α, IL-6 were determined by ELISA and the uptake of EVs was monitored by confocal laser scanning microscope. Western blotting detected changes in the SLC4A1 and NF-κB signaling pathway. The results indicated that miR-1249–5p played an important role in ALI, and further investigation of its target gene SLC4A1 and NF-κB signaling pathway provides ideas for new therapeutic targets and strategies for ALI. [ABSTRACT FROM AUTHOR]

Alkaline flooding is a promising enhanced heavy oil recovery method. In this paper, this enhanced heavy oil recovery method is investigated in terms of its tertiary oil recovery potential and its effects on wettability alteration of the porous medium. Core flood tests show that the tertiary oil recovery can be greater than 10% of the initial oil in place (IOIP) by NaOH-only flooding or with NaOH and Na 2 CO 3 mixed flooding systems. Micromodel experiments show that the wettability of the pore walls in the micromodel had been changed from water-wet to oil-wet, which can block water channeling and thereby improve the sweep efficiency. In order to prove the effect of wettability alteration on the enhanced heavy oil recovery, the contact angles of the heavy oil in alkaline solution on a quartz substrate have been measured. The results show that the contact angle can change from 24°, without alkali, to about 160°, under the action of NaOH or Na 2 CO 3 . The wettability alteration from water-wet to oil-wet can make the heavy oil adsorb at the rock surface and block the water channel. Therefore, the wettability alteration is one of the important mechanisms for enhanced heavy oil recovery by alkaline flooding. [ABSTRACT FROM AUTHOR]

As an important part of intelligent transportation systems, path planning algorithms have been extensively studied in the literature. Most of existing studies are focused on the global optimization of paths to find the optimal path between Origin-Destination (OD) pairs. However, in urban road networks, the optimal path may not be always available when some unknown emergent events occur on the path. Thus a more practical method is to calculate several suboptimal paths instead of finding only one optimal path. In this paper, a cooperative Q-learning path planning algorithm is proposed to seek a suboptimal multipath set for OD pairs in urban road networks. The road model is abstracted to the form that Q-learning can be applied firstly. Then the gray prediction algorithm is combined into Q-learning to find the suboptimal paths with reliable constraints. Simulation results are provided to show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Cognitive spectrum management can improve the utilization efficiency of spectrum while increasing the energy consumption of sensor network nodes. Hence, how to balance the energy consumption and spectrum efficiency has become a critical challenge in the resource-constrained cognitive radio sensor networks. In this paper, by analyzing the channel characteristics and the energy efficiency of networks, a joint channel selection and power control spectrum decision algorithm based on distributed Q learning is proposed. To evaluate the performance of the proposed framework, an optimal Q value subject to communication efficiency index is formulated. Then, the learning strategy selection scheme is designed to solve the optimization problem by establishing a learning model. In this learning model, each node can get the strategy of other nodes to select the optimal strategy by introducing distributed strategy estimation. The simulation results show that the proposed algorithm has better performance than the existing methods. [ABSTRACT FROM AUTHOR]

For heavy haul trains, it is difficult to get global information due to the limited range of communication. This paper proposed a novel distributed optimal control based on game strategy, in which the global optimization is achieved by equilibrizing subsystems’ performance just utilizing local information. To online solve the game control, an efficient multivariable extremum seeking algorithm was adapted to approximate the partial differential equation deduced by optimal condition. The convergence of the proposed approximate algorithm is proved by constructing a fictitious Lie bracket system using Lyapunov function. Finally, the proposed distributed optimal control is valuated rigorously by case study according to the configuration of Daqin railway in China. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries have been employed extensively in many important applications in the electronics industry. For safety and reliability, it is extremely critical to get an accurate and early-stage remaining useful life prognostic of lithium-ion batteries. However, battery lifetime predictions are challenging due to the nonlinear battery degradation and the operational diversity among batteries. To increase the prediction accuracy, this paper proposes a hybrid framework combining the model-based method and data-driven method. In this framework, after estimating the battery capacity using online operating data, battery lifetime is predicted by the model-based empirical model as well as the data-driven support vector regression model. For the empirical model, its adaptability is improved by updating the parameters dynamically with particle filters. For the support vector regression model, its performance is optimized by an artificial bee colony algorithm. Finally, a fusion method with cascaded structure is proposed to integrate predictions from these two models, which boosts the prediction accuracy by iteratively exerting two concatenated Kalman filters. The generality and effectiveness of the proposed method are verified on battery data sets provided by NASA and our testing bench, respectively. The experimental results illustrate that the proposed method can improve the prediction accuracy of battery remaining lifetime, especially at the early stage. RMSE and MAE of the proposed hybrid framework are within 4 and 3.5. Compared with two existed hybrid methods, RMSE of prediction can be reduced by at least 7.6%. A reduction of not less than 5.9% in MAE of prediction is achieved. • A hybrid prediction method is proposed to predict RUL at the early stage of batteries. • The proposed method integrates the empirical model and data-driven model through cascaded Kalman filters. • Empirical model is updated by particle filters online to adapt to battery dynamically. • Data-driven model is optimized by artificial bee colony algorithm. [ABSTRACT FROM AUTHOR]

Abstract: The heavy-haul trains consist of a large number of locomotives and wagons which usually cover a very long range of train tracks with different slopes and curvatures. As a consequence, the control design for the heavy-haul trains has posed a significant challenge due to the modeling complexity and the complicated traveling conditions, under which the problems of communication delays and input constraints naturally arise. In this paper, a novel decentralized control design is proposed for the heavy-haul trains by explicitly addressing the issues of input constraints and time-varying communication delays. Specifically, the complicated longitudinal dynamical model of the heavy-haul trains is first converted into a double integrator form by introducing a set of state and input transformations. Then, a decentralized cooperative control is proposed to regulate the speeds of individual train units (locomotives and wagons) to the desired profile with the objective of maintaining the minimum in-train force for safety consideration. It is rigorously proved that the closed-loop system is uniformly asymptotically cooperative stable under the least communication conditions among individual train units. Extensive simulations are conducted to validate the performance of the proposed new design. [Copyright &y& Elsevier]

With continuous development in the scales of cities, the role of the metro in urban transportation is becoming more and more important. When running at a high speed, the safety of the train in the tunnel is significantly affected by any foreign objects. To address this problem, we propose a foreign object intrusion detection method based on WiFi technology, which uses radio frequency (RF) signals to sense environmental changes and is suitable for lightless tunnel environments. Firstly, based on extensive experiments, the abnormal phase offset between the RF chains of the WiFi network card and its offset law was observed. Based on this observation, a fast phase calibration method is proposed. This method only needs the azimuth information between the transmitter and the receiver to calibrate the the phase offset rapidly through the compensation of the channel state information (CSI) data. The time complexity of the algorithm is lower than the existing algorithm. Secondly, a method combining the MUSIC algorithm and static clutter suppression is proposed. This method utilizes the incoherence of the dynamic reflection signal to improve the efficiency of foreign object detection and localization in the tunnel with a strong multipath effect. Finally, experiments were conducted using Intel 5300 NIC in the indoor environment that was close to the tunnel environment. The performance of the detection probability and localization accuracy of the proposed method is tested. [ABSTRACT FROM AUTHOR]

A challenging rescue task for the underground disaster is to guide survivors in getting away from the dangerous area quickly. To address the issue, an escape guidance path developing method is proposed based on anisotropic underground wireless sensor networks under the condition of sparse anchor nodes. Firstly, a hybrid channel model was constructed to reflect the relationship between distance and receiving signal strength, which incorporates the underground complex communication characteristics, including the analytical ray wave guide model, the Shadowing effect, the tunnel size, and the penetration effect of obstacles. Secondly, a trustable anchor node selection algorithm with node movement detection is proposed, which solves the problem of high-precision node location in anisotropic networks with sparse anchor nodes after the disaster. Consequently, according to the node location and the obstacles, the optimal guidance path is developed by using the modified minimum spanning tree algorithm. Finally, the simulations in the 3D scene are conducted to verify the performance of the proposed method on the localization accuracy, guidance path effectiveness, and scalability. [ABSTRACT FROM AUTHOR]