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Many scientific applications can be well modeled as large-scale workflows. Cloud computing has become a suitable platform for hosting and executing them. Workflow scheduling has gained much attention in recent years. However, since cloud service providers must offer services for multiple users with various QoS demands, scheduling multiple applications with different QoS requirements is highly challenging. This work proposes a Multi-swarm Co-evolutionary-based Hybrid Optimization (MCHO) algorithm for multiple-workflow scheduling to minimize total makespan and cost while workflow deadline constraints. First, we design a multi-swarm co-evolutionary mechanism where three swarms are adopted to sufficiently search for various elite solutions. Second, to improving global search and convergence performance, we embed local and global guiding information into the updating process of a Particle Swarm Optimizer, and develop a swarm cooperation technique. Third, we propose a Genetic Algorithm-based elite enhancement strategy to exploit more non-dominated individuals, and apply the Metropolis Acceptance rule of Simulated Annealing to update the local guiding solution for each swarm so as to prevent it from being stuck into a local optimum at an early stage. Extensive experimental results demonstrate that MCHO outperforms the state-of-art scheduling algorithms with better distributed non-dominated solutions.

For taking out the adjustment process of sparse auto-encoder for broad learning system, S. Feng et al. proposed fuzzy broad learning system by replacing the feature nodes of broad learning system with Takagi-Sugeno fuzzy systems. In fuzzy broad learning system, artificial parameters selection of ridge regression might result in the decrease in testing accuracy. To overcome this shortcoming of fuzzy broad learning system, this paper builds a novel model of fuzzy broad learning system based on accelerating amount by introducing the accelerating amount into fuzzy broad learning system. The theoretical result on the universal approximation property of fuzzy broad learning system based on accelerating amount is presented. Three experiment studies on the regression problems of UCI, fashion MNIST and medical MNIST data-sets are performed to show the improvement in testing accuracy.To overcome this shortcoming of FBLS, this paper builds a novel model of FBLS based on accelerating amount (AA-FBLS) by introducing the accelerating amount into FBLS. The theoretical result on the universal approximation property of AA-FBLS is presented. Two experiment studies on the approximation of Fashion-MNIST and MedMNIST data-sets are performed to show the improvement in testing accuracy.

Cloud container services as the new norm of cloud resource provisioning are more flexible and widely used in workflows scheduling. However, it is challenging to minimize the cost for workflows scheduling in cloud container services, especially when workflows arrive time and tasks execution time are uncertain. In this article, a stochastic hybrid workflows [i.e., off-line batch workflows (DIWs) and online stream workflows (DSWs)] scheduling problem in cloud container services is solved. A stochastic hybrid workflows scheduling system (SHWS), which consists of a workflow analyzer, workflow classifier, runtime estimator, workflow scheduler, and resource manager, is designed to manage and schedule DIWs and DSWs. Based on the SHWS, a stochastic hybrid workflows scheduling algorithm (SHWSA) is proposed to minimize the cost and improve resource utilization. We conduct the experiments using both synthetic data and real-world data to evaluate the proposed SHWSA approach. The results demonstrate the superiority of SHWSA compared with the state-of-the-art algorithms.

In this paper, the problem of optimal time-varying attitude tracking control for rigid spacecraft with system constraints and unknown additive disturbances is considered. Through the design of a new non-linear tube-based robust model predictive control (TRMPC) algorithm, a dual-loop cascaded tracking control framework is established. The proposed TRMPC algorithm explicitly considers the effect of disturbances and applies tightened system constraints to predict the motion of the nominal system. The obtained optimal control action is then combined with a non-linear feedback law such that the actual system trajectories can always be steered within a tube region centred around the nominal solution. To facilitate the recursive feasibility of the optimization process and guarantee the input-to-state stability of the tracking control process, the terminal controller and the corresponding terminal invariant set are also constructed. The effectiveness of using the proposed dual-loop TRMPC control scheme to track reference attitude trajectories is validated by experimental studies. A number of comparative studies were carried out, and the obtained results reveal that the proposed design is able to achieve more promising constraint handling and attitude tracking performance than that of the other newly developed methods investigated in this research.

This paper investigates the distributed fixed-time attitude coordinated control problem for multiple spacecraft subject to actuator saturation under the directed topology. First, a distributed fixed-time observer is presented for each follower spacecraft to estimate the leader spacecraft’s states. Compared with the commonly used fixed-time observer, the settling time of the proposed fixed-time observer can be easily adjusted by some free design parameters. Next, a distributed fixed-time control scheme is derived by using the estimates of the leader spacecraft's states and the adding a power integrator technique. When considering actuator saturation, an auxiliary system is utilized to compensate the saturation. Further, a rigorous theoretical proof is provided to show that the practical fixed-time stability of the closed-loop system is ensured. Finally, simulation results illustrate the benefits and effectiveness of the developed control scheme.

Path planning and formation control are both challenging and critical issues in robotics, which involve computing an optimal path from the initial position to target while keeping the desired formation. This paper studies the path planning and formation control problem for multiple unmanned aerial vehicles (multi-UAVs) in 3-D constrained space. Considering the local minimum of artificial potential function (APF), an effective improved artificial potential function (IAPF) based path planning approach is proposed for the multi-UAV systems. By introducing a rotating potential field, the UAVs can escape from the common local minimum and oscillations efficiently. Afterwards, by using the leader-follower model, a formation controller based on potential function method is developed to ensure that the follower UAVs keep the desired angles and distances with the leader, and a Lyapunov function is designed to analyze the closed-system stability. Finally, simulation studies under different environmental constraints confirm the efficiency of the proposed approaches in addressing the the path planning and formation control issues in 3-D space.

This article considers a cyber-physical system with multiple remote state estimation subsystems under denial-of-service (DoS) attack. Suppose that there are multiple distributed estimation systems, in each of which, a sensor monitors the system and sends its local estimation to a remote estimator over one of multiple wireless channels. A DoS attacker emits noise power to jam the wireless channels. A scheduler is installed to dispatch each sensor to transmit information through the specific channel to minimize the total estimation error covariance on account of energy-saving. Whereas, the DoS attacker attempts to jam a channel to realize an opposite objective. With considering interference from other individuals, a multisensor multichannel remote state estimation model is constructed. A myopic policy and a long-term online interaction strategy under varying network environment are investigated and compared by solving a two-player zero-sum game and a Markov game, respectively. Simulations are provided to demonstrate our results.

This article proposes two kinds of terminal sliding mode control (TSMC) strategies for implementing the finite-time attitude tracking of spacecraft under environmental disturbances and model uncertainties. First, the integral disturbance observer (IDO) is designed to estimate the disturbances and uncertainties. Second, the IDO-based continuous TSMC (CTSMC) method is developed to achieve active disturbance rejection and better tracking performance. Third, to further mitigate the chattering, a modified TSMC (MTSMC) method is constructed by employing an adaptive method and incorporating a piecewise smooth function. Finally, simulations are performed to show the feasibility of the proposed TSMC laws.

Recently, the correlation filter (CF) has been catching significant attention in visual tracking for its high efficiency in most state-of-the-art algorithms. However, the tracker easily fails when facing the distractions caused by background clutter, occlusion, and other challenging situations. These distractions commonly exist in the visual object tracking of real applications. Keep tracking under these circumstances is the bottleneck in the field. To improve tracking performance under complex interference, a combination of least absolute shrinkage and selection operator (LASSO) regression and contextual information is introduced to the CF framework through the learning stage in this article to ignore these distractions. Moreover, an elastic net regression is proposed to regroup the features, and an adaptive scale method is implemented to deal with the scale changes during tracking. Theoretical analysis and exhaustive experimental analysis show that the proposed peak strength context-aware (PSCA) CF significantly improves the kernelized CF (KCF) and achieves better performance than other state-of-the-art trackers.

We consider a remote state estimation process under an active eavesdropper for cyber-physical system. A smart sensor transmits its local state estimates to a remote estimator over an unreliable network, which is eavesdropped by an adversary. The intelligent adversary can work in passive eavesdropping mode and active jamming mode. An active jamming mode enables the adversary to interfere the data transmission from sensor to estimator, and meanwhile improve the data reception of itself. To protect the transmission data from being wiretapped, the sensor with two antennas injects noise to the eavesdropping link with different power levels. Aiming at minimizing the estimation error covariance and power cost of themselves while maximizing the estimation error covariance of their opponents, a two-player nonzero-sum game is constructed for sensor and active eavesdropper. For an open-loop case, the mixed Nash equilibrium is obtained by solving an one-stage nonzero-sum game. For a long term consideration, a Markov stochastic game is introduced and a Nash Q-learning method is given to find the Nash equilibrium strategies for two players. Numerical results are provided to show the effectiveness of our theoretical conclusions.

In this paper, a predictive cloud control scheme is investigated for a networked multiagent system (NMAS) with quantized signals under denial-of-service (DoS) attacks. An arbitrary region quantizer is applied to take effective values of control signals. A Stackelberg game is adopted to model the DoS attacks which result in attack-induced packet dropouts. Based on cloud computing and predictive control, the predictive cloud control scheme is designed to compensate for network-induced delays and the attack-induced packet dropouts actively. By a “zoom” strategy, sufficient conditions are given to achieve consensus for the NMAS. Effectiveness and computing superiority of the proposed scheme are demonstrated by simulation results.

The paper concentrates on the distributed fusion estimation issue of a bandwidth-constrained multi-sensor nonlinear networked system suffering from jamming attacks. For each communication channel, a stochastic event-triggered transmission scheme is developed to reduce excessive communication between smart sensors and local estimators, and a Stackelberg game framework is established to analyze interactions between the smart jammer and smart sensors. Utilizing a sequential fast covariance intersection fusion rule, a distributed fusion estimation algorithm is designed by fusing local estimations from event-triggered unscented Kalman filter-based local estimators. Then convergence conditions are derived by analyzing behaviors of the fusion estimation error covariance, and the boundedness of communication rate for each communication channel is further discussed. Finally, a comparative simulation is given to testify the validity of the proposed fusion technique.

There is a lot of interest in problems of multivehicle systems (MVSs) in recent years due to their wide range of applications. In this paper, we are closely examining three approaches for dealing with problems of coordination control strategies in (MVSs). First, a distributed control approach is discussed where the controller here is designed using bilinear matrix inequality (BMI) technique. Second, a two-level control approach is provided in which a global controller is used to supervise the overall performance of the (MVSs) by sending corrective signals to the local controllers of the agents. In this approach, there is no direct communication among agents but all of the communication with the global controller. Third, a cloud-based control approach is addressed which benefits of the technological advances in networked control systems theory. Computer simulations are performed on autonomous underwater vehicles (AUV) and comparative studies among the approaches are elucidated throughout the paper.

This brief presents an integrated trajectory planning and attitude control framework for six-degree-of-freedom (6-DOF) hypersonic vehicle (HV) reentry flight. The proposed framework utilizes a bilevel structure incorporating desensitized trajectory optimization and deep neural network (DNN)-based control. In the upper level, a trajectory data set containing optimal system control and state trajectories is generated, while in the lower level control system, DNNs are constructed and trained using the pregenerated trajectory ensemble in order to represent the functional relationship between the optimized system states and controls. These well-trained networks are then used to produce optimal feedback actions online. A detailed simulation analysis was performed to validate the real-time applicability and the optimality of the designed bilevel framework. Moreover, a comparative analysis was also carried out between the proposed DNN-driven controller and other optimization-based techniques existing in related works. Our results verify the reliability of using the proposed bilevel design for the control of HV reentry flight in real time.

Color variation in stained histopathology images prevents the development of computer-assisted diagnosis (CAD) algorithms for whole slide imaging systems. Therefore, stain normalization methods are studied to reduce the influence of color variation combined with digital image processing algorithms. The Structure Preserve Color Normalization (SPCN) method is a promising stain normalization method, utilizing the sparse non-negative matrix factorization to estimate the stain feature appearance matrix. However, the SPCN method suffers from the high computational complexity of dictionary learning, and its official implementation relies on Matlab and CPU. This research proposes the StainCNNs method to simplify the process of stain feature extraction, and imply a GPU-enabled realization to accelerate the learning of stain features in the Tensorflow Framework. What’s more, the StainCNNs method is able to perform the stain normalization quickly in dataset level, more efficient than the SPCN method which is unable to make use of the stain feature distribution in dataset. Stain normalization experiments are conducted on the Camelyon16 dataset and the ICPR2014 dataset, evaluated by the QSSIM score and the FSIM score. Results demonstrate that the proposed StainCNNs method achieves a state-of-the-art performance compared with many conventional stain normalization methods.

Objective:To explore if there is association between vitamin D supplementation through cod liver oil ingestion around the periconceptional period and the risk of developing severe CHD in offspring. Furthermore, we would examine the interaction between vitamin D and folic acid supplementation in the association.Methods:A case–control study was conducted in Shanghai Children’s Medical Center, in which, a total of 262 severe CHD cases versus 262 controls were recruited through June 2016 to December 2017. All children were younger than 2 years. To reduce potential selection bias and to minimise confounding effects, propensity score matching was applied.Results:After propensity score matching, vitamin D supplementation seemed to be associated with decreased odds ratio of severe CHD (odds ratio = 0.666; 95% confidence intervals: 0.449–0.990) in the multivariable conditional logistic analysis. Furthermore, we found an additive interaction between vitamin D and folic acid supplementation (relative excess risk due to interaction = 0.810, 95% confidence intervals: 0.386–1.235) in the association.Conclusion:The results suggested that maternal vitamin D supplementation could decrease the risk of offspring severe CHD; moreover, it could strengthen the protective effect of folic acid. The significance of this study lies in providing epidemiological evidence that vitamin D supplementation around the periconceptional period could be a potential nutritional intervention strategy to meet the challenge of increasing CHD.

Crowdsensing has emerged as a powerful tool to collect IoT big data. Moving big data to the cloud for analysis is time consuming and has the risk of data privacy leakage. An alternative is to leave the training data distributed on mobile devices, and learn a shared model by aggregating locally computed updates. In this article, we propose a CrowdLearning system, which employs MUs for big data collection and deep learning training. We propose a game-based incentive mechanism to optimize the utilities of MUs and accuracy of the training model by exploiting the various sensing and training capabilities of MUs. Experiments have been conducted to evaluate the performance of proposed CrowdLearning system and the results validate the effectiveness of the proposed mechanism.

This article addresses the distributed finite-time attitude tracking control problem for a group of uncertain rigid spacecraft in the presence of unavailable angular velocity under the directed topology condition. First, a finite-time adaptive neural network observer is proposed for each follower to estimate its own unavailable angular velocity. Unlike existing velocity-observer-based design methods, the proposed one does not need the exact knowledge of the system model, and works well for the systems with both vanishing and nonvanishing uncertainties. Further, another finite-time observer is provided to obtain the precise angular velocity information of the dynamic leader in a distributed manner. Based on these two observers and adding a power integrator technique, a continuous distributed finite-time control scheme with only attitude measurements is finally established. A rigorous theoretical proof shows that the entire finite-time stability of the combined observer–controller closed-loop system is ensured. Simulation results illustrate the benefits and effectiveness of the developed control scheme.

Background Animal studies suggested that maternal sleep during pregnancy was associated with sleep pattern in offspring; however, it has not been clear in human populations. Aim Our study discusses the relationships of maternal sleep duration with sleep characteristics in their offspring through an epidemiological study. Methods A retrospective cross-sectional study including 6236 mother-child dyads was conducted in 31 preschools in May 2019, in Shanghai, China. Information regarding maternal sleep duration in three trimesters of pregnancy was collected retrospectively. Children's current sleep characteristics were evaluated through the Children's Sleep Habits Questionnaire (CSHQ). Linear regressions and logistic regression models were applied to estimate β and adjusted odds ratios with 95% confidence intervals (95% CI). Results Maternal sleep duration was positively associated with childhood sleep duration, which was shown in the first (β=0.113), second (β=0.131), and third trimesters (β=0.088). Meanwhile, insufficient maternal sleep duration could increase the risk of children's short sleep duration (first trimester: AOR=1.25; second trimester: AOR=1.33; third trimester: AOR=1.33). Maternal sleep duration was also associated with childhood CSHQ score: β=-0.308, -0.392, and -0.300 for the first, second, and third trimesters, respectively. Similarly, insufficient maternal sleep duration could predict childhood sleep disturbance as AOR=1.28 in the second trimester and AOR=1.26 in the third trimester. Conclusion Our findings established a relationship between maternal sleep during pregnancy and their children's sleep pattern through a population-based epidemiology study. Poor childhood sleep was found when their mother experienced less sleep duration during pregnancy, especially in the second and third trimesters.

In this paper, the security problem for a cloud control system (CCS) is studied. In the CCS, so-called advanced persistent threats (APTs) can be launched by a malicious attacker to reduce the quality of service of cloud and deteriorate the system performance further. To defend against APTs and create a security as a service scheme, a defender needs to allocate defense resource to different units serving to different plants for improving the overall system performance when the CCS accommodates multiple physical plants simultaneously. After observing the defender's action, the attacker decides which serve units to comprise. Considering that both defender and attacker are subject to resource constraints, the interaction of two sides is modeled by a Stackelberg game. The optimal solutions for two players under different types of budget constraints are investigated. Simulation examples and comparison results are provided to verify the main results of this paper.

In this article, resilient strategy design is investigated for a cloud control system (CCS) subject to jamming attack. In the presence of jammer, signals, including measurements and control inputs between cloud server and physical plant may be interfered, which degrades the signal-to-interference-plus-noise ratio and further leads to packet dropout of signals. A Stackelberg game is applied to depict the interaction of transmitter and jammer. On account of game equilibria of two players, controller is devised for CCS with time-varying delay. A novel cross-layer dynamic pricing mechanism is devised to drive the disturbance attention performance of CCS to a desired region under constrained information and time-varying networks. The simulation results are provided to verify the effectiveness of the proposed methodology.

Novel generalized predictive control (GPC) is proposed to deal with the problems of cumulative deviation of the predicted value, long control process and large noise that often encountered in conventional GPC. First, the objective function of GPC is improved based on integral time square error (ITSE) criterion, and the output expression under soft constraint is deduced. Then, the predicted value is corrected by adopting the integral weighting method, the convergence of which is demonstrated. Finally, both the simulations and hardware experiments are conducted in a practical photoelectric tracking system. It turns out that the superiority of the improved GPC in control precision, dynamic response and robustness is verified.

Power-efficient Data Center Networks (DCNs) have been proposed to save power of DCNs using OpenFlow. In these DCNs, the OpenFlow controller adaptively turns on/off links and OpenFlow switches to form a minimum-power subnet that satisfies the traffic demand. As the subnet changes, flows are dynamically routed and rerouted to the routes composed of active switches and links. However, existing flow scheduling schemes could cause undesired results: (1) power inefficiency: due to unbalanced traffic allocation on active routes, extra switches and links may be activated to cater to bursty traffic surges on congested routes, and (2) Quality of Service (QoS) fluctuation: because of the limited flow entry processing ability, switches may not be able to timely install/delete/update flow entries to properly route/reroute flows. In this paper, we propose AggreFlow, a dynamic flow scheduling scheme that achieves power efficiency and QoS improvement using three techniques: Flow-set Routing , Lazy Rerouting , and Adaptive Rerouting . Flow-set Routing achieves load balancing with a small number of flow entry operations by routing flows in a coarse-grained flow-set fashion. Lazy Rerouting spreads rerouting operations over a relatively long period of time, reducing the burstiness of entry operation on switches. Adaptive Rerouting selectively reroutes flow-sets to maintain load balancing. We built an NS3 based fat-tree network simulation platform to evaluate AggreFlow’s performance. The simulation results show that AggreFlow reduces power consumption by about 18%, yet achieving load balancing and improved QoS (low packet loss rate and reducing the number of processing entries for flow scheduling by 98%), compared with baseline schemes.

This article investigates the attitude stabilization problem of a rigid spacecraft with actuator saturation and failures. Two neural network-based control schemes are proposed using anti-saturation adaptive strategies. To satisfy the input constraint, we design two controllers in a saturation function structure. Taking into account the modeling uncertainties, external disturbances, and adverse effects from actuator faults and failures, the first anti-saturation adaptive controller is implemented based on radial basis function neural networks (RBFNNs) with a fixed-time terminal sliding mode (FTTSM) containing a tunable parameter. Then, we upgrade the proposed controller to a fully adaptive-gain anti-saturation version, in order to strengthen the robustness and adaptivity with respect to actuator faults and failures, unknown mass properties, and external disturbances. In the two schemes, all of the designed adaptive parameters are scalars, thus they only require light computational load and can avoid the redesign process of the controller during spacecraft operation. Finally, the feasibility of the proposed methods is illustrated via two numerical examples.

This paper is concerned with model predictive control (MPC) for multi-loop networked control systems over shared bandwidth-limited communication networks. The plants are described by linear discrete-time systems with additive disturbances and input constraints. At each time instant, only a limited number of loops can communicate over the networks, and the scheduling sequence is determined by a network manager. Algorithms are proposed for co-designing the MPC and the scheduling protocol to stabilize all loops with conflict-free transmissions. We give conditions to ensure recursive feasibility of the optimization problems and the stability of all loops. Finally, two numerical examples show that the algorithms work well.

Background Previous studies suggested that maternal subjective feeling of stress seemed to be involved in the incidence of congenial heart disease in offspring. To better understand the findings, our study would discuss the relationships of maternal exposure to stressful life event and social support, which are more objective and comprehensive indicators of stress, around periconceptional period with the risk of ventricular septal defect (VSD), the most popular subtype of congenital heart disease. Methods A hospital-based case-control study was conducted through June, 2016 to December, 2017. We collected maternal self-reports of 8 social support questions in 3 aspects and 8 stressful life events among mothers of 202 VSD cases and 262 controls. Social support was categorized into low, medium high, and high (higher is better), and stressful life event was indexed into low, medium low, and high (higher is worse). Logistic regression models were applied to estimate adjusted odds ratios and 95% confidence intervals (95% CI). Results The adjusted odds ratio of high stressful life event was 2.342 (95% CI: 1.348, 4.819) compared with low stressful life event. After crossover analysis, compared with low event & high support, the adjusted odds ratio of low event & low support, high event & high support, and high event & low support were 2.059 (95% CI: 1.104, 3.841), 2.699 (95% CI: 1.042, 6.988) and 2.781 (95% CI: 1.033, 7.489), respectively. Conclusions In summary, we observed an increased risk of VSD when pregnant women exposed to stressful life events, however, social support could, to some extent, reduce the risk of stressful life event.

This paper deals with the problem of fixed-time attitude tracking control for spacecraft subject to model uncertainties and external disturbances. Firstly, by using a fixed-time extended state observer (FxTESO), the synthetic uncertainties generated by external disturbances and model deviations can be estimated and compensated accordingly. We propose a novel FxTESO aimed to improve previous methods. Compared with the existing extended state observer (ESO), the proposed FxTESO provides faster convergence and higher accuracy. Then, we design a fixed-time adaptive attitude tracking controller based on the strategy combining the FxTESO and fast non-singular terminal sliding mode control (FNTSMC), such that a desired attitude can be achieved accurately, which does not only allow providing fast and accurate responses, and acceptable chattering suppression, but also avoiding singularity. Finally, the numerical simulation results are discussed to verify the efficiency and merits of the proposed control strategy.

In this paper, a monocular visual-inertial odometry that utilize both point and line features is deduced. Compared with point features, line features provide more geometric information of the environment, which are more reliable in textureless scenes. However, extracting line segment features from the image are very time consuming, which will affect the real-time performance of the system. To deal with this problem, EDLines line segment detector is introduced to replace the LSD algorithm. Geometric properties of lines are utilized to reject the mismatching of line segment feature. Plucker coordinates and orthonormal representation of lines are used to represent 3D lines. Afterwards, we optimize the state by minimizing a cost function consists of pre-integrated IMU residuals and visual feature re-projection residuals in a sliding window optimization framework. The proposed odometry was tested on the public datasets. The results demonstrate that the presented system can operate in real time with high accuracy.

As a new computing paradigm for hosting and executing large-scale scientific applications, cloud computing can provide users with scalable and elastic computational resources. Workflow scheduling is NP-complete and becomes even more challenging under the dynamic environment of the cloud since certain quality of service (QoS) needs to be met in most cases. In this paper, a meta-heuristic scheduling algorithm, named EMPSO (Elimination Mechanism-based PSO), is proposed to minimize the makespan while satisfying the budget constraint. Firstly, a new technique for choosing the global best solution is developed to avoid the premature convergence caused by the original totally greedy selection method, which improves the global search performance of our algorithm. Then, for accelerating the convergence speed towards the global optima, time-varying parameters are introduced to keep a good balance between global search and convergence by updating particles’ velocity adaptively during different optimizing stages. Finally, an elimination mechanism is embedded to further improve the overall performance by promoting the quality of inferior particles. Extensive experiments are conducted on well-known scientific workflows of different sizes and types. The experimental results demonstrate that EMPSO outperforms GA and PSO with respect to meeting the budget constraint and minimizing the makespan.

Cloud has gradually become a more popular platform for executing scientific workflow applications due to its unique features. However, how to effectively schedule scientific workflows in clouds to minimize execution cost under a deadline constraint remains a critical problem. And in this problem, deadline distribution strategy plays a key role. In this paper, a new deadline distribution strategy with level importance rate is proposed. Based on this, we propose a heuristic scheduling method, named LIRHM. LIRHM prioritizes tasks by their upward ranks and distributes the deadline to each task as its sub-deadline using the proposed deadline distribution strategy, then maps each task to the best VM instance which meets its sub-deadline and minimizes workflow execution cost. Compared to four methods with different deadline distribution strategies, experiments on five real-word scientific workflows show the effectiveness of LIRHM and the importance of earlier tasks on deadline distribution strategy. In addition, we analyze the performance difference of different deadline distribution strategies on each workflow.

Software-Defined Networking (SDN) promises good network performance in Wide Area Networks (WANs) with the logically centralized control using physically distributed controllers. In Software-Defined WANs (SD-WANs), maintaining path programmability, which enables flexible path change on flows, is crucial for maintaining network performance under traffic variation. However, when controllers fail, existing solutions are essentially coarse-grained switch-controller mapping solutions and only recover the path programmability of a limited number of offline flows, which traverse offline switches controlled by failed controllers. In this paper, we propose ProgrammabilityMedic (PM) to provide predictable path programmability recovery under controller failures in SD-WANs. The key idea of PM is to approximately realize flow-controller mappings using hybrid SDN/legacy routing supported by high-end commercial SDN switches. Using the hybrid routing, we can recover programmability by fine-grainedly selecting a routing mode for each offline flow at each offline switch to fit the given control resource from active controllers. Thus, PM can effectively map offline switches to active controllers to improve recovery efficiency. Simulation results show that PM outperforms existing switch-level solutions by maintaining balanced programmability and increasing the total programmability of recovered offline flows up to 315% under two controller failures and 340% under three controller failures.

We have witnessed the great advances in the Internet of Things (IoT) that bring great opportunities for promoting industrial upgrades and even allow the introduction of the fourth industrial revolution. In IoT, data is generated at an unprecedented scale, and it needs to be analyzed efficiently to draw meaningful insights. In recent years, deep learning has emerged as a powerful tool for implementing this analysis. Unfortunately, the traditional deep learning algorithm running in the cloud data center needs a large amount of data for effective training, which takes a substantial amount of time. In this article, we propose a concept of crowdtraining, which employs edge computing units (ECUs) for big data training in IoT and pays rewards to ECUs using a gamebased incentive mechanism. Simulations have been performed to evaluate the performance of a crowdtraining game.

Background In general, the existing evidence points to a role for maternal sleep in pregnancy complications and fetal growth, however, little has been focused on birth defects. We aimed to explore the association between periconceptional poor sleep and the risk of congenital heart disease (CHD), and to examine if daytime napping could to some extent change the association. Methods A case–control study was conducted in Shanghai Children's Medical Center, in which, a total of 524 cases (262 simple CHD vs. 262 severe CHD), along with 262 controls. Results In the multivariable logistic analysis, poor sleep could increase the risk of both simple CHD (OR = 2.486, 95% CI = 1.619–3.818) and severe CHD (OR = 1.950, 95% CI = 1.269–2.997), while routine daytime nap could decrease risk of simple CHD (OR = 0.634, 95% CI = 0.435–0.923). In the stratified analysis, the concurrence with routine daytime nap could weaken the risk of simple CHD caused by poor sleep (OR = 3.183, 95% CI: 1.830–5.537 decreased to OR = 2.236, 95% CI: 1.200–4.165). The examinations were repeated in ventricular septal defect and tetralogy of Fallot, and the established associations can be verified. Moreover, all these findings were also similarly observed in both propensity‐score‐adjusted and propensity‐score‐matched analyses. Conclusions Poor maternal sleep around periconceptional period seems to be an independent risk factor for CHD. The concurrence with daytime nap could to some extent reduce the risk in simple CHD. The results individually and collectively put forward the importance of maternal sleep in embryonic heart development.

In this paper, a countermeasure for wireless networked control systems suffering from jamming attacks is studied by a variable sampling approach. A Stackelberg game framework is utilized to analyze interactions between a smart jammer and a legitimate user. The variable sampling approach is exploited to deal with data packets dropout between a sensor and a controller. Moreover, a resilient variable sampling controller is designed by a delta operator method. Besides, stability conditions are provided for systems with proposed controller. Finally, a numerical simulation is provided to validate the effectiveness of the proposed method.

This article addresses the finite-time attitude formation-containment control problem for networked uncertain rigid spacecraft under directed topology. A unified distributed finite-time attitude control framework, based on the sliding-mode control (SMC) principle, is developed. Different from the current state of the art, the proposed attitude control method is suitable for not only the leader spacecraft but also the follower spacecraft, and only the neighbor state information among spacecraft is required, allowing the resulting control scheme to be truly distributed. Furthermore, the proposed method is inherently continuous, which eliminates the undesired chattering problem. Such features are deemed favorable in practical spacecraft applications. In addition, upon using the proposed neuro-adaptive control technique, the attitude formation-containment deployment can be achieved in finite time with sufficient accuracy, despite the involvement of both the uncertain inertia matrices and external disturbances. The effectiveness of the developed control scheme is confirmed by numerical simulations.

Deep learning suffers from many notorious issues such as low convergence rate, over-fitting, and time-consuming. To alleviate these problems, an alternative learning framework with a non-iterative training mechanism named Broad Learning System (BLS) was proposed, which randomly assigns the parameters of hidden nodes and frozen them throughout the training process and then obtains its output weights using the ridge regression theory. This training method makes BLS have very high training efficiency. However, using ridge regression to solve the output weights cannot guarantee the stability of the solution in many cases, especially when the number of training samples is large, which may cause over-fitting and instability of BLS models. To solve this problem, we propose an improved BLS with a dense architecture and use the Proportional-Integral-Differential (PID) and Adaptive moment estimation (Adam) to replace the ridge regression operation. The new algorithm is called PID-A-DBLS, and its advantages include: 1) dense architecture can improve the feature extraction ability of the model; 2) using PID and Adam to solve the output weights can avoid the disadvantages of ridge regression. Extensive experimental results on four benchmark data sets show that PID-A-DBLS can achieve much better generalization ability and stability than BLS and its variants.

With the improvement of information technology, the performance of sensors has been greatly improved, and a large number of multi-sensor systems for various applications have emerged. The diversification and complexity of modern war threat also put forward higher requirements for traditional data or information processing systems.

In this part, Kalman filtering fusion for linear dynamic systems will be introduced in five chapters. They are studied mainly aimed to solve the problems caused by network or wireless communication.

In this article, we propose a novel economic model-predictive control (MPC) algorithm for a group of disturbed linear systems and implement it in a distributed manner. The system consists of multiple subsystems interacting with each other via dynamics and aims to optimize an economic objective. Each subsystem is subject to constraints both on states and inputs as well as unknown but bounded disturbances. First, we divide the computation of control inputs into several local optimization problems based on each subsystem's local information. This is done by introducing compatibility constraints to confine the difference between the actual information and the previously published reference information of each subsystem, which is the key feature of the proposed distributed algorithm. Then, to ensure the satisfaction of both state and input constraints under disturbances, constraints are tightened on the state and the input of nominal systems by considering explicitly the effect of uncertainties. Moreover, based on an overall optimal steady state, a dissipativity constraint and a terminal constraint are designed and incorporated in the local optimization problems to establish recursive feasibility and guarantee stability for the resulting closed-loop system. Finally, the efficiency of the distributed economic MPC algorithm is demonstrated in a building temperature control case study.

New cloud services are being developed to support a varied of technology applications. In this paper, a new cloud service: Intelligent Manufacturing Cloud Control is given, which includes different functionalities of intelligent manufacturing system as a model: cloud feedback control, cloud worflow scheduling, cloud containers. This paper focuses on the fact that cloud control system is most time-critical and demanding functionality. Hence, a novel intelligent manufacturing cloud control systems architecture is proposed, and time savings under such proposed architecture is fully analyzed. It is shown that signficant cost and time savings could be achieved, mainly due to the virtualization of controllers and the reduction of hardware cost. Moreover, a task-based delay triggered cloud worflow scheduling strategy is proposed to minimize the makespan of intelligent manufacturing cloud control worflows. To experimentally evaluate our approach, the controllers are implemented on cloud environment and control industry-standard plant. The experimental results show that the proposed cloud controllers perform indistinguishably from the local controllers.

Key Points Question Are maternal prepregnancy weight and gestational weight gain associated with childhood allergies? Findings In this cross-sectional study evaluating 8877 children, excessive gestational weight gain was associated with a risk of asthma and/or wheezing, allergic rhinitis, eczema, and food and/or drug allergy, and this risk appeared to be higher when coexisting with maternal prepregnancy overweight or obesity. Low gestational weight gain was associated with a reduced risk of allergies in children of women with low or normal prepregnancy weight, while there was no association in women who were overweight or obese. Meaning The findings of this study suggest that, to meet the challenge of increasing childhood allergies, the management of prepregnancy weight and gestational weight gain must be considered., Importance Information on the association between maternal prepregnancy body weight, gestational weight change, and childhood allergies is not consistent. Little is known on whether there is a combined association with comprehensive childhood allergies. Objective To examine the association of maternal prepregnancy body mass index and gestational weight gain (GWG) with the risk of childhood allergic diseases including asthma and/or wheezing, allergic rhinitis, eczema, and food and/or drug allergy. Design, Setting, and Participants This cross-sectional study was conducted from April 12 to June 1, 2019, using the Shanghai Children Allergy Study (SCAS), a large, population-based survey. A multistage and multistrata sampling approach was applied to conduct the survey in 13 districts across Shanghai, China, with an enrollment of 15 145 mother-child pairs. Exposures Reported maternal weight before pregnancy and at the time of delivery were requested in the survey; GWG was examined in absolute terms and with reference to the Institute of Medicine guideline. Main Outcomes and Measures The ascertainment of allergic diseases was based on the International Study of Asthma and Allergies in Childhood questionnaire. Results Among 15 145 children aged 3 to 14 years (7911 [52.2%] boys) within the SCAS, 8877 children (58.6%) were screened for allergic diseases. Multivariable log-binomial regression models suggested that excessive GWG was associated with risks of 19% for asthma/wheezing, 11% for allergic rhinitis, and 10% for eczema in the children. Gestational weight gain extremely above the Institute of Medicine guideline in women who were overweight/obese before pregnancy was associated with the highest risk of childhood asthma/wheezing (adjusted prevalence ratio, 1.42; 95% CI, 1.16-1.74; P = .001), allergic rhinitis (adjusted prevalence ratio, 1.32; 95% CI, 1.12-1.56; P = .001), and eczema (adjusted prevalence ratio, 1.24; 95% CI, 1.08-1.41; P = .002). Gestational weight gain below the Institute of Medicine guideline was associated with an attenuated risk of 13% for childhood asthma/wheezing, 11% for allergic rhinitis, 14% for eczema, and 15% food/drug allergy when mothers were of prepregnancy normal weight; similar associations were observed in underweight mothers, but there was no association in overweight mothers. Generally, the associations were similar in the stratified analysis according to children’s age group. Moreover, the results were similar in the allergen test subgroup. Conclusions and Relevance The findings of this study suggest that excessive GWG may be a risk factor for childhood allergic diseases. Further studies on the long-term effects of the intrauterine environment on children’s health may take maternal weight management during pregnancy into consideration since GWG could be a controllable and modifiable risk factor., This cross-sectional study examines the incidence of childhood allergies in women with varying levels of prepregnancy weight and gestational weight gain.

Background: Early life exposure in the uterus had long-term effect on children's health. As the prevalence of allergies is increasing with a remarkable gender difference, very few studies have traced back to their early origins. We sought to investigate if maternal behavioral exposure, herein sleep, physical activity and screen time during pregnancy, is associated childhood respiratory allergies. The sex difference would be examined. Methods: 6236 mother-child pairs from Shanghai Children Allergy Study (SCAS) were enrolled, The International Study of Asthma and Allergies in Childhood questionnaire was adopted to evaluate respiratory allergic diseases. Results: 14.6%, 16.2% and 21.0% of children had asthma, wheeze, and allergic rhinitis, respectively. Maternal short sleep duration, lack of physical activity and too much screen exposure during pregnancy could increase the risk of childhood respiratory allergies. Moreover, a dose-response trend was clearly shown. However, the significance was found only in males, any two of the three combined could increase the risk (OR,1.921; 95%CI,1.217-3.033), and the coexistence of all three further amplified the risk (OR,2.412; 95%CI,1.489-3.906). The findings can be verified in allergen test subgroup and single type of respiratory allergies in most cases. And gender difference was identified, all above associations were mainly observed in males. Conclusions: Maternal unhealthy behaviors during pregnancy could increase the risk of childhood respiratory allergies with a dose-response pattern. Males were more susceptible to the association. The identification of modifiable maternal risk behaviors lies in the emphasis of intervention in early life to face up increasing childhood allergies.

Recent studies of obstacle avoidance under the Dynamic Movement Primitives (DMPs) framework are limited on the dotted obstacles. A novel obstacle avoidance algorithm based on DMPs is proposed for shaped obstacle avoidance. By combining the steering behavior method and the potential field method with the evaluation of Euclidean distance, while absorbing the switching strategy, this new algorithm performs well in mimicking the learned DMPs trajectory and handles the fluctuations occur in the initial stage of motion. The simulations of 2D DMPs trajectory learning of one-obstacle avoidance and multi-obstacle avoidance show that the algorithm is feasible with figurate obstacles, and acquires great conformability with the learned trajectory and smoothness.

In this paper, a robot grasping method with object recognition and autonomous grasping ability based on RGB-D camera is designed. For object recognition, foreground extraction and point cloud clustering are proposed to realize object segmentation based on point cloud data and a set of object dataset. A kind of multiple modal convolution neural network model with dual channel is designed based on VGG and tested with homemade training dataset. For grasping planning, a heuristic nonuniform random grasp sample algorithm is presented according to the local reference frame and the local mean curvature of point clouds. The grasp candidates are scaled up from sample grasp pose by grid searching. The internal points in close region of every grasp hypothesis are encoded to an image and then the image is inputted into a simple convolutional neural network to evaluate the grasp success rate to rank the candidate set. The experimental results show that the proposed robot grasping method can realize object recognition and grasp objects accurately.