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From a cradle of Austronesian expansion to the dynamic economic powerhouse and successful democracy it is today, Taiwan is layered in colonial histories. In Taiwan Lives, Niki J. P. Alsford presents a comprehensive examination of the island nation’s rich and complex past, told through the life stories of those who have lived it. A merchant, an exile, an activist, a pop star, a doctor, and a president are just some of the twenty-four individuals whose lives populate this people's history of Taiwan. Ranging across time, social strata, ethnicity, and political alliance, these tales offer snapshots of historical eras and illustrate the interwoven fabric of colonialism. Chapters can be read in sequence or individually. With clear and accessible prose, Taiwan Lives is ideal for undergraduate course use.

This paper studies the distributed multi-agent resilient optimization problem under the f-total Byzantine attacks. Compared with the previous work on Byzantineresilient multi-agent exact optimization problems, we do not require the communication topology to be fully connected. Under the redundancy of cost functions, we propose the distributed comparative gradient elimination resilient optimization algorithm based on the traditional assumptions on strongly convex global costs and Lipschitz continuous gradients. Under this algorithm, we successfully prove that if the number of inneighbors of each normal agent is greater than some constant and the parameter f satisfies certain conditions, all normal agents' local estimations of the global variable will finally reach consensus and converge to the optimized solution. Finally, the numerical experiments successfully verify the correctness of the results., Comment: There are some errors in the provement of this paper

In the past decade, convolutional neural networks (CNNs) have shown prominence for semantic segmentation. Although CNN models have very impressive performance, the ability to capture global representation is still insufficient, which results in suboptimal results. Recently, Transformer achieved huge success in NLP tasks, demonstrating its advantages in modeling long-range dependency. Recently, Transformer has also attracted tremendous attention from computer vision researchers who reformulate the image processing tasks as a sequence-to-sequence prediction but resulted in deteriorating local feature details. In this work, we propose a lightweight real-time semantic segmentation network called LETNet. LETNet combines a U-shaped CNN with Transformer effectively in a capsule embedding style to compensate for respective deficiencies. Meanwhile, the elaborately designed Lightweight Dilated Bottleneck (LDB) module and Feature Enhancement (FE) module cultivate a positive impact on training from scratch simultaneously. Extensive experiments performed on challenging datasets demonstrate that LETNet achieves superior performances in accuracy and efficiency balance. Specifically, It only contains 0.95M parameters and 13.6G FLOPs but yields 72.8\% mIoU at 120 FPS on the Cityscapes test set and 70.5\% mIoU at 250 FPS on the CamVid test dataset using a single RTX 3090 GPU. The source code will be available at https://github.com/IVIPLab/LETNet., Comment: IEEE Transactions on Intelligent Transportation Systems, 10 pages

Containment control is an important issue in the consensus problem of multiagent systems (MASs). This article presents an output-based containment control strategy for a class of nonaffine nonlinear MASs with uncertainies and directed topology. With the help of differential homeomorphism transform and the idea of active disturbance rejection control (ADRC), a nonaffine nonlinear MAS is transformed into an affine one with uncertainties. Two filters with extended states in each follower are designed with the idea of extended state observer to reconstruct the states of the transformed MAS. Then, uncertainties of the MAS are compensated with the help of the estimations of the extended states. Moreover, the derivative of the virtual control signal in the backstepping technique is replaced by a tracking differentiator (TD), overcoming the so-called ``explosion of complexity'' problem. By means of Lyapunov theory, the containment errors of the followers are proven to converge to a small neighborhood around the origin via an appropriate choice of parameters. Simulation examples are provided to demonstrate the proposed control strategy.

Measuring the quasar distance through joint analysis of spectroastrometry (SA) and reverberation mapping (RM) observations is a new method for driving the development of cosmology. In this paper, we carry out detailed simulation and analysis to study the effect of four basic observational parameters (baseline length, exposure time, equivalent diameter and spectral resolution) on the data quality of differential phase curves (DPCs), furthermore on the accuracy of distance measurement. In our simulation, we adopt an axis symmetrical disc model of broad line region (BLR) to generate differential phase signals. We find that the differential phases and their Poisson errors could be amplified by extending the baseline, while the influence of OPD (optical path difference) errors can be reduced during fitting the BLR model. Longer exposure time or larger equivalent diameter helps reduce the absolute Poisson error. Therefore, the relative error of DPCs could be reduce by increasing any of the above three parameters, then the accuracy of distance measurement could be improved. In contrast, the uncertainty of $D_{\rm{A}}$ ( absolute angular distances) could be improved with higher spectral resolution, although the relative error of DPCs would be amplified. We show how the uncertainty of distance measurement varies with the relative error of DPCs. For our specific set of model parameters, without considering more complicated structures and kinematics of BLRs in our simulation, it is found that the relative error of DPCs $<$ 20$\%$ is a limit for accurate distance measurement. The relative error of DPCs have a lower limit (roughly 5$\%$) and the uncertainty of distance measurement can be better than 2$\%$.

Formation tracking is a critical issue in the consensus control of multiagent systems (MASs). This article presents a time-varying formation tracking strategy with predefined performance for a class of uncertain nonaffine nonlinear MASs connected through a directed topology. The nonaffine nonlinear MASs are transformed into affine nonlinear ones with uncertainties via the idea of active disturbance rejection control (ADRC). The uncertainties in the MASs are approximated and compensated by extended state observers (ESOs) in real time. Tracking differentiators (TDs) are introduced to reduce the complexity in the computation of the derivatives of virtual control variables. Employing funnel variables, our strategy guarantees the formation of tracking errors to stay within the desired ranges, thus improving the control performance of the closed-loop system. It is proved that all signals of the system are bounded and the formation errors can be made arbitrarily small within a residue around the origin by appropriate choices of control parameters. Case studies are carried out to demonstrate the effectiveness of the proposed control strategy. Note to Practitioners-The motivation of this article is to present a time-varying formation tracking strategy with predefined performance for a class of uncertain nonaffine nonlinear MASs within a directed topology. To simplify the process of solving the formation tracking problem, the presented strategy incorporates ADRC with the backstepping technique. Employing ADRC, our strategy approximates the uncertainties of the MAS followers via ESOs. The uncertainties are then compensated through real-Time estimations of extended states. Moreover, with ADRC, TDs are used to estimate the derivatives of complex nonlinear functions, eliminating the requirement of the operations of higher order derivatives of virtual control variables. It provides a feasible strategy for industrial applications.

Voltage regulation is imperative for the successful operation of electricity distribution networks, especially with a high penetration level of photovoltaic (PV) systems. Power compensation control (PCC) that uses both reactive power compensation and active power curtailment has shown promising results in alleviating voltage rise problems. It crucially relies on real-time communications among distributed PV systems. However, the transmission of state measurements and control signals in PCC is hampered by inevitable communication delays. Therefore, it is important to not only estimate the maximum tolerable communication delay (MTCD) but also develop an alternative technique for PCC under abnormal communication delay (ACD) conditions. This article presents a delay-tolerant predictive PCC for voltage regulation in distribution feeders. After estimating the MTCD based on voltage and power mutation, it uses normal PCC for effective operation when communication delay is within MTCD, or switches to predictive PCC under ACD conditions. An accurate prediction is achieved using a double neural network with online adjustment of weights and samples. Simulations on a sample distribution network demonstrate the effectiveness of our presented approach.

Fully Autonomous Public Turing test to tell Computers and Humans Apart (CAPTCHA) is an essential component for network security resisting attacks, such as collision attack and password blasting.As a recently emerged CAPTCHA technology, drag-and-drop interactive CAPTCHA has been successfully employed in great number of practical applications. However, there are still some problems involved in the architecture and back-end anomaly detection model of the interactive CAPTCHA that need to be addressed: excessive concentration of computing pressure on cloud system, poor accuracy of anomaly detection model, and huge cost of the labelling for the attack sample. To this end, a novel cloud endpoint coordinating CAPTCHA based on multi-view stacking ensemble (MVSE) is proposed in this paper. In particular, a novel cloud endpoint coordinating CAPTCHA architecture is designed to make most use of the computing power of endpoint devices and reduce the calculation pressure of cloud system. Meanwhile, a multi-view stacking ensemble learning-based user action anomaly detection model is proposed for the cloud endpoint coordinating CAPTCHA architecture. Finally, an iterative top-k training (ITK-training) semi-supervised learning algorithm is employed for data enhancement and make the most use of un-labeled samples in order to reduce the deploy cost of drag-and-drop CAPTCHA system. A real-world data from one of the biggest Internet companies of China is used to validate the effectiveness of our proposed model. We can obtain that the computing pressure of the cloud can reduce nearly 95% and the accuracy of the proposed CAPTCHA system can reach 96.77% using MVSE learning and 98.67% using MVSE learning with the ITK-training based data enhancement.

Consensus control of multiagent systems (MASs) has applications in various domains. As MASs work in networked environments, their security control becomes critically desirable in response to various cyberattacks, such as denial of service (DoS). Efforts have been made in the development of both time- and event-triggered consensus control of MASs. However, there is a lack of precise calculation of control input during the attacking periods. To address this issue, a distributed secure consensus control with event triggering is developed for linear leader-following MASs under DoS attacks. It is designed with a dual-terminal event-triggered mechanism, which schedules information transmission through two triggered functions for each follower: one on the measurement channel (sensor-to-controller) and the other on the control channel (controller-to-actuator). To deal with DoS attacks, the combined states in the triggered functions are replaced by their estimations from an observer. Sufficient conditions are established for the duration and frequency of DoS attacks. To remove continuous monitoring of the measurement errors, a self-triggered secure control scheme is further developed, which combines the system states and other information at past triggered instants. Theoretical analysis shows that the followers in MASs under DoS attacks are able to track the leader and meanwhile the Zeno behavior is excluded. Case studies are conducted to demonstrate the effectiveness of our distributed secure consensus control of MASs.

Neural dynamic surface control (NDSC) is an effective technique for the tracking control of nonlinear systems. The objective of this article is to improve closed-loop transient performance and reduce the number of learning parameters for a strict-feedback nonlinear system with unknown control gains. For this purpose, a predictor-based NDSC (PNDSC) approach is presented. It introduces Nussbaum functions and predictors into the traditional NDSC for nonlinear systems with unknown control gains. Unlike NDSC that uses surface errors to update the learning parameters of neural networks (NNs), the PNDSC employs prediction errors for the same purpose, leading to improved transient performance of closed-loop control systems. To reduce the number of learning parameters, the PNDSC is further embedded with the technique of the minimal number of learning parameters (MNLPs). This avoids the problem of the ``explosion of learning parameters'' as the order of the system increases. A Lyapunov-based stability analysis shows that all signals are bounded in the closed-loop systems under PNDSC embedded with MNLPs. Simulations are conducted to demonstrate the effectiveness of the PNDSC approach presented in this article.

Measuring the quasar distance through joint analysis of spectroastrometry (SA) and reverberation mapping (RM) observations is a new method for driving the development of cosmology. In this paper, we carry out detailed simulation and analysis to study the effect of four basic observational parameters (baseline length, exposure time, equivalent diameter and spectral resolution) on the data quality of differential phase curves (DPCs), furthermore on the accuracy of distance measurement. In our simulation, we adopt an axis symmetrical disc model of broad line region (BLR) to generate differential phase signals. We find that the differential phases and their Poisson errors could be amplified by extending the baseline, while the influence of OPD (optical path difference) errors can be reduced during fitting the BLR model. Longer exposure time or larger equivalent diameter helps reduce the absolute Poisson error. Therefore, the relative error of DPCs could be reduce by increasing any of the above three parameters, then the accuracy of distance measurement could be improved. In contrast, the uncertainty of $D_{\rm{A}}$ ( absolute angular distances) could be improved with higher spectral resolution, although the relative error of DPCs would be amplified. We show how the uncertainty of distance measurement varies with the relative error of DPCs. For our specific set of model parameters, without considering more complicated structures and kinematics of BLRs in our simulation, it is found that the relative error of DPCs $<$ 20$\%$ is a limit for accurate distance measurement. The relative error of DPCs have a lower limit (roughly 5$\%$) and the uncertainty of distance measurement can be better than 2$\%$.

Since hydrogen has many advantages (e.g., free pollution, extensive sources, convenient storage and transportation), hydrogen-based multi-energy systems (HMESs) have received wide attention. However, existing works on the optimal operation of HMESs neglect building thermal dynamics, which means that the flexibility of building thermal loads can not be utilized for reducing system operation cost. In this paper, we investigate an optimal operation problem of an HMES with the consideration of building thermal dynamics. Specifically, we first formulate an expected operational cost minimization problem related to an HMES. Due to the existence of uncertain parameters, inexplicit building thermal dynamics models, temporally coupled operational constraints related to three kinds of energy storage systems and indoor temperatures, as well as the coupling between electric energy subsystems and thermal energy subsystems, it is challenging to solve the formulated problem. To overcome the challenge, we reformulate the problem as a Markov game and propose an energy management algorithm to solve it based on multi-agent discrete actor-critic with rules (MADACR). Note that the proposed algorithm does not require any prior knowledge of uncertain parameters, parameter prediction, and explicit building thermal dynamics model. Simulation results based on real-world traces show the effectiveness of the proposed algorithm., Comment: 13 pages, 15 figures

In recent years, how to strike a good trade-off between accuracy and inference speed has become the core issue for real-time semantic segmentation applications, which plays a vital role in real-world scenarios such as autonomous driving systems and drones. In this study, we devise a novel lightweight network using a multi-scale context fusion (MSCFNet) scheme, which explores an asymmetric encoder-decoder architecture to dispose this problem. More specifically, the encoder adopts some developed efficient asymmetric residual (EAR) modules, which are composed of factorization depth-wise convolution and dilation convolution. Meanwhile, instead of complicated computation, simple deconvolution is applied in the decoder to further reduce the amount of parameters while still maintaining high segmentation accuracy. Also, MSCFNet has branches with efficient attention modules from different stages of the network to well capture multi-scale contextual information. Then we combine them before the final classification to enhance the expression of the features and improve the segmentation efficiency. Comprehensive experiments on challenging datasets have demonstrated that the proposed MSCFNet, which contains only 1.15M parameters, achieves 71.9\% Mean IoU on the Cityscapes testing dataset and can run at over 50 FPS on a single Titan XP GPU configuration., Comment: IEEE Transactions on Intelligent Transportation Systems, 11 pages, 7 figures

The curtailment of photovoltaic (PV) power injection is one of the ways to mitigate the overvoltage issue in residential distribution networks with high levels of PVs. The reactive power compensation (RPC) of PV inverter, when applied in conjunction with the active power curtailment (APC) of battery energy storage system (BESS), is more effective in improving the voltage profile. Hence, a coordinated power compensation algorithm (PCA) is proposed in this article to regulate the voltage and reduce the power curtailment while serving requisite load demand. Both RPC and APC are integrated to improve the voltage regulation margin in a low-voltage distribution network. Using bus voltage sensitivities, the integrated RPC and APC are performed on the most sensitive PV inverter and BESS for voltage regulation. The proposed PCA has a large capability in voltage regulation as it performs RPC before APC, reducing the amount of total PV power curtailment. Simulation studies are conducted on distribution feeders to demonstrate the effectiveness of the proposed approach.

An event-triggered resilient control framework is proposed for a class of uncertain nonlinear systems under DoS attacks. Unknown dynamics are approximated by fuzzy logic systems (FLSs), while a switched adaptive state observer is presented to reconstruct unavailable states in the presence of intermittent attacks. Then, the backstepping method is developed for the control scheme; additionally, a relative threshold triggered scheme is designed. Furthermore, in theory, we prove the proposed control scheme guarantees all closed-loop signals remain bounded, and the error signal converges to a small area of the origin under DoS attacks. Finally, a power networked system and a numerical example are used to verify the effectiveness of the theoretical claims in our proposed scheme.

In commercial buildings, about 40%-50% of the total electricity consumption is attributed to Heating, Ventilation, and Air Conditioning (HVAC) systems, which places an economic burden on building operators. In this paper, we intend to minimize the energy cost of an HVAC system in a multi-zone commercial building under dynamic pricing with the consideration of random zone occupancy, thermal comfort, and indoor air quality comfort. Due to the existence of unknown thermal dynamics models, parameter uncertainties (e.g., outdoor temperature, electricity price, and number of occupants), spatially and temporally coupled constraints associated with indoor temperature and CO2 concentration, a large discrete solution space, and a non-convex and non-separable objective function, it is very challenging to achieve the above aim. To this end, the above energy cost minimization problem is reformulated as a Markov game. Then, an HVAC control algorithm is proposed to solve the Markov game based on multi-agent deep reinforcement learning with attention mechanism. The proposed algorithm does not require any prior knowledge of uncertain parameters and can operate without knowing building thermal dynamics models. Simulation results based on real-world traces show the effectiveness, robustness and scalability of the proposed algorithm., Comment: 14 pages, 21 figures, accepted by IEEE Transactions on Smart Grid

With the increase of connectivity in power grid, a cascading failure may be triggered by the failure of a transmission line, which can lead to substantial economic losses and serious negative social impacts. Therefore, it is very important to identify the critical lines under various types of attacks that may initiate a cascading failure and deploy defense resources to protect them. Since coordinated multistage line attacks can lead to larger negative impacts compared with a single-stage attack or a multistage attack without coordination, this paper intends to identify the critical lines under coordinated multistage attacks that may initiate a cascading failure and deploy limited defense resources optimally. To this end, we first formulate a total generation loss maximization problem with the consideration of multiple attackers and multiple stages. Due to the large size of solution space, it is very challenging to solve the formulated problem. To overcome the challenge, we reformulate the problem as a Markov game and design its components, e.g., state, action, and reward. Next, we propose a scalable algorithm to solve the Markov game based on multi-agent deep reinforcement learning and prioritized experience replay, which can determine the optimal attacking line sequences. Then, we design a defense strategy to decide the optimal defense line set. Extensive simulation results show the effectiveness of the proposed algorithm and the designed defense strategy., Comment: 11pages, 10 figures

The basic theory was studied on recovery of platinum group metals from spent auto catalysts by the reduction-smelting copper trapping method and the experimental verification was carried out. The equations of possible reactions in the whole process were obtained through theoretical calculation, and the analysis shows that the oxides of platinum group metals are reduced to metallic state in preference to copper oxide, and the actual reduction-smelting temperature should be lower than 1 500 degrees C. The phase diagram and viscosity of CaO-Al2O3-SiO2 smelting slag containing MgO system were calculated, and the amount of CaO and SiO2 should be controlled in the smelting process to implement choice of slag type. It can be seen from the phase diagrams of the copper-platinum group metals that copper can form a continuous solid solution with platinum, palladium and rhodium. The feasibility of recovering platinum group metals from spent auto catalysts by the copper trapping method is proved theoretically. The recoveries of platinum, palladium and rhodium are 98.2%, 99.2% and 97.6%, respectively, under the appropriate conditions by practical experimental verification., The basic theory was studied on recovery of platinum group metals from spent auto catalysts by the reduction-smelting copper trapping method and the experimental verification was carried out. The equations of possible reactions in the whole process were obtained through theoretical calculation, and the analysis shows that the oxides of platinum group metals are reduced to metallic state in preference to copper oxide, and the actual reduction-smelting temperature should be lower than 1 500 degrees C. The phase diagram and viscosity of CaO-Al2O3-SiO2 smelting slag containing MgO system were calculated, and the amount of CaO and SiO2 should be controlled in the smelting process to implement choice of slag type. It can be seen from the phase diagrams of the copper-platinum group metals that copper can form a continuous solid solution with platinum, palladium and rhodium. The feasibility of recovering platinum group metals from spent auto catalysts by the copper trapping method is proved theoretically. The recoveries of platinum, palladium and rhodium are 98.2%, 99.2% and 97.6%, respectively, under the appropriate conditions by practical experimental verification.

An adaptive neural output consensus control issue is considered for stochastic nonlinear strict-feedback multi-agent systems (MASs). The traditional backstepping framework is employed combing with the graph theory, as well as neural networks (NNs) technology. NNs are utilized for the approximation of unknown functions, and the Itô's lemma is used to deal with stochastic dynamics of the system. It is proved that all signals remain bounded in probability and that the tracking errors of all followers converge to a small neighborhood of the origin in the sense of mean quartic value by suitable choice of parameters. A simulation example is provided.

Nonlocal image representation methods, including group-based sparse coding and block-matching 3-D filtering, have shown their great performance in application to low-level tasks. The nonlocal prior is extracted from each group consisting of patches with similar intensities. Grouping patches based on intensity similarity, however, gives rise to disturbance and inaccuracy in estimation of the true images. To address this problem, we propose a structure-based low-rank model with graph nuclear norm regularization. We exploit the local manifold structure inside a patch and group the patches by the distance metric of manifold structure. With the manifold structure information, a graph nuclear norm regularization is established and incorporated into a low-rank approximation model. We then prove that the graph-based regularization is equivalent to a weighted nuclear norm and the proposed model can be solved by a weighted singular-value thresholding algorithm. Extensive experiments on additive white Gaussian noise removal and mixed noise removal demonstrate that the proposed method achieves a better performance than several state-of-the-art algorithms., QC 20170614

Hong-Xia Wu,1,* Hua Zhong,2,3,* Yue-Dong Xu,1 Cui-Ping Xu,4 Ying Zhang,5 Wei Zhang1 1Department of Endocrinology, Shandong Provincial Qianfoshan Hospital, Shandong University, 2Department of Oncology, Shandong University of Traditional Chinese Medicine, 3Department of Traditional Chinese Medicine, Shandong Provincial Qianfoshan Hospital, Shandong University, 4Department of Nursing, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, Shandong, 5Department of Nursing, Tianjin Chest Hospital, Tianjing, People’s Republic of China *These authors contributed equally to this work Background: We examined the effects of psychological and behavioral intervention on health-related quality of life and mental health among patients suffering from differentiated thyroid cancer (DTC) treated with postoperative radioactive iodine-131 (RAI).Methods: Sixty patients with DTC, undergoing RAI, were randomly assigned to receive either conventional nursing (n=30) or a 1-year psychological and behavioral intervention based on conventional nursing (n=30). Health-related quality of life and mental health issues, depression, and anxiety were measured using the Quality of Life Core Questionnaire, Self-rating Depression Scale, and Self-rating Anxiety Score, respectively.Results: After RAI treatment, patients in both groups showed improved functional capacities (ie, physical, role, cognitive, emotional, and social) and global quality of life, along with reduced depression and anxiety (P<0.05). At 1-year follow-up, compared with patients in the routine nursing group, those in the psychological and behavioral intervention group demonstrated greater improvements in functional capacities, global quality of life, and depression and anxiety symptoms (P<0.05).Conclusion: Psychological and behavioral interventions for patients with DTC undergoing RAI facilitated positive outcomes, suggesting that nursing care models that include psychological and behavior

Regulatory T (Treg) cells are potent suppressors that maintain immune homeostasis. Accumulation of Treg can inhibit effective immune responses in cancer patients, leading to tumor development and progression. Despite direct cytotoxicity, several chemotherapeutic drugs have been reported to deplete Treg cells for better prognosis for cancer patients. Treg cells are a heterogenous population with at least three different subsets, nonsuppressive, resting, and activated Treg cells. However, the characteristics of Treg cell subsets in lung cancer patients and how chemotherapy affects Treg cells remain elusive. In this study, we first analyzed Treg cell subsets in peripheral blood samples from 40 nonsmall cell lung cancer (NSCLC) patients and 20 healthy donors. Treg cells, specifically activated Treg cell subset, significantly increased in patients with NSCLC. Compared to nonsuppressive Treg cells, activated Treg cells expressed higher level of CD39 and predominantly produced inhibitory cytokines. In vitro assay showed that docetaxel reduced all three subsets of Treg cells. More importantly, we found docetaxel-based chemotherapy significantly decreased all three Treg subsets after 4 cycles of treatment in 17 NSCLC patients. Taken together, this study revealed dynamic changes of various Treg cell subsets in NSCLC patients before and after chemotherapy, providing activated Treg cells as a potential target for chemotherapy.

Regulatory T (Treg) cells are potent suppressors that maintain immune homeostasis. Accumulation of Treg can inhibit effective immune responses in cancer patients, leading to tumor development and progression. Despite direct cytotoxicity, several chemotherapeutic drugs have been reported to deplete Treg cells for better prognosis for cancer patients. Treg cells are a heterogenous population with at least three different subsets, nonsuppressive, resting, and activated Treg cells. However, the characteristics of Treg cell subsets in lung cancer patients and how chemotherapy affects Treg cells remain elusive. In this study, we first analyzed Treg cell subsets in peripheral blood samples from 40 nonsmall cell lung cancer (NSCLC) patients and 20 healthy donors. Treg cells, specifically activated Treg cell subset, significantly increased in patients with NSCLC. Compared to nonsuppressive Treg cells, activated Treg cells expressed higher level of CD39 and predominantly produced inhibitory cytokines. In vitro assay showed that docetaxel reduced all three subsets of Treg cells. More importantly, we found docetaxel-based chemotherapy significantly decreased all three Treg subsets after 4 cycles of treatment in 17 NSCLC patients. Taken together, this study revealed dynamic changes of various Treg cell subsets in NSCLC patients before and after chemotherapy, providing activated Treg cells as a potential target for chemotherapy.

This correspondence paper addresses the problem of output feedback stabilization of control systems in networked environments with quality-of-service (QoS) constraints. The problem is investigated in discrete-time state space using Lyapunov’s stability theory and the linear inequality matrix technique. A new discrete-time modeling approach is developed to describe a networked control system (NCS) with parameter uncertainties and nonideal network QoS. It integrates a network-induced delay, packet dropout, and other network behaviors into a unified framework. With this modeling, an improved stability condition, which is dependent on the lower and upper bounds of the equivalent network-induced delay, is established for the NCS with norm-bounded parameter uncertainties. It is further extended for the output feedback stabilization of the NCS with nonideal QoS. Numerical examples are given to demonstrate the main results of the theoretical development.

Network induced delay in networked control systems (NCS) is inherently non-uniformly distributed and behaves with multifractal nature. However, such network characteristics have not been well considered in NCS analysis and synthesis. Making use of the information of the statistical distribution of NCS network induced delay, a delay distribution based stochastic model is adopted to link Quality-of-Control and network Quality-of-Service for NCS with uncertainties. From this model together with a tighter bounding technology for cross terms, H∞ NCS analysis is carried out with significantly improved stability results. Furthermore, a memoryless H∞ controller is designed to stabilize the NCS and to achieve the prescribed disturbance attenuation level. Numerical examples are given to demonstrate the effectiveness of the proposed method.

This paper investigates the robust H∞ control for Takagi-Sugeno (T-S) fuzzy systems with interval time-varying delay. By employing a new and tighter integral inequality and constructing an appropriate type of Lyapunov functional, delay-dependent stability criteria are derived for the control problem. Because neither any model transformation nor free weighting matrices are employed in our theoretical derivation, the developed stability criteria significantly improve and simplify the existing stability conditions. Also, the maximum allowable upper delay bound and controller feedback gains can be obtained simultaneously from the developed approach by solving a constrained convex optimization problem. Numerical examples are given to demonstrate the effectiveness of the proposed methods.

This paper addresses the robust stability analysis of uncertain neutral systems with discrete and distributed delays. An innovative approach is developed for the derivation of delaydependent stability criteria. Like other methods, the approach reported here also employes the technique of linear matrix inequalities. However, unlike the existing methods that are popularly used in recent publications, our approach employes neither free weighing matrices nor model transformation, and is thus much simpler.With the proposed approach, delay-dependent robust stability criteria are derived for the systems under consideration, leading to significant improvement in the allowable delay bound. Numerical simulations are given to demonstrate the effectiveness of the proposed approach.

Control over communication networks has received increasing interest in recent years. The performance of the feedback control implemented in networked control systems (NCSs) is directly dependent on network Quality-of-Service (QoS). The time-varying characteristics of the QoS degrade the control performance and introduce distortion to controller signals. Addressing this problem, this paper proposed guaranteed cost control for NCSs based on network QoS. An approach was developed for codesign of feedback control and network QoS. The effectiveness of the proposed method was illustrated through a numerical example.

Toxoplasma gondii is an obligatory intracellular apicomplexan protozoan which can infect any warm-blooded animal and causes severe diseases in immunocompromised individuals or infants infected in utero. The survival and success of this parasite require that it colonizes the host cell, avoids host immune defenses, replicates within an appropriate niche, and exits the infected host cell to spread to neighboring non-infected cells. All of these processes depend on the parasite ability to synthesis and export secreted proteins. Amongst the secreted proteins, rhoptry organelle proteins (ROPs) are essential for the parasite invasion and host cell manipulation. Even though the functions of most ROPs have been elucidated in the less virulent T. gondii (type II), the roles of ROPs in the highly virulent type I strain remain largely un-characterized. Herein, we investigated the contributions of 15 ROPs (ROP10, ROP11, ROP15, ROP20, ROP23, ROP31, ROP32, ROP33, ROP34, ROP35, ROP36, ROP40, ROP41, ROP46, and ROP47) to the infectivity of the high virulent type I T. gondii (RH strain). Using CRISPR-Cas9, these 15 ROPs genes were successfully disrupted and the effects of gene knockout on the parasite’s ability to infect cells in vitro and BALB/c mice in vivo were investigated. These results showed that deletions of these ROPs did not interfere with the parasite ability to grow in cultured human foreskin fibroblast cells and did not significantly alter parasite pathogenicity for BALB/c mice. Although these ROPs did not seem to be essential for the acute infectious stage of type I T. gondii in the mouse model, they might have different functions in other intermediate hosts or play different roles in other life cycle forms of this parasite due to the different expression patterns; this warrants further investigations.

A method is described for the processing of a low-grade, high Mg content nickeliferous laterite ore containing 0.80% Ni, 20.15% Mg, 10.81% Fe, 0.025% Co, 0.06% Al and 16.59% Si for the production of Ni-Fe alloy powder. Optimum reduction conditions were a feed size of 0.09-0.12 mm, reductant dosage of 3%, additive dosage of 2.5%, temperature of 1 300 degrees C and reaction time of 3 h. Following ball milling and magnetic separation a Ni-Fe alloy with Ni content and recovery of more than 7% and 87%, respectively, was produced., A method is described for the processing of a low-grade, high Mg content nickeliferous laterite ore containing 0.80% Ni, 20.15% Mg, 10.81% Fe, 0.025% Co, 0.06% Al and 16.59% Si for the production of Ni-Fe alloy powder. Optimum reduction conditions were a feed size of 0.09-0.12 mm, reductant dosage of 3%, additive dosage of 2.5%, temperature of 1 300 degrees C and reaction time of 3 h. Following ball milling and magnetic separation a Ni-Fe alloy with Ni content and recovery of more than 7% and 87%, respectively, was produced.

The effects of particle size of spent catalyst, dosage of nitric acid, leaching temperature, leaching time and number of washings on the leaching rate of silver from the catalyst were investigated. The results showed that the particle size of the spent catalyst had a large effect on the leaching rate of silver which could be improved by increasing the number of washings. Using a dosage of nitric acid of 70% of the quality of spent catalyst, a leaching temperature of 65 degrees C, a leaching time of 3 h, a particle size of 0.125-0.18 mm, and number of washings 3, the leaching rate of silver was 99.52% and the silver content in the leaching residues was 0.072%. The spent catalyst could be transferred to sponge silver products using this process with a direct recovery rate of silver reaching 99.27%., The effects of particle size of spent catalyst, dosage of nitric acid, leaching temperature, leaching time and number of washings on the leaching rate of silver from the catalyst were investigated. The results showed that the particle size of the spent catalyst had a large effect on the leaching rate of silver which could be improved by increasing the number of washings. Using a dosage of nitric acid of 70% of the quality of spent catalyst, a leaching temperature of 65 degrees C, a leaching time of 3 h, a particle size of 0.125-0.18 mm, and number of washings 3, the leaching rate of silver was 99.52% and the silver content in the leaching residues was 0.072%. The spent catalyst could be transferred to sponge silver products using this process with a direct recovery rate of silver reaching 99.27%.

Toxoplasma gondii is an obligatory intracellular apicomplexan protozoan which can infect any warm-blooded animal and causes severe diseases in immunocompromised individuals or infants infected in utero. The survival and success of this parasite require that it colonizes the host cell, avoids host immune defenses, replicates within an appropriate niche, and exits the infected host cell to spread to neighboring non-infected cells. All of these processes depend on the parasite ability to synthesis and export secreted proteins. Amongst the secreted proteins, rhoptry organelle proteins (ROPs) are essential for the parasite invasion and host cell manipulation. Even though the functions of most ROPs have been elucidated in the less virulent T. gondii (type II), the roles of ROPs in the highly virulent type I strain remain largely un-characterized. Herein, we investigated the contributions of 15 ROPs (ROP10, ROP11, ROP15, ROP20, ROP23, ROP31, ROP32, ROP33, ROP34, ROP35, ROP36, ROP40, ROP41, ROP46, and ROP47) to the infectivity of the high virulent type I T. gondii (RH strain). Using CRISPR-Cas9, these 15 ROPs genes were successfully disrupted and the effects of gene knockout on the parasite’s ability to infect cells in vitro and BALB/c mice in vivo were investigated. These results showed that deletions of these ROPs did not interfere with the parasite ability to grow in cultured human foreskin fibroblast cells and did not significantly alter parasite pathogenicity for BALB/c mice. Although these ROPs did not seem to be essential for the acute infectious stage of type I T. gondii in the mouse model, they might have different functions in other intermediate hosts or play different roles in other life cycle forms of this parasite due to the different expression patterns; this warrants further investigations.

Toxoplasma gondii is an obligatory intracellular apicomplexan protozoan which can infect any warm-blooded animal and causes severe diseases in immunocompromised individuals or infants infected in utero. The survival and success of this parasite require that it colonizes the host cell, avoids host immune defenses, replicates within an appropriate niche, and exits the infected host cell to spread to neighboring non-infected cells. All of these processes depend on the parasite ability to synthesis and export secreted proteins. Amongst the secreted proteins, rhoptry organelle proteins (ROPs) are essential for the parasite invasion and host cell manipulation. Even though the functions of most ROPs have been elucidated in the less virulent T. gondii (type II), the roles of ROPs in the highly virulent type I strain remain largely un-characterized. Herein, we investigated the contributions of 15 ROPs (ROP10, ROP11, ROP15, ROP20, ROP23, ROP31, ROP32, ROP33, ROP34, ROP35, ROP36, ROP40, ROP41, ROP46, and ROP47) to the infectivity of the high virulent type I T. gondii (RH strain). Using CRISPR-Cas9, these 15 ROPs genes were successfully disrupted and the effects of gene knockout on the parasite’s ability to infect cells in vitro and BALB/c mice in vivo were investigated. These results showed that deletions of these ROPs did not interfere with the parasite ability to grow in cultured human foreskin fibroblast cells and did not significantly alter parasite pathogenicity for BALB/c mice. Although these ROPs did not seem to be essential for the acute infectious stage of type I T. gondii in the mouse model, they might have different functions in other intermediate hosts or play different roles in other life cycle forms of this parasite due to the different expression patterns; this warrants further investigations.

Toxoplasma gondii is an obligatory intracellular apicomplexan protozoan which can infect any warm-blooded animal and causes severe diseases in immunocompromised individuals or infants infected in utero. The survival and success of this parasite require that it colonizes the host cell, avoids host immune defenses, replicates within an appropriate niche, and exits the infected host cell to spread to neighboring non-infected cells. All of these processes depend on the parasite ability to synthesis and export secreted proteins. Amongst the secreted proteins, rhoptry organelle proteins (ROPs) are essential for the parasite invasion and host cell manipulation. Even though the functions of most ROPs have been elucidated in the less virulent T. gondii (type II), the roles of ROPs in the highly virulent type I strain remain largely un-characterized. Herein, we investigated the contributions of 15 ROPs (ROP10, ROP11, ROP15, ROP20, ROP23, ROP31, ROP32, ROP33, ROP34, ROP35, ROP36, ROP40, ROP41, ROP46, and ROP47) to the infectivity of the high virulent type I T. gondii (RH strain). Using CRISPR-Cas9, these 15 ROPs genes were successfully disrupted and the effects of gene knockout on the parasite’s ability to infect cells in vitro and BALB/c mice in vivo were investigated. These results showed that deletions of these ROPs did not interfere with the parasite ability to grow in cultured human foreskin fibroblast cells and did not significantly alter parasite pathogenicity for BALB/c mice. Although these ROPs did not seem to be essential for the acute infectious stage of type I T. gondii in the mouse model, they might have different functions in other intermediate hosts or play different roles in other life cycle forms of this parasite due to the different expression patterns; this warrants further investigations.

Toxoplasma gondii is an obligatory intracellular apicomplexan protozoan which can infect any warm-blooded animal and causes severe diseases in immunocompromised individuals or infants infected in utero. The survival and success of this parasite require that it colonizes the host cell, avoids host immune defenses, replicates within an appropriate niche, and exits the infected host cell to spread to neighboring non-infected cells. All of these processes depend on the parasite ability to synthesis and export secreted proteins. Amongst the secreted proteins, rhoptry organelle proteins (ROPs) are essential for the parasite invasion and host cell manipulation. Even though the functions of most ROPs have been elucidated in the less virulent T. gondii (type II), the roles of ROPs in the highly virulent type I strain remain largely un-characterized. Herein, we investigated the contributions of 15 ROPs (ROP10, ROP11, ROP15, ROP20, ROP23, ROP31, ROP32, ROP33, ROP34, ROP35, ROP36, ROP40, ROP41, ROP46, and ROP47) to the infectivity of the high virulent type I T. gondii (RH strain). Using CRISPR-Cas9, these 15 ROPs genes were successfully disrupted and the effects of gene knockout on the parasite’s ability to infect cells in vitro and BALB/c mice in vivo were investigated. These results showed that deletions of these ROPs did not interfere with the parasite ability to grow in cultured human foreskin fibroblast cells and did not significantly alter parasite pathogenicity for BALB/c mice. Although these ROPs did not seem to be essential for the acute infectious stage of type I T. gondii in the mouse model, they might have different functions in other intermediate hosts or play different roles in other life cycle forms of this parasite due to the different expression patterns; this warrants further investigations.