Enabling controlling complex networks with local topological information

Complex networks characterize the nature of internal/external interactions in real-world systems including social, economic, biological, ecological, and technological networks. Two issues keep as obstacles to fulflling control of large-scale networks: structural controllability which describes the ability to guide a dynamical system from any initial state to any desired fnal state in fnite time, with a suitable choice of inputs; and optimal control, which is a typical control approach to minimize the cost for driving the network to a predefned state with a given number of control inputs. For large complex networks without global information of network topology, both problems remain essentially open. Here we combine graph theory and control theory for tackling the two problems in one go, using only local network topology information. For the structural controllability problem, a distributed local-game matching method is proposed, where every node plays a simple Bayesian game with local information and local interactions with adjacent nodes, ensuring a suboptimal solution at a linear complexity. Starring from any structural controllability solution, a minimizing longest control path method can efciently reach a good solution for the optimal control in large networks. Our results provide solutions for distributed complex network control and demonstrate a way to link the structural controllability and optimal control together.The work was partially supported by National Science Foundation of China (61603209), and Beijing Natural Science Foundation (4164086), and the Study of Brain-Inspired Computing System of Tsinghua University program (20151080467), and Ministry of Education, Singapore, under contracts RG28/14, MOE2014-T2-1-028 and MOE2016-T2-1-119. Part of this work is an outcome of the Future Resilient Systems project at the Singapore-ETH Centre (SEC), which is funded by the National Research Foundation of Singapore (NRF) under its Campus for Research Excellence and Technological Enterprise (CREATE) programme. (61603209 - National Science Foundation of China; 4164086 - Beijing Natural Science Foundation; 20151080467 - Study of Brain-Inspired Computing System of Tsinghua University program; RG28/14 - Ministry of Education, Singapore; MOE2014-T2-1-028 - Ministry of Education, Singapore; MOE2016-T2-1-119 - Ministry of Education, Singapore; National Research Foundation of Singapore (NRF) under Campus for Research Excellence and Technological Enterprise (CREATE) programme)Published versio

Author correction: Enabling controlling complex networks with local topological information

Correction to: Scientific Reports https://doi.org/10.1038/s41598-018-22655-5, published online 15 March 2018. The Acknowledgements section in this Article is incomplete.The work was partially supported by National Science Foundation of China (61603209, 61327902), and Beijing Natural Science Foundation (4164086), and the Study of Brain-Inspired Computing System of Tsinghua University program (20151080467), and SuZhou-Tsinghua innovation leading program 2016SZ0102, and Ministry of Education, Singapore, under contracts RG28/14, MOE2014-T2-1-028 and MOE2016-T2-1-119. Part of this work is an outcome of the Future Resilient Systems project at the Singapore-ETH Centre (SEC), which is funded by the National Research Foundation of Singapore (NRF) under its Campus for Research Excellence and Technological Enterprise (CREATE) program. (61603209 - National Science Foundation of China; 61327902 - National Science Foundation of China; 4164086 - Beijing Natural Science Foundation; 20151080467 - Study of Brain-Inspired Computing System of Tsinghua University program; 2016SZ0102 - SuZhou-Tsinghua innovation leading program; RG28/14 - Ministry of Education, Singapore; MOE2014-T2-1-028 - Ministry of Education, Singapore; MOE2016-T2-1-119 - Ministry of Education, Singapore; National Research Foundation of Singapore (NRF) under its Campus for Research Excellence and Technological Enterprise (CREATE) program)Published versio

Research on the grinding performance of high pressure sintering SiCp/Al matrix composites

The two-dimensional vertical grinding test equipment was used to grind SiCp/Al composites which were prepared by high pressure sintering method. The SEM observation of grinding morphology showed that grinding damage can be prevented by SiC particles reinforcement, 60% volume fraction of SiC particles of SiCp/Al composite can hinder grinding depth and grinding performance was improved with the sintering pressure and temperature increasing. In addition, some scratches and exfoliated pits of SiC particles were observed on the surface of 60% volume SiCp/Al composite as the increase of grinding grain, while the depth of these scratches was shallower, there was no large area exfoliated pits of SiC reinforcements

IgM-associated gut bacteria in obesity and type 2 diabetes in C57BL/6 mice and humans

Abstract: Aims/hypothesis: IgM is the primary antibody produced by B cells and we hypothesise that IgM antibodies to gut microbiota may play a role in immunometabolism in obesity and type 2 diabetes. To test our hypothesis, we used B6 mice deficient in activation-induced cytidine deaminase (Aid−/− [also known as Aicda−/−]) which secrete only IgM antibodies, and human faecal samples. Methods: We studied the immunometabolic effects and gut microbial changes in high-fat-diet-induced obesity (HFDIO) in Aid−/− B6 mice compared with wild-type mice. To determine similarities between mice and humans, human stool samples were collected from children and adolescents who were obese with normal glucose tolerance (NGT), obese with glucose intolerance (IGT), or obese and newly diagnosed with type 2 diabetes, for faecal microbiota transplant (FMT) into germ-free (GF) B6 mice and we assessed IgM-bound bacteria and immune responses. Results: Compared with wild-type mice, Aid−/− B6 mice developed exacerbated HFDIO due to abundant levels of IgM. FMT from Aid−/− B6 to GF B6 mice promoted greater weight gain in recipient mice compared with FMT using wild-type mouse faecal microbiota. Obese youth with type 2 diabetes had more IgM-bound gut bacteria. Using the stools from the obese youth with type 2 diabetes for FMT to GF B6 mice, we observed that the gut microbiota promoted body weight gain and impaired glucose tolerance in the recipient GF B6 mice. Importantly, some clinical features of these obese young individuals were mirrored in the GF B6 mice following FMT. Conclusions/interpretation: Our results suggest that IgM-bound gut microbiota may play an important role in the immuno-pathogenesis of obesity and type 2 diabetes, and provide a novel link between IgM in obesity and type 2 diabetes in both mice and humans. Data availability: The 16s rRNA sequencing datasets supporting the current study have been deposited in the NCBI SRA public repository (https://www.ncbi.nlm.nih.gov/sra; accession no. SAMN18796639). Graphical abstract

Infections with Immunogenic Trypanosomes Reduce Tsetse Reproductive Fitness: Potential Impact of Different Parasite Strains on Vector Population Structure

The parasite Trypanosoma brucei rhodesiense and its insect vector Glossina morsitans morsitans were used to evaluate the effect of parasite clearance (resistance) as well as the cost of midgut infections on tsetse host fitness. Tsetse flies are viviparous and have a low reproductive capacity, giving birth to only 6–8 progeny during their lifetime. Thus, small perturbations to their reproductive fitness can have a major impact on population densities. We measured the fecundity (number of larval progeny deposited) and mortality in parasite-resistant tsetse females and untreated controls and found no differences. There was, however, a typanosome-specific impact on midgut infections. Infections with an immunogenic parasite line that resulted in prolonged activation of the tsetse immune system delayed intrauterine larval development resulting in the production of fewer progeny over the fly's lifetime. In contrast, parasitism with a second line that failed to activate the immune system did not impose a fecundity cost. Coinfections favored the establishment of the immunogenic parasites in the midgut. We show that a decrease in the synthesis of Glossina Milk gland protein (GmmMgp), a major female accessory gland protein associated with larvagenesis, likely contributed to the reproductive lag observed in infected flies. Mathematical analysis of our empirical results indicated that infection with the immunogenic trypanosomes reduced tsetse fecundity by 30% relative to infections with the non-immunogenic strain. We estimate that a moderate infection prevalence of about 26% with immunogenic parasites has the potential to reduce tsetse populations. Potential repercussions for vector population growth, parasite–host coevolution, and disease prevalence are discussed

Adaptive minimum-entropy hybrid compensation for compound faults of non-gaussian stochastic systems

This study investigated minimum-entropy hybrid fault-tolerant control (FTC) theory for non-Gaussian stochastic systems with compound faults. After fuzzy linearization for the singular systems, the output probability density function (PDF) is generated by rational square root B-splines. To deal with the compound faults consisting of single sensor fault and intermittent multiple actuator faults, an active-passive hybrid adaptive FTC scheme is proposed: A passive compensation function can directly reconstruct the algorithm to mask the sensor fault; then, actuator fault estimation accurately tracks the multiple actuator faults. Hence, the hybrid FTC combines estimated information and passive compensation simultaneously implements active actuator fault repair and passive sensor fault shielding. A novel variable parameter algorithm that mimics animal predation behavior is designed and incorporated into learning rates, making the controller more sensitive to the incipient deviations in actuator faults. Finally, with the optimal indicators containing entropy and mean of non-Gaussian PDF, the minimum-entropy FTC is achieved. Lyapunov and indicator functions prove the stability, simulation verifies the effectiveness of the methods.Published versio

Multiple-step fault estimation for interval type-II T-S fuzzy system of hypersonic vehicle with time-varying elevator faults

This article proposes a multiple-step fault estimation algorithm for hypersonic flight vehicles that uses an interval type-II Takagi–Sugeno fuzzy model. An interval type-II Takagi–Sugeno fuzzy model is developed to approximate the nonlinear dynamic system and handle the parameter uncertainties of hypersonic firstly. Then, a multiple-step time-varying additive fault estimation algorithm is designed to estimate time-varying additive elevator fault of hypersonic flight vehicles. Finally, the simulation is conducted in both aspects of modeling and fault estimation; the validity and availability of such method are verified by a series of the comparison of numerical simulation results.Published versio

To B or not to B - pathogenic and regulatory B cells in autoimmune diabetes

B cells have a vitally important function to produce antibodies which are directly pathogenic in some autoimmune diseases. However, it is clear that a number of other B cell functions are also critical in the pathogenesis of organ-specific autoimmune diseases that were previously thought to be mainly T cell mediated. Therapeutic agents that target B cells and their functions may therefore be of considerable importance in these autoimmune diseases. In this review, we will focus on B cell characteristics and functions that contribute to type 1 diabetes (T1D) and discuss why anti-B cell treatment may be effective in T1D, a disease that was previously considered to be primarily T cell mediated

Distributed adaptive containment control for a class of nonlinear multiagent systems with input quantization

This paper is devoted to distributed adaptive containment control for a class of nonlinear multiagent systems with input quantization. By employing a matrix factorization and a novel matrix normalization technique, some assumptions involving control gain matrices in existing results are relaxed. By fusing the techniques of sliding mode control and backstepping control, a two-step design method is proposed to construct controllers and, with the aid of neural networks, all system nonlinearities are allowed to be unknown. Moreover, a linear time-varying model and a similarity transformation are introduced to circumvent the obstacle brought by quantization, and the controllers need no information about the quantizer parameters. The proposed scheme is able to ensure the boundedness of all closed-loop signals and steer the containment errors into an arbitrarily small residual set. The simulation results illustrate the effectiveness of the scheme