Resilient Autonomous Control of Distributed Multi-agent Systems in Contested Environments

An autonomous and resilient controller is proposed for leader-follower multi-agent systems under uncertainties and cyber-physical attacks. The leader is assumed non-autonomous with a nonzero control input, which allows changing the team behavior or mission in response to environmental changes. A resilient learning-based control protocol is presented to find optimal solutions to the synchronization problem in the presence of attacks and system dynamic uncertainties. An observer-based distributed H_infinity controller is first designed to prevent propagating the effects of attacks on sensors and actuators throughout the network, as well as to attenuate the effect of these attacks on the compromised agent itself. Non-homogeneous game algebraic Riccati equations are derived to solve the H_infinity optimal synchronization problem and off-policy reinforcement learning is utilized to learn their solution without requiring any knowledge of the agent's dynamics. A trust-confidence based distributed control protocol is then proposed to mitigate attacks that hijack the entire node and attacks on communication links. A confidence value is defined for each agent based solely on its local evidence. The proposed resilient reinforcement learning algorithm employs the confidence value of each agent to indicate the trustworthiness of its own information and broadcast it to its neighbors to put weights on the data they receive from it during and after learning. If the confidence value of an agent is low, it employs a trust mechanism to identify compromised agents and remove the data it receives from them from the learning process. Simulation results are provided to show the effectiveness of the proposed approach

Optimal adaptive control of time-delay dynamical systems with known and uncertain dynamics

Delays are found in many industrial pneumatic and hydraulic systems, and as a result, the performance of the overall closed-loop system deteriorates unless they are explicitly accounted. It is also possible that the dynamics of such systems are uncertain. On the other hand, optimal control of time-delay systems in the presence of known and uncertain dynamics by using state and output feedback is of paramount importance. Therefore, in this research, a suite of novel optimal adaptive control (OAC) techniques are undertaken for linear and nonlinear continuous time-delay systems in the presence of uncertain system dynamics using state and/or output feedback. First, the optimal regulation of linear continuous-time systems with state and input delays by utilizing a quadratic cost function over infinite horizon is addressed using state and output feedback. Next, the optimal adaptive regulation is extended to uncertain linear continuous-time systems under a mild assumption that the bounds on system matrices are known. Subsequently, the event-triggered optimal adaptive regulation of partially unknown linear continuous time systems with state-delay is addressed by using integral reinforcement learning (IRL). It is demonstrated that the optimal control policy renders asymptotic stability of the closed-loop system provided the linear time-delayed system is controllable and observable. The proposed event-triggered approach relaxed the need for continuous availability of state vector and proven to be zeno-free. Finally, the OAC using IRL neural network based control of uncertain nonlinear time-delay systems with input and state delays is investigated. An identifier is proposed for nonlinear time-delay systems to approximate the system dynamics and relax the need for the control coefficient matrix in generating the control policy. Lyapunov analysis is utilized to design the optimal adaptive controller, derive parameter/weight tuning law and verify stability of the closed-loop system”--Abstract, page iv

Online Optimal Adaptive Control of Partially Uncertain Nonlinear Discrete-Time Systems using Multilayer Neural Networks

This article intends to address an online optimal adaptive regulation of nonlinear discrete-time systems in affine form and with partially uncertain dynamics using a multilayer neural network (MNN). The actor-critic framework estimates both the optimal control input and value function. Instantaneous control input error and temporal difference are used to tune the weights of the critic and actor networks, respectively. The selection of the basis functions and their derivatives are not required in the proposed approach. The state vector, critic, and actor NN weights are proven to be bounded using the Lyapunov method. Our approach can be extended to neural networks with an arbitrary number of hidden layers. We have demonstrated our approach via a simulation example

Using Ash Prepared from Almond Shell for Removing Acid Red 18 from Aqueous Solutions

زمینه و هدف: رنگ اسید رد 18 یکی از پر مصرف ترین رنگ در صنایع نساجی به شمار می روند که افزایش خطرات زیست محیطی را به دنبال دارند. بنابراین لازم است که پساب صنایع نساجی قبل از تخلیه به محیط با استفاده از روش های موثر مورد تصفیه قرار گیرند. هدف از این مطالعه، ارزیابی حذف رنگ اسید رد 18 از محلول های آبی با استفاده از خاکستر تهیه شده از پوسته بادام می باشد. روش بررسی: در این مطالعه تجربی از اسید رد 18 به عنوان نماینده رنگ های آزو استفاده شد. پوسته بادام به روش حرارتی فعال شد (تهیه خاکستر) و در یک سیستم منقطع برای حذف رنگ از محلول های آبی به کار گرفته شد. عملیات آزمایشگاهی در شرایط: pH (12-2)، زمان تماس (120-15 دقیقه)، مقدار جاذب (2-2/0 گرم بر لیتر)، غلظت اولیه رنگ (100-25 میلی گرم بر لیتر) و دمای آزمایشگاهی (Cº 1±25) انجام شد. یافته ها: نتایج نشان داد که بیشترین میزان حذف اسید رد 18 (90) به وسیله خاکستر تهیه شده از پوسته بادام در 2=pH، زمان تماس 60 دقیقه و دوز جاذب 6/1 گرم بر لیتر به دست می آید. همچنین داده های تعادل با مدل های ایزوترم لانگمویر و فروندلیچ تطبیق داده شد که حاصل امر نشانگر تطابق بهتر داده های حاصل از مطالعه حاضر با مدل جذب لانگمیر (0/9782=R2) نسبت به معادله جذب فروندلیچ (0/9579=R2) می باشد. نتیجه گیری: مطالعه حاضر نشان داد که فعال سازی حرارتی پوسته بادام و کاربرد آن در شرایط کنترل شده، روش موثری در حذف رنگ های آزو از پساب های نساجی می باشد

Optimal Adaptive Tracking Control Of Partially Uncertain Nonlinear Discrete-Time Systems Using Lifelong Hybrid Learning

This article addresses a multilayer neural network (MNN)-based optimal adaptive tracking of partially uncertain nonlinear discrete-time (DT) systems in affine form. By employing an actor–critic neural network (NN) to approximate the value function and optimal control policy, the critic NN is updated via a novel hybrid learning scheme, where its weights are adjusted once at a sampling instant and also in a finite iterative manner within the instants to enhance the convergence rate. Moreover, to deal with the persistency of excitation (PE) condition, a replay buffer is incorporated into the critic update law through concurrent learning. To address the vanishing gradient issue, the actor and critic MNN weights are tuned using control input and temporal difference errors (TDEs), respectively. In addition, a weight consolidation scheme is incorporated into the critic MNN update law to attain lifelong learning and overcome catastrophic forgetting, thus lowering the cumulative cost. The tracking error, and the actor and critic weight estimation errors are shown to be bounded using the Lyapunov analysis. Simulation results using the proposed approach on a two-link robot manipulator show a significant reduction in tracking error by $44\%$ and cumulative cost by $31\%$ in a multitask environment

Prevalence of qnr, intI, and intII genes in extendedspectrum beta-lactamase (ESBL)-producing Escherichia coli isolated from clinical samples in Iran

Purpose: To investigate the prevalence of qnr, intI, and intII genes in extended spectrum betalactamase (ESBL)-producing Escherichia coli isolated from clinical samples in Kerman, Iran.Methods: A total of 127 E. coli were collected from clinical samples in Kerman hospitals. The antibiotic susceptibility test was performed using disc diffusion method, while the presence of ESBL-producing E. coli was determined by phenotypic confirmatory test. Furthermore, the presence of qnrA, qnrB, qnrS, intI, intII, and β-lactamase-encoding genes was detected by polymerase chain reaction (PCR). Finally, the data were analyzed and associations between different genes and antibiotic resistance were evaluated.Results: The highest and lowest rates of resistance were observed against ampicillin (72.4 %) and imipenem (2.3 %), respectively. Also, 41.7 % of the isolates produced ESBL-enzymes. The qnrS and genes were detected in 6.3 and 0.78 %, respectively, of the isolates, while qnrA gene was not detected in the current study. The results revealed that 64.5 and 10.2 % of isolates carried intI and intII genes, respectively. Data analysis showed a significant association between ESBL production and class I integrin gene in E. coli isolates.Conclusions: Due to the variation in the resistance patterns of E. coli against antibiotics in different geographical regions, antimicrobial treatments should be based on local experience. Also, the coexistence of ESBL and intI gene in the majority of E. coli isolates suggests that care should be taken in choosing antibiotic therapy.Keywords: Extended-spectrum β-lactamase, E. coli, Integrin, Imipenem, Bacterial genes, Antibiotic resistanc

Mapping 123 million neonatal, infant and child deaths between 2000 and 2017

Since 2000, many countries have achieved considerable success in improving child survival, but localized progress remains unclear. To inform efforts towards United Nations Sustainable Development Goal 3.2—to end preventable child deaths by 2030—we need consistently estimated data at the subnational level regarding child mortality rates and trends. Here we quantified, for the period 2000–2017, the subnational variation in mortality rates and number of deaths of neonates, infants and children under 5 years of age within 99 low- and middle-income countries using a geostatistical survival model. We estimated that 32% of children under 5 in these countries lived in districts that had attained rates of 25 or fewer child deaths per 1,000 live births by 2017, and that 58% of child deaths between 2000 and 2017 in these countries could have been averted in the absence of geographical inequality. This study enables the identification of high-mortality clusters, patterns of progress and geographical inequalities to inform appropriate investments and implementations that will help to improve the health of all populations

Global injury morbidity and mortality from 1990 to 2017 : results from the Global Burden of Disease Study 2017

Correction:Background Past research in population health trends has shown that injuries form a substantial burden of population health loss. Regular updates to injury burden assessments are critical. We report Global Burden of Disease (GBD) 2017 Study estimates on morbidity and mortality for all injuries. Methods We reviewed results for injuries from the GBD 2017 study. GBD 2017 measured injury-specific mortality and years of life lost (YLLs) using the Cause of Death Ensemble model. To measure non-fatal injuries, GBD 2017 modelled injury-specific incidence and converted this to prevalence and years lived with disability (YLDs). YLLs and YLDs were summed to calculate disability-adjusted life years (DALYs). Findings In 1990, there were 4 260 493 (4 085 700 to 4 396 138) injury deaths, which increased to 4 484 722 (4 332 010 to 4 585 554) deaths in 2017, while age-standardised mortality decreased from 1079 (1073 to 1086) to 738 (730 to 745) per 100 000. In 1990, there were 354 064 302 (95% uncertainty interval: 338 174 876 to 371 610 802) new cases of injury globally, which increased to 520 710 288 (493 430 247 to 547 988 635) new cases in 2017. During this time, age-standardised incidence decreased non-significantly from 6824 (6534 to 7147) to 6763 (6412 to 7118) per 100 000. Between 1990 and 2017, age-standardised DALYs decreased from 4947 (4655 to 5233) per 100 000 to 3267 (3058 to 3505). Interpretation Injuries are an important cause of health loss globally, though mortality has declined between 1990 and 2017. Future research in injury burden should focus on prevention in high-burden populations, improving data collection and ensuring access to medical care.Peer reviewe