Output mini-max control for polynomial systems: analysis and applications

This paper presents a solution to a robust optimal regulation problem for a nonlinear polynomial system affected by parametric and matched uncertainties, which is based only on partial state information. The parameters describing the dynamics of the nonlinear polynomial plant depend on a vector of unknown parameters, which belongs to a finite parametric set, and the application of a certain control input is associated with the worst or least favourable value of the unknown parameter. A high-order sliding mode state reconstructor is designed for the nonlinear plant in such a way that the previously designed control can be applied for a system with incomplete information. Additionally, the matched uncertainty is also compensated by means of the same output-based regulator. The obtained algorithm is applied to control an uncertain nonlinear inductor circuit of the third order and a mechanical pendulum of the third order, successfully verifying the effectiveness of the developed approach.     

Effects of Lifestyle Intervention in Tissue-Specific Lipidomic Profile of Formerly Obese Mice

Lipids are highly diverse in their composition, properties and distribution in different biological entities. We aim to establish the lipidomes of several insulin-sensitive tissues and to test their plasticity when divergent feeding regimens and lifestyles are imposed. Here, we report a proton nuclear magnetic resonance (¹H-NMR) study of lipid abundance across 4 tissues of C57Bl6J male mice that includes the changes in the lipid profile after every lifestyle intervention. Every tissue analysed presented a specific lipid profile irrespective of interventions. Glycerolipids and fatty acids were most abundant in epididymal white adipose tissue (eWAT) followed by liver, whereas sterol lipids and phosphoglycerolipids were highly enriched in hypothalamus, and gastrocnemius had the lowest content in all lipid species compared to the other tissues. Both when subjected to a high-fat diet (HFD) and after a subsequent lifestyle intervention (INT), the lipidome of hypothalamus showed no changes. Gastrocnemius and liver revealed a pattern of increase in content in many lipid species after HFD followed by a regression to basal levels after INT, while eWAT lipidome was affected mainly by the fat composition of the administered diets and not their caloric density. Thus, the present study demonstrates a unique lipidome for each tissue modulated by caloric intake and dietary composition.     

Architecting Graphene Oxide Rolled‐Up Micromotors: A Simple Paper‐Based Manufacturing Technology

A graphene oxide rolled‐up tube production process is reported using wax‐printed membranes for the fabrication of on‐demand engineered micromotors at different levels of oxidation, thickness, and lateral dimensions. The resultant graphene oxide rolled‐up tubes can show magnetic and catalytic movement within the addition of magnetic nanoparticles or sputtered platinum in the surface of graphene‐oxide‐modified wax‐printed membranes prior to the scrolling process. As a proof of concept, the as‐prepared catalytic graphene oxide rolled‐up micromotors are successfully exploited for oil removal from water. This micromotor production technology relies on an easy, operator‐friendly, fast, and cost‐efficient wax‐printed paper‐based method and may offer a myriad of hybrid devices and applications.     

Solving multi-objective rescheduling problems in dynamic permutation flow shop environments with disruptions

In multi-objective optimisation problems, optimal decisions need to be made in the presence of trade-offs among conflicting objectives which may sometimes be expressed in different units of measure. This makes it difficult to reduce the problem to a single-objective optimisation. Furthermore, when disruptive changes emerge in manufacturing environments, such as the arrival of new jobs or machine breakdowns, the scheduling system should be adapted by responding quickly. In this paper, we propose a rescheduling architecture for solving the problem based on a predictive-reactive strategy and a new method to calculate the reactive schedule in each rescheduling period. Additionally, we developed a methodology that allows the use of multi-objective performance metrics to evaluate dispatching rules. These rules are applied at a benchmark specifically designed for this paper considering three objective functions: makespan, total weighted tardiness and stability. Three types of disruptions are also considered: arrivals of new jobs, machine breakdowns and variations in job processing times. Results showed that the RANDOM rule provides a better behaviour compared to other evaluated rules and a lower ratio of non-dominated solutions compared to ATC (apparent tardiness cost) and FIFO (first-in-first-out) rules. Moreover, the behaviour of the hypervolume metric depends on the problem dimensions.     

High Electrical Conductivity of Single Metal–Organic Chains

Molecular wires are essential components for future nanoscale electronics. However, the preparation of individual long conductive molecules is still a challenge. MMX metal–organic polymers are quasi‐1D sequences of single halide atoms (X) bridging subunits with two metal ions (MM) connected by organic ligands. They are excellent electrical conductors as bulk macroscopic crystals and as nanoribbons. However, according to theoretical calculations, the electrical conductance found in the experiments should be even higher. Here, a novel and simple drop‐casting procedure to isolate bundles of few to single MMX chains is demonstrated. Furthermore, an exponential dependence of the electrical resistance of one or two MMX chains as a function of their length that does not agree with predictions based on their theoretical band structure is reported. This dependence is attributed to strong Anderson localization originated by structural defects. Theoretical modeling confirms that the current is limited by structural defects, mainly vacancies of iodine atoms, through which the current is constrained to flow. Nevertheless, measurable electrical transport along distances beyond 250 nm surpasses that of all other molecular wires reported so far. This work places in perspective the role of defects in 1D wires and their importance for molecular electronics.     

Manipulation and Steering of Hyperbolic Surface Polaritons in Hexagonal Boron Nitride

Hexagonal boron nitride (hBN) is a natural hyperbolic material that supports both volume‐confined hyperbolic polaritons and sidewall‐confined hyperbolic surface polaritons (HSPs). In this work, efficient excitation, control, and steering of HSPs are demonstrated in hBN through engineering the geometry and orientation of hBN sidewalls. By combining infrared nanoimaging and numerical simulations, the reflection, transmission, and scattering of HSPs are investigated at the hBN corners with various apex angles. It is also shown that the sidewall‐confined nature of HSPs enables a high degree of control over their propagation by designing the geometry of hBN nanostructures.     

Early prediction for physical human robot collaboration in the operating room

To enable a natural and fluent human robot collaboration flow, it is critical for a robot to comprehend their human peers’ on-going actions, predict their behaviors in the near future, and plan its actions correspondingly. Specifically, the capability of making early predictions is important, so that the robot can foresee the precise timing of a turn-taking event and start motion planning and execution early enough to smooth the turn-taking transition. Such proactive behavior would reduce human’s waiting time, increase efficiency and enhance naturalness in collaborative task. To that end, this paper presents the design and implementation of an early turn-taking prediction algorithm, catered for physical human robot collaboration scenarios. Specifically, a robotic scrub nurse system which can comprehend surgeon’s multimodal communication cues and perform turn-taking prediction is presented. The developed algorithm was tested on a collected data set of simulated surgical procedures in a surgeon–nurse tandem. The proposed turn-taking prediction algorithm is found to be significantly superior to its algorithmic counterparts, and is more accurate than human baseline when little partial input is given (less than 30% of full action). After observing more information, the algorithm can achieve comparable performances as humans with a F1 score of 0.90.     

An experimental study on the applicability of SYN cookies to networked constrained devices

The Internet protocol suite is increasingly used on devices with constrained resources that operate as both clients and servers within the Internet of Things paradigm. However, these devices usually apply few—if any—security measures. Therefore, they are vulnerable to network attacks, particularly to denial of service attacks. The well‐known SYN flood attack works by filling up the connection queue with fake SYN requests. When the queue is full, new connections cannot be opened until some entries are removed after a time‐out. Class 2 constrained devices—according to the RFC 7228—are highly vulnerable to this attack because of their limited available memory, even in low‐rate attacks. This paper analyses and compares in a class 2 constrained device the performance of 2 commonly used defence mechanisms (ie, recycle half‐open connections and SYN cookies) during a low‐rate SYN flood. We first review 2 SYN cookies implementations (ie, Linux and FreeBSD) and compare them with a hybrid approach in a class 2 device. Finally, experimental results prove that the proposed SYN cookies implementation is more effective than recycling the oldest half‐open connections.     

Robust Navigation and Seamless Localization for Carlike Robots in Indoor‐outdoor Environments

This paper describes a navigation and seamless localization system that permits carlike robots to move safely in heterogeneous scenarios within indoor and outdoor environments. The robot localization integrates different sensor (GPS, odometry, laser rangefinders) information depending on the kind of area (indoors, outdoors, and areas between) or on the sensor uncertainty in such a way that there are no discontinuities in the localization, and a bounded uncertainty is constantly maintained. Transitions through indoor and outdoor environments are thoroughly considered to assure a smooth change in‐between. The paper addresses a navigation technique that combines two well‐known obstacle avoidance techniques, namely the nearness diagram and the dynamic window approaches, exploiting the advantages and properties of both, and integrating the seamless localization technique. The navigation technique is developed for carlike robots by considering their shape and kinodynamic constraints, and the restrictions imposed by the environment. Forward‐backward maneuvers are also integrated in the method, allowing difficult situations in dense scenarios to be managed. The whole system has been tested in simulations and experiments in real large‐scale scenarios.     

<Emphasis FontCategory="NonProportional">umd-verification</Emphasis>: Automation of Software Validation for the EGI Federated e-Infrastructure

Supporting e-Science in the EGI e-Infrastructure requires extensive and reliable software, for advanced computing use, deployed across over approximately 300 European and worldwide data centers. The Unified Middleware Distribution (UMD) and Cloud Middleware Distribution (CMD) are the channels to deliver the software for the EGI e-Infrastructure consumption. The software is compiled, validated and distributed following the Software Provisioning Process (SWPP), where the Quality Criteria (QC) definition sets the minimum quality requirements for EGI acceptance. The growing number of software components currently existing within UMD and CMD distributions hinders the application of the traditional, manual-based validation mechanisms, thus driving the adoption of automated solutions. This paper presents umd-verification, an open-source tool that enforces the fulfillment of the QC requirements in an automated way for the continuous validation of the software products for scientific disposal. The umd-verification tool has been successfully integrated within the SWPP pipeline and is progressively supporting the full validation of the products in the UMD and CMD repositories. While the cost of supporting new products is dependant on the availability of Infrastructure as Code solutions to take over the deployment and high test coverage, the results obtained for the already integrated products are promising, as the time invested in the validation of products has been drastically reduced. Furthermore, automation adoption has brought along benefits for the reliability of the process, such as the removal of human-associated errors or the risk of regression of previously tested functionalities.