استكشف المجموعة الواسعة من العناوين المتاحة.

Ben wants to show Bella how his remote-controlled fire engine works, but the buttons aren't working properly and strange things are happening to Bella's dog.

This work proposes and realizes a control architecture that can support the deployment of a large‐scale robot skin in a Human‐Robot Collaboration scenario. It is shown, how whole‐body tactile feedback can extend the capabilities of robots during dynamic interactions by providing information about multiple contacts across the robot's surface. Specifically, an uncalibrated skin system is used to implement stable force control while simultaneously handling the multi‐contact interactions of a user. The system formulates control tasks for force control, tactile guidance, collision avoidance, and compliance, and fuses them with a multi‐priority redundancy resolution strategy. The approach is evaluated on an omnidirectional mobile‐manipulator with dual arms covered with robot skin. Results are assessed under dynamic conditions, showing that multi‐modal tactile information enables robust force control while at the same time remaining responsive to a user's interactions. The sense of touch is an important aspect of natural collaboration between humans and robots. This work extends the capabilities of robots during dynamic interactions with humans by using the tactile feedback of a large‐area skin system. The implemented control architecture is validated on a mobile manipulator and enables stable force control while performing a collaborative task with a human.

The use of high compression ratios on spark ignition engines enables the increase of thermal efficiency, but also contributes to the reduction of high load limit because of the higher auto-ignition tendency in the end-gas. Gaseous fuels provide a good option to expand the high load limits because of their high octane ratings, mostly in small engines. Biogas is a renewable fuel, mainly composed by CH 4 and CO 2 that exhibits high auto-ignition temperature and slow laminar flame speed. When biogas is burned in spark ignition engines partial and total misfire at low loads counteract the benefits achieved at high loads in which knocking combustion is reduced, hence the design of an effective control of the operating range based on the real-time observation of combustion instabilities is desirable. This paper presents an interactive control of the safe operating range through the modification of the spark time, equivalent ratio and throttle valve opening, taking as feedback the combustion instabilities, which are calculated from the in-cylinder pressure evolution for a biogas-fueled high compression ratio spark ignition engine. The interactive control was tested on a modified diesel engine converted to spark-ignition, original compression ratio of 15.5:1 and fueled with biogas. Control was able to keep a safe operating range with a maximum throttle valve opening of 39%, equivalence ratios within 0.6 and 1, and spark advances in the range of 329–358 crank angle degree. The coefficient of variation of IMEP was lower than 8%, whereas the maximum average knocking intensity was close to 2.5.

Much contemporary rhetoric regards the prospects and pitfalls of using artificial intelligence techniques to automate an increasing range of tasks, especially those once considered the purview of people alone. These accounts are often wildly optimistic, understating outstanding challenges while turning a blind eye to the human labor that undergirds and sustains ostensibly “automated” services. This long-standing focus on purely automated methods unnecessarily cedes a promising design space: one in which computational assistance augments and enriches, rather than replaces, people’s intellectual work. This tension between human agency and machine automation poses vital challenges for design and engineering. In this work, we consider the design of systems that enable rich, adaptive interaction between people and algorithms. We seek to balance the often-complementary strengths and weaknesses of each, while promoting human control and skillful action. We share case studies of interactive systems we have developed in three arenas—data wrangling, exploratory analysis, and natural language translation—that integrate proactive computational support into interactive systems. To improve outcomes and support learning by both people and machines, we describe the use of shared representations of tasks augmented with predictive models of human capabilities and actions. We conclude with a discussion of future prospects and scientific frontiers for intelligence augmentation research.

We need to rapidly detect and respond to public rumours, perceptions, attitudes and behaviours around COVID-19 and control measures. The creation of an interactive platform and dashboard to provide real-time alerts of rumours and concerns about coronavirus spreading globally would enable public health officials and relevant stakeholders to respond rapidly with a proactive and engaging narrative that can mitigate misinformation.

Many animal species organise into disordered swarms, polarised flocks or swirls to protect from predators or optimise foraging. Previous studies suggest that such collective states are related to a critical point, which could explain their balance between robustness to noise and high responsiveness regarding external perturbations. Here we provide experimental evidence for this idea by investigating the stability of swirls formed by light-responsive active colloids which adjust their individual motion to positions and orientations of neighbours. Because their behaviour can be precisely tuned, controlled changes between different collective states can be achieved. During the transition between stable swirls and swarms we observe a maximum of the group's susceptibility indicating the vicinity of a critical point. Our results support the idea of system-independent organisation principles of collective states and provide useful strategies for the realisation of responsive yet stable ensembles in microrobotic systems.

The death toll and economic loss resulting from the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic are stark reminders that we are vulnerable to zoonotic viral threats. Strategies are needed to identify and characterize animal viruses that pose the greatest risk of spillover and spread in humans and inform public health interventions. Using expert opinion and scientific evidence, we identified host, viral, and environmental risk factors contributing to zoonotic virus spillover and spread in humans. We then developed a risk ranking framework and interactive web tool, SpillOver, that estimates a risk score for wildlife-origin viruses, creating a comparative risk assessment of viruses with uncharacterized zoonotic spillover potential alongside those already known to be zoonotic. Using data from testing 509,721 samples from 74,635 animals as part of a virus discovery project and public records of virus detections around the world, we ranked the spillover potential of 887 wildlife viruses. Validating the risk assessment, the top 12 were known zoonotic viruses, including SARS-CoV-2. Several newly detected wildlife viruses ranked higher than known zoonotic viruses. Using a scientifically informed process, we capitalized on the recent wealth of virus discovery data to systematically identify and prioritize targets for investigation. The publicly accessible SpillOver platform can be used by policy makers and health scientists to inform research and public health interventions for prevention and rapid control of disease outbreaks. SpillOver is a living, interactive database that can be refined over time to continue to improve the quality and public availability of information on viral threats to human health.

Due to the under-actuated and strong coupling characteristics of quadrotor aircraft, traditional trajectory tracking methods have low control precision, and poor anti-interference ability. A novel fuzzy proportional-interactive-derivative (PID)-type iterative learning control (ILC) was designed for a quadrotor unmanned aerial vehicle (UAV). The control method combined PID-ILC control and fuzzy control, so it inherited the robustness to disturbances and system model uncertainties of the ILC control. A new control law based on the PID-ILC algorithm was introduced to solve the problem of chattering caused by an external disturbance in the ILC control alone. Fuzzy control was used to set the PID parameters of three learning gain matrices to restrain the influence of uncertain factors on the system and improve the control precision. The system stability with the new design was verified using Lyapunov stability theory. The Gazebo simulation showed that the proposed design method creates effective ILC controllers for quadrotor aircraft.

Summary Background Cardiopulmonary bypass initiates a systemic inflammatory response syndrome that is associated with postoperative morbidity and mortality. Steroids suppress inflammatory responses and might improve outcomes in patients at high risk of morbidity and mortality undergoing cardiopulmonary bypass. We aimed to assess the effects of steroids in patients at high risk of morbidity and mortality undergoing cardiopulmonary bypass. Methods The Steroids In caRdiac Surgery (SIRS) study is a double-blind, randomised, controlled trial. We used a central computerised phone or interactive web system to randomly assign (1:1) patients at high risk of morbidity and mortality from 80 hospital or cardiac surgery centres in 18 countries undergoing cardiac surgery with the use of cardiopulmonary bypass to receive either methylprednisolone (250 mg at anaesthetic induction and 250 mg at initiation of cardiopulmonary bypass) or placebo. Patients were assigned with block randomisation with random block sizes of 2, 4, or 6 and stratified by centre. Patients aged 18 years or older were eligible if they had a European System for Cardiac Operative Risk Evaluation of at least 6. Patients were excluded if they were taking or expected to receive systemic steroids in the immediate postoperative period or had a history of bacterial or fungal infection in the preceding 30 days. Patients, caregivers, and those assessing outcomes were masked to allocation. The primary outcomes were 30-day mortality and a composite of death and major morbidity (ie, myocardial injury, stroke, renal failure, or respiratory failure) within 30 days, both analysed by intention to treat. Safety outcomes were also analysed by intention to treat. This study is registered with ClinicalTrials.gov , number NCT00427388. Findings Patients were recruited between June 21, 2007, and Dec 19, 2013. Complete 30-day data was available for all 7507 patients randomly assigned to methylprednisolone (n=3755) and to placebo (n=3752). Methylprednisolone, compared with placebo, did not reduce the risk of death at 30 days (154 [4%] vs 177 [5%] patients; relative risk [RR] 0·87, 95% CI 0·70–1·07, p=0·19) or the risk of death or major morbidity (909 [24%] vs 885 [24%]; RR 1·03, 95% CI 0·95–1·11, p=0·52). The most common safety outcomes in the methylprednisolone and placebo group were infection (465 [12%] vs 493 [13%]), surgical site infection (151 [4%] vs 151 [4%]), and delirium (295 [8%] vs 289 [8%]). Interpretation Methylprednisolone did not have a significant effect on mortality or major morbidity after cardiac surgery with cardiopulmonary bypass. The SIRS trial does not support the routine use of methylprednisolone for patients undergoing cardiopulmonary bypass. Funding Canadian Institutes of Health Research.

Abstract Based on the operation process of dry bulk terminal and according to the unique process characteristics of dry bulk terminal, this study combs the specific distribution of key objects and key nodes of dry bulk terminal. Based on the analysis of the process mechanism and interactive information of dry bulk terminal, the collaborative control mechanism of key objects and key nodes is studied, which creates a certain theoretical framework for the automation and intelligent design of dry bulk terminal.