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Highlights Electrodeposited amorphous IrO x thin electrodes are first developed for proton exchange membrane electrolyzer cells. An ultralow loading of 0.075 mg cm−2 archives superior performance to catalyst-coated membrane (2 mg cm−2). > 96% of catalyst savings and > 42-fold higher catalyst utilization are demonstrated.

Perovskite-type oxide SrSn1-xVxO3 thin films with different concentrations x = 0.1–0.9 were fabricated by using pulsed-laser deposition, and the effects of V doping on the structure, optical band gap and the third-order optical nonlinearity were systematically investigated. With the increase of x value, the lattice parameters of SrSn1-xVxO3 decrease from 3.997 to 3.862 Å gradually, while the optical band gaps firstly increase and then decrease with boundary at x = 0.3. The third-order nonlinear optical responses were studied via the z-scan technique. The closed-aperture measurements show a negative nonlinear refractive index n2, and the open-aperture measurements demonstrate a saturable absorption β. Both the n2 and β responses vary with the increase of V doping level. The metal-oxygen chemical bond along with the localized V5+Sn2+V5+ complex contribute to the enhancement of optical nonlinearity, and the highest value of third-order susceptibility χ(3) is observed in SrSn0.5V0.5O3 film.

Highlights 4-nm-thick Pt nanosheets (Pt-NSs) are electrochemically grown on Ti substrates for hydrogen evolution reactions. Highly uniform Pt-NS surface coverage with an ultralow loading of 0.015 mgPt cm−2 is achieved. 99.5% catalyst savings and about 237-fold higher catalyst utilization are demonstrated.

Abstract Background Fluorescence image analysis in biochemical science often involves the complex tasks of identifying samples for analysis and calculating the desired information from the intensity traces. Analyzing giant unilamellar vesicles (GUVs) is one of these tasks. Researchers need to identify many vesicles to statistically analyze the degree of molecular interaction or state of molecular organization on the membranes. This analysis is complicated, requiring a careful manual examination by researchers, so automating the analysis can significantly aid in improving its efficiency and reliability. Results We developed a convolutional neural network (CNN) assisted intelligent analysis routine based on the whole 3D z-stack images. The programs identify the vesicles with desired morphology and analyzes the data automatically. The programs can perform protein binding analysis on the membranes or state decision analysis of domain phase separation. We also show that the method can easily be applied to similar problems, such as intensity analysis of phase-separated protein droplets. CNN-based classification approach enables the identification of vesicles even from relatively complex samples. We demonstrate that the proposed artificial intelligence-assisted classification can further enhance the accuracy of the analysis close to the performance of manual examination in vesicle selection and vesicle state determination analysis. Conclusions We developed a MATLAB based software capable of efficiently analyzing confocal fluorescence image data of giant unilamellar vesicles. The program can automatically identify GUVs with desired morphology and perform intensity-based calculation and state decision for each vesicle. We expect our method of CNN implementation can be expanded and applied to many similar problems in image data analysis.

Abstract With the increasing employment of robots in multiple areas such as smart manufacturing and intelligent transportation, both undergraduate and graduate students from computing related majors (e.g., computer science and information technology) demonstrated strong interests in learning robotics technology to broaden their career opportunities. However, instilling computing students with robotics knowledge remains a challenge since most of them have limited pre-training in engineering subjects such as electronics and mechatronics. Therefore, robotics education for computing students demands an immersive real-world learning environment by considering both theories and intensive hands-on projects. Different from traditional textbook-directed robotics learning, in this study, a situated learning-based robotics education pedagogy is proposed for computing students to equip them with robotics expertise and foster their problem-solving skills in real-world human–robot interaction contexts. To create a realistic human–robot collaboration situation, a multi-modal collaborative robot is employed in the classroom-based learning community for the whole semester. Mini-project-based homework and team projects are designed for students to practice their critical thinking and hands-on experiences. The bidirectional-evaluation approach is utilized by the instructor and students to assess the quality of the proposed pedagogy. Practice results and student evaluations suggested that the proposed situated learning-based pedagogy and robotics curriculum provided computing students to learn robotics in an effective way, which was well recognized and accepted by students even most of them were beginners. Future work of this study is also discussed.

Robots are increasingly being employed for diverse applications where they must work and coexist with humans. The trust in human–robot collaboration (HRC) is a critical aspect of any shared-task performance for both the human and the robot. The study of a human-trusting robot has been investigated by numerous researchers. However, a robot-trusting human, which is also a significant issue in HRC, is seldom explored in the field of robotics. Motivated by this gap, we propose a novel trust-assist framework for human–robot co-carry tasks in this study. This framework allows the robot to determine a trust level for its human co-carry partner. The calculations of this trust level are based on human motions, past interactions between the human–robot pair, and the human’s current performance in the co-carry task. The trust level between the human and the robot is evaluated dynamically throughout the collaborative task, and this allows the trust to change if the human performs false positive actions, which can help the robot avoid making unpredictable movements and causing injury to the human. Additionally, the proposed framework can enable the robot to generate and perform assisting movements to follow human-carrying motions and paces when the human is considered trustworthy in the co-carry task. The results of our experiments suggest that the robot effectively assists the human in real-world collaborative tasks through the proposed trust-assist framework.

Most robots are programmed to carry out specific tasks routinely with minor variations. However, more and more applications from SMEs require robots work alongside their counterpart human workers. To smooth the collaboration task flow and improve the collaboration efficiency, a better way is to formulate the robot to surmise what kind of assistance a human coworker needs and naturally take the right action at the right time. This paper proposes a prediction-based human-robot collaboration model for assembly scenarios. An embedded learning from demonstration technique enables the robot to understand various task descriptions and customized working preferences. A state-enhanced convolutional long short-term memory (ConvLSTM)-based framework is formulated for extracting the high-level spatiotemporal features from the shared workspace and predicting the future actions to facilitate the fluent task transition. This model allows the robot to adapt itself to predicted human actions and enables proactive assistance during collaboration. We applied our model to the seats assembly experiment for a scale model vehicle and it can obtain a human worker’s intentions, predict a coworker’s future actions, and provide assembly parts correspondingly. It has been verified that the proposed framework yields higher smoothness and shorter idle times, and meets more working styles, compared to the state-of-the-art methods without prediction awareness.

An autonomous household robot passed a self-awareness test in 2015, proving that the cognitive capabilities of robots are heading towards those of humans. While this is a milestone in AI, it raises questions about legal implications. If robots are progressively developing cognition, it is important to discuss whether they are entitled to justice pursuant to conventional notions of human rights. This paper offers a comprehensive discussion of this complex question through cross-disciplinary scholarly sources from computer science, ethics, and law. The computer science perspective dissects hardware and software of robots to unveil whether human behavior can be efficiently replicated. The ethics perspective utilizes insights from robot ethics scholars to help decide whether robots can act morally enough to be endowed with human rights. The legal perspective provides an in-depth discussion of human rights with an emphasis on eligibility. The article concludes with recommendations including open research issues.

Intelligent robotic vehicles are more and more fully automated, without steering wheels, gas/brake pedals, or gearshifts. However, allowing the human driver to step in and maneuver the robotic vehicle under specific driving requirements is a necessary issue that should be considered. To this end, we propose a wearable-sensing-based hands-free maneuver intention understanding approach to assist the human to naturally operate the robotic vehicle without physical contact. The human intentions are interpreted and modeled based on the fuzzy control using the forearm postures and muscle activities information detected by a wearable sensory system, which incorporates electromyography (EMG) sensors and inertial measurement unit (IMU). Based on the maneuver intention understanding model, the human can flexibly, intuitively, and conveniently control diverse vehicle maneuvers only using his intention expressions. This approach was implemented by a series of experiments in the practical situations on a lab-based 1/10 robotic vehicle research platform. Experimental results and evaluations demonstrated that, by taking advantage of the nonphysical contact and natural handleability of this approach, the robotic vehicle was successfully and effectively maneuvered to finish the driving tasks with considerable accuracy and robustness in human-robotic vehicle interaction.

Perovskite-type oxide SrSn1-xVxO3 thin films with different concentrations x = 0.1-0.9 were fabricated by using pulsed-laser deposition, and the effects of V doping on the structure, optical band gap and the third-order optical nonlinearity were systematically investigated. With the increase of x value, the lattice parameters of SrSn1-xVxO3 decrease from 3.997 to 3.862 Å gradually, while the optical band gaps firstly increase and then decrease with boundary at x = 0.3. The third-order nonlinear optical responses were studied via the z-scan technique. The closed-aperture measurements show a negative nonlinear refractive index n2, and the open-aperture measurements demonstrate a saturable absorption ß. Both the n2 and ß responses vary with the increase of V doping level. The metal-oxygen chemical bond along with the localized V5+Sn2+V5+ complex contribute to the enhancement of optical nonlinearity, and the highest value of third-order susceptibility ?(3) is observed in SrSn0.5V0.5O3 film. [ABSTRACT FROM AUTHOR]