Single channel based interference-free and self-powered human-machine interactive interface using eigenfrequency-dominant mechanism

The recent development of wearable devices is revolutionizing the way of human-machine interaction (HMI). Nowadays, an interactive interface that carries more embedded information is desired to fulfil the increasing demand in era of Internet of Things. However, present approach normally relies on sensor arrays for memory expansion, which inevitably brings the concern of wiring complexity, signal differentiation, power consumption, and miniaturization. Herein, a one-channel based self-powered HMI interface, which uses the eigenfrequency of magnetized micropillar (MMP) as identification mechanism, is reported. When manually vibrated, the inherent recovery of the MMP caused a damped oscillation that generates current signals because of Faraday's Law of induction. The time-to-frequency conversion explores the MMP-related eigenfrequency, which provides a specific solution to allocate diverse commands in an interference-free behavior even with one electric channel. A cylindrical cantilever model was built to regulate the MMP eigenfrequencies via precisely designing the dimensional parameters and material properties. We show that using one device and two electrodes, high-capacity HMI interface can be realized when the MMPs with different eigenfrequencies have been integrated. This study provides the reference value to design the future HMI system especially for situations that require a more intuitive and intelligent communication experience with high-memory demand.Comment: 35 pages, 6 figure

Planning urban energy systems adapting to extreme weather

In the context of increasing urbanization and climate change globally, urban energy systems (UES) planning needs adequate consideration of climate change, particularly to ensure energy supply during extreme weather events (EWE) such as heatwaves, floods, and typhoons. Here we propose a two-layer modeling framework for UES planning considering the impact of EWE. An application of the framework to a typical coastal city of Xiamen, China reveals that deploying energy storage (i.e., pumped hydro and battery) offers significant flexibility to ensure the critical demand is met during typhoon as a typical EWE and avoids over investment in supply technologies. This requires an extra 2.8% total cost on investment and operation of UES for 20 years. Planning energy systems with proper consideration of EWE can ensure robust urban energy services even with increasing penetration of fluctuating renewables, and we offer a flexible and computationally efficient paradigm for UES planning considering the impact of EWE

Cost-efficient decarbonization of local energy systems by whole-system based design optimization

On the way toward Net Zero 2050, the UK government set the 2035 target by slashing 78 % emissions compared to the 1990-level. To help understand how an electrified local energy system could contribute to this target and the associated cost, we develop a whole-system based local energy optimization (LEO) model. The model captures a series of state-of-the-art technologies including building fabric retrofit, battery storage, electro-mobility, electro-heating, demand response, distributed renewable, and Peer-to-Peer (P2P) energy trading. And the model enables trade-off assessment between cost and emissions minimization, compares two system operating modes, i.e., cost-oriented and grid-impact-oriented, and evaluates the impacts from weather risks and capital cost assumptions. A case study in Wales reveals (1) capital cost assumptions can lead up to 30.8 % overall cost difference of the local energy system; (2) operating the system in cost-oriented mode can save up to 5 % cost than in the grid-impact-oriented mode; (3) electro-heating by heat pumps has the highest priority among all investigated technologies. Overall, this study demonstrates how to design and operate a cost-efficient and electrified UK local energy system by the whole-system incorporation of near-term technical and business model advances towards a decarbonized future

Engineering human ventricular heart muscles based on a highly efficient system for purification of human pluripotent stem cell-derived ventricular cardiomyocytes

Background Most infarctions occur in the left anterior descending coronary artery and cause myocardium damage of the left ventricle. Although current pluripotent stem cells (PSCs) and directed cardiac differentiation techniques are able to generate fetal-like human cardiomyocytes, isolation of pure ventricular cardiomyocytes has been challenging. For repairing ventricular damage, we aimed to establish a highly efficient purification system to obtain homogeneous ventricular cardiomyocytes and prepare engineered human ventricular heart muscles in a dish. Methods The purification system used TALEN-mediated genomic editing techniques to insert the neomycin or EGFP selection marker directly after the myosin light chain 2 (MYL2) locus in human pluripotent stem cells. Purified early ventricular cardiomyocytes were estimated by immunofluorescence, fluorescence-activated cell sorting, quantitative PCR, microelectrode array, and patch clamp. In subsequent experiments, the mixture of mature MYL2-positive ventricular cardiomyocytes and mesenchymal cells were cocultured with decellularized natural heart matrix. Histological and electrophysiology analyses of the formed tissues were performed 2 weeks later. Results Human ventricular cardiomyocytes were efficiently isolated based on the purification system using G418 or flow cytometry selection. When combined with the decellularized natural heart matrix as the scaffold, functional human ventricular heart muscles were prepared in a dish. Conclusions These engineered human ventricular muscles can be great tools for regenerative therapy of human ventricular damage as well as drug screening and ventricular-specific disease modeling in the future. Electronic supplementary material The online version of this article (doi:10.1186/s13287-017-0651-x) contains supplementary material, which is available to authorized users

Effects of Low-Level Autonomic Stimulation on Prevention of Atrial Fibrillation Induced by Acute Electrical Remodeling

Background. Rapid atrial pacing (RAP) can induce electrical and autonomic remodeling and facilitate atrial fibrillation (AF). Recent reports showed that low-level vagosympathetic nerve stimulation (LLVNS) can suppress AF, as an antiarrhythmic effect. We hypothesized that LLVNS can reverse substrate heterogeneity induced by RAP. Methods and Results. Mongrel dogs were divided into (LLVNS+RAP) and RAP groups. Electrode catheters were sutured to multiple atrial sites, and LLVNS was applied to cervical vagosympathetic trunks with voltage 50% below the threshold slowing sinus rate by ⩽30 msec. RAP induced a significant decrease in effective refractory period (ERP) and increase in the window of vulnerability at all sites, characterized by descending and elevated gradient differences towards the ganglionic plexi (GP) sites, respectively. The ERP dispersion was obviously enlarged by RAP and more significant when the ERP of GP-related sites was considered. Recovery time from AF was also prolonged significantly as a result of RAP. LLVNS could reverse all these changes induced by RAP and recover the heterogeneous substrate to baseline. Conclusions. LLVNS can reverse the electrical and autonomic remodeling and abolish the GP-central gradient differences induced by RAP, and thus it can recover the homogeneous substrate, which may be the underlying mechanism of its antiarrhythmic effect

Artificial intelligence for diagnosis and Gleason grading of prostate cancer: The PANDA challenge

Through a community-driven competition, the PANDA challenge provides a curated diverse dataset and a catalog of models for prostate cancer pathology, and represents a blueprint for evaluating AI algorithms in digital pathology. Artificial intelligence (AI) has shown promise for diagnosing prostate cancer in biopsies. However, results have been limited to individual studies, lacking validation in multinational settings. Competitions have been shown to be accelerators for medical imaging innovations, but their impact is hindered by lack of reproducibility and independent validation. With this in mind, we organized the PANDA challenge-the largest histopathology competition to date, joined by 1,290 developers-to catalyze development of reproducible AI algorithms for Gleason grading using 10,616 digitized prostate biopsies. We validated that a diverse set of submitted algorithms reached pathologist-level performance on independent cross-continental cohorts, fully blinded to the algorithm developers. On United States and European external validation sets, the algorithms achieved agreements of 0.862 (quadratically weighted kappa, 95% confidence interval (CI), 0.840-0.884) and 0.868 (95% CI, 0.835-0.900) with expert uropathologists. Successful generalization across different patient populations, laboratories and reference standards, achieved by a variety of algorithmic approaches, warrants evaluating AI-based Gleason grading in prospective clinical trials.KWF Kankerbestrijding ; Netherlands Organization for Scientific Research (NWO) ; Swedish Research Council European Commission ; Swedish Cancer Society ; Swedish eScience Research Center ; Ake Wiberg Foundation ; Prostatacancerforbundet ; Academy of Finland ; Cancer Foundation Finland ; Google Incorporated ; MICCAI board challenge working group ; Verily Life Sciences ; EIT Health ; Karolinska Institutet ; MICCAI 2020 satellite event team ; ERAPerMe

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure