Guest Editorial: Special Issue on "Advance in Mobile Edge Computing"

Cloud computing has a problem for communication-intensive applications, which need to meet the delay requirements. The problem becomes more intense with the huge application of the Internet of Things. Mobile Edge Computing processes data at the nearest available nodes and is emerging as a promising computation architecture to handle ever-increasing demands. In this special issue, we selected five papers from ChinaCom 2020 in their extended versions and public call for papers

High-performance electrochemical CO2 reduction cells based on non-noble metal catalysts

The promise and challenge of electrochemical mitigation of CO2 calls for innovations on both catalyst and reactor levels. In this work, enabled by our high-performance and earth-abundant CO2 electroreduction catalyst materials, we developed alkaline microflow electrolytic cells for energy-efficient, selective, fast, and durable CO2 conversion to CO and HCOO-. With a cobalt phthalocyanine-based cathode catalyst, the CO-selective cell starts to operate at a 0.26 V overpotential and reaches a Faradaic efficiency of 94% and a partial current density of 31 mA/cm2 at a 0.56 V overpotential. With a SnO2-based cathode catalyst, the HCOO--selective cell starts to operate at a 0.76 V overpotential and reaches a Faradaic efficiency of 82% and a partial current density of 113 mA/cm2 at a 1.36 V overpotential. In contrast to previous studies, we found that the overpotential reduction from using the alkaline electrolyte is mostly contributed by a pH gradient near the cathode surface

Spontaneous rotational symmetry breaking in KTaO3_3 interface superconductors

Strongly correlated electrons could display intriguing spontaneous broken symmetries in the ground state. Understanding these symmetry breaking states is fundamental to elucidate the various exotic quantum phases in condensed matter physics. Here, we report an experimental observation of spontaneous rotational symmetry breaking of the superconductivity at the interface of YAlO$_3$/KTaO$_3$ (111) with a superconducting transition temperature of 1.86 K. Both the magnetoresistance and upper critical field in an in-plane field manifest striking twofold symmetric oscillations deep inside the superconducting state, whereas the anisotropy vanishes in the normal state, demonstrating that it is an intrinsic property of the superconducting phase. We attribute this behavior to the mixed-parity superconducting state, which is an admixture of $s$-wave and $p$-wave pairing components induced by strong spin-orbit coupling. Our work demonstrates an unconventional nature of the pairing interaction in the KTaO$_3$ interface superconductor, and provides a new platform to clarify a delicate interplay of electron correlation and spin-orbit coupling.Comment: 7 pages, 4 figure

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Non-standard errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Social Network Supported Process Recommender System

Process recommendation technologies have gained more and more attention in the field of intelligent business process modeling to assist the process modeling. However, most of the existing technologies only use the process structure analysis and do not take the social features of processes into account, while the process modeling is complex and comprehensive in most situations. This paper studies the feasibility of social network research technologies on process recommendation and builds a social network system of processes based on the features similarities. Then, three process matching degree measurements are presented and the system implementation is discussed subsequently. Finally, experimental evaluations and future works are introduced