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Multi-access edge computing (MEC) enables end devices with limited computing power to provide effective solutions while dealing with tasks that are computationally challenging. When each end device in an MEC scenario generates multiple tasks, how to reasonably and effectively schedule these tasks is a large-scale discrete action space problem. In addition, how to exploit the objectively existing spatial structure relationships in the given scenario is also an important factor to be considered in task-scheduling algorithms. In this work, we consider indivisible, time-sensitive tasks under this scenario and formalize the task-scheduling problem to minimize the long-term losses. We propose a multiagent collaborative deep reinforcement learning (DRL)-based distributed scheduling algorithm based on graph attention neural networks (GATs) to solve task-scheduling problems in the MEC scenario. Each end device creates a graph representation agent to extract potential spatial features in the scenario and a scheduling agent to extract the timing-related features of the tasks and make scheduling decisions using a gated recurrent unit (GRU). The simulation results show that, compared with several baseline algorithms, our proposed algorithm can take advantage of the spatial positional relationship of devices in the environment, significantly reduce the average delay and drop rate, and improve link utilization. [ABSTRACT FROM AUTHOR]

Human extended pluripotent stem (hEPS) cell is a newly established human embryonic stem cell (hESC) line with the capacity of chimerizing both embryonic and extraembryonic tissues compared with primed hESCs which are inefficient to contribute to the inner cell mass (ICM). The molecular mechanism underlying the pluripotency of hEPS cells is still not clear. We conducted RNA-seq and ATAC-seq analysis to investigate the differential expression profiling and genomic chromatin accessibility features. According to our data, more than 2000 genes were specially up-regulated in hEPS cells. Furthermore, the open chromatin regions in these two human embryonic stem cell lines were quite different. In hEPS cells, transcriptional factors binding motifs associated with pluripotency maintenance were enriched in chromatin accessible regions. Integrating the results from ATAC-seq and RNA-seq, we identified new regulatory features which were important for pluripotency maintenance and cell development in hEPS cells. Together, these results provided a new perspective on the understanding of molecular features of hESCs in different pluripotent states and a novel resource for further studies on regenerative medicine by using hEPS cells. • In RNA-seq analysis, hEPS cells have a specific transcription profile different from primed hESCs. • ATAC-seq reveals the distinct chromatin accessibility features in hEPS cells. • Remarkable differences in transcriptional factor binding motifs between hEPS cells and primed hESCs. • By integration of RNA-seq and ATAC-seq results, a number of genes regulating cell development in hEPS cells were identified. [ABSTRACT FROM AUTHOR]

As the next-generation power grid system, the smart grid can realize the balance of supply and demand and help in communication security and privacy protection. However, real-time power consumption data collection might expose the users' privacy information, such as their living habits and economic conditions. In addition, during the process of data transmission, it may lead to data inconsistency between the user side and the storage side. Blockchain provides tamper-resistant and traceable characteristics for solving these problems, and ring signature schemes provide an anonymous authentication mechanism. Therefore, in this work, we consider the applications of ring signature scheme in smart grid based on blockchain. We introduce the notion of multi-authority traceable ring signature (MA-TRS) scheme for distributed setting. In our scheme, there is an auditing node that can distinguish the identity of the real signer from the ring without any secret information. Last but not least, we prove that the proposed scheme is unforgeable, anonymous, and traceable. [ABSTRACT FROM AUTHOR]

The IoT era observes the increasing demand for data to support various applications and services. The Mobile Crowdsensing (MCS) system then emerged. By utilizing the hybrid intelligence of humans and sensors, it is significantly beneficial to keep collecting high-quality sensing data for all kinds of IoT applications, such as environmental monitoring, intelligent healthcare services, and traffic management. However, the service quality of MCS systems relies on a dedicated designed task allocation framework, which needs to consider the participant resource bottleneck and system utility at the same time. Recent studies tend to use a different solution to solve the two challenges. The incentive mechanism is for resolving the participant shortage problem, and task assignment methods are studied to find the best match of participants and system utility goal of MCS. Thus, existing task allocation frameworks fail to consider the participant's expectations deeply. We propose a semiopportunistic concept-based solution to overcome this issue. Similar to the "shared mobility" concept, our proposed task allocation framework can offer the participants routing advice without disturbing their original travel plan. The participant can accomplish the sensing request on his route. We further consider the system constraints to determine a subgroup of participants that can obtain the utility optimization goal. Specifically, we use the Graph Attention Network (GAT) to produce the target sensing area's virtual representation and provide the participant with a payoff-maximized route. Such a method makes our solution adapt to most of MCS scenarios' conditions instead of using fixed system settings. Then, a reinforcement learning- (RL-) based task assignment is adopted, which can help the MCS system towards better performance improvements while support different utility functions. The simulation results on various conditions demonstrate the superior performance of the proposed solution. [ABSTRACT FROM AUTHOR]

Abstract The electrochemical behavior of hydroquinone in unbuffered aqueous solution was investigated using cyclic voltammetry. Subsequent cathodic scan showed a new cathodic peak at the negative potential and another similar cathodic peak for catechol. The peak current increased with the increasing pH of the bulk solution and concentration of hydroquinone. We observed that only the new cathodic peak increased, whereas the other peaks remained constant with the addition of quinone to the test solution. This new cathodic peak was caused by the ultrafast diffusion of proton from the electrode surface to the bulk solution. The results can help us understand the unique characteristics of hydroquinone in unbuffered solution. Graphical abstract The protons released by QH 2 rapidly diffused into the unbuffered bulk solution. Unlabelled Image Highlights • CVs of hydroquinone or catechol in unbuffered solution exhibit an extra cathodic peak at negative potential. • The pH gradient between the electrode surface and the bulk solution promoted the magnitude of the cathodic peak. • The ultrafast diffusion of proton caused a new cathodic preak in unbuffered solution. [ABSTRACT FROM AUTHOR]