Strangeness-driven exploration in multi-agent reinforcement learning.

In this study, a novel exploration method for centralized training and decentralized execution (CTDE)-based multi-agent reinforcement learning (MARL) is introduced. The method uses the concept of strangeness, which is determined by evaluating (1) the level of the unfamiliarity of the observations an agent encounters and (2) the level of the unfamiliarity of the entire state the agents visit. An exploration bonus, which is derived from the concept of strangeness, is combined with the extrinsic reward obtained from the environment to form a mixed reward, which is then used for training CTDE-based MARL algorithms.

Intelligent Task Dispatching and Scheduling Using a Deep Q-Network in a Cluster Edge Computing System.

Recently, intelligent IoT applications based on artificial intelligence (AI) have been deployed with mobile edge computing (MEC). Intelligent IoT applications demand more computing resources and lower service latencies for AI tasks in dynamic MEC environments. Thus, in this paper, considering the resource scalability and resource optimization of edge computing, an intelligent task dispatching model using a deep Q-network, which can efficiently use the computing resource of edge nodes is proposed to maximize the computation ability of the cluster edge system, which consists of multiple edge nodes.

Compound Context-Aware Bayesian Inference Scheme for Smart IoT Environment.

The objective of smart cities is to improve the quality of life for citizens by using Information and Communication Technology (ICT). The smart IoT environment consists of multiple sensor devices that continuously produce a large amount of data. In the IoT system, accurate inference from multi-sensor data is imperative to make a correct decision.

A Dual-Connectivity Mobility Link Service for Producer Mobility in the Named Data Networking.

With the exponential growth of Cyber-Physical Systems (CPSs) technologies, the Internet of Things (IoT) infrastructure has evolved from built-in static infrastructure to a flexible structure applicable to various mobile environments. In this Internet of Mobile Things (IoMT) environment, each IoT device could operate simultaneously as a provider and consumer of information, and could provide new services through the exchange of such information. Named Data Networking (NDN), which could request data by content name rather than location (IP address), is suitable for such mobile IoT environments.

Sortation Control Using Multi-Agent Deep Reinforcement Learning in -Grid Sortation System.

Intralogistics is a technology that optimizes, integrates, automates, and manages the logistics flow of goods within a logistics transportation and sortation center. As the demand for parcel transportation increases, many sortation systems have been developed. In general, the goal of sortation systems is to route (or sort) parcels correctly and quickly.

Federated Reinforcement Learning for Training Control Policies on Multiple IoT Devices.

Reinforcement learning has recently been studied in various fields and also used to optimally control IoT devices supporting the expansion of Internet connection beyond the usual standard devices. In this paper, we try to allow multiple reinforcement learning agents to learn optimal control policy on their own IoT devices of the same type but with slightly different dynamics. For such multiple IoT devices, there is no guarantee that an agent who interacts only with one IoT device and learns the optimal control policy will also control another IoT device well.

An Authentication and Key Management Mechanism for Resource Constrained Devices in IEEE 802.11-based IoT Access Networks.

Many Internet of Things (IoT) services utilize an IoT access network to connect small devices with remote servers. They can share an access network with standard communication technology, such as IEEE 802.11ah.

A socially aware routing based on local contact information in delay-tolerant networks.

In delay-tolerant networks, network topology changes dynamically and there is no guarantee of continuous connectivity between any two nodes. These features make DTN routing one of important research issues, and the application of social network metrics has led to the design of recent DTN routing schemes. In this paper, we propose an efficient routing scheme by using a node's local contact history and social network metrics.

A target coverage scheduling scheme based on genetic algorithms in directional sensor networks.

As a promising tool for monitoring the physical world, directional sensor networks (DSNs) consisting of a large number of directional sensors are attracting increasing attention. As directional sensors in DSNs have limited battery power and restricted angles of sensing range, maximizing the network lifetime while monitoring all the targets in a given area remains a challenge. A major technique to conserve the energy of directional sensors is to use a node wake-up scheduling protocol by which some sensors remain active to provide sensing services, while the others are inactive to conserve their energy.

Aripiprazole inhibits superoxide generation from phorbol-myristate-acetate (PMA)-stimulated microglia in vitro: implication for antioxidative psychotropic actions via microglia.

Altered antioxidant status has been implicated in schizophrenia. Microglia, major sources of free radicals such as superoxide (•O(2)(-)), play crucial roles in various brain pathologies. Recent postmortem and imaging studies have indicated microglial activation in the brain of schizophrenic patients.

Phosphatidylserine and phosphatidylcholine-containing liposomes inhibit amyloid beta and interferon-gamma-induced microglial activation.

There is increasing evidence that microglial activation is one of the major pathogenic factors for Alzheimer's disease (AD) and the inhibition of the inflammatory activation of the microglia thus appears to be neuroprotective and a potentially useful treatment for AD. Phospholipids such as phosphatidylserine (PS) and phosphatidylcholine (PC) have been reported to modulate the immune function of phagocytes. In addition, PS has been reported to be a nootropics that can be used as nonprescription memory or cognitive enhancers.

Phospholipids modulate superoxide and nitric oxide production by lipopolysaccharide and phorbol 12-myristate-13-acetate-activated microglia.

Microglial activation and inflammatory processes have been implicated in the pathogenesis of a number of neurodegenerative disorders. Recently, peroxynitrite (ONOO(-)), the reaction product of superoxide (O(2)(-)) and nitric oxide (NO) both of which can be generated by activated microglia, has been demonstrated to act as a major mediator in the neurotoxicity induced by activated microglia. On the other hand, phospholipids such as phosphatidylserine (PS) and phosphatidylcholine (PC) have been reported to modulate the immune function of phagocytes.

Nox4 as the major catalytic component of an endothelial NAD(P)H oxidase.

Background: Recent evidence has suggested that reactive oxygen species are important signaling molecules in vascular cells and play a pivotal role in the development of vascular diseases. The activity of NAD(P)H oxidase has been identified as the major source of reactive oxygen species in vascular endothelial cells. However, the precise molecular structure and the mechanism of activation of the oxidase have remained poorly understood.

A kinetic study of 3-chlorophenol enhanced hydroxyl radical generation during ozonation.

Hydroxyl (OH) radical is proposed as an important factor in the ozonation of water. In the present study, the enhancing effect of 3-chlorophenol on OH radical generation was mathematically evaluated using electron spin resonance (ESR)/spin-trapping technique. OH radical was trapped with a 5,5-dimethyl-1-pyrroline-N-oxide (DMPO) as a stable adduct, DMPO-OH.