Smart Meters Integration in Distribution System State Estimation with Collaborative Filtering and Deep Gaussian Process

The problem of state estimations for electric distribution system is considered. A collaborative filtering approach is proposed in this paper to integrate the slow time-scale smart meter measurements in the distribution system state estimation, in which the deep Gaussian process is incorporated to infer the fast time-scale pseudo measurements and avoid anomalies. Numerical tests have demonstrated the higher estimation accuracy of the proposed method

LogEvent2vec : LogEvent-to-vector based anomaly detection for large-scale logs in internet of things

Funding: This work was funded by the National Natural Science Foundation of China (Nos. 61802030), the Research Foundation of Education Bureau of Hunan Province, China (No. 19B005), and the International Cooperative Project for “Double First-Class”, CSUST (No. 2018IC24), the open research fund of Key Lab of Broadband Wireless Communication and Sensor Network Technology (Nanjing University of Posts and Telecommunications), Ministry of Education (No. JZNY201905), the Open Research Fund of the Hunan Provincial Key Laboratory of Network Investigational Technology (No. 2018WLZC003). This work was funded by the Researchers Supporting Project No. (RSP-2019/102) King Saud University, Riyadh, Saudi Arabia. Acknowledgments: We thank Researchers Supporting Project No. (RSP-2019/102) King Saud University, Riyadh, Saudi Arabia, for funding this research. We thank Francesco Cauteruccio for proofreading this paper.Peer reviewedPublisher PD

Multi-UAV-Assisted Offloading for Joint Optimization of Energy Consumption and Latency in Mobile Edge Computing

To address the performance limitations caused by the insufficient computing capacity and energy of edge internet of things devices (IoTDs), we proposed a multi-unmanned aerial vehicles (UAV)-assisted mobile edge computing (MEC) application framework in this article. In this framework, UAVs equipped with high-performance computing devices act as aerial servers deployed in the target area to support data offloading and task computing for IoTDs. We formulated an optimization problem to jointly optimize the connection scheduling, computing resource allocation, and UAVs' flying trajectories, considering the device offloading priority, to achieve a joint optimization of energy consumption and latency for all IoTDs during a given time period. Subsequently, to address this problem, we employed deep reinforcement learning for dynamic trajectory planning, supplemented by optimization theory and heuristic algorithm based on matching theory to assist in solving connection scheduling and computing resource allocation. To evaluate the performance of proposed algorithm, we compared it with deep deterministic policy gradient, particle swarm optimization, random moving, and local execution schemes. Simulation results demonstrated that the multi-UAV-assisted MEC significantly reduces the computing cost of IoTDs. Moreover, our proposed solution exhibited effectiveness in terms of convergence and optimization of computing costs compared to other benchmark schemes

A large calcium-imaging dataset reveals a systematic V4 organization for natural scenes

The visual system evolved to process natural scenes, yet most of our understanding of the topology and function of visual cortex derives from studies using artificial stimuli. To gain deeper insights into visual processing of natural scenes, we utilized widefield calcium-imaging of primate V4 in response to many natural images, generating a large dataset of columnar-scale responses. We used this dataset to build a digital twin of V4 via deep learning, generating a detailed topographical map of natural image preferences at each cortical position. The map revealed clustered functional domains for specific classes of natural image features. These ranged from surface-related attributes like color and texture to shape-related features such as edges, curvature, and facial features. We validated the model-predicted domains with additional widefield calcium-imaging and single-cell resolution two-photon imaging. Our study illuminates the detailed topological organization and neural codes in V4 that represent natural scenes.Comment: 39 pages, 14 figure

Plant-microbial interactions facilitate grassland species coexistence at the community level

Interspecific competition and plant-soil feedbacks are powerful drivers of plant community structure. However, across a range of edaphic conditions the interactive effects of these drivers on complex plant communities remain unclear. For example, plant-soil feedback studies focus on soil trained by a single plant species. We developed a method to assess effects of plant-microbial interactions (PMI) on a complex plant community. We established mesocosms with 13 grassland species, grown individually or together, in overgrazed or restored soil, with or without soil microbial inoculum collected from a productive and diverse native grassland. We assessed biomass production as influenced by edaphic conditions, interspecific competition and PMI. Furthermore, we assessed potential influences of interspecific competition and edaphic conditions on strength and direction of PMI. Our results indicate PMI drives negative growth responses for graminoids while forbs experience positive growth responses. Generally, interspecific competition did not alter the magnitude or direction of PMI-mediated growth responses. Edaphic conditions altered the influence of soil microbial communities on individual plant growth while PMI facilitated plant evenness. In plant community mesocosms, PMI-associated benefits were observed in overgrazed soil. However, interspecific competition overwhelmed plant growth benefits associated with soil microbial communities when plant communities were grown in restored soil. In mesocosms containing dominant grass species, interspecific competition had negative effects on species coexistence, but both positive and negative PMI partially counterbalanced this influence on plant species evenness. Understanding these mechanisms may improve our capacity to manage diverse and productive grasslands by enabling prediction of plant community composition following disturbance and subsequent restoration

Emission color tuning and white-light generation based on photochromic control of energy transfer reactions in polymer micelles

We encapsulate a fluorescent donor molecule and a photochromic acceptor unit (photoswitch) in polymer micelles and show that the color of the emitted fluorescence is continuously changed from blue to yellow upon light-induced isomerization of the acceptor. Interestingly, white-light generation is achieved in between. With the photoswitch in the colorless form, intense blue emission from the donor is observed, while UV-induced isomerization to the colored form induces an energy transfer reaction that quenches the donor emission and sensitizes the yellow emission from the colored photoswitch. The process is reversed by exposure to visible light, triggering isomerization to the colorless form