Data Security Storage Method for Power Distribution Internet of Things in Cyber-Physical Energy Systems

The existing cloud storage methods cannot meet the delay requirements of intelligent devices in the power distribution Internet of Things (IoT), and it is difficult to ensure the data security in the complex network environment. Therefore, a data Security Storage method for the power distribution IoT is proposed. Firstly, based on the “cloud tube edge end” power distribution IoT structure, a cloud edge collaborative centralized distributed joint control mode is proposed, which makes full use of the collaborative advantages of cloud computing and edge computing to meet the real-time requirements. Then, a distributed data storage method based on the Kademlia algorithm is proposed, and the homomorphic encryption and secret sharing algorithm are used to store the data in the cloud as ciphertext and perform data query directly on the ciphertext. Finally, considering the heterogeneity of edge nodes, the security protection model of edge nodes based on noncooperative differential game is established, and the algorithm of optimal defense strategy of edge nodes is designed to ensure the security of edge nodes. The experimental results show that the proposed method obtained excellent query performance, and the ability to resist network attacks is better than other comparison methods. It can reduce the data storage and query delay and ensure the data security of the system

An Orderly EV Charging Scheduling Method Based on Deep Learning in Cloud-Edge Collaborative Environment

The rapid increase of the number of electric vehicles (EVs) has posed great challenges to the safe operation of the distribution network. Therefore, this paper proposes an ordered charging scheduling method for EV in the cloud-edge collaborative environment. Firstly, the uncertainty of user load demands, charging station requirements, and renewable outputs are taken into consideration. Correspondingly, the residential distribution points, EV charging stations, and renewable plants are regarded as the edge nodes. Then, the load demands and renewable outputs are predicted by a model combined with the convolutional neural network and deep belief network (CNN-DBN). Secondly, the power supply plans for charging stations are determined at the cloud side aiming at minimizing the operating cost of the distribution network via collecting the forecasting results. Finally, the charging station formulates the personalized charging scheduling strategies according to EV’s charging plans and the charging demands in order to follow the supply plan. The simulation results show that the load peak-to-valley difference and standard deviation of the proposed algorithm are reduced by 30.13% and 16.94%, respectively, compared with the disorderly charging and discharging behavior, which has verified the effectiveness and feasibility of the proposed method

Electron acceleration in a coil target-driven low-β magnetic reconnection simulation

Magnetic reconnection driven by a capacitor coil target is an innovative way to investigate low-β magnetic reconnection in the laboratory, where β is the ratio of particle thermal pressure to magnetic pressure. Low-β magnetic reconnection frequently occurs in the Earth’s magnetosphere, where the plasma is characterized by β ≲ 0.01. In this paper, we analyze electron acceleration during magnetic reconnection and its effects on the electron energy spectrum via particle-in-cell simulations informed by parameters obtained from experiments. We note that magnetic reconnection starts when the current sheet is down to about three electron inertial lengths. From a quantitative comparison of the different mechanisms underlying the electron acceleration in low-β reconnection driven by coil targets, we find that the electron acceleration is dominated by the betatron mechanism, whereas the parallel electric field plays a cooling role and Fermi acceleration is negligible. The accelerated electrons produce a hardened power-law spectrum with a high-energy bump. We find that injecting electrons into the current sheet is likely to be essential for further acceleration. In addition, we perform simulations for both a double-coil co-directional magnetic field and a single-coil one to eliminate the possibility of direct acceleration of electrons beyond thermal energies by the coil current. The squeeze between the two coil currents can only accelerate electrons inefficiently before reconnection. The simulation results provide insights to guide future experimental improvements in low-β magnetic reconnection driven by capacitor coil targets

Bioinspired Closed-loop CPG-based Control of a Robot Fish for Obstacle Avoidance and Direction Tracking

This paper presents a study on bioinspired closed-loop Central Pattern Generator (CPG) based control of a robot fish for obstacle avoidance and direction tracking. The biomimetic robot fish is made of a rigid head with a pair of pectoral fins, a wire-driven active body covered with soft skin, and a compliant tail. The CPG model consists of four input parameters: the flapping amplitude, the flapping angular velocity, the flapping offset, and the time ratio between the beat phase and the restore phase in flapping. The robot fish is equipped with three infrared sensors mounted on the left, front and right of the robot fish, as well as an inertial measurement unit, from which the surrounding obstacles and moving direction can be sensed. Based on these sensor signals, the closed-loop CPG-based control can drive the robot fish to avoid obstacles and to track designated directions. Four sets of experiments are presented, including avoiding a static obstacle, avoiding a moving obstacle, tracking a designated direction and tracking a designated direction with an obstacle in the path. The experiment results indicated that the presented control strategy worked well and the robot fish can accomplish the obstacle avoidance and direction tracking effectively

Distinguishing Kawasaki Disease from Febrile Infectious Disease Using Gene Pair Signatures

Kawasaki disease (KD) is an acute systemic vasculitis of childhood with prolonged fever, and the diagnosis of KD is mainly based on clinical criteria, which is prone to misdiagnosis with other febrile infectious (FI) diseases. Currently, there remain no effective molecular markers for KD diagnosis. In this study, we aimed to use a relative-expression-based method k-TSP and resampling framework to identify robust gene pair signatures to distinguish KD from bacterial and virus febrile infectious diseases. Our study pool consisted of 808 childhood patients from several studies and assigned to three groups, namely, the discovery set (n=224), validation set-1 (n=197), and validation set-2 (n=387). We had identified 60 biologically relevant gene pairs and developed a top-ranked gene pair classifier (TRGP) using the first seven signatures, with the area under the receiver-operating characteristic curves (AUROC) of 0.947 (95% CI, 0.918-0.976), a sensitivity of 0.936 (95% CI, 0.872-0.987), and a specificity of 0.774 (95% CI, 0.705-0.836) in the discovery set. In the validation set-1, the TRGP classifier distinguished KD from FI with AUROC of 0.955 (95% CI, 0.919-0.991), a sensitivity of 0.959 (95% CI, 0.925-0.986), and a specificity of 0.863 (95% CI, 0.764-0.961). In the validation set-2, the predictive performance of classification was with an AUROC of 0.796 (95% CI, 0.747-0.845), a sensitivity of 0.797 (95% CI, 0.720-0.864), and a specificity of 0.661 (95% CI, 0.606-0.717). Our study reveals that gene pair signatures are robust across diverse studies and can be utilized as objective biomarkers to distinguish KD from FI, helping to develop a fast, simple, and effective molecular approach to improve the diagnosis of KD

Transfer RNAs Mediate the Rapid Adaptation of Escherichia coli to Oxidative Stress.

Translational systems can respond promptly to sudden environmental changes to provide rapid adaptations to environmental stress. Unlike the well-studied translational responses to oxidative stress in eukaryotic systems, little is known regarding how prokaryotes respond rapidly to oxidative stress in terms of translation. In this study, we measured protein synthesis from the entire Escherichia coli proteome and found that protein synthesis was severely slowed down under oxidative stress. With unchanged translation initiation, this slowdown was caused by decreased translation elongation speed. We further confirmed by tRNA sequencing and qRT-PCR that this deceleration was caused by a global, enzymatic downregulation of almost all tRNA species shortly after exposure to oxidative agents. Elevation in tRNA levels accelerated translation and protected E. coli against oxidative stress caused by hydrogen peroxide and the antibiotic ciprofloxacin. Our results showed that the global regulation of tRNAs mediates the rapid adjustment of the E. coli translation system for prompt adaptation to oxidative stress

Modeling simulation on amplifying magnetic fields in supernova remnants with an intense laser

Local magnetic field enhancement in supernova remnants (SNRs) is a natural laboratory for studying the amplification effect of turbulent magnetic fields. In recent years, high-power laser devices have gradually matured as a tool for astronomical research that perfects observations and theoretical models. In this study, a model of the amplification effect of the turbulent magnetic field in SNRs by an intense laser is simulated using the radiation magnetohydrodynamic simulation program. We investigate and compare the evolutionary processes of unstable turbulence under different initial disturbance modes, directions, and intensities of external magnetic fields and obtain the magnetic energy spectrum and magnetic field magnification. The results demonstrate that the fluid motion associated with Rayleigh–Taylor instability will stretch the environmental magnetic field significantly, with an intensity amplified by two orders of magnitude. The environmental magnetic field perpendicular to the laser injection direction is decisive during magnetic field amplification which is necessary to clarify the physical mechanism of magnetic field amplification in SNRs. Furthermore, it will deepen the understanding of the interstellar magnetic field’s evolution. The results also establish a reference for laser-driven magnetized plasma experiments in a robust magnetic environment

Protein-Level Integration Strategy of Multiengine MS Spectra Search Results for Higher Confidence and Sequence Coverage

Multiple search engines based on various models have been developed to search MS/MS spectra against a reference database, providing different results for the same data set. How to integrate these results efficiently with minimal compromise on false discoveries is an open question due to the lack of an independent, reliable, and highly sensitive standard. We took the advantage of the translating mRNA sequencing (RNC-seq) result as a standard to evaluate the integration strategies of the protein identifications from various search engines. We used seven mainstream search engines (Andromeda, Mascot, OMSSA, X!Tandem, pFind, InsPecT, and ProVerB) to search the same label-free MS data sets of human cell lines Hep3B, MHCCLM3, and MHCC97H from the Chinese C-HPP Consortium for Chromosomes 1, 8, and 20. As expected, the union of seven engines resulted in a boosted false identification, whereas the intersection of seven engines remarkably decreased the identification power. We found that identifications of at least two out of seven engines resulted in maximizing the protein identification power while minimizing the ratio of suspicious/translation-supported identifications (STR), as monitored by our STR index, based on RNC-Seq. Furthermore, this strategy also significantly improves the peptides coverage of the protein amino acid sequence. In summary, we demonstrated a simple strategy to significantly improve the performance for shotgun mass spectrometry by protein-level integrating multiple search engines, maximizing the utilization of the current MS spectra without additional experimental work