Replay Attack Detection Based on Parity Space Method for Cyber-Physical Systems

The replay attack detection problem is studied from a new perspective based on parity space method in this paper. The proposed detection methods have the ability to distinguish system fault and replay attack, handle both input and output data replay, maintain certain control performance, and can be implemented conveniently and efficiently. First, the replay attack effect on the residual is derived and analyzed. The residual change induced by replay attack is characterized explicitly and the detection performance analysis based on two different test statistics are given. Second, based on the replay attack effect characterization, targeted passive and active design for detection performance enhancement are proposed. Regarding the passive design, four optimization schemes regarding different cost functions are proposed with optimal parity matrix solutions, and the unified solution to the passive optimization schemes is obtained; the active design is enabled by a marginally stable filter so as to enlarge the replay attack effect on the residual for detection. Simulations and comparison studies are given to show the effectiveness of the proposed methods

Mapping knowledge of the stem cell in traumatic brain injury: a bibliometric and visualized analysis

BackgroundTraumatic brain injury (TBI) is a brain function injury caused by external mechanical injury. Primary and secondary injuries cause neurological deficits that mature brain tissue cannot repair itself. Stem cells can self-renewal and differentiate, the research of stem cells in the pathogenesis and treatment of TBI has made significant progress in recent years. However, numerous articles must be summarized to analyze hot spots and predict trends. This study aims to provide a panorama of knowledge and research hotspots through bibliometrics.MethodWe searched in the Web of Science Core Collection (WoSCC) database to identify articles pertaining to TBI and stem cells published between 2000 and 2022. Visualization knowledge maps, including co-authorship, co-citation, and co-occurrence analysis were generated by VOSviewer, CiteSpace, and the R package “bibliometrix.”ResultsWe retrieved a total of 459 articles from 45 countries. The United States and China contributed the majority of publications. The number of publications related to TBI and stem cells is increasing yearly. Tianjin Medical University was the most prolific institution, and Professor Charles S. Cox, Jr. from the University of Texas Health Science Center at Houston was the most influential author. The Journal of Neurotrauma has published the most research articles on TBI and stem cells. Based on the burst references, “immunomodulation,” “TBI,” and “cellular therapy” have been regarded as research hotspots in the field. The keywords co-occurrence analysis revealed that “exosomes,” “neuroinflammation,” and “microglia” were essential research directions in the future.ConclusionResearch on TBI and stem cells has shown a rapid growth trend in recent years. Existing studies mainly focus on the activation mechanism of endogenous neural stem cells and how to make exogenous stem cell therapy more effective. The combination with bioengineering technology is the trend in this field. Topics related to exosomes and immune regulation may be the future focus of TBI and stem cell research

Far Off-Resonance Laser Frequency Stabilization Technology

In atomic physics experiments, a frequency-stabilized or ‘locked’ laser source is commonly required. Many established techniques are available for locking close to an atomic resonance. However, in many instances, such as atomic magnetometer and magic wavelength optical lattices in ultra-cold atoms, it is desirable to lock the frequency of the laser far away from the resonance. This review presents several far off-resonance laser frequency stabilization methods, by which the frequency of the probe beam can be locked on the detuning as far as several tens of gigahertz (GHz) away from atomic resonance line, and discusses existing challenges and possible future directions in this field

An Intelligent Advanced Classification Method for Tunnel-Surrounding Rock Mass Based on the Particle Swarm Optimization Least Squares Support Vector Machine

The fast and accurate classification of surrounding rock mass is the basis for tunnel design and construction and has significant value in engineering applications. Therefore, this paper proposes a method for classifying and predicting surrounding rock mass based on particle swarm optimization (PSO)–least squares support vector machine (LSSVM). The premise of the research is that the data acquired from digital drilling technology are divided into a training group and a test group; the training group continuously optimizes the algorithm for the particle swarm optimization least squares support vector machine, and then the test group is used for verification. Moreover, the fast searching abilities of the particle swarm significantly accelerate the computational power and computational accuracy of the least squares support vector machine, making it a high-speed analog search tool. Taking the Jiaozhou Bay undersea tunnel in China as an example, a comparison of the evaluation results of PSO-LSSVM and QGA-RBF (quantum genetic algorithm-radical basis function neural network) is undertaken. The results show that PSO-LSSVM matches well with the field-measured surrounding rock grade. Applying the method in an engineering context proves that it has good self-learning abilities, even when the sample size is small and the prediction accuracy is high; as such, it meets the engineering requirements. The technique has the advantages of small sample prediction, pattern recognition, and nonlinear prediction

The Genome-Wide Identification of Long Non-Coding RNAs Involved in Floral Thermogenesis in Nelumbo nucifera Gaertn

The sacred lotus (Nelumbo nucifera Gaertn.) can maintain a stable floral chamber temperature when blooming, despite ambient temperature fluctuations; however, the long non-coding RNAs (lncRNAs) involved in floral thermogenesis remain unclear. In the present study, we obtain comprehensive lncRNAs expression profiles from receptacles at five developmental stages by strand-specific RNA sequencing to reveal the lncRNAs regulatory mechanism of the floral thermogenesis of N. nucifera. A total of 22,693 transcripts were identified as lncRNAs, of which approximately 44.78% had stage-specific expression patterns. Subsequently, we identified 2579 differential expressed lncRNAs (DELs) regulating 2367 protein-coding genes mainly involved in receptacle development and reproductive process. Then, lncRNAs with floral thermogenesis identified by weighted gene co-expression network analysis (WGCNA) were mainly related to sulfur metabolism and mitochondrial electron transport chains. Meanwhile, 70 lncRNAs were predicted to act as endogenous target mimics (eTMs) for 29 miRNAs and participate in the regulation of 16 floral thermogenesis-related genes. Our dual luciferase reporter assays indicated that lncRNA LTCONS_00068702 acted as eTMs for miR164a_4 to regulate the expression of TrxL2 gene. These results deepen our understanding of the regulation mechanism of floral thermogenesis by lncRNAs and accumulate data for further research