Cyber Resilience through Real-Time Threat Analysis in Information Security

This paper examines how cybersecurity is developing and how it relates to more conventional information security. Although information security and cyber security are sometimes used synonymously, this study contends that they are not the same. The concept of cyber security is explored, which goes beyond protecting information resources to include a wider variety of assets, including people [1]. Protecting information assets is the main goal of traditional information security, with consideration to the human element and how people fit into the security process. On the other hand, cyber security adds a new level of complexity, as people might unintentionally contribute to or become targets of cyberattacks. This aspect presents moral questions since it is becoming more widely accepted that society has a duty to protect weaker members of society, including children [1]. The study emphasizes how important cyber security is on a larger scale, with many countries creating plans and laws to counteract cyberattacks. Nevertheless, a lot of these sources frequently neglect to define the differences or the relationship between information security and cyber security [1]. The paper focus on differentiating between cybersecurity and information security on a larger scale. The study also highlights other areas of cybersecurity which includes defending people, social norms, and vital infrastructure from threats that arise from online in addition to information and technology protection. It contends that ethical issues and the human factor are becoming more and more important in protecting assets in the digital age, and that cyber security is a paradigm shift in this regard [1].

Research on Multi-Core Processor Analysis for WCET Estimation

Real-time system timing analysis is crucial for estimating the worst-case execution time(WCET)of a program.To achieve this,static or dynamic analysis methods are used,along with targeted modeling of the actual hardware system.This literature review focuses on calculating WCET for multi-core processors,providing a survey of traditional methods used for static and dynamic analysis and highlighting the major challenges that arise from different program execution scenarios on multi-core platforms.This paper outlines the strengths and weaknesses of current methodologies and offers insights into prospective areas of research on multi-core analysis.By presenting a comprehensive analysis of the current state of research on multi-core processor analysis for WCET estimation,this review aims to serve as a valuable resource for researchers and practitioners in the field.

Data network traffic analysis and optimization strategy of real-time power grid dynamic monitoring system for wide-frequency measurements

The application and development of a wide-area measurement system(WAMS)has enabled many applications and led to several requirements based on dynamic measurement data.Such data are transmitted as big data information flow.To ensure effective transmission of wide-frequency electrical information by the communication protocol of a WAMS,this study performs real-time traffic monitoring and analysis of the data network of a power information system,and establishes corresponding network optimization strategies to solve existing transmission problems.This study utilizes the traffic analysis results obtained using the current real-time dynamic monitoring system to design an optimization strategy,covering the optimization in three progressive levels:the underlying communication protocol,source data,and transmission process.Optimization of the system structure and scheduling optimization of data information are validated to be feasible and practical via tests.

Real-Time Multimodal Biometric Authentication of Human Using Face Feature Analysis

As multimedia data sharing increases,data security in mobile devices and its mechanism can be seen as critical.Biometrics combines the physiological and behavioral qualities of an individual to validate their character in real-time.Humans incorporate physiological attributes like a fingerprint,face,iris,palm print,finger knuckle print,Deoxyribonucleic Acid(DNA),and behavioral qualities like walk,voice,mark,or keystroke.The main goal of this paper is to design a robust framework for automatic face recognition.Scale Invariant Feature Transform(SIFT)and Speeded-up Robust Features(SURF)are employed for face recognition.Also,we propose a modified Gabor Wavelet Transform for SIFT/SURF(GWT-SIFT/GWT-SURF)to increase the recognition accuracy of human faces.The proposed scheme is composed of three steps.First,the entropy of the image is removed using Discrete Wavelet Transform(DWT).Second,the computational complexity of the SIFT/SURF is reduced.Third,the accuracy is increased for authentication by the proposed GWT-SIFT/GWT-SURF algorithm.A comparative analysis of the proposed scheme is done on real-time Olivetti Research Laboratory(ORL)and Poznan University of Technology(PUT)databases.When compared to the traditional SIFT/SURF methods,we verify that the GWT-SIFT achieves the better accuracy of 99.32%and the better approach is the GWT-SURF as the run time of the GWT-SURF for 100 images is 3.4 seconds when compared to the GWT-SIFT which has a run time of 4.9 seconds for 100 images.

A Mathematical Model of Real-Time Simulation and the Convergence Analysis on Real-Time Runge-Kutta Algorithms

In this paper, a mathematical model of real-time simulation is given, and the problem of convergence on real-time Runge-Kutta algorithms is analysed. At last a theorem on the relation between the order of compensation and the convergent order of real-time algorithm is proved.

Integrated Real-Time Big Data Stream Sentiment Analysis Service

Opinion (sentiment) analysis on big data streams from the constantly generated text streams on social media networks to hundreds of millions of online consumer reviews provides many organizations in every field with opportunities to discover valuable intelligence from the massive user generated text streams. However, the traditional content analysis frameworks are inefficient to handle the unprecedentedly big volume of unstructured text streams and the complexity of text analysis tasks for the real time opinion analysis on the big data streams. In this paper, we propose a parallel real time sentiment analysis system: Social Media Data Stream Sentiment Analysis Service (SMDSSAS) that performs multiple phases of sentiment analysis of social media text streams effectively in real time with two fully analytic opinion mining models to combat the scale of text data streams and the complexity of sentiment analysis processing on unstructured text streams. We propose two aspect based opinion mining models: Deterministic and Probabilistic sentiment models for a real time sentiment analysis on the user given topic related data streams. Experiments on the social media Twitter stream traffic captured during the pre-election weeks of the 2016 Presidential election for real-time analysis of public opinions toward two presidential candidates showed that the proposed system was able to predict correctly Donald Trump as the winner of the 2016 Presidential election. The cross validation results showed that the proposed sentiment models with the real-time streaming components in our proposed framework delivered effectively the analysis of the opinions on two presidential candidates with average 81% accuracy for the Deterministic model and 80% for the Probabilistic model, which are 1% - 22% improvements from the results of the existing literature.

Electricity Price Influence Factors Analysis Using Stochastic Matrix for Real-Time Electricity Price Forecasting

Real-time electricity price( RTEP) influence factor extraction is essential to forecasting accurate power system electricity prices. At present,new electricity price forecasting models have been studied to improve predictive accuracy,ignoring the extraction and analysis of RTEP influence factors. In this study,a correlation analysis method is proposed based on stochastic matrix theory.Firstly, an augmented matrix is formulated, including RTEP influence factor data and RTEP state data. Secondly, data correlation analysis results are obtained given the statistical characteristics of source data based on stochastic matrix theory.Mean spectral radius( MSR) is used as the measure of correlativity.Finally,the proposed method is evaluated in New England electricity markets and compared with the BP neural network forecasting method. Experimental results show that the extracted index system comprehensively generalizes RTEP influence factors,which play a significant role in improving RTEP forecasting accuracy.

Real-Time Computer Analytical Application in Pressure Transient Analysis of Petroleum Reservoirs

Today with certainty, the petroleum industry is fostering sanguinely the fields’ development programs for the optimization of reservoir characterization through worth-full appliances of computer analysis techniques. The time element is of prime importance for optimistic petroleum development projects. Therefore, the frontline of “Real-time Analysis” is added into the applications of computer solving techniques for achieving and sketching up the real-time cost effectiveness in analyzing field development programs. It focuses on the phases of real-time well test data acquisition system, real-time secure access to the well test data either on field or in office and real-time data interpretation unit. This interface will yield the productive results for the field of reservoir’s pressure transient analysis and wells’ systems analysis by following the up-to-date preferred, accurate and effective well test analytical principles with modern real-time computer applications and techniques. It also lays emphasis for the comfort and reliability of data in creating best interpersonal working modes within a reputable and esteemed petroleum development organization.

Real-time prediction of mechanical behaviors of underwater shield tunnel structure using machine learning method based on structural health monitoring data

Predicting the mechanical behaviors of structure and perceiving the anomalies in advance are essential to ensuring the safe operation of infrastructures in the long run.In addition to the incomplete consideration of influencing factors,the prediction time scale of existing studies is rough.Therefore,this study focuses on the development of a real-time prediction model by coupling the spatio-temporal correlation with external load through autoencoder network(ATENet)based on structural health monitoring(SHM)data.An autoencoder mechanism is performed to acquire the high-level representation of raw monitoring data at different spatial positions,and the recurrent neural network is applied to understanding the temporal correlation from the time series.Then,the obtained temporal-spatial information is coupled with dynamic loads through a fully connected layer to predict structural performance in next 12 h.As a case study,the proposed model is formulated on the SHM data collected from a representative underwater shield tunnel.The robustness study is carried out to verify the reliability and the prediction capability of the proposed model.Finally,the ATENet model is compared with some typical models,and the results indicate that it has the best performance.ATENet model is of great value to predict the realtime evolution trend of tunnel structure.

实时xCELLigence细胞分析系统在药物心脏毒性筛选中的应用

药物的心脏毒性越来越受到关注,大量临床上使用的药物由于心脏毒性而被撤市或监管。因此,药物安全评价是研发过程中非常重要的一步。实时xCELLigence细胞分析系统是一种基于电子阻抗的非标记性、非侵入性、实时动态的细胞分析检测技术,它可以广泛地应用于药物的毒性筛选中。xCELLigence RTCA Cardio系统具有高的时间分辨率,能灵敏地、精确地监测到药物作用于心肌细胞后心肌细胞搏动频率、节律、幅度等指标的改变,并通过Poincaréplots分析其心率变异性。这些指标均与药物的心脏毒性有着密切的关系。因此,该系统具有高的特异性,能在药物研发的早期阶段筛选其心脏毒性。本文对RTCA的基本原理和其在药物心脏毒性筛选中的应用作一综述。

Delay-dependent stability and added damping of SDOF real-time dynamic hybrid testing

It is well-recognized that a transfer system response delay that reduces the test stability inevitably exists in real-time dynamic hybrid testing （RTDHT）. This paper focuses on the delay-dependent stability and added damping of SDOF systems in RTDHT. The exponential delay term is transferred into a rational fraction by the Pad6 approximation, and the delay-dependent stability conditions and instability mechanism of SDOF RTDHT systems are investigated by the root locus technique. First, the stability conditions are discussed separately for the cases of stiffness, mass, and damping experimental substructure. The use of root locus plots shows that the added damping effect and instability mechanism for mass are different from those for stiffness. For the stiffness experimental substructure case, the instability results from the inherent mode because of an obvious negative damping effect of the delay. For the mass case, the delay introduces an equivalent positive damping into the inherent mode, and instability occurs at an added high frequency mode. Then, the compound stability condition is investigated for a general case and the results show that the mass ratio may have both upper and lower limits to remain stable. Finally, a high-emulational virtual shaking table model is built to validate the stability conclusions.

Genome-wide analysis and identification of cytokinin oxidase/dehydrogenase(CKX) gene family in foxtail millet(Setaria italica)

Cytokinin oxidase/dehydrogenase(CKX; EC.1.5.99.12) regulates cytokinin(CK) level in plants and plays an essential role in CK regulatory processes. CKX proteins are encoded by a small gene family with a varying number of members in different plants. In spite of their physiological importance, systematic analyses of SiCKX genes in foxtail millet have not yet been examined. In this paper, we report the genome wide isolation and characterization of SiCKXs using bioinformatic methods. A total of 11 members of the family were identified in the foxtail millet genome. SiCKX genes were distributed in seven chromosomes(chromosome 1, 3, 4, 5, 6, 7, and 11). The coding sequences of all the SiCKX genes were disrupted by introns, with numbers varying from one to four. These genes expanded in the genome mainly due to segmental duplication events. Multiple alignment and motif display results showed that all SiCKX proteins share FAD- and CK-binding domains. Putative cis-elements involved in Ca2+-response, abiotic stress response, light and circadian rhythm regulation, disease resistance and seed development were present in the promoters of SiCKX genes. Expression data mining suggested that SiCKX genes have diverse expression patterns. Real-time PCR analysis indicated that all 11 SiCKX genes were up-regulated in embryos under 6-BA treatment, and some were NaCl or PEG inducible. Collectively, these results provide molecular insights into CKX research in plants.

Comparative transcriptome analysis on the alteration of gene expression in ayu （Plecoglossus altivelis）larvae associated with salinity change

Ayu （Plecoglossus altivelis） fish, which are an amphidromous species distributed in East Asia, live in brackish water （BW） during their larval stage and in fresh water （FW） during their adult stage. In this study, we found that FW-acclimated ayu larvae exhibited a slower growth ratio compared with that of BW-acclimated larvae. However, the mechanism underlying FW acclimation on growth suppression is poorly known. We employed transcriptome analysis to investigate the differential gene expression of FW acclimation by RNA sequencing. We identified 158 upregulated and 139 downregulated transcripts in FW-acclimated ayu larvae compared with that in BW-acclimated larvae. As determined by Gene Ontology annotation and Kyoto Encyclopedia of Genes and Genomes pathway mapping, functional annotation of the genes covered diverse biological functions and processes, and included neuroendo- crinology, osmotic regulation, energy metabolism, and the cytoskeleton. Transcriptional expression of several differentially expressed genes in response to FW acclimation was further confirmed by real-time quantitative PCR. In accordance with transcriptome analysis, iodothyronine deiodinase （ID）, pro-opiom- elanocortin （POMC）, betaine-homocysteine S-meth- yltransferase 1 （BHMT）, fructose-bisphosphate aldolase B （aldolase B）, tyrosine aminotransferase （TAT）, and Na＋-K＋ ATPase （NKA） were upregulated after FW acclimation. Furthermore, the mRNA expressions of b-type natriurefic peptide （BNP） and transgelin were downregulated after FW acclimation. Our data indicate that FW acclimation reduced the growth rate of ayu larvae, which might result from the expression alteration of genes related to endocrine hormones, energy metabolism, and direct osmoregulation.

Detection of Lactobacillus acidophilus in Fermented Material by Real-time Fluorescent Quantitative PCR

The species distinctive PCR primer of Lactobacillus acidophilus （ L. acidophilus） was designed according to 16S rRNA gene sequences of conunon Lac- tobacillus species in fermented material. Bacterial genome DNA of separated L. acidophilus in fermented sample was taken as template, and L. acidophilus in fer- mented material was conducted the quantitative determination by real-time quantitative PCR （RT-PCR）. Analysis on RT-PCR results shown that contents of L. aci- dophilus in the test sample reached 1.5 billion CFU / g. Test results shown that contents of L. acidophilus in fermented material could be detected accurately by the established RT-PCR method in the test. indicating that the established RT-PCR method could be aookued to the detection of L. acidophilus in fermented material.

Real-time Design Constraints in Implementing Active Vibration Control Algorithms

Although computer architectures incorporate fast processing hardware resources, high performance real-time implementation of a complex control algorithm requires an efficient design and software coding of the algorithm so as to exploit special features of the hardware and avoid associated architecture shortcomings. This paper presents an investigation into the analysis and design mechanisms that will lead to reduction in the execution time in implementing real-time control algorithms. The proposed mechanisms are exemplified by means of one algorithm, which demonstrates their applicability to real-time applications. An active vibration control （AVC） algorithm for a flexible beam system simulated using the finite difference （FD） method is considered to demonstrate the effectiveness of the proposed methods. A comparative performance evaluation of the proposed design mechanisms is presented and discussed through a set of experiments.

Real-Time Monitoring of Climactic and Geotechnical Variables during Landslides on the Slopes of Serra do Mar and Serra da Mantiqueira (São Paulo State, Brazil)

The municipalities of Ubatuba, Campos do Jord?o, and S?o José dos Campos are located in the region of S?o Paulo State (Brazil). These municipalities are recognized nationally for having an elevated number of recorded landslides on slopes and embankments. In addition, these municipalities contain multiple areas that are at risk for landslides. Various soil landslides occurred in these municipalities in January 2013, when real-time climactic and geotechnical variables were monitored by automatic rain gauges, humidity sensors and soil temperature and suction devices. The resulting data were used to understand the functions of each variable in the occurrence of land- slides. Analyses of rainfall, humidity and soil temperature were used with field investigations to formulate a hypothesis regarding the predominant rupture mechanism and the role of each monitored variable in the deflagration of the soil landslides that occurred in the three studied municipalities. The geotechnical variable data revealed that both temperature and soil moisture contents played fundamental roles in the deflagration of shallow planar landslides in urban areas. The hourly rain intensity and/or rainfall accumulation for 24 and/or 72 h were responsible for the deflagration of the landslides that occurred in the studied areas, along with the existing anthropic constraints in the risk areas. Significant variations did not occur in the soil suction data during the landslides, principally due to the unsatisfactory sensor precision when reading field suction between –10 and?–100 kPA (±25%).

Quantum Operator Model for Data Analysis and Forecast

A new dynamic model identification method is developed for continuous-time series analysis and forward prediction applications. The quantum of data is defined over moving time intervals in sliding window coordinates for compressing the size of stored data while retaining the resolution of information. Quantum vectors are introduced as the basis of a linear space for defining a Dynamic Quantum Operator (DQO) model of the system defined by its data stream. The transport of the quantum of compressed data is modeled between the time interval bins during the movement of the sliding time window. The DQO model is identified from the samples of the real-time flow of data over the sliding time window. A least-square-fit identification method is used for evaluating the parameters of the quantum operator model, utilizing the repeated use of the sampled data through a number of time steps. The method is tested to analyze, and forward-predict air temperature variations accessed from weather data as well as methane concentration variations obtained from measurements of an operating mine. The results show efficient forward prediction capabilities, surpassing those using neural networks and other methods for the same task.

Design of Intelligent Network Performance Analysis & Forecast Support System

A system designed for supporting the network performance analysis and forecast effort is presented, based on the combination of offline network analysis and online real-time performance forecast. The off-line analysis will perform analysis of specific network node performance, correlation analysis of relative network nodes performance and evolutionary mathematical modeling of long-term network performance measurements. The online real-time network performance forecast will be based on one so-called hybrid prediction modeling approach for short-term network, performance prediction and trend analysis. Based on the module design, the system proposed has good intelligence, scalability and self-adaptability, which will offer highly effective network performance analysis and forecast tools for network managers, and is one ideal support platform for network performance analysis and forecast effort.

Proteomic Analysis of Chrysanthemum Lateral Buds after Removing Apical Dominance Based on Label-Free Technology

Studying the genetic basis and regulatory mechanism of chrysanthemum lateral bud outgrowth is of great significance for reduction the production cost of cut chrysanthemum.To clarify the molecular basis of lateral bud elongation after removal of apical dominance in chrysanthemum,label-free quantification analysis was used to analyze the proteome changes after apical bud removal.Quantitative real-time PCR(qPCR)was used to analyze the changes in the expression of three plant hormone-related genes.A total of 440 differentially expressed proteins were successfully identified at three time points during the lateral bud elongation.The number of differentially expressed proteins in the three stages(24 h/0 h,48 h/0 h,48 h/24 h)were 219,332,and 97,respectively.The difference in expressed proteins in the three comparison stages mainly involves RNA processing and modification;translation,ribosomal structure and biogenesis;Posttranslational modification,protein turnover,and chaperones.Path analysis showed that there was various physiological activities in the process of lateral bud dormancy breaking and elongation,which involved energy metabolism,biosynthesis,signal transduction and stress response in the growth process of lateral buds.qPCR indicated that the expression of cytokinin synthesis related gene was significantly increased after the removal of apical dominance,while the expression of strigolactones synthesis related gene experiences a dramatic fall to promote the development of the lateral buds.However,there was a drop before a slight increase in the expression of the auxin synthesis related gene,which was mainly due to the removal of apical dominance that led to the loss of indoleacetic acid in the main stem.However,with formation of the new apical source,indoleacetic acid can be released again.

Developmental Changes of GH Gene in Tan Sheep and Correlation Analysis with Slaughter Traits

[ Objective] The paper was to study the differential expression of growth hormone （GH） gene in different ages and different tissues of Tan sheep. [ Method] Using real-time fluorescent quantitative PCR （RT-PCR） assay, specific primers were designed according to the GH gene sequence published on Gen- Bank. With the total RNA of Tan sheep tissue as the template, the expression levels of GH gene in pectoral muscle, leg muscle and longissimus dorsi muscle at dif- ferent ages were analyzed. [ Result] In three types of muscle tissues, the expression patterns of GH gene in different tissues at the same month of age were pectoral muscle 〉 leg muscle 〉 longissimus dorsi muscle. The expression levels of GH gene were positively correlated with live weight before slaughter, carcass weight and net meat weight, and had significant positive correlation only with net meat weight （ P 〈 0.05 ）. [ Conclusion ] The content of GH gene in different tissues gradually increased with the increasing months of age, and there were extremely significant differences in expression level of GH gene among different months of age or different sites （P〈0. 01）.