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.

Design of a Technology Verification Platform for Space Electromagnetic Interference Signal Testing and Analysis

This paper designs a space electromagnetic interference signal test and analysis technology verification platform.The article firstly introduces the general scheme of the technical verification platform and then describes each component unit of the hardware and the overall structure of the software in detail.The platform can achieve a 10 MHz~50 GHz working frequency band,1.2 GHz acquisition and real-time recording bandwidth,6 GB/s recording rate,and 12 TB recording capacity.

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.

Performance analysis model for real-time Ethernet-based computer numerical control system

In order to optimize the embedded system implementation for Ethernet-based computer numerical control （CNC） system, it is very necessary to establish the performance analysis model and further adopt the codesign method from the control, communication and computing perspectives. On the basis of analyzing real-time Ethemet, system architecture, time characteristic parameters of control-loop ere, a performance analysis model for real-time Ethemet-based CNC system was proposed, which is able to include the timing effects caused by the implementation platform in the simulation. The key for establishing the model is accomplished by designing the error analysis module and the controller nodes. Under the restraint of CPU resource and communication bandwidth, the experiment with a case study was conducted, and the results show that if the deadline miss ratio of data packets is 0.2%, then the percentage error is 1.105%. The proposed model can be used at several stages of CNC system development.

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.

Real-time lane departure warning system based on principal component analysis of grayscale distribution and risk evaluation model

A technology for unintended lane departure warning was proposed. As crucial information, lane boundaries were detected based on principal component analysis of grayscale distribution in search bars of given number and then each search bar was tracked using Kalman filter between frames. The lane detection performance was evaluated and demonstrated in ways of receiver operating characteristic, dice similarity coefficient and real-time performance. For lane departure detection, a lane departure risk evaluation model based on lasting time and frequency was effectively executed on the ARM-based platform. Experimental results indicate that the algorithm generates satisfactory lane detection results under different traffic and lighting conditions, and the proposed warning mechanism sends effective warning signals, avoiding most false warning.

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 Grouting Monitoring and Visualization Analysis System for Dam Foundation Curtain Grouting

A real-time monitoring and 3D visualization analysis system is proposed for dam foundation curtain grouting. Based on the real-time control technology, the optimization method and the set theory, a mathematical model of the system is established. The real-time collection and transmission technology of the grouting data provides a data foundation for the system. The real-time grouting monitoring and dynamic alarming method helps the system control the grouting quality during the grouting process, thus, the abnormalities of grouting, such as jacking and hydraulic uplift, can be effectively controlled. In addition, the 3D grouting visualization analysis technology is proposed to establish the grouting information model(GIM). The GIM provides a platform to visualize and analyze the grouting process and results. The system has been applied to a hydraulic project of China as a case study, and the application results indicate that the real-time grouting monitoring and 3D visualization analysis for the grouting process can help engineers control the grouting quality more efficiently.

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.

Causal role of immune cells in obstructive sleep apnea hypopnea syndrome:Mendelian randomization study

BACKGROUND Despite being one of the most prevalent sleep disorders,obstructive sleep apnea hypoventilation syndrome(OSAHS)has limited information on its immunologic foundation.The immunological underpinnings of certain major psychiatric diseases have been uncovered in recent years thanks to the extensive use of genome-wide association studies(GWAS)and genotyping techniques using highdensity genetic markers(e.g.,SNP or CNVs).But this tactic hasn't yet been applied to OSAHS.Using a Mendelian randomization analysis,we analyzed the causal link between immune cells and the illness in order to comprehend the immunological bases of OSAHS.AIM To investigate the immune cells'association with OSAHS via genetic methods,guiding future clinical research.METHODS A comprehensive two-sample mendelian randomization study was conducted to investigate the causal relationship between immune cell characteristics and OSAHS.Summary statistics for each immune cell feature were obtained from the GWAS catalog.Information on 731 immune cell properties,such as morphologic parameters,median fluorescence intensity,absolute cellular,and relative cellular,was compiled using publicly available genetic databases.The results'robustness,heterogeneity,and horizontal pleiotropy were confirmed using extensive sensitivity examination.RESULTS Following false discovery rate(FDR)correction,no statistically significant effect of OSAHS on immunophenotypes was observed.However,two lymphocyte subsets were found to have a significant association with the risk of OSAHS:Basophil%CD33dim HLA DR-CD66b-(OR=1.03,95%CI=1.01-1.03,P<0.001);CD38 on IgD+CD24-B cell(OR=1.04,95%CI=1.02-1.04,P=0.019).CONCLUSION This study shows a strong link between immune cells and OSAHS through a gene approach,thus offering direction for potential future medical research.

Numerical analysis for the effcacy of nasal surgery in obstructive sleep apnea hypopnea syndrome

In the present study, we reconstructed upper airway and soft palate models of 3 obstructive sleep apnea-hypopnea syndrome（OSAHS） patients with nasal obstruction. The airflow distribution and movement of the soft palate before and after surgery were described by a numerical simulation method. The curative effect of nasal surgery was evaluated for the three patients with OSAHS. The degree of nasal obstruction in the 3 patients was improved after surgery. For 2 patients with mild OSAHS, the upper airway resistance and soft palate displacement were reduced after surgery. These changes contributed to the mitigation of respiratory airflow limitation. For the patient with severe OSAHS, the upper airway resistance and soft palate displacement increased after surgery, which aggravated the airway obstruction. The effcacy of nasal surgery for patients with OSAHS is determined by the degree of improvement in nasal obstruction and whether the effects on the pharynx are beneficial. Numerical simulation results are consistent with the polysomnogram（PSG） test results, chief complaints, and clinical findings, and can indirectly reflect the degree of nasal patency and improvement of snoring symptoms, and further,provide a theoretical basis to solve relevant clinical problems.

Neuropsychological Functioning after Adenotonsillectomy in Children with Obstructive Sleep Apnea:A Meta-analysis

The present study aimed to investigate the efficacy of adenotonsillectomy(AT) for children with obstructive sleep apnea syndrome(OSAS) and the improvement of their cognitive function. Studies on cognitive performance of OSAS children treated with or without AT were identified by searching the Pubmed, EMBASE and Cochrane library. A meta-analysis was conducted to analyze the literature. The random-effects model was used to evaluate 11 eligible studies using an inverse-variance method. The neuropsychological test results of 4 cognitive domains(general intelligence, memory, attention-executive function and verbal ability) were obtained and analyzed. By comparison of cognitive function between OSAS children and healthy controls, the effect sizes of each domain were achieved as follows: general intelligence, –0.5(P<0.0001); memory, –0.18(P=0.02); attention-executive function, –0.21(P=0.002); and verbal ability, –0.48(P=0.0006). The effect sizes of general intelligence, memory, attention-executive function, and verbal ability after AT compared to baseline level were –0.37(P=0.008), –0.36(P=0.0005), –0.02(P=0.88), and –0.45(P=0.009), respectively. Comparing the cognitive ability between OSAS children after AT and healthy controls showed that the effect sizes were –0.54(P=0.0009), –0.24(P=0.12), –0.17(P=0.35), and –0.45(P=0.009) in general intelligence, memory, attention-executive function, and verbal ability, respectively. Our results confirmed that OSAS children performed worse than healthy children in terms of the 4 cognitive domains investigated. After 6–12 months of observation, significant improvement in attention-executive function and verbal ability were found in OSAS children treated with AT compared to their baseline level; restoration of attention-executive function and memory were observed in OSAS children after AT in comparison to healthy controls. Further rigorous randomized controlled trials should be conducted to obtain definitive conclusions.

Electrical Stimulation Therapy Improves Sleep Respiratory Parameters in Obstructive Sleep Apnea Syndrome: A Meta-analysis

Recent clinical trials have shown that electrical stimulation has beneficial effects in obstruc- tive sleep apnea syndrome （OSAS）. The purpose of this study was to evaluate the efficacy of electrical stimulation therapy for OSAS with a meta-analysis. The meta-analysis of all relative studies was per- formed through searching international literature, including PUBMED, CNKI, and EMBASE databases. This literature analysis compared all patients undergoing electrical stimulation therapy with respect to the respiratory disturbance index （RDI） and changes in sleep structure. Six studies were selected in- volving a total of 91 patients. The meta-analysis indicated that electrical stimulation therapy reduced RDI, longest apnea time, and improved the minimum SaO2. Based on the evidence found, electrical stimulation may be a potential therapy for OSAS, warranting further clinical trials.

Obstructive Sleep Apnea Syndrome Phenotyping by Cluster Analysis: Typical Sleepy, Obese Middle-aged Men with Desaturating Events are A Minority of Patients in A Multi-ethnic Cohort of 33% Women

Objective Several clinical obstructive sleep apnea syndrome(OSAS)phenotypes associated with heterogeneous cardiovascular risk profiles have been recently identified.The purpose of this study was to identify clusters amongst these profiles that allow for the differentiation of patients.Methods This retrospective study included all moderate-to-severe OSAS patients referred to the sleep unit over a 5-year period.Demographic,symptom,comorbidity,polysomnographic,and continuous positive airway pressure(CPAP)adherence data were collected.Statistical analyses were performed to identify clusters of patients.Results A total of 567 patients were included(67%men,54±13 years,body mass index:32±7 kg/m2,65%Caucasian,32%European African).Five clusters were identified:less severe OSAS(n=172);healthier severe OSAS(n=160);poorly sleeping OSAS patients with cardiometabolic comorbidities(n=87);younger obese men with sleepiness at the wheel(n=94);sleepy obese men with very severe desaturating OSAS and cardiometabolic comorbidities(n=54).Patients in clusters 3 and 5 were older than those in clusters 2 and 4(P=0.034).Patients in clusters 4 and 5 were significantly more obese than those in the other clusters(P=0.04).No significant differences were detected in terms of symptoms and comorbidities.Polysomnographic profiles were very discriminating between clusters.CPAP adherence was similar in all clusters but,among adherent patients,daily usage was more important in cluster 1(less severe patients)than in cluster 5.Conclusion This study highlights that the typical sleepy obese middle-aged men with desaturating events represent only a minority of patients in our multi-ethnic moderate-to-severe OSAS cohort of 33%females.

Retinal nerve fiber layer thickness changes in obstructive sleep apnea syndrome:a systematic review and Metaanalysis

AIM： To evaluate the retinal nerve fiber layer（RNFL）thickness changes in patients with obstructive sleep apnoea syndrome（OSAS）, and detect possible prevalence of glaucoma in this population.METHODS： Comprehensive studies were conducted on the Cochrane Library, Pub Med and Embase through March, 2015. Only studies that fit the selection criteria about RNFL and OSAS would be included. For the measures, we calculated the 95% confidence interval（CI）and weighted mean differences（WMD）. The systematic review and Meta-analysis was performed by Rev Man 5.2software.RESULTS： Nine case-control studies were analyzed containing a total of 1086 cases and 580 controls.Average RNFL thickness in OSAS was reduced significantly compared with healthy controls in random effects model（WMD =-2.56, 95% CI：-4.82 to-0.31, P =0.003, I2=57%）. A significant RNFL thickness reduction were found between the two groups in inferior quadrant（WMD =-3.11, 95% CI：-5.53 to-0.69, P =0.01）, superior quadrant（WMD =-2.37, 95% CI：-4.7 to 0.04, P =0.05）. In nasal quadrant（WMD =-2.54, 95% CI：-6.53 to 1.45, P =0.21） and temporal quadrant（WMD=-1.26, 95% CI：-2.19 to 0.47, P =0.15） there was no difference of RNFL thickness between the two groups.CONCLUSION： The results show that RNFL thickness is lower in patients with moderate or severe OSAS than in normal subjects or patients with mild OSAS according to the nine homogeneity studies.

A Sleep Scoring System Using EEG Combined Spectral and Detrended Fluctuation Analysis Features

Most of sleep disorders are diagnosed based on the sleep scoring and assessments. The purpose of this study is to combine detrended fluctuation analysis features and spectral features of single electroencephalograph (EEG) channel for the purpose of building an automated sleep staging system based on the hybrid prediction engine model. The testing results of the model were promising as the classification accuracies were 98.85%, 92.26%, 94.4%, 95.16% and 93.68% for the wake, non-rapid eye movement S1, non-rapid eye movement S2, non-rapid eye movement S3 and rapid eye movement sleep stages, respectively. The overall classification accuracy was 85.18%. We concluded that it might be possible to employ this approach to build an industrial sleep assessment system that reduced the number of channels that affected the sleep quality and the effort excreted by sleep specialists through the process of the sleep scoring.