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].

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.

Trends and hotspots in gastrointestinal neoplasms risk assessment: A bibliometric analysis from 1984 to 2022

BACKGROUND Gastrointestinal neoplasm(GN)significantly impact the global cancer burden and mortality,necessitating early detection and treatment.Understanding the evolution and current state of research in this field is vital.AIM To conducts a comprehensive bibliometric analysis of publications from 1984 to 2022 to elucidate the trends and hotspots in the GN risk assessment research,focusing on key contributors,institutions,and thematic evolution.METHODS This study conducted a bibliometric analysis of data from the Web of Science Core Collection database using the"bibliometrix"R package,VOSviewer,and CiteSpace.The analysis focused on the distribution of publications,contributions by institutions and countries,and trends in keywords.The methods included data synthesis,network analysis,and visualization of international collaboration networks.RESULTS This analysis of 1371 articles on GN risk assessment revealed a notable evolution in terms of research focus and collaboration.It highlights the United States'critical role in advancing this field,with significant contributions from institutions such as Brigham and Women's Hospital and the National Cancer Institute.The last five years,substantial advancements have been made,representing nearly 45%of the examined literature.Publication rates have dramatically increased,from 20 articles in 2002 to 112 in 2022,reflecting intensified research efforts.This study underscores a growing trend toward interdisciplinary and international collaboration,with the Journal of Clinical Oncology standing out as a key publication outlet.This shift toward more comprehensive and collaborative research methods marks a significant step in addressing GN risks.CONCLUSION This study underscores advancements in GN risk assessment through genetic analyses and machine learning and reveals significant geographical disparities in research emphasis.This calls for enhanced global collaboration and integration of artificial intelligence to improve cancer prevention and treatment accuracy,ultimately enhancing worldwide patient care.

Modeling,Simulation,and Risk Analysis of Battery Energy Storage Systems in New Energy Grid Integration Scenarios

Energy storage batteries can smooth the volatility of renewable energy sources.The operating conditions during power grid integration of renewable energy can affect the performance and failure risk of battery energy storage system(BESS).However,the current modeling of grid-connected BESS is overly simplistic,typically only considering state of charge(SOC)and power constraints.Detailed lithium(Li)-ion battery cell models are computationally intensive and impractical for real-time applications and may not be suitable for power grid operating conditions.Additionally,there is a lack of real-time batteries risk assessment frameworks.To address these issues,in this study,we establish a thermal-electric-performance(TEP)coupling model based on a multitime scale BESS model,incorporating the electrical and thermal characteristics of Li-ion batteries along with their performance degradation to achieve detailed simulation of grid-connected BESS.Additionally,considering the operating characteristics of energy storage batteries and electrical and thermal abuse factors,we developed a battery pack operational riskmodel,which takes into account SOCand charge-discharge rate(Cr),using amodified failure rate to represent the BESS risk.By integrating detailed simulation of energy storage with predictive failure risk analysis,we obtained a detailed model for BESS risk analysis.This model offers a multi-time scale integrated simulation that spans month-level energy storage simulation times,day-level performance degradation,minutescale failure rate,and second-level BESS characteristics.It offers a critical tool for the study of BESS.Finally,the performance and risk of energy storage batteries under three scenarios—microgrid energy storage,wind power smoothing,and power grid failure response—are simulated,achieving a real-time state-dependent operational risk analysis of the BESS.

Risk Analysis of Passalora sequoiae Invasion to China with Imported Coniferous Wood

According to international standard plant quarantine measures and principle risk analysis(ISPM No.11),a risk assessment was carried out for Passalora sequoiae through geographical distribution,possibility of colonization,probability of diffusion,economic importance and difficulty in risk management.Results show that P.sequoiae has a greater risk of introduction and diffusion,and it has distributed in parts of China.It is suggested that P.sequoiae should be added to the list of forest dangerous pests in China.Besides,porting departments should focus on the pathogen on imported host seedlings like Cryptomeria.

Analysis of the Tragedy of Vila Socóin Cubatão (São Paulo, Brazil) Using the Haddon Matrix: A Comprehensive Examination of Socio-Environmental Factors and Risk Mitigation Strategies

The tragedy of Vila Socó epitomizes the socio-environmental repercussions of rapid industrialization in Cubatão. Beginning in the 1940s with the construction of the Anchieta highway, the city experienced an influx of migrants drawn by burgeoning industries, leading to unplanned urban growth and the emergence of vulnerable communities like Vila Socó. This article examines the interconnected factors—such as demographic shifts, inadequate planning, and regulatory oversight—that culminated in the devastating fire of 1984, claiming numerous lives and highlighting systemic failures. Utilizing the Haddon Matrix, this study dissects the Vila Socó incident, emphasizing the roles of human error, infrastructure integrity, and socio-economic disparities in disaster causation. By contextualizing the tragedy within Cubatão’s industrial trajectory, it underscores the urgent need for comprehensive risk assessment and proactive mitigation strategies in rapidly developing regions globally. Beyond its immediate focus, this work offers broader insights into the dynamics of industrial disasters and their socio-economic implications. As pipelines continue to play a vital role in global energy infrastructure, the lessons drawn from Vila Socó’s tragedy resonate deeply, emphasizing the imperative of robust safety protocols and accountable governance to prevent similar catastrophes in the future.

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.

Severity Risk Analysis Matrix Ranking (SRAMR) for Oil-Spill Contingency Planning: Asemoku-Agip Pipeline in Perspective

One of the down sides of crude oil exploration and exploitation in the developing nations is its impacts on the environment. A major manifestation of poor crude oil management is oil-spillages. Mitigation strategies have been too expensive, but a cheaper recent way of managing crude-spills is by developing a severity risk analysis matrix ranking (SRAMR). The spatial data-sets deployed in this study were acquired from the USGS, Google Earth Pro, and NOSDRA. A buffer zone of 100 - 400 meters was created to characterize the LULC characteristics of the area. Also, this was to help develop a risk sensitivity characteristic. The study found that the vegetal cover was the environmental resource at high risk to crude-spills in the area, while other land-uses were at low risk of crude-spill. It is hoped that the finding from this study informs policy development and planning for crude oil spill incidents.

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.

Cusp Model for Risk Analysis

Aim To assess simultaneously various risk states of a system. Methods\ Using the catastrophe and fuzzy theory, the energy and uncertainty in a system are set as two control variables and the function of the system is used as the state variable for analysis. Results and Conclusion\ A risk analysis model named the cusp model is presented. Various states regarding the safety of the system such as the accident state, no accident state and miss state can be represented at will on the cusp model.

Analysis on Ecological Risk in Northwest China

Based on scenario analysis method, assuming different economic develop- ment policies, and AHP method, the ecological indicators and risk prediction models were established. The trends of ecological risk in the northwest region were ana- lyzed. The results showed that： in the northwest, the potential ecological risks of the population economy, water resources, and land resources and natural disasters still existed. However, the environment and respond to ecological risks were decreasing. Therefore, a regional balanced development measures had been taken to curb the western trend of ecological deterioration. So that socio-economic development, re- sources, environment, and economy were expected to a virtuous cycle, and the eco- logical risks would be reduced. This study provides the basis for land and water use planning and environmental protection of government decision-making and management.

Risk Assessment and Analysis of Bursaphelenchus Species in the Coniferous Forest in Yuyao, Ningbo

The method for pest risk analysis was used to make the potential risk analysis and assessment to the 8 Bursaphelenchus species found in the pines in Yuyao, Ningbao, and the corresponding risk management strategies were put forward.

risk analysis

According to the developing of international trade,people began to pay more attention on foreign currency and its exchange.Currency risk is coming into being with changes in foreign exchange value,which affect the return on loan or investments by other currencies.So,at this time,people will think about how to control the currency risk,that is risk analysis.In my opinion,risk analysis is the actions of man-

FAULT TREE ANALYSIS OF SPONTANEOUS COMBUSTION OF SULPHIDE ORES AND ITS RISK ASSESSMENT

A logic fault tree of mine spontaneous combustion of sulphide ores was built by the fault tree analysis (FTA) based on a lot of mechanism investigation of sulphide ore spontaneous combustion in more than ten mines and review of a great amount of relevant