Traditional Assessment Methods Research Articles

Multi-Criteria Analysis in Circular Economy Principles: Using AHP Model for Risk Assessment in Sustainable Whisky Production

As the whisky industry applies circular economy principles to maximize resource utilization and minimize environmental impact, companies become exposed to several risks, which require complex assessments to ensure reliable outcomes. This study provides an organized framework to identify, prioritize, and rank various risk factors commonly observed in the whisky industry through the development of an analytical hierarchy process (AHP) multi-criteria analysis model. Experts from 18 small European distilleries identified five main risk criteria and nineteen sub-criteria from brainstorming workplace observations and categorized them as: environmental (5), operational (4), technological innovation (3), food safety (3), and economical (4) risks. The analytical hierarchy process (AHP) approach was used to determine the weights and ranks of the main criteria and sub-criteria based on the survey responses received from experts from each distillery. The final judgements are consistent, as indicated by consistency values (CR) of less than 0.1 for all risk criteria. Unlike traditional risk assessment methods, the AHP model effectively integrates qualitative and quantitative data, aiding strategic decision making in the whisky industry by breaking down complex problems into manageable sub-problems. Future research directions may expand the criteria and explore additional sustainable practices.

Study on Fractal Damage of Concrete Cracks Based on U-Net

The damage degree of a reinforced concrete structure is closely related to the generation and expansion of cracks. However, the traditional damage assessment methods of reinforced concrete structures have defects, including low efficiency of crack detection, low accuracy of crack extraction, and dependence on the experience of inspectors to evaluate the damage of structures. Because of the above problems, this paper proposes a damage assessment method for concrete members combining the U-Net convolutional neural network and crack fractal features. Firstly, the collected test crack images are input into U-Net for segmenting and extracting the output cracks. The damage to the concrete structure is then classified into four empirical levels according to the damage index (DI). Subsequently, a linear regression equation is constructed between the fractal dimension (D) of the cracks and the damage index (DI) of the reinforced concrete members. The damage assessment is then performed by predicting the damage index using linear regression. The method was subsequently employed to predict the damage level of a reinforced concrete shear wall–beam combination specimen, which was then compared with the actual damage level. The results demonstrate that the damage assessment method for concrete members proposed in this study is capable of effectively identifying the damage degree of the concrete members, indicating that the method is both robust and generalizable.

Research on the Evaluation of College English Teaching Based on Data Mining Technology From the Perspective of “Internet Plus Education”

This paper explores using data mining in English teaching assessment in higher education within the 'Internet + Education' era. Traditional assessment methods struggle to meet modern teaching needs. By collecting diverse data like student performance and learning behavior, and employing data mining, a comprehensive assessment model is built. This model tracks student progress, uncovers data patterns, and offers personalized teaching guidance. Empirical analysis validates the model's feasibility, enabling colleges to better understand English teaching quality and adjust methods accordingly. In essence, this study offers a novel approach to assessment and contributes to education digitization and teaching enhancement.

Evaluation of Minimum-to-Severe Global and Macrovesicular Steatosis in Human Liver Specimens: A Portable Ambient Light-Compatible Spectroscopic Probe.

Hepatic steatosis (HS), particularly macrovesicular steatosis (MaS), influences transplant outcomes. Accurate assessment of MaS is crucial for graft selection. While traditional assessment methods have limitations, non-invasive spectroscopic techniques like Raman and reflectance spectroscopy offer promise. This study aimed to evaluate the efficacy of a portable ambient light-compatible spectroscopic system in assessing global HS and MaS in human liver specimens. A two-stage approach was employed on thawed snap-frozen human liver specimens under ambient room light: biochemical validation involving a comparison of fat content from Raman and reflectance intensities with triglyceride (TG) quantifications and histopathological validation, contrasting Raman-derived fat content with evaluations by an expert pathologist and a "Positive Pixel Count" algorithm. Raman and reflectance intensities were combined to discern significant (≥ 10%) discrepancies in global HS and MaS. The initial set of 16 specimens showed a positive correlation between Raman and reflectance-derived fat content and TG quantifications. The Raman system effectively differentiated minimum-to-severe global and macrovesicular steatosis in the subsequent 66 specimens. A dual-variable prediction algorithm was developed, effectively classifying significant discrepancies (> 10%) between algorithm-estimated global HS and pathologist-estimated MaS. Our study established the viability and reliability of a portable spectroscopic system for non-invasive HS and MaS assessment in human liver specimens. The compatibility with ambient light conditions and the ability to address limitations of previous methods marks a significant advancement in this field. By offering promising differentiation between global HS and MaS, our system introduces an innovative approach to real-time and quantitative donor HS assessments. The proposed method holds the promise of refining donor liver assessment during liver recovery and ultimately enhancingtransplantation outcomes.

Carbon Footprint Assessment Based on Agricultural Traceability System Records: A Case Study of Onion Production in Southern Taiwan

This study proposes an improved methodology based on life cycle assessment (LCA), which is used to calculate the carbon footprint of agriculture, provides a simple and feasible calculation path, and constructs a streamlined framework for calculating the carbon footprint based on the agricultural traceability system records. Using important economic crop (Onion) as research subject, and choose the largest planting area in R.O.C. (southern Taiwan) as a case study. A total of 64 farm production history records have been collected, includes all of farms certified with a traceable agricultural products (TAP) label. Through a detailed analysis of the traditional carbon footprint calculation method, found that agricultural traceability system records could replace the data source in carbon footprint verification (CFV) process, and system records could be used as activity data after being organized. With our method, no need to go through a complicated CFV process, just download the existing data on agricultural traceability system, can start calculating carbon footprint as soon as possible. To compared to traditional assessment method, results show a margin of error is less than 6% compared to traditional assessment methods. Advantages of improved method were be found, such as easy data acquisition, simplified calculation steps, and improved data transparency and accuracy. From statistical data, show that at least seven categories of carbon emission sources for carbon footprints, the most significant of carbon emission impact are fertilizers. The result of improved methodology based on life cycle assessment (LCA), show that using the improved methods can help promote the carbon footprint management efficiency of agricultural organizations such as Farmers’ Association or Agricultural Production Marketing Group, promptly monitor the carbon footprint status of their fields and adjust strategies to reduce carbon footprints in real-time, advancing towards the goal of net-zero carbon emissions.

Evaluating Students' Academic Progress in the Role of Fuzzy Logic

This paper explores the role of fuzzy logic in the assessment of student performance in local high school, Badr, Libya, positing that it offers a more flexible and accurate alternative to traditional categorization methods of academic achievement. Conventional assessment tools, such as standardized test scores and GPAs, often yield a distorted and overly simplistic view of student performance. Utilizing a mixed-methods research design, this study compares the performance, attendance, and self-reported satisfaction of students assessed through fuzzy logic with those evaluated by traditional methods. The results demonstrate that students assessed using fuzzy logic achieved higher average scores and expressed greater satisfaction with the assessment process. Additionally, educators employing fuzzy logic provided more detailed and constructive feedback, enriching the learning experience. Thus, this research indicates that fuzzy logic can effectively mitigate the shortcomings of traditional assessment methods, enhancing the recognition of diverse student abilities. The findings advocate for the integration of fuzzy logic in educational assessment practices to promote inclusivity and improved learning outcomes.

MONGOLIA IN INTERNATIONAL FINANCIAL AND LOGISTICS CHAINS

The article is devoted to the methodological and practical aspects of assessing the specifics of developing countries, including Mongolia, in estimate of the performance of financial and logistics chains. The relevance and significance of the study is that decisions on the investment program (Vision 2050) are made on the basis of traditional assessments of investment projects, its’ economic and social effects. Based on a generalization of available information and data, the article presents the author’s approach to justifying the need to change traditional assessment methods. The necessity of taking into account in models the costs of diversification and forecasting changes in the place in value chains when implicit costs arise in the future has been proven. Ignoring the fact that the costs of international trade are passed on to consumers in small commodity economies often leads to recessions and the need for constant debt refinancing. The article examines the structure of Mongolia’s exports and imports not in traditional estimates, but from the position of influence on future growth of expenses in the absence of sources to cover them. Particular attention is paid to the growing inefficiency and irrationality of budget expenditures in the traditional forecasting of the effects and benefits from the implementation of the project. In relation to the research topic, the use of the terms «budget liquidity», «fictitious financial flows», «disguise of commodity flows» is justified. An approach to the classification of factors that distort the picture of the movement of financial and logistics flows from the point of view of influence on national economic interests is proposed. In conclusion, a conclusion is drawn about the possibility of practical application of the proposed approaches for the analysis of financial and logistics flows of other countries, as well as the existing assumptions in the application of this model.

Safety-risk assessment system for prefabricated building construction in China

PurposePrefabricated building (PB) uses factory production and onsite assembly, which differs from traditional construction methods. This special construction approach may lead to dissimilar safety risks and challenges. Traditional safety assessment methods may not adequately and accurately assess the safety risks of PB construction. This paper aims to develop a new concept and methodology for targeted improvement in assessing PB safety risks.Design/methodology/approachRisk factors and indicators were established based on literature review and expert inputs. A structural equation model (SEM) was developed to investigate the relationships among three main risk categories: objects, workers and management. SEM analyzed the intricate associations between indicators and deepened understanding of safety risks. The model was tailored for China’s PB construction projects to enhance safety-risk management.FindingsThe cloud model evaluation validated the SEM model. A PB case study project tested and verified the model, evaluated its efficacy and quantified its safety performance and grade. We identified significant safety risk impacts across the three risk categories, safety-control level and specific areas that require improvement. The SEM model established a robust safety evaluation indicator system for comprehensive safety assessment of PB construction.Practical implicationsPractical recommendations provide valuable insights for decision-makers to enhance construction efficiency without compromising safety. This study contributed to the conceptual foundation and devised a novel method for evaluating safety performance in PB construction for safer and more efficient practices.Originality/valueThis study departed from the traditional method of calculating weights, opting instead for the SEM method to determine the weights of individual risk indicators. Additionally, we leveraged the cloud model to mitigate the influence of subjective factors in analyzing questionnaire survey responses. The feasibility and reliability of our proposed method were rigorously tested and verified by applying it to the PB case.

Classification of green tea and quantitative prediction of catechin content based on indicator displacement array sensors

Green tea, renowned for its health benefits and cultural significance, exhibits variations in quality and biochemical composition across different grades and varieties. Traditional quality assessment methods are often labour-intensive and time-consuming, rendering them impractical for rapid analysis. This study employed a sensitive indicator displacement array sensor along with supervised learning algorithms—Extreme learning machine, Support vector machine, Random forest, and Back propagation neural network—to accurately classify green tea varieties and grades, as well as predict catechin content. Sensor components were initially screened based on their responsiveness to epigallocatechin gallate, and subsequently developed using specific indicator-receptor combinations: Pyrocatechol Violet/Fe2+, Alizarin Red S/Phenylboronic Acid, and Bromopyrogallol Red/3-Nitrophenylboronic Acid. Validation was performed through monitoring changes in RGB values and UV–visible spectroscopy. Discrimination among Anji white tea grades was assessed using principal component analysis and further refined through supervised algorithms, achieving high accuracy rates, especially with Random forest and Extreme learning machine models, which excelled in both calibration and prediction sets. These methodologies were extended to classify ten green tea varieties, demonstrating robustness and high accuracy in a multiclass setting. Independent validation confirmed the models’ efficacy in-class differentiation, with most achieving an accuracy of over 90%. Moreover, the quantitative prediction of catechin content was enhanced using nonlinear models, specifically Support vector regression optimized with the Particle swarm optimization algorithm. These models exhibited superior performance compared to linear models, particularly in effectively predicting individual catechin levels. These findings highlight the potential of combining sensor technologies with supervised learning algorithms to enhance tea quality assessment and categorization, establishing a robust framework for future applications in tea quality control.

Real-time risk assessment of distribution systems based on Unscented Kalman Filter

The continuous growth of renewable energy and the load level has posed increasingly severe operational risks to distribution systems. In view of this, this paper combines state estimation with risk assessment, and uses the results of distribution system state estimation based on Unscented Kalman Filter as the input of risk assessment. With the combination, the sampling based on probability distributions in traditional risk assessment methods is no longer needed, thus avoiding the difficulty of updating probability distributions timely according to the latest information in real-time operation. By applying the proposed risk assessment method, the real-time assessment of operational risks in perspectives of bus voltage, branch power, and renewable energy utilization is achieved. Meanwhile, the weight of each risk index is properly determined according to both subjective and objective knowledge by using Analytic Hierarchy Process method and entropy weight method. Case studies show that the proposed method achieves effective assessment of comprehensive risks in the operation of distribution system.

Resilience assessment of FPSO leakage emergency response based on quantitative FRAM

FPSO production process is prone to leakage, and failure to respond promptly and effectively will lead to accident escalation and serious consequences. However, traditional safety assessment methods cannot handle the nonlinear relationships between human, technology, and organization in emergency response process. This study proposes a quantitative FRAM to evaluate the emergency response resilience of FPSO leakage. This method establishes a resilience evaluation framework including three tiers: function, ability, and system, which can quantify system resilience based on the variability of function. First, identify basic functions according to the four stages of monitoring, response, learning and anticipation in the emergency response process, and establish the FRAM model of FPSO leakage emergency response. Then, the quantitative FRAM and Monte Carlo simulation are combined to calculate the variabilities of functions under different operating conditions. Finally, based on the simulation results, the variabilities of basic functions are aggregated and statistically analyzed to quantify system resilience. The implementation process of this method is illustrated by a case study. The influence of different factors on the system resilience is analyzed by setting various operation scenarios, and critical functions are identified by sensitivity analysis, which can provide reference for improving system resilience and ensuring FPSO safety production.

Quantification of uncertainty in short-term tropospheric column density risks for a wide range of carbon monoxide

The short-term risks associated with atmospheric trace gases, particularly carbon monoxide (CO), are critical for ecological security and human health. Traditional statistical methods, which still dominate the assessment of these risks, limit the potential for enhanced accuracy and reliability. This study evaluates the performance of traditional models (ARIMA), machine learning models (LightGBM, ConvLSTM2D), and optimized machine learning solutions (Bayes residual optimization ConvLSTM2D LightGBM, Bayes_CL) in predicting Sentinel 5P columnar CO levels. This study findings demonstrate that machine learning models and their optimized versions significantly outperform traditional ARIMA models in cross-validation (CV), visualization, and overall prediction performance. Notably, machine learning model based on Bayes and residual optimization (Bayes_CL) achieved the highest CV score (Bayes_CL R2 = 0.8, LightGBM R2 = 0.79, ConvLSTM2D R2 = 0.75, ARIMA R2 = 0.61), along with superior visualization and other metrics. Using Bayes_CL, we effectively quantified a 2.4% increase in columnar CO levels in mainland China in the second half of 2023, following the complete lifting of COVID-19 lockdowns. This study confirms that machine learning models can effectively replace traditional methods for short-term risk assessment of Sentinel 5P columnar CO. This transition holds significant implications for policy formulation, greenhouse effect assessment, and population health risk evaluation, especially in uncertain situations where human activities are severely disrupted, thereby affecting environmental safety.

Virtual reality analytics map (VRAM): A conceptual framework for detecting mental disorders using virtual reality data

Virtual reality (VR) is an emerging tool in mental health care yet its potential in diagnostic assessments remains underexplored. Recognizing the growing need of technological advancements that support traditional methods for mental health assessment, this paper introduces the Virtual Reality Analytics Map (VRAM), a novel conceptual framework designed to leverage VR analytics for the detection of symptoms of mental disorders. The VRAM framework integrates psychological constructs with VR technology, systematically mapping and quantifying behavioral domains through specific VR tasks. This approach potentially allows for the precise capture and identification of nuanced behavioral, cognitive, and affective digital biomarkers associated with symptoms of mental disorders. The benefits of the VRAM framework are demonstrated with its example application across various mental disorders ensuring the utility and versatility of the framework. By bridging the gap between psychology and technology, the VRAM framework aims to contribute to the early detection and assessment of mental disorders.

Comparative Study Of Keratometer And Autorefractometer For Non-Invasive Tear Break-Up Time Measurement In Rural Settings

Tear break-up time (TBUT) is a crucial indicator of ocular surface health, often used in diagnosing dry eye syndrome. Traditional assessment methods may not be accessible in rural areas due to the high cost of advanced diagnostic machines. This study investigates the reliability and accuracy of keratometers and autorefractometers for measuring non-invasive TBUT in a cohort of 200 patients. Our findings indicate a strong correlation between TBUT values from these tools and the conventional invasive TBUT test, demonstrating their potential utility in resource-limited settings

AI-Based Risk Assessments in Forensic Auditing: Benefits, Challenges and Future Implications

Forensic auditing is a critical component of ensuring financial integrity and detecting fraud within organizations. Traditional methods of risk assessment in forensic auditing often rely on manual processes, which can be time-consuming, labour-intensive, and prone to human error. In recent years, the integration of artificial intelligence (AI) techniques has revolutionized the field, offering more efficient and accurate risk assessment capabilities. This abstract explores the role of AI-based risk assessment in forensic auditing, highlighting its benefits, challenges, and future implications. AI-based risk assessment leverages advanced algorithms and machine learning models to analyse large volumes of financial data, identify patterns, anomalies, and potential red flags indicative of fraudulent activities. By automating repetitive tasks such as data collection, classification, and analysis, AI streamlines the auditing process, enabling forensic auditors to focus on interpreting results and making informed decisions. One of the primary advantages of AI-based risk assessment is its ability to detect complex fraud schemes that may go unnoticed by traditional methods. Machine learning algorithms can detect subtle deviations from expected behaviour, flagging transactions or activities that exhibit unusual patterns or characteristics. Moreover, AI systems can adapt and learn from new data, continuously improving their detection capabilities over time. Another benefit of AI-based risk assessment is its scalability and efficiency. With the increasing volume and complexity of financial transactions, manual auditing processes struggle to keep pace. AI, on the other hand, can analyse vast datasets in a fraction of the time it would take a human auditor, allowing organizations to conduct more comprehensive and timely audits.

Evolving Assessment in Medical Education: Exploring the Role of Open-Book Examinations.

The landscape of medical education has witnessed significant transformations over the past decades, particularly with the advent of active teaching methodologies. However, despite these advancements, the traditional theoretical assessment methods have remained largely unchanged. This lack of evolution in assessment systems poses a challenge as it is crucial for assessment methods to evolve in tandem with teaching approaches to ensure a comprehensive and effective learning process in medical education. This paper reviews the integration and effectiveness of open-book examinations (OBEs) in medical education, reflecting their growing significance. An integrative review of the literature was conducted, drawing from a range of relevant publications over the last decade, sourced from databases such as PubMed, Web of Science, Scopus, and ERIC. The inclusion criteria focused on full-text articles in English, with search terms including "medicine," "assessment," "open book examination," "open book exam," and "open book assessment," combined using Boolean operators. Thirteen publications were selected and critically appraised using The Critical Appraisal Skills Program checklist. The analysis identified three primary thematic categories: "Teaching Strategy for Pandemic and Challenging Conditions," "Tool of Learning & Educational Impact", and "Operational Challenges & Future Directions". These themes were explored to understand the role and impact of open-book examinations in medical education. The findings indicate that open-book examinations are a crucial component in the evolving landscape of medical education. While certain reservations remain, open-book examinations have shown significant potential in fostering critical thinking, argumentation skills, and lifelong learning among medical students. They reflect the ongoing evolution of knowledge in the medical field and contribute to the development of professionals' adept at navigating and applying complex information. Further research is recommended to solidify these findings and expand the understanding of open-book examinations in medical education.

Development and User Experience Evaluation of an Experience Sampling-Based Dietary Assessment Method

BackgroundMost technology-based dietary assessment methods use the same methodology as traditional dietary assessment methods resulting in similar limitations and biases. Experience sampling methodology (ESM) is a real-life real-time data-capturing method that is explored as an alternative methodology for dietary assessment to improve feasibility and data accuracy. ObjectivesThis research aimed to develop and evaluate an experience sampling-based dietary assessment method (ESDAM) measuring habitual dietary intake. MethodsStarting from a food frequency questionnaire, experience sampling principles were implemented resulting in a pilot ESDAM. Second, the pilot ESDAM was evaluated for feasibility and convergent validity compared with a 3-d food record. Mean intake with standard deviations was compared between the pilot ESDAM, food record (FR), and food frequency questionnaire (FFQ), and Spearman correlation coefficients (SCCs) were calculated. Third, following a literature review and expert opinion, the questions and design of the pilot ESDAM were further adapted to ESM and implemented in an experience sampling survey application. The resulting prototype ESDAM underwent 2 rounds of user experience (UX) evaluation in which 10 persons tested ESDAM for 1 wk followed by a structured evaluation interview. ResultsThe pilot ESDAM, FR, and FFQ were completed by 28 participants and the evaluation questionnaire by 78 participants. Mean energy intake by the FFQ, pilot ESDAM, and FR was 1272.2 ± 308.9 kcal/d, 1592.3 ± 358.9 kcal/d, and 1664.6 ± 257.8 kcal, respectively. The evaluation revealed the limited time window (19:00–23:00) to respond was inconvenient, good acceptability, and ease of use of the pilot ESDAM. The UX evaluation study revealed overall good acceptability, ease of use, and low burden of the different prototypes of ESDAM. ConclusionsESM could advance the field beyond traditional methodologies and improve feasibility. ESDAM is unique in assessing dietary intake quantitatively through ESM. Additional assessment of validity might shed light on the data accuracy of ESDAM.

A Kinematic Deviation Index (KDI) for Evaluation of Forelimb Function in Rodents.

Rodent models are widely used to study neurological conditions and assess forelimb movement to measure function performance, deficit, recovery and treatment effectiveness. Traditional assessment methods based on endpoints such as whether the task is accomplished, while easy to implement, provide limited information on movement patterns important to assess different functional strategies. On the other side, detailed kinematic analysis provides granular information on the movement patterns but is difficult to compare across laboratories, and may not translate to clinical metrics of upper limb function. To address these limitations, we developed and validated a kinematic deviation index (KDI) for rodents that mimics current trends in clinical research. The KDI is a unitless summary score that quantifies the difference between an animal movement during a task and its optimal performance derived from spatiotemporal marker sequences without pre-specifying movements. We demonstrate the utility of KDI in assessing reaching and grasping in mice and validate its discrimination between trial endpoints in healthy animals. Furthermore, we show KDI sensitivity to interventions, including acute and chronic spinal cord injury and optogenetic disruption of sensorimotor circuits. The KDI provides a comprehensive measure of motor function that bridges the gap between detailed kinematic analysis and simple success/failure metrics, offering a valuable tool for assessing recovery and compensation in rodent models of neurological disorders.

Research Trends and Usability Challenges in Behavioral Data-Based Cognitive Function Assessment

The prevalence of dementia, a condition associated with high social costs, is rising alongside the aging population. Early diagnosis of mild cognitive impairment (MCI), a precursor to dementia, is essential for effective intervention. Recent research has focused on diagnosing cognitive function in the elderly by analyzing behavioral data, such as gait and hand movements. Compared to traditional neuropsychological assessment methods, behavioral data-based assessments offer advantages, including reduced fatigue for patients and examiners, faster testing procedures, and more objective evaluation of results. This study reviews 15 research projects from the past five years (2018–2023) that have utilized behavioral data to assess cognitive function. It examines the specific gait and hand movement variables used, the technologies implemented, and user experiences reported in these studies. As these types of assessments require new technologies or environments, we analyzed usability issues that should be considered for accurate cognitive assessment. Based on this analysis, the paper proposes future directions for research in the field of behavioral data-based cognitive function assessment.

A Novel Fractional Model and Its Application in Network Security Situation Assessment

The evaluation process of the Fractional Order Model is as follows. To address the commonly observed issue of low accuracy in traditional situational assessment methods, a novel evaluation algorithm model, the fractional-order BP neural network optimized by the chaotic sparrow search algorithm (TESA-FBP), is proposed. The fractional-order BP neural network, by incorporating fractional calculus, demonstrates enhanced dynamic response characteristics and historical dependency, showing exceptional potential for handling complex nonlinear problems, particularly in the field of network security situational awareness. However, the performance of this network is highly dependent on the precise selection of network parameters, including the fractional order and initial values of the weights. Traditional optimization methods often suffer from slow convergence, a tendency to be trapped in local optima, and insufficient optimization accuracy, which significantly limits the practical effectiveness of the fractional-order BP neural network. By introducing cubic chaotic mapping to generate an initial population with high randomness and global coverage capability, the exploration ability of the sparrow search algorithm in the search space is effectively enhanced, reducing the risk of falling into local optima. Additionally, the Estimation of Distribution Algorithm (EDA) constructs a probabilistic model to guide the population toward the globally optimal region, further improving the efficiency and accuracy of the search process. The organic combination of these three approaches not only leverages their respective strengths, but also significantly improves the training performance of the fractional-order BP neural network in complex environments, enhancing its generalization ability and stability. Ultimately, in the network security situational awareness system, this integration markedly enhances the prediction accuracy and response speed.