Assessment Methodology Research Articles

Harnessing adversarial machine learning for advanced threat detection: AI-driven strategies in cybersecurity risk assessment and fraud prevention

The abstract is "The rapid evolution of cyber threats necessitates innovative defenses, particularly in the domains of risk assessment and fraud detection. This paper explores the integration of Artificial Intelligence (AI) and Adversarial Machine Learning (ML) techniques as a formidable strategy against increasingly sophisticated cyber-attacks. We present a comprehensive framework that leverages AI to dynamically assess cybersecurity risks and detect fraudulent activities with unprecedented accuracy and speed. Firstly, we delve into the foundational principles of adversarial machine learning, outlining how these techniques can be employed to simulate potential cyber threats, thereby enabling the development of more resilient AI-driven cybersecurity systems. We highlight the dual role of adversarial ML in both enhancing security defenses and potentially serving as a vector for sophisticated attacks, underscoring the importance of developing robust, adversarial-resistant models. Subsequently, we introduce a novel adaptive risk assessment methodology that incorporates real-time data analysis, machine learning algorithms, and predictive modeling to accurately identify and prioritize threats. This method adapts to the evolving digital landscape, ensuring that cybersecurity measures are always one step ahead of potential attackers. In the context of fraud detection, we explore -how AI algorithms can sift through vast datasets to detect anomalies and patterns indicative of fraudulent behavior. Through case studies and empirical analysis, we demonstrate the effectiveness of AI in identifying fraud across various sectors, from financial transactions to online identity verification processes. Our research contributes to the cybersecurity field by providing a detailed examination of how AI and adversarial ML can be harnessed to fortify digital defenses, improve risk assessment techniques, and enhance fraud detection capabilities. The insights garnered from this study not only advance theoretical understanding but also offer practical guidance for organizations seeking to implement AI-driven security solutions. As cyber threats continue to evolve, the integration of AI and adversarial ML in cybersecurity strategies will be paramount in safeguarding digital assets and maintaining the integrity of online systems."

European Society of Intensive Care Medicine clinical practice guideline on fluid therapy in adult critically ill patients. Part 1: the choice of resuscitation fluids.

This is the first of three parts of the clinical practice guideline from the European Society of Intensive Care Medicine (ESICM) on resuscitation fluids in adult critically ill patients. This part addresses fluid choice and the other two will separately address fluid amount and fluid removal. This guideline was formulated by an international panel of clinical experts and methodologists. The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) methodology was applied to evaluate the certainty of evidence and to move from evidence to decision. For volume expansion, the guideline provides conditional recommendations for using crystalloids rather than albumin in critically ill patients in general (moderate certainty of evidence), in patients with sepsis (moderate certainty of evidence), in patients with acute respiratory failure (very low certainty of evidence) and in patients in the perioperative period and patients at risk for bleeding (very low certainty of evidence). There is a conditional recommendation for using isotonic saline rather than albumin in patients with traumatic brain injury (very low certainty of evidence). There is a conditional recommendation for using albumin rather than crystalloids in patients with cirrhosis (very low certainty of evidence). The guideline provides conditional recommendations for using balanced crystalloids rather than isotonic saline in critically ill patients in general (low certainty of evidence), in patients with sepsis (low certainty of evidence) and in patients with kidney injury (very low certainty of evidence). There is a conditional recommendation for using isotonic saline rather than balanced crystalloids in patients with traumatic brain injury (very low certainty of evidence). There is a conditional recommendation for using isotonic crystalloids rather than small-volume hypertonic crystalloidsin critically ill patients in general (very low certainty of evidence). This guideline provides eleven recommendations to inform clinicians on resuscitation fluid choice in critically ill patients.

Assessing occupational pesticide exposure in Latin America and the Caribbean: Methods review for agricultural vulnerability reduction.

B A rapid review was conducted to describe pesticide exposure assessment methodologies in Agricultural Work Contexts (AWC)s in Latin America and the Caribbean (LAC), in order to provide elements to reduce vulnerabilities within the agricultural population. A final number of 79 articles were selected from Medline via Pubmed, LILACS, and SciELO. In the region, family agriculture is the predominant form of agricultural production (72%) in informal agricultural work contexts (AWCs), which are characterized by small-scale diversified agricultural production, lower educational attainment, limited income, and a lack of social and healthcare security for the families. Indirect exposure assessment methods were the most commonly reported (58%), and its use was associated with informal AWCs (p = 0.011). Understanding AWCs and employing appropriate assessment methods can contribute to addressing vulnerabilities in the agricultural sector.

The Challenges of Blood Sample Delivery via Drones in Urban Environment: A Feasibility Study through Specific Operation Risk Assessment Methodology

In recent years, Unmanned Aircraft System (UAS) usage in the medical sector as an alternative to traditional means of goods transport has grown significantly. Even though the reduced response time achieved with UASs can be lifesaving in critical situations, their usage must comply with technological constraints such as range, speed and capacity, while minimizing potential risks. In this paper, the feasibility of a drone operation dedicated to the transport of blood samples in an urban area is studied through a safety risk analysis. The assessment utilizes the Specific Operation Risk Assessment (SORA) framework, in line with current European regulations, and extends it to define flight trajectories with minimal risk. A case study in the Helsinki urban area is used as a reference, with an exemplary case of commercial drone transportation of blood samples between the Töölö and Malmi Hospitals. By leveraging the drone performance capabilities and minimizing the risk for people on the ground, this approach demonstrates that medical delivery using drones in densely populated urban environments remains challenging. Nonetheless, it argues that the proposed method can enhance risk awareness and support the planning of feasible operations.

Ultrasound Elastography: Methods, Clinical Applications, and Limitations: A Review Article

Ultrasound is a highly adaptable medical imaging modality that offers several applications and a wide range of uses, both for diagnostic and therapeutic purposes. The principles of sound wave propagation and reflection enable ultrasound imaging to function as a highly secure modality. This technique facilitates the production of real-time visual representations, thereby assisting in the evaluation of various medical conditions such as cardiac, gynecologic, and abdominal diseases, among others. The ultrasound modality encompasses a diverse range of modes and mechanisms that serve to enhance the methodology of pathology and physiology assessment. Doppler imaging and US elastography, in particular, are two such techniques that contribute to this expansion. Elastography-based imaging methods have attracted significant interest in recent years for the non-invasive evaluation of tissue mechanical characteristics. These techniques utilize the changes in soft tissue elasticity in various diseases to generate both qualitative and quantitative data for diagnostic purposes. Specialized imaging techniques collect data by identifying tissue stiffness under mechanical forces such as compression or shear waves. However, in this review paper, we provide a comprehensive examination of the fundamental concepts, underlying physics, and limitations associated with ultrasound elastography. Additionally, we present a concise overview of its present-day clinical utilization and ongoing advancements across many clinical domains.

Network equivalent-based VQ Curve method and its application in a novel real-time voltage stability assessment methodology

The PV and VQ curves have been widely accepted tools for monitoring voltage stability. Although these curves have been used in offline studies, their online applications are not trivial, involving specific issues and complexities concerning real-time environment. The literature presents some approaches to determine quantities related to voltage stability limit in real-time analysis of the PV curve. However, it does not occur with the corresponding quantities for the VQ curve, making it difficult to analyze these curves jointly. This work presents two significant innovative contributions, based on the use of real-time network equivalents estimated through measurement data. The first is a technique suitable for real-time applications to calculate the complete VQ curve, called Network Equivalent-based VQ Curve. The second is a methodology for determining voltage stability margins using information from both PV and VQ curves. Simulations were carried out in RVS Test System, and 9, 14, 30 and 118 Bus Test Cases available in MATPOWER. The results of Network Equivalent-based VQ Curve application were validated by power flow solution. The voltage stability assessment methodology proposed in this paper represents a significant advance, going beyond the traditional approaches.

Assessing clinical pathological characteristics and gene expression patterns associated with hepatoid adenocarcinoma of the stomach.

The objective of this study is to assess the clinical pathological attributes of Hepatoid Adenocarcinoma of the Stomach (HAS) and to delineate the differential diagnostic considerations about it. The investigation involved analyzing 31 HAS cases using histomorphological assessment, immunohistochemical profiling, and relevant gene detection methodologies. Among the 31 HAS cases, 9 (29.0%) were of trabecular hepatoid adenocarcinoma of the stomach, 7 (22.6%) were of glandular hepatoid adenocarcinoma of the stomach, 4 (12.9%) were of nesting hepatoid adenocarcinoma of the stomach, 3 (9.7%) were of clear cell hepatoid adenocarcinoma of the stomach, and 8 (25.8%) were of diverse hepatoid adenocarcinoma of the stomach. Of these 31 cases, 24 were male, accounting for 77.4% of the cases. Serum alpha-fetoprotein (AFP) levels were notably elevated, with radioimmunoassay results reaching 1240ng/ml; 28 out of 31 cases had AFP levels below 25µg/l, accounting for 90.3%. Related genes: HER2 protein indicated positive expression on the cell membrane in 35.5% (11/31) of the cases; HER2 gene amplification detected by the FISH technique was 12.9% (4/31). Tumoral stromal lymphocytes exhibited a PD-1 positive expression rate of 58.1% (18/31). In gastric cancer tissues, the PD-L1 positive rate was 45.1% (14/31). HAS represents a distinctive subtype of gastric cancer with a propensity for mimicking other forms of tumors, underscoring the significance of discerning its unique histopathological attributes for accurate differential diagnosis and tailored therapeutic interventions.

Multi-scale coupled attention for visual object detection.

The application of deep neural network has achieved remarkable success in object detection. However, the network structures should be still evolved consistently and tuned finely to acquire better performance. This gears to the continuous demands on high performance in those complex scenes, where multi-scale objects to be detected are located here and there. To this end, this paper proposes a network structure called Multi-Scale Coupled Attention (MSCA) under the framework of self-attention learning with methodologies of importance assessment. Architecturally, it consists of a Multi-Scale Coupled Channel Attention (MSCCA) module, and a Multi-Scale Coupled Spatial Attention (MSCSA) module. Specifically, the MSCCA module is developed to achieve the goal of self-attention learning linearly on the multi-scale channels. In parallel, the MSCSA module is constructed to achieve this goal nonlinearly on the multi-scale spatial grids. The MSCCA and MSSCA modules can be connected together into a sequence, which can be used as a plugin to develop end-to-end learning models for object detection. Finally, our proposed network is compared on two public datasets with 13 classical or state-of-the-art models, including the Faster R-CNN, Cascade R-CNN, RetinaNet, SSD, PP-YOLO, YOLO v3, YOLO v5, YOLO v7, YOLOX, DETR, conditional DETR, UP-DETR and FP-DETR. Comparative experimental results with numerical scores, the ablation study, and the performance behaviour all demonstrate the effectiveness of our proposed model.

Enhancing Loan Prediction Accuracy: A Comparative Analysis of Machine Learning Algorithms with XAI Integration

The contemporary financial landscape necessitates loan recommendation systems that offer both accuracy and transparency. Conventional assessment methodologies often suffer from limitations in efficiency and transparency, leading to potential risks for both lenders and borrowers. This research proposes the development of a novel loan recommendation system that leverages the power of machine learning (ML) and Explainable Artificial Intelligence (XAI). The paper delves into the processes of data collection, preprocessing, model training, evaluation, and subsequent integration into a web application using the Flask framework. The employed datasets encompass a variety of loan types, with the study aiming to identify the most effective ML algorithms from a selection that includes XGBoost, CatBoost, Random Forest, Gradient Boosting, and Logistic Regression. To enhance the system's transparency, Explainable AI methods, such as LIME, are incorporated. The culmination of this research is a web application that facilitates personalized predictions regarding loan eligibility, accompanied by clear explanations. Index terms - Loan Recommendation System, Machine Learning, Explainable AI, XGBoost, CatBoost, Random Forest, Gradient Boost, Logistic Regression,LIME.

"Action and reaction" - a new public sector effectiveness assessment methodology

The main contribution of the paper is the development of a new way of assessing the efficiency of public administration using statistical methods - multivariate regression (identification of exogenous factors) and residual analysis (identification of regression residuals). The methodology presented is intuitive, practical and, if appropriately set up, globally applicable for the evaluation of a wide range of processes taking place in the public sector on the principle of "action and reaction". Putting the methodology into practice can go some way to satisfying the "taxpayer hunger" for information about whether the money they pay in taxes is being used wisely. It makes it possible to assess efficiency in a particular institution or to compare similar processes in several institutions over a given period of time. The methodology has been developed on the basis of audit findings (prepared by the supreme audit institution for the public sector in a democratic state) and their implications. The developed methodology brings a new perspective to the traditional claim that public sector effectiveness is virtually impossible to measure and that factors for objective evaluation of public sector managers cannot be easily established.

Moving Trend Analysis Methodology for Hydro-meteorology Time Series Dynamic Assessment

AbstractIn the last 30 years, there are many publications in the literature due to global warming and climate change impacts exhibiting non-stationary behaviors in hydro-meteorology time series records especially in the forms of increasing or decreasing trends. The conventional trend analyzes cover the entire recording time with a single straight-line trend and slope. These methods do not provide information about up and down partial moving trends evolution at shorter durations along the entire record length. This paper proposes a dynamic methodology for identifying such evolutionary finite duration moving trend method (MTM) identifications and interpretations. The purpose of choosing MTM was to investigate the dynamic partial trend evolution over the recording period so that dry (decreasing trend) and wet (increasing trend) segments could be objectively identified and these trends could assist in water resources management in the study area. The moving trend analysis is like the classical moving average methodology with one important digression that instead of arithmetic averages and their horizontal line representations, a series of finite duration successive increasing and decreasing trends are identified over a given hydro-meteorology time series record. In general, partial moving trends of 10-year, 20-year, 30-year and 40-year occur above or below the overall trend and thus provide practical insight into the dynamic trend pattern with important implications. The moving trend methodology is applied to annual records of Danube River discharges, New Jersey state wise temperatures and precipitation time series from the City of Istanbul.

Identifying Core Issues for Basin Management: The Issue Generating Assessment (IGA) Methodology.

Effective stakeholder engagement is essential for basin management, requiring structured approaches to foster collaboration and consensus. This paper applies the Issue Generating Assessment (IGA) method, which identifies core issues for stakeholder discussion, to basin management. Focusing on the Israeli part of the Hadera Basin, we identify the core issues that should be discussed by stakeholders using the IGA method. To this end 39 participants across 14 sectors evaluating three generic basin management strategies were asked to qualitatively explain their evaluations. By analyzing these explanations utilizing the IGA method, four core issues emerged: (1) Managing uncertainty: addressing climate change and land use impacts on stream flow; (2) Mutual impacts management: handling interactions between the stream and its surroundings; (3) Integration of uses: balancing various stream utilization priorities; (4) Defining natural system functions: determining the role of natural systems. For each core issue, we proposed questions to guide stakeholder discussions. The IGA method is thus found to be useful, and has the potential to foster meaningful dialogue in structured stakeholder meetings, thereby focusing discussions and allowing understandings among stakeholders to be reached as a basis for basin management plans. Such early understandings may contribute to the development of strategies for sustainable basin management.

Role of Vena Cava Filter in the Prophylaxis and Treatment of Venous Thromboembolism in Injured Adult Patients: A Systematic Review, Meta-Analysis, and Practice Management Guideline from the Eastern Association for the Surgery of Trauma [RETRACTED

Injured patients are at an increased risk of developing deep vein thrombosis (DVT) and pulmonary embolism (PE). Inferior vena cava (IVC) filters have been used in injured patients to prevent venous thromboembolism (VTE), but current evidence-based guidelines are lacking. Questions regarding IVC filter use in injured patients with clearly defined Population(s), Intervention(s), Comparison(s), and appropriately selected Outcomes (PICO) were formulated. The study sought to understand the evidence behind use of ultra short term IVC filters and use of IVC filters in injured patients with and without known VTE who are unable to receive therapeutic anticoagulation and chemoprophylaxis, respectively. A literature search and review was conducted, followed by meta-analysis. The quality of evidence was assessed per Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology. There were twenty-one studies that were analyzed. Three studies were randomized controlled trials (RCTs), three were observational studies, and fifteen studies were retrospective studies. In injured patients without known acute VTE who cannot receive chemoprophylaxis, we recommend against placement of an IVC filter due to associated higher rate of mortality, DVT, PE, and length of stay. The quality of evidence was assessed to be low. In injured patients with known DVT who cannot receive chemoprophylaxis we conditionally recommend against placement of an IVC filter. The quality of evidence was assessed to be very low. No recommendations can be made with respect to placement of ultra short term IVC filters based upon available data. IVC filters should not be placed routinely for prophylaxis in injured adult patients without known VTE who cannot receive chemoprophylaxis. The taskforce conditionally recommends against the placement of IVC filter in injured adult patients with known DVT who cannot receive chemoprophylaxis. Guideline; Systematic review/meta-analysis, level IV.

A Measure That Really Works? Impact Evaluation of the Contribution for Self-Employment as a Tool of Active Labour Market Policy in Slovakia

Unemployment presents a significant challenge requiring attention not only in developing countries but also in economically developed ones. Active labour market policies offer a potential solution to address this issue. This study focuses on assessing the impact of the intervention called Contribution for Self-employment provided under the Act on Employment Services No. 5/2004 Coll in the Slovak Republic. This financial support is extended voluntarily to unemployed individuals seeking jobs and aims to partially defray the expenses associated with launching business ventures. The primary objective of this research is to quantify the effectiveness of the Contribution for Self-employment in enhancing the employment of its recipients, thereby gauging its efficacy in reducing unemployment. The evaluation employs a counterfactual impact assessment methodology, utilising propensity score matching for analysis, with propensity score estimated by the logistic regression. Data from the registry of jobseekers maintained by the Central Labour Office of Social Affairs and Family in Slovakia are utilised in this analysis. This study’s findings indicate a favourable impact of the contribution on the employment of its participants compared to the comparable non-participants. Consequently, this intervention emerges as a viable mechanism for supporting entrepreneurship and mitigating unemployment in Slovakia.

Theoretical semi-quantitative risk assessment methodology for tunnel design and construction processes

Theoretical semi-quantitative risk assessment methodology for tunnel design and construction processes

Q-Rung orthopair fuzzy 2-tuple linguistic WASPAS algorithm for patients’ prioritization based on prioritized Maclaurin symmetric mean aggregation operators

Due to the fuzziness of the medical field, q-rung orthopair fuzzy 2-tuple linguistic (q-RF2L) set is the privileged way to aid medical professionals in conveying their assessments in the patient prioritization problem. The theme of the present study is to put forward a novel approach centered around the merging of prioritized averaging (PA) and the Maclaurin symmetric mean (MSM) operator within q-RF2L context. According to the prioritization of the professionals and the correlation among the defined criteria, we apply both PA and MSM to assess priority degrees and relationships, respectively. Keeping the pluses of the PA and MSM operators in mind, we introduce two aggregation operators (AOs), namely q-RF2L prioritized Maclaurin symmetric mean and q-RF2L prioritized dual Maclaurin symmetric mean operators. Meanwhile, some essential features and remarks of the proposed AOs are discussed at length. Based on the formulated AOs, we extend the weighted aggregated sum product assessment methodology to cope with q-RF2L decision-making problems. Ultimately, to illustrate the practicality and effectiveness of the stated methodology, a real-world example of patients’ prioritization problem is addressed, and an in-depth analysis with prevailing methods is performed.

A Methodology for Susceptibility Assessment of Wave-Induced Seabed Liquefaction in Silt-Dominated Nearshore Environments

Wave-induced seabed liquefaction significantly jeopardizes the stability of marine structures and the safety of human life. Susceptibility assessment is key to enabling spatial predictions and establishing a solid foundation for effective risk analysis and management. However, the current research encounters various challenges, involving an incomplete evaluation system, poor applicability of methods, and insufficient databases. These issues collectively hinder the accuracy of susceptibility assessments, undermining their utility in engineering projects. To address these challenges, a susceptibility assessment method with the safety factor was developed as the key assessment parameter, allowing for a comprehensive susceptibility assessment across the silt-dominated nearshore environment using Empirical Bayesian Kriging (EBK). The safety factor is determined by combining the cyclic stress ratio (CSR) and the cyclic resistance ratio (CRR), which characterize wave loadings and sediment properties in the study area, respectively. This method was applied in the Chengdao region of the Yellow River Estuary, China, a typical silt-dominated nearshore environment where wave-induced liquefaction events have been reported as being responsible for multiple oil platform and pipeline accidents. By collecting the regional wave and seabed sediment data from cores spanning from 1998 to 2017, the safety factors were calculated, and a zonal map depicting the susceptibility assessment of wave-induced seabed liquefaction was created. This study can serve as a valuable reference for the construction and maintenance of marine engineering in liquefaction-prone areas.

Snow Disaster Risk Assessment Based on Long-Term Remote Sensing Data: A Case Study of the Qinghai–Tibet Plateau Region in Xizang

The risk analysis and assessment of snow disasters are essential foundational tasks in natural disaster management and profoundly impact the scientific and precise formulation of disaster prevention, preparedness, and mitigation strategies. Employing the theory and methodology of snow disaster assessment, this research focuses on historical and potential snow disasters in the Qinghai–Tibetan Plateau (QTP) Region. Utilizing a long-time-series snow depth remote sensing dataset, we extracted six assessment indicators for historical snow disaster risk factors and potential snow disaster risk factors. We determined the weights of these six assessment indicators using the entropy weight method. Subsequently, we established a snow disaster assessment model to evaluate the grade distribution of snow disasters in the study area. This method can effectively solve the problem of the sparse data distribution of meteorological stations and reflect degrees of snow disaster risk on a large spatial scale. The findings reveal that areas with a relatively high snow disaster risk are primarily concentrated in the western part of the Ali Region, the central part of Chamdo, and near the border in Southern Xizang. Additionally, regions with a high frequency of snow disasters are predominantly located at the junction of Nagchu, Chamdo, and Nyingchi in the eastern part of Xizang. These results contribute valuable insights into the risk assessment of snow disasters and facilitate the development of effective strategies for disaster management in the region.

Emerging Technology Integration - Artificial Intelligence (AI) and Machine Learning (ML) for Predictive Analysis for Safety and Toxicity Assessment in Environmental Toxicology

Environmental toxicology is facing unprecedented challenges due to the escalating complexity of environmental issues. Traditional methodologies for safety and toxicity assessment are struggling to keep pace with the rapid evolution of pollutants and their impacts on ecosystems and human health. In response to this pressing need, there has been a paradigm shift in the field towards the integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques. This research article delves into the transformative potential of AI and ML in environmental toxicology, elucidating how these advanced computational tools offer promising solutions for predictive analysis. By leveraging vast datasets and sophisticated algorithms, AI and ML enable more efficient, accurate, and timely assessment of environmental risks. They empower researchers and policymakers to anticipate and mitigate potential hazards before they escalate into crises. This paper provides a comprehensive overview of the diverse applications of AI and ML in environmental toxicology, ranging from predictive modeling of chemical hazards to early detection of environmental threats. Furthermore, it highlights the potential of AI and ML to revolutionize decision-making processes in environmental management and policy formulation. By synthesizing vast amounts of data and identifying complex patterns and correlations, these technologies offer invaluable insights for crafting evidence-based policies and strategies to safeguard the environment and public health. This research article underscores the pivotal role of AI and ML in addressing the multifaceted challenges of environmental toxicology. Through their integration, we have the opportunity to enhance our understanding of environmental risks, optimize resource allocation, and ultimately, pave the way towards a more sustainable and resilient future.

A Novel Approach for Dynamic Analysis of Wind-Integrated Multi-Machine Power Systems

Indeed, the rapid expansion of Renewable Energy Sources (RES) in recent years has brought about numerous benefits, including reduced carbon emissions, increased energy independence, and the creation of new economic opportunities. However, integrating these variable and intermittent sources into existing power systems poses several challenges for power system management. Faults in electrical networks are among the key factors and sources of network disturbances. Control and automation strategies are among the key fault clearing techniques responsible for the safe operation of the system. In recent years, the increasing penetration of wind energy in multi-machine power systems has posed unique challenges to power grid stability and reliability. Accurate assessment methodologies are required to ensure the effective integration of wind energy sources while maintaining grid stability. Several researchers have revealed various constraints of control and automation strategies such as a slow dynamic response, the inability to switch the network on and off remotely, a high fault clearing time and loss minimization. It's important to note that the impact of wind energy on system inertia is a complex and dynamic aspect of power system operation. Ongoing research and technological advancements aim to improve the integration of wind and other renewable energy sources while ensuring grid stability and reliability. As the energy transition continues, addressing these technical challenges is crucial for building a sustainable and resilient power system. In this paper, the influence of doubly-fed induction generator (DFIG) penetration is analyzed to examine the transient stability of power system networks. The concept of a Coupling Strength Index (CSI) derived from Network Structural Characteristics Theory sounds intriguing, especially in the context of identifying critical elements susceptible to the impact of a three-phase fault in a network. The investigation involves studying the transient stability of a power system under different conditions, specifically with and without doubly-fed induction generators (DFIGs) connected to a weak bus. Additionally, a three-phase fault is applied at the middle of the identified weakest line for both the IEEE 9 and 39 bus systems. The investigation of generator speed, rotor angle, and electric power during transient stability analysis provides a holistic view of how a power system responds to disturbances. This information is crucial for ensuring the reliability and stability of the system, especially when studying the integration of renewable energy sources and addressing potential challenges associated with faults and weak buses. This paper presents a pioneering non-iterative framework for dynamically assessing wind energy dominated multi-machine power systems. The proposed framework aims to address the shortcomings of traditional iterative methods, providing a more efficient and reliable approach to power system analysis.