Risk Evaluation Method Research Articles

Evaluating Areal Windspeeds and Wave Heights by Gaidai Risk Evaluation Method

Evaluating Areal Windspeeds and Wave Heights by Gaidai Risk Evaluation Method

Abstract Versus Concrete Risk Identification in Clinical Research in Japan: Randomized and Prospective Pilot Research on the Effect of Risk Reduction Activities in a Risk-Based Approach.

The risk-based approach (RBA) of clinical trial was first introduced in 2011-2012. RBA necessitates implementing risk reduction activities that are proportionate to risk in order to reduce avoidable quality issues. However, there is no consistent methodology or research for identifying and evaluating risks and planning risk reduction activities. We aimed to evaluate risk reduction activities and their effects by using two risk identification and evaluation methods. Among the risk identification and evaluation methods, we selected one method with the lowest number of categories for identifying risks [risk assessment form (RAF)] and one with the highest number [risk assessment tool (RAT)]. Each method was used to identify and evaluate risks in and plan risk reduction activities for the research on ponatinib blood concentration and treatment outcome in patients with chronic phase chronic myelogenous leukemia. RAF and RAT can identify risk using abstract questions and a list of concrete risks, respectively. The sites were randomized into two groups to implement planned risk reduction activities using RAF and RAT and to compare the mean of errors and protocol deviation per subject visit between the two groups. The mean of errors per subject visit and the mean of protocol deviation per subject visit were lower in the RAF group than in the RAT group. Our study indicates that risk reductions can be successfully implemented by using a method to identify and evaluate risks in a small number of abstract categories that are critical to quality of clinical research.

Heart Disease Prediction Using Machine Learning

Heart disease continues to be a major global cause of death, highlighting the urgent need for more effective methods of early detection and risk evaluation. This paper explores the use of machine learning techniques for predicting heart disease, focusing on five key algorithms: Naïve Bayes, k-Nearest Neighbor (KNN), Decision Tree, Artificial Neural Network (ANN), and Random Forest. Through a comprehensive review of existing research and data, we assess the performance of these algorithms in heart disease risk prediction. The analysis reveals that machine learning approaches offer substantial improvements in both accuracy and efficiency over traditional diagnostic techniques. Among the algorithms studied, Random Forest showed the best overall results, with some studies indicating accuracy rates as high as 95% in detecting potential heart disease cases. This review underscores the transformative potential of machine learning in reshaping cardiovascular healthcare through more individualized risk assessments and the promotion of early intervention strategies. Incorporating these advanced predictive models into routine clinical workflows could lead to significantly better patient outcomes and help alleviate the worldwide impact of heart disease. Keywords: Cardiovascular Risk Assessment, Machine Learning Models, Health Data Analytics, Random Forest, Neural Networks, Feature Significance, Decision Support Systems, Precision Medicine, Predictive Healthcare Analytics, Early Diagnosis.

Collision probability evaluation of SRM slag during orbital lifetime

At the end of combustion, a solid rocket motor (SRM) in orbit emits combustion products with relatively large particle sizes (slag). ISO 24113:2023 “Space debris mitigation requirements” defines criteria to limit debris emission 1 mm or larger in the LEO protected region. JAXA's “Space debris mitigation standard,” JMR-003E, sets out the same requirements. Hence, JAXA has begun an improvement study on SRM slag emissions as it develops the next generation of SRMs to conserve the orbital environment by significantly reducing SRM slag larger than 1 mm. However, ensuring the complete prevention of larger SRM slag emission is technically challenging. Thus, we are alternatively working to establish a risk evaluation method and criteria based on the collision probability (Pc) of SRM slag during its orbital lifetime. Based on ground firing tests, this study estimated the number and particle size of SRM slag in orbit emitted during the Epsilon and Epsilon S launch missions. The Pc per mission during the orbital lifetime was calculated and provisionally set to 10–3 for currently operational spacecraft. This paper discusses its acceptability to JAXA.

Landscape ecological risk evaluation coupled with ecosystem service improvement and its spatial heterogeneity analysis: a case study of the Yellow River Source Area

The ecological environment of the Yellow River Source Area (YRSA) is crucial to the Qinghai-Tibet Plateau, and its ecological risk management is important for the ecological protection and sustainable development of the Qinghai-Tibet Plateau. this study improves the traditional evaluation method of landscape ecological risk(LER) based on ecosystem services and detects its spatial heterogeneity by using Geodetector. The study results are as follows: (1) The temporal distribution of LER in the YRSA is similar to its landscape vulnerability. In the past 20 years, the areas of its high, medium-high, medium, and medium-low ecological risk areas have been converted to low ecological risk areas to varying degrees. (2) Overall, the spatial difference in LER in the YRSA is significant, mainly dominated by HH and HL clustering. Meanwhile, the Moran’I index of the LER Index decreases year by year, indicating that the spatial aggregation of the LER decreases and the spatial autocorrelation weakens year by year. (3) Climatic factors have the most significant influence on the ecological risk of the landscape in the YRSA, followed by topographic factors and anthropogenic factors.

Future worldwide coronavirus disease 2019 epidemic predictions by Gaidai multivariate risk evaluation method.

Accurate estimation of pandemic likelihood in every US state of interest and at any time. Coronavirus disease 2019 (COVID-19) is an infectious illness with a high potential for global dissemination and low rates of fatality and morbidity, placing some strains on national public health systems. This research intends to benchmark a novel technique, that enables hazard assessment, based on available clinical data, and dynamically observed patient numbers while taking into account pertinent territorial and temporal mapping. Multicentre, population-based, and biostatistical strategies have been utilized to process raw/unfiltered medical survey data. The expansion of extreme value statistics from the univariate to the bivariate situation meets with numerous challenges. First, the univariate extreme value types theorem cannot be directly extended to the bivariate (2D) case,-not to mention challenges with system dimensionality higher than 2D. Assessing outbreak risks of future outbreaks in any nation/region of interest. Existing bio-statistical approaches do not always have the benefits of effectively handling large regional dimensionality and cross-correlation between various regional observations. These methods deal with temporal observations of multi-regional phenomena. Apply contemporary, novel statistical/reliability techniques directly to raw/unfiltered clinical data. The current study outlines a novel bio-system hazard assessment technique that is particularly suited for multi-regional environmental, bio, and public health systems, observed over a representative period. With the use of the Gaidai multivariate hazard assessment approach, epidemic outbreak spatiotemporal risks may be properly assessed. Based on raw/unfiltered clinical survey data, the Gaidai multivariate hazard assessment approach may be applied to a variety of public health applications. The study's primary finding was an assessment of the risks of epidemic outbreaks, along with a matching confidence range. Future global COVID-19/severe acute respiratory syndrome coronavirus 2 (SARS-COV2) epidemic risks have been examined in the current study; however, COVID-19/SARS-COV2 infection transmission mechanisms have not been discussed.

Multivariate ocean waves dynamics in North Sea and Norwegian Sea by Gaidai reliability method

Current study presents state-of-the-art approach for evaluating spatiotemporal multivariate environmental risks, especially suitable for complex environmental systems that have been either numerically simulated or physically observed. Advocated methodology provides accurate hazard risk forecasts, based on real-time in situ environmental data. Design of offshore structures requires multivariate risk assessment – offshore and naval operations require both short- and long-term risk and reliability analyses. Contemporary risk evaluation techniques often struggle with raw timeseries multivariate data due to intrinsic multidimensionality and nonlinear interconnections among critical system’s dimensions/components. In the current study effectiveness of the Gaidai multivariate risk evaluation method is illustrated by utilizing significant wave heights dataset, measured within two offshore zones: Heidrun and Troll Norwegian oil fields. Analyzing offshore waves being particularly challenging due to their complexity, high nonlinearity, multidimensionality, yet dynamic inter-correlations. Global warming being among several significant factors, affecting ocean and sea wave heights. For naval, offshore and marine structures operating in harsh weather conditions, robust multivariate environmental risk evaluation methods being crucial for both design and safe operations. Current study aims to validate and benchmark state-of-the-art methodology, enabling extraction. Advocated approach allows for efficient yet accurate assessment of global damages, failures or hazard risks for a wide range of complex nonlinear multivariate environmental and energy systems. Primary advantage of presented multivariate reliability methodology lies within its ability to treat complex systems with practically unlimited number of dimensions, while existing reliability methods being mostly limited to univariate and bivariate analyses.

A machine learning-based simulation metamodeling method for dynamic scheduling in smart manufacturing systems

Conventional Digital Twins (DTs) in smart manufacturing rely on complex and time-intensive simulation models, hindering real-time DT-based decision-making. However, the availability of big data in Manufacturing Execution Systems (MES) enables training different Machine Learning (ML) models for fast and accurate predictions and decision assessments. Accordingly, this paper proposes an ML-Based Simulation Metamodeling Method (MLBSM) to facilitate DT-based decision-making for dynamic production scheduling in complex Stochastic Flexible Job Shop (SFJS) environments. The proposed MLBSM integrates three modules: a novel data vectorizing method (SPBM), multi-output Adaptive Boosting Regressor (ABR) models, and a new statistical risk evaluation method. SPBM converts unstructured production log data into numerical vectors for ABR training by calculating numeric penalty scores for each job based on the position of operations in the schedule queue. Each trained ABR predicts mean job completion times for various dynamic scenarios based on shift schedules. The risk evaluation method estimates the standard deviation of job completion times and calculates the delay probability scores for each job, aiding DT in promptly evaluating production schedules. Working seamlessly together, MLBSM modules present a novel way of using production log data for ML training and ultimately bypassing several computationally intensive simulation replications. In this research, a simulation model generates the synthetic MES data, focusing on the machining process at a photolithography workstation in the semiconductor manufacturing. Experiments demonstrate the MLBSM’s accuracy and efficiency, predicting high-risk jobs with over 80% recall and being at least 70 times faster than conventional simulation runs. Sensitivity analyses also confirm the MLBSM’s consistency under different workstation conditions.

A risk evaluation method for human-machine interaction in emergencies based on multiple mental models-driven situation assessment

In the process of human-machine interaction (HMI) under abnormal situations, situation assessment (SAS) is a critical component as incorrect SAS can feed into inappropriate actions and cause severe accidents. Therefore, it is essential to model SAS for HMI risk evaluation. During emergency situations, SAS is predominantly affected by time pressure. However, current researches focus on deliberative SAS with sufficient time and have limitations in quantifying the time pressure effect on SAS during emergencies. This paper proposes a risk evaluation method for HMI in emergencies by quantitatively modeling the time pressure effect on SAS under the recognition-primed decision (RPD) model framework. Firstly, SAS process is characterized as identifying the first plausible cause of an abnormal phenomenon driven by multiple Bayesian network-based mental models, and a simulation algorithm for SAS process is proposed to evaluate HMI risk. Secondly, two effects of time pressure on SAS activities, i.e., filtration effect on information perception and impair effect on knowledge retrieval, are quantified based on the Saliency-Effort-Expectancy-Value (SEEV) model and the Adaptive Control of Thought-Rational (ACT-R) theory. Finally, this method is applied to a Boeing 737-31S accident with high time pressure and results demonstrate the effectiveness of the method.

Intelligent measurement method of transition resistance of insulation fasteners in urban rail transit system based on SSA-optimized algorithm

Rail-to-earth transition resistance characterizes the amplitude of stray current leakage in urban rail transit system using electric traction, which is critical for evaluating failure risk of metal infrastructures caused by electrochemical corrosion. However, detection method commonly used in the field exhibit measurement errors resulting from the limitation of measuring principle and measuring distance, which will seriously affect the accuracy of electrical insulation performance evaluation of the whole system. In view of this, this paper proposes a data-driven method for accurately measuring rail-to-earth transition resistance in urban rail transit system based on CDEGS-based simulation and SSA-optimized algorithm. CDEGS model was built to construct database for machine learning of SSA-RF network structure. SSA algorithm was used in the measurement model to optimize the topology of the random forest (RF) structure. Proposed ensemble in this paper was proved to conduct transition resistance regression with a mean relative accuracy rate of 98.85 %. Results of sensitivity analysis indicate that proposed measurement method shows acceptable robustness, which allows the algorithm parameters to fluctuate within a certain range when applied in the field. Besides, in the case of uniform and non-uniform insulation degradation, the proposed method shows good performance in assessing insulation performance. This provides a feasible foundation for future applications of the proposed method in failure risk evaluation of buried infrastructures.

Evaluation of ecological and health risks of trace elements in soils of olive orchards and apportionment of their sources using the APCS-MLR receptor model

İzmir, Turkey’s third most populous city, is in an important position in terms of both agriculture and industry. The province, which contributes 9.3% to the country’s industrial production, also has an important potential in terms of olive cultivation. However, until now, no research has been undertaken to analyze the content of trace elements (TEs) in the soil of olive orchards in İzmir. This study was carried out to determine the pollution level and ecological risks of TEs in the olive orchards soils of İzmir province, to reveal their potential sources and to evaluate their health risks. Among the TEs, the average content of only Ni (37.9 mg/kg) exceeded the world soil average content (29 mg/kg), while the average content of only Cd (0.176 mg/kg) exceeded the upper continental crust content (0.09 mg/kg). Enrichment factor revealed that there was significant enrichment for Cd in 73.6%, Ni in 11.6% and Cr in 5.4% of olive orchards, respectively, due to polluted irrigation water and agrochemicals. Similarly, ecological risk factor indicated that there were moderate and considerable ecological risks for Cd in 48.8% and 23.3% of olive orchards, respectively. Absolute principal component scores-multiple linear regression (APCS-MLR) model showed that Ni and Cr in the study area are affected by agricultural sources, Al, Co, Cu, Fe, Mn, Pb and Zn originate from lithogenic sources, and Cd originates from mixed sources. Based on health risk evaluation methods, non-carcinogenic and carcinogenic effects would not be expected for residents. This study provides significant knowledge for evaluating soil TE pollution in olive orchards and serves a model for source apportionment and human health risk evaluation of TEs in other agricultural regions.

Data-Driven Smart Assessment for Enterprise Audit Risks Based on Radial Base Function Neural Network and Grey Correlation Analysis

In the era of big data, audit risk assessment is facing increasing business volume. Hence, it is important to develop automatic audit risk assessment methods for enterprises with the assistance of intelligent algorithms. As a consequence, this paper proposes data-driven smart assessment for enterprise audit risks-based radial basis function (RBF) neural networks and grey correlation analysis (GCA). First, evaluation data are collected and analyzed to understand the characteristics and influencing factors of enterprise audit risks. Secondly, RBF interpolation is introduced to establish the network structure for RBF neural networks. Then, GCA is integrated with RBF Interpolation (RBFNN) to formulate the automatic audit risk evaluation method, and the proposal is named RBF and GCA. Such a combined methodology can improve audit risk assessment efficiency. Finally, we make some experiments to evaluate the proposed TBF-GCA on a real-world dataset, and some evaluation indicators are specified based on the actual audit risk situation of the enterprises. The obtained results reveal that RBF–GCA has high accuracy in identity precision and can effectively identify audit risks for enterprises.

Evaluation of Outdoor Sports Safety Risk Management Based on Embedded Systems

With the increasing popularity of outdoor sports, sports safety issues have gradually become prominent. In order to effectively manage safety risks in outdoor sports, this paper proposes an embedded system-based outdoor sports safety risk management and evaluation method. Intended to provide safety assurance for outdoor sports participants through real-time data collection, intelligent analysis, and early warning. This method identifies safety risk patterns in different sports scenarios through training and learning historical data. This paper also studied the construction method of an outdoor tourism security system and proposed an embedded system for encrypting the outdoor sports security database. An embedded system for encrypting outdoor sports security databases has been proposed. The simulation results show that the recall rate of the model is 92.8%, and the accuracy rate is 95.7%. The model can predict potential security risks based on real-time collected data and generate corresponding warning messages.

In silico degradation of fluoroquinolones by a microalgae-based constructed wetland system

Fluoroquinolone antibiotics (FQs) have been used worldwide due to their extended antimicrobial spectrum. However, the overuse of FQs leads to frequent detection in the environment and cannot be efficiently removed. Microalgae-based constructed wetland systems have been proven to be a relatively proper method to treat FQs, mainly by microalgae, plants, microorganisms, and sediments. To improve the removal efficiency of microalgae-constructed wetland, a systematic molecular design, screening, functional, and risk evaluation method was developed using three-dimensional quantitative structure–activity relationship models, molecular dynamics simulation, molecular docking, and TOPKAT approaches. Five designed ciprofloxacin alternatives with improved bactericidal effects and lower human health risks were found to be more easily degraded by microalgae (16.11–167.88 %), plants (6.72–58.86 %), microorganisms (9.10–15.02 %), and sediments (435.83 %–1763.51 %) compared with ciprofloxacin. According to the mechanism analysis, the removal effect of the FQs can be affected via changes in the number, bond energy, and molecular descriptors of favorable and unfavorable amino acids. To the best of our knowledge, this is the first comprehensive study of improving the microalgae, plants, microorganisms, and sediment removal efficiency of FQs in constructed wetlands, which provides theoretical support for the treatment of FQ pollution.

Lifetime assessment of semi-submersible wind turbines by Gaidai risk evaluation method

As the global agenda turns more towards the so-called challenge of climate change and lowering carbon emissions, research into green, renewable energy sources becoming nowadays more and more popular. Offshore wind power, produced by FOWTs (i.e., Floating Offshore Wind Turbines), is one such substitute. It is a significant industrial part of the contemporary offshore wind energy industry and produces clean, renewable electricity. Accurate operational lifetime assessment for FOWTs is an important technical safety issue, as environmental in situ loads can lead to fatigue damage as well as extreme structural dynamics, which can cause structural damage. In this study, in situ environmental hydro and aerodynamic environmental loads, that act on FOWT, given actual local sea conditions have been numerically assessed, using the FAST coupled nonlinear aero-hydro-servo-elastic software package. FAST combines aerodynamics and hydrodynamics models for FOWTs, control and electrical system dynamics models, along with structural dynamics models, enabling coupled nonlinear MC simulation in the real time. The FAST software tool enables analysis of a range of FOWT configurations, including 2- or 3-bladed horizontal-axis rotor, pitch and stall regulation, rigid and teetering hub, upwind and downwind rotors. FAST relies on advanced engineering models—derived from the fundamental laws, however with appropriate assumptions and simplifications, supplemented where applicable with experimental data. Recently developed Gaidai reliability lifetime assessment method, being well suitable for risks evaluation of a variety of sustainable energy systems, experiencing nonlinear, potentially extreme in situ environmental loads, throughout their designed service life. The main advantage of the advocated Gaidai risks evaluation methodology being its ability to tackle simultaneously a large number of dynamic systems' degrees of freedom, corresponding to the system's critical components.

Embracing epistemic uncertainty: a risk evaluation method for pollutants in stormwater.

In this study, we show that pollutants of emerging concern are, by nature, prone to the emergence of epistemic uncertainty. We also show that the current uncertainty quantification methods used for pollutant modelling rely almost exclusively on parameter uncertainty, which is not adequate to tackle epistemic uncertainty affecting the model structure. We, therefore, suggest a paradigm shift in the current pollutant modelling approaches by adding a term explicitly accounting for epistemic uncertainties. In a proof-of-concept, we use this approach to investigate the impact of epistemic uncertainty in the fluctuation of pollutants during wet-weather discharge (input information) on the distribution of mass of pollutants (output distributions). We found that the range of variability negatively impacts the tail of output distributions. The fluctuation time, associated with high covariance between discharge and concentration, is a major driver for the output distributions. Adapting to different levels of epistemic uncertainty, our approach helps to identify critical unknown information in the fluctuation of pollutant concentration. Such information can be used in a risk management context and to design smart monitoring campaigns.

Production Forecasting at Natural Gas Wells

In Romania, natural gas production is concentrated in two large producers, OMV Petrom and Romgaz. However, there are also smaller companies in the natural gas production area. In these companies, the deposits are mostly mature, or new deposits have low production capacity. Thus, the production forecast is very important for the continued existence of these companies. The model is based on the pressure variation in the gas reservoir, and the exponential model with production decline is currently used by gas and oil producers. Following the variation in the production of the gas wells, we found that in many cases, the Gaussian and Hubbert forecast models are more suitable for simulating the production pattern of gas wells. The models used to belong to the category of poorly conditioned models, with little data, usually called gray models. Papers published in this category are based on data collected over a period of time and provide a forecast of the model for the next period. The mathematical method can lead to a very good approximation of the known data, as well as short-term forecasting in the continuation of the time interval, for which we have these data. The neural network method requires more data for the network learning stage. Increasing the number of known variables is conducive to a successful model. Often, we do not have this data, or obtaining it is expensive and uneconomical for short periods of possible exploitation. The network model sometimes captures a fairly local pattern and changing conditions require the model to be remade. The model is not valid for a large category of gas wells. The Hubbert and Gauss models used in the article have a more comprehensive character, including a wide category of gas wells whose behavior as evolutionary stages is similar. The model is adapted according to practical observations by reducing the production growth period; the layout is asymmetric around the production peak; and the production range is reduced. Thus, an attempt is made to replace the exponential model with the Hubbert and Gauss models, which were found to be in good agreement with the production values. These models were completed using the Monte Carlo method and matrix of risk evaluation. A better appreciation of monthly production, which is an important aspect of supply contracts, and cumulative production, which is important for evaluating the utility of the investment, is ensured. In addition, we can determine the risk associated with the realization of production at a certain moment of exploitation, generating a complete picture of the forecast over the entire operating interval. A comparison with production results on a case study confirms the benefits of the forecasting procedure used.

Safety risk perception and control of water inrush during tunnel excavation in karst areas: An improved uncertain information fusion method

Water inrush disasters seriously endanger the construction safety of karst tunnels. To accurately assess the water inrush risk level of karst tunnels, actively prevent and control water inrush disasters. A risk evaluation method based on an improved cloud model (CM) and evidence reasoning (ER) combined with Monte Carlo simulation is proposed, which is applied in a water inrush risk evaluation of the Yaguan tunnel. The results are as follows. (1) This method considers the uncertainty and reliability of the water inrush risk information for karst tunnels. (2) Through an iterative evaluation with Monte Carlo simulation, the dynamic feedback mechanism of the risk state of the karst tunnel construction system is established. (3) Local sensitivity analysis is performed to identify risk factors and related measures are proposed. Hence, the research method can be used to effectively evaluate the water inrush risk level and provide references for similar project risk evaluations.

A Novel Intuitionistic Fuzzy Set-Based Risk Priority Number Method for Solving Chemical Experiment Risk Evaluation

Scientific experiments cover a wide range of fields—from basic to applied scientific research. Chemical experiments are the basis for cultivating chemical knowledge in scientific experiments and are an important way to cultivate scientific thinking and methods. However, due to the toxicity or flammability of the chemical substances in the experiments, hazardous events often lead to personal injuries and environmental damage. Exactly assessing risk factors and reducing the risk of hazards to protect the experimenters and ensure environmental safety are crucial in chemical experiments. However, while the traditional risk evaluation method cannot consider the weight of risk evaluation criteria, it also cannot effectively address problems through hierarchical analysis, as well as imprecise and ambiguous information inherent in human cognition. Therefore, this paper proposed an approach based on failure mode and effects analysis (FMEA) to assess the risk of chemical experiments in a fuzzy information environment. The approach combines the typical analytic hierarchy process (AHP), the risk priority number (RPN) of FMEA, and the intuitionistic fuzzy set (IFS) methods to evaluate risks associated with chemical experiments and consider the damage recovery in chemical experiments. This study applied the case of a university chemistry experiment, “preparation of hydrogels”, to validate the reasonableness and correctness of the proposed approach and compare its numerical verification results with those from the typical RPN, the AHP-RPN, and the AHP-fuzzy risk priority number (AHP-FRPN) methods. The finding demonstrates that the proposed method can more effectively address risk evaluation problems in chemical experiments than the other methods. This result serves as an important reference for reducing chemical experiment risk occurrences.

Long-term and combined heavy-metal contamination forms a unique microbiome and resistome: A case study in a Yellow River tributary sediments

Microorganisms have developed mechanisms to adapt to environmental stress, but how microbial communities adapt to long-term and combined heavy-metal contamination under natural environmental conditions remains unclear. Specifically, this study analyzed the characteristics of heavy metal composition, microbial community, and heavy metal resistance genes (MRGs) in sediments along Mang River, a tributary of the Yellow River, which has been heavily polluted by industrial production for more than 40 years. The results showed that the concentrations of Cr, Zn, Pb, Cu and As in most sediments were higher than the ambient background values. Bringing the heavy metals speciation and concentration into the risk evaluation method, two-thirds of the sediment samples were at or above the moderate risk level, and the ecological risk of combined heavy metals in the sediments decreased along the river stream. The high ecological risk of heavy metals affected the microbial community structure, metabolic pathways and MRG distribution. The formation of a HM-resistant microbiome possibly occurred through the spread of insertion sequences (ISs) carrying multiple MRGs, the types of ISs carrying MRGs outnumber those of plasmids, and the quantity of MRGs on ISs is also higher than that on plasmids. These findings could improve our understanding of the adaptation mechanism of microbial communities to long-term combined heavy metal contamination.