Level Of Accuracy Research Articles

Multi-step carbon emissions forecasting using an interpretable framework of new data preprocessing techniques and improved grey multivariable convolution model

As China stands at a critical juncture in its transition towards a low-carbon future, the precise prediction and analysis of provincial carbon emissions have emerged as paramount tasks. Focusing on forecasting in this intricate and vital domain, an updated grey multivariable convolution model is designed by employing the unified new-information-oriented accumulating generation operator (UNAGO) technique to process raw sequences. Equipped with UNAGO's hyper-parameters that enable the independent scaling effects and prioritize new information, the newly-designed model offers high flexibility and adaptability for handling complex provincial carbon emissions. Subsequently, four different restricted carbon emission sequences are utilized as case studies for validation purposes, and the new model's performance is scrutinized against five contrasting methods across three prediction forecasting horizons. Comparative experimental results reveal the new model's superior level of accuracy in predicting carbon emissions across four different provinces, with MAPE values of <4 % and 10 % in the in-sample and out-of-sample periods, respectively. Furthermore, rigorous evaluations with Diebold-Mariano (DM) and Probability Density Analysis (PDA) tests confirm the model's robust and general forecasting capabilities.

A rigorous workflow and comparative analysis for accurate determination of vitrinite reflectance using data-driven approaches in the Persian Gulf region

Vitrinite reflectance (VR) is a critical measure of source rock maturity in geochemistry. Although VR is a widely accepted measure of maturity, its accurate measurement often proves challenging and costly. Rock–Eval pyrolysis offers the advantages of being cost-effective, fast, and providing accurate data. Previous studies have employed empirical equations and traditional machine learning methods using T-max data for VR prediction, but these approaches often yielded subpar results. Therefore, the quest to develop a precise method for predicting vitrinite reflectance based on Rock–Eval data becomes particularly valuable. This study presents a novel approach to predicting VR using advanced machine learning models, namely ExtraTree and XGBoost, along with new ways to prepare the data, such as winsorization for outlier treatment and principal component analysis (PCA) for dimensionality reduction. The depth and three Rock–Eval parameters (T-max, S1/TOC, and HI) were used as input variables. Three model sets were examined: Set 1, which involved both Winsorization and PCA; Set 2, which only included Winsorization; and Set 3, which did not include either. The results indicate that the ExtraTree model in Set 1 demonstrated the highest level of predictive accuracy, whereas Set 3 exhibited the lowest level of accuracy, confirming the methodology's effectiveness. The ExtraTree model obtained an overall R2 score of 0.997, surpassing traditional methods by a significant margin. This approach improves the accuracy and dependability of virtual reality predictions, showing significant advancements compared to conventional empirical equations and traditional machine learning methods.

Correlation between salivary and serum biomarkers of kidney function in chronic kidney disease patients attending the nephrology clinics at Teaching Hospital Karapitiya, Sri Lanka

The present study was designed to determine the correlation between salivary and serum levels of creatinine and urea in patients with chronic kidney disease (CKD) and to propose the use of saliva as an alternative medium for the analysis of biochemical parameters of kidney function. Accordingly, an analytical cross-sectional study was carried out using 100 CKD patients attending the Nephrology Clinics at Teaching Hospital Karapitiya, Sri Lanka. Saliva and serum samples were collected from the patients for the estimation of creatinine and urea using spectrophotometric assay kits. Of the total, the majority of the patients were found within CKD stage 3 (82%). The salivary and serum concentrations of creatinine varied within the range of 0.20 - 2.10 mg/dL and 1.54 – 11.6 mg/dL, whereas the salivary and serum concentrations of urea varied within the range of 68.04 – 127.59 mg/dL and 60.14 – 168.25 mg/dL respectively within the study population. Significant positive correlations were observed between the salivary and serum concentrations of creatinine (r= 0.985, p=0.01), and urea (r= 0.942, p=0.01), and between salivary concentrations of creatinine and urea (r=0.887, p=0.01). A significant difference was observed in the salivary biomarkers between mild to moderate stages of CKD patients and severe to end-stage CKD patients. No significant association was observed in the salivary markers with the age and sex of the patients. The area under curve values in the receiver operating characteristic analysis was&gt;0.9 (95% CI). These findings revealed high levels of diagnostic accuracy in salivary biomarkers suggesting the potential use of saliva for the assessment of kidney function in CKD patients.

Advanced Tuberculosis Detection System for Preserving Public Health

Tuberculosis is a major public health threat that requires efficient and early detection technologies. Challenges associated with real-world implementation include information variability, model availability, and operations in different clinical circumstances. In this paper, we introduce a Crow Search Driven Tuned Logistic Regression (CS-TLR) tuberculosis identification system to sustain early identification of the disease and increase the levels of diagnostic accuracy to help sustain public health. The model attempts to enhance the overall prediction performance of logistic regression by optimizing its parameters by using CSA's optimization capability. We gathered the Kaggle dataset with a variety of clinical and demographic characteristics of tuberculosis patients. We evaluate the performance of the suggested method employing standard parameters including AUC (90.5%), precision (89.9%), recall (90.1%), and F1-score (89.7%). The findings suggest that it could be an important public health surveillance tool by enabling early diagnosis and treatment of TB.

Automatic Detection of Stalling Events using Machine Learning Algorithms

With the proliferation of new video streaming OTT service applications, content providers must guarantee an efficient, effective and satisfactory interaction between the user and the service application, to measure this they use the quality of experience (QoE). QoE in the context of telecommunications can be understood as the acceptability of a service perceived by end users. However, given the variability of the network and the different factors that can intervene during the service, this perception can be altered and one of these causes is stalling events. It is necessary to have an accurate method to detect stalling events, allowing content providers to make better decisions in the design of their OTT applications and thus reduce customer desertion and the economic losses of service or content providers. For this reason, in this paper firstly we show a comparative analysis of supervised algorithms showing its invalidity given the imbalance of the data despite the high levels of accuracy. Therfore, we propose as a contribution a novel method based on algorithms provided by SSAD (Semi Supervised Anomaly Detection) for the detection of stalling events. Finally, we observe that in our method the Isolation Forest model has the best performance, closer to 1 and we will show more detail about its performance in future works.

Comparative Analysis of Machine Learning Techniques for Enhanced Vehicle Tracking and Analysis

A The past few years have seen a marvellous growth in technology and science. This rapid improvement has proven to be a blessing, making human life easier. Technological developments such as autonomous driving systems and electric cars have made it easier to travel in a dependable and economical manner, satisfying the increasing need for convenient and environmentally friendly travel. However, the increase in traffic has led to a surge in accidents and road casualties. Despite efforts to enhance automobile design and traffic control, there remains a significant need for implementing a system for vehicle tracking, accident detection, and notification. Delays in information and unfulfilled medical needs often result in the loss of lives following accidents. This study reviews and compares different automatic accident detection and notification systems that use accelerometers, vibration detectors, and GPS technology to notify registered contacts of an accident's location via SMS or email. The analysis that follows will specifically look at the benefits, drawbacks, and future uses of various technologies that are used in these systems. In this study, different machine learning-based methods for improving the accuracy of car tracking and cutting down on reaction times in accident situations will be looked at and compared. For testing their usefulness, we used deep learning models like CNN, SVM, and YOLOv3 on a number of different datasets. According to our data, these methods greatly enhance the accuracy of spotting, with YOLOv3 showing the best level of accuracy. Furthermore, the study talks about the pros, cons, and possible future uses of these technologies. It stresses the need for more research into improving model performance in different situations.

Artificial Neural Network-based Models for Short Term Forecasting of Solar PV Power Output and Battery State of Charge of Solar Electric Vehicle Charging Station

The main objective of this study is to develop ANN-based predictive models for short-term forecasting of solar PV power output and battery state of charge. The 3Ds energy model that integrates Decarbonization, Digitalization, and Decentralization of the energy system to facilitate the shift towards sustainable energy sources is used for this electric vehicle project. The experimental set up includes solar PV panels, a solar inverter, a battery inverter, and a battery bank. Additionally, smart meters were installed to collect real-time performance data from the solar PV-powered electric vehicle (EV) charging station. The weather and real-time system performance data were used to develop short term forecasting models based on Artificial Neural Networks (ANN) and the Levenberg-Marquardt method, to predict the performance of the system (power output and state of the charge) ahead. The R values for the prediction models of solar photovoltaic (PV) specific power and battery state of charge fall within the range of 0.9957 to 0.9969 and 0.9990 to 0.9996, respectively. Furthermore, the mean squared errors (MSEs) for the artificial neural network (ANN) models pertaining to solar photovoltaic (PV) power output and battery state of charge exhibit a variety of values. Specifically, the MSEs for solar PV power output range from 1.242 x 10ˆ-4 to 1.579 x 10ˆ-4, while the MSEs for battery status of charge range from 0.1889 to 0.3402. Artificial neural network (ANN) models possess considerable promise for practical implementations as they simplify intricate connections among inputs, parameters, and outputs in real-world scenarios. The level of accuracy and short-term predictive models of the solar PV powered EV charging station are very important for achieving a balance between the supply of solar photovoltaic (PV) system and the demand for electric vehicles (EVs). Predictive models will help build complex control mechanisms for controlling and optimizing solar PV-powered charging stations, supervising their operation and maintenance, and simplifying renewable energy pre-purchase. Future works will include the development of an energy management system to improve the efficiency of the solar stations charging the EVs, the establishment of blockchain networks for both the solar PV system and battery bank, and the integration of cybersecurity protocols for the charging station.

Quality control of traditional Chinese medicine Chelidonii herba based on 2D-Q-NMR

Chelidonii herba is a traditional Chinese medicinal herb with effects including antispasmodic, analgesic, antitussive, and bronchodilator properties. Alkaloids are the main bioactive ingredients in Chelidonii herba. In this study, a two-dimensional nuclear magnetic resonance (Heteronuclear Singular Quantum Correlation, HSQC-2D-NMR) technique was employed to quantitatively analyze the total alkaloid content and three major active alkaloid monomers in Chelidonii herba from eleven different sources. The quantification results of the three monomeric alkaloids were also verified using conventional quantitative control methods such as HPLC. Experimental findings indicate that the total alkaloid content is not directly correlated with the content of the three monomeric alkaloids. Furthermore, the content of any individual monomeric alkaloid does not accurately reflect the overall quality of Chelidonii herba. It was demonstrated that the 2D-Q-NMR NMR can be applied as an alternative method. While maintaining the same levels of accuracy and precision, the 2D-Q-NMR method is simpler to operate and provides more comprehensive results with higher reproducibility in some cases.

Combination certainty factor method and fuzzy expert system module to determine the dose of leukemia drugs

Leukemia is a type of blood cancer. Treatment for leukemia patients can last for years because the dose of medication given is adjusted to the patient's immune system. The aim of this research is the use of information technology through a combination of certainty factors and the development of a fuzzy expert system (FES) module to determine the therapeutic schedule for administering leukemia drugs. The urgency of this research is to help medical personnel in measuring the dose of leukemia medication to be given to patients so as to increase the cure rate for leukemia patients. The method used is certainty factor and fuzzy logic. The combination of the certainty factor method and the FES module which is carried out using input variables in the form of the severity of the leukemia suffered by the patient is to produce an appropriate therapeutic schedule for administering leukemia drugs. The result of this research is a combination of the factor certainty method and the FES module which has been tested and the accuracy level is 95.17%, the same as recommendations from experts.

The calculation method of blood flow pressure based on four-dimensional flow magnetic resonance imaging and deep learning

Utilizing artificial intelligence methods for blood flow pressure estimation can significantly enhance the computational speed of blood flow pressure. However, current related research can only calculate the blood flow pressure parameters of vessels with different geometric shapes under fixed boundary conditions, thus fail to achieve transient flow field calculation and consider the hemodynamic differences formed by patients' varying physiological and pathological conditions. In view of this, this study proposes a method for relative pressure estimation based on four-dimensional flow magnetic resonance imaging (4D flow MRI) of patient blood flow and deep learning. 4D flow MRI was used to obtain the patient's blood flow velocity gradient data, and feature engineering processing is performed on the sampled data. Then, a novel neural network was proposed to acquire the characteristic relationship between velocity gradient and pressure gradient in the vicinity of the point to be measured and within adjacent sampling time periods, thereby achieving the calculation of the relative pressure in the vicinity of the point to be measured. Statistical analysis was performed to evaluate the efficacy of the method, comparing it with computational fluid dynamics methods and catheter pressure measurement techniques. The accuracy of the proposed method exceeded 96%, while computational efficiency was improved by several tens of times, and no manual setting of physiological parameters was required. Furthermore, the results were compared with clinical catheter-measured pressure results, r2 = 0.9053, indicating a significant consistency between the two methods. Compared to previous research, the method proposed in this study can take the blood flow velocity conditions of different patients at different times as input features via 4D flow MRI, thus enabling the calculation of pressure in transient flow fields, which significantly improved computational efficiency and reduced costs while maintaining a high level of calculation accuracy. This provides new direction for future research on machine learning prediction of blood flow pressure.

Standardized IETA criteria enhance accuracy of junior and intermediate ultrasound radiologists in diagnosing malignant endometrial and intrauterine lesions.

To transform the standardized descriptions of the ultrasound characteristics of endometrial and intrauterine lesions devised by the International Endometrial Tumor Analysis (IETA) group into a practical scoring method and to investigate whether application of this method enhances the diagnostic accuracy of ultrasound radiologists with different levels of experience in detecting malignancy compared with subjective assessment. This was a retrospective study of 855 patients with endometrial and/or intrauterine lesions, who were divided into a training (n = 600) and a validation (n = 255) set. Ultrasound radiologists with varying levels of experience (expert, intermediate and junior) evaluated all lesions by subjective assessment and according to IETA rules. Using IETA rules, the experts identified signs of malignancy in the training set, assigned scores for each indicator and validated the scoring method in the validation set. The intermediate-level and junior ultrasound radiologists reassessed the malignancy of the lesions using the IETA scoring method and compared their classifications with those made previously by subjective assessment. Postsurgical pathological evaluation was used as the reference standard. Using subjective assessment, the experts demonstrated the highest level of diagnostic accuracy, with a sensitivity of 85.0%, specificity of 94.3% and an area under the receiver-operating-characteristics curve (AUC) of 0.897. Applying the IETA scoring method (comprising eight ultrasound characteristics that contributed to the total score) with a threshold of > 25 points for the diagnosis of malignancy achieved a sensitivity of 84.7%, specificity of 94.7% and AUC of 0.9533 in the training set, with similar performance in the validation set, when performed by experts. Using the IETA scoring method, both junior and intermediate ultrasound radiologists showed improvement in sensitivity (from 55.5% to 74.8% and from 70.2% to 77.1%, respectively), specificity (from 88.4% to 91.5% and from 87.4% to 92.2%, respectively) and AUC (from 0.704 to 0.827 and from 0.793 to 0.841, respectively) for diagnosing malignant lesions. The IETA scoring method exhibits high diagnostic efficacy for malignant endometrial and intrauterine lesions. This method compensates for the lack of experience among junior and intermediate-level ultrasound radiologists, enhancing their diagnostic skill to a level nearing that of experienced senior ultrasound radiologists. Further research is essential to validate the practicality of implementing this method and to confirm its clinical value. © 2024 International Society of Ultrasound in Obstetrics and Gynecology.

SSGCNet: A Sparse Spectra Graph Convolutional Network for Epileptic EEG Signal Classification.

In this article, we propose a sparse spectra graph convolutional network (SSGCNet) for epileptic electroencephalogram (EEG) signal classification. The goal is to develop a lightweighted deep learning model while retaining a high level of classification accuracy. To do so, we propose a weighted neighborhood field graph (WNFG) to represent EEG signals. The WNFG reduces redundant edges between graph nodes and has lower graph generation time and memory usage than the baseline solution. The sequential graph convolutional network is further developed from a WNFG by combining sparse weight pruning and the alternating direction method of multipliers (ADMM). Compared with the state-of-the-art method, our method has the same classification accuracy on the Bonn public dataset and the spikes and slow waves (SSW) clinical real dataset when the connection rate is ten times smaller.

Intelligent classification and performance prediction of multi-text assessment with recurrent neural networks-long short-term memory

&lt;p&gt;The assessment document at the time of study program accreditation shows performance achievements that will have an impact on the development of the study program in the future. The description in the assessment document contains unstructured data, making it difficult to identify target indicators. Apart from that, the number of Indonesian-based assessment documents is quite large, and there has been no research on these assessment documents. Therefore, this research aims to classify and predict target indicator categories into 4 categories: deficient, enough, good, and very. Learning testing of the Indonesian language assessment sentence classification model using recurrent neural networks-long short-term memory (RNN-LSTM) using 5 layers and 3 parameters produces performance with an accuracy value of 94.24% and a loss of 10%. In the evaluation with the Adamax optimizer, it had a high level of accuracy, namely 79%, followed by stochastic gradient descent (SGD) of 78%. For the Adam optimizer, Adadelta, and root mean squared propagation (RMSProp) have an accuracy rate of 77%.&lt;/p&gt;

Stability analysis of MHD Jeffery–Hamel flow using artificial neural network

The current research explores the useful impact of artificial neural networks (ANN) back-propagation along Levenberg-Marquardt Method (ANN-BLMM), for findng the influence of dimensionless numbers on the flow distribution pertaining magnetohydrodynamics (MHD) Jeffery–Hamel fluid amid two plates which are enclined at angles 2α. We have employed a numerical approach, ANN-BLMM to assess various aspects of our data, including testing, training, validation, Mean Square Errors (MSE), performance, and fitting. The methodology being used has been tested and validated through comparison with other results obtain numerically, showing extreme level of accuracy. Moreover, we have confirmed our findings through error histograms and regression tests. We used OHAM for the data set. Furthermore, we have also explored the influence of Reynolds number (R) on both flow and pressure distribution, visually representing our findings through graphical analysis. We have discussed about the nature and variations of velocity profiles within MHD Jefery–Hamel flow (MHDJHF), taking into account various values of Ha and Re in both convergent and divergent channels. It was discovered that a significant stabilizing influence of an increase in the magnetic field intensity was observed for both diverging and converging channel geometries and as the Hartman numbers rise, so does the fluid velocity. The absolute error is reduced to 10–2 to 10–6.

System approximation from later time samples on a generalized temporal grid

Let {fn}n=1∞ be a Schauder basis for L2([0,1]). We consider a function u(x,t) which can be represented as(1)u(x,t)=∑n=1∞anfn(x)gn(t). In our manuscript, our primary focus is to investigate under what conditions the initial condition function f(x)=u(x,0) can be reasonably approximated from the provided samples {u(x0,tk)}k=1N. Our research encompasses a diverse spectrum of partial differential equations (PDEs) that describe the Initial Value Problems, particularly emphasizing the initial condition functions. Assuming that the initial condition function belongs to a predefined function class in Sobolev space, we develop conditions required to achieve the desired level of accuracy N−r in reconstructing the solution. Effectively controlling errors of recovered coefficients of the approximated initial condition function, to contain a linear growth term, we enhanced the efficiency of the recovery process compared to previously known reconstruction algorithms.

An analytical method for broadband acoustic analysis of 2D cavities containing or bounded by porous materials

Real-life vibro-acoustic systems often involve porous treatments, resulting in complex-valued and frequency-dependent models that are challenging to solve. Traditional prediction techniques like the finite element (FE) method requires huge computational cost, especially in the mid to high frequency ranges. This paper develops a novel spectral dynamic stiffness (SDS) formulation, using very few number of degrees of freedom but describing the broadband acoustic behaviour of acoustic cavities with porous materials highly accurately. The method employs frequency-dependent shape function that satisfies exactly the (damped) Helmholtz equation to describe the (equivalent) acoustic pressure field, and also features an innovative approach to use the fast-convergent Modified Fourier series to describe any arbitrary acoustic BCs. Finally, the SDS matrices for cavities containing or bounded by porous materials are formulated in an analytical manner. It is demonstrated that the method exhibits a much higher computational efficiency over the FE package COMSOL, at least 6 times faster than the COMSOL with a similar level of accuracy, and benchmark solutions are provided. This promising method can provide a powerful tool for systems with porous materials that have frequency-dependent characteristics, paving the way for efficient and accurate acoustic analysis in complex engineering applications.

Identification of municipal solid waste disposal sites for sustainable solid waste management in Cooch Behar Municipality, West Bengal: A RS-GIS based MCDA approach

The proper disposal of solid waste represents one of the most intricate environmental challenges encountered by the municipalities of West Bengal. The municipality of Cooch Behar, a rapidly developing part of the Koch Bihar district of West Bengal, was selected as a case study. A health and environmental threat is created by the disposal of solid waste from the municipality area in an open place in Guriahati G.P., 6 km from the town and near the Torsa River. This study aimsto identify the most suitable areas for the disposal and dumping of municipal solid waste. Using the Analytic Hierarchy Process, the weights of the seventeen causative factors were allocated based on their relevance to identify the appropriate locations. The present study also introduces an innovative methodology employing GIS-based sensitivity analyses to enhance the optimisation of factors influencing suitable solid waste disposal site selection. Three possible waste disposal sites have been identified based on low land costs, the availability of barren land and the distance to settlements and sensitive places. The area under the ROC curve reveals that the AUC value is 0.895, which indicates that the level of accuracy of the model is 89.50 %. With a weight of 0.184, land value was found to be the most important criterion in the model, followed by distance to settlement (0.135) and distance to road (0.123). By carefully choosing suitable locations, this research could assist urban planners in developing a waste management system that is resilient and environmentally sustainable.

House Price Prediction Taking Account of the Influence of COVID-19

Machine learning algorithms are utilized in various practical applications and academic research. Due to the advent of digital technology, there is now a greater availability of massive, organized, and georeferenced datasets. This has simplified the use of algorithms for trend analysis and detection as well as forecasting, which helps users make decisions. The purpose of this study is to determine which Machine Learning (ML) algorithms are most effective in projecting housing price and to assess how the COVID-19 has affected these prices. The method includes the stages of data preprocessing, feature manipulation, hyperparameter tuning, evaluation, selection, and, in the end, interpretation of the model. Another objective is to assess and compare several ensemble learning strategies, comprising bagging techniques like random forest, and boosting techniques like Gradient Boosting Regressor, using a linear regression model. The results of this study confirm that the real estate sector is very resistant to pandemics, as the decline in prices was not as severe as initially anticipated. Among the several models that were examined, extreme gradient boosting demonstrated the highest level of accuracy, albeit with a minimal difference.

Control Allocation Optimization for an Over-Actuated Tandem Tiltwing eVTOL Aircraft Considering Aerodynamic Interactions

This paper describes control allocation strategies for a tandem tiltwing electric vertical take-off and landing (eVTOL) aircraft with distributed propulsion. The control mappings are constructed as local surrogate models that relate the control effectors to the aircraft's aeropropulsive forces and moments across the full flight envelope, including interactional aerodynamics effects. Control allocation optimization problems are formulated based on force and moment commands for specific flight conditions. Specifically, a nonlinear programming formulation based on nonlinear control mapping is proposed for control allocation optimization, and the problem is solved using a sparse nonlinear optimizer. The solutions obtained from different formulations such as the minimization of force and moment residuals and the minimization of control effort are investigated. Furthermore, these solutions are compared with the solutions obtained using linear control mapping-based formulations. Results presented for the cases of forward flight, low speed flight & hover, and flight in the transition corridor suggest that considering aerodynamic interactions while formulating and solving the control allocation problem is necessary for higher levels of accuracy compared to linear approaches. One-propeller-out scenarios and moment and force generation cases are also investigated. Finally, control allocation for steady and accelerated operation in the transition corridor is discussed. The contribution of the methods and results in the paper is a nonlinear control allocation methodology for the entire flight envelope of an over-actuated tandem tiltwing eVTOL aircraft.

Agar dilution test and its Merits and Drawbacks in evolving dynamics of colistin susceptibility

ObjectivesAutomated results for determining colistin susceptibility tests are unreliable, micro broth dilution requires expertise, and CBDE has limited dilutions. International Consensus Document of 2022 suggested that agar dilution was unacceptable due to varying results in the literature. MethodsThe study was designed to evaluate the agar dilution method for colistin susceptibility in CRE isolates compared to CBDE. In the study, 108 carbapenem-resistant isolates were tested for Colistin susceptibility by Microbroth Dilution, agar dilution, and colistin broth disc elution. The comparisons were made using various statistical parameters. ResultsThe results of the agar dilution method revealed an essential agreement of 75 % and a categorical agreement of 92.5 %. The method showed a sensitivity of 75 % and a specificity of 97.7 %. The positive and negative predictive values were 88.2 % and 94.5 %, respectively. Youden's index was 0.727, indicating a moderate level of accuracy. Meanwhile, CBDE diagnostic accuracy tests were better, with Youden's index at 0.939. ConclusionsWhile CBDE demonstrated better accuracy parameters, it did not offer a broader MIC range. In contrast, agar dilution showed reasonable specificity and reliability for isolates with high MIC. Therefore, we propose using CBDE for screening and agar dilution as a supplementary test in the approach to colistin susceptibility.