Ratio Of Deviation Research Articles

Prediction of Aging Population in Guangdong Province Based on Metabolic GM (1,1) Model

With the deepening of reform and opening up, Guangdong’s economic vitality continues to be maintained. People’s living standards and medical conditions have further improved, the population’s life expectancy has gradually increased, and the aging of the population has intensified. With the data of the elderly population aged 65 and above in Guangdong Province from 2011 to 2020, a metabolic GM (1,1) model is established based on the grey system theory, and the predictive value of the established model is tested. The results show: Q= 0.005189 ≤ 0.01; the mean square deviation ratio c= 0.064 ≤ 0.35; the test accuracy of the model is class I. The metabolic GM (1,1) model predicts that the population aged 65 and over in Guangdong Province will continue to increase in the next five years, and the problem of population aging is still severe. Therefore, it is necessary to change the situation. In the past, solutions to population aging only focused on the aspects of changing the population policy and the pension system. Now, we should fully consider the interrelationship of population aging on economic growth, savings and consumption, macroeconomic balance, and other indicators. We should actively respond to the urgent reality of the population aging in Guangdong with a systematic strategy.

A diffuse reflectance portable near infrared spectroscopy system for the determination of biuret content in urea fertilizer

Simple, rapid, and reliable determination of the biuret content in urea fertilizer is very important for the development of fertilizer industry. A near infrared diffuse reflectance measurement system with a portable spectrometer was developed, in which the reference and dark background spectrum could also be recorded automatically in addition to the absorbance data. The key performances of the proposed NIR system have been tested on urea fertilizer. Numerical experiments showed that the coefficient of determination (R2) of the external validation set was 0.97, with a root mean square error (RMSE) of 0.04%. The ratios of the performance deviation (RPD) value in the calibration and validation sets were 12 and 3.5, respectively. It can be concluded that this NIR system for the determination of biuret content in urea fertilizer may potentially be used as an alternative method to traditional wet chemical methods due to its simplicity, sensitivity, and portability.

Portfolios under Different Methods and Scenarios: A Case of Fiji’s South Pacific Stock Exchange

In this study, we analyze portfolio performance under different methods and scenarios for the small island economy of Fiji. In addition to documenting the historical performance and the smallness of the stock market, the study looks at the possibility of opting for an equally weighted (naïve) portfolio against market and minimum variance portfolios. To this end, we extract monthly stock price data of 17/19 listed companies from August 2019 to July 2022 and invoke different approaches to develop portfolios under different scenarios. We consider the mean-variance, minimum variance, semi-variance, utility maximization, and minimum turbulence portfolios, based on beta-adjusted (CAPM-based) returns. The different portfolios presented in the study should provide some insights on asset allocation in Fiji’s stock market. Interestingly, unlike average returns, the beta-adjusted returns indicate that an equally weighted portfolio can yield relatively higher expected returns than market portfolios, although, with a relatively higher standard deviation and lower Sharpe ratio than the optimized results. In a semi-variance analysis (where we account for downside risk only), equally weighted portfolio yields superior returns, albeit with a relatively lower Sortino ratio. Given that Fiji’s stock market is currently a small, with a relatively small number of listed companies, potential and less sophisticated investors and analysts considering portfolios based on beta-adjusted returns, may simply opt for 1/N (naïve) portfolios as a diversification strategy while realizing decent expected returns. The optimized portfolio under mean-variance, semi-variance, and utility are presented as alternative considerations for nuanced investors. Additionally, equally weighted turbulence-adjusted and minimum-turbulence portfolios are constructed to capture periods of unusualness and calmness in the market. The methodologies and the results presented can be adjusted and applied to other small markets and hence can influence investment decisions of investors in creating diversified portfolios under different scenarios.

Estimating soil salinity using Gaofen-2 imagery: A novel application of combined spectral and textural features

Gaofen-2 (GF-2) imagery data has been playing an important role in environmental monitoring. However, the scarcity of spectral bands makes GF-2 difficult to use in soil salinity estimation. In this paper, we combined spectral and textual features for soil salinity estimation from GF-2 imagery. The spectral features comprised five classes of predictors: spectral value, vegetation index, salinity index, brightness index, and intensity index. Four gray-level co-occurrence matrix (GLCM) indices were used as the textural features. The least absolute shrinkage and selection operator (LASSO) was applied to select features. Four methods, namely, Random forest (RF), support vector machine (SVM), back propagation neural network (BPNN), and partial least squares regression (PLSR) were applied and compared. To this end, 211 soil samples were collected in the Yellow River Delta through field investigation. The results showed that GF-2 imagery could successfully estimate soil salinity by integrating spectral and texture features, and among the four methods, the RF had the highest accuracy with the determination coefficient for cross-validation (R2CV), a root mean square error for cross-validation (RMSECV), and the ratio of the standard deviation to the root mean square error of prediction (RPD) of 0.82, 2.36 g kg−1, and 2.28, respectively. Especially, the impact of different scale features on the soil salinity estimation accuracy was evaluated. The optimal window size for features was 9 × 9 pixels, and increasing or decreasing the window size will decrease the estimation accuracy. The study provides a novel application to soil salinity estimation from remote sensing imagery.

Gender and age structure of mortality caused by COVID-19

Introduction: The policy of mortality decline of the population of Russia, pursued by the state, requires consideration of the regional peculiarities of mortality in territorial subjects of the Russian Federation by gender and age. The regional situation on male and female mortality also should be considered when developing the measures directed to prevent various diseases including infectious ones. In this regard, the studies of gender and age indicators of mortality during the periods of infectious pandemics become relevant.Objective: The analysis of gender and age rates of COVID-19 mortality in 2021 in order to substantiate the best management decisions in the sphere of the organization of medical care and statistical registration of lethal outcomes during the pandemic spread of dangerous viral infections.Material and methods: The main source of information on mortality in 2020 is data of the State Statistics of Mortality of Rosstat. The data on mortality in 2021 are presented in the records on death cases in Federal State Information System “Unified State Register of Registration of Acts of Current State”. To evaluate the obtained data, simple (unweighted) arithmetic mean values, specific indicators, a root mean square (standard) deviation of the mean values, coefficient of variation were used (a measure of the spread of attribute values – the ratio of a standard deviation to the arithmetic-mean).Results: In 2021 17.3% of total number of all deaths had COVID-19 as an initial cause of death. 248,134 cases of those were women (58.5%), 176,116 cases were men (41.5%). The mortality rate from COVID-19 increased by an average 1.7 times upon transition from one five-year-old age group to another, 85% of those who died from COVID-19 were aged 60 years and older. In 2021 the coronavirus infection of COVID-19 contributed significantly to female mortality more, than male mortality, which is inconsistent with the data on gender structure of COVID-19 mortality in other countries, as well as with the widespread belief that men are more vulnerable to this infection.Conclusions: Growth rates of COVID-19 mortality depending on age resembles the aging rate, reflecting the biological nature of mortality. Total excess mortality is the better basis for assessment of changes in the population health during pandemics, than mortality from COVID-19 alone. The organization of systematic checks of correctness of coding the causes of death at the state registration of death is necessary.Restrictions: Data of the Federal State Information System “Unified State Register of Registration of Acts of Current State” can differ from data of Rosstat which in 2022 became available to professional researchers later, than data of the Federal State Information System “Unified State Register of Registration of Acts of Current State”.

A comparative analysis of maximum likelihood estimation and artificial neural network modeling to assess electrical component reliability

AbstractThis study focuses on accurately predicting the behavior of new power function distribution using neural network and optimizing it using maximum likelihood estimation. The main motivation of this study is that there is no study in the literature that optimizes and predicts the reliability analysis of lifetime models by combining artificial neural networks and maximum likelihood estimation methods. The numerical findings of the reliability investigations and the values got from maximum likelihood estimation and artificial neural network modeling have been examined and investigated carefully. For the artificial neural network models, the R value was 0.99999 and the deviation ratios were lower than 0.08%. The findings reveal that artificial neural networks are a powerful and useful mathematical tool for analyzing the reliability of lifetime models and numerical study findings via maximum likelihood estimation are completely in accord with artificial neural network prediction results.

Predicting starch content in cassava fresh roots using near-infrared spectroscopy.

The cassava starch market is promising in sub-Saharan Africa and increasing rapidly due to the numerous uses of starch in food industries. More accurate, high-throughput, and cost-effective phenotyping approaches could hasten the development of cassava varieties with high starch content to meet the growing market demand. This study investigated the effectiveness of a pocket-sized SCiO™ molecular sensor (SCiO) (740-1070 nm) to predict starch content in freshly ground cassava roots. A set of 344 unique genotypes from 11 field trials were evaluated. The predictive ability of individual trials was compared using partial least squares regression (PLSR). The 11 trials were aggregated to capture more variability, and the performance of the combined data was evaluated using two additional algorithms, random forest (RF) and support vector machine (SVM). The effect of pretreatment on model performance was examined. The predictive ability of SCiO was compared to that of two commercially available near-infrared (NIR) spectrometers, the portable ASD QualitySpec® Trek (QST) (350-2500 nm) and the benchtop FOSS XDS Rapid Content™ Analyzer (BT) (400-2490 nm). The heritability of NIR spectra was investigated, and important spectral wavelengths were identified. Model performance varied across trials and was related to the amount of genetic diversity captured in the trial. Regardless of the chemometric approach, a satisfactory and consistent estimate of starch content was obtained across pretreatments with the SCiO (correlation between the predicted and the observed test set, (R2 P): 0.84-0.90; ratio of performance deviation (RPD): 2.49-3.11, ratio of performance to interquartile distance (RPIQ): 3.24-4.08, concordance correlation coefficient (CCC): 0.91-0.94). While PLSR and SVM showed comparable prediction abilities, the RF model yielded the lowest performance. The heritability of the 331 NIRS spectra varied across trials and spectral regions but was highest (H2 > 0.5) between 871-1070 nm in most trials. Important wavelengths corresponding to absorption bands associated with starch and water were identified from 815 to 980 nm. Despite its limited spectral range, SCiO provided satisfactory prediction, as did BT, whereas QST showed less optimal calibration models. The SCiO spectrometer may be a cost-effective solution for phenotyping the starch content of fresh roots in resource-limited cassava breeding programs.

Integration of NIR spectroscopy and chemometrics for authentication and quantitation of adulteration in sweet marjoram (Origanum majorana L.)

Origanum majorana L. (marjoram) has considerable promise as a folk remedy for digestive ailments in addition to its ethnomedicinal use in gynecological disorders. Due to the high demand for the botanical raw material, it is prone to intentional and inadvertent adulteration by morphologically similar herbs. In the current study, the adulteration of marjoram with its common adulterants; oregano, thyme, olive, basil and sage has been thoroughly investigated. To distinguish between marjoram and its prevalent adulterants, an integrated near infrared (NIR) spectroscopic method coupled with chemometric data analysis was developed. For exploratory pattern recognition, unsupervised multivariate models such as principal component analysis (PCA) and hierarchical cluster analysis (HCA) were implemented, followed by supervised models. Authentication of marjoram was successfully assessed using soft independent modelling of class analogy (SIMCA) with 100% sensitivity. Orthogonal projection to latent structures-discriminant analysis (OPLS-DA) modeling was employed to discriminate pure marjoram from its deliberately adulterated mixtures. Further, amounts of the adulterants were quantified in the botanical mixtures using partial least squares regression (PLS-R). The calibration and validation goodness of fit were determined to be greater than 0.9 and almost one, with a low root mean square error of prediction (RMSEP) and high ratio of performance to deviation (RPD) and range error ratio (RER) which highlight the model significant potential. Moreover, the optimal number of latent variables was found to be 5 as computed by permutation test. In the current study, the validity and reliability of the models employed to assess marjoram authenticity and purity was evidenced by both internal and external validation methods with a little to no data pre-processing.

Signal Quality Assessment of Compressively Sensed Electrocardiogram.

Develop a signal quality index (SQI) to determine the quality of compressively sensed electrocardiogram (ECG) by estimating the signal-to-noise ratio (SNR). The SQI used random forests, with the ratio of the standard deviations of an ECG segment and a clean ECG and the Wasserstein metric between the amplitude distributions of an ECG segment and a clean ECG, as features. The SQI was tested using the Long-Term Atrial Fibrillation Database (LTAFDB) and the PhysioNet/CinC Challenge 2011 Database Set A (CinCDB). Clean ECG segments from the LTAFDB were corrupted using simulated motion artifact, with preset SNR between -12 dB and 12 dB. The CinCDB was used as-it-is. The databases were compressively sensed using three types of sensing matrices at three compression ratios (50%, 75%, and 95%). For LTAFDB, the RMSE and Spearman correlation between the SQI and the preset SNR were used for evaluation, while for CinCDB, accuracy and F1 score were used. The average RMSE was 3.18 dB and 3.47 dB in normal and abnormal ECG. The average Spearman correlation was 0.94 and 0.93 in normal and abnormal ECG, respectively. The average accuracy and F1 score were 0.90 and 0.88, respectively. The SQI determined the quality of compressively sensed ECG and generalized across different databases. There was no consequential effect on the SQI due to abnormal ECG or compression using different sensing matrices and compression ratios. Without reconstruction, the SQI can inform which ECG should be analyzed to reduce false alarms due to contamination.

A guideline for spatio‐temporal consistency in water quality modelling in rural areas

A guideline for spatio‐temporal consistency in water quality modelling in rural areas

Investment Analysis of the Logistics Industry

After Covid-19, the American stock market experienced a big impact. This paper mainly focuses on the consequent influence on the logistics industry in US and the investment portfolio on them. This paper also aims to compare the average return, standard deviation and sharp ratio of two classic investment portfolio models: the Markowitz model and the Index model in logistics industry in US before and after covid-19. Through analyzing the data of 10 logistics industry stocks in America and the data of Standard Poor 500 as a reference from 2019 to 2021 using these two models, it is found that the average return was much higher with a corresponding higher standard deviation after covid-19, disregard of the investment models on the portfolio. It is also found that the Markowitz model had a better overall performance (smaller standard deviation and higher sharp ratio) than the Index model (although they always had the same average return), disregard of the impact of the pandemic.

Contrasting toxicity of polystyrene nanoplastics to the rotifer Brachionus koreanus in the presence of zinc oxide nanoparticles and zinc ions

Emerging contaminants such as nanoplastics and nanoparticles likely experience similar environmental behaviours, fate and effects but our knowledge of their combined toxicity is scanty. This study, therefore, investigated the joint toxicity of polystyrene nanoplastics (PNPs) and zinc oxide nanoparticles (ZnO-NPs) to an ecologically important rotifer Brachionus koreanus, and compared with the joint toxicity of PNPs and Zn ions (Zn-IONs from ZnSO4·7H2O). With increasing concentration, ZnO-NPs formed significant agglomeration with PNPs for up to 1.3 times of the original hydrodynamic size of ZnO-NPs, alongside doubling in their sedimentation and thereby losing 58% of their released Zn ions. In contrast, the availability of Zn-IONs was less affected by the agglomeration and sedimentation of PNPs, with only a loss of 18% of Zn ions at the highest concentration of PNPs. Consequently, as suggested by Concentration Addition and Independent Action models and the Model Deviation Ratios, ZnO-NPs and PNPs exerted an antagonistic interaction whereas Zn-IONs and PNPs exhibited an additive effect. We also advocate the use of the Nonparametric Response Surface method, which is more useful to predict the toxicity of chemical mixtures with interacting effects. Our findings suggested a potential difference between particle-particle and particle-ion interactions, especially at higher test concentrations, which may eventually affect their toxicity. We, therefore, call for a more systematic evaluation of commonly coexisting chemical mixtures which consist of nanoplastics and manufactured nanomaterials.

Acute toxicity of pesticide mixtures to honey bees is generally additive, and well predicted by Concentration Addition

Understanding the frequency of non-additive effects of pesticides (synergism and antagonism) is important in the context of risk assessment. The goal of this study was to investigate the prevalence of non-additive effects of pesticides to honey bees (Apis mellifera). We investigated a large set of mixtures including insecticides and fungicides of different chemical modes of action and classes. The mixtures included represent a relevant sample of pesticides that are currently used globally. We investigated whether the experimental toxicity of the mixtures could be predicted based on the Concentration Addition (CA) model for acute contact and oral adult bee toxicity tests. We measured the degree of deviation from the additivity predictions of the experimental toxicity based on the well-known Mixture Deviation Ratio (MDR). Further, we investigated the appropriate MDR thresholds that should be used for the identification of non-additive effects based on acceptable rates for false positive (alpha) and true positive (beta) findings. We found that a deviation factor of MDR = 5 is a sound reference for labeling potential non-additive effects in acute adult bee experimental designs when assuming a typical Coefficient of Variation (CV%) = 100 in the determination of the LD50 of a pesticide (a factor of 2× deviation in the LD 50 resulting from inter-experimental variability). We found that only 2.4 % and 9 % of the mixtures evaluated had an MDR > 5 and MDR < 0.2, respectively. The frequency and magnitude of deviation from additivity found for bees in this study are consistent with those of other terrestrial and aquatic taxa. Our findings suggest that additivity is a good baseline for predicting the toxicity of pesticide mixtures to bees, and that the rare cases of synergy of pesticide mixtures to bees are not random but have a mechanistic basis.

A Comparative Study on The Performance Analysis of Feature Extractors Used in Augmented Reality Applications Under Various Image Conditions

Until today, various approaches have been proposed in order to create Augmented Reality (AR) environment where virtual-real integration takes place. One of these approaches is vision-based model and it is divided into two branches as Marker-Based Augmented Reality (MBAR) and Markerless Augmented Reality (MAR). In the use of MBAR model, a reference image is introduced to the system before and when this image enters the camera view, an AR environment is created. However, in MAR model, no image is introduced to the system before. Instead, it uses natural characteristics present in the image such as edges, corners and geometrical shapes to create an AR environment. In order to use MAR model, it is necessary to use algorithms which require high processing power and memory capacity. Within the scope of this study, MAR model was chosen as reference and an evaluation on combinations of descriptive extractors (such as ORB, SIFT and SURF) and matchers (such as Bruteforce, Bruteforce-Hamming and Flannbase) was presented. In this context, it was aimed to obtain knowledge about i) the number of keypoints and detection time with use of different descriptor extractors, ii) the number of matching keypoints and the amount of positional deviation of a virtual object placed on a real world scene with use of different matchers. In line with this goal, analyzes were made using different image scales and brightness levels on both PC and mobile platforms. Results showed that, for both platforms, combinations using ORB method could work faster with less deviation than the combinations using other methods in all conditions. In addition, RANSAC algorithm was also used to reduce the total mean deviation ratio and it was seen that the rate could be reduced from 70% to 4.5%.

“Surgical Wings”: Forceps-Based Device Improves Surgeon Hand Ergonomics

Introduction: Surgical instruments require ongoing improvement for optimal ergonomics. Our novel “surgical wings” (SW) device is designed to improve steadiness of forceps and decrease hand fatigue and discomfort. Methods: A total of 21 surgical trainees and attending surgeons participated in this study at a single academic center. The effect of SW on ergonomics of various tasks using forceps was evaluated. Each participant performed tasks with and without SW attached to the forceps. Surface electromyography (EMG) was used to record thenar muscle activity during all tasks. Post testing survey was conducted to evaluate participants’ assessment of the SW effect on task performance, hand fatigue and comfort. Results: Continuous Pinch test: The steadiness index (ratio of standard deviation to the mean of the EMG voltage) significantly improved with the device use (p value 0.031). Repetitive Pinch test: EMG values during rest periods were significantly lower with the device (p value 0.031). Pegboard test: No significant difference in the speed of completion or number of peg drops was detected in trials performed with and without the device. Post-testing survey: Participants scored favorably the device performance during pinch tests and neutral during the pegboard task favorably. Conclusion: Based on our data, SW effectively decreases hand muscle effort and allows for better hand relaxation. The device significantly improves the steadiness of forceps use during sustained pinch. The device use did not negatively affect speed or tasks performance precision. Favorable subjective assessment of device ergonomics by participants correlated well with the EMG data.

Prediction of shear stress induced by shoaling internal solitary waves based on machine learning method

Recently, the interactions between internal solitary waves (ISWs) and the seabed have directed increasing attention to ocean engineering and offshore energy. In particular, ISWs induce bottom currents and pressure fluctuations in deep water. In this paper, we propose a method for predicting the shear stress induced by shoaling ISWs based on machine learning, and the developed approach can be used to quickly determine the safety and stability of ocean engineering. First, we provided a basic dataset for model training. Four machine learning models were selected to predict the shear stress induced by shoaling ISWs under different trim conditions. The results indicated that the performance of the convolutional neural network-long short-term memory (CNN-LSTM) forest prediction model was significantly better than the three other tested models, including long short-term memory (LSTM), support vector regression (SVR) and deep neural network (DNN) models. Therefore, the CNN-LSTM forest prediction model was the optimal model for predicting the shear stress induced by shoaling ISWs. Specifically, each metric of the CNN-LSTM model was smaller than that of the other three, and the root mean squared error to the standard deviation ratio was closest to 0.7. In addition, the CNN-LSTM model significantly outperformed the SVR and DNN models in terms of the length of prediction time. The predicted values by the CNN-LSTM model were consistent with the experimental values. The method for predicting shear stress based on machine learning in this paper can be used to predict the shear stress induced by shoaling ISWs, guide future field experiment designs, reduce damage to the seabed caused by ISWs, and promote the development of ocean engineering in deep water.

Fusion of three spectroscopic techniques for prediction of fatty acid in processed lamb

The application of individual spectroscopic techniques for meat analysis has been widely explored. Attempts to fuse data from multiple spectroscopic instruments for meat analysis are still lacking. Comparative assessment of the performance of mid infrared (MIR), near infrared (NIR) and Raman spectroscopy to estimate fatty acid (FA) composition in processed lamb was investigated. The acquired data from these individual techniques were then utilised in estimating similar parameters using a multi-block partial least square data fusion approach. Model performance was assessed with respect to the determination coefficient and ratio of predictive deviation upon cross-validation of the model. The fused data had slight improvements for the prediction of four FA parameters including MUFA, C18:0, C18:1 c9 and C9, t11- CLA), suggesting possible information enhancement with use of multiple instruments. However, MIR offered better predictability (RPD values) across the FA parameters considered.

Multifracture initiation from a series of noncircular convex wellbores

In this study, a numerical model is proposed based on the dual toughness and strength criterion for fracture initiation to determine the fracture initiation location and initiation pressure. In the model, hydraulic fractures emanate from a noncircular convex wellbore, which is considered as a jointed cross between an ellipse and a circle. Numerical studies were performed to explore the behaviors associated with fracture initiation under different stress combinations. The results indicate that the initiation pressure is sensitive to the geometric parameters of the wellbore. The initiation location is mainly controlled by the wellbore shape under a near-equal stress state characterized by the ratio of deviation (S) to mean (P) stresses (S/P < 0.10); if S/P > 0.50, the initiation location is dominated by the stress state. For an intermediate stress state (0.10 ≤ S/P ≤ 0.50), the initiation location is affected by both the stress state and wellbore shape; multiple fractures are easier to initiate in the presence of a noncircular wellbore and tend to develop into an X shape. In addition, X-shaped fractures prefer externally expanded wellbores. The subsequent propagations of the X-shaped fractures were also modeled, and these fractures exhibited a strong near-well tortuosity.

Cultural involvement and preference in ethnic accommodation

The enduring connection between migration and ethnic identity becomes more salient with each passing decade of the 21st century. Ethnic identity often entails a strong migration component bringing people from disparate cultures into new interaction and generating awareness of group distinctions. This close link between migration and ethnic identity suggests that social science tools used to study one of these subjects may prove useful in studying the other. In this spirit, we apply concepts originating in the study of immigrant acculturation to the wider subject of ethnic accommodation. A continuous-case approach applies Berry's bi-dimensional theoretical perspective on acculturation using measures of cultural involvement and cultural preference for respondents from a range of different ethnic groups. These groups are defined by visible minority status, linguistic contrasts, and different national origins as sampled in the 2013 Canadian General Social Survey. Multivariate statistical models control for other possible sources of variation in the experiences of individuals in order to identify how CI and CP vary in relation to ethnic identity itself. We consider relative positions of different ethnic groups in Canadian society using mean values of our measures, and different distributions of members of each group around such mean values using a new ratio of standard deviations of cultural involvement and cultural preference scores. These results give us new insights into comparative ethnic accommodation patterns, applicable in a wide range of other societal contexts beyond the Canadian data examined here.

Visualizing Bivariate Statistics Using Ellipses Over a Scatter Plot

A scatter plot shows the relationship between two quantitative variables x and y. Sometimes, we can predict one variable as a linear function of the other using the least-squares regression lines of y on x or x on y. These two regression lines together suffice to identify the mean vector, the coefficient of determination, the correlation coefficient, and the ratio of the standard deviations (SD). So, do our proposed summary ellipses. Additionally, the inner ellipse reveals the SDs and the outer ellipse flags potential outliers.&#x0D; &#x0D;