Tree Method Research Articles

A Combined Strategy for Path Planning of Tensegrity Manipulators Considering Structural Stability

Abstract A strategy combining an improved rapidly exploring random tree (RRT) method with the artificial potential field (APF) method is proposed for the path planning of flexible tensegrity manipulators considering typical complex constraints, such as structural stability, obstacle avoidance, and cable no-slackening. The relationship between the node displacements and the elongations of active members is established for the kinematic analysis of tensegrity manipulators. The rest lengths of active members are taken as the design variables for the generation of a random tree. The guide node is utilized for goal-biased samplings to expedite the exploration toward the final configuration, while the APF method is introduced to ensure that the obtained elongations of active members can satisfy the constraint of obstacle avoidance. The proportion of random and goal-biased samplings is adaptively adjusted based on the sampling success rate. Futile random samplings can be significantly reduced by dynamically modifying the random sampling range according to the obtained configuration nearest to the final configuration. The computational procedure of the proposed method is presented. An illustrative flexible tensegrity manipulator is employed to demonstrate the adaptability of the proposed path planning method to complex constraints, especially structural stability.

Application of machine learning in predicting health perception through military personnel's sense of empowerment.

The promotion of health and provision of care services for new recruits are issues of constant concern for military leaders and healthcare providers, as they are crucial to maintaining and operating military forces. The enhancement of military personnel's empowerment has been recognized as a core value in promoting health perception. However, the pathways between military personnel's sense of empowerment and health perception have not been thoroughly explored. The primary aim of this study is to examine the predictive power of different dimensions of empowerment (personal, interpersonal, and socio-political) on new recruits' health perception, and to further observe differences among subgroups, which will help us grasp the nuances of future health intervention measures. The research data were extracted from the "Military Career Development Study," analyzing personal empowerment data from Wave 1 (W1) and perceived health data from Wave 2 (W2) (N = 2,232). In terms of analytical methods, five ML classifiers, including Decision Tree, Random Forest, Support Vector Machine, AdaBoost, and k-Nearest Neighbors (KNN) algorithms, were used for prediction in both the full sample and subsamples (gender and socioeconomic status). Results show that among the five ML classifiers, the Decision Tree performed best overall, achieving a prediction accuracy of 95.4%. The results by gender show that the ML models perform best for both males and females with the Decision Tree and Random Forest methods. For the Decision Tree, the accuracy rates were 94.9% for males and 95.1% for females; the F1 scores were 92.9% for males and 93.2% for females. For the Random Forest, the accuracy rates were 94.9% for males and 95.4% for females; the F1 scores were 92.7% for males and 93.2% for females. Regarding SES, the Decision Tree and Random Forest methods performed best. In the SES Low group, both methods achieved a prediction accuracy of 95.6% and an F1 score of 93.7%; in the SES high group, they achieved a prediction accuracy of 95.4% and an F1 score of 93.3%. However, the contribution of different dimensions of empowerment features varied significantly among subgroups. These findings can provide important information on the differences in health perception among military personnel.

Population Structure and Selection Signatures in Chinese Indigenous Zhaotong Pigs Revealed by Whole-Genome Resequencing

Zhaotong pig (ZTP) is a Chinese indigenous pig breed in Yunnan Province, known for its unique body shape and appearance, good meat quality, strong foraging ability, and adaptability. However, there is still a lack of research on its genome. In order to investigate the genetic diversity, population structure, and selection signatures of the breed, we conducted a comprehensive analysis by resequencing on 30 ZTPs and comparing them with genomic data from 10 Asian wild boars (AWBs). A total of 45,514,452 autosomal SNPs were detected in the 40 pigs, and 23,649,650 SNPs were retained for further analysis after filtering. The HE, HO, PN, MAF, π, and Fis values were calculated to evaluate the genetic diversity, and the results showed that ZTPs had higher genetic diversity and lower inbreeding coefficient compared with AWBs. Population structure was analyzed using NJ tree, PCA, ADMIXTURE, and LD methods. It was found that ZTPs were population independent of AWBs and had a lower LD decay compared to AWBs. Moreover, the results of the IBS genetic distance and G matrix showed that most of the individuals had large genetic distances and distant genetic relationships in ZTPs. Selection signatures were detected between ZTPs and AWBs by using two methods, FST and π ratio. Totals of 1104 selected regions and 275 candidate genes were identified. Finally, functional enrichment analysis identified some annotated genes that might affect fat deposition (NPY1R, NPY5R, and NMU), reproduction (COL3A1, COL5A2, GLRB, TAC3, and MAP3K12), growth (STAT6 and SQOR), tooth development (AMBN, ENAM, and ODAM), and immune response (MBL2, IL1A, and DNAJA3). Our results will provide a valuable basis for the future effective protection, breeding, and utilization of ZTPs.

APPLICATION OF THE DNA BARCODING METHOD FOR IDENTIFICATION AND TAXONOMIC ANALYSIS OF THE LAMIACEAE FAMILY

The Lamiaceae family is one of the largest and most important among the aromatic plants, including many species with pronounced pharmacological properties. Plants of this family, such as mint, rosemary and thyme, are widely used in traditional and modern medicine due to their therapeutic and biochemical properties. The importance of correct species identification is emphasized by the need to preserve the biodiversity and sustainable use of the resources of this family. In this work, plants of Lamiaceae family growing in the territory of the Karaganda region were studied by DNA barcoding. The studies were conducted on the basis of nucleotide sequences of rbcL and trnH-psbA chloroplast DNA barcodes using the methods of paired genetic distances, BLASTn and phylogenetic tree based on the analysis of maximum parsimony. The high efficiency of the rbcL locus was demonstrated for the identification of higher taxonomic groups, such as subfamilies and tribes, while the intergenic spacer trnH-psbAhasproved to be effectiveat the level of speciesandgenus level.

A Study of Self Similar Vector Fields in Bianchi Type III Spacetime Via Riftree Approach

Abstract In this research, we study self-similar vector fields in Bianchi type III spacetime by applying
 the Rif tree method. This study uses a computer approach to convert symmetry equations
 into a simplified involutive form, which separates the integration problem into several cases
 that are shown as a tree structure. Certain restrictions apply to the metric functions in
 certain circumstances. Explicit formulations for the metrics and their corresponding self
similar vector fields are obtained by solving the system of equations governing self-similar
 symmetries using these conditions. Additionally, physical significance are taken into ac
count for an in-depth analysis of the generated models.

An Artificial Intelligence approach to monitor infectious diseases: lessons learned from COVID-19

Abstract Background The World Health Organization declared the start (March 11th 2020) and the end (May 5th 2023) of COVID19 pandemic, while in Italy it has been present since February 2020. Indicators such as number of new positive cases, deaths and hospitalizations are used to monitor epidemiological trends, but they suffer from biases limiting their effectiveness. Methods We used data from the Emergency Medical Services Activities as an alternative and studied three types of contributions: COVID19, flu and baseline during three time frames: -period 1 (July 1st-Oct 11th 2016; Feb 10th-May 20th 2017) used to model the baseline, when flu contribution should be negligible. -Period 2: (Dec 15th-Feb 14th 2017), when flu in 2017 is very evident. -Period 3: (March 11th-31st 2020), when the first COVID19 wave is dominant. To extract the pure contribution from flu and COVID19, the baseline contribution was properly subtracted. Results From these data we developed a machine learning approach (MLA) that offers a simple and powerful tool to monitor COVID19 or future infectious diseases. To maximize the identification power of COVID19, we used the Toolkit for multivariate analysis package. An artificial neural network, multilayer perceptron, deep neural network and boosted decision tree methods have been trained, considering the events in period 3 as signal and period 1 as background. To avoid overtraining, the samples were divided in two: one half to train the algorithm, and the other to check the performance. The results are stable over time and able to efficiently discriminate COVID19. With 50% efficiency of accepting COVID19 patients, roughly 95% of baseline patients can be rejected. Conclusions MLA can be used to early assign a probability of COVID19 without specific test, only relying on standard triage and emergency call details. This tool could be very useful to early detect the presence of new pandemics and tag positive patients before the official healthcare reporting system. Key messages • AI model using data from Emergency Medical Services Activities can be a useful tool to early detect new pandemics and tag positive patients before the official healthcare reporting system. • Covid19 pandemic gave us the possibility to develop new digital tools to use in the analysis of epidemiological trends.

The solution of the task of dynamic interpretation of seismic data using machine learning methods

This article examines the problem of dynamically interpreting seismic data using machine learning models, which include Extremely Randomized Trees (Extra Trees), Gradient Boosting (GB), and Adaptive Boosting (AdaBoost) for the given problem. The study analyzes some existing solutions of the problem and describes the advantages of these machine learning models. Accuracy is estimated using the root mean square error metric. The authors found that dynamic interpretation and prediction of seismic data using these machine learning methods had not been extensively explored in research on related topics, which became the main focus of the study. The article formalizes the use of the mentioned models and highlights features and advantages for the given problem. Several common machine learning methods were investigated to find functional relationships between input parameters. Computational experiments were conducted to evaluate their applicability and compare the algorithms. The results show that the Extra Trees method is the most suitable for practical use for the given problem, as it demonstrates the highest accuracy in determining functional relationships and dynamic interpretation.

Examining the relationship between the built environment and carbon emissions from operating vehicles: enlightenment from nonlinear models.

Carbon emissions from urban transportation significantly contribute to overall transportation emissions and are a major cause of the continuous rise in global temperatures. Understanding the spatial distribution and influencing factors of carbon emissions from operating vehicles can aid in formulating targeted policies and promoting emission reduction. To analyze the factors influencing urban traffic carbon emissions, we calculated emissions using trajectory data from operating vehicles in Shenzhen. We then used gradient boosting regression tree methods, specifically RF, XGBoost, and LightGBM models, to analyze the impact of the built environment on vehicle emissions. We used the XGBoost model for detailed factor analysis by comparing the models. The results indicate that bus stops, intersections, housing density, metro stops, and land use mix are the top five factors influencing emissions. When road density is 0-15 km/km2, the distance from the city center is 0-6 km, and the population exceeds 2000/km2, the built environment significantly reduces vehicle emissions.

Using mathematical models in the assessment of the circulatory system in employees working under harmful occupational conditions

Introduction. According to the results of medical examinations of metallurgists, in the structure of newly identified somatic morbidity, circulatory system diseases (CSD) are one of the leading pathologies. To assess the prevalence of CSD in workers in harmful working conditions, it is possible to use various mathematical solutions as a tool. The objective of the study was to substantiate the possibility of using variance analysis and classification tree (CT) methods to assess the prevalence of circulatory diseases in industrial workers. Materials and methods. Data of medical examinations and a questionnaire-based survey of three hundred eight workers of steel production were input into a database. The interrelationship between CSD and individual and occupational risk factors was examined using a one- and two-way ANOVA. The classification tree method was used for a multivariate analysis. Results. The one- and two-way ANOVA revealed a statistically significant correlation between the CSD family history (p=0.001) and age, years of occupational exposure, overweight, blood glucose level, total cholesterol (p<0.05) and the CSD prevalence in the steel workers. We applied the classification tree method to make decisions on referring the subjects to potentially “healthy” and “sick” workers by determining the CSD prevalence in relation to various combinations of risk factors. Limitations. This study is limited to the results of medical examinations of the workers collected over a one-year period. Conclusions. Our experience in using the analysis of variance and classification tree to evaluate the CSD prevalence in ferrous industry employees allows recommending them for use in assessing the prevalence of various diseases in workers exposed to occupational hazards.

Role of Artificial intelligence model in prediction of low back pain using T2 weighted MRI of Lumbar spine.

Low back pain (LBP), the primary cause of disability, is the most common musculoskeletal disorder globally and the primary cause of disability. Magnetic resonance imaging (MRI) studies are inconclusive and less sensitive for identifying and classifying patients with LBP. Hence, this study aimed to investigate the role of artificial intelligence (AI) models in the prediction of LBP using T2 weighted MRI image of the lumbar spine. This was a prospective case-control study. A total of 200 MRI patients (100 cases and controls each) referred for lumbar spine and whole spine screening were included. The scans were performed using 3.0 Tesla MRI (United Imaging Healthcare). T2 weighted images of the lumbar spine were segmented to extract radiomic features. Machine learning (ML) models, such as random forest, decision tree, logistic regression, K-nearest neighbors, adaboost, and deep learning methods (DL), such as ResNet and GoogleNet, were used, and performance measures were calculated. Our study showed that Random forest and AdaBoost are the most reliable ML models for predicting LBP. Random forest showed high performance with area under curve (AUC) values from 0.83 to 0.88 across all lumbar vertebrae and L2-L3, L3-L4, and L4-L5 intervertebral discs (IVDs), with AUCs of 0.88 the highest at L5-S1 IVD (0.92). Adaboost demonstrated high performance at the L2-L5 vertebrae with AUC values of 0.82 to 0.90, with the highest AUC (0.97) at the L5-S1 IVD. Among the DL models, GoogleNet outperformed the other models at 30 epochs with an accuracy of 0.85, followed by ResNet 18 (30 epochs) with an accuracy of 0.84. The study demonstrated that ML and DL models can effectively predict LBP from MRI T2 weighted image of the lumbar spine. ML and DL models could also enhance the diagnostic accuracy of LBP, potentially leading to better patient management and outcomes.

METHOD OF RECOGNITION OF FPV-UAV RADIO SIGNALS FORMED ACCORDING TO CROSSFIRE AND EXPRESSLRS STANDARDS

Unmanned aerial vehicles (UAVs) with the First Person View (FPV) system are the most common type of attack UAVs used at the tactical level by the armed forces of the russian federation. Timely detection of facts of their using by the enemy and countering them are important tasks solved by units of the Ukrainian Defense Forces. An effective countermeasure way for FPV-UAVs is to create interference in their radio control channels frequency bands. To increase the radio suppression range, it is necessary to use interference matched in frequency and structure, the necessary condition for the formation of which is the presence of a priori information about the structure of the radio signal. Analysis of the available information on the construction of enemy FPV-UAVs shows that their radio control and telemetry channels are usually combined, have time division multiple access, the signals are formed according to CrossFire and ExpressLRS standards and have fixed sets of parameter values that can be used as features in recognition. The proposed FPV-UAV radio signal recognition technique is based on decision tree and minimum metric methods and uses a priori information on frequency, time and modulation parameters of radio signals. The combination of the two decision-making methods made it possible to reduce the computational complexity by eliminating operations for determining the parameters of radio signals that do not correspond to the decisions taken at the previous stages, and provides the possibility of adding new time parameters without changing the developed recognition technique. The method consists of a sequence of operations for estimation radio signal parameters, comparing them with known values, making decisions about the type of standard and command- telemetry radio line operation mode. Only one fragment with a duration of one frequency element is sufficient to recognize the FPV-UAV radio signal, which reduces the technical requirements for detection means in which the method can be used.

A Bi‐level stacked LSTM‐DNN‐based decoder network for AGC dispatch under regulation market framework in presence of VPP and EV aggregators

AbstractThe consideration of mileage settlement in the frequency regulation market has encouraged fast‐acting units, such as converter‐interfaced generators (CIG) and electric vehicle stations, to actively participate in load‐generation balancing through automatic generation control (AGC). Conventional frequency regulation faces challenges in coping with the growing variability of CIGs and also lacks effective incentives for rapid‐responding units. In this context, a bi‐level AGC dispatch approach based on a stacked long short‐term memory (LSTM)‐deep neural network (DNN)‐based decoder framework is proposed for a power system comprising diverse CIGs forming a virtual power plant and electric vehicle aggregators. The proposed decoder network is comprised of stacked LSTM and DNN, wherein the cascaded LSTM layers are introduced to accurately capture temporal information from time series input. The inclusion of a dropout mechanism further enhances the model’s generalisability in unforeseen environments. The proposed dispatch framework uses mileage‐based compensation criteria to optimally allocate instructions among various participating units with differing regulation characteristics. The performance of the proposed method is analysed by considering packet loss, delay, unexpected generation failure, and denial of service attacks. The evaluation of the proposed approach reveals its superior performance compared to proportionality, particle swarm optimisation, decision tree, and DNN methods.

Application of the Decision Tree Method for Differentiating Human Groups

One of the tasks of modern biological anthropology is to develop a system that could objectively classify humanity on the basis of measurements. Here, the decision tree algorithm was chosen to create a classification of groups. The method helps to evaluate the differentiating power of specific dimensions for separating samples and to assess the composition of clusters at each step of the analysis. Standard cranial measurements were used, and the entropy index was chosen as a heterogeneity measure. Classification units were 39 ethno-territorial groups from 13 major regions of the Old World. At the first step, differentiation is made between broad-faced and narrow-faced groups, demonstrating the classificatory value of this trait. The first cluster includes only Mongoloids, admixed Southern Siberian populations, and Ainu. The second cluster is heterogeneous, but its further subdivision is more in line with the traditional classification. Traits underlying the branching of the tree may be the same in different branches, evidencing their taxonomic importance. Capabilities of the decision tree method proved sufficient to construct a system largely similar to the traditional one. Certain traits separate large groups of populations, while others are efficient at the regional level. The method, therefore, can be recommended as a supplementary tool at the intraspecific level.

Tabular deep learning: a comparative study applied to multi-task genome-wide prediction

PurposeMore accurate prediction of phenotype traits can increase the success of genomic selection in both plant and animal breeding studies and provide more reliable disease risk prediction in humans. Traditional approaches typically use regression models based on linear assumptions between the genetic markers and the traits of interest. Non-linear models have been considered as an alternative tool for modeling genomic interactions (i.e. non-additive effects) and other subtle non-linear patterns between markers and phenotype. Deep learning has become a state-of-the-art non-linear prediction method for sound, image and language data. However, genomic data is better represented in a tabular format. The existing literature on deep learning for tabular data proposes a wide range of novel architectures and reports successful results on various datasets. Tabular deep learning applications in genome-wide prediction (GWP) are still rare. In this work, we perform an overview of the main families of recent deep learning architectures for tabular data and apply them to multi-trait regression and multi-class classification for GWP on real gene datasets.MethodsThe study involves an extensive overview of recent deep learning architectures for tabular data learning: NODE, TabNet, TabR, TabTransformer, FT-Transformer, AutoInt, GANDALF, SAINT and LassoNet. These architectures are applied to multi-trait GWP. Comprehensive benchmarks of various tabular deep learning methods are conducted to identify best practices and determine their effectiveness compared to traditional methods.ResultsExtensive experimental results on several genomic datasets (three for multi-trait regression and two for multi-class classification) highlight LassoNet as a standout performer, surpassing both other tabular deep learning models and the highly efficient tree based LightGBM method in terms of both best prediction accuracy and computing efficiency.ConclusionThrough series of evaluations on real-world genomic datasets, the study identifies LassoNet as a standout performer, surpassing decision tree methods like LightGBM and other tabular deep learning architectures in terms of both predictive accuracy and computing efficiency. Moreover, the inherent variable selection property of LassoNet provides a systematic way to find important genetic markers that contribute to phenotype expression.

Phylogenomics reveal species limits and inter-relationships in the narrow-range endemic lycian salamanders

Salamanders of the genus Lyciasalamandra are represented by as many as 20 narrow-range endemic taxa inhabiting the Mediterranean coast of Turkey and a handful of Aegean Islands. Despite recent molecular phylogenetic studies, the genus is rife with uncertainty about the number of contained species and their phylogenetic relationships, both of which can interfere with needed conservation actions. To test species limits and infer interrelationships we generated as many as 113,176 RAD loci containing 229,427 single nucleotide polymorphisms (SNPs), for 110 specimens of Lyciasalamandra representing 19 of the 20 described taxa. Through a conservative species delimitation approach, we found support for eight species in the genus which broadly agree with currently described species-level diversity. We then use multiple coalescent-based species tree methods to resolve relationships in this relatively old, synchronous species radiation. We recommend synonymization of the largely over-split subspecific taxa, and the elevation of L. luschani finikensis to full species status as L. finikensis. Our hope is that this revised taxonomic framework provides a stable foundation for conservation management in these fragile, microendemic taxa.

Classification and regression tree (CART) for predicting cadmium (Cd) uptake by rice (Oryza sativa L.) and its application to derive soil Cd threshold based on field data

The entry of Cd into soil-rice systems is a growing concern as it can pose potential risks to public health. To derive regional soil Cd threshold, a total of 333 paired soil and rice samples was collected in Anhui Province, Eastern China. The results showed that the total soil Cd and soil Zn/Cd were the most significant variables contributing to Cd content in polished rice. The Chinese Soil Quality Standards might overestimate risk posed by Cd-contaminated soil for rice production in the mining area due to high Zn/Cd values of some mining-associated soils. Cd levels in polished rice can be predictable using stepwise multiple linear regression (MLR) model. However, the derived soil Cd threshold based on the MLR model would be unrealistically high. The classification and regression tree method (CART) performed well in simulating Cd levels in polished rice and can be used to derive soil Cd threshold instead of MLR to minimize the uncertainty.

Batik Pattern Classification Using Machine Learning Approaches

Batik is one of Malaysia traditional textile art form that is well known. By using Artificial Neural Network (ANN), k-Nearest Neighbors (k-NN) and Decision Tree methods, this study aims to classify Kelantan batik designs according to flora, fauna and geometry motifs. 133 images of the three categories were collected from social media and underwent pre-processing techniques. Image augmentation was done to enhance the diversity and quality of available training data for machine learning models. Digital transformation on the images based on colour features was developed to classify the three types of batik motifs. Image embedding was employed which returned a vector representation of each image in a data table in Orange software. The data divided into training and testing dataset, with a ratio of 8:2. The performance of each machine learning techniques were compared. The findings showed that ANN produced an excellent performance as indicated by classification accuracy (CA) of 87.9% for original images and 82.2% using Luminance Gradient image transformation technique. ANN also returns the highest AUC score indicating it is the best in distinguishing the images motif. Results from confusion matrix showed that ANN have the least misclassification for batik classification. Based on the result, it signifies that ANN performs the best classification among the three approaches.

A new lattice approach for risk-minimization hedging under generalized autoregressive conditional heteroskedasticity models

This paper explores the calculation of risk-minimization hedging strategies, specifically local and global risk minimization strategies for contingent claims under affine and non-affine GARCH models with the known closed forms of its first four moments across times under the physical measure. A unified and efficient willow tree method is introduced for various GARCH models. Unlike methods that provide option values and hedging ratios solely at the inception time, the proposed willow tree method generates a comprehensive table of option values and hedging ratios at each discrete time step across possible asset prices. Additionally, the method showcases robust performance in hedging at lower frequencies than the underlying asset’s modeling frequency (e.g., weekly or monthly hedging using a daily GARCH model). Lastly, the willow tree method outperforms the Monte Carlo method, offering greater efficiency, accuracy, and flexibility in solving risk-minimization hedging problems.

Air Quality Prediction using Ensemble Classifiers and Single Decision Tree

Air pollution is a major concern nowadays that needs immediate action. Various acts have already been initiated by the government in controlling it. The harmful gases present in the air are Nitrogen dioxide, Sulphur dioxide, Ozone, and Carbon monoxide that is causing air pollution and are becoming the major cause of harmful diseases. It does not affect human beings only but the entire environment. According to World Health Organization, air pollution is killing seven million people around the world. Planting trees and avoiding using plastics is the possible solution to control air pollution. The air quality index is the measure through which we categorize the air pollution of various cities. In this paper, we will discuss the bagging and boosting method, the extra trees method of machine learning in predicting air quality index, and its comparison with Decision trees. The dataset is collected from the Open Weather Application Program Interface. In this, the prediction of the Air Quality Index is done based on previous data. Time i.e., hourly prediction and space series prediction are done on the dataset. After implementing each algorithm confusion matrix is obtained and from this precision and recall are calculated for each category of the Air quality index ranging from one to five with one being good and five being very unhealthy. It has been observed that Gradient Boosting achieves the highest accuracy with 98.89%.

Comparison of Classification Results of SVM, KNN, Decision Tree, and Ensemble Methods in Diabetes Diagnosis

This study aims to determine which algorithms and test techniques are the most optimal in detecting diabetes mellitus and obtaining the best results based on the value of accuracy, precision, and recall. In this study, approaches were used in early diagnosis of diabetes using KNN, SVM, Decision Tree, and Ensemble Majority Voting methods in Percentage Split and K-Fold Cross Validation methods. Diabetes is a disease characterized by high blood sugar (glucose) levels and can cause a variety of disease complications and damage to the body's organs if not treated immediately. Early diagnosis of diabetes is becoming crucial so that people can take immediate action to the hospital for immediate treatment. The data used is Healthcare-Diabetes from Kaggle. The results of this study have found that the K-Fold Cross Validation method is better because it can provide an average improvement in Ensemble accuracy of 13.42% compared to the Percentage Split method which only gives an average increase in Ensamble accuracy of 9.15%. The best algorithm for classifying diabetes disease is the Ensemble Majority Voting algorithm using the K-Fold Cross Validation method with a 98.81% accuracy rate. These excellent research results may contribute to detecting early symptoms of diabetes before it become too severe.